From 2d4ac21fcb25d06dfe2ff82f28c14acef4c71ef0 Mon Sep 17 00:00:00 2001 From: aho Date: Mon, 13 Dec 2021 18:16:19 +0100 Subject: [PATCH 01/83] Add area weighting (undone) --- R/ACC.R | 15 +++++++++++++++ 1 file changed, 15 insertions(+) diff --git a/R/ACC.R b/R/ACC.R index 0f1040c..9e92da4 100644 --- a/R/ACC.R +++ b/R/ACC.R @@ -293,6 +293,9 @@ ACC <- function(exp, obs, dat_dim = 'dataset', space_dim = c('lat', 'lon'), c(space_dim, dat_dim)), fun = .ACC, dat_dim = dat_dim, avg_dim = avg_dim, +#------------NEW-------------- + lat = lat, +#------------NEW_END-------------- conftype = conftype, pval = pval, conf = conf, conf.lev = conf.lev, ncores_input = ncores, ncores = ncores) @@ -321,6 +324,9 @@ ACC <- function(exp, obs, dat_dim = 'dataset', space_dim = c('lat', 'lon'), c(space_dim, avg_dim, dat_dim)), fun = .ACC, dat_dim = dat_dim, avg_dim = avg_dim, +#------------NEW-------------- + lat = lat, +#------------NEW_END-------------- conftype = conftype, pval = pval, conf = conf, conf.lev = conf.lev, ncores_input = ncores, ncores = ncores) @@ -409,6 +415,15 @@ ACC <- function(exp, obs, dat_dim = 'dataset', space_dim = c('lat', 'lon'), if (is.null(avg_dim)) { # ACC +#-----------NEW-------------- + # area weighted + wt <- cos(lat * pi/180) + wt <- rep(wt, as.numeric(dim(exp_sub)[2])) ## dim(exp): [lat, lon] + exp_sub <- as.vector(t(exp_sub)) ## first longitudes and then latitudes + obs_sub <- as.vector(t(obs_sub)) ## first longitudes and then latitudes + exp_sub <- sqrt(wt) * (exp_sub - mean(exp_sub)) + obs_sub <- sqrt(wt) * (obs_sub - mean(obs_sub)) +#----------NEW_END----------- top <- sum(exp_sub*obs_sub, na.rm = TRUE) #a number bottom <- sqrt(sum(exp_sub^2, na.rm = TRUE) * sum(obs_sub^2, na.rm = TRUE)) acc[iexp, iobs] <- top/bottom #a number -- GitLab From 56462824cc0ba8bea4f4807e9cd7a91e4d5b4788 Mon Sep 17 00:00:00 2001 From: aho Date: Thu, 16 Dec 2021 14:35:22 +0100 Subject: [PATCH 02/83] Add area weighting and allow dat_dim = NULL --- R/ACC.R | 281 +++++++++++++++++++++++++++++++++-------------------- man/ACC.Rd | 47 +++++---- 2 files changed, 202 insertions(+), 126 deletions(-) diff --git a/R/ACC.R b/R/ACC.R index 9e92da4..340dd77 100644 --- a/R/ACC.R +++ b/R/ACC.R @@ -1,37 +1,40 @@ -#'Compute the anomaly correlation coefficient between the forecast and corresponding observation +#'Compute the spatial anomaly correlation coefficient between the forecast and corresponding observation #' -#'Calculate the anomaly correlation coefficient for the ensemble mean of each -#'model and the corresponding references over a spatial domain. It can return a -#'forecast time series if the data contain forest time dimension, and also the -#'start date mean if the data contain start date dimension. -#'The domain of interest can be specified by providing the list -#'of longitudes/latitudes (lon/lat) of the data together with the corners -#'of the domain: lonlatbox = c(lonmin, lonmax, latmin, latmax). +#'Calculate the spatial anomaly correlation coefficient (ACC) for the ensemble +#'mean of each model and the corresponding references over a spatial domain. It +#'can return a forecast time series if the data contain forest time dimension, +#'and also the ACC mean over one dimension, e.g., start date dimension. +#'The domain of interest can be specified by providing the list of longitudes/ +#'latitudes of the data together with the corners of the domain: lonlatbox = +#'c(lonmin, lonmax, latmin, latmax). The data will be adjusted to have a spatial +#'mean of zero, then area weighting is applied. The formula is referenced from +#'Wilks (2011; section 7.6.4; https://doi.org/10.1016/B978-0-12-385022-5.00008-7). #' #'@param exp A numeric array of experimental anomalies with named dimensions. -#' It must have at least 'dat_dim' and 'space_dim'. +#' The dimension must have at least 'space_dim'. #'@param obs A numeric array of observational anomalies with named dimensions. -#' It must have the same dimensions as 'exp' except the length of 'dat_dim' +#' The dimension should be the same as 'exp' except the length of 'dat_dim' #' and 'memb_dim'. #'@param dat_dim A character string indicating the name of dataset (nobs/nexp) -#' dimension. The default value is 'dataset'. +#' dimension. The default value is 'dataset'. If there is no dataset +#' dimension, set NULL. #'@param space_dim A character string vector of 2 indicating the name of the #' latitude and longitude dimensions (in order) along which ACC is computed. #' The default value is c('lat', 'lon'). #'@param avg_dim A character string indicating the name of the dimension to be -#' averaged. It must be one of 'time_dim'. The mean ACC is calculated along -#' averaged. If no need to calculate mean ACC, set as NULL. The default value -#' is 'sdate'. +#' averaged, which is usually the time dimension. If no need to calculate mean +#' ACC, set as NULL. The default value is 'sdate'. #'@param memb_dim A character string indicating the name of the member #' dimension. If the data are not ensemble ones, set as NULL. The default #' value is 'member'. -#'@param lat A vector of the latitudes of the exp/obs grids. Only required when -#' the domain of interested is specified. The default value is NULL. +#'@param lat A vector of the latitudes of the exp/obs grids. It is used for +#' area weighting and when the domain of interested 'lonlatbox' is specified. #'@param lon A vector of the longitudes of the exp/obs grids. Only required when -#' the domain of interested is specified. The default value is NULL. +#' the domain of interested 'lonlatbox' is specified. The default value is +#' NULL. #'@param lonlatbox A numeric vector of 4 indicating the corners of the domain of -#' interested: c(lonmin, lonmax, latmin, latmax). Only required when the domain -#' of interested is specified. The default value is NULL. +#' interested: c(lonmin, lonmax, latmin, latmax). The default value is NULL +#' and the whole data will be used. #'@param conf A logical value indicating whether to retrieve the confidence #' intervals or not. The default value is TRUE. #'@param conftype A charater string of "parametric" or "bootstrap". @@ -55,7 +58,8 @@ #'\item{acc}{ #' The ACC with the dimensions c(nexp, nobs, the rest of the dimension except #' space_dim and memb_dim). nexp is the number of experiment (i.e., dat_dim in -#' exp), and nobs is the number of observation (i.e., dat_dim in obs). +#' exp), and nobs is the number of observation (i.e., dat_dim in obs). If +#' dat_dim is NULL, nexp and nobs are omitted. #'} #'\item{conf.lower (if conftype = "parametric") or acc_conf.lower (if #' conftype = "bootstrap")}{ @@ -113,7 +117,8 @@ #'PlotACC(res_bootstrap, startDates) #' } #'@references Joliffe and Stephenson (2012). Forecast Verification: A -#' Practitioner's Guide in Atmospheric Science. Wiley-Blackwell. +#' Practitioner's Guide in Atmospheric Science. Wiley-Blackwell.; +#' Wilks (2011; section 7.6.4; https://doi.org/10.1016/B978-0-12-385022-5.00008-7). #'@import multiApply #'@importFrom abind abind #'@importFrom stats qt qnorm quantile @@ -146,11 +151,14 @@ ACC <- function(exp, obs, dat_dim = 'dataset', space_dim = c('lat', 'lon'), stop("Parameter 'exp' and 'obs' must have same dimension names.") } ## dat_dim - if (!is.character(dat_dim) | length(dat_dim) > 1) { - stop("Parameter 'dat_dim' must be a character string.") - } - if (!dat_dim %in% names(dim(exp)) | !dat_dim %in% names(dim(obs))) { - stop("Parameter 'dat_dim' is not found in 'exp' or 'obs' dimension.") + if (!is.null(dat_dim)) { + if (!is.character(dat_dim) | length(dat_dim) > 1) { + stop("Parameter 'dat_dim' must be a character string or NULL.") + } + if (!dat_dim %in% names(dim(exp)) | !dat_dim %in% names(dim(obs))) { + stop("Parameter 'dat_dim' is not found in 'exp' or 'obs' dimension.", + "Set it as NULL if there is no dataset dimension.") + } } ## space_dim if (!is.character(space_dim) | length(space_dim) != 2) { @@ -178,11 +186,12 @@ ACC <- function(exp, obs, dat_dim = 'dataset', space_dim = c('lat', 'lon'), } } ## lat - if (!is.null(lat)) { - if (!is.numeric(lat) | length(lat) != dim(exp)[space_dim[1]]) { - stop(paste0("Parameter 'lat' must be a numeric vector with the same ", - "length as the latitude dimension of 'exp' and 'obs'.")) - } + if (is.null(lat)) { + stop("Parameter 'lat' cannot be NULL. It is required for area weighting.") + } + if (!is.numeric(lat) | length(lat) != dim(exp)[space_dim[1]]) { + stop(paste0("Parameter 'lat' must be a numeric vector with the same ", + "length as the latitude dimension of 'exp' and 'obs'.")) } ## lon if (!is.null(lon)) { @@ -196,16 +205,22 @@ ACC <- function(exp, obs, dat_dim = 'dataset', space_dim = c('lat', 'lon'), if (!is.numeric(lonlatbox) | length(lonlatbox) != 4) { stop("Parameter 'lonlatbox' must be a numeric vector of 4.") } - } - ## lat, lon, and lonlatbox - if (!is.null(lon) & !is.null(lat) & !is.null(lonlatbox)) { + if (is.null(lon)) { + stop("Parameter 'lat' and 'lon' are required if 'lonlatbox' is specified.") + } select_lonlat <- TRUE - } else if (is.null(lon) & is.null(lat) & is.null(lonlatbox)) { - select_lonlat <- FALSE } else { - stop(paste0("Parameters 'lon', 'lat', and 'lonlatbox' must be used or be ", - "NULL at the same time.")) + select_lonlat <- FALSE } +# ## lat, lon, and lonlatbox +# if (!is.null(lon) & !is.null(lat) & !is.null(lonlatbox)) { +# select_lonlat <- TRUE +# } else if (is.null(lon) & is.null(lat) & is.null(lonlatbox)) { +# select_lonlat <- FALSE +# } else { +# stop(paste0("Parameters 'lon', 'lat', and 'lonlatbox' must be used or be ", +# "NULL at the same time.")) +# } ## conf if (!is.logical(conf) | length(conf) > 1) { stop("Parameter 'conf' must be one logical value.") @@ -219,7 +234,8 @@ ACC <- function(exp, obs, dat_dim = 'dataset', space_dim = c('lat', 'lon'), stop("Parameter 'memb_dim' cannot be NULL when parameter 'conftype' is 'bootstrap'.") } ## conf.lev - if (!is.numeric(conf.lev) | conf.lev < 0 | conf.lev > 1 | length(conf.lev) > 1) { + if (!is.numeric(conf.lev) | any(conf.lev < 0) | any(conf.lev > 1) | + length(conf.lev) > 1) { stop("Parameter 'conf.lev' must be a numeric number between 0 and 1.") } } @@ -229,8 +245,8 @@ ACC <- function(exp, obs, dat_dim = 'dataset', space_dim = c('lat', 'lon'), } ## ncores if (!is.null(ncores)) { - if (!is.numeric(ncores) | ncores %% 1 != 0 | ncores < 0 | - length(ncores) > 1) { + if (!is.numeric(ncores) | any(ncores %% 1 != 0) | any(ncores < 0) | + length(ncores) > 1) { stop("Parameter 'ncores' must be a positive integer.") } } @@ -293,9 +309,7 @@ ACC <- function(exp, obs, dat_dim = 'dataset', space_dim = c('lat', 'lon'), c(space_dim, dat_dim)), fun = .ACC, dat_dim = dat_dim, avg_dim = avg_dim, -#------------NEW-------------- lat = lat, -#------------NEW_END-------------- conftype = conftype, pval = pval, conf = conf, conf.lev = conf.lev, ncores_input = ncores, ncores = ncores) @@ -324,9 +338,7 @@ ACC <- function(exp, obs, dat_dim = 'dataset', space_dim = c('lat', 'lon'), c(space_dim, avg_dim, dat_dim)), fun = .ACC, dat_dim = dat_dim, avg_dim = avg_dim, -#------------NEW-------------- lat = lat, -#------------NEW_END-------------- conftype = conftype, pval = pval, conf = conf, conf.lev = conf.lev, ncores_input = ncores, ncores = ncores) @@ -354,23 +366,27 @@ ACC <- function(exp, obs, dat_dim = 'dataset', space_dim = c('lat', 'lon'), return(res) } -.ACC <- function(exp, obs, dat_dim = 'dataset', #space_dim = c('lat', 'lon'), - avg_dim = 'sdate', #memb_dim = NULL, - lon = NULL, lat = NULL, lonlatbox = NULL, +.ACC <- function(exp, obs, dat_dim = 'dataset', avg_dim = 'sdate', lat, conf = TRUE, conftype = "parametric", conf.lev = 0.95, pval = TRUE, ncores_input = NULL) { - -# if (is.null(avg_dim)) - # exp: [space_dim, dat_exp] - # obs: [space_dim, dat_obs] -# if (!is.null(avg_dim)) - # exp: [space_dim, avg_dim, dat_exp] - # obs: [space_dim, avg_dim, dat_obs] - # .ACC() should use all the spatial points to calculate ACC. It returns [nexp, nobs]. - - nexp <- as.numeric(dim(exp)[length(dim(exp))]) - nobs <- as.numeric(dim(obs)[length(dim(obs))]) + # If dat_dim = NULL, it returns a number. + + # if (is.null(avg_dim)) + ## exp: [lat, lon, (dat_exp)] + ## obs: [lat, lon, (dat_obs)] + # if (!is.null(avg_dim)) + ## exp: [lat, lon, avg_dim, (dat_exp)] + ## obs: [lat, lon, avg_dim, (dat_obs)] + + # Add dat_dim temporarily if dat_dim = NULL + if (is.null(dat_dim)) { + nexp <- 1 + nobs <- 1 + } else { + nexp <- as.numeric(dim(exp)[length(dim(exp))]) + nobs <- as.numeric(dim(obs)[length(dim(obs))]) + } if (is.null(avg_dim)) { acc <- array(dim = c(nexp = nexp, nobs = nobs)) @@ -378,34 +394,67 @@ ACC <- function(exp, obs, dat_dim = 'dataset', space_dim = c('lat', 'lon'), if (conf) { conf.upper <- array(dim = c(nexp = nexp, nobs = nobs)) conf.lower <- array(dim = c(nexp = nexp, nobs = nobs)) - if (conftype == 'bootstrap') { - ndraw <- 100 - acc_draw <- array(dim = c(nexp = nexp, nobs = nobs, ndraw)) - } } } else { - acc <- array(dim = c(nexp = nexp, nobs = nobs, dim(exp)[length(dim(exp)) - 1])) - names(dim(acc))[3] <- avg_dim - macc <- array(dim = c(nexp = nexp, nobs = nobs)) - if (pval) p.val <- array(dim = c(nexp = nexp, nobs = nobs, dim(exp)[length(dim(exp)) - 1])) + acc <- array(dim = c(nexp = nexp, nobs = nobs, dim(exp)[avg_dim])) + names(dim(acc))[3] <- avg_dim + macc <- array(dim = c(nexp = nexp, nobs = nobs)) + if (pval) p.val <- array(dim = c(nexp = nexp, nobs = nobs, dim(exp)[avg_dim])) if (conf) { - conf.upper <- array(dim = c(nexp = nexp, nobs = nobs, dim(exp)[length(dim(exp)) - 1])) - conf.lower <- array(dim = c(nexp = nexp, nobs = nobs, dim(exp)[length(dim(exp)) - 1])) - if (conftype == 'bootstrap') { - ndraw <- 100 - acc_draw <- array(dim = c(nexp = nexp, nobs = nobs, dim(exp)[length(dim(exp)) - 1], ndraw)) - macc_draw <- array(dim = c(nexp = nexp, nobs = nobs, ndraw)) - } + conf.upper <- array(dim = c(nexp = nexp, nobs = nobs, dim(exp)[avg_dim])) + conf.lower <- array(dim = c(nexp = nexp, nobs = nobs, dim(exp)[avg_dim])) } } + # centralize & area weighted + ## spatial centralization for each [avg_dim, dat] + dim_exp <- dim(exp) + dim_obs <- dim(obs) + wt <- cos(lat * pi/180) + wt <- rep(wt, times = prod(dim_exp[2:length(dim_exp)])) + + if (is.null(avg_dim)) { # [lat, lon, (dat)] + if (is.null(dat_dim)) { #[lat, lon] + # turn exp and obs into vector, first latitudes and then longitudes + exp <- as.vector(exp) + obs <- as.vector(obs) + exp <- array(sqrt(wt) * (exp - mean(exp, na.rm = TRUE)), dim = dim_exp) + obs <- array(sqrt(wt) * (obs - mean(obs, na.rm = TRUE)), dim = dim_obs) + } else { #[lat, lon, dat] + # exp + exp <- array(exp, dim = c(prod(dim_exp[1:2]), dim_exp[3])) + mean_exp <- rep(apply(exp, 2, mean, na.rm = TRUE), each = prod(dim_exp[1:2])) + exp <- array(sqrt(wt) * (as.vector(exp) - mean_exp), dim = dim_exp) + # obs + obs <- array(obs, dim = c(prod(dim_obs[1:2]), dim_obs[3])) + mean_obs <- rep(apply(obs, 2, mean, na.rm = TRUE), each = prod(dim_obs[1:2])) + obs <- array(sqrt(wt) * (as.vector(obs) - mean_obs), dim = dim_obs) + } + } else { #[lat, lon, avg_dim, (dat)] + # exp + exp <- array(exp, dim = c(prod(dim_exp[1:2]), dim_exp[3:length(dim_exp)])) + mean_exp <- apply(exp, 2:length(dim(exp)), mean, na.rm = TRUE) # [avg_dim, (dat)] + mean_exp <- rep(as.vector(mean_exp), each = prod(dim_exp[1:2])) + exp <- array(sqrt(wt) * (as.vector(exp) - mean_exp), dim = dim_exp) + # obs + obs <- array(obs, dim = c(prod(dim_obs[1:2]), dim_obs[3:length(dim_obs)])) + mean_obs <- apply(obs, 2:length(dim(obs)), mean, na.rm = TRUE) # [avg_dim, (dat)] + mean_obs <- rep(as.vector(mean_obs), each = prod(dim_obs[1:2])) + obs <- array(sqrt(wt) * (as.vector(obs) - mean_obs), dim = dim_obs) + } + # Per-paired exp and obs. NAs should be in the same position in both exp and obs for (iobs in 1:nobs) { for (iexp in 1:nexp) { - exp_sub <- ClimProjDiags::Subset(exp, dat_dim, iexp, drop = 'selected') - obs_sub <- ClimProjDiags::Subset(obs, dat_dim, iobs, drop = 'selected') - # dim: [space_dim] + if (!is.null(dat_dim)) { + exp_sub <- ClimProjDiags::Subset(exp, dat_dim, iexp, drop = 'selected') + obs_sub <- ClimProjDiags::Subset(obs, dat_dim, iobs, drop = 'selected') + } else { + exp_sub <- exp + obs_sub <- obs + } + # dim: [lat, lon, (avg_dim)] # Variance(iexp) should not take into account any point # that is not available in iobs and therefore not accounted for @@ -413,29 +462,26 @@ ACC <- function(exp, obs, dat_dim = 'dataset', space_dim = c('lat', 'lon'), exp_sub[is.na(obs_sub)] <- NA obs_sub[is.na(exp_sub)] <- NA + # area weights + wt <- cos(lat * pi/180) + if (is.null(avg_dim)) { # ACC -#-----------NEW-------------- - # area weighted - wt <- cos(lat * pi/180) - wt <- rep(wt, as.numeric(dim(exp_sub)[2])) ## dim(exp): [lat, lon] - exp_sub <- as.vector(t(exp_sub)) ## first longitudes and then latitudes - obs_sub <- as.vector(t(obs_sub)) ## first longitudes and then latitudes - exp_sub <- sqrt(wt) * (exp_sub - mean(exp_sub)) - obs_sub <- sqrt(wt) * (obs_sub - mean(obs_sub)) -#----------NEW_END----------- - top <- sum(exp_sub*obs_sub, na.rm = TRUE) #a number + top <- sum(exp_sub * obs_sub, na.rm = TRUE) #a number bottom <- sqrt(sum(exp_sub^2, na.rm = TRUE) * sum(obs_sub^2, na.rm = TRUE)) acc[iexp, iobs] <- top/bottom #a number # handle bottom = 0 if (is.infinite(acc[iexp, iobs])) acc[iexp, iobs] <- NA + # pval and conf if (pval | conf) { if (conftype == "parametric") { - # calculate effective sample size along space_dim - # combine space_dim into one dim first - obs_tmp <- array(obs_sub, dim = c(space = length(obs_sub))) - eno <- Eno(obs_tmp, 'space', ncores = ncores_input) # a number + # calculate effective sample size +# # combine space_dim into one dim first +# obs_tmp <- array(obs_sub, dim = c(space = length(obs_sub))) +# eno <- Eno(obs_tmp, 'space', ncores = ncores_input) # a number + eno <- .Eno(as.vector(obs_sub), na.action = na.pass) + if (pval) { t <- qt(conf.lev, eno - 2) # a number p.val[iexp, iobs] <- sqrt(t^2 / (t^2 + eno - 2)) @@ -448,18 +494,22 @@ ACC <- function(exp, obs, dat_dim = 'dataset', space_dim = c('lat', 'lon'), } } else { #avg_dim is not NULL + # exp_sub and obs_sub: [lat, lon, avg_dim] + # MACC - top <- sum(exp_sub*obs_sub, na.rm = TRUE) #a number +# ## area weighted + top <- sum(exp_sub * obs_sub, na.rm = TRUE) #a number bottom <- sqrt(sum(exp_sub^2, na.rm = TRUE) * sum(obs_sub^2, na.rm = TRUE)) macc[iexp, iobs] <- top/bottom #a number + # handle bottom = 0 if (is.infinite(macc[iexp, iobs])) macc[iexp, iobs] <- NA + # ACC - for (i in 1:dim(acc)[3]) { #NOTE: use sapply!!! - exp_sub_i <- ClimProjDiags::Subset(exp_sub, avg_dim, i, drop = 'selected') - obs_sub_i <- ClimProjDiags::Subset(obs_sub, avg_dim, i, drop = 'selected') - #dim: [space_dim] - top <- sum(exp_sub_i*obs_sub_i, na.rm = TRUE) #a number + for (i in 1:dim(acc)[3]) { + exp_sub_i <- exp_sub[, , i] + obs_sub_i <- obs_sub[, , i] + top <- sum(exp_sub_i * obs_sub_i, na.rm = TRUE) #a number bottom <- sqrt(sum(exp_sub_i^2, na.rm = TRUE) * sum(obs_sub_i^2, na.rm = TRUE)) acc[iexp, iobs, i] <- top/bottom #a number # handle bottom = 0 @@ -471,9 +521,10 @@ ACC <- function(exp, obs, dat_dim = 'dataset', space_dim = c('lat', 'lon'), if (conftype == "parametric") { # calculate effective sample size along space_dim # combine space_dim into one dim first - obs_tmp <- array(obs_sub, dim = c(space = prod(dim(obs_sub)[-length(dim(obs_sub))]), - dim(obs_sub)[length(dim(obs_sub))])) - eno <- Eno(obs_tmp, 'space', ncores = ncores_input) # a vector of avg_dim + obs_tmp <- array(obs_sub, + dim = c(space = prod(dim(obs_sub)[1:2]), + dim(obs_sub)[3])) + eno <- apply(obs_tmp, 2, .Eno, na.action = na.pass) # a vector of avg_dim if (pval) { t <- qt(conf.lev, eno - 2) # a vector of avg_dim p.val[iexp, iobs, ] <- sqrt(t^2 / (t^2 + eno - 2)) @@ -491,8 +542,29 @@ ACC <- function(exp, obs, dat_dim = 'dataset', space_dim = c('lat', 'lon'), } #------------------------------------------------ - - + # Remove nexp and nobs if dat_dim = NULL + if (is.null(dat_dim)) { + if (is.null(avg_dim)) { + acc <- as.vector(acc) + if (conf) { + conf.lower <- as.vector(conf.lower) + conf.upper <- as.vector(conf.upper) + } + if (pval) { + p.val <- as.vector(p.val) + } + } else { + dim(acc) <- dim(acc)[3:length(dim(acc))] + macc <- as.vector(macc) + if (conf) { + dim(conf.lower) <- dim(conf.lower)[3:length(dim(conf.lower))] + dim(conf.upper) <- dim(conf.upper)[3:length(dim(conf.upper))] + } + if (pval) { + dim(p.val) <- dim(p.val)[3:length(dim(p.val))] + } + } + } # Return output if (is.null(avg_dim)) { @@ -524,9 +596,8 @@ ACC <- function(exp, obs, dat_dim = 'dataset', space_dim = c('lat', 'lon'), } -.ACC_bootstrap <- function(exp, obs, dat_dim = 'dataset', #space_dim = c('lat', 'lon'), - avg_dim = 'sdate', memb_dim = NULL, - lon = NULL, lat = NULL, lonlatbox = NULL, +.ACC_bootstrap <- function(exp, obs, dat_dim = 'dataset', + avg_dim = 'sdate', memb_dim = NULL, lat = NULL, conf = TRUE, conftype = "parametric", conf.lev = 0.95, pval = TRUE, ncores_input = NULL) { # if (is.null(avg_dim)) diff --git a/man/ACC.Rd b/man/ACC.Rd index d1389fd..a9039d6 100644 --- a/man/ACC.Rd +++ b/man/ACC.Rd @@ -2,7 +2,7 @@ % Please edit documentation in R/ACC.R \name{ACC} \alias{ACC} -\title{Compute the anomaly correlation coefficient between the forecast and corresponding observation} +\title{Compute the spatial anomaly correlation coefficient between the forecast and corresponding observation} \usage{ ACC( exp, @@ -23,37 +23,38 @@ ACC( } \arguments{ \item{exp}{A numeric array of experimental anomalies with named dimensions. -It must have at least 'dat_dim' and 'space_dim'.} +The dimension must have at least 'space_dim'.} \item{obs}{A numeric array of observational anomalies with named dimensions. -It must have the same dimensions as 'exp' except the length of 'dat_dim' +The dimension should be the same as 'exp' except the length of 'dat_dim' and 'memb_dim'.} \item{dat_dim}{A character string indicating the name of dataset (nobs/nexp) -dimension. The default value is 'dataset'.} +dimension. The default value is 'dataset'. If there is no dataset +dimension, set NULL.} \item{space_dim}{A character string vector of 2 indicating the name of the latitude and longitude dimensions (in order) along which ACC is computed. The default value is c('lat', 'lon').} \item{avg_dim}{A character string indicating the name of the dimension to be -averaged. It must be one of 'time_dim'. The mean ACC is calculated along -averaged. If no need to calculate mean ACC, set as NULL. The default value -is 'sdate'.} +averaged, which is usually the time dimension. If no need to calculate mean +ACC, set as NULL. The default value is 'sdate'.} \item{memb_dim}{A character string indicating the name of the member dimension. If the data are not ensemble ones, set as NULL. The default value is 'member'.} -\item{lat}{A vector of the latitudes of the exp/obs grids. Only required when -the domain of interested is specified. The default value is NULL.} +\item{lat}{A vector of the latitudes of the exp/obs grids. It is used for +area weighting and when the domain of interested 'lonlatbox' is specified.} \item{lon}{A vector of the longitudes of the exp/obs grids. Only required when -the domain of interested is specified. The default value is NULL.} +the domain of interested 'lonlatbox' is specified. The default value is +NULL.} \item{lonlatbox}{A numeric vector of 4 indicating the corners of the domain of -interested: c(lonmin, lonmax, latmin, latmax). Only required when the domain -of interested is specified. The default value is NULL.} +interested: c(lonmin, lonmax, latmin, latmax). The default value is NULL +and the whole data will be used.} \item{conf}{A logical value indicating whether to retrieve the confidence intervals or not. The default value is TRUE.} @@ -82,7 +83,8 @@ A list containing the numeric arrays:\cr \item{acc}{ The ACC with the dimensions c(nexp, nobs, the rest of the dimension except space_dim and memb_dim). nexp is the number of experiment (i.e., dat_dim in - exp), and nobs is the number of observation (i.e., dat_dim in obs). + exp), and nobs is the number of observation (i.e., dat_dim in obs). If + dat_dim is NULL, nexp and nobs are omitted. } \item{conf.lower (if conftype = "parametric") or acc_conf.lower (if conftype = "bootstrap")}{ @@ -113,13 +115,15 @@ A list containing the numeric arrays:\cr } } \description{ -Calculate the anomaly correlation coefficient for the ensemble mean of each -model and the corresponding references over a spatial domain. It can return a -forecast time series if the data contain forest time dimension, and also the -start date mean if the data contain start date dimension. -The domain of interest can be specified by providing the list -of longitudes/latitudes (lon/lat) of the data together with the corners -of the domain: lonlatbox = c(lonmin, lonmax, latmin, latmax). +Calculate the spatial anomaly correlation coefficient (ACC) for the ensemble +mean of each model and the corresponding references over a spatial domain. It +can return a forecast time series if the data contain forest time dimension, +and also the ACC mean over one dimension, e.g., start date dimension. +The domain of interest can be specified by providing the list of longitudes/ +latitudes of the data together with the corners of the domain: lonlatbox = +c(lonmin, lonmax, latmin, latmax). The data will be adjusted to have a spatial +mean of zero, then area weighting is applied. The formula is referenced from +Wilks (2011; section 7.6.4; https://doi.org/10.1016/B978-0-12-385022-5.00008-7). } \examples{ \dontshow{ @@ -151,5 +155,6 @@ PlotACC(res_bootstrap, startDates) } \references{ Joliffe and Stephenson (2012). Forecast Verification: A - Practitioner's Guide in Atmospheric Science. Wiley-Blackwell. + Practitioner's Guide in Atmospheric Science. Wiley-Blackwell.; + Wilks (2011; section 7.6.4; https://doi.org/10.1016/B978-0-12-385022-5.00008-7). } -- GitLab From 7f0cd2a474c9992774bafe161824b23f9eecd29a Mon Sep 17 00:00:00 2001 From: aho Date: Thu, 16 Dec 2021 15:04:02 +0100 Subject: [PATCH 03/83] Delete unneeded lines and simplified code --- R/ACC.R | 142 ++++++++++++++++++----------------------------------- man/ACC.Rd | 4 +- 2 files changed, 51 insertions(+), 95 deletions(-) diff --git a/R/ACC.R b/R/ACC.R index 340dd77..55220d7 100644 --- a/R/ACC.R +++ b/R/ACC.R @@ -105,8 +105,8 @@ #'clim <- Clim(sampleData$mod, sampleData$obs) #'ano_exp <- Ano(sampleData$mod, clim$clim_exp) #'ano_obs <- Ano(sampleData$obs, clim$clim_obs) -#'acc <- ACC(ano_exp, ano_obs) -#'acc_bootstrap <- ACC(ano_exp, ano_obs, conftype = 'bootstrap') +#'acc <- ACC(ano_exp, ano_obs, lat = sampleData$lat) +#'acc_bootstrap <- ACC(ano_exp, ano_obs, conftype = 'bootstrap', lat = sampleData$lat) #'# Combine acc results for PlotACC #'res <- array(c(acc$conf.lower, acc$acc, acc$conf.upper, acc$p.val), #' dim = c(dim(acc$acc), 4)) @@ -212,15 +212,6 @@ ACC <- function(exp, obs, dat_dim = 'dataset', space_dim = c('lat', 'lon'), } else { select_lonlat <- FALSE } -# ## lat, lon, and lonlatbox -# if (!is.null(lon) & !is.null(lat) & !is.null(lonlatbox)) { -# select_lonlat <- TRUE -# } else if (is.null(lon) & is.null(lat) & is.null(lonlatbox)) { -# select_lonlat <- FALSE -# } else { -# stop(paste0("Parameters 'lon', 'lat', and 'lonlatbox' must be used or be ", -# "NULL at the same time.")) -# } ## conf if (!is.logical(conf) | length(conf) > 1) { stop("Parameter 'conf' must be one logical value.") @@ -303,72 +294,50 @@ ACC <- function(exp, obs, dat_dim = 'dataset', space_dim = c('lat', 'lon'), obs <- MeanDims(obs, memb_dim, na.rm = TRUE) } - if (is.null(avg_dim)) { - res <- Apply(list(exp, obs), - target_dims = list(c(space_dim, dat_dim), - c(space_dim, dat_dim)), - fun = .ACC, - dat_dim = dat_dim, avg_dim = avg_dim, - lat = lat, - conftype = conftype, pval = pval, conf = conf, conf.lev = conf.lev, - ncores_input = ncores, - ncores = ncores) - - if (conftype == 'bootstrap') { - res_conf <- Apply(list(exp_ori, obs_ori), - target_dims = list(c(memb_dim, dat_dim, space_dim), - c(memb_dim, dat_dim, space_dim)), - fun = .ACC_bootstrap, - dat_dim = dat_dim, memb_dim = memb_dim, avg_dim = avg_dim, - conftype = conftype, pval = pval, conf = conf, conf.lev = conf.lev, - ncores_input = ncores, - ncores = ncores) - #NOTE: pval? - res <- list(acc = res$acc, - acc_conf.lower = res_conf$acc_conf.lower, - acc_conf.upper = res_conf$acc_conf.upper, - macc = res$macc, - macc_conf.lower = res_conf$macc_conf.lower, - macc_conf.upper = res_conf$macc_conf.upper) - } + if (is.null(avg_dim)) { + target_dims <- list(c(space_dim, dat_dim), c(space_dim, dat_dim)) } else { - res <- Apply(list(exp, obs), - target_dims = list(c(space_dim, avg_dim, dat_dim), - c(space_dim, avg_dim, dat_dim)), + target_dims <- list(c(space_dim, avg_dim, dat_dim), c(space_dim, avg_dim, dat_dim)) + } + res <- Apply(list(exp, obs), + target_dims = target_dims, fun = .ACC, dat_dim = dat_dim, avg_dim = avg_dim, lat = lat, conftype = conftype, pval = pval, conf = conf, conf.lev = conf.lev, - ncores_input = ncores, ncores = ncores) - if (conftype == 'bootstrap') { - res_conf <- Apply(list(exp_ori, obs_ori), - target_dims = list(c(memb_dim, dat_dim, avg_dim, space_dim), - c(memb_dim, dat_dim, avg_dim, space_dim)), - fun = .ACC_bootstrap, - dat_dim = dat_dim, memb_dim = memb_dim, avg_dim = avg_dim, - conftype = conftype, pval = pval, conf = conf, conf.lev = conf.lev, - ncores_input = ncores, - ncores = ncores) - res <- list(acc = res$acc, - acc_conf.lower = res_conf$acc_conf.lower, - acc_conf.upper = res_conf$acc_conf.upper, - macc = res$macc, - macc_conf.lower = res_conf$macc_conf.lower, - macc_conf.upper = res_conf$macc_conf.upper) - + # If bootstrap, calculate confidence level + if (conftype == 'bootstrap') { + if (is.null(avg_dim)) { + target_dims_bs <- list(c(memb_dim, dat_dim, space_dim), + c(memb_dim, dat_dim, space_dim)) + } else { + target_dims_bs <- list(c(memb_dim, dat_dim, avg_dim, space_dim), + c(memb_dim, dat_dim, avg_dim, space_dim)) } - - } - - return(res) + res_conf <- Apply(list(exp_ori, obs_ori), + target_dims = target_dims_bs, + fun = .ACC_bootstrap, + dat_dim = dat_dim, memb_dim = memb_dim, avg_dim = avg_dim, + lat = lat, + conftype = conftype, pval = pval, conf = conf, conf.lev = conf.lev, + ncores = ncores) + #NOTE: pval? + res <- list(acc = res$acc, + acc_conf.lower = res_conf$acc_conf.lower, + acc_conf.upper = res_conf$acc_conf.upper, + macc = res$macc, + macc_conf.lower = res_conf$macc_conf.lower, + macc_conf.upper = res_conf$macc_conf.upper) + } + + return(res) } .ACC <- function(exp, obs, dat_dim = 'dataset', avg_dim = 'sdate', lat, - conf = TRUE, conftype = "parametric", conf.lev = 0.95, pval = TRUE, - ncores_input = NULL) { + conf = TRUE, conftype = "parametric", conf.lev = 0.95, pval = TRUE) { # .ACC() should use all the spatial points to calculate ACC. It returns [nexp, nobs]. # If dat_dim = NULL, it returns a number. @@ -414,24 +383,13 @@ ACC <- function(exp, obs, dat_dim = 'dataset', space_dim = c('lat', 'lon'), wt <- cos(lat * pi/180) wt <- rep(wt, times = prod(dim_exp[2:length(dim_exp)])) - if (is.null(avg_dim)) { # [lat, lon, (dat)] - if (is.null(dat_dim)) { #[lat, lon] - # turn exp and obs into vector, first latitudes and then longitudes - exp <- as.vector(exp) - obs <- as.vector(obs) - exp <- array(sqrt(wt) * (exp - mean(exp, na.rm = TRUE)), dim = dim_exp) - obs <- array(sqrt(wt) * (obs - mean(obs, na.rm = TRUE)), dim = dim_obs) - } else { #[lat, lon, dat] - # exp - exp <- array(exp, dim = c(prod(dim_exp[1:2]), dim_exp[3])) - mean_exp <- rep(apply(exp, 2, mean, na.rm = TRUE), each = prod(dim_exp[1:2])) - exp <- array(sqrt(wt) * (as.vector(exp) - mean_exp), dim = dim_exp) - # obs - obs <- array(obs, dim = c(prod(dim_obs[1:2]), dim_obs[3])) - mean_obs <- rep(apply(obs, 2, mean, na.rm = TRUE), each = prod(dim_obs[1:2])) - obs <- array(sqrt(wt) * (as.vector(obs) - mean_obs), dim = dim_obs) - } - } else { #[lat, lon, avg_dim, (dat)] + if (is.null(avg_dim) & is.null(dat_dim)) { #[lat, lon] + # turn exp and obs into vector, first latitudes and then longitudes + exp <- as.vector(exp) + obs <- as.vector(obs) + exp <- array(sqrt(wt) * (exp - mean(exp, na.rm = TRUE)), dim = dim_exp) + obs <- array(sqrt(wt) * (obs - mean(obs, na.rm = TRUE)), dim = dim_obs) + } else { # [lat, lon, dat], [lat, lon, avg_dim], or [lat, lon, avg_dim, dat] # exp exp <- array(exp, dim = c(prod(dim_exp[1:2]), dim_exp[3:length(dim_exp)])) mean_exp <- apply(exp, 2:length(dim(exp)), mean, na.rm = TRUE) # [avg_dim, (dat)] @@ -477,9 +435,6 @@ ACC <- function(exp, obs, dat_dim = 'dataset', space_dim = c('lat', 'lon'), if (pval | conf) { if (conftype == "parametric") { # calculate effective sample size -# # combine space_dim into one dim first -# obs_tmp <- array(obs_sub, dim = c(space = length(obs_sub))) -# eno <- Eno(obs_tmp, 'space', ncores = ncores_input) # a number eno <- .Eno(as.vector(obs_sub), na.action = na.pass) if (pval) { @@ -497,7 +452,6 @@ ACC <- function(exp, obs, dat_dim = 'dataset', space_dim = c('lat', 'lon'), # exp_sub and obs_sub: [lat, lon, avg_dim] # MACC -# ## area weighted top <- sum(exp_sub * obs_sub, na.rm = TRUE) #a number bottom <- sqrt(sum(exp_sub^2, na.rm = TRUE) * sum(obs_sub^2, na.rm = TRUE)) macc[iexp, iobs] <- top/bottom #a number @@ -530,8 +484,10 @@ ACC <- function(exp, obs, dat_dim = 'dataset', space_dim = c('lat', 'lon'), p.val[iexp, iobs, ] <- sqrt(t^2 / (t^2 + eno - 2)) } if (conf) { - conf.upper[iexp, iobs, ] <- tanh(atanh(acc[iexp, iobs, ]) + qnorm(1 - (1 - conf.lev) / 2) / sqrt(eno - 3)) - conf.lower[iexp, iobs, ] <- tanh(atanh(acc[iexp, iobs, ]) + qnorm((1 - conf.lev) / 2) / sqrt(eno - 3)) + conf.upper[iexp, iobs, ] <- tanh(atanh(acc[iexp, iobs, ]) + + qnorm(1 - (1 - conf.lev) / 2) / sqrt(eno - 3)) + conf.lower[iexp, iobs, ] <- tanh(atanh(acc[iexp, iobs, ]) + + qnorm((1 - conf.lev) / 2) / sqrt(eno - 3)) } } } @@ -597,9 +553,9 @@ ACC <- function(exp, obs, dat_dim = 'dataset', space_dim = c('lat', 'lon'), .ACC_bootstrap <- function(exp, obs, dat_dim = 'dataset', - avg_dim = 'sdate', memb_dim = NULL, lat = NULL, - conf = TRUE, conftype = "parametric", conf.lev = 0.95, pval = TRUE, - ncores_input = NULL) { + avg_dim = 'sdate', memb_dim = NULL, lat, + conf = TRUE, conftype = "parametric", conf.lev = 0.95, + pval = TRUE) { # if (is.null(avg_dim)) # exp: [memb_exp, dat_exp, space_dim] # obs: [memb_obs, dat_obs, space_dim] @@ -657,7 +613,7 @@ ACC <- function(exp, obs, dat_dim = 'dataset', space_dim = c('lat', 'lon'), #calculate the ACC of the randomized field tmpACC <- .ACC(drawexp, drawobs, conf = FALSE, pval = FALSE, avg_dim = avg_dim, - ncores_input = ncores_input) + lat = lat) if (is.null(avg_dim)) { acc_draw[, , jdraw] <- tmpACC$acc } else { diff --git a/man/ACC.Rd b/man/ACC.Rd index a9039d6..979afcb 100644 --- a/man/ACC.Rd +++ b/man/ACC.Rd @@ -141,8 +141,8 @@ sampleData$obs <- Season(sampleData$obs, monini = 11, moninf = 12, monsup = 2) clim <- Clim(sampleData$mod, sampleData$obs) ano_exp <- Ano(sampleData$mod, clim$clim_exp) ano_obs <- Ano(sampleData$obs, clim$clim_obs) -acc <- ACC(ano_exp, ano_obs) -acc_bootstrap <- ACC(ano_exp, ano_obs, conftype = 'bootstrap') +acc <- ACC(ano_exp, ano_obs, lat = sampleData$lat) +acc_bootstrap <- ACC(ano_exp, ano_obs, conftype = 'bootstrap', lat = sampleData$lat) # Combine acc results for PlotACC res <- array(c(acc$conf.lower, acc$acc, acc$conf.upper, acc$p.val), dim = c(dim(acc$acc), 4)) -- GitLab From 560ee5c8a3b548c62eab771a630c659805e32c8d Mon Sep 17 00:00:00 2001 From: aho Date: Thu, 16 Dec 2021 15:28:24 +0100 Subject: [PATCH 04/83] Small docfix --- R/ACC.R | 6 ++++-- man/ACC.Rd | 3 ++- 2 files changed, 6 insertions(+), 3 deletions(-) diff --git a/R/ACC.R b/R/ACC.R index 55220d7..ab8185c 100644 --- a/R/ACC.R +++ b/R/ACC.R @@ -78,7 +78,8 @@ #'\item{macc}{ #' The mean anomaly correlation coefficient with dimensions #' c(nexp, nobs, the rest of the dimension except space_dim, memb_dim, and -#' avg_dim). Only present if 'avg_dim' is not NULL. +#' avg_dim). Only present if 'avg_dim' is not NULL. If dat_dim is NULL, nexp +#' and nobs are omitted. #'} #'\item{macc_conf.lower}{ #' The lower confidence interval of MACC with the same dimensions as MACC. @@ -182,7 +183,8 @@ ACC <- function(exp, obs, dat_dim = 'dataset', space_dim = c('lat', 'lon'), stop("Parameter 'memb_dim' must be a character string.") } if (!memb_dim %in% names(dim(exp)) | !memb_dim %in% names(dim(obs))) { - stop("Parameter 'memb_dim' is not found in 'exp' or 'obs' dimension.") + stop("Parameter 'memb_dim' is not found in 'exp' or 'obs' dimension.", + "Set it as NULL if there is no member dimension.") } } ## lat diff --git a/man/ACC.Rd b/man/ACC.Rd index 979afcb..5bd17f6 100644 --- a/man/ACC.Rd +++ b/man/ACC.Rd @@ -103,7 +103,8 @@ A list containing the numeric arrays:\cr \item{macc}{ The mean anomaly correlation coefficient with dimensions c(nexp, nobs, the rest of the dimension except space_dim, memb_dim, and - avg_dim). Only present if 'avg_dim' is not NULL. + avg_dim). Only present if 'avg_dim' is not NULL. If dat_dim is NULL, nexp + and nobs are omitted. } \item{macc_conf.lower}{ The lower confidence interval of MACC with the same dimensions as MACC. -- GitLab From 75cf9835164545d02a2424afcdb665529db3bcb4 Mon Sep 17 00:00:00 2001 From: aho Date: Thu, 16 Dec 2021 15:32:32 +0100 Subject: [PATCH 05/83] Remove unneeded line --- R/ACC.R | 3 --- 1 file changed, 3 deletions(-) diff --git a/R/ACC.R b/R/ACC.R index ab8185c..113d7ac 100644 --- a/R/ACC.R +++ b/R/ACC.R @@ -422,9 +422,6 @@ ACC <- function(exp, obs, dat_dim = 'dataset', space_dim = c('lat', 'lon'), exp_sub[is.na(obs_sub)] <- NA obs_sub[is.na(exp_sub)] <- NA - # area weights - wt <- cos(lat * pi/180) - if (is.null(avg_dim)) { # ACC top <- sum(exp_sub * obs_sub, na.rm = TRUE) #a number -- GitLab From 257c6ec62705749cb1d579fa5cabdf06ce2cfb65 Mon Sep 17 00:00:00 2001 From: aho Date: Mon, 3 Jan 2022 18:23:50 +0100 Subject: [PATCH 06/83] Change space_dim to lat_dim and lon_dim --- R/ACC.R | 84 ++++++++++++++++----------- man/ACC.Rd | 18 ++++-- tests/testthat/test-ACC.R | 117 ++++++++++++++++++++++++++------------ 3 files changed, 144 insertions(+), 75 deletions(-) diff --git a/R/ACC.R b/R/ACC.R index 113d7ac..ec71d03 100644 --- a/R/ACC.R +++ b/R/ACC.R @@ -11,16 +11,19 @@ #'Wilks (2011; section 7.6.4; https://doi.org/10.1016/B978-0-12-385022-5.00008-7). #' #'@param exp A numeric array of experimental anomalies with named dimensions. -#' The dimension must have at least 'space_dim'. +#' The dimension must have at least 'lat_dim' and 'lon_dim'. #'@param obs A numeric array of observational anomalies with named dimensions. #' The dimension should be the same as 'exp' except the length of 'dat_dim' #' and 'memb_dim'. #'@param dat_dim A character string indicating the name of dataset (nobs/nexp) #' dimension. The default value is 'dataset'. If there is no dataset #' dimension, set NULL. -#'@param space_dim A character string vector of 2 indicating the name of the -#' latitude and longitude dimensions (in order) along which ACC is computed. -#' The default value is c('lat', 'lon'). +#'@param lat_dim A character string indicating the name of the latitude +#' dimension of 'exp' and 'obs' along which ACC is computed. The default value +#' is 'lat'. +#'@param lon_dim A character string indicating the name of the longitude +#' dimension of 'exp' and 'obs' along which ACC is computed. The default value +#' is 'lon'. #'@param avg_dim A character string indicating the name of the dimension to be #' averaged, which is usually the time dimension. If no need to calculate mean #' ACC, set as NULL. The default value is 'sdate'. @@ -57,7 +60,7 @@ #'A list containing the numeric arrays:\cr #'\item{acc}{ #' The ACC with the dimensions c(nexp, nobs, the rest of the dimension except -#' space_dim and memb_dim). nexp is the number of experiment (i.e., dat_dim in +#' lat_dim, lon_dim and memb_dim). nexp is the number of experiment (i.e., dat_dim in #' exp), and nobs is the number of observation (i.e., dat_dim in obs). If #' dat_dim is NULL, nexp and nobs are omitted. #'} @@ -77,7 +80,7 @@ #'} #'\item{macc}{ #' The mean anomaly correlation coefficient with dimensions -#' c(nexp, nobs, the rest of the dimension except space_dim, memb_dim, and +#' c(nexp, nobs, the rest of the dimension except lat_dim, lon_dim, memb_dim, and #' avg_dim). Only present if 'avg_dim' is not NULL. If dat_dim is NULL, nexp #' and nobs are omitted. #'} @@ -125,8 +128,8 @@ #'@importFrom stats qt qnorm quantile #'@importFrom ClimProjDiags Subset #'@export -ACC <- function(exp, obs, dat_dim = 'dataset', space_dim = c('lat', 'lon'), - avg_dim = 'sdate', memb_dim = 'member', +ACC <- function(exp, obs, dat_dim = 'dataset', lat_dim = 'lat', lon_dim = 'lon', + space_dim = c('lat', 'lon'), avg_dim = 'sdate', memb_dim = 'member', lat = NULL, lon = NULL, lonlatbox = NULL, conf = TRUE, conftype = "parametric", conf.lev = 0.95, pval = TRUE, ncores = NULL) { @@ -141,7 +144,7 @@ ACC <- function(exp, obs, dat_dim = 'dataset', space_dim = c('lat', 'lon'), } if (is.null(dim(exp)) | is.null(dim(obs))) { stop(paste0("Parameter 'exp' and 'obs' must have at least dimensions ", - "dat_dim and space_dim.")) + "lat_dim and lon_dim.")) } if(any(is.null(names(dim(exp))))| any(nchar(names(dim(exp))) == 0) | any(is.null(names(dim(obs))))| any(nchar(names(dim(obs))) == 0)) { @@ -161,12 +164,25 @@ ACC <- function(exp, obs, dat_dim = 'dataset', space_dim = c('lat', 'lon'), "Set it as NULL if there is no dataset dimension.") } } - ## space_dim - if (!is.character(space_dim) | length(space_dim) != 2) { - stop("Parameter 'space_dim' must be a character vector of 2.") + ## space_dim (deprecated) + if (!missing("space_dim")) { + warning("Parameter 'space_dim' is deprecated. Use 'lat_dim' and 'lon_dim' instead.") + lat_dim <- space_dim[1] + lon_dim <- space_dim[2] } - if (any(!space_dim %in% names(dim(exp))) | any(!space_dim %in% names(dim(obs)))) { - stop("Parameter 'space_dim' is not found in 'exp' or 'obs' dimension.") + ## lat_dim + if (!is.character(lat_dim) | length(lat_dim) != 1) { + stop("Parameter 'lat_dim' must be a character string.") + } + if (!lat_dim %in% names(dim(exp)) | !lat_dim %in% names(dim(obs))) { + stop("Parameter 'lat_dim' is not found in 'exp' or 'obs' dimension.") + } + ## lon_dim + if (!is.character(lon_dim) | length(lon_dim) != 1) { + stop("Parameter 'lon_dim' must be a character string.") + } + if (!lon_dim %in% names(dim(exp)) | !lon_dim %in% names(dim(obs))) { + stop("Parameter 'lon_dim' is not found in 'exp' or 'obs' dimension.") } ## avg_dim if (!is.null(avg_dim)) { @@ -191,13 +207,13 @@ ACC <- function(exp, obs, dat_dim = 'dataset', space_dim = c('lat', 'lon'), if (is.null(lat)) { stop("Parameter 'lat' cannot be NULL. It is required for area weighting.") } - if (!is.numeric(lat) | length(lat) != dim(exp)[space_dim[1]]) { + if (!is.numeric(lat) | length(lat) != dim(exp)[lat_dim]) { stop(paste0("Parameter 'lat' must be a numeric vector with the same ", "length as the latitude dimension of 'exp' and 'obs'.")) } ## lon if (!is.null(lon)) { - if (!is.numeric(lon) | length(lon) != dim(exp)[space_dim[2]]) { + if (!is.numeric(lon) | length(lon) != dim(exp)[lon_dim]) { stop(paste0("Parameter 'lon' must be a numeric vector with the same ", "length as the longitude dimension of 'exp' and 'obs'.")) } @@ -246,13 +262,15 @@ ACC <- function(exp, obs, dat_dim = 'dataset', space_dim = c('lat', 'lon'), ## exp and obs (2) name_exp <- sort(names(dim(exp))) name_obs <- sort(names(dim(obs))) - name_exp <- name_exp[-which(name_exp == dat_dim)] - name_obs <- name_obs[-which(name_obs == dat_dim)] + if (!is.null(dat_dim)) { + name_exp <- name_exp[-which(name_exp == dat_dim)] + name_obs <- name_obs[-which(name_obs == dat_dim)] + } if (!is.null(memb_dim)) { name_exp <- name_exp[-which(name_exp == memb_dim)] name_obs <- name_obs[-which(name_obs == memb_dim)] } - if(!all(dim(exp)[name_exp] == dim(obs)[name_obs])) { + if (!all(dim(exp)[name_exp] == dim(obs)[name_obs])) { stop(paste0("Parameter 'exp' and 'obs' must have same length of ", "all the dimensions expect 'dat_dim' and 'memb_dim'.")) } @@ -282,8 +300,8 @@ ACC <- function(exp, obs, dat_dim = 'dataset', space_dim = c('lat', 'lon'), (lonlatbox[1] > lonlatbox[2] & (lon > lonlatbox[1] | lon < lonlatbox[2]))) indlat <- which(lat >= lonlatbox[3] & lat <= lonlatbox[4]) - exp <- ClimProjDiags::Subset(exp, space_dim, list(indlat, indlon), drop = FALSE) - obs <- ClimProjDiags::Subset(obs, space_dim, list(indlat, indlon), drop = FALSE) + exp <- ClimProjDiags::Subset(exp, c(lat_dim, lon_dim), list(indlat, indlon), drop = FALSE) + obs <- ClimProjDiags::Subset(obs, c(lat_dim, lon_dim), list(indlat, indlon), drop = FALSE) } # Ensemble mean @@ -298,9 +316,9 @@ ACC <- function(exp, obs, dat_dim = 'dataset', space_dim = c('lat', 'lon'), if (is.null(avg_dim)) { - target_dims <- list(c(space_dim, dat_dim), c(space_dim, dat_dim)) + target_dims <- list(c(lat_dim, lon_dim, dat_dim), c(lat_dim, lon_dim, dat_dim)) } else { - target_dims <- list(c(space_dim, avg_dim, dat_dim), c(space_dim, avg_dim, dat_dim)) + target_dims <- list(c(lat_dim, lon_dim, avg_dim, dat_dim), c(lat_dim, lon_dim, avg_dim, dat_dim)) } res <- Apply(list(exp, obs), target_dims = target_dims, @@ -313,11 +331,11 @@ ACC <- function(exp, obs, dat_dim = 'dataset', space_dim = c('lat', 'lon'), # If bootstrap, calculate confidence level if (conftype == 'bootstrap') { if (is.null(avg_dim)) { - target_dims_bs <- list(c(memb_dim, dat_dim, space_dim), - c(memb_dim, dat_dim, space_dim)) + target_dims_bs <- list(c(memb_dim, dat_dim, lat_dim, lon_dim), + c(memb_dim, dat_dim, lat_dim, lon_dim)) } else { - target_dims_bs <- list(c(memb_dim, dat_dim, avg_dim, space_dim), - c(memb_dim, dat_dim, avg_dim, space_dim)) + target_dims_bs <- list(c(memb_dim, dat_dim, avg_dim, lat_dim, lon_dim), + c(memb_dim, dat_dim, avg_dim, lat_dim, lon_dim)) } res_conf <- Apply(list(exp_ori, obs_ori), target_dims = target_dims_bs, @@ -472,8 +490,8 @@ ACC <- function(exp, obs, dat_dim = 'dataset', space_dim = c('lat', 'lon'), # pval and conf if (pval | conf) { if (conftype == "parametric") { - # calculate effective sample size along space_dim - # combine space_dim into one dim first + # calculate effective sample size along lat_dim and lon_dim + # combine lat_dim and lon_dim into one dim first obs_tmp <- array(obs_sub, dim = c(space = prod(dim(obs_sub)[1:2]), dim(obs_sub)[3])) @@ -556,11 +574,11 @@ ACC <- function(exp, obs, dat_dim = 'dataset', space_dim = c('lat', 'lon'), conf = TRUE, conftype = "parametric", conf.lev = 0.95, pval = TRUE) { # if (is.null(avg_dim)) - # exp: [memb_exp, dat_exp, space_dim] - # obs: [memb_obs, dat_obs, space_dim] + # exp: [memb_exp, dat_exp, lat, lon] + # obs: [memb_obs, dat_obs, lat, lon] # if (!is.null(avg_dim)) - # exp: [memb_exp, dat_exp, avg_dim, space_dim] - # obs: [memb_obs, dat_obs, avg_dim, space_dim] + # exp: [memb_exp, dat_exp, avg_dim, lat, lon] + # obs: [memb_obs, dat_obs, avg_dim, lat, lon] nexp <- as.numeric(dim(exp)[2]) nobs <- as.numeric(dim(obs)[2]) diff --git a/man/ACC.Rd b/man/ACC.Rd index 5bd17f6..1402808 100644 --- a/man/ACC.Rd +++ b/man/ACC.Rd @@ -8,6 +8,8 @@ ACC( exp, obs, dat_dim = "dataset", + lat_dim = "lat", + lon_dim = "lon", space_dim = c("lat", "lon"), avg_dim = "sdate", memb_dim = "member", @@ -23,7 +25,7 @@ ACC( } \arguments{ \item{exp}{A numeric array of experimental anomalies with named dimensions. -The dimension must have at least 'space_dim'.} +The dimension must have at least 'lat_dim' and 'lon_dim'.} \item{obs}{A numeric array of observational anomalies with named dimensions. The dimension should be the same as 'exp' except the length of 'dat_dim' @@ -33,9 +35,13 @@ and 'memb_dim'.} dimension. The default value is 'dataset'. If there is no dataset dimension, set NULL.} -\item{space_dim}{A character string vector of 2 indicating the name of the -latitude and longitude dimensions (in order) along which ACC is computed. -The default value is c('lat', 'lon').} +\item{lat_dim}{A character string indicating the name of the latitude +dimension of 'exp' and 'obs' along which ACC is computed. The default value +is 'lat'.} + +\item{lon_dim}{A character string indicating the name of the longitude +dimension of 'exp' and 'obs' along which ACC is computed. The default value +is 'lon'.} \item{avg_dim}{A character string indicating the name of the dimension to be averaged, which is usually the time dimension. If no need to calculate mean @@ -82,7 +88,7 @@ computation. The default value is NULL.} A list containing the numeric arrays:\cr \item{acc}{ The ACC with the dimensions c(nexp, nobs, the rest of the dimension except - space_dim and memb_dim). nexp is the number of experiment (i.e., dat_dim in + lat_dim, lon_dim and memb_dim). nexp is the number of experiment (i.e., dat_dim in exp), and nobs is the number of observation (i.e., dat_dim in obs). If dat_dim is NULL, nexp and nobs are omitted. } @@ -102,7 +108,7 @@ A list containing the numeric arrays:\cr } \item{macc}{ The mean anomaly correlation coefficient with dimensions - c(nexp, nobs, the rest of the dimension except space_dim, memb_dim, and + c(nexp, nobs, the rest of the dimension except lat_dim, lon_dim, memb_dim, and avg_dim). Only present if 'avg_dim' is not NULL. If dat_dim is NULL, nexp and nobs are omitted. } diff --git a/tests/testthat/test-ACC.R b/tests/testthat/test-ACC.R index 7631401..5c36725 100644 --- a/tests/testthat/test-ACC.R +++ b/tests/testthat/test-ACC.R @@ -11,6 +11,8 @@ context("s2dv::ACC tests") set.seed(2) obs1 <- array(rnorm(30), dim = c(dataset = 1, member = 1, sdate = 5, ftime = 1, lat = 2, lon = 3)) + lat1 <- c(30, 35) + lon1 <- c(0, 5, 10) # dat2 set.seed(1) exp2 <- array(rnorm(60), dim = c(dataset = 2, sdate = 5, @@ -21,10 +23,12 @@ context("s2dv::ACC tests") set.seed(2) na <- floor(runif(2, min = 1, max = 30)) obs2[na] <- NA + lat2 <- c(30, 35) + lon2 <- c(0, 5, 10) ############################################## test_that("1. Input checks", { - + # exp and obs (1) expect_error( ACC(c(), c()), "Parameter 'exp' and 'obs' cannot be NULL." @@ -36,7 +40,7 @@ test_that("1. Input checks", { expect_error( ACC(c(1:10), c(2:4)), paste0("Parameter 'exp' and 'obs' must have at least dimensions ", - "dat_dim and space_dim.") + "lat_dim and lon_dim.") ) expect_error( ACC(array(1:10, dim = c(2, 5)), array(1:4, dim = c(2, 2))), @@ -46,6 +50,7 @@ test_that("1. Input checks", { ACC(array(1:10, dim = c(a = 2, c = 5)), array(1:4, dim = c(a = 2, b = 2))), "Parameter 'exp' and 'obs' must have same dimension name" ) + # dat_dim expect_error( ACC(exp1, obs1, dat_dim = 1), "Parameter 'dat_dim' must be a character string." @@ -54,14 +59,30 @@ test_that("1. Input checks", { ACC(exp1, obs1, dat_dim = 'a'), "Parameter 'dat_dim' is not found in 'exp' or 'obs' dimension." ) + # space_dim (deprecated) + expect_warning( + ACC(exp1, obs1, space_dim = c('lat', 'lon'), lat = c(1, 2)), + "Parameter 'space_dim' is deprecated. Use 'lat_dim' and 'lon_dim' instead." + ) + # lat_dim + expect_error( + ACC(exp1, obs1, lat_dim = 1), + "Parameter 'lat_dim' must be a character string." + ) + expect_error( + ACC(exp1, obs1, lat_dim = 'latitude'), + "Parameter 'lat_dim' is not found in 'exp' or 'obs' dimension." + ) + # lon_dim expect_error( - ACC(exp1, obs1, space_dim = c('lon')), - "Parameter 'space_dim' must be a character vector of 2." + ACC(exp1, obs1, lon_dim = 2), + "Parameter 'lon_dim' must be a character string." ) expect_error( - ACC(exp1, obs1, space_dim = c('lon', 'lev')), - "Parameter 'space_dim' is not found in 'exp' or 'obs' dimension." + ACC(exp1, obs1, lon_dim = 'lonn'), + "Parameter 'lon_dim' is not found in 'exp' or 'obs' dimension." ) + # avg_dim expect_error( ACC(exp1, obs1, avg_dim = 1), "Parameter 'avg_dim' must be a character string." @@ -70,6 +91,7 @@ test_that("1. Input checks", { ACC(exp1, obs1, avg_dim = c('lev')), "Parameter 'avg_dim' is not found in 'exp' or 'obs' dimension." ) + # memb_dim expect_error( ACC(exp1, obs1, memb_dim = TRUE), "Parameter 'memb_dim' must be a character string." @@ -78,52 +100,57 @@ test_that("1. Input checks", { ACC(exp1, obs1, memb_dim = 'memb'), "Parameter 'memb_dim' is not found in 'exp' or 'obs' dimension." ) + # lat expect_error( ACC(exp1, obs1, lat = c(1, 2, 3)), paste0("Parameter \'lat\' must be a numeric vector with the same ", "length as the latitude dimension of \'exp\' and \'obs\'.") ) + # lon expect_error( - ACC(exp1, obs1, lon = c(1, 3)), + ACC(exp1, obs1, lat = c(1, 2), lon = c(1, 3)), paste0("Parameter \'lon\' must be a numeric vector with the same ", "length as the longitude dimension of \'exp\' and \'obs\'.") ) + # lonlatbox expect_error( - ACC(exp1, obs1, lonlatbox = c(-90, 90)), + ACC(exp1, obs1, lat = c(1, 2), lon = c(1, 2, 3), lonlatbox = c(-90, 90)), "Parameter 'lonlatbox' must be a numeric vector of 4." ) + # conf expect_error( - ACC(exp1, obs1, lat = c(1, 2), lon = c(1, 2, 3)), - paste0("Parameters 'lon', 'lat', and 'lonlatbox' must be used or be ", - "NULL at the same time.") - ) - expect_error( - ACC(exp1, obs1, conf = 1), + ACC(exp1, obs1, lat = c(1, 2), lon = c(1, 2, 3), conf = 1), "Parameter 'conf' must be one logical value." ) + # conftype expect_error( - ACC(exp1, obs1, conftype = 'a'), + ACC(exp1, obs1, lat = c(1, 2), lon = c(1, 2, 3), conftype = 'a'), "Parameter \'conftype\' must be either \'parametric\' or \'bootstrap\'." ) expect_error( - ACC(exp1, obs1, memb_dim = NULL, conftype = 'bootstrap'), + ACC(exp1, obs1, lat = c(1, 2), lon = c(1, 2, 3), memb_dim = NULL, conftype = 'bootstrap'), "Parameter 'memb_dim' cannot be NULL when parameter 'conftype' is 'bootstrap'." ) + # conf.lev expect_error( - ACC(exp1, obs1, conf.lev = -1), + ACC(exp1, obs1, lat = c(1, 2), lon = c(1, 2, 3), conf.lev = -1), "Parameter 'conf.lev' must be a numeric number between 0 and 1." ) + # pval expect_error( - ACC(exp1, obs1, pval = 'TRUE'), + ACC(exp1, obs1, lat = c(1, 2), lon = c(1, 2, 3), pval = 'TRUE'), "Parameter 'pval' must be one logical value." ) + # ncores expect_error( - ACC(exp1, obs1, ncores = 1.5), + ACC(exp1, obs1, lat = c(1, 2), lon = c(1, 2, 3), ncores = 1.5), "Parameter 'ncores' must be a positive integer." ) + # exp and obs (2) expect_error( ACC(exp = array(1:10, dim = c(dataset = 5, member = 1, lat = 2, lon = 1)), obs = array(1:4, dim = c(dataset = 2, member = 2, lat = 1, lon = 1)), + lat = c(1, 2), lon = c(1), avg_dim = NULL), "Parameter 'exp' and 'obs' must have same length of all the dimensions expect 'dat_dim' and 'memb_dim'." ) @@ -134,66 +161,84 @@ test_that("1. Input checks", { test_that("2. Output checks: dat1", { expect_equal( - dim(ACC(exp1, obs1)$acc), + dim(ACC(exp1, obs1, lat = lat1, lon = lon1)$acc), c(nexp = 1, nobs = 1, sdate = 5, ftime = 1) ) expect_equal( - names(ACC(exp1, obs1)), + names(ACC(exp1, obs1, lat = lat1)), c("acc", "conf.lower", "conf.upper", "p.val", "macc") ) expect_equal( - mean(ACC(exp1, obs1)$acc), - -0.2001352, + as.vector(ACC(exp1, obs1, lat = lat1)$acc), + c(-0.006384684, -0.664580451, -0.637292180, 0.320974249, -0.453246995), tolerance = 0.00001 ) expect_equal( - as.vector(ACC(exp1, obs1)$p.val), + as.vector(ACC(exp1, obs1, lat = lat1)$p.val), c(0.7292993, 0.7292993, 0.7292993, 0.7292993, 0.7292993), tolerance = 0.00001 ) expect_equal( - as.vector(ACC(exp1, obs1)$conf.lower), - c(-0.8595534, -0.9644555, -0.9408508, -0.6887500, -0.7619374), + as.vector(ACC(exp1, obs1, lat = lat1)$conf.lower), + c(-0.8137296, -0.9589397, -0.9549511, -0.6633950, -0.9246771), tolerance = 0.00001 ) expect_equal( - as.vector(ACC(exp1, obs1)$conf.upper), - c(0.7493799, 0.2515608, 0.4759707, 0.8890967, 0.8517117), + as.vector(ACC(exp1, obs1, lat = lat1)$conf.upper), + c(0.8093704, 0.3190711, 0.3609563, 0.8984881, 0.5668074), tolerance = 0.00001 ) expect_equal( - names(ACC(exp1, obs1, avg_dim = NULL)), + names(ACC(exp1, obs1, lat = lat1, avg_dim = NULL)), c("acc", "conf.lower", "conf.upper", "p.val") ) expect_equal( - dim(ACC(exp1, obs1, dat_dim = 'member', memb_dim = NULL)$acc), + dim(ACC(exp1, obs1, lat = lat1, dat_dim = 'member', memb_dim = NULL)$acc), c(nexp = 2, nobs = 1, sdate = 5, dataset = 1, ftime = 1) ) expect_equal( - names(ACC(exp1, obs1, conf = FALSE)), + names(ACC(exp1, obs1, lat = lat1, conf = FALSE)), c("acc", "p.val", "macc") ) expect_equal( - names(ACC(exp1, obs1, pval = FALSE)), + names(ACC(exp1, obs1, lat = lat1, pval = FALSE)), c("acc", "conf.lower", "conf.upper", "macc") ) expect_equal( - names(ACC(exp1, obs1, conf = FALSE, pval = FALSE)), + names(ACC(exp1, obs1, lat = lat1, conf = FALSE, pval = FALSE)), c("acc", "macc") ) expect_equal( - as.vector(ACC(exp1, obs1, conf = FALSE, avg_dim = NULL, conf.lev = 0.9)$p.val), + as.vector(ACC(exp1, obs1, lat = lat1, conf = FALSE, avg_dim = NULL, conf.lev = 0.9)$p.val), c(0.6083998, 0.6083998, 0.6083998, 0.6083998, 0.6083998), tolerance = 0.00001 ) expect_equal( - mean(ACC(exp1, obs1, lat = c(10, 20), lon = c(20, 30, 40), lonlatbox = c(20, 30, 10, 20))$acc), - -0.1681097, + as.vector(ACC(exp1, obs1, lat = c(10, 20), lon = c(20, 30, 40), lonlatbox = c(20, 30, 10, 20))$acc), + c(0.3555372, -0.5205672, -0.3403301, 0.2690648, -0.5146910), tolerance = 0.00001 ) + }) +############################################## +test_that("3. Output checks: dat2", { +expect_equal( + dim(ACC(exp2, obs2, lat = lat2, lon = lon2, memb_dim = NULL)$acc), + c(nexp = 2, nobs = 1, sdate = 5, ftime = 1) +) +expect_equal( + as.vector(ACC(exp2, obs2, lat = lat2, memb_dim = NULL)$acc)[3:7], + c(-0.3601880, -0.5624773, -0.4603762, -0.6997169, -0.1336961), + tolerance = 0.00001 +) +expect_equal( + mean(ACC(exp2, obs2, lat = lat2, memb_dim = NULL)$acc), + -0.1484762, + tolerance = 0.00001 +) +}) -- GitLab From 3af3504e5d11c63b7da1291c86fc53f9bc5225f3 Mon Sep 17 00:00:00 2001 From: aho Date: Mon, 3 Jan 2022 18:32:18 +0100 Subject: [PATCH 07/83] Revise example due to new development of ACC() --- R/PlotACC.R | 4 ++-- man/PlotACC.Rd | 4 ++-- 2 files changed, 4 insertions(+), 4 deletions(-) diff --git a/R/PlotACC.R b/R/PlotACC.R index 3bffa68..a674ff6 100644 --- a/R/PlotACC.R +++ b/R/PlotACC.R @@ -65,8 +65,8 @@ #'ano_exp <- Ano(sampleData$mod, clim$clim_exp) #'ano_obs <- Ano(sampleData$obs, clim$clim_obs) -#'acc <- ACC(ano_exp, ano_obs) -#'acc_bootstrap <- ACC(ano_exp, ano_obs, conftype = 'bootstrap') +#'acc <- ACC(ano_exp, ano_obs, lat = sampleData$lat) +#'acc_bootstrap <- ACC(ano_exp, ano_obs, lat = sampleData$lat, conftype = 'bootstrap') #'# Combine acc results for PlotACC #'res <- array(c(acc$conf.lower, acc$acc, acc$conf.upper, acc$p.val), #' dim = c(dim(acc$acc), 4)) diff --git a/man/PlotACC.Rd b/man/PlotACC.Rd index cd2b357..2764de3 100644 --- a/man/PlotACC.Rd +++ b/man/PlotACC.Rd @@ -110,8 +110,8 @@ sampleData$obs <- Season(sampleData$obs, monini = 11, moninf = 12, monsup = 2) clim <- Clim(sampleData$mod, sampleData$obs) ano_exp <- Ano(sampleData$mod, clim$clim_exp) ano_obs <- Ano(sampleData$obs, clim$clim_obs) -acc <- ACC(ano_exp, ano_obs) -acc_bootstrap <- ACC(ano_exp, ano_obs, conftype = 'bootstrap') +acc <- ACC(ano_exp, ano_obs, lat = sampleData$lat) +acc_bootstrap <- ACC(ano_exp, ano_obs, lat = sampleData$lat, conftype = 'bootstrap') # Combine acc results for PlotACC res <- array(c(acc$conf.lower, acc$acc, acc$conf.upper, acc$p.val), dim = c(dim(acc$acc), 4)) -- GitLab From 597b48e1640186fa4f0eb15f0527f8bc1bf311a1 Mon Sep 17 00:00:00 2001 From: aho Date: Mon, 17 Jan 2022 12:58:38 +0100 Subject: [PATCH 08/83] Add 'useRaster = TRUE' in image() to avoid artifect lines --- R/PlotEquiMap.R | 2 +- R/PlotSection.R | 6 +++--- 2 files changed, 4 insertions(+), 4 deletions(-) diff --git a/R/PlotEquiMap.R b/R/PlotEquiMap.R index 3de30e9..db1829f 100644 --- a/R/PlotEquiMap.R +++ b/R/PlotEquiMap.R @@ -849,7 +849,7 @@ PlotEquiMap <- function(var, lon, lat, varu = NULL, varv = NULL, if (square) { image(lonb$x, latb$x, var[lonb$ix, latb$ix], col = c(col_inf_image, cols, col_sup_image), breaks = c(-.Machine$double.xmax, brks, .Machine$double.xmax), - axes = FALSE, xlab = "", ylab = "", add = TRUE) + axes = FALSE, xlab = "", ylab = "", add = TRUE, useRaster = TRUE) } else { .filled.contour(lonb$x, latb$x, var[lonb$ix, latb$ix], levels = c(.Machine$double.xmin, brks, .Machine$double.xmax), diff --git a/R/PlotSection.R b/R/PlotSection.R index 29bdd49..1cceaab 100644 --- a/R/PlotSection.R +++ b/R/PlotSection.R @@ -139,9 +139,9 @@ PlotSection <- function(var, horiz, depth, toptitle = '', sizetit = 1, mgp = c(2.5, 0.5, 0), las = 1) image(horizb$x, depthb$x, array(0, dims), col = 'grey', breaks = c(-1, 1), axes = FALSE, xlab = "", ylab = "", main = toptitle, - cex.main = 1.5 * sizetit) + cex.main = 1.5 * sizetit, useRaster = TRUE) image(horizb$x, depthb$x, var[horizb$ix, depthb$ix], col = cols, - breaks = brks, axes = FALSE, xlab = "", ylab = "", add = TRUE) + breaks = brks, axes = FALSE, xlab = "", ylab = "", add = TRUE, useRaster = TRU, useRaster = TRUE) if (axelab) { minhoriz <- ceiling(round(min(horizb$x), 0) / 10) * 10 maxhoriz <- floor(round(max(horizb$x), 0) / 10) * 10 @@ -158,7 +158,7 @@ PlotSection <- function(var, horiz, depth, toptitle = '', sizetit = 1, par(mar = c(1.5, ymargin, 2.5, 0.5), mgp = c(1.5, 0.3, 0), las = 1, cex = 1.2) image(1:length(cols), 1, t(t(1:length(cols))), axes = FALSE, col = cols, - xlab = units, ylab = '') + xlab = units, ylab = '', useRaster = TRUE) box() axis(1, at = seq(0.5, length(brks) - 0.5, 1), labels = brks, cex.axis = 1) } -- GitLab From 6b8c1e6e25e383039dbbfaef8d6a07d60c2fe706 Mon Sep 17 00:00:00 2001 From: aho Date: Mon, 17 Jan 2022 13:13:10 +0100 Subject: [PATCH 09/83] Remove 'useRaster = TRUE' in PlotSection because it only can be used with regular-spaced grid --- R/PlotSection.R | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/R/PlotSection.R b/R/PlotSection.R index 1cceaab..29bdd49 100644 --- a/R/PlotSection.R +++ b/R/PlotSection.R @@ -139,9 +139,9 @@ PlotSection <- function(var, horiz, depth, toptitle = '', sizetit = 1, mgp = c(2.5, 0.5, 0), las = 1) image(horizb$x, depthb$x, array(0, dims), col = 'grey', breaks = c(-1, 1), axes = FALSE, xlab = "", ylab = "", main = toptitle, - cex.main = 1.5 * sizetit, useRaster = TRUE) + cex.main = 1.5 * sizetit) image(horizb$x, depthb$x, var[horizb$ix, depthb$ix], col = cols, - breaks = brks, axes = FALSE, xlab = "", ylab = "", add = TRUE, useRaster = TRU, useRaster = TRUE) + breaks = brks, axes = FALSE, xlab = "", ylab = "", add = TRUE) if (axelab) { minhoriz <- ceiling(round(min(horizb$x), 0) / 10) * 10 maxhoriz <- floor(round(max(horizb$x), 0) / 10) * 10 @@ -158,7 +158,7 @@ PlotSection <- function(var, horiz, depth, toptitle = '', sizetit = 1, par(mar = c(1.5, ymargin, 2.5, 0.5), mgp = c(1.5, 0.3, 0), las = 1, cex = 1.2) image(1:length(cols), 1, t(t(1:length(cols))), axes = FALSE, col = cols, - xlab = units, ylab = '', useRaster = TRUE) + xlab = units, ylab = '') box() axis(1, at = seq(0.5, length(brks) - 0.5, 1), labels = brks, cex.axis = 1) } -- GitLab From b9763e1210a47ab4719770ddf63fa9e401da9436 Mon Sep 17 00:00:00 2001 From: aho Date: Mon, 17 Jan 2022 17:14:30 +0100 Subject: [PATCH 10/83] Add error handling for 'useRaster' --- R/PlotEquiMap.R | 16 +++++++++++++--- 1 file changed, 13 insertions(+), 3 deletions(-) diff --git a/R/PlotEquiMap.R b/R/PlotEquiMap.R index db1829f..9981ed3 100644 --- a/R/PlotEquiMap.R +++ b/R/PlotEquiMap.R @@ -847,9 +847,19 @@ PlotEquiMap <- function(var, lon, lat, varu = NULL, varv = NULL, col_inf_image <- ifelse(is.null(col_inf), colNA, col_inf) col_sup_image <- ifelse(is.null(col_sup), colNA, col_sup) if (square) { - image(lonb$x, latb$x, var[lonb$ix, latb$ix], col = c(col_inf_image, cols, col_sup_image), - breaks = c(-.Machine$double.xmax, brks, .Machine$double.xmax), - axes = FALSE, xlab = "", ylab = "", add = TRUE, useRaster = TRUE) + # If lat and lon are both regular-spaced, "useRaster = TRUE" can avoid + # artifact white lines on the figure. If not, useRaster has to be FALSE (default) + tryCatch({ + image(lonb$x, latb$x, var[lonb$ix, latb$ix], + col = c(col_inf_image, cols, col_sup_image), + breaks = c(-.Machine$double.xmax, brks, .Machine$double.xmax), + axes = FALSE, xlab = "", ylab = "", add = TRUE, useRaster = TRUE) + }, error = function(x) { + image(lonb$x, latb$x, var[lonb$ix, latb$ix], + col = c(col_inf_image, cols, col_sup_image), + breaks = c(-.Machine$double.xmax, brks, .Machine$double.xmax), + axes = FALSE, xlab = "", ylab = "", add = TRUE) + }) } else { .filled.contour(lonb$x, latb$x, var[lonb$ix, latb$ix], levels = c(.Machine$double.xmin, brks, .Machine$double.xmax), -- GitLab From 3507a8d7e9310e5759e9d7d00fafdaa38e520c4f Mon Sep 17 00:00:00 2001 From: aho Date: Thu, 20 Jan 2022 09:44:58 +0100 Subject: [PATCH 11/83] Add 'space_dim' back to documentation --- R/ACC.R | 4 ++++ man/ACC.Rd | 5 +++++ 2 files changed, 9 insertions(+) diff --git a/R/ACC.R b/R/ACC.R index ec71d03..44c724c 100644 --- a/R/ACC.R +++ b/R/ACC.R @@ -24,6 +24,10 @@ #'@param lon_dim A character string indicating the name of the longitude #' dimension of 'exp' and 'obs' along which ACC is computed. The default value #' is 'lon'. +#'@param space_dim A character string vector of 2 indicating the name of the +#' latitude and longitude dimensions (in order) along which ACC is computed. +#' The default value is c('lat', 'lon'). This argument has been deprecated. +#' Use 'lat_dim' and 'lon_dim' instead. #'@param avg_dim A character string indicating the name of the dimension to be #' averaged, which is usually the time dimension. If no need to calculate mean #' ACC, set as NULL. The default value is 'sdate'. diff --git a/man/ACC.Rd b/man/ACC.Rd index 1402808..7df6abb 100644 --- a/man/ACC.Rd +++ b/man/ACC.Rd @@ -43,6 +43,11 @@ is 'lat'.} dimension of 'exp' and 'obs' along which ACC is computed. The default value is 'lon'.} +\item{space_dim}{A character string vector of 2 indicating the name of the +latitude and longitude dimensions (in order) along which ACC is computed. +The default value is c('lat', 'lon'). This argument has been deprecated. +Use 'lat_dim' and 'lon_dim' instead.} + \item{avg_dim}{A character string indicating the name of the dimension to be averaged, which is usually the time dimension. If no need to calculate mean ACC, set as NULL. The default value is 'sdate'.} -- GitLab From 6562c4e0f92eb31d46fac608f9898fbfc316d572 Mon Sep 17 00:00:00 2001 From: aho Date: Thu, 20 Jan 2022 11:59:40 +0100 Subject: [PATCH 12/83] Clarify the 'method' options in documentation --- R/Clim.R | 3 ++- man/Clim.Rd | 3 ++- 2 files changed, 4 insertions(+), 2 deletions(-) diff --git a/R/Clim.R b/R/Clim.R index d01cf5d..6380bd2 100644 --- a/R/Clim.R +++ b/R/Clim.R @@ -23,7 +23,8 @@ #' complete along 'dat_dim', this startdate along 'dat_dim' will be discarded. #' The default value is "c('dataset', 'member')". #'@param method A character string indicating the method to be used. The -#' options include 'clim', 'kharin', and 'NDV'. The default value is 'clim'. +#' options include 'clim' (per-pair method), 'kharin' (Kharin method), and +#' 'NDV' (Fuckar method). The default value is 'clim'. #'@param ftime_dim A character string indicating the name of forecast time #' dimension. Only used when method = 'NDV'. The default value is 'ftime'. #'@param memb A logical value indicating whether to remain 'memb_dim' dimension diff --git a/man/Clim.Rd b/man/Clim.Rd index 50ec0d9..3237786 100644 --- a/man/Clim.Rd +++ b/man/Clim.Rd @@ -33,7 +33,8 @@ complete along 'dat_dim', this startdate along 'dat_dim' will be discarded. The default value is "c('dataset', 'member')".} \item{method}{A character string indicating the method to be used. The -options include 'clim', 'kharin', and 'NDV'. The default value is 'clim'.} +options include 'clim' (per-pair method), 'kharin' (Kharin method), and +'NDV' (Fuckar method). The default value is 'clim'.} \item{ftime_dim}{A character string indicating the name of forecast time dimension. Only used when method = 'NDV'. The default value is 'ftime'.} -- GitLab From 1fcc74a1b3d3150d3968e72602d844035514022c Mon Sep 17 00:00:00 2001 From: aho Date: Thu, 20 Jan 2022 13:02:54 +0100 Subject: [PATCH 13/83] Add citation link --- R/Clim.R | 4 ++-- man/Clim.Rd | 4 ++-- 2 files changed, 4 insertions(+), 4 deletions(-) diff --git a/R/Clim.R b/R/Clim.R index 6380bd2..52fe7c7 100644 --- a/R/Clim.R +++ b/R/Clim.R @@ -4,8 +4,8 @@ #'and observational data using one of the following methods: #'\enumerate{ #' \item{per-pair method (Garcia-Serrano and Doblas-Reyes, CD, 2012)} -#' \item{Kharin method (Karin et al, GRL, 2012)} -#' \item{Fuckar method (Fuckar et al, GRL, 2014)} +#' \item{Kharin method (Kharin et al, GRL, 2012 https://doi.org/10.1029/2012GL052647)} +#' \item{Fuckar method (Fuckar et al, GRL, 2014 https://doi.org/10.1002/2014GL060815)} #'} #'Per-pair climatology means that only the startdates covered by the #'whole experiments/observational dataset will be used. In other words, the diff --git a/man/Clim.Rd b/man/Clim.Rd index 3237786..b499e88 100644 --- a/man/Clim.Rd +++ b/man/Clim.Rd @@ -72,8 +72,8 @@ This function computes per-pair climatologies for the experimental and observational data using one of the following methods: \enumerate{ \item{per-pair method (Garcia-Serrano and Doblas-Reyes, CD, 2012)} - \item{Kharin method (Karin et al, GRL, 2012)} - \item{Fuckar method (Fuckar et al, GRL, 2014)} + \item{Kharin method (Kharin et al, GRL, 2012 https://doi.org/10.1029/2012GL052647)} + \item{Fuckar method (Fuckar et al, GRL, 2014 https://doi.org/10.1002/2014GL060815)} } Per-pair climatology means that only the startdates covered by the whole experiments/observational dataset will be used. In other words, the -- GitLab From 364e582bda029dbb8d1cd0f134ecd9e64f9ed99b Mon Sep 17 00:00:00 2001 From: aho Date: Thu, 20 Jan 2022 15:29:16 +0100 Subject: [PATCH 14/83] Supplement the details for each method --- R/Clim.R | 5 ++++- man/Clim.Rd | 5 ++++- 2 files changed, 8 insertions(+), 2 deletions(-) diff --git a/R/Clim.R b/R/Clim.R index 52fe7c7..59f8bc9 100644 --- a/R/Clim.R +++ b/R/Clim.R @@ -3,7 +3,7 @@ #'This function computes per-pair climatologies for the experimental #'and observational data using one of the following methods: #'\enumerate{ -#' \item{per-pair method (Garcia-Serrano and Doblas-Reyes, CD, 2012)} +#' \item{per-pair method (Garcia-Serrano and Doblas-Reyes, CD, 2012 https://doi.org/10.1007/s00382-012-1413-1)} #' \item{Kharin method (Kharin et al, GRL, 2012 https://doi.org/10.1029/2012GL052647)} #' \item{Fuckar method (Fuckar et al, GRL, 2014 https://doi.org/10.1002/2014GL060815)} #'} @@ -11,6 +11,9 @@ #'whole experiments/observational dataset will be used. In other words, the #'startdates which are not all available along 'dat_dim' dimension of both #'the 'exp' and 'obs' are excluded when computing the climatologies. +#'Kharin method is the linear trend bias correction method, and Fuckar method +#'is the initial condition bias correction method. The two methods both do the +#'per-pair correction beforehand. #' #'@param exp A named numeric array of experimental data, with at least two #' dimensions 'time_dim' and 'dat_dim'. diff --git a/man/Clim.Rd b/man/Clim.Rd index b499e88..cf58525 100644 --- a/man/Clim.Rd +++ b/man/Clim.Rd @@ -71,7 +71,7 @@ A list of 2: This function computes per-pair climatologies for the experimental and observational data using one of the following methods: \enumerate{ - \item{per-pair method (Garcia-Serrano and Doblas-Reyes, CD, 2012)} + \item{per-pair method (Garcia-Serrano and Doblas-Reyes, CD, 2012 https://doi.org/10.1007/s00382-012-1413-1)} \item{Kharin method (Kharin et al, GRL, 2012 https://doi.org/10.1029/2012GL052647)} \item{Fuckar method (Fuckar et al, GRL, 2014 https://doi.org/10.1002/2014GL060815)} } @@ -79,6 +79,9 @@ Per-pair climatology means that only the startdates covered by the whole experiments/observational dataset will be used. In other words, the startdates which are not all available along 'dat_dim' dimension of both the 'exp' and 'obs' are excluded when computing the climatologies. +Kharin method is the linear trend bias correction method, and Fuckar method +is the initial condition bias correction method. The two methods both do the +per-pair correction beforehand. } \examples{ # Load sample data as in Load() example: -- GitLab From 7d28ee1490cad8326ce91b5e24a8f3ea58b9d525 Mon Sep 17 00:00:00 2001 From: Carlos Delgado Date: Fri, 4 Mar 2022 16:44:05 +0100 Subject: [PATCH 15/83] first version adapted from SpecsVerification --- R/DiffCorr.R | 101 +++++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 101 insertions(+) create mode 100644 R/DiffCorr.R diff --git a/R/DiffCorr.R b/R/DiffCorr.R new file mode 100644 index 0000000..eea3184 --- /dev/null +++ b/R/DiffCorr.R @@ -0,0 +1,101 @@ + +library(s2dv) +library(multiApply) + +DiffCorr <- function(exp, ref, obs, conf.lev = NULL, handle.na = "na.fail", time_dim = 'year', member_dim = NULL, ncores = NULL){ + + ## Checkings + if (!is.array(exp)){stop('"exp" must be an array.')} + if (!is.array(obs)){stop('"obs" must be an array.')} + if (!is.array(ref)){stop('"ref" must be an array.')} + if (!identical(as.numeric(dim(exp)[time_dim]), as.numeric(dim(ref)[time_dim]))){stop('"time_dim" must have the same length in "exp" and "ref".')} + if (!identical(as.numeric(dim(exp)[time_dim]), as.numeric(dim(obs)[time_dim]))){stop('"time_dim" must have the same length in "exp" and "obs".')} + if (!is.null(conf.lev)){ + if (!is.numeric(conf.lev) | conf.lev <= 0 | conf.lev >= 1){stop('"conf.lev" must be either NULL or a number between 0 and 1.')} + } + if (!is.character(time_dim)){stop('"time_dim" must be a string.')} + if (!is.null(member_dim)){ + if (!is.character(member_dim)){stop('"member_dim" must be either NULL or a string.')} + } + if (!is.null(ncores)){ + if (!is.numeric(ncores) | ncores < 1){stop('"ncores" must be either NULL or a positive integer.')} + } + + ## Ensemble means + if (!is.null(member_dim)){ + exp <- s2dv::MeanDims(data = exp, dims = member_dim, na.rm = FALSE, drop = 'selected') + ref <- s2dv::MeanDims(data = ref, dims = member_dim, na.rm = FALSE, drop = 'selected') + } + + ## Correlation difference + output <- multiApply::Apply(data = list(exp = exp, ref = ref, obs = obs), + target_dims = time_dim, fun = .DiffCorr, + conf.lev = conf.lev, handle.na = handle.na, + time_dim = time_dim, ncores = ncores) + return(output) +} + +.DiffCorr <- function(exp, ref, obs, conf.lev, handle.na, time_dim) { + + ## handle NA's + if (handle.na == "na.fail") { + if (any(is.na(c(exp, ref, obs)))) { + stop("There are missing values.") + } + } else if (handle.na == "only.complete.triplets") { + nna <- !is.na(fcst) & !is.na(fcst.ref) & !is.na(obs) + if (all(nna == FALSE)) { + stop("There are no complete sets of forecasts and observations") + } + exp <- exp[nna] + ref <- ref[nna] + obs <- obs[nna] + } else { + stop("unknown 'handle.na' argument") + } + + ## Number of effective degrees of freedom + N <- s2dv::Eno(data = obs, time_dim = time_dim, na.action = handle.na, ncores = 1) + + ## Correlation coefficients and their confidence intervals + cor.exp <- cor(exp, obs) + cor.ref <- cor(ref, obs) + + ## Correlation difference + output <- NULL + output$corr.diff <- cor.exp - cor.ref + + ## auxiliary quantities + r12 <- cor.exp + r13 <- cor.ref + r23 <- cor(exp, ref) + + ## confidence interval, according to Zou (2007) + if (!is.null(conf.lev)){ + + ## individual confidence limits of cor.exp and cor.ref (not used) + # z.exp <- 0.5 * log((1 + cor.exp) / (1 - cor.exp)) + # z.ref <- 0.5 * log((1 + cor.ref) / (1 - cor.ref)) + + alpha <- (1. - conf.lev) / 2. + l1 <- tanh(atanh(r12) + qnorm(alpha)/sqrt(N-3)) + u1 <- tanh(atanh(r12) + qnorm(1-alpha)/sqrt(N-3)) + l2 <- tanh(atanh(r13) + qnorm(alpha)/sqrt(N-3)) + u2 <- tanh(atanh(r13) + qnorm(1-alpha)/sqrt(N-3)) + + ## Correlation between the two corr.coef r12 and r13 + c.12.13 <- ((r23 - 0.5 * r12 * r13) * (1 - r12*r12 - r13*r13 - r23*r23) + r23*r23*r23) / ((1 - r12*r12) * (1 - r13*r13)) + + ## Confidence interval + output$conf.lower <- r12 - r13 - sqrt((r12-l1)^2 + (u2 - r13)^2 - 2*c.12.13*(r12-l1)*(u2-r13)) + output$conf.upper <- r12 - r13 + sqrt((u1 - r12)^2 + (r13-l2)^2 - 2*c.12.13*(u1-r12)*(r13-l2)) + + } + + ## p-value of one-sided test for equality of dependent correlation coefficients (Steiger, 1980) + R <- (1-r12*r12-r13*r13-r23*r23) + 2*r12*r13*r23 + t <- (r12 - r13) * sqrt((N-1)*(1+r23) / (2 * ((N-1)/(N-3)) * R + 0.25*(r12+r13)^2 * (1-r23)^3)) + output$p.value <- 1 - pt(t, df=N-3) + + return(output) +} -- GitLab From 809eb885c4dcc5963c8343f87c288ba2bc8e8fcc Mon Sep 17 00:00:00 2001 From: Carlos Delgado Date: Fri, 4 Mar 2022 16:49:53 +0100 Subject: [PATCH 16/83] first version (without documentation) --- R/RPS.R | 99 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++ R/RPSS.R | 64 ++++++++++++++++++++++++++++++++++++ 2 files changed, 163 insertions(+) create mode 100644 R/RPS.R create mode 100644 R/RPSS.R diff --git a/R/RPS.R b/R/RPS.R new file mode 100644 index 0000000..0081d8e --- /dev/null +++ b/R/RPS.R @@ -0,0 +1,99 @@ + +library(multiApply) +library(easyVerification) + +RPS <- function(exp, obs, n_categories, indices_for_clim, Fair = FALSE, time_dim = 'year', member_dim = 'member', ncores = NULL){ + + ## Checkings + if (!is.array(exp)){stop('"exp" must be an array.')} + if (!is.array(obs)){stop('"obs" must be an array.')} + if (!identical(as.numeric(dim(exp)[time_dim]), as.numeric(dim(obs)[time_dim]))){stop('"time_dim" must have the same length in "exp" and "obs".')} + if (!is.numeric(n_categories) | n_categories < 2){stop('"n_categories" must be an integer greater than 1.')} + if (!is.logical(Fair)){stop('"Fair" must be TRUE or FALSE.')} + if (!is.character(time_dim)){stop('"time_dim" must be a string.')} + if (!is.character(member_dim)){stop('"member_dim" must be a string.')} + if (!is.null(ncores)){ + if (!is.numeric(ncores) | ncores < 1){stop('"ncores" must be either NULL or a positive integer.')} + } + + ## RPS + rps <- multiApply::Apply(data = list(exp = exp, obs = obs), + target_dims = list(exp = c(time_dim, member_dim), obs = time_dim), + n_categories = n_categories, indices_for_clim = indices_for_clim, + Fair = Fair, time_dim = time_dim, member_dim = member_dim, + fun = .RPS, ncores = ncores)$output1 + return(rps) +} + +.RPS <- function(exp, obs, n_categories, indices_for_clim, Fair = FALSE, time_dim = 'year', member_dim = 'member'){ + + exp_probs <- .get_probs(data = exp, type = 'exp', indices_for_quartiles = indices_for_clim, + time_dim = time_dim, member_dim = member_dim, n_categories = n_categories)$probs + + obs_probs <- .get_probs(data = obs, type = 'obs', indices_for_quartiles = indices_for_clim, + time_dim = time_dim, member_dim = NULL, n_categories = n_categories)$probs + + rps <- .RPS_from_probs(probs_exp = exp_probs, probs_obs = obs_probs, n_categories = n_categories, Fair = Fair, n_members = as.numeric(dim(exp)[member_dim])) + + return(rps) +} + +.get_probs <- function(data, type, indices_for_quartiles, time_dim, member_dim, n_categories){ + + if (type == 'exp'){ + target_dims_quantiles = c(time_dim, member_dim) + target_dims_probs = list(member_dim, 'bin') + } else if (type == 'obs'){ + target_dims_quantiles = time_dim + target_dims_probs = list(NULL,'bin') + } else {stop("Parameter 'type' must be 'exp' or 'obs'")} + + quantiles <- multiApply::Apply(data = ClimProjDiags::Subset(x = data, along = time_dim, indices = indices_for_quartiles), + target_dims = target_dims_quantiles, + fun = .quantile_aux, n_categories = n_categories, + output_dims = 'bin', ncores = NULL)$output1 + + probs <- multiApply::Apply(data = list(data = data, quantiles = quantiles), + target_dims = target_dims_probs, + fun = .c2p, + output_dims = 'bin', ncores = NULL)$output1 + + return(list('probs' = probs, 'quantiles' = quantiles)) +} + +.quantile_aux = function(data, n_categories){ + data = as.vector(data) + q = quantile(x = data, probs = 1:(n_categories - 1) / n_categories, type = 8, na.rm = TRUE) + return(q) +} + +.c2p <- function(data, quantiles) { + if (anyNA(data)){ + output <- array(data = NA, dim = length(quantiles) + 1) + } else { + output <- colMeans(easyVerification::convert2prob(as.vector(data), threshold = as.vector(quantiles))) + } + return(output) +} + + +.RPS_from_probs <- function(probs_exp, probs_obs, n_categories, Fair, n_members){ + + rps <- NULL + for (i in 1:n_categories){ + rps[i] <- (cumsum(probs_exp)[i]-cumsum(probs_obs)[i])^2 ## (Wilks 2011, pp.348-350) + } + rps <- sum(rps) + + if (Fair == TRUE){ ## FairRPS + ## adjustment <- rowSums(-1 * (1/R - 1/R.new) * ens.cum * (R - ens.cum)/R/(R - 1)) [formula taken from SpecsVerification::EnsRps] + R <- n_members + R_new <- Inf + probs_cum <- cumsum(probs_exp) + adjustment <- -1 / (R-1) * probs_cum * (1 - probs_cum) + adjustment <- sum(adjustment) + rps <- rps + adjustment + } + + return(rps) +} diff --git a/R/RPSS.R b/R/RPSS.R new file mode 100644 index 0000000..b9d59d0 --- /dev/null +++ b/R/RPSS.R @@ -0,0 +1,64 @@ + +library(multiApply) +library(easyVerification) +library(s2dv) + +RPSS <- function(exp, obs, ref = NULL, n_categories, indices_for_clim, Fair = FALSE, time_dim = 'year', member_dim = 'member', ncores = NULL){ + + ## Checkings + if (!is.array(exp)){stop('"exp" must be an array.')} + if (!is.array(obs)){stop('"obs" must be an array.')} + if (!is.null(ref)){ + if (!is.array(ref)){stop('"ref" must be an array.')} + if (!identical(as.numeric(dim(exp)[time_dim]), as.numeric(dim(ref)[time_dim]))){stop('"time_dim" must have the same length in "exp" and "ref".')} + } + if (!identical(as.numeric(dim(exp)[time_dim]), as.numeric(dim(obs)[time_dim]))){stop('"time_dim" must have the same length in "exp" and "obs".')} + if (!is.numeric(n_categories) | n_categories < 2){stop('"n_categories" must be an integer greater than 1.')} + if (!is.logical(Fair)){stop('"Fair" must be TRUE or FALSE.')} + if (!is.character(time_dim)){stop('"time_dim" must be a string.')} + if (!is.character(member_dim)){stop('"member_dim" must be a string.')} + if (!is.null(ncores)){ + if (!is.numeric(ncores) | ncores < 1){stop('"ncores" must be either NULL or a positive integer.')} + } + + if (!is.null(ref)){ ## using "ref" as reference forecast + output <- multiApply::Apply(data = list(exp = exp, obs = obs, ref = ref), + target_dims = list(exp = c(time_dim, member_dim), obs = time_dim, ref = c(time_dim, member_dim)), + n_categories = n_categories, indices_for_clim = indices_for_clim, + Fair = Fair, time_dim = time_dim, member_dim = member_dim, + fun = .RPSS, ncores = ncores) + } else { ## using climatology as reference forecast + output <- multiApply::Apply(data = list(exp = exp, obs = obs), + target_dims = list(exp = c(time_dim, member_dim), obs = time_dim), + ref = ref, n_categories = n_categories, indices_for_clim = indices_for_clim, + Fair = Fair, time_dim = time_dim, member_dim = member_dim, + fun = .RPSS, ncores = ncores) + } + + return(output) +} + +.RPSS <- function(exp, obs, ref, n_categories, indices_for_clim, Fair, time_dim, member_dim){ + + ## RPS of the forecast + rps_exp <- .RPS(exp = exp, obs = obs, n_categories = n_categories, indices_for_clim = indices_for_clim, Fair = Fair, time_dim = time_dim, member_dim = member_dim) + + ## RPS of the reference forecast + if (is.null(ref)){ ## using climatology as reference forecast + clim_probs <- array(data = 1 / n_categories, dim = c(time = length(obs))) + obs_probs <- .get_probs(data = obs, type = 'obs', indices_for_quartiles = indices_for_clim, time_dim = time_dim, member_dim = NULL, n_categories = n_categories)$probs + rps_ref <- .RPS_from_probs(probs_exp = clim_probs, probs_obs = obs_probs, n_categories = n_categories, Fair = FALSE) + } else { ## using "ref" as reference forecast + rps_ref <- .RPS(exp = ref, obs = obs, n_categories = n_categories, indices_for_clim = indices_for_clim, Fair = Fair, time_dim = time_dim, member_dim = member_dim) + } + + ## RPSS + rpss <- 1 - mean(rps_exp) / mean(rps_ref) + + ## Significance + rps_exp <- array(data = rps_exp, dim = c(time = length(rps_exp))) + rps_ref <- array(data = rps_ref, dim = c(time = length(rps_ref))) + sign <- s2dv::RandomWalkTest(skill_A = rps_exp, skill_B = rps_ref, time_dim = 'time', ncores = NULL)$signif + + return(list(rpss = rpss, sign = sign)) +} -- GitLab From 8ec54099762aa2bf7d310d6c472abc402589aec1 Mon Sep 17 00:00:00 2001 From: Carlos Delgado Date: Sat, 5 Mar 2022 15:44:29 +0100 Subject: [PATCH 17/83] fixed dim(clim_probs) --- R/RPSS.R | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/R/RPSS.R b/R/RPSS.R index b9d59d0..fc25631 100644 --- a/R/RPSS.R +++ b/R/RPSS.R @@ -45,8 +45,8 @@ RPSS <- function(exp, obs, ref = NULL, n_categories, indices_for_clim, Fair = FA ## RPS of the reference forecast if (is.null(ref)){ ## using climatology as reference forecast - clim_probs <- array(data = 1 / n_categories, dim = c(time = length(obs))) obs_probs <- .get_probs(data = obs, type = 'obs', indices_for_quartiles = indices_for_clim, time_dim = time_dim, member_dim = NULL, n_categories = n_categories)$probs + clim_probs <- array(data = 1 / n_categories, dim = dim(obs_probs)) rps_ref <- .RPS_from_probs(probs_exp = clim_probs, probs_obs = obs_probs, n_categories = n_categories, Fair = FALSE) } else { ## using "ref" as reference forecast rps_ref <- .RPS(exp = ref, obs = obs, n_categories = n_categories, indices_for_clim = indices_for_clim, Fair = Fair, time_dim = time_dim, member_dim = member_dim) -- GitLab From 1b79f4171bbd132ded21c10269b635420a162c6d Mon Sep 17 00:00:00 2001 From: Carlos Delgado Date: Thu, 10 Mar 2022 17:02:23 +0100 Subject: [PATCH 18/83] return.na --- R/DiffCorr.R | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/R/DiffCorr.R b/R/DiffCorr.R index eea3184..3aae37f 100644 --- a/R/DiffCorr.R +++ b/R/DiffCorr.R @@ -2,7 +2,7 @@ library(s2dv) library(multiApply) -DiffCorr <- function(exp, ref, obs, conf.lev = NULL, handle.na = "na.fail", time_dim = 'year', member_dim = NULL, ncores = NULL){ +DiffCorr <- function(exp, ref, obs, conf.lev = NULL, handle.na = 'return.na', time_dim = 'year', member_dim = NULL, ncores = NULL){ ## Checkings if (!is.array(exp)){stop('"exp" must be an array.')} @@ -23,8 +23,8 @@ DiffCorr <- function(exp, ref, obs, conf.lev = NULL, handle.na = "na.fail", time ## Ensemble means if (!is.null(member_dim)){ - exp <- s2dv::MeanDims(data = exp, dims = member_dim, na.rm = FALSE, drop = 'selected') - ref <- s2dv::MeanDims(data = ref, dims = member_dim, na.rm = FALSE, drop = 'selected') + exp <- multiApply::Apply(data = exp, target_dims = member_dim, fun = mean, na.rm = FALSE, ncores = ncores)$output1 + ref <- multiApply::Apply(data = ref, target_dims = member_dim, fun = mean, na.rm = FALSE, ncores = ncores)$output1 } ## Correlation difference @@ -50,12 +50,12 @@ DiffCorr <- function(exp, ref, obs, conf.lev = NULL, handle.na = "na.fail", time exp <- exp[nna] ref <- ref[nna] obs <- obs[nna] - } else { + } else if (handle.na != "return.na") { stop("unknown 'handle.na' argument") } ## Number of effective degrees of freedom - N <- s2dv::Eno(data = obs, time_dim = time_dim, na.action = handle.na, ncores = 1) + N <- s2dv::Eno(data = obs, time_dim = time_dim, na.action = na.pass, ncores = 1) ## Correlation coefficients and their confidence intervals cor.exp <- cor(exp, obs) -- GitLab From 3a30a3f67b4100267ea743c70431b3f6fed9fcf6 Mon Sep 17 00:00:00 2001 From: Carlos Delgado Date: Thu, 10 Mar 2022 17:06:58 +0100 Subject: [PATCH 19/83] . --- R/RPS.R | 5 +++-- R/RPSS.R | 3 ++- 2 files changed, 5 insertions(+), 3 deletions(-) diff --git a/R/RPS.R b/R/RPS.R index 0081d8e..c7e7e0b 100644 --- a/R/RPS.R +++ b/R/RPS.R @@ -33,8 +33,9 @@ RPS <- function(exp, obs, n_categories, indices_for_clim, Fair = FALSE, time_dim obs_probs <- .get_probs(data = obs, type = 'obs', indices_for_quartiles = indices_for_clim, time_dim = time_dim, member_dim = NULL, n_categories = n_categories)$probs - rps <- .RPS_from_probs(probs_exp = exp_probs, probs_obs = obs_probs, n_categories = n_categories, Fair = Fair, n_members = as.numeric(dim(exp)[member_dim])) - + rps <- multiApply::Apply(data = list(probs_exp = exp_probs, probs_obs = obs_probs), target_dims = 'bin', + fun = .RPS_from_probs, n_categories = n_categories, Fair = Fair, + n_members = as.numeric(dim(exp)[member_dim]), ncores = NULL)$output1 return(rps) } diff --git a/R/RPSS.R b/R/RPSS.R index fc25631..ae6881f 100644 --- a/R/RPSS.R +++ b/R/RPSS.R @@ -47,7 +47,8 @@ RPSS <- function(exp, obs, ref = NULL, n_categories, indices_for_clim, Fair = FA if (is.null(ref)){ ## using climatology as reference forecast obs_probs <- .get_probs(data = obs, type = 'obs', indices_for_quartiles = indices_for_clim, time_dim = time_dim, member_dim = NULL, n_categories = n_categories)$probs clim_probs <- array(data = 1 / n_categories, dim = dim(obs_probs)) - rps_ref <- .RPS_from_probs(probs_exp = clim_probs, probs_obs = obs_probs, n_categories = n_categories, Fair = FALSE) + rps_ref <- multiApply::Apply(data = list(probs_exp = clim_probs, probs_obs = obs_probs), target_dims = 'bin', + fun = .RPS_from_probs, n_categories = n_categories, Fair = FALSE, ncores = NULL)$output1 } else { ## using "ref" as reference forecast rps_ref <- .RPS(exp = ref, obs = obs, n_categories = n_categories, indices_for_clim = indices_for_clim, Fair = Fair, time_dim = time_dim, member_dim = member_dim) } -- GitLab From 9c99215cf2f49aaa6624c3be06fdd9c788adcdf7 Mon Sep 17 00:00:00 2001 From: Carlos Delgado Date: Thu, 17 Mar 2022 11:19:55 +0100 Subject: [PATCH 20/83] first version (without documentation) --- R/ResidualCorr.R | 62 ++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 62 insertions(+) create mode 100644 R/ResidualCorr.R diff --git a/R/ResidualCorr.R b/R/ResidualCorr.R new file mode 100644 index 0000000..74e6a25 --- /dev/null +++ b/R/ResidualCorr.R @@ -0,0 +1,62 @@ + +library(s2dv) +library(multiApply) + +ResidualCorr <- function(exp, ref, obs, time_dim = 'year', member_dim = NULL, alpha = 0.05, ncores = 1){ + + ## Checkings + if (!is.array(exp)){stop('"exp" must be an array.')} + if (!is.array(obs)){stop('"obs" must be an array.')} + if (!is.array(ref)){stop('"ref" must be an array.')} + if (!identical(as.numeric(dim(exp)[time_dim]), as.numeric(dim(ref)[time_dim]))){stop('"time_dim" must have the same length in "exp" and "ref".')} + if (!identical(as.numeric(dim(exp)[time_dim]), as.numeric(dim(obs)[time_dim]))){stop('"time_dim" must have the same length in "exp" and "obs".')} + if (!is.numeric(alpha) | alpha <= 0 | alpha >= 1){stop('"alpha" must be a number between 0 and 1.')} + if (!is.character(time_dim)){stop('"time_dim" must be a string.')} + if (!is.null(member_dim)){ + if (!is.character(member_dim)){stop('"member_dim" must be either NULL or a string.')} + } + if (!is.null(ncores)){ + if (!is.numeric(ncores) | ncores < 1){stop('"ncores" must be either NULL or a positive integer.')} + } + + ## Ensemble mean + if (!is.null(member_dim)){ + exp <- multiApply::Apply(data = exp, target_dims = member_dim, fun = mean, na.rm = FALSE, ncores = ncores)$output1 + ref <- multiApply::Apply(data = ref, target_dims = member_dim, fun = mean, na.rm = FALSE, ncores = ncores)$output1 + } + + ## Residual correlation and its significance + output <- multiApply::Apply(data = list(exp = exp, ref = ref, obs = obs), + target_dims = time_dim, fun = .ResidualCorr, + time_dim = time_dim, alpha = alpha, ncores = ncores) + return(output) +} + +.ResidualCorr <- function(exp, ref, obs, time_dim, alpha){ + + if (!anyNA(exp) & !anyNA(ref) & !anyNA(obs)){ + + ## Residuals of 'exp' and 'obs' (regressing 'ref' out in both 'exp' and 'obs') + exp_res <- lm(formula = y ~ x, data = list(y = exp, x = ref), na.action = NULL)$residuals + obs_res <- lm(formula = y ~ x, data = list(y = obs, x = ref), na.action = NULL)$residuals + + ## Residual correlation (and significance) + residual_cor <- NULL + residual_cor$r <- cor(exp_res,obs_res) + + ## Significance + residual_cor$n_eff <- s2dv::Eno(data = obs_res, time_dim = time_dim, na.action = na.pass, ncores = 1) + t_alpha2_n2 <- qt(p=alpha/2, df = residual_cor$n_eff-2, lower.tail = FALSE) + t <- abs(residual_cor$r) * sqrt(residual_cor$n_eff-2) / sqrt(1-residual_cor$r^2) + if (anyNA(c(t,t_alpha2_n2)) == FALSE & t >= t_alpha2_n2){ + residual_cor$sign <- TRUE + } else { + residual_cor$sign <- FALSE + } + + } else { + residual_cor <- list(r = NA, n_eff = NA, sign = NA) + } + + return(residual_cor) +} -- GitLab From 61ece2ebabd2b69f82ee38153d1ad4276fcf65c5 Mon Sep 17 00:00:00 2001 From: Carlos Delgado Date: Thu, 17 Mar 2022 17:20:12 +0100 Subject: [PATCH 21/83] included documentation --- R/ResidualCorr.R | 59 ++++++++++++++++++++++++++++++++++++++++-------- 1 file changed, 50 insertions(+), 9 deletions(-) diff --git a/R/ResidualCorr.R b/R/ResidualCorr.R index 74e6a25..b9cae9d 100644 --- a/R/ResidualCorr.R +++ b/R/ResidualCorr.R @@ -1,8 +1,49 @@ - -library(s2dv) -library(multiApply) - -ResidualCorr <- function(exp, ref, obs, time_dim = 'year', member_dim = NULL, alpha = 0.05, ncores = 1){ +#'Compute the residual correlation +#' +#'The residual correlation assesses whether a forecast captures any of the observed variability that is not +#'already captured by a reference forecast (Smith et al., 2019; https://doi.org/10.1038/s41612-019-0071-y.). +#'The procedure is as follows: the residuals of the forecasts and observations are computed by linearly regressing out +#'the reference forecast's ensemble mean from the forecasts’ ensemble mean and observations, respectively. Then, the +#'residual correlation is computed as the correlation between both residuals. Positive values of the residual correlation +#'indicate that the forecast capture more observed variability than the reference forecast, while negative values mean +#'that the reference forecast capture more. The significance of the residual correlation is computed with a two-sided t-test +#'(Wilks, 2011; https://doi.org/10.1016/B978-0-12-385022-5.00008-7) using a effective degrees of freedom to account for +#'the time series' autocorrelation (von Storch and Zwiers, 1999; https://doi.org/10.1017/CBO9780511612336). +#' +#'@param exp A numerical array with the forecast with, at least, time dimension. +#'@param ref A numerical array with the reference forecast data with, at least, time dimension. +#' The length of the time dimension must be the same as for "exp". +#'@param obs A numerical array with the observations with, at least, time dimension. +#' The length of the time dimension must be the same as for "exp". +#'@param time_dim A character string indicating the name of the time dimension. +#' The default value is 'year'. +#'@param member_dim A character string indicating the name of the member dimension to compute the +#' ensemble means of the forecast and reference forecast. If it is NULL, the ensemble means should +#' be provided directly to the function. The default value is NULL. +#'@param alpha Significance level to be used in the statistical significance test. The default value +#' is 0.05. +#'@param ncores An integer indicating the number of cores to use for parallel +#' computation. The default value is NULL. +#' +#'@return A list of 2: +#'\item{$r}{ +#' A numerical array with the residual correlation with the same dimensions as the input arrays +#' except "time_dim" (and "member_dim" if provided). +#'} +#'\item{$sign}{ +#' A logical array with the statistical significance of the residual correlation with the same +#' dimensions as the input arrays except "time_dim" (and "member_dim" if provided). +#'} +#' +#'@examples +#' exp <- array(rnorm(1000), dim = c(lat = 3, lon = 2, member = 10, year = 50)) +#' ref <- array(rnorm(1000), dim = c(lat = 3, lon = 2, member = 10, year = 50)) +#' obs <- array(rnorm(1000), dim = c(lat = 3, lon = 2, year = 50)) +#' ResidualCorr(exp, ref, obs, member_dim = 'member') +#' +#'@import multiApply +#'@export +ResidualCorr <- function(exp, ref, obs, time_dim = 'year', member_dim = NULL, alpha = 0.05, ncores = NULL){ ## Checkings if (!is.array(exp)){stop('"exp" must be an array.')} @@ -45,9 +86,9 @@ ResidualCorr <- function(exp, ref, obs, time_dim = 'year', member_dim = NULL, al residual_cor$r <- cor(exp_res,obs_res) ## Significance - residual_cor$n_eff <- s2dv::Eno(data = obs_res, time_dim = time_dim, na.action = na.pass, ncores = 1) - t_alpha2_n2 <- qt(p=alpha/2, df = residual_cor$n_eff-2, lower.tail = FALSE) - t <- abs(residual_cor$r) * sqrt(residual_cor$n_eff-2) / sqrt(1-residual_cor$r^2) + n_eff <- s2dv::Eno(data = obs_res, time_dim = time_dim, na.action = na.pass, ncores = 1) + t_alpha2_n2 <- qt(p = alpha/2, df = n_eff-2, lower.tail = FALSE) + t <- abs(residual_cor$r) * sqrt(n_eff-2) / sqrt(1-residual_cor$r^2) if (anyNA(c(t,t_alpha2_n2)) == FALSE & t >= t_alpha2_n2){ residual_cor$sign <- TRUE } else { @@ -55,7 +96,7 @@ ResidualCorr <- function(exp, ref, obs, time_dim = 'year', member_dim = NULL, al } } else { - residual_cor <- list(r = NA, n_eff = NA, sign = NA) + residual_cor <- list(r = NA, sign = NA) } return(residual_cor) -- GitLab From 3b4232a3bcf139bee665635fc226561ba71d66de Mon Sep 17 00:00:00 2001 From: Carlos Delgado Date: Thu, 17 Mar 2022 17:55:48 +0100 Subject: [PATCH 22/83] included documentation --- R/DiffCorr.R | 134 ++++++++++++++++++++++++++------------------------- 1 file changed, 68 insertions(+), 66 deletions(-) diff --git a/R/DiffCorr.R b/R/DiffCorr.R index 3aae37f..325f16f 100644 --- a/R/DiffCorr.R +++ b/R/DiffCorr.R @@ -1,8 +1,47 @@ - -library(s2dv) -library(multiApply) - -DiffCorr <- function(exp, ref, obs, conf.lev = NULL, handle.na = 'return.na', time_dim = 'year', member_dim = NULL, ncores = NULL){ +#'Compute the correlation difference and its significance +#' +#'Positive values of the correlation difference indicate that the forecast is more skilful +#'than the reference forecast, while negative values mean that the reference forecast is +#'more skilful. The statistical significance of the correlation differences is computed with +#'a one-sided test for equality of dependent correlation coefficients (Steiger, 1980; +#'https://content.apa.org/doi/10.1037/0033-2909.87.2.245; #'Siegert et al., 2017; +#'https://doi.org/10.1175/MWR-D-16-0037.1) using a effective degrees of freedom to account for +#'the time series' autocorrelation (von Storch and Zwiers, 1999; https://doi.org/10.1017/CBO9780511612336). +#' +#'@param exp A numerical array with the forecast with, at least, time dimension. +#'@param ref A numerical array with the reference forecast data with, at least, time dimension. +#' The length of the time dimension must be the same as for "exp". +#'@param obs A numerical array with the observations with, at least, time dimension. +#' The length of the time dimension must be the same as for "exp". +#'@param time_dim A character string indicating the name of the time dimension. +#' The default value is 'year'. +#'@param member_dim A character string indicating the name of the member dimension to compute the +#' ensemble means of the forecast and reference forecast. If it is NULL, the ensemble means should +#' be provided directly to the function. The default value is NULL. +#'@param alpha Significance level to be used in the statistical significance test. The default value +#' is 0.05. +#'@param ncores An integer indicating the number of cores to use for parallel +#' computation. The default value is NULL. +#' +#'@return A list of 2: +#'\item{$r}{ +#' A numerical array with the correlation differences with the same dimensions as the input arrays +#' except "time_dim" (and "member_dim" if provided). +#'} +#'\item{$sign}{ +#' A logical array with the statistical significance of the correlation differences with the same +#' dimensions as the input arrays except "time_dim" (and "member_dim" if provided). +#'} +#' +#'@examples +#' exp <- array(rnorm(1000), dim = c(lat = 3, lon = 2, member = 10, year = 50)) +#' ref <- array(rnorm(1000), dim = c(lat = 3, lon = 2, member = 10, year = 50)) +#' obs <- array(rnorm(1000), dim = c(lat = 3, lon = 2, year = 50)) +#' DiffCorr(exp, ref, obs, member_dim = 'member') +#' +#'@import multiApply +#'@export +DiffCorr <- function(exp, ref, obs, time_dim = 'year', member_dim = NULL, alpha = 0.05, ncores = NULL){ ## Checkings if (!is.array(exp)){stop('"exp" must be an array.')} @@ -10,13 +49,11 @@ DiffCorr <- function(exp, ref, obs, conf.lev = NULL, handle.na = 'return.na', ti if (!is.array(ref)){stop('"ref" must be an array.')} if (!identical(as.numeric(dim(exp)[time_dim]), as.numeric(dim(ref)[time_dim]))){stop('"time_dim" must have the same length in "exp" and "ref".')} if (!identical(as.numeric(dim(exp)[time_dim]), as.numeric(dim(obs)[time_dim]))){stop('"time_dim" must have the same length in "exp" and "obs".')} - if (!is.null(conf.lev)){ - if (!is.numeric(conf.lev) | conf.lev <= 0 | conf.lev >= 1){stop('"conf.lev" must be either NULL or a number between 0 and 1.')} - } if (!is.character(time_dim)){stop('"time_dim" must be a string.')} if (!is.null(member_dim)){ if (!is.character(member_dim)){stop('"member_dim" must be either NULL or a string.')} } + if (!is.numeric(alpha) | alpha <= 0 | alpha >= 1){stop('"alpha" must be a number between 0 and 1.')} if (!is.null(ncores)){ if (!is.numeric(ncores) | ncores < 1){stop('"ncores" must be either NULL or a positive integer.')} } @@ -30,72 +67,37 @@ DiffCorr <- function(exp, ref, obs, conf.lev = NULL, handle.na = 'return.na', ti ## Correlation difference output <- multiApply::Apply(data = list(exp = exp, ref = ref, obs = obs), target_dims = time_dim, fun = .DiffCorr, - conf.lev = conf.lev, handle.na = handle.na, - time_dim = time_dim, ncores = ncores) + time_dim = time_dim, alpha = alpha, ncores = ncores) return(output) } -.DiffCorr <- function(exp, ref, obs, conf.lev, handle.na, time_dim) { - - ## handle NA's - if (handle.na == "na.fail") { - if (any(is.na(c(exp, ref, obs)))) { - stop("There are missing values.") - } - } else if (handle.na == "only.complete.triplets") { - nna <- !is.na(fcst) & !is.na(fcst.ref) & !is.na(obs) - if (all(nna == FALSE)) { - stop("There are no complete sets of forecasts and observations") - } - exp <- exp[nna] - ref <- ref[nna] - obs <- obs[nna] - } else if (handle.na != "return.na") { - stop("unknown 'handle.na' argument") - } - - ## Number of effective degrees of freedom - N <- s2dv::Eno(data = obs, time_dim = time_dim, na.action = na.pass, ncores = 1) +.DiffCorr <- function(exp, ref, obs, time_dim, alpha) { - ## Correlation coefficients and their confidence intervals - cor.exp <- cor(exp, obs) - cor.ref <- cor(ref, obs) - - ## Correlation difference - output <- NULL - output$corr.diff <- cor.exp - cor.ref - - ## auxiliary quantities - r12 <- cor.exp - r13 <- cor.ref - r23 <- cor(exp, ref) - - ## confidence interval, according to Zou (2007) - if (!is.null(conf.lev)){ - - ## individual confidence limits of cor.exp and cor.ref (not used) - # z.exp <- 0.5 * log((1 + cor.exp) / (1 - cor.exp)) - # z.ref <- 0.5 * log((1 + cor.ref) / (1 - cor.ref)) - - alpha <- (1. - conf.lev) / 2. - l1 <- tanh(atanh(r12) + qnorm(alpha)/sqrt(N-3)) - u1 <- tanh(atanh(r12) + qnorm(1-alpha)/sqrt(N-3)) - l2 <- tanh(atanh(r13) + qnorm(alpha)/sqrt(N-3)) - u2 <- tanh(atanh(r13) + qnorm(1-alpha)/sqrt(N-3)) + if (!anyNA(exp) & !anyNA(ref) & !anyNA(obs)){ - ## Correlation between the two corr.coef r12 and r13 - c.12.13 <- ((r23 - 0.5 * r12 * r13) * (1 - r12*r12 - r13*r13 - r23*r23) + r23*r23*r23) / ((1 - r12*r12) * (1 - r13*r13)) + ## Correlation difference + cor.exp <- cor(exp, obs) + cor.ref <- cor(ref, obs) + output <- NULL + output$corr.diff <- cor.exp - cor.ref - ## Confidence interval - output$conf.lower <- r12 - r13 - sqrt((r12-l1)^2 + (u2 - r13)^2 - 2*c.12.13*(r12-l1)*(u2-r13)) - output$conf.upper <- r12 - r13 + sqrt((u1 - r12)^2 + (r13-l2)^2 - 2*c.12.13*(u1-r12)*(r13-l2)) + ## Significance with one-sided test for equality of dependent correlation coefficients (Steiger, 1980) + r12 <- cor.exp + r13 <- cor.ref + r23 <- cor(exp, ref) + N <- s2dv::Eno(data = obs, time_dim = time_dim, na.action = na.pass, ncores = 1) ## effective degrees of freedom + R <- (1-r12*r12-r13*r13-r23*r23) + 2*r12*r13*r23 + t <- (r12 - r13) * sqrt((N-1)*(1+r23) / (2 * ((N-1)/(N-3)) * R + 0.25*(r12+r13)^2 * (1-r23)^3)) + p.value <- 1 - pt(t, df = N-3) + if (p.value <= alpha){ + output$sign <- TRUE + } else { + output$sign <- FALSE + } + } else { + output <- list(corr.diff = NA, sign = NA) } - ## p-value of one-sided test for equality of dependent correlation coefficients (Steiger, 1980) - R <- (1-r12*r12-r13*r13-r23*r23) + 2*r12*r13*r23 - t <- (r12 - r13) * sqrt((N-1)*(1+r23) / (2 * ((N-1)/(N-3)) * R + 0.25*(r12+r13)^2 * (1-r23)^3)) - output$p.value <- 1 - pt(t, df=N-3) - return(output) } -- GitLab From 6e50719a3978f29cad701afcf85ea1bc187de60a Mon Sep 17 00:00:00 2001 From: Carlos Delgado Date: Thu, 17 Mar 2022 17:56:29 +0100 Subject: [PATCH 23/83] . --- R/ResidualCorr.R | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/R/ResidualCorr.R b/R/ResidualCorr.R index b9cae9d..a69178a 100644 --- a/R/ResidualCorr.R +++ b/R/ResidualCorr.R @@ -1,4 +1,4 @@ -#'Compute the residual correlation +#'Compute the residual correlation and its significance #' #'The residual correlation assesses whether a forecast captures any of the observed variability that is not #'already captured by a reference forecast (Smith et al., 2019; https://doi.org/10.1038/s41612-019-0071-y.). -- GitLab From a41f8bb1cba9567adc154245c7286563dd06debd Mon Sep 17 00:00:00 2001 From: Carlos Delgado Torres Date: Thu, 17 Mar 2022 18:21:56 +0100 Subject: [PATCH 24/83] . --- R/DiffCorr.R | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/R/DiffCorr.R b/R/DiffCorr.R index 325f16f..15538e0 100644 --- a/R/DiffCorr.R +++ b/R/DiffCorr.R @@ -4,7 +4,7 @@ #'than the reference forecast, while negative values mean that the reference forecast is #'more skilful. The statistical significance of the correlation differences is computed with #'a one-sided test for equality of dependent correlation coefficients (Steiger, 1980; -#'https://content.apa.org/doi/10.1037/0033-2909.87.2.245; #'Siegert et al., 2017; +#'https://content.apa.org/doi/10.1037/0033-2909.87.2.245; Siegert et al., 2017; #'https://doi.org/10.1175/MWR-D-16-0037.1) using a effective degrees of freedom to account for #'the time series' autocorrelation (von Storch and Zwiers, 1999; https://doi.org/10.1017/CBO9780511612336). #' -- GitLab From 178932ae938d67aa1d9dbb3f25c212f0b028ea61 Mon Sep 17 00:00:00 2001 From: Carlos Delgado Date: Thu, 17 Mar 2022 18:46:42 +0100 Subject: [PATCH 25/83] corrected typo in documentation --- R/RandomWalkTest.R | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/R/RandomWalkTest.R b/R/RandomWalkTest.R index 494be65..adeadc1 100644 --- a/R/RandomWalkTest.R +++ b/R/RandomWalkTest.R @@ -1,8 +1,8 @@ #'Random walk test for skill differences #' #'Forecast comparison of the skill obtained with 2 forecasts (with respect to a -#'common reference) based on Random Walks, with significance estimate at the 5% -#'confidence level, as in DelSole and Tippett (2015). +#'common reference) based on Random Walks, with significance estimate at the 95% +#'confidence level, as in DelSole and Tippett (2016). #' #'@param skill_A A numerical array of the time series of the skill with the #' forecaster A's. -- GitLab From 43c6d69b5e6ddb57e546d8a732c32d90d5768764 Mon Sep 17 00:00:00 2001 From: Carlos Delgado Date: Thu, 17 Mar 2022 18:55:35 +0100 Subject: [PATCH 26/83] included documentation --- R/RPS.R | 47 ++++++++++++++++++++++++++++++++++++++++++----- R/RPSS.R | 52 +++++++++++++++++++++++++++++++++++++++++++++++----- 2 files changed, 89 insertions(+), 10 deletions(-) diff --git a/R/RPS.R b/R/RPS.R index c7e7e0b..1cf25a4 100644 --- a/R/RPS.R +++ b/R/RPS.R @@ -1,8 +1,41 @@ - -library(multiApply) -library(easyVerification) - -RPS <- function(exp, obs, n_categories, indices_for_clim, Fair = FALSE, time_dim = 'year', member_dim = 'member', ncores = NULL){ +#'Compute the Ranked Probability Score +#' +#'The Ranked Probability Score (RPS; Wilks, 2011; https://doi.org/10.1016/B978-0-12-385022-5.00008-7) is defined +#'as the sum of the squared differences between the cumulative forecast probabilities (computed from the ensemble +#'members) and the observations (defined as 0% if the category did not happen and 100% if it happened). It can be +#'used to evaluate the skill of multi-categorical probabilistic forecasts. The RPS ranges between 0 (perfect +#'forecast) and n-1 (worst possible forecast), where n is the number of categories. In the case of a forecast +#'divided into two categories (the lowest number of categories that a probabilistic forecast can provide), the +#'RPS corresponds to the Brier Score (BS; Wilks, 2011) and, therefore, ranges between 0 and 1. +#' +#'@param exp A numerical array with the forecast with, at least, time dimension. +#'@param obs A numerical array with the observations with, at least, time dimension. +#' The length of the time dimension must be the same as for "exp". +#'@param indices_for_clim A vector with the indices to be taken along "time_dim" dimension for computing the thresholds +#' between the probabilistic categories. +#'@param n_categories An integer with the number of probabilistic categories. The default value is 3 (tercile categories). +#'@param Fair A logical indicating whether to compute the FairRPSS (the potential RPSS that the forecast would have with +#'an infinite ensemble size). The default value is FALSE. +#'@param time_dim A character string indicating the name of the time dimension. +#' The default value is 'year'. +#'@param member_dim A character string indicating the name of the member dimension to compute the +#' ensemble means of the forecast and reference forecast. If it is NULL, the ensemble means should +#' be provided directly to the function. The default value is 'member'. +#'@param ncores An integer indicating the number of cores to use for parallel +#' computation. The default value is NULL. +#' +#'@return +#' A numerical array with the RPS with the same dimensions as "exp" except the "time_dim" and "member_dim" dimensions. +#' +#'@examples +#' exp <- array(rnorm(1000), dim = c(lat = 3, lon = 2, member = 10, year = 50)) +#' obs <- array(rnorm(1000), dim = c(lat = 3, lon = 2, year = 50)) +#' RPS(exp, obs, indices_for_clim = 1:50) +#' +#'@import multiApply +#'@importFrom easyVerification convert2prob +#'@export +RPS <- function(exp, obs, indices_for_clim, n_categories = 3, Fair = FALSE, time_dim = 'year', member_dim = 'member', ncores = NULL){ ## Checkings if (!is.array(exp)){stop('"exp" must be an array.')} @@ -22,6 +55,10 @@ RPS <- function(exp, obs, n_categories, indices_for_clim, Fair = FALSE, time_dim n_categories = n_categories, indices_for_clim = indices_for_clim, Fair = Fair, time_dim = time_dim, member_dim = member_dim, fun = .RPS, ncores = ncores)$output1 + + ## Returning only the mean RPS + rps <- multiApply::Apply(data = rps, target_dims = time_dim, fun = mean, na.rm = FALSE, ncores = ncores)$output1 + return(rps) } diff --git a/R/RPSS.R b/R/RPSS.R index ae6881f..a06de09 100644 --- a/R/RPSS.R +++ b/R/RPSS.R @@ -1,9 +1,51 @@ +#'Compute the Ranked Probability Skill Score +#' +#'The Ranked Probability Skill Score (RPSS; Wilks, 2011; https://doi.org/10.1016/B978-0-12-385022-5.00008-7) +#'is the skill score based on the Ranked Probability Score (RPS; Wilks, 2011). It can be used to assess whether +#'a forecast presents an improvement or worsening with respect to a reference forecast. The RPSS ranges between +#'minus infinite and 1. If the RPSS is positive, it indicates that the forecast has higher skill than the +#'reference forecast, while a negative value means that it has a lower skill. Examples of reference forecasts +#'are the climatological forecast (same probabilities for all categories for all time steps), persistence, a previous model version, +#'and another model. It is computed as RPSS = 1 - RPS_exp / RPS_ref. The statistical significance is obtained based +#'on a Random Walk test at the 95% confidence level (DelSole and Tippett, 2016; https://doi.org/10.1175/MWR-D-15-0218.1) +#' +#'@param exp A numerical array with the forecast with, at least, time dimension. +#'@param obs A numerical array with the observations with, at least, time dimension. +#' The length of the time dimension must be the same as for "exp". +#'@param ref NULL or a numerical array with the reference forecast with, at least, time dimension. +#' The length of the time dimension must be the same as for "exp". If it is NULL, the climatological forecast is +#' used as reference forecast. The default value is NULL. +#'@param indices_for_clim A vector with the indices to be taken along "time_dim" dimension for computing the thresholds +#' between the probabilistic categories. +#'@param n_categories An integer with the number of probabilistic categories. The default value is 3 (tercile categories). +#'@param Fair A logical indicating whether to compute the FairRPSS (the potential RPSS that the forecast would have with +#'an infinite ensemble size). The default value is FALSE. +#'@param time_dim A character string indicating the name of the time dimension. +#' The default value is 'year'. +#'@param member_dim A character string indicating the name of the member dimension to compute the +#' ensemble means of the forecast and reference forecast. If it is NULL, the ensemble means should +#' be provided directly to the function. The default value is 'member'. +#'@param ncores An integer indicating the number of cores to use for parallel +#' computation. The default value is NULL. +#' +#'@return +#'\item{$rpss}{ +#' A numerical array with the RPSS with the same dimensions as "exp" except the "time_dim" +#' and "member_dim" dimensions. +#'} +#'\item{$sign}{ +#' A logical array with the statistical significance of the RPSS with the same dimensions +#' as "exp" except the "time_dim" and "member_dim" dimensions. +#'} -library(multiApply) -library(easyVerification) -library(s2dv) - -RPSS <- function(exp, obs, ref = NULL, n_categories, indices_for_clim, Fair = FALSE, time_dim = 'year', member_dim = 'member', ncores = NULL){ +#'@examples +#' exp <- array(rnorm(1000), dim = c(lat = 3, lon = 2, member = 10, year = 50)) +#' obs <- array(rnorm(1000), dim = c(lat = 3, lon = 2, year = 50)) +#' RPSS(exp, obs, indices_for_clim = 1:50) +#' +#'@import multiApply +#'@export +RPSS <- function(exp, obs, ref = NULL, indices_for_clim, n_categories = 3, Fair = FALSE, time_dim = 'year', member_dim = 'member', ncores = NULL){ ## Checkings if (!is.array(exp)){stop('"exp" must be an array.')} -- GitLab From bc63bef7eb49cfbc8854e749c415605cf852cbb8 Mon Sep 17 00:00:00 2001 From: Carlos Delgado Date: Thu, 24 Mar 2022 13:29:13 +0100 Subject: [PATCH 27/83] added method parameter to allow computing pearson, kendall and spearman coefficients --- R/ResidualCorr.R | 12 ++++++++---- 1 file changed, 8 insertions(+), 4 deletions(-) diff --git a/R/ResidualCorr.R b/R/ResidualCorr.R index a69178a..bd551e5 100644 --- a/R/ResidualCorr.R +++ b/R/ResidualCorr.R @@ -20,6 +20,8 @@ #'@param member_dim A character string indicating the name of the member dimension to compute the #' ensemble means of the forecast and reference forecast. If it is NULL, the ensemble means should #' be provided directly to the function. The default value is NULL. +#'@param method A character string indicating the correlation coefficient to be computed ("pearson", +#' "kendall", or "spearman"). The default value is "pearson". #'@param alpha Significance level to be used in the statistical significance test. The default value #' is 0.05. #'@param ncores An integer indicating the number of cores to use for parallel @@ -43,7 +45,7 @@ #' #'@import multiApply #'@export -ResidualCorr <- function(exp, ref, obs, time_dim = 'year', member_dim = NULL, alpha = 0.05, ncores = NULL){ +ResidualCorr <- function(exp, ref, obs, time_dim = 'year', member_dim = NULL, method = 'pearson', alpha = 0.05, ncores = NULL){ ## Checkings if (!is.array(exp)){stop('"exp" must be an array.')} @@ -56,6 +58,7 @@ ResidualCorr <- function(exp, ref, obs, time_dim = 'year', member_dim = NULL, al if (!is.null(member_dim)){ if (!is.character(member_dim)){stop('"member_dim" must be either NULL or a string.')} } + if (!method %in% c("pearson","kendall","spearman")){stop('"method" must be "pearson", "kendall", or "spearman".')} if (!is.null(ncores)){ if (!is.numeric(ncores) | ncores < 1){stop('"ncores" must be either NULL or a positive integer.')} } @@ -69,11 +72,12 @@ ResidualCorr <- function(exp, ref, obs, time_dim = 'year', member_dim = NULL, al ## Residual correlation and its significance output <- multiApply::Apply(data = list(exp = exp, ref = ref, obs = obs), target_dims = time_dim, fun = .ResidualCorr, - time_dim = time_dim, alpha = alpha, ncores = ncores) + time_dim = time_dim, method = method, + alpha = alpha, ncores = ncores) return(output) } -.ResidualCorr <- function(exp, ref, obs, time_dim, alpha){ +.ResidualCorr <- function(exp, ref, obs, time_dim, method, alpha){ if (!anyNA(exp) & !anyNA(ref) & !anyNA(obs)){ @@ -83,7 +87,7 @@ ResidualCorr <- function(exp, ref, obs, time_dim = 'year', member_dim = NULL, al ## Residual correlation (and significance) residual_cor <- NULL - residual_cor$r <- cor(exp_res,obs_res) + residual_cor$r <- cor(x = exp_res, y = obs_res, method = method) ## Significance n_eff <- s2dv::Eno(data = obs_res, time_dim = time_dim, na.action = na.pass, ncores = 1) -- GitLab From 69809a06d380fb457ab6ff972390e4c4dedd4575 Mon Sep 17 00:00:00 2001 From: Carlos Delgado Date: Thu, 24 Mar 2022 13:56:41 +0100 Subject: [PATCH 28/83] added method parameter to allow computing pearson, kendall and spearman coefficients --- R/DiffCorr.R | 14 +++++++++----- 1 file changed, 9 insertions(+), 5 deletions(-) diff --git a/R/DiffCorr.R b/R/DiffCorr.R index 15538e0..3ebe6dd 100644 --- a/R/DiffCorr.R +++ b/R/DiffCorr.R @@ -18,6 +18,8 @@ #'@param member_dim A character string indicating the name of the member dimension to compute the #' ensemble means of the forecast and reference forecast. If it is NULL, the ensemble means should #' be provided directly to the function. The default value is NULL. +#'@param method A character string indicating the correlation coefficient to be computed ("pearson", +#' "kendall", or "spearman"). The default value is "pearson". #'@param alpha Significance level to be used in the statistical significance test. The default value #' is 0.05. #'@param ncores An integer indicating the number of cores to use for parallel @@ -41,7 +43,7 @@ #' #'@import multiApply #'@export -DiffCorr <- function(exp, ref, obs, time_dim = 'year', member_dim = NULL, alpha = 0.05, ncores = NULL){ +DiffCorr <- function(exp, ref, obs, time_dim = 'year', member_dim = NULL, method = 'pearson', alpha = 0.05, ncores = NULL){ ## Checkings if (!is.array(exp)){stop('"exp" must be an array.')} @@ -53,6 +55,7 @@ DiffCorr <- function(exp, ref, obs, time_dim = 'year', member_dim = NULL, alpha if (!is.null(member_dim)){ if (!is.character(member_dim)){stop('"member_dim" must be either NULL or a string.')} } + if (!method %in% c("pearson","kendall","spearman")){stop('"method" must be "pearson", "kendall", or "spearman".')} if (!is.numeric(alpha) | alpha <= 0 | alpha >= 1){stop('"alpha" must be a number between 0 and 1.')} if (!is.null(ncores)){ if (!is.numeric(ncores) | ncores < 1){stop('"ncores" must be either NULL or a positive integer.')} @@ -67,17 +70,18 @@ DiffCorr <- function(exp, ref, obs, time_dim = 'year', member_dim = NULL, alpha ## Correlation difference output <- multiApply::Apply(data = list(exp = exp, ref = ref, obs = obs), target_dims = time_dim, fun = .DiffCorr, - time_dim = time_dim, alpha = alpha, ncores = ncores) + time_dim = time_dim, method = method, + alpha = alpha, ncores = ncores) return(output) } -.DiffCorr <- function(exp, ref, obs, time_dim, alpha) { +.DiffCorr <- function(exp, ref, obs, time_dim, method, alpha) { if (!anyNA(exp) & !anyNA(ref) & !anyNA(obs)){ ## Correlation difference - cor.exp <- cor(exp, obs) - cor.ref <- cor(ref, obs) + cor.exp <- cor(x = exp, y = obs, method = method) + cor.ref <- cor(x = ref, y = obs, method = method) output <- NULL output$corr.diff <- cor.exp - cor.ref -- GitLab From 7fa4531030744435bdee06a87993766136bcac14 Mon Sep 17 00:00:00 2001 From: Carlos Delgado Date: Fri, 25 Mar 2022 12:02:00 +0100 Subject: [PATCH 29/83] indices_for_clim = NULL by default (to use the whole period for climatology) --- R/RPS.R | 9 +++++++-- R/RPSS.R | 9 +++++++-- 2 files changed, 14 insertions(+), 4 deletions(-) diff --git a/R/RPS.R b/R/RPS.R index 1cf25a4..4cd5120 100644 --- a/R/RPS.R +++ b/R/RPS.R @@ -12,7 +12,7 @@ #'@param obs A numerical array with the observations with, at least, time dimension. #' The length of the time dimension must be the same as for "exp". #'@param indices_for_clim A vector with the indices to be taken along "time_dim" dimension for computing the thresholds -#' between the probabilistic categories. +#' between the probabilistic categories. If NULL, the whole period is used. The default value is NULL. #'@param n_categories An integer with the number of probabilistic categories. The default value is 3 (tercile categories). #'@param Fair A logical indicating whether to compute the FairRPSS (the potential RPSS that the forecast would have with #'an infinite ensemble size). The default value is FALSE. @@ -35,12 +35,17 @@ #'@import multiApply #'@importFrom easyVerification convert2prob #'@export -RPS <- function(exp, obs, indices_for_clim, n_categories = 3, Fair = FALSE, time_dim = 'year', member_dim = 'member', ncores = NULL){ +RPS <- function(exp, obs, indices_for_clim = NULL, n_categories = 3, Fair = FALSE, time_dim = 'year', member_dim = 'member', ncores = NULL){ ## Checkings if (!is.array(exp)){stop('"exp" must be an array.')} if (!is.array(obs)){stop('"obs" must be an array.')} if (!identical(as.numeric(dim(exp)[time_dim]), as.numeric(dim(obs)[time_dim]))){stop('"time_dim" must have the same length in "exp" and "obs".')} + if (is.null(indices_for_clim)){ + indices_for_clim <- 1:as.numeric(dim(obs)[time_dim]) + } else if (!is.numeric(indices_for_clim)){ + stop('"indices_for_clim" must be NULL or a numeric vector.') + } if (!is.numeric(n_categories) | n_categories < 2){stop('"n_categories" must be an integer greater than 1.')} if (!is.logical(Fair)){stop('"Fair" must be TRUE or FALSE.')} if (!is.character(time_dim)){stop('"time_dim" must be a string.')} diff --git a/R/RPSS.R b/R/RPSS.R index a06de09..59005c2 100644 --- a/R/RPSS.R +++ b/R/RPSS.R @@ -16,7 +16,7 @@ #' The length of the time dimension must be the same as for "exp". If it is NULL, the climatological forecast is #' used as reference forecast. The default value is NULL. #'@param indices_for_clim A vector with the indices to be taken along "time_dim" dimension for computing the thresholds -#' between the probabilistic categories. +#' between the probabilistic categories. If NULL, the whole period is used. The default value is NULL. #'@param n_categories An integer with the number of probabilistic categories. The default value is 3 (tercile categories). #'@param Fair A logical indicating whether to compute the FairRPSS (the potential RPSS that the forecast would have with #'an infinite ensemble size). The default value is FALSE. @@ -45,7 +45,7 @@ #' #'@import multiApply #'@export -RPSS <- function(exp, obs, ref = NULL, indices_for_clim, n_categories = 3, Fair = FALSE, time_dim = 'year', member_dim = 'member', ncores = NULL){ +RPSS <- function(exp, obs, ref = NULL, indices_for_clim = NULL, n_categories = 3, Fair = FALSE, time_dim = 'year', member_dim = 'member', ncores = NULL){ ## Checkings if (!is.array(exp)){stop('"exp" must be an array.')} @@ -55,6 +55,11 @@ RPSS <- function(exp, obs, ref = NULL, indices_for_clim, n_categories = 3, Fair if (!identical(as.numeric(dim(exp)[time_dim]), as.numeric(dim(ref)[time_dim]))){stop('"time_dim" must have the same length in "exp" and "ref".')} } if (!identical(as.numeric(dim(exp)[time_dim]), as.numeric(dim(obs)[time_dim]))){stop('"time_dim" must have the same length in "exp" and "obs".')} + if (is.null(indices_for_clim)){ + indices_for_clim <- 1:as.numeric(dim(obs)[time_dim]) + } else if (!is.numeric(indices_for_clim)){ + stop('"indices_for_clim" must be NULL or a numeric vector.') + } if (!is.numeric(n_categories) | n_categories < 2){stop('"n_categories" must be an integer greater than 1.')} if (!is.logical(Fair)){stop('"Fair" must be TRUE or FALSE.')} if (!is.character(time_dim)){stop('"time_dim" must be a string.')} -- GitLab From 147a91b8e254fa0f7fa25e205f6497cbbab4a84f Mon Sep 17 00:00:00 2001 From: Carlos Delgado Date: Fri, 25 Mar 2022 17:03:15 +0100 Subject: [PATCH 30/83] p.val can be return. N.eff can be defined --- R/DiffCorr.R | 31 +++++++++++++++++++++---------- 1 file changed, 21 insertions(+), 10 deletions(-) diff --git a/R/DiffCorr.R b/R/DiffCorr.R index 3ebe6dd..1def12d 100644 --- a/R/DiffCorr.R +++ b/R/DiffCorr.R @@ -13,6 +13,8 @@ #' The length of the time dimension must be the same as for "exp". #'@param obs A numerical array with the observations with, at least, time dimension. #' The length of the time dimension must be the same as for "exp". +#'@param N.eff Effective sample size to be used in the statistical significance test. +#' If NA, it is computed with the Eno() function. The default value is NA. #'@param time_dim A character string indicating the name of the time dimension. #' The default value is 'year'. #'@param member_dim A character string indicating the name of the member dimension to compute the @@ -20,6 +22,7 @@ #' be provided directly to the function. The default value is NULL. #'@param method A character string indicating the correlation coefficient to be computed ("pearson", #' "kendall", or "spearman"). The default value is "pearson". +#'@param p.val Logical indicating whether to regurn the p-value. The default value is TRUE. #'@param alpha Significance level to be used in the statistical significance test. The default value #' is 0.05. #'@param ncores An integer indicating the number of cores to use for parallel @@ -34,6 +37,10 @@ #' A logical array with the statistical significance of the correlation differences with the same #' dimensions as the input arrays except "time_dim" (and "member_dim" if provided). #'} +#'\item{$p.val}{ +#' A numeric array with the p-values with the same dimensions as the input arrays except "time_dim" +#' (and "member_dim" if provided). +#'} #' #'@examples #' exp <- array(rnorm(1000), dim = c(lat = 3, lon = 2, member = 10, year = 50)) @@ -43,7 +50,7 @@ #' #'@import multiApply #'@export -DiffCorr <- function(exp, ref, obs, time_dim = 'year', member_dim = NULL, method = 'pearson', alpha = 0.05, ncores = NULL){ +DiffCorr <- function(exp, ref, obs, N.eff = NA, time_dim = 'year', member_dim = NULL, method = 'pearson', p.val = TRUE, alpha = 0.05, ncores = NULL){ ## Checkings if (!is.array(exp)){stop('"exp" must be an array.')} @@ -51,11 +58,11 @@ DiffCorr <- function(exp, ref, obs, time_dim = 'year', member_dim = NULL, method if (!is.array(ref)){stop('"ref" must be an array.')} if (!identical(as.numeric(dim(exp)[time_dim]), as.numeric(dim(ref)[time_dim]))){stop('"time_dim" must have the same length in "exp" and "ref".')} if (!identical(as.numeric(dim(exp)[time_dim]), as.numeric(dim(obs)[time_dim]))){stop('"time_dim" must have the same length in "exp" and "obs".')} + if (!is.na(N.eff) & !is.numeric(N.eff)){stop('"N.eff" must be NA or numeric.')} if (!is.character(time_dim)){stop('"time_dim" must be a string.')} - if (!is.null(member_dim)){ - if (!is.character(member_dim)){stop('"member_dim" must be either NULL or a string.')} - } + if (!is.null(member_dim) & !is.character(member_dim)){stop('"member_dim" must be either NULL or a string.')} if (!method %in% c("pearson","kendall","spearman")){stop('"method" must be "pearson", "kendall", or "spearman".')} + if (!is.logical(p.val)){stop('"p.val" must be TRUE or FALSE.')} if (!is.numeric(alpha) | alpha <= 0 | alpha >= 1){stop('"alpha" must be a number between 0 and 1.')} if (!is.null(ncores)){ if (!is.numeric(ncores) | ncores < 1){stop('"ncores" must be either NULL or a positive integer.')} @@ -70,12 +77,12 @@ DiffCorr <- function(exp, ref, obs, time_dim = 'year', member_dim = NULL, method ## Correlation difference output <- multiApply::Apply(data = list(exp = exp, ref = ref, obs = obs), target_dims = time_dim, fun = .DiffCorr, - time_dim = time_dim, method = method, - alpha = alpha, ncores = ncores) + N.eff = N.eff, time_dim = time_dim, method = method, + p.val = p.val, alpha = alpha, ncores = ncores) return(output) } -.DiffCorr <- function(exp, ref, obs, time_dim, method, alpha) { +.DiffCorr <- function(exp, ref, obs, N.eff, time_dim, method, p.val, alpha) { if (!anyNA(exp) & !anyNA(ref) & !anyNA(obs)){ @@ -89,18 +96,22 @@ DiffCorr <- function(exp, ref, obs, time_dim = 'year', member_dim = NULL, method r12 <- cor.exp r13 <- cor.ref r23 <- cor(exp, ref) - N <- s2dv::Eno(data = obs, time_dim = time_dim, na.action = na.pass, ncores = 1) ## effective degrees of freedom + if (is.na(N.eff)){ + N.eff <- s2dv::Eno(data = obs, time_dim = time_dim, na.action = na.pass, ncores = 1) ## effective degrees of freedom + } R <- (1-r12*r12-r13*r13-r23*r23) + 2*r12*r13*r23 - t <- (r12 - r13) * sqrt((N-1)*(1+r23) / (2 * ((N-1)/(N-3)) * R + 0.25*(r12+r13)^2 * (1-r23)^3)) - p.value <- 1 - pt(t, df = N-3) + t <- (r12 - r13) * sqrt((N.eff-1)*(1+r23) / (2 * ((N.eff-1)/(N.eff-3)) * R + 0.25*(r12+r13)^2 * (1-r23)^3)) + p.value <- 1 - pt(t, df = N.eff-3) if (p.value <= alpha){ output$sign <- TRUE } else { output$sign <- FALSE } + if (isTRUE(p.val)){output$p.val <- p.value} } else { output <- list(corr.diff = NA, sign = NA) + if (isTRUE(p.val)){output$p.val <- NA} } return(output) -- GitLab From dc9d0784af3157ef03c6452ab9fc45e3df4195b6 Mon Sep 17 00:00:00 2001 From: Carlos Delgado Date: Fri, 25 Mar 2022 17:14:10 +0100 Subject: [PATCH 31/83] sign=FALSE is return if p.val=NA (e.g., if there is variance is zero) --- R/DiffCorr.R | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/R/DiffCorr.R b/R/DiffCorr.R index 1def12d..c35ee91 100644 --- a/R/DiffCorr.R +++ b/R/DiffCorr.R @@ -102,7 +102,7 @@ DiffCorr <- function(exp, ref, obs, N.eff = NA, time_dim = 'year', member_dim = R <- (1-r12*r12-r13*r13-r23*r23) + 2*r12*r13*r23 t <- (r12 - r13) * sqrt((N.eff-1)*(1+r23) / (2 * ((N.eff-1)/(N.eff-3)) * R + 0.25*(r12+r13)^2 * (1-r23)^3)) p.value <- 1 - pt(t, df = N.eff-3) - if (p.value <= alpha){ + if (!is.na(p.value) & p.value <= alpha){ output$sign <- TRUE } else { output$sign <- FALSE -- GitLab From 36a67e5e8f6b9638cd062fd2687ed640b9c971f4 Mon Sep 17 00:00:00 2001 From: Carlos Delgado Date: Mon, 28 Mar 2022 17:59:51 +0200 Subject: [PATCH 32/83] several modification taking into account Eleftheria feedback --- R/DiffCorr.R | 73 +++++++++++++++++++++++++++++++++------------------- 1 file changed, 46 insertions(+), 27 deletions(-) diff --git a/R/DiffCorr.R b/R/DiffCorr.R index c35ee91..4286082 100644 --- a/R/DiffCorr.R +++ b/R/DiffCorr.R @@ -13,44 +13,46 @@ #' The length of the time dimension must be the same as for "exp". #'@param obs A numerical array with the observations with, at least, time dimension. #' The length of the time dimension must be the same as for "exp". -#'@param N.eff Effective sample size to be used in the statistical significance test. -#' If NA, it is computed with the Eno() function. The default value is NA. +#'@param N.eff Effective sample size to be used in the statistical significance test. It can be NA +#' (in this case, it is computed with the s2dv::Eno() function), a numeric (which is used for all +#' cases), or an array with the same dimensions as "obs" except "time_dim" (for a particular N.eff +#' to be used for each case). The default value is NA. #'@param time_dim A character string indicating the name of the time dimension. -#' The default value is 'year'. +#' The default value is 'sdate'. #'@param member_dim A character string indicating the name of the member dimension to compute the #' ensemble means of the forecast and reference forecast. If it is NULL, the ensemble means should #' be provided directly to the function. The default value is NULL. #'@param method A character string indicating the correlation coefficient to be computed ("pearson", #' "kendall", or "spearman"). The default value is "pearson". -#'@param p.val Logical indicating whether to regurn the p-value. The default value is TRUE. -#'@param alpha Significance level to be used in the statistical significance test. The default value -#' is 0.05. +#'@param alpha Significance level to be used in the statistical significance test. If NULL, the p-value +#' is returned instead. The default value is NULL. #'@param ncores An integer indicating the number of cores to use for parallel #' computation. The default value is NULL. #' -#'@return A list of 2: +#'@return A list with: #'\item{$r}{ #' A numerical array with the correlation differences with the same dimensions as the input arrays #' except "time_dim" (and "member_dim" if provided). #'} #'\item{$sign}{ #' A logical array with the statistical significance of the correlation differences with the same -#' dimensions as the input arrays except "time_dim" (and "member_dim" if provided). +#' dimensions as the input arrays except "time_dim" (and "member_dim" if provided). Returned only +#' if "alpha" is a numeric. #'} #'\item{$p.val}{ #' A numeric array with the p-values with the same dimensions as the input arrays except "time_dim" -#' (and "member_dim" if provided). +#' (and "member_dim" if provided). Returned only if "alpha" is NULL. #'} #' #'@examples -#' exp <- array(rnorm(1000), dim = c(lat = 3, lon = 2, member = 10, year = 50)) -#' ref <- array(rnorm(1000), dim = c(lat = 3, lon = 2, member = 10, year = 50)) -#' obs <- array(rnorm(1000), dim = c(lat = 3, lon = 2, year = 50)) +#' exp <- array(rnorm(1000), dim = c(lat = 3, lon = 2, member = 10, sdate = 50)) +#' ref <- array(rnorm(1000), dim = c(lat = 3, lon = 2, member = 10, sdate = 50)) +#' obs <- array(rnorm(1000), dim = c(lat = 3, lon = 2, sdate = 50)) #' DiffCorr(exp, ref, obs, member_dim = 'member') #' #'@import multiApply #'@export -DiffCorr <- function(exp, ref, obs, N.eff = NA, time_dim = 'year', member_dim = NULL, method = 'pearson', p.val = TRUE, alpha = 0.05, ncores = NULL){ +DiffCorr <- function(exp, ref, obs, N.eff = NA, time_dim = 'sdate', member_dim = NULL, method = 'pearson', alpha = NULL, ncores = NULL){ ## Checkings if (!is.array(exp)){stop('"exp" must be an array.')} @@ -58,12 +60,19 @@ DiffCorr <- function(exp, ref, obs, N.eff = NA, time_dim = 'year', member_dim = if (!is.array(ref)){stop('"ref" must be an array.')} if (!identical(as.numeric(dim(exp)[time_dim]), as.numeric(dim(ref)[time_dim]))){stop('"time_dim" must have the same length in "exp" and "ref".')} if (!identical(as.numeric(dim(exp)[time_dim]), as.numeric(dim(obs)[time_dim]))){stop('"time_dim" must have the same length in "exp" and "obs".')} - if (!is.na(N.eff) & !is.numeric(N.eff)){stop('"N.eff" must be NA or numeric.')} + if (is.array(N.eff)){ + if (!all(names(dim(N.eff)) %in% names(dim(obs)))){ + stop('If "N.eff" is provided with an array, it must have the same dimensions as "obs" except "time_dim".') + } + } else if (!is.na(N.eff) & !is.numeric(N.eff)){ + stop('"N.eff" must be NA, a numeric, or an array with the same dimensions as "obs" except "time_dim".') + } if (!is.character(time_dim)){stop('"time_dim" must be a string.')} if (!is.null(member_dim) & !is.character(member_dim)){stop('"member_dim" must be either NULL or a string.')} if (!method %in% c("pearson","kendall","spearman")){stop('"method" must be "pearson", "kendall", or "spearman".')} - if (!is.logical(p.val)){stop('"p.val" must be TRUE or FALSE.')} - if (!is.numeric(alpha) | alpha <= 0 | alpha >= 1){stop('"alpha" must be a number between 0 and 1.')} + if (!is.null(alpha)){ + if (!is.numeric(alpha) | alpha <= 0 | alpha >= 1){stop('"alpha" must be NULL or a number between 0 and 1.')} + } if (!is.null(ncores)){ if (!is.numeric(ncores) | ncores < 1){stop('"ncores" must be either NULL or a positive integer.')} } @@ -75,14 +84,16 @@ DiffCorr <- function(exp, ref, obs, N.eff = NA, time_dim = 'year', member_dim = } ## Correlation difference - output <- multiApply::Apply(data = list(exp = exp, ref = ref, obs = obs), - target_dims = time_dim, fun = .DiffCorr, - N.eff = N.eff, time_dim = time_dim, method = method, - p.val = p.val, alpha = alpha, ncores = ncores) + output <- multiApply::Apply(data = list(exp = exp, ref = ref, + obs = obs, N.eff = N.eff), + target_dims = list(exp = time_dim, ref = time_dim, + obs = time_dim, N.eff = NULL), + fun = .DiffCorr, time_dim = time_dim, + method = method, alpha = alpha, ncores = ncores) return(output) } -.DiffCorr <- function(exp, ref, obs, N.eff, time_dim, method, p.val, alpha) { +.DiffCorr <- function(exp, ref, obs, N.eff, time_dim, method, alpha) { if (!anyNA(exp) & !anyNA(ref) & !anyNA(obs)){ @@ -102,16 +113,24 @@ DiffCorr <- function(exp, ref, obs, N.eff = NA, time_dim = 'year', member_dim = R <- (1-r12*r12-r13*r13-r23*r23) + 2*r12*r13*r23 t <- (r12 - r13) * sqrt((N.eff-1)*(1+r23) / (2 * ((N.eff-1)/(N.eff-3)) * R + 0.25*(r12+r13)^2 * (1-r23)^3)) p.value <- 1 - pt(t, df = N.eff-3) - if (!is.na(p.value) & p.value <= alpha){ - output$sign <- TRUE + if (is.null(alpha)){ + output$p.val <- p.value } else { - output$sign <- FALSE + if (!is.na(p.value) & p.value <= alpha){ + output$sign <- TRUE + } else { + output$sign <- FALSE + } } - if (isTRUE(p.val)){output$p.val <- p.value} } else { - output <- list(corr.diff = NA, sign = NA) - if (isTRUE(p.val)){output$p.val <- NA} + + if (is.null(alpha)){ + output <- list(corr.diff = NA, p.val = NA) + } else { + output <- list(corr.diff = NA, sign = NA) + } + } return(output) -- GitLab From db13be8e56430f30be24289a2898ca921de4cfe9 Mon Sep 17 00:00:00 2001 From: Carlos Delgado Date: Tue, 29 Mar 2022 17:15:38 +0200 Subject: [PATCH 33/83] added handle.na parameter --- R/DiffCorr.R | 95 ++++++++++++++++++++++++++++++++++------------------ 1 file changed, 62 insertions(+), 33 deletions(-) diff --git a/R/DiffCorr.R b/R/DiffCorr.R index 4286082..5b067a5 100644 --- a/R/DiffCorr.R +++ b/R/DiffCorr.R @@ -26,6 +26,10 @@ #' "kendall", or "spearman"). The default value is "pearson". #'@param alpha Significance level to be used in the statistical significance test. If NULL, the p-value #' is returned instead. The default value is NULL. +#'@handle.na How to handle missing values. If "return.na", NA will be returned for the cases that contain +#' at least one NA in "exp", "ref" or "obs". If "only.complete.triplets", only the time steps with no +#' missing values in all "exp", "ref" and "obs" will be used. If "na.fail", an error will arise if any of +#' "exp", "ref" or "obs" contains any NA. The default value is "return.na". #'@param ncores An integer indicating the number of cores to use for parallel #' computation. The default value is NULL. #' @@ -52,7 +56,7 @@ #' #'@import multiApply #'@export -DiffCorr <- function(exp, ref, obs, N.eff = NA, time_dim = 'sdate', member_dim = NULL, method = 'pearson', alpha = NULL, ncores = NULL){ +DiffCorr <- function(exp, ref, obs, N.eff = NA, time_dim = 'sdate', member_dim = NULL, method = 'pearson', alpha = NULL, handle.na = 'return.na', ncores = NULL){ ## Checkings if (!is.array(exp)){stop('"exp" must be an array.')} @@ -73,6 +77,7 @@ DiffCorr <- function(exp, ref, obs, N.eff = NA, time_dim = 'sdate', member_dim = if (!is.null(alpha)){ if (!is.numeric(alpha) | alpha <= 0 | alpha >= 1){stop('"alpha" must be NULL or a number between 0 and 1.')} } + if (!handle.na %in% c('return.na','only.complete.triplets','na.fail')){stop('"handle.na" must be "return.na", "only.complete.triplets" or "na.fail".')} if (!is.null(ncores)){ if (!is.numeric(ncores) | ncores < 1){stop('"ncores" must be either NULL or a positive integer.')} } @@ -88,49 +93,73 @@ DiffCorr <- function(exp, ref, obs, N.eff = NA, time_dim = 'sdate', member_dim = obs = obs, N.eff = N.eff), target_dims = list(exp = time_dim, ref = time_dim, obs = time_dim, N.eff = NULL), - fun = .DiffCorr, time_dim = time_dim, - method = method, alpha = alpha, ncores = ncores) + fun = .DiffCorr, time_dim = time_dim, method = method, + alpha = alpha, handle.na = handle.na, ncores = ncores) return(output) } -.DiffCorr <- function(exp, ref, obs, N.eff, time_dim, method, alpha) { +.DiffCorr <- function(exp, ref, obs, N.eff, time_dim, method, alpha, handle.na) { - if (!anyNA(exp) & !anyNA(ref) & !anyNA(obs)){ + if (anyNA(exp) | anyNA(ref) | anyNA(obs)){ ## There are NAs - ## Correlation difference - cor.exp <- cor(x = exp, y = obs, method = method) - cor.ref <- cor(x = ref, y = obs, method = method) - output <- NULL - output$corr.diff <- cor.exp - cor.ref - - ## Significance with one-sided test for equality of dependent correlation coefficients (Steiger, 1980) - r12 <- cor.exp - r13 <- cor.ref - r23 <- cor(exp, ref) - if (is.na(N.eff)){ - N.eff <- s2dv::Eno(data = obs, time_dim = time_dim, na.action = na.pass, ncores = 1) ## effective degrees of freedom - } - R <- (1-r12*r12-r13*r13-r23*r23) + 2*r12*r13*r23 - t <- (r12 - r13) * sqrt((N.eff-1)*(1+r23) / (2 * ((N.eff-1)/(N.eff-3)) * R + 0.25*(r12+r13)^2 * (1-r23)^3)) - p.value <- 1 - pt(t, df = N.eff-3) - if (is.null(alpha)){ - output$p.val <- p.value - } else { - if (!is.na(p.value) & p.value <= alpha){ - output$sign <- TRUE + if (identical(handle.na,'na.fail')){ + + stop('The data contain NAs.') + + } else if (identical(handle.na,'only.complete.triplets')){ + + nna <- !is.na(exp) & !is.na(ref) & !is.na(obs) + if (all(nna == FALSE)){stop("there are no complete sets of forecasts and observations")} + exp <- exp[nna] + ref <- ref[nna] + obs <- obs[nna] + output <- .diff.corr(exp = exp, ref = ref, obs = obs, method = method, N.eff = N.eff, time_dim = time_dim, alpha = alpha) + + } else if (identical(handle.na,'return.na')){ + + if (is.null(alpha)){ + output <- list(corr.diff = NA, p.val = NA) } else { - output$sign <- FALSE + output <- list(corr.diff = NA, sign = NA) } - } + + } else {stop('Incorrect "handle.na"')} - } else { + } else { ## There are no NAs - if (is.null(alpha)){ - output <- list(corr.diff = NA, p.val = NA) + output <- .diff.corr(exp = exp, ref = ref, obs = obs, method = method, N.eff = N.eff, time_dim = time_dim, alpha = alpha) + + } + + return(output) +} + +.diff.corr <- function(exp, ref, obs, method, N.eff, time_dim, alpha){ + + ## Correlation difference + cor.exp <- cor(x = exp, y = obs, method = method) + cor.ref <- cor(x = ref, y = obs, method = method) + output <- NULL + output$corr.diff <- cor.exp - cor.ref + + ## Significance with one-sided test for equality of dependent correlation coefficients (Steiger, 1980) + r12 <- cor.exp + r13 <- cor.ref + r23 <- cor(exp, ref) + if (is.na(N.eff)){ + N.eff <- s2dv::Eno(data = obs, time_dim = time_dim, na.action = na.pass, ncores = 1) ## effective degrees of freedom + } + R <- (1-r12*r12-r13*r13-r23*r23) + 2*r12*r13*r23 + t <- (r12 - r13) * sqrt((N.eff-1)*(1+r23) / (2 * ((N.eff-1)/(N.eff-3)) * R + 0.25*(r12+r13)^2 * (1-r23)^3)) + p.value <- 1 - pt(t, df = N.eff-3) + if (is.null(alpha)){ + output$p.val <- p.value + } else { + if (!is.na(p.value) & p.value <= alpha){ + output$sign <- TRUE } else { - output <- list(corr.diff = NA, sign = NA) + output$sign <- FALSE } - } return(output) -- GitLab From 8f72f8b8ddca314abb93f75d5b0e7c7f1f139fb7 Mon Sep 17 00:00:00 2001 From: Carlos Delgado Date: Wed, 30 Mar 2022 13:15:02 +0200 Subject: [PATCH 34/83] replaced n_categories with prob_thresholds --- R/RPS.R | 57 ++++++++++++++++++++++++-------------------------------- R/RPSS.R | 45 +++++++++++++++++++++++++++++++------------- 2 files changed, 56 insertions(+), 46 deletions(-) diff --git a/R/RPS.R b/R/RPS.R index 4cd5120..b28791e 100644 --- a/R/RPS.R +++ b/R/RPS.R @@ -5,15 +5,16 @@ #'members) and the observations (defined as 0% if the category did not happen and 100% if it happened). It can be #'used to evaluate the skill of multi-categorical probabilistic forecasts. The RPS ranges between 0 (perfect #'forecast) and n-1 (worst possible forecast), where n is the number of categories. In the case of a forecast -#'divided into two categories (the lowest number of categories that a probabilistic forecast can provide), the +#'divided into two categories (the lowest number of categories that a probabilistic forecast can have), the #'RPS corresponds to the Brier Score (BS; Wilks, 2011) and, therefore, ranges between 0 and 1. #' #'@param exp A numerical array with the forecast with, at least, time dimension. #'@param obs A numerical array with the observations with, at least, time dimension. #' The length of the time dimension must be the same as for "exp". +#'@param prob_thresholds A numeric vector with the relative thresholds (from 0 to 1) between the categories. +#' The default value is c(1/3,2/3), which corresponds to tercile equiprobable categories. #'@param indices_for_clim A vector with the indices to be taken along "time_dim" dimension for computing the thresholds #' between the probabilistic categories. If NULL, the whole period is used. The default value is NULL. -#'@param n_categories An integer with the number of probabilistic categories. The default value is 3 (tercile categories). #'@param Fair A logical indicating whether to compute the FairRPSS (the potential RPSS that the forecast would have with #'an infinite ensemble size). The default value is FALSE. #'@param time_dim A character string indicating the name of the time dimension. @@ -30,23 +31,26 @@ #'@examples #' exp <- array(rnorm(1000), dim = c(lat = 3, lon = 2, member = 10, year = 50)) #' obs <- array(rnorm(1000), dim = c(lat = 3, lon = 2, year = 50)) -#' RPS(exp, obs, indices_for_clim = 1:50) +#' RPS(exp = exp, obs = obs) #' #'@import multiApply +#'@importFrom ClimProjDiags Subset #'@importFrom easyVerification convert2prob #'@export -RPS <- function(exp, obs, indices_for_clim = NULL, n_categories = 3, Fair = FALSE, time_dim = 'year', member_dim = 'member', ncores = NULL){ +RPS <- function(exp, obs, prob_thresholds = c(1/3,2/3), indices_for_clim = NULL, Fair = FALSE, time_dim = 'year', member_dim = 'member', ncores = NULL){ ## Checkings if (!is.array(exp)){stop('"exp" must be an array.')} if (!is.array(obs)){stop('"obs" must be an array.')} if (!identical(as.numeric(dim(exp)[time_dim]), as.numeric(dim(obs)[time_dim]))){stop('"time_dim" must have the same length in "exp" and "obs".')} + if (!is.numeric(prob_thresholds) | !is.vector(prob_thresholds) | any(prob_thresholds <= 0) | any(prob_thresholds >= 1)){ + stop('"prob_thresholds" must be a numeric vector with the relative thresholds (from 0 to 1) between the categories.') + } if (is.null(indices_for_clim)){ indices_for_clim <- 1:as.numeric(dim(obs)[time_dim]) } else if (!is.numeric(indices_for_clim)){ stop('"indices_for_clim" must be NULL or a numeric vector.') } - if (!is.numeric(n_categories) | n_categories < 2){stop('"n_categories" must be an integer greater than 1.')} if (!is.logical(Fair)){stop('"Fair" must be TRUE or FALSE.')} if (!is.character(time_dim)){stop('"time_dim" must be a string.')} if (!is.character(member_dim)){stop('"member_dim" must be a string.')} @@ -57,7 +61,7 @@ RPS <- function(exp, obs, indices_for_clim = NULL, n_categories = 3, Fair = FALS ## RPS rps <- multiApply::Apply(data = list(exp = exp, obs = obs), target_dims = list(exp = c(time_dim, member_dim), obs = time_dim), - n_categories = n_categories, indices_for_clim = indices_for_clim, + prob_thresholds = prob_thresholds, indices_for_clim = indices_for_clim, Fair = Fair, time_dim = time_dim, member_dim = member_dim, fun = .RPS, ncores = ncores)$output1 @@ -67,47 +71,34 @@ RPS <- function(exp, obs, indices_for_clim = NULL, n_categories = 3, Fair = FALS return(rps) } -.RPS <- function(exp, obs, n_categories, indices_for_clim, Fair = FALSE, time_dim = 'year', member_dim = 'member'){ +.RPS <- function(exp, obs, prob_thresholds, indices_for_clim, Fair, time_dim, member_dim){ - exp_probs <- .get_probs(data = exp, type = 'exp', indices_for_quartiles = indices_for_clim, - time_dim = time_dim, member_dim = member_dim, n_categories = n_categories)$probs + exp_probs <- .get_probs(data = exp, type = 'exp', indices_for_quantiles = indices_for_clim, + time_dim = time_dim, member_dim = member_dim, prob_thresholds = prob_thresholds) - obs_probs <- .get_probs(data = obs, type = 'obs', indices_for_quartiles = indices_for_clim, - time_dim = time_dim, member_dim = NULL, n_categories = n_categories)$probs + obs_probs <- .get_probs(data = obs, type = 'obs', indices_for_quantiles = indices_for_clim, + time_dim = time_dim, member_dim = NULL, prob_thresholds = prob_thresholds) + ## RPS for each time step rps <- multiApply::Apply(data = list(probs_exp = exp_probs, probs_obs = obs_probs), target_dims = 'bin', - fun = .RPS_from_probs, n_categories = n_categories, Fair = Fair, + fun = .RPS_from_probs, n_categories = length(prob_thresholds)+1, Fair = Fair, n_members = as.numeric(dim(exp)[member_dim]), ncores = NULL)$output1 return(rps) } -.get_probs <- function(data, type, indices_for_quartiles, time_dim, member_dim, n_categories){ +.get_probs <- function(data, type, indices_for_quantiles, time_dim, member_dim, prob_thresholds){ if (type == 'exp'){ - target_dims_quantiles = c(time_dim, member_dim) - target_dims_probs = list(member_dim, 'bin') + target_dims = member_dim } else if (type == 'obs'){ - target_dims_quantiles = time_dim - target_dims_probs = list(NULL,'bin') + target_dims = NULL } else {stop("Parameter 'type' must be 'exp' or 'obs'")} - quantiles <- multiApply::Apply(data = ClimProjDiags::Subset(x = data, along = time_dim, indices = indices_for_quartiles), - target_dims = target_dims_quantiles, - fun = .quantile_aux, n_categories = n_categories, - output_dims = 'bin', ncores = NULL)$output1 - - probs <- multiApply::Apply(data = list(data = data, quantiles = quantiles), - target_dims = target_dims_probs, - fun = .c2p, - output_dims = 'bin', ncores = NULL)$output1 + quantiles <- quantile(x = as.vector(data), probs = prob_thresholds, type = 8, na.rm = TRUE) ## Absolute thresholds - return(list('probs' = probs, 'quantiles' = quantiles)) -} - -.quantile_aux = function(data, n_categories){ - data = as.vector(data) - q = quantile(x = data, probs = 1:(n_categories - 1) / n_categories, type = 8, na.rm = TRUE) - return(q) + probs <- multiApply::Apply(data = data, target_dims = target_dims, fun = .c2p, output_dims = 'bin', + quantiles = quantiles, ncores = NULL)$output1 ## Probabilities + return(probs) } .c2p <- function(data, quantiles) { diff --git a/R/RPSS.R b/R/RPSS.R index 59005c2..f7dc80d 100644 --- a/R/RPSS.R +++ b/R/RPSS.R @@ -15,9 +15,10 @@ #'@param ref NULL or a numerical array with the reference forecast with, at least, time dimension. #' The length of the time dimension must be the same as for "exp". If it is NULL, the climatological forecast is #' used as reference forecast. The default value is NULL. +#'@param prob_thresholds A numeric vector with the relative thresholds (between 0 and 1) between the categories. +#' The default value is c(1/3,2/3), which corresponds to tercile equiprobable categories. #'@param indices_for_clim A vector with the indices to be taken along "time_dim" dimension for computing the thresholds #' between the probabilistic categories. If NULL, the whole period is used. The default value is NULL. -#'@param n_categories An integer with the number of probabilistic categories. The default value is 3 (tercile categories). #'@param Fair A logical indicating whether to compute the FairRPSS (the potential RPSS that the forecast would have with #'an infinite ensemble size). The default value is FALSE. #'@param time_dim A character string indicating the name of the time dimension. @@ -40,27 +41,31 @@ #'@examples #' exp <- array(rnorm(1000), dim = c(lat = 3, lon = 2, member = 10, year = 50)) +#' ref <- array(rnorm(1000), dim = c(lat = 3, lon = 2, member = 10, year = 50)) #' obs <- array(rnorm(1000), dim = c(lat = 3, lon = 2, year = 50)) -#' RPSS(exp, obs, indices_for_clim = 1:50) +#' RPSS(exp = exp, obs = obs) ## climatology as reference forecast +#' RPSS(exp = exp, obs = obs, ref = ref) ## ref as reference forecast #' #'@import multiApply #'@export -RPSS <- function(exp, obs, ref = NULL, indices_for_clim = NULL, n_categories = 3, Fair = FALSE, time_dim = 'year', member_dim = 'member', ncores = NULL){ +RPSS <- function(exp, obs, ref = NULL, prob_thresholds = c(1/3,2/3), indices_for_clim = NULL, Fair = FALSE, time_dim = 'year', member_dim = 'member', ncores = NULL){ ## Checkings if (!is.array(exp)){stop('"exp" must be an array.')} if (!is.array(obs)){stop('"obs" must be an array.')} + if (!identical(as.numeric(dim(exp)[time_dim]), as.numeric(dim(obs)[time_dim]))){stop('"time_dim" must have the same length in "exp" and "obs".')} if (!is.null(ref)){ if (!is.array(ref)){stop('"ref" must be an array.')} if (!identical(as.numeric(dim(exp)[time_dim]), as.numeric(dim(ref)[time_dim]))){stop('"time_dim" must have the same length in "exp" and "ref".')} } - if (!identical(as.numeric(dim(exp)[time_dim]), as.numeric(dim(obs)[time_dim]))){stop('"time_dim" must have the same length in "exp" and "obs".')} + if (!is.numeric(prob_thresholds) | !is.vector(prob_thresholds) | any(prob_thresholds <= 0) | any(prob_thresholds >= 1)){ + stop('"prob_thresholds" must be a numeric vector with the relative thresholds (from 0 to 1) between the categories.') + } if (is.null(indices_for_clim)){ indices_for_clim <- 1:as.numeric(dim(obs)[time_dim]) } else if (!is.numeric(indices_for_clim)){ stop('"indices_for_clim" must be NULL or a numeric vector.') } - if (!is.numeric(n_categories) | n_categories < 2){stop('"n_categories" must be an integer greater than 1.')} if (!is.logical(Fair)){stop('"Fair" must be TRUE or FALSE.')} if (!is.character(time_dim)){stop('"time_dim" must be a string.')} if (!is.character(member_dim)){stop('"member_dim" must be a string.')} @@ -71,13 +76,13 @@ RPSS <- function(exp, obs, ref = NULL, indices_for_clim = NULL, n_categories = 3 if (!is.null(ref)){ ## using "ref" as reference forecast output <- multiApply::Apply(data = list(exp = exp, obs = obs, ref = ref), target_dims = list(exp = c(time_dim, member_dim), obs = time_dim, ref = c(time_dim, member_dim)), - n_categories = n_categories, indices_for_clim = indices_for_clim, + prob_thresholds = prob_thresholds, indices_for_clim = indices_for_clim, Fair = Fair, time_dim = time_dim, member_dim = member_dim, fun = .RPSS, ncores = ncores) } else { ## using climatology as reference forecast output <- multiApply::Apply(data = list(exp = exp, obs = obs), target_dims = list(exp = c(time_dim, member_dim), obs = time_dim), - ref = ref, n_categories = n_categories, indices_for_clim = indices_for_clim, + ref = ref, prob_thresholds = prob_thresholds, indices_for_clim = indices_for_clim, Fair = Fair, time_dim = time_dim, member_dim = member_dim, fun = .RPSS, ncores = ncores) } @@ -85,19 +90,19 @@ RPSS <- function(exp, obs, ref = NULL, indices_for_clim = NULL, n_categories = 3 return(output) } -.RPSS <- function(exp, obs, ref, n_categories, indices_for_clim, Fair, time_dim, member_dim){ +.RPSS <- function(exp, obs, ref, prob_thresholds, indices_for_clim, Fair, time_dim, member_dim){ ## RPS of the forecast - rps_exp <- .RPS(exp = exp, obs = obs, n_categories = n_categories, indices_for_clim = indices_for_clim, Fair = Fair, time_dim = time_dim, member_dim = member_dim) + rps_exp <- .RPS(exp = exp, obs = obs, prob_thresholds = prob_thresholds, indices_for_clim = indices_for_clim, Fair = Fair, time_dim = time_dim, member_dim = member_dim) ## RPS of the reference forecast if (is.null(ref)){ ## using climatology as reference forecast - obs_probs <- .get_probs(data = obs, type = 'obs', indices_for_quartiles = indices_for_clim, time_dim = time_dim, member_dim = NULL, n_categories = n_categories)$probs - clim_probs <- array(data = 1 / n_categories, dim = dim(obs_probs)) + obs_probs <- .get_probs(data = obs, type = 'obs', indices_for_quantiles = indices_for_clim, time_dim = time_dim, member_dim = NULL, prob_thresholds = prob_thresholds) + clim_probs <- .get_probs_clim(prob_thresholds = prob_thresholds, dims = dim(obs_probs), time_dim = time_dim) rps_ref <- multiApply::Apply(data = list(probs_exp = clim_probs, probs_obs = obs_probs), target_dims = 'bin', - fun = .RPS_from_probs, n_categories = n_categories, Fair = FALSE, ncores = NULL)$output1 + fun = .RPS_from_probs, n_categories = length(prob_thresholds)+1, Fair = FALSE, ncores = NULL)$output1 } else { ## using "ref" as reference forecast - rps_ref <- .RPS(exp = ref, obs = obs, n_categories = n_categories, indices_for_clim = indices_for_clim, Fair = Fair, time_dim = time_dim, member_dim = member_dim) + rps_ref <- .RPS(exp = ref, obs = obs, prob_thresholds = prob_thresholds, indices_for_clim = indices_for_clim, Fair = Fair, time_dim = time_dim, member_dim = member_dim) } ## RPSS @@ -110,3 +115,17 @@ RPSS <- function(exp, obs, ref = NULL, indices_for_clim = NULL, n_categories = 3 return(list(rpss = rpss, sign = sign)) } + +.get_probs_clim <- function(prob_thresholds, dims, time_dim){ + + probs <- array(data = NA, dim = c(bin = length(prob_thresholds)+1)) + probs[1] <- prob_thresholds[1] + if (length(prob_thresholds) > 1){ + probs[2:(length(probs)-1)] <- diff(prob_thresholds) + } + probs[length(probs)] <- 1 - prob_thresholds[length(prob_thresholds)] + stopifnot(identical(sum(probs),1)) + probs <- s2dv::InsertDim(data = probs, posdim = 2, lendim = dims[[time_dim]], name = time_dim) + + return(probs) +} -- GitLab From 43665468fc0a4a93ffc60161f5a52ec7b2b273d7 Mon Sep 17 00:00:00 2001 From: aho Date: Wed, 6 Apr 2022 16:13:27 +0200 Subject: [PATCH 35/83] Combine .diff.corr() with .DiffCorr() and refine the code format --- NAMESPACE | 1 + R/DiffCorr.R | 334 ++++++++++++++++++++++++++++++------------------ man/DiffCorr.Rd | 98 ++++++++++++++ 3 files changed, 310 insertions(+), 123 deletions(-) create mode 100644 man/DiffCorr.Rd diff --git a/NAMESPACE b/NAMESPACE index ca5d500..8e17668 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -25,6 +25,7 @@ export(ConfigShowSimilarEntries) export(ConfigShowTable) export(Consist_Trend) export(Corr) +export(DiffCorr) export(EOF) export(Eno) export(EuroAtlanticTC) diff --git a/R/DiffCorr.R b/R/DiffCorr.R index 5b067a5..74421eb 100644 --- a/R/DiffCorr.R +++ b/R/DiffCorr.R @@ -1,166 +1,254 @@ #'Compute the correlation difference and its significance #' -#'Positive values of the correlation difference indicate that the forecast is more skilful -#'than the reference forecast, while negative values mean that the reference forecast is -#'more skilful. The statistical significance of the correlation differences is computed with -#'a one-sided test for equality of dependent correlation coefficients (Steiger, 1980; +#'Compute the correlation difference between two deterministic forecasts. +#'Positive values of the correlation difference indicate that the forecast is +#'more skillful than the reference forecast, while negative values mean that +#'the reference forecast is more skillful. The statistical significance of the +#'correlation differences is computed with a one-sided test for equality of +#'dependent correlation coefficients (Steiger, 1980; #'https://content.apa.org/doi/10.1037/0033-2909.87.2.245; Siegert et al., 2017; -#'https://doi.org/10.1175/MWR-D-16-0037.1) using a effective degrees of freedom to account for -#'the time series' autocorrelation (von Storch and Zwiers, 1999; https://doi.org/10.1017/CBO9780511612336). +#'https://doi.org/10.1175/MWR-D-16-0037.1) using effective degrees of freedom +#'to account for the autocorrelation of the time series (von Storch and Zwiers, +#'1999; https://doi.org/10.1017/CBO9780511612336). #' -#'@param exp A numerical array with the forecast with, at least, time dimension. -#'@param ref A numerical array with the reference forecast data with, at least, time dimension. -#' The length of the time dimension must be the same as for "exp". -#'@param obs A numerical array with the observations with, at least, time dimension. -#' The length of the time dimension must be the same as for "exp". -#'@param N.eff Effective sample size to be used in the statistical significance test. It can be NA -#' (in this case, it is computed with the s2dv::Eno() function), a numeric (which is used for all -#' cases), or an array with the same dimensions as "obs" except "time_dim" (for a particular N.eff -#' to be used for each case). The default value is NA. +#'@param exp A named numerical array of the forecast data with at least time +#' dimension. +#'@param ref A named numerical array of the reference forecast data with at +#' least time dimension. The dimensions must be the same as "exp" except +#' 'memb_dim'. +#'@param obs A named numerical array with the observations with at least time +#' dimension. The dimensions must be the same as "exp" except 'memb_dim'. +#'@param N.eff Effective sample size to be used in the statistical significance +#' test. It can be NA (and it will be computed with the s2dv:::.Eno), a +#' numeric (which is used for all cases), or an array with the same dimensions +#' as "obs" except "time_dim" (for a particular N.eff to be used for each case) +#' . The default value is NA. #'@param time_dim A character string indicating the name of the time dimension. #' The default value is 'sdate'. -#'@param member_dim A character string indicating the name of the member dimension to compute the -#' ensemble means of the forecast and reference forecast. If it is NULL, the ensemble means should -#' be provided directly to the function. The default value is NULL. -#'@param method A character string indicating the correlation coefficient to be computed ("pearson", -#' "kendall", or "spearman"). The default value is "pearson". -#'@param alpha Significance level to be used in the statistical significance test. If NULL, the p-value -#' is returned instead. The default value is NULL. -#'@handle.na How to handle missing values. If "return.na", NA will be returned for the cases that contain -#' at least one NA in "exp", "ref" or "obs". If "only.complete.triplets", only the time steps with no -#' missing values in all "exp", "ref" and "obs" will be used. If "na.fail", an error will arise if any of -#' "exp", "ref" or "obs" contains any NA. The default value is "return.na". +#'@param memb_dim A character string indicating the name of the member +#' dimension to compute the ensemble mean of the forecast and reference +#' forecast. If it is NULL (default), the ensemble mean should be provided +#' directly to the function. +#'@param method A character string indicating the correlation coefficient to be +#' computed ("pearson", "kendall", or "spearman"). The default value is +#' "pearson". +#'@param alpha A numeric of the significance level to be used in the statistical +#' significance test. If it is a numeric, "sign" will be returned. If NULL, the +#' p-value will be returned instead. The default value is NULL. +#'@param handle.na A charcater string indicating how to handle missing values. +#' If "return.na", NAs will be returned for the cases that contain at least one +#' NA in "exp", "ref", or "obs". If "only.complete.triplets", only the time +#' steps with no missing values in all "exp", "ref", and "obs" will be used. If +#' "na.fail", an error will arise if any of "exp", "ref", or "obs" contains any +#' NA. The default value is "return.na". #'@param ncores An integer indicating the number of cores to use for parallel #' computation. The default value is NULL. #' #'@return A list with: -#'\item{$r}{ -#' A numerical array with the correlation differences with the same dimensions as the input arrays -#' except "time_dim" (and "member_dim" if provided). +#'\item{$corr.diff}{ +#' A numerical array of the correlation differences with the same dimensions as +#' the input arrays except "time_dim" (and "memb_dim" if provided). #'} #'\item{$sign}{ -#' A logical array with the statistical significance of the correlation differences with the same -#' dimensions as the input arrays except "time_dim" (and "member_dim" if provided). Returned only -#' if "alpha" is a numeric. +#' A logical array of the statistical significance of the correlation +#' differences with the same dimensions as the input arrays except "time_dim" +#' (and "memb_dim" if provided). Returned only if "alpha" is a numeric. #'} #'\item{$p.val}{ -#' A numeric array with the p-values with the same dimensions as the input arrays except "time_dim" -#' (and "member_dim" if provided). Returned only if "alpha" is NULL. +#' A numeric array of the p-values with the same dimensions as the input arrays +#' except "time_dim" (and "memb_dim" if provided). Returned only if "alpha" is +#' NULL. #'} #' #'@examples #' exp <- array(rnorm(1000), dim = c(lat = 3, lon = 2, member = 10, sdate = 50)) #' ref <- array(rnorm(1000), dim = c(lat = 3, lon = 2, member = 10, sdate = 50)) #' obs <- array(rnorm(1000), dim = c(lat = 3, lon = 2, sdate = 50)) -#' DiffCorr(exp, ref, obs, member_dim = 'member') +#' res <- DiffCorr(exp, ref, obs, memb_dim = 'member') #' #'@import multiApply #'@export -DiffCorr <- function(exp, ref, obs, N.eff = NA, time_dim = 'sdate', member_dim = NULL, method = 'pearson', alpha = NULL, handle.na = 'return.na', ncores = NULL){ +DiffCorr <- function(exp, ref, obs, N.eff = NA, time_dim = 'sdate', + memb_dim = NULL, method = 'pearson', alpha = NULL, + handle.na = 'return.na', ncores = NULL) { - ## Checkings - if (!is.array(exp)){stop('"exp" must be an array.')} - if (!is.array(obs)){stop('"obs" must be an array.')} - if (!is.array(ref)){stop('"ref" must be an array.')} - if (!identical(as.numeric(dim(exp)[time_dim]), as.numeric(dim(ref)[time_dim]))){stop('"time_dim" must have the same length in "exp" and "ref".')} - if (!identical(as.numeric(dim(exp)[time_dim]), as.numeric(dim(obs)[time_dim]))){stop('"time_dim" must have the same length in "exp" and "obs".')} - if (is.array(N.eff)){ - if (!all(names(dim(N.eff)) %in% names(dim(obs)))){ - stop('If "N.eff" is provided with an array, it must have the same dimensions as "obs" except "time_dim".') + # Check inputs + ## exp, ref, and obs (1) + if (!is.array(exp) | !is.numeric(exp)) + stop('Parameter "exp" must be a numeric array.') + if (!is.array(ref) | !is.numeric(ref)) + stop('Parameter "ref" must be a numeric array.') + if (!is.array(obs) | !is.numeric(obs)) + stop('Parameter "obs" must be a numeric array.') + ## N.eff + if (is.array(N.eff)) { + if (!is.numeric(N.eff)) stop("Parameter 'N.eff' must be numeric.") + if (!all(names(dim(N.eff)) %in% names(dim(obs)))) { + stop(paste0('If parameter "N.eff" is provided with an array, it must ', + 'have the same dimensions as "obs" except "time_dim".')) + } else if (any(dim(obs)[match(names(dim(N.eff)), names(dim(obs)))] != dim(N.eff))) { + stop("Parameter 'N.eff' must have the same dimensions as 'obs' except 'time_dim'.") } - } else if (!is.na(N.eff) & !is.numeric(N.eff)){ - stop('"N.eff" must be NA, a numeric, or an array with the same dimensions as "obs" except "time_dim".') + } else if (any((!is.na(N.eff) & !is.numeric(N.eff)) | length(N.eff) != 1)) { + stop(paste0('Parameter "N.eff" must be NA, a numeric, or an array with ', + 'the same dimensions as "obs" except "time_dim".')) } - if (!is.character(time_dim)){stop('"time_dim" must be a string.')} - if (!is.null(member_dim) & !is.character(member_dim)){stop('"member_dim" must be either NULL or a string.')} - if (!method %in% c("pearson","kendall","spearman")){stop('"method" must be "pearson", "kendall", or "spearman".')} - if (!is.null(alpha)){ - if (!is.numeric(alpha) | alpha <= 0 | alpha >= 1){stop('"alpha" must be NULL or a number between 0 and 1.')} + + ## time_dim + if (!is.character(time_dim) | length(time_dim) != 1) + stop('Parameter "time_dim" must be a character string.') + if (!time_dim %in% names(dim(exp)) | !time_dim %in% names(dim(ref)) | + !time_dim %in% names(dim(obs))) { + stop("Parameter 'time_dim' is not found in 'exp', 'ref', or 'obs' dimension.") } - if (!handle.na %in% c('return.na','only.complete.triplets','na.fail')){stop('"handle.na" must be "return.na", "only.complete.triplets" or "na.fail".')} - if (!is.null(ncores)){ - if (!is.numeric(ncores) | ncores < 1){stop('"ncores" must be either NULL or a positive integer.')} + ## memb_dim + if (!is.null(memb_dim)) { + if (!is.character(memb_dim) | length(memb_dim) > 1) { + stop("Parameter 'memb_dim' must be a character string.") + } + if (!memb_dim %in% names(dim(exp)) | !memb_dim %in% names(dim(ref))) { + stop("Parameter 'memb_dim' is not found in 'exp' or 'ref' dimension.") + } } - - ## Ensemble means - if (!is.null(member_dim)){ - exp <- multiApply::Apply(data = exp, target_dims = member_dim, fun = mean, na.rm = FALSE, ncores = ncores)$output1 - ref <- multiApply::Apply(data = ref, target_dims = member_dim, fun = mean, na.rm = FALSE, ncores = ncores)$output1 + ## exp, ref, and obs (2) + name_exp <- sort(names(dim(exp))) + name_ref <- sort(names(dim(ref))) + name_obs <- sort(names(dim(obs))) + if (!is.null(memb_dim)) { + name_exp <- name_exp[-which(name_exp == memb_dim)] + name_ref <- name_ref[-which(name_ref == memb_dim)] + } + if (!all.equal(dim(exp)[name_exp], dim(ref)[name_ref], dim(obs)[name_obs])) { + stop(paste0("Parameter 'exp', 'ref', and 'obs' must have same length of ", + "all dimensions expect 'memb_dim'.")) + } + ## method + if (!method %in% c("pearson", "kendall", "spearman")) { + stop('Parameter "method" must be "pearson", "kendall", or "spearman".') + } + ## alpha + if (!is.null(alpha)) { + if (any(!is.numeric(alpha) | alpha <= 0 | alpha >= 1 | + length(alpha) > 1)) { + stop('Parameter "alpha" must be NULL or a number between 0 and 1.') + } + } + ## handle.na + if (!handle.na %in% c('return.na', 'only.complete.triplets', 'na.fail')) { + stop('Parameter "handle.na" must be "return.na", "only.complete.triplets" or "na.fail".') + } + if (!is.null(ncores)) { + if (any(!is.numeric(ncores) | ncores %% 1 != 0 | ncores <= 0 | + length(ncores) > 1)) { + stop('Parameter "ncores" must be either NULL or a positive integer.') + } } - ## Correlation difference - output <- multiApply::Apply(data = list(exp = exp, ref = ref, - obs = obs, N.eff = N.eff), - target_dims = list(exp = time_dim, ref = time_dim, - obs = time_dim, N.eff = NULL), - fun = .DiffCorr, time_dim = time_dim, method = method, - alpha = alpha, handle.na = handle.na, ncores = ncores) + ############################### + + # NA check: na.fail + if (handle.na == "na.fail" & (anyNA(exp) | anyNA(ref) | anyNA(obs))) + stop('The data contain NAs.') + + # Calculate ensemble means + dim_exp <- dim(exp) + dim_ref <- dim(ref) + dim_obs <- dim(obs) + + if (!is.null(memb_dim)) { + exp_memb_dim_ind <- which(names(dim_exp) == memb_dim) + ref_memb_dim_ind <- which(names(dim_ref) == memb_dim) + exp <- apply(exp, c(1:length(dim_exp))[-exp_memb_dim_ind], mean, na.rm = FALSE) + ref <- apply(ref, c(1:length(dim_ref))[-ref_memb_dim_ind], mean, na.rm = FALSE) + if (is.null(dim(exp))) exp <- array(exp, dim = c(dim_exp[time_dim])) + if (is.null(dim(ref))) ref <- array(ref, dim = c(dim_ref[time_dim])) + +# exp <- Apply(data = exp, target_dims = memb_dim, fun = mean, na.rm = FALSE, ncores = ncores)$output1 +# ref <- Apply(data = ref, target_dims = memb_dim, fun = mean, na.rm = FALSE, ncores = ncores)$output1 + } + + # output_dims + if (is.null(alpha)) { + output_dims <- list(corr.diff = NULL, p.val = NULL) + } else { + output_dims <- list(corr.diff = NULL, sign = NULL) + } + # Correlation difference + if (is.array(N.eff)) { + output <- Apply(data = list(exp = exp, ref = ref, + obs = obs, N.eff = N.eff), + target_dims = list(exp = time_dim, ref = time_dim, + obs = time_dim, N.eff = NULL), + output_dims = output_dims, + fun = .DiffCorr, method = method, + alpha = alpha, handle.na = handle.na, ncores = ncores) + } else { + output <- Apply(data = list(exp = exp, ref = ref, + obs = obs), + target_dims = list(exp = time_dim, ref = time_dim, + obs = time_dim), + output_dims = output_dims, N.eff = N.eff, + fun = .DiffCorr, method = method, + alpha = alpha, handle.na = handle.na, ncores = ncores) + } + return(output) } -.DiffCorr <- function(exp, ref, obs, N.eff, time_dim, method, alpha, handle.na) { +.DiffCorr <- function(exp, ref, obs, N.eff, method, alpha, handle.na) { + + .diff.corr <- function(exp, ref, obs, method, N.eff, alpha) { + + # Correlation difference + cor.exp <- cor(x = exp, y = obs, method = method) + cor.ref <- cor(x = ref, y = obs, method = method) + output <- NULL + output$corr.diff <- cor.exp - cor.ref + + # Significance with one-sided test for equality of dependent correlation coefficients (Steiger, 1980) + r12 <- cor.exp + r13 <- cor.ref + r23 <- cor(exp, ref) + if (is.na(N.eff)) { + N.eff <- .Eno(x = obs, na.action = na.pass) ## effective degrees of freedom + } + R <- (1 - r12 * r12 - r13 * r13 - r23 * r23) + 2 * r12 * r13 * r23 + t <- (r12 - r13) * sqrt((N.eff - 1) * (1 + r23) / (2 * ((N.eff - 1) / (N.eff - 3)) * R + 0.25 * (r12 + r13)^2 * (1 - r23)^3)) + p.value <- 1 - pt(t, df = N.eff - 3) + if (is.null(alpha)) { + output$p.val <- p.value + } else { + output$sign <- ifelse(!is.na(p.value) & p.value <= alpha, TRUE, FALSE) + } + return(output) + } + + #================================================== - if (anyNA(exp) | anyNA(ref) | anyNA(obs)){ ## There are NAs - - if (identical(handle.na,'na.fail')){ - - stop('The data contain NAs.') - - } else if (identical(handle.na,'only.complete.triplets')){ - - nna <- !is.na(exp) & !is.na(ref) & !is.na(obs) - if (all(nna == FALSE)){stop("there are no complete sets of forecasts and observations")} - exp <- exp[nna] - ref <- ref[nna] - obs <- obs[nna] - output <- .diff.corr(exp = exp, ref = ref, obs = obs, method = method, N.eff = N.eff, time_dim = time_dim, alpha = alpha) - - } else if (identical(handle.na,'return.na')){ + if (anyNA(exp) | anyNA(ref) | anyNA(obs)) { ## There are NAs + if (handle.na == 'only.complete.triplets') { + nna <- is.na(exp) | is.na(ref) | is.na(obs) # A vector of T/F + if (all(nna)) stop("There is no complete set of forecasts and observations.") + # Remove the incomplete set + exp <- exp[!nna] + ref <- ref[!nna] + obs <- obs[!nna] + + output <- .diff.corr(exp = exp, ref = ref, obs = obs, method = method, + N.eff = N.eff, alpha = alpha) - if (is.null(alpha)){ + } else if (handle.na == 'return.na') { + # Data contain NA, return NAs directly without passing to .diff.corr + if (is.null(alpha)) { output <- list(corr.diff = NA, p.val = NA) } else { output <- list(corr.diff = NA, sign = NA) } - - } else {stop('Incorrect "handle.na"')} - - } else { ## There are no NAs - - output <- .diff.corr(exp = exp, ref = ref, obs = obs, method = method, N.eff = N.eff, time_dim = time_dim, alpha = alpha) - - } - - return(output) -} - -.diff.corr <- function(exp, ref, obs, method, N.eff, time_dim, alpha){ - - ## Correlation difference - cor.exp <- cor(x = exp, y = obs, method = method) - cor.ref <- cor(x = ref, y = obs, method = method) - output <- NULL - output$corr.diff <- cor.exp - cor.ref - - ## Significance with one-sided test for equality of dependent correlation coefficients (Steiger, 1980) - r12 <- cor.exp - r13 <- cor.ref - r23 <- cor(exp, ref) - if (is.na(N.eff)){ - N.eff <- s2dv::Eno(data = obs, time_dim = time_dim, na.action = na.pass, ncores = 1) ## effective degrees of freedom - } - R <- (1-r12*r12-r13*r13-r23*r23) + 2*r12*r13*r23 - t <- (r12 - r13) * sqrt((N.eff-1)*(1+r23) / (2 * ((N.eff-1)/(N.eff-3)) * R + 0.25*(r12+r13)^2 * (1-r23)^3)) - p.value <- 1 - pt(t, df = N.eff-3) - if (is.null(alpha)){ - output$p.val <- p.value - } else { - if (!is.na(p.value) & p.value <= alpha){ - output$sign <- TRUE - } else { - output$sign <- FALSE } + + } else { ## There is no NA + output <- .diff.corr(exp = exp, ref = ref, obs = obs, method = method, + N.eff = N.eff, alpha = alpha) } - return(output) } diff --git a/man/DiffCorr.Rd b/man/DiffCorr.Rd new file mode 100644 index 0000000..5671908 --- /dev/null +++ b/man/DiffCorr.Rd @@ -0,0 +1,98 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/DiffCorr.R +\name{DiffCorr} +\alias{DiffCorr} +\title{Compute the correlation difference and its significance} +\usage{ +DiffCorr( + exp, + ref, + obs, + N.eff = NA, + time_dim = "sdate", + memb_dim = NULL, + method = "pearson", + alpha = NULL, + handle.na = "return.na", + ncores = NULL +) +} +\arguments{ +\item{exp}{A named numerical array of the forecast data with at least time +dimension.} + +\item{ref}{A named numerical array of the reference forecast data with at +least time dimension. The dimensions must be the same as "exp" except +'memb_dim'.} + +\item{obs}{A named numerical array with the observations with at least time +dimension. The dimensions must be the same as "exp" except 'memb_dim'.} + +\item{N.eff}{Effective sample size to be used in the statistical significance +test. It can be NA (and it will be computed with the s2dv:::.Eno), a +numeric (which is used for all cases), or an array with the same dimensions +as "obs" except "time_dim" (for a particular N.eff to be used for each case) +. The default value is NA.} + +\item{time_dim}{A character string indicating the name of the time dimension. +The default value is 'sdate'.} + +\item{memb_dim}{A character string indicating the name of the member +dimension to compute the ensemble mean of the forecast and reference +forecast. If it is NULL (default), the ensemble mean should be provided +directly to the function.} + +\item{method}{A character string indicating the correlation coefficient to be +computed ("pearson", "kendall", or "spearman"). The default value is +"pearson".} + +\item{alpha}{A numeric of the significance level to be used in the statistical +significance test. If it is a numeric, "sign" will be returned. If NULL, the +p-value will be returned instead. The default value is NULL.} + +\item{handle.na}{A charcater string indicating how to handle missing values. +If "return.na", NAs will be returned for the cases that contain at least one +NA in "exp", "ref", or "obs". If "only.complete.triplets", only the time +steps with no missing values in all "exp", "ref", and "obs" will be used. If +"na.fail", an error will arise if any of "exp", "ref", or "obs" contains any +NA. The default value is "return.na".} + +\item{ncores}{An integer indicating the number of cores to use for parallel +computation. The default value is NULL.} +} +\value{ +A list with: +\item{$corr.diff}{ + A numerical array of the correlation differences with the same dimensions as + the input arrays except "time_dim" (and "memb_dim" if provided). +} +\item{$sign}{ + A logical array of the statistical significance of the correlation + differences with the same dimensions as the input arrays except "time_dim" + (and "memb_dim" if provided). Returned only if "alpha" is a numeric. +} +\item{$p.val}{ + A numeric array of the p-values with the same dimensions as the input arrays + except "time_dim" (and "memb_dim" if provided). Returned only if "alpha" is + NULL. +} +} +\description{ +Compute the correlation difference between two deterministic forecasts. +Positive values of the correlation difference indicate that the forecast is +more skillful than the reference forecast, while negative values mean that +the reference forecast is more skillful. The statistical significance of the +correlation differences is computed with a one-sided test for equality of +dependent correlation coefficients (Steiger, 1980; +https://content.apa.org/doi/10.1037/0033-2909.87.2.245; Siegert et al., 2017; +https://doi.org/10.1175/MWR-D-16-0037.1) using effective degrees of freedom +to account for the autocorrelation of the time series (von Storch and Zwiers, +1999; https://doi.org/10.1017/CBO9780511612336). +} +\examples{ +exp <- array(rnorm(1000), dim = c(lat = 3, lon = 2, member = 10, sdate = 50)) +ref <- array(rnorm(1000), dim = c(lat = 3, lon = 2, member = 10, sdate = 50)) +obs <- array(rnorm(1000), dim = c(lat = 3, lon = 2, sdate = 50)) +res <- DiffCorr(exp, ref, obs, memb_dim = 'member') + +} -- GitLab From c7a87618a84302edb959436756249351988b867b Mon Sep 17 00:00:00 2001 From: Carlos Delgado Date: Thu, 7 Apr 2022 16:03:49 +0200 Subject: [PATCH 36/83] p.val is returned if alpha=NULL --- R/ResidualCorr.R | 51 ++++++++++++++++++++++++++++++++++++------------ 1 file changed, 38 insertions(+), 13 deletions(-) diff --git a/R/ResidualCorr.R b/R/ResidualCorr.R index bd551e5..645e70c 100644 --- a/R/ResidualCorr.R +++ b/R/ResidualCorr.R @@ -22,19 +22,25 @@ #' be provided directly to the function. The default value is NULL. #'@param method A character string indicating the correlation coefficient to be computed ("pearson", #' "kendall", or "spearman"). The default value is "pearson". -#'@param alpha Significance level to be used in the statistical significance test. The default value -#' is 0.05. +#'@param alpha Significance level to be used in the statistical significance test. +#' If it is a numeric, "sign" will be returned. If NULL, the p-value will be returned instead. +#' The default value is NULL. #'@param ncores An integer indicating the number of cores to use for parallel #' computation. The default value is NULL. #' -#'@return A list of 2: +#'@return A list with: #'\item{$r}{ #' A numerical array with the residual correlation with the same dimensions as the input arrays #' except "time_dim" (and "member_dim" if provided). #'} #'\item{$sign}{ #' A logical array with the statistical significance of the residual correlation with the same -#' dimensions as the input arrays except "time_dim" (and "member_dim" if provided). +#' dimensions as the input arrays except "time_dim" (and "member_dim" if provided). Returned only +#' if "alpha" is a numeric. +#'} +#'\item{$p.val}{ +#' A numeric array of the p-values with the same dimensions as the input arrays +#' except "time_dim" (and "memb_dim" if provided). Returned only if "alpha" is NULL. #'} #' #'@examples @@ -45,7 +51,7 @@ #' #'@import multiApply #'@export -ResidualCorr <- function(exp, ref, obs, time_dim = 'year', member_dim = NULL, method = 'pearson', alpha = 0.05, ncores = NULL){ +ResidualCorr <- function(exp, ref, obs, time_dim = 'year', member_dim = NULL, method = 'pearson', alpha = NULL, ncores = NULL){ ## Checkings if (!is.array(exp)){stop('"exp" must be an array.')} @@ -53,7 +59,9 @@ ResidualCorr <- function(exp, ref, obs, time_dim = 'year', member_dim = NULL, me if (!is.array(ref)){stop('"ref" must be an array.')} if (!identical(as.numeric(dim(exp)[time_dim]), as.numeric(dim(ref)[time_dim]))){stop('"time_dim" must have the same length in "exp" and "ref".')} if (!identical(as.numeric(dim(exp)[time_dim]), as.numeric(dim(obs)[time_dim]))){stop('"time_dim" must have the same length in "exp" and "obs".')} - if (!is.numeric(alpha) | alpha <= 0 | alpha >= 1){stop('"alpha" must be a number between 0 and 1.')} + if (!is.null(alpha)){ + if (!is.numeric(alpha) | alpha <= 0 | alpha >= 1){stop('"alpha" must be NULL or a number between 0 and 1.')} + } if (!is.character(time_dim)){stop('"time_dim" must be a string.')} if (!is.null(member_dim)){ if (!is.character(member_dim)){stop('"member_dim" must be either NULL or a string.')} @@ -89,18 +97,35 @@ ResidualCorr <- function(exp, ref, obs, time_dim = 'year', member_dim = NULL, me residual_cor <- NULL residual_cor$r <- cor(x = exp_res, y = obs_res, method = method) - ## Significance + ## Effective degrees of freedom n_eff <- s2dv::Eno(data = obs_res, time_dim = time_dim, na.action = na.pass, ncores = 1) - t_alpha2_n2 <- qt(p = alpha/2, df = n_eff-2, lower.tail = FALSE) + + ## Statistic t <- abs(residual_cor$r) * sqrt(n_eff-2) / sqrt(1-residual_cor$r^2) - if (anyNA(c(t,t_alpha2_n2)) == FALSE & t >= t_alpha2_n2){ - residual_cor$sign <- TRUE + + if(is.null(alpha)){ ## p-value + + residual_cor$p.val <- pt(q = t, df = n_eff-2, lower.tail = FALSE) + + } else { + + t_alpha2_n2 <- qt(p = alpha/2, df = n_eff-2, lower.tail = FALSE) + if (anyNA(c(t,t_alpha2_n2)) == FALSE & t >= t_alpha2_n2){ + residual_cor$sign <- TRUE + } else { + residual_cor$sign <- FALSE + } + + } + + } else {## Significance + + if (is.null(alpha)){ + residual_cor <- list(r = NA, p.val = NA) } else { - residual_cor$sign <- FALSE + residual_cor <- list(r = NA, sign = NA) } - } else { - residual_cor <- list(r = NA, sign = NA) } return(residual_cor) -- GitLab From 003a4a10f61fbe9230fb5a366b3881ca955209fa Mon Sep 17 00:00:00 2001 From: aho Date: Mon, 25 Apr 2022 17:26:48 +0200 Subject: [PATCH 37/83] Format adjustment --- NAMESPACE | 1 + R/ResidualCorr.R | 207 +++++++++++++++++++++++++++++--------------- man/ResidualCorr.Rd | 87 +++++++++++++++++++ 3 files changed, 223 insertions(+), 72 deletions(-) create mode 100644 man/ResidualCorr.Rd diff --git a/NAMESPACE b/NAMESPACE index ca5d500..841814e 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -60,6 +60,7 @@ export(RatioRMS) export(RatioSDRMS) export(Regression) export(Reorder) +export(ResidualCorr) export(SPOD) export(Season) export(SignalNoiseRatio) diff --git a/R/ResidualCorr.R b/R/ResidualCorr.R index 645e70c..2171da0 100644 --- a/R/ResidualCorr.R +++ b/R/ResidualCorr.R @@ -1,126 +1,189 @@ #'Compute the residual correlation and its significance #' -#'The residual correlation assesses whether a forecast captures any of the observed variability that is not -#'already captured by a reference forecast (Smith et al., 2019; https://doi.org/10.1038/s41612-019-0071-y.). -#'The procedure is as follows: the residuals of the forecasts and observations are computed by linearly regressing out -#'the reference forecast's ensemble mean from the forecasts’ ensemble mean and observations, respectively. Then, the -#'residual correlation is computed as the correlation between both residuals. Positive values of the residual correlation -#'indicate that the forecast capture more observed variability than the reference forecast, while negative values mean -#'that the reference forecast capture more. The significance of the residual correlation is computed with a two-sided t-test -#'(Wilks, 2011; https://doi.org/10.1016/B978-0-12-385022-5.00008-7) using a effective degrees of freedom to account for -#'the time series' autocorrelation (von Storch and Zwiers, 1999; https://doi.org/10.1017/CBO9780511612336). +#'The residual correlation assesses whether a forecast captures any of the +#'observed variability that is not already captured by a reference forecast +#'(Smith et al., 2019; https://doi.org/10.1038/s41612-019-0071-y.). +#'The procedure is as follows: the residuals of the forecasts and observations +#'are computed by linearly regressing out the reference forecast's ensemble mean +#'from the forecasts' ensemble mean and observations, respectively. Then, the +#'residual correlation is computed as the correlation between both residuals. +#'Positive values of the residual correlation indicate that the forecast capture +#'more observed variability than the reference forecast, while negative values +#'mean that the reference forecast capture more. The significance of the +#'residual correlation is computed with a two-sided t-test +#'(Wilks, 2011; https://doi.org/10.1016/B978-0-12-385022-5.00008-7) using an +#'effective degrees of freedom to account for the time series' autocorrelation +#'(von Storch and Zwiers, 1999; https://doi.org/10.1017/CBO9780511612336). #' -#'@param exp A numerical array with the forecast with, at least, time dimension. -#'@param ref A numerical array with the reference forecast data with, at least, time dimension. -#' The length of the time dimension must be the same as for "exp". -#'@param obs A numerical array with the observations with, at least, time dimension. -#' The length of the time dimension must be the same as for "exp". +#'@param exp A named numerical array of the forecast with at least time +#' dimension. +#'@param ref A named numerical array of the reference forecast data with at +#' least time dimension. The dimensions must be the same as "exp" except +#' 'memb_dim'. +#'@param obs A named numerical array of the observations with at least time +#' dimension. The dimensions must be the same as "exp" except 'memb_dim'. #'@param time_dim A character string indicating the name of the time dimension. #' The default value is 'year'. -#'@param member_dim A character string indicating the name of the member dimension to compute the -#' ensemble means of the forecast and reference forecast. If it is NULL, the ensemble means should -#' be provided directly to the function. The default value is NULL. -#'@param method A character string indicating the correlation coefficient to be computed ("pearson", -#' "kendall", or "spearman"). The default value is "pearson". -#'@param alpha Significance level to be used in the statistical significance test. -#' If it is a numeric, "sign" will be returned. If NULL, the p-value will be returned instead. -#' The default value is NULL. +#'@param memb_dim A character string indicating the name of the member dimension +#' to compute the ensemble mean of the forecast and reference forecast. If it +#' is NULL, the ensemble mean should be provided directly to the function. The +#' default value is NULL. +#'@param method A character string indicating the correlation coefficient to be +#' computed ("pearson", "kendall", or "spearman"). The default value is +#' "pearson". +#'@param alpha A numeric of the significance level to be used in the statistical +#' significance test. If it is a numeric, "sign" will be returned. If NULL, the +#' p-value will be returned instead. The default value is NULL. #'@param ncores An integer indicating the number of cores to use for parallel #' computation. The default value is NULL. #' #'@return A list with: #'\item{$r}{ -#' A numerical array with the residual correlation with the same dimensions as the input arrays -#' except "time_dim" (and "member_dim" if provided). +#' A numerical array of the residual correlation with the same dimensions as +#' the input arrays except "time_dim" (and "memb_dim" if provided). #'} #'\item{$sign}{ -#' A logical array with the statistical significance of the residual correlation with the same -#' dimensions as the input arrays except "time_dim" (and "member_dim" if provided). Returned only -#' if "alpha" is a numeric. +#' A logical array of the statistical significance of the residual correlation +#' with the same dimensions as the input arrays except "time_dim" (and +#' "memb_dim" if provided). Returned only if "alpha" is a numeric. #'} #'\item{$p.val}{ #' A numeric array of the p-values with the same dimensions as the input arrays -#' except "time_dim" (and "memb_dim" if provided). Returned only if "alpha" is NULL. +#' except "time_dim" (and "memb_dim" if provided). Returned only if "alpha" is +#' NULL. #'} #' #'@examples #' exp <- array(rnorm(1000), dim = c(lat = 3, lon = 2, member = 10, year = 50)) #' ref <- array(rnorm(1000), dim = c(lat = 3, lon = 2, member = 10, year = 50)) #' obs <- array(rnorm(1000), dim = c(lat = 3, lon = 2, year = 50)) -#' ResidualCorr(exp, ref, obs, member_dim = 'member') +#' res <- ResidualCorr(exp, ref, obs, memb_dim = 'member') #' #'@import multiApply #'@export -ResidualCorr <- function(exp, ref, obs, time_dim = 'year', member_dim = NULL, method = 'pearson', alpha = NULL, ncores = NULL){ +ResidualCorr <- function(exp, ref, obs, time_dim = 'year', memb_dim = NULL, + method = 'pearson', alpha = NULL, ncores = NULL) { - ## Checkings - if (!is.array(exp)){stop('"exp" must be an array.')} - if (!is.array(obs)){stop('"obs" must be an array.')} - if (!is.array(ref)){stop('"ref" must be an array.')} - if (!identical(as.numeric(dim(exp)[time_dim]), as.numeric(dim(ref)[time_dim]))){stop('"time_dim" must have the same length in "exp" and "ref".')} - if (!identical(as.numeric(dim(exp)[time_dim]), as.numeric(dim(obs)[time_dim]))){stop('"time_dim" must have the same length in "exp" and "obs".')} - if (!is.null(alpha)){ - if (!is.numeric(alpha) | alpha <= 0 | alpha >= 1){stop('"alpha" must be NULL or a number between 0 and 1.')} + # Check inputs + ## exp, ref, and obs (1) + if (!is.array(exp) | !is.numeric(exp)) + stop('Parameter "exp" must be a numeric array.') + if (!is.array(ref) | !is.numeric(ref)) + stop('Parameter "ref" must be a numeric array.') + if (!is.array(obs) | !is.numeric(obs)) + stop('Parameter "obs" must be a numeric array.') + + ## time_dim + if (!is.character(time_dim) | length(time_dim) != 1) + stop('Parameter "time_dim" must be a character string.') + if (!time_dim %in% names(dim(exp)) | !time_dim %in% names(dim(ref)) | + !time_dim %in% names(dim(obs))) { + stop("Parameter 'time_dim' is not found in 'exp', 'ref', or 'obs' dimension.") } - if (!is.character(time_dim)){stop('"time_dim" must be a string.')} - if (!is.null(member_dim)){ - if (!is.character(member_dim)){stop('"member_dim" must be either NULL or a string.')} + ## memb_dim + if (!is.null(memb_dim)) { + if (!is.character(memb_dim) | length(memb_dim) > 1) { + stop("Parameter 'memb_dim' must be a character string.") + } + if (!memb_dim %in% names(dim(exp)) | !memb_dim %in% names(dim(ref))) { + stop("Parameter 'memb_dim' is not found in 'exp' or 'ref' dimension.") + } } - if (!method %in% c("pearson","kendall","spearman")){stop('"method" must be "pearson", "kendall", or "spearman".')} - if (!is.null(ncores)){ - if (!is.numeric(ncores) | ncores < 1){stop('"ncores" must be either NULL or a positive integer.')} + ## exp, ref, and obs (2) + name_exp <- sort(names(dim(exp))) + name_ref <- sort(names(dim(ref))) + name_obs <- sort(names(dim(obs))) + if (!is.null(memb_dim)) { + name_exp <- name_exp[-which(name_exp == memb_dim)] + name_ref <- name_ref[-which(name_ref == memb_dim)] } - - ## Ensemble mean - if (!is.null(member_dim)){ - exp <- multiApply::Apply(data = exp, target_dims = member_dim, fun = mean, na.rm = FALSE, ncores = ncores)$output1 - ref <- multiApply::Apply(data = ref, target_dims = member_dim, fun = mean, na.rm = FALSE, ncores = ncores)$output1 + if (!all.equal(dim(exp)[name_exp], dim(ref)[name_ref], dim(obs)[name_obs])) { + stop(paste0("Parameter 'exp', 'ref', and 'obs' must have same length of ", + "all dimensions expect 'memb_dim'.")) } - + ## method + if (!method %in% c("pearson", "kendall", "spearman")) { + stop('Parameter "method" must be "pearson", "kendall", or "spearman".') + } + ## alpha + if (!is.null(alpha)) { + if (any(!is.numeric(alpha) | alpha <= 0 | alpha >= 1 | + length(alpha) > 1)) { + stop('Parameter "alpha" must be NULL or a number between 0 and 1.') + } + } + ## ncores + if (!is.null(ncores)) { + if (any(!is.numeric(ncores) | ncores %% 1 != 0 | ncores <= 0 | + length(ncores) > 1)) { + stop('Parameter "ncores" must be either NULL or a positive integer.') + } + } + + ############################### + + # Calculate ensemble mean + dim_exp <- dim(exp) + dim_ref <- dim(ref) + dim_obs <- dim(obs) + + if (!is.null(memb_dim)) { + exp_memb_dim_ind <- which(names(dim_exp) == memb_dim) + ref_memb_dim_ind <- which(names(dim_ref) == memb_dim) + exp <- apply(exp, c(1:length(dim_exp))[-exp_memb_dim_ind], mean, na.rm = FALSE) + ref <- apply(ref, c(1:length(dim_ref))[-ref_memb_dim_ind], mean, na.rm = FALSE) + if (is.null(dim(exp))) exp <- array(exp, dim = c(dim_exp[time_dim])) + if (is.null(dim(ref))) ref <- array(ref, dim = c(dim_ref[time_dim])) + } + + # output_dims + if (is.null(alpha)) { + output_dims <- list(r = NULL, p.val = NULL) + } else { + output_dims <- list(r = NULL, sign = NULL) + } + ## Residual correlation and its significance - output <- multiApply::Apply(data = list(exp = exp, ref = ref, obs = obs), - target_dims = time_dim, fun = .ResidualCorr, - time_dim = time_dim, method = method, - alpha = alpha, ncores = ncores) + output <- Apply(data = list(exp = exp, ref = ref, obs = obs), + target_dims = time_dim, fun = .ResidualCorr, + method = method, + alpha = alpha, ncores = ncores) return(output) } -.ResidualCorr <- function(exp, ref, obs, time_dim, method, alpha){ - - if (!anyNA(exp) & !anyNA(ref) & !anyNA(obs)){ +.ResidualCorr <- function(exp, ref, obs, method, alpha) { + # exp and ref and obs: [time] + + if (!anyNA(exp) & !anyNA(ref) & !anyNA(obs)) { - ## Residuals of 'exp' and 'obs' (regressing 'ref' out in both 'exp' and 'obs') + # Residuals of 'exp' and 'obs' (regressing 'ref' out in both 'exp' and 'obs') exp_res <- lm(formula = y ~ x, data = list(y = exp, x = ref), na.action = NULL)$residuals obs_res <- lm(formula = y ~ x, data = list(y = obs, x = ref), na.action = NULL)$residuals - ## Residual correlation (and significance) + # Residual correlation (and significance) residual_cor <- NULL residual_cor$r <- cor(x = exp_res, y = obs_res, method = method) - ## Effective degrees of freedom - n_eff <- s2dv::Eno(data = obs_res, time_dim = time_dim, na.action = na.pass, ncores = 1) + # Effective degrees of freedom + n_eff <- .Eno(x = obs_res, na.action = na.pass) ## Statistic - t <- abs(residual_cor$r) * sqrt(n_eff-2) / sqrt(1-residual_cor$r^2) + t <- abs(residual_cor$r) * sqrt(n_eff - 2) / sqrt(1 - residual_cor$r^2) - if(is.null(alpha)){ ## p-value - - residual_cor$p.val <- pt(q = t, df = n_eff-2, lower.tail = FALSE) - + if (is.null(alpha)) { # p-value + residual_cor$p.val <- pt(q = t, df = n_eff - 2, lower.tail = FALSE) } else { - - t_alpha2_n2 <- qt(p = alpha/2, df = n_eff-2, lower.tail = FALSE) - if (anyNA(c(t,t_alpha2_n2)) == FALSE & t >= t_alpha2_n2){ + t_alpha2_n2 <- qt(p = alpha / 2, df = n_eff - 2, lower.tail = FALSE) + if (!anyNA(c(t,t_alpha2_n2)) & t >= t_alpha2_n2) { residual_cor$sign <- TRUE } else { residual_cor$sign <- FALSE } - } - } else {## Significance + } else { # There is NA - if (is.null(alpha)){ + if (is.null(alpha)) { residual_cor <- list(r = NA, p.val = NA) } else { residual_cor <- list(r = NA, sign = NA) diff --git a/man/ResidualCorr.Rd b/man/ResidualCorr.Rd new file mode 100644 index 0000000..6bc489c --- /dev/null +++ b/man/ResidualCorr.Rd @@ -0,0 +1,87 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/ResidualCorr.R +\name{ResidualCorr} +\alias{ResidualCorr} +\title{Compute the residual correlation and its significance} +\usage{ +ResidualCorr( + exp, + ref, + obs, + time_dim = "year", + memb_dim = NULL, + method = "pearson", + alpha = NULL, + ncores = NULL +) +} +\arguments{ +\item{exp}{A named numerical array of the forecast with at least time +dimension.} + +\item{ref}{A named numerical array of the reference forecast data with at +least time dimension. The dimensions must be the same as "exp" except +'memb_dim'.} + +\item{obs}{A named numerical array of the observations with at least time +dimension. The dimensions must be the same as "exp" except 'memb_dim'.} + +\item{time_dim}{A character string indicating the name of the time dimension. +The default value is 'year'.} + +\item{memb_dim}{A character string indicating the name of the member dimension +to compute the ensemble mean of the forecast and reference forecast. If it +is NULL, the ensemble mean should be provided directly to the function. The +default value is NULL.} + +\item{method}{A character string indicating the correlation coefficient to be +computed ("pearson", "kendall", or "spearman"). The default value is +"pearson".} + +\item{alpha}{A numeric of the significance level to be used in the statistical +significance test. If it is a numeric, "sign" will be returned. If NULL, the +p-value will be returned instead. The default value is NULL.} + +\item{ncores}{An integer indicating the number of cores to use for parallel +computation. The default value is NULL.} +} +\value{ +A list with: +\item{$r}{ + A numerical array of the residual correlation with the same dimensions as + the input arrays except "time_dim" (and "memb_dim" if provided). +} +\item{$sign}{ + A logical array of the statistical significance of the residual correlation + with the same dimensions as the input arrays except "time_dim" (and + "memb_dim" if provided). Returned only if "alpha" is a numeric. +} +\item{$p.val}{ + A numeric array of the p-values with the same dimensions as the input arrays + except "time_dim" (and "memb_dim" if provided). Returned only if "alpha" is + NULL. +} +} +\description{ +The residual correlation assesses whether a forecast captures any of the +observed variability that is not already captured by a reference forecast +(Smith et al., 2019; https://doi.org/10.1038/s41612-019-0071-y.). +The procedure is as follows: the residuals of the forecasts and observations +are computed by linearly regressing out the reference forecast's ensemble mean +from the forecasts' ensemble mean and observations, respectively. Then, the +residual correlation is computed as the correlation between both residuals. +Positive values of the residual correlation indicate that the forecast capture +more observed variability than the reference forecast, while negative values +mean that the reference forecast capture more. The significance of the +residual correlation is computed with a two-sided t-test +(Wilks, 2011; https://doi.org/10.1016/B978-0-12-385022-5.00008-7) using an +effective degrees of freedom to account for the time series' autocorrelation +(von Storch and Zwiers, 1999; https://doi.org/10.1017/CBO9780511612336). +} +\examples{ +exp <- array(rnorm(1000), dim = c(lat = 3, lon = 2, member = 10, year = 50)) +ref <- array(rnorm(1000), dim = c(lat = 3, lon = 2, member = 10, year = 50)) +obs <- array(rnorm(1000), dim = c(lat = 3, lon = 2, year = 50)) +res <- ResidualCorr(exp, ref, obs, memb_dim = 'member') + +} -- GitLab From 6df8a7b9a64254b35b08df95332a63777e1cadb4 Mon Sep 17 00:00:00 2001 From: aho Date: Tue, 26 Apr 2022 10:56:34 +0200 Subject: [PATCH 38/83] Change any(is.na( to anyNA --- R/CDORemap.R | 4 ++-- R/Cluster.R | 2 +- R/ColorBar.R | 4 ++-- R/EOF.R | 2 +- R/Eno.R | 2 +- R/Histo2Hindcast.R | 2 +- R/Load.R | 14 +++++++------- R/Utils.R | 4 ++-- man/Cluster.Rd | 2 +- 9 files changed, 18 insertions(+), 18 deletions(-) diff --git a/R/CDORemap.R b/R/CDORemap.R index 00824de..56dcec7 100644 --- a/R/CDORemap.R +++ b/R/CDORemap.R @@ -222,10 +222,10 @@ CDORemap <- function(data_array = NULL, lons, lats, grid, method, if (!is.numeric(lons) || !is.numeric(lats)) { stop("Expected numeric 'lons' and 'lats'.") } - if (any(is.na(lons > 0))) { + if (anyNA(lons > 0)) { stop("Found invalid values in 'lons'.") } - if (any(is.na(lats > 0))) { + if (anyNA(lats > 0)) { stop("Found invalid values in 'lats'.") } if (is.null(dim(lons))) { diff --git a/R/Cluster.R b/R/Cluster.R index 8f40118..d6c5dff 100644 --- a/R/Cluster.R +++ b/R/Cluster.R @@ -53,7 +53,7 @@ #' are same as 'data' without 'space_dim'. #'} #'\item{$centers}{ -#' A nemeric array of cluster centres or centroids (e.g. [1:K, 1:spatial degrees +#' A numeric array of cluster centres or centroids (e.g. [1:K, 1:spatial degrees #' of freedom]). The rest dimensions are same as 'data' except 'time_dim' #' and 'space_dim'. #'} diff --git a/R/ColorBar.R b/R/ColorBar.R index 206f68d..286b115 100644 --- a/R/ColorBar.R +++ b/R/ColorBar.R @@ -180,7 +180,7 @@ ColorBar <- function(brks = NULL, cols = NULL, vertical = TRUE, if (!is.null(var_limits)) { if (!(is.numeric(var_limits) && (length(var_limits) == 2))) { stop("Parameter 'var_limits' must be a numeric vector of length 2.") - } else if (any(is.na(var_limits))) { + } else if (anyNA(var_limits)) { stop("Parameter 'var_limits' must not contain NA values.") } else if (any(is.infinite(var_limits))) { stop("Parameter 'var_limits' must not contain infinite values.") @@ -210,7 +210,7 @@ ColorBar <- function(brks = NULL, cols = NULL, vertical = TRUE, brks <- length(cols) + 1 } } - if (is.null(bar_limits) || any(is.na(bar_limits))) { + if (is.null(bar_limits) || anyNA(bar_limits)) { # var_limits is defined if (is.null(bar_limits)) { bar_limits <- c(NA, NA) diff --git a/R/EOF.R b/R/EOF.R index 8f8d640..d5c79a6 100644 --- a/R/EOF.R +++ b/R/EOF.R @@ -200,7 +200,7 @@ EOF <- function(ano, lat, lon, time_dim = 'sdate', space_dim = c('lat', 'lon'), # Check if all the time steps at one grid point are NA-consistent. # The grid point should have all NAs or no NA along time dim. - if (any(is.na(ano))) { + if (anyNA(ano)) { ano_latlon <- array(ano, dim = c(nt, ny * nx)) # [time, lat*lon] na_ind <- which(is.na(ano_latlon), arr.ind = T) if (dim(na_ind)[1] != nt * length(unique(na_ind[, 2]))) { diff --git a/R/Eno.R b/R/Eno.R index 8c8d16b..e2324de 100644 --- a/R/Eno.R +++ b/R/Eno.R @@ -58,7 +58,7 @@ Eno <- function(data, time_dim = 'sdate', na.action = na.pass, ncores = NULL) { as.character(substitute(na.action)) != c("na.fail")) { stop("Parameter 'na.action' must be a function either na.pass or na.fail.") } - if(as.character(substitute(na.action))== c("na.fail") && any(is.na(data))) { + if(as.character(substitute(na.action))== c("na.fail") && anyNA(data)) { stop(paste0("Calculation fails because NA is found in paratemter 'data', ", "which is not accepted when ", "parameter 'na.action' = na.fail.")) diff --git a/R/Histo2Hindcast.R b/R/Histo2Hindcast.R index 5657457..f910a9a 100644 --- a/R/Histo2Hindcast.R +++ b/R/Histo2Hindcast.R @@ -80,7 +80,7 @@ Histo2Hindcast <- function(data, sdatesin, sdatesout, nleadtimesout, if (!is.character(sdatesout) | !is.vector(sdatesout)) { stop(paste0("Parameter 'sdatesout' must be a vector of character in the ", "format 'YYYYMMDD' or 'YYYYMM'.")) - } else if (!all(nchar(sdatesout) %in% c(6, 8)) | any(is.na(as.numeric(sdatesin)))) { + } else if (!all(nchar(sdatesout) %in% c(6, 8)) | anyNA(as.numeric(sdatesin))) { stop(paste0("Parameter 'sdatesout' must be a vector of character in the ", "format 'YYYYMMDD' or 'YYYYMM'.")) } diff --git a/R/Load.R b/R/Load.R index 955c894..cdb30be 100644 --- a/R/Load.R +++ b/R/Load.R @@ -992,7 +992,7 @@ Load <- function(var, exp = NULL, obs = NULL, sdates, nmember = NULL, if (is.null(sdates)) { stop("Error: parameter 'sdates' must be provided.") } - if (!is.character(sdates) || !all(nchar(sdates) == 8) || any(is.na(strtoi(sdates)))) { + if (!is.character(sdates) || !all(nchar(sdates) == 8) || anyNA(strtoi(sdates))) { stop("Error: parameter 'sdates' is incorrect. All starting dates should be a character string in the format 'YYYYMMDD'.") } @@ -1007,7 +1007,7 @@ Load <- function(var, exp = NULL, obs = NULL, sdates, nmember = NULL, } if (length(nmember) != length(exp)) { stop("Error: 'nmember' must contain as many values as 'exp'.") - } else if (any(is.na(nmember))) { + } else if (anyNA(nmember)) { nmember[which(is.na(nmember))] <- max(nmember, na.rm = TRUE) } } @@ -1023,7 +1023,7 @@ Load <- function(var, exp = NULL, obs = NULL, sdates, nmember = NULL, } if (length(nmemberobs) != length(obs)) { stop("Error: 'nmemberobs' must contain as many values as 'obs'.") - } else if (any(is.na(nmemberobs))) { + } else if (anyNA(nmemberobs)) { nmemberobs[which(is.na(nmemberobs))] <- max(nmemberobs, na.rm = TRUE) } } @@ -1532,7 +1532,7 @@ Load <- function(var, exp = NULL, obs = NULL, sdates, nmember = NULL, } dims <- dim_exp[na.omit(match(c('dataset', 'member', 'sdate', 'ftime', 'lat', 'lon'), names(dim_exp)))] - if (is.null(dims[['member']]) || any(is.na(unlist(dims))) || any(unlist(dims) == 0)) { + if (is.null(dims[['member']]) || anyNA(unlist(dims)) || any(unlist(dims) == 0)) { dims <- 0 dim_exp <- NULL } @@ -1844,7 +1844,7 @@ Load <- function(var, exp = NULL, obs = NULL, sdates, nmember = NULL, .warning("no data found in file system for any observational dataset.") } dims <- dim_obs[na.omit(match(c('dataset', 'member', 'sdate', 'ftime', 'lat', 'lon'), names(dim_obs)))] - if (is.null(dims[['member']]) || any(is.na(unlist(dims))) || any(unlist(dims) == 0)) { + if (is.null(dims[['member']]) || anyNA(unlist(dims)) || any(unlist(dims) == 0)) { dims <- 0 dim_obs <- NULL } @@ -1864,12 +1864,12 @@ Load <- function(var, exp = NULL, obs = NULL, sdates, nmember = NULL, # So [1, 1, 1, 1, 1, 1] will be next to [2, 1, 1, 1, 1, 1] in memory pointer_var_exp <- pointer_var_obs <- NULL if (!is.null(dim_exp) && (length(unlist(dim_exp)) == length(dim_exp)) && - !any(is.na(unlist(dim_exp))) && !any(unlist(dim_exp) == 0)) { + !anyNA(unlist(dim_exp)) && !any(unlist(dim_exp) == 0)) { var_exp <- big.matrix(nrow = prod(unlist(dim_exp)), ncol = 1) pointer_var_exp <- describe(var_exp) } if (!is.null(dim_obs) && (length(unlist(dim_obs)) == length(dim_obs)) && - !any(is.na(unlist(dim_obs))) && !any(unlist(dim_obs) == 0)) { + !anyNA(unlist(dim_obs)) && !any(unlist(dim_obs) == 0)) { var_obs <- big.matrix(nrow = prod(unlist(dim_obs)), ncol = 1) pointer_var_obs <- describe(var_obs) } diff --git a/R/Utils.R b/R/Utils.R index 76ef63a..1f39694 100644 --- a/R/Utils.R +++ b/R/Utils.R @@ -255,7 +255,7 @@ NA } result <- FALSE - if (!any(is.na(c(nlons, nlats)))) { + if (!anyNA(c(nlons, nlats))) { if ((nlons == length(lon)) && (nlats == length(lat))) { result <- TRUE @@ -583,7 +583,7 @@ } if (length(expected_dims) > 0) { dim_matches <- match(expected_dims, var_dimnames) - if (any(is.na(dim_matches))) { + if (anyNA(dim_matches)) { if (!is.null(old_members_dimname)) { expected_dims[which(expected_dims == 'lev')] <- old_members_dimname } diff --git a/man/Cluster.Rd b/man/Cluster.Rd index 7ea25de..8c46b3d 100644 --- a/man/Cluster.Rd +++ b/man/Cluster.Rd @@ -63,7 +63,7 @@ A list containing: are same as 'data' without 'space_dim'. } \item{$centers}{ - A nemeric array of cluster centres or centroids (e.g. [1:K, 1:spatial degrees + A numeric array of cluster centres or centroids (e.g. [1:K, 1:spatial degrees of freedom]). The rest dimensions are same as 'data' except 'time_dim' and 'space_dim'. } -- GitLab From 7d0833b866bc4ade28efdb9e6a54a56611741789 Mon Sep 17 00:00:00 2001 From: aho Date: Tue, 26 Apr 2022 19:01:58 +0200 Subject: [PATCH 39/83] Add params 'N.eff' and 'handle.na'; Change time_dim default to 'sdate'; create unit tests --- R/ResidualCorr.R | 144 +++++++++++++++------ man/ResidualCorr.Rd | 25 +++- tests/testthat/test-ResidualCorr.R | 197 +++++++++++++++++++++++++++++ 3 files changed, 324 insertions(+), 42 deletions(-) create mode 100644 tests/testthat/test-ResidualCorr.R diff --git a/R/ResidualCorr.R b/R/ResidualCorr.R index 2171da0..ba00994 100644 --- a/R/ResidualCorr.R +++ b/R/ResidualCorr.R @@ -22,6 +22,11 @@ #' 'memb_dim'. #'@param obs A named numerical array of the observations with at least time #' dimension. The dimensions must be the same as "exp" except 'memb_dim'. +#'@param N.eff Effective sample size to be used in the statistical significance +#' test. It can be NA (and it will be computed with the s2dv:::.Eno), a +#' numeric (which is used for all cases), or an array with the same dimensions +#' as "obs" except "time_dim" (for a particular N.eff to be used for each case) +#' . The default value is NA. #'@param time_dim A character string indicating the name of the time dimension. #' The default value is 'year'. #'@param memb_dim A character string indicating the name of the member dimension @@ -34,11 +39,17 @@ #'@param alpha A numeric of the significance level to be used in the statistical #' significance test. If it is a numeric, "sign" will be returned. If NULL, the #' p-value will be returned instead. The default value is NULL. +#'@param handle.na A charcater string indicating how to handle missing values. +#' If "return.na", NAs will be returned for the cases that contain at least one +#' NA in "exp", "ref", or "obs". If "only.complete.triplets", only the time +#' steps with no missing values in all "exp", "ref", and "obs" will be used. If +#' "na.fail", an error will arise if any of "exp", "ref", or "obs" contains any +#' NA. The default value is "return.na". #'@param ncores An integer indicating the number of cores to use for parallel #' computation. The default value is NULL. #' #'@return A list with: -#'\item{$r}{ +#'\item{$res.corr}{ #' A numerical array of the residual correlation with the same dimensions as #' the input arrays except "time_dim" (and "memb_dim" if provided). #'} @@ -54,16 +65,17 @@ #'} #' #'@examples -#' exp <- array(rnorm(1000), dim = c(lat = 3, lon = 2, member = 10, year = 50)) -#' ref <- array(rnorm(1000), dim = c(lat = 3, lon = 2, member = 10, year = 50)) -#' obs <- array(rnorm(1000), dim = c(lat = 3, lon = 2, year = 50)) +#' exp <- array(rnorm(1000), dim = c(lat = 3, lon = 2, member = 10, sdate = 50)) +#' ref <- array(rnorm(1000), dim = c(lat = 3, lon = 2, member = 5, sdate = 50)) +#' obs <- array(rnorm(1000), dim = c(lat = 3, lon = 2, sdate = 50)) #' res <- ResidualCorr(exp, ref, obs, memb_dim = 'member') #' #'@import multiApply #'@export -ResidualCorr <- function(exp, ref, obs, time_dim = 'year', memb_dim = NULL, - method = 'pearson', alpha = NULL, ncores = NULL) { - +ResidualCorr <- function(exp, ref, obs, N.eff = NA, time_dim = 'sdate', + memb_dim = NULL, method = 'pearson', alpha = NULL, + handle.na = 'return.na', ncores = NULL) { + # Check inputs ## exp, ref, and obs (1) if (!is.array(exp) | !is.numeric(exp)) @@ -73,6 +85,19 @@ ResidualCorr <- function(exp, ref, obs, time_dim = 'year', memb_dim = NULL, if (!is.array(obs) | !is.numeric(obs)) stop('Parameter "obs" must be a numeric array.') + ## N.eff + if (is.array(N.eff)) { + if (!is.numeric(N.eff)) stop("Parameter 'N.eff' must be numeric.") + if (!all(names(dim(N.eff)) %in% names(dim(obs))) | + any(dim(obs)[match(names(dim(N.eff)), names(dim(obs)))] != dim(N.eff))) { + stop(paste0('If parameter "N.eff" is provided with an array, it must ', + 'have the same dimensions as "obs" except "time_dim".')) + } + } else if (any((!is.na(N.eff) & !is.numeric(N.eff)) | length(N.eff) != 1)) { + stop(paste0('Parameter "N.eff" must be NA, a numeric, or an array with ', + 'the same dimensions as "obs" except "time_dim".')) + } + ## time_dim if (!is.character(time_dim) | length(time_dim) != 1) stop('Parameter "time_dim" must be a character string.') @@ -97,7 +122,8 @@ ResidualCorr <- function(exp, ref, obs, time_dim = 'year', memb_dim = NULL, name_exp <- name_exp[-which(name_exp == memb_dim)] name_ref <- name_ref[-which(name_ref == memb_dim)] } - if (!all.equal(dim(exp)[name_exp], dim(ref)[name_ref], dim(obs)[name_obs])) { + if (!identical(length(name_exp), length(name_ref), length(name_obs)) | + !identical(dim(exp)[name_exp], dim(ref)[name_ref], dim(obs)[name_obs])) { stop(paste0("Parameter 'exp', 'ref', and 'obs' must have same length of ", "all dimensions expect 'memb_dim'.")) } @@ -112,6 +138,10 @@ ResidualCorr <- function(exp, ref, obs, time_dim = 'year', memb_dim = NULL, stop('Parameter "alpha" must be NULL or a number between 0 and 1.') } } + ## handle.na + if (!handle.na %in% c('return.na', 'only.complete.triplets', 'na.fail')) { + stop('Parameter "handle.na" must be "return.na", "only.complete.triplets" or "na.fail".') + } ## ncores if (!is.null(ncores)) { if (any(!is.numeric(ncores) | ncores %% 1 != 0 | ncores <= 0 | @@ -122,6 +152,10 @@ ResidualCorr <- function(exp, ref, obs, time_dim = 'year', memb_dim = NULL, ############################### + # NA check: na.fail + if (handle.na == "na.fail" & (anyNA(exp) | anyNA(ref) | anyNA(obs))) + stop('The data contain NAs.') + # Calculate ensemble mean dim_exp <- dim(exp) dim_ref <- dim(ref) @@ -138,58 +172,94 @@ ResidualCorr <- function(exp, ref, obs, time_dim = 'year', memb_dim = NULL, # output_dims if (is.null(alpha)) { - output_dims <- list(r = NULL, p.val = NULL) + output_dims <- list(res.corr = NULL, p.val = NULL) } else { - output_dims <- list(r = NULL, sign = NULL) + output_dims <- list(res.corr = NULL, sign = NULL) } - ## Residual correlation and its significance - output <- Apply(data = list(exp = exp, ref = ref, obs = obs), - target_dims = time_dim, fun = .ResidualCorr, - method = method, - alpha = alpha, ncores = ncores) + + # Residual correlation + if (is.array(N.eff)) { + output <- Apply(data = list(exp = exp, ref = ref, + obs = obs, N.eff = N.eff), + target_dims = list(exp = time_dim, ref = time_dim, + obs = time_dim, N.eff = NULL), + output_dims = output_dims, + fun = .ResidualCorr, method = method, + alpha = alpha, handle.na = handle.na, ncores = ncores) + } else { + output <- Apply(data = list(exp = exp, ref = ref, + obs = obs), + target_dims = list(exp = time_dim, ref = time_dim, + obs = time_dim), + output_dims = output_dims, N.eff = N.eff, + fun = .ResidualCorr, method = method, + alpha = alpha, handle.na = handle.na, ncores = ncores) + } + return(output) } -.ResidualCorr <- function(exp, ref, obs, method, alpha) { +.ResidualCorr <- function(exp, ref, obs, N.eff, method, alpha, handle.na) { # exp and ref and obs: [time] + .residual.corr <- function(exp, ref, obs, method, N.eff, alpha) { - if (!anyNA(exp) & !anyNA(ref) & !anyNA(obs)) { - # Residuals of 'exp' and 'obs' (regressing 'ref' out in both 'exp' and 'obs') exp_res <- lm(formula = y ~ x, data = list(y = exp, x = ref), na.action = NULL)$residuals obs_res <- lm(formula = y ~ x, data = list(y = obs, x = ref), na.action = NULL)$residuals # Residual correlation (and significance) - residual_cor <- NULL - residual_cor$r <- cor(x = exp_res, y = obs_res, method = method) + output <- NULL + output$res.corr <- cor(x = exp_res, y = obs_res, method = method) # Effective degrees of freedom - n_eff <- .Eno(x = obs_res, na.action = na.pass) - - ## Statistic - t <- abs(residual_cor$r) * sqrt(n_eff - 2) / sqrt(1 - residual_cor$r^2) + if (is.na(N.eff)) { + N.eff <- .Eno(x = obs_res, na.action = na.pass) + } + t <- abs(output$res.corr) * sqrt(N.eff - 2) / sqrt(1 - output$res.corr^2) if (is.null(alpha)) { # p-value - residual_cor$p.val <- pt(q = t, df = n_eff - 2, lower.tail = FALSE) + output$p.val <- pt(q = t, df = N.eff - 2, lower.tail = FALSE) } else { - t_alpha2_n2 <- qt(p = alpha / 2, df = n_eff - 2, lower.tail = FALSE) - if (!anyNA(c(t,t_alpha2_n2)) & t >= t_alpha2_n2) { - residual_cor$sign <- TRUE + t_alpha2_n2 <- qt(p = alpha / 2, df = N.eff - 2, lower.tail = FALSE) + if (!anyNA(c(t, t_alpha2_n2)) & t >= t_alpha2_n2) { + output$sign <- TRUE } else { - residual_cor$sign <- FALSE + output$sign <- FALSE } } - } else { # There is NA - - if (is.null(alpha)) { - residual_cor <- list(r = NA, p.val = NA) - } else { - residual_cor <- list(r = NA, sign = NA) + return(output) + } + + + #================================================== + + if (anyNA(exp) | anyNA(ref) | anyNA(obs)) { ## There are NAs + if (handle.na == 'only.complete.triplets') { + nna <- is.na(exp) | is.na(ref) | is.na(obs) # A vector of T/F + if (all(nna)) stop("There is no complete set of forecasts and observations.") + # Remove the incomplete set + exp <- exp[!nna] + ref <- ref[!nna] + obs <- obs[!nna] + + output <- .residual.corr(exp = exp, ref = ref, obs = obs, method = method, + N.eff = N.eff, alpha = alpha) + + } else if (handle.na == 'return.na') { + # Data contain NA, return NAs directly without passing to .diff.corr + if (is.null(alpha)) { + output <- list(res.corr = NA, p.val = NA) + } else { + output <- list(res.corr = NA, sign = NA) + } } + } else { ## There is no NA + output <- .residual.corr(exp = exp, ref = ref, obs = obs, method = method, + N.eff = N.eff, alpha = alpha) } - - return(residual_cor) + + return(output) } diff --git a/man/ResidualCorr.Rd b/man/ResidualCorr.Rd index 6bc489c..1e0adad 100644 --- a/man/ResidualCorr.Rd +++ b/man/ResidualCorr.Rd @@ -8,10 +8,12 @@ ResidualCorr( exp, ref, obs, - time_dim = "year", + N.eff = NA, + time_dim = "sdate", memb_dim = NULL, method = "pearson", alpha = NULL, + handle.na = "return.na", ncores = NULL ) } @@ -26,6 +28,12 @@ least time dimension. The dimensions must be the same as "exp" except \item{obs}{A named numerical array of the observations with at least time dimension. The dimensions must be the same as "exp" except 'memb_dim'.} +\item{N.eff}{Effective sample size to be used in the statistical significance +test. It can be NA (and it will be computed with the s2dv:::.Eno), a +numeric (which is used for all cases), or an array with the same dimensions +as "obs" except "time_dim" (for a particular N.eff to be used for each case) +. The default value is NA.} + \item{time_dim}{A character string indicating the name of the time dimension. The default value is 'year'.} @@ -42,12 +50,19 @@ computed ("pearson", "kendall", or "spearman"). The default value is significance test. If it is a numeric, "sign" will be returned. If NULL, the p-value will be returned instead. The default value is NULL.} +\item{handle.na}{A charcater string indicating how to handle missing values. +If "return.na", NAs will be returned for the cases that contain at least one +NA in "exp", "ref", or "obs". If "only.complete.triplets", only the time +steps with no missing values in all "exp", "ref", and "obs" will be used. If +"na.fail", an error will arise if any of "exp", "ref", or "obs" contains any +NA. The default value is "return.na".} + \item{ncores}{An integer indicating the number of cores to use for parallel computation. The default value is NULL.} } \value{ A list with: -\item{$r}{ +\item{$res.corr}{ A numerical array of the residual correlation with the same dimensions as the input arrays except "time_dim" (and "memb_dim" if provided). } @@ -79,9 +94,9 @@ effective degrees of freedom to account for the time series' autocorrelation (von Storch and Zwiers, 1999; https://doi.org/10.1017/CBO9780511612336). } \examples{ -exp <- array(rnorm(1000), dim = c(lat = 3, lon = 2, member = 10, year = 50)) -ref <- array(rnorm(1000), dim = c(lat = 3, lon = 2, member = 10, year = 50)) -obs <- array(rnorm(1000), dim = c(lat = 3, lon = 2, year = 50)) +exp <- array(rnorm(1000), dim = c(lat = 3, lon = 2, member = 10, sdate = 50)) +ref <- array(rnorm(1000), dim = c(lat = 3, lon = 2, member = 5, sdate = 50)) +obs <- array(rnorm(1000), dim = c(lat = 3, lon = 2, sdate = 50)) res <- ResidualCorr(exp, ref, obs, memb_dim = 'member') } diff --git a/tests/testthat/test-ResidualCorr.R b/tests/testthat/test-ResidualCorr.R new file mode 100644 index 0000000..e1eec1a --- /dev/null +++ b/tests/testthat/test-ResidualCorr.R @@ -0,0 +1,197 @@ +context("s2dv::ResidualCorr tests") + +############################################## + +# dat1 +set.seed(1) +exp1 <- array(rnorm(180), dim = c(lat = 3, lon = 2, memb = 3, sdate = 10)) +set.seed(2) +ref1 <- array(rnorm(120), dim = c(lat = 3, lon = 2, memb = 2, sdate = 10)) +set.seed(3) +obs1 <- array(rnorm(60), dim = c(lat = 3, lon = 2, sdate = 10)) +Neff1 <- array(rep(9:10, 3), dim = c(lat = 3, lon = 2)) + +# dat2 +set.seed(1) +exp2 <- array(rnorm(10), dim = c(sdate = 10)) +set.seed(2) +ref2 <- array(rnorm(10), dim = c(sdate = 10)) +set.seed(3) +obs2 <- array(rnorm(10), dim = c(sdate = 10)) + + + +############################################## +test_that("1. Input checks", { + # exp and obs (1) + expect_error( + ResidualCorr(c()), + 'Parameter "exp" must be a numeric array.' + ) + expect_error( + ResidualCorr(exp1, c('a')), + 'Parameter "ref" must be a numeric array.' + ) + expect_error( + ResidualCorr(exp1, ref1, c()), + 'Parameter "obs" must be a numeric array.' + ) + # N.eff + expect_error( + ResidualCorr(exp1, ref1, obs1, N.eff = array(1, dim = c(lat = 2, lon = 2))), +'If parameter "N.eff" is provided with an array, it must have the same dimensions as "obs" except "time_dim".' + ) + expect_error( + ResidualCorr(exp1, ref1, obs1, N.eff = 1:3), + 'Parameter "N.eff" must be NA, a numeric, or an array with the same dimensions as "obs" except "time_dim".' + ) + # time_dim + expect_error( + ResidualCorr(exp1, ref1, obs1, time_dim = 1), + 'Parameter "time_dim" must be a character string.' + ) + expect_error( + ResidualCorr(exp1, ref1, obs1, time_dim = 'time'), + "Parameter 'time_dim' is not found in 'exp', 'ref', or 'obs' dimension." + ) + # memb_dim + expect_error( + ResidualCorr(exp1, ref1, obs1, memb_dim = TRUE), + "Parameter 'memb_dim' must be a character string." + ) + expect_error( + ResidualCorr(exp1, ref1, obs1, memb_dim = 'member'), + "Parameter 'memb_dim' is not found in 'exp' or 'ref' dimension." + ) + # exp, ref, and obs (2) + expect_error( + ResidualCorr(exp1, array(1:10, dim = c(sdate = 10, memb = 1)), obs1, memb_dim = 'memb'), + "Parameter 'exp', 'ref', and 'obs' must have same length of all dimensions expect 'memb_dim'." + ) + # method + expect_error( + ResidualCorr(exp1, ref1, obs1, method = 'asd', memb_dim = 'memb'), + 'Parameter "method" must be "pearson", "kendall", or "spearman".' + ) + # alpha + expect_error( + ResidualCorr(exp2, ref2, obs2, alpha = 1), + 'Parameter "alpha" must be NULL or a number between 0 and 1.' + ) + # handle.na + expect_error( + ResidualCorr(exp2, ref2, obs2, handle.na = TRUE), + 'Parameter "handle.na" must be "return.na", "only.complete.triplets" or "na.fail".' + ) + # ncores + expect_error( + ResidualCorr(exp2, ref2, obs2, ncores = 1.5), + 'Parameter "ncores" must be either NULL or a positive integer.' + ) + +}) + +############################################## +test_that("2. Output checks: dat1", { +expect_equal( +names(ResidualCorr(exp1, ref1, obs1, memb_dim = 'memb')), +c("res.corr", "p.val") +) +expect_equal( +dim(ResidualCorr(exp1, ref1, obs1, memb_dim = 'memb')$res.corr), +c(lat = 3, lon = 2) +) +expect_equal( +dim(ResidualCorr(exp1, ref1, obs1, memb_dim = 'memb')$p.val), +c(lat = 3, lon = 2) +) +expect_equal( +as.vector(ResidualCorr(exp1, ref1, obs1, memb_dim = 'memb')$res.corr), +c(0.002784318, 0.537697647, -0.240071018, 0.258706464, 0.338160748, 0.432107476), +tolerance = 0.0001 +) +expect_equal( +as.vector(ResidualCorr(exp1, ref1, obs1, memb_dim = 'memb')$p), +c(0.49695468, 0.05446055, 0.25203961, 0.23522967, 0.16960864, 0.10618145), +tolerance = 0.0001 +) +expect_equal( +names(ResidualCorr(exp1, ref1, obs1, memb_dim = 'memb', alpha = 0.05)), +c("res.corr", "sign") +) +expect_equal( +as.vector(ResidualCorr(exp1, ref1, obs1, memb_dim = 'memb', alpha = 0.05)$res.corr), +c(0.002784318, 0.537697647, -0.240071018, 0.258706464, 0.338160748, 0.432107476), +tolerance = 0.0001 +) +expect_equal( +as.vector(ResidualCorr(exp1, ref1, obs1, memb_dim = 'memb', alpha = 0.05)$sign), +rep(FALSE, 6) +) +expect_equal( +as.vector(ResidualCorr(exp1, ref1, obs1, memb_dim = 'memb', N.eff = Neff1)$res), +c(0.002784318, 0.537697647, -0.240071018, 0.258706464, 0.338160748, 0.432107476), +tolerance = 0.0001 +) +expect_equal( +as.vector(ResidualCorr(exp1, ref1, obs1, memb_dim = 'memb', N.eff = Neff1)$p), +c(0.49716394, 0.05446055, 0.26690777, 0.23522967, 0.18671025, 0.10618145), +tolerance = 0.0001 +) + + +#--------------------------- +exp1[1] <- NA +expect_equal( +as.vector(ResidualCorr(exp1, ref1, obs1, memb_dim = 'memb')$res.corr), +c(NA, 0.537697647, -0.240071018, 0.258706464, 0.338160748, 0.432107476), +tolerance = 0.0001 +) +expect_equal( +as.vector(ResidualCorr(exp1, ref1, obs1, memb_dim = 'memb', handle.na = 'only.complete.triplets')$res.corr), +c(-0.1082588, 0.537697647, -0.240071018, 0.258706464, 0.338160748, 0.432107476), +tolerance = 0.0001 +) +expect_error( +ResidualCorr(exp1, ref1, obs1, memb_dim = 'memb', handle.na = 'na.fail'), +"The data contain NAs." +) + +exp1[1,1,1,1:3] <- NA +ref1[1,1,1,4:8] <- NA +obs1[1,1,9:10] <- NA +expect_error( +ResidualCorr(exp1, ref1, obs1, memb_dim = 'memb', handle.na = 'only.complete.triplets'), +"There is no complete set of forecasts and observations." +) + + +}) + +############################################## + +test_that("3. Output checks: dat2", { +expect_equal( +names(ResidualCorr(exp2, ref2, obs2)), +c("res.corr", "p.val") +) +expect_equal( +dim(ResidualCorr(exp2, ref2, obs2)$res.corr), +NULL +) +expect_equal( +dim(ResidualCorr(exp2, ref2, obs2)$p), +NULL +) +expect_equal( +ResidualCorr(exp2, ref2, obs2)$p, +0.2209847, +tolerance = 0.0001 +) +expect_equal( +ResidualCorr(exp2, ref2, obs2)$res, +-0.274962, +tolerance = 0.0001 +) + +}) -- GitLab From c27f3bb6210b04fe3ff545a1010019a631d7312b Mon Sep 17 00:00:00 2001 From: aho Date: Tue, 26 Apr 2022 19:15:11 +0200 Subject: [PATCH 40/83] Add unit tests for method kendall and spearman --- tests/testthat/test-ResidualCorr.R | 22 ++++++++++++++++++++++ 1 file changed, 22 insertions(+) diff --git a/tests/testthat/test-ResidualCorr.R b/tests/testthat/test-ResidualCorr.R index e1eec1a..c9ac4b9 100644 --- a/tests/testthat/test-ResidualCorr.R +++ b/tests/testthat/test-ResidualCorr.R @@ -128,6 +128,28 @@ expect_equal( as.vector(ResidualCorr(exp1, ref1, obs1, memb_dim = 'memb', alpha = 0.05)$sign), rep(FALSE, 6) ) +expect_equal( +as.vector(ResidualCorr(exp1, ref1, obs1, memb_dim = 'memb', method = "kendall")$res), +c(0.11111111, 0.42222222, -0.20000000, 0.02222222, 0.20000000, 0.42222222), +tolerance = 0.0001 +) +expect_equal( +as.vector(ResidualCorr(exp1, ref1, obs1, memb_dim = 'memb', method = "kendall")$p), +c(0.3799615, 0.1120891, 0.2897920, 0.4757064, 0.2897920, 0.1120891), +tolerance = 0.0001 +) +expect_equal( +as.vector(ResidualCorr(exp1, ref1, obs1, memb_dim = 'memb', method = "spearman")$res), +c(0.13939394, 0.61212121, -0.27272727, 0.07878788, 0.28484848, 0.47878788), +tolerance = 0.0001 +) +expect_equal( +as.vector(ResidualCorr(exp1, ref1, obs1, memb_dim = 'memb', method = "spearman")$p), +c(0.35046594, 0.02998607, 0.22291917, 0.41435870, 0.21251908, 0.08076146), +tolerance = 0.0001 +) + + expect_equal( as.vector(ResidualCorr(exp1, ref1, obs1, memb_dim = 'memb', N.eff = Neff1)$res), c(0.002784318, 0.537697647, -0.240071018, 0.258706464, 0.338160748, 0.432107476), -- GitLab From 090925f232660f25eeb246671adf1459633d0dea Mon Sep 17 00:00:00 2001 From: Carlos Delgado Torres Date: Wed, 27 Apr 2022 17:13:37 +0200 Subject: [PATCH 41/83] clarified "$sign" output --- R/ResidualCorr.R | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/R/ResidualCorr.R b/R/ResidualCorr.R index ba00994..dae0cb8 100644 --- a/R/ResidualCorr.R +++ b/R/ResidualCorr.R @@ -54,9 +54,9 @@ #' the input arrays except "time_dim" (and "memb_dim" if provided). #'} #'\item{$sign}{ -#' A logical array of the statistical significance of the residual correlation -#' with the same dimensions as the input arrays except "time_dim" (and -#' "memb_dim" if provided). Returned only if "alpha" is a numeric. +#' A logical array indicating whether the residual correlation is statistically +#' significant or not with the same dimensions as the input arrays except "time_dim" +#' (and "memb_dim" if provided). Returned only if "alpha" is a numeric. #'} #'\item{$p.val}{ #' A numeric array of the p-values with the same dimensions as the input arrays -- GitLab From b0bebd4af6e3372bfcb1a38545e619a8a9e6f714 Mon Sep 17 00:00:00 2001 From: aho Date: Thu, 28 Apr 2022 16:03:39 +0200 Subject: [PATCH 42/83] Change output name from 'corr.diff' to 'diff.corr'; add unit test; improve a few conditional statement --- R/DiffCorr.R | 21 ++-- man/DiffCorr.Rd | 2 +- man/ResidualCorr.Rd | 6 +- tests/testthat/test-DiffCorr.R | 217 +++++++++++++++++++++++++++++++++ 4 files changed, 231 insertions(+), 15 deletions(-) create mode 100644 tests/testthat/test-DiffCorr.R diff --git a/R/DiffCorr.R b/R/DiffCorr.R index 74421eb..511dd3a 100644 --- a/R/DiffCorr.R +++ b/R/DiffCorr.R @@ -45,7 +45,7 @@ #' computation. The default value is NULL. #' #'@return A list with: -#'\item{$corr.diff}{ +#'\item{$diff.corr}{ #' A numerical array of the correlation differences with the same dimensions as #' the input arrays except "time_dim" (and "memb_dim" if provided). #'} @@ -83,17 +83,15 @@ DiffCorr <- function(exp, ref, obs, N.eff = NA, time_dim = 'sdate', ## N.eff if (is.array(N.eff)) { if (!is.numeric(N.eff)) stop("Parameter 'N.eff' must be numeric.") - if (!all(names(dim(N.eff)) %in% names(dim(obs)))) { + if (!all(names(dim(N.eff)) %in% names(dim(obs))) | + any(dim(obs)[match(names(dim(N.eff)), names(dim(obs)))] != dim(N.eff))) { stop(paste0('If parameter "N.eff" is provided with an array, it must ', 'have the same dimensions as "obs" except "time_dim".')) - } else if (any(dim(obs)[match(names(dim(N.eff)), names(dim(obs)))] != dim(N.eff))) { - stop("Parameter 'N.eff' must have the same dimensions as 'obs' except 'time_dim'.") } } else if (any((!is.na(N.eff) & !is.numeric(N.eff)) | length(N.eff) != 1)) { stop(paste0('Parameter "N.eff" must be NA, a numeric, or an array with ', 'the same dimensions as "obs" except "time_dim".')) } - ## time_dim if (!is.character(time_dim) | length(time_dim) != 1) stop('Parameter "time_dim" must be a character string.') @@ -118,7 +116,8 @@ DiffCorr <- function(exp, ref, obs, N.eff = NA, time_dim = 'sdate', name_exp <- name_exp[-which(name_exp == memb_dim)] name_ref <- name_ref[-which(name_ref == memb_dim)] } - if (!all.equal(dim(exp)[name_exp], dim(ref)[name_ref], dim(obs)[name_obs])) { + if (!identical(length(name_exp), length(name_ref), length(name_obs)) | + !identical(dim(exp)[name_exp], dim(ref)[name_ref], dim(obs)[name_obs])) { stop(paste0("Parameter 'exp', 'ref', and 'obs' must have same length of ", "all dimensions expect 'memb_dim'.")) } @@ -169,9 +168,9 @@ DiffCorr <- function(exp, ref, obs, N.eff = NA, time_dim = 'sdate', # output_dims if (is.null(alpha)) { - output_dims <- list(corr.diff = NULL, p.val = NULL) + output_dims <- list(diff.corr = NULL, p.val = NULL) } else { - output_dims <- list(corr.diff = NULL, sign = NULL) + output_dims <- list(diff.corr = NULL, sign = NULL) } # Correlation difference if (is.array(N.eff)) { @@ -203,7 +202,7 @@ DiffCorr <- function(exp, ref, obs, N.eff = NA, time_dim = 'sdate', cor.exp <- cor(x = exp, y = obs, method = method) cor.ref <- cor(x = ref, y = obs, method = method) output <- NULL - output$corr.diff <- cor.exp - cor.ref + output$diff.corr <- cor.exp - cor.ref # Significance with one-sided test for equality of dependent correlation coefficients (Steiger, 1980) r12 <- cor.exp @@ -240,9 +239,9 @@ DiffCorr <- function(exp, ref, obs, N.eff = NA, time_dim = 'sdate', } else if (handle.na == 'return.na') { # Data contain NA, return NAs directly without passing to .diff.corr if (is.null(alpha)) { - output <- list(corr.diff = NA, p.val = NA) + output <- list(diff.corr = NA, p.val = NA) } else { - output <- list(corr.diff = NA, sign = NA) + output <- list(diff.corr = NA, sign = NA) } } diff --git a/man/DiffCorr.Rd b/man/DiffCorr.Rd index 5671908..7f56289 100644 --- a/man/DiffCorr.Rd +++ b/man/DiffCorr.Rd @@ -62,7 +62,7 @@ computation. The default value is NULL.} } \value{ A list with: -\item{$corr.diff}{ +\item{$diff.corr}{ A numerical array of the correlation differences with the same dimensions as the input arrays except "time_dim" (and "memb_dim" if provided). } diff --git a/man/ResidualCorr.Rd b/man/ResidualCorr.Rd index 1e0adad..e98ea7a 100644 --- a/man/ResidualCorr.Rd +++ b/man/ResidualCorr.Rd @@ -67,9 +67,9 @@ A list with: the input arrays except "time_dim" (and "memb_dim" if provided). } \item{$sign}{ - A logical array of the statistical significance of the residual correlation - with the same dimensions as the input arrays except "time_dim" (and - "memb_dim" if provided). Returned only if "alpha" is a numeric. + A logical array indicating whether the residual correlation is statistically + significant or not with the same dimensions as the input arrays except "time_dim" + (and "memb_dim" if provided). Returned only if "alpha" is a numeric. } \item{$p.val}{ A numeric array of the p-values with the same dimensions as the input arrays diff --git a/tests/testthat/test-DiffCorr.R b/tests/testthat/test-DiffCorr.R new file mode 100644 index 0000000..2d1fbaf --- /dev/null +++ b/tests/testthat/test-DiffCorr.R @@ -0,0 +1,217 @@ +context("s2dv::DiffCorr tests") + +############################################## + +# dat1 +set.seed(1) +exp1 <- array(rnorm(180), dim = c(lat = 3, lon = 2, memb = 3, sdate = 10)) +set.seed(2) +ref1 <- array(rnorm(120), dim = c(lat = 3, lon = 2, memb = 2, sdate = 10)) +set.seed(3) +obs1 <- array(rnorm(60), dim = c(lat = 3, lon = 2, sdate = 10)) +Neff1 <- array(rep(9:10, 3), dim = c(lat = 3, lon = 2)) + +# dat2 +set.seed(1) +exp2 <- array(rnorm(10), dim = c(sdate = 10)) +set.seed(2) +ref2 <- array(rnorm(10), dim = c(sdate = 10)) +set.seed(3) +obs2 <- array(rnorm(10), dim = c(sdate = 10)) + + + +############################################## +test_that("1. Input checks", { + # exp and obs (1) + expect_error( + DiffCorr(c()), + 'Parameter "exp" must be a numeric array.' + ) + expect_error( + DiffCorr(exp1, c('a')), + 'Parameter "ref" must be a numeric array.' + ) + expect_error( + DiffCorr(exp1, ref1, c()), + 'Parameter "obs" must be a numeric array.' + ) + # N.eff + expect_error( + DiffCorr(exp1, ref1, obs1, N.eff = array(1, dim = c(lat = 2, lon = 2))), + "If parameter \"N.eff\" is provided with an array, it must have the same dimensions as \"obs\" except \"time_dim\"." + ) + expect_error( + DiffCorr(exp1, ref1, obs1, N.eff = 1:3), + 'Parameter "N.eff" must be NA, a numeric, or an array with the same dimensions as "obs" except "time_dim".' + ) + # time_dim + expect_error( + DiffCorr(exp1, ref1, obs1, time_dim = 1), + 'Parameter "time_dim" must be a character string.' + ) + expect_error( + DiffCorr(exp1, ref1, obs1, time_dim = 'time'), + "Parameter 'time_dim' is not found in 'exp', 'ref', or 'obs' dimension." + ) + # memb_dim + expect_error( + DiffCorr(exp1, ref1, obs1, memb_dim = TRUE), + "Parameter 'memb_dim' must be a character string." + ) + expect_error( + DiffCorr(exp1, ref1, obs1, memb_dim = 'member'), + "Parameter 'memb_dim' is not found in 'exp' or 'ref' dimension." + ) + # exp, ref, and obs (2) + expect_error( + DiffCorr(exp1, array(1:10, dim = c(sdate = 10, memb = 1)), obs1, memb_dim = 'memb'), + "Parameter 'exp', 'ref', and 'obs' must have same length of all dimensions expect 'memb_dim'." + ) + # method + expect_error( + DiffCorr(exp1, ref1, obs1, method = 'asd', memb_dim = 'memb'), + 'Parameter "method" must be "pearson", "kendall", or "spearman".' + ) + # alpha + expect_error( + DiffCorr(exp2, ref2, obs2, alpha = 1), + 'Parameter "alpha" must be NULL or a number between 0 and 1.' + ) + # handle.na + expect_error( + DiffCorr(exp2, ref2, obs2, handle.na = TRUE), + 'Parameter "handle.na" must be "return.na", "only.complete.triplets" or "na.fail".' + ) + # ncores + expect_error( + DiffCorr(exp2, ref2, obs2, ncores = 1.5), + 'Parameter "ncores" must be either NULL or a positive integer.' + ) + +}) + +############################################## +test_that("2. Output checks: dat1", { +expect_equal( +names(DiffCorr(exp1, ref1, obs1, memb_dim = 'memb')), +c("diff.corr", "p.val") +) +expect_equal( +dim(DiffCorr(exp1, ref1, obs1, memb_dim = 'memb')$diff), +c(lat = 3, lon = 2) +) +expect_equal( +dim(DiffCorr(exp1, ref1, obs1, memb_dim = 'memb')$p.val), +c(lat = 3, lon = 2) +) +expect_equal( +as.vector(DiffCorr(exp1, ref1, obs1, memb_dim = 'memb')$diff), +c(0.27347087, 0.50556882, 0.08855968, 0.24199701, 0.22935182, 0.88336336), +tolerance = 0.0001 +) +expect_equal( +as.vector(DiffCorr(exp1, ref1, obs1, memb_dim = 'memb')$p), +c(0.26166060, 0.15899774, 0.39264452, 0.27959883, 0.34736305, 0.07479832), +tolerance = 0.0001 +) +expect_equal( +names(DiffCorr(exp1, ref1, obs1, memb_dim = 'memb', alpha = 0.05)), +c("diff.corr", "sign") +) +expect_equal( +as.vector(DiffCorr(exp1, ref1, obs1, memb_dim = 'memb', alpha = 0.05)$diff.corr), +c(0.27347087, 0.50556882, 0.08855968, 0.24199701, 0.22935182, 0.88336336), +tolerance = 0.0001 +) +expect_equal( +as.vector(DiffCorr(exp1, ref1, obs1, memb_dim = 'memb', alpha = 0.05)$sign), +rep(FALSE, 6) +) +expect_equal( +as.vector(DiffCorr(exp1, ref1, obs1, memb_dim = 'memb', method = "kendall")$diff.corr), +c(0.08888889, 0.35555556, 0.00000000, -0.04444444, 0.00000000, 0.93333333), +tolerance = 0.0001 +) +expect_equal( +as.vector(DiffCorr(exp1, ref1, obs1, memb_dim = 'memb', method = "kendall")$p), +c(0.42259038, 0.25373083, 0.50000000, 0.54183730, 0.50000000, 0.06307063), +tolerance = 0.0001 +) +expect_equal( +as.vector(DiffCorr(exp1, ref1, obs1, memb_dim = 'memb', method = "spearman")$diff.corr), +c(0.07272727, 0.54545455, 0.10909091, -0.01212121, -0.03636364, 1.01818182), +tolerance = 0.0001 +) +expect_equal( +as.vector(DiffCorr(exp1, ref1, obs1, memb_dim = 'memb', method = "spearman")$p), +c(0.4358970, 0.1341575, 0.3448977, 0.5114262, 0.5264872, 0.0437861), +tolerance = 0.0001 +) +expect_equal( +as.vector(DiffCorr(exp1, ref1, obs1, memb_dim = 'memb', N.eff = Neff1)$diff.corr), +c(0.27347087, 0.50556882, 0.08855968, 0.24199701, 0.22935182, 0.88336336), +tolerance = 0.0001 +) +expect_equal( +as.vector(DiffCorr(exp1, ref1, obs1, memb_dim = 'memb', N.eff = Neff1)$p), +c(0.27841537, 0.15899774, 0.40096749, 0.27959883, 0.35889690, 0.07479832), +tolerance = 0.0001 +) + + +#--------------------------- +exp1[1] <- NA +expect_equal( +as.vector(DiffCorr(exp1, ref1, obs1, memb_dim = 'memb')$diff.corr), +c(NA, 0.50556882, 0.08855968, 0.24199701, 0.22935182, 0.88336336), +tolerance = 0.0001 +) +expect_equal( +as.vector(DiffCorr(exp1, ref1, obs1, memb_dim = 'memb', handle.na = 'only.complete.triplets')$diff.corr), +c(0.17418065, 0.50556882, 0.08855968, 0.24199701, 0.22935182, 0.88336336), +tolerance = 0.0001 +) +expect_error( +DiffCorr(exp1, ref1, obs1, memb_dim = 'memb', handle.na = 'na.fail'), +"The data contain NAs." +) + +exp1[1,1,1,1:3] <- NA +ref1[1,1,1,4:8] <- NA +obs1[1,1,9:10] <- NA +expect_error( +DiffCorr(exp1, ref1, obs1, memb_dim = 'memb', handle.na = 'only.complete.triplets'), +"There is no complete set of forecasts and observations." +) + + +}) + +############################################## + +test_that("3. Output checks: dat2", { +expect_equal( +names(DiffCorr(exp2, ref2, obs2)), +c("diff.corr", "p.val") +) +expect_equal( +dim(DiffCorr(exp2, ref2, obs2)$diff.corr), +NULL +) +expect_equal( +dim(DiffCorr(exp2, ref2, obs2)$p), +NULL +) +expect_equal( +DiffCorr(exp2, ref2, obs2)$p, +0.6577392, +tolerance = 0.0001 +) +expect_equal( +DiffCorr(exp2, ref2, obs2)$diff, +-0.2434725, +tolerance = 0.0001 +) + +}) -- GitLab From 66d85e3d3244656ba010888131ddfaabe782e85a Mon Sep 17 00:00:00 2001 From: aho Date: Mon, 2 May 2022 14:56:13 +0200 Subject: [PATCH 43/83] Allow dat_dim to be NULL; fix the output dimension name for some cases --- R/Clim.R | 151 +++++++++------ man/Clim.Rd | 15 +- man/ResidualCorr.Rd | 6 +- tests/testthat/test-Clim.R | 364 ++++++++++++++++++++++++++++++++++--- 4 files changed, 442 insertions(+), 94 deletions(-) diff --git a/R/Clim.R b/R/Clim.R index 59f8bc9..7d04f60 100644 --- a/R/Clim.R +++ b/R/Clim.R @@ -15,23 +15,26 @@ #'is the initial condition bias correction method. The two methods both do the #'per-pair correction beforehand. #' -#'@param exp A named numeric array of experimental data, with at least two -#' dimensions 'time_dim' and 'dat_dim'. -#'@param obs A named numeric array of observational data, same dimensions as -#' parameter 'exp' except along 'dat_dim'. +#'@param exp A named numeric array of experimental data with at least dimension +#' 'time_dim'. +#'@param obs A named numeric array of observational data that has the same +#' dimension as 'exp' except 'dat_dim'. #'@param time_dim A character string indicating the name of dimension along #' which the climatologies are computed. The default value is 'sdate'. #'@param dat_dim A character vector indicating the name of the dataset and #' member dimensions. If data at one startdate (i.e., 'time_dim') are not #' complete along 'dat_dim', this startdate along 'dat_dim' will be discarded. -#' The default value is "c('dataset', 'member')". -#'@param method A character string indicating the method to be used. The +#' If there is no dataset dimension, it can be NULL, however, it will be more +#' efficient to simply use mean() to do the calculation. The default value is +#' "c('dataset', 'member')". +#'@param method A character string indicating the method to be used. The #' options include 'clim' (per-pair method), 'kharin' (Kharin method), and #' 'NDV' (Fuckar method). The default value is 'clim'. #'@param ftime_dim A character string indicating the name of forecast time #' dimension. Only used when method = 'NDV'. The default value is 'ftime'. #'@param memb A logical value indicating whether to remain 'memb_dim' dimension -#' (TRUE) or do ensemble mean over 'memb_dim' (FALSE). The default value is TRUE. +#' (TRUE) or do ensemble mean over 'memb_dim' (FALSE). The default value is +#' TRUE. #'@param memb_dim A character string indicating the name of the member #' dimension. Only used when parameter 'memb' is FALSE. It must be one element #' in 'dat_dim'. The default value is 'member'. @@ -86,14 +89,10 @@ Clim <- function(exp, obs, time_dim = 'sdate', dat_dim = c('dataset', 'member'), stop(paste0("Parameter 'exp' and 'obs' must be at least two dimensions ", "containing time_dim and dat_dim.")) } - if(any(is.null(names(dim(exp))))| any(nchar(names(dim(exp))) == 0) | - any(is.null(names(dim(obs))))| any(nchar(names(dim(obs))) == 0)) { + if (any(is.null(names(dim(exp))))| any(nchar(names(dim(exp))) == 0) | + any(is.null(names(dim(obs))))| any(nchar(names(dim(obs))) == 0)) { stop("Parameter 'exp' and 'obs' must have dimension names.") } - if(!all(names(dim(exp)) %in% names(dim(obs))) | - !all(names(dim(obs)) %in% names(dim(exp)))) { - stop("Parameter 'exp' and 'obs' must have same dimension name") - } ## time_dim if (!is.character(time_dim) | length(time_dim) > 1) { stop("Parameter 'time_dim' must be a character string.") @@ -102,12 +101,26 @@ Clim <- function(exp, obs, time_dim = 'sdate', dat_dim = c('dataset', 'member'), stop("Parameter 'time_dim' is not found in 'exp' or 'obs' dimension.") } ## dat_dim - if (!is.character(dat_dim)) { - stop("Parameter 'dat_dim' must be a character vector.") - } - if (!all(dat_dim %in% names(dim(exp))) | !all(dat_dim %in% names(dim(obs)))) { - stop("Parameter 'dat_dim' is not found in 'exp' or 'obs' dimension.") - } + if (!is.null(dat_dim)) { + if (!is.character(dat_dim)) { + stop("Parameter 'dat_dim' must be a character vector.") + } + # Check if dat_dim is in exp + if (!all(dat_dim %in% names(dim(exp)))) { + stop("Parameter 'dat_dim' is not found in 'exp' dimensions.") + } + # If dat_dim is not in obs, add it in + if (any(!dat_dim %in% names(dim(obs)))) { + reset_obs_dim <- TRUE + ori_obs_dim <- dim(obs) + dim(obs) <- c(dim(obs), rep(1, length(dat_dim[which(!dat_dim %in% names(dim(obs)))]))) + names(dim(obs)) <- c(names(ori_obs_dim), dat_dim[which(!dat_dim %in% names(dim(obs)))]) + } else { + reset_obs_dim <- FALSE + } + } else { + reset_obs_dim <- FALSE + } ## method if (!(method %in% c("clim", "kharin", "NDV"))) { stop("Parameter 'method' must be one of 'clim', 'kharin' or 'NDV'.") @@ -126,13 +139,15 @@ Clim <- function(exp, obs, time_dim = 'sdate', dat_dim = c('dataset', 'member'), stop("Parameter 'memb' must be one logical value.") } ## memb_dim - if (!memb) { + if (!is.null(memb_dim) & !memb) { if (!is.character(memb_dim) | length(memb_dim) > 1) { stop("Parameter 'memb_dim' must be a character string.") } if (!memb_dim %in% names(dim(exp)) | !memb_dim %in% names(dim(obs))) { - stop("Parameter 'memb_dim' is not found in 'exp' or 'obs' dimension.") + stop("Parameter 'memb_dim' is not found in 'exp' dimension.") } + if (!memb_dim %in% dat_dim) + stop("Parameter 'memb_dim' must be one element in parameter 'dat_dim'.") } ## na.rm if (!is.logical(na.rm) | length(na.rm) > 1) { @@ -148,13 +163,15 @@ Clim <- function(exp, obs, time_dim = 'sdate', dat_dim = c('dataset', 'member'), ## exp and obs (2) name_exp <- sort(names(dim(exp))) name_obs <- sort(names(dim(obs))) - for (i in 1:length(dat_dim)) { - name_exp <- name_exp[-which(name_exp == dat_dim[i])] - name_obs <- name_obs[-which(name_obs == dat_dim[i])] + if (!is.null(dat_dim)) { + for (i in 1:length(dat_dim)) { + name_exp <- name_exp[-which(name_exp == dat_dim[i])] + name_obs <- name_obs[-which(name_obs == dat_dim[i])] + } } if(!all(dim(exp)[name_exp] == dim(obs)[name_obs])) { - stop(paste0("Parameter 'exp' and 'obs' must have same length of ", - "all dimension expect 'dat_dim'.")) + stop(paste0("Parameter 'exp' and 'obs' must have the same dimensions ", + "expect 'dat_dim'.")) } ############################### @@ -169,8 +186,8 @@ Clim <- function(exp, obs, time_dim = 'sdate', dat_dim = c('dataset', 'member'), # Calculate Clim #---------------------------------- - # Remove all sdate if not complete along dat_dim - + # Per-pair: Remove all sdate if not complete along dat_dim + if (!is.null(dat_dim)) { pos <- rep(0, length(dat_dim)) for (i in 1:length(dat_dim)) { #[dat, sdate] ## dat_dim: [dataset, member] @@ -186,7 +203,7 @@ Clim <- function(exp, obs, time_dim = 'sdate', dat_dim = c('dataset', 'member'), } exp[which(is.na(outrows_exp))] <- NA obs[which(is.na(outrows_obs))] <- NA - + } #----------------------------------- if (method == 'clim') { @@ -197,11 +214,17 @@ Clim <- function(exp, obs, time_dim = 'sdate', dat_dim = c('dataset', 'member'), dat_dim = dat_dim, memb_dim = memb_dim, memb = memb, na.rm = na.rm, ncores_input = ncores, ncores = ncores) - # Add member dimension name back - if (memb) { - if(is.null(names(dim(clim$clim_exp))[1])) { - names(dim(clim$clim_exp))[1] <- memb_dim - names(dim(clim$clim_obs))[1] <- memb_dim + + # Remove dat_dim in obs if obs doesn't have at first place + if (reset_obs_dim) { + clim_obs_dim <- ori_obs_dim[-which(names(ori_obs_dim) == time_dim)] + if (!memb & memb_dim %in% names(clim_obs_dim)) { + clim_obs_dim <- clim_obs_dim[-which(names(clim_obs_dim) == memb_dim)] + } + if (is.integer(clim_obs_dim) & length(clim_obs_dim) == 0) { + clim$clim_obs <- as.vector(clim$clim_obs) + } else { + clim$clim_obs <- array(clim$clim_obs, dim = clim_obs_dim) } } @@ -234,32 +257,44 @@ Clim <- function(exp, obs, time_dim = 'sdate', dat_dim = c('dataset', 'member'), na.rm = TRUE, ncores_input = NULL) { if (method == 'clim') { - # exp: [sdate, dat_dim_exp] - # obs: [sdate, dat_dim_obs] - - clim_exp <- apply(exp, which(names(dim(exp)) != time_dim), - mean, na.rm = na.rm) #average out time_dim - clim_obs <- apply(obs, which(names(dim(obs)) != time_dim), - mean, na.rm = na.rm) #[dat_dim] - - ## member mean - if (!memb) { - if (length(dim(clim_exp)) == 1) { #dim: [member] - clim_exp <- mean(clim_exp, na.rm = TRUE) - clim_obs <- mean(clim_obs, na.rm = TRUE) - } else { - pos <- which(names(dim(clim_exp)) == memb_dim) - pos <- c(1:length(dim(clim_exp)))[-pos] - dim_name <- names(dim(clim_exp)) - clim_exp <- apply(clim_exp, pos, mean, na.rm = TRUE) - clim_obs <- apply(clim_obs, pos, mean, na.rm = TRUE) - if (is.null(names(dim(as.array(clim_exp))))) { - clim_exp <- as.array(clim_exp) - clim_obs <- as.array(clim_obs) - names(dim(clim_exp)) <- dim_name[pos] - names(dim(clim_obs)) <- dim_name[pos] + if (!is.null(dat_dim)) { + # exp: [sdate, dat_dim_exp] + # obs: [sdate, dat_dim_obs] + + clim_exp <- apply(exp, which(names(dim(exp)) != time_dim), + mean, na.rm = na.rm) #average out time_dim + clim_obs <- apply(obs, which(names(dim(obs)) != time_dim), + mean, na.rm = na.rm) #[dat_dim] + + if (is.null(dim(clim_exp))) { + dim(clim_exp) <- length(clim_exp) + names(dim(clim_exp)) <- dat_dim + dim(clim_obs) <- length(clim_obs) + names(dim(clim_obs)) <- dat_dim + } + + ## ensemble mean + if (!memb) { + if (length(dim(clim_exp)) == 1 | is.null(dim(clim_exp))) { #dim: [member] + clim_exp <- mean(clim_exp, na.rm = TRUE) + clim_obs <- mean(clim_obs, na.rm = TRUE) + } else { + pos <- which(names(dim(clim_exp)) == memb_dim) + pos <- c(1:length(dim(clim_exp)))[-pos] + dim_name <- names(dim(clim_exp)) + clim_exp <- apply(clim_exp, pos, mean, na.rm = TRUE) + clim_obs <- apply(clim_obs, pos, mean, na.rm = TRUE) + if (is.null(names(dim(as.array(clim_exp))))) { + clim_exp <- as.array(clim_exp) + clim_obs <- as.array(clim_obs) + names(dim(clim_exp)) <- dim_name[pos] + names(dim(clim_obs)) <- dim_name[pos] + } } } + } else { #dat_dim = NULL + clim_exp <- mean(exp) + clim_obs <- mean(obs) } } else if (method == 'kharin') { diff --git a/man/Clim.Rd b/man/Clim.Rd index cf58525..311e699 100644 --- a/man/Clim.Rd +++ b/man/Clim.Rd @@ -18,11 +18,11 @@ Clim( ) } \arguments{ -\item{exp}{A named numeric array of experimental data, with at least two -dimensions 'time_dim' and 'dat_dim'.} +\item{exp}{A named numeric array of experimental data with at least dimension +'time_dim'.} -\item{obs}{A named numeric array of observational data, same dimensions as -parameter 'exp' except along 'dat_dim'.} +\item{obs}{A named numeric array of observational data that has the same +dimension as 'exp' except 'dat_dim'.} \item{time_dim}{A character string indicating the name of dimension along which the climatologies are computed. The default value is 'sdate'.} @@ -30,7 +30,9 @@ which the climatologies are computed. The default value is 'sdate'.} \item{dat_dim}{A character vector indicating the name of the dataset and member dimensions. If data at one startdate (i.e., 'time_dim') are not complete along 'dat_dim', this startdate along 'dat_dim' will be discarded. -The default value is "c('dataset', 'member')".} +If there is no dataset dimension, it can be NULL, however, it will be more +efficient to simply use mean() to do the calculation. The default value is +"c('dataset', 'member')".} \item{method}{A character string indicating the method to be used. The options include 'clim' (per-pair method), 'kharin' (Kharin method), and @@ -40,7 +42,8 @@ options include 'clim' (per-pair method), 'kharin' (Kharin method), and dimension. Only used when method = 'NDV'. The default value is 'ftime'.} \item{memb}{A logical value indicating whether to remain 'memb_dim' dimension -(TRUE) or do ensemble mean over 'memb_dim' (FALSE). The default value is TRUE.} +(TRUE) or do ensemble mean over 'memb_dim' (FALSE). The default value is +TRUE.} \item{memb_dim}{A character string indicating the name of the member dimension. Only used when parameter 'memb' is FALSE. It must be one element diff --git a/man/ResidualCorr.Rd b/man/ResidualCorr.Rd index 1e0adad..e98ea7a 100644 --- a/man/ResidualCorr.Rd +++ b/man/ResidualCorr.Rd @@ -67,9 +67,9 @@ A list with: the input arrays except "time_dim" (and "memb_dim" if provided). } \item{$sign}{ - A logical array of the statistical significance of the residual correlation - with the same dimensions as the input arrays except "time_dim" (and - "memb_dim" if provided). Returned only if "alpha" is a numeric. + A logical array indicating whether the residual correlation is statistically + significant or not with the same dimensions as the input arrays except "time_dim" + (and "memb_dim" if provided). Returned only if "alpha" is a numeric. } \item{$p.val}{ A numeric array of the p-values with the same dimensions as the input arrays diff --git a/tests/testthat/test-Clim.R b/tests/testthat/test-Clim.R index 1d2ff3e..f4a5cb9 100644 --- a/tests/testthat/test-Clim.R +++ b/tests/testthat/test-Clim.R @@ -26,6 +26,42 @@ context("s2dv::Clim tests") obs3 <- array(rnorm(40), dim = c(dat = 1, ensemble = 1, lon = 2, lat = 4, date = 5)) + # dat4 + set.seed(1) + exp4 <- array(rnorm(30), dim = c(dataset = 1, member = 3, sdate = 5)) + set.seed(2) + obs4 <- array(rnorm(10), dim = c(dataset = 1, member = 1, sdate = 5)) + + # dat5 + set.seed(1) + exp5 <- array(rnorm(30), dim = c(member = 3, sdate = 5)) + set.seed(2) + obs5 <- array(rnorm(10), dim = c(member = 1, sdate = 5)) + + # dat6 + set.seed(1) + exp6 <- array(rnorm(30), dim = c(dataset = 1, member = 3, sdate = 5)) + set.seed(2) + obs6 <- array(rnorm(10), dim = c(dataset = 1, sdate = 5)) + + # dat7 + set.seed(1) + exp7 <- array(rnorm(30), dim = c(dataset = 1, member = 3, sdate = 5)) + set.seed(2) + obs7 <- array(rnorm(10), dim = c(sdate = 5)) + + # dat8 + set.seed(1) + exp8 <- array(rnorm(30), dim = c(member = 3, sdate = 5)) + set.seed(2) + obs8 <- array(rnorm(10), dim = c(sdate = 5)) + + # dat9 + set.seed(1) + exp9 <- array(rnorm(30), dim = c(sdate = 5)) + set.seed(2) + obs9 <- array(rnorm(10), dim = c(sdate = 5)) + ############################################## test_that("1. Input checks", { @@ -47,10 +83,6 @@ test_that("1. Input checks", { "Parameter 'exp' and 'obs' must have dimension names." ) expect_error( - Clim(array(1:10, dim = c(a = 2, c = 5)), array(1:4, dim = c(a = 2, b = 2))), - "Parameter 'exp' and 'obs' must have same dimension name" - ) - expect_error( Clim(exp1, obs1, method = TRUE), "Parameter 'method' must be one of 'clim', 'kharin' or 'NDV'." ) @@ -68,7 +100,7 @@ test_that("1. Input checks", { ) expect_error( Clim(exp1, obs1, dat_dim = c('member', 'dat')), - "Parameter 'dat_dim' is not found in 'exp' or 'obs' dimension." + "Parameter 'dat_dim' is not found in 'exp' dimensions." ) expect_error( Clim(exp1, obs1, method = 'NDV', ftime_dim = 4), @@ -88,7 +120,7 @@ test_that("1. Input checks", { ) expect_error( Clim(exp1, obs1, memb = FALSE, memb_dim = 'memb'), - "Parameter 'memb_dim' is not found in 'exp' or 'obs' dimension." + "Parameter 'memb_dim' is not found in 'exp' dimension." ) expect_error( Clim(exp1, obs1, na.rm = na.omit), @@ -101,8 +133,7 @@ test_that("1. Input checks", { expect_error( Clim(array(1:10, dim = c(dataset = 2, member = 5, sdate = 4, ftime = 3)), array(1:4, dim = c(dataset = 2, member = 2, sdate = 5, ftime = 3))), - paste0("Parameter 'exp' and 'obs' must have same length of ", - "all dimension expect 'dat_dim'.") + "Parameter 'exp' and 'obs' must have the same dimensions expect 'dat_dim'." ) }) @@ -110,58 +141,79 @@ test_that("1. Input checks", { ############################################## test_that("2. Output checks: dat1", { + # dimension expect_equal( dim(Clim(exp1, obs1)$clim_exp), c(dataset = 1, member = 3, ftime = 3, lon = 2, lat = 4) ) expect_equal( - dim(Clim(exp1, obs1, memb = FALSE)$clim_exp), - c(dataset = 1, ftime = 3, lon = 2, lat = 4) + dim(Clim(exp1, obs1)$clim_obs), + c(dataset = 1, member = 1, ftime = 3, lon = 2, lat = 4) ) expect_equal( - dim(Clim(exp1, obs1, time_dim = 'lon')$clim_exp), - c(dataset = 1, member = 3, sdate = 5, ftime = 3, lat = 4) + dim(Clim(exp1, obs1, memb = FALSE)$clim_exp), + c(dataset = 1, ftime = 3, lon = 2, lat = 4) ) expect_equal( - dim(Clim(exp1, obs1, method = 'kharin')$clim_exp), - c(sdate = 5, dataset = 1, member = 3, ftime = 3, lon = 2, lat = 4) + dim(Clim(exp1, obs1, memb = FALSE)$clim_obs), + c(dataset = 1, ftime = 3, lon = 2, lat = 4) ) expect_equal( - dim(Clim(exp1, obs1, method = 'NDV')$clim_exp), - c(sdate = 5, dataset = 1, member = 3, ftime = 3, lon = 2, lat = 4) + dim(Clim(exp1, obs1, time_dim = 'lon')$clim_exp), + c(dataset = 1, member = 3, sdate = 5, ftime = 3, lat = 4) ) + # clim_exp expect_equal( - dim(Clim(exp1, obs1)$clim_obs), - c(dataset = 1, member = 1, ftime = 3, lon = 2, lat = 4) + (Clim(exp1, obs1, memb = TRUE)$clim_exp)[1:5], + c(0.10592542, 0.10971142, 0.08689170, 0.14149945, 0.02359945), + tolerance = 0.001 ) expect_equal( - dim(Clim(exp1, obs1, method = 'kharin')$clim_obs), - c(dataset = 1, member = 1, ftime = 3, lon = 2, lat = 4) + (Clim(exp1, obs1, memb = FALSE)$clim_exp)[1:5], + c(0.10084284, 0.06407350, 0.09028584, 0.17526332, 0.18004387), + tolerance = 0.001 ) expect_equal( - dim(Clim(exp1, obs1, method = 'NDV')$clim_obs), - c(dataset = 1, member = 1, ftime = 3, lon = 2, lat = 4) + max(Clim(exp1, obs1)$clim_exp, na.rm = T), + 1.026186, + tolerance = 0.001 ) + # clim_obs expect_equal( (Clim(exp1, obs1)$clim_obs)[1:5], c(0.14831161, -0.60462627, 0.06609153, -0.23378059, 0.50553522), tolerance = 0.001 ) expect_equal( - (Clim(exp1, obs1, memb = FALSE)$clim_exp)[1:5], - c(0.10084284, 0.06407350, 0.09028584, 0.17526332, 0.18004387), + (Clim(exp1, obs1, memb = F)$clim_obs)[1:5], + c(0.14831161, -0.60462627, 0.06609153, -0.23378059, 0.50553522), tolerance = 0.001 ) + + # method = 'kharin' expect_equal( - max(Clim(exp1, obs1)$clim_exp, na.rm = T), - 1.026186, - tolerance = 0.001 + dim(Clim(exp1, obs1, method = 'kharin')$clim_exp), + c(sdate = 5, dataset = 1, member = 3, ftime = 3, lon = 2, lat = 4) + ) + expect_equal( + dim(Clim(exp1, obs1, method = 'kharin')$clim_obs), + c(dataset = 1, member = 1, ftime = 3, lon = 2, lat = 4) ) expect_equal( max(Clim(exp1, obs1, method = 'kharin')$clim_exp, na.rm = T), 2.282634, tolerance = 0.001 ) + + # method = 'NDV' + expect_equal( + dim(Clim(exp1, obs1, method = 'NDV')$clim_exp), + c(sdate = 5, dataset = 1, member = 3, ftime = 3, lon = 2, lat = 4) + ) + expect_equal( + dim(Clim(exp1, obs1, method = 'NDV')$clim_obs), + c(dataset = 1, member = 1, ftime = 3, lon = 2, lat = 4) + ) expect_equal( min(Clim(exp1, obs1, method = 'NDV')$clim_exp, na.rm = T), -4.025745, @@ -212,9 +264,267 @@ test_that("4. Output checks: dat3", { tolerance = 0.00001 ) +}) +############################################## +test_that("5. Output checks: dat4", { +res1 <- Clim(exp4, obs4, memb = T) +res2 <- Clim(exp4, obs4, memb = F) +expect_equal( +dim(res1$clim_exp), +c(dataset = 1, member = 3) +) +expect_equal( +dim(res1$clim_obs), +c(dataset = 1, member = 1) +) +expect_equal( +dim(res2$clim_exp), +c(dataset = 1) +) +expect_equal( +dim(res2$clim_obs), +c(dataset = 1) +) +expect_equal( +as.vector(res1$clim_exp), +c(0.1059254, 0.1097114, 0.0868917), +tolerance = 0.0001 +) +expect_equal( +as.vector(res1$clim_obs), +-0.06696949, +tolerance = 0.0001 +) +expect_equal( +as.vector(res2$clim_exp), +0.1008428, +tolerance = 0.0001 +) +expect_equal( +as.vector(res2$clim_obs), +-0.06696949, +tolerance = 0.0001 +) + +}) + + +############################################## +test_that("6. Output checks: dat5", { +res1 <- Clim(exp5, obs5, dat_dim = 'member', memb = T) +res2 <- Clim(exp5, obs5, dat_dim = 'member', memb = F) +expect_equal( +dim(res1$clim_exp), +c(member = 3) +) +expect_equal( +dim(res1$clim_obs), +c(member = 1) +) +expect_equal( +dim(res2$clim_exp), +NULL +) +expect_equal( +dim(res2$clim_obs), +NULL +) +expect_equal( +as.vector(res1$clim_exp), +c(0.1059254, 0.1097114, 0.0868917), +tolerance = 0.0001 +) +expect_equal( +as.vector(res1$clim_obs), +-0.06696949, +tolerance = 0.0001 +) +expect_equal( +as.vector(res2$clim_exp), +0.1008428, +tolerance = 0.0001 +) +expect_equal( +as.vector(res2$clim_obs), +-0.06696949, +tolerance = 0.0001 +) +}) + +############################################## +test_that("7. Output checks: dat6", { +res1 <- Clim(exp6, obs6, memb = T) +res2 <- Clim(exp6, obs6, memb = F) +expect_equal( +dim(res1$clim_exp), +c(dataset = 1, member = 3) +) +expect_equal( +dim(res1$clim_obs), +c(dataset = 1) +) +expect_equal( +dim(res2$clim_exp), +c(dataset = 1) +) +expect_equal( +dim(res2$clim_obs), +c(dataset = 1) +) +expect_equal( +as.vector(res1$clim_exp), +c(0.1059254, 0.1097114, 0.0868917), +tolerance = 0.0001 +) +expect_equal( +as.vector(res1$clim_obs), +-0.06696949, +tolerance = 0.0001 +) +expect_equal( +as.vector(res2$clim_exp), +0.1008428, +tolerance = 0.0001 +) +expect_equal( +as.vector(res2$clim_obs), +-0.06696949, +tolerance = 0.0001 +) }) +############################################## +test_that("8. Output checks: dat7", { +res1 <- Clim(exp7, obs7, memb = T) +res2 <- Clim(exp7, obs7, memb = F) +expect_equal( +dim(res1$clim_exp), +c(dataset = 1, member = 3) +) +expect_equal( +dim(res1$clim_obs), +NULL +) +expect_equal( +dim(res2$clim_exp), +c(dataset = 1) +) +expect_equal( +dim(res2$clim_obs), +NULL +) +expect_equal( +as.vector(res1$clim_exp), +c(0.1059254, 0.1097114, 0.0868917), +tolerance = 0.0001 +) +expect_equal( +as.vector(res1$clim_obs), +-0.06696949, +tolerance = 0.0001 +) +expect_equal( +as.vector(res2$clim_exp), +0.1008428, +tolerance = 0.0001 +) +expect_equal( +as.vector(res2$clim_obs), +-0.06696949, +tolerance = 0.0001 +) + +}) + + +############################################## +test_that("9. Output checks: dat8", { +res1 <- Clim(exp8, obs8, dat_dim = 'member', memb = T) +res2 <- Clim(exp8, obs8, dat_dim = 'member', memb = F) +expect_equal( +dim(res1$clim_exp), +c(member = 3) +) +expect_equal( +dim(res1$clim_obs), +NULL +) +expect_equal( +dim(res2$clim_exp), +NULL +) +expect_equal( +dim(res2$clim_obs), +NULL +) +expect_equal( +as.vector(res1$clim_exp), +c(0.1059254, 0.1097114, 0.0868917), +tolerance = 0.0001 +) +expect_equal( +as.vector(res1$clim_obs), +-0.06696949, +tolerance = 0.0001 +) +expect_equal( +as.vector(res2$clim_exp), +0.1008428, +tolerance = 0.0001 +) +expect_equal( +as.vector(res2$clim_obs), +-0.06696949, +tolerance = 0.0001 +) + +}) + +############################################## +test_that("10. Output checks: dat9", { +res1 <- Clim(exp9, obs9, dat_dim = NULL, memb = T) +res2 <- Clim(exp9, obs9, dat_dim = NULL, memb_dim = NULL, memb = F) +expect_equal( +dim(res1$clim_exp), +NULL +) +expect_equal( +dim(res1$clim_obs), +NULL +) +expect_equal( +dim(res2$clim_exp), +NULL +) +expect_equal( +dim(res2$clim_obs), +NULL +) +expect_equal( +as.vector(res1$clim_exp), +0.1292699, +tolerance = 0.0001 +) +expect_equal( +as.vector(res1$clim_obs), +-0.06696949, +tolerance = 0.0001 +) +expect_equal( +as.vector(res2$clim_exp), +0.1292699, +tolerance = 0.0001 +) +expect_equal( +as.vector(res2$clim_obs), +-0.06696949, +tolerance = 0.0001 +) + +}) + +############################################## -- GitLab From e12f814085636874e2bfeeba69148a7f2ca81b3d Mon Sep 17 00:00:00 2001 From: aho Date: Mon, 2 May 2022 18:42:39 +0200 Subject: [PATCH 44/83] Revise method NDV and kharin for output dimensions --- R/Clim.R | 172 ++++++++++++++++--------- man/Clim.Rd | 2 +- tests/testthat/test-Clim.R | 256 ++++++++++++++++++++++++++++++++++++- 3 files changed, 367 insertions(+), 63 deletions(-) diff --git a/R/Clim.R b/R/Clim.R index 7d04f60..1093c99 100644 --- a/R/Clim.R +++ b/R/Clim.R @@ -53,7 +53,7 @@ #' dimension 'memb_dim' is also removed. #'} #'\item{$clim_obs}{ -#' A numeric array with the same dimensions as parameter 'exp' +#' A numeric array with the same dimensions as parameter 'obs' #' except dimension 'time_dim' is removed. If parameter 'memb' is FALSE, #' dimension 'memb_dim' is also removed. #'} @@ -148,6 +148,8 @@ Clim <- function(exp, obs, time_dim = 'sdate', dat_dim = c('dataset', 'member'), } if (!memb_dim %in% dat_dim) stop("Parameter 'memb_dim' must be one element in parameter 'dat_dim'.") + } else if (is.null(memb_dim) & !memb) { + memb <- TRUE } ## na.rm if (!is.logical(na.rm) | length(na.rm) > 1) { @@ -215,19 +217,6 @@ Clim <- function(exp, obs, time_dim = 'sdate', dat_dim = c('dataset', 'member'), memb = memb, na.rm = na.rm, ncores_input = ncores, ncores = ncores) - # Remove dat_dim in obs if obs doesn't have at first place - if (reset_obs_dim) { - clim_obs_dim <- ori_obs_dim[-which(names(ori_obs_dim) == time_dim)] - if (!memb & memb_dim %in% names(clim_obs_dim)) { - clim_obs_dim <- clim_obs_dim[-which(names(clim_obs_dim) == memb_dim)] - } - if (is.integer(clim_obs_dim) & length(clim_obs_dim) == 0) { - clim$clim_obs <- as.vector(clim$clim_obs) - } else { - clim$clim_obs <- array(clim$clim_obs, dim = clim_obs_dim) - } - } - } else if (method == 'kharin') { clim <- Apply(list(exp, obs), target_dims = c(time_dim, dat_dim), @@ -245,6 +234,19 @@ Clim <- function(exp, obs, time_dim = 'sdate', dat_dim = c('dataset', 'member'), dat_dim = dat_dim, ftime_dim = ftime_dim, memb_dim = memb_dim, memb = memb, na.rm = na.rm, ncores_input = ncores, ncores = ncores) + } + + # Remove dat_dim in obs if obs doesn't have at first place + if (reset_obs_dim) { + clim_obs_dim <- ori_obs_dim[-which(names(ori_obs_dim) == time_dim)] + if (!memb & memb_dim %in% names(clim_obs_dim)) { + clim_obs_dim <- clim_obs_dim[-which(names(clim_obs_dim) == memb_dim)] + } + if (is.integer(clim_obs_dim) & length(clim_obs_dim) == 0) { + clim$clim_obs <- as.vector(clim$clim_obs) + } else { + clim$clim_obs <- array(clim$clim_obs, dim = clim_obs_dim) + } } return(clim) @@ -302,8 +304,16 @@ Clim <- function(exp, obs, time_dim = 'sdate', dat_dim = c('dataset', 'member'), # obs: [sdate, dat_dim_obs] # obs clim - clim_obs <- apply(obs, which(names(dim(obs)) != time_dim), - mean, na.rm = na.rm) #[dat_dim] + if (!is.null(dat_dim)) { + clim_obs <- apply(obs, which(names(dim(obs)) != time_dim), + mean, na.rm = na.rm) #[dat_dim] + if (is.null(dim(clim_obs))) { + dim(clim_obs) <- length(clim_obs) + names(dim(clim_obs)) <- dat_dim + } + } else { + clim_obs <- mean(obs) + } # exp clim ##--- NEW trend ---## @@ -312,17 +322,21 @@ Clim <- function(exp, obs, time_dim = 'sdate', dat_dim = c('dataset', 'member'), tmp_exp <- Trend(data = exp, time_dim = time_dim, interval = 1, polydeg = 1, conf = FALSE, ncores = ncores_input)$trend # tmp_exp: [stats, dat_dim)] - tmp_obs_mean <- apply(tmp_obs, 1, mean) #average out dat_dim (dat and member) #tmp_obs_mean: [stats = 2] - - intercept_exp <- Subset(tmp_exp, 1, 1, drop = 'selected') #[dat_dim] - slope_exp <- Subset(tmp_exp, 1, 2, drop = 'selected') #[dat_dim] - intercept_obs <- array(tmp_obs_mean[1], dim = dim(exp)[-1]) #[dat_dim] - slope_obs <- array(tmp_obs_mean[2], dim = dim(exp)[-1]) #[dat_dim] + if (!is.null(dat_dim)) { + intercept_exp <- Subset(tmp_exp, 1, 1, drop = 'selected') #[dat_dim] + slope_exp <- Subset(tmp_exp, 1, 2, drop = 'selected') #[dat_dim] + intercept_obs <- array(tmp_obs_mean[1], dim = dim(exp)[-1]) #[dat_dim] + slope_obs <- array(tmp_obs_mean[2], dim = dim(exp)[-1]) #[dat_dim] + } else { + intercept_exp <- tmp_exp[1] + slope_exp <- tmp_exp[2] + intercept_obs <- tmp_obs_mean[1] + slope_obs <- tmp_obs_mean[2] + } trend_exp <- list() trend_obs <- list() - for (jdate in 1:dim(exp)[time_dim]) { trend_exp[[jdate]] <- intercept_exp + jdate * slope_exp trend_obs[[jdate]] <- intercept_obs + jdate * slope_obs @@ -330,26 +344,40 @@ Clim <- function(exp, obs, time_dim = 'sdate', dat_dim = c('dataset', 'member'), # turn list into array trend_exp <- array(unlist(trend_exp), dim = c(dim(exp)[-1], dim(exp)[1])) trend_obs <- array(unlist(trend_obs), dim = c(dim(exp)[-1], dim(exp)[1])) - len <- length(dim(exp)) - trend_exp <- Reorder(trend_exp, c(len, 1:(len - 1))) - trend_obs <- Reorder(trend_obs, c(len, 1:(len - 1))) + if (!is.null(dat_dim)) { + len <- length(dim(exp)) + trend_exp <- Reorder(trend_exp, c(len, 1:(len - 1))) + trend_obs <- Reorder(trend_obs, c(len, 1:(len - 1))) + } - clim_obs_mean <- mean(apply(clim_obs, 1, mean)) #average out dat_dim, get a number + # average out dat_dim, get a number + if (is.null(dim(clim_obs))) { + clim_obs_mean <- mean(clim_obs) + } else { + clim_obs_mean <- mean(apply(clim_obs, 1, mean)) + } clim_obs_mean <- array(clim_obs_mean, dim = dim(exp)) #enlarge it for the next line clim_exp <- trend_exp - trend_obs + clim_obs_mean - - ## member mean + + ## member mean if (!memb) { - pos <- which(names(dim(clim_exp)) == memb_dim) - pos <- c(1:length(dim(clim_exp)))[-pos] - dim_name <- names(dim(clim_exp)) - clim_exp <- apply(clim_exp, pos, mean, na.rm = TRUE) - clim_obs <- apply(clim_obs, pos, mean, na.rm = TRUE) - if (is.null(names(dim(as.array(clim_exp))))) { + pos_exp <- c(1:length(dim(clim_exp)))[-which(names(dim(clim_exp)) == memb_dim)] + pos_obs <- c(1:length(dim(clim_obs)))[-which(names(dim(clim_obs)) == memb_dim)] + tmp_dim_exp <- dim(clim_exp) + tmp_dim_obs <- dim(clim_obs) + clim_exp <- apply(clim_exp, pos_exp, mean, na.rm = TRUE) + if (is.integer(pos_obs) & length(pos_obs) == 0) { + clim_obs <- mean(clim_obs) + } else { + clim_obs <- apply(clim_obs, pos_obs, mean, na.rm = TRUE) + } + if (is.null(dim(clim_exp))) { clim_exp <- as.array(clim_exp) + dim(clim_exp) <- tmp_dim_exp[pos_exp] + } + if (is.null(dim(clim_obs)) & !(is.integer(pos_obs) & length(pos_obs) == 0)) { clim_obs <- as.array(clim_obs) - names(dim(clim_exp)) <- dim_name[pos] - names(dim(clim_obs)) <- dim_name[pos] + dim(clim_obs) <- tmp_dim_obs[pos_obs] } } @@ -358,15 +386,24 @@ Clim <- function(exp, obs, time_dim = 'sdate', dat_dim = c('dataset', 'member'), # exp: [sdate, dat_dim, ftime] # obs: [sdate, dat_dim, ftime] - # obs clim + # obs clim clim_obs <- apply(obs, which(names(dim(obs)) != time_dim), - mean, na.rm = na.rm) #[dat_dim, ftime] + mean, na.rm = na.rm) #[(dat_dim), ftime] - # exp clim + if (is.null(dim(clim_obs))) { + dim(clim_obs) <- length(clim_obs) + names(dim(clim_obs)) <- c(dat_dim, ftime_dim) + } + + # exp clim pos_ftime <- length(dim(exp)) #a number dim_ftime <- dim(exp)[pos_ftime] #c(ftime = 4) - pos_dat <- 2:(length(dim(exp)) - 1) #1 is sdate, last is ftime - dim_dat <- dim(exp)[pos_dat] #c(dataset = 1, member = 3) + if (!is.null(dat_dim)) { + pos_dat <- 2:(length(dim(exp)) - 1) #1 is sdate, last is ftime + dim_dat <- dim(exp)[pos_dat] #c(dataset = 1, member = 3) + } else { + dim_dat <- NULL + } # Create initial data set (i.e., only first ftime) tmp <- Subset(exp, ftime_dim, 1, drop = 'selected') @@ -389,14 +426,14 @@ Clim <- function(exp, obs, time_dim = 'sdate', dat_dim = c('dataset', 'member'), # Find intercept and slope intercept_exp <- Subset(tmp_exp, 1, 1, drop = 'selected') #[dat_dim, ftime] slope_exp <- Subset(tmp_exp, 1, 2, drop = 'selected') #[dat_dim, ftime] - intercept_obs <- array(tmp_obs_mean[1, ], dim = c(dim_ftime, dim_dat)) - #[ftime, dat_dim] exp - intercept_obs <- Reorder(intercept_obs, c(2:length(dim(intercept_obs)), 1)) - #[dat_dim, ftime] exp - slope_obs <- array(tmp_obs_mean[2, ], dim = c(dim_ftime, dim_dat)) - #[ftime, dat_dim] exp - slope_obs <- Reorder(slope_obs, c(2:length(dim(slope_obs)), 1)) - #[dat_dim, ftime] exp + intercept_obs <- array(tmp_obs_mean[1, ], dim = c(dim_ftime, dim_dat)) #[ftime, dat_dim] exp + if (!is.null(dat_dim)) { + intercept_obs <- Reorder(intercept_obs, c(2:length(dim(intercept_obs)), 1)) #[dat_dim, ftime] exp + } + slope_obs <- array(tmp_obs_mean[2, ], dim = c(dim_ftime, dim_dat)) #[ftime, dat_dim] exp + if (!is.null(dat_dim)) { + slope_obs <- Reorder(slope_obs, c(2:length(dim(slope_obs)), 1)) #[dat_dim, ftime] exp + } trend_exp <- list() trend_obs <- list() @@ -405,7 +442,9 @@ Clim <- function(exp, obs, time_dim = 'sdate', dat_dim = c('dataset', 'member'), trend_exp[[jdate]] <- intercept_exp + tmp * slope_exp #[dat_dim, ftime] tmp <- array(ini_obs_mean[jdate, ], dim = c(dim_ftime, dim_dat)) #[ftime, dat_dim] - tmp <- Reorder(tmp, c(2:length(dim(tmp)), 1)) #[dat_dim, ftime] + if (!is.null(dat_dim)) { + tmp <- Reorder(tmp, c(2:length(dim(tmp)), 1)) #[dat_dim, ftime] + } trend_obs[[jdate]] <- intercept_obs + tmp * slope_obs } # turn list into array @@ -416,8 +455,11 @@ Clim <- function(exp, obs, time_dim = 'sdate', dat_dim = c('dataset', 'member'), trend_exp <- Reorder(trend_exp, c(len, 1:(len - 1))) trend_obs <- Reorder(trend_obs, c(len, 1:(len - 1))) #trend_: [sdate, dat_dim, ftime] - + if (is.null(dim(clim_obs))) { + clim_obs_mean <- clim_obs #[ftime] + } else { clim_obs_mean <- apply(clim_obs, length(dim(clim_obs)), mean) #average out dat_dim, [ftime] + } clim_obs_mean <- array(clim_obs_mean, dim = c(dim_ftime, dim(exp)[1], dim_dat)) #[ftime, sdate, dat_dim] len <- length(dim(clim_obs_mean)) @@ -426,18 +468,26 @@ Clim <- function(exp, obs, time_dim = 'sdate', dat_dim = c('dataset', 'member'), clim_exp <- trend_exp - trend_obs + clim_obs_mean - ## member mean + ## member mean if (!memb) { - pos <- which(names(dim(clim_exp)) == memb_dim) - pos <- c(1:length(dim(clim_exp)))[-pos] - dim_name <- names(dim(clim_exp)) - clim_exp <- apply(clim_exp, pos, mean, na.rm = TRUE) - clim_obs <- apply(clim_obs, pos, mean, na.rm = TRUE) - if (is.null(names(dim(as.array(clim_exp))))) { + pos_exp <- c(1:length(dim(clim_exp)))[-which(names(dim(clim_exp)) == memb_dim)] + pos_obs <- c(1:length(dim(clim_obs)))[-which(names(dim(clim_obs)) == memb_dim)] + + tmp_dim_exp <- dim(clim_exp) + tmp_dim_obs <- dim(clim_obs) + clim_exp <- apply(clim_exp, pos_exp, mean, na.rm = TRUE) + if (is.integer(pos_obs) & length(pos_obs) == 0) { + clim_obs <- mean(clim_obs) + } else { + clim_obs <- apply(clim_obs, pos_obs, mean, na.rm = TRUE) + } + if (is.null(dim(clim_exp))) { clim_exp <- as.array(clim_exp) + dim(clim_exp) <- tmp_dim_exp[pos_exp] + } + if (is.null(dim(clim_obs)) & !(is.integer(pos_obs) & length(pos_obs) == 0)) { clim_obs <- as.array(clim_obs) - names(dim(clim_exp)) <- dim_name[pos] - names(dim(clim_obs)) <- dim_name[pos] + dim(clim_obs) <- tmp_dim_obs[pos_obs] } } diff --git a/man/Clim.Rd b/man/Clim.Rd index 311e699..a5a6f19 100644 --- a/man/Clim.Rd +++ b/man/Clim.Rd @@ -65,7 +65,7 @@ A list of 2: dimension 'memb_dim' is also removed. } \item{$clim_obs}{ - A numeric array with the same dimensions as parameter 'exp' + A numeric array with the same dimensions as parameter 'obs' except dimension 'time_dim' is removed. If parameter 'memb' is FALSE, dimension 'memb_dim' is also removed. } diff --git a/tests/testthat/test-Clim.R b/tests/testthat/test-Clim.R index f4a5cb9..7751afb 100644 --- a/tests/testthat/test-Clim.R +++ b/tests/testthat/test-Clim.R @@ -7,7 +7,7 @@ context("s2dv::Clim tests") ftime = 3, lon = 2, lat = 4)) set.seed(2) obs1 <- array(rnorm(120), dim = c(dataset = 1, member = 1, - ftime = 3, lon = 2, lat = 4, sdate = 5)) + ftime = 3, lon = 2, lat = 4, sdate = 5)) # dat2 exp2 <- exp1 set.seed(1) @@ -62,6 +62,7 @@ context("s2dv::Clim tests") set.seed(2) obs9 <- array(rnorm(10), dim = c(sdate = 5)) + ############################################## test_that("1. Input checks", { @@ -307,6 +308,47 @@ as.vector(res2$clim_obs), -0.06696949, tolerance = 0.0001 ) +# method = 'kharin' +res1 <- Clim(exp4, obs4, memb = T, method = 'kharin') +res2 <- Clim(exp4, obs4, memb = F, method = 'kharin') +expect_equal( +dim(res1$clim_exp), +c(sdate = 5, dataset = 1, member = 3) +) +expect_equal( +dim(res1$clim_obs), +c(dataset = 1, member = 1) +) +expect_equal( +dim(res2$clim_exp), +c(sdate = 5, dataset = 1) +) +expect_equal( +dim(res2$clim_obs), +c(dataset = 1) +) + +# method = 'NDV' +exp4 <- array(exp4, dim = c(dim(exp4), ftime = 1)) +obs4 <- array(obs4, dim = c(dim(obs4), ftime = 1)) +res1 <- Clim(exp4, obs4, memb = T, method = 'NDV') +res2 <- Clim(exp4, obs4, memb = F, method = 'NDV') +expect_equal( +dim(res1$clim_exp), +c(sdate = 5, dataset = 1, member = 3, ftime = 1) +) +expect_equal( +dim(res1$clim_obs), +c(dataset = 1, member = 1, ftime = 1) +) +expect_equal( +dim(res2$clim_exp), +c(sdate = 5, dataset = 1, ftime = 1) +) +expect_equal( +dim(res2$clim_obs), +c(dataset = 1, ftime = 1) +) }) @@ -352,6 +394,48 @@ as.vector(res2$clim_obs), tolerance = 0.0001 ) +# method = 'kharin' +res1 <- Clim(exp5, obs5, memb = T, dat_dim = 'member', method = 'kharin') +res2 <- Clim(exp5, obs5, memb = F, dat_dim = 'member', method = 'kharin') +expect_equal( +dim(res1$clim_exp), +c(sdate = 5, member = 3) +) +expect_equal( +dim(res1$clim_obs), +c(member = 1) +) +expect_equal( +dim(res2$clim_exp), +c(sdate = 5) +) +expect_equal( +dim(res2$clim_obs), +NULL +) + +# method = 'NDV' +exp5 <- array(exp5, dim = c(dim(exp5), ftime = 1)) +obs5 <- array(obs5, dim = c(dim(obs5), ftime = 1)) +res1 <- Clim(exp5, obs5, memb = T, dat_dim = 'member', method = 'NDV') +res2 <- Clim(exp5, obs5, memb = F, dat_dim = 'member', method = 'NDV') +expect_equal( +dim(res1$clim_exp), +c(sdate = 5, member = 3, ftime = 1) +) +expect_equal( +dim(res1$clim_obs), +c(member = 1, ftime = 1) +) +expect_equal( +dim(res2$clim_exp), +c(sdate = 5, ftime = 1) +) +expect_equal( +dim(res2$clim_obs), +c(ftime = 1) +) + }) ############################################## @@ -395,6 +479,48 @@ as.vector(res2$clim_obs), tolerance = 0.0001 ) +# method = 'kharin' +res1 <- Clim(exp6, obs6, memb = T, method = 'kharin') +res2 <- Clim(exp6, obs6, memb = F, method = 'kharin') +expect_equal( +dim(res1$clim_exp), +c(sdate = 5, dataset = 1, member = 3) +) +expect_equal( +dim(res1$clim_obs), +c(dataset = 1) +) +expect_equal( +dim(res2$clim_exp), +c(sdate = 5, dataset = 1) +) +expect_equal( +dim(res2$clim_obs), +c(dataset = 1) +) + +# method = 'NDV' +exp6 <- array(exp6, dim = c(dim(exp6), ftime = 1)) +obs6 <- array(obs6, dim = c(dim(obs6), ftime = 1)) +res1 <- Clim(exp6, obs6, memb = T, method = 'NDV') +res2 <- Clim(exp6, obs6, memb = F, method = 'NDV') +expect_equal( +dim(res1$clim_exp), +c(sdate = 5, dataset = 1, member = 3, ftime = 1) +) +expect_equal( +dim(res1$clim_obs), +c(dataset = 1, ftime = 1) +) +expect_equal( +dim(res2$clim_exp), +c(sdate = 5, dataset = 1, ftime = 1) +) +expect_equal( +dim(res2$clim_obs), +c(dataset = 1, ftime = 1) +) + }) ############################################## @@ -438,6 +564,49 @@ as.vector(res2$clim_obs), tolerance = 0.0001 ) +# method = 'kharin' +res1 <- Clim(exp7, obs7, memb = T, method = 'kharin') +res2 <- Clim(exp7, obs7, memb = F, method = 'kharin') +expect_equal( +dim(res1$clim_exp), +c(sdate = 5, dataset = 1, member = 3) +) +expect_equal( +dim(res1$clim_obs), +NULL +) +expect_equal( +dim(res2$clim_exp), +c(sdate = 5, dataset = 1) +) +expect_equal( +dim(res2$clim_obs), +NULL +) + +# method = 'NDV' +exp7 <- array(exp7, dim = c(dim(exp7), ftime = 1)) +obs7 <- array(obs7, dim = c(dim(obs7), ftime = 1)) +res1 <- Clim(exp7, obs7, memb = T, method = 'NDV') +res2 <- Clim(exp7, obs7, memb = F, method = 'NDV') +expect_equal( +dim(res1$clim_exp), +c(sdate = 5, dataset = 1, member = 3, ftime = 1) +) +expect_equal( +dim(res1$clim_obs), +c(ftime = 1) +) +expect_equal( +dim(res2$clim_exp), +c(sdate = 5, dataset = 1, ftime = 1) +) +expect_equal( +dim(res2$clim_obs), +c(ftime = 1) +) + + }) @@ -482,6 +651,48 @@ as.vector(res2$clim_obs), tolerance = 0.0001 ) +# method = 'kharin' +res1 <- Clim(exp8, obs8, dat_dim = 'member', memb = T, method = 'kharin') +res2 <- Clim(exp8, obs8, dat_dim = 'member', memb = F, method = 'kharin') +expect_equal( +dim(res1$clim_exp), +c(sdate = 5, member = 3) +) +expect_equal( +dim(res1$clim_obs), +NULL +) +expect_equal( +dim(res2$clim_exp), +c(sdate = 5) +) +expect_equal( +dim(res2$clim_obs), +NULL +) + +# method = 'NDV' +exp8 <- array(exp8, dim = c(dim(exp8), ftime = 1)) +obs8 <- array(obs8, dim = c(dim(obs8), ftime = 1)) +res1 <- Clim(exp8, obs8, dat_dim = 'member', memb = T, method = 'NDV') +res2 <- Clim(exp8, obs8, dat_dim = 'member', memb = F, method = 'NDV') +expect_equal( +dim(res1$clim_exp), +c(sdate = 5, member = 3, ftime = 1) +) +expect_equal( +dim(res1$clim_obs), +c(ftime = 1) +) +expect_equal( +dim(res2$clim_exp), +c(sdate = 5, ftime = 1) +) +expect_equal( +dim(res2$clim_obs), +c(ftime = 1) +) + }) ############################################## @@ -525,6 +736,49 @@ as.vector(res2$clim_obs), tolerance = 0.0001 ) +# method = 'kharin' +res1 <- Clim(exp9, obs9, dat_dim = NULL, memb = T, method = 'kharin') +res2 <- Clim(exp9, obs9, dat_dim = NULL, memb_dim = NULL, memb = F, method = 'kharin') +expect_equal( +dim(res1$clim_exp), +c(sdate = 5) +) +expect_equal( +dim(res1$clim_obs), +NULL +) +expect_equal( +dim(res2$clim_exp), +c(sdate = 5) +) +expect_equal( +dim(res2$clim_obs), +NULL +) + +# method = 'NDV' +exp9 <- array(exp9, dim = c(dim(exp9), ftime = 1)) +obs9 <- array(obs9, dim = c(dim(obs9), ftime = 1)) +res1 <- Clim(exp9, obs9, dat_dim = NULL, memb = T, method = 'NDV') +res2 <- Clim(exp9, obs9, dat_dim = NULL, memb_dim = NULL, memb = F, method = 'NDV') +expect_equal( +dim(res1$clim_exp), +c(sdate = 5, ftime = 1) +) +expect_equal( +dim(res1$clim_obs), +c(ftime = 1) +) +expect_equal( +dim(res2$clim_exp), +c(sdate = 5, ftime = 1) +) +expect_equal( +dim(res2$clim_obs), +c(ftime = 1) +) + + }) ############################################## -- GitLab From 610ec5ffe5b7e16389bd7444cab88aa5ddcc9f63 Mon Sep 17 00:00:00 2001 From: aho Date: Tue, 3 May 2022 19:25:11 +0200 Subject: [PATCH 45/83] Improve efficiency --- R/Clim.R | 44 ++++++++++++++++++++++++-------------------- 1 file changed, 24 insertions(+), 20 deletions(-) diff --git a/R/Clim.R b/R/Clim.R index 1093c99..d4773d5 100644 --- a/R/Clim.R +++ b/R/Clim.R @@ -190,21 +190,26 @@ Clim <- function(exp, obs, time_dim = 'sdate', dat_dim = c('dataset', 'member'), #---------------------------------- # Per-pair: Remove all sdate if not complete along dat_dim if (!is.null(dat_dim)) { - pos <- rep(0, length(dat_dim)) - for (i in 1:length(dat_dim)) { #[dat, sdate] - ## dat_dim: [dataset, member] - pos[i] <- which(names(dim(obs)) == dat_dim[i]) + pos <- which(names(dim(obs)) %in% dat_dim) + + na_array <- array(0, dim = dim(exp)[-pos]) + dat_dim_comb <- plyr::alply(expand.grid(lapply(dim(exp)[dat_dim], seq, 1)), 1) + for (i_dat in 1:length(dat_dim_comb)) { + na_array <- na_array + is.na(Subset(exp, dat_dim, dat_dim_comb[[i_dat]], drop = 'selected')) + } + dat_dim_comb <- plyr::alply(expand.grid(lapply(dim(obs)[dat_dim], seq, 1)), 1) + for (i_dat in 1:length(dat_dim_comb)) { + na_array <- na_array + is.na(Subset(obs, dat_dim, dat_dim_comb[[i_dat]], drop = 'selected')) } - outrows_exp <- MeanDims(exp, pos, na.rm = FALSE) + - MeanDims(obs, pos, na.rm = FALSE) - outrows_obs <- outrows_exp + na_array_2 <- na_array for (i in 1:length(pos)) { - outrows_exp <- InsertDim(outrows_exp, pos[i], dim(exp)[pos[i]]) - outrows_obs <- InsertDim(outrows_obs, pos[i], dim(obs)[pos[i]]) + na_array <- InsertDim(na_array, pos[i], dim(exp)[pos[i]]) + na_array_2 <- InsertDim(na_array_2, pos[i], dim(obs)[pos[i]]) } - exp[which(is.na(outrows_exp))] <- NA - obs[which(is.na(outrows_obs))] <- NA + exp[which(na_array != 0)] <- NA + obs[which(na_array_2 != 0)] <- NA + } #----------------------------------- @@ -277,18 +282,17 @@ Clim <- function(exp, obs, time_dim = 'sdate', dat_dim = c('dataset', 'member'), ## ensemble mean if (!memb) { - if (length(dim(clim_exp)) == 1 | is.null(dim(clim_exp))) { #dim: [member] + if (length(dim(clim_exp)) == 1) { #dim: [member] clim_exp <- mean(clim_exp, na.rm = TRUE) clim_obs <- mean(clim_obs, na.rm = TRUE) } else { - pos <- which(names(dim(clim_exp)) == memb_dim) - pos <- c(1:length(dim(clim_exp)))[-pos] dim_name <- names(dim(clim_exp)) + pos <- c(1:length(dim(clim_exp)))[-which(dim_name == memb_dim)] clim_exp <- apply(clim_exp, pos, mean, na.rm = TRUE) clim_obs <- apply(clim_obs, pos, mean, na.rm = TRUE) - if (is.null(names(dim(as.array(clim_exp))))) { - clim_exp <- as.array(clim_exp) - clim_obs <- as.array(clim_obs) + if (is.null(dim(clim_exp))) { + dim(clim_exp) <- length(clim_exp) + dim(clim_obs) <- length(clim_obs) names(dim(clim_exp)) <- dim_name[pos] names(dim(clim_obs)) <- dim_name[pos] } @@ -317,11 +321,11 @@ Clim <- function(exp, obs, time_dim = 'sdate', dat_dim = c('dataset', 'member'), # exp clim ##--- NEW trend ---## - tmp_obs <- Trend(data = obs, time_dim = time_dim, interval = 1, - polydeg = 1, conf = FALSE, ncores = ncores_input)$trend + tmp_obs <- Trend(data = obs, time_dim = time_dim, interval = 1, + polydeg = 1, conf = FALSE, ncores = ncores_input)$trend tmp_exp <- Trend(data = exp, time_dim = time_dim, interval = 1, polydeg = 1, conf = FALSE, ncores = ncores_input)$trend - # tmp_exp: [stats, dat_dim)] + # tmp_exp: [stats, dat_dim] tmp_obs_mean <- apply(tmp_obs, 1, mean) #average out dat_dim (dat and member) #tmp_obs_mean: [stats = 2] if (!is.null(dat_dim)) { -- GitLab From ad25390670387abe807b4b64e3c9ac6c90f91a76 Mon Sep 17 00:00:00 2001 From: aho Date: Fri, 6 May 2022 11:33:52 +0200 Subject: [PATCH 46/83] Improve efficiency of MeanDims by substituting apply(, mean) with colMeans() --- R/Clim.R | 28 ++++++++++++---------------- R/MeanDims.R | 4 ++-- 2 files changed, 14 insertions(+), 18 deletions(-) diff --git a/R/Clim.R b/R/Clim.R index d4773d5..ce9ba2e 100644 --- a/R/Clim.R +++ b/R/Clim.R @@ -190,27 +190,23 @@ Clim <- function(exp, obs, time_dim = 'sdate', dat_dim = c('dataset', 'member'), #---------------------------------- # Per-pair: Remove all sdate if not complete along dat_dim if (!is.null(dat_dim)) { - pos <- which(names(dim(obs)) %in% dat_dim) - - na_array <- array(0, dim = dim(exp)[-pos]) - dat_dim_comb <- plyr::alply(expand.grid(lapply(dim(exp)[dat_dim], seq, 1)), 1) - for (i_dat in 1:length(dat_dim_comb)) { - na_array <- na_array + is.na(Subset(exp, dat_dim, dat_dim_comb[[i_dat]], drop = 'selected')) - } - dat_dim_comb <- plyr::alply(expand.grid(lapply(dim(obs)[dat_dim], seq, 1)), 1) - for (i_dat in 1:length(dat_dim_comb)) { - na_array <- na_array + is.na(Subset(obs, dat_dim, dat_dim_comb[[i_dat]], drop = 'selected')) + pos <- rep(0, length(dat_dim)) + for (i in 1:length(dat_dim)) { #[dat, sdate] + ## dat_dim: [dataset, member] + pos[i] <- which(names(dim(obs)) == dat_dim[i]) } + outrows_exp <- MeanDims(exp, pos, na.rm = FALSE) + + MeanDims(obs, pos, na.rm = FALSE) + outrows_obs <- outrows_exp - na_array_2 <- na_array for (i in 1:length(pos)) { - na_array <- InsertDim(na_array, pos[i], dim(exp)[pos[i]]) - na_array_2 <- InsertDim(na_array_2, pos[i], dim(obs)[pos[i]]) + outrows_exp <- InsertDim(outrows_exp, pos[i], dim(exp)[pos[i]]) + outrows_obs <- InsertDim(outrows_obs, pos[i], dim(obs)[pos[i]]) } - exp[which(na_array != 0)] <- NA - obs[which(na_array_2 != 0)] <- NA - + exp[which(is.na(outrows_exp))] <- NA + obs[which(is.na(outrows_obs))] <- NA } + #----------------------------------- if (method == 'clim') { diff --git a/R/MeanDims.R b/R/MeanDims.R index 9e5dd49..0684895 100644 --- a/R/MeanDims.R +++ b/R/MeanDims.R @@ -79,8 +79,8 @@ MeanDims <- function(data, dims, na.rm = FALSE, drop = TRUE) { if (is.character(dims)) { dims <- which(names(dim_data) %in% dims) } - pos <- (1:length(dim_data))[-dims] - data <- apply(data, pos, mean, na.rm = na.rm) + data <- aperm(data, c(dims, (1:length(dim_data))[-dims])) + data <- colMeans(data, dims = length(dims), na.rm = na.rm) # If data is vector if (is.null(dim(data))) { -- GitLab From 27b1f498bc42393c3b8f6a30c94f6e7f83fd638c Mon Sep 17 00:00:00 2001 From: aho Date: Fri, 6 May 2022 16:01:59 +0200 Subject: [PATCH 47/83] If output is a number and drop = T, return a number instead of an array --- R/MeanDims.R | 9 +++++---- man/MeanDims.Rd | 7 ++++--- tests/testthat/test-MeanDims.R | 14 +++++++++++++- 3 files changed, 22 insertions(+), 8 deletions(-) diff --git a/R/MeanDims.R b/R/MeanDims.R index 0684895..56a304d 100644 --- a/R/MeanDims.R +++ b/R/MeanDims.R @@ -10,9 +10,10 @@ #' not (FALSE). #'@param drop A logical value indicating whether to keep the averaged #' dimension (FALSE) or drop it (TRUE). The default value is TRUE. -#'@return An array with the same dimension as parameter 'data' except the -#' 'dims' dimensions. If 'drop' is TRUE, 'dims' will be removed; if 'drop' is -#' FALSE, 'dims' will be preserved and the length will be 1. +#'@return A numeric or an array with the same dimension as parameter 'data' +#' except the 'dims' dimensions. If 'drop' is TRUE, 'dims' will be removed; if +#' 'drop' is FALSE, 'dims' will be preserved and the length will be 1. If all +#' the dimensions are averaged out, a numeric is returned. #' #'@examples #'a <- array(rnorm(24), dim = c(dat = 2, member = 3, time = 4)) @@ -69,7 +70,7 @@ MeanDims <- function(data, dims, na.rm = FALSE, drop = TRUE) { if (length(dims) == length(dim_data)) { if (drop) { - data <- as.array(mean(data, na.rm = na.rm)) + data <- mean(data, na.rm = na.rm) } else { data <- array(mean(data, na.rm = na.rm), dim = rep(1, length(dim_data))) diff --git a/man/MeanDims.Rd b/man/MeanDims.Rd index 721866b..94369da 100644 --- a/man/MeanDims.Rd +++ b/man/MeanDims.Rd @@ -19,9 +19,10 @@ not (FALSE).} dimension (FALSE) or drop it (TRUE). The default value is TRUE.} } \value{ -An array with the same dimension as parameter 'data' except the - 'dims' dimensions. If 'drop' is TRUE, 'dims' will be removed; if 'drop' is - FALSE, 'dims' will be preserved and the length will be 1. +A numeric or an array with the same dimension as parameter 'data' + except the 'dims' dimensions. If 'drop' is TRUE, 'dims' will be removed; if +'drop' is FALSE, 'dims' will be preserved and the length will be 1. If all + the dimensions are averaged out, a numeric is returned. } \description{ This function returns the mean of an array along a set of dimensions and diff --git a/tests/testthat/test-MeanDims.R b/tests/testthat/test-MeanDims.R index a431b99..c043c78 100644 --- a/tests/testthat/test-MeanDims.R +++ b/tests/testthat/test-MeanDims.R @@ -78,6 +78,14 @@ test_that("2. Output checks: dat1", { dim(MeanDims(dat1, dims = c('sdate', 'ftime'))), c(dat = 1) ) + expect_equal( + dim(MeanDims(dat1, dims = c('sdate', 'ftime', 'dat'))), + NULL + ) + expect_equal( + dim(MeanDims(dat1, dims = 1:3)), + NULL + ) expect_equal( MeanDims(dat1, dims = c('sdate'))[1:2], c(3, 8) @@ -96,7 +104,7 @@ test_that("2. Output checks: dat1", { ) expect_equal( dim(MeanDims(dat1, dims = 1:3, drop = T)), - 1 + NULL ) expect_equal( as.vector(drop(MeanDims(dat1, dims = 1:3, drop = F))), @@ -153,6 +161,10 @@ test_that("5. Output checks: dat4", { dim(MeanDims(dat4, dims = 1, drop = F)), 1 ) + expect_equal( + dim(MeanDims(dat4, dims = 1, drop = T)), + NULL + ) }) ############################################## -- GitLab From 006b41b4f557a461d359645754bcd4a4a076eb82 Mon Sep 17 00:00:00 2001 From: aho Date: Wed, 11 May 2022 11:38:23 +0200 Subject: [PATCH 48/83] Add time metadata to avoid cdo warning --- R/CDORemap.R | 16 +++++++++++++--- 1 file changed, 13 insertions(+), 3 deletions(-) diff --git a/R/CDORemap.R b/R/CDORemap.R index 56dcec7..6a9a7e5 100644 --- a/R/CDORemap.R +++ b/R/CDORemap.R @@ -697,6 +697,8 @@ CDORemap <- function(data_array = NULL, lons, lats, grid, method, if (!is.null(unlimited_dim)) { # This will make ArrayToNc create this dim as unlimited. names(dim(data_array))[unlimited_dim] <- 'time' + # create time variable. The value is random since CDORemap() doesn't support time remapping now and we just want to avoid cdo warning + time_attr <- array(c(1:dim(data_array)[unlimited_dim]), dim = c(dim(data_array)[unlimited_dim])) } if (length(dim(lons)) == 1) { names(dim(lons)) <- lon_dim @@ -758,7 +760,11 @@ CDORemap <- function(data_array = NULL, lons, lats, grid, method, } # dims_before_crop <- dim(subset) # Make sure subset goes along with metadata - easyNCDF::ArrayToNc(setNames(list(subset, lons, lats), c('var', lon_var_name, lat_var_name)), tmp_file) + if (is.null(unlimited_dim)) { + easyNCDF::ArrayToNc(setNames(list(subset, lons, lats), c('var', lon_var_name, lat_var_name)), tmp_file) + } else { + easyNCDF::ArrayToNc(setNames(list(subset, lons, lats, time_attr), c('var', lon_var_name, lat_var_name, 'time')), tmp_file) + } } else { if (is_irregular) { pos_lon <- which(names(dim(data_array)) == lon_dim) @@ -774,7 +780,11 @@ CDORemap <- function(data_array = NULL, lons, lats, grid, method, } } # dims_before_crop <- dim(data_array) - easyNCDF::ArrayToNc(setNames(list(data_array, lons, lats), c('var', lon_var_name, lat_var_name)), tmp_file) + if (is.null(unlimited_dim)) { + easyNCDF::ArrayToNc(setNames(list(data_array, lons, lats), c('var', lon_var_name, lat_var_name)), tmp_file) + } else { + easyNCDF::ArrayToNc(setNames(list(data_array, lons, lats, time_attr), c('var', lon_var_name, lat_var_name, 'time')), tmp_file) + } } sellonlatbox <- '' if (crop) { @@ -787,7 +797,7 @@ CDORemap <- function(data_array = NULL, lons, lats, grid, method, system(paste0("cdo -s ", sellonlatbox, "remap", method, ",", grid, " ", tmp_file, " ", tmp_file2)) }) file.remove(tmp_file) - if (('try-error' %in% class(err)) || err > 0) { + if (is(err, 'try-error') || err > 0) { stop("CDO remap failed.") } ncdf_remapped <- nc_open(tmp_file2) -- GitLab From e2990e4edddf003d258901a91e0cf77e6c269080 Mon Sep 17 00:00:00 2001 From: aho Date: Wed, 11 May 2022 17:56:08 +0200 Subject: [PATCH 49/83] Formatting and combine small functions --- R/RPS.R | 172 +++++++++++++++++++++++++++++++------------------------- 1 file changed, 96 insertions(+), 76 deletions(-) diff --git a/R/RPS.R b/R/RPS.R index b28791e..886ecec 100644 --- a/R/RPS.R +++ b/R/RPS.R @@ -1,45 +1,60 @@ #'Compute the Ranked Probability Score #' -#'The Ranked Probability Score (RPS; Wilks, 2011; https://doi.org/10.1016/B978-0-12-385022-5.00008-7) is defined -#'as the sum of the squared differences between the cumulative forecast probabilities (computed from the ensemble -#'members) and the observations (defined as 0% if the category did not happen and 100% if it happened). It can be -#'used to evaluate the skill of multi-categorical probabilistic forecasts. The RPS ranges between 0 (perfect -#'forecast) and n-1 (worst possible forecast), where n is the number of categories. In the case of a forecast -#'divided into two categories (the lowest number of categories that a probabilistic forecast can have), the -#'RPS corresponds to the Brier Score (BS; Wilks, 2011) and, therefore, ranges between 0 and 1. +#'The Ranked Probability Score (RPS; Wilks, 2011) is defined as the sum of the +#'squared differences between the cumulative forecast probabilities (computed +#'from the ensemble members) and the observations (defined as 0% if the category +#'did not happen and 100% if it happened). It can be used to evaluate the skill +#'of multi-categorical probabilistic forecasts. The RPS ranges between 0 +#'(perfect forecast) and n-1 (worst possible forecast), where n is the number of +#'categories. In the case of a forecast divided into two categories (the lowest +#'number of categories that a probabilistic forecast can have), the RPS +#'corresponds to the Brier Score (BS; Wilks, 2011), therefore, ranges between 0 +#'and 1. #' -#'@param exp A numerical array with the forecast with, at least, time dimension. -#'@param obs A numerical array with the observations with, at least, time dimension. -#' The length of the time dimension must be the same as for "exp". -#'@param prob_thresholds A numeric vector with the relative thresholds (from 0 to 1) between the categories. -#' The default value is c(1/3,2/3), which corresponds to tercile equiprobable categories. -#'@param indices_for_clim A vector with the indices to be taken along "time_dim" dimension for computing the thresholds -#' between the probabilistic categories. If NULL, the whole period is used. The default value is NULL. -#'@param Fair A logical indicating whether to compute the FairRPSS (the potential RPSS that the forecast would have with -#'an infinite ensemble size). The default value is FALSE. +#'@param exp A named numerical array of the forecast with at least time +#' dimension. +#'@param obs A named numerical array of the observation with at least time +#' dimension. The dimensions must be the same as 'exp' expect 'memb_dim'. #'@param time_dim A character string indicating the name of the time dimension. -#' The default value is 'year'. -#'@param member_dim A character string indicating the name of the member dimension to compute the -#' ensemble means of the forecast and reference forecast. If it is NULL, the ensemble means should -#' be provided directly to the function. The default value is 'member'. +#' The default value is 'sdate'. +#'@param memb_dim A character string indicating the name of the member dimension +#' to compute the ensemble mean of the forecast. If it is NULL, the ensemble +#' mean should be provided directly to the function. The default value is NULL. +#'@param prob_thresholds A numeric vector of the relative thresholds (from 0 to +#' 1) between the categories. The default value is c(1/3, 2/3), which +#' corresponds to tercile equiprobable categories. +#'@param indices_for_clim A vector of the indices to be taken along "time_dim" +#' for computing the thresholds between the probabilistic categories. If NULL, +#' the whole period is used. The default value is NULL. +#'@param Fair A logical indicating whether to compute the FairRPSS (the +#' potential RPSS that the forecast would have with an infinite ensemble size). +#' The default value is FALSE. #'@param ncores An integer indicating the number of cores to use for parallel #' computation. The default value is NULL. #' #'@return -#' A numerical array with the RPS with the same dimensions as "exp" except the "time_dim" and "member_dim" dimensions. +#'A numerical array of RPS with the same dimensions as "exp" except the +#'"time_dim" and "memb_dim" dimensions. +#' +#'@references +#'Wilks, 2011; https://doi.org/10.1016/B978-0-12-385022-5.00008-7 #' #'@examples -#' exp <- array(rnorm(1000), dim = c(lat = 3, lon = 2, member = 10, year = 50)) -#' obs <- array(rnorm(1000), dim = c(lat = 3, lon = 2, year = 50)) -#' RPS(exp = exp, obs = obs) +#' exp <- array(rnorm(1000), dim = c(lat = 3, lon = 2, member = 10, sdate = 50)) +#' obs <- array(rnorm(1000), dim = c(lat = 3, lon = 2, sdate = 50)) +#' res <- RPS(exp = exp, obs = obs, memb_dim = 'member') #' #'@import multiApply #'@importFrom ClimProjDiags Subset #'@importFrom easyVerification convert2prob #'@export -RPS <- function(exp, obs, prob_thresholds = c(1/3,2/3), indices_for_clim = NULL, Fair = FALSE, time_dim = 'year', member_dim = 'member', ncores = NULL){ +RPS <- function(exp, obs, time_dim = 'sdate', memb_dim = NULL, + prob_thresholds = c(1/3, 2/3), indices_for_clim = NULL, Fair = FALSE, + ncores = NULL) { - ## Checkings + # Check inputs + ## exp and obs (1) + if (!is.array(exp)){stop('"exp" must be an array.')} if (!is.array(obs)){stop('"obs" must be an array.')} if (!identical(as.numeric(dim(exp)[time_dim]), as.numeric(dim(obs)[time_dim]))){stop('"time_dim" must have the same length in "exp" and "obs".')} @@ -53,81 +68,86 @@ RPS <- function(exp, obs, prob_thresholds = c(1/3,2/3), indices_for_clim = NULL, } if (!is.logical(Fair)){stop('"Fair" must be TRUE or FALSE.')} if (!is.character(time_dim)){stop('"time_dim" must be a string.')} - if (!is.character(member_dim)){stop('"member_dim" must be a string.')} + if (!is.character(memb_dim)){stop('"memb_dim" must be a string.')} if (!is.null(ncores)){ if (!is.numeric(ncores) | ncores < 1){stop('"ncores" must be either NULL or a positive integer.')} } ## RPS - rps <- multiApply::Apply(data = list(exp = exp, obs = obs), - target_dims = list(exp = c(time_dim, member_dim), obs = time_dim), - prob_thresholds = prob_thresholds, indices_for_clim = indices_for_clim, - Fair = Fair, time_dim = time_dim, member_dim = member_dim, - fun = .RPS, ncores = ncores)$output1 - - ## Returning only the mean RPS - rps <- multiApply::Apply(data = rps, target_dims = time_dim, fun = mean, na.rm = FALSE, ncores = ncores)$output1 + rps <- Apply(data = list(exp = exp, obs = obs), + target_dims = list(exp = c(time_dim, memb_dim), obs = time_dim), + fun = .RPS, + prob_thresholds = prob_thresholds, indices_for_clim = indices_for_clim, + Fair = Fair, + ncores = ncores)$output1 return(rps) } -.RPS <- function(exp, obs, prob_thresholds, indices_for_clim, Fair, time_dim, member_dim){ - - exp_probs <- .get_probs(data = exp, type = 'exp', indices_for_quantiles = indices_for_clim, - time_dim = time_dim, member_dim = member_dim, prob_thresholds = prob_thresholds) - - obs_probs <- .get_probs(data = obs, type = 'obs', indices_for_quantiles = indices_for_clim, - time_dim = time_dim, member_dim = NULL, prob_thresholds = prob_thresholds) - + +.RPS <- function(exp, obs, prob_thresholds, indices_for_clim, Fair) { + # exp: [sdate, memb] + # obs: [sdate] + + exp_probs <- .get_probs(data = exp, indices_for_quantiles = indices_for_clim, + prob_thresholds = prob_thresholds) + # exp_probs: [bin, sdate] + obs_probs <- .get_probs(data = obs, indices_for_quantiles = indices_for_clim, + prob_thresholds = prob_thresholds) + # obs_probs: [bin, sdate] + ## RPS for each time step - rps <- multiApply::Apply(data = list(probs_exp = exp_probs, probs_obs = obs_probs), target_dims = 'bin', - fun = .RPS_from_probs, n_categories = length(prob_thresholds)+1, Fair = Fair, - n_members = as.numeric(dim(exp)[member_dim]), ncores = NULL)$output1 + rps <- Apply(data = list(probs_exp = exp_probs, probs_obs = obs_probs), target_dims = 'bin', + fun = .RPS_from_probs, Fair = Fair, + n_members = dim(exp)[2], ncores = NULL)$output1 + # rps: [sdate] + + # Return only the mean RPS + rps <- mean(rps, na.rm = FALSE) + return(rps) } -.get_probs <- function(data, type, indices_for_quantiles, time_dim, member_dim, prob_thresholds){ - - if (type == 'exp'){ - target_dims = member_dim - } else if (type == 'obs'){ - target_dims = NULL - } else {stop("Parameter 'type' must be 'exp' or 'obs'")} - - quantiles <- quantile(x = as.vector(data), probs = prob_thresholds, type = 8, na.rm = TRUE) ## Absolute thresholds - - probs <- multiApply::Apply(data = data, target_dims = target_dims, fun = .c2p, output_dims = 'bin', - quantiles = quantiles, ncores = NULL)$output1 ## Probabilities - return(probs) -} -.c2p <- function(data, quantiles) { - if (anyNA(data)){ - output <- array(data = NA, dim = length(quantiles) + 1) - } else { - output <- colMeans(easyVerification::convert2prob(as.vector(data), threshold = as.vector(quantiles))) +.get_probs <- function(data, indices_for_quantiles, prob_thresholds) { + # if exp: [sdate, memb] + # if obs: [sdate] + if (length(dim(data)) == 1) dim(data) <- c(dim(data), 1) + + # Absolute thresholds + quantiles <- quantile(x = as.vector(data), probs = prob_thresholds, type = 8, na.rm = TRUE) + + # Probabilities + probs <- array(dim = c(bin = length(quantiles) + 1, dim(data)[1])) # [bin, sdate] + for (i_time in 1:dim(data)[1]) { + if (anyNA(data[i_time, ])) { + probs[, i_time] <- rep(NA, dim = length(quantiles) + 1) + } else { + probs[, i_time] <- colMeans(easyVerification::convert2prob(data[i_time, ], threshold = quantiles)) + } } - return(output) + + return(probs) } -.RPS_from_probs <- function(probs_exp, probs_obs, n_categories, Fair, n_members){ - - rps <- NULL - for (i in 1:n_categories){ - rps[i] <- (cumsum(probs_exp)[i]-cumsum(probs_obs)[i])^2 ## (Wilks 2011, pp.348-350) - } - rps <- sum(rps) - - if (Fair == TRUE){ ## FairRPS +.RPS_from_probs <- function(probs_exp, probs_obs, Fair, n_members) { + # probs_exp: [bin] + # probs_obs: [bin] + + rps <- sum((cumsum(probs_exp) - cumsum(probs_obs))^2) + + if (Fair) { # FairRPS ## adjustment <- rowSums(-1 * (1/R - 1/R.new) * ens.cum * (R - ens.cum)/R/(R - 1)) [formula taken from SpecsVerification::EnsRps] R <- n_members R_new <- Inf probs_cum <- cumsum(probs_exp) - adjustment <- -1 / (R-1) * probs_cum * (1 - probs_cum) + adjustment <- -1 / (R - 1) * probs_cum * (1 - probs_cum) adjustment <- sum(adjustment) rps <- rps + adjustment } return(rps) } + + -- GitLab From 1379da555e2fc3c112d4e649aab5e5ae8e146b86 Mon Sep 17 00:00:00 2001 From: aho Date: Thu, 12 May 2022 14:49:15 +0200 Subject: [PATCH 50/83] Improve RPS() efficiency and correct the uusage of indices_for_clim; unit test created --- NAMESPACE | 3 + R/RPS.R | 149 +++++++++++++++++++++++--------------- man/RPS.Rd | 70 ++++++++++++++++++ man/RPSS.Rd | 75 +++++++++++++++++++ man/RandomWalkTest.Rd | 4 +- tests/testthat/test-RPS.R | 129 +++++++++++++++++++++++++++++++++ 6 files changed, 368 insertions(+), 62 deletions(-) create mode 100644 man/RPS.Rd create mode 100644 man/RPSS.Rd create mode 100644 tests/testthat/test-RPS.R diff --git a/NAMESPACE b/NAMESPACE index 841814e..1449ad4 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -54,6 +54,8 @@ export(ProjectField) export(REOF) export(RMS) export(RMSSS) +export(RPS) +export(RPSS) export(RandomWalkTest) export(RatioPredictableComponents) export(RatioRMS) @@ -92,6 +94,7 @@ importFrom(ClimProjDiags,WeightedMean) importFrom(abind,abind) importFrom(abind,adrop) importFrom(easyNCDF,ArrayToNc) +importFrom(easyVerification,convert2prob) importFrom(grDevices,bmp) importFrom(grDevices,col2rgb) importFrom(grDevices,colorRampPalette) diff --git a/R/RPS.R b/R/RPS.R index 886ecec..744b9c7 100644 --- a/R/RPS.R +++ b/R/RPS.R @@ -14,16 +14,15 @@ #'@param exp A named numerical array of the forecast with at least time #' dimension. #'@param obs A named numerical array of the observation with at least time -#' dimension. The dimensions must be the same as 'exp' expect 'memb_dim'. +#' dimension. The dimensions must be the same as 'exp' except 'memb_dim'. #'@param time_dim A character string indicating the name of the time dimension. #' The default value is 'sdate'. #'@param memb_dim A character string indicating the name of the member dimension -#' to compute the ensemble mean of the forecast. If it is NULL, the ensemble -#' mean should be provided directly to the function. The default value is NULL. +#' to compute the probabilities of the forecast. The default value is 'member'. #'@param prob_thresholds A numeric vector of the relative thresholds (from 0 to #' 1) between the categories. The default value is c(1/3, 2/3), which #' corresponds to tercile equiprobable categories. -#'@param indices_for_clim A vector of the indices to be taken along "time_dim" +#'@param indices_for_clim A vector of the indices to be taken along 'time_dim' #' for computing the thresholds between the probabilistic categories. If NULL, #' the whole period is used. The default value is NULL. #'@param Fair A logical indicating whether to compute the FairRPSS (the @@ -34,51 +33,93 @@ #' #'@return #'A numerical array of RPS with the same dimensions as "exp" except the -#'"time_dim" and "memb_dim" dimensions. +#''time_dim' and 'memb_dim' dimensions. #' #'@references #'Wilks, 2011; https://doi.org/10.1016/B978-0-12-385022-5.00008-7 #' #'@examples -#' exp <- array(rnorm(1000), dim = c(lat = 3, lon = 2, member = 10, sdate = 50)) -#' obs <- array(rnorm(1000), dim = c(lat = 3, lon = 2, sdate = 50)) -#' res <- RPS(exp = exp, obs = obs, memb_dim = 'member') +#'exp <- array(rnorm(1000), dim = c(lat = 3, lon = 2, member = 10, sdate = 50)) +#'obs <- array(rnorm(1000), dim = c(lat = 3, lon = 2, sdate = 50)) +#'res <- RPS(exp = exp, obs = obs) #' #'@import multiApply -#'@importFrom ClimProjDiags Subset #'@importFrom easyVerification convert2prob #'@export -RPS <- function(exp, obs, time_dim = 'sdate', memb_dim = NULL, +RPS <- function(exp, obs, time_dim = 'sdate', memb_dim = 'member', prob_thresholds = c(1/3, 2/3), indices_for_clim = NULL, Fair = FALSE, ncores = NULL) { # Check inputs ## exp and obs (1) - - if (!is.array(exp)){stop('"exp" must be an array.')} - if (!is.array(obs)){stop('"obs" must be an array.')} - if (!identical(as.numeric(dim(exp)[time_dim]), as.numeric(dim(obs)[time_dim]))){stop('"time_dim" must have the same length in "exp" and "obs".')} - if (!is.numeric(prob_thresholds) | !is.vector(prob_thresholds) | any(prob_thresholds <= 0) | any(prob_thresholds >= 1)){ - stop('"prob_thresholds" must be a numeric vector with the relative thresholds (from 0 to 1) between the categories.') + if (!is.array(exp) | !is.numeric(exp)) + stop('Parameter "exp" must be a numeric array.') + if (!is.array(obs) | !is.numeric(obs)) + stop('Parameter "obs" must be a numeric array.') + if(any(is.null(names(dim(exp))))| any(nchar(names(dim(exp))) == 0) | + any(is.null(names(dim(obs))))| any(nchar(names(dim(obs))) == 0)) { + stop("Parameter 'exp' and 'obs' must have dimension names.") } - if (is.null(indices_for_clim)){ - indices_for_clim <- 1:as.numeric(dim(obs)[time_dim]) - } else if (!is.numeric(indices_for_clim)){ - stop('"indices_for_clim" must be NULL or a numeric vector.') + ## time_dim + if (!is.character(time_dim) | length(time_dim) != 1) + stop('Parameter "time_dim" must be a character string.') + if (!time_dim %in% names(dim(exp)) | !time_dim %in% names(dim(obs))) { + stop("Parameter 'time_dim' is not found in 'exp' or 'obs' dimension.") } - if (!is.logical(Fair)){stop('"Fair" must be TRUE or FALSE.')} - if (!is.character(time_dim)){stop('"time_dim" must be a string.')} - if (!is.character(memb_dim)){stop('"memb_dim" must be a string.')} - if (!is.null(ncores)){ - if (!is.numeric(ncores) | ncores < 1){stop('"ncores" must be either NULL or a positive integer.')} + ## memb_dim + if (!is.character(memb_dim) | length(memb_dim) > 1) { + stop("Parameter 'memb_dim' must be a character string.") } - - ## RPS + if (!memb_dim %in% names(dim(exp))) { + stop("Parameter 'memb_dim' is not found in 'exp' dimension.") + } + ## exp and obs (2) + name_exp <- sort(names(dim(exp))) + name_obs <- sort(names(dim(obs))) + name_exp <- name_exp[-which(name_exp == memb_dim)] + if (!identical(length(name_exp), length(name_obs)) | + !identical(dim(exp)[name_exp], dim(obs)[name_obs])) { + stop(paste0("Parameter 'exp' and 'obs' must have same length of ", + "all dimensions expect 'memb_dim'.")) + } + ## prob_thresholds + if (!is.numeric(prob_thresholds) | !is.vector(prob_thresholds) | + any(prob_thresholds <= 0) | any(prob_thresholds >= 1)) { + stop("Parameter 'prob_thresholds' must be a numeric vector between 0 and 1.") + } + ## indices_for_clim + if (is.null(indices_for_clim)) { + indices_for_clim <- 1:dim(obs)[time_dim] + } else { + if (!is.numeric(indices_for_clim) | !is.vector(indices_for_clim)) { + stop("Parameter 'indices_for_clim' must be NULL or a numeric vector.") + } else if (length(indices_for_clim) > dim(obs)[time_dim] | + max(indices_for_clim) > dim(obs)[time_dim] | + any(indices_for_clim) < 1) { + stop("Parameter 'indices_for_clim' should be the indices of 'time_dim'.") + } + } + ## Fair + if (!is.logical(Fair) | length(Fair) > 1) { + stop("Parameter 'Fair' must be either TRUE or FALSE.") + } + ## ncores + if (!is.null(ncores)) { + if (!is.numeric(ncores) | ncores %% 1 != 0 | ncores <= 0 | + length(ncores) > 1) { + stop("Parameter 'ncores' must be either NULL or a positive integer.") + } + } + + ############################### + + # Compute RPS rps <- Apply(data = list(exp = exp, obs = obs), - target_dims = list(exp = c(time_dim, memb_dim), obs = time_dim), + target_dims = list(exp = c(time_dim, memb_dim), + obs = time_dim), fun = .RPS, - prob_thresholds = prob_thresholds, indices_for_clim = indices_for_clim, - Fair = Fair, + prob_thresholds = prob_thresholds, + indices_for_clim = indices_for_clim, Fair = Fair, ncores = ncores)$output1 return(rps) @@ -95,59 +136,47 @@ RPS <- function(exp, obs, time_dim = 'sdate', memb_dim = NULL, obs_probs <- .get_probs(data = obs, indices_for_quantiles = indices_for_clim, prob_thresholds = prob_thresholds) # obs_probs: [bin, sdate] - - ## RPS for each time step - rps <- Apply(data = list(probs_exp = exp_probs, probs_obs = obs_probs), target_dims = 'bin', - fun = .RPS_from_probs, Fair = Fair, - n_members = dim(exp)[2], ncores = NULL)$output1 + + probs_exp_cumsum <- apply(exp_probs, 2, cumsum) + probs_obs_cumsum <- apply(obs_probs, 2, cumsum) + rps <- apply((probs_exp_cumsum - probs_obs_cumsum)^2, 2, sum) # rps: [sdate] + if (Fair) { # FairRPS + ## adjustment <- rowSums(-1 * (1/R - 1/R.new) * ens.cum * (R - ens.cum)/R/(R - 1)) [formula taken from SpecsVerification::EnsRps] + R <- dim(exp)[2] #memb + R_new <- Inf + adjustment <- (-1) / (R - 1) * probs_exp_cumsum * (1 - probs_exp_cumsum) + adjustment <- apply(adjustment, 2, sum) + rps <- rps + adjustment + } + # Return only the mean RPS rps <- mean(rps, na.rm = FALSE) return(rps) } - .get_probs <- function(data, indices_for_quantiles, prob_thresholds) { # if exp: [sdate, memb] # if obs: [sdate] + + # Add dim [memb = 1] to obs if (length(dim(data)) == 1) dim(data) <- c(dim(data), 1) # Absolute thresholds - quantiles <- quantile(x = as.vector(data), probs = prob_thresholds, type = 8, na.rm = TRUE) + quantiles <- quantile(x = as.vector(data[indices_for_quantiles, ]), probs = prob_thresholds, type = 8, na.rm = TRUE) # Probabilities probs <- array(dim = c(bin = length(quantiles) + 1, dim(data)[1])) # [bin, sdate] for (i_time in 1:dim(data)[1]) { if (anyNA(data[i_time, ])) { - probs[, i_time] <- rep(NA, dim = length(quantiles) + 1) + probs[, i_time] <- rep(NA, dim = length(quantiles) + 1) } else { - probs[, i_time] <- colMeans(easyVerification::convert2prob(data[i_time, ], threshold = quantiles)) + probs[, i_time] <- colMeans(easyVerification::convert2prob(data[i_time, ], threshold = quantiles)) } } return(probs) } - -.RPS_from_probs <- function(probs_exp, probs_obs, Fair, n_members) { - # probs_exp: [bin] - # probs_obs: [bin] - - rps <- sum((cumsum(probs_exp) - cumsum(probs_obs))^2) - - if (Fair) { # FairRPS - ## adjustment <- rowSums(-1 * (1/R - 1/R.new) * ens.cum * (R - ens.cum)/R/(R - 1)) [formula taken from SpecsVerification::EnsRps] - R <- n_members - R_new <- Inf - probs_cum <- cumsum(probs_exp) - adjustment <- -1 / (R - 1) * probs_cum * (1 - probs_cum) - adjustment <- sum(adjustment) - rps <- rps + adjustment - } - - return(rps) -} - - diff --git a/man/RPS.Rd b/man/RPS.Rd new file mode 100644 index 0000000..d1acc02 --- /dev/null +++ b/man/RPS.Rd @@ -0,0 +1,70 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/RPS.R +\name{RPS} +\alias{RPS} +\title{Compute the Ranked Probability Score} +\usage{ +RPS( + exp, + obs, + time_dim = "sdate", + memb_dim = "member", + prob_thresholds = c(1/3, 2/3), + indices_for_clim = NULL, + Fair = FALSE, + ncores = NULL +) +} +\arguments{ +\item{exp}{A named numerical array of the forecast with at least time +dimension.} + +\item{obs}{A named numerical array of the observation with at least time +dimension. The dimensions must be the same as 'exp' except 'memb_dim'.} + +\item{time_dim}{A character string indicating the name of the time dimension. +The default value is 'sdate'.} + +\item{memb_dim}{A character string indicating the name of the member dimension +to compute the probabilities of the forecast. The default value is 'member'.} + +\item{prob_thresholds}{A numeric vector of the relative thresholds (from 0 to +1) between the categories. The default value is c(1/3, 2/3), which +corresponds to tercile equiprobable categories.} + +\item{indices_for_clim}{A vector of the indices to be taken along 'time_dim' +for computing the thresholds between the probabilistic categories. If NULL, +the whole period is used. The default value is NULL.} + +\item{Fair}{A logical indicating whether to compute the FairRPSS (the +potential RPSS that the forecast would have with an infinite ensemble size). +The default value is FALSE.} + +\item{ncores}{An integer indicating the number of cores to use for parallel +computation. The default value is NULL.} +} +\value{ +A numerical array of RPS with the same dimensions as "exp" except the +'time_dim' and 'memb_dim' dimensions. +} +\description{ +The Ranked Probability Score (RPS; Wilks, 2011) is defined as the sum of the +squared differences between the cumulative forecast probabilities (computed +from the ensemble members) and the observations (defined as 0% if the category +did not happen and 100% if it happened). It can be used to evaluate the skill +of multi-categorical probabilistic forecasts. The RPS ranges between 0 +(perfect forecast) and n-1 (worst possible forecast), where n is the number of +categories. In the case of a forecast divided into two categories (the lowest +number of categories that a probabilistic forecast can have), the RPS +corresponds to the Brier Score (BS; Wilks, 2011), therefore, ranges between 0 +and 1. +} +\examples{ +exp <- array(rnorm(1000), dim = c(lat = 3, lon = 2, member = 10, sdate = 50)) +obs <- array(rnorm(1000), dim = c(lat = 3, lon = 2, sdate = 50)) +res <- RPS(exp = exp, obs = obs) + +} +\references{ +Wilks, 2011; https://doi.org/10.1016/B978-0-12-385022-5.00008-7 +} diff --git a/man/RPSS.Rd b/man/RPSS.Rd new file mode 100644 index 0000000..47136f7 --- /dev/null +++ b/man/RPSS.Rd @@ -0,0 +1,75 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/RPSS.R +\name{RPSS} +\alias{RPSS} +\title{Compute the Ranked Probability Skill Score} +\usage{ +RPSS( + exp, + obs, + ref = NULL, + prob_thresholds = c(1/3, 2/3), + indices_for_clim = NULL, + Fair = FALSE, + time_dim = "year", + member_dim = "member", + ncores = NULL +) +} +\arguments{ +\item{exp}{A numerical array with the forecast with, at least, time dimension.} + +\item{obs}{A numerical array with the observations with, at least, time dimension. +The length of the time dimension must be the same as for "exp".} + +\item{ref}{NULL or a numerical array with the reference forecast with, at least, time dimension. +The length of the time dimension must be the same as for "exp". If it is NULL, the climatological forecast is +used as reference forecast. The default value is NULL.} + +\item{prob_thresholds}{A numeric vector with the relative thresholds (between 0 and 1) between the categories. +The default value is c(1/3,2/3), which corresponds to tercile equiprobable categories.} + +\item{indices_for_clim}{A vector with the indices to be taken along "time_dim" dimension for computing the thresholds +between the probabilistic categories. If NULL, the whole period is used. The default value is NULL.} + +\item{Fair}{A logical indicating whether to compute the FairRPSS (the potential RPSS that the forecast would have with +an infinite ensemble size). The default value is FALSE.} + +\item{time_dim}{A character string indicating the name of the time dimension. +The default value is 'year'.} + +\item{member_dim}{A character string indicating the name of the member dimension to compute the +ensemble means of the forecast and reference forecast. If it is NULL, the ensemble means should +be provided directly to the function. The default value is 'member'.} + +\item{ncores}{An integer indicating the number of cores to use for parallel +computation. The default value is NULL.} +} +\value{ +\item{$rpss}{ + A numerical array with the RPSS with the same dimensions as "exp" except the "time_dim" + and "member_dim" dimensions. +} +\item{$sign}{ + A logical array with the statistical significance of the RPSS with the same dimensions + as "exp" except the "time_dim" and "member_dim" dimensions. +} +} +\description{ +The Ranked Probability Skill Score (RPSS; Wilks, 2011; https://doi.org/10.1016/B978-0-12-385022-5.00008-7) +is the skill score based on the Ranked Probability Score (RPS; Wilks, 2011). It can be used to assess whether +a forecast presents an improvement or worsening with respect to a reference forecast. The RPSS ranges between +minus infinite and 1. If the RPSS is positive, it indicates that the forecast has higher skill than the +reference forecast, while a negative value means that it has a lower skill. Examples of reference forecasts +are the climatological forecast (same probabilities for all categories for all time steps), persistence, a previous model version, +and another model. It is computed as RPSS = 1 - RPS_exp / RPS_ref. The statistical significance is obtained based +on a Random Walk test at the 95% confidence level (DelSole and Tippett, 2016; https://doi.org/10.1175/MWR-D-15-0218.1) +} +\examples{ +exp <- array(rnorm(1000), dim = c(lat = 3, lon = 2, member = 10, year = 50)) +ref <- array(rnorm(1000), dim = c(lat = 3, lon = 2, member = 10, year = 50)) +obs <- array(rnorm(1000), dim = c(lat = 3, lon = 2, year = 50)) +RPSS(exp = exp, obs = obs) ## climatology as reference forecast +RPSS(exp = exp, obs = obs, ref = ref) ## ref as reference forecast + +} diff --git a/man/RandomWalkTest.Rd b/man/RandomWalkTest.Rd index 1110648..bd1460a 100644 --- a/man/RandomWalkTest.Rd +++ b/man/RandomWalkTest.Rd @@ -37,8 +37,8 @@ A list of 2: } \description{ Forecast comparison of the skill obtained with 2 forecasts (with respect to a -common reference) based on Random Walks, with significance estimate at the 5% -confidence level, as in DelSole and Tippett (2015). +common reference) based on Random Walks, with significance estimate at the 95% +confidence level, as in DelSole and Tippett (2016). } \examples{ fcst_A <- array(c(11:50), dim = c(sdate = 10, lat = 2, lon = 2)) diff --git a/tests/testthat/test-RPS.R b/tests/testthat/test-RPS.R new file mode 100644 index 0000000..7b1f77e --- /dev/null +++ b/tests/testthat/test-RPS.R @@ -0,0 +1,129 @@ +context("s2dv::RPS tests") + +############################################## + +# dat1 +set.seed(1) +exp1 <- array(rnorm(60), dim = c(member = 3, sdate = 10, lat = 2)) +set.seed(2) +obs1 <- array(rnorm(20), dim = c(sdate = 10, lat = 2)) + +# dat2 +set.seed(1) +exp2 <- array(rnorm(20), dim = c(member = 2, sdate = 10)) +set.seed(2) +obs2 <- array(rnorm(10), dim = c(sdate = 10)) + + + +############################################## +test_that("1. Input checks", { + # exp and obs (1) + expect_error( + RPS(c()), + 'Parameter "exp" must be a numeric array.' + ) + expect_error( + RPS(exp1, c()), + 'Parameter "obs" must be a numeric array.' + ) + + # time_dim + expect_error( + RPS(exp1, obs1, time_dim = 1), + 'Parameter "time_dim" must be a character string.' + ) + expect_error( + RPS(exp1, obs1, time_dim = 'time'), + "Parameter 'time_dim' is not found in 'exp' or 'obs' dimension." + ) + # memb_dim + expect_error( + RPS(exp1, obs1, memb_dim = TRUE), + "Parameter 'memb_dim' must be a character string." + ) + expect_error( + RPS(exp1, obs1, memb_dim = 'memb'), + "Parameter 'memb_dim' is not found in 'exp' dimension." + ) + # exp, ref, and obs (2) + expect_error( + RPS(exp1, array(1:9, dim = c(sdate = 9))), + "Parameter 'exp' and 'obs' must have same length of all dimensions expect 'memb_dim'." + ) + # prob_thresholds + expect_error( + RPS(exp1, obs1, prob_thresholds = 1), + "Parameter 'prob_thresholds' must be a numeric vector between 0 and 1." + ) + # indices_for_clim + expect_error( + RPS(exp1, obs1, indices_for_clim = array(1:6, dim = c(2, 3))), + "Parameter 'indices_for_clim' must be NULL or a numeric vector." + ) + expect_error( + RPS(exp1, obs1, indices_for_clim = 3:11), + "Parameter 'indices_for_clim' should be the indices of 'time_dim'." + ) + # Fair + expect_error( + RPS(exp1, obs1, Fair = 1), + "Parameter 'Fair' must be either TRUE or FALSE." + ) + # ncores + expect_error( + RPS(exp2, obs2, ncores = 1.5), + "Parameter 'ncores' must be either NULL or a positive integer." + ) + +}) + +############################################## +test_that("2. Output checks: dat1", { + +expect_equal( +dim(RPS(exp1, obs1)), +c(lat = 2) +) +expect_equal( +as.vector(RPS(exp1, obs1)), +c(0.3555556, 0.4444444), +tolerance = 0.0001 +) +expect_equal( +as.vector(RPS(exp1, obs1, Fair = T)), +c(0.2000000, 0.2666667), +tolerance = 0.0001 +) +expect_equal( +as.vector(RPS(exp1, obs1, indices_for_clim = 3:5)), +c(0.5000000, 0.4666667), +tolerance = 0.0001 +) +expect_equal( +as.vector(RPS(exp1, obs1, prob_thresholds = seq(0.1, 0.9, 0.1))), +c(1.600000, 1.888889), +tolerance = 0.0001 +) + +}) + +############################################## +test_that("3. Output checks: dat2", { + +expect_equal( +dim(RPS(exp2, obs2)), +NULL +) +expect_equal( +RPS(exp2, obs2), +0.75, +tolerance = 0.0001 +) +expect_equal( +RPS(exp2, obs2, indices_for_clim = 2:5, prob_thresholds = seq(0.1, 0.9, 0.1)), +2.75, +tolerance = 0.0001 +) + +}) -- GitLab From 298bb734d1fce1a171b72b76334c799837df8120 Mon Sep 17 00:00:00 2001 From: aho Date: Thu, 12 May 2022 15:24:06 +0200 Subject: [PATCH 51/83] Change the order of 'obs' and 'ref' --- R/ResidualCorr.R | 61 ++++++++++++++++++++++----------------------- man/ResidualCorr.Rd | 12 ++++----- 2 files changed, 36 insertions(+), 37 deletions(-) diff --git a/R/ResidualCorr.R b/R/ResidualCorr.R index dae0cb8..541cfe8 100644 --- a/R/ResidualCorr.R +++ b/R/ResidualCorr.R @@ -17,11 +17,11 @@ #' #'@param exp A named numerical array of the forecast with at least time #' dimension. +#'@param obs A named numerical array of the observations with at least time +#' dimension. The dimensions must be the same as "exp" except 'memb_dim'. #'@param ref A named numerical array of the reference forecast data with at #' least time dimension. The dimensions must be the same as "exp" except #' 'memb_dim'. -#'@param obs A named numerical array of the observations with at least time -#' dimension. The dimensions must be the same as "exp" except 'memb_dim'. #'@param N.eff Effective sample size to be used in the statistical significance #' test. It can be NA (and it will be computed with the s2dv:::.Eno), a #' numeric (which is used for all cases), or an array with the same dimensions @@ -66,13 +66,13 @@ #' #'@examples #' exp <- array(rnorm(1000), dim = c(lat = 3, lon = 2, member = 10, sdate = 50)) -#' ref <- array(rnorm(1000), dim = c(lat = 3, lon = 2, member = 5, sdate = 50)) #' obs <- array(rnorm(1000), dim = c(lat = 3, lon = 2, sdate = 50)) -#' res <- ResidualCorr(exp, ref, obs, memb_dim = 'member') +#' ref <- array(rnorm(1000), dim = c(lat = 3, lon = 2, member = 5, sdate = 50)) +#' res <- ResidualCorr(exp = exp, obs = obs, ref = ref, memb_dim = 'member') #' #'@import multiApply #'@export -ResidualCorr <- function(exp, ref, obs, N.eff = NA, time_dim = 'sdate', +ResidualCorr <- function(exp, obs, ref, N.eff = NA, time_dim = 'sdate', memb_dim = NULL, method = 'pearson', alpha = NULL, handle.na = 'return.na', ncores = NULL) { @@ -80,10 +80,10 @@ ResidualCorr <- function(exp, ref, obs, N.eff = NA, time_dim = 'sdate', ## exp, ref, and obs (1) if (!is.array(exp) | !is.numeric(exp)) stop('Parameter "exp" must be a numeric array.') - if (!is.array(ref) | !is.numeric(ref)) - stop('Parameter "ref" must be a numeric array.') if (!is.array(obs) | !is.numeric(obs)) stop('Parameter "obs" must be a numeric array.') + if (!is.array(ref) | !is.numeric(ref)) + stop('Parameter "ref" must be a numeric array.') ## N.eff if (is.array(N.eff)) { @@ -101,9 +101,9 @@ ResidualCorr <- function(exp, ref, obs, N.eff = NA, time_dim = 'sdate', ## time_dim if (!is.character(time_dim) | length(time_dim) != 1) stop('Parameter "time_dim" must be a character string.') - if (!time_dim %in% names(dim(exp)) | !time_dim %in% names(dim(ref)) | - !time_dim %in% names(dim(obs))) { - stop("Parameter 'time_dim' is not found in 'exp', 'ref', or 'obs' dimension.") + if (!time_dim %in% names(dim(exp)) | !time_dim %in% names(dim(obs)) | + !time_dim %in% names(dim(ref))) { + stop("Parameter 'time_dim' is not found in 'exp', 'obs', or 'ref' dimension.") } ## memb_dim if (!is.null(memb_dim)) { @@ -116,15 +116,15 @@ ResidualCorr <- function(exp, ref, obs, N.eff = NA, time_dim = 'sdate', } ## exp, ref, and obs (2) name_exp <- sort(names(dim(exp))) - name_ref <- sort(names(dim(ref))) name_obs <- sort(names(dim(obs))) + name_ref <- sort(names(dim(ref))) if (!is.null(memb_dim)) { name_exp <- name_exp[-which(name_exp == memb_dim)] name_ref <- name_ref[-which(name_ref == memb_dim)] } - if (!identical(length(name_exp), length(name_ref), length(name_obs)) | - !identical(dim(exp)[name_exp], dim(ref)[name_ref], dim(obs)[name_obs])) { - stop(paste0("Parameter 'exp', 'ref', and 'obs' must have same length of ", + if (!identical(length(name_exp), length(name_obs), length(name_ref)) | + !identical(dim(exp)[name_exp], dim(obs)[name_obs], dim(ref)[name_ref])) { + stop(paste0("Parameter 'exp', 'obs', and 'ref' must have same length of ", "all dimensions expect 'memb_dim'.")) } ## method @@ -153,13 +153,13 @@ ResidualCorr <- function(exp, ref, obs, N.eff = NA, time_dim = 'sdate', ############################### # NA check: na.fail - if (handle.na == "na.fail" & (anyNA(exp) | anyNA(ref) | anyNA(obs))) + if (handle.na == "na.fail" & (anyNA(exp) | anyNA(obs) | anyNA(ref))) stop('The data contain NAs.') # Calculate ensemble mean dim_exp <- dim(exp) - dim_ref <- dim(ref) dim_obs <- dim(obs) + dim_ref <- dim(ref) if (!is.null(memb_dim)) { exp_memb_dim_ind <- which(names(dim_exp) == memb_dim) @@ -180,18 +180,17 @@ ResidualCorr <- function(exp, ref, obs, N.eff = NA, time_dim = 'sdate', # Residual correlation if (is.array(N.eff)) { - output <- Apply(data = list(exp = exp, ref = ref, - obs = obs, N.eff = N.eff), - target_dims = list(exp = time_dim, ref = time_dim, - obs = time_dim, N.eff = NULL), + output <- Apply(data = list(exp = exp, obs = obs, ref = ref, + N.eff = N.eff), + target_dims = list(exp = time_dim, obs = time_dim, + ref = time_dim, N.eff = NULL), output_dims = output_dims, fun = .ResidualCorr, method = method, alpha = alpha, handle.na = handle.na, ncores = ncores) } else { - output <- Apply(data = list(exp = exp, ref = ref, - obs = obs), - target_dims = list(exp = time_dim, ref = time_dim, - obs = time_dim), + output <- Apply(data = list(exp = exp, obs = obs, ref = ref), + target_dims = list(exp = time_dim, obs = time_dim, + ref = time_dim), output_dims = output_dims, N.eff = N.eff, fun = .ResidualCorr, method = method, alpha = alpha, handle.na = handle.na, ncores = ncores) @@ -200,9 +199,9 @@ ResidualCorr <- function(exp, ref, obs, N.eff = NA, time_dim = 'sdate', return(output) } -.ResidualCorr <- function(exp, ref, obs, N.eff, method, alpha, handle.na) { +.ResidualCorr <- function(exp, obs, ref, N.eff, method, alpha, handle.na) { # exp and ref and obs: [time] - .residual.corr <- function(exp, ref, obs, method, N.eff, alpha) { + .residual.corr <- function(exp, obs, ref, method, N.eff, alpha) { # Residuals of 'exp' and 'obs' (regressing 'ref' out in both 'exp' and 'obs') exp_res <- lm(formula = y ~ x, data = list(y = exp, x = ref), na.action = NULL)$residuals @@ -235,16 +234,16 @@ ResidualCorr <- function(exp, ref, obs, N.eff = NA, time_dim = 'sdate', #================================================== - if (anyNA(exp) | anyNA(ref) | anyNA(obs)) { ## There are NAs + if (anyNA(exp) | anyNA(obs) | anyNA(ref)) { ## There are NAs if (handle.na == 'only.complete.triplets') { - nna <- is.na(exp) | is.na(ref) | is.na(obs) # A vector of T/F + nna <- is.na(exp) | is.na(obs) | is.na(ref) # A vector of T/F if (all(nna)) stop("There is no complete set of forecasts and observations.") # Remove the incomplete set exp <- exp[!nna] - ref <- ref[!nna] obs <- obs[!nna] + ref <- ref[!nna] - output <- .residual.corr(exp = exp, ref = ref, obs = obs, method = method, + output <- .residual.corr(exp = exp, obs = obs, ref = ref, method = method, N.eff = N.eff, alpha = alpha) } else if (handle.na == 'return.na') { @@ -257,7 +256,7 @@ ResidualCorr <- function(exp, ref, obs, N.eff = NA, time_dim = 'sdate', } } else { ## There is no NA - output <- .residual.corr(exp = exp, ref = ref, obs = obs, method = method, + output <- .residual.corr(exp = exp, obs = obs, ref = ref, method = method, N.eff = N.eff, alpha = alpha) } diff --git a/man/ResidualCorr.Rd b/man/ResidualCorr.Rd index e98ea7a..fe7dd10 100644 --- a/man/ResidualCorr.Rd +++ b/man/ResidualCorr.Rd @@ -6,8 +6,8 @@ \usage{ ResidualCorr( exp, - ref, obs, + ref, N.eff = NA, time_dim = "sdate", memb_dim = NULL, @@ -21,13 +21,13 @@ ResidualCorr( \item{exp}{A named numerical array of the forecast with at least time dimension.} +\item{obs}{A named numerical array of the observations with at least time +dimension. The dimensions must be the same as "exp" except 'memb_dim'.} + \item{ref}{A named numerical array of the reference forecast data with at least time dimension. The dimensions must be the same as "exp" except 'memb_dim'.} -\item{obs}{A named numerical array of the observations with at least time -dimension. The dimensions must be the same as "exp" except 'memb_dim'.} - \item{N.eff}{Effective sample size to be used in the statistical significance test. It can be NA (and it will be computed with the s2dv:::.Eno), a numeric (which is used for all cases), or an array with the same dimensions @@ -95,8 +95,8 @@ effective degrees of freedom to account for the time series' autocorrelation } \examples{ exp <- array(rnorm(1000), dim = c(lat = 3, lon = 2, member = 10, sdate = 50)) -ref <- array(rnorm(1000), dim = c(lat = 3, lon = 2, member = 5, sdate = 50)) obs <- array(rnorm(1000), dim = c(lat = 3, lon = 2, sdate = 50)) -res <- ResidualCorr(exp, ref, obs, memb_dim = 'member') +ref <- array(rnorm(1000), dim = c(lat = 3, lon = 2, member = 5, sdate = 50)) +res <- ResidualCorr(exp = exp, obs = obs, ref = ref, memb_dim = 'member') } -- GitLab From 026cb55714c2aca94a909ef813bbb000557d4520 Mon Sep 17 00:00:00 2001 From: aho Date: Thu, 12 May 2022 17:41:11 +0200 Subject: [PATCH 52/83] Correct conditional statement and unit test --- R/ResidualCorr.R | 4 +- tests/testthat/test-ResidualCorr.R | 76 +++++++++++++++--------------- 2 files changed, 40 insertions(+), 40 deletions(-) diff --git a/R/ResidualCorr.R b/R/ResidualCorr.R index 541cfe8..7428d1b 100644 --- a/R/ResidualCorr.R +++ b/R/ResidualCorr.R @@ -122,8 +122,8 @@ ResidualCorr <- function(exp, obs, ref, N.eff = NA, time_dim = 'sdate', name_exp <- name_exp[-which(name_exp == memb_dim)] name_ref <- name_ref[-which(name_ref == memb_dim)] } - if (!identical(length(name_exp), length(name_obs), length(name_ref)) | - !identical(dim(exp)[name_exp], dim(obs)[name_obs], dim(ref)[name_ref])) { + if (length(name_exp) != length(name_obs) | length(name_exp) != length(name_ref) | + any(dim(exp)[name_exp] != dim(obs)[name_obs]) | any(dim(exp)[name_exp] != dim(ref)[name_ref])) { stop(paste0("Parameter 'exp', 'obs', and 'ref' must have same length of ", "all dimensions expect 'memb_dim'.")) } diff --git a/tests/testthat/test-ResidualCorr.R b/tests/testthat/test-ResidualCorr.R index c9ac4b9..c15276c 100644 --- a/tests/testthat/test-ResidualCorr.R +++ b/tests/testthat/test-ResidualCorr.R @@ -29,63 +29,63 @@ test_that("1. Input checks", { 'Parameter "exp" must be a numeric array.' ) expect_error( - ResidualCorr(exp1, c('a')), + ResidualCorr(exp1, obs1, c('a')), 'Parameter "ref" must be a numeric array.' ) expect_error( - ResidualCorr(exp1, ref1, c()), + ResidualCorr(exp1, c()), 'Parameter "obs" must be a numeric array.' ) # N.eff expect_error( - ResidualCorr(exp1, ref1, obs1, N.eff = array(1, dim = c(lat = 2, lon = 2))), + ResidualCorr(exp1, obs1, ref1, N.eff = array(1, dim = c(lat = 2, lon = 2))), 'If parameter "N.eff" is provided with an array, it must have the same dimensions as "obs" except "time_dim".' ) expect_error( - ResidualCorr(exp1, ref1, obs1, N.eff = 1:3), + ResidualCorr(exp1, obs1, ref1, N.eff = 1:3), 'Parameter "N.eff" must be NA, a numeric, or an array with the same dimensions as "obs" except "time_dim".' ) # time_dim expect_error( - ResidualCorr(exp1, ref1, obs1, time_dim = 1), + ResidualCorr(exp1, obs1, ref1, time_dim = 1), 'Parameter "time_dim" must be a character string.' ) expect_error( - ResidualCorr(exp1, ref1, obs1, time_dim = 'time'), - "Parameter 'time_dim' is not found in 'exp', 'ref', or 'obs' dimension." + ResidualCorr(exp1, obs1, ref1, time_dim = 'time'), + "Parameter 'time_dim' is not found in 'exp', 'obs', or 'ref' dimension." ) # memb_dim expect_error( - ResidualCorr(exp1, ref1, obs1, memb_dim = TRUE), + ResidualCorr(exp1, obs1, ref1, memb_dim = TRUE), "Parameter 'memb_dim' must be a character string." ) expect_error( - ResidualCorr(exp1, ref1, obs1, memb_dim = 'member'), + ResidualCorr(exp1, obs1, ref1, memb_dim = 'member'), "Parameter 'memb_dim' is not found in 'exp' or 'ref' dimension." ) # exp, ref, and obs (2) expect_error( - ResidualCorr(exp1, array(1:10, dim = c(sdate = 10, memb = 1)), obs1, memb_dim = 'memb'), - "Parameter 'exp', 'ref', and 'obs' must have same length of all dimensions expect 'memb_dim'." + ResidualCorr(exp1, obs1, array(1:10, dim = c(sdate = 10, memb = 1)), memb_dim = 'memb'), + "Parameter 'exp', 'obs', and 'ref' must have same length of all dimensions expect 'memb_dim'." ) # method expect_error( - ResidualCorr(exp1, ref1, obs1, method = 'asd', memb_dim = 'memb'), + ResidualCorr(exp1, obs1, ref1, method = 'asd', memb_dim = 'memb'), 'Parameter "method" must be "pearson", "kendall", or "spearman".' ) # alpha expect_error( - ResidualCorr(exp2, ref2, obs2, alpha = 1), + ResidualCorr(exp2, obs2, ref2, alpha = 1), 'Parameter "alpha" must be NULL or a number between 0 and 1.' ) # handle.na expect_error( - ResidualCorr(exp2, ref2, obs2, handle.na = TRUE), + ResidualCorr(exp2, obs2, ref2, handle.na = TRUE), 'Parameter "handle.na" must be "return.na", "only.complete.triplets" or "na.fail".' ) # ncores expect_error( - ResidualCorr(exp2, ref2, obs2, ncores = 1.5), + ResidualCorr(exp2, obs2, ref2, ncores = 1.5), 'Parameter "ncores" must be either NULL or a positive integer.' ) @@ -94,69 +94,69 @@ test_that("1. Input checks", { ############################################## test_that("2. Output checks: dat1", { expect_equal( -names(ResidualCorr(exp1, ref1, obs1, memb_dim = 'memb')), +names(ResidualCorr(exp1, obs1, ref1, memb_dim = 'memb')), c("res.corr", "p.val") ) expect_equal( -dim(ResidualCorr(exp1, ref1, obs1, memb_dim = 'memb')$res.corr), +dim(ResidualCorr(exp1, obs1, ref1, memb_dim = 'memb')$res.corr), c(lat = 3, lon = 2) ) expect_equal( -dim(ResidualCorr(exp1, ref1, obs1, memb_dim = 'memb')$p.val), +dim(ResidualCorr(exp1, obs1, ref1, memb_dim = 'memb')$p.val), c(lat = 3, lon = 2) ) expect_equal( -as.vector(ResidualCorr(exp1, ref1, obs1, memb_dim = 'memb')$res.corr), +as.vector(ResidualCorr(exp1, obs1, ref1, memb_dim = 'memb')$res.corr), c(0.002784318, 0.537697647, -0.240071018, 0.258706464, 0.338160748, 0.432107476), tolerance = 0.0001 ) expect_equal( -as.vector(ResidualCorr(exp1, ref1, obs1, memb_dim = 'memb')$p), +as.vector(ResidualCorr(exp1, obs1, ref1, memb_dim = 'memb')$p), c(0.49695468, 0.05446055, 0.25203961, 0.23522967, 0.16960864, 0.10618145), tolerance = 0.0001 ) expect_equal( -names(ResidualCorr(exp1, ref1, obs1, memb_dim = 'memb', alpha = 0.05)), +names(ResidualCorr(exp1, obs1, ref1, memb_dim = 'memb', alpha = 0.05)), c("res.corr", "sign") ) expect_equal( -as.vector(ResidualCorr(exp1, ref1, obs1, memb_dim = 'memb', alpha = 0.05)$res.corr), +as.vector(ResidualCorr(exp1, obs1, ref1, memb_dim = 'memb', alpha = 0.05)$res.corr), c(0.002784318, 0.537697647, -0.240071018, 0.258706464, 0.338160748, 0.432107476), tolerance = 0.0001 ) expect_equal( -as.vector(ResidualCorr(exp1, ref1, obs1, memb_dim = 'memb', alpha = 0.05)$sign), +as.vector(ResidualCorr(exp1, obs1, ref1, memb_dim = 'memb', alpha = 0.05)$sign), rep(FALSE, 6) ) expect_equal( -as.vector(ResidualCorr(exp1, ref1, obs1, memb_dim = 'memb', method = "kendall")$res), +as.vector(ResidualCorr(exp1, obs1, ref1, memb_dim = 'memb', method = "kendall")$res), c(0.11111111, 0.42222222, -0.20000000, 0.02222222, 0.20000000, 0.42222222), tolerance = 0.0001 ) expect_equal( -as.vector(ResidualCorr(exp1, ref1, obs1, memb_dim = 'memb', method = "kendall")$p), +as.vector(ResidualCorr(exp1, obs1, ref1, memb_dim = 'memb', method = "kendall")$p), c(0.3799615, 0.1120891, 0.2897920, 0.4757064, 0.2897920, 0.1120891), tolerance = 0.0001 ) expect_equal( -as.vector(ResidualCorr(exp1, ref1, obs1, memb_dim = 'memb', method = "spearman")$res), +as.vector(ResidualCorr(exp1, obs1, ref1, memb_dim = 'memb', method = "spearman")$res), c(0.13939394, 0.61212121, -0.27272727, 0.07878788, 0.28484848, 0.47878788), tolerance = 0.0001 ) expect_equal( -as.vector(ResidualCorr(exp1, ref1, obs1, memb_dim = 'memb', method = "spearman")$p), +as.vector(ResidualCorr(exp1, obs1, ref1, memb_dim = 'memb', method = "spearman")$p), c(0.35046594, 0.02998607, 0.22291917, 0.41435870, 0.21251908, 0.08076146), tolerance = 0.0001 ) expect_equal( -as.vector(ResidualCorr(exp1, ref1, obs1, memb_dim = 'memb', N.eff = Neff1)$res), +as.vector(ResidualCorr(exp1, obs1, ref1, memb_dim = 'memb', N.eff = Neff1)$res), c(0.002784318, 0.537697647, -0.240071018, 0.258706464, 0.338160748, 0.432107476), tolerance = 0.0001 ) expect_equal( -as.vector(ResidualCorr(exp1, ref1, obs1, memb_dim = 'memb', N.eff = Neff1)$p), +as.vector(ResidualCorr(exp1, obs1, ref1, memb_dim = 'memb', N.eff = Neff1)$p), c(0.49716394, 0.05446055, 0.26690777, 0.23522967, 0.18671025, 0.10618145), tolerance = 0.0001 ) @@ -165,17 +165,17 @@ tolerance = 0.0001 #--------------------------- exp1[1] <- NA expect_equal( -as.vector(ResidualCorr(exp1, ref1, obs1, memb_dim = 'memb')$res.corr), +as.vector(ResidualCorr(exp1, obs1, ref1, memb_dim = 'memb')$res.corr), c(NA, 0.537697647, -0.240071018, 0.258706464, 0.338160748, 0.432107476), tolerance = 0.0001 ) expect_equal( -as.vector(ResidualCorr(exp1, ref1, obs1, memb_dim = 'memb', handle.na = 'only.complete.triplets')$res.corr), +as.vector(ResidualCorr(exp1, obs1, ref1, memb_dim = 'memb', handle.na = 'only.complete.triplets')$res.corr), c(-0.1082588, 0.537697647, -0.240071018, 0.258706464, 0.338160748, 0.432107476), tolerance = 0.0001 ) expect_error( -ResidualCorr(exp1, ref1, obs1, memb_dim = 'memb', handle.na = 'na.fail'), +ResidualCorr(exp1, obs1, ref1, memb_dim = 'memb', handle.na = 'na.fail'), "The data contain NAs." ) @@ -183,7 +183,7 @@ exp1[1,1,1,1:3] <- NA ref1[1,1,1,4:8] <- NA obs1[1,1,9:10] <- NA expect_error( -ResidualCorr(exp1, ref1, obs1, memb_dim = 'memb', handle.na = 'only.complete.triplets'), +ResidualCorr(exp1, obs1, ref1, memb_dim = 'memb', handle.na = 'only.complete.triplets'), "There is no complete set of forecasts and observations." ) @@ -194,24 +194,24 @@ ResidualCorr(exp1, ref1, obs1, memb_dim = 'memb', handle.na = 'only.complete.tri test_that("3. Output checks: dat2", { expect_equal( -names(ResidualCorr(exp2, ref2, obs2)), +names(ResidualCorr(exp2, obs2, ref2)), c("res.corr", "p.val") ) expect_equal( -dim(ResidualCorr(exp2, ref2, obs2)$res.corr), +dim(ResidualCorr(exp2, obs2, ref2)$res.corr), NULL ) expect_equal( -dim(ResidualCorr(exp2, ref2, obs2)$p), +dim(ResidualCorr(exp2, obs2, ref2)$p), NULL ) expect_equal( -ResidualCorr(exp2, ref2, obs2)$p, +ResidualCorr(exp2, obs2, ref2)$p, 0.2209847, tolerance = 0.0001 ) expect_equal( -ResidualCorr(exp2, ref2, obs2)$res, +ResidualCorr(exp2, obs2, ref2)$res, -0.274962, tolerance = 0.0001 ) -- GitLab From cb78b52add85f65281c67cbb23a0826402ff3699 Mon Sep 17 00:00:00 2001 From: aho Date: Thu, 12 May 2022 18:06:35 +0200 Subject: [PATCH 53/83] Improve RPSS() formatting and efficiency --- R/RPS.R | 17 ++- R/RPSS.R | 280 ++++++++++++++++++++++++------------- man/RPS.Rd | 3 +- man/RPSS.Rd | 85 ++++++----- tests/testthat/test-RPS.R | 2 +- tests/testthat/test-RPSS.R | 272 +++++++++++++++++++++++++++++++++++ 6 files changed, 516 insertions(+), 143 deletions(-) create mode 100644 tests/testthat/test-RPSS.R diff --git a/R/RPS.R b/R/RPS.R index 744b9c7..7b4af7c 100644 --- a/R/RPS.R +++ b/R/RPS.R @@ -14,7 +14,8 @@ #'@param exp A named numerical array of the forecast with at least time #' dimension. #'@param obs A named numerical array of the observation with at least time -#' dimension. The dimensions must be the same as 'exp' except 'memb_dim'. +#' dimension. The dimensions must be the same as 'exp' except 'obs' doesn't +#' have 'memb_dim'. #'@param time_dim A character string indicating the name of the time dimension. #' The default value is 'sdate'. #'@param memb_dim A character string indicating the name of the member dimension @@ -80,7 +81,7 @@ RPS <- function(exp, obs, time_dim = 'sdate', memb_dim = 'member', if (!identical(length(name_exp), length(name_obs)) | !identical(dim(exp)[name_exp], dim(obs)[name_obs])) { stop(paste0("Parameter 'exp' and 'obs' must have same length of ", - "all dimensions expect 'memb_dim'.")) + "all dimensions expect 'obs' doesn't have 'memb_dim'.")) } ## prob_thresholds if (!is.numeric(prob_thresholds) | !is.vector(prob_thresholds) | @@ -117,16 +118,21 @@ RPS <- function(exp, obs, time_dim = 'sdate', memb_dim = 'member', rps <- Apply(data = list(exp = exp, obs = obs), target_dims = list(exp = c(time_dim, memb_dim), obs = time_dim), + output_dims = time_dim, fun = .RPS, prob_thresholds = prob_thresholds, indices_for_clim = indices_for_clim, Fair = Fair, ncores = ncores)$output1 - + + # Return only the mean RPS + rps <- MeanDims(rps, time_dim, na.rm = FALSE) + return(rps) } -.RPS <- function(exp, obs, prob_thresholds, indices_for_clim, Fair) { +.RPS <- function(exp, obs, prob_thresholds = c(1/3, 2/3), + indices_for_clim = NULL, Fair = FALSE) { # exp: [sdate, memb] # obs: [sdate] @@ -151,9 +157,6 @@ RPS <- function(exp, obs, time_dim = 'sdate', memb_dim = 'member', rps <- rps + adjustment } - # Return only the mean RPS - rps <- mean(rps, na.rm = FALSE) - return(rps) } diff --git a/R/RPSS.R b/R/RPSS.R index f7dc80d..1498456 100644 --- a/R/RPSS.R +++ b/R/RPSS.R @@ -1,131 +1,215 @@ #'Compute the Ranked Probability Skill Score #' -#'The Ranked Probability Skill Score (RPSS; Wilks, 2011; https://doi.org/10.1016/B978-0-12-385022-5.00008-7) -#'is the skill score based on the Ranked Probability Score (RPS; Wilks, 2011). It can be used to assess whether -#'a forecast presents an improvement or worsening with respect to a reference forecast. The RPSS ranges between -#'minus infinite and 1. If the RPSS is positive, it indicates that the forecast has higher skill than the -#'reference forecast, while a negative value means that it has a lower skill. Examples of reference forecasts -#'are the climatological forecast (same probabilities for all categories for all time steps), persistence, a previous model version, -#'and another model. It is computed as RPSS = 1 - RPS_exp / RPS_ref. The statistical significance is obtained based -#'on a Random Walk test at the 95% confidence level (DelSole and Tippett, 2016; https://doi.org/10.1175/MWR-D-15-0218.1) +#'The Ranked Probability Skill Score (RPSS; Wilks, 2011) is the skill score +#'based on the Ranked Probability Score (RPS; Wilks, 2011). It can be used to +#'assess whether a forecast presents an improvement or worsening with respect to +#'a reference forecast. The RPSS ranges between minus infinite and 1. If the +#'RPSS is positive, it indicates that the forecast has higher skill than the +#'reference forecast, while a negative value means that it has a lower skill. +#'Examples of reference forecasts are the climatological forecast (same +#'probabilities for all categories for all time steps), persistence, a previous +#'model version, and another model. It is computed as RPSS = 1 - RPS_exp / RPS_ref. +#'The statistical significance is obtained based on a Random Walk test at the +#'95% confidence level (DelSole and Tippett, 2016). #' -#'@param exp A numerical array with the forecast with, at least, time dimension. -#'@param obs A numerical array with the observations with, at least, time dimension. -#' The length of the time dimension must be the same as for "exp". -#'@param ref NULL or a numerical array with the reference forecast with, at least, time dimension. -#' The length of the time dimension must be the same as for "exp". If it is NULL, the climatological forecast is -#' used as reference forecast. The default value is NULL. -#'@param prob_thresholds A numeric vector with the relative thresholds (between 0 and 1) between the categories. -#' The default value is c(1/3,2/3), which corresponds to tercile equiprobable categories. -#'@param indices_for_clim A vector with the indices to be taken along "time_dim" dimension for computing the thresholds -#' between the probabilistic categories. If NULL, the whole period is used. The default value is NULL. -#'@param Fair A logical indicating whether to compute the FairRPSS (the potential RPSS that the forecast would have with -#'an infinite ensemble size). The default value is FALSE. +#'@param exp A named numerical array of the forecast with at least time +#' dimension. +#'@param obs A named numerical array of the observation with at least time +#' dimension. The dimensions must be the same as 'exp' except 'obs' doesn't +#' have 'memb_dim'. +#'@param ref A named numerical array of the reference forecast data with at +#' least time dimension. The dimensions must be the same as 'exp' except +#' 'memb_dim'. If it is NULL, the climatological forecast is used as reference +#' forecast. The default value is NULL. #'@param time_dim A character string indicating the name of the time dimension. -#' The default value is 'year'. -#'@param member_dim A character string indicating the name of the member dimension to compute the -#' ensemble means of the forecast and reference forecast. If it is NULL, the ensemble means should -#' be provided directly to the function. The default value is 'member'. +#' The default value is 'sdate'. +#'@param memb_dim A character string indicating the name of the member dimension +#' to compute the probabilities of the forecast and the reference forecast. The +#' default value is 'member'. +#'@param prob_thresholds A numeric vector of the relative thresholds (from 0 to +#' 1) between the categories. The default value is c(1/3, 2/3), which +#' corresponds to tercile equiprobable categories. +#'@param indices_for_clim A vector of the indices to be taken along 'time_dim' +#' for computing the thresholds between the probabilistic categories. If NULL, +#' the whole period is used. The default value is NULL. +#'@param Fair A logical indicating whether to compute the FairRPSS (the +#' potential RPSS that the forecast would have with an infinite ensemble size). +#' The default value is FALSE. #'@param ncores An integer indicating the number of cores to use for parallel #' computation. The default value is NULL. #' #'@return #'\item{$rpss}{ -#' A numerical array with the RPSS with the same dimensions as "exp" except the "time_dim" -#' and "member_dim" dimensions. +#' A numerical array of the RPSS with the same dimensions as "exp" except the +#' 'time_dim' and 'memb_dim' dimensions. #'} #'\item{$sign}{ -#' A logical array with the statistical significance of the RPSS with the same dimensions -#' as "exp" except the "time_dim" and "member_dim" dimensions. +#' A logical array of the statistical significance of the RPSS with the same +#' dimensions as 'exp' except the 'time_dim' and 'memb_dim' dimensions. #'} - +#' +#'@references +#'Wilks, 2011; https://doi.org/10.1016/B978-0-12-385022-5.00008-7 +#'DelSole and Tippett, 2016; https://doi.org/10.1175/MWR-D-15-0218.1 +#' #'@examples -#' exp <- array(rnorm(1000), dim = c(lat = 3, lon = 2, member = 10, year = 50)) -#' ref <- array(rnorm(1000), dim = c(lat = 3, lon = 2, member = 10, year = 50)) -#' obs <- array(rnorm(1000), dim = c(lat = 3, lon = 2, year = 50)) -#' RPSS(exp = exp, obs = obs) ## climatology as reference forecast -#' RPSS(exp = exp, obs = obs, ref = ref) ## ref as reference forecast +#'exp <- array(rnorm(1000), dim = c(lat = 3, lon = 2, member = 10, sdate = 50)) +#'obs <- array(rnorm(1000), dim = c(lat = 3, lon = 2, sdate = 50)) +#'ref <- array(rnorm(1000), dim = c(lat = 3, lon = 2, member = 10, sdate = 50)) +#'res <- RPSS(exp = exp, obs = obs) ## climatology as reference forecast +#'res <- RPSS(exp = exp, obs = obs, ref = ref) ## ref as reference forecast #' #'@import multiApply #'@export -RPSS <- function(exp, obs, ref = NULL, prob_thresholds = c(1/3,2/3), indices_for_clim = NULL, Fair = FALSE, time_dim = 'year', member_dim = 'member', ncores = NULL){ - - ## Checkings - if (!is.array(exp)){stop('"exp" must be an array.')} - if (!is.array(obs)){stop('"obs" must be an array.')} - if (!identical(as.numeric(dim(exp)[time_dim]), as.numeric(dim(obs)[time_dim]))){stop('"time_dim" must have the same length in "exp" and "obs".')} - if (!is.null(ref)){ - if (!is.array(ref)){stop('"ref" must be an array.')} - if (!identical(as.numeric(dim(exp)[time_dim]), as.numeric(dim(ref)[time_dim]))){stop('"time_dim" must have the same length in "exp" and "ref".')} +RPSS <- function(exp, obs, ref = NULL, time_dim = 'sdate', memb_dim = 'member', + prob_thresholds = c(1/3, 2/3), indices_for_clim = NULL, Fair = FALSE, + ncores = NULL){ + + # Check inputs + ## exp, obs, and ref (1) + if (!is.array(exp) | !is.numeric(exp)) + stop('Parameter "exp" must be a numeric array.') + if (!is.array(obs) | !is.numeric(obs)) + stop('Parameter "obs" must be a numeric array.') + if (!is.null(ref)) { + if (!is.array(ref) | !is.numeric(ref)) + stop('Parameter "ref" must be a numeric array.') } - if (!is.numeric(prob_thresholds) | !is.vector(prob_thresholds) | any(prob_thresholds <= 0) | any(prob_thresholds >= 1)){ - stop('"prob_thresholds" must be a numeric vector with the relative thresholds (from 0 to 1) between the categories.') + ## time_dim + if (!is.character(time_dim) | length(time_dim) != 1) + stop('Parameter "time_dim" must be a character string.') + if (!time_dim %in% names(dim(exp)) | !time_dim %in% names(dim(obs))) { + stop("Parameter 'time_dim' is not found in 'exp' or 'obs' dimension.") } - if (is.null(indices_for_clim)){ - indices_for_clim <- 1:as.numeric(dim(obs)[time_dim]) - } else if (!is.numeric(indices_for_clim)){ - stop('"indices_for_clim" must be NULL or a numeric vector.') + if (!is.null(ref) & !time_dim %in% names(dim(ref))) { + stop("Parameter 'time_dim' is not found in 'ref' dimension.") } - if (!is.logical(Fair)){stop('"Fair" must be TRUE or FALSE.')} - if (!is.character(time_dim)){stop('"time_dim" must be a string.')} - if (!is.character(member_dim)){stop('"member_dim" must be a string.')} - if (!is.null(ncores)){ - if (!is.numeric(ncores) | ncores < 1){stop('"ncores" must be either NULL or a positive integer.')} - } - - if (!is.null(ref)){ ## using "ref" as reference forecast - output <- multiApply::Apply(data = list(exp = exp, obs = obs, ref = ref), - target_dims = list(exp = c(time_dim, member_dim), obs = time_dim, ref = c(time_dim, member_dim)), - prob_thresholds = prob_thresholds, indices_for_clim = indices_for_clim, - Fair = Fair, time_dim = time_dim, member_dim = member_dim, - fun = .RPSS, ncores = ncores) - } else { ## using climatology as reference forecast - output <- multiApply::Apply(data = list(exp = exp, obs = obs), - target_dims = list(exp = c(time_dim, member_dim), obs = time_dim), - ref = ref, prob_thresholds = prob_thresholds, indices_for_clim = indices_for_clim, - Fair = Fair, time_dim = time_dim, member_dim = member_dim, - fun = .RPSS, ncores = ncores) + ## memb_dim + if (!is.character(memb_dim) | length(memb_dim) > 1) { + stop("Parameter 'memb_dim' must be a character string.") + } + if (!memb_dim %in% names(dim(exp))) { + stop("Parameter 'memb_dim' is not found in 'exp' dimension.") + } + if (!is.null(ref) & !memb_dim %in% names(dim(ref))) { + stop("Parameter 'memb_dim' is not found in 'ref' dimension.") + } + ## exp and obs (2) + name_exp <- sort(names(dim(exp))) + name_obs <- sort(names(dim(obs))) + name_exp <- name_exp[-which(name_exp == memb_dim)] + if (!identical(length(name_exp), length(name_obs)) | + !identical(dim(exp)[name_exp], dim(obs)[name_obs])) { + stop(paste0("Parameter 'exp' and 'obs' must have same length of ", + "all dimensions expect 'obs' doesn't have 'memb_dim'.")) + } + if (!is.null(ref)) { + name_ref <- sort(names(dim(ref))) + name_ref <- name_ref[-which(name_ref == memb_dim)] + if (!identical(length(name_exp), length(name_ref)) | + !identical(dim(exp)[name_exp], dim(ref)[name_ref])) { + stop(paste0("Parameter 'exp' and 'obs' must have same length of ", + "all dimensions expect 'memb_dim'.")) + } + } + ## prob_thresholds + if (!is.numeric(prob_thresholds) | !is.vector(prob_thresholds) | + any(prob_thresholds <= 0) | any(prob_thresholds >= 1)) { + stop("Parameter 'prob_thresholds' must be a numeric vector between 0 and 1.") + } + ## indices_for_clim + if (is.null(indices_for_clim)) { + indices_for_clim <- 1:dim(obs)[time_dim] + } else { + if (!is.numeric(indices_for_clim) | !is.vector(indices_for_clim)) { + stop("Parameter 'indices_for_clim' must be NULL or a numeric vector.") + } else if (length(indices_for_clim) > dim(obs)[time_dim] | + max(indices_for_clim) > dim(obs)[time_dim] | + any(indices_for_clim) < 1) { + stop("Parameter 'indices_for_clim' should be the indices of 'time_dim'.") + } + } + ## Fair + if (!is.logical(Fair) | length(Fair) > 1) { + stop("Parameter 'Fair' must be either TRUE or FALSE.") + } + ## ncores + if (!is.null(ncores)) { + if (!is.numeric(ncores) | ncores %% 1 != 0 | ncores <= 0 | + length(ncores) > 1) { + stop("Parameter 'ncores' must be either NULL or a positive integer.") + } } + + ############################### + + # Compute RPSS + if (!is.null(ref)) { # use "ref" as reference forecast + data <- list(exp = exp, obs = obs, ref = ref) + target_dims = list(exp = c(time_dim, memb_dim), + obs = time_dim, + ref = c(time_dim, memb_dim)) + } else { + data <- list(exp = exp, obs = obs) + target_dims = list(exp = c(time_dim, memb_dim), + obs = time_dim) + } + output <- Apply(data, + target_dims = target_dims, + fun = .RPSS, + prob_thresholds = prob_thresholds, + indices_for_clim = indices_for_clim, Fair = Fair, + ncores = ncores) return(output) } -.RPSS <- function(exp, obs, ref, prob_thresholds, indices_for_clim, Fair, time_dim, member_dim){ - - ## RPS of the forecast - rps_exp <- .RPS(exp = exp, obs = obs, prob_thresholds = prob_thresholds, indices_for_clim = indices_for_clim, Fair = Fair, time_dim = time_dim, member_dim = member_dim) +.RPSS <- function(exp, obs, ref = NULL, prob_thresholds = c(1/3, 2/3), + indices_for_clim = NULL, Fair = FALSE) { + # exp: [sdate, memb] + # obs: [sdate] + # ref: [sdate, memb] or NULL + + # RPS of the forecast + rps_exp <- .RPS(exp = exp, obs = obs, prob_thresholds = prob_thresholds, + indices_for_clim = indices_for_clim, Fair = Fair) - ## RPS of the reference forecast - if (is.null(ref)){ ## using climatology as reference forecast - obs_probs <- .get_probs(data = obs, type = 'obs', indices_for_quantiles = indices_for_clim, time_dim = time_dim, member_dim = NULL, prob_thresholds = prob_thresholds) - clim_probs <- .get_probs_clim(prob_thresholds = prob_thresholds, dims = dim(obs_probs), time_dim = time_dim) - rps_ref <- multiApply::Apply(data = list(probs_exp = clim_probs, probs_obs = obs_probs), target_dims = 'bin', - fun = .RPS_from_probs, n_categories = length(prob_thresholds)+1, Fair = FALSE, ncores = NULL)$output1 - } else { ## using "ref" as reference forecast - rps_ref <- .RPS(exp = ref, obs = obs, prob_thresholds = prob_thresholds, indices_for_clim = indices_for_clim, Fair = Fair, time_dim = time_dim, member_dim = member_dim) + # RPS of the reference forecast + if (is.null(ref)) { ## using climatology as reference forecast + obs_probs <- .get_probs(data = obs, indices_for_quantiles = indices_for_clim, + prob_thresholds = prob_thresholds) + # obs_probs: [bin, sdate] + + clim_probs <- c(prob_thresholds[1], diff(prob_thresholds), 1 - prob_thresholds[length(prob_thresholds)]) + clim_probs <- array(clim_probs, dim = dim(obs_probs)) + # clim_probs: [bin, sdate] + + # Calculate RPS for each time step + probs_clim_cumsum <- apply(clim_probs, 2, cumsum) + probs_obs_cumsum <- apply(obs_probs, 2, cumsum) + rps_ref <- apply((probs_clim_cumsum - probs_obs_cumsum)^2, 2, sum) + # rps_ref: [sdate] + +# if (Fair) { # FairRPS +# ## adjustment <- rowSums(-1 * (1/R - 1/R.new) * ens.cum * (R - ens.cum)/R/(R - 1)) [formula taken from SpecsVerification::EnsRps] +# R <- dim(exp)[2] #memb +# R_new <- Inf +# adjustment <- (-1) / (R - 1) * probs_clim_cumsum * (1 - probs_clim_cumsum) +# adjustment <- apply(adjustment, 2, sum) +# rps_ref <- rps_ref + adjustment +# } + + } else { # use "ref" as reference forecast + rps_ref <- .RPS(exp = ref, obs = obs, prob_thresholds = prob_thresholds, + indices_for_clim = indices_for_clim, Fair = Fair) } - ## RPSS + # RPSS rpss <- 1 - mean(rps_exp) / mean(rps_ref) - ## Significance - rps_exp <- array(data = rps_exp, dim = c(time = length(rps_exp))) - rps_ref <- array(data = rps_ref, dim = c(time = length(rps_ref))) - sign <- s2dv::RandomWalkTest(skill_A = rps_exp, skill_B = rps_ref, time_dim = 'time', ncores = NULL)$signif + # Significance + sign <- .RandomWalkTest(skill_A = rps_exp, skill_B = rps_ref)$signif return(list(rpss = rpss, sign = sign)) } -.get_probs_clim <- function(prob_thresholds, dims, time_dim){ - - probs <- array(data = NA, dim = c(bin = length(prob_thresholds)+1)) - probs[1] <- prob_thresholds[1] - if (length(prob_thresholds) > 1){ - probs[2:(length(probs)-1)] <- diff(prob_thresholds) - } - probs[length(probs)] <- 1 - prob_thresholds[length(prob_thresholds)] - stopifnot(identical(sum(probs),1)) - probs <- s2dv::InsertDim(data = probs, posdim = 2, lendim = dims[[time_dim]], name = time_dim) - - return(probs) -} diff --git a/man/RPS.Rd b/man/RPS.Rd index d1acc02..cbb0e7a 100644 --- a/man/RPS.Rd +++ b/man/RPS.Rd @@ -20,7 +20,8 @@ RPS( dimension.} \item{obs}{A named numerical array of the observation with at least time -dimension. The dimensions must be the same as 'exp' except 'memb_dim'.} +dimension. The dimensions must be the same as 'exp' except 'obs' doesn't +have 'memb_dim'.} \item{time_dim}{A character string indicating the name of the time dimension. The default value is 'sdate'.} diff --git a/man/RPSS.Rd b/man/RPSS.Rd index 47136f7..6871d80 100644 --- a/man/RPSS.Rd +++ b/man/RPSS.Rd @@ -8,68 +8,81 @@ RPSS( exp, obs, ref = NULL, + time_dim = "sdate", + memb_dim = "member", prob_thresholds = c(1/3, 2/3), indices_for_clim = NULL, Fair = FALSE, - time_dim = "year", - member_dim = "member", ncores = NULL ) } \arguments{ -\item{exp}{A numerical array with the forecast with, at least, time dimension.} +\item{exp}{A named numerical array of the forecast with at least time +dimension.} -\item{obs}{A numerical array with the observations with, at least, time dimension. -The length of the time dimension must be the same as for "exp".} +\item{obs}{A named numerical array of the observation with at least time +dimension. The dimensions must be the same as 'exp' except 'obs' doesn't +have 'memb_dim'.} -\item{ref}{NULL or a numerical array with the reference forecast with, at least, time dimension. -The length of the time dimension must be the same as for "exp". If it is NULL, the climatological forecast is -used as reference forecast. The default value is NULL.} +\item{ref}{A named numerical array of the reference forecast data with at +least time dimension. The dimensions must be the same as 'exp' except +'memb_dim'. If it is NULL, the climatological forecast is used as reference +forecast. The default value is NULL.} -\item{prob_thresholds}{A numeric vector with the relative thresholds (between 0 and 1) between the categories. -The default value is c(1/3,2/3), which corresponds to tercile equiprobable categories.} +\item{time_dim}{A character string indicating the name of the time dimension. +The default value is 'sdate'.} -\item{indices_for_clim}{A vector with the indices to be taken along "time_dim" dimension for computing the thresholds -between the probabilistic categories. If NULL, the whole period is used. The default value is NULL.} +\item{memb_dim}{A character string indicating the name of the member dimension +to compute the probabilities of the forecast and the reference forecast. The +default value is 'member'.} -\item{Fair}{A logical indicating whether to compute the FairRPSS (the potential RPSS that the forecast would have with -an infinite ensemble size). The default value is FALSE.} +\item{prob_thresholds}{A numeric vector of the relative thresholds (from 0 to +1) between the categories. The default value is c(1/3, 2/3), which +corresponds to tercile equiprobable categories.} -\item{time_dim}{A character string indicating the name of the time dimension. -The default value is 'year'.} +\item{indices_for_clim}{A vector of the indices to be taken along 'time_dim' +for computing the thresholds between the probabilistic categories. If NULL, +the whole period is used. The default value is NULL.} -\item{member_dim}{A character string indicating the name of the member dimension to compute the -ensemble means of the forecast and reference forecast. If it is NULL, the ensemble means should -be provided directly to the function. The default value is 'member'.} +\item{Fair}{A logical indicating whether to compute the FairRPSS (the +potential RPSS that the forecast would have with an infinite ensemble size). +The default value is FALSE.} \item{ncores}{An integer indicating the number of cores to use for parallel computation. The default value is NULL.} } \value{ \item{$rpss}{ - A numerical array with the RPSS with the same dimensions as "exp" except the "time_dim" - and "member_dim" dimensions. + A numerical array of the RPSS with the same dimensions as "exp" except the + 'time_dim' and 'memb_dim' dimensions. } \item{$sign}{ - A logical array with the statistical significance of the RPSS with the same dimensions - as "exp" except the "time_dim" and "member_dim" dimensions. + A logical array of the statistical significance of the RPSS with the same + dimensions as 'exp' except the 'time_dim' and 'memb_dim' dimensions. } } \description{ -The Ranked Probability Skill Score (RPSS; Wilks, 2011; https://doi.org/10.1016/B978-0-12-385022-5.00008-7) -is the skill score based on the Ranked Probability Score (RPS; Wilks, 2011). It can be used to assess whether -a forecast presents an improvement or worsening with respect to a reference forecast. The RPSS ranges between -minus infinite and 1. If the RPSS is positive, it indicates that the forecast has higher skill than the -reference forecast, while a negative value means that it has a lower skill. Examples of reference forecasts -are the climatological forecast (same probabilities for all categories for all time steps), persistence, a previous model version, -and another model. It is computed as RPSS = 1 - RPS_exp / RPS_ref. The statistical significance is obtained based -on a Random Walk test at the 95% confidence level (DelSole and Tippett, 2016; https://doi.org/10.1175/MWR-D-15-0218.1) +The Ranked Probability Skill Score (RPSS; Wilks, 2011) is the skill score +based on the Ranked Probability Score (RPS; Wilks, 2011). It can be used to +assess whether a forecast presents an improvement or worsening with respect to +a reference forecast. The RPSS ranges between minus infinite and 1. If the +RPSS is positive, it indicates that the forecast has higher skill than the +reference forecast, while a negative value means that it has a lower skill. +Examples of reference forecasts are the climatological forecast (same +probabilities for all categories for all time steps), persistence, a previous +model version, and another model. It is computed as RPSS = 1 - RPS_exp / RPS_ref. +The statistical significance is obtained based on a Random Walk test at the +95% confidence level (DelSole and Tippett, 2016). } \examples{ -exp <- array(rnorm(1000), dim = c(lat = 3, lon = 2, member = 10, year = 50)) -ref <- array(rnorm(1000), dim = c(lat = 3, lon = 2, member = 10, year = 50)) -obs <- array(rnorm(1000), dim = c(lat = 3, lon = 2, year = 50)) -RPSS(exp = exp, obs = obs) ## climatology as reference forecast -RPSS(exp = exp, obs = obs, ref = ref) ## ref as reference forecast +exp <- array(rnorm(1000), dim = c(lat = 3, lon = 2, member = 10, sdate = 50)) +obs <- array(rnorm(1000), dim = c(lat = 3, lon = 2, sdate = 50)) +ref <- array(rnorm(1000), dim = c(lat = 3, lon = 2, member = 10, sdate = 50)) +res <- RPSS(exp = exp, obs = obs) ## climatology as reference forecast +res <- RPSS(exp = exp, obs = obs, ref = ref) ## ref as reference forecast } +\references{ +Wilks, 2011; https://doi.org/10.1016/B978-0-12-385022-5.00008-7 +DelSole and Tippett, 2016; https://doi.org/10.1175/MWR-D-15-0218.1 +} diff --git a/tests/testthat/test-RPS.R b/tests/testthat/test-RPS.R index 7b1f77e..00f89dc 100644 --- a/tests/testthat/test-RPS.R +++ b/tests/testthat/test-RPS.R @@ -49,7 +49,7 @@ test_that("1. Input checks", { # exp, ref, and obs (2) expect_error( RPS(exp1, array(1:9, dim = c(sdate = 9))), - "Parameter 'exp' and 'obs' must have same length of all dimensions expect 'memb_dim'." + "Parameter 'exp' and 'obs' must have same length of all dimensions expect 'obs' doesn't have 'memb_dim'." ) # prob_thresholds expect_error( diff --git a/tests/testthat/test-RPSS.R b/tests/testthat/test-RPSS.R new file mode 100644 index 0000000..88fa18e --- /dev/null +++ b/tests/testthat/test-RPSS.R @@ -0,0 +1,272 @@ +context("s2dv::RPSS tests") + +############################################## + +# dat1 +set.seed(1) +exp1 <- array(rnorm(60), dim = c(member = 3, sdate = 10, lat = 2)) +set.seed(2) +obs1 <- array(rnorm(20), dim = c(sdate = 10, lat = 2)) +set.seed(3) +ref1 <- array(rnorm(40), dim = c(member = 2, sdate = 10, lat = 2)) + +# dat2 +set.seed(1) +exp2 <- array(rnorm(20), dim = c(member = 2, sdate = 10)) +set.seed(2) +obs2 <- array(rnorm(10), dim = c(sdate = 10)) +set.seed(3) +ref2 <- array(rnorm(20), dim = c(member = 2, sdate = 10)) + + +############################################## +test_that("1. Input checks", { + # exp and obs (1) + expect_error( + RPSS(c()), + 'Parameter "exp" must be a numeric array.' + ) + expect_error( + RPSS(exp1, c()), + 'Parameter "obs" must be a numeric array.' + ) + + # time_dim + expect_error( + RPSS(exp1, obs1, time_dim = 1), + 'Parameter "time_dim" must be a character string.' + ) + expect_error( + RPSS(exp1, obs1, time_dim = 'time'), + "Parameter 'time_dim' is not found in 'exp' or 'obs' dimension." + ) + expect_error( + RPSS(exp2, obs2, array(rnorm(20), dim = c(member = 2, time = 10))), + "Parameter 'time_dim' is not found in 'ref' dimension." + ) + # memb_dim + expect_error( + RPSS(exp1, obs1, memb_dim = TRUE), + "Parameter 'memb_dim' must be a character string." + ) + expect_error( + RPSS(exp1, obs1, memb_dim = 'memb'), + "Parameter 'memb_dim' is not found in 'exp' dimension." + ) + expect_error( + RPSS(exp2, obs2, array(rnorm(20), dim = c(memb = 2, sdate = 10))), + "Parameter 'memb_dim' is not found in 'ref' dimension." + ) + # exp, ref, and obs (2) + expect_error( + RPSS(exp1, array(1:9, dim = c(sdate = 9))), + "Parameter 'exp' and 'obs' must have same length of all dimensions expect 'obs' doesn't have 'memb_dim'." + ) + expect_error( + RPSS(exp1, obs1, ref2), + "Parameter 'exp' and 'obs' must have same length of all dimensions expect 'memb_dim'." + ) + # prob_thresholds + expect_error( + RPSS(exp1, obs1, ref1, prob_thresholds = 1), + "Parameter 'prob_thresholds' must be a numeric vector between 0 and 1." + ) + # indices_for_clim + expect_error( + RPSS(exp1, obs1, ref1, indices_for_clim = array(1:6, dim = c(2, 3))), + "Parameter 'indices_for_clim' must be NULL or a numeric vector." + ) + expect_error( + RPSS(exp1, obs1, indices_for_clim = 3:11), + "Parameter 'indices_for_clim' should be the indices of 'time_dim'." + ) + # Fair + expect_error( + RPSS(exp1, obs1, Fair = 1), + "Parameter 'Fair' must be either TRUE or FALSE." + ) + # ncores + expect_error( + RPSS(exp2, obs2, ncores = 1.5), + "Parameter 'ncores' must be either NULL or a positive integer." + ) + +}) + +############################################## +test_that("2. Output checks: dat1", { + +expect_equal( +names(RPSS(exp1, obs1)), +c("rpss", "sign") +) +expect_equal( +names(RPSS(exp1, obs1, ref1)), +c("rpss", "sign") +) +expect_equal( +dim(RPSS(exp1, obs1)$rpss), +c(lat = 2) +) +expect_equal( +dim(RPSS(exp1, obs1)$sign), +c(lat = 2) +) +expect_equal( +dim(RPSS(exp1, obs1, ref1)$rpss), +c(lat = 2) +) +expect_equal( +dim(RPSS(exp1, obs1, ref1)$sign), +c(lat = 2) +) +# ref = NULL +expect_equal( +as.vector(RPSS(exp1, obs1)$rpss), +c(0.15789474, -0.05263158), +tolerance = 0.0001 +) +expect_equal( +as.vector(RPSS(exp1, obs1)$sign), +c(FALSE, FALSE), +) +expect_equal( +as.vector(RPSS(exp1, obs1, Fair = T)$rpss), +c(0.5263158, 0.3684211), +tolerance = 0.0001 +) +expect_equal( +as.vector(RPSS(exp1, obs1, indices_for_clim = 3:5)$rpss), +c(-0.4062500, -0.1052632), +tolerance = 0.0001 +) +expect_equal( +as.vector(RPSS(exp1, obs1, prob_thresholds = seq(0.1, 0.9, 0.1))$rpss), +c(0.03030303, -0.14478114), +tolerance = 0.0001 +) +# ref = ref +expect_equal( +as.vector(RPSS(exp1, obs1, ref1)$rpss), +c(0.5259259, 0.4771242), +tolerance = 0.0001 +) +expect_equal( +as.vector(RPSS(exp1, obs1, ref1)$sign), +c(FALSE, FALSE) +) +expect_equal( +as.vector(RPSS(exp1, obs1, ref1, Fair = T)$rpss), +c(0.6000000, 0.6190476), +tolerance = 0.0001 +) +expect_equal( +as.vector(RPSS(exp1, obs1, ref1, indices_for_clim = 3:5)$rpss), +c(0.2857143, 0.4166667), +tolerance = 0.0001 +) +expect_equal( +as.vector(RPSS(exp1, obs1, ref1, indices_for_clim = 3:5)$sign), +c(FALSE, FALSE) +) +expect_equal( +as.vector(RPSS(exp1, obs1, ref1, prob_thresholds = seq(0.1, 0.9, 0.1))$rpss), +c(0.4754098, 0.3703704), +tolerance = 0.0001 +) +expect_equal( +as.vector(RPSS(exp1, obs1, ref1, prob_thresholds = seq(0.1, 0.9, 0.1))$sign), +c(T, F) +) + +}) + +############################################## +test_that("3. Output checks: dat2", { + +expect_equal( +names(RPSS(exp2, obs2)), +c("rpss", "sign") +) +expect_equal( +names(RPSS(exp2, obs2, ref2)), +c("rpss", "sign") +) +expect_equal( +dim(RPSS(exp2, obs2)$rpss), +NULL +) +expect_equal( +dim(RPSS(exp2, obs2)$sign), +NULL +) +expect_equal( +dim(RPSS(exp2, obs2, ref2)$rpss), +NULL +) +expect_equal( +dim(RPSS(exp2, obs2, ref2)$sign), +NULL +) +# ref = NULL +expect_equal( +as.vector(RPSS(exp2, obs2)$rpss), +c(-0.7763158), +tolerance = 0.0001 +) +expect_equal( +as.vector(RPSS(exp2, obs2)$sign), +FALSE, +) +expect_equal( +as.vector(RPSS(exp2, obs2, Fair = T)$rpss), +c(-0.1842105), +tolerance = 0.0001 +) +expect_equal( +as.vector(RPSS(exp2, obs2, indices_for_clim = 3:5)$rpss), +-0.8984375, +tolerance = 0.0001 +) +expect_equal( +as.vector(RPSS(exp2, obs2, prob_thresholds = seq(0.1, 0.9, 0.1))$rpss), +c(-0.7272727), +tolerance = 0.0001 +) +# ref = ref +expect_equal( +as.vector(RPSS(exp2, obs2, ref2)$rpss), +0, +tolerance = 0.0001 +) +expect_equal( +as.vector(RPSS(exp2, obs2, ref2)$sign), +FALSE +) +expect_equal( +as.vector(RPSS(exp2, obs2, ref2, Fair = T)$rpss), +0, +tolerance = 0.0001 +) +expect_equal( +as.vector(RPSS(exp2, obs2, ref2, indices_for_clim = 3:5)$rpss), +0.03571429, +tolerance = 0.0001 +) +expect_equal( +as.vector(RPSS(exp2, obs2, ref2, indices_for_clim = 3:5)$sign), +FALSE +) +expect_equal( +as.vector(RPSS(exp2, obs2, ref2, prob_thresholds = seq(0.1, 0.9, 0.1))$rpss), +0.06557377, +tolerance = 0.0001 +) +expect_equal( +as.vector(RPSS(exp2, obs2, ref2, prob_thresholds = seq(0.1, 0.9, 0.1))$sign), +FALSE +) + + + +}) -- GitLab From 8c50012f7c8b1f9d33370cd4a67d509a5168ed43 Mon Sep 17 00:00:00 2001 From: aho Date: Thu, 12 May 2022 18:22:36 +0200 Subject: [PATCH 54/83] Move easyVerification to Import --- DESCRIPTION | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/DESCRIPTION b/DESCRIPTION index f2577bf..9690f44 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -37,9 +37,9 @@ Imports: NbClust, multiApply (>= 2.1.1), SpecsVerification (>= 0.5.0), - easyNCDF + easyNCDF, + easyVerification Suggests: - easyVerification, testthat License: Apache License 2.0 URL: https://earth.bsc.es/gitlab/es/s2dv/ -- GitLab From 02cfb4067cc7be76f0ede438d0ae0245385751c4 Mon Sep 17 00:00:00 2001 From: aho Date: Fri, 13 May 2022 09:42:44 +0200 Subject: [PATCH 55/83] Change the order of input obs and ref. --- R/DiffCorr.R | 60 ++++++++++++-------------- man/DiffCorr.Rd | 12 +++--- tests/testthat/test-DiffCorr.R | 78 +++++++++++++++++----------------- 3 files changed, 73 insertions(+), 77 deletions(-) diff --git a/R/DiffCorr.R b/R/DiffCorr.R index 511dd3a..c83b7b8 100644 --- a/R/DiffCorr.R +++ b/R/DiffCorr.R @@ -13,11 +13,11 @@ #' #'@param exp A named numerical array of the forecast data with at least time #' dimension. +#'@param obs A named numerical array with the observations with at least time +#' dimension. The dimensions must be the same as "exp" except 'memb_dim'. #'@param ref A named numerical array of the reference forecast data with at #' least time dimension. The dimensions must be the same as "exp" except #' 'memb_dim'. -#'@param obs A named numerical array with the observations with at least time -#' dimension. The dimensions must be the same as "exp" except 'memb_dim'. #'@param N.eff Effective sample size to be used in the statistical significance #' test. It can be NA (and it will be computed with the s2dv:::.Eno), a #' numeric (which is used for all cases), or an array with the same dimensions @@ -62,13 +62,13 @@ #' #'@examples #' exp <- array(rnorm(1000), dim = c(lat = 3, lon = 2, member = 10, sdate = 50)) -#' ref <- array(rnorm(1000), dim = c(lat = 3, lon = 2, member = 10, sdate = 50)) #' obs <- array(rnorm(1000), dim = c(lat = 3, lon = 2, sdate = 50)) -#' res <- DiffCorr(exp, ref, obs, memb_dim = 'member') +#' ref <- array(rnorm(1000), dim = c(lat = 3, lon = 2, member = 10, sdate = 50)) +#' res <- DiffCorr(exp, obs, ref, memb_dim = 'member') #' #'@import multiApply #'@export -DiffCorr <- function(exp, ref, obs, N.eff = NA, time_dim = 'sdate', +DiffCorr <- function(exp, obs, ref, N.eff = NA, time_dim = 'sdate', memb_dim = NULL, method = 'pearson', alpha = NULL, handle.na = 'return.na', ncores = NULL) { @@ -76,10 +76,10 @@ DiffCorr <- function(exp, ref, obs, N.eff = NA, time_dim = 'sdate', ## exp, ref, and obs (1) if (!is.array(exp) | !is.numeric(exp)) stop('Parameter "exp" must be a numeric array.') - if (!is.array(ref) | !is.numeric(ref)) - stop('Parameter "ref" must be a numeric array.') if (!is.array(obs) | !is.numeric(obs)) stop('Parameter "obs" must be a numeric array.') + if (!is.array(ref) | !is.numeric(ref)) + stop('Parameter "ref" must be a numeric array.') ## N.eff if (is.array(N.eff)) { if (!is.numeric(N.eff)) stop("Parameter 'N.eff' must be numeric.") @@ -95,9 +95,9 @@ DiffCorr <- function(exp, ref, obs, N.eff = NA, time_dim = 'sdate', ## time_dim if (!is.character(time_dim) | length(time_dim) != 1) stop('Parameter "time_dim" must be a character string.') - if (!time_dim %in% names(dim(exp)) | !time_dim %in% names(dim(ref)) | - !time_dim %in% names(dim(obs))) { - stop("Parameter 'time_dim' is not found in 'exp', 'ref', or 'obs' dimension.") + if (!time_dim %in% names(dim(exp)) | !time_dim %in% names(dim(obs)) | + !time_dim %in% names(dim(ref))) { + stop("Parameter 'time_dim' is not found in 'exp', 'obs', or 'ref' dimension.") } ## memb_dim if (!is.null(memb_dim)) { @@ -110,15 +110,15 @@ DiffCorr <- function(exp, ref, obs, N.eff = NA, time_dim = 'sdate', } ## exp, ref, and obs (2) name_exp <- sort(names(dim(exp))) - name_ref <- sort(names(dim(ref))) name_obs <- sort(names(dim(obs))) + name_ref <- sort(names(dim(ref))) if (!is.null(memb_dim)) { name_exp <- name_exp[-which(name_exp == memb_dim)] name_ref <- name_ref[-which(name_ref == memb_dim)] } - if (!identical(length(name_exp), length(name_ref), length(name_obs)) | - !identical(dim(exp)[name_exp], dim(ref)[name_ref], dim(obs)[name_obs])) { - stop(paste0("Parameter 'exp', 'ref', and 'obs' must have same length of ", + if (length(name_exp) != length(name_obs) | length(name_exp) != length(name_ref) | + !identical(dim(exp)[name_exp], dim(obs)[name_obs]) | !identical(dim(exp)[name_exp], dim(ref)[name_ref])) { + stop(paste0("Parameter 'exp', 'obs', and 'ref' must have same length of ", "all dimensions expect 'memb_dim'.")) } ## method @@ -161,9 +161,6 @@ DiffCorr <- function(exp, ref, obs, N.eff = NA, time_dim = 'sdate', ref <- apply(ref, c(1:length(dim_ref))[-ref_memb_dim_ind], mean, na.rm = FALSE) if (is.null(dim(exp))) exp <- array(exp, dim = c(dim_exp[time_dim])) if (is.null(dim(ref))) ref <- array(ref, dim = c(dim_ref[time_dim])) - -# exp <- Apply(data = exp, target_dims = memb_dim, fun = mean, na.rm = FALSE, ncores = ncores)$output1 -# ref <- Apply(data = ref, target_dims = memb_dim, fun = mean, na.rm = FALSE, ncores = ncores)$output1 } # output_dims @@ -174,18 +171,17 @@ DiffCorr <- function(exp, ref, obs, N.eff = NA, time_dim = 'sdate', } # Correlation difference if (is.array(N.eff)) { - output <- Apply(data = list(exp = exp, ref = ref, - obs = obs, N.eff = N.eff), - target_dims = list(exp = time_dim, ref = time_dim, - obs = time_dim, N.eff = NULL), + output <- Apply(data = list(exp = exp, obs = obs, ref = ref, + N.eff = N.eff), + target_dims = list(exp = time_dim, obs = time_dim, + ref = time_dim, N.eff = NULL), output_dims = output_dims, fun = .DiffCorr, method = method, alpha = alpha, handle.na = handle.na, ncores = ncores) } else { - output <- Apply(data = list(exp = exp, ref = ref, - obs = obs), - target_dims = list(exp = time_dim, ref = time_dim, - obs = time_dim), + output <- Apply(data = list(exp = exp, obs = obs, ref = ref), + target_dims = list(exp = time_dim, obs = time_dim, + ref = time_dim), output_dims = output_dims, N.eff = N.eff, fun = .DiffCorr, method = method, alpha = alpha, handle.na = handle.na, ncores = ncores) @@ -194,9 +190,9 @@ DiffCorr <- function(exp, ref, obs, N.eff = NA, time_dim = 'sdate', return(output) } -.DiffCorr <- function(exp, ref, obs, N.eff, method, alpha, handle.na) { +.DiffCorr <- function(exp, obs, ref, N.eff = NA, method = 'pearson', alpha = NULL, handle.na = 'return.na') { - .diff.corr <- function(exp, ref, obs, method, N.eff, alpha) { + .diff.corr <- function(exp, obs, ref, method = 'pearson', N.eff = NA, alpha = NULL) { # Correlation difference cor.exp <- cor(x = exp, y = obs, method = method) @@ -224,16 +220,16 @@ DiffCorr <- function(exp, ref, obs, N.eff = NA, time_dim = 'sdate', #================================================== - if (anyNA(exp) | anyNA(ref) | anyNA(obs)) { ## There are NAs + if (anyNA(exp) | anyNA(obs) | anyNA(ref)) { ## There are NAs if (handle.na == 'only.complete.triplets') { - nna <- is.na(exp) | is.na(ref) | is.na(obs) # A vector of T/F + nna <- is.na(exp) | is.na(obs) | is.na(ref) # A vector of T/F if (all(nna)) stop("There is no complete set of forecasts and observations.") # Remove the incomplete set exp <- exp[!nna] - ref <- ref[!nna] obs <- obs[!nna] + ref <- ref[!nna] - output <- .diff.corr(exp = exp, ref = ref, obs = obs, method = method, + output <- .diff.corr(exp = exp, obs = obs, ref = ref, method = method, N.eff = N.eff, alpha = alpha) } else if (handle.na == 'return.na') { @@ -246,7 +242,7 @@ DiffCorr <- function(exp, ref, obs, N.eff = NA, time_dim = 'sdate', } } else { ## There is no NA - output <- .diff.corr(exp = exp, ref = ref, obs = obs, method = method, + output <- .diff.corr(exp = exp, obs = obs, ref = ref, method = method, N.eff = N.eff, alpha = alpha) } return(output) diff --git a/man/DiffCorr.Rd b/man/DiffCorr.Rd index 7f56289..d920536 100644 --- a/man/DiffCorr.Rd +++ b/man/DiffCorr.Rd @@ -6,8 +6,8 @@ \usage{ DiffCorr( exp, - ref, obs, + ref, N.eff = NA, time_dim = "sdate", memb_dim = NULL, @@ -21,13 +21,13 @@ DiffCorr( \item{exp}{A named numerical array of the forecast data with at least time dimension.} +\item{obs}{A named numerical array with the observations with at least time +dimension. The dimensions must be the same as "exp" except 'memb_dim'.} + \item{ref}{A named numerical array of the reference forecast data with at least time dimension. The dimensions must be the same as "exp" except 'memb_dim'.} -\item{obs}{A named numerical array with the observations with at least time -dimension. The dimensions must be the same as "exp" except 'memb_dim'.} - \item{N.eff}{Effective sample size to be used in the statistical significance test. It can be NA (and it will be computed with the s2dv:::.Eno), a numeric (which is used for all cases), or an array with the same dimensions @@ -91,8 +91,8 @@ to account for the autocorrelation of the time series (von Storch and Zwiers, } \examples{ exp <- array(rnorm(1000), dim = c(lat = 3, lon = 2, member = 10, sdate = 50)) -ref <- array(rnorm(1000), dim = c(lat = 3, lon = 2, member = 10, sdate = 50)) obs <- array(rnorm(1000), dim = c(lat = 3, lon = 2, sdate = 50)) -res <- DiffCorr(exp, ref, obs, memb_dim = 'member') +ref <- array(rnorm(1000), dim = c(lat = 3, lon = 2, member = 10, sdate = 50)) +res <- DiffCorr(exp, obs, ref, memb_dim = 'member') } diff --git a/tests/testthat/test-DiffCorr.R b/tests/testthat/test-DiffCorr.R index 2d1fbaf..952e14f 100644 --- a/tests/testthat/test-DiffCorr.R +++ b/tests/testthat/test-DiffCorr.R @@ -30,62 +30,62 @@ test_that("1. Input checks", { ) expect_error( DiffCorr(exp1, c('a')), - 'Parameter "ref" must be a numeric array.' + 'Parameter "obs" must be a numeric array.' ) expect_error( - DiffCorr(exp1, ref1, c()), - 'Parameter "obs" must be a numeric array.' + DiffCorr(exp1, obs1, c()), + 'Parameter "ref" must be a numeric array.' ) # N.eff expect_error( - DiffCorr(exp1, ref1, obs1, N.eff = array(1, dim = c(lat = 2, lon = 2))), + DiffCorr(exp1, obs1, ref1, N.eff = array(1, dim = c(lat = 2, lon = 2))), "If parameter \"N.eff\" is provided with an array, it must have the same dimensions as \"obs\" except \"time_dim\"." ) expect_error( - DiffCorr(exp1, ref1, obs1, N.eff = 1:3), + DiffCorr(exp1, obs1, ref1, N.eff = 1:3), 'Parameter "N.eff" must be NA, a numeric, or an array with the same dimensions as "obs" except "time_dim".' ) # time_dim expect_error( - DiffCorr(exp1, ref1, obs1, time_dim = 1), + DiffCorr(exp1, obs1, ref1, time_dim = 1), 'Parameter "time_dim" must be a character string.' ) expect_error( - DiffCorr(exp1, ref1, obs1, time_dim = 'time'), - "Parameter 'time_dim' is not found in 'exp', 'ref', or 'obs' dimension." + DiffCorr(exp1, obs1, ref1, time_dim = 'time'), + "Parameter 'time_dim' is not found in 'exp', 'obs', or 'ref' dimension." ) # memb_dim expect_error( - DiffCorr(exp1, ref1, obs1, memb_dim = TRUE), + DiffCorr(exp1, obs1, ref1, memb_dim = TRUE), "Parameter 'memb_dim' must be a character string." ) expect_error( - DiffCorr(exp1, ref1, obs1, memb_dim = 'member'), + DiffCorr(exp1, obs1, ref1, memb_dim = 'member'), "Parameter 'memb_dim' is not found in 'exp' or 'ref' dimension." ) # exp, ref, and obs (2) expect_error( - DiffCorr(exp1, array(1:10, dim = c(sdate = 10, memb = 1)), obs1, memb_dim = 'memb'), - "Parameter 'exp', 'ref', and 'obs' must have same length of all dimensions expect 'memb_dim'." + DiffCorr(exp1, array(1:10, dim = c(sdate = 10, memb = 1)), ref1, memb_dim = 'memb'), + "Parameter 'exp', 'obs', and 'ref' must have same length of all dimensions expect 'memb_dim'." ) # method expect_error( - DiffCorr(exp1, ref1, obs1, method = 'asd', memb_dim = 'memb'), + DiffCorr(exp1, obs1, ref1, method = 'asd', memb_dim = 'memb'), 'Parameter "method" must be "pearson", "kendall", or "spearman".' ) # alpha expect_error( - DiffCorr(exp2, ref2, obs2, alpha = 1), + DiffCorr(exp2, obs2, ref2, alpha = 1), 'Parameter "alpha" must be NULL or a number between 0 and 1.' ) # handle.na expect_error( - DiffCorr(exp2, ref2, obs2, handle.na = TRUE), + DiffCorr(exp2, obs2, ref2, handle.na = TRUE), 'Parameter "handle.na" must be "return.na", "only.complete.triplets" or "na.fail".' ) # ncores expect_error( - DiffCorr(exp2, ref2, obs2, ncores = 1.5), + DiffCorr(exp2, obs2, ref2, ncores = 1.5), 'Parameter "ncores" must be either NULL or a positive integer.' ) @@ -94,67 +94,67 @@ test_that("1. Input checks", { ############################################## test_that("2. Output checks: dat1", { expect_equal( -names(DiffCorr(exp1, ref1, obs1, memb_dim = 'memb')), +names(DiffCorr(exp1, obs1, ref1, memb_dim = 'memb')), c("diff.corr", "p.val") ) expect_equal( -dim(DiffCorr(exp1, ref1, obs1, memb_dim = 'memb')$diff), +dim(DiffCorr(exp1, obs1, ref1, memb_dim = 'memb')$diff), c(lat = 3, lon = 2) ) expect_equal( -dim(DiffCorr(exp1, ref1, obs1, memb_dim = 'memb')$p.val), +dim(DiffCorr(exp1, obs1, ref1, memb_dim = 'memb')$p.val), c(lat = 3, lon = 2) ) expect_equal( -as.vector(DiffCorr(exp1, ref1, obs1, memb_dim = 'memb')$diff), +as.vector(DiffCorr(exp1, obs1, ref1, memb_dim = 'memb')$diff), c(0.27347087, 0.50556882, 0.08855968, 0.24199701, 0.22935182, 0.88336336), tolerance = 0.0001 ) expect_equal( -as.vector(DiffCorr(exp1, ref1, obs1, memb_dim = 'memb')$p), +as.vector(DiffCorr(exp1, obs1, ref1, memb_dim = 'memb')$p), c(0.26166060, 0.15899774, 0.39264452, 0.27959883, 0.34736305, 0.07479832), tolerance = 0.0001 ) expect_equal( -names(DiffCorr(exp1, ref1, obs1, memb_dim = 'memb', alpha = 0.05)), +names(DiffCorr(exp1, obs1, ref1, memb_dim = 'memb', alpha = 0.05)), c("diff.corr", "sign") ) expect_equal( -as.vector(DiffCorr(exp1, ref1, obs1, memb_dim = 'memb', alpha = 0.05)$diff.corr), +as.vector(DiffCorr(exp1, obs1, ref1, memb_dim = 'memb', alpha = 0.05)$diff.corr), c(0.27347087, 0.50556882, 0.08855968, 0.24199701, 0.22935182, 0.88336336), tolerance = 0.0001 ) expect_equal( -as.vector(DiffCorr(exp1, ref1, obs1, memb_dim = 'memb', alpha = 0.05)$sign), +as.vector(DiffCorr(exp1, obs1, ref1, memb_dim = 'memb', alpha = 0.05)$sign), rep(FALSE, 6) ) expect_equal( -as.vector(DiffCorr(exp1, ref1, obs1, memb_dim = 'memb', method = "kendall")$diff.corr), +as.vector(DiffCorr(exp1, obs1, ref1, memb_dim = 'memb', method = "kendall")$diff.corr), c(0.08888889, 0.35555556, 0.00000000, -0.04444444, 0.00000000, 0.93333333), tolerance = 0.0001 ) expect_equal( -as.vector(DiffCorr(exp1, ref1, obs1, memb_dim = 'memb', method = "kendall")$p), +as.vector(DiffCorr(exp1, obs1, ref1, memb_dim = 'memb', method = "kendall")$p), c(0.42259038, 0.25373083, 0.50000000, 0.54183730, 0.50000000, 0.06307063), tolerance = 0.0001 ) expect_equal( -as.vector(DiffCorr(exp1, ref1, obs1, memb_dim = 'memb', method = "spearman")$diff.corr), +as.vector(DiffCorr(exp1, obs1, ref1, memb_dim = 'memb', method = "spearman")$diff.corr), c(0.07272727, 0.54545455, 0.10909091, -0.01212121, -0.03636364, 1.01818182), tolerance = 0.0001 ) expect_equal( -as.vector(DiffCorr(exp1, ref1, obs1, memb_dim = 'memb', method = "spearman")$p), +as.vector(DiffCorr(exp1, obs1, ref1, memb_dim = 'memb', method = "spearman")$p), c(0.4358970, 0.1341575, 0.3448977, 0.5114262, 0.5264872, 0.0437861), tolerance = 0.0001 ) expect_equal( -as.vector(DiffCorr(exp1, ref1, obs1, memb_dim = 'memb', N.eff = Neff1)$diff.corr), +as.vector(DiffCorr(exp1, obs1, ref1, memb_dim = 'memb', N.eff = Neff1)$diff.corr), c(0.27347087, 0.50556882, 0.08855968, 0.24199701, 0.22935182, 0.88336336), tolerance = 0.0001 ) expect_equal( -as.vector(DiffCorr(exp1, ref1, obs1, memb_dim = 'memb', N.eff = Neff1)$p), +as.vector(DiffCorr(exp1, obs1, ref1, memb_dim = 'memb', N.eff = Neff1)$p), c(0.27841537, 0.15899774, 0.40096749, 0.27959883, 0.35889690, 0.07479832), tolerance = 0.0001 ) @@ -163,17 +163,17 @@ tolerance = 0.0001 #--------------------------- exp1[1] <- NA expect_equal( -as.vector(DiffCorr(exp1, ref1, obs1, memb_dim = 'memb')$diff.corr), +as.vector(DiffCorr(exp1, obs1, ref1, memb_dim = 'memb')$diff.corr), c(NA, 0.50556882, 0.08855968, 0.24199701, 0.22935182, 0.88336336), tolerance = 0.0001 ) expect_equal( -as.vector(DiffCorr(exp1, ref1, obs1, memb_dim = 'memb', handle.na = 'only.complete.triplets')$diff.corr), +as.vector(DiffCorr(exp1, obs1, ref1, memb_dim = 'memb', handle.na = 'only.complete.triplets')$diff.corr), c(0.17418065, 0.50556882, 0.08855968, 0.24199701, 0.22935182, 0.88336336), tolerance = 0.0001 ) expect_error( -DiffCorr(exp1, ref1, obs1, memb_dim = 'memb', handle.na = 'na.fail'), +DiffCorr(exp1, obs1, ref1, memb_dim = 'memb', handle.na = 'na.fail'), "The data contain NAs." ) @@ -181,7 +181,7 @@ exp1[1,1,1,1:3] <- NA ref1[1,1,1,4:8] <- NA obs1[1,1,9:10] <- NA expect_error( -DiffCorr(exp1, ref1, obs1, memb_dim = 'memb', handle.na = 'only.complete.triplets'), +DiffCorr(exp1, obs1, ref1, memb_dim = 'memb', handle.na = 'only.complete.triplets'), "There is no complete set of forecasts and observations." ) @@ -192,24 +192,24 @@ DiffCorr(exp1, ref1, obs1, memb_dim = 'memb', handle.na = 'only.complete.triplet test_that("3. Output checks: dat2", { expect_equal( -names(DiffCorr(exp2, ref2, obs2)), +names(DiffCorr(exp2, obs2, ref2)), c("diff.corr", "p.val") ) expect_equal( -dim(DiffCorr(exp2, ref2, obs2)$diff.corr), +dim(DiffCorr(exp2, obs2, ref2)$diff.corr), NULL ) expect_equal( -dim(DiffCorr(exp2, ref2, obs2)$p), +dim(DiffCorr(exp2, obs2, ref2)$p), NULL ) expect_equal( -DiffCorr(exp2, ref2, obs2)$p, +DiffCorr(exp2, obs2, ref2)$p, 0.6577392, tolerance = 0.0001 ) expect_equal( -DiffCorr(exp2, ref2, obs2)$diff, +DiffCorr(exp2, obs2, ref2)$diff, -0.2434725, tolerance = 0.0001 ) -- GitLab From 07de22d56c2832db72911f66b7eb22cdcb767ddc Mon Sep 17 00:00:00 2001 From: aho Date: Fri, 13 May 2022 17:05:18 +0200 Subject: [PATCH 56/83] Fix the bug of calculating nclusters --- R/Cluster.R | 56 +++++++++++-------- man/Cluster.Rd | 1 + tests/testthat/test-Cluster.R | 101 ++++++++++++++++++++++++++++------ 3 files changed, 117 insertions(+), 41 deletions(-) diff --git a/R/Cluster.R b/R/Cluster.R index d6c5dff..2fac687 100644 --- a/R/Cluster.R +++ b/R/Cluster.R @@ -37,7 +37,7 @@ #' "ptbiserial", "gap", "frey", "mcclain", "gamma", "gplus", "tau", "dunn", #' "hubert", "sdindex", and "sdbw". #' One can also use all of them with the option 'alllong' or almost all indices -# except gap, gamma, gplus and tau with 'all', when the optimal number of +#' except gap, gamma, gplus and tau with 'all', when the optimal number of #' clusters K is detremined by the majority rule (the maximum of histogram of #' the results of all indices with finite solutions). Use of some indices on #' a big and/or unstructured dataset can be computationally intense and/or @@ -214,8 +214,23 @@ Cluster <- function(data, weights = NULL, time_dim = 'sdate', space_dim = NULL, } ############################### - # Calculate Cluster + # Compute nclusters + if (is.null(nclusters)) { + pdf(file = NULL) + nbclust.results <- NbClust::NbClust(data, distance = 'euclidean', + min.nc = 2, max.nc = 20, + method = 'kmeans', index = index) + dev.off() + if (index == 'all' || index == 'alllong') { + kmc <- hist(nbclust.results$Best.nc[1, ], breaks = seq(0, 20), + plot = FALSE)$counts + nclusters <- which(kmc == max(kmc)) + } else { + nclusters <- nbclust.results$Best.nc[1] + } + } + # Calculate Cluster output <- Apply(list(data), target_dims = c(time_dim, space_dim), fun = .Cluster, @@ -225,7 +240,7 @@ Cluster <- function(data, weights = NULL, time_dim = 'sdate', space_dim = NULL, return(output) } -.Cluster <- function(data, weights = NULL, nclusters = NULL, index = 'sdindex') { +.Cluster <- function(data, weights = NULL, nclusters, index = 'sdindex') { # data: [time, (lat, lon)] dat_dim <- dim(data) @@ -241,27 +256,22 @@ Cluster <- function(data, weights = NULL, time_dim = 'sdate', space_dim = NULL, data <- do.call(abind::abind, c(data_list, along = 0)) } } + + kmeans.results <- kmeans(data, centers = nclusters, iter.max = 300, + nstart = 30) - if (!is.null(nclusters)) { - kmeans.results <- kmeans(data, centers = nclusters, iter.max = 300, - nstart = 30) - } else { - pdf(file = NULL) - nbclust.results <- NbClust::NbClust(data, distance = 'euclidean', - min.nc = 2, max.nc = 20, - method = 'kmeans', index = index) - dev.off() - - if (index == 'all' || index == 'alllong') { - kmc <- hist(nbclust.results$Best.nc[1, ], breaks = seq(0, 20), - plot = FALSE)$counts - kmc1 <- which(kmc == max(kmc)) - } else { - kmc1 <- nbclust.results$Best.nc[1] - } +#---------------NEW--------------- + # Add dimension names and shape space_dim back + kmeans.results$cluster <- as.array(kmeans.results$cluster) + names(dim(kmeans.results$cluster)) <- names(dat_dim)[1] + kmeans.results$centers <- array(kmeans.results$centers, + dim = c(nclusters, dat_dim[-1])) + names(dim(kmeans.results$centers)) <- c('K', names(dat_dim)[-1]) + kmeans.results$withinss <- as.array(kmeans.results$withinss) + names(dim(kmeans.results$withinss)) <- 'K' + kmeans.results$size <- as.array(kmeans.results$size) + names(dim(kmeans.results$size)) <- 'K' - kmeans.results <- kmeans(data, centers = kmc1, iter.max = 300, - nstart = 30) - } +#----------NEW_END---------------- invisible(kmeans.results) } diff --git a/man/Cluster.Rd b/man/Cluster.Rd index 8c46b3d..1fa6a1f 100644 --- a/man/Cluster.Rd +++ b/man/Cluster.Rd @@ -47,6 +47,7 @@ Other indices available in NBClust are "kl", "ch", "hartigan", "ccc", "ptbiserial", "gap", "frey", "mcclain", "gamma", "gplus", "tau", "dunn", "hubert", "sdindex", and "sdbw". One can also use all of them with the option 'alllong' or almost all indices + except gap, gamma, gplus and tau with 'all', when the optimal number of clusters K is detremined by the majority rule (the maximum of histogram of the results of all indices with finite solutions). Use of some indices on a big and/or unstructured dataset can be computationally intense and/or diff --git a/tests/testthat/test-Cluster.R b/tests/testthat/test-Cluster.R index 4e0a7b1..13297d8 100644 --- a/tests/testthat/test-Cluster.R +++ b/tests/testthat/test-Cluster.R @@ -10,7 +10,7 @@ context("s2dv::Cluster tests") # dat2 set.seed(2) dat2 <- array(rnorm(300), - dim = c(sdate = 50, lat = 2, lon = 3)) + dim = c(dat = 1, sdate = 50, lat = 2, lon = 3)) weights2 <- array(c(0.9, 1.1), dim = c(lat = 2, lon = 3)) ############################################## @@ -78,24 +78,76 @@ test_that("1. Input checks", { ############################################## test_that("2. Output checks: dat1", { -# The output is random. Only check dimensions. expect_equal( - length(Cluster(dat1, weights1, space_dim = 'space')$cluster), - 50 + names(Cluster(dat1, weights1, space_dim = 'space')), + c("cluster", "centers", "totss", "withinss", "tot.withinss", + "betweenss", "size", "iter", "ifault") ) expect_equal( - length(Cluster(dat1)$cluster), - 100 + dim(Cluster(dat1, weights1, space_dim = 'space')$cluster), + c(sdate = 50) + ) + expect_equal( + dim(Cluster(dat1)$cluster), + c(sdate = 50, space = 2) ) expect_equal( dim(Cluster(dat1, weights1, space_dim = 'space')$centers), - c(8, 2) + c(K = 7, space = 2) ) expect_equal( dim(Cluster(dat1, weights1, nclusters = 3, space_dim = 'space')$centers), - c(3, 2) + c(K = 3, space = 2) + ) + expect_equal( + dim(Cluster(dat1, weights1, space_dim = 'space')$withinss), + c(K = 7) + ) + expect_equal( + dim(Cluster(dat1, weights1, space_dim = 'space')$size), + c(K = 7) + ) + expect_equal( + as.vector(Cluster(dat1, weights1, space_dim = 'space')$cluster)[1:5], + c(7, 5, 7, 5, 4) + ) + expect_equal( + as.vector(Cluster(dat1, weights1, space_dim = 'space')$centers[1:5, 2]), + c(-0.08553708, -1.55834000, 1.60550527, 1.79873789, -0.74919031), + tolerance = 0.0001 + ) + expect_equal( + as.vector(Cluster(dat1, weights1, space_dim = 'space')$totss), + 83.08558, + tolerance = 0.0001 + ) + expect_equal( + as.vector(Cluster(dat1, weights1, space_dim = 'space')$withinss)[1:3], + c(1.8509071, 0.6242121, 0.7800697), + tolerance = 0.0001 + ) + expect_equal( + as.vector(Cluster(dat1, weights1, space_dim = 'space')$tot.withinss), + 11.71979, + tolerance = 0.0001 + ) + expect_equal( + as.vector(Cluster(dat1, weights1, space_dim = 'space')$betweenss), + 71.36579, + tolerance = 0.0001 + ) + expect_equal( + as.vector(Cluster(dat1, weights1, space_dim = 'space')$size), + c(4, 5, 4, 6, 10, 8, 13) + ) + expect_equal( + as.vector(Cluster(dat1, weights1, space_dim = 'space')$iter), + 2 + ) + expect_equal( + as.vector(Cluster(dat1, weights1, space_dim = 'space')$ifault), + 0 ) - }) @@ -103,24 +155,37 @@ test_that("2. Output checks: dat1", { test_that("3. Output checks: dat2", { expect_equal( - length(Cluster(dat2, weights2, space_dim = c('lat', 'lon'))$cluster), - 50 + names(Cluster(dat2, weights2, space_dim = c('lat', 'lon'))), + c("cluster", "centers", "totss", "withinss", "tot.withinss", + "betweenss", "size", "iter", "ifault") ) expect_equal( - length(Cluster(dat2)$cluster), - 300 + dim(Cluster(dat2, weights2, space_dim = c('lat', 'lon'))$cluster), + c(sdate = 50, dat = 1) ) expect_equal( - length(Cluster(dat2, space_dim = c('lon', 'lat'))$cluster), - 50 + dim(Cluster(dat2)$cluster), + c(sdate = 50, dat = 1, lat = 2, lon = 3) ) expect_equal( dim(Cluster(dat2, weights2, space_dim = c('lat', 'lon'))$centers), - c(7, 6) + c(K = 6, lat = 2, lon = 3, dat = 1) + ) + expect_equal( + dim(Cluster(dat2, weights2, nclusters = 3, space_dim = c('lat', 'lon'))$centers), + c(K = 3, lat = 2, lon = 3, dat = 1) + ) + expect_equal( + dim(Cluster(dat2, weights2, space_dim = c('lat', 'lon'))$withinss), + c(K = 6, dat = 1) + ) + expect_equal( + dim(Cluster(dat2, weights2, space_dim = c('lat', 'lon'))$size), + c(K = 6, dat = 1) ) expect_equal( - dim(Cluster(dat2, weights2, nclusters = 5, space_dim = c('lat', 'lon'))$centers), - c(5, 6) + as.vector(Cluster(dat2, weights2, space_dim = c('lat', 'lon'))$cluster)[1:5], + c(5, 1, 2, 1, 5) ) }) -- GitLab From ed5a8f3ef529fda86c4baa856afd6cec456ddeb9 Mon Sep 17 00:00:00 2001 From: aho Date: Mon, 16 May 2022 15:35:33 +0200 Subject: [PATCH 57/83] Allow obs to have memb_dim --- R/RPS.R | 22 +++++++++++++++------- R/RPSS.R | 22 +++++++++++++++------- man/RPS.Rd | 3 +-- man/RPSS.Rd | 3 +-- tests/testthat/test-RPS.R | 13 +++++++++++-- tests/testthat/test-RPSS.R | 10 +++++++--- 6 files changed, 50 insertions(+), 23 deletions(-) diff --git a/R/RPS.R b/R/RPS.R index 7b4af7c..84956ac 100644 --- a/R/RPS.R +++ b/R/RPS.R @@ -14,8 +14,7 @@ #'@param exp A named numerical array of the forecast with at least time #' dimension. #'@param obs A named numerical array of the observation with at least time -#' dimension. The dimensions must be the same as 'exp' except 'obs' doesn't -#' have 'memb_dim'. +#' dimension. The dimensions must be the same as 'exp' except 'memb_dim'. #'@param time_dim A character string indicating the name of the time dimension. #' The default value is 'sdate'. #'@param memb_dim A character string indicating the name of the member dimension @@ -78,10 +77,13 @@ RPS <- function(exp, obs, time_dim = 'sdate', memb_dim = 'member', name_exp <- sort(names(dim(exp))) name_obs <- sort(names(dim(obs))) name_exp <- name_exp[-which(name_exp == memb_dim)] + if (memb_dim %in% name_obs) { + name_obs <- name_obs[-which(name_obs == memb_dim)] + } if (!identical(length(name_exp), length(name_obs)) | !identical(dim(exp)[name_exp], dim(obs)[name_obs])) { stop(paste0("Parameter 'exp' and 'obs' must have same length of ", - "all dimensions expect 'obs' doesn't have 'memb_dim'.")) + "all dimensions expect 'memb_dim'.")) } ## prob_thresholds if (!is.numeric(prob_thresholds) | !is.vector(prob_thresholds) | @@ -115,9 +117,15 @@ RPS <- function(exp, obs, time_dim = 'sdate', memb_dim = 'member', ############################### # Compute RPS + if (!memb_dim %in% names(dim(obs))) { + target_dims_obs <- time_dim + } else { + target_dims_obs <- c(time_dim, memb_dim) + } + rps <- Apply(data = list(exp = exp, obs = obs), target_dims = list(exp = c(time_dim, memb_dim), - obs = time_dim), + obs = target_dims_obs), output_dims = time_dim, fun = .RPS, prob_thresholds = prob_thresholds, @@ -134,7 +142,7 @@ RPS <- function(exp, obs, time_dim = 'sdate', memb_dim = 'member', .RPS <- function(exp, obs, prob_thresholds = c(1/3, 2/3), indices_for_clim = NULL, Fair = FALSE) { # exp: [sdate, memb] - # obs: [sdate] + # obs: [sdate, (memb)] exp_probs <- .get_probs(data = exp, indices_for_quantiles = indices_for_clim, prob_thresholds = prob_thresholds) @@ -162,9 +170,9 @@ RPS <- function(exp, obs, time_dim = 'sdate', memb_dim = 'member', .get_probs <- function(data, indices_for_quantiles, prob_thresholds) { # if exp: [sdate, memb] - # if obs: [sdate] + # if obs: [sdate, (memb)] - # Add dim [memb = 1] to obs + # Add dim [memb = 1] to obs if it doesn't have memb_dim if (length(dim(data)) == 1) dim(data) <- c(dim(data), 1) # Absolute thresholds diff --git a/R/RPSS.R b/R/RPSS.R index 1498456..04c3137 100644 --- a/R/RPSS.R +++ b/R/RPSS.R @@ -15,8 +15,7 @@ #'@param exp A named numerical array of the forecast with at least time #' dimension. #'@param obs A named numerical array of the observation with at least time -#' dimension. The dimensions must be the same as 'exp' except 'obs' doesn't -#' have 'memb_dim'. +#' dimension. The dimensions must be the same as 'exp' except 'memb_dim'. #'@param ref A named numerical array of the reference forecast data with at #' least time dimension. The dimensions must be the same as 'exp' except #' 'memb_dim'. If it is NULL, the climatological forecast is used as reference @@ -63,7 +62,7 @@ #'@export RPSS <- function(exp, obs, ref = NULL, time_dim = 'sdate', memb_dim = 'member', prob_thresholds = c(1/3, 2/3), indices_for_clim = NULL, Fair = FALSE, - ncores = NULL){ + ncores = NULL) { # Check inputs ## exp, obs, and ref (1) @@ -98,10 +97,13 @@ RPSS <- function(exp, obs, ref = NULL, time_dim = 'sdate', memb_dim = 'member', name_exp <- sort(names(dim(exp))) name_obs <- sort(names(dim(obs))) name_exp <- name_exp[-which(name_exp == memb_dim)] + if (memb_dim %in% name_obs) { + name_obs <- name_obs[-which(name_obs == memb_dim)] + } if (!identical(length(name_exp), length(name_obs)) | !identical(dim(exp)[name_exp], dim(obs)[name_obs])) { stop(paste0("Parameter 'exp' and 'obs' must have same length of ", - "all dimensions expect 'obs' doesn't have 'memb_dim'.")) + "all dimensions expect 'memb_dim'.")) } if (!is.null(ref)) { name_ref <- sort(names(dim(ref))) @@ -144,15 +146,21 @@ RPSS <- function(exp, obs, ref = NULL, time_dim = 'sdate', memb_dim = 'member', ############################### # Compute RPSS + if (!memb_dim %in% names(dim(obs))) { + target_dims_obs <- time_dim + } else { + target_dims_obs <- c(time_dim, memb_dim) + } + if (!is.null(ref)) { # use "ref" as reference forecast data <- list(exp = exp, obs = obs, ref = ref) target_dims = list(exp = c(time_dim, memb_dim), - obs = time_dim, + obs = target_dims_obs, ref = c(time_dim, memb_dim)) } else { data <- list(exp = exp, obs = obs) target_dims = list(exp = c(time_dim, memb_dim), - obs = time_dim) + obs = target_dims_obs) } output <- Apply(data, target_dims = target_dims, @@ -167,7 +175,7 @@ RPSS <- function(exp, obs, ref = NULL, time_dim = 'sdate', memb_dim = 'member', .RPSS <- function(exp, obs, ref = NULL, prob_thresholds = c(1/3, 2/3), indices_for_clim = NULL, Fair = FALSE) { # exp: [sdate, memb] - # obs: [sdate] + # obs: [sdate, (memb)] # ref: [sdate, memb] or NULL # RPS of the forecast diff --git a/man/RPS.Rd b/man/RPS.Rd index cbb0e7a..d1acc02 100644 --- a/man/RPS.Rd +++ b/man/RPS.Rd @@ -20,8 +20,7 @@ RPS( dimension.} \item{obs}{A named numerical array of the observation with at least time -dimension. The dimensions must be the same as 'exp' except 'obs' doesn't -have 'memb_dim'.} +dimension. The dimensions must be the same as 'exp' except 'memb_dim'.} \item{time_dim}{A character string indicating the name of the time dimension. The default value is 'sdate'.} diff --git a/man/RPSS.Rd b/man/RPSS.Rd index 6871d80..6324ea2 100644 --- a/man/RPSS.Rd +++ b/man/RPSS.Rd @@ -21,8 +21,7 @@ RPSS( dimension.} \item{obs}{A named numerical array of the observation with at least time -dimension. The dimensions must be the same as 'exp' except 'obs' doesn't -have 'memb_dim'.} +dimension. The dimensions must be the same as 'exp' except 'memb_dim'.} \item{ref}{A named numerical array of the reference forecast data with at least time dimension. The dimensions must be the same as 'exp' except diff --git a/tests/testthat/test-RPS.R b/tests/testthat/test-RPS.R index 00f89dc..e5a5f0c 100644 --- a/tests/testthat/test-RPS.R +++ b/tests/testthat/test-RPS.R @@ -14,6 +14,12 @@ exp2 <- array(rnorm(20), dim = c(member = 2, sdate = 10)) set.seed(2) obs2 <- array(rnorm(10), dim = c(sdate = 10)) +# dat2_1 +set.seed(1) +exp2_1 <- array(rnorm(20), dim = c(member = 2, sdate = 10)) +set.seed(2) +obs2_1 <- array(rnorm(10), dim = c(member = 1, sdate = 10)) + ############################################## @@ -49,7 +55,7 @@ test_that("1. Input checks", { # exp, ref, and obs (2) expect_error( RPS(exp1, array(1:9, dim = c(sdate = 9))), - "Parameter 'exp' and 'obs' must have same length of all dimensions expect 'obs' doesn't have 'memb_dim'." + "Parameter 'exp' and 'obs' must have same length of all dimensions expect 'memb_dim'." ) # prob_thresholds expect_error( @@ -125,5 +131,8 @@ RPS(exp2, obs2, indices_for_clim = 2:5, prob_thresholds = seq(0.1, 0.9, 0.1)), 2.75, tolerance = 0.0001 ) - +expect_equal( +RPS(exp2, obs2), +RPS(exp2_1, obs2_1) +) }) diff --git a/tests/testthat/test-RPSS.R b/tests/testthat/test-RPSS.R index 88fa18e..b4c0b7a 100644 --- a/tests/testthat/test-RPSS.R +++ b/tests/testthat/test-RPSS.R @@ -15,6 +15,8 @@ set.seed(1) exp2 <- array(rnorm(20), dim = c(member = 2, sdate = 10)) set.seed(2) obs2 <- array(rnorm(10), dim = c(sdate = 10)) +set.seed(2) +obs2_1 <- array(rnorm(10), dim = c(member = 1, sdate = 10)) set.seed(3) ref2 <- array(rnorm(20), dim = c(member = 2, sdate = 10)) @@ -60,7 +62,7 @@ test_that("1. Input checks", { # exp, ref, and obs (2) expect_error( RPSS(exp1, array(1:9, dim = c(sdate = 9))), - "Parameter 'exp' and 'obs' must have same length of all dimensions expect 'obs' doesn't have 'memb_dim'." + "Parameter 'exp' and 'obs' must have same length of all dimensions expect 'memb_dim'." ) expect_error( RPSS(exp1, obs1, ref2), @@ -266,7 +268,9 @@ expect_equal( as.vector(RPSS(exp2, obs2, ref2, prob_thresholds = seq(0.1, 0.9, 0.1))$sign), FALSE ) - - +expect_equal( +RPSS(exp2, obs2), +RPSS(exp2, obs2_1) +) }) -- GitLab From a28ee0ea87c1d200a5f27119e010807fc876da07 Mon Sep 17 00:00:00 2001 From: aho Date: Tue, 17 May 2022 12:27:51 +0200 Subject: [PATCH 58/83] Remove method 'kendall' and add a warning if method == 'spearman' --- R/DiffCorr.R | 27 +++++++++++++++++---------- man/DiffCorr.Rd | 16 +++++++++------- tests/testthat/test-DiffCorr.R | 24 ++++++++++-------------- 3 files changed, 36 insertions(+), 31 deletions(-) diff --git a/R/DiffCorr.R b/R/DiffCorr.R index c83b7b8..c250814 100644 --- a/R/DiffCorr.R +++ b/R/DiffCorr.R @@ -5,11 +5,9 @@ #'more skillful than the reference forecast, while negative values mean that #'the reference forecast is more skillful. The statistical significance of the #'correlation differences is computed with a one-sided test for equality of -#'dependent correlation coefficients (Steiger, 1980; -#'https://content.apa.org/doi/10.1037/0033-2909.87.2.245; Siegert et al., 2017; -#'https://doi.org/10.1175/MWR-D-16-0037.1) using effective degrees of freedom -#'to account for the autocorrelation of the time series (von Storch and Zwiers, -#'1999; https://doi.org/10.1017/CBO9780511612336). +#'dependent correlation coefficients (Steiger, 1980; Siegert et al., 2017) using +#'effective degrees of freedom to account for the autocorrelation of the time +#'series (von Storch and Zwiers, 1999). #' #'@param exp A named numerical array of the forecast data with at least time #' dimension. @@ -30,8 +28,7 @@ #' forecast. If it is NULL (default), the ensemble mean should be provided #' directly to the function. #'@param method A character string indicating the correlation coefficient to be -#' computed ("pearson", "kendall", or "spearman"). The default value is -#' "pearson". +#' computed ("pearson" or "spearman"). The default value is "pearson". #'@param alpha A numeric of the significance level to be used in the statistical #' significance test. If it is a numeric, "sign" will be returned. If NULL, the #' p-value will be returned instead. The default value is NULL. @@ -60,6 +57,11 @@ #' NULL. #'} #' +#'@references +#'Steiger, 1980; https://content.apa.org/doi/10.1037/0033-2909.87.2.245 +#'Siegert et al., 2017; https://doi.org/10.1175/MWR-D-16-0037.1 +#'von Storch and Zwiers, 1999; https://doi.org/10.1017/CBO9780511612336 +#' #'@examples #' exp <- array(rnorm(1000), dim = c(lat = 3, lon = 2, member = 10, sdate = 50)) #' obs <- array(rnorm(1000), dim = c(lat = 3, lon = 2, sdate = 50)) @@ -119,11 +121,16 @@ DiffCorr <- function(exp, obs, ref, N.eff = NA, time_dim = 'sdate', if (length(name_exp) != length(name_obs) | length(name_exp) != length(name_ref) | !identical(dim(exp)[name_exp], dim(obs)[name_obs]) | !identical(dim(exp)[name_exp], dim(ref)[name_ref])) { stop(paste0("Parameter 'exp', 'obs', and 'ref' must have same length of ", - "all dimensions expect 'memb_dim'.")) + "all dimensions except 'memb_dim'.")) } ## method - if (!method %in% c("pearson", "kendall", "spearman")) { - stop('Parameter "method" must be "pearson", "kendall", or "spearman".') + if (!method %in% c("pearson", "spearman")) { + stop('Parameter "method" must be "pearson" or "spearman".') + } + if (method == "spearman") { + warning(paste0("The test used in this function is built on Pearson method. ", + "To verify if Spearman method is reliable, you can run the ", + "Monte-Carlo simulations that are done in Siegert et al., 2017")) } ## alpha if (!is.null(alpha)) { diff --git a/man/DiffCorr.Rd b/man/DiffCorr.Rd index d920536..d8ff65c 100644 --- a/man/DiffCorr.Rd +++ b/man/DiffCorr.Rd @@ -43,8 +43,7 @@ forecast. If it is NULL (default), the ensemble mean should be provided directly to the function.} \item{method}{A character string indicating the correlation coefficient to be -computed ("pearson", "kendall", or "spearman"). The default value is -"pearson".} +computed ("pearson" or "spearman"). The default value is "pearson".} \item{alpha}{A numeric of the significance level to be used in the statistical significance test. If it is a numeric, "sign" will be returned. If NULL, the @@ -83,11 +82,9 @@ Positive values of the correlation difference indicate that the forecast is more skillful than the reference forecast, while negative values mean that the reference forecast is more skillful. The statistical significance of the correlation differences is computed with a one-sided test for equality of -dependent correlation coefficients (Steiger, 1980; -https://content.apa.org/doi/10.1037/0033-2909.87.2.245; Siegert et al., 2017; -https://doi.org/10.1175/MWR-D-16-0037.1) using effective degrees of freedom -to account for the autocorrelation of the time series (von Storch and Zwiers, -1999; https://doi.org/10.1017/CBO9780511612336). +dependent correlation coefficients (Steiger, 1980; Siegert et al., 2017) using +effective degrees of freedom to account for the autocorrelation of the time +series (von Storch and Zwiers, 1999). } \examples{ exp <- array(rnorm(1000), dim = c(lat = 3, lon = 2, member = 10, sdate = 50)) @@ -96,3 +93,8 @@ ref <- array(rnorm(1000), dim = c(lat = 3, lon = 2, member = 10, sdate = 50)) res <- DiffCorr(exp, obs, ref, memb_dim = 'member') } +\references{ +Steiger, 1980; https://content.apa.org/doi/10.1037/0033-2909.87.2.245 +Siegert et al., 2017; https://doi.org/10.1175/MWR-D-16-0037.1 +von Storch and Zwiers, 1999; https://doi.org/10.1017/CBO9780511612336 +} diff --git a/tests/testthat/test-DiffCorr.R b/tests/testthat/test-DiffCorr.R index 952e14f..e0834e1 100644 --- a/tests/testthat/test-DiffCorr.R +++ b/tests/testthat/test-DiffCorr.R @@ -66,12 +66,18 @@ test_that("1. Input checks", { # exp, ref, and obs (2) expect_error( DiffCorr(exp1, array(1:10, dim = c(sdate = 10, memb = 1)), ref1, memb_dim = 'memb'), - "Parameter 'exp', 'obs', and 'ref' must have same length of all dimensions expect 'memb_dim'." + "Parameter 'exp', 'obs', and 'ref' must have same length of all dimensions except 'memb_dim'." ) # method expect_error( DiffCorr(exp1, obs1, ref1, method = 'asd', memb_dim = 'memb'), - 'Parameter "method" must be "pearson", "kendall", or "spearman".' + 'Parameter "method" must be "pearson" or "spearman".' + ) + expect_warning( + DiffCorr(exp1, obs1, ref1, memb_dim = 'memb', method = 'spearman'), + paste0("The test used in this function is built on Pearson method. ", + "To verify if Spearman method is reliable, you can run the ", + "Monte-Carlo simulations that are done in Siegert et al., 2017") ) # alpha expect_error( @@ -129,22 +135,12 @@ as.vector(DiffCorr(exp1, obs1, ref1, memb_dim = 'memb', alpha = 0.05)$sign), rep(FALSE, 6) ) expect_equal( -as.vector(DiffCorr(exp1, obs1, ref1, memb_dim = 'memb', method = "kendall")$diff.corr), -c(0.08888889, 0.35555556, 0.00000000, -0.04444444, 0.00000000, 0.93333333), -tolerance = 0.0001 -) -expect_equal( -as.vector(DiffCorr(exp1, obs1, ref1, memb_dim = 'memb', method = "kendall")$p), -c(0.42259038, 0.25373083, 0.50000000, 0.54183730, 0.50000000, 0.06307063), -tolerance = 0.0001 -) -expect_equal( -as.vector(DiffCorr(exp1, obs1, ref1, memb_dim = 'memb', method = "spearman")$diff.corr), +suppressWarnings(as.vector(DiffCorr(exp1, obs1, ref1, memb_dim = 'memb', method = "spearman")$diff.corr)), c(0.07272727, 0.54545455, 0.10909091, -0.01212121, -0.03636364, 1.01818182), tolerance = 0.0001 ) expect_equal( -as.vector(DiffCorr(exp1, obs1, ref1, memb_dim = 'memb', method = "spearman")$p), +suppressWarnings(as.vector(DiffCorr(exp1, obs1, ref1, memb_dim = 'memb', method = "spearman")$p)), c(0.4358970, 0.1341575, 0.3448977, 0.5114262, 0.5264872, 0.0437861), tolerance = 0.0001 ) -- GitLab From d27cb2465e12e018e2fd0950123a384c52b2e1ba Mon Sep 17 00:00:00 2001 From: Roberto Bilbao Date: Tue, 17 May 2022 13:30:40 +0200 Subject: [PATCH 59/83] Added xlonshft and xlabels parameters to modify labels --- R/PlotEquiMap.R | 16 ++++++++++++++-- 1 file changed, 14 insertions(+), 2 deletions(-) diff --git a/R/PlotEquiMap.R b/R/PlotEquiMap.R index 9981ed3..c725a10 100644 --- a/R/PlotEquiMap.R +++ b/R/PlotEquiMap.R @@ -261,7 +261,7 @@ PlotEquiMap <- function(var, lon, lat, varu = NULL, varv = NULL, margin_scale = rep(1, 4), title_scale = 1, numbfig = NULL, fileout = NULL, width = 8, height = 5, size_units = 'in', - res = 100, ...) { + res = 100, xlonshft = 0, xlabels = NULL, ...) { # Process the user graphical parameters that may be passed in the call ## Graphical parameters to exclude excludedArgs <- c("cex", "cex.axis", "cex.lab", "cex.main", "col", "din", "fig", "fin", "lab", "las", "lty", "lwd", "mai", "mar", "mgp", "new", "oma", "ps", "tck") @@ -635,6 +635,12 @@ PlotEquiMap <- function(var, lon, lat, varu = NULL, varv = NULL, } else { intxlon <- round(intxlon) } + if (!is.numeric(xlonshft)) { + stop("Parameter 'xlonshft' must be numeric.") + } else { + xlonshft <- round(xlonshft) + } + # Check legend parameters if (!is.logical(drawleg)) { @@ -761,7 +767,7 @@ PlotEquiMap <- function(var, lon, lat, varu = NULL, varv = NULL, spaceticklab <- 0 if (axelab) { ypos <- seq(latmin, latmax, intylat) - xpos <- seq(lonmin, lonmax, intxlon) + xpos <- seq(lonmin, lonmax, intxlon) + xlonshft letters <- array('', length(ypos)) if (degree_sym == FALSE) { letters[ypos < 0] <- 'S' @@ -771,6 +777,10 @@ PlotEquiMap <- function(var, lon, lat, varu = NULL, varv = NULL, letters[ypos > 0] <- paste(intToUtf8(176), 'N') } ylabs <- paste(as.character(abs(ypos)), letters, sep = '') + + if (!is.null(xlabels)) { + xlabs = abind(xlabels,c('')) + } else { letters <- array('', length(xpos)) if (labW) { xpos2 <- xpos @@ -795,6 +805,8 @@ PlotEquiMap <- function(var, lon, lat, varu = NULL, varv = NULL, } else { xlabs <- paste(as.character(abs(xpos)), letters, sep = '') } + } + spaceticklab <- max(-cex_axes_ticks, 0) margins[1] <- margins[1] + 1.2 * cex_axes_labels + spaceticklab margins[2] <- margins[2] + 1.2 * cex_axes_labels + spaceticklab -- GitLab From 102232ed4f5301b2026c4ca2faf71a119168e1bc Mon Sep 17 00:00:00 2001 From: aho Date: Tue, 17 May 2022 17:22:58 +0200 Subject: [PATCH 60/83] Add documentatiion to new parameters and add 'ylabels' and 'ylatshft' --- R/PlotEquiMap.R | 121 +++++++++++++++++++++++++++++--------------- man/PlotEquiMap.Rd | 23 ++++++++- man/ResidualCorr.Rd | 6 +-- 3 files changed, 105 insertions(+), 45 deletions(-) diff --git a/R/PlotEquiMap.R b/R/PlotEquiMap.R index c725a10..9e1121a 100644 --- a/R/PlotEquiMap.R +++ b/R/PlotEquiMap.R @@ -134,11 +134,24 @@ #'@param lab_dist_y A numeric of the distance of the latitude labels to the #' box borders. The default value is NULL and is automatically adjusted by #' the function. -#'@param degree_sym A logical indicating whether to include degree symbol (30° N) or not (30N; default). +#'@param degree_sym A logical indicating whether to include degree symbol (30° N) +#' or not (30N; default). #'@param intylat Interval between latitude ticks on y-axis, in degrees. #' Defaults to 20. #'@param intxlon Interval between latitude ticks on x-axis, in degrees. #' Defaults to 20. +#'@param xlonshft A numeric of the degrees to shift the latitude ticks. The +#' default value is 0. +#'@param ylatshft A numeric of the degrees to shift the longitude ticks. The +#' default value is 0. +#'@param xlabels A vector of character string of the custumized x-axis labels. +#' The values should correspond to each tick, which is decided by the longitude +#' and parameter 'intxlon'. The default value is NULL and the labels will be +#' automatically generated. +#'@param ylabels A vector of character string of the custumized y-axis labels. +#' The values should correspond to each tick, which is decided by the latitude +#' and parameter 'intylat'. The default value is NULL and the labels will be +#' automatically generated. #'@param axes_tick_scale Scale factor for the tick lines along the longitude #' and latitude axes. #'@param axes_label_scale Scale factor for the labels along the longitude @@ -249,7 +262,8 @@ PlotEquiMap <- function(var, lon, lat, varu = NULL, varv = NULL, arr_scale_shaft = 1, arr_scale_shaft_angle = 1, axelab = TRUE, labW = FALSE, lab_dist_x = NULL, lab_dist_y = NULL, degree_sym = FALSE, - intylat = 20, intxlon = 20, + intylat = 20, intxlon = 20, + xlonshft = 0, ylatshft = 0, xlabels = NULL, ylabels = NULL, axes_tick_scale = 1, axes_label_scale = 1, drawleg = TRUE, subsampleg = NULL, bar_extra_labels = NULL, draw_bar_ticks = TRUE, @@ -261,7 +275,7 @@ PlotEquiMap <- function(var, lon, lat, varu = NULL, varv = NULL, margin_scale = rep(1, 4), title_scale = 1, numbfig = NULL, fileout = NULL, width = 8, height = 5, size_units = 'in', - res = 100, xlonshft = 0, xlabels = NULL, ...) { + res = 100, ...) { # Process the user graphical parameters that may be passed in the call ## Graphical parameters to exclude excludedArgs <- c("cex", "cex.axis", "cex.lab", "cex.main", "col", "din", "fig", "fin", "lab", "las", "lty", "lwd", "mai", "mar", "mgp", "new", "oma", "ps", "tck") @@ -635,12 +649,22 @@ PlotEquiMap <- function(var, lon, lat, varu = NULL, varv = NULL, } else { intxlon <- round(intxlon) } - if (!is.numeric(xlonshft)) { - stop("Parameter 'xlonshft' must be numeric.") - } else { - xlonshft <- round(xlonshft) + if (!is.numeric(xlonshft) | length(xlonshft) != 1) { + stop("Parameter 'xlonshft' must be a number.") + } + if (!is.numeric(ylatshft) | length(ylatshft) != 1) { + stop("Parameter 'ylatshft' must be a number.") + } + if (!is.null(xlabels)) { + if (!is.character(xlabels) | !is.vector(xlabels)) { + stop("Parameter 'xlabels' must be a vector of character string.") + } + } + if (!is.null(ylabels)) { + if (!is.character(ylabels) | !is.vector(ylabels)) { + stop("Parameter 'ylabels' must be a vector of character string.") + } } - # Check legend parameters if (!is.logical(drawleg)) { @@ -766,47 +790,62 @@ PlotEquiMap <- function(var, lon, lat, varu = NULL, varv = NULL, cex_axes_ticks <- -0.5 * axes_tick_scale spaceticklab <- 0 if (axelab) { - ypos <- seq(latmin, latmax, intylat) - xpos <- seq(lonmin, lonmax, intxlon) + xlonshft - letters <- array('', length(ypos)) - if (degree_sym == FALSE) { - letters[ypos < 0] <- 'S' - letters[ypos > 0] <- 'N' + # Y axis label + if (!is.null(ylabels)) { + ypos <- seq(latmin, latmax, intylat) + ylatshft + if (length(ypos) != length(ylabels)) { + stop(paste0("Parameter 'ylabels' must have the same length as the latitude ", + "vector spaced by 'intylat'.")) + } + ylabs <- ylabels } else { - letters[ypos < 0] <- paste(intToUtf8(176), 'S') - letters[ypos > 0] <- paste(intToUtf8(176), 'N') + ypos <- seq(latmin, latmax, intylat) + ylatshft + letters <- array('', length(ypos)) + if (degree_sym == FALSE) { + letters[ypos < 0] <- 'S' + letters[ypos > 0] <- 'N' + } else { + letters[ypos < 0] <- paste(intToUtf8(176), 'S') + letters[ypos > 0] <- paste(intToUtf8(176), 'N') + } + ylabs <- paste(as.character(abs(ypos)), letters, sep = '') } - ylabs <- paste(as.character(abs(ypos)), letters, sep = '') + # X axis label if (!is.null(xlabels)) { - xlabs = abind(xlabels,c('')) - } else { - letters <- array('', length(xpos)) - if (labW) { - xpos2 <- xpos - xpos2[xpos2 > 180] <- 360 - xpos2[xpos2 > 180] - } - if (degree_sym == FALSE) { - letters[xpos < 0] <- 'W' - letters[xpos > 0] <- 'E' + xpos <- seq(lonmin, lonmax, intxlon) + xlonshft + if (length(xpos) != length(xlabels)) { + stop(paste0("Parameter 'xlabels' must have the same length as the longitude ", + "vector spaced by 'intxlon'.")) + } + xlabs <- xlabels } else { - letters[xpos < 0] <- paste(intToUtf8(176), 'W') - letters[xpos > 0] <- paste(intToUtf8(176), 'E') - } - if (labW) { - letters[xpos == 0] <- ' ' - letters[xpos == 180] <- ' ' + xpos <- seq(lonmin, lonmax, intxlon) + xlonshft + letters <- array('', length(xpos)) + if (labW) { + xpos2 <- xpos + xpos2[xpos2 > 180] <- 360 - xpos2[xpos2 > 180] + } if (degree_sym == FALSE) { - letters[xpos > 180] <- 'W' + letters[xpos < 0] <- 'W' + letters[xpos > 0] <- 'E' } else { - letters[xpos > 180] <- paste(intToUtf8(176), 'W') - } - xlabs <- paste(as.character(abs(xpos2)), letters, sep = '') - } else { - xlabs <- paste(as.character(abs(xpos)), letters, sep = '') - } + letters[xpos < 0] <- paste(intToUtf8(176), 'W') + letters[xpos > 0] <- paste(intToUtf8(176), 'E') + } + if (labW) { + letters[xpos == 0] <- ' ' + letters[xpos == 180] <- ' ' + if (degree_sym == FALSE) { + letters[xpos > 180] <- 'W' + } else { + letters[xpos > 180] <- paste(intToUtf8(176), 'W') + } + xlabs <- paste(as.character(abs(xpos2)), letters, sep = '') + } else { + xlabs <- paste(as.character(abs(xpos)), letters, sep = '') + } } - spaceticklab <- max(-cex_axes_ticks, 0) margins[1] <- margins[1] + 1.2 * cex_axes_labels + spaceticklab margins[2] <- margins[2] + 1.2 * cex_axes_labels + spaceticklab diff --git a/man/PlotEquiMap.Rd b/man/PlotEquiMap.Rd index 31fe4d8..47a0d98 100644 --- a/man/PlotEquiMap.Rd +++ b/man/PlotEquiMap.Rd @@ -54,6 +54,10 @@ PlotEquiMap( degree_sym = FALSE, intylat = 20, intxlon = 20, + xlonshft = 0, + ylatshft = 0, + xlabels = NULL, + ylabels = NULL, axes_tick_scale = 1, axes_label_scale = 1, drawleg = TRUE, @@ -247,7 +251,8 @@ the function.} box borders. The default value is NULL and is automatically adjusted by the function.} -\item{degree_sym}{A logical indicating whether to include degree symbol (30° N) or not (30N; default).} +\item{degree_sym}{A logical indicating whether to include degree symbol (30° N) +or not (30N; default).} \item{intylat}{Interval between latitude ticks on y-axis, in degrees. Defaults to 20.} @@ -255,6 +260,22 @@ Defaults to 20.} \item{intxlon}{Interval between latitude ticks on x-axis, in degrees. Defaults to 20.} +\item{xlonshft}{A numeric of the degrees to shift the latitude ticks. The +default value is 0.} + +\item{ylatshft}{A numeric of the degrees to shift the longitude ticks. The +default value is 0.} + +\item{xlabels}{A vector of character string of the custumized x-axis labels. +The values should correspond to each tick, which is decided by the longitude +and parameter 'intxlon'. The default value is NULL and the labels will be +automatically generated.} + +\item{ylabels}{A vector of character string of the custumized y-axis labels. +The values should correspond to each tick, which is decided by the latitude +and parameter 'intylat'. The default value is NULL and the labels will be +automatically generated.} + \item{axes_tick_scale}{Scale factor for the tick lines along the longitude and latitude axes.} diff --git a/man/ResidualCorr.Rd b/man/ResidualCorr.Rd index 1e0adad..e98ea7a 100644 --- a/man/ResidualCorr.Rd +++ b/man/ResidualCorr.Rd @@ -67,9 +67,9 @@ A list with: the input arrays except "time_dim" (and "memb_dim" if provided). } \item{$sign}{ - A logical array of the statistical significance of the residual correlation - with the same dimensions as the input arrays except "time_dim" (and - "memb_dim" if provided). Returned only if "alpha" is a numeric. + A logical array indicating whether the residual correlation is statistically + significant or not with the same dimensions as the input arrays except "time_dim" + (and "memb_dim" if provided). Returned only if "alpha" is a numeric. } \item{$p.val}{ A numeric array of the p-values with the same dimensions as the input arrays -- GitLab From 269b8791c70b05b24d310c406c1794b29cdfa95b Mon Sep 17 00:00:00 2001 From: jcos Date: Wed, 18 May 2022 18:04:46 +0200 Subject: [PATCH 61/83] first attempt at weighted quantile and probs --- R/RPS.R | 52 ++++++++++++++++++++++++++++++++++++++++++---------- 1 file changed, 42 insertions(+), 10 deletions(-) diff --git a/R/RPS.R b/R/RPS.R index 84956ac..b2065cd 100644 --- a/R/RPS.R +++ b/R/RPS.R @@ -48,7 +48,7 @@ #'@export RPS <- function(exp, obs, time_dim = 'sdate', memb_dim = 'member', prob_thresholds = c(1/3, 2/3), indices_for_clim = NULL, Fair = FALSE, - ncores = NULL) { + ncores = NULL, weights = NULL) { # Check inputs ## exp and obs (1) @@ -130,7 +130,7 @@ RPS <- function(exp, obs, time_dim = 'sdate', memb_dim = 'member', fun = .RPS, prob_thresholds = prob_thresholds, indices_for_clim = indices_for_clim, Fair = Fair, - ncores = ncores)$output1 + ncores = ncores, weights = weights)$output1 # Return only the mean RPS rps <- MeanDims(rps, time_dim, na.rm = FALSE) @@ -140,12 +140,12 @@ RPS <- function(exp, obs, time_dim = 'sdate', memb_dim = 'member', .RPS <- function(exp, obs, prob_thresholds = c(1/3, 2/3), - indices_for_clim = NULL, Fair = FALSE) { + indices_for_clim = NULL, Fair = FALSE, weights = NULL) { # exp: [sdate, memb] # obs: [sdate, (memb)] exp_probs <- .get_probs(data = exp, indices_for_quantiles = indices_for_clim, - prob_thresholds = prob_thresholds) + prob_thresholds = prob_thresholds, weights = weights) # exp_probs: [bin, sdate] obs_probs <- .get_probs(data = obs, indices_for_quantiles = indices_for_clim, prob_thresholds = prob_thresholds) @@ -168,7 +168,7 @@ RPS <- function(exp, obs, time_dim = 'sdate', memb_dim = 'member', return(rps) } -.get_probs <- function(data, indices_for_quantiles, prob_thresholds) { +.get_probs <- function(data, indices_for_quantiles, prob_thresholds, weigths = NULL) { # if exp: [sdate, memb] # if obs: [sdate, (memb)] @@ -176,18 +176,50 @@ RPS <- function(exp, obs, time_dim = 'sdate', memb_dim = 'member', if (length(dim(data)) == 1) dim(data) <- c(dim(data), 1) # Absolute thresholds - quantiles <- quantile(x = as.vector(data[indices_for_quantiles, ]), probs = prob_thresholds, type = 8, na.rm = TRUE) - + if (is.null(weights)){ + quantiles <- quantile(x = as.vector(data[indices_for_quantiles, ]), probs = prob_thresholds, type = 8, na.rm = TRUE) + } else { + # linear approach to compute weighted distribution thresholds. + sorted_data, sorted_weights <- .sorted_distributions(data[indices_for_quantiles, ], weights[indices_for_quantiles, ]) + # approach 1 (interpolate from discrete cumulative weights distribution. NOT type 8) + cumulative_weights <- cumsum(sorted_weights) - 0.5 * sorted_weights + cumulative_weights <- cumulative_weights - cumulative_weights[1] # fix the 0 + cumulative_weights <- cumulative_weights / cumulative_weights[length(cumulative_weights)] # fix the 1 + quantiles <- interp1(cumulative_weights, sorted_data, c(1/3,2/3), "cubic") + # approach 2 (interpolate from the polynomial fit of the cumulative weights distribution) + fit_model <- lm(cumsum(sorted_weights) ~ poly(sorted_data,10)) # order 10, but we should think what order we want to use + predicted.intervals <- predict(fit_model,data.frame(x=sorted_data)) + quantiles <- interp1(predicted.intervals, sorted_data, c(1/3,2/3), "cubic") + } # Probabilities probs <- array(dim = c(bin = length(quantiles) + 1, dim(data)[1])) # [bin, sdate] for (i_time in 1:dim(data)[1]) { - if (anyNA(data[i_time, ])) { - probs[, i_time] <- rep(NA, dim = length(quantiles) + 1) + if (is.null(weights)){ + if (anyNA(data[i_time, ])) { + probs[, i_time] <- rep(NA, dim = length(quantiles) + 1) + } else { + probs[, i_time] <- colMeans(easyVerification::convert2prob(data[i_time, ], threshold = quantiles)) + } } else { - probs[, i_time] <- colMeans(easyVerification::convert2prob(data[i_time, ], threshold = quantiles)) + sorted_data, sorted_weights <- .sorted_distributions(data[i_time, ], weights[i_time, ]) + + fit_model <- lm(cumsum(sorted_weights) ~ poly(sorted_data,10)) + predicted.intervals <- predict(fit_model,data.frame(x=sorted_data)) + integrated_probs <- interp1(sorted_data, predicted.intervals, quantiles, "cubic") + probs[, i_time] <- c(integrated_probs[1], integrated_probs[2]-integrated_probs[1], 1-integrated_probs[2]) } + } return(probs) } +.sorted_distributions <- function(data_vector, weights_vector){ + weights_vector <- as.vector(weights_vector) + data_vector <- as.vector(data_vector) + weights_vector <- weights_vector / sum(weights_vector) # normalize to 1 + sorter <- order(data_vector) + sorted_data <- data_vector[sorter] + sorted_weights <- weights_vector[sorter] + return(sorted_data, sorted_weights) +} \ No newline at end of file -- GitLab From 1283a29dad4b39c6eacf315c8755128c991f5b2d Mon Sep 17 00:00:00 2001 From: jcos Date: Thu, 19 May 2022 08:30:49 +0200 Subject: [PATCH 62/83] quantile and prob from poly fit coeff, not interp --- R/RPS.R | 27 +++++++++++++-------------- 1 file changed, 13 insertions(+), 14 deletions(-) diff --git a/R/RPS.R b/R/RPS.R index b2065cd..1afdf6e 100644 --- a/R/RPS.R +++ b/R/RPS.R @@ -185,11 +185,11 @@ RPS <- function(exp, obs, time_dim = 'sdate', memb_dim = 'member', cumulative_weights <- cumsum(sorted_weights) - 0.5 * sorted_weights cumulative_weights <- cumulative_weights - cumulative_weights[1] # fix the 0 cumulative_weights <- cumulative_weights / cumulative_weights[length(cumulative_weights)] # fix the 1 - quantiles <- interp1(cumulative_weights, sorted_data, c(1/3,2/3), "cubic") + quantiles <- interp1(cumulative_weights, sorted_data, c(1/3,2/3), "cubic") # library(signal) # approach 2 (interpolate from the polynomial fit of the cumulative weights distribution) fit_model <- lm(cumsum(sorted_weights) ~ poly(sorted_data,10)) # order 10, but we should think what order we want to use - predicted.intervals <- predict(fit_model,data.frame(x=sorted_data)) - quantiles <- interp1(predicted.intervals, sorted_data, c(1/3,2/3), "cubic") + a <- fit_model$coefficients + quantiles <- a[1]+a[2]*prob_threshold+a[3]*prob_threshold^2+a[4]*prob_threshold^3 } # Probabilities probs <- array(dim = c(bin = length(quantiles) + 1, dim(data)[1])) # [bin, sdate] @@ -202,10 +202,9 @@ RPS <- function(exp, obs, time_dim = 'sdate', memb_dim = 'member', } } else { sorted_data, sorted_weights <- .sorted_distributions(data[i_time, ], weights[i_time, ]) - - fit_model <- lm(cumsum(sorted_weights) ~ poly(sorted_data,10)) - predicted.intervals <- predict(fit_model,data.frame(x=sorted_data)) - integrated_probs <- interp1(sorted_data, predicted.intervals, quantiles, "cubic") + fit_model <- lm(cumsum(sorted_weights) ~ poly(sorted_data,10)) # poly(sorted_data,10,raw=TRUE)? + a <- fit_model$coefficients + integrated_probs <- a[1]+a[2]*quantiles+a[3]*quantiles^2+a[4]*quantiles^3 probs[, i_time] <- c(integrated_probs[1], integrated_probs[2]-integrated_probs[1], 1-integrated_probs[2]) } @@ -215,11 +214,11 @@ RPS <- function(exp, obs, time_dim = 'sdate', memb_dim = 'member', } .sorted_distributions <- function(data_vector, weights_vector){ - weights_vector <- as.vector(weights_vector) - data_vector <- as.vector(data_vector) - weights_vector <- weights_vector / sum(weights_vector) # normalize to 1 - sorter <- order(data_vector) - sorted_data <- data_vector[sorter] - sorted_weights <- weights_vector[sorter] - return(sorted_data, sorted_weights) + weights_vector <- as.vector(weights_vector) + data_vector <- as.vector(data_vector) + weights_vector <- weights_vector / sum(weights_vector) # normalize to 1 + sorter <- order(data_vector) + sorted_data <- data_vector[sorter] + sorted_weights <- weights_vector[sorter] + return(sorted_data, sorted_weights) } \ No newline at end of file -- GitLab From 762a4f529d4f4e040505a6ed585e4674578f0731 Mon Sep 17 00:00:00 2001 From: Pep Cos Date: Thu, 19 May 2022 10:01:55 +0200 Subject: [PATCH 63/83] Revert "quantile and prob from poly fit coeff, not interp" This reverts commit 1283a29dad4b39c6eacf315c8755128c991f5b2d --- R/RPS.R | 27 ++++++++++++++------------- 1 file changed, 14 insertions(+), 13 deletions(-) diff --git a/R/RPS.R b/R/RPS.R index 1afdf6e..b2065cd 100644 --- a/R/RPS.R +++ b/R/RPS.R @@ -185,11 +185,11 @@ RPS <- function(exp, obs, time_dim = 'sdate', memb_dim = 'member', cumulative_weights <- cumsum(sorted_weights) - 0.5 * sorted_weights cumulative_weights <- cumulative_weights - cumulative_weights[1] # fix the 0 cumulative_weights <- cumulative_weights / cumulative_weights[length(cumulative_weights)] # fix the 1 - quantiles <- interp1(cumulative_weights, sorted_data, c(1/3,2/3), "cubic") # library(signal) + quantiles <- interp1(cumulative_weights, sorted_data, c(1/3,2/3), "cubic") # approach 2 (interpolate from the polynomial fit of the cumulative weights distribution) fit_model <- lm(cumsum(sorted_weights) ~ poly(sorted_data,10)) # order 10, but we should think what order we want to use - a <- fit_model$coefficients - quantiles <- a[1]+a[2]*prob_threshold+a[3]*prob_threshold^2+a[4]*prob_threshold^3 + predicted.intervals <- predict(fit_model,data.frame(x=sorted_data)) + quantiles <- interp1(predicted.intervals, sorted_data, c(1/3,2/3), "cubic") } # Probabilities probs <- array(dim = c(bin = length(quantiles) + 1, dim(data)[1])) # [bin, sdate] @@ -202,9 +202,10 @@ RPS <- function(exp, obs, time_dim = 'sdate', memb_dim = 'member', } } else { sorted_data, sorted_weights <- .sorted_distributions(data[i_time, ], weights[i_time, ]) - fit_model <- lm(cumsum(sorted_weights) ~ poly(sorted_data,10)) # poly(sorted_data,10,raw=TRUE)? - a <- fit_model$coefficients - integrated_probs <- a[1]+a[2]*quantiles+a[3]*quantiles^2+a[4]*quantiles^3 + + fit_model <- lm(cumsum(sorted_weights) ~ poly(sorted_data,10)) + predicted.intervals <- predict(fit_model,data.frame(x=sorted_data)) + integrated_probs <- interp1(sorted_data, predicted.intervals, quantiles, "cubic") probs[, i_time] <- c(integrated_probs[1], integrated_probs[2]-integrated_probs[1], 1-integrated_probs[2]) } @@ -214,11 +215,11 @@ RPS <- function(exp, obs, time_dim = 'sdate', memb_dim = 'member', } .sorted_distributions <- function(data_vector, weights_vector){ - weights_vector <- as.vector(weights_vector) - data_vector <- as.vector(data_vector) - weights_vector <- weights_vector / sum(weights_vector) # normalize to 1 - sorter <- order(data_vector) - sorted_data <- data_vector[sorter] - sorted_weights <- weights_vector[sorter] - return(sorted_data, sorted_weights) + weights_vector <- as.vector(weights_vector) + data_vector <- as.vector(data_vector) + weights_vector <- weights_vector / sum(weights_vector) # normalize to 1 + sorter <- order(data_vector) + sorted_data <- data_vector[sorter] + sorted_weights <- weights_vector[sorter] + return(sorted_data, sorted_weights) } \ No newline at end of file -- GitLab From d172207b5941974abf23500db1bb7536d1d22996 Mon Sep 17 00:00:00 2001 From: Pep Cos Date: Thu, 19 May 2022 10:07:20 +0200 Subject: [PATCH 64/83] Revert "first attempt at weighted quantile and probs" This reverts commit 269b8791c70b05b24d310c406c1794b29cdfa95b --- R/RPS.R | 52 ++++++++++------------------------------------------ 1 file changed, 10 insertions(+), 42 deletions(-) diff --git a/R/RPS.R b/R/RPS.R index b2065cd..84956ac 100644 --- a/R/RPS.R +++ b/R/RPS.R @@ -48,7 +48,7 @@ #'@export RPS <- function(exp, obs, time_dim = 'sdate', memb_dim = 'member', prob_thresholds = c(1/3, 2/3), indices_for_clim = NULL, Fair = FALSE, - ncores = NULL, weights = NULL) { + ncores = NULL) { # Check inputs ## exp and obs (1) @@ -130,7 +130,7 @@ RPS <- function(exp, obs, time_dim = 'sdate', memb_dim = 'member', fun = .RPS, prob_thresholds = prob_thresholds, indices_for_clim = indices_for_clim, Fair = Fair, - ncores = ncores, weights = weights)$output1 + ncores = ncores)$output1 # Return only the mean RPS rps <- MeanDims(rps, time_dim, na.rm = FALSE) @@ -140,12 +140,12 @@ RPS <- function(exp, obs, time_dim = 'sdate', memb_dim = 'member', .RPS <- function(exp, obs, prob_thresholds = c(1/3, 2/3), - indices_for_clim = NULL, Fair = FALSE, weights = NULL) { + indices_for_clim = NULL, Fair = FALSE) { # exp: [sdate, memb] # obs: [sdate, (memb)] exp_probs <- .get_probs(data = exp, indices_for_quantiles = indices_for_clim, - prob_thresholds = prob_thresholds, weights = weights) + prob_thresholds = prob_thresholds) # exp_probs: [bin, sdate] obs_probs <- .get_probs(data = obs, indices_for_quantiles = indices_for_clim, prob_thresholds = prob_thresholds) @@ -168,7 +168,7 @@ RPS <- function(exp, obs, time_dim = 'sdate', memb_dim = 'member', return(rps) } -.get_probs <- function(data, indices_for_quantiles, prob_thresholds, weigths = NULL) { +.get_probs <- function(data, indices_for_quantiles, prob_thresholds) { # if exp: [sdate, memb] # if obs: [sdate, (memb)] @@ -176,50 +176,18 @@ RPS <- function(exp, obs, time_dim = 'sdate', memb_dim = 'member', if (length(dim(data)) == 1) dim(data) <- c(dim(data), 1) # Absolute thresholds - if (is.null(weights)){ - quantiles <- quantile(x = as.vector(data[indices_for_quantiles, ]), probs = prob_thresholds, type = 8, na.rm = TRUE) - } else { - # linear approach to compute weighted distribution thresholds. - sorted_data, sorted_weights <- .sorted_distributions(data[indices_for_quantiles, ], weights[indices_for_quantiles, ]) - # approach 1 (interpolate from discrete cumulative weights distribution. NOT type 8) - cumulative_weights <- cumsum(sorted_weights) - 0.5 * sorted_weights - cumulative_weights <- cumulative_weights - cumulative_weights[1] # fix the 0 - cumulative_weights <- cumulative_weights / cumulative_weights[length(cumulative_weights)] # fix the 1 - quantiles <- interp1(cumulative_weights, sorted_data, c(1/3,2/3), "cubic") - # approach 2 (interpolate from the polynomial fit of the cumulative weights distribution) - fit_model <- lm(cumsum(sorted_weights) ~ poly(sorted_data,10)) # order 10, but we should think what order we want to use - predicted.intervals <- predict(fit_model,data.frame(x=sorted_data)) - quantiles <- interp1(predicted.intervals, sorted_data, c(1/3,2/3), "cubic") - } + quantiles <- quantile(x = as.vector(data[indices_for_quantiles, ]), probs = prob_thresholds, type = 8, na.rm = TRUE) + # Probabilities probs <- array(dim = c(bin = length(quantiles) + 1, dim(data)[1])) # [bin, sdate] for (i_time in 1:dim(data)[1]) { - if (is.null(weights)){ - if (anyNA(data[i_time, ])) { - probs[, i_time] <- rep(NA, dim = length(quantiles) + 1) - } else { - probs[, i_time] <- colMeans(easyVerification::convert2prob(data[i_time, ], threshold = quantiles)) - } + if (anyNA(data[i_time, ])) { + probs[, i_time] <- rep(NA, dim = length(quantiles) + 1) } else { - sorted_data, sorted_weights <- .sorted_distributions(data[i_time, ], weights[i_time, ]) - - fit_model <- lm(cumsum(sorted_weights) ~ poly(sorted_data,10)) - predicted.intervals <- predict(fit_model,data.frame(x=sorted_data)) - integrated_probs <- interp1(sorted_data, predicted.intervals, quantiles, "cubic") - probs[, i_time] <- c(integrated_probs[1], integrated_probs[2]-integrated_probs[1], 1-integrated_probs[2]) + probs[, i_time] <- colMeans(easyVerification::convert2prob(data[i_time, ], threshold = quantiles)) } - } return(probs) } -.sorted_distributions <- function(data_vector, weights_vector){ - weights_vector <- as.vector(weights_vector) - data_vector <- as.vector(data_vector) - weights_vector <- weights_vector / sum(weights_vector) # normalize to 1 - sorter <- order(data_vector) - sorted_data <- data_vector[sorter] - sorted_weights <- weights_vector[sorter] - return(sorted_data, sorted_weights) -} \ No newline at end of file -- GitLab From 2c9ca69f6859d7cc77fb5035f346733451b3c1c2 Mon Sep 17 00:00:00 2001 From: jcos Date: Fri, 20 May 2022 15:59:28 +0200 Subject: [PATCH 65/83] basic workflow --- R/RPS.R | 84 ++++++++++++++++++++++++++++++++++++++++++++++++--------- 1 file changed, 71 insertions(+), 13 deletions(-) diff --git a/R/RPS.R b/R/RPS.R index 84956ac..a5d54d7 100644 --- a/R/RPS.R +++ b/R/RPS.R @@ -48,7 +48,7 @@ #'@export RPS <- function(exp, obs, time_dim = 'sdate', memb_dim = 'member', prob_thresholds = c(1/3, 2/3), indices_for_clim = NULL, Fair = FALSE, - ncores = NULL) { + ncores = NULL, weights = NULL) { # Check inputs ## exp and obs (1) @@ -130,8 +130,8 @@ RPS <- function(exp, obs, time_dim = 'sdate', memb_dim = 'member', fun = .RPS, prob_thresholds = prob_thresholds, indices_for_clim = indices_for_clim, Fair = Fair, - ncores = ncores)$output1 - + ncores = ncores, weights = weights)$output1 + browser() # Return only the mean RPS rps <- MeanDims(rps, time_dim, na.rm = FALSE) @@ -140,12 +140,10 @@ RPS <- function(exp, obs, time_dim = 'sdate', memb_dim = 'member', .RPS <- function(exp, obs, prob_thresholds = c(1/3, 2/3), - indices_for_clim = NULL, Fair = FALSE) { + indices_for_clim = NULL, Fair = FALSE, weights = NULL) { # exp: [sdate, memb] # obs: [sdate, (memb)] - - exp_probs <- .get_probs(data = exp, indices_for_quantiles = indices_for_clim, - prob_thresholds = prob_thresholds) + exp_probs <- .get_probs(data = exp, indices_for_quantiles = indices_for_clim, prob_thresholds = prob_thresholds, weights = weights) # exp_probs: [bin, sdate] obs_probs <- .get_probs(data = obs, indices_for_quantiles = indices_for_clim, prob_thresholds = prob_thresholds) @@ -168,7 +166,7 @@ RPS <- function(exp, obs, time_dim = 'sdate', memb_dim = 'member', return(rps) } -.get_probs <- function(data, indices_for_quantiles, prob_thresholds) { +.get_probs <- function(data, indices_for_quantiles, prob_thresholds, weights = NULL) { # if exp: [sdate, memb] # if obs: [sdate, (memb)] @@ -176,18 +174,78 @@ RPS <- function(exp, obs, time_dim = 'sdate', memb_dim = 'member', if (length(dim(data)) == 1) dim(data) <- c(dim(data), 1) # Absolute thresholds - quantiles <- quantile(x = as.vector(data[indices_for_quantiles, ]), probs = prob_thresholds, type = 8, na.rm = TRUE) - + if (is.null(weights)){ + quantiles <- quantile(x = as.vector(data[indices_for_quantiles, ]), probs = prob_thresholds, type = 8, na.rm = TRUE) + } else { + # linear approach to compute weighted distribution thresholds. + weights <- s2dv::Reorder(data = weights, order = names(dim(data))) # move to check or in .rps ******* + sorted_arrays <- .sorted_distributions(data[indices_for_quantiles, ], weights[indices_for_quantiles, ]) + sorted_data <- sorted_arrays$data + sorted_weights <- sorted_arrays$weights + # approach 1 (interpolate from discrete cumulative weights distribution. NOT type 8) + # function vvvvvvvvvvvvvvvvvvv + # cumulative_weights <- cumsum(sorted_weights) - 0.5 * sorted_weights + # cumulative_weights <- cumulative_weights - cumulative_weights[1] # fix the 0 + # cumulative_weights <- cumulative_weights / cumulative_weights[length(cumulative_weights)] # fix the 1 + # # function ^^^^^^^^^^^^^^^^^^^ + # quantiles <- interp1(cumulative_weights, sorted_data, probs, "cubic") # library(pracma) + # approach 2 (interpolate from the polynomial fit of the cumulative weights distribution) + order <- 5 + fit_model <- lm(sorted_data ~ poly(cumsum(sorted_weights), order, raw=TRUE)) # order 10, but we should think what order we want to use + quantiles <- prob_thresholds*0 # make a list of zeros the length of prob_thresholds + for (i in 1:(order+1)) {quantiles <- quantiles+fit_model$coefficients[i]*prob_thresholds^(i-1)} + } # Probabilities probs <- array(dim = c(bin = length(quantiles) + 1, dim(data)[1])) # [bin, sdate] for (i_time in 1:dim(data)[1]) { - if (anyNA(data[i_time, ])) { - probs[, i_time] <- rep(NA, dim = length(quantiles) + 1) + if (is.null(weights)){ + if (anyNA(data[i_time, ])) { + probs[, i_time] <- rep(NA, dim = length(quantiles) + 1) + } else { + probs[, i_time] <- colMeans(easyVerification::convert2prob(data[i_time, ], threshold = quantiles)) + } } else { - probs[, i_time] <- colMeans(easyVerification::convert2prob(data[i_time, ], threshold = quantiles)) + sorted_arrays <- .sorted_distributions(data[i_time, ], weights[i_time, ]) + sorted_data <- sorted_arrays$data + sorted_weights <- sorted_arrays$weights + # approach 1. interpolate + cumulative_weights <- cumsum(sorted_weights) - 0.5 * sorted_weights + cumulative_weights <- cumulative_weights - cumulative_weights[1] # fix the 0 + cumulative_weights <- cumulative_weights / cumulative_weights[length(cumulative_weights)] # fix the 1 + # Solve issues for quantiles outside the sorted_data range: + xtra_length <- 10 + if (min(quantiles)max(sorted_data)){ + xtra <- seq(max(sorted_data), max(quantiles), length.out=xtra_length) + sorted_data <- c(sorted_data,xtra) + cumulative_weights <- c(cumulative_weights,numeric(xtra_length)) + } + integrated_probs <- interp1(sorted_data, cumulative_weights, quantiles, "cubic") + probs[, i_time] <- c(integrated_probs[1], integrated_probs[2]-integrated_probs[1], 1-integrated_probs[2]) + if (min(probs[, i_time])<0) {print(paste0("ERROR negative probability in i_time=",i_time))} ######################## checks + if (max(probs[, i_time])>1) {print(paste0("ERROR >1 probability in i_time=",i_time))} ######################## checks + + # approach 2. might give erroneous results for quantiles that fall outside the data range. Try normal distro + # order <- 5 + # fit_model <- lm(cumsum(sorted_weights) ~ poly(sorted_data, order, raw=TRUE)) # order 10, but we should think what order we want to use + # integrated_probs <- quantiles*0 + # for (i in 1:(order+1)) {integrated_probs <- integrated_probs+fit_model$coefficients[i]*quantiles^(i-1)} + # probs[, i_time] <- c(integrated_probs[1], integrated_probs[2]-integrated_probs[1], 1-integrated_probs[2]) } + } return(probs) } +.sorted_distributions <- function(data_vector, weights_vector){ + weights_vector <- as.vector(weights_vector) + data_vector <- as.vector(data_vector) + weights_vector <- weights_vector / sum(weights_vector) # normalize to 1 + sorter <- order(data_vector) + return(list("data" = data_vector[sorter], "weights" = weights_vector[sorter])) +} \ No newline at end of file -- GitLab From 75592c3620c065daa99c05a97f632fc9a118b450 Mon Sep 17 00:00:00 2001 From: aho Date: Mon, 23 May 2022 15:48:12 +0200 Subject: [PATCH 66/83] Fix bugs for R/4.x.x and add Load unit test --- R/Load.R | 18 ++--- R/Utils.R | 12 ++-- tests/testthat/test-Load.R | 133 +++++++++++++++++++++++++++++++++++++ 3 files changed, 148 insertions(+), 15 deletions(-) create mode 100644 tests/testthat/test-Load.R diff --git a/R/Load.R b/R/Load.R index cdb30be..3c3aa8e 100644 --- a/R/Load.R +++ b/R/Load.R @@ -1878,11 +1878,11 @@ Load <- function(var, exp = NULL, obs = NULL, sdates, nmember = NULL, } # We calculate the % of total progress that each work piece represents so # that progress bar can be updated properly - exp_work_piece_percent <- prod(dim_exp) / (prod(dim_obs) + prod(dim_exp)) - obs_work_piece_percent <- prod(dim_obs) / (prod(dim_obs) + prod(dim_exp)) + exp_work_piece_percent <- prod(unlist(dim_exp)) / (prod(unlist(dim_obs)) + prod(unlist(dim_exp))) + obs_work_piece_percent <- prod(unlist(dim_obs)) / (prod(unlist(dim_obs)) + prod(unlist(dim_exp))) # Add some important extra fields in the work pieces before sending - exp_work_pieces <- lapply(exp_work_pieces, function (x) c(x, list(dataset_type = 'exp', dims = dim_exp, out_pointer = pointer_var_exp)))###, progress_amount = exp_work_piece_progress))) - obs_work_pieces <- lapply(obs_work_pieces, function (x) c(x, list(dataset_type = 'obs', dims = dim_obs, out_pointer = pointer_var_obs)))###, progress_amount = obs_work_piece_progress))) + exp_work_pieces <- lapply(exp_work_pieces, function (x) c(x, list(dataset_type = 'exp', dims = unlist(dim_exp), out_pointer = pointer_var_exp)))###, progress_amount = exp_work_piece_progress))) + obs_work_pieces <- lapply(obs_work_pieces, function (x) c(x, list(dataset_type = 'obs', dims = unlist(dim_obs), out_pointer = pointer_var_obs)))###, progress_amount = obs_work_piece_progress))) work_pieces <- c(exp_work_pieces, obs_work_pieces) # Calculate the progress %s that will be displayed and assign them to the # appropriate work pieces @@ -1953,15 +1953,15 @@ Load <- function(var, exp = NULL, obs = NULL, sdates, nmember = NULL, bytes_obs <- 0 obs_dim_sizes <- '0' } else { - bytes_obs <- prod(c(dim_obs, 8)) - obs_dim_sizes <- paste(na.omit(as.vector(dim_obs[c('dataset', 'member', 'sdate', 'ftime', 'lat', 'lon')])), collapse = ' x ') + bytes_obs <- prod(c(unlist(dim_obs), 8)) + obs_dim_sizes <- paste(na.omit(as.vector(unlist(dim_obs)[c('dataset', 'member', 'sdate', 'ftime', 'lat', 'lon')])), collapse = ' x ') } if (length(dim_exp) == 0) { bytes_exp <- 0 exp_dim_sizes <- '0' } else { - bytes_exp <- prod(c(dim_exp, 8)) - exp_dim_sizes <- paste(na.omit(as.vector(dim_exp[c('dataset', 'member', 'sdate', 'ftime', 'lat', 'lon')])), collapse = ' x ') + bytes_exp <- prod(c(unlist(dim_exp), 8)) + exp_dim_sizes <- paste(na.omit(as.vector(unlist(dim_exp)[c('dataset', 'member', 'sdate', 'ftime', 'lat', 'lon')])), collapse = ' x ') } .message(paste("Total size of requested data: ", bytes_obs + bytes_exp, "bytes.")) .message(paste("- Experimental data: (", exp_dim_sizes, ") x 8 bytes =", bytes_exp, "bytes."), indent = 2) @@ -2146,7 +2146,7 @@ Load <- function(var, exp = NULL, obs = NULL, sdates, nmember = NULL, old_dims <- dim_exp dim_exp <- dim_exp[dim_reorder] mod_data <- - aperm(array(bigmemory::as.matrix(var_exp), dim = old_dims), dim_reorder) + aperm(array(bigmemory::as.matrix(var_exp), dim = unlist(old_dims)), dim_reorder) attr(mod_data, 'dimensions') <- names(dim_exp) names(dim(mod_data)) <- names(dim_exp) number_ftime <- dim_exp[["ftime"]] diff --git a/R/Utils.R b/R/Utils.R index 1f39694..c136914 100644 --- a/R/Utils.R +++ b/R/Utils.R @@ -349,11 +349,11 @@ if (!is.null(work_piece[['progress_amount']])) { cat("\n") } - cat(paste0("! Warning: the dataset with index ", + .warning(paste0("The dataset with index ", tail(work_piece[['indices']], 1), " in '", work_piece[['dataset_type']], "' doesn't start at longitude 0 and will be re-interpolated in order to align its longitudes with the standard CDO grids definable with the names 'tgrid' or 'rx', which are by definition starting at the longitude 0.\n")) if (!is.null(mask)) { - cat(paste0("! Warning: a mask was provided for the dataset with index ", + .warning(paste0("A mask was provided for the dataset with index ", tail(work_piece[['indices']], 1), " in '", work_piece[['dataset_type']], "'. This dataset has been re-interpolated to align its longitudes to start at 0. You must re-interpolate the corresponding mask to align its longitudes to start at 0 as well, if you haven't done so yet. Running cdo remapcon,", common_grid_name, " original_mask_file.nc new_mask_file.nc will fix it.\n")) } @@ -363,7 +363,7 @@ cat("\n") } if (!explore_dims) { - cat(paste0("! Warning: the dataset with index ", tail(work_piece[['indices']], 1), + .warning(paste0("The dataset with index ", tail(work_piece[['indices']], 1), " in '", work_piece[['dataset_type']], "' is originally on ", "a grid coarser than the common grid and it has been ", "extrapolated. Check the results carefully. It is ", @@ -836,7 +836,7 @@ if (!all(dim_matches == sort(dim_matches))) { if (!found_disordered_dims && rev(work_piece[['indices']])[2] == 1 && rev(work_piece[['indices']])[3] == 1) { found_disordered_dims <- TRUE - cat(paste0("! Warning: the dimensions for the variable ", namevar, " in the files of the experiment with index ", tail(work_piece[['indices']], 1), " are not in the optimal order for loading with Load(). The optimal order would be '", paste(expected_dims, collapse = ', '), "'. One of the files of the dataset is stored in ", filename)) + .warning(paste0("The dimensions for the variable ", namevar, " in the files of the experiment with index ", tail(work_piece[['indices']], 1), " are not in the optimal order for loading with Load(). The optimal order would be '", paste(expected_dims, collapse = ', '), "'. One of the files of the dataset is stored in ", filename)) } tmp <- aperm(tmp, dim_matches) } @@ -879,13 +879,13 @@ } if (output == 'areave' || output == 'lon') { - weights <- InsertDim(cos(final_lats * pi / 180), 1, length(final_lons)) + weights <- InsertDim(cos(final_lats * pi / 180), 1, length(final_lons), name = 'lon') weights[which(is.na(x))] <- NA if (output == 'areave') { weights <- weights / mean(weights, na.rm = TRUE) mean(x * weights, na.rm = TRUE) } else { - weights <- weights / InsertDim(MeanDims(weights, 2, na.rm = TRUE), 2, length(final_lats)) + weights <- weights / InsertDim(MeanDims(weights, 2, na.rm = TRUE), 2, length(final_lats), name = 'lat') MeanDims(x * weights, 2, na.rm = TRUE) } } else if (output == 'lat') { diff --git a/tests/testthat/test-Load.R b/tests/testthat/test-Load.R new file mode 100644 index 0000000..6a99873 --- /dev/null +++ b/tests/testthat/test-Load.R @@ -0,0 +1,133 @@ +context("s2dv::Load tests") + +############################################## + +test_that("1-1.", { + +path <- "/esarchive/exp/meteofrance/system6c3s/$STORE_FREQ$_mean/$VAR_NAME$_f6h/$VAR_NAME$_$START_DATE$.nc" +dateseq <- c("19930501", "19940501", "19950501") +suppressWarnings( +res1 <- Load(var = 'sfcWind', + exp = list(list(name = 'meteofrance/system6c3s', path = path)), + obs = 'erainterim', + sdates = dateseq, leadtimemin = 2, leadtimemax = 3, + lonmin = -9, lonmax = 1.5, latmin = 75, latmax = 79.5, + storefreq = "daily", sampleperiod = 1, nmember = 3, + output = "lonlat", method = "bilinear", + grid = "r360x180", nprocs = 1) +) +#suppressWarnings( +#res2 <- Load(var = 'sfcWind', +# exp = list(list(name = 'meteofrance/system6c3s', path = path)), +# obs = 'erainterim', +# sdates = dateseq, leadtimemin = 2, leadtimemax = 3, +# lonmin = -9, lonmax = 1.5, latmin = 75, latmax = 79.5, +# storefreq = "daily", sampleperiod = 1, nmember = 3, +# output = "lonlat", method = "bilinear", +# grid = "r360x180") +#) +#expect_equal( +#res1$mod, res2$mod +#) +#expect_equal( +#res1$obs, res2$obs +#) +#expect_equal( +#names(res1), names(res2) +#) +expect_equal( +dim(res1$mod), +c(dataset = 1, member = 3, sdate = 3, ftime = 2, lat = 5, lon = 11) +) +expect_equal( +dim(res1$obs), +c(dataset = 1, member = 1, sdate = 3, ftime = 2, lat = 5, lon = 11) +) +expect_equal( +as.vector(drop(res1$mod)[1, , 2, 3, 4]), +c(2.0504, 2.2746, 3.4189), +tolerance = 0.0001 +) +expect_equal( +as.vector(drop(res1$obs)[, 2, 3, 4]), +c(5.9923, 2.7927, 2.5408), +tolerance = 0.0001 +) +expect_equal( +mean(res1$mod, na.rm = T), +6.007487, +tolerance = 0.0001 +) +expect_equal( +mean(res1$obs, na.rm = T), +5.043017, +tolerance = 0.0001 +) + +}) + + +test_that("1-2.", { + +path <- "/esarchive/exp/meteofrance/system6c3s/$STORE_FREQ$_mean/$VAR_NAME$_f6h/$VAR_NAME$_$START_DATE$.nc" +dateseq <- c("19930501", "19940501", "19950501") +suppressWarnings( +res1 <- Load(var = 'sfcWind', + exp = list(list(name = 'meteofrance/system6c3s', path = path)), + obs = 'erainterim', + sdates = dateseq, leadtimemin = 2, leadtimemax = 3, + lonmin = -9, lonmax = 1.5, latmin = 75, latmax = 79.5, + storefreq = "daily", sampleperiod = 1, nmember = 3, + output = "areave", + nprocs = 1) +) +#suppressWarnings( +#res2 <- Load(var = 'sfcWind', +# exp = list(list(name = 'meteofrance/system6c3s', path = path)), +# obs = 'erainterim', +# sdates = dateseq, leadtimemin = 2, leadtimemax = 3, +# lonmin = -9, lonmax = 1.5, latmin = 75, latmax = 79.5, +# storefreq = "daily", sampleperiod = 1, nmember = 3, +# output = "areave") +#) +#expect_equal( +#res1$mod, res2$mod +#) +#expect_equal( +#res1$obs, res2$obs +#) +#expect_equal( +#names(res1), names(res2) +#) +expect_equal( +dim(res1$mod), +c(dataset = 1, member = 3, sdate = 3, ftime = 2) +) +expect_equal( +dim(res1$obs), +c(dataset = 1, member = 1, sdate = 3, ftime = 2) +) +expect_equal( +as.vector(drop(res1$mod)[1, , 2]), +c(5.037364, 2.395024, 5.418090), +tolerance = 0.0001 +) +expect_equal( +as.vector(drop(res1$obs)[, 2]), +c(8.728424, 2.931754, 3.918583), +tolerance = 0.0001 +) +expect_equal( +mean(res1$mod, na.rm = T), +6.162087, +tolerance = 0.0001 +) +expect_equal( +mean(res1$obs, na.rm = T), +5.178091, +tolerance = 0.0001 +) + +}) + + -- GitLab From 7536e289fd004435716c600611bafa05273ae37b Mon Sep 17 00:00:00 2001 From: jcos Date: Thu, 26 May 2022 12:34:59 +0200 Subject: [PATCH 67/83] switch to approx() and use it in the quantiles and probs --- R/RPS.R | 105 ++++++++++++++++++++++++++++++-------------------------- 1 file changed, 56 insertions(+), 49 deletions(-) diff --git a/R/RPS.R b/R/RPS.R index a5d54d7..e323936 100644 --- a/R/RPS.R +++ b/R/RPS.R @@ -131,7 +131,7 @@ RPS <- function(exp, obs, time_dim = 'sdate', memb_dim = 'member', prob_thresholds = prob_thresholds, indices_for_clim = indices_for_clim, Fair = Fair, ncores = ncores, weights = weights)$output1 - browser() + # browser() # Return only the mean RPS rps <- MeanDims(rps, time_dim, na.rm = FALSE) @@ -178,67 +178,70 @@ RPS <- function(exp, obs, time_dim = 'sdate', memb_dim = 'member', quantiles <- quantile(x = as.vector(data[indices_for_quantiles, ]), probs = prob_thresholds, type = 8, na.rm = TRUE) } else { # linear approach to compute weighted distribution thresholds. + # browser() + #weights<-as.array(weights) weights <- s2dv::Reorder(data = weights, order = names(dim(data))) # move to check or in .rps ******* + # is there a better way of doing this: vvvvvvvvvv sorted_arrays <- .sorted_distributions(data[indices_for_quantiles, ], weights[indices_for_quantiles, ]) sorted_data <- sorted_arrays$data - sorted_weights <- sorted_arrays$weights - # approach 1 (interpolate from discrete cumulative weights distribution. NOT type 8) - # function vvvvvvvvvvvvvvvvvvv - # cumulative_weights <- cumsum(sorted_weights) - 0.5 * sorted_weights - # cumulative_weights <- cumulative_weights - cumulative_weights[1] # fix the 0 - # cumulative_weights <- cumulative_weights / cumulative_weights[length(cumulative_weights)] # fix the 1 - # # function ^^^^^^^^^^^^^^^^^^^ - # quantiles <- interp1(cumulative_weights, sorted_data, probs, "cubic") # library(pracma) + cumulative_weights <- sorted_arrays$cumulative_weights + quantiles <- approx(cumulative_weights, sorted_data, prob_thresholds, "linear")$y # library(pracma) + # approach 2 (interpolate from the polynomial fit of the cumulative weights distribution) - order <- 5 - fit_model <- lm(sorted_data ~ poly(cumsum(sorted_weights), order, raw=TRUE)) # order 10, but we should think what order we want to use - quantiles <- prob_thresholds*0 # make a list of zeros the length of prob_thresholds - for (i in 1:(order+1)) {quantiles <- quantiles+fit_model$coefficients[i]*prob_thresholds^(i-1)} + # order <- 5 # should it be fixed? should it be provided? should it be computed according to the number of points available? + # fit_model <- lm(sorted_data ~ poly(cumsum(sorted_weights), order, raw=TRUE)) + # quantiles <- numeric(length(prob_thresholds)) # make a list of zeros the length of prob_thresholds + # for (i in 1:(order+1)) {quantiles <- quantiles+fit_model$coefficients[i]*prob_thresholds^(i-1)} } # Probabilities + # browser() probs <- array(dim = c(bin = length(quantiles) + 1, dim(data)[1])) # [bin, sdate] for (i_time in 1:dim(data)[1]) { - if (is.null(weights)){ - if (anyNA(data[i_time, ])) { + if (anyNA(data[i_time, ])) { probs[, i_time] <- rep(NA, dim = length(quantiles) + 1) - } else { - probs[, i_time] <- colMeans(easyVerification::convert2prob(data[i_time, ], threshold = quantiles)) - } } else { - sorted_arrays <- .sorted_distributions(data[i_time, ], weights[i_time, ]) - sorted_data <- sorted_arrays$data - sorted_weights <- sorted_arrays$weights - # approach 1. interpolate - cumulative_weights <- cumsum(sorted_weights) - 0.5 * sorted_weights - cumulative_weights <- cumulative_weights - cumulative_weights[1] # fix the 0 - cumulative_weights <- cumulative_weights / cumulative_weights[length(cumulative_weights)] # fix the 1 - # Solve issues for quantiles outside the sorted_data range: - xtra_length <- 10 - if (min(quantiles)max(sorted_data)){ - xtra <- seq(max(sorted_data), max(quantiles), length.out=xtra_length) - sorted_data <- c(sorted_data,xtra) - cumulative_weights <- c(cumulative_weights,numeric(xtra_length)) + if (is.null(weights)){ + probs[, i_time] <- colMeans(easyVerification::convert2prob(data[i_time, ], threshold = quantiles)) + print("............probs...................") + print(probs[, i_time]) + print("...............................") + } else { + # function vvvv + sorted_arrays <- .sorted_distributions(data[i_time, ], weights[i_time, ]) + sorted_data <- sorted_arrays$data + cumulative_weights <- sorted_arrays$cumulative_weights + # find any quantiles that are outside the data range + integrated_probs <- array(dim = c(bin=length(quantiles))) + for (i_quant in 1:length(quantiles)) { + if (quantiles[i_quant] < min(sorted_data)) { + integrated_probs[i_quant] = 0 # append + } else if (max(sorted_data) < quantiles[i_quant]) { + integrated_probs[i_quant] = 1 # append + } else { + integrated_probs[i_quant] = approx(sorted_data, cumulative_weights, quantiles[i_quant], "linear")$y + } + } + probs[, i_time] <- append(integrated_probs, 1) - append(0, integrated_probs) + # print("..........quantiles.........") + # print(quantiles) + # print("..........integrated probs.........") + # print(integrated_probs) + print("..........probs.........") + print(probs[, i_time]) + print("..............................") + print("..............................") + if (min(probs[, i_time])<0) {print(paste0("ERROR negative probability in i_time=",i_time))} ######################## checks + if (max(probs[, i_time])>1) {print(paste0("ERROR >1 probability in i_time=",i_time))} ######################## checks + # approach 2. might give erroneous results for quantiles that fall outside the data range. Try normal distro + # order <- 5 + # fit_model <- lm(cumsum(sorted_weights) ~ poly(sorted_data, order, raw=TRUE)) # order 10, but we should think what order we want to use + # integrated_probs <- quantiles*0 + # for (i in 1:(order+1)) {integrated_probs <- integrated_probs+fit_model$coefficients[i]*quantiles^(i-1)} + # probs[, i_time] <- c(integrated_probs[1], integrated_probs[2]-integrated_probs[1], 1-integrated_probs[2]) } - integrated_probs <- interp1(sorted_data, cumulative_weights, quantiles, "cubic") - probs[, i_time] <- c(integrated_probs[1], integrated_probs[2]-integrated_probs[1], 1-integrated_probs[2]) - if (min(probs[, i_time])<0) {print(paste0("ERROR negative probability in i_time=",i_time))} ######################## checks - if (max(probs[, i_time])>1) {print(paste0("ERROR >1 probability in i_time=",i_time))} ######################## checks - - # approach 2. might give erroneous results for quantiles that fall outside the data range. Try normal distro - # order <- 5 - # fit_model <- lm(cumsum(sorted_weights) ~ poly(sorted_data, order, raw=TRUE)) # order 10, but we should think what order we want to use - # integrated_probs <- quantiles*0 - # for (i in 1:(order+1)) {integrated_probs <- integrated_probs+fit_model$coefficients[i]*quantiles^(i-1)} - # probs[, i_time] <- c(integrated_probs[1], integrated_probs[2]-integrated_probs[1], 1-integrated_probs[2]) } } - return(probs) } @@ -247,5 +250,9 @@ RPS <- function(exp, obs, time_dim = 'sdate', memb_dim = 'member', data_vector <- as.vector(data_vector) weights_vector <- weights_vector / sum(weights_vector) # normalize to 1 sorter <- order(data_vector) - return(list("data" = data_vector[sorter], "weights" = weights_vector[sorter])) + sorted_weights <- weights_vector[sorter] + cumulative_weights <- cumsum(sorted_weights) - 0.5 * sorted_weights + cumulative_weights <- cumulative_weights - cumulative_weights[1] # fix the 0 + cumulative_weights <- cumulative_weights / cumulative_weights[length(cumulative_weights)] # fix the 1 + return(list("data" = data_vector[sorter], "cumulative_weights" = cumulative_weights)) } \ No newline at end of file -- GitLab From 788eb44996b6de7303b97e30bd41679b56ef6dd0 Mon Sep 17 00:00:00 2001 From: aho Date: Mon, 30 May 2022 15:34:45 +0200 Subject: [PATCH 68/83] Add one more test --- tests/testthat/test-Load.R | 49 ++++++++++++++++++++++++++++++++++++++ 1 file changed, 49 insertions(+) diff --git a/tests/testthat/test-Load.R b/tests/testthat/test-Load.R index 6a99873..8266139 100644 --- a/tests/testthat/test-Load.R +++ b/tests/testthat/test-Load.R @@ -131,3 +131,52 @@ tolerance = 0.0001 }) +test_that("1-3.", { + + ecearth <- list(name = 'm04o', + path = file.path('/esarchive/exp/ecearth', + '/$EXP_NAME$/monthly_mean', + '/$VAR_NAME$_f6h/$VAR_NAME$_$START_DATE$.nc')) + + ecmwf <- list(name = 'system5c3s', + path = file.path('/esarchive/exp/ecmwf', + '/$EXP_NAME$/monthly_mean', + '/$VAR_NAME$_f6h/$VAR_NAME$_$START_DATE$.nc')) + + era5 <- list(name = 'era5', + path = file.path('/esarchive/recon/ecmwf', + '$OBS_NAME$/monthly_mean', + '$VAR_NAME$_f1h/$VAR_NAME$_$YEAR$$MONTH$.nc')) + + startDates <- format(seq(as.POSIXct('1999-01-01'), as.POSIXct('1999-12-01'), by = 'months'), '%Y%m%d') + time_length <- 2 + +suppressWarnings( + data <- Load(var = 'psl', + exp = list(ecearth, ecmwf), + obs = list(era5), + sdates = startDates, + storefreq = "monthly", + leadtimemax = time_length, + output = 'lonlat', + lonmin = -2, lonmax = 2, + latmin = -5, latmax = 5, + nprocs = 1) +) + +expect_equal( +dim(data$mod), +c(dataset = 2, member = 10, sdate = 12, ftime = 2, lat = 14, lon = 5) +) +expect_equal( +dim(data$obs), +c(dataset = 1, member = 1, sdate = 12, ftime = 2, lat = 14, lon = 5) +) +expect_equal( +as.vector(data$mod[1, 1, , 2, 2, 3]), +c(rep(NA, 4), 101250, rep(NA, 5), 100940, NA), +tolerance = 0.0001 +) + + +}) -- GitLab From 5001796129269f9ceb03ff5f03b460d2446ea695 Mon Sep 17 00:00:00 2001 From: aho Date: Wed, 1 Jun 2022 17:16:30 +0200 Subject: [PATCH 69/83] Map plotting are flexible with any longitude range --- R/PlotEquiMap.R | 30 ++++++++---------------------- 1 file changed, 8 insertions(+), 22 deletions(-) diff --git a/R/PlotEquiMap.R b/R/PlotEquiMap.R index 9e1121a..2aadda7 100644 --- a/R/PlotEquiMap.R +++ b/R/PlotEquiMap.R @@ -758,11 +758,6 @@ PlotEquiMap <- function(var, lon, lat, varu = NULL, varv = NULL, latmax <- ceiling(max(lat) / 10) * 10 lonmin <- floor(min(lon) / 10) * 10 lonmax <- ceiling(max(lon) / 10) * 10 - if (min(lon) < 0) { - continents <- 'world' - } else { - continents <- 'world2' - } # # Plotting the map @@ -947,24 +942,15 @@ PlotEquiMap <- function(var, lon, lat, varu = NULL, varv = NULL, # Plotting continents # ~~~~~~~~~~~~~~~~~~~~~ # - # maps::map has the global map range [0, 360] or [-180, 180]. - # Set xlim so lon = 0 won't show a straight line when lon = [0, 359]. - # NOTE: It works except Antartic area. Don't know why. ylim is also set - # but it doesn't work. - if (continents == 'world') { # [-180, 180] - xlim_conti <- c(-179.99, 179.99) - } else { # [0, 360] - xlim_conti <- c(0.01, 359.99) - } + wrap_vec <- c(lon[1], lon[1] + 360) old_lwd <- par('lwd') par(lwd = coast_width) # If [0, 360], use GEOmap; if [-180, 180], use maps::map # UPDATE: Use maps::map for both cases. The difference between GEOmap and # maps is trivial. The only thing we can see for now is that # GEOmap has better lakes. - coast <- maps::map(continents, interior = country.borders, wrap = TRUE, - xlim = xlim_conti, ylim = c(-89.99, 89.99), - fill = filled.continents, add = TRUE, plot = FALSE) + coast <- maps::map(interior = country.borders, wrap = wrap_vec, + fill = filled.continents, add = TRUE, plot = FALSE) if (filled.continents) { polygon(coast, col = continent_color, border = coast_color, lwd = coast_width) @@ -972,7 +958,7 @@ PlotEquiMap <- function(var, lon, lat, varu = NULL, varv = NULL, lines(coast, col = coast_color, lwd = coast_width) } if (!is.null(lake_color)) { - maps::map('lakes', add = TRUE, fill = filled.continents, col = lake_color) + maps::map('lakes', add = TRUE, wrap = wrap_vec, fill = filled.continents, col = lake_color) } par(lwd = old_lwd) @@ -981,8 +967,8 @@ PlotEquiMap <- function(var, lon, lat, varu = NULL, varv = NULL, old_lwd <- par('lwd') par(lwd = coast_width) - outline <- maps::map(continents, fill = T, plot = FALSE) # must be fill = T - xbox <- xlim_conti + c(-2, 2) + outline <- maps::map(wrap = wrap_vec, fill = T, plot = FALSE) # must be fill = T + xbox <- wrap_vec + c(-2, 2) ybox <- c(-92, 92) outline$x <- c(outline$x, NA, c(xbox, rev(xbox), xbox[1])) outline$y <- c(outline$y, NA, rep(ybox, each = 2), ybox[1]) @@ -992,9 +978,9 @@ PlotEquiMap <- function(var, lon, lat, varu = NULL, varv = NULL, } # Plot shapefile + #NOTE: the longitude range cannot cut shapefile range, or not all the shapefile will be plotted. if (!is.null(shapefile)) { - maps::map(shape, interior = country.borders, #wrap = TRUE, - xlim = xlim_conti, ylim = c(-89.99, 89.99), + maps::map(shape, interior = country.borders, #wrap = wrap_vec, fill = filled.continents, add = TRUE, plot = TRUE, lwd = shapefile_lwd, col = shapefile_color) } -- GitLab From 281ff94a074846ace6d65a5ab2c1c06d1b54858d Mon Sep 17 00:00:00 2001 From: jcos Date: Wed, 8 Jun 2022 16:39:54 +0200 Subject: [PATCH 70/83] cleanup --- R/RPS.R | 39 ++++++--------------------------------- 1 file changed, 6 insertions(+), 33 deletions(-) diff --git a/R/RPS.R b/R/RPS.R index e323936..e0e19b4 100644 --- a/R/RPS.R +++ b/R/RPS.R @@ -122,7 +122,6 @@ RPS <- function(exp, obs, time_dim = 'sdate', memb_dim = 'member', } else { target_dims_obs <- c(time_dim, memb_dim) } - rps <- Apply(data = list(exp = exp, obs = obs), target_dims = list(exp = c(time_dim, memb_dim), obs = target_dims_obs), @@ -177,24 +176,13 @@ RPS <- function(exp, obs, time_dim = 'sdate', memb_dim = 'member', if (is.null(weights)){ quantiles <- quantile(x = as.vector(data[indices_for_quantiles, ]), probs = prob_thresholds, type = 8, na.rm = TRUE) } else { - # linear approach to compute weighted distribution thresholds. - # browser() - #weights<-as.array(weights) weights <- s2dv::Reorder(data = weights, order = names(dim(data))) # move to check or in .rps ******* - # is there a better way of doing this: vvvvvvvvvv sorted_arrays <- .sorted_distributions(data[indices_for_quantiles, ], weights[indices_for_quantiles, ]) sorted_data <- sorted_arrays$data cumulative_weights <- sorted_arrays$cumulative_weights - quantiles <- approx(cumulative_weights, sorted_data, prob_thresholds, "linear")$y # library(pracma) - - # approach 2 (interpolate from the polynomial fit of the cumulative weights distribution) - # order <- 5 # should it be fixed? should it be provided? should it be computed according to the number of points available? - # fit_model <- lm(sorted_data ~ poly(cumsum(sorted_weights), order, raw=TRUE)) - # quantiles <- numeric(length(prob_thresholds)) # make a list of zeros the length of prob_thresholds - # for (i in 1:(order+1)) {quantiles <- quantiles+fit_model$coefficients[i]*prob_thresholds^(i-1)} + quantiles <- approx(cumulative_weights, sorted_data, prob_thresholds, "linear")$y } # Probabilities - # browser() probs <- array(dim = c(bin = length(quantiles) + 1, dim(data)[1])) # [bin, sdate] for (i_time in 1:dim(data)[1]) { if (anyNA(data[i_time, ])) { @@ -202,42 +190,27 @@ RPS <- function(exp, obs, time_dim = 'sdate', memb_dim = 'member', } else { if (is.null(weights)){ probs[, i_time] <- colMeans(easyVerification::convert2prob(data[i_time, ], threshold = quantiles)) - print("............probs...................") - print(probs[, i_time]) - print("...............................") } else { - # function vvvv sorted_arrays <- .sorted_distributions(data[i_time, ], weights[i_time, ]) sorted_data <- sorted_arrays$data cumulative_weights <- sorted_arrays$cumulative_weights # find any quantiles that are outside the data range integrated_probs <- array(dim = c(bin=length(quantiles))) for (i_quant in 1:length(quantiles)) { + # for thresholds falling under the distribution if (quantiles[i_quant] < min(sorted_data)) { - integrated_probs[i_quant] = 0 # append + integrated_probs[i_quant] = 0 + # for thresholds falling over the distribution } else if (max(sorted_data) < quantiles[i_quant]) { - integrated_probs[i_quant] = 1 # append + integrated_probs[i_quant] = 1 } else { integrated_probs[i_quant] = approx(sorted_data, cumulative_weights, quantiles[i_quant], "linear")$y } } probs[, i_time] <- append(integrated_probs, 1) - append(0, integrated_probs) - # print("..........quantiles.........") - # print(quantiles) - # print("..........integrated probs.........") - # print(integrated_probs) - print("..........probs.........") - print(probs[, i_time]) - print("..............................") - print("..............................") + # This shouldn't happen, idk if this cehck is needed then if (min(probs[, i_time])<0) {print(paste0("ERROR negative probability in i_time=",i_time))} ######################## checks if (max(probs[, i_time])>1) {print(paste0("ERROR >1 probability in i_time=",i_time))} ######################## checks - # approach 2. might give erroneous results for quantiles that fall outside the data range. Try normal distro - # order <- 5 - # fit_model <- lm(cumsum(sorted_weights) ~ poly(sorted_data, order, raw=TRUE)) # order 10, but we should think what order we want to use - # integrated_probs <- quantiles*0 - # for (i in 1:(order+1)) {integrated_probs <- integrated_probs+fit_model$coefficients[i]*quantiles^(i-1)} - # probs[, i_time] <- c(integrated_probs[1], integrated_probs[2]-integrated_probs[1], 1-integrated_probs[2]) } } -- GitLab From 4f878edcb99f1f23546624169a8b083e4a8c6002 Mon Sep 17 00:00:00 2001 From: aho Date: Fri, 10 Jun 2022 13:47:53 +0200 Subject: [PATCH 71/83] Improve error message to facilitate xlabels correction. --- R/PlotEquiMap.R | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/R/PlotEquiMap.R b/R/PlotEquiMap.R index 2aadda7..ee10ec3 100644 --- a/R/PlotEquiMap.R +++ b/R/PlotEquiMap.R @@ -790,7 +790,7 @@ PlotEquiMap <- function(var, lon, lat, varu = NULL, varv = NULL, ypos <- seq(latmin, latmax, intylat) + ylatshft if (length(ypos) != length(ylabels)) { stop(paste0("Parameter 'ylabels' must have the same length as the latitude ", - "vector spaced by 'intylat'.")) + "vector spaced by 'intylat' (length = ", length(ypos), ").")) } ylabs <- ylabels } else { @@ -811,7 +811,7 @@ PlotEquiMap <- function(var, lon, lat, varu = NULL, varv = NULL, xpos <- seq(lonmin, lonmax, intxlon) + xlonshft if (length(xpos) != length(xlabels)) { stop(paste0("Parameter 'xlabels' must have the same length as the longitude ", - "vector spaced by 'intxlon'.")) + "vector spaced by 'intxlon' (length = ", length(xpos), ").")) } xlabs <- xlabels } else { -- GitLab From f4c5f17a6969c9fa40879cf4146f3ba0a02b5c4f Mon Sep 17 00:00:00 2001 From: aho Date: Fri, 10 Jun 2022 17:28:52 +0200 Subject: [PATCH 72/83] Add 'weights' documentation --- R/RPS.R | 53 ++++++++++++++++++++++++-------------- R/RPSS.R | 26 +++++++++++++++---- man/RPS.Rd | 5 ++++ man/RPSS.Rd | 5 ++++ tests/testthat/test-RPS.R | 21 ++++++++++++++- tests/testthat/test-RPSS.R | 21 ++++++++++++++- 6 files changed, 105 insertions(+), 26 deletions(-) diff --git a/R/RPS.R b/R/RPS.R index e0e19b4..00d0c10 100644 --- a/R/RPS.R +++ b/R/RPS.R @@ -28,6 +28,9 @@ #'@param Fair A logical indicating whether to compute the FairRPSS (the #' potential RPSS that the forecast would have with an infinite ensemble size). #' The default value is FALSE. +#'@param weights A named two-dimensional numerical array of the weights for each +#' member and time. The dimension names should include 'memb_dim' and +#' 'time_dim'. The default value is NULL. #'@param ncores An integer indicating the number of cores to use for parallel #' computation. The default value is NULL. #' @@ -47,8 +50,8 @@ #'@importFrom easyVerification convert2prob #'@export RPS <- function(exp, obs, time_dim = 'sdate', memb_dim = 'member', - prob_thresholds = c(1/3, 2/3), indices_for_clim = NULL, Fair = FALSE, - ncores = NULL, weights = NULL) { + prob_thresholds = c(1/3, 2/3), indices_for_clim = NULL, Fair = FALSE, + weights = NULL, ncores = NULL) { # Check inputs ## exp and obs (1) @@ -106,6 +109,18 @@ RPS <- function(exp, obs, time_dim = 'sdate', memb_dim = 'member', if (!is.logical(Fair) | length(Fair) > 1) { stop("Parameter 'Fair' must be either TRUE or FALSE.") } + ## weights + if (!is.null(weights)) { + if (!is.array(weights) | !is.numeric(weights)) + stop('Parameter "weights" must be a two-dimensional numeric array.') + if (length(dim(weights)) != 2 | any(!names(dim(weights)) %in% c(memb_dim, time_dim))) + stop("Parameter 'weights' must have two dimensions with the names of memb_dim and time_dim.") + if (dim(weights)[memb_dim] != dim(exp)[memb_dim] | + dim(weights)[time_dim] != dim(exp)[time_dim]) { + stop("Parameter 'weights' must have the same dimension lengths as memb_dim and time_dim in 'exp'.") + } + weights <- Reorder(weights, c(time_dim, memb_dim)) + } ## ncores if (!is.null(ncores)) { if (!is.numeric(ncores) | ncores %% 1 != 0 | ncores <= 0 | @@ -129,7 +144,7 @@ RPS <- function(exp, obs, time_dim = 'sdate', memb_dim = 'member', fun = .RPS, prob_thresholds = prob_thresholds, indices_for_clim = indices_for_clim, Fair = Fair, - ncores = ncores, weights = weights)$output1 + weights = weights, ncores = ncores)$output1 # browser() # Return only the mean RPS rps <- MeanDims(rps, time_dim, na.rm = FALSE) @@ -145,7 +160,7 @@ RPS <- function(exp, obs, time_dim = 'sdate', memb_dim = 'member', exp_probs <- .get_probs(data = exp, indices_for_quantiles = indices_for_clim, prob_thresholds = prob_thresholds, weights = weights) # exp_probs: [bin, sdate] obs_probs <- .get_probs(data = obs, indices_for_quantiles = indices_for_clim, - prob_thresholds = prob_thresholds) + prob_thresholds = prob_thresholds, weights = NULL) # obs_probs: [bin, sdate] probs_exp_cumsum <- apply(exp_probs, 2, cumsum) @@ -173,52 +188,52 @@ RPS <- function(exp, obs, time_dim = 'sdate', memb_dim = 'member', if (length(dim(data)) == 1) dim(data) <- c(dim(data), 1) # Absolute thresholds - if (is.null(weights)){ + if (is.null(weights)) { quantiles <- quantile(x = as.vector(data[indices_for_quantiles, ]), probs = prob_thresholds, type = 8, na.rm = TRUE) } else { - weights <- s2dv::Reorder(data = weights, order = names(dim(data))) # move to check or in .rps ******* + # weights: [sdate, memb] sorted_arrays <- .sorted_distributions(data[indices_for_quantiles, ], weights[indices_for_quantiles, ]) sorted_data <- sorted_arrays$data cumulative_weights <- sorted_arrays$cumulative_weights quantiles <- approx(cumulative_weights, sorted_data, prob_thresholds, "linear")$y } + # Probabilities probs <- array(dim = c(bin = length(quantiles) + 1, dim(data)[1])) # [bin, sdate] for (i_time in 1:dim(data)[1]) { if (anyNA(data[i_time, ])) { - probs[, i_time] <- rep(NA, dim = length(quantiles) + 1) + probs[, i_time] <- rep(NA, dim = length(quantiles) + 1) } else { - if (is.null(weights)){ - probs[, i_time] <- colMeans(easyVerification::convert2prob(data[i_time, ], threshold = quantiles)) + if (is.null(weights)) { + probs[, i_time] <- colMeans(easyVerification::convert2prob(data[i_time, ], threshold = quantiles)) } else { sorted_arrays <- .sorted_distributions(data[i_time, ], weights[i_time, ]) sorted_data <- sorted_arrays$data cumulative_weights <- sorted_arrays$cumulative_weights # find any quantiles that are outside the data range - integrated_probs <- array(dim = c(bin=length(quantiles))) + integrated_probs <- array(dim = c(bin = length(quantiles))) for (i_quant in 1:length(quantiles)) { # for thresholds falling under the distribution if (quantiles[i_quant] < min(sorted_data)) { - integrated_probs[i_quant] = 0 + integrated_probs[i_quant] <- 0 # for thresholds falling over the distribution } else if (max(sorted_data) < quantiles[i_quant]) { - integrated_probs[i_quant] = 1 + integrated_probs[i_quant] <- 1 } else { - integrated_probs[i_quant] = approx(sorted_data, cumulative_weights, quantiles[i_quant], "linear")$y + integrated_probs[i_quant] <- approx(sorted_data, cumulative_weights, quantiles[i_quant], "linear")$y } } probs[, i_time] <- append(integrated_probs, 1) - append(0, integrated_probs) - # This shouldn't happen, idk if this cehck is needed then - if (min(probs[, i_time])<0) {print(paste0("ERROR negative probability in i_time=",i_time))} ######################## checks - if (max(probs[, i_time])>1) {print(paste0("ERROR >1 probability in i_time=",i_time))} ######################## checks + if (min(probs[, i_time]) < 0 | max(probs[, i_time]) > 1) { + stop(paste0("Probability in i_time = ", i_time, " is out of [0, 1].")) + } } } - } return(probs) } -.sorted_distributions <- function(data_vector, weights_vector){ +.sorted_distributions <- function(data_vector, weights_vector) { weights_vector <- as.vector(weights_vector) data_vector <- as.vector(data_vector) weights_vector <- weights_vector / sum(weights_vector) # normalize to 1 @@ -228,4 +243,4 @@ RPS <- function(exp, obs, time_dim = 'sdate', memb_dim = 'member', cumulative_weights <- cumulative_weights - cumulative_weights[1] # fix the 0 cumulative_weights <- cumulative_weights / cumulative_weights[length(cumulative_weights)] # fix the 1 return(list("data" = data_vector[sorter], "cumulative_weights" = cumulative_weights)) -} \ No newline at end of file +} diff --git a/R/RPSS.R b/R/RPSS.R index 04c3137..c4e5744 100644 --- a/R/RPSS.R +++ b/R/RPSS.R @@ -34,6 +34,9 @@ #'@param Fair A logical indicating whether to compute the FairRPSS (the #' potential RPSS that the forecast would have with an infinite ensemble size). #' The default value is FALSE. +#'@param weights A named two-dimensional numerical array of the weights for each +#' member and time. The dimension names should include 'memb_dim' and +#' 'time_dim'. The default value is NULL. #'@param ncores An integer indicating the number of cores to use for parallel #' computation. The default value is NULL. #' @@ -62,7 +65,7 @@ #'@export RPSS <- function(exp, obs, ref = NULL, time_dim = 'sdate', memb_dim = 'member', prob_thresholds = c(1/3, 2/3), indices_for_clim = NULL, Fair = FALSE, - ncores = NULL) { + weights = NULL, ncores = NULL) { # Check inputs ## exp, obs, and ref (1) @@ -135,6 +138,18 @@ RPSS <- function(exp, obs, ref = NULL, time_dim = 'sdate', memb_dim = 'member', if (!is.logical(Fair) | length(Fair) > 1) { stop("Parameter 'Fair' must be either TRUE or FALSE.") } + ## weights + if (!is.null(weights)) { + if (!is.array(weights) | !is.numeric(weights)) + stop('Parameter "weights" must be a two-dimensional numeric array.') + if (length(dim(weights)) != 2 | any(!names(dim(weights)) %in% c(memb_dim, time_dim))) + stop("Parameter 'weights' must have two dimensions with the names of memb_dim and time_dim.") + if (dim(weights)[memb_dim] != dim(exp)[memb_dim] | + dim(weights)[time_dim] != dim(exp)[time_dim]) { + stop("Parameter 'weights' must have the same dimension lengths as memb_dim and time_dim in 'exp'.") + } + weights <- Reorder(weights, c(time_dim, memb_dim)) + } ## ncores if (!is.null(ncores)) { if (!is.numeric(ncores) | ncores %% 1 != 0 | ncores <= 0 | @@ -167,25 +182,26 @@ RPSS <- function(exp, obs, ref = NULL, time_dim = 'sdate', memb_dim = 'member', fun = .RPSS, prob_thresholds = prob_thresholds, indices_for_clim = indices_for_clim, Fair = Fair, + weights = weights, ncores = ncores) return(output) } .RPSS <- function(exp, obs, ref = NULL, prob_thresholds = c(1/3, 2/3), - indices_for_clim = NULL, Fair = FALSE) { + indices_for_clim = NULL, Fair = FALSE, weights = NULL) { # exp: [sdate, memb] # obs: [sdate, (memb)] # ref: [sdate, memb] or NULL # RPS of the forecast rps_exp <- .RPS(exp = exp, obs = obs, prob_thresholds = prob_thresholds, - indices_for_clim = indices_for_clim, Fair = Fair) + indices_for_clim = indices_for_clim, Fair = Fair, weights = weights) # RPS of the reference forecast if (is.null(ref)) { ## using climatology as reference forecast obs_probs <- .get_probs(data = obs, indices_for_quantiles = indices_for_clim, - prob_thresholds = prob_thresholds) + prob_thresholds = prob_thresholds, weights = NULL) # obs_probs: [bin, sdate] clim_probs <- c(prob_thresholds[1], diff(prob_thresholds), 1 - prob_thresholds[length(prob_thresholds)]) @@ -209,7 +225,7 @@ RPSS <- function(exp, obs, ref = NULL, time_dim = 'sdate', memb_dim = 'member', } else { # use "ref" as reference forecast rps_ref <- .RPS(exp = ref, obs = obs, prob_thresholds = prob_thresholds, - indices_for_clim = indices_for_clim, Fair = Fair) + indices_for_clim = indices_for_clim, Fair = Fair, weights = weights) } # RPSS diff --git a/man/RPS.Rd b/man/RPS.Rd index d1acc02..5062671 100644 --- a/man/RPS.Rd +++ b/man/RPS.Rd @@ -12,6 +12,7 @@ RPS( prob_thresholds = c(1/3, 2/3), indices_for_clim = NULL, Fair = FALSE, + weights = NULL, ncores = NULL ) } @@ -40,6 +41,10 @@ the whole period is used. The default value is NULL.} potential RPSS that the forecast would have with an infinite ensemble size). The default value is FALSE.} +\item{weights}{A named two-dimensional numerical array of the weights for each +member and time. The dimension names should include 'memb_dim' and +'time_dim'. The default value is NULL.} + \item{ncores}{An integer indicating the number of cores to use for parallel computation. The default value is NULL.} } diff --git a/man/RPSS.Rd b/man/RPSS.Rd index 6324ea2..ca33c07 100644 --- a/man/RPSS.Rd +++ b/man/RPSS.Rd @@ -13,6 +13,7 @@ RPSS( prob_thresholds = c(1/3, 2/3), indices_for_clim = NULL, Fair = FALSE, + weights = NULL, ncores = NULL ) } @@ -47,6 +48,10 @@ the whole period is used. The default value is NULL.} potential RPSS that the forecast would have with an infinite ensemble size). The default value is FALSE.} +\item{weights}{A named two-dimensional numerical array of the weights for each +member and time. The dimension names should include 'memb_dim' and +'time_dim'. The default value is NULL.} + \item{ncores}{An integer indicating the number of cores to use for parallel computation. The default value is NULL.} } diff --git a/tests/testthat/test-RPS.R b/tests/testthat/test-RPS.R index e5a5f0c..04df1bb 100644 --- a/tests/testthat/test-RPS.R +++ b/tests/testthat/test-RPS.R @@ -7,7 +7,8 @@ set.seed(1) exp1 <- array(rnorm(60), dim = c(member = 3, sdate = 10, lat = 2)) set.seed(2) obs1 <- array(rnorm(20), dim = c(sdate = 10, lat = 2)) - +set.seed(3) +weights1 <- array(abs(rnorm(30)), dim = c(member = 3, sdate = 10)) # dat2 set.seed(1) exp2 <- array(rnorm(20), dim = c(member = 2, sdate = 10)) @@ -76,6 +77,19 @@ test_that("1. Input checks", { RPS(exp1, obs1, Fair = 1), "Parameter 'Fair' must be either TRUE or FALSE." ) + # weights + expect_error( + RPS(exp1, obs1, weights = c(0, 1)), + 'Parameter "weights" must be a two-dimensional numeric array.' + ) + expect_error( + RPS(exp1, obs1, weights = array(1, dim = c(member = 3, time = 10))), + "Parameter 'weights' must have two dimensions with the names of memb_dim and time_dim." + ) + expect_error( + RPS(exp1, obs1, weights = array(1, dim = c(member = 3, sdate = 1))), + "Parameter 'weights' must have the same dimension lengths as memb_dim and time_dim in 'exp'." + ) # ncores expect_error( RPS(exp2, obs2, ncores = 1.5), @@ -111,6 +125,11 @@ as.vector(RPS(exp1, obs1, prob_thresholds = seq(0.1, 0.9, 0.1))), c(1.600000, 1.888889), tolerance = 0.0001 ) +expect_equal( +as.vector(RPS(exp1, obs1, weights = weights1)), +c(0.3692964, 0.5346627), +tolerance = 0.0001 +) }) diff --git a/tests/testthat/test-RPSS.R b/tests/testthat/test-RPSS.R index b4c0b7a..c63add0 100644 --- a/tests/testthat/test-RPSS.R +++ b/tests/testthat/test-RPSS.R @@ -9,7 +9,8 @@ set.seed(2) obs1 <- array(rnorm(20), dim = c(sdate = 10, lat = 2)) set.seed(3) ref1 <- array(rnorm(40), dim = c(member = 2, sdate = 10, lat = 2)) - +set.seed(4) +weights1 <- array(abs(rnorm(30)), dim = c(member = 3, sdate = 10)) # dat2 set.seed(1) exp2 <- array(rnorm(20), dim = c(member = 2, sdate = 10)) @@ -87,6 +88,19 @@ test_that("1. Input checks", { RPSS(exp1, obs1, Fair = 1), "Parameter 'Fair' must be either TRUE or FALSE." ) + # weights + expect_error( + RPS(exp1, obs1, weights = c(0, 1)), + 'Parameter "weights" must be a two-dimensional numeric array.' + ) + expect_error( + RPS(exp1, obs1, weights = array(1, dim = c(member = 3, time = 10))), + "Parameter 'weights' must have two dimensions with the names of memb_dim and time_dim." + ) + expect_error( + RPS(exp1, obs1, weights = array(1, dim = c(member = 3, sdate = 1))), + "Parameter 'weights' must have the same dimension lengths as memb_dim and time_dim in 'exp'." + ) # ncores expect_error( RPSS(exp2, obs2, ncores = 1.5), @@ -154,6 +168,11 @@ c(0.5259259, 0.4771242), tolerance = 0.0001 ) expect_equal( +as.vector(RPSS(exp1, obs1, ref1, weights = weights1)$rpss), +c(0.6596424, 0.4063579), +tolerance = 0.0001 +) +expect_equal( as.vector(RPSS(exp1, obs1, ref1)$sign), c(FALSE, FALSE) ) -- GitLab From dc4eed7d5c3b326ffefef3288afe62cef7119706 Mon Sep 17 00:00:00 2001 From: aho Date: Wed, 15 Jun 2022 09:48:19 +0200 Subject: [PATCH 73/83] Bugfix for 'var' as list --- DESCRIPTION | 2 +- R/PlotLayout.R | 15 ++++++++++++++- man/PlotLayout.Rd | 15 ++++++++++++++- 3 files changed, 29 insertions(+), 3 deletions(-) diff --git a/DESCRIPTION b/DESCRIPTION index 9690f44..68a4320 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -47,4 +47,4 @@ BugReports: https://earth.bsc.es/gitlab/es/s2dv/-/issues LazyData: true SystemRequirements: cdo Encoding: UTF-8 -RoxygenNote: 7.0.1 +RoxygenNote: 7.2.0 diff --git a/R/PlotLayout.R b/R/PlotLayout.R index 50a87f2..b6bc467 100644 --- a/R/PlotLayout.R +++ b/R/PlotLayout.R @@ -56,6 +56,19 @@ #' b) providing a list of named sub-lists in 'special_args', where the names #' of each sub-list match the names of the parameters to be adjusted, and #' each value in a sub-list contains the value of the corresponding parameter. +#' For example, if the plots are two maps with different arguments, the +#' structure would be like:\n +#' var:\n +#' List of 2\n +#' $ : num [1:360, 1:181] 1 3.82 5.02 6.63 8.72 ...\n +#' $ : num [1:360, 1:181] 2.27 2.82 4.82 7.7 10.32 ...\n +#' special_args:\n +#' List of 2\n +#' $ :List of 2\n +#' ..$ arg1: ...\n +#' ..$ arg2: ...\n +#' $ :List of 1\n +#' ..$ arg1: ...\n #'@param nrow Numeric value to force the number of rows in the automatically #' generated layout. If higher than the required, this will yield blank cells #' in the layout (which can then be populated). If lower than the required @@ -225,7 +238,7 @@ PlotLayout <- function(fun, plot_dims, var, ..., special_args = NULL, is_single_na <- function(x) ifelse(length(x) > 1, FALSE, is.na(x)) # Check var - if (is.array(var) || (is_single_na(var))) { + if (!is.list(var) & (is.array(var) || (is_single_na(var)))) { var <- list(var) } else if (is.list(var)) { if (!all(sapply(var, is.array) | sapply(var, is_single_na))) { diff --git a/man/PlotLayout.Rd b/man/PlotLayout.Rd index 10bb39e..eaa2197 100644 --- a/man/PlotLayout.Rd +++ b/man/PlotLayout.Rd @@ -94,7 +94,20 @@ a) splitting your array into a list of sub-arrays (each with the data for one plot) and providing it as parameter 'var', b) providing a list of named sub-lists in 'special_args', where the names of each sub-list match the names of the parameters to be adjusted, and -each value in a sub-list contains the value of the corresponding parameter.} +each value in a sub-list contains the value of the corresponding parameter. +For example, if the plots are two maps with different arguments, the +structure would be like:\n +var:\n + List of 2\n + $ : num [1:360, 1:181] 1 3.82 5.02 6.63 8.72 ...\n + $ : num [1:360, 1:181] 2.27 2.82 4.82 7.7 10.32 ...\n +special_args:\n + List of 2\n + $ :List of 2\n + ..$ arg1: ...\n + ..$ arg2: ...\n + $ :List of 1\n + ..$ arg1: ...\n} \item{nrow}{Numeric value to force the number of rows in the automatically generated layout. If higher than the required, this will yield blank cells -- GitLab From 01b077d150ce38dc35f12c66b8f56a81ede1aa1b Mon Sep 17 00:00:00 2001 From: aho Date: Thu, 16 Jun 2022 15:45:17 +0200 Subject: [PATCH 74/83] Version bumping to 1.2.0 --- DESCRIPTION | 2 +- NEWS.md | 14 ++++++++++++++ R/PlotLayout.R | 24 ++++++++++++------------ man/PlotLayout.Rd | 24 ++++++++++++------------ man/s2dv-package.Rd | 10 +--------- man/sampleDepthData.Rd | 6 ++++-- man/sampleMap.Rd | 6 ++++-- man/sampleTimeSeries.Rd | 6 ++++-- 8 files changed, 52 insertions(+), 40 deletions(-) diff --git a/DESCRIPTION b/DESCRIPTION index 68a4320..a768066 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -1,6 +1,6 @@ Package: s2dv Title: A Set of Common Tools for Seasonal to Decadal Verification -Version: 1.1.0 +Version: 1.2.0 Authors@R: c( person("BSC-CNS", role = c("aut", "cph")), person("An-Chi", "Ho", , "an.ho@bsc.es", role = c("aut", "cre")), diff --git a/NEWS.md b/NEWS.md index 71993f0..df06e6b 100644 --- a/NEWS.md +++ b/NEWS.md @@ -1,3 +1,17 @@ +# s2dv 1.2.0 (Release date: 2022-06-16) +- Cluster(): Fix a bug of calculating nclusters ("K"): the function didn't use the whole data to calculate "K" if parameter "nclusters" is NULL.; Add missing output dimension names +- Clim(): Correct the output dimensions for some cases; allow dat_dim to be NULL; obs doesn't need to have dat_dim. +- MeanDims(): if the result is a number and drop = T, return a numeric instead of an array +- Load(): Bugfix for R >= 4.0.0 regarding list and vector confusion +- PlotLayout(): Bugfix when param "var" is a list +- ACC(): Add area-weighting into the calculation and ensure the data has a spatial mean of zero. "space_dim" is deprecated and replaced by "lat_dim" and "lon_dim". "dat_dim" can be NULL. +- PlotEquiMap(): Add useRaster = TRUE in image() if possible (i.e., latitude and longitude are regularly spaced.) +- PlotEquiMap(): New parameters xlonshft ylatshft xlabels ylabels for self-defined axis +- PlotEquiMap(): Flexible map longitude range +- New function: WeightCells, DiffCorr, ResidualCorr, RPSS, RPSSS +- Clim() and MeanDims() efficiency improvement +- CDORemap(): Add time metadata to avoid cdo warning like Warning (find_time_vars): Time variable >time< not found! + # s2dv 1.1.0 (Release date: 2021-12-14) - New functions: RatioPredictableComponents, SignalNoiseRatio - CDORemap(): Able to interpolate irregular grid to regular grid; include new cdo methods 'con2', 'laf' and 'nn' diff --git a/R/PlotLayout.R b/R/PlotLayout.R index b6bc467..742478e 100644 --- a/R/PlotLayout.R +++ b/R/PlotLayout.R @@ -57,18 +57,18 @@ #' of each sub-list match the names of the parameters to be adjusted, and #' each value in a sub-list contains the value of the corresponding parameter. #' For example, if the plots are two maps with different arguments, the -#' structure would be like:\n -#' var:\n -#' List of 2\n -#' $ : num [1:360, 1:181] 1 3.82 5.02 6.63 8.72 ...\n -#' $ : num [1:360, 1:181] 2.27 2.82 4.82 7.7 10.32 ...\n -#' special_args:\n -#' List of 2\n -#' $ :List of 2\n -#' ..$ arg1: ...\n -#' ..$ arg2: ...\n -#' $ :List of 1\n -#' ..$ arg1: ...\n +#' structure would be like:\cr +#' var:\cr +#' List of 2\cr +#' $ : num [1:360, 1:181] 1 3.82 5.02 6.63 8.72 ...\cr +#' $ : num [1:360, 1:181] 2.27 2.82 4.82 7.7 10.32 ...\cr +#' special_args:\cr +#' List of 2\cr +#' $ :List of 2\cr +#' ..$ arg1: ...\cr +#' ..$ arg2: ...\cr +#' $ :List of 1\cr +#' ..$ arg1: ...\cr #'@param nrow Numeric value to force the number of rows in the automatically #' generated layout. If higher than the required, this will yield blank cells #' in the layout (which can then be populated). If lower than the required diff --git a/man/PlotLayout.Rd b/man/PlotLayout.Rd index eaa2197..f8e7bae 100644 --- a/man/PlotLayout.Rd +++ b/man/PlotLayout.Rd @@ -96,18 +96,18 @@ b) providing a list of named sub-lists in 'special_args', where the names of each sub-list match the names of the parameters to be adjusted, and each value in a sub-list contains the value of the corresponding parameter. For example, if the plots are two maps with different arguments, the -structure would be like:\n -var:\n - List of 2\n - $ : num [1:360, 1:181] 1 3.82 5.02 6.63 8.72 ...\n - $ : num [1:360, 1:181] 2.27 2.82 4.82 7.7 10.32 ...\n -special_args:\n - List of 2\n - $ :List of 2\n - ..$ arg1: ...\n - ..$ arg2: ...\n - $ :List of 1\n - ..$ arg1: ...\n} +structure would be like:\cr +var:\cr + List of 2\cr + $ : num [1:360, 1:181] 1 3.82 5.02 6.63 8.72 ...\cr + $ : num [1:360, 1:181] 2.27 2.82 4.82 7.7 10.32 ...\cr +special_args:\cr + List of 2\cr + $ :List of 2\cr + ..$ arg1: ...\cr + ..$ arg2: ...\cr + $ :List of 1\cr + ..$ arg1: ...\cr} \item{nrow}{Numeric value to force the number of rows in the automatically generated layout. If higher than the required, this will yield blank cells diff --git a/man/s2dv-package.Rd b/man/s2dv-package.Rd index 5576921..871a8b6 100644 --- a/man/s2dv-package.Rd +++ b/man/s2dv-package.Rd @@ -6,15 +6,7 @@ \alias{s2dv-package} \title{s2dv: A Set of Common Tools for Seasonal to Decadal Verification} \description{ -The advanced version of package 's2dverification'. It is - intended for 'seasonal to decadal' (s2d) climate forecast verification, but - it can also be used in other kinds of forecasts or general climate analysis. - This package is specially designed for the comparison between the experimental - and observational datasets. The functionality of the included functions covers - from data retrieval, data post-processing, skill scores against observation, - to visualization. Compared to 's2dverification', 's2dv' is more compatible - with the package 'startR', able to use multiple cores for computation and - handle multi-dimensional arrays with a higher flexibility. +The advanced version of package 's2dverification'. It is intended for 'seasonal to decadal' (s2d) climate forecast verification, but it can also be used in other kinds of forecasts or general climate analysis. This package is specially designed for the comparison between the experimental and observational datasets. The functionality of the included functions covers from data retrieval, data post-processing, skill scores against observation, to visualization. Compared to 's2dverification', 's2dv' is more compatible with the package 'startR', able to use multiple cores for computation and handle multi-dimensional arrays with a higher flexibility. } \references{ \url{https://earth.bsc.es/gitlab/es/s2dv/} diff --git a/man/sampleDepthData.Rd b/man/sampleDepthData.Rd index 77e4a7a..47e2f1b 100644 --- a/man/sampleDepthData.Rd +++ b/man/sampleDepthData.Rd @@ -5,7 +5,8 @@ \alias{sampleDepthData} \title{Sample of Experimental Data for Forecast Verification In Function Of Latitudes And Depths} -\format{The data set provides with a variable named 'sampleDepthData'.\cr\cr +\format{ +The data set provides with a variable named 'sampleDepthData'.\cr\cr sampleDepthData$exp is an array that contains the experimental data and the dimension meanings and values are:\cr @@ -18,7 +19,8 @@ but in this sample is not defined (NULL).\cr\cr sampleDepthData$depths is an array with the 7 longitudes covered by the data.\cr\cr -sampleDepthData$lat is an array with the 21 latitudes covered by the data.\cr\cr} +sampleDepthData$lat is an array with the 21 latitudes covered by the data.\cr\cr +} \usage{ data(sampleDepthData) } diff --git a/man/sampleMap.Rd b/man/sampleMap.Rd index eaf8aa5..e4ec5a5 100644 --- a/man/sampleMap.Rd +++ b/man/sampleMap.Rd @@ -4,7 +4,8 @@ \name{sampleMap} \alias{sampleMap} \title{Sample Of Observational And Experimental Data For Forecast Verification In Function Of Longitudes And Latitudes} -\format{The data set provides with a variable named 'sampleMap'.\cr\cr +\format{ +The data set provides with a variable named 'sampleMap'.\cr\cr sampleMap$mod is an array that contains the experimental data and the dimension meanings and values are:\cr c(# of experimental datasets, # of members, # of starting dates, # of lead-times, # of latitudes, # of longitudes)\cr @@ -16,7 +17,8 @@ sampleMap$obs is an array that contains the observational data and the dimension sampleMap$lat is an array with the 2 latitudes covered by the data (see examples on Load() for details on why such low resolution).\cr\cr - sampleMap$lon is an array with the 3 longitudes covered by the data (see examples on Load() for details on why such low resolution).} + sampleMap$lon is an array with the 3 longitudes covered by the data (see examples on Load() for details on why such low resolution). +} \usage{ data(sampleMap) } diff --git a/man/sampleTimeSeries.Rd b/man/sampleTimeSeries.Rd index 05a8e79..7f058e2 100644 --- a/man/sampleTimeSeries.Rd +++ b/man/sampleTimeSeries.Rd @@ -4,7 +4,8 @@ \name{sampleTimeSeries} \alias{sampleTimeSeries} \title{Sample Of Observational And Experimental Data For Forecast Verification As Area Averages} -\format{The data set provides with a variable named 'sampleTimeSeries'.\cr\cr +\format{ +The data set provides with a variable named 'sampleTimeSeries'.\cr\cr sampleTimeSeries$mod is an array that contains the experimental data and the dimension meanings and values are:\cr c(# of experimental datasets, # of members, # of starting dates, # of lead-times)\cr @@ -16,7 +17,8 @@ sampleTimeSeries$obs is an array that contains the observational data and the di sampleTimeSeries$lat is an array with the 2 latitudes covered by the data that was area averaged to calculate the time series (see examples on Load() for details on why such low resolution).\cr\cr -sampleTimeSeries$lon is an array with the 3 longitudes covered by the data that was area averaged to calculate the time series (see examples on Load() for details on why such low resolution).} +sampleTimeSeries$lon is an array with the 3 longitudes covered by the data that was area averaged to calculate the time series (see examples on Load() for details on why such low resolution). +} \usage{ data(sampleTimeSeries) } -- GitLab From 7d2c324dae7639a6a996816dc298521a74182551 Mon Sep 17 00:00:00 2001 From: jcos Date: Thu, 16 Jun 2022 17:39:41 +0200 Subject: [PATCH 75/83] docu: add thresholds to use the W method consistently --- R/RPS.R | 4 +++- R/RPSS.R | 4 +++- 2 files changed, 6 insertions(+), 2 deletions(-) diff --git a/R/RPS.R b/R/RPS.R index 00d0c10..8ded53e 100644 --- a/R/RPS.R +++ b/R/RPS.R @@ -30,7 +30,9 @@ #' The default value is FALSE. #'@param weights A named two-dimensional numerical array of the weights for each #' member and time. The dimension names should include 'memb_dim' and -#' 'time_dim'. The default value is NULL. +#' 'time_dim'. The default value is NULL. The ensemble should have at least 70 +#' members or span at least 10 time steps and have more than 45 members if +#' consistency between the weighted and unweighted methodologies is desired. #'@param ncores An integer indicating the number of cores to use for parallel #' computation. The default value is NULL. #' diff --git a/R/RPSS.R b/R/RPSS.R index c4e5744..3b24777 100644 --- a/R/RPSS.R +++ b/R/RPSS.R @@ -36,7 +36,9 @@ #' The default value is FALSE. #'@param weights A named two-dimensional numerical array of the weights for each #' member and time. The dimension names should include 'memb_dim' and -#' 'time_dim'. The default value is NULL. +#' 'time_dim'. The default value is NULL. The ensemble should have at least 70 +#' members or span at least 10 time steps and have more than 45 members if +#' consistency between the weighted and unweighted methodologies is desired. #'@param ncores An integer indicating the number of cores to use for parallel #' computation. The default value is NULL. #' -- GitLab From fdc803bbfee91697f9da7d11bb3ca4d452be6175 Mon Sep 17 00:00:00 2001 From: aho Date: Thu, 16 Jun 2022 18:06:45 +0200 Subject: [PATCH 76/83] Fix 'class(x == y)' by inherits() --- R/AMV.R | 4 ++-- R/GMST.R | 4 ++-- R/GSAT.R | 4 ++-- R/Load.R | 2 +- R/Persistence.R | 6 +++--- R/Regression.R | 2 +- R/SPOD.R | 4 ++-- R/TPI.R | 6 +++--- 8 files changed, 16 insertions(+), 16 deletions(-) diff --git a/R/AMV.R b/R/AMV.R index a51a819..e14897b 100644 --- a/R/AMV.R +++ b/R/AMV.R @@ -98,10 +98,10 @@ AMV <- function(data, data_lats, data_lons, type, lat_dim = 'lat', lon_dim = 'lo stop("Parameter 'data' must be a numeric array.") } # data_lats and data_lons part1 - if (!(class(data_lats) == 'numeric' | class(data_lats) == 'integer')) { + if (!(inherits(data_lats, 'numeric') | inherits(data_lats, 'integer'))) { stop("Parameter 'data_lats' must be a numeric vector.") } - if (!(class(data_lons) == 'numeric' | class(data_lons) == 'integer')) { + if (!(inherits(data_lons, 'numeric') | inherits(data_lons, 'integer'))) { stop("Parameter 'data_lons' must be a numeric vector.") } # type diff --git a/R/GMST.R b/R/GMST.R index 0d6c49e..85b382d 100644 --- a/R/GMST.R +++ b/R/GMST.R @@ -125,10 +125,10 @@ GMST <- function(data_tas, data_tos, data_lats, data_lons, mask_sea_land, sea_va stop("The dimension of data_tas and data_tos must be identical.") } # data_lats and data_lons part1 - if (!(class(data_lats) == 'numeric' | class(data_lats) == 'integer')) { + if (!(inherits(data_lats, 'numeric') | inherits(data_lats, 'integer'))) { stop("Parameter 'data_lats' must be a numeric vector.") } - if (!(class(data_lons) == 'numeric' | class(data_lons) == 'integer')) { + if (!(inherits(data_lons, 'numeric') | inherits(data_lons, 'integer'))) { stop("Parameter 'data_lons' must be a numeric vector.") } # lat_dim diff --git a/R/GSAT.R b/R/GSAT.R index 1774bd6..30975b9 100644 --- a/R/GSAT.R +++ b/R/GSAT.R @@ -94,10 +94,10 @@ GSAT <- function(data, data_lats, data_lons, type, lat_dim = 'lat', lon_dim = 'l stop("Parameter 'data' must be a numeric array.") } # data_lats and data_lons part1 - if (!(class(data_lats) == 'numeric' | class(data_lats) == 'integer')) { + if (!(inherits(data_lats, 'numeric') | inherits(data_lats, 'integer'))) { stop("Parameter 'data_lats' must be a numeric vector.") } - if (!(class(data_lons) == 'numeric' | class(data_lons) == 'integer')) { + if (!(inherits(data_lons, 'numeric') | inherits(data_lons, 'integer'))) { stop("Parameter 'data_lons' must be a numeric vector.") } # type diff --git a/R/Load.R b/R/Load.R index 3c3aa8e..e188299 100644 --- a/R/Load.R +++ b/R/Load.R @@ -2121,7 +2121,7 @@ Load <- function(var, exp = NULL, obs = NULL, sdates, nmember = NULL, }) - if (class(errors) == 'try-error') { + if (inherits(errors, 'try-error')) { invisible(list(load_parameters = load_parameters)) } else { # Before ending, the data is arranged in the common format, with the following diff --git a/R/Persistence.R b/R/Persistence.R index 5a53857..9895f47 100644 --- a/R/Persistence.R +++ b/R/Persistence.R @@ -122,7 +122,7 @@ Persistence <- function(data, dates, time_dim = 'time', start, end, ft_start, stop(paste0("Parameter 'dates' must be a sequence of integer (YYYY) or ", "string (YYYY-MM-DD) in class 'Date'.")) } - } else if (class(dates) == 'Date') { #(YYYY-MM-DD) + } else if (inherits(dates, 'Date')) { #(YYYY-MM-DD) } else { stop(paste0("Parameter 'dates' must be a sequence of integer (YYYY) or ", @@ -148,7 +148,7 @@ Persistence <- function(data, dates, time_dim = 'time', start, end, ft_start, stop(paste0("Parameter 'start' must start at least 40 time steps after ", "the first 'dates'.")) } - } else if (class(start) == 'Date') { + } else if (inherits(start, 'Date')) { if (length(start) > 1 | any(start < as.Date(ISOdate(1850, 1, 1))) | any(start > as.Date(ISOdate(2021, 1, 1)))) { stop(paste0("Parameter 'start' must be an integer or a string in class ", @@ -176,7 +176,7 @@ Persistence <- function(data, dates, time_dim = 'time', start, end, ft_start, stop(paste0("Parameter 'end' must end at most 1 time steps after ", "the last 'dates'.")) } - } else if (class(end) == 'Date') { + } else if (inherits(end, 'Date')) { if (length(end) > 1 | any(end < as.Date(ISOdate(1850, 1, 1))) | any(end > as.Date(ISOdate(2020, 12, 31)))) { stop(paste0("Parameter 'end' must be an integer or a string in class ", diff --git a/R/Regression.R b/R/Regression.R index 244ddc7..1cd12e6 100644 --- a/R/Regression.R +++ b/R/Regression.R @@ -123,7 +123,7 @@ Regression <- function(datay, datax, reg_dim = 'sdate', formula = y ~ x, stop("Parameter 'reg_dim' is not found in 'datay' or 'datax' dimension.") } ## formula - if (class(formula) != 'formula') { + if (!inherits(formula, 'formula')) { stop("Parameter 'formula' must the an object of class 'formula'.") } ## pval diff --git a/R/SPOD.R b/R/SPOD.R index 51717b3..2599477 100644 --- a/R/SPOD.R +++ b/R/SPOD.R @@ -97,10 +97,10 @@ SPOD <- function(data, data_lats, data_lons, type, lat_dim = 'lat', lon_dim = 'l stop("Parameter 'data' must be a numeric array.") } # data_lats and data_lons part1 - if (!(class(data_lats) == 'numeric' | class(data_lats) == 'integer')) { + if (!(inherits(data_lats, 'numeric') | inherits(data_lats, 'integer'))) { stop("Parameter 'data_lats' must be a numeric vector.") } - if (!(class(data_lons) == 'numeric' | class(data_lons) == 'integer')) { + if (!(inherits(data_lons, 'numeric') | inherits(data_lons, 'integer'))) { stop("Parameter 'data_lons' must be a numeric vector.") } # type diff --git a/R/TPI.R b/R/TPI.R index 80d958e..d3f0550 100644 --- a/R/TPI.R +++ b/R/TPI.R @@ -96,10 +96,10 @@ TPI <- function(data, data_lats, data_lons, type, lat_dim = 'lat', lon_dim = 'lo stop("Parameter 'data' must be a numeric array.") } # data_lats and data_lons part1 - if (!(class(data_lats) == 'numeric' | class(data_lats) == 'integer')) { + if (!(inherits(data_lats, 'numeric') | inherits(data_lats, 'integer'))) { stop("Parameter 'data_lats' must be a numeric vector.") } - if (!(class(data_lons) == 'numeric' | class(data_lons) == 'integer')) { + if (!(inherits(data_lons, 'numeric') | inherits(data_lons, 'integer'))) { stop("Parameter 'data_lons' must be a numeric vector.") } # type @@ -181,7 +181,7 @@ TPI <- function(data, data_lats, data_lons, type, lat_dim = 'lat', lon_dim = 'lo } # indices_for_clim if (!is.null(indices_for_clim)) { - if (!class(indices_for_clim) %in% c('numeric', 'integer') + if (!(inherits(indices_for_clim, 'numeric') | inherits(indices_for_clim, 'integer')) & !(is.logical(indices_for_clim) & !any(indices_for_clim))) { stop(paste0("The parameter 'indices_for_clim' must be a numeric vector ", "or NULL to compute the anomalies based on the whole period, ", -- GitLab From 41d516bdf2f1bc3b709cf56134406e5d9c21d767 Mon Sep 17 00:00:00 2001 From: aho Date: Thu, 16 Jun 2022 18:10:37 +0200 Subject: [PATCH 77/83] Update document --- man/RPS.Rd | 4 +++- man/RPSS.Rd | 4 +++- 2 files changed, 6 insertions(+), 2 deletions(-) diff --git a/man/RPS.Rd b/man/RPS.Rd index 5062671..ee5c241 100644 --- a/man/RPS.Rd +++ b/man/RPS.Rd @@ -43,7 +43,9 @@ The default value is FALSE.} \item{weights}{A named two-dimensional numerical array of the weights for each member and time. The dimension names should include 'memb_dim' and -'time_dim'. The default value is NULL.} +'time_dim'. The default value is NULL. The ensemble should have at least 70 +members or span at least 10 time steps and have more than 45 members if +consistency between the weighted and unweighted methodologies is desired.} \item{ncores}{An integer indicating the number of cores to use for parallel computation. The default value is NULL.} diff --git a/man/RPSS.Rd b/man/RPSS.Rd index ca33c07..5b8bd7e 100644 --- a/man/RPSS.Rd +++ b/man/RPSS.Rd @@ -50,7 +50,9 @@ The default value is FALSE.} \item{weights}{A named two-dimensional numerical array of the weights for each member and time. The dimension names should include 'memb_dim' and -'time_dim'. The default value is NULL.} +'time_dim'. The default value is NULL. The ensemble should have at least 70 +members or span at least 10 time steps and have more than 45 members if +consistency between the weighted and unweighted methodologies is desired.} \item{ncores}{An integer indicating the number of cores to use for parallel computation. The default value is NULL.} -- GitLab From 2ebc648ad959bcbf9909765ab0c39ef2770d11ec Mon Sep 17 00:00:00 2001 From: aho Date: Thu, 16 Jun 2022 18:14:05 +0200 Subject: [PATCH 78/83] Update author list --- DESCRIPTION | 1 + 1 file changed, 1 insertion(+) diff --git a/DESCRIPTION b/DESCRIPTION index a768066..e8382cd 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -6,6 +6,7 @@ Authors@R: c( person("An-Chi", "Ho", , "an.ho@bsc.es", role = c("aut", "cre")), person("Nuria", "Perez-Zanon", , "nuria.perez@bsc.es", role = "aut"), person("Roberto", "Bilbao", , "roberto.bilbao@bsc.es", role = "ctb"), + person("Josep", "Cos", , "josep.cos@bsc.es", role = "ctb"), person("Carlos", "Delgado", , "carlos.delgado@bsc.es", role = "ctb"), person("Llorenç", "Lledó", , "llorenc.lledo@bsc.es", role = "ctb"), person("Andrea", "Manrique", , "andrea.manrique@bsc.es", role = "ctb"), -- GitLab From c4669ae02fb5d626a778d8a4ed984468664a1bae Mon Sep 17 00:00:00 2001 From: aho Date: Thu, 16 Jun 2022 20:13:59 +0200 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ze(`C5ggnLmzgf_Y=rB4VaeHnCK(!*o3$0tHqQv;wk41@YV@0C@e<^_e7Vac~ zYTvIZfVta`9KPbLh>+_yH|iCDjFBe5x1Xy6^CTR6hEg9MZH4L_#(Re498TMWOBXJE zhISN=3P00U|MK(|<%_KHO%>J?=|&OLU%d!e+?h5v&-I9F2FBCD$NVQu&i+N5EDcr+ TT0Buv0U;0$E32}$3eNuleP+*= -- GitLab From 7a8687bfeb4bab04b128b957c74b7d8a5ab56c94 Mon Sep 17 00:00:00 2001 From: aho Date: Fri, 17 Jun 2022 09:43:50 +0200 Subject: [PATCH 80/83] Fix typo --- NEWS.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/NEWS.md b/NEWS.md index df06e6b..704763c 100644 --- a/NEWS.md +++ b/NEWS.md @@ -8,9 +8,9 @@ - PlotEquiMap(): Add useRaster = TRUE in image() if possible (i.e., latitude and longitude are regularly spaced.) - PlotEquiMap(): New parameters xlonshft ylatshft xlabels ylabels for self-defined axis - PlotEquiMap(): Flexible map longitude range -- New function: WeightCells, DiffCorr, ResidualCorr, RPSS, RPSSS +- New function: WeightCells, DiffCorr, ResidualCorr, RPS, RPSS - Clim() and MeanDims() efficiency improvement -- CDORemap(): Add time metadata to avoid cdo warning like Warning (find_time_vars): Time variable >time< not found! +- CDORemap(): Add time metadata to avoid cdo warning like "Warning (find_time_vars): Time variable >time< not found!" # s2dv 1.1.0 (Release date: 2021-12-14) - New functions: RatioPredictableComponents, SignalNoiseRatio -- GitLab From 4ae74c6e272303fc29b6b1dc9d2bb833f31ad76b Mon Sep 17 00:00:00 2001 From: aho Date: Wed, 22 Jun 2022 11:58:23 +0200 Subject: [PATCH 81/83] Stop printing cdo command messages. Improve 'crop' documentation --- NEWS.md | 3 ++- R/CDORemap.R | 48 +++++++++++++++++++++++++++++---------------- man/CDORemap.Rd | 41 ++++++++++++++++++++++++++------------ man/s2dv-package.Rd | 1 + 4 files changed, 63 insertions(+), 30 deletions(-) diff --git a/NEWS.md b/NEWS.md index 704763c..558b667 100644 --- a/NEWS.md +++ b/NEWS.md @@ -10,7 +10,8 @@ - PlotEquiMap(): Flexible map longitude range - New function: WeightCells, DiffCorr, ResidualCorr, RPS, RPSS - Clim() and MeanDims() efficiency improvement -- CDORemap(): Add time metadata to avoid cdo warning like "Warning (find_time_vars): Time variable >time< not found!" +- CDORemap(): Add arbitrary time metadata to avoid cdo warning like "Warning (find_time_vars): Time variable >time< not found!" +- CDORemap(): Stop printing messages from cdo command. # s2dv 1.1.0 (Release date: 2021-12-14) - New functions: RatioPredictableComponents, SignalNoiseRatio diff --git a/R/CDORemap.R b/R/CDORemap.R index 6a9a7e5..ce3ed35 100644 --- a/R/CDORemap.R +++ b/R/CDORemap.R @@ -42,18 +42,35 @@ #' for the price of writing more intermediate files (whis usually is #' unconvenient) by setting the parameter \code{avoid_writes = TRUE}. #'@param crop Whether to crop the data after interpolation with -#' 'cdo sellonlatbox' (TRUE) or to extend interpolated data to the whole -#' world as CDO does by default (FALSE). If \code{crop = TRUE} then the -#' longitude and latitude borders which to crop at are taken as the limits of -#' the cells at the borders ('lons' and 'lats' are perceived as cell centers), -#' i.e. the resulting array will contain data that covers the same area as -#' the input array. This is equivalent to specifying \code{crop = 'preserve'}, -#' i.e. preserving area. If \code{crop = 'tight'} then the borders which to -#' crop at are taken as the minimum and maximum cell centers in 'lons' and -#' 'lats', i.e. the area covered by the resulting array may be smaller if -#' interpolating from a coarse grid to a fine grid. The parameter 'crop' also -#' accepts a numeric vector of custom borders which to crop at: -#' c(western border, eastern border, southern border, northern border). +#' 'cdo sellonlatbox' (TRUE) or to extend interpolated data to the whole +#' world as CDO does by default (FALSE). The default value is TRUE.\cr +#' \itemize{ +#' \item{ +#' If \code{crop = TRUE}, the longitude and latitude borders to be cropped +#' at are taken as the limits of the cells at the borders (not the values +#' of 'lons' and 'lats', which are perceived as cell centers), i.e., the +#' resulting array will contain data that covers the same area as the input +#' array. This is equivalent to specifying \code{crop = 'preserve'}, i.e., +#' preserving area. Notice that the longitude range of returning array will +#' follow the original data 'lons' instead of the target grid 'grid'. +#' } +#' \item{ +#' If \code{crop = FALSE}, the returning array is not cropped, i.e., a +#' global domain, and the longitude range will be the same as the target +#' grid 'grid'. +#' } +#' \item{ +#' If \code{crop = 'tight'}, the borders to be cropped at are taken as the +#' minimum and maximum cell centers in 'lons' and 'lats', i.e., the area +#' covered by the resulting array may be smaller if interpolating from a +#' coarse grid to a fine grid. +#' } +#' \item{ +#' The parameter 'crop' also accepts a numeric vector of customized borders +#' to be cropped at:\cr +#' c(western border, eastern border, southern border, northern border). +#' } +#' } #'@param force_remap Whether to force remapping, even if the input data array #' is already on the target grid. #'@param write_dir Path to the directory where to create the intermediate @@ -414,7 +431,6 @@ CDORemap <- function(data_array = NULL, lons, lats, grid, method, } if (is.logical(crop)) { if (crop) { - warning("Parameter 'crop' = 'TRUE'. The output grid range will follow the input lons and lats.") if (length(lons) == 1 || length(lats) == 1) { stop("CDORemap cannot remap if crop = TRUE and values for only one ", "longitude or one latitude are provided. Either a) provide ", @@ -525,8 +541,6 @@ CDORemap <- function(data_array = NULL, lons, lats, grid, method, } ###--- } - } else if (crop == FALSE) { - warning("Parameter 'crop' = 'FALSE'. The output grid range will follow parameter 'grid'.") } } else if (is.numeric(crop)) { if (length(crop) != 4) { @@ -794,11 +808,11 @@ CDORemap <- function(data_array = NULL, lons, lats, grid, method, ',', format(lat_extremes[2], scientific = FALSE), ' -') } err <- try({ - system(paste0("cdo -s ", sellonlatbox, "remap", method, ",", grid, " ", tmp_file, " ", tmp_file2)) + system(paste0("cdo -s ", sellonlatbox, "remap", method, ",", grid, " ", tmp_file, " ", tmp_file2), ignore.stdout = T, ignore.stderr = T) }) file.remove(tmp_file) if (is(err, 'try-error') || err > 0) { - stop("CDO remap failed.") + stop("CDO remap failed. Possible problem: parameter 'grid'.") } ncdf_remapped <- nc_open(tmp_file2) if (!lons_lats_taken) { diff --git a/man/CDORemap.Rd b/man/CDORemap.Rd index b49f944..61f6101 100644 --- a/man/CDORemap.Rd +++ b/man/CDORemap.Rd @@ -49,18 +49,35 @@ for the price of writing more intermediate files (whis usually is unconvenient) by setting the parameter \code{avoid_writes = TRUE}.} \item{crop}{Whether to crop the data after interpolation with -'cdo sellonlatbox' (TRUE) or to extend interpolated data to the whole -world as CDO does by default (FALSE). If \code{crop = TRUE} then the -longitude and latitude borders which to crop at are taken as the limits of -the cells at the borders ('lons' and 'lats' are perceived as cell centers), -i.e. the resulting array will contain data that covers the same area as -the input array. This is equivalent to specifying \code{crop = 'preserve'}, -i.e. preserving area. If \code{crop = 'tight'} then the borders which to -crop at are taken as the minimum and maximum cell centers in 'lons' and -'lats', i.e. the area covered by the resulting array may be smaller if -interpolating from a coarse grid to a fine grid. The parameter 'crop' also -accepts a numeric vector of custom borders which to crop at: -c(western border, eastern border, southern border, northern border).} +'cdo sellonlatbox' (TRUE) or to extend interpolated data to the whole +world as CDO does by default (FALSE). The default value is TRUE.\cr +\itemize{ + \item{ + If \code{crop = TRUE}, the longitude and latitude borders to be cropped + at are taken as the limits of the cells at the borders (not the values + of 'lons' and 'lats', which are perceived as cell centers), i.e., the + resulting array will contain data that covers the same area as the input + array. This is equivalent to specifying \code{crop = 'preserve'}, i.e., + preserving area. Notice that the longitude range of returning array will + follow the original data 'lons' instead of the target grid 'grid'. + } + \item{ + If \code{crop = FALSE}, the returning array is not cropped, i.e., a + global domain, and the longitude range will be the same as the target + grid 'grid'. + } + \item{ + If \code{crop = 'tight'}, the borders to be cropped at are taken as the + minimum and maximum cell centers in 'lons' and 'lats', i.e., the area + covered by the resulting array may be smaller if interpolating from a + coarse grid to a fine grid. + } + \item{ + The parameter 'crop' also accepts a numeric vector of customized borders + to be cropped at:\cr + c(western border, eastern border, southern border, northern border). + } +}} \item{force_remap}{Whether to force remapping, even if the input data array is already on the target grid.} diff --git a/man/s2dv-package.Rd b/man/s2dv-package.Rd index 871a8b6..3c98a95 100644 --- a/man/s2dv-package.Rd +++ b/man/s2dv-package.Rd @@ -31,6 +31,7 @@ Authors: Other contributors: \itemize{ \item Roberto Bilbao \email{roberto.bilbao@bsc.es} [contributor] + \item Josep Cos \email{josep.cos@bsc.es} [contributor] \item Carlos Delgado \email{carlos.delgado@bsc.es} [contributor] \item Llorenç Lledó \email{llorenc.lledo@bsc.es} [contributor] \item Andrea Manrique \email{andrea.manrique@bsc.es} [contributor] -- GitLab From dbb6719d648e87eaa95de373112db7f80f7a2974 Mon Sep 17 00:00:00 2001 From: aho Date: Wed, 22 Jun 2022 14:23:32 +0200 Subject: [PATCH 82/83] Update publish date --- NEWS.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/NEWS.md b/NEWS.md index 558b667..d65a824 100644 --- a/NEWS.md +++ b/NEWS.md @@ -1,4 +1,4 @@ -# s2dv 1.2.0 (Release date: 2022-06-16) +# s2dv 1.2.0 (Release date: 2022-06-22) - Cluster(): Fix a bug of calculating nclusters ("K"): the function didn't use the whole data to calculate "K" if parameter "nclusters" is NULL.; Add missing output dimension names - Clim(): Correct the output dimensions for some cases; allow dat_dim to be NULL; obs doesn't need to have dat_dim. - MeanDims(): if the result is a number and drop = T, return a numeric instead of an array -- GitLab From 2b5455510367845f6ad780e758d482f42fa1912f Mon Sep 17 00:00:00 2001 From: aho Date: Wed, 22 Jun 2022 14:31:27 +0200 Subject: [PATCH 83/83] Update pdf --- s2dv-manual.pdf | Bin 402805 -> 404372 bytes 1 file changed, 0 insertions(+), 0 deletions(-) diff --git a/s2dv-manual.pdf b/s2dv-manual.pdf index dab70611f5a50086733b604b43015acbc0240458..2b6da42257033ded7f67d7307378f372ae3a10fe 100644 GIT binary patch delta 282422 zcmZtsQ*Rl#ab7I zIp@<9PU5pvd&aCEb5nYGRw$==diD6oYvSYC-5K+d-hU+JL zy-k2Jc1JHg#aLwUSX&!!Yv_4lsu@$f!9>S6q(J2w`Zydg5pRc{8Ao6q_r7=!ZiGLC zE@|sLn07-79c!)Ct)GEhk-$D)aKJ`dx#69=-WMb|;kaEf-Fpv}smWrD3oX_d!bUnN-J>7D6*f|3<~=z!_Ny zsh-3K64(MZBC#smgOx9+v`@gX_06JMQ^IpV{!C^~L?IoKGpY&Bl!98cwVF%H4$1d~ z(6E&5PtZ!8fLyyw0o5n$?z1T{utyjEB`nqWr8}9xB&kQ>u}>*HnUM*itL96iW=$}2 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