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#'Compute the 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).
#'
#'@param exp A numeric array of experimental anomalies with named dimensions.
#' It must have at least 'dat_dim' and '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'
#' and 'memb_dim'.
#'@param dat_dim A character string indicating the name of dataset (nobs/nexp)
#' dimension. The default value is 'dataset'.
#'@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'.
#'@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 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.
#'@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.
#'@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".
#' "parametric" provides a confidence interval for the ACC computed by a
#' Fisher transformation and a significance level for the ACC from a one-sided
#' student-T distribution. "bootstrap" provides a confidence interval for the
#' ACC and MACC computed from bootstrapping on the members with 100 drawings
#' with replacement. To guarantee the statistical robustness of the result,
#' make sure that your experiment and observation always have the same number
#' of members. "bootstrap" requires 'memb_dim' has value. The default value is
#' 'parametric'.
#'@param conf.lev A numeric indicating the confidence level for the
#' regression computation. The default value is 0.95.
#'@param pval A logical value indicating whether to compute the p-value or not.
#' The default value is TRUE.
#'@param ncores An integer indicating the number of cores to use for parallel
#' computation. The default value is NULL.
#'
#'@return
#'A list containing the numeric arrays:\cr
#' 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).
#'\item{conf.lower (if conftype = "parametric") or acc_conf.lower (if
#' conftype = "bootstrap")}{
#' The lower confidence interval of ACC with the same dimensions as ACC. Only
#' present if \code{conf = TRUE}.
#'\item{conf.upper (if conftype = "parametric") or acc_conf.upper (if
#' conftype = "bootstrap")}{
#' The upper confidence interval of ACC with the same dimensions as ACC. Only
#' present if \code{conf = TRUE}.
#'}
#'\item{p.val}{
#' The p-value with the same dimensions as ACC. Only present if
#' \code{pval = TRUE} and code{conftype = "parametric"}.
#' 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.
#'}
#'\item{macc_conf.lower}{
#' The lower confidence interval of MACC with the same dimensions as MACC.
#' Only present if \code{conftype = "bootstrap"}.
#'}
#'\item{macc_conf.upper}{
#' The upper confidence interval of MACC with the same dimensions as MACC.
#' Only present if \code{conftype = "bootstrap"}.
#'}
#'
#'@examples
#' \dontshow{
#'startDates <- c('19851101', '19901101', '19951101', '20001101', '20051101')
#'sampleData <- s2dv:::.LoadSampleData('tos', c('experiment'),
#' c('observation'), startDates,
#' leadtimemin = 1,
#' leadtimemax = 4,
#' output = 'lonlat',
#' latmin = 27, latmax = 48,
#' lonmin = -12, lonmax = 40)
#' }
#'sampleData$mod <- Season(sampleData$mod, monini = 11, moninf = 12, monsup = 2)
#'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_bootstrap <- ACC(ano_exp, ano_obs, conftype = 'bootstrap')
#'res <- array(c(acc$conf.lower, acc$acc, acc$conf.upper, acc$p.val),
#' dim = c(dim(acc$acc), 4))
#'res_bootstrap <- array(c(acc$acc_conf.lower, acc$acc, acc$acc_conf.upper, acc$p.val),
#' dim = c(dim(acc$acc), 4))
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#' }
#'@references Joliffe and Stephenson (2012). Forecast Verification: A
#' Practitioner's Guide in Atmospheric Science. Wiley-Blackwell.
#'@import multiApply
#'@importFrom abind abind
#'@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',
lat = NULL, lon = NULL, lonlatbox = NULL,
conf = TRUE, conftype = "parametric", conf.lev = 0.95, pval = TRUE,
ncores = NULL) {
# Check inputs
## exp and obs (1)
if (is.null(exp) | is.null(obs)) {
stop("Parameter 'exp' and 'obs' cannot be NULL.")
}
if (!is.numeric(exp) | !is.numeric(obs)) {
stop("Parameter 'exp' and 'obs' must be a numeric array.")
}
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."))
}
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 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.")
}
## space_dim
if (!is.character(space_dim) | length(space_dim) != 2) {
stop("Parameter 'space_dim' must be a character vector of 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.")
}
## avg_dim
if (!is.null(avg_dim)) {
if (!is.character(avg_dim) | length(avg_dim) > 1) {
stop("Parameter 'avg_dim' must be a character string.")
}
if (!avg_dim %in% names(dim(exp)) | !avg_dim %in% names(dim(obs))) {
stop("Parameter 'avg_dim' is not found in 'exp' or 'obs' dimension.")
}
}
## 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(obs))) {
stop("Parameter 'memb_dim' is not found in 'exp' or 'obs' dimension.")
}
}
## 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'."))
}
}
## lon
if (!is.null(lon)) {
if (!is.numeric(lon) | length(lon) != dim(exp)[space_dim[2]]) {
stop(paste0("Parameter 'lon' must be a numeric vector with the same ",
"length as the longitude dimension of 'exp' and 'obs'."))
}
}
## lonlatbox
if (!is.null(lonlatbox)) {
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)) {
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.")
}
if (conf) {
## conftype
if (!conftype %in% c('parametric', 'bootstrap')) {
stop("Parameter 'conftype' must be either 'parametric' or 'bootstrap'.")
}
if (conftype == 'bootstrap' & is.null(memb_dim)) {
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) {
stop("Parameter 'conf.lev' must be a numeric number between 0 and 1.")
}
}
## pval
if (!is.logical(pval) | length(pval) > 1) {
stop("Parameter 'pval' must be one logical value.")
}
## ncores
if (!is.null(ncores)) {
if (!is.numeric(ncores) | ncores %% 1 != 0 | ncores < 0 |
length(ncores) > 1) {
stop("Parameter 'ncores' must be a positive integer.")
}
}
## 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(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])) {
stop(paste0("Parameter 'exp' and 'obs' must have same length of ",
"all the dimensions expect 'dat_dim' and 'memb_dim'."))
}
#-----------------------------------------------------------------
###############################
# Sort dimension
name_exp <- names(dim(exp))
name_obs <- names(dim(obs))
order_obs <- match(name_exp, name_obs)
obs <- Reorder(obs, order_obs)
###############################
# Select the domain
if (select_lonlat) {
for (jind in 1:2) {
while (lonlatbox[jind] < 0) {
lonlatbox[jind] <- lonlatbox[jind] + 360
}
while (lonlatbox[jind] > 360) {
lonlatbox[jind] <- lonlatbox[jind] - 360
}
}
indlon <- which((lon >= lonlatbox[1] & lon <= lonlatbox[2]) |
(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)
}
# Ensemble mean
if (!is.null(memb_dim)) {
if (conftype == 'bootstrap') {
exp_ori <- exp
obs_ori <- obs
}
exp <- MeanDims(exp, memb_dim, na.rm = TRUE, ncores = ncores)
obs <- MeanDims(obs, memb_dim, na.rm = TRUE, ncores = ncores)
}
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,
conftype = conftype, pval = pval, conf = conf, conf.lev = conf.lev,
ncores_input = 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$acc_conf.upper,
macc_conf.lower = res_conf$macc_conf.lower,
macc_conf.upper = res_conf$macc_conf.upper)
} else {
res <- Apply(list(exp, obs),
target_dims = list(c(space_dim, avg_dim, dat_dim),
c(space_dim, avg_dim, dat_dim)),
fun = .ACC,
dat_dim = dat_dim, avg_dim = avg_dim,
conftype = conftype, pval = pval, conf = conf, conf.lev = conf.lev,
ncores_input = ncores,
if (conftype == 'bootstrap') {
res_conf <- Apply(list(exp_ori, obs_ori),
target_dims = list(c(memb_dim, dat_dim, avg_dim, space_dim),
c(memb_dim, dat_dim, avg_dim, space_dim)),
fun = .ACC_bootstrap,
dat_dim = dat_dim, memb_dim = memb_dim, avg_dim = avg_dim,
conftype = conftype, pval = pval, conf = conf, conf.lev = conf.lev,
ncores_input = ncores,
ncores = ncores)
acc_conf.lower = res_conf$acc_conf.lower,
acc_conf.upper = res_conf$acc_conf.upper,
macc_conf.lower = res_conf$macc_conf.lower,
macc_conf.upper = res_conf$macc_conf.upper)
}
return(res)
}
.ACC <- function(exp, obs, dat_dim = 'dataset', #space_dim = c('lat', 'lon'),
avg_dim = 'sdate', #memb_dim = NULL,
lon = NULL, lat = NULL, lonlatbox = NULL,
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 (is.null(avg_dim)) {
acc <- array(dim = c(nexp = nexp, nobs = nobs))
if (pval) p.val <- array(dim = c(nexp = nexp, nobs = nobs))
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]))
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))
}
}
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}
# 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]
# Variance(iexp) should not take into account any point
# that is not available in iobs and therefore not accounted for
# in covariance(iexp, iobs) and vice-versa
exp_sub[is.na(obs_sub)] <- NA
obs_sub[is.na(exp_sub)] <- NA
if (is.null(avg_dim)) {
# ACC
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
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if (pval) {
t <- qt(conf.lev, eno - 2) # a number
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))
}
}
}
} else { #avg_dim is not NULL
# MACC
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
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
if (is.infinite(acc[iexp, iobs, i])) acc[iexp, iobs, i] <- 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 = 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
if (pval) {
t <- qt(conf.lev, eno - 2) # a vector of avg_dim
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))
}
}
}
} # if avg_dim is not NULL
}
}
#------------------------------------------------
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if (is.null(avg_dim)) {
if (conf & pval) {
return(list(acc = acc, conf.lower = conf.lower, conf.upper = conf.upper,
p.val = p.val))
} else if (conf & !pval) {
return(list(acc = acc, conf.lower = conf.lower, conf.upper = conf.upper,
macc = macc))
} else if (!conf & pval) {
return(list(acc = acc, p.val = p.val))
} else {
return(list(acc = acc))
}
} else {
if (conf & pval) {
return(list(acc = acc, conf.lower = conf.lower, conf.upper = conf.upper,
p.val = p.val, macc = macc))
} else if (conf & !pval) {
return(list(acc = acc, conf.lower = conf.lower, conf.upper = conf.upper,
macc = macc))
} else if (!conf & pval) {
return(list(acc = acc, p.val = p.val, macc = macc))
} else {
return(list(acc = acc, macc = macc))
}
}
}
.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,
conf = TRUE, conftype = "parametric", conf.lev = 0.95, pval = TRUE,
ncores_input = NULL) {
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# if (is.null(avg_dim))
# exp: [memb_exp, dat_exp, space_dim]
# obs: [memb_obs, dat_obs, space_dim]
# if (!is.null(avg_dim))
# exp: [memb_exp, dat_exp, avg_dim, space_dim]
# obs: [memb_obs, dat_obs, avg_dim, space_dim]
nexp <- as.numeric(dim(exp)[2])
nobs <- as.numeric(dim(obs)[2])
nmembexp <- as.numeric(dim(exp)[1])
nmembobs <- as.numeric(dim(obs)[1])
ndraw <- 100
if (is.null(avg_dim)) {
acc_draw <- array(dim = c(nexp = nexp, nobs = nobs, ndraw))
} else {
acc_draw <- array(dim = c(nexp = nexp, nobs = nobs, dim(exp)[3], ndraw))
macc_draw <- array(dim = c(nexp = nexp, nobs = nobs, ndraw))
}
for (jdraw in 1:ndraw) {
#choose a randomly member index for each point of the matrix
indexp <- array(sample(nmembexp, size = prod(dim(exp)[-c(length(dim(exp)) - 1, length(dim(exp)))]),
replace = TRUE),
dim = dim(exp))
indobs <- array(sample(nmembobs, size = prod(dim(obs)[-c(length(dim(obs)) - 1, length(dim(obs)))]),
replace = TRUE),
dim = dim(obs))
#combine maxtrix of data and random index
varindexp <- abind::abind(exp, indexp, along = length(dim(exp)) + 1)
varindobs <- abind::abind(obs, indobs, along = length(dim(obs)) + 1)
#select randomly the members for each point of the matrix
# if (is.null(avg_dim)) {
drawexp <- array(
apply(varindexp, c(2:length(dim(exp))), function(x) x[,1][x[,2]] ),
dim = dim(exp))
drawobs <- array(
apply(varindobs, c(2:length(dim(obs))), function(x) x[,1][x[,2]] ),
dim = dim(obs))
# ensemble mean before .ACC
drawexp <- MeanDims(drawexp, memb_dim, na.rm = TRUE, ncores = ncores_input)
drawobs <- MeanDims(drawobs, memb_dim, na.rm = TRUE, ncores = ncores_input)
# Reorder
if (is.null(avg_dim)) {
drawexp <- Reorder(drawexp, c(2, 3, 1))
drawobs <- Reorder(drawobs, c(2, 3, 1))
} else {
drawexp <- Reorder(drawexp, c(3, 4, 2, 1))
drawobs <- Reorder(drawobs, c(3, 4, 2, 1))
}
#calculate the ACC of the randomized field
tmpACC <- .ACC(drawexp, drawobs, conf = FALSE, pval = FALSE, avg_dim = avg_dim,
ncores_input = ncores_input)
if (is.null(avg_dim)) {
acc_draw[, , jdraw] <- tmpACC$acc
} else {
acc_draw[, , , jdraw] <- tmpACC$acc
macc_draw[, , jdraw] <- tmpACC$macc
}
}
#calculate the confidence interval
if (is.null(avg_dim)) {
acc_conf.upper <- apply(acc_draw, c(1, 2),
function (x) {
quantile(x, 1 - (1 - conf.lev) / 2, na.rm = TRUE)})
acc_conf.lower <- apply(acc_draw, c(1, 2),
function (x) {
quantile(x, (1 - conf.lev) / 2, na.rm = TRUE)})
} else {
acc_conf.upper <- apply(acc_draw, c(1, 2, 3),
function (x) {
quantile(x, 1 - (1 - conf.lev) / 2, na.rm = TRUE)})
acc_conf.lower <- apply(acc_draw, c(1, 2, 3),
function (x) {
quantile(x, (1 - conf.lev) / 2, na.rm = TRUE)})
macc_conf.upper <- apply(macc_draw, c(1, 2),
function (x) {
quantile(x, 1 - (1 - conf.lev) / 2, na.rm = TRUE)})
macc_conf.lower <- apply(macc_draw, c(1, 2),
function (x) {
quantile(x, (1 - conf.lev) / 2, na.rm = TRUE)})
}
# Return output
if (is.null(avg_dim)) {
return(list(acc_conf.lower = acc_conf.lower,
acc_conf.upper = acc_conf.upper))
} else {
return(list(acc_conf.lower = acc_conf.lower,
acc_conf.upper = acc_conf.upper,
macc_conf.lower = macc_conf.lower,
macc_conf.upper = macc_conf.upper))
}
}