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#'Compute the correlation coefficient between an array of forecast and their corresponding observation
#'
#'Calculate the correlation coefficient (Pearson, Kendall or Spearman) for
#'an array of forecast and an array of observation. The correlations are
#'computed along time_dim, the startdate dimension. If comp_dim is given,
#'the correlations are computed only if obs along the comp_dim dimension are
#'complete between limits[1] and limits[2], i.e., there is no NA between
#'limits[1] and limits[2]. This option can be activated if the user wants to
#'account only for the forecasts which the corresponding observations are
#'available at all leadtimes.\cr
#'The confidence interval is computed by the Fisher transformation and the
#'significance level relies on an one-sided student-T distribution.\cr
#'If the dataset has more than one member, ensemble mean is necessary necessary
#'before using this function since it only allows one dimension 'dat_dim' to
#'have inconsistent length between 'exp' and 'obs'. If all the dimensions of
#''exp' and 'obs' are identical, you can simply use apply() and cor() to
#'compute the correlation.
#'
#'@param exp A named numeric array of experimental data, with at least two
#'@param obs A named numeric array of observational data, same dimensions as
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#' parameter 'exp' except along 'dat_dim' and 'memb_dim'.
#'@param time_dim A character string indicating the name of dimension along
#' which the correlations are computed. The default value is 'sdate'.
#'@param dat_dim A character string indicating the name of dataset (nobs/nexp)
#' dimension. The default value is 'dataset'.
#'@param comp_dim A character string indicating the name of dimension along which
#' obs is taken into account only if it is complete. The default value
#' is NULL.
#'@param limits A vector of two integers indicating the range along comp_dim to
#' be completed. The default is c(1, length(comp_dim dimension)).
#'@param method A character string indicating the type of correlation:
#' 'pearson', 'spearman', or 'kendall'. The default value is 'pearson'.
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#'@param memb_dim A character string indicating the name of the member
#' dimension. It must be one dimension in 'exp' and 'obs'. If there is no
#' member dimension, set NULL. The default value is NULL.
#'@param memb A logical value indicating whether to remain 'memb_dim' dimension
#' (TRUE) or do ensemble mean over 'memb_dim' (FALSE). Only functional when
#' 'memb_dim' is not NULL. The default value is TRUE.
#'@param pval A logical value indicating whether to compute or not the p-value
#' of the test Ho: Corr = 0. The default value is TRUE.
#'@param conf A logical value indicating whether to retrieve the confidence
#' intervals or not. The default value is TRUE.
#'@param conf.lev A numeric indicating the confidence level for the
#' regression computation. The default value is 0.95.
#'@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 with dimension:\cr
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#' c(nexp, nobs, exp_memb, obs_memb, all other dimensions of exp except
#' time_dim and memb_dim).\cr
#'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_memb is the number of
#'member in experiment (i.e., 'memb_dim' in exp) and obs_memb is the number of
#'member in observation (i.e., 'memb_dim' in obs).\cr\cr
#'\item{$corr}{
#' The correlation coefficient.
#'}
#'\item{$p.val}{
#' The p-value. Only present if \code{pval = TRUE}.
#'}
#'\item{$conf.lower}{
#' The lower confidence interval. Only present if \code{conf = TRUE}.
#'}
#'\item{$conf.upper}{
#' The upper confidence interval. Only present if \code{conf = TRUE}.
#'}
#'
#'@examples
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#'# Case 1: Load sample data as in Load() example:
#'example(Load)
#'clim <- Clim(sampleData$mod, sampleData$obs)
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#'ano_exp <- Ano(sampleData$mod, clim$clim_exp)
#'ano_obs <- Ano(sampleData$obs, clim$clim_obs)
#'runmean_months <- 12
#'
#'# Smooth along lead-times
#'smooth_ano_exp <- Smoothing(ano_exp, runmeanlen = runmean_months)
#'smooth_ano_obs <- Smoothing(ano_obs, runmeanlen = runmean_months)
#'required_complete_row <- 3 # Discard start dates which contain any NA lead-times
#'leadtimes_per_startdate <- 60
#'corr <- Corr(MeanDims(smooth_ano_exp, 'member'),
#' MeanDims(smooth_ano_obs, 'member'),
#' comp_dim = 'ftime',
#' limits = c(ceiling((runmean_months + 1) / 2),
#' leadtimes_per_startdate - floor(runmean_months / 2)))
#'
#'# Case 2: Keep member dimension
#'corr <- Corr(smooth_ano_exp, smooth_ano_obs, memb_dim = 'member')
#'# ensemble mean
#'corr <- Corr(smooth_ano_exp, smooth_ano_obs, memb_dim = 'member', memb = FALSE)
#'
#'@import multiApply
#'@importFrom stats cor pt qnorm
#'@export
Corr <- function(exp, obs, time_dim = 'sdate', dat_dim = 'dataset',
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comp_dim = NULL, limits = NULL, method = 'pearson',
memb_dim = NULL, memb = TRUE,
pval = TRUE, conf = TRUE,
conf.lev = 0.95, 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 be at least two dimensions ",
}
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.")
}
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.")
}
## 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.")
}
## comp_dim
if (!is.null(comp_dim)) {
if (!is.character(comp_dim) | length(comp_dim) > 1) {
stop("Parameter 'comp_dim' must be a character string.")
}
if (!comp_dim %in% names(dim(exp)) | !comp_dim %in% names(dim(obs))) {
stop("Parameter 'comp_dim' is not found in 'exp' or 'obs' dimension.")
}
}
## limits
if (!is.null(limits)) {
if (is.null(comp_dim)) {
stop("Paramter 'comp_dim' cannot be NULL if 'limits' is assigned.")
}
if (!is.numeric(limits) | any(limits %% 1 != 0) | any(limits < 0) |
length(limits) != 2 | any(limits > dim(exp)[comp_dim])) {
stop(paste0("Parameter 'limits' must be a vector of two positive ",
"integers smaller than the length of paramter 'comp_dim'."))
}
}
## method
if (!(method %in% c("kendall", "spearman", "pearson"))) {
stop("Parameter 'method' must be one of 'kendall', 'spearman' or 'pearson'.")
}
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## 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.")
}
}
## memb
if (!is.logical(memb) | length(memb) > 1) {
stop("Parameter 'memb' must be one logical value.")
}
## pval
if (!is.logical(pval) | length(pval) > 1) {
stop("Parameter 'pval' must be one logical value.")
}
## conf
if (!is.logical(conf) | length(conf) > 1) {
stop("Parameter 'conf' must be one logical value.")
}
## 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.")
}
## 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)]
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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 ",
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"all dimension expect 'dat_dim' and 'memb_dim'."))
}
if (dim(exp)[time_dim] < 3) {
stop("The length of time_dim must be at least 3 to compute correlation.")
}
###############################
# Sort dimension
name_exp <- names(dim(exp))
name_obs <- names(dim(obs))
order_obs <- match(name_exp, name_obs)
###############################
# Calculate Corr
# Remove data along comp_dim dim if there is at least one NA between limits
if (!is.null(comp_dim)) {
pos <- which(names(dim(obs)) == comp_dim)
if (is.null(limits)) {
obs_sub <- obs
} else {
obs_sub <- ClimProjDiags::Subset(obs, pos, list(limits[1]:limits[2]))
outrows <- is.na(MeanDims(obs_sub, pos, na.rm = FALSE, ncores = ncores))
outrows <- InsertDim(outrows, pos, dim(obs)[comp_dim])
obs[which(outrows)] <- NA
}
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if (is.null(memb_dim)) {
# Define output_dims
if (conf & pval) {
output_dims <- list(corr = c('nexp', 'nobs'),
p.val = c('nexp', 'nobs'),
conf.lower = c('nexp', 'nobs'),
conf.upper = c('nexp', 'nobs'))
} else if (conf & !pval) {
output_dims <- list(corr = c('nexp', 'nobs'),
conf.lower = c('nexp', 'nobs'),
conf.upper = c('nexp', 'nobs'))
} else if (!conf & pval) {
output_dims <- list(corr = c('nexp', 'nobs'),
p.val = c('nexp', 'nobs'))
} else {
output_dims <- list(corr = c('nexp', 'nobs'))
}
res <- Apply(list(exp, obs),
target_dims = list(c(time_dim, dat_dim),
c(time_dim, dat_dim)),
output_dims = output_dims,
fun = .Corr,
time_dim = time_dim, method = method,
pval = pval, conf = conf, conf.lev = conf.lev,
ncores = ncores)
} else {
if (!memb) { #ensemble mean
name_exp <- names(dim(exp))
margin_dims_ind <- c(1:length(name_exp))[-which(name_exp == memb_dim)]
exp <- apply(exp, margin_dims_ind, mean, na.rm = TRUE) #NOTE: remove NAs here
obs <- apply(obs, margin_dims_ind, mean, na.rm = TRUE)
# Define output_dims
if (conf & pval) {
output_dims <- list(corr = c('nexp', 'nobs'),
p.val = c('nexp', 'nobs'),
conf.lower = c('nexp', 'nobs'),
conf.upper = c('nexp', 'nobs'))
} else if (conf & !pval) {
output_dims <- list(corr = c('nexp', 'nobs'),
conf.lower = c('nexp', 'nobs'),
conf.upper = c('nexp', 'nobs'))
} else if (!conf & pval) {
output_dims <- list(corr = c('nexp', 'nobs'),
p.val = c('nexp', 'nobs'))
} else {
output_dims <- list(corr = c('nexp', 'nobs'))
}
res <- Apply(list(exp, obs),
target_dims = list(c(time_dim, dat_dim),
c(time_dim, dat_dim)),
output_dims = output_dims,
fun = .Corr,
time_dim = time_dim, method = method,
pval = pval, conf = conf, conf.lev = conf.lev, ncores_input = ncores,
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ncores = ncores)
} else { # correlation for each member
# Define output_dims
if (conf & pval) {
output_dims <- list(corr = c('nexp', 'nobs', 'exp_memb', 'obs_memb'),
p.val = c('nexp', 'nobs', 'exp_memb', 'obs_memb'),
conf.lower = c('nexp', 'nobs', 'exp_memb', 'obs_memb'),
conf.upper = c('nexp', 'nobs', 'exp_memb', 'obs_memb'))
} else if (conf & !pval) {
output_dims <- list(corr = c('nexp', 'nobs', 'exp_memb', 'obs_memb'),
conf.lower = c('nexp', 'nobs', 'exp_memb', 'obs_memb'),
conf.upper = c('nexp', 'nobs', 'exp_memb', 'obs_memb'))
} else if (!conf & pval) {
output_dims <- list(corr = c('nexp', 'nobs', 'exp_memb', 'obs_memb'),
p.val = c('nexp', 'nobs', 'exp_memb', 'obs_memb'))
} else {
output_dims <- list(corr = c('nexp', 'nobs', 'exp_memb', 'obs_memb'))
}
res <- Apply(list(exp, obs),
target_dims = list(c(time_dim, dat_dim, memb_dim),
c(time_dim, dat_dim, memb_dim)),
output_dims = output_dims,
fun = .Corr,
time_dim = time_dim, method = method,
pval = pval, conf = conf, conf.lev = conf.lev, ncores_input = ncores,
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ncores = ncores)
}
}
return(res)
}
.Corr <- function(exp, obs, time_dim = 'sdate', method = 'pearson',
conf = TRUE, pval = TRUE, conf.lev = 0.95, ncores_input = NULL) {
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if (length(dim(exp)) == 2) {
# exp: [sdate, dat_exp]
# obs: [sdate, dat_obs]
nexp <- as.numeric(dim(exp)[2])
nobs <- as.numeric(dim(obs)[2])
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# NOTE: Use sapply to replace the for loop
CORR <- sapply(1:nobs, function(i) {
sapply(1:nexp, function (x) {
if (any(!is.na(exp[, x])) && sum(!is.na(obs[, i])) > 2) { #NOTE: Is this necessary?
cor(exp[, x], obs[, i],
use = "pairwise.complete.obs",
method = method)
} else {
NA #CORR[, i] <- NA
}
})
})
if (is.null(dim(CORR))) {
CORR <- array(CORR, dim = c(1, 1))
}
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} else { # member
# exp: [sdate, dat_exp, memb_exp]
# obs: [sdate, dat_obs, memb_obs]
nexp <- as.numeric(dim(exp)[2])
nobs <- as.numeric(dim(obs)[2])
exp_memb <- as.numeric(dim(exp)[3])
obs_memb <- as.numeric(dim(obs)[3])
CORR <- array(dim = c(nexp = nexp, nobs = nobs, exp_memb = exp_memb, obs_memb = obs_memb))
for (j in 1:obs_memb) {
for (y in 1:exp_memb) {
CORR[, , y, j] <- sapply(1:nobs, function(i) {
sapply(1:nexp, function (x) {
if (any(!is.na(exp[, x, y])) && sum(!is.na(obs[, i, j])) > 2) {
cor(exp[, x, y], obs[, i, j],
use = "pairwise.complete.obs",
method = method)
} else {
NA #CORR[, i] <- NA
}
})
})
}
}
}
# if (pval) {
# for (i in 1:nobs) {
# p.val[, i] <- try(sapply(1:nexp,
# function(x) {(cor.test(exp[, x], obs[, i],
# use = "pairwise.complete.obs",
# method = method)$p.value)/2}), silent = TRUE)
# if (class(p.val[, i]) == 'character') {
# p.val[, i] <- NA
# }
# }
# }
if (pval | conf) {
if (method == "kendall" | method == "spearman") {
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tmp <- apply(obs, c(1:length(dim(obs)))[-1], rank) # for memb_dim = NULL, 2; for memb_dim, c(2, 3)
names(dim(tmp))[1] <- time_dim
eno <- Eno(tmp, time_dim, ncores = ncores_input)
} else if (method == "pearson") {
eno <- Eno(obs, time_dim, ncores = ncores_input)
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if (length(dim(exp)) == 2) {
eno_expand <- array(dim = c(nexp = nexp, nobs = nobs))
for (i in 1:nexp) {
eno_expand[i, ] <- eno
}
} else { #member
eno_expand <- array(dim = c(nexp = nexp, nobs = nobs, exp_memb = exp_memb, obs_memb = obs_memb))
for (i in 1:nexp) {
for (j in 1:exp_memb) {
eno_expand[i, , j, ] <- eno
}
}
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#############old#################
#This doesn't return error but it's diff from cor.test() when method is spearman and kendall
if (pval) {
t <-sqrt(CORR * CORR * (eno_expand - 2) / (1 - (CORR ^ 2)))
p.val <- pt(t, eno_expand - 2, lower.tail = FALSE)
}
###################################
if (conf) {
conf.lower <- (1 - conf.lev) / 2
conf.upper <- 1 - conf.lower
conflow <- tanh(atanh(CORR) + qnorm(conf.lower) / sqrt(eno_expand - 3))
confhigh <- tanh(atanh(CORR) + qnorm(conf.upper) / sqrt(eno_expand - 3))
}
if (pval & conf) {
res <- list(corr = CORR, p.val = p.val,
conf.lower = conflow, conf.upper = confhigh)
} else if (pval & !conf) {
res <- list(corr = CORR, p.val = p.val)
} else if (!pval & conf) {
res <- list(corr = CORR,
conf.lower = conflow, conf.upper = confhigh)
} else {
res <- list(corr = CORR)
}
return(res)
}