# File rSpearman.R
# Part of the hydroGOF R package, https://github.com/hzambran/hydroGOF
# https://cran.r-project.org/package=hydroGOF
# http://www.rforge.net/hydroGOF/ ;
# Copyright 2024-2024 Mauricio Zambrano-Bigiarini
# Distributed under GPL 2 or later
# From https://en.wikipedia.org/wiki/Spearman%27s_rank_correlation_coefficient:
# In statistics, Spearman's rank correlation coefficient or Spearman's rho, named after
# Charles Spearman[1] and often denoted by the Greek letter \rho (rho) or as r_{s},
# is a nonparametric measure of rank correlation (statistical dependence between the
# rankings of two variables). It assesses how well the relationship between two variables
# can be described using a monotonic function.
# The Spearman correlation between two variables is equal to the Pearson correlation between
# the rank values of those two variables; while Pearson's correlation assesses linear
# relationships, Spearman's correlation assesses monotonic relationships (whether linear
# or not).
# If there are no repeated data values, a perfect Spearman correlation of +1 or -1 occurs
# when each of the variables is a perfect monotone function of the other.
# Intuitively, the Spearman correlation between two variables will be high when observations
# have a similar (or identical for a correlation of 1) rank (i.e. relative position label
# of the observations within the variable: 1st, 2nd, 3rd, etc.) between the two variables,
# and low when observations have a dissimilar (or fully opposed for a correlation of -1)
# rank between the two variables.
################################################################################
# Author: Mauricio Zambrano-Bigiarini #
################################################################################
# Started: 20-Jan-2024 #
# Updates: #
################################################################################
rSpearman <-function(sim, obs, ...) UseMethod("rSpearman")
rSpearman.default <- function(sim, obs, fun=NULL, ...,
epsilon.type=c("none", "Pushpalatha2012", "otherFactor", "otherValue"),
epsilon.value=NA) {
if ( is.na(match(class(sim), c("integer", "numeric", "ts", "zoo"))) |
is.na(match(class(obs), c("integer", "numeric", "ts", "zoo")))
) stop("Invalid argument type: 'sim' & 'obs' have to be of class: c('integer', 'numeric', 'ts', 'zoo')")
vi <- valindex(sim, obs)
if (length(vi) > 0) {
obs <- as.numeric(obs[vi])
sim <- as.numeric(sim[vi])
if (!is.null(fun)) {
fun1 <- match.fun(fun)
new <- preproc(sim=sim, obs=obs, fun=fun1, ...,
epsilon.type=epsilon.type, epsilon.value=epsilon.value)
sim <- new[["sim"]]
obs <- new[["obs"]]
} # IF end
rSpearman <- cor(sim, obs, method="spearman", use="pairwise.complete.obs")
# if 'sim' and 'obs' were matrixs or data.frame, then the correlation
# between observed and simulated values for each variable is given by the diagonal of 'rSpearman'
} else {
rSpearman <- NA
warning("There are no pairs of 'sim' and 'obs' without missing values !")
} # ELSE end
return(rSpearman)
} # 'rSpearman.default' end
################################################################################
# Author: Mauricio Zambrano-Bigiarini #
################################################################################
# Started: 20-Jan-2024 #
# Updates: #
################################################################################
rSpearman.matrix <- function(sim, obs, na.rm=TRUE, fun=NULL, ...,
epsilon.type=c("none", "Pushpalatha2012", "otherFactor", "otherValue"),
epsilon.value=NA){
rSpearman <- rep(NA, ncol(obs))
rSpearman <- sapply(1:ncol(obs), function(i,x,y) {
rSpearman[i] <- rSpearman.default( x[,i], y[,i], na.rm=na.rm, fun=fun, ...,
epsilon.type=epsilon.type, epsilon.value=epsilon.value)
}, x=sim, y=obs )
return(rSpearman)
} # 'rSpearman.matrix' END
################################################################################
# Author: Mauricio Zambrano-Bigiarini #
################################################################################
# Started: 20-Jan-2024 #
# Updates: #
################################################################################
rSpearman.data.frame <- function(sim, obs, na.rm=TRUE, fun=NULL, ...,
epsilon.type=c("none", "Pushpalatha2012", "otherFactor", "otherValue"),
epsilon.value=NA){
sim <- as.matrix(sim)
obs <- as.matrix(obs)
rSpearman.matrix(sim, obs, na.rm=na.rm, fun=fun, ...,
epsilon.type=epsilon.type, epsilon.value=epsilon.value)
} # 'rSpearman.data.frame' END
################################################################################
# Author: Mauricio Zambrano-Bigiarini #
################################################################################
# Started: 20-Jan-2024 #
# Updates: #
################################################################################
rSpearman.zoo <- function(sim, obs, na.rm=TRUE, fun=NULL, ...,
epsilon.type=c("none", "Pushpalatha2012", "otherFactor", "otherValue"),
epsilon.value=NA){
sim <- zoo::coredata(sim)
if (is.zoo(obs)) obs <- zoo::coredata(obs)
if (is.matrix(sim) | is.data.frame(sim)) {
rSpearman.matrix(sim, obs, na.rm=na.rm, fun=fun, ...,
epsilon.type=epsilon.type, epsilon.value=epsilon.value)
} else NextMethod(sim, obs, na.rm=na.rm, fun=fun, ...,
epsilon.type=epsilon.type, epsilon.value=epsilon.value)
} # 'rSpearman.zoo' end
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