# File rsr.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 2010-2023 Mauricio Zambrano-Bigiarini
# Distributed under GPL 2 or later
################################################################################
# 'rsr': Ratio of RMSE to the Standard Deviation of the Observations #
################################################################################
# Author: Mauricio Zambrano-Bigiarini #
################################################################################
# Started: 03-Feb-2010; #
# Updates: 16-Jan-2023 #
################################################################################
# 'obs' : numeric 'data.frame', 'matrix' or 'vector' with observed values
# 'sim' : numeric 'data.frame', 'matrix' or 'vector' with simulated values
# 'Result': Ratio of RMSE to the Standard Deviation of the Observations
# It varies from 0 (its optimal value), which means zero RMSE and
# therefore a perfect model simulation, to +Inf. The lower the RSR,
# the better the model performance. Moriasi+al2007 suggest that
# a good performance is obtained for RSR < 0.7
# Ref: Moriasi, D.N., Arnold, J.G., Van Liew, M.W., Bingner, R.L., Harmel,
# R.D., Veith, T.L. 2007. Model evaluation guidelines for systematic
# quantification of accuracy in watershed simulations.
# Transactions of the ASABE. 50(3):885-900.
rsr <-function(sim, obs, ...) UseMethod("rsr")
rsr.default <- function(sim, obs, na.rm=TRUE, 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')")
epsilon.type <- match.arg(epsilon.type)
# index of those elements that are present both in 'sim' and 'obs' (NON- NA values)
vi <- valindex(sim, obs)
if (length(vi) > 0) {
# Filtering 'obs' and 'sim', selecting only those pairs of elements
# that are present both in 'x' and 'y' (NON- NA values)
obs <- obs[vi]
sim <- 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
#Root mean squared error
rmse <- rmse(sim=sim, obs=obs, na.rm=na.rm, fun=NULL, ...,
epsilon.type=c("none", "Pushpalatha2012", "otherFactor", "otherValue"),
epsilon.value=NA)
#Standard deviation of the observations
sd.obs <- sd(obs, na.rm=na.rm)
if ( sd.obs > 0 ) {
rsr <- rmse / sd.obs
} else {
rsr <- NA
warning("'sd(obs)=0' -> it is not possible to compute 'RSR' !")
}
} else {
rsr <- NA
warning("There are no pairs of 'sim' and 'obs' without missing values !")
} # ELSE end
return( rsr )
} # 'rsr.default' end
################################################################################
# 'rsr': Ratio of RMSE to the Standard Deviation of the Observations #
################################################################################
# Started: 03-Feb-2010; #
# Updates: 16-Jan-2023 #
################################################################################
rsr.matrix <- function(sim, obs, na.rm=TRUE, fun=NULL, ...,
epsilon.type=c("none", "Pushpalatha2012", "otherFactor", "otherValue"),
epsilon.value=NA){
# Checking that 'sim' and 'obs' have the same dimensions
if ( all.equal(dim(sim), dim(obs)) != TRUE )
stop( paste("Invalid argument: dim(sim) != dim(obs) ( [",
paste(dim(sim), collapse=" "), "] != [",
paste(dim(obs), collapse=" "), "] )", sep="") )
rsr <- rep(NA, ncol(obs))
rsr <- sapply(1:ncol(obs), function(i,x,y) {
rsr[i] <- rsr.default( x[,i], y[,i], na.rm=na.rm, fun=fun, ...,
epsilon.type=epsilon.type,
epsilon.value=epsilon.value)
}, x=sim, y=obs )
return(rsr)
} # 'rsr.matrix' end
################################################################################
# 'rsr': Ratio of RMSE to the Standard Deviation of the Observations #
################################################################################
# Started: 03-Feb-2010; #
# Updates: 16-Jan-2023 #
################################################################################
rsr.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)
rsr.matrix(sim, obs, na.rm=na.rm, fun=fun, ...,
epsilon.type=epsilon.type, epsilon.value=epsilon.value)
} # 'rsr.data.frame' end
################################################################################
# Author: Mauricio Zambrano-Bigiarini #
################################################################################
# Started: 22-Mar-2013 #
# Updates: 16-Jan-2023 #
################################################################################
rsr.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)) {
rsr.matrix(sim, obs, na.rm=na.rm, ...)
} else NextMethod(sim, obs, na.rm=na.rm, ...)
} # 'rsr.zoo' end
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