# File mNSE.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 2011-2024 Mauricio Zambrano-Bigiarini
# Distributed under GPL 2 or later
##################################################
# 'mNSE': Modified Nash-sutcliffe Efficiency #
##################################################
# Started: July 28th, 2009; 06-Sep-09 #
# Updates: 01-Jun-2011 #
# 29-Jul-2022 #
##################################################
# Ref:
# Krause, P., Boyle, D. P., and Base, F. (2005). Comparison of different efficiency criteria for hydrological model assessment, Adv. Geosci., 5, 89-97
# Legates and McCabe, 1999. Evaluating the use of "goodness-of-fit" measures
# in hydrologic and hydroclimatic model validation.
# Water Resources Research. v35 i1. 233-241.
# Nash-Sutcliffe efficiency not "inflated" by squared values
# Essentially, the closer the model efficiency is to 1, the more accurate the model is.
# 'obs' : numeric 'data.frame', 'matrix' or 'vector' with observed values
# 'sim' : numeric 'data.frame', 'matrix' or 'vector' with simulated values
# 'j' : numeric, with the exponent to be used in the computation of the modified Nash-Sutcliffe effciency. The default value is j=1
# 'Result': Modified Nash-sutcliffe Efficiency between 'sim' and 'obs'
mNSE <-function(sim, obs, ...) UseMethod("mNSE")
mNSE.default <- function(sim, obs, j=1, 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", "xts"))) |
is.na(match(class(obs), c("integer", "numeric", "ts", "zoo", "xts")))
) stop("Invalid argument type: 'sim' & 'obs' have to be of class: c('integer', 'numeric', 'ts', 'zoo', 'xts')")
epsilon.type <- match.arg(epsilon.type)
# Checking that the provided exponent is positive
if (j < 0 ) stop("Invalid argument: 'j' must be positive")
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
denominator <- sum( abs(obs - mean(obs, na.rm=na.rm))^j )
if ( (denominator != 0) & (!is.na(denominator)) ) {
NS1 <- 1 - ( sum( abs(obs - sim)^j ) / denominator )
} else {
NS1 <- NA
warning("'sum(abs(obs - mean(obs))^j)=0', it is not possible to compute 'mNSE'")
} # ELSE end
} else {
NS1 <- NA
warning("There are no pairs of 'sim' and 'obs' without missing values !")
} # ELSE end
return(NS1)
} # 'mNSE.default' end
mNSE.matrix <- function (sim, obs, j=1, 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="") )
NS1 <- rep(NA, ncol(obs))
NS1 <- sapply(1:ncol(obs), function(i,x,y) {
NS1[i] <- mNSE.default( x[,i], y[,i], j=j, na.rm=na.rm, fun=fun, ...,
epsilon.type=epsilon.type, epsilon.value=epsilon.value)
}, x=sim, y=obs)
names(NS1) <- colnames(obs)
return(NS1)
} # 'mNSE.matrix' end
mNSE.data.frame <- function(sim, obs, j=1, na.rm=TRUE,
fun=NULL, ...,
epsilon.type=c("none", "Pushpalatha2012", "otherFactor", "otherValue"),
epsilon.value=NA){
sim <- as.matrix(sim)
obs <- as.matrix(obs)
mNSE.matrix(sim, obs, j, na.rm=na.rm, fun=fun, ...,
epsilon.type=epsilon.type, epsilon.value=epsilon.value)
} # 'mNSE.data.frame' end
mNSeff <-function(sim, obs, ...) UseMethod("mNSE")
################################################################################
# Author: Mauricio Zambrano-Bigiarini #
################################################################################
# Started: 22-Mar-2013 #
# Updates: 29-Jul-2022 #
################################################################################
mNSE.zoo <- function(sim, obs, j=1, 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)) {
mNSE.matrix(sim, obs, j=j, na.rm=na.rm, ...)
} else NextMethod(sim, obs, j=j, na.rm=na.rm, fun=fun, ...,
epsilon.type=epsilon.type, epsilon.value=epsilon.value)
} # 'mNSE.zoo' end
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