Nothing
R/gof.R
In hydroGOF: Goodness-of-Fit Functions for Comparison of Simulated and Observed Hydrological Time Series
Defines functions
gof.zoo
gof.data.frame
gof.matrix
gof.default
gof
Documented in
gof
gof.data.frame
gof.default
gof.matrix
gof.zoo
# File gof.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 2008-2024 Mauricio Zambrano-Bigiarini
# Distributed under GPL 2 or later
################################################################################
# Author: Mauricio Zambrano-Bigiarini #
################################################################################
# Started: 15-Dic-2008 -> 03 Feb 2009; #
# Updates: 06-Sep-09 #
# 2010 #
# 21-Jan-2011 #
# 08-May-2012 #
# 14-Jan-2023 ; 15-Jan-2023 ; 16-Jan-2023 #
# 19-Jan-2024 ; 20-Jan-2024 ; 23-Mar-2024 ; 08-May-2024 #
################################################################################
# It computes:
# 1) 'me' : Mean Error
# 2) 'mae' : Mean Absolute Error
# 3) 'mse' : Mean Square Error
# 4) 'rmse' : Root Mean Square Error
# 5) 'ubRMSE : unbiased Root Mean Square Error
# 6) 'nrms' : Normalized Root Mean Square Error
# 7) 'PBIAS' : Percent Bias ( -1 <= PBIAS <= 1 )
# 8) 'RSR' : Ratio of the RMSE to the standard deviation of the observations
# 9) 'rSD' : Ratio of Standard Deviations, rSD = SD(sim) / SD(obs)
# 10) 'NSE' : Nash-Sutcliffe Efficiency ( -Inf <= NSE <= 1 )
# 11) 'mNSE' : Modified Nash-Sutcliffe Efficiency
# 12) 'rNSE' : Relative Nash-Sutcliffe Efficiency
# 13) 'wNSE' : Weighted Nash-Sutcliffe Efficiency
# 14) 'wsNSE' : Weighted Seasonal Nash-Sutcliffe Efficiency ( -Inf <= wsNSE <= 1 )
# 15) 'd' : Index of Agreement( 0 <= d <= 1 )
# 16) 'dr' : Refined Index of Agreement( -1 <= dr <= 1 )
# 17) 'md' : Modified Index of Agreement( 0 <= md <= 1 )
# 18) 'rd' : Relative Index of Agreement( 0 <= rd <= 1 )
# 29) 'cp' : Coefficient of Persistence ( 0 <= cp <= 1 )
# 20) 'r' : Pearson Correlation coefficient ( -1 <= r <= 1 )
# 21) 'R2' : Coefficient of Determination ( 0 <= R2 <= 1 )
# 22) 'bR2' : Weighted coefficient of determination
# 23) 'VE' : Volumetric efficiency
# 24) 'KGE' : Kling-Gupta efficiency (-Inf < KGE <= 1)
# 25) 'KGElf' : Kling-Gupta efficiency with focus on low values (-Inf < KGElf <= 1)
# 26) 'KGEnp' : Non-parametric Kling-Gupta efficiency (-Inf < KGEnp <= 1)
# 27) 'KGEkm' : Knowable Moments Kling-Gupta efficiency (-Inf < KGEkm <= 1)
# 28) 'APFB' : Annual Peak Flow Bias ( 0 <= APFB <= Inf )
# 29) 'HFB' : High Flow Bias ( 0 <= HFB <= Inf )
# 30) 'sKGE' : Split Kling-Gupta efficiency (-Inf < sKGE <= 1)
# 31) 'rSpearman' : Spearman correlation coefficient ( -1 <= r <= 1 )
# 32) 'pbiasFDC' : PBIAS in the slope of the midsegment of the Flow Duration Curve ( 0 <= pbiasFDC )
gof <-function(sim, obs, ...) UseMethod("gof")
gof.default <- function(sim, obs, na.rm=TRUE, do.spearman=FALSE, do.pbfdc=FALSE,
j=1, lambda=0.95, norm="sd", s=c(1,1,1),
method=c("2009", "2012", "2021"), lQ.thr=0.6, hQ.thr=0.1,
start.month=1, digits=2, fun=NULL, ...,
epsilon.type=c("none", "Pushpalatha2012", "otherFactor", "otherValue"),
epsilon.value=NA){
method <- match.arg(method)
epsilon.type <- match.arg(epsilon.type)
ME <- me(sim, obs, na.rm=na.rm, fun=fun, ...,
epsilon.type=epsilon.type, epsilon.value=epsilon.value)
MAE <- mae(sim, obs, na.rm=na.rm, fun=fun, ...,
epsilon.type=epsilon.type, epsilon.value=epsilon.value)
MSE <- mse(sim, obs, na.rm=na.rm, fun=fun, ...,
epsilon.type=epsilon.type, epsilon.value=epsilon.value)
RMSE <- rmse(sim, obs, na.rm=na.rm, fun=fun, ...,
epsilon.type=epsilon.type, epsilon.value=epsilon.value)
ubRMSE <- ubRMSE(sim, obs, na.rm=na.rm, fun=fun, ...,
epsilon.type=epsilon.type, epsilon.value=epsilon.value)
NRMSE <- nrmse(sim, obs, na.rm=na.rm, norm=norm, fun=fun, ...,
epsilon.type=epsilon.type, epsilon.value=epsilon.value)
RSR <- rsr(sim, obs, na.rm=na.rm, fun=fun, ...,
epsilon.type=epsilon.type, epsilon.value=epsilon.value)
rSD <- rSD(sim, obs, na.rm=na.rm, fun=fun, ...,
epsilon.type=epsilon.type, epsilon.value=epsilon.value)
PBIAS <- pbias(sim, obs, na.rm=na.rm, fun=fun, ...,
epsilon.type=epsilon.type, epsilon.value=epsilon.value)
NSE <- NSE(sim, obs, na.rm=na.rm, fun=fun, ...,
epsilon.type=epsilon.type, epsilon.value=epsilon.value)
mNSE <- mNSE(sim, obs, na.rm=na.rm, j=j, fun=fun, ...,
epsilon.type=epsilon.type, epsilon.value=epsilon.value)
rNSE <- rNSE(sim, obs, na.rm=na.rm, fun=fun, ...,
epsilon.type=epsilon.type, epsilon.value=epsilon.value)
wNSE <- wNSE(sim, obs, na.rm=na.rm, fun=fun, ...,
epsilon.type=epsilon.type, epsilon.value=epsilon.value)
wsNSE <- wsNSE(sim, obs, na.rm=na.rm, j=j, lambda=lambda,
lQ.thr=lQ.thr, hQ.thr=hQ.thr, fun=fun, ...,
epsilon.type=epsilon.type, epsilon.value=epsilon.value)
d <- d(sim, obs, na.rm=na.rm, fun=fun, ...,
epsilon.type=epsilon.type, epsilon.value=epsilon.value)
dr <- dr(sim, obs, na.rm=na.rm, fun=fun, ...,
epsilon.type=epsilon.type, epsilon.value=epsilon.value)
md <- md(sim, obs, na.rm=na.rm, fun=fun, ...,
epsilon.type=epsilon.type, epsilon.value=epsilon.value)
rd <- rd(sim, obs, na.rm=na.rm, fun=fun, ...,
epsilon.type=epsilon.type, epsilon.value=epsilon.value)
cp <- cp(sim, obs, na.rm=na.rm, fun=fun, ...,
epsilon.type=epsilon.type, epsilon.value=epsilon.value)
r <- .rPearson(sim, obs, fun=fun, ...,
epsilon.type=epsilon.type, epsilon.value=epsilon.value)
R2 <- R2(sim, obs, na.rm=na.rm, fun=fun, ...,
epsilon.type=epsilon.type, epsilon.value=epsilon.value)
bR2 <- br2(sim, obs, na.rm=na.rm, fun=fun, ...,
epsilon.type=epsilon.type, epsilon.value=epsilon.value)
VE <- VE(sim, obs, na.rm=na.rm, fun=fun, ...,
epsilon.type=epsilon.type, epsilon.value=epsilon.value)
KGE <- KGE(sim, obs, na.rm=na.rm, s=s, method=method, out.type="single",
fun=fun, ..., epsilon.type=epsilon.type, epsilon.value=epsilon.value)
KGElf <- KGElf(sim, obs, na.rm=na.rm, s=s, method=method,
epsilon.type=epsilon.type, epsilon.value=epsilon.value)
KGEnp <- KGEnp(sim, obs, na.rm=na.rm, out.type="single", fun=fun, ...,
epsilon.type=epsilon.type, epsilon.value=epsilon.value)
KGEkm <- KGEkm(sim, obs, na.rm=na.rm, s=s, method=method, out.type="single",
fun=fun, ..., epsilon.type=epsilon.type, epsilon.value=epsilon.value)
if ( zoo::is.zoo(sim) & zoo::is.zoo(obs) ) {
do.sKGE <- TRUE
sKGE <- sKGE(sim, obs, s=s, na.rm=na.rm, method=method,
start.month=start.month, out.PerYear=FALSE, fun=fun, ...,
epsilon.type=epsilon.type, epsilon.value=epsilon.value)
do.HFB <- TRUE
HFB <- HFB(sim, obs, na.rm=na.rm, hQ.thr=hQ.thr, start.month=start.month,
out.PerYear=FALSE, fun=fun, ...,
epsilon.type=epsilon.type, epsilon.value=epsilon.value)
do.APFB <- TRUE
APFB <- APFB(sim, obs, na.rm=na.rm, start.month=start.month,
out.PerYear=FALSE, fun=fun, ...,
epsilon.type=epsilon.type, epsilon.value=epsilon.value)
} else {
do.sKGE <- FALSE
do.HFB <- FALSE
do.APFB <- FALSE
sKGE <- NA
HFB <- NA
APFB <- NA
} # ELSE end
# Creating the basic output object
gof <- rbind( ME, MAE, MSE, RMSE, ubRMSE,
NRMSE, PBIAS, RSR, rSD, NSE,
mNSE, rNSE, wNSE, wsNSE, d,
dr, md, rd, cp, r,
R2, bR2, VE, KGE, KGElf,
KGEnp, KGEkm)
# Changing names to NRMSE and PBIAS
rownames(gof)[6] <- "NRMSE %"
rownames(gof)[7] <- "PBIAS %"
# Adding Split KGE
if (do.sKGE) { # 'sKGE' is presented after 'KGEkm'
gof <- rbind(gof, sKGE)
rownames(gof)[length(rownames(gof))] <- "sKGE"
} # IF end
# Adding APFB
if (do.APFB) { # 'APFB' is presented after 'sKGE'
gof <- rbind(gof, APFB)
rownames(gof)[length(rownames(gof))] <- "APFB"
} # IF end
# Adding HFB
if (do.HFB) { # 'HFB' is presented after 'APFB'
gof <- rbind(gof, HFB)
rownames(gof)[length(rownames(gof))] <- "HFB"
} # IF end
# Adding r.Spearman
if (do.spearman) {
r.Spearman <- rSpearman(sim, obs, na.rm=na.rm, fun=fun, ...,
epsilon.type=epsilon.type, epsilon.value=epsilon.value)
gof <- rbind(gof, r.Spearman)
rownames(gof)[length(rownames(gof))] <- "rSpearman"
} # IF end
# Adding pbiasfdc
if (do.pbfdc) {
pbfdc <- pbiasfdc(sim, obs, na.rm=na.rm, lQ.thr=lQ.thr, hQ.thr=hQ.thr, plot=FALSE,
fun=fun, ..., epsilon.type=epsilon.type, epsilon.value=epsilon.value)
gof <- rbind(gof, pbfdc)
rownames(gof)[length(rownames(gof))] <- "pbiasFDC %"
} # IF end
# Rounding the final results, for avoiding scientific notation
gof <- round(gof, digits)
return(gof)
} # 'gof.default' end
################################################################################
# Author: Mauricio Zambrano-Bigiarini #
################################################################################
# Started: 15-Dic-2008 -> 03 Feb 2009; #
# Updates: 06-Sep-09 #
# 2010 #
# 21-Jan-2011 #
# 08-May-2012 #
# 15-Jan-2023 #
# 19-Jan-2024 ; 08-May-2024 #
################################################################################
gof.matrix <- function(sim, obs, na.rm=TRUE, do.spearman=FALSE, do.pbfdc=FALSE,
j=1, lambda=0.95, norm="sd", s=c(1,1,1),
method=c("2009", "2012", "2021"), lQ.thr=0.6, hQ.thr=0.1,
start.month=1, digits=2, fun=NULL, ...,
epsilon.type=c("none", "Pushpalatha2012", "otherFactor", "otherValue"),
epsilon.value=NA){
# Temporal variable for some computations
tmp <- gof(1:10,1:10)
# Number of objective functions currently computed by gof
ngof <- nrow(tmp)
# Name of the objective functions computed by 'gof'
gofnames <- rownames(tmp)
# Creating the matrix that will store the final results
gof <- matrix(NA, ncol(obs), nrow=ngof)
# Computing the goodness-of-fit measures for each column of 'sim' and 'obs'
gof <- sapply(1:ncol(obs), function(i,x,y) {
gof[, i] <- gof.default( x[,i], y[,i], na.rm=na.rm,
do.spearman=do.spearman, do.pbfdc=do.pbfdc,
j=j, lambda=lambda, norm=norm, s=s,
method=method, lQ.thr=lQ.thr, hQ.thr=hQ.thr,
start.month=start.month, digits=digits, fun=fun, ...,
epsilon.type=epsilon.type,
epsilon.value=epsilon.value)
}, x=sim, y=obs )
rownames(gof) <- gofnames
colnames(gof) <- colnames(sim)
return(gof)
} # 'gof.matrix' end
################################################################################
# Author: Mauricio Zambrano-Bigiarini #
################################################################################
# Started: 15-Dic-2008 -> 03 Feb 2009; #
# Updates: 06-Sep-09 #
# 2010 #
# 21-Jan-2011 #
# 08-May-2012 ; #
# 15-Jan-2023 #
# 19-Jan-2024 ; 08-May-2024 #
################################################################################
gof.data.frame <- function(sim, obs, na.rm=TRUE, do.spearman=FALSE, do.pbfdc=FALSE,
j=1, lambda=0.95, norm="sd", s=c(1,1,1),
method=c("2009", "2012", "2021"), lQ.thr=0.6, hQ.thr=0.1,
start.month=1, digits=2, fun=NULL, ...,
epsilon.type=c("none", "Pushpalatha2012", "otherFactor", "otherValue"),
epsilon.value=NA){
sim <- as.matrix(sim)
obs <- as.matrix(obs)
gof.matrix(sim, obs, na.rm=na.rm, do.spearman=do.spearman, do.pbfdc=do.pbfdc,
j=j, lambda=lambda, norm=norm, s=s,
method=method, lQ.thr=lQ.thr, hQ.thr=hQ.thr,
start.month=start.month, digits=digits, fun=fun, ...,
epsilon.type=epsilon.type,
epsilon.value=epsilon.value)
} # 'gof.data.frame' end
################################################################################
# Author: Mauricio Zambrano-Bigiarini #
################################################################################
# Started: 05-Nov-2012 #
# Updates: 22-Mar-2013 #
# 15-Jan-2023 #
# 19-Jan-2024 ; 08-May-2024 #
################################################################################
gof.zoo <- function(sim, obs, na.rm=TRUE, do.spearman=FALSE, do.pbfdc=FALSE,
j=1, lambda=0.95, norm="sd", s=c(1,1,1),
method=c("2009", "2012", "2021"), lQ.thr=0.6, hQ.thr=0.1,
start.month=1, digits=2, 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)) {
gof.matrix(sim, obs, na.rm=na.rm,
do.spearman=do.spearman, do.pbfdc=do.pbfdc,
j=j, lambda=lambda, norm=norm, s=s,
method=method, lQ.thr=lQ.thr, hQ.thr=hQ.thr,
start.month=start.month, digits=digits, fun=fun, ...,
epsilon.type=epsilon.type,
epsilon.value=epsilon.value)
} else
NextMethod(sim, obs, na.rm=na.rm,
do.spearman=do.spearman, do.pbfdc=do.pbfdc,
j=j, lambda=lambda, norm=norm, s=s,
method=method, lQ.thr=lQ.thr, hQ.thr=hQ.thr,
start.month=start.month, digits=digits, fun=fun, ...,
epsilon.type=epsilon.type,
epsilon.value=epsilon.value)
} # 'gof.zoo' end
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Defines functions gof.zoo gof.data.frame gof.matrix gof.default gof
Documented in gof gof.data.frame gof.default gof.matrix gof.zoo
# File gof.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 2008-2024 Mauricio Zambrano-Bigiarini
# Distributed under GPL 2 or later
################################################################################
# Author: Mauricio Zambrano-Bigiarini #
################################################################################
# Started: 15-Dic-2008 -> 03 Feb 2009; #
# Updates: 06-Sep-09 #
# 2010 #
# 21-Jan-2011 #
# 08-May-2012 #
# 14-Jan-2023 ; 15-Jan-2023 ; 16-Jan-2023 #
# 19-Jan-2024 ; 20-Jan-2024 ; 23-Mar-2024 ; 08-May-2024 #
################################################################################
# It computes:
# 1) 'me' : Mean Error
# 2) 'mae' : Mean Absolute Error
# 3) 'mse' : Mean Square Error
# 4) 'rmse' : Root Mean Square Error
# 5) 'ubRMSE : unbiased Root Mean Square Error
# 6) 'nrms' : Normalized Root Mean Square Error
# 7) 'PBIAS' : Percent Bias ( -1 <= PBIAS <= 1 )
# 8) 'RSR' : Ratio of the RMSE to the standard deviation of the observations
# 9) 'rSD' : Ratio of Standard Deviations, rSD = SD(sim) / SD(obs)
# 10) 'NSE' : Nash-Sutcliffe Efficiency ( -Inf <= NSE <= 1 )
# 11) 'mNSE' : Modified Nash-Sutcliffe Efficiency
# 12) 'rNSE' : Relative Nash-Sutcliffe Efficiency
# 13) 'wNSE' : Weighted Nash-Sutcliffe Efficiency
# 14) 'wsNSE' : Weighted Seasonal Nash-Sutcliffe Efficiency ( -Inf <= wsNSE <= 1 )
# 15) 'd' : Index of Agreement( 0 <= d <= 1 )
# 16) 'dr' : Refined Index of Agreement( -1 <= dr <= 1 )
# 17) 'md' : Modified Index of Agreement( 0 <= md <= 1 )
# 18) 'rd' : Relative Index of Agreement( 0 <= rd <= 1 )
# 29) 'cp' : Coefficient of Persistence ( 0 <= cp <= 1 )
# 20) 'r' : Pearson Correlation coefficient ( -1 <= r <= 1 )
# 21) 'R2' : Coefficient of Determination ( 0 <= R2 <= 1 )
# 22) 'bR2' : Weighted coefficient of determination
# 23) 'VE' : Volumetric efficiency
# 24) 'KGE' : Kling-Gupta efficiency (-Inf < KGE <= 1)
# 25) 'KGElf' : Kling-Gupta efficiency with focus on low values (-Inf < KGElf <= 1)
# 26) 'KGEnp' : Non-parametric Kling-Gupta efficiency (-Inf < KGEnp <= 1)
# 27) 'KGEkm' : Knowable Moments Kling-Gupta efficiency (-Inf < KGEkm <= 1)
# 28) 'APFB' : Annual Peak Flow Bias ( 0 <= APFB <= Inf )
# 29) 'HFB' : High Flow Bias ( 0 <= HFB <= Inf )
# 30) 'sKGE' : Split Kling-Gupta efficiency (-Inf < sKGE <= 1)
# 31) 'rSpearman' : Spearman correlation coefficient ( -1 <= r <= 1 )
# 32) 'pbiasFDC' : PBIAS in the slope of the midsegment of the Flow Duration Curve ( 0 <= pbiasFDC )
gof <-function(sim, obs, ...) UseMethod("gof")
gof.default <- function(sim, obs, na.rm=TRUE, do.spearman=FALSE, do.pbfdc=FALSE,
j=1, lambda=0.95, norm="sd", s=c(1,1,1),
method=c("2009", "2012", "2021"), lQ.thr=0.6, hQ.thr=0.1,
start.month=1, digits=2, fun=NULL, ...,
epsilon.type=c("none", "Pushpalatha2012", "otherFactor", "otherValue"),
epsilon.value=NA){
method <- match.arg(method)
epsilon.type <- match.arg(epsilon.type)
ME <- me(sim, obs, na.rm=na.rm, fun=fun, ...,
epsilon.type=epsilon.type, epsilon.value=epsilon.value)
MAE <- mae(sim, obs, na.rm=na.rm, fun=fun, ...,
epsilon.type=epsilon.type, epsilon.value=epsilon.value)
MSE <- mse(sim, obs, na.rm=na.rm, fun=fun, ...,
epsilon.type=epsilon.type, epsilon.value=epsilon.value)
RMSE <- rmse(sim, obs, na.rm=na.rm, fun=fun, ...,
epsilon.type=epsilon.type, epsilon.value=epsilon.value)
ubRMSE <- ubRMSE(sim, obs, na.rm=na.rm, fun=fun, ...,
epsilon.type=epsilon.type, epsilon.value=epsilon.value)
NRMSE <- nrmse(sim, obs, na.rm=na.rm, norm=norm, fun=fun, ...,
epsilon.type=epsilon.type, epsilon.value=epsilon.value)
RSR <- rsr(sim, obs, na.rm=na.rm, fun=fun, ...,
epsilon.type=epsilon.type, epsilon.value=epsilon.value)
rSD <- rSD(sim, obs, na.rm=na.rm, fun=fun, ...,
epsilon.type=epsilon.type, epsilon.value=epsilon.value)
PBIAS <- pbias(sim, obs, na.rm=na.rm, fun=fun, ...,
epsilon.type=epsilon.type, epsilon.value=epsilon.value)
NSE <- NSE(sim, obs, na.rm=na.rm, fun=fun, ...,
epsilon.type=epsilon.type, epsilon.value=epsilon.value)
mNSE <- mNSE(sim, obs, na.rm=na.rm, j=j, fun=fun, ...,
epsilon.type=epsilon.type, epsilon.value=epsilon.value)
rNSE <- rNSE(sim, obs, na.rm=na.rm, fun=fun, ...,
epsilon.type=epsilon.type, epsilon.value=epsilon.value)
wNSE <- wNSE(sim, obs, na.rm=na.rm, fun=fun, ...,
epsilon.type=epsilon.type, epsilon.value=epsilon.value)
wsNSE <- wsNSE(sim, obs, na.rm=na.rm, j=j, lambda=lambda,
lQ.thr=lQ.thr, hQ.thr=hQ.thr, fun=fun, ...,
epsilon.type=epsilon.type, epsilon.value=epsilon.value)
d <- d(sim, obs, na.rm=na.rm, fun=fun, ...,
epsilon.type=epsilon.type, epsilon.value=epsilon.value)
dr <- dr(sim, obs, na.rm=na.rm, fun=fun, ...,
epsilon.type=epsilon.type, epsilon.value=epsilon.value)
md <- md(sim, obs, na.rm=na.rm, fun=fun, ...,
epsilon.type=epsilon.type, epsilon.value=epsilon.value)
rd <- rd(sim, obs, na.rm=na.rm, fun=fun, ...,
epsilon.type=epsilon.type, epsilon.value=epsilon.value)
cp <- cp(sim, obs, na.rm=na.rm, fun=fun, ...,
epsilon.type=epsilon.type, epsilon.value=epsilon.value)
r <- .rPearson(sim, obs, fun=fun, ...,
epsilon.type=epsilon.type, epsilon.value=epsilon.value)
R2 <- R2(sim, obs, na.rm=na.rm, fun=fun, ...,
epsilon.type=epsilon.type, epsilon.value=epsilon.value)
bR2 <- br2(sim, obs, na.rm=na.rm, fun=fun, ...,
epsilon.type=epsilon.type, epsilon.value=epsilon.value)
VE <- VE(sim, obs, na.rm=na.rm, fun=fun, ...,
epsilon.type=epsilon.type, epsilon.value=epsilon.value)
KGE <- KGE(sim, obs, na.rm=na.rm, s=s, method=method, out.type="single",
fun=fun, ..., epsilon.type=epsilon.type, epsilon.value=epsilon.value)
KGElf <- KGElf(sim, obs, na.rm=na.rm, s=s, method=method,
epsilon.type=epsilon.type, epsilon.value=epsilon.value)
KGEnp <- KGEnp(sim, obs, na.rm=na.rm, out.type="single", fun=fun, ...,
epsilon.type=epsilon.type, epsilon.value=epsilon.value)
KGEkm <- KGEkm(sim, obs, na.rm=na.rm, s=s, method=method, out.type="single",
fun=fun, ..., epsilon.type=epsilon.type, epsilon.value=epsilon.value)
if ( zoo::is.zoo(sim) & zoo::is.zoo(obs) ) {
do.sKGE <- TRUE
sKGE <- sKGE(sim, obs, s=s, na.rm=na.rm, method=method,
start.month=start.month, out.PerYear=FALSE, fun=fun, ...,
epsilon.type=epsilon.type, epsilon.value=epsilon.value)
do.HFB <- TRUE
HFB <- HFB(sim, obs, na.rm=na.rm, hQ.thr=hQ.thr, start.month=start.month,
out.PerYear=FALSE, fun=fun, ...,
epsilon.type=epsilon.type, epsilon.value=epsilon.value)
do.APFB <- TRUE
APFB <- APFB(sim, obs, na.rm=na.rm, start.month=start.month,
out.PerYear=FALSE, fun=fun, ...,
epsilon.type=epsilon.type, epsilon.value=epsilon.value)
} else {
do.sKGE <- FALSE
do.HFB <- FALSE
do.APFB <- FALSE
sKGE <- NA
HFB <- NA
APFB <- NA
} # ELSE end
# Creating the basic output object
gof <- rbind( ME, MAE, MSE, RMSE, ubRMSE,
NRMSE, PBIAS, RSR, rSD, NSE,
mNSE, rNSE, wNSE, wsNSE, d,
dr, md, rd, cp, r,
R2, bR2, VE, KGE, KGElf,
KGEnp, KGEkm)
# Changing names to NRMSE and PBIAS
rownames(gof)[6] <- "NRMSE %"
rownames(gof)[7] <- "PBIAS %"
# Adding Split KGE
if (do.sKGE) { # 'sKGE' is presented after 'KGEkm'
gof <- rbind(gof, sKGE)
rownames(gof)[length(rownames(gof))] <- "sKGE"
} # IF end
# Adding APFB
if (do.APFB) { # 'APFB' is presented after 'sKGE'
gof <- rbind(gof, APFB)
rownames(gof)[length(rownames(gof))] <- "APFB"
} # IF end
# Adding HFB
if (do.HFB) { # 'HFB' is presented after 'APFB'
gof <- rbind(gof, HFB)
rownames(gof)[length(rownames(gof))] <- "HFB"
} # IF end
# Adding r.Spearman
if (do.spearman) {
r.Spearman <- rSpearman(sim, obs, na.rm=na.rm, fun=fun, ...,
epsilon.type=epsilon.type, epsilon.value=epsilon.value)
gof <- rbind(gof, r.Spearman)
rownames(gof)[length(rownames(gof))] <- "rSpearman"
} # IF end
# Adding pbiasfdc
if (do.pbfdc) {
pbfdc <- pbiasfdc(sim, obs, na.rm=na.rm, lQ.thr=lQ.thr, hQ.thr=hQ.thr, plot=FALSE,
fun=fun, ..., epsilon.type=epsilon.type, epsilon.value=epsilon.value)
gof <- rbind(gof, pbfdc)
rownames(gof)[length(rownames(gof))] <- "pbiasFDC %"
} # IF end
# Rounding the final results, for avoiding scientific notation
gof <- round(gof, digits)
return(gof)
} # 'gof.default' end
################################################################################
# Author: Mauricio Zambrano-Bigiarini #
################################################################################
# Started: 15-Dic-2008 -> 03 Feb 2009; #
# Updates: 06-Sep-09 #
# 2010 #
# 21-Jan-2011 #
# 08-May-2012 #
# 15-Jan-2023 #
# 19-Jan-2024 ; 08-May-2024 #
################################################################################
gof.matrix <- function(sim, obs, na.rm=TRUE, do.spearman=FALSE, do.pbfdc=FALSE,
j=1, lambda=0.95, norm="sd", s=c(1,1,1),
method=c("2009", "2012", "2021"), lQ.thr=0.6, hQ.thr=0.1,
start.month=1, digits=2, fun=NULL, ...,
epsilon.type=c("none", "Pushpalatha2012", "otherFactor", "otherValue"),
epsilon.value=NA){
# Temporal variable for some computations
tmp <- gof(1:10,1:10)
# Number of objective functions currently computed by gof
ngof <- nrow(tmp)
# Name of the objective functions computed by 'gof'
gofnames <- rownames(tmp)
# Creating the matrix that will store the final results
gof <- matrix(NA, ncol(obs), nrow=ngof)
# Computing the goodness-of-fit measures for each column of 'sim' and 'obs'
gof <- sapply(1:ncol(obs), function(i,x,y) {
gof[, i] <- gof.default( x[,i], y[,i], na.rm=na.rm,
do.spearman=do.spearman, do.pbfdc=do.pbfdc,
j=j, lambda=lambda, norm=norm, s=s,
method=method, lQ.thr=lQ.thr, hQ.thr=hQ.thr,
start.month=start.month, digits=digits, fun=fun, ...,
epsilon.type=epsilon.type,
epsilon.value=epsilon.value)
}, x=sim, y=obs )
rownames(gof) <- gofnames
colnames(gof) <- colnames(sim)
return(gof)
} # 'gof.matrix' end
################################################################################
# Author: Mauricio Zambrano-Bigiarini #
################################################################################
# Started: 15-Dic-2008 -> 03 Feb 2009; #
# Updates: 06-Sep-09 #
# 2010 #
# 21-Jan-2011 #
# 08-May-2012 ; #
# 15-Jan-2023 #
# 19-Jan-2024 ; 08-May-2024 #
################################################################################
gof.data.frame <- function(sim, obs, na.rm=TRUE, do.spearman=FALSE, do.pbfdc=FALSE,
j=1, lambda=0.95, norm="sd", s=c(1,1,1),
method=c("2009", "2012", "2021"), lQ.thr=0.6, hQ.thr=0.1,
start.month=1, digits=2, fun=NULL, ...,
epsilon.type=c("none", "Pushpalatha2012", "otherFactor", "otherValue"),
epsilon.value=NA){
sim <- as.matrix(sim)
obs <- as.matrix(obs)
gof.matrix(sim, obs, na.rm=na.rm, do.spearman=do.spearman, do.pbfdc=do.pbfdc,
j=j, lambda=lambda, norm=norm, s=s,
method=method, lQ.thr=lQ.thr, hQ.thr=hQ.thr,
start.month=start.month, digits=digits, fun=fun, ...,
epsilon.type=epsilon.type,
epsilon.value=epsilon.value)
} # 'gof.data.frame' end
################################################################################
# Author: Mauricio Zambrano-Bigiarini #
################################################################################
# Started: 05-Nov-2012 #
# Updates: 22-Mar-2013 #
# 15-Jan-2023 #
# 19-Jan-2024 ; 08-May-2024 #
################################################################################
gof.zoo <- function(sim, obs, na.rm=TRUE, do.spearman=FALSE, do.pbfdc=FALSE,
j=1, lambda=0.95, norm="sd", s=c(1,1,1),
method=c("2009", "2012", "2021"), lQ.thr=0.6, hQ.thr=0.1,
start.month=1, digits=2, 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)) {
gof.matrix(sim, obs, na.rm=na.rm,
do.spearman=do.spearman, do.pbfdc=do.pbfdc,
j=j, lambda=lambda, norm=norm, s=s,
method=method, lQ.thr=lQ.thr, hQ.thr=hQ.thr,
start.month=start.month, digits=digits, fun=fun, ...,
epsilon.type=epsilon.type,
epsilon.value=epsilon.value)
} else
NextMethod(sim, obs, na.rm=na.rm,
do.spearman=do.spearman, do.pbfdc=do.pbfdc,
j=j, lambda=lambda, norm=norm, s=s,
method=method, lQ.thr=lQ.thr, hQ.thr=hQ.thr,
start.month=start.month, digits=digits, fun=fun, ...,
epsilon.type=epsilon.type,
epsilon.value=epsilon.value)
} # 'gof.zoo' end
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