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v1.0.6.5 ErrorCrit_KGE and ErrorCrit_KGE2 now return clean subcriteria names #4538

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DESCRIPTION
View file @ 38968946
Package: airGR
Type: Package
Title: Suite of GR Hydrological Models for Precipitation-Runoff Modelling
Version: 1.0.6.4
Version: 1.0.6.6
Date: 2017-04-05
Authors@R: c(
person("Laurent", "Coron", role = c("aut", "trl")),
......
R/ErrorCrit_KGE.R
View file @ 38968946
......@@ -48,16 +48,18 @@ ErrorCrit_KGE <- function(InputsCrit,OutputsModel, warnings = TRUE, verbose = TR
##Other_variables_preparation
meanVarObs <- mean(VarObs[!TS_ignore]);
meanVarSim <- mean(VarSim[!TS_ignore]);
iCrit <- 0;
SubCritNames <- NULL;
SubCritValues <- NULL;
iCrit <- 0
SubCritPrint <- NULL
SubCritNames <- NULL
SubCritValues <- NULL
##SubErrorCrit_____KGE_rPearson__________________
iCrit <- iCrit+1;
SubCritNames[iCrit] <- paste(CritName," cor(sim, obs, \"pearson\") =", sep = "")
SubCritPrint[iCrit] <- paste(CritName," cor(sim, obs, \"pearson\") =", sep = "")
SubCritValues[iCrit] <- NA;
SubCritNames[iCrit] <- "r"
Numer <- sum( (VarObs[!TS_ignore]-meanVarObs)*(VarSim[!TS_ignore]-meanVarSim) );
Deno1 <- sqrt( sum((VarObs[!TS_ignore]-meanVarObs)^2) );
Deno2 <- sqrt( sum((VarSim[!TS_ignore]-meanVarSim)^2) );
......@@ -68,8 +70,9 @@ ErrorCrit_KGE <- function(InputsCrit,OutputsModel, warnings = TRUE, verbose = TR
##SubErrorCrit_____KGE_alpha_____________________
iCrit <- iCrit+1;
SubCritNames[iCrit] <- paste(CritName," sd(sim)/sd(obs) =", sep = "")
SubCritPrint[iCrit] <- paste(CritName," sd(sim)/sd(obs) =", sep = "")
SubCritValues[iCrit] <- NA;
SubCritNames[iCrit] <- "alpha"
Numer <- sd(VarSim[!TS_ignore]);
Denom <- sd(VarObs[!TS_ignore]);
if(Numer==0 & Denom==0){ Crit <- 1; } else { Crit <- Numer/Denom ; }
......@@ -78,8 +81,9 @@ ErrorCrit_KGE <- function(InputsCrit,OutputsModel, warnings = TRUE, verbose = TR
##SubErrorCrit_____KGE_beta______________________
iCrit <- iCrit+1;
SubCritNames[iCrit] <- paste(CritName," mean(sim)/mean(obs) =", sep = "")
SubCritPrint[iCrit] <- paste(CritName," mean(sim)/mean(obs) =", sep = "")
SubCritValues[iCrit] <- NA;
SubCritNames[iCrit] <- "beta"
if(meanVarSim==0 & meanVarObs==0){ Crit <- 1; } else { Crit <- meanVarSim/meanVarObs ; }
if(is.numeric(Crit) & is.finite(Crit)){ SubCritValues[iCrit] <- Crit; }
......@@ -93,7 +97,7 @@ ErrorCrit_KGE <- function(InputsCrit,OutputsModel, warnings = TRUE, verbose = TR
##Verbose______________________________________
if(verbose) {
message("Crit. ", CritName, " = ", sprintf("%.4f", CritValue))
message(paste("\tSubCrit.", SubCritNames, sprintf("%.4f", SubCritValues), "\n", sep = " "))
message(paste("\tSubCrit.", SubCritPrint, sprintf("%.4f", SubCritValues), "\n", sep = " "))
}
......
R/ErrorCrit_KGE2.R
View file @ 38968946
......@@ -48,14 +48,16 @@ ErrorCrit_KGE2 <- function(InputsCrit,OutputsModel, warnings = TRUE, verbose = T
##Other_variables_preparation
meanVarObs <- mean(VarObs[!TS_ignore]);
meanVarSim <- mean(VarSim[!TS_ignore]);
iCrit <- 0;
SubCritNames <- NULL;
SubCritValues <- NULL;
iCrit <- 0
SubCritPrint <- NULL
SubCritNames <- NULL
SubCritValues <- NULL
##SubErrorCrit_____KGE_rPearson__________________
iCrit <- iCrit+1;
SubCritNames[iCrit] <- paste(CritName," cor(sim, obs, \"pearson\") =", sep = "")
SubCritPrint[iCrit] <- paste(CritName," cor(sim, obs, \"pearson\") =", sep = "")
SubCritNames[iCrit] <- "r"
SubCritValues[iCrit] <- NA;
Numer <- sum( (VarObs[!TS_ignore]-meanVarObs)*(VarSim[!TS_ignore]-meanVarSim) );
Deno1 <- sqrt( sum((VarObs[!TS_ignore]-meanVarObs)^2) );
......@@ -67,7 +69,8 @@ ErrorCrit_KGE2 <- function(InputsCrit,OutputsModel, warnings = TRUE, verbose = T
##SubErrorCrit_____KGE_gamma______________________
iCrit <- iCrit+1;
SubCritNames[iCrit] <- paste(CritName," cv(sim)/cv(obs) =", sep = "")
SubCritPrint[iCrit] <- paste(CritName," cv(sim)/cv(obs) =", sep = "")
SubCritNames[iCrit] <- "gamma"
SubCritValues[iCrit] <- NA;
if(meanVarSim==0){ if(sd(VarSim[!TS_ignore])==0){ CVsim <- 1; } else { CVsim <- 99999; } } else { CVsim <- sd(VarSim[!TS_ignore])/meanVarSim; }
if(meanVarObs==0){ if(sd(VarObs[!TS_ignore])==0){ CVobs <- 1; } else { CVobs <- 99999; } } else { CVobs <- sd(VarObs[!TS_ignore])/meanVarObs; }
......@@ -77,7 +80,8 @@ ErrorCrit_KGE2 <- function(InputsCrit,OutputsModel, warnings = TRUE, verbose = T
##SubErrorCrit_____KGE_beta______________________
iCrit <- iCrit+1;
SubCritNames[iCrit] <- paste(CritName," mean(sim)/mean(obs) =", sep = "")
SubCritPrint[iCrit] <- paste(CritName," mean(sim)/mean(obs) =", sep = "")
SubCritNames[iCrit] <- "beta"
SubCritValues[iCrit] <- NA;
if(meanVarSim==0 & meanVarObs==0){ Crit <- 1; } else { Crit <- meanVarSim/meanVarObs ; }
if(is.numeric(Crit) & is.finite(Crit)){ SubCritValues[iCrit] <- Crit; }
......@@ -92,7 +96,7 @@ ErrorCrit_KGE2 <- function(InputsCrit,OutputsModel, warnings = TRUE, verbose = T
##Verbose______________________________________
if(verbose) {
message("Crit. ", CritName, " = ", sprintf("%.4f", CritValue))
message(paste("\tSubCrit.", SubCritNames, sprintf("%.4f", SubCritValues), "\n", sep = " "))
message(paste("\tSubCrit.", SubCritPrint, sprintf("%.4f", SubCritValues), "\n", sep = " "))
}
......
man/ErrorCrit_KGE.Rd
View file @ 38968946
\encoding{UTF-8}
\name{ErrorCrit_KGE}
\alias{ErrorCrit_KGE}
\title{Error criterion based on the KGE formula}
\usage{
ErrorCrit_KGE(InputsCrit, OutputsModel, warnings = TRUE, verbose = TRUE)
}
\arguments{
\item{InputsCrit}{[object of class \emph{InputsCrit}] see \code{\link{CreateInputsCrit}} for details}
......@@ -26,25 +30,37 @@ ErrorCrit_KGE(InputsCrit, OutputsModel, warnings = TRUE, verbose = TRUE)
\emph{$Ind_notcomputed} \tab [numeric] indices of the time steps where InputsCrit$BoolCrit=FALSE or no data is available \cr
}
}
\description{
Function which computes an error criterion based on the KGE formula proposed by Gupta et al. (2009).
}
\details{
In addition to the criterion value, the function outputs include a multiplier (-1 or +1) which allows
the use of the function for model calibration: the product CritValue*Multiplier is the criterion to be minimised (Multiplier=-1 for KGE).
the use of the function for model calibration: the product CritValue*Multiplier is the criterion to be minimised (Multiplier=-1 for KGE).\cr\cr
The KGE formula is
\deqn{KGE = 1 - \sqrt(r - 1)^2 + (\alpha - 1)^2 + (\beta - 1)^2}{KGE = 1 - sqrt((r - 1)² + (\alpha - 1)² + (\beta - 1)²)}
with the following sub-criteria:
\deqn{r = the linear correlation coefficient between Q_s and Q_o}{r = the linear correlation coefficient between Q[s] and Q[o]}
\deqn{\alpha = \frac{\sigma_s}{\sigma_o}}{\alpha = \sigma[s] / \sigma[o]}
\deqn{\beta = \frac{\mu_s}{\mu_o}}{\beta = \mu[s] / \mu[o]}
}
\examples{
## see example of the ErrorCrit function
}
\author{
Laurent Coron (June 2014)
Laurent Coron
}
\references{
Gupta, H. V., Kling, H., Yilmaz, K. K. and Martinez, G. F. (2009),
Decomposition of the mean squared error and NSE performance criteria: Implications
for improving hydrological modelling, Journal of Hydrology, 377(1-2), 80-91, doi:10.1016/j.jhydrol.2009.08.003. \cr
}
\seealso{
\code{\link{ErrorCrit_RMSE}}, \code{\link{ErrorCrit_NSE}}, \code{\link{ErrorCrit_KGE2}}
\code{\link{ErrorCrit}}, \code{\link{ErrorCrit_RMSE}}, \code{\link{ErrorCrit_NSE}}, \code{\link{ErrorCrit_KGE2}}
}
man/ErrorCrit_KGE2.Rd
View file @ 38968946
\encoding{UTF-8}
\name{ErrorCrit_KGE2}
\alias{ErrorCrit_KGE2}
\title{Error criterion based on the KGE' formula}
\usage{
ErrorCrit_KGE2(InputsCrit, OutputsModel, warnings = TRUE, verbose = TRUE)
}
\arguments{
\item{InputsCrit}{[object of class \emph{InputsCrit}] see \code{\link{CreateInputsCrit}} for details}
......@@ -26,19 +30,30 @@ ErrorCrit_KGE2(InputsCrit, OutputsModel, warnings = TRUE, verbose = TRUE)
\emph{$Ind_notcomputed} \tab [numeric] indices of the time steps where InputsCrit$BoolCrit=FALSE or no data is available \cr
}
}
\description{
Function which computes an error criterion based on the KGE' formula proposed by Kling et al. (2012).
}
\details{
In addition to the criterion value, the function outputs include a multiplier (-1 or +1) which allows
the use of the function for model calibration: the product CritValue*Multiplier is the criterion to be minimised (Multiplier=-1 for KGE2).
the use of the function for model calibration: the product CritValue*Multiplier is the criterion to be minimised (Multiplier=-1 for KGE2).\cr\cr
The KGE' formula is
\deqn{KGE' = 1 - \sqrt{(r - 1)^2 + (\gamma - 1)^2 + (\beta - 1)^2}}{KGE' = 1 - sqrt((r - 1)² + (\gamma - 1)² + (\beta - 1)²)}
with the following sub-criteria:
\deqn{r = the linear correlation coefficient between Q_s and Q_o}{r = is the linear correlation coefficient between Q[s] and Q[o]}
\deqn{\alpha = \frac{CV_s}{CV_o}}{\alpha = CV[s] / CV[o]}
\deqn{\beta = \frac{\mu_s}{\mu_o}}{\beta = \mu[s] / \mu[o]}
}
\examples{
## see example of the ErrorCrit function
}
\author{
Laurent Coron (June 2014)
Laurent Coron
}
\references{
Gupta, H. V., Kling, H., Yilmaz, K. K. and Martinez, G. F. (2009),
Decomposition of the mean squared error and NSE performance criteria: Implications
......@@ -47,7 +62,8 @@ Gupta, H. V., Kling, H., Yilmaz, K. K. and Martinez, G. F. (2009),
Runoff conditions in the upper Danube basin under an ensemble of climate change scenarios,
Journal of Hydrology, 424-425, 264-277, doi:10.1016/j.jhydrol.2012.01.011.
}
\seealso{
\code{\link{ErrorCrit_RMSE}}, \code{\link{ErrorCrit_NSE}}, \code{\link{ErrorCrit_KGE}}
\code{\link{ErrorCrit}}, \code{\link{ErrorCrit_RMSE}}, \code{\link{ErrorCrit_NSE}}, \code{\link{ErrorCrit_KGE}}
}
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