# File cp.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
################################################################################
# 'cp': Coefficient of Persistence #
################################################################################
# Author: Mauricio Zambrano-Bigiarini #
################################################################################
# Started: 18-Dec-2008; #
# Updates: 06-Sep-2009; #
# 16-Jan-2023 #
# 20-Jan-2024 #
################################################################################
# Persistence Index (Kitadinis and Bras, 1980; Corradini et al., 1986)
# is used to compare the model performance agains a simple model using
# the observed value of the previous day as the prediction for the current day.
#Kitanidis, P.K., and Bras, R.L. 1980. Real-time forecasting with a conceptual
#hydrologic model. 2. Applications and results. Water Resources Research,
#Vol. 16, No. 6, pp. 1034:1044.
# The coefficient of persistencec omparest he predictions of the model
# with the predictions obtained by assuming that the process is a Wiener
# process(variance increasing linearly with time), in which case,
# the best estimate for the future is given by the latest measurement
# (Kitadinis and Bras, 1980)
# Persistence model efficiency (PME) is a normalized model evaluation statistic
# that quantifies the relative magnitude of the residual variance (noise)
# to the variance of the errors obtained by the use of a simple persistence
# model
# ("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..
# PME ranges from 0 to 1, with PME = 1 being the optimal value.
# PME values should be larger than 0.0 to indicate a minimally acceptable
# model performance (Gupta et al., 1999
# 'obs' : numeric 'data.frame', 'matrix' or 'vector' with observed values
# 'sim' : numeric 'data.frame', 'matrix' or 'vector' with simulated values
# 'Result': Persistence Index Efficiency between 'sim' and 'obs'
cp <-function(sim, obs, ...) UseMethod("cp")
cp.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')")
# the next two lines are required for avoiding an strange behaviour
# of the difference function when sim and obs are time series.
if ( !is.na(match(class(sim), c("ts", "zoo"))) ) sim <- as.numeric(sim)
if ( !is.na(match(class(obs), c("ts", "zoo"))) ) obs <- as.numeric(obs)
# Checking 'epsilon.type'
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
# lenght of the data sets that will be ocnsidered for the ocmputations
n <- length(obs)
denominator <- sum( ( obs[2:n] - obs[1:(n-1)] )^2 )
if ( (denominator != 0) & (!is.na(denominator)) ) {
cp <- ( 1 - ( sum( (obs[2:n] - sim[2:n])^2 ) / denominator ) )
} else {
cp <- NA
warning("'sum((obs[2:n]-obs[1:(n-1))^2)=0' -> it is not possible to compute 'cp' !")
}
} else {
cp <- NA
warning("There are no pairs of 'sim' and 'obs' without missing values !")
} # ELSE end
return(cp)
} # 'cp.default' end
################################################################################
# 'cp': Coefficient of Persistence #
################################################################################
# Started: 18-Dec-2008; #
# Updates: 06-Sep-2009; #
# 16-Jan-2023 #
################################################################################
cp.matrix <- function(sim, obs, na.rm=TRUE, fun=NULL, ...,
epsilon.type=c("none", "Pushpalatha2012", "otherFactor", "otherValue"),
epsilon.value=NA){
# Checking 'epsilon.type'
epsilon.type <- match.arg(epsilon.type)
# 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="") )
cp <- rep(NA, ncol(obs))
cp <- sapply(1:ncol(obs), function(i,x,y) {
cp[i] <- cp.default( x[,i], y[,i], na.rm=na.rm, fun=fun, ...,
epsilon.type=epsilon.type,
epsilon.value=epsilon.value)
}, x=sim, y=obs )
names(cp) <- colnames(obs)
return(cp)
} # 'cp.matrix' end
################################################################################
# 'cp': Coefficient of Persistence #
################################################################################
# Started: 18-Dec-2008; #
# Updates: 06-Sep-2009; #
# 16-Jan-2023 #
################################################################################
cp.data.frame <- function(sim, obs, na.rm=TRUE, fun=NULL, ...,
epsilon.type=c("none", "Pushpalatha2012", "otherFactor", "otherValue"),
epsilon.value=NA){
# Checking 'epsilon.type'
epsilon.type <- match.arg(epsilon.type)
sim <- as.matrix(sim)
obs <- as.matrix(obs)
cp.matrix(sim, obs, na.rm=na.rm, fun=fun, ...,
epsilon.type=epsilon.type, epsilon.value=epsilon.value)
} # 'cp.data.frame' end
################################################################################
# Author: Mauricio Zambrano-Bigiarini #
################################################################################
# Started: 22-Mar-2013 #
# Updates: 16-Jan-2023 #
################################################################################
cp.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)) {
cp.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)
} # 'cp.zoo' end
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.