# TODO: Add comment
#
# Author: ecor
###############################################################################
rm(list=ls())
library(MASS)
library(RMAWGEN)
### additional function
## fitting with a parametric probability distribution
qqfit <- function(x,FUN=qexp,par=list()) {
if (!is.list(par)) {
names <- names(par)
par <- as.list(par)
names(par) <- names
}
pval <- ecdf(x)(x)
args <- par
args[["p"]] <- as.vector(pval)
qval <- do.call(what=FUN,args=args)
return(qval)
}
## modified signature for 'ks.test' or other test function suitably to run this script
gof.test.mod <- function(x,y="pexp",what="ks.test",par=list(),...){
x <- sort(x)
if (is.numeric(y)) y <- sort(y)
if (!is.list(par)) {
names <- names(par)
par <- as.list(par)
names(par) <- names
}
args <- list(...)
if (length(par)>1) args[names(par)] <- par
if (length(par)==1) args[[names(par)]] <- par[[1]]
args[["y"]] <- y
args[["x"]] <- x
htest <- do.call(what,args)
return(htest)
}
#val <- prec_mes[,it]
#pval <- ecdf(val[prec_mes[,it]>0])(val[prec_mes[,it]>0])
#qval <- qexp(pval,rate=list_exp[[it]]["rate"])
#val[prec_mes[,it]>0] <- qval
#prec_exp[,it] <- val
data(trentino)
# investigation period
year_min <- 1961
year_max <- 1990
period <- PRECIPITATION$year>=year_min & PRECIPITATION$year<=year_max
# investigation season
season <- PRECIPITATION$month %in% c(3,4,5) ## SPRING!
station <- names(PRECIPITATION)[!(names(PRECIPITATION) %in% c("day","month","year"))]
prec_mes <- PRECIPITATION[period & season ,station]
## removing nonworking stations (e.g. time series with NA)
accepted <- array(TRUE,length(names(prec_mes)))
names(accepted) <- names(prec_mes)
for (it in names(prec_mes)) {
accepted[it] <- (length(which(!is.na(prec_mes[,it])))==length(prec_mes[,it]))
}
prec_mes <- prec_mes[,accepted]
prec_exp <- prec_mes
prec_gamma <- prec_mes
prec_weibull <- prec_mes
list_exp <- list() ## dexp
list_gamma <- list() ## dgamma
list_weibull <- list() ## dweibull
for (it in names(prec_mes)) {
x <- prec_mes[,it]
x <- x[x>0]
start <- list(shape=1,scale=1,rate=1)
if (length(x)>0) {
list_exp[[it]] <- list()
list_gamma[[it]] <- list()
list_weibull[[it]] <- list()
list_exp[[it]]$par <- fitdistr(x,dexp,start=start[names(start) %in% names(formals(dexp))])
list_gamma[[it]]$par <- fitdistr(x,dgamma,start=start[names(start) %in% names(formals(dgamma))][1:2])
list_weibull[[it]]$par <- fitdistr(x,dweibull,start=start[names(start) %in% names(formals(dweibull))])
# Kolgomorov-Smirnov tests
list_exp[[it]]$htest <- gof.test.mod(x=x,y="pexp",par=list_exp[[it]]$par$estimate)
list_gamma[[it]]$htest <- gof.test.mod(x=x,y="pgamma",par=list_gamma[[it]]$par$estimate)
list_weibull[[it]]$htest <- gof.test.mod(x=x,y="pweibull",par=list_weibull[[it]]$par$estimate)
### Theoretical Quantile Precipitation
prec_exp[,it][prec_mes[,it]>0] <- qqfit(prec_mes[,it][prec_mes[,it]>0],FUN=qexp,par=list_exp[[it]]$par$estimate)
prec_gamma[,it][prec_mes[,it]>0] <- qqfit(prec_mes[,it][prec_mes[,it]>0],FUN=qgamma,par=list_gamma[[it]]$par$estimate)
prec_weibull[,it][prec_mes[,it]>0] <- qqfit(prec_mes[,it][prec_mes[,it]>0],FUN=qweibull,par=list_weibull[[it]]$par$estimate)
x <- prec_exp[,it][prec_mes[,it]>0]
list_exp[[it]]$htest0 <- gof.test.mod(x=x,y="pexp",par=list_exp[[it]]$par$estimate)
x <- prec_gamma[,it][prec_mes[,it]>0]
list_gamma[[it]]$htest0 <- gof.test.mod(x=x,y="pgamma",par=list_gamma[[it]]$par$estimate)
x <- prec_weibull[,it][prec_mes[,it]>0]
list_weibull[[it]]$htest0 <- gof.test.mod(x=x,y="pweibull",par=list_weibull[[it]]$par$estimate)
} else {
list_exp[it] <- NULL
list_gamma[it] <- NULL
list_weibull[it] <- NULL
}
}
## resume p-Values
pvalues_exp <- unlist(lapply(X=list_exp,FUN=function(x) {x$htest$p.value}))
pvalues_gamma <- unlist(lapply(X=list_gamma,FUN=function(x) {x$htest$p.value}))
pvalues_weibull <- unlist(lapply(X=list_weibull,FUN=function(x) {x$htest$p.value}))
pvalues0_exp <- unlist(lapply(X=list_exp,FUN=function(x) {x$htest0$p.value}))
pvalues0_gamma <- unlist(lapply(X=list_gamma,FUN=function(x) {x$htest0$p.value}))
pvalues0_weibull <- unlist(lapply(X=list_weibull,FUN=function(x) {x$htest0$p.value}))
## scratch
# val <- prec_mes[,it]
# probs <- pexp(val,rate=list_exp[[it]]["rate"])
# val[prec_mes[,it]>0] <- quantile(val[prec_mes[,it]>0],probs=probs[prec_mes[,it]>0])
# prec_exp[,it] <- val
# val <- prec_mes[,it]
# pval <- ecdf(val[prec_mes[,it]>0])(val[prec_mes[,it]>0])
# qval <- qexp(pval,rate=list_exp[[it]]["rate"])
# val[prec_mes[,it]>0] <- qval
# prec_exp[,it] <- val
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