initial values now work for link functions

NAMESPACE

@@ -5,10 +5,8 @@ export(IDF.plot)
 export(dgev.d)
 export(gev.d.diag)
 export(gev.d.fit)
+export(gev.d.nll)
 export(gev.d.params)
-export(gev.d.rl)
-export(gev.d.rl2)
-export(gev.d2stdgumbel)
 export(pgev.d)
 export(qgev.d)
 export(rgev.d)
@@ -29,5 +27,7 @@ importFrom(graphics,title)
 importFrom(ismev,gev.fit)
 importFrom(pbapply,pbsapply)
 importFrom(stats,lm)
+importFrom(stats,make.link)
 importFrom(stats,optim)
-importFrom(zoo,rollapply)
+importFrom(zoo,as.zoo)
+importFrom(zoo,rollapplyr)

R/IDF.R

@@ -34,7 +34,8 @@
 #' 
 #' @export
 #' @importFrom pbapply pbsapply 
-#' @importFrom zoo rollapply 
+#' @importFrom zoo rollapplyr 
+#' @importFrom zoo as.zoo 
 #'
 #' @examples
 #' dates <- as.Date("2019-01-01")+0:729
@@ -68,7 +69,7 @@ IDF.agg <- function(data,ds,na.accept = 0,
     
     # function 1: aggregate over single durations and find annual maxima:
     agg.ts <- function(ds){
-      runmean <- rollapply(data.s[,names[2]],ds/dtime,FUN=sum,fill =NA,align='right')
+      runmean <- rollapplyr(as.zoo(data.s[,names[2]]),ds/dtime,FUN=sum,fill =NA,align='right')
       runmean <- runmean/ds #intensity per hour
       subset <- is.element(as.POSIXlt(data.s[,names[1]])$mon,which.mon)
       max <- tapply(runmean[subset],(as.POSIXlt(data.s[,names[1]])$year+1900)[subset],
@@ -133,8 +134,9 @@ IDF.plot <- function(durations,fitparams,probs=c(0.5,0.9,0.99),
   if(length(cols)!=length(probs))cols <- rep_len(cols,length.out=length(probs))
   
   ## calculate IDF values for given probability and durations
-  idf.array <- simplify2array(qgev.d(probs,mut=fitparams[1],sigma0=fitparams[2],xi=fitparams[3],
-                                     theta=fitparams[4],eta=fitparams[5],d=durations)) # array[probs,durs]
+  qs <- lapply(durations,qgev.d,p=probs,mut=fitparams[1],sigma0=fitparams[2],xi=fitparams[3],
+         theta=fitparams[4],eta=fitparams[5])
+  idf.array <- simplify2array(qs) # array[probs,durs]
   if(!add){ #new plot
     ## initialize plot window
     # check if limits were passed
@@ -166,4 +168,4 @@ IDF.plot <- function(durations,fitparams,probs=c(0.5,0.9,0.99),
     legend(x="topright",title = 'p-quantile',legend=probs,
            col=cols,lty=lty,lwd=lwd)
   }
-}
\ No newline at end of file
+}

R/d-gev.R

@@ -28,7 +28,12 @@
 #'
 #' @examples
 #' x <- seq(4,20,0.1)
-#' dens <- dgev.d(q=x,mut=4,sigma0=2,xi=0,theta=0.1,eta=0.1,d=1:4)
+#' # calculate probability density for one duration
+#' dgev.d(q=x,mut=4,sigma0=2,xi=0,theta=0.1,eta=0.1,d=1)
+#' 
+#' # calculate probability density for different durations
+#' ds <- 1:4
+#' dens <- lapply(ds,dgev.d,q=x,mut=4,sigma0=2,xi=0,theta=0.1,eta=0.1)
 #' 
 #' plot(x,dens[[1]],type='l',ylim = c(0,0.21),ylab = 'Probability Density')
 #' for(i in 2:4){
@@ -43,15 +48,9 @@ dgev.d <- function(q,mut,sigma0,xi,theta,eta,d,...) {
     warning('Some shape parameters are negative,resulting from a negativ theta '
             ,theta, ' this will prododuce NAs.')}
   # if denominator is negative NAs will be returned
-  densfun <- function(d){
-    if(d+theta<=0){return(rep(NA,length(q)))}else{
-      sigma.d <-sigma0/(d+theta)^eta
-      return(dgev(q,loc=mut*sigma.d,scale=sigma.d,shape=xi,...))}
-  }
-  # calculate for each duration in d
-  durations <- as.list(d)
-  names(durations) <- d
-  return(sapply(durations,densfun,simplify = FALSE))
+  if(d+theta<=0){return(rep(NA,length(q)))}else{
+    sigma.d <-sigma0/(d+theta)^eta
+    return(dgev(q,loc=mut*sigma.d,scale=sigma.d,shape=xi,...))}
 }
 
 
@@ -83,7 +82,7 @@ dgev.d <- function(q,mut,sigma0,xi,theta,eta,d,...) {
 #'
 #' @examples
 #' x <- seq(4,20,0.1)
-#' prob <- pgev.d(q=x,mu=4,sigma=2,xi=0,theta=0.1,eta=0.1,d=c(1,2))
+#' prob <- pgev.d(q=x,mu=4,sigma0=2,xi=0,theta=0.1,eta=0.1,d=1)
 pgev.d <- function(q,mut,sigma0,xi,theta,eta,d,...) {
   if(any(c(length(mut),length(sigma0),length(xi),length(theta),length(eta))>1)){
     message('One of the parameters mut, sigma0, xi, theta, eta is a vector. ',
@@ -92,15 +91,9 @@ pgev.d <- function(q,mut,sigma0,xi,theta,eta,d,...) {
     warning('Some shape parameters are negative,resulting from a negativ theta '
             ,theta, ' this will prododuce NAs.')}
   # if denominator is negative NAs will be returned
-  pfun <- function(d){
-    if(d+theta<=0){return(rep(NA,length(q)))}else{
-      sigma.d <-sigma0/(d+theta)^eta
-      return(pgev(q,loc=mut*sigma.d,scale=sigma.d,shape=xi,...))}
-  }
-  # calculate for each duration in d
-  durations <- as.list(d)
-  names(durations) <- d
-  return(sapply(durations,pfun,simplify = FALSE))
+  if(d+theta<=0){return(rep(NA,length(q)))}else{
+    sigma.d <-sigma0/(d+theta)^eta
+    return(pgev(q,loc=mut*sigma.d,scale=sigma.d,shape=xi,...))}
 }
 
 
@@ -132,15 +125,19 @@ pgev.d <- function(q,mut,sigma0,xi,theta,eta,d,...) {
 #'
 #' @examples
 #' p <- c(0.5,0.9,0.99)
-#' d <- 2^seq(0,4,0.1)
-#' qs <- qgev.d(p=p,mu=4,sigma=2,xi=0,theta=0.1,eta=0.3,d=d)
+#' # calulate quantiles for one duration
+#' qgev.d(p=p,mu=4,sigma0=2,xi=0,theta=0.1,eta=0.3,d=1)
+#' 
+#' # calculate quantiles for sequence of durations
+#' ds <- 2^seq(0,4,0.1)
+#' qs <- lapply(ds,qgev.d,p=p,mu=4,sigma0=2,xi=0,theta=0.1,eta=0.3)
 #' qs <- simplify2array(qs)
 #' 
-#' plot(d,qs[1,],ylim=c(3,20),type='l',log = 'xy',ylab='Intensity',xlab = 'Duration')
+#' plot(ds,qs[1,],ylim=c(3,20),type='l',log = 'xy',ylab='Intensity',xlab = 'Duration')
 #' for(i in 2:3){
-#'   lines(d,qs[i,],lty=i)
+#'   lines(ds,qs[i,],lty=i)
 #' }
-#' legend(8,19,title = 'annual frequency \n of exceedance 1/T',
+#' legend('topright',title = 'Annual frequency of exceedance',
 #'        legend = 1-p,lty=1:3,bty = 'n')
 qgev.d <- function(p,mut,sigma0,xi,theta,eta,d,...) {
   if(any(c(length(mut),length(sigma0),length(xi),length(theta),length(eta))>1)){
@@ -150,16 +147,10 @@ qgev.d <- function(p,mut,sigma0,xi,theta,eta,d,...) {
     warning('Some shape parameters are negative,resulting from a negativ theta '
             ,theta, ' this will prododuce NAs.')}
   # if denominator is negative NAs will be returned
-  qfun <- function(d){
-    if(d+theta<=0){return(rep(NA,length(p)))}else{
-      sigma.d <-sigma0/(d+theta)^eta
-      return(qgev(p,loc=as.numeric(mut*sigma.d)
-                  ,scale=as.numeric(sigma.d),shape=as.numeric(xi),...))}
-  }
-  # calculate for each duration in d
-  durations <- as.list(d)
-  names(durations) <- d
-  return(sapply(durations,qfun,simplify = FALSE))
+  if(d+theta<=0){return(rep(NA,length(p)))}else{
+    sigma.d <-sigma0/(d+theta)^eta
+    return(qgev(p,loc=as.numeric(mut*sigma.d)
+                ,scale=as.numeric(sigma.d),shape=as.numeric(xi),...))}
 }
 
 
@@ -189,8 +180,13 @@ qgev.d <- function(p,mut,sigma0,xi,theta,eta,d,...) {
 #' @importFrom evd rgev 
 #'
 #' @examples
-#' samp <- rgev.d(100,4,2,0,0,0.5,c(1,2))
-#'
+#' # random sample for one duration
+#' rgev.d(n=100,mut=4,sigma=2,xi=0,theta=0.1,eta=0.3,d=1)
+#' 
+#' # compare randomn samples for different durations
+#' ds <- c(1,4)
+#' samp <- lapply(ds,rgev.d,n=100,mut=4,sigma=2,xi=0,theta=0.1,eta=0.3)
+#' 
 #' hist(samp[[1]],breaks = 10,col=rgb(1,0,0,0.5),freq = FALSE
 #'      ,ylim=c(0,0.3),xlab='x',main = 'd-GEV samples for two different durations')
 #' hist(samp[[2]],breaks = 10,add=TRUE,col=rgb(0,0,1,0.5),freq = FALSE)
@@ -202,15 +198,9 @@ rgev.d <- function(n,mut,sigma0,xi,theta,eta,d) {
     warning('Some shape parameters are negative,resulting from a negativ theta '
             ,theta, ' this will prododuce NAs.')}
   # if denominator is negative NAs will be returned
-  rfun <- function(d){
-    if(d+theta<=0){return(rep(NA,n))}else{
-      sigma.d <-sigma0/(d+theta)^eta
-      return(rgev(n,loc=mut*sigma.d,scale=sigma.d,shape=xi))}
-  }
-  # calculate for each duration in d
-  durations <- as.list(d)
-  names(durations) <- d
-  return(sapply(durations,rfun,simplify = FALSE))
+  if(d+theta<=0){return(rep(NA,n))}else{
+    sigma.d <-sigma0/(d+theta)^eta
+    return(rgev(n,loc=mut*sigma.d,scale=sigma.d,shape=xi))}
 }
 
 

man/dgev.d.Rd

@@ -35,7 +35,12 @@ modified location parameter \eqn{\mu_t=\mu/\sigma(d)}.
 }
 \examples{
 x <- seq(4,20,0.1)
-dens <- dgev.d(q=x,mut=4,sigma0=2,xi=0,theta=0.1,eta=0.1,d=1:4)
+# calculate probability density for one duration
+dgev.d(q=x,mut=4,sigma0=2,xi=0,theta=0.1,eta=0.1,d=1)
+
+# calculate probability density for different durations
+ds <- 1:4
+dens <- lapply(ds,dgev.d,q=x,mut=4,sigma0=2,xi=0,theta=0.1,eta=0.1)
 
 plot(x,dens[[1]],type='l',ylim = c(0,0.21),ylab = 'Probability Density')
 for(i in 2:4){

man/gev.d.fit.Rd

@@ -5,10 +5,11 @@
 \title{Maximum-likelihood Fitting of the duration dependent GEV Distribution}
 \usage{
 gev.d.fit(xdat, ds, ydat = NULL, mul = NULL, sigl = NULL,
-  shl = NULL, thetal = NULL, etal = NULL, mulink = identity,
-  siglink = identity, shlink = identity, thetalink = identity,
-  etalink = identity, muinit = NULL, siginit = NULL, shinit = NULL,
-  thetainit = NULL, etainit = NULL, show = TRUE,
+  shl = NULL, thetal = NULL, etal = NULL,
+  mulink = make.link("identity"), siglink = make.link("identity"),
+  shlink = make.link("identity"), thetalink = make.link("identity"),
+  etalink = make.link("identity"), muinit = NULL, siginit = NULL,
+  shinit = NULL, thetainit = NULL, etainit = NULL, show = TRUE,
   method = "Nelder-Mead", maxit = 10000, init.vals = NULL, ...)
 }
 \arguments{
@@ -26,14 +27,12 @@ covariates for generalized linear modelling of the parameters (or NULL (the defa
 if the corresponding parameter is stationary).
 Parameters are: modified location, scale_0, shape, duration offset, duration exponent repectively.}
 
-\item{mulink, siglink, shlink, thetalink, etalink}{Inverse link functions for generalized linear 
-modelling of the parameters.}
+\item{mulink, siglink, shlink, thetalink, etalink}{Link functions for generalized linear 
+modelling of the parameters, created with \code{\link{make.link}}.}
 
-\item{muinit, siginit, shinit, thetainit, etainit}{initial values as numeric of length 
-equal to total number of parameters 
-used to model the parameters. If NULL (the default) is given, initial parameters are obtained 
-internally by fitting the GEV seperately for each duration and applying a linear model to optain the 
-duration dependency of the location and shape parameter.}
+\item{muinit, siginit, shinit, thetainit, etainit}{Initial values as numeric of length 
+equal to total number of parameters. Alternatively initial values for only the parameter intercepts 
+can be passed to \code{init.vals}.}
 
 \item{show}{Logical; if TRUE (the default), print details of the fit.}
 
@@ -41,7 +40,10 @@ duration dependency of the location and shape parameter.}
 
 \item{maxit}{The maximum number of iterations.}
 
-\item{init.vals}{vector of length 5, giving initial values for parameter intercepts}
+\item{init.vals}{vector of length 5, giving initial values for parameter intercepts
+used to model the parameters. If NULL (the default) is given, initial parameters are obtained 
+internally by fitting the GEV seperately for each duration and applying a linear model to optain the 
+duration dependency of the location and shape parameter.}
 
 \item{...}{Other control parameters for the optimization.}
 }
@@ -50,10 +52,10 @@ A list containing the following components.
 A subset of these components are printed after the fit. 
 If show is TRUE, then assuming that successful convergence is indicated, 
 the components nllh, mle and se are always printed. 
-\item{nllh}{single numeric giving the negative log-likelihood value.} 
+\item{nllh}{single numeric giving the negative log-likelihood value} 
 \item{mle}{numeric vector giving the MLE's for the modified location, scale_0, shape, 
 duration offset and duration exponent, resp.} 
-\item{se}{numeric vector giving the standard errors for the MLE's (in the same order).}
+\item{se}{numeric vector giving the standard errors for the MLE's (in the same order)}
 \item{trans}{An logical indicator for a non-stationary fit.}
 \item{model}{A list with components mul, sigl, shl, thetal and etal.}
 \item{link}{A character vector giving inverse link functions.}

man/gev.d.init.Rd

@@ -4,13 +4,15 @@
 \alias{gev.d.init}
 \title{get initial values for gev.d.fit}
 \usage{
-gev.d.init(xdat, ds, thetainit)
+gev.d.init(xdat, ds, link)
 }
 \arguments{
 \item{xdat}{vector of maxima for differnt durations}
 
 \item{ds}{vector of durations belonging to maxima in xdat}
 
+\item{link}{list of 5, link functions for parameters, created with \code{\link{make.link}}}
+
 \item{thetainit}{initial parameter for theta}
 }
 \value{

man/gev.d.nll.Rd

+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/gevdfit.R
+\name{gev.d.nll}
+\alias{gev.d.nll}
+\title{computes negative log-likelihood of d-gev model}
+\usage{
+gev.d.nll(xdat, ds, mut, sig0, xi, theta, eta)
+}
+\arguments{
+\item{xdat}{numeric vector containing observations}
+
+\item{ds}{numeric vector containing coresponding durations}
+
+\item{mut, sig0, xi, theta, eta}{numeric vectors containing corresponding mles for each of the parameters}
+}
+\value{
+single value containing negative log likelihood
+}
+\description{
+computes negative log-likelihood of d-gev model
+}
+\examples{
+# compute nll of values not included in fit
+train.set <- example[example$d!=2,]
+test.set <- example[example$d==2,]
+fit <- gev.d.fit(train.set$dat,train.set$d,mul = c(1,2),sigl = 1
+          ,ydat = as.matrix(train.set[c('cov1','cov2')]))
+params <- gev.d.params(fit,ydat = as.matrix(test.set[c('cov1','cov2')]))
+gev.d.nll(xdat = test.set$dat,ds = test.set$d,mut = params[,1],sig0 = params[,2],xi = params[,3]
+          ,theta = params[,4],eta = params[,5])
+}

man/gev.d.rl.Rd

-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/gevdfit.R
-\name{gev.d.rl}
-\alias{gev.d.rl}
-\title{Calculate Returnlevel}
-\usage{
-gev.d.rl(params, p.d)
-}
-\arguments{
-\item{params}{list of parameters mu_tilde, sigma0, xi, theta, eta (single values and/or compatible matrices)
-as obtained from \code{\link{gev.d.fit}} or \code{\link{gev.d.params}}}
-
-\item{p.d}{numeric vector of length 2 containing one value the for exeedance probability p = 1-1/RP 
-and one value for the duration at which to calculate the return level}
-}
-\value{
-one return level value or matrix with return levels (depending on input to params)
-unit: e.g. mm/h
-}
-\description{
-calculate Returnlevel for chosen duration and return period 
-from \code{\link{gev.d.fit}} parameters
-}
-\examples{
-data('example',package = 'IDF')
-fit <- gev.d.fit(example$dat,example$d,ydat = as.matrix(example[,c("cov1","cov2")])
-                 ,mul = c(1,2),sigl = 1)
-### calculate rl on grid:
-# covariates values
-cov1 <- rep(seq(-1,1,0.1),11)
-cov2 <- rep(seq(0,1,0.1),each=21)
-# calculate parameters of d-gev on grid, based on output of gev.d.fit
-par <- gev.d.params(fit = fit,ydat = cbind(cov1,cov2))
-# calculate 100 year (p=0.99) rl for a duration of d=24 hours
-rl <- gev.d.rl(params = par,p.d = c(0.99,24))
-dim(rl) <- c(21,11)
-# rl map:
-image(x=seq(-1,1,0.1),y=seq(0,1,0.1),z=rl,xlab = 'cov1', ylab = 'cov2')
-}

man/gev.d.rl2.Rd

-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/gevdfit.R
-\name{gev.d.rl2}
-\alias{gev.d.rl2}
-\title{Calculate Returnlevel with standart deviation (through delta method)}
-\usage{
-gev.d.rl2(p, fit, ds, ydat, sd = FALSE)
-}
-\arguments{
-\item{p}{single numeric value containing the non-exceedance probability p=1-1/T}
-
-\item{fit}{\code{\link{gev.d.fit}} object}
-
-\item{ds}{numeric vector of durations for which to calculate the return levels}
-
-\item{ydat}{matrix containing the covariates in the same order as used in \code{\link{gev.d.fit}}}
-
-\item{sd}{logical, if TRUE, standart deviation of the returnlevels obtained with the delta method
-are also returned}
-}
-\value{
-either vector or matrix containing the p-quantile or list containing both the p-quantile and its standart deviation
-for durations in ds (and different rows for the covariates in ydat)
-}
-\description{
-calculate returnlevel for chosen duration and return period 
-for \code{\link{gev.d.fit}} objects
-}
-\examples{
-data('example',package = 'IDF')
-fit <- gev.d.fit(example$dat,example$d,ydat = as.matrix(example[,c("cov1","cov2")])
-                 ,mul = c(1,2),sigl = 1)
-# calculate 100 year (p=0.99) rl for a duration of d=24 hours 
-rl <- gev.d.rl2(p = 0.99,fit = fit,ds = 1:30,ydat = matrix(0.7,1,2),sd=TRUE)
-plot(1:30,rl$q,log='xy',type = 'l',xlab = 'Duration',ylab = 'Intensity',ylim = c(5,100))
-# add 0.95-confidence intervals
-lines(1:30,rl$q-1.96*rl$sd,lty=2)
-lines(1:30,rl$q+1.96*rl$sd,lty=2)
-}

man/gev.d2stdgumbel.Rd

-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/gevdfit.R
-\name{gev.d2stdgumbel}
-\alias{gev.d2stdgumbel}
-\title{Transform data to standart gumbel}
-\usage{
-gev.d2stdgumbel(xdat, ds, params)
-}
-\arguments{
-\item{xdat}{A vector containing maxima for different durations.}
-
-\item{ds}{A vector of aggregation levels corresponding to the maxima in xdat.}
-
-\item{params}{list of parameters mu_tilde, sigma0, xi, theta, eta 
-as obtained from \code{\link{gev.d.params}}}
-}
-\value{
-Vector containing transformed data.
-}
-\description{
-Transform data to standart gumbel
-}
-\examples{
-data('example',package = 'IDF')
-# fit subset
-ind <- sample(1:10, length(example$dat), replace=TRUE)
-fit.subs <- gev.d.fit(example$dat[ind!=1],example$d[ind!=1]
-                      ,ydat = as.matrix(example[ind!=1,c("cov1","cov2")])
-                      ,mul = c(1,2),sigl = 1)
-# calculate parameters for unfitted values
-par <- gev.d.params(fit = fit.subs
-                      ,ydat = as.matrix(example[ind==1,c("cov1","cov2")]))
-# transform unfitted values to standart gumbel
-sg.data <- gev.d2stdgumbel(xdat = example$dat[ind==1]
-                      ,ds = example$d[ind==1],params = par)
-# check unfitted values agains standart gumbel
-gev.d.diag(data.frame(data=sg.data,ds=example$d[ind==1]),pch=20)
-}

man/pgev.d.Rd

@@ -35,7 +35,7 @@ modified location parameter \eqn{\mu_t=\mu/\sigma(d)}.
 }
 \examples{
 x <- seq(4,20,0.1)
-prob <- pgev.d(q=x,mu=4,sigma=2,xi=0,theta=0.1,eta=0.1,d=c(1,2))
+prob <- pgev.d(q=x,mu=4,sigma0=2,xi=0,theta=0.1,eta=0.1,d=1)
 }
 \references{
 Koutsoyannis et al., 1998, doi:10.1016/S0022-1694(98)00097-3

man/qgev.d.Rd

@@ -35,15 +35,19 @@ modified location parameter \eqn{\mu_t=\mu/\sigma(d)}.
 }
 \examples{
 p <- c(0.5,0.9,0.99)
-d <- 2^seq(0,4,0.1)
-qs <- qgev.d(p=p,mu=4,sigma=2,xi=0,theta=0.1,eta=0.3,d=d)
+# calulate quantiles for one duration
+qgev.d(p=p,mu=4,sigma0=2,xi=0,theta=0.1,eta=0.3,d=1)
+
+# calculate quantiles for sequence of durations
+ds <- 2^seq(0,4,0.1)
+qs <- lapply(ds,qgev.d,p=p,mu=4,sigma0=2,xi=0,theta=0.1,eta=0.3)
 qs <- simplify2array(qs)
 
-plot(d,qs[1,],ylim=c(3,20),type='l',log = 'xy',ylab='Intensity',xlab = 'Duration')
+plot(ds,qs[1,],ylim=c(3,20),type='l',log = 'xy',ylab='Intensity',xlab = 'Duration')
 for(i in 2:3){
-  lines(d,qs[i,],lty=i)
+  lines(ds,qs[i,],lty=i)
 }
-legend(8,19,title = 'annual frequency \\n of exceedance 1/T',
+legend('topright',title = 'Annual frequency of exceedance',
        legend = 1-p,lty=1:3,bty = 'n')
 }
 \references{

man/rgev.d.Rd

@@ -32,7 +32,12 @@ with the duration dependent scale \eqn{\sigma(d)=\sigma_0/(d+\theta)^\eta} and
 modified location parameter \eqn{\mu_t=\mu/\sigma(d)}.
 }
 \examples{
-samp <- rgev.d(100,4,2,0,0,0.5,c(1,2))
+# random sample for one duration
+rgev.d(n=100,mut=4,sigma=2,xi=0,theta=0.1,eta=0.3,d=1)
+
+# compare randomn samples for different durations
+ds <- c(1,4)
+samp <- lapply(ds,rgev.d,n=100,mut=4,sigma=2,xi=0,theta=0.1,eta=0.3)
 
 hist(samp[[1]],breaks = 10,col=rgb(1,0,0,0.5),freq = FALSE
      ,ylim=c(0,0.3),xlab='x',main = 'd-GEV samples for two different durations')