Nothing
plot.gumbelp <-
function(x, type = c("histogram","predictive", "retlevel"), t=2, k=100, ...){
if(type=="histogram"){
par(mfrow=c(1,2))
hist(x$posterior[,1],freq=FALSE,main=expression(mu), xlab=NULL)
abline(v=quantile(x$posterior[,1],0.025),lwd=2)
abline(v=quantile(x$posterior[,1],0.975),lwd=2)
hist(x$posterior[,2],freq=FALSE,main=expression(sigma), xlab=NULL)
abline(v=quantile(x$posterior[,2],0.025),lwd=2)
abline(v=quantile(x$posterior[,2],0.975),lwd=2)
par(mfrow=c(1,1))
}
if(type=="predictive"){
linf=max(min(x$data)-1,0)
lsup=11*max(x$data)/10
dat1=seq(linf,lsup,(lsup-linf)/300)
n=length(dat1)
int=length(x$posterior[,1])
mu=x$posterior[,1]
sigma=x$posterior[,2]
res=array(0,c(n))
for (i in 1:n)
{for (j in 1:int)
{
res[i]=res[i]+(1/int)*(1/sigma[j])*exp(-(dat1[i]-mu[j])/sigma[j])*exp(-exp(-(dat1[i]-mu[j])/sigma[j]))}}
hist(x$data,freq=F,ylim=c(min(res),max(res)),xlab="data",ylab="density", ...)
lines(dat1,res)
out<-list(pred=res)
return(out)
}
if(type=="retlevel"){
sampl=x$posterior[,1]-x$posterior[,2]*log(-log(1-1/t))
res=quantile(sampl,0.5)
t=seq(1,k,1)
n=length(t)
li=array(0,c(n))
ls=array(0,c(n))
pred=array(0,c(n))
for (s in 1:n)
{sampl=x$posterior[,1]-x$posterior[,2]*log(-log(1-1/s))
li[s]=quantile(sampl,0.025)
ls[s]=quantile(sampl,0.975)
pred[s]=quantile(sampl,0.5)
}
plot(t,pred,type="l",ylim=c(li[2],max(ls)),ylab="returns", ...)
lines(t,li,lty=2)
lines(t,ls,lty=2)
out<-list(retmedian=res, retpred=pred)
return(out)
}
}
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