#'Altman plot
#'
#'This function provides a plot from Altman (2004) that allows the assessment
#'of goodness-of-fit. Assuming the observed process is stationary, it plots
#'the marginal CDF of the data evaluated at the maximum likelihood estimates
#'against the empirical CDF values. The plot should be close to a 45 degree straight
#'line.
#'
#'@param move.HSMM A move.HSMM object containing a fitted HSMM model.
#'@include Distributions.R
#'@include move.HSMM.state_probs.R
#'@include gen.Gamma.R
#'@return A plot
#'@export
#'
move.HSMM.Altman=function(move.HSMM){
obs=move.HSMM$obs
params=move.HSMM$params
nstates=move.HSMM$nstates
dists=move.HSMM$dists
m=move.HSMM$m1
ndist=length(dists)-1
out=Distributions(dists,nstates)
PDFs=out[[3]]
CDFs=out[[4]]
Gamma <- gen.Gamma(m,params,PDFs,CDFs)
if(nstates>2){
params[[1]]=NULL
}
delta <- solve(t(diag(sum(m))-Gamma+1),rep(1,sum(m)))
n=nrow(obs)
Ffit=vector("list",2)
Femp=vector("list",2)
for(i in 1:ndist){
Ffit[[i]]=numeric(n)
}
circ=c("wrpcauchy","wrpnorm")
for(k in 2:length(dists)){
if(match(dists[k],circ,nomatch=0)>0){
if(any(obs[,k-1]>pi,na.rm=T)){
obs[,k-1][(obs[,k-1]>pi)&(!is.na(obs[,k-1]))]=obs[,k-1][(obs[,k-1]>pi)&(!is.na(obs[,k-1]))]-2*pi
}
}
}
#Calculate fitted marginal distribution
mstart=c(1,cumsum(m)+1)
mstart=mstart[-length(mstart)]
mstop=cumsum(m)
for(k in 1:n){
#for each distribution
for(j in 1:nstates){
for(i in 1:ndist){
nparam=max(1,ncol(params[[i+1]]))
if(nparam==2){
Ffit[[i]][k]=Ffit[[i]][k]+sum(CDFs[[i+1]](obs[k,i],params[[i+1]][j,1],params[[i+1]][j,2])*delta[mstart[j]:mstop[j]])
}else if(nparam==1){
Ffit[[i]][k]=Ffit[[i]][k]+sum(CDFs[[i+1]](obs[k,i],params[[i+1]][j])*delta[mstart[j]:mstop[j]])
}else if(nparam==3){
Ffit[[i]][k]=Ffit[[i]][k]+sum(CDFs[[i+1]](obs[k,i],params[[i+1]][j],params[[i+1]][j,2],params[[i+1]][j,3])*delta[mstart[j]:mstop[j]])
}
}
}
}
label=names(CDFs)[2:length(CDFs)]
par(mfrow=c(ndist,1))
for(i in 1:ndist){
Femp[[i]]=numeric(nrow(obs))
if(out[[6]][i]==1){
a=ecdf(obs[,i])
Femp[[i]]=a(obs[,i])
}else if(out[[6]][i]==2){
a=ecdf(obs[,i])
Femp[[i]]=a(obs[,i])
}
plot(Ffit[[i]],Femp[[i]],main=paste(label[i],"Altman Plot"),xlab="Fitted",ylab="Empirical",xlim=c(0,1),ylim=c(0,1))
abline(a=0,b=1)
}
}
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.