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#' Plot results of an AME object
#'
#' A set of plots summarizing the MCMC routine for an AME fit, as well as some
#' posterior predictive checks.
#'
#'
#' @param x the result of fitting an AME model
#' @param ... additional parameters (not used)
#' @return a series of plots
#' @author Peter Hoff
#' @exportS3Method amen::plot
plot.ame <-
function(x, ...)
{
fit<-x
gof<-nrow(fit$GOF)>1
p<-ncol(fit$BETA)
if(!gof | p==0 )
{
par(mfrow=c(1+2*gof,2),mar=c(3,3,1,1),mgp=c(1.75,0.75,0))
}
if(gof & p>0 )
{
par(mar=c(3,3,1,1),mgp=c(1.75,0.75,0))
layout(matrix(c(1,3,5,1,3,5,2,4,6,2,4,7),3,4) )
}
mVC<-apply(fit$VC,2,median)
matplot(fit$VC,type="l",lty=1,ylab="VC")
abline(h=mVC,col=1:length(mVC) )
if(p>0)
{
mBETA<-apply(fit$BETA,2,median)
matplot(fit$BETA,type="l",lty=1,col=1:p,ylab="BETA")
abline(h=mBETA,col=1:p )
abline(h=0,col="gray")
}
if(gof)
{
## ame
if(length(dim(fit$GOF))==2)
{
for(k in 1:5)
{
hist(fit$GOF[-1,k],xlim=range(fit$GOF[,k]),main="",prob=TRUE,
xlab=colnames(fit$GOF)[k],col="lightblue",ylab="",yaxt="n")
abline(v=fit$GOF[1,k],col="red")
}
}
## ame_rep
if(length(dim(fit$GOF))==3)
{
DG<-sweep( fit$GOF[,,-1,drop=FALSE],c(1,2),fit$GOF[,,1],"-")
DG<-zapsmall(DG)
for(k in 1:5)
{
boxplot(t(DG[k,,]),col="lightblue",ylab=dimnames(fit$GOF)[[1]][k])
abline(h=0,col="gray")
}
}
}
}
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