Nothing
### LDDPtwopl.R
### Fit a two parameter logistic model with a Linear Dependent DP prior
### for the random effect distribution
###
### Copyright: Alejandro Jara, 2012.
###
### Last modification: 28-08-2012.
###
### This program is free software; you can redistribute it and/or modify
### it under the terms of the GNU General Public License as published by
### the Free Software Foundation; either version 2 of the License, or (at
### your option) any later version.
###
### This program is distributed in the hope that it will be useful, but
### WITHOUT ANY WARRANTY; without even the implied warranty of
### MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
### General Public License for more details.
###
### You should have received a copy of the GNU General Public License
### along with this program; if not, write to the Free Software
### Foundation, Inc., 675 Mass Ave, Cambridge, MA 02139, USA.
###
### The author's contact information:
###
### Alejandro Jara
### Department of Statistics
### Facultad de Matematicas
### Pontificia Universidad Catolica de Chile
### Casilla 306, Correo 22
### Santiago
### Chile
### Voice: +56-2-3544506 URL : http://www.mat.puc.cl/~ajara
### Fax : +56-2-3547729 Email: atjara@uc.cl
###
LDDPtwopl <- function(formula,prior,mcmc,state,status,
grid=seq(-10,10,length=1000),zpred,data=sys.frame(sys.parent()),compute.band=FALSE)
UseMethod("LDDPtwopl")
LDDPtwopl.default <-
function(formula,
prior,
mcmc,
state,
status,
grid=seq(-10,10,length=1000),
zpred,
data=sys.frame(sys.parent()),
compute.band=FALSE)
{
#########################################################################################
# call parameters
#########################################################################################
cl <- match.call()
mf <- match.call(expand.dots = FALSE)
m <- match(c("formula", "data","na.action"), names(mf), 0)
mf <- mf[c(1, m)]
mf$drop.unused.levels <- TRUE
mf[[1]] <- as.name("model.frame")
mf <- eval(mf, parent.frame())
y <- model.response(mf,"numeric")
#########################################################################################
# data structure
#########################################################################################
nsubject <- nrow(y)
p <- ncol(y)
z <- as.matrix(model.matrix(formula))
q <- ncol(z)
if(ncol(zpred) != q)
{
stop("The design matrix for prediction must have the same number of columns than the data desing matrix.\n")
}
#########################################################################################
# prediction
#########################################################################################
npred <- nrow(zpred)
ngrid <- length(grid)
#########################################################################################
# prior information
#########################################################################################
if(is.null(prior$a0))
{
a0 <- -1
b0 <- -1
alpha <- prior$alpha
alpharand <- 0
}
else
{
a0 <- prior$a0
b0 <- prior$b0
alpha <- 1
alpharand <- 1
}
a0b0 <- c(a0,b0)
tau1 <- prior$tau1
if(tau1 < 0)
{
stop("The parameter of the Gamma prior for the kernel variance must be possitive.\n")
}
if(is.null(prior$tau2))
{
taus1 <- prior$taus1
taus2 <- prior$taus2
if(taus1 < 0 || taus2 < 0)
{
stop("The parameters of the Gamma prior for the gamma centering distribution must be possitive.\n")
}
tau2 <- 2.01
tau2rand <- 1
}
else
{
taus1 <- -1
taus2 <- -1
tau2 <- prior$tau2
tau2rand <- 0
}
b0 <- prior$beta0
prec1 <- solve(prior$Sbeta0)
sb <- prec1%*%b0
if(length(b0)!=(p-1))
{
stop("Error in the dimension of the mean of the normal prior for the difficulty parameters.\n")
}
if(nrow(prec1)!=(p-1) || ncol(prec1)!=(p-1))
{
stop("Error in the dimension of the covariance of the normal prior for the difficulty parameters.\n")
}
if(is.null(prior$mu0))
{
mu0 <- rep(0,q)
mu <- prior$mub
prec2 <- diag(1,q)
murand <- 0
}
else
{
mu0 <- prior$mu0
prec2 <- solve(prior$S0)
mu <- rep(0,q)
murand <- 1
}
smu <- prec2%*%mu0
if(length(mu0)!=q)
{
stop("Error in the dimension of the prior mean of the mean of the normal centering distribution.\n")
}
if(length(mu)!=q)
{
stop("Error in the dimension of the mean of the normal centering distribution.\n")
}
if(nrow(prec2)!=q || ncol(prec2)!=q)
{
stop("Error in the dimension of the variance of the mean of the normal centering distribution.\n")
}
if(is.null(prior$nu))
{
nu <- -1
tinv <- diag(1,q)
sigma <- prior$sb
sigmarand <- 0
}
else
{
nu <- prior$nu
tinv <- prior$psiinv
sigma <- diag(1,q)
sigmarand<-1
}
if(nrow(tinv)!=q || ncol(tinv)!=q)
{
stop("Error in the dimension of the Wishart prior for the variance of the normal centering distribution.\n")
}
if(nrow(sigma)!=q || ncol(sigma)!=q)
{
stop("Error in the dimension of the normal cenetering covariance matrix.\n")
}
r1 <- prior$r1
r2 <- prior$r2
if(r2 < 0)
{
stop("The scale parameter of the normal prior for the discrimination parameters must be possitive.\n")
}
a0b0 <- c(a0b0,tau1,taus1,taus2,nu)
disprior <- c(r1,r2)
#########################################################################################
# mcmc specification
#########################################################################################
if(missing(mcmc))
{
nburn <- 1000
nsave <- 1000
nskip <- 0
ndisplay <- 100
mcmcvec <- c(nburn,nskip,ndisplay)
}
else
{
mcmcvec <- c(mcmc$nburn,mcmc$nskip,mcmc$ndisplay)
nsave <- mcmc$nsave
}
if(is.null(mcmc$lsdv))
{
lsdv <- -0.7
}
else
{
lsdv <- mcmc$lsdv
}
#########################################################################################
# output
#########################################################################################
acrate <- rep(0,p-1)
cpo <- matrix(0,nrow=nsubject,ncol=p)
denspm <- matrix(0,nrow=npred,ncol=ngrid)
randsave <- matrix(0,nrow=nsave,ncol=nsubject+npred)
thetasave <- matrix(0,nrow=nsave,ncol=2*p-1+q+(q*(q+1)/2)+2)
densave <- matrix(0,nrow=nsave,ncol=npred*ngrid)
#########################################################################################
# parameters depending on status
#########################################################################################
if(status==TRUE)
{
beta <- rep(0,p-1)
b <- rep(0,nsubject)
dp <- rep(0,p-1)
ncluster <- 1
ss <- rep(1,nsubject)
alphaclus <- matrix(0,nrow=nsubject+100,ncol=q)
sigmaclus <- rep(0,nsubject+100)
sigmaclus[1] <- 1
}
if(status==FALSE)
{
beta <- state$beta
b <- state$b
db <- log(state$gamma)
alpha <- state$alpha
ncluster <- state$ncluster
ss <- state$ss
alphaclus <- state$alphaclus
sigmaclus <- state$sigmaclus
mu <- state$mub
sigma <- state$sb
tau2 <- state$tau2
}
#########################################################################################
# working space
#########################################################################################
seed1 <- sample(1:29000,1)
seed2 <- sample(1:29000,1)
seed <- c(seed1,seed2)
adapt <- rep(0,p-1)
lsd <- rep(lsdv,p-1)
cstrt <- matrix(0,nrow=nsubject,ncol=nsubject)
ccluster <- rep(0,nsubject)
iflagq <- rep(0,q)
alphawork <- rep(0,q)
densw <- matrix(0,nrow=npred,ncol=ngrid)
prob <- rep(0,nsubject+100)
quadf <- matrix(0,nrow=q,ncol=q)
sigmainv <- matrix(0,nrow=q,ncol=q)
workmhq1 <- rep(0,q*(q+1)/2)
workmhq2 <- rep(0,q*(q+1)/2)
workvq1 <- rep(0,q)
workvq2 <- rep(0,q)
ztz <- matrix(0,nrow=q,ncol=q)
zty <- rep(0,q)
#########################################################################################
# calling the Fortran code
#########################################################################################
foo <- .Fortran("lddptwopl",
ngrid =as.integer(ngrid),
npred =as.integer(npred),
nsubject =as.integer(nsubject),
p =as.integer(p),
q =as.integer(q),
y =as.integer(y),
grid =as.double(grid),
z =as.double(z),
zpred =as.double(zpred),
murand =as.integer(murand),
a0b0 =as.double(a0b0),
b0 =as.double(b0),
prec1 =as.double(prec1),
mu0 =as.double(mu0),
prec2 =as.double(prec2),
smu =as.double(smu),
tinv =as.double(tinv),
acrate =as.double(acrate),
cpo =as.double(cpo),
denspm =as.double(denspm),
randsave =as.double(randsave),
thetasave =as.double(thetasave),
densave =as.double(densave),
ncluster =as.integer(ncluster),
ss =as.integer(ss),
beta =as.double(beta),
b =as.double(b),
dp =as.double(dp),
alphaclus =as.double(alphaclus),
sigmaclus =as.double(sigmaclus),
alpha =as.double(alpha),
mu =as.double(mu),
sigma =as.double(sigma),
tau2 =as.double(tau2),
disprior =as.double(disprior),
mcmc =as.integer(mcmcvec),
nsave =as.integer(nsave),
cstrt =as.integer(cstrt),
ccluster =as.integer(ccluster),
iflagq =as.integer(iflagq),
adapt =as.double(adapt),
lsd =as.double(lsd),
alphawork =as.double(alphawork),
densw =as.double(densw),
prob =as.double(prob),
quadf =as.double(quadf),
sigmainv =as.double(sigmainv),
workmhq1 =as.double(workmhq1),
workmhq2 =as.double(workmhq2),
workvq1 =as.double(workvq1),
workvq2 =as.double(workvq2),
ztz =as.double(ztz),
zty =as.double(zty),
seed =as.integer(seed),
PACKAGE ="DPpackage")
#########################################################################################
# save state
#########################################################################################
hpdf <- function(x)
{
alpha <- 0.05
vec <- x
n <- length(x)
alow <- rep(0,2)
aupp <- rep(0,2)
a <- .Fortran("hpd",n=as.integer(n),alpha=as.double(alpha),x=as.double(vec),
alow=as.double(alow),aupp=as.double(aupp),PACKAGE="DPpackage")
return(c(a$alow[1],a$aupp[1]))
}
pdf <- function(x)
{
alpha <- 0.05
vec <- x
n <- length(x)
alow<-rep(0,2)
aupp<-rep(0,2)
a <- .Fortran("hpd",n=as.integer(n),alpha=as.double(alpha),x=as.double(vec),
alow=as.double(alow),aupp=as.double(aupp),PACKAGE="DPpackage")
return(c(a$alow[2],a$aupp[2]))
}
model.name<-"Bayesian Semiparametric Two Parameter Logistic Model using a LDDP prior"
state <- list(alpha=foo$alpha,
b=foo$b,
beta=foo$beta,
dp=foo$dp,
ncluster=foo$ncluster,
ss=foo$ss,
alphaclus=matrix(foo$alphaclus,nrow=nsubject+100,ncol=q),
sigmaclus=foo$sigmaclus,
mub=foo$mu,
sb=matrix(foo$sigma,nrow=q,ncol=q),
tau2=foo$tau2)
cpo <- matrix(foo$cpo,nrow=nsubject,ncol=p)
randsave <- matrix(foo$randsave,nrow=nsave,ncol=nsubject+npred)
thetasave <- matrix(foo$thetasave,nrow=nsave,ncol=2*p-1+q+(q*(q+1)/2)+2)
densave <- matrix(foo$densave,nrow=nsave,ncol=npred*ngrid)
dens.m <- matrix(foo$denspm,nrow=npred,ncol=ngrid)
dens.l <- NULL
dens.u <- NULL
if(compute.band)
{
limm <- apply(densave, 2, hpdf)
dens.l <- limm[1,]
dens.u <- limm[2,]
dens.l <- matrix(dens.l,nrow=npred,ncol=ngrid,byrow=TRUE)
dens.u <- matrix(dens.u,nrow=npred,ncol=ngrid,byrow=TRUE)
}
pnames <- paste("beta",2:p,sep="")
pnames <- c(pnames,paste("gamma",2:p,sep=""))
pnames <- c(pnames,"tau2",paste("mub",seq(1,q),sep="-"))
for(i in 1:q)
{
for(j in i:q)
{
pnames <- c(pnames,paste("sb",i,j,sep=""))
}
}
pnames <- c(pnames,"ncluster","alpha")
colnames(thetasave) <- pnames
qnames <- NULL
for(i in 1:nsubject)
{
temp <- paste("theta(ID=",i,sep="")
temp <- paste(temp,")",sep="")
qnames <- c(qnames,temp)
}
qnames <- c(qnames,paste("prediction",seq(1,npred),sep="-"))
dimnames(randsave) <- list(NULL,qnames)
coeff <- apply(thetasave, 2, mean)
save.state <- list(thetasave=thetasave,
randsave=randsave,
densave=densave)
z <- list(call=cl,
acrate=foo$acrate,
modelname=model.name,
cpo=cpo,
prior=prior,
mcmc=mcmc,
state=state,
save.state=save.state,
nsubject=nsubject,
p=p,
q=q,
npred=npred,
zpred=zpred,
coefficients=coeff,
dens.m=dens.m,
dens.l=dens.l,
dens.u=dens.u,
grid=grid,
alpharand=alpharand,
murand=murand,
sigmarand=sigmarand,
tau2rand=tau2rand,
compute.band=compute.band)
cat("\n\n")
class(z) <- c("LDDPtwopl")
return(z)
}
###
### Tools
###
### Copyright: Alejandro Jara, 2009
### Last modification: 25-09-2009.
###
"print.LDDPtwopl" <- function (x, digits = max(3, getOption("digits") - 3), ...)
{
cat("\n",x$modelname,"\n\nCall:\n", sep = "")
print(x$call)
cat("\n")
if (length(x$coefficients))
{
cat("Posterior Inference of Parameters:\n")
print.default(format(x$coefficients, digits = digits), print.gap = 2,
quote = FALSE)
}
else cat("No coefficients\n")
cat("\nNumber of subjects:",x$nsubject)
cat("\nNumber of items:",x$p)
cat("\n\n")
invisible(x)
}
"summary.LDDPtwopl"<-function(object, hpd=TRUE, ...)
{
stde<-function(x)
{
n<-length(x)
return(sd(x)/sqrt(n))
}
hpdf<-function(x)
{
alpha<-0.05
vec<-x
n<-length(x)
alow<-rep(0,2)
aupp<-rep(0,2)
a<-.Fortran("hpd",n=as.integer(n),alpha=as.double(alpha),x=as.double(vec),
alow=as.double(alow),aupp=as.double(aupp),PACKAGE="DPpackage")
return(c(a$alow[1],a$aupp[1]))
}
pdf<-function(x)
{
alpha<-0.05
vec<-x
n<-length(x)
alow<-rep(0,2)
aupp<-rep(0,2)
a<-.Fortran("hpd",n=as.integer(n),alpha=as.double(alpha),x=as.double(vec),
alow=as.double(alow),aupp=as.double(aupp),PACKAGE="DPpackage")
return(c(a$alow[2],a$aupp[2]))
}
thetasave <- object$save.state$thetasave
### Difficulty and discrimination parameters
dimen1 <- 2*object$p-2
if(dimen1>1)
{
mat <- thetasave[,1:dimen1]
}
else
{
mat <- matrix(thetasave[,1:dimen1],ncol=1)
}
coef.p <- object$coefficients[1:dimen1]
coef.m <- apply(mat, 2, median)
coef.sd <- apply(mat, 2, sd)
coef.se <- apply(mat, 2, stde)
if(hpd){
limm <- apply(mat, 2, hpdf)
coef.l <- limm[1,]
coef.u <- limm[2,]
}
else
{
limm <- apply(mat, 2, pdf)
coef.l <- limm[1,]
coef.u <- limm[2,]
}
names(coef.m) <- names(object$coefficients[1:dimen1])
names(coef.sd) <- names(object$coefficients[1:dimen1])
names(coef.se) <- names(object$coefficients[1:dimen1])
names(coef.l) <- names(object$coefficients[1:dimen1])
names(coef.u) <- names(object$coefficients[1:dimen1])
coef.table <- cbind(coef.p, coef.m, coef.sd, coef.se , coef.l , coef.u)
if(hpd)
{
dimnames(coef.table) <- list(names(coef.p), c("Mean", "Median", "Std. Dev.", "Naive Std.Error",
"95%HPD-Low","95%HPD-Upp"))
}
else
{
dimnames(coef.table) <- list(names(coef.p), c("Mean", "Median", "Std. Dev.", "Naive Std.Error",
"95%CI-Low","95%CI-Upp"))
}
ans <- c(object[c("call", "modelname")])
ans$coefficients <- coef.table
### CPO
ans$cpo<-object$cpo
### Baseline Information
q <- object$q
dimen2 <- 1 + q + q*(q+1)/2
mat <- thetasave[,(dimen1+1):(dimen1+dimen2)]
coef.p <- object$coefficients[(dimen1+1):(dimen1+dimen2)]
coef.m <-apply(mat, 2, median)
coef.sd<-apply(mat, 2, sd)
coef.se<-apply(mat, 2, stde)
if(hpd){
limm<-apply(mat, 2, hpdf)
coef.l<-limm[1,]
coef.u<-limm[2,]
}
else
{
limm<-apply(mat, 2, pdf)
coef.l<-limm[1,]
coef.u<-limm[2,]
}
coef.table <- cbind(coef.p, coef.m, coef.sd, coef.se , coef.l , coef.u)
if(hpd)
{
dimnames(coef.table) <- list(names(coef.p), c("Mean", "Median", "Std. Dev.", "Naive Std.Error",
"95%HPD-Low","95%HPD-Upp"))
}
else
{
dimnames(coef.table) <- list(names(coef.p), c("Mean", "Median", "Std. Dev.", "Naive Std.Error",
"95%CI-Low","95%CI-Upp"))
}
ans$base<-coef.table
### Precision parameter
if(object$alpharand==0)
{
coef.p <- object$coefficients[(dimen1+dimen2+1)]
mat <- matrix(thetasave[,(dimen1+dimen2+1)],ncol=1)
}
else
{
coef.p <- object$coefficients[(dimen1+dimen2+1):(dimen1+dimen2+2)]
mat <- thetasave[,(dimen1+dimen2+1):(dimen1+dimen2+2)]
}
coef.m <-apply(mat, 2, median)
coef.sd<-apply(mat, 2, sd)
coef.se<-apply(mat, 2, stde)
if(hpd){
limm<-apply(mat, 2, hpdf)
coef.l<-limm[1,]
coef.u<-limm[2,]
}
else
{
limm<-apply(mat, 2, pdf)
coef.l<-limm[1,]
coef.u<-limm[2,]
}
coef.table <- cbind(coef.p, coef.m, coef.sd, coef.se , coef.l , coef.u)
if(hpd)
{
dimnames(coef.table) <- list(names(coef.p), c("Mean", "Median", "Std. Dev.", "Naive Std.Error",
"95%HPD-Low","95%HPD-Upp"))
}
else
{
dimnames(coef.table) <- list(names(coef.p), c("Mean", "Median", "Std. Dev.", "Naive Std.Error",
"95%CI-Low","95%CI-Upp"))
}
ans$prec<-coef.table
ans$nrec <- object$nrec
ans$nsubject <- object$nsubject
ans$p <- object$p
ans$q <- object$q
ans$acrate <- object$acrate
class(ans) <- "summaryLDDPtwopl"
return(ans)
}
"print.summaryLDDPtwopl"<-function (x, digits = max(3, getOption("digits") - 3), ...)
{
cat("\n",x$modelname,"\n\nCall:\n", sep = "")
print(x$call)
cat("\n")
cat("Posterior Predictive Distributions (log):\n")
print.default(format(summary(log(as.vector(x$cpo))), digits = digits), print.gap = 2,
quote = FALSE)
if (length(x$coefficients)) {
cat("\nDifficulty parameters:\n")
print.default(format(x$coefficients, digits = digits), print.gap = 2,
quote = FALSE)
}
if (length(x$base)) {
cat("\nBaseline distribution:\n")
print.default(format(x$base, digits = digits), print.gap = 2,
quote = FALSE)
}
else cat("No baseline parameters\n")
if (length(x$prec)) {
cat("\nPrecision parameter:\n")
print.default(format(x$prec, digits = digits), print.gap = 2,
quote = FALSE)
}
cat("\nNumber of subjects:",x$nsubject)
cat("\nNumber of items:",x$p,"\n")
cat("\nNumber of predictors:",x$q,"\n")
cat("\n\n")
invisible(x)
}
"plot.LDDPtwopl"<-function(x, hpd=TRUE, ask=TRUE, nfigr=2, nfigc=2, param=NULL, col="#bdfcc9", ...)
{
fancydensplot1<-function(x, hpd=TRUE, npts=200, xlab="", ylab="", main="",col="#bdfcc9", ...)
# Author: AJV, 2006
#
{
dens <- density(x,n=npts)
densx <- dens$x
densy <- dens$y
meanvar <- mean(x)
densx1 <- max(densx[densx<=meanvar])
densx2 <- min(densx[densx>=meanvar])
densy1 <- densy[densx==densx1]
densy2 <- densy[densx==densx2]
ymean <- densy1 + ((densy2-densy1)/(densx2-densx1))*(meanvar-densx1)
if(hpd==TRUE)
{
alpha<-0.05
alow<-rep(0,2)
aupp<-rep(0,2)
n<-length(x)
a<-.Fortran("hpd",n=as.integer(n),alpha=as.double(alpha),x=as.double(x),
alow=as.double(alow),aupp=as.double(aupp),PACKAGE="DPpackage")
xlinf<-a$alow[1]
xlsup<-a$aupp[1]
}
else
{
xlinf <- quantile(x,0.025)
xlsup <- quantile(x,0.975)
}
densx1 <- max(densx[densx<=xlinf])
densx2 <- min(densx[densx>=xlinf])
densy1 <- densy[densx==densx1]
densy2 <- densy[densx==densx2]
ylinf <- densy1 + ((densy2-densy1)/(densx2-densx1))*(xlinf-densx1)
densx1 <- max(densx[densx<=xlsup])
densx2 <- min(densx[densx>=xlsup])
densy1 <- densy[densx==densx1]
densy2 <- densy[densx==densx2]
ylsup <- densy1 + ((densy2-densy1)/(densx2-densx1))*(xlsup-densx1)
plot(0.,0.,xlim = c(min(densx), max(densx)), ylim = c(min(densy), max(densy)),
axes = F,type = "n" , xlab=xlab, ylab=ylab, main=main, cex=1.2)
xpol<-c(xlinf,xlinf,densx[densx>=xlinf & densx <=xlsup],xlsup,xlsup)
ypol<-c(0,ylinf,densy[densx>=xlinf & densx <=xlsup] ,ylsup,0)
polygon(xpol, ypol, border = FALSE,col=col)
lines(c(min(densx), max(densx)),c(0,0),lwd=1.2)
segments(min(densx),0, min(densx),max(densy),lwd=1.2)
lines(densx,densy,lwd=1.2)
segments(meanvar, 0, meanvar, ymean,lwd=1.2)
segments(xlinf, 0, xlinf, ylinf,lwd=1.2)
segments(xlsup, 0, xlsup, ylsup,lwd=1.2)
axis(1., at = round(c(xlinf, meanvar,xlsup), 2.), labels = T,pos = 0.)
axis(1., at = round(seq(min(densx),max(densx),length=15), 2.), labels = F,pos = 0.)
axis(2., at = round(seq(0,max(densy),length=5), 2.), labels = T,pos =min(densx))
}
if(is(x, "LDDPtwopl"))
{
if(is.null(param))
{
nn <- length(x$coefficients)
coef.p <- x$coefficients[-nn]
n <- length(coef.p)
pnames <- names(coef.p)
par(ask = ask)
layout(matrix(seq(1,nfigr*nfigc,1), nrow=nfigr , ncol=nfigc ,byrow=TRUE))
for(i in 1:n)
{
title1 <- paste("Trace of",pnames[i],sep=" ")
title2 <- paste("Density of",pnames[i],sep=" ")
plot(ts(x$save.state$thetasave[,i]),main=title1,xlab="MCMC scan",ylab=" ")
if(pnames[i]=="ncluster")
{
hist(x$save.state$thetasave[,i],main=title2,xlab="values", ylab="probability",probability=TRUE)
}
else
{
fancydensplot1(x$save.state$thetasave[,i],hpd=hpd,main=title2,xlab="values", ylab="density",col=col)
}
}
if(x$alpharand==1)
{
title1 <- paste("Trace of","alpha",sep=" ")
title2 <- paste("Density of","alpha",sep=" ")
plot(ts(x$save.state$thetasave[,nn]),main=title1,xlab="MCMC scan",ylab=" ")
fancydensplot1(x$save.state$thetasave[,nn],hpd=hpd,main=title2,xlab="values", ylab="density",col=col)
}
q <- nrow(x$zpred)
for(i in 1:q)
{
yylim <- max(c(x$dens.m[i,],x$dens.u[i,]))
title1 <- paste("Density Estimate for Prediction",i,sep=" - ")
plot(x$grid,x$dens.m[i,],ylab="density",main=title1,lty=1,type='l',lwd=2,xlab=expression(theta),ylim=c(0,yylim+0.1))
if(is.null(x$dens.u))
{
}
else
{
lines(x$grid,x$dens.u[i,],lty=2,lwd=2)
lines(x$grid,x$dens.l[i,],lty=2,lwd=2)
}
}
}
else
{
coef.p <- x$coefficients
n <- length(coef.p)
pnames <- names(coef.p)
poss <- 0
for(i in 1:n)
{
if(pnames[i]==param)poss <- i
}
if(poss==0 && param !="prediction")
{
stop("This parameter is not present in the original model.\n")
}
par(ask = ask)
layout(matrix(seq(1,nfigr*nfigc,1), nrow=nfigr, ncol=nfigc, byrow = TRUE))
if(param !="prediction")
{
par(ask = ask)
layout(matrix(seq(1,nfigr*nfigc,1), nrow=nfigr, ncol=nfigc, byrow = TRUE))
title1<-paste("Trace of",pnames[poss],sep=" ")
title2<-paste("Density of",pnames[poss],sep=" ")
plot(ts(x$save.state$thetasave[,poss]),main=title1,xlab="MCMC scan",ylab=" ")
if(param=="ncluster")
{
hist(x$save.state$thetasave[,poss],main=title2,xlab="values", ylab="probability",probability=TRUE)
}
else
{
fancydensplot1(x$save.state$thetasave[,poss],hpd=hpd,main=title2,xlab="values", ylab="density",col=col)
}
}
else
{
layout(matrix(seq(1,nfigr*nfigc,1), nrow=nfigr , ncol=nfigc ,byrow=TRUE))
q <- nrow(x$zpred)
for(i in 1:q)
{
yylim <- max(c(x$dens.m[i,],x$dens.u[i,]))
title1 <- paste("Density Estimate for Prediction",i,sep=" - ")
plot(x$grid,x$dens.m[i,],ylab="density",main=title1,lty=1,type='l',lwd=2,xlab=expression(theta),ylim=c(0,yylim+0.1))
if(is.null(x$dens.u))
{
}
else
{
lines(x$grid,x$dens.u[i,],lty=2,lwd=2)
lines(x$grid,x$dens.l[i,],lty=2,lwd=2)
}
}
}
}
}
}
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