Nothing
### Pbinary.R
### Fit a parametric bernoulli regression model.
###
### Copyright: Alejandro Jara, 2006-2012.
###
### Last modification: 15-12-2006.
###
### 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
###
"Pbinary"<-
function(formula,link="logit",prior,mcmc,state,status,misc=NULL,
data=sys.frame(sys.parent()),na.action=na.fail)
UseMethod("Pbinary")
"Pbinary.default"<-
function(formula,
link="logit",
prior,
mcmc,
state,
status,
misc=NULL,
data=sys.frame(sys.parent()),
na.action=na.fail)
{
#########################################################################################
# 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$drop.unused.levels <- TRUE
mf[[1]] <- as.name("model.frame")
mf <- eval(mf, parent.frame())
#########################################################################################
# data structure
#########################################################################################
yobs<- model.response(mf,"numeric")
nrec<-length(yobs)
x<-as.matrix(model.matrix(formula))
p<-dim(x)[2]
#########################################################################################
# Elements for Pseudo Countour Probabilities' computation
#########################################################################################
tt<-terms(formula,data=data)
mat<-attr(tt,"factors")
namfact<-colnames(mat)
nvar<-dim(mat)[1]
nfact<-dim(mat)[2]
possiP<-matrix(0,ncol=2,nrow=nfact)
dataF<-model.frame(formula,data,xlev=NULL)
namD<-names(dataF)
isF<-sapply(dataF, function(x) is.factor(x) || is.logical(x))
nlevel<-rep(0,nvar)
for(i in 1:nvar)
{
if(isF[i])
{
nlevel[i]<-length(table(dataF[[i]]))
}
else
{
nlevel[i]<-1
}
}
startp<-1+attr(tt, "intercept")
for(i in 1:nfact)
{
tmp1<-1
for(j in 1:nvar)
{
if(mat[j,i]==1 && isF[j])
{
tmp1<-tmp1*(nlevel[j]-1)
}
}
endp<-startp+tmp1-1
possiP[i,1]<-startp
possiP[i,2]<-endp
startp<-endp+1
}
dimnames(possiP)<-list(namfact,c("Start","End"))
#########################################################################################
# misclassification
#########################################################################################
if(is.null(misc))
{
sens<-rep(1,nrec)
spec<-rep(1,nrec)
}
else
{
sens<-misc$sens
spec<-misc$spec
if(length(sens)==1)sens<-rep(sens,nrec)
if(length(spec)==1)spec<-rep(spec,nrec)
}
#########################################################################################
# mcmc specification
#########################################################################################
MLElogit<-function(x,y,sens,spec)
{
fn<-function(theta)
{
eta<-x%*%theta
p<-plogis(eta)
like <- sens*p+(1-spec)*(1-p)
if (all(like > 0))
eval<- -sum(log(like[y==1]))-sum(log(1-like[y==0]))
else eval<-Inf
return(eval)
}
start<-coefficients(glm(y~x-1,family=binomial(logit)))
foo<-optim(start,fn=fn,method="BFGS",hessian=TRUE)
out<-NULL
out$beta<-foo$par
out$stderr<-sqrt(diag(-solve(-foo$hessian)))
out$covb<-(-solve(-foo$hessian))
return(out)
}
MLEprobit<-function(x,y,sens,spec)
{
fn<-function(theta)
{
eta<-x%*%theta
p<-pnorm(eta)
like <- sens*p+(1-spec)*(1-p)
if (all(like > 0))
eval<- -sum(log(like[y==1]))-sum(log(1-like[y==0]))
else eval<-Inf
return(eval)
}
start<-coefficients(glm(y~x-1,family=binomial(logit)))
foo<-optim(start,fn=fn,method="BFGS",hessian=TRUE)
out<-NULL
out$beta<-foo$par
out$stderr<-sqrt(diag(-solve(-foo$hessian)))
out$covb<-(-solve(-foo$hessian))
return(out)
}
pcloglog<-function(x)
{
return(1-exp(-exp(x)))
}
MLEcloglog<-function(x,y,sens,spec)
{
fn<-function(theta)
{
eta<-x%*%theta
p<-pcloglog(eta)
like <- sens*p+(1-spec)*(1-p)
if (all(like > 0))
eval<- -sum(log(like[y==1]))-sum(log(1-like[y==0]))
else eval<-Inf
return(eval)
}
start<-coefficients(glm(y~x-1,family=binomial(logit)))
foo<-optim(start,fn=fn,method="BFGS",hessian=TRUE)
out<-NULL
out$beta<-foo$par
out$stderr<-sqrt(diag(-solve(-foo$hessian)))
out$covb<-(-solve(-foo$hessian))
return(out)
}
MLEcauchy<-function(x,y,sens,spec)
{
fn<-function(theta)
{
eta<-x%*%theta
p<-pcauchy(eta)
like <- sens*p+(1-spec)*(1-p)
if (all(like > 0))
eval<- -sum(log(like[y==1]))-sum(log(1-like[y==0]))
else eval<-Inf
return(eval)
}
start<-coefficients(glm(y~x-1,family=binomial(logit)))
foo<-optim(start,fn=fn,method="BFGS",hessian=TRUE)
out<-NULL
out$beta<-foo$par
out$stderr<-sqrt(diag(-solve(-foo$hessian)))
out$covb<-(-solve(-foo$hessian))
return(out)
}
mcmcvec<-c(mcmc$nburn,mcmc$nskip,mcmc$ndisplay)
nsave<-mcmc$nsave
xmatrix<-x
if(link=="logit")
{
linkn<-1
fit0<- MLElogit(xmatrix,yobs,sens,spec)
}
if(link=="probit")
{
linkn<-2
fit0<- MLEprobit(xmatrix,yobs,sens,spec)
}
if(link=="cloglog")
{
linkn<-3
fit0<- MLEprobit(xmatrix,yobs,sens,spec)
}
if(link=="cauchy")
{
linkn<-4
fit0<- MLEcauchy(xmatrix,yobs,sens,spec)
}
propv<- fit0$covb
if(is.null(mcmc$tune))
{
tune=1
}
else
{
tune<-mcmc$tune
}
#########################################################################################
# prior information
#########################################################################################
betapm<-prior$beta0
betapv<-prior$Sbeta0
propv<-diag(mcmc$tune,p)%*%solve(solve(betapv)+solve(propv))%*%diag(mcmc$tune,p)
#########################################################################################
# parameters depending on status
#########################################################################################
if(status)
{
beta<-fit0$beta
eta <- x %*% beta
}
else
{
beta<-state$beta
eta <- x %*% beta
}
#########################################################################################
# output
#########################################################################################
thetasave <- matrix(0, nrow=mcmc$nsave, ncol=p)
#########################################################################################
# working space
#########################################################################################
acrate<-0
betac<-rep(0,p)
etan<-rep(0,nrec)
iflag<-rep(0,p)
workm1<-matrix(0,nrow=p,ncol=p)
workm2<-matrix(0,nrow=p,ncol=p)
workmh1<-rep(0,(p*(p+1)/2))
workv1<-rep(0,p)
workv2<-rep(0,p)
seed1<-sample(1:29000,1)
seed2<-sample(1:29000,1)
cpo<-rep(0,nrec)
#########################################################################################
# calling the fortran code
#########################################################################################
foo <- .Fortran("pbinary",
link =as.integer(linkn),
nrec =as.integer(nrec),
p =as.integer(p),
sens =as.double(sens),
spec =as.double(spec),
x =as.double(x),
yobs =as.integer(yobs),
betapm =as.double(betapm),
betapv =as.double(betapv),
mcmc =as.integer(mcmcvec),
nsave =as.integer(nsave),
propv =as.double(propv),
acrate =as.double(acrate),
thetasave =as.double(thetasave),
cpo =as.double(cpo),
beta =as.double(beta),
betac =as.double(betac),
eta =as.double(eta),
etan =as.double(etan),
iflag =as.integer(iflag),
seed1 =as.integer(seed1),
seed2 =as.integer(seed2),
workm1 =as.double(workm1),
workm2 =as.double(workm2),
workmh1 =as.double(workmh1),
workv1 =as.double(workv1),
workv2 =as.double(workv2),
PACKAGE="DPpackage")
#########################################################################################
# save state
#########################################################################################
thetasave<-matrix(foo$thetasave,nrow=mcmc$nsave, ncol=(p))
model.name<-"Bayesian parametric binary regression model"
colnames(thetasave)<-c(dimnames(x)[[2]])
coeff<-rep(0,p)
for(i in 1:p){
coeff[i]<-mean(thetasave[,i])
}
names(coeff)<-c(dimnames(x)[[2]])
state <- list(beta=foo$beta)
save.state <- list(thetasave=thetasave)
z<-list(modelname=model.name,coefficients=coeff,acrate=foo$acrate,call=cl,
prior=prior,mcmc=mcmc,state=state,save.state=save.state,nrec=foo$nrec,
cpo=foo$cpo,p=p,link=link,x=x,possiP=possiP)
cat("\n\n")
class(z)<-c("Pbinary")
return(z)
}
###
### Estimate the probability curve for a fitted parametric binary
### regression model.
###
### Copyright: Alejandro Jara Vallejos, 2006
### Last modification: 01-07-2006.
predict.Pbinary<-function(object,xnew=NULL,hpd=TRUE, ...)
{
pcloglog<-function(x)
{
return(1-exp(-exp(x)))
}
sd<-function(x)
{
return(sqrt(var(x)))
}
std<-function(x)
{
n<-length(x)
return(sqrt(var(x))/sqrt(n))
}
hpdf<-function(x)
{
alow<-rep(0,2)
aupp<-rep(0,2)
sig<-0.05
n<-length(x)
a<-.Fortran("hpd",n=as.integer(n),alpha=as.double(sig),x=as.double(x),
alow=as.double(alow),aupp=as.double(aupp),PACKAGE="DPpackage")
return(c(a$alow[1],a$aupp[1]))
}
nhpdf<-function(x)
{
return(c(quantile(x,0.025),quantile(x,0.975)))
}
if(is.null(xnew))
{
xnew<-object$x
}
if(is(object, "Pbinary"))
{
npred<-dim(xnew)[1]
pnew<-dim(xnew)[2]
nrec<-object$nrec
if (object$p != pnew)
{
stop("Dimension of xnew is not the same that the design matrix
in the original model.\n")
}
plinf<-rep(0,npred)
plsup<-rep(0,npred)
lp<-xnew%*%t(object$save.state$thetasave[,1:object$p])
if(object$link=="logit")
{
prob<-plogis(lp)
}
if(object$link=="probit")
{
prob<-pnorm(lp)
}
if(object$link=="cloglog")
{
prob<-pcloglog(lp)
}
if(object$link=="cauchy")
{
prob<-pcauchy(lp)
}
pm<-apply(prob,1,mean)
pmed<-apply(prob,1,median)
psd<-apply(prob,1,sd)
pstd<-apply(prob,1,std)
if(hpd=="TRUE")
{
phpd<-apply(prob,1,hpdf)
}
else
{
phpd<-apply(prob,1,nhpdf)
}
covn<-rep(0,npred)
for(i in 1:npred)
{
covnw<-round(xnew[i,1],3)
for(j in 2:pnew){
covnw<-paste(covnw,round(xnew[i,j],3),sep=";")
}
covn[i]<-covnw
}
plinf<-phpd[1,]
plsup<-phpd[2,]
names(pm)<-covn
names(pmed)<-covn
names(psd)<-covn
names(pstd)<-covn
names(plinf)<-covn
names(plsup)<-covn
out<-NULL
out$pmean<-pm
out$pmedian<-pmed
out$psd<-psd
out$pstd<-pstd
out$plinf<-plinf
out$plsup<-plsup
return(out)
}
}
###
### Tools for Pbinary: anova, print, summary, plot
###
### Copyright: Alejandro Jara Vallejos, 2006
### Last modification: 15-12-2006.
"anova.Pbinary"<-function(object, ...)
{
######################################################################################
cregion<-function(x,probs=c(0.90,0.975))
######################################################################################
# Function to compute a simultaneous credible region for a vector
# parameter from the MCMC sample
#
# Reference: Besag, J., Green, P., Higdon, D. and Mengersen, K. (1995)
# Bayesian computation and stochastic systems (with Discussion)
# Statistical Science, vol. 10, 3 - 66, page 30
# and Held, L. (2004) Simultaneous inference in risk assessment; a Bayesian
# perspective In: COMPSTAT 2004, Proceedings in Computational
# Statistics (J. Antoch, Ed.) 213 - 222, page 214
#
# Arguments
# sample : a data frame or matrix with sampled values (one column = one parameter).
# probs : probabilities for which the credible regions are computed.
######################################################################################
{
#Basic information
nmonte<-dim(x)[1]
p<-dim(x)[2]
#Ranks for each component
ranks <- apply(x, 2, rank, ties.method="first")
#Compute the set S={max(nmonte+1-min r_i(t) , max r_i(t)): t=1,..,nmonte}
left <- nmonte + 1 - apply(ranks, 1, min)
right <- apply(ranks, 1, max)
S <- apply(cbind(left, right), 1, max)
S <- S[order(S)]
#Compute the credible region
k <- floor(nmonte*probs)
tstar <- S[k]
out<-list()
for(i in 1:length(tstar))
{
upelim <- x[ranks == tstar[i]]
lowlim <- x[ranks == nmonte + 1 - tstar[i]]
out[[i]] <- rbind(lowlim, upelim)
rownames(out[[i]]) <- c("Lower", "Upper")
colnames(out[[i]]) <- colnames(x)
}
names(out) <- paste(probs)
return(out)
}
######################################################################################
cint<-function(x,probs=c(0.90,0.975))
######################################################################################
# Function to compute a credible interval from the MCMC sample
#
# Arguments
# sample : a data frame or matrix with sampled values (one column = one parameter).
# probs : probabilities for which the credible regions are to be computed.
######################################################################################
{
#Compute the credible interval
delta<-(1-probs)/2
lprobs<-cbind(delta,probs+delta)
out<-matrix(quantile(x,probs=lprobs),ncol=2)
colnames(out) <- c("Lower","Upper")
rownames(out) <- paste(probs)
return(out)
}
######################################################################################
hnulleval<-function(mat,hnull)
######################################################################################
# Evaluate H0
# AJV, 2006
######################################################################################
{
npar<-dim(mat)[2]
lower<-rep(0,npar)
upper<-rep(0,npar)
for(i in 1:npar)
{
lower[i]<-mat[1,i]< hnull[i]
upper[i]<-mat[2,i]> hnull[i]
}
total<-lower+upper
out<-(sum(total==2) == npar)
return(out)
}
######################################################################################
hnulleval2<-function(vec,hnull)
######################################################################################
# Evaluate H0
# AJV, 2006
######################################################################################
{
lower<-vec[1]< hnull
upper<-vec[2]> hnull
total<-lower+upper
out<-(total==2)
return(out)
}
######################################################################################
pcp<-function(x,hnull=NULL,precision=0.001,prob=0.95,digits=digits)
######################################################################################
# Function to compute Pseudo Countour Probabilities (Region)
# AJV, 2006
######################################################################################
{
if(is.null(hnull))hnull<-rep(0,dim(x)[2])
if (dim(x)[2]!=length(hnull)) stop("Dimension of x and hnull must be equal!!")
probs <- seq(precision, 1-precision, by=precision)
neval <- length(probs)
probsf <- c(prob,probs)
cr <- cregion(x,probs=probsf)
is.hnull <- hnulleval(cr[[2]],hnull)
if(is.hnull)
{
pval <- 1-precision
}
else
{
is.hnull <- hnulleval(cr[[length(cr)]],hnull)
if (!is.hnull)
{
pval <- precision
}
else
{
is.hnull<-rep(0,neval+1)
for(i in 1:(neval+1))
{
is.hnull[i] <- hnulleval(cr[[i]],hnull)
}
is.hnull <- is.hnull[-1]
first <- neval - sum(is.hnull) + 1
pval <- 1 - probs[first]
}
}
output <- list(cr=cr[[1]], prob=prob, pval=pval,hnull=hnull)
return(output)
}
######################################################################################
pcp2<-function(x,hnull=NULL,precision=0.001,prob=0.95)
######################################################################################
# Function to compute Pseudo Countour Probabilities (Interval)
# AJV, 2006
######################################################################################
{
if(is.null(hnull))hnull<-0
probs <- seq(precision, 1-precision, by=precision)
neval <- length(probs)
probsf <- c(prob,probs)
cr <- cint(x,probs=probsf)
is.hnull <- hnulleval2(cr[2,],hnull)
if(is.hnull)
{
pval <- 1-precision
}
else
{
is.hnull <- hnulleval2(cr[(neval+1),],hnull)
if (!is.hnull)
{
pval <- precision
}
else
{
is.hnull<-rep(0,neval+1)
for(i in 1:(neval+1))
{
is.hnull[i] <- hnulleval2(cr[i,],hnull)
}
is.hnull <- is.hnull[-1]
first <- neval - sum(is.hnull) + 1
pval <- 1-probs[first]
}
}
output <- list(cr=cr[1,], prob=prob, pval=pval,hnull=hnull)
return(output)
}
######################################################################################
######################################################################################
######################################################################################
possiP<-object$possiP
nfact<-dim(possiP)[1]
P<-rep(0,nfact)
df<-rep(0,nfact)
for(i in 1:nfact)
{
df[i]<-1
if((possiP[i,2]-possiP[i,1])>0)
{
x<-matrix(object$save.state$thetasave[,possiP[i,1]:possiP[i,2]])
foo<-pcp(x=x)
P[i]<-foo$pval
df[i]<-(possiP[i,2]-possiP[i,1])+1
}
else
{
x<-object$save.state$thetasave[,possiP[i,1]:possiP[i,2]]
foo<-pcp2(x=x)
P[i]<-foo$pval
}
}
table <- data.frame(df,P)
dimnames(table) <- list(rownames(possiP), c("Df","PsCP"))
structure(table, heading = c("Table of Pseudo Contour Probabilities\n",
paste("Response:", deparse(formula(object)[[2]]))), class = c("anovaPsCP",
"data.frame"))
}
"print.Pbinary"<-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(x$cpo)), digits = digits), print.gap = 2,
quote = FALSE)
cat("\nPosterior Inference of Parameters:\n")
print.default(format(x$coefficients, digits = digits), print.gap = 2,
quote = FALSE)
cat("\nAcceptance Rate for Metropolis Step = ",x$acrate,"\n")
cat("\nNumber of Observations:",x$nrec)
cat("\n\n")
invisible(x)
}
"summary.Pbinary"<-function(object, hpd=TRUE, ...)
{
dimen<-object$p
coef.p<-object$coefficients[1:dimen]
coef.sd<-rep(0,dimen)
coef.se<-rep(0,dimen)
coef.l<-rep(0,dimen)
coef.u<-rep(0,dimen)
coef.m<-rep(0,dimen)
names(coef.sd)<-names(object$coefficients[1:dimen])
names(coef.l)<-names(object$coefficients[1:dimen])
names(coef.u)<-names(object$coefficients[1:dimen])
alpha<-0.05
for(i in 1:dimen){
alow<-rep(0,2)
aupp<-rep(0,2)
coef.sd[i]<-sqrt(var(object$save.state$thetasave[,i]))
coef.m[i]<-median(object$save.state$thetasave[,i])
vec<-object$save.state$thetasave[,i]
n<-length(vec)
if(hpd==TRUE)
{
a<-.Fortran("hpd",n=as.integer(n),alpha=as.double(alpha),x=as.double(vec),
alow=as.double(alow),aupp=as.double(aupp),PACKAGE="DPpackage")
coef.l[i]<-a$alow[1]
coef.u[i]<-a$aupp[1]
}
else
{
coef.l[i]<-quantile(vec,0.025)
coef.u[i]<-quantile(vec,0.975)
}
}
coef.se<-coef.sd/sqrt(n)
coef.table <- cbind(coef.p, coef.m, coef.sd, coef.se , coef.l , coef.u)
if(hpd==TRUE)
{
dimnames(coef.table) <- list(names(coef.p), c("Mean", "Median", "Std. Dev.", "Naive Std.Error",
"95%HPD-Low","95%HPD-Upp"))
}
else
{
dimnames(coef.table) <- list(names(coef.p), c("Mean", "Median", "Std. Dev.", "Naive Std.Error",
"95%CI-Low","95%CI-Upp"))
}
ans <- c(object[c("call", "modelname")])
ans$coefficients<-coef.table
ans$cpo<-object$cpo
ans$acrate<-object$acrate
ans$nrec<-object$nrec
class(ans) <- "summaryPbinary"
return(ans)
}
"print.summaryPbinary"<-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(x$cpo)), digits = digits), print.gap = 2,
quote = FALSE)
if (length(x$coefficients)) {
cat("\nRegression coefficients:\n")
print.default(format(x$coefficients, digits = digits), print.gap = 2,
quote = FALSE)
}
else cat("No coefficients\n")
cat("\nAcceptance Rate for Metropolis Step = ",x$acrate,"\n")
cat("\nNumber of Observations:",x$nrec)
cat("\n\n")
invisible(x)
}
"plot.Pbinary"<-function(x, hpd=TRUE, ask=TRUE, nfigr=2, nfigc=2, param=NULL, col="#bdfcc9", ...)
{
fancydensplot<-function(x, hpd=TRUE, npts=200, xlab="", ylab="", main="",col="#bdfcc9", ...)
# Author: AJV, 2006
#
{
dens <- density(x,n=npts)
densx <- dens$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$aupp[1]
}
else
{
xlinf <- quantile(x,0.025)
xlsup <- quantile(x,0.975)
}
densx1 <- max(densx[densx<=xlinf])
densx2 <- min(densx[densx>=xlinf])
densy1 <- densy[densx==densx1]
densy2 <- densy[densx==densx2]
ylinf <- densy1 + ((densy2-densy1)/(densx2-densx1))*(xlinf-densx1)
densx1 <- max(densx[densx<=xlsup])
densx2 <- min(densx[densx>=xlsup])
densy1 <- densy[densx==densx1]
densy2 <- densy[densx==densx2]
ylsup <- densy1 + ((densy2-densy1)/(densx2-densx1))*(xlsup-densx1)
plot(0.,0.,xlim = c(min(densx), max(densx)), ylim = c(min(densy), max(densy)),
axes = F,type = "n" , xlab=xlab, ylab=ylab, main=main, cex=1.2)
xpol<-c(xlinf,xlinf,densx[densx>=xlinf & densx <=xlsup],xlsup,xlsup)
ypol<-c(0,ylinf,densy[densx>=xlinf & densx <=xlsup] ,ylsup,0)
polygon(xpol, ypol, border = FALSE,col=col)
lines(c(min(densx), max(densx)),c(0,0),lwd=1.2)
segments(min(densx),0, min(densx),max(densy),lwd=1.2)
lines(densx,densy,lwd=1.2)
segments(meanvar, 0, meanvar, ymean,lwd=1.2)
segments(xlinf, 0, xlinf, ylinf,lwd=1.2)
segments(xlsup, 0, xlsup, ylsup,lwd=1.2)
axis(1., at = round(c(xlinf, meanvar,xlsup), 2.), labels = T,pos = 0.)
axis(1., at = round(seq(min(densx),max(densx),length=15), 2.), labels = F,pos = 0.)
axis(2., at = round(seq(0,max(densy),length=5), 2.), labels = T,pos =min(densx))
}
if(is(x, "Pbinary")){
if(is.null(param))
{
coef.p<-x$coefficients
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(x$save.state$thetasave[,i],type='l',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
{
fancydensplot(x$save.state$thetasave[,i],hpd=hpd,main=title2,xlab="values", ylab="density",col=col)
}
}
}
else
{
coef.p<-x$coefficients
n<-length(coef.p)
pnames<-names(coef.p)
poss<-0
for(i in 1:n)
{
if(pnames[i]==param)poss=i
}
if (poss==0)
{
stop("This parameter is not present in the original model.\n")
}
par(ask = ask)
layout(matrix(seq(1,nfigr*nfigc,1), nrow=nfigr, ncol=nfigc, byrow = TRUE))
title1<-paste("Trace of",pnames[poss],sep=" ")
title2<-paste("Density of",pnames[poss],sep=" ")
plot(x$save.state$thetasave[,poss],type='l',main=title1,xlab="MCMC scan",ylab=" ")
fancydensplot(x$save.state$thetasave[,poss],hpd=hpd,main=title2,xlab="values", ylab="density",col=col)
}
}
}
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