Nothing
### DPbetabinom.R
### Fit a semiparametric beta binomial model using a DP prior.
###
### Copyright: Alejandro Jara, 2009 - 2012.
### Last modification: 09-03-2009.
###
### 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 authors' 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
###
### Fernando Quintana
### Departamento de Estadistica
### Facultad de Matematicas
### Pontificia Universidad Catolica de Chile
### Casilla 306, Correo 22
### Santiago
### Voice: +56-2-3544464 URL : http://www.mat.puc.cl/~quintana
### Fax : +56-2-3547229 Email: quintana@mat.puc.cl
###
"DPbetabinom"<-
function(y,ngrid=100,prior,mcmc,state,status,data=sys.frame(sys.parent()),work.dir=NULL)
UseMethod("DPbetabinom")
DPbetabinom.default<-
function(y,
ngrid=100,
prior,
mcmc,
state,
status,
data=sys.frame(sys.parent()),
work.dir=NULL)
{
#########################################################################################
# call parameters
#########################################################################################
cl <- match.call()
#########################################################################################
# response
#########################################################################################
nrec <- nrow(y)
q <- ncol(y)
if(is.null(q))
{
stop("The respons must be a nrec*2 matrix.\n")
}
#########################################################################################
# change working directory (if requested..)
#########################################################################################
if(!is.null(work.dir))
{
cat("\n Changing working directory to ",work.dir,"\n")
old.dir <- getwd() # by default work in current working directory
setwd(work.dir)
}
#########################################################################################
# prediction
#########################################################################################
grid <- seq(from=0,to=1,length.out=ngrid)
#########################################################################################
# prior information
#########################################################################################
if(is.null(prior$a0))
{
a0 <--1
b0 <--1
alpha<-prior$alpha
}
else
{
a0 <- prior$a0
b0 <- prior$b0
alpha <- 1
}
a1 <- prior$a1
b1 <- prior$b1
#########################################################################################
# mcmc specification
#########################################################################################
mcmcvec <- c(mcmc$nburn,mcmc$nskip,mcmc$ndisplay)
nsave <- mcmc$nsave
#########################################################################################
# output
#########################################################################################
cpo <- matrix(0,nrow=nrec,ncol=2)
thetasave <- matrix(0,nrow=nsave,ncol=3)
randsave <- matrix(0,nrow=nsave,ncol=(nrec+1))
densm <- rep(0,ngrid)
#########################################################################################
# parameters depending on status
#########################################################################################
if(status==TRUE)
{
ncluster <- 1
ss <- rep(1,nrec)
p <- rep(0,nrec+1)
p[1] <- mean(y[,1]/y[,2])
}
if(status==FALSE)
{
alpha <- state$alpha
ncluster <- state$ncluster
ss <- state$ss
p <- state$p
}
#########################################################################################
# working space
#########################################################################################
cstrt <- matrix(0,nrow=nrec,ncol=nrec)
ccluster <- rep(0,nrec)
prob <- rep(0,(nrec+1))
lprob <- rep(0,(nrec+1))
workcpo <- rep(0,nrec)
seed1 <- sample(1:29000,1)
seed2 <- sample(1:29000,1)
seed <- c(seed1,seed2)
#########################################################################################
# calling the fortran code
#########################################################################################
y <- cbind(y[,1],y[,2]-y[,1])
foo <- .Fortran("dpbetabinom",
nrec =as.integer(nrec),
y =as.double(y),
ngrid =as.integer(ngrid),
grid =as.double(grid),
a0 =as.double(a0),
b0 =as.double(b0),
a1 =as.double(a1),
b1 =as.double(b1),
ncluster =as.integer(ncluster),
ss =as.integer(ss),
alpha =as.double(alpha),
p =as.double(p),
mcmc =as.integer(mcmcvec),
nsave =as.integer(nsave),
cpo =as.double(cpo),
densm =as.double(densm),
thetasave =as.double(thetasave),
randsave =as.double(randsave),
ccluster =as.integer(ccluster),
cstrt =as.integer(cstrt),
prob =as.double(prob),
lprob =as.double(lprob),
workcpo =as.double(workcpo),
seed =as.integer(seed),
PACKAGE ="DPpackage")
#########################################################################################
# save state
#########################################################################################
model.name <- "Bayesian Semiparametric Beta-Binomial Model"
cpom<-matrix(foo$cpo,nrow=nrec,ncol=2)
cpo<-cpom[,1]
fso<-cpom[,2]
state <- list(alpha=foo$alpha,
ncluster=foo$ncluster,
ss=foo$ss,
p=foo$p)
pnames <- c("ncluster","alpha","LPML")
thetasave <- matrix(foo$thetasave,nrow=nsave,ncol=3)
colnames(thetasave) <- pnames
coeff <- apply(thetasave,2,mean)[1:2]
randsave<-matrix(foo$randsave,nrow=nsave,ncol=(nrec+1))
densp.m <- foo$densm
save.state <- list(thetasave=thetasave,
randsave=randsave)
z <- list(call=cl,
y=y,
coefficients=coeff,
modelname=model.name,
cpo=cpo,
fso=fso,
prior=prior,
mcmc=mcmc,
state=state,
save.state=save.state,
nrec=foo$nrec,
densp.m=densp.m,
ngrid=ngrid,
grid=grid)
cat("\n\n")
class(z) <- "DPbetabinom"
return(z)
}
###
### Tools
###
### Copyright: Alejandro Jara, 2009
### Last modification: 09-03-2009.
###
"print.DPbetabinom"<-function (x, digits = max(3, getOption("digits") - 3), ...)
{
cat("\n",x$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$coefficients, digits = digits), print.gap = 2,
quote = FALSE)
cat("\nNumber of Observations:",x$nrec)
cat("\n\n")
invisible(x)
}
"summary.DPbetabinom"<-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
ans <- c(object[c("call", "modelname")])
### CPO
ans$cpo <- object$cpo
### Precision parameter
if(is.null(object$prior$a0))
{
coef.p <- object$coefficients[1]
mat <- matrix(thetasave[,1],ncol=1)
}
else
{
coef.p <- object$coefficients
mat <- thetasave[,1:2]
}
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$nrec
class(ans) <- "summaryDPbetabinom"
return(ans)
}
"print.summaryDPbetabinom"<-function (x, digits = max(3, getOption("digits") - 3), ...)
{
cat("\n",x$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$prec)) {
cat("\nPrecision parameter:\n")
print.default(format(x$prec, digits = digits), print.gap = 2,
quote = FALSE)
}
cat("\nNumber of Observations:",x$nrec)
cat("\n\n")
invisible(x)
}
"plot.DPbetabinom"<-function(x, ask=TRUE, output="density", param=NULL, hpd=TRUE, nfigr=1, nfigc=1, col="#bdfcc9", ...)
{
fancydensplot1<-function(x, hpd=TRUE, npts=200, xlab="", ylab="", main="",col="#bdfcc9", ...)
# Author: AJV, 2006
#
{
dens <- density(x,n=npts)
densx <- dens$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$aupp[1]
}
else
{
xlinf <- quantile(x,0.025)
xlsup <- quantile(x,0.975)
}
densx1 <- max(densx[densx<=xlinf])
densx2 <- min(densx[densx>=xlinf])
densy1 <- densy[densx==densx1]
densy2 <- densy[densx==densx2]
ylinf <- densy1 + ((densy2-densy1)/(densx2-densx1))*(xlinf-densx1)
densx1 <- max(densx[densx<=xlsup])
densx2 <- min(densx[densx>=xlsup])
densy1 <- densy[densx==densx1]
densy2 <- densy[densx==densx2]
ylsup <- densy1 + ((densy2-densy1)/(densx2-densx1))*(xlsup-densx1)
plot(0.,0.,xlim = c(min(densx), max(densx)), ylim = c(min(densy), max(densy)),
axes = F,type = "n" , xlab=xlab, ylab=ylab, main=main, cex=1.2)
xpol<-c(xlinf,xlinf,densx[densx>=xlinf & densx <=xlsup],xlsup,xlsup)
ypol<-c(0,ylinf,densy[densx>=xlinf & densx <=xlsup] ,ylsup,0)
polygon(xpol, ypol, border = FALSE,col=col)
lines(c(min(densx), max(densx)),c(0,0),lwd=1.2)
segments(min(densx),0, min(densx),max(densy),lwd=1.2)
lines(densx,densy,lwd=1.2)
segments(meanvar, 0, meanvar, ymean,lwd=1.2)
segments(xlinf, 0, xlinf, ylinf,lwd=1.2)
segments(xlsup, 0, xlsup, ylsup,lwd=1.2)
axis(1., at = round(c(xlinf, meanvar,xlsup), 2.), labels = T,pos = 0.)
axis(1., at = round(seq(min(densx),max(densx),length=15), 2.), labels = F,pos = 0.)
axis(2., at = round(seq(0,max(densy),length=5), 2.), labels = T,pos =min(densx))
}
if(is(x, "DPbetabinom"))
{
if(output=="density")
{
# Density estimation
par(ask = ask)
layout(matrix(seq(1,nfigr*nfigc,1),nrow=nfigr,ncol=nfigc,byrow=TRUE))
title1 <- "Posterior density estimate"
plot(x$grid,x$densp.m,lwd=2,type="l",lty=1,main=title1,xlab="values",ylab="density")
}
else
{
if(is.null(param))
{
pnames <- colnames(x$save.state$thetasave)
cnames <- names(x$coefficients)
par(ask = ask)
layout(matrix(seq(1,nfigr*nfigc,1), nrow=nfigr , ncol=nfigc ,byrow=TRUE))
for(i in 1:1)
{
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
{
fancydensplot1(x$save.state$thetasave[,i],hpd=hpd,main=title2,xlab="values", ylab="density",col=col)
}
}
if(is.null(x$prior$a0))
{
cat("")
}
else
{
title1<-paste("Trace of",pnames[2],sep=" ")
title2<-paste("Density of",pnames[2],sep=" ")
plot(x$save.state$thetasave[,2],type='l',main=title1,xlab="MCMC scan",ylab=" ")
fancydensplot1(x$save.state$thetasave[,2],hpd=hpd,main=title2,xlab="values", ylab="density",col=col)
}
title1<-paste("Trace Plot of",pnames[3],sep=" ")
plot(x$save.state$thetasave[,3],type='l',main=title1,xlab="MCMC scan",ylab="LPML")
}
else
{
pnames <- colnames(x$save.state$thetasave)
n <- ncol(x$save.state$thetasave)
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=" ")
if(pnames[poss]=="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)
}
}
}
}
}
Any scripts or data that you put into this service are public.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.