Nothing
### DPdensity.R
### Fit a linear Dirichlet Process mixture of normal model for
### density estimation
###
### Copyright: Alejandro Jara, 2006-2012.
###
### Last modification: 05-10-2009.
###
### 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 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
###
DPdensity<-function(y,ngrid=1000,grid=NULL,prior,mcmc,state,status,method="neal",data=sys.frame(sys.parent()),na.action=na.fail)
UseMethod("DPdensity")
DPdensity.default <- function(y,ngrid=1000,grid=NULL,prior,mcmc,state,status,method="neal",data,na.action=na.fail)
{
#########################################################################################
# call parameters
#########################################################################################
cl <- match.call()
y <- na.action(as.matrix(y))
#########################################################################################
# data structure
#########################################################################################
nrec <- nrow(y)
nvar <- ncol(y)
left <- rep(0,2)
right <- rep(0,2)
if(nvar==1)
{
left[1] <- min(y)-0.5*sqrt(var(y))
right[1] <- max(y)+0.5*sqrt(var(y))
}
else
{
left[1] <- min(y[,1])-0.5*sqrt(var(y[,1]))
right[1] <- max(y[,1])+0.5*sqrt(var(y[,1]))
left[2] <- min(y[,2])-0.5*sqrt(var(y[,2]))
right[2] <- max(y[,2])+0.5*sqrt(var(y[,2]))
}
#########################################################################################
# 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)
if(is.null(prior$nu2))
{
psiinv1 <- matrix(prior$psiinv1,nvar,nvar)
psiinv2 <- psiinv1
psi1 <- matrix(solve(psiinv1),nvar,nvar)
nuvec <- c(prior$nu1,-1)
psi1rand <- 0
}
else
{
psiinv1 <- matrix(var(y),nvar,nvar)
psi1 <- matrix(solve(psiinv1),nvar,nvar)
psiinv2 <- matrix(prior$psiinv2,nvar,nvar)
nuvec <- c(prior$nu1,prior$nu2)
psi1rand <- 1
}
if(is.null(prior$m2) && is.null(prior$s2))
{
s2inv <- matrix(0,nrow=nvar,ncol=nvar)
s2invm2 <- matrix(0,nrow=nvar,ncol=1)
m1 <- prior$m1
m1rand <- 0
}
else
{
s2inv <- solve(prior$s2)
s2invm2 <- s2inv%*%prior$m2
m1 <- rep(0,nvar)
for(i in 1:nvar)
{
m1[i] <- mean(y[,i])+rnorm(1,0,100)
}
m1rand <- 1
}
if(is.null(prior$tau1) && is.null(prior$tau2))
{
tau <- c(-2,-2)
k0 <- prior$k0
k0rand <- 0
}
else
{
tau <- c(prior$tau1,prior$tau2)
k0 <- rgamma(1,shape=prior$tau1,scale=prior$tau2)
k0rand <- 1
}
#########################################################################################
# mcmc specification
#########################################################################################
mcmcvec <- c(mcmc$nburn,mcmc$nskip,mcmc$ndisplay)
nsave <- mcmc$nsave
#########################################################################################
# output
#########################################################################################
if(is.null(grid))
{
if(nvar>1) ngrid <- as.integer(sqrt(ngrid))
grid1 <- seq(left[1],right[1],length=ngrid)
grid2 <- seq(left[2],right[2],length=ngrid)
}
else
{
grid <- as.matrix(grid)
ngrid <- nrow(grid)
if(nvar==1)
{
grid1 <- grid[,1]
grid2 <- rep(0,ngrid)
}
else
{
grid1 <- grid[,1]
grid2 <- grid[,2]
}
}
f <- matrix(0,nrow=ngrid,ncol=ngrid)
fun1 <- rep(0,ngrid)
fun2 <- rep(0,ngrid)
thetasave <- matrix(0,nrow=nsave,ncol=nvar+nvar*(nvar+1)/2+3)
randsave <- matrix(0,nrow=nsave,ncol=(nrec+2)*nvar+(nrec+1)*nvar*(nvar+1)/2)
#########################################################################################
# parameters depending on status
#########################################################################################
nuniq <- nvar*(nvar+1)/2
if(status==TRUE)
{
muclus <- matrix(0,nrow=nrec+100,ncol=nvar)
sigmaclus <- matrix(0,nrow=nrec+100,ncol=nuniq)
for(i in 1:1)
{
counter<-0
for(j in 1:nvar)
{
muclus[i,j] <- m1[j]
for(k in j:nvar)
{
counter <- counter+1
sigmaclus[i,counter] <- psiinv1[j,k]
}
}
}
ncluster <- 1
ss <- rep(1,nrec)
}
if(status==FALSE)
{
alpha <- state$alpha
m1 <- state$m1
muclus <- state$muclus
ncluster <- state$ncluster
psi1 <- state$psi1
psiinv1 <- solve(psi1)
k0 <- state$k0
sigmaclus <- state$sigmaclus
ss <- state$ss
}
#########################################################################################
# working space
#########################################################################################
ccluster <- rep(0,nrec)
cpo <- rep(0,nrec)
iflag <- rep(0,nvar)
muwork <- rep(0,nvar)
muwork2 <- rep(0,nvar)
prob <- rep(0,(nrec+100))
s1 <- matrix(0,nvar,nvar)
seed1 <- sample(1:29000,1)
seed2 <- sample(1:29000,1)
seed3 <- sample(1:29000,1)
seed <- c(seed1,seed2,seed2)
sigmawork <- matrix(0,nrow=nvar,ncol=nvar)
sigmawork2 <- matrix(0,nrow=nvar,ncol=nvar)
sigworkinv <- matrix(0,nrow=nvar,ncol=nvar)
theta <- rep(0,nvar)
workm1 <- matrix(0,nrow=nvar,ncol=nvar)
workm2 <- matrix(0,nrow=nvar,ncol=nvar)
workm3 <- matrix(0,nrow=nvar,ncol=nvar)
workmh1 <- rep(0,nvar*(nvar+1)/2)
workmh2 <- rep(0,nvar*(nvar+1)/2)
workv1 <- rep(0,nvar)
workv2 <- rep(0,nvar)
workv3 <- rep(0,nvar)
ywork <- rep(0,nvar)
workcpo <- rep(0,nrec)
#########################################################################################
# calling the fortran code
#########################################################################################
if(method=="no-gaps")
{
if(nvar<=2)
{
foo <- .Fortran("bivspdeng",
ngrid =as.integer(ngrid),
nrec =as.integer(nrec),
nvar =as.integer(nvar),
y =as.double(y),
a0b0 =as.double(a0b0),
k0 =as.double(k0),
nuvec =as.integer(nuvec),
m1rand =as.integer(m1rand),
s2inv =as.double(s2inv),
s2invm2 =as.double(s2invm2),
psiinv2 =as.double(psiinv2),
tau =as.double(tau),
mcmc =as.integer(mcmcvec),
nsave =as.integer(nsave),
cpo =as.double(cpo),
f =as.double(f),
fun1 =as.double(fun1),
fun2 =as.double(fun2),
randsave =as.double(randsave),
thetasave =as.double(thetasave),
alpha =as.double(alpha),
m1 =as.double(m1),
muclus =as.double(muclus),
ncluster =as.integer(ncluster),
psi1 =as.double(psi1),
psiinv1 =as.double(psiinv1),
s1 =as.double(s1),
sigmaclus =as.double(sigmaclus),
ss =as.integer(ss),
ccluster =as.integer(ccluster),
grid1 =as.double(grid1),
grid2 =as.double(grid2),
iflag =as.integer(iflag),
muwork =as.double(muwork),
muwork2 =as.double(muwork2),
prob =as.double(prob),
seed =as.integer(seed),
sigmawork =as.double(sigmawork),
sigmawork2 =as.double(sigmawork2),
sigworkinv =as.double(sigworkinv),
theta =as.double(theta),
workm1 =as.double(workm1),
workm2 =as.double(workm2),
workm3 =as.double(workm3),
workmh1 =as.double(workmh1),
workmh2 =as.double(workmh2),
workv1 =as.double(workv1),
workv2 =as.double(workv2),
workv3 =as.double(workv3),
ywork =as.double(ywork),
workcpo =as.double(workcpo),
PACKAGE ="DPpackage")
}
else
{
foo <- .Fortran("spdeng",
nrec =as.integer(nrec),
nvar =as.integer(nvar),
y =as.double(y),
a0b0 =as.double(a0b0),
k0 =as.double(k0),
nuvec =as.integer(nuvec),
m1rand =as.integer(m1rand),
s2inv =as.double(s2inv),
s2invm2 =as.double(s2invm2),
psiinv2 =as.double(psiinv2),
tau =as.double(tau),
mcmc =as.integer(mcmcvec),
nsave =as.integer(nsave),
cpo =as.double(cpo),
randsave =as.double(randsave),
thetasave =as.double(thetasave),
alpha =as.double(alpha),
m1 =as.double(m1),
muclus =as.double(muclus),
ncluster =as.integer(ncluster),
psi1 =as.double(psi1),
psiinv1 =as.double(psiinv1),
s1 =as.double(s1),
sigmaclus =as.double(sigmaclus),
ss =as.integer(ss),
ccluster =as.integer(ccluster),
iflag =as.integer(iflag),
muwork =as.double(muwork),
prob =as.double(prob),
seed =as.integer(seed),
sigmawork =as.double(sigmawork),
sigworkinv =as.double(sigworkinv),
theta =as.double(theta),
workm1 =as.double(workm1),
workm2 =as.double(workm2),
workm3 =as.double(workm3),
workmh1 =as.double(workmh1),
workmh2 =as.double(workmh2),
workv1 =as.double(workv1),
workv2 =as.double(workv2),
workv3 =as.double(workv3),
ywork =as.double(ywork),
workcpo =as.double(workcpo),
PACKAGE ="DPpackage")
}
}
if(method=="neal")
{
if(nvar<=2)
{
foo <- .Fortran("bivspdenn",
ngrid =as.integer(ngrid),
nrec =as.integer(nrec),
nvar =as.integer(nvar),
y =as.double(y),
a0b0 =as.double(a0b0),
k0 =as.double(k0),
nuvec =as.integer(nuvec),
m1rand =as.integer(m1rand),
s2inv =as.double(s2inv),
s2invm2 =as.double(s2invm2),
psiinv2 =as.double(psiinv2),
tau =as.double(tau),
mcmc =as.integer(mcmcvec),
nsave =as.integer(nsave),
cpo =as.double(cpo),
f =as.double(f),
fun1 =as.double(fun1),
fun2 =as.double(fun2),
randsave =as.double(randsave),
thetasave =as.double(thetasave),
alpha =as.double(alpha),
m1 =as.double(m1),
muclus =as.double(muclus),
ncluster =as.integer(ncluster),
psi1 =as.double(psi1),
psiinv1 =as.double(psiinv1),
s1 =as.double(s1),
sigmaclus =as.double(sigmaclus),
ss =as.integer(ss),
ccluster =as.integer(ccluster),
grid1 =as.double(grid1),
grid2 =as.double(grid2),
iflag =as.integer(iflag),
muwork =as.double(muwork),
muwork2 =as.double(muwork2),
prob =as.double(prob),
seed =as.integer(seed),
sigmawork =as.double(sigmawork),
sigmawork2 =as.double(sigmawork2),
sigworkinv =as.double(sigworkinv),
theta =as.double(theta),
workm1 =as.double(workm1),
workm2 =as.double(workm2),
workm3 =as.double(workm3),
workmh1 =as.double(workmh1),
workmh2 =as.double(workmh2),
workv1 =as.double(workv1),
workv2 =as.double(workv2),
workv3 =as.double(workv3),
ywork =as.double(ywork),
workcpo =as.double(workcpo),
PACKAGE ="DPpackage")
}
else
{
foo <- .Fortran("spdenn",
nrec =as.integer(nrec),
nvar =as.integer(nvar),
y =as.double(y),
a0b0 =as.double(a0b0),
k0 =as.double(k0),
nuvec =as.integer(nuvec),
m1rand =as.integer(m1rand),
s2inv =as.double(s2inv),
s2invm2 =as.double(s2invm2),
psiinv2 =as.double(psiinv2),
tau =as.double(tau),
mcmc =as.integer(mcmcvec),
nsave =as.integer(nsave),
cpo =as.double(cpo),
randsave =as.double(randsave),
thetasave =as.double(thetasave),
alpha =as.double(alpha),
m1 =as.double(m1),
muclus =as.double(muclus),
ncluster =as.integer(ncluster),
psi1 =as.double(psi1),
psiinv1 =as.double(psiinv1),
s1 =as.double(s1),
sigmaclus =as.double(sigmaclus),
ss =as.integer(ss),
ccluster =as.integer(ccluster),
iflag =as.integer(iflag),
muwork =as.double(muwork),
prob =as.double(prob),
seed =as.integer(seed),
sigmawork =as.double(sigmawork),
sigworkinv =as.double(sigworkinv),
theta =as.double(theta),
workm1 =as.double(workm1),
workm2 =as.double(workm2),
workm3 =as.double(workm3),
workmh1 =as.double(workmh1),
workmh2 =as.double(workmh2),
workv1 =as.double(workv1),
workv2 =as.double(workv2),
workv3 =as.double(workv3),
ywork =as.double(ywork),
workcpo =as.double(workcpo),
PACKAGE ="DPpackage")
}
}
#########################################################################################
# save state
#########################################################################################
model.name <- "DPM of normals model for density estimation"
varnames <- colnames(y)
state <- list(
alpha=foo$alpha,
m1=matrix(foo$m1,nrow=nvar,ncol=1),
muclus=matrix(foo$muclus,nrow=nrec+100,ncol=nvar),
ncluster=foo$ncluster,
psi1=matrix(foo$psi1,nrow=nvar,ncol=nvar),
k0=foo$k0,
sigmaclus=matrix(foo$sigmaclus,nrow=nrec+100,ncol=nuniq),
ss=foo$ss
)
randsave <- matrix(foo$randsave,nrow=nsave,ncol=(nrec+2)*nvar+(nrec+1)*nvar*(nvar+1)/2)
thetasave <- matrix(foo$thetasave,nrow=nsave,ncol=nvar+nvar*(nvar+1)/2+3)
indip <- rep(0,(nvar+nvar*(nvar+1)/2+3))
if(is.null(varnames))
{
varnames<-all.vars(cl)[1:nvar]
}
coeff<-NULL
pnames1<-NULL
if(is.null(prior$m2) && is.null(prior$s2))
{
}
else
{
for(i in 1:nvar)
{
coeff<-c(coeff,mean(thetasave[,i]))
pnames1<-c(pnames1,paste("m1",varnames[i],sep="-"))
indip[i]<-1
}
}
if(is.null(prior$tau1))
{
}
else
{
coeff<-c(coeff,mean(thetasave[,(nvar+1)]))
pnames1<-c(pnames1,"k0")
indip[nvar+1]<-1
}
if(is.null(prior$nu2))
{
}
else
{
for(i in 1:(nvar*(nvar+1)/2))
{
coeff<-c(coeff,mean(thetasave[,(nvar+1+i)]))
indip[(nvar+1+i)]<-1
}
for(i in 1:nvar)
{
for(j in i:nvar)
{
if(i==j)pnames1<-c(pnames1,paste("psi1",varnames[i],sep="-"))
if(i!=j)
{
tempname<-paste(varnames[i],varnames[j],sep="-")
pnames1<-c(pnames1,paste("psi1",tempname,sep="-"))
}
}
}
}
coeff<-c(coeff,mean(thetasave[,(nvar+nvar*(nvar+1)/2+2)]))
pnames2<-c("ncluster")
indip[(nvar+nvar*(nvar+1)/2+2)]<-1
if(alpharand==1)
{
coeff<-c(coeff,mean(thetasave[,(nvar+nvar*(nvar+1)/2+3)]))
pnames2<-c(pnames2,"alpha")
indip[(nvar+nvar*(nvar+1)/2+3)]<-1
}
names(coeff)<-c(pnames1,pnames2)
pnames1<-NULL
for(i in 1:nvar)
{
pnames1<-c(pnames1,paste("m1",varnames[i],sep="-"))
}
pnames1<-c(pnames1,"k0")
for(i in 1:nvar)
{
for(j in i:nvar)
{
if(i==j)pnames1<-c(pnames1,paste("psi1",varnames[i],sep="-"))
if(i!=j)
{
tempname<-paste(varnames[i],varnames[j],sep="-")
pnames1<-c(pnames1,paste("psi1",tempname,sep="-"))
}
}
}
pnames2<-c("ncluster","alpha")
dimnames(thetasave)<-list(NULL,c(pnames1,pnames2))
pnamesre<-NULL
for(i in 1:nrec)
{
for(j in 1:nvar)
{
tmpn<-paste("mu",varnames[j],sep="-")
tmpn<-paste(tmpn," (Subject=",sep="")
tmpn<-paste(tmpn,i,sep="")
tmpn<-paste(tmpn,")",sep="")
pnamesre<-c(pnamesre,tmpn)
}
for(j in 1:nvar)
{
for(k in j:nvar)
{
tmpn<-paste("sigma",varnames[j],sep="-")
tmpn<-paste(tmpn,varnames[k],sep="-")
tmpn<-paste(tmpn," (Subject=",sep="")
tmpn<-paste(tmpn,i,sep="")
tmpn<-paste(tmpn,")",sep="")
pnamesre<-c(pnamesre,tmpn)
}
}
}
for(j in 1:nvar)
{
tmpn<-paste("mu",varnames[j],sep="-")
tmpn<-paste(tmpn," (Prediction)",sep="")
tmpn<-paste(tmpn,")",sep="")
pnamesre<-c(pnamesre,tmpn)
}
for(j in 1:nvar)
{
for(k in j:nvar)
{
tmpn<-paste("sigma",varnames[j],sep="-")
tmpn<-paste(tmpn,varnames[k],sep="-")
tmpn<-paste(tmpn," (Prediction)",sep="")
tmpn<-paste(tmpn,")",sep="")
pnamesre<-c(pnamesre,tmpn)
}
}
for(j in 1:nvar)
{
tmpn<-paste(varnames[j]," (Prediction)",sep="")
pnamesre<-c(pnamesre,tmpn)
}
dimnames(randsave)<-list(NULL,pnamesre)
save.state <- list(thetasave=thetasave,randsave=randsave)
x1<-NULL
x2<-NULL
dens<-NULL
if(nvar==1)
{
x1 <- foo$grid1
dens <- foo$fun1
f <- matrix(foo$f,nrow=ngrid,ncol=ngrid)
grid1 <- foo$grid1
grid2 <- foo$grid2
fun1 <- foo$fun1
fun2 <- foo$fun2
}
if(nvar==2)
{
x1 <- foo$grid1
x2 <- foo$grid2
dens <- matrix(foo$f,nrow=ngrid,ncol=ngrid)
f <- matrix(foo$f,nrow=ngrid,ncol=ngrid)
grid1 <- foo$grid1
grid2 <- foo$grid2
fun1 <- foo$fun1
fun2 <- foo$fun2
}
if(nvar>2)
{
x1 <- NULL
x2 <- NULL
dens <- NULL
f <- NULL
grid1 <- NULL
grid2 <- NULL
fun1 <- NULL
fun2 <- NULL
}
z <- list(call=cl,
y=y,
varnames=varnames,
modelname=model.name,
cpo=foo$cpo,
prior=prior,
mcmc=mcmc,
state=state,
save.state=save.state,
nrec=foo$nrec,
nvar=foo$nvar,
alpharand=alpharand,
psi1rand=psi1rand,
m1rand=m1rand,
k0rand=k0rand,
coefficients=coeff,
indip=indip,
f=f,
grid1=grid1,
grid2=grid2,
fun1=fun1,
fun2=fun2,
x1=x1,
x2=x2,
dens=dens)
cat("\n\n")
class(z)<-"DPdensity"
return(z)
}
###
### Tools
###
### Copyright: Alejandro Jara Vallejos, 2006
### Last modification: 05-10-2006.
###
"print.DPdensity"<-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$nrec)
cat("\nNumber of Variables:",x$nvar,"\n")
cat("\n\n")
invisible(x)
}
"summary.DPdensity"<-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]))
}
#nsave<-object$nsave
#dimen<-length(object$coefficients)
#thetasave<-matrix(object$save.state$thetasave,nrow=nsave, ncol=dimen)
thetasave<-object$save.state$thetasave
ans <- c(object[c("call", "modelname")])
### CPO
ans$cpo<-object$cpo
### Baseline Information
mat<-NULL
coef.p<-NULL
dimen1<-object$nvar+object$nvar*(object$nvar+1)/2+1
for(i in 1:dimen1)
{
if(object$indip[i]==1)
{
coef.p<-c(coef.p,object$coefficients[i])
mat<-cbind(mat,thetasave[,i])
}
}
dimen1<-dim(mat)[2]
if(dimen1>0){
coef.m <-apply(mat, 2, median)
coef.sd<-apply(mat, 2, sd)
coef.se<-apply(mat, 2, stde)
if(hpd){
limm<-apply(mat, 2, hpdf)
coef.l<-limm[1,]
coef.u<-limm[2,]
}
else
{
limm<-apply(mat, 2, pdf)
coef.l<-limm[1,]
coef.u<-limm[2,]
}
coef.table <- cbind(coef.p, coef.m, coef.sd, coef.se , coef.l , coef.u)
if(hpd)
{
dimnames(coef.table) <- list(names(coef.p), c("Mean", "Median", "Std. Dev.", "Naive Std.Error",
"95%HPD-Low","95%HPD-Upp"))
}
else
{
dimnames(coef.table) <- list(names(coef.p), c("Mean", "Median", "Std. Dev.", "Naive Std.Error",
"95%CI-Low","95%CI-Upp"))
}
ans$base<-coef.table
}
### Precision parameter
dimen1<-object$nvar+object$nvar*(object$nvar+1)/2+1
if(is.null(object$prior$a0))
{
dimen2<-1
coef.p<-object$coefficients[(dimen1+1)]
mat<-matrix(thetasave[,(dimen1+1)],ncol=1)
}
else
{
dimen2<-2
coef.p<-object$coefficients[(dimen1+1):(dimen1+2)]
mat<-thetasave[,(dimen1+1):(dimen1+2)]
}
coef.m <-apply(mat, 2, median)
coef.sd<-apply(mat, 2, sd)
coef.se<-apply(mat, 2, stde)
if(hpd){
limm<-apply(mat, 2, hpdf)
coef.l<-limm[1,]
coef.u<-limm[2,]
}
else
{
limm<-apply(mat, 2, pdf)
coef.l<-limm[1,]
coef.u<-limm[2,]
}
coef.table <- cbind(coef.p, coef.m, coef.sd, coef.se , coef.l , coef.u)
if(hpd)
{
dimnames(coef.table) <- list(names(coef.p), c("Mean", "Median", "Std. Dev.", "Naive Std.Error",
"95%HPD-Low","95%HPD-Upp"))
}
else
{
dimnames(coef.table) <- list(names(coef.p), c("Mean", "Median", "Std. Dev.", "Naive Std.Error",
"95%CI-Low","95%CI-Upp"))
}
ans$prec<-coef.table
ans$nrec<-object$nrec
ans$nvar<-object$nvar
class(ans) <- "summaryDPdensity"
return(ans)
}
"print.summaryDPdensity"<-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$cpo))), digits = digits), print.gap = 2,
quote = FALSE)
if (length(x$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$prec, digits = digits), print.gap = 2,
quote = FALSE)
}
cat("\nNumber of Observations:",x$nrec)
cat("\nNumber of Variables:",x$nvar,"\n")
cat("\n\n")
invisible(x)
}
"plot.DPdensity"<-function(x, ask=TRUE, output="density", param=NULL, hpd=TRUE, nfigr=1, nfigc=1, col="#bdfcc9", ...)
{
fancydensplot1<-function(x, hpd=TRUE, npts=200, xlab="", ylab="", main="",col="#bdfcc9", ...)
# Author: AJV, 2006
#
{
dens <- density(x,n=npts)
densx <- dens$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))
}
"bivk"<-function(x, y, h, n = 25, lims = c(range(x), range(y)))
{
nx <- length(x)
if (length(y) != nx)
stop("Data vectors must be the same length")
gx <- seq(lims[1], lims[2], length = n)
gy <- seq(lims[3], lims[4], length = n)
if (missing(h))
h <- c(band(x), band(y))
h <- h/4
ax <- outer(gx, x, "-")/h[1]
ay <- outer(gy, y, "-")/h[2]
z <- matrix(dnorm(ax), n, nx) %*% t(matrix(dnorm(ay), n,
nx))/(nx * h[1] * h[2])
return(list(x = gx, y = gy, z = z))
}
"band"<-function(x)
{
r <- quantile(x, c(0.25, 0.75))
h <- (r[2] - r[1])/1.34
4 * 1.06 * min(sqrt(var(x)), h) * length(x)^(-1/5)
}
if(is(x, "DPdensity"))
{
if(output=="density")
{
# Density estimation
par(ask = ask)
layout(matrix(seq(1,nfigr*nfigc,1),nrow=nfigr,ncol=nfigc,byrow=TRUE))
start<-(x$nrec+1)*x$nvar+(x$nrec+1)*x$nvar*(x$nvar+1)/2
if(x$nvar==1)
{
title1<-paste("Density of",x$varnames[1],sep=' ')
aa<-hist(x$y[,1],plot=F,)
maxx<-max(aa$intensities+aa$density)+0.1*max(aa$intensities+aa$density)
miny<-min(x$y[,1])
maxy<-max(x$y[,1])
deltay<-(maxy-miny)*0.2
miny<-miny-deltay
maxy<-maxy+deltay
hist(x$y[,1],probability=T,xlim=c(min(x$grid1),max(x$grid1)),ylim=c(0,maxx),nclas=25,main=title1,xlab="values", ylab="density")
lines(x$grid1,x$fun1,lwd=2)
}
if(x$nvar==2)
{
title1<-paste("Density of",x$varnames[1],sep=' ')
aa<-hist(x$y[,1],plot=F,)
maxx<-max(aa$intensities+aa$density)+0.1*max(aa$intensities+aa$density)
miny<-min(x$y[,1])
maxy<-max(x$y[,1])
deltay<-(maxy-miny)*0.2
miny<-miny-deltay
maxy<-maxy+deltay
hist(x$y[,1],probability=T,xlim=c(min(x$grid1),max(x$grid1)),ylim=c(0,maxx),nclas=25,main=title1,xlab="values", ylab="density")
lines(x$grid1,x$fun1,lwd=2)
title1<-paste("Density of",x$varnames[2],sep=' ')
aa<-hist(x$y[,2],plot=F,)
maxx<-max(aa$intensities+aa$density)+0.1*max(aa$intensities+aa$density)
miny<-min(x$y[,2])
maxy<-max(x$y[,2])
deltay<-(maxy-miny)*0.2
miny<-miny-deltay
maxy<-maxy+deltay
hist(x$y[,2],probability=T,xlim=c(min(x$grid2),max(x$grid2)),ylim=c(0,maxx),nclas=25,main=title1,xlab="values", ylab="density")
lines(x$grid2,x$fun2,lwd=2)
varsn<-paste(x$varnames[1],x$varnames[2],sep="-")
title1<-paste("Predictive Density of ",varsn,sep='')
xx<-matrix(x$grid1,ncol=1)
yy<-matrix(x$grid2,ncol=1)
z<-x$f
colnames(xx)<-x$varnames[1]
colnames(yy)<-x$varnames[2]
contour(xx,yy,z,main=title1,xlab=x$varnames[1],ylab=x$varnames[2])
persp(xx,yy,z,xlab=x$varnames[1],ylab=x$varnames[2],zlab="density",theta=-30,phi=15,expand = 0.9, ltheta = 120,main=title1)
}
if(x$nvar>2)
{
for(i in 1:x$nvar)
{
title1<-paste("Density of",x$varnames[i],sep=' ')
aa<-hist(x$y[,i],plot=F)
maxx<-max(aa$intensities+aa$density)+0.1*max(aa$intensities+aa$density)
miny<-min(x$y[,i])-0.5*sqrt(var(x$y[,i]))
maxy<-max(x$y[,i])+0.5*sqrt(var(x$y[,i]))
deltay<-(maxy-miny)*0.2
miny<-miny-deltay
maxy<-maxy+deltay
hist(x$y[,i],probability=T,xlim=c(miny,maxy),ylim=c(0,maxx),nclas=25,main=title1,xlab="values", ylab="density")
lines(density(x$save.state$randsave[,(start+i)]),lwd=2)
}
for(i in 1:(x$nvar-1))
{
vectmp<-x$y[,i]
xlim<-c(min(vectmp)-0.5*sqrt(var(vectmp)),max(vectmp)+0.125*sqrt(var(vectmp)))
for(j in (i+1):x$nvar)
{
vectmp<-x$y[,j]
ylim<-c(min(vectmp)-0.5*sqrt(var(vectmp)),max(vectmp)+0.125*sqrt(var(vectmp)))
varsn<-paste(x$varnames[i],x$varnames[j],sep="-")
title1<-paste("Predictive Density of ",varsn,sep='')
xx<-x$save.state$randsave[,(start+i)]
yy<-x$save.state$randsave[,(start+j)]
est<-bivk(xx,yy,n=200)
contour(est,xlim=xlim,ylim=ylim,main=title1,xlab=x$varnames[i],ylab=x$varnames[j])
persp(est,theta=-30,phi=15,expand = 0.9, ltheta = 120,main=title1,
xlab=x$varnames[i],ylab=x$varnames[j],zlab="density")
}
}
}
}
else
{
if(is.null(param))
{
pnames<-colnames(x$save.state$thetasave)
n<-dim(x$save.state$thetasave)[2]
cnames<-names(x$coefficients)
par(ask = ask)
layout(matrix(seq(1,nfigr*nfigc,1), nrow=nfigr , ncol=nfigc ,byrow=TRUE))
for(i in 1:(n-1))
{
if(x$indip[i]==1)
{
title1<-paste("Trace of",pnames[i],sep=" ")
title2<-paste("Density of",pnames[i],sep=" ")
plot(x$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$a0))
{
cat("")
}
else
{
title1<-paste("Trace of",pnames[n],sep=" ")
title2<-paste("Density of",pnames[n],sep=" ")
plot(x$save.state$thetasave[,n],type='l',main=title1,xlab="MCMC scan",ylab=" ")
fancydensplot1(x$save.state$thetasave[,n],hpd=hpd,main=title2,xlab="values", ylab="density",col=col)
}
}
else
{
pnames<-colnames(x$save.state$thetasave)
n<-dim(x$save.state$thetasave)[2]
poss<-0
for(i in 1:n)
{
if(pnames[i]==param)poss=i
}
if (poss==0)
{
stop("This parameter is not present in the original model.\n")
}
par(ask = ask)
layout(matrix(seq(1,nfigr*nfigc,1), nrow=nfigr, ncol=nfigc, byrow = TRUE))
title1<-paste("Trace of",pnames[poss],sep=" ")
title2<-paste("Density of",pnames[poss],sep=" ")
plot(x$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)
}
}
}
}
}
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