Nothing
R/DPbinary.R
In DPpackage: Bayesian Nonparametric Modeling in R
### DPbinary.R
### Fit a semiparametric bernoulli regression model.
###
### Copyright: Alejandro Jara, 2006-2012.
###
### Last modification: 22-12-2006.
###
### 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
###
"DPbinary"<-
function(formula,inter=TRUE,baseline="logistic",prior,mcmc,state,status,misc=NULL,
data=sys.frame(sys.parent()),na.action=na.fail)
UseMethod("DPbinary")
"DPbinary.default"<-
function(formula,
inter=TRUE,
baseline="logistic",
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$drop.unused.levels <- TRUE
mf[[1]] <- as.name("model.frame")
mf <- eval(mf, parent.frame())
#########################################################################################
# data structure
#########################################################################################
yobs<- model.response(mf,"numeric")
nrec<-length(yobs)
x<-as.matrix(model.matrix(formula))
p<-dim(x)[2]
if(inter)
{
x<-x
p<-p
}
else
{
x<-x[,2:p]
p<-(p-1)
}
#########################################################################################
# Elements for Pseudo Countour Probabilities' computation
#########################################################################################
tt<-terms(formula,data=data)
mat<-attr(tt,"factors")
namfact<-colnames(mat)
nvar<-dim(mat)[1]
nfact<-dim(mat)[2]
possiP<-matrix(0,ncol=2,nrow=nfact)
dataF<-model.frame(formula,data,xlev=NULL)
namD<-names(dataF)
isF<-sapply(dataF, function(x) is.factor(x) || is.logical(x))
nlevel<-rep(0,nvar)
for(i in 1:nvar)
{
if(isF[i])
{
nlevel[i]<-length(table(dataF[[i]]))
}
else
{
nlevel[i]<-1
}
}
startp<-1+inter
for(i in 1:nfact)
{
tmp1<-1
for(j in 1:nvar)
{
if(mat[j,i]==1 && isF[j])
{
tmp1<-tmp1*(nlevel[j]-1)
}
}
endp<-startp+tmp1-1
possiP[i,1]<-startp
possiP[i,2]<-endp
startp<-endp+1
}
dimnames(possiP)<-list(namfact,c("Start","End"))
#########################################################################################
# misclassification
#########################################################################################
if(is.null(misc))
{
sens<-rep(1,nrec)
spec<-rep(1,nrec)
model<-0
}
else
{
sens<-misc$sens
spec<-misc$spec
if(length(sens)==1)sens<-rep(sens,nrec)
if(length(spec)==1)spec<-rep(spec,nrec)
model<-1
}
#########################################################################################
# mcmc specification
#########################################################################################
MLElogit<-function(x,y,sens,spec)
{
fn<-function(theta)
{
eta<-x%*%theta
p<-plogis(eta)
like <- sens*p+(1-spec)*(1-p)
if (all(like > 0))
eval<- -sum(log(like[y==1]))-sum(log(1-like[y==0]))
else eval<-Inf
return(eval)
}
start<-coefficients(glm(y~x-1,family=binomial(logit)))
foo<-optim(start,fn=fn,method="BFGS",hessian=TRUE)
out<-NULL
out$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$hessian))
return(out)
}
MLEcauchy<-function(x,y,sens,spec)
{
fn<-function(theta)
{
eta<-x%*%theta
p<-pcauchy(eta)
like <- sens*p+(1-spec)*(1-p)
if (all(like > 0))
eval<- -sum(log(like[y==1]))-sum(log(1-like[y==0]))
else eval<-Inf
return(eval)
}
start<-coefficients(glm(y~x-1,family=binomial(logit)))
foo<-optim(start,fn=fn,method="BFGS",hessian=TRUE)
out<-NULL
out$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$nsave
if(inter)
{
xmatrix<-x
}
else
{
xmatrix<-cbind(rep(1,nrec),x)
}
if(baseline=="logistic")
{
fit0<- MLElogit(xmatrix,yobs,sens,spec)
}
if(baseline=="normal")
{
fit0<- MLEprobit(xmatrix,yobs,sens,spec)
}
if(baseline=="cauchy")
{
fit0<- MLEcauchy(xmatrix,yobs,sens,spec)
}
if(inter)
{
propv<- fit0$covb
}
else
{
propv<- fit0$covb[2:(p+1),2:(p+1)]
}
if(is.null(mcmc$tune))
{
tune=1.1
}
else
{
tune<-mcmc$tune
}
resop<-1
#########################################################################################
# prior information
#########################################################################################
if(is.null(prior$a0))
{
a0b0<-c(-1,-1)
alpha<-prior$alpha
}
else
{
a0b0<-c(prior$a0,prior$b0)
alpha<-rgamma(1,prior$a0,prior$b0)
}
betapm<-prior$beta0
betapv<-prior$Sbeta0
propv<-diag(tune,p)%*%solve(solve(betapv)+solve(propv))%*%diag(tune,p)
#########################################################################################
# parameters depending on status
#########################################################################################
if(status)
{
if(inter)
{
beta<-fit0$beta
}
else
{
beta<-fit0$beta[2:(p+1)]
}
eta <- x %*% beta
v <- rep(0,nrec)
v[yobs==1]<-eta[yobs==1]-0.5
v[yobs==0]<-eta[yobs==0]+0.5
y<-yobs
}
else
{
beta<-state$beta
eta <- x %*% beta
v<-state$v
y<-state$y
alpha<-state$alpha
}
#########################################################################################
# output
#########################################################################################
xlink=rev(seq(-6,6,length=34))
nlink<-length(xlink)
fsave <- matrix(0, nrow=mcmc$nsave, ncol=nlink)
thetasave <- matrix(0, nrow=mcmc$nsave, ncol=p+2)
randsave <- matrix(0, nrow=mcmc$nsave, ncol=nrec+1)
#########################################################################################
# working space
#########################################################################################
extra<-1
ntotal<-nrec
maxint<-2*ntotal+1+nlink
maxend<-2*ntotal+nlink
maxm<-as.integer(log(0.0001)/log((ntotal+1)/(ntotal+2)))
acrate<-0
betac<-rep(0,p)
clusts<-matrix(0,nrow=nrec,ncol=(nrec+1))
endp<-rep(0,maxend)
endp2<-rep(0,maxend)
etan<-rep(0,nrec)
fsavet<-rep(0,nlink)
index<-rep(0,maxm)
intcount<-rep(0,maxint)
intcount2<-rep(0,maxint)
intind<-rep(0,(nrec+1))
intind2<-rep(0,(nrec+1))
iflag<-rep(0,p)
intposso<-matrix(0,nrow=maxint,ncol=(nrec+1))
intpossn<-matrix(0,nrow=maxint,ncol=(nrec+1))
limr<-matrix(0,nrow=nrec,ncol=2)
lpsav<-rep(0,nrec)
lpsavc<-rep(0,nrec)
mass<-rep(0,maxint)
ncluster<-nrec
prob<-rep(0,maxint)
s<-rep(0,nrec)
uvec<-rep(0,maxm)
vvec<-rep(0,maxm)
vnew<-rep(0,(nrec+1))
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)
wvec<-rep(0,maxm)
seed1<-sample(1:29000,1)
seed2<-sample(1:29000,1)
cpo<-rep(0,nrec)
#########################################################################################
# calling the fortran code
#########################################################################################
if(baseline=="logistic")
{
foo <- .Fortran("dpbinaryl",
model =as.integer(model),
nrec =as.integer(nrec),
p =as.integer(p),
sens =as.double(sens),
spec =as.double(spec),
x =as.double(x),
yobs =as.integer(yobs),
nlink =as.integer(nlink),
xlink =as.double(xlink),
a0b0 =as.double(a0b0),
betapm =as.double(betapm),
betapv =as.double(betapv),
mcmcvec =as.integer(mcmcvec),
nsave =as.integer(nsave),
propv =as.double(propv),
extra =as.integer(extra),
acrate =as.double(acrate),
fsave =as.double(fsave),
randsave =as.double(randsave),
thetasave =as.double(thetasave),
cpo =as.double(cpo),
alpha =as.double(alpha),
beta =as.double(beta),
ncluster =as.integer(ncluster),
y =as.integer(y),
v =as.double(v),
betac =as.double(betac),
endp =as.double(endp),
endp2 =as.double(endp2),
eta =as.double(eta),
etan =as.double(etan),
fsavet =as.integer(fsavet),
clusts =as.integer(clusts),
index =as.integer(index),
intcount =as.integer(intcount),
intcount2 =as.integer(intcount2),
intind =as.integer(intind),
intind2 =as.integer(intind2),
iflag =as.integer(iflag),
intposso =as.integer(intposso),
intpossn =as.integer(intpossn),
limr =as.double(limr),
lpsav =as.integer(lpsav),
lpsavc =as.integer(lpsavc),
maxint =as.integer(maxint),
maxend =as.integer(maxend),
maxm =as.integer(maxm),
mass =as.double(mass),
prob =as.double(prob),
s =as.integer(s),
seed1 =as.integer(seed1),
seed2 =as.integer(seed2),
uvec =as.double(uvec),
vvec =as.double(vvec),
vnew =as.double(vnew),
workm1 =as.double(workm1),
workm2 =as.double(workm2),
workmh1 =as.double(workmh1),
workv1 =as.double(workv1),
workv2 =as.double(workv2),
wvec =as.double(wvec),
PACKAGE="DPpackage")
}
if(baseline=="normal")
{
foo <- .Fortran("dpbinaryp",
model =as.integer(model),
nrec =as.integer(nrec),
p =as.integer(p),
sens =as.double(sens),
spec =as.double(spec),
x =as.double(x),
yobs =as.integer(yobs),
nlink =as.integer(nlink),
xlink =as.double(xlink),
a0b0 =as.double(a0b0),
betapm =as.double(betapm),
betapv =as.double(betapv),
mcmcvec =as.integer(mcmcvec),
nsave =as.integer(nsave),
propv =as.double(propv),
extra =as.integer(extra),
acrate =as.double(acrate),
fsave =as.double(fsave),
randsave =as.double(randsave),
thetasave =as.double(thetasave),
cpo =as.double(cpo),
alpha =as.double(alpha),
beta =as.double(beta),
ncluster =as.integer(ncluster),
y =as.integer(y),
v =as.double(v),
betac =as.double(betac),
endp =as.double(endp),
endp2 =as.double(endp2),
eta =as.double(eta),
etan =as.double(etan),
fsavet =as.integer(fsavet),
clusts =as.integer(clusts),
index =as.integer(index),
intcount =as.integer(intcount),
intcount2 =as.integer(intcount2),
intind =as.integer(intind),
intind2 =as.integer(intind2),
iflag =as.integer(iflag),
intposso =as.integer(intposso),
intpossn =as.integer(intpossn),
limr =as.double(limr),
lpsav =as.integer(lpsav),
lpsavc =as.integer(lpsavc),
maxint =as.integer(maxint),
maxend =as.integer(maxend),
maxm =as.integer(maxm),
mass =as.double(mass),
prob =as.double(prob),
s =as.integer(s),
seed1 =as.integer(seed1),
seed2 =as.integer(seed2),
uvec =as.double(uvec),
vvec =as.double(vvec),
vnew =as.double(vnew),
workm1 =as.double(workm1),
workm2 =as.double(workm2),
workmh1 =as.double(workmh1),
workv1 =as.double(workv1),
workv2 =as.double(workv2),
wvec =as.double(wvec),
PACKAGE="DPpackage")
}
if(baseline=="cauchy")
{
foo <- .Fortran("dpbinaryc",
model =as.integer(model),
nrec =as.integer(nrec),
p =as.integer(p),
sens =as.double(sens),
spec =as.double(spec),
x =as.double(x),
yobs =as.integer(yobs),
nlink =as.integer(nlink),
xlink =as.double(xlink),
a0b0 =as.double(a0b0),
betapm =as.double(betapm),
betapv =as.double(betapv),
mcmcvec =as.integer(mcmcvec),
nsave =as.integer(nsave),
propv =as.double(propv),
extra =as.integer(extra),
acrate =as.double(acrate),
fsave =as.double(fsave),
randsave =as.double(randsave),
thetasave =as.double(thetasave),
cpo =as.double(cpo),
alpha =as.double(alpha),
beta =as.double(beta),
ncluster =as.integer(ncluster),
y =as.integer(y),
v =as.double(v),
betac =as.double(betac),
endp =as.double(endp),
endp2 =as.double(endp2),
eta =as.double(eta),
etan =as.double(etan),
fsavet =as.integer(fsavet),
clusts =as.integer(clusts),
index =as.integer(index),
intcount =as.integer(intcount),
intcount2 =as.integer(intcount2),
intind =as.integer(intind),
intind2 =as.integer(intind2),
iflag =as.integer(iflag),
intposso =as.integer(intposso),
intpossn =as.integer(intpossn),
limr =as.double(limr),
lpsav =as.integer(lpsav),
lpsavc =as.integer(lpsavc),
maxint =as.integer(maxint),
maxend =as.integer(maxend),
maxm =as.integer(maxm),
mass =as.double(mass),
prob =as.double(prob),
s =as.integer(s),
seed1 =as.integer(seed1),
seed2 =as.integer(seed2),
uvec =as.double(uvec),
vvec =as.double(vvec),
vnew =as.double(vnew),
workm1 =as.double(workm1),
workm2 =as.double(workm2),
workmh1 =as.double(workmh1),
workv1 =as.double(workv1),
workv2 =as.double(workv2),
wvec =as.double(wvec),
PACKAGE="DPpackage")
}
#########################################################################################
# save state
#########################################################################################
fsave<-matrix(foo$fsave,nrow=mcmc$nsave, ncol=nlink)
thetasave<-matrix(foo$thetasave,nrow=mcmc$nsave, ncol=(p+2))
randsave<-matrix(foo$randsave,nrow=mcmc$nsave, ncol=(nrec+1))
model.name<-"Bayesian semiparametric binary regression model"
colnames(thetasave)<-c(dimnames(x)[[2]],"ncluster","alpha")
coeff<-apply(thetasave, 2, mean)
names(coeff)<-c(dimnames(x)[[2]],"ncluster","alpha")
qnames<-NULL
for(i in 1:nrec){
idname<-paste("(Subject",i,sep="=")
idname<-paste(idname,")",sep="")
qnames<-c(qnames,idname)
}
qnames<-c(qnames,"Prediction")
colnames(randsave)<-qnames
state <- list(beta=foo$beta,v=foo$v,y=foo$y,alpha=foo$alpha)
save.state <- list(thetasave=thetasave,fsave=fsave,randsave=randsave)
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$cpo,p=p,nlink=nlink,xlink=xlink,baseline=baseline,x=x,
possiP=possiP)
cat("\n\n")
class(z)<-c("DPbinary")
return(z)
}
###
### Estimate the probability curve for a fitted semiparametric binary
### regression model.
###
### Copyright: Alejandro Jara Vallejos, 2006
### Last modification: 21-08-2006.
"predict.DPbinary"<-
function(object,xnew=NULL,hpd=TRUE, ...)
{
stde<-function(x)
{
n<-length(x)
return(sd(x)/sqrt(n))
}
hpdf<-function(x)
{
alpha<-0.05
vec<-x
n<-length(x)
alow<-rep(0,2)
aupp<-rep(0,2)
a<-.Fortran("hpd",n=as.integer(n),alpha=as.double(alpha),x=as.double(vec),
alow=as.double(alow),aupp=as.double(aupp),PACKAGE="DPpackage")
return(c(a$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$aupp[2]))
}
if(is.null(xnew))
{
xnew<-object$x
}
if(is.null(xnew))
{
xnew<-object$x
}
seed1<-sample(1:29000,1)
seed2<-sample(1:29000,1)
if(is(object, "DPbinary"))
{
npred<-dim(xnew)[1]
pnew<-dim(xnew)[2]
nrec<-object$nrec
baseline<-object$baseline
alpha<-object$save.state$thetasave[,(object$p+2)]
nsave<-length(alpha)
v<-matrix(object$save.state$randsave,nsave,nrec+1)
vpred<-v[,(nrec+1)]
v<-v[,1:nrec]
if (object$p != pnew)
{
stop("Dimension of xnew is not the same that the design matrix
in the original model.\n")
}
covn<-rep(0,npred)
for(i in 1:npred)
{
covnw<-round(xnew[i,1],3)
for(j in 2:pnew){
covnw<-paste(covnw,round(xnew[i,j],3),sep=";")
}
covn[i]<-covnw
}
lp<-xnew%*%t(object$save.state$thetasave[,1:object$p])
out<-matrix(0,nrow=npred,ncol=nsave)
if(baseline=="logistic")
{
foo <- .Fortran("dppredl",
nsave =as.integer(nsave),
nrec =as.integer(nrec),
npred =as.integer(npred),
alpha =as.double(alpha),
v =as.double(v),
lp =as.double(lp),
out =as.double(out),
seed1 =as.integer(seed1),
seed2 =as.integer(seed2),
PACKAGE="DPpackage")
}
if(baseline=="normal")
{
foo <- .Fortran("dppredp",
nsave =as.integer(nsave),
nrec =as.integer(nrec),
npred =as.integer(npred),
alpha =as.double(alpha),
v =as.double(v),
lp =as.double(lp),
out =as.double(out),
seed1 =as.integer(seed1),
seed2 =as.integer(seed2),
PACKAGE="DPpackage")
}
if(baseline=="cauchy")
{
foo <- .Fortran("dppredc",
nsave =as.integer(nsave),
nrec =as.integer(nrec),
npred =as.integer(npred),
alpha =as.double(alpha),
v =as.double(v),
lp =as.double(lp),
out =as.double(out),
seed1 =as.integer(seed1),
seed2 =as.integer(seed2),
PACKAGE="DPpackage")
}
out<-t(matrix(foo$out,nrow=npred,ncol=nsave))
pm <-apply(out, 2, mean)
pmed <-apply(out, 2, median)
psd<-apply(out, 2, sd)
pstd<-apply(out, 2, stde)
if(hpd){
limm<-apply(out, 2, hpdf)
plinf<-limm[1,]
plsup<-limm[2,]
}
else
{
plinf<-apply(out, 2, pdf)
coef.l<-limm[1,]
plsup<-limm[2,]
}
names(pm)<-covn
names(pmed)<-covn
names(psd)<-covn
names(pstd)<-covn
names(plinf)<-covn
names(plsup)<-covn
out<-NULL
out$pmean<-pm
out$pmedian<-pmed
out$psd<-psd
out$pstd<-pstd
out$plinf<-plinf
out$plsup<-plsup
out$vpred<-vpred
out$npred<-npred
out$covn<-covn
}
out
}
###
### Tools for DPbinary: anova, print, summary, plot
###
### Copyright: Alejandro Jara Vallejos, 2006
### Last modification: 15-12-2006.
"anova.DPbinary"<-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$pval
}
}
table <- data.frame(df,P)
dimnames(table) <- list(rownames(possiP), c("Df","PsCP"))
structure(table, heading = c("Table of Pseudo Contour Probabilities\n",
paste("Response:", deparse(formula(object)[[2]]))), class = c("anovaPsCP",
"data.frame"))
}
"print.DPbinary"<-function (x, digits = max(3, getOption("digits") - 3), ...)
{
cat("\n",x$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$nrec)
cat("\n\n")
invisible(x)
}
"summary.DPbinary"<-function(object, hpd=TRUE, ...)
{
stde<-function(x)
{
n<-length(x)
return(sd(x)/sqrt(n))
}
hpdf<-function(x)
{
alpha<-0.05
vec<-x
n<-length(x)
alow<-rep(0,2)
aupp<-rep(0,2)
a<-.Fortran("hpd",n=as.integer(n),alpha=as.double(alpha),x=as.double(vec),
alow=as.double(alow),aupp=as.double(aupp),PACKAGE="DPpackage")
return(c(a$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$thetasave
### Fixed part of the model
dimen1<-object$p
if(dimen1==1)
{
mat<-matrix(thetasave[,1:dimen1],ncol=1)
}
else
{
mat<-thetasave[,1:dimen1]
}
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$cpo
### Baseline Information
if(is.null(object$prior$a0))
{
dimen2<-1
}
else
{
dimen2<-2
}
if(dimen2==1)
{
mat<-matrix(thetasave[,(dimen1+1):(dimen1+dimen2)],ncol=1)
}
else
{
mat<-thetasave[,(dimen1+1):(dimen1+dimen2)]
}
coef.p<-object$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$prec<-coef.table
ans$acrate<-object$acrate
ans$nrec<-object$nrec
class(ans) <- "summaryDPbinary"
return(ans)
}
"print.summaryDPbinary"<-function (x, digits = max(3, getOption("digits") - 3), ...)
{
cat("\n",x$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")
if (length(x$prec)) {
cat("\nPrecision parameter:\n")
print.default(format(x$prec, digits = digits), print.gap = 2,
quote = FALSE)
}
else cat("No precision parameter\n")
cat("\nAcceptance Rate for Metropolis Step = ",x$acrate,"\n")
cat("\nNumber of Observations:",x$nrec)
cat("\n\n")
invisible(x)
}
"plot.DPbinary"<-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))
}
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]))
}
if(is(x, "DPbinary")){
if(is.null(param))
{
coef.p<-x$coefficients
n<-length(coef.p)
pnames<-names(coef.p)
par(ask = ask)
layout(matrix(seq(1,nfigr*nfigc,1), nrow=nfigr, ncol=nfigc, byrow = TRUE))
for(i in 1:(n-1)){
title1<-paste("Trace of",pnames[i],sep=" ")
title2<-paste("Density of",pnames[i],sep=" ")
plot(ts(x$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$thetasave[,i],hpd=hpd,main=title2,xlab="values", ylab="density",col=col)
}
}
if(is.null(x$prior$a0))
{
cat("")
}
else
{
title1<-paste("Trace of",pnames[n],sep=" ")
title2<-paste("Density of",pnames[n],sep=" ")
plot(ts(x$save.state$thetasave[,n]),type='l',main=title1,xlab="MCMC scan",ylab=" ")
fancydensplot(x$save.state$thetasave[,n],hpd=hpd,main=title2,xlab="values", ylab="density",col=col)
}
title1<-c("Predictive Error Density")
title2<-c("Link Function")
fancydensplot(x$save.state$randsave[,(x$nrec+1)],hpd=hpd,main=title1,xlab="values", ylab="density",col=col)
pml <-apply(x$save.state$fsave, 2, mean)
if(hpd){
limm<-apply(x$save.state$fsave, 2, hpdf)
pll<-limm[1,]
plu<-limm[2,]
}
else
{
limm<-apply(x$save.state$fsave, 2, pdf)
pll<-limm[1,]
plu<-limm[2,]
}
plot(x$xlink,pml,xlab="x",ylab="probability",main=title2,lty=1,type='l',lwd=2,ylim=c(0,1))
lines(x$xlink,pll,lty=2,lwd=2)
lines(x$xlink,plu,lty=2,lwd=2)
}
else
{
coef.p<-x$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 !="link")
{
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 !="link")
{
title1<-paste("Trace of",pnames[poss],sep=" ")
title2<-paste("Density of",pnames[poss],sep=" ")
plot(ts(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)
}
else
{
title1<-c("Predictive Error Density")
title2<-c("Link Function")
fancydensplot(x$save.state$randsave[,(x$nrec+1)],hpd=hpd,main=title1,xlab="values", ylab="density",col=col)
pml <-apply(x$save.state$fsave, 2, mean)
if(hpd){
limm<-apply(x$save.state$fsave, 2, hpdf)
pll<-limm[1,]
plu<-limm[2,]
}
else
{
limm<-apply(x$save.state$fsave, 2, pdf)
pll<-limm[1,]
plu<-limm[2,]
}
plot(x$xlink,pml,xlab="x",ylab="probability",main=title2,lty=1,type='l',lwd=2,ylim=c(0,1))
lines(x$xlink,pll,lty=2,lwd=2)
lines(x$xlink,plu,lty=2,lwd=2)
}
}
}
}
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### DPbinary.R
### Fit a semiparametric bernoulli regression model.
###
### Copyright: Alejandro Jara, 2006-2012.
###
### Last modification: 22-12-2006.
###
### 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
###
"DPbinary"<-
function(formula,inter=TRUE,baseline="logistic",prior,mcmc,state,status,misc=NULL,
data=sys.frame(sys.parent()),na.action=na.fail)
UseMethod("DPbinary")
"DPbinary.default"<-
function(formula,
inter=TRUE,
baseline="logistic",
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$drop.unused.levels <- TRUE
mf[[1]] <- as.name("model.frame")
mf <- eval(mf, parent.frame())
#########################################################################################
# data structure
#########################################################################################
yobs<- model.response(mf,"numeric")
nrec<-length(yobs)
x<-as.matrix(model.matrix(formula))
p<-dim(x)[2]
if(inter)
{
x<-x
p<-p
}
else
{
x<-x[,2:p]
p<-(p-1)
}
#########################################################################################
# Elements for Pseudo Countour Probabilities' computation
#########################################################################################
tt<-terms(formula,data=data)
mat<-attr(tt,"factors")
namfact<-colnames(mat)
nvar<-dim(mat)[1]
nfact<-dim(mat)[2]
possiP<-matrix(0,ncol=2,nrow=nfact)
dataF<-model.frame(formula,data,xlev=NULL)
namD<-names(dataF)
isF<-sapply(dataF, function(x) is.factor(x) || is.logical(x))
nlevel<-rep(0,nvar)
for(i in 1:nvar)
{
if(isF[i])
{
nlevel[i]<-length(table(dataF[[i]]))
}
else
{
nlevel[i]<-1
}
}
startp<-1+inter
for(i in 1:nfact)
{
tmp1<-1
for(j in 1:nvar)
{
if(mat[j,i]==1 && isF[j])
{
tmp1<-tmp1*(nlevel[j]-1)
}
}
endp<-startp+tmp1-1
possiP[i,1]<-startp
possiP[i,2]<-endp
startp<-endp+1
}
dimnames(possiP)<-list(namfact,c("Start","End"))
#########################################################################################
# misclassification
#########################################################################################
if(is.null(misc))
{
sens<-rep(1,nrec)
spec<-rep(1,nrec)
model<-0
}
else
{
sens<-misc$sens
spec<-misc$spec
if(length(sens)==1)sens<-rep(sens,nrec)
if(length(spec)==1)spec<-rep(spec,nrec)
model<-1
}
#########################################################################################
# mcmc specification
#########################################################################################
MLElogit<-function(x,y,sens,spec)
{
fn<-function(theta)
{
eta<-x%*%theta
p<-plogis(eta)
like <- sens*p+(1-spec)*(1-p)
if (all(like > 0))
eval<- -sum(log(like[y==1]))-sum(log(1-like[y==0]))
else eval<-Inf
return(eval)
}
start<-coefficients(glm(y~x-1,family=binomial(logit)))
foo<-optim(start,fn=fn,method="BFGS",hessian=TRUE)
out<-NULL
out$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$hessian))
return(out)
}
MLEcauchy<-function(x,y,sens,spec)
{
fn<-function(theta)
{
eta<-x%*%theta
p<-pcauchy(eta)
like <- sens*p+(1-spec)*(1-p)
if (all(like > 0))
eval<- -sum(log(like[y==1]))-sum(log(1-like[y==0]))
else eval<-Inf
return(eval)
}
start<-coefficients(glm(y~x-1,family=binomial(logit)))
foo<-optim(start,fn=fn,method="BFGS",hessian=TRUE)
out<-NULL
out$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$nsave
if(inter)
{
xmatrix<-x
}
else
{
xmatrix<-cbind(rep(1,nrec),x)
}
if(baseline=="logistic")
{
fit0<- MLElogit(xmatrix,yobs,sens,spec)
}
if(baseline=="normal")
{
fit0<- MLEprobit(xmatrix,yobs,sens,spec)
}
if(baseline=="cauchy")
{
fit0<- MLEcauchy(xmatrix,yobs,sens,spec)
}
if(inter)
{
propv<- fit0$covb
}
else
{
propv<- fit0$covb[2:(p+1),2:(p+1)]
}
if(is.null(mcmc$tune))
{
tune=1.1
}
else
{
tune<-mcmc$tune
}
resop<-1
#########################################################################################
# prior information
#########################################################################################
if(is.null(prior$a0))
{
a0b0<-c(-1,-1)
alpha<-prior$alpha
}
else
{
a0b0<-c(prior$a0,prior$b0)
alpha<-rgamma(1,prior$a0,prior$b0)
}
betapm<-prior$beta0
betapv<-prior$Sbeta0
propv<-diag(tune,p)%*%solve(solve(betapv)+solve(propv))%*%diag(tune,p)
#########################################################################################
# parameters depending on status
#########################################################################################
if(status)
{
if(inter)
{
beta<-fit0$beta
}
else
{
beta<-fit0$beta[2:(p+1)]
}
eta <- x %*% beta
v <- rep(0,nrec)
v[yobs==1]<-eta[yobs==1]-0.5
v[yobs==0]<-eta[yobs==0]+0.5
y<-yobs
}
else
{
beta<-state$beta
eta <- x %*% beta
v<-state$v
y<-state$y
alpha<-state$alpha
}
#########################################################################################
# output
#########################################################################################
xlink=rev(seq(-6,6,length=34))
nlink<-length(xlink)
fsave <- matrix(0, nrow=mcmc$nsave, ncol=nlink)
thetasave <- matrix(0, nrow=mcmc$nsave, ncol=p+2)
randsave <- matrix(0, nrow=mcmc$nsave, ncol=nrec+1)
#########################################################################################
# working space
#########################################################################################
extra<-1
ntotal<-nrec
maxint<-2*ntotal+1+nlink
maxend<-2*ntotal+nlink
maxm<-as.integer(log(0.0001)/log((ntotal+1)/(ntotal+2)))
acrate<-0
betac<-rep(0,p)
clusts<-matrix(0,nrow=nrec,ncol=(nrec+1))
endp<-rep(0,maxend)
endp2<-rep(0,maxend)
etan<-rep(0,nrec)
fsavet<-rep(0,nlink)
index<-rep(0,maxm)
intcount<-rep(0,maxint)
intcount2<-rep(0,maxint)
intind<-rep(0,(nrec+1))
intind2<-rep(0,(nrec+1))
iflag<-rep(0,p)
intposso<-matrix(0,nrow=maxint,ncol=(nrec+1))
intpossn<-matrix(0,nrow=maxint,ncol=(nrec+1))
limr<-matrix(0,nrow=nrec,ncol=2)
lpsav<-rep(0,nrec)
lpsavc<-rep(0,nrec)
mass<-rep(0,maxint)
ncluster<-nrec
prob<-rep(0,maxint)
s<-rep(0,nrec)
uvec<-rep(0,maxm)
vvec<-rep(0,maxm)
vnew<-rep(0,(nrec+1))
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)
wvec<-rep(0,maxm)
seed1<-sample(1:29000,1)
seed2<-sample(1:29000,1)
cpo<-rep(0,nrec)
#########################################################################################
# calling the fortran code
#########################################################################################
if(baseline=="logistic")
{
foo <- .Fortran("dpbinaryl",
model =as.integer(model),
nrec =as.integer(nrec),
p =as.integer(p),
sens =as.double(sens),
spec =as.double(spec),
x =as.double(x),
yobs =as.integer(yobs),
nlink =as.integer(nlink),
xlink =as.double(xlink),
a0b0 =as.double(a0b0),
betapm =as.double(betapm),
betapv =as.double(betapv),
mcmcvec =as.integer(mcmcvec),
nsave =as.integer(nsave),
propv =as.double(propv),
extra =as.integer(extra),
acrate =as.double(acrate),
fsave =as.double(fsave),
randsave =as.double(randsave),
thetasave =as.double(thetasave),
cpo =as.double(cpo),
alpha =as.double(alpha),
beta =as.double(beta),
ncluster =as.integer(ncluster),
y =as.integer(y),
v =as.double(v),
betac =as.double(betac),
endp =as.double(endp),
endp2 =as.double(endp2),
eta =as.double(eta),
etan =as.double(etan),
fsavet =as.integer(fsavet),
clusts =as.integer(clusts),
index =as.integer(index),
intcount =as.integer(intcount),
intcount2 =as.integer(intcount2),
intind =as.integer(intind),
intind2 =as.integer(intind2),
iflag =as.integer(iflag),
intposso =as.integer(intposso),
intpossn =as.integer(intpossn),
limr =as.double(limr),
lpsav =as.integer(lpsav),
lpsavc =as.integer(lpsavc),
maxint =as.integer(maxint),
maxend =as.integer(maxend),
maxm =as.integer(maxm),
mass =as.double(mass),
prob =as.double(prob),
s =as.integer(s),
seed1 =as.integer(seed1),
seed2 =as.integer(seed2),
uvec =as.double(uvec),
vvec =as.double(vvec),
vnew =as.double(vnew),
workm1 =as.double(workm1),
workm2 =as.double(workm2),
workmh1 =as.double(workmh1),
workv1 =as.double(workv1),
workv2 =as.double(workv2),
wvec =as.double(wvec),
PACKAGE="DPpackage")
}
if(baseline=="normal")
{
foo <- .Fortran("dpbinaryp",
model =as.integer(model),
nrec =as.integer(nrec),
p =as.integer(p),
sens =as.double(sens),
spec =as.double(spec),
x =as.double(x),
yobs =as.integer(yobs),
nlink =as.integer(nlink),
xlink =as.double(xlink),
a0b0 =as.double(a0b0),
betapm =as.double(betapm),
betapv =as.double(betapv),
mcmcvec =as.integer(mcmcvec),
nsave =as.integer(nsave),
propv =as.double(propv),
extra =as.integer(extra),
acrate =as.double(acrate),
fsave =as.double(fsave),
randsave =as.double(randsave),
thetasave =as.double(thetasave),
cpo =as.double(cpo),
alpha =as.double(alpha),
beta =as.double(beta),
ncluster =as.integer(ncluster),
y =as.integer(y),
v =as.double(v),
betac =as.double(betac),
endp =as.double(endp),
endp2 =as.double(endp2),
eta =as.double(eta),
etan =as.double(etan),
fsavet =as.integer(fsavet),
clusts =as.integer(clusts),
index =as.integer(index),
intcount =as.integer(intcount),
intcount2 =as.integer(intcount2),
intind =as.integer(intind),
intind2 =as.integer(intind2),
iflag =as.integer(iflag),
intposso =as.integer(intposso),
intpossn =as.integer(intpossn),
limr =as.double(limr),
lpsav =as.integer(lpsav),
lpsavc =as.integer(lpsavc),
maxint =as.integer(maxint),
maxend =as.integer(maxend),
maxm =as.integer(maxm),
mass =as.double(mass),
prob =as.double(prob),
s =as.integer(s),
seed1 =as.integer(seed1),
seed2 =as.integer(seed2),
uvec =as.double(uvec),
vvec =as.double(vvec),
vnew =as.double(vnew),
workm1 =as.double(workm1),
workm2 =as.double(workm2),
workmh1 =as.double(workmh1),
workv1 =as.double(workv1),
workv2 =as.double(workv2),
wvec =as.double(wvec),
PACKAGE="DPpackage")
}
if(baseline=="cauchy")
{
foo <- .Fortran("dpbinaryc",
model =as.integer(model),
nrec =as.integer(nrec),
p =as.integer(p),
sens =as.double(sens),
spec =as.double(spec),
x =as.double(x),
yobs =as.integer(yobs),
nlink =as.integer(nlink),
xlink =as.double(xlink),
a0b0 =as.double(a0b0),
betapm =as.double(betapm),
betapv =as.double(betapv),
mcmcvec =as.integer(mcmcvec),
nsave =as.integer(nsave),
propv =as.double(propv),
extra =as.integer(extra),
acrate =as.double(acrate),
fsave =as.double(fsave),
randsave =as.double(randsave),
thetasave =as.double(thetasave),
cpo =as.double(cpo),
alpha =as.double(alpha),
beta =as.double(beta),
ncluster =as.integer(ncluster),
y =as.integer(y),
v =as.double(v),
betac =as.double(betac),
endp =as.double(endp),
endp2 =as.double(endp2),
eta =as.double(eta),
etan =as.double(etan),
fsavet =as.integer(fsavet),
clusts =as.integer(clusts),
index =as.integer(index),
intcount =as.integer(intcount),
intcount2 =as.integer(intcount2),
intind =as.integer(intind),
intind2 =as.integer(intind2),
iflag =as.integer(iflag),
intposso =as.integer(intposso),
intpossn =as.integer(intpossn),
limr =as.double(limr),
lpsav =as.integer(lpsav),
lpsavc =as.integer(lpsavc),
maxint =as.integer(maxint),
maxend =as.integer(maxend),
maxm =as.integer(maxm),
mass =as.double(mass),
prob =as.double(prob),
s =as.integer(s),
seed1 =as.integer(seed1),
seed2 =as.integer(seed2),
uvec =as.double(uvec),
vvec =as.double(vvec),
vnew =as.double(vnew),
workm1 =as.double(workm1),
workm2 =as.double(workm2),
workmh1 =as.double(workmh1),
workv1 =as.double(workv1),
workv2 =as.double(workv2),
wvec =as.double(wvec),
PACKAGE="DPpackage")
}
#########################################################################################
# save state
#########################################################################################
fsave<-matrix(foo$fsave,nrow=mcmc$nsave, ncol=nlink)
thetasave<-matrix(foo$thetasave,nrow=mcmc$nsave, ncol=(p+2))
randsave<-matrix(foo$randsave,nrow=mcmc$nsave, ncol=(nrec+1))
model.name<-"Bayesian semiparametric binary regression model"
colnames(thetasave)<-c(dimnames(x)[[2]],"ncluster","alpha")
coeff<-apply(thetasave, 2, mean)
names(coeff)<-c(dimnames(x)[[2]],"ncluster","alpha")
qnames<-NULL
for(i in 1:nrec){
idname<-paste("(Subject",i,sep="=")
idname<-paste(idname,")",sep="")
qnames<-c(qnames,idname)
}
qnames<-c(qnames,"Prediction")
colnames(randsave)<-qnames
state <- list(beta=foo$beta,v=foo$v,y=foo$y,alpha=foo$alpha)
save.state <- list(thetasave=thetasave,fsave=fsave,randsave=randsave)
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$cpo,p=p,nlink=nlink,xlink=xlink,baseline=baseline,x=x,
possiP=possiP)
cat("\n\n")
class(z)<-c("DPbinary")
return(z)
}
###
### Estimate the probability curve for a fitted semiparametric binary
### regression model.
###
### Copyright: Alejandro Jara Vallejos, 2006
### Last modification: 21-08-2006.
"predict.DPbinary"<-
function(object,xnew=NULL,hpd=TRUE, ...)
{
stde<-function(x)
{
n<-length(x)
return(sd(x)/sqrt(n))
}
hpdf<-function(x)
{
alpha<-0.05
vec<-x
n<-length(x)
alow<-rep(0,2)
aupp<-rep(0,2)
a<-.Fortran("hpd",n=as.integer(n),alpha=as.double(alpha),x=as.double(vec),
alow=as.double(alow),aupp=as.double(aupp),PACKAGE="DPpackage")
return(c(a$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$aupp[2]))
}
if(is.null(xnew))
{
xnew<-object$x
}
if(is.null(xnew))
{
xnew<-object$x
}
seed1<-sample(1:29000,1)
seed2<-sample(1:29000,1)
if(is(object, "DPbinary"))
{
npred<-dim(xnew)[1]
pnew<-dim(xnew)[2]
nrec<-object$nrec
baseline<-object$baseline
alpha<-object$save.state$thetasave[,(object$p+2)]
nsave<-length(alpha)
v<-matrix(object$save.state$randsave,nsave,nrec+1)
vpred<-v[,(nrec+1)]
v<-v[,1:nrec]
if (object$p != pnew)
{
stop("Dimension of xnew is not the same that the design matrix
in the original model.\n")
}
covn<-rep(0,npred)
for(i in 1:npred)
{
covnw<-round(xnew[i,1],3)
for(j in 2:pnew){
covnw<-paste(covnw,round(xnew[i,j],3),sep=";")
}
covn[i]<-covnw
}
lp<-xnew%*%t(object$save.state$thetasave[,1:object$p])
out<-matrix(0,nrow=npred,ncol=nsave)
if(baseline=="logistic")
{
foo <- .Fortran("dppredl",
nsave =as.integer(nsave),
nrec =as.integer(nrec),
npred =as.integer(npred),
alpha =as.double(alpha),
v =as.double(v),
lp =as.double(lp),
out =as.double(out),
seed1 =as.integer(seed1),
seed2 =as.integer(seed2),
PACKAGE="DPpackage")
}
if(baseline=="normal")
{
foo <- .Fortran("dppredp",
nsave =as.integer(nsave),
nrec =as.integer(nrec),
npred =as.integer(npred),
alpha =as.double(alpha),
v =as.double(v),
lp =as.double(lp),
out =as.double(out),
seed1 =as.integer(seed1),
seed2 =as.integer(seed2),
PACKAGE="DPpackage")
}
if(baseline=="cauchy")
{
foo <- .Fortran("dppredc",
nsave =as.integer(nsave),
nrec =as.integer(nrec),
npred =as.integer(npred),
alpha =as.double(alpha),
v =as.double(v),
lp =as.double(lp),
out =as.double(out),
seed1 =as.integer(seed1),
seed2 =as.integer(seed2),
PACKAGE="DPpackage")
}
out<-t(matrix(foo$out,nrow=npred,ncol=nsave))
pm <-apply(out, 2, mean)
pmed <-apply(out, 2, median)
psd<-apply(out, 2, sd)
pstd<-apply(out, 2, stde)
if(hpd){
limm<-apply(out, 2, hpdf)
plinf<-limm[1,]
plsup<-limm[2,]
}
else
{
plinf<-apply(out, 2, pdf)
coef.l<-limm[1,]
plsup<-limm[2,]
}
names(pm)<-covn
names(pmed)<-covn
names(psd)<-covn
names(pstd)<-covn
names(plinf)<-covn
names(plsup)<-covn
out<-NULL
out$pmean<-pm
out$pmedian<-pmed
out$psd<-psd
out$pstd<-pstd
out$plinf<-plinf
out$plsup<-plsup
out$vpred<-vpred
out$npred<-npred
out$covn<-covn
}
out
}
###
### Tools for DPbinary: anova, print, summary, plot
###
### Copyright: Alejandro Jara Vallejos, 2006
### Last modification: 15-12-2006.
"anova.DPbinary"<-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$pval
}
}
table <- data.frame(df,P)
dimnames(table) <- list(rownames(possiP), c("Df","PsCP"))
structure(table, heading = c("Table of Pseudo Contour Probabilities\n",
paste("Response:", deparse(formula(object)[[2]]))), class = c("anovaPsCP",
"data.frame"))
}
"print.DPbinary"<-function (x, digits = max(3, getOption("digits") - 3), ...)
{
cat("\n",x$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$nrec)
cat("\n\n")
invisible(x)
}
"summary.DPbinary"<-function(object, hpd=TRUE, ...)
{
stde<-function(x)
{
n<-length(x)
return(sd(x)/sqrt(n))
}
hpdf<-function(x)
{
alpha<-0.05
vec<-x
n<-length(x)
alow<-rep(0,2)
aupp<-rep(0,2)
a<-.Fortran("hpd",n=as.integer(n),alpha=as.double(alpha),x=as.double(vec),
alow=as.double(alow),aupp=as.double(aupp),PACKAGE="DPpackage")
return(c(a$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$thetasave
### Fixed part of the model
dimen1<-object$p
if(dimen1==1)
{
mat<-matrix(thetasave[,1:dimen1],ncol=1)
}
else
{
mat<-thetasave[,1:dimen1]
}
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$cpo
### Baseline Information
if(is.null(object$prior$a0))
{
dimen2<-1
}
else
{
dimen2<-2
}
if(dimen2==1)
{
mat<-matrix(thetasave[,(dimen1+1):(dimen1+dimen2)],ncol=1)
}
else
{
mat<-thetasave[,(dimen1+1):(dimen1+dimen2)]
}
coef.p<-object$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$prec<-coef.table
ans$acrate<-object$acrate
ans$nrec<-object$nrec
class(ans) <- "summaryDPbinary"
return(ans)
}
"print.summaryDPbinary"<-function (x, digits = max(3, getOption("digits") - 3), ...)
{
cat("\n",x$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")
if (length(x$prec)) {
cat("\nPrecision parameter:\n")
print.default(format(x$prec, digits = digits), print.gap = 2,
quote = FALSE)
}
else cat("No precision parameter\n")
cat("\nAcceptance Rate for Metropolis Step = ",x$acrate,"\n")
cat("\nNumber of Observations:",x$nrec)
cat("\n\n")
invisible(x)
}
"plot.DPbinary"<-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))
}
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]))
}
if(is(x, "DPbinary")){
if(is.null(param))
{
coef.p<-x$coefficients
n<-length(coef.p)
pnames<-names(coef.p)
par(ask = ask)
layout(matrix(seq(1,nfigr*nfigc,1), nrow=nfigr, ncol=nfigc, byrow = TRUE))
for(i in 1:(n-1)){
title1<-paste("Trace of",pnames[i],sep=" ")
title2<-paste("Density of",pnames[i],sep=" ")
plot(ts(x$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$thetasave[,i],hpd=hpd,main=title2,xlab="values", ylab="density",col=col)
}
}
if(is.null(x$prior$a0))
{
cat("")
}
else
{
title1<-paste("Trace of",pnames[n],sep=" ")
title2<-paste("Density of",pnames[n],sep=" ")
plot(ts(x$save.state$thetasave[,n]),type='l',main=title1,xlab="MCMC scan",ylab=" ")
fancydensplot(x$save.state$thetasave[,n],hpd=hpd,main=title2,xlab="values", ylab="density",col=col)
}
title1<-c("Predictive Error Density")
title2<-c("Link Function")
fancydensplot(x$save.state$randsave[,(x$nrec+1)],hpd=hpd,main=title1,xlab="values", ylab="density",col=col)
pml <-apply(x$save.state$fsave, 2, mean)
if(hpd){
limm<-apply(x$save.state$fsave, 2, hpdf)
pll<-limm[1,]
plu<-limm[2,]
}
else
{
limm<-apply(x$save.state$fsave, 2, pdf)
pll<-limm[1,]
plu<-limm[2,]
}
plot(x$xlink,pml,xlab="x",ylab="probability",main=title2,lty=1,type='l',lwd=2,ylim=c(0,1))
lines(x$xlink,pll,lty=2,lwd=2)
lines(x$xlink,plu,lty=2,lwd=2)
}
else
{
coef.p<-x$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 !="link")
{
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 !="link")
{
title1<-paste("Trace of",pnames[poss],sep=" ")
title2<-paste("Density of",pnames[poss],sep=" ")
plot(ts(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)
}
else
{
title1<-c("Predictive Error Density")
title2<-c("Link Function")
fancydensplot(x$save.state$randsave[,(x$nrec+1)],hpd=hpd,main=title1,xlab="values", ylab="density",col=col)
pml <-apply(x$save.state$fsave, 2, mean)
if(hpd){
limm<-apply(x$save.state$fsave, 2, hpdf)
pll<-limm[1,]
plu<-limm[2,]
}
else
{
limm<-apply(x$save.state$fsave, 2, pdf)
pll<-limm[1,]
plu<-limm[2,]
}
plot(x$xlink,pml,xlab="x",ylab="probability",main=title2,lty=1,type='l',lwd=2,ylim=c(0,1))
lines(x$xlink,pll,lty=2,lwd=2)
lines(x$xlink,plu,lty=2,lwd=2)
}
}
}
}
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