===== 2010 Sep 20 =====
geweke<-function(x, frac1 = 0.2, frac2 = 0.5){
xstart <- c(start(x), end(x) - frac2 * (end(x) - start(x)))[c(1,3)]
xend <- c(start(x) + frac1 * (end(x) - start(x)), end(x))[c(1,3)]
y.variance <- y.mean <- vector("list", 2)
for (i in 1:2) {
y <- window(x, start = xstart[i], end = xend[i])
y.mean[[i]] <- apply(as.matrix(y), 2, mean)
y.variance[[i]] <- apply(as.matrix(y), 2, var)
}
z <- (y.mean[[1]] - y.mean[[2]])/sqrt(y.variance[[1]] + y.variance[[2]])
z
}
HPD <- function(obj, prob = 0.95, ...)
{
obj <- as.matrix(obj)
vals <- apply(obj, 2, sort)
if (!is.matrix(vals)) stop("obj must have nsamp > 1")
nsamp <- nrow(vals)
npar <- ncol(vals)
gap <- max(1, min(nsamp - 1, round(nsamp * prob)))
init <- 1:(nsamp - gap)
inds <- apply(vals[init + gap, ,drop=FALSE] - vals[init, ,drop=FALSE],
2, which.min)
ans <- cbind(vals[cbind(inds, 1:npar)],
vals[cbind(inds + gap, 1:npar)])
ans
}
res<-NULL
stem<-'Tdata'
for (i in 1001:2000){
codafile<-paste("/pscratch/zzhang4/Private/student-grm/CODA/TdataR/TdataCODAN300I",i,".txt",sep='')
if (file.exists(codafile)){
codasamp<-read.table(codafile)
par<-matrix(c(codasamp[,2]),ncol=7)
gew.max<- max(geweke(par))
m.par<-apply(par,2,mean)
sd.par<-apply(par,2,sd)
p025.par<-apply(par,2,quantile,.025)
p975.par<-apply(par,2,quantile,.975)
hpd.ci<-c(t(HPD(par)))
par.i<-c(m.par,sd.par,p025.par,p975.par,hpd.ci, gew.max,i)
res<-rbind(res,par.i)
}
file.res<-paste('res-Tdata-T-N-300-Wishart.txt', sep='')
}
write.table(res, file.res)
* T model results
## process the results
## 1) convergence diagnostic
## geweke function
geweke<-function(x, frac1 = 0.2, frac2 = 0.5){
xstart <- c(start(x), end(x) - frac2 * (end(x) - start(x)))[c(1,3)]
xend <- c(start(x) + frac1 * (end(x) - start(x)), end(x))[c(1,3)]
y.variance <- y.mean <- vector("list", 2)
for (i in 1:2) {
y <- window(x, start = xstart[i], end = xend[i])
y.mean[[i]] <- apply(as.matrix(y), 2, mean)
y.variance[[i]] <- apply(as.matrix(y), 2, var)
}
z <- (y.mean[[1]] - y.mean[[2]])/sqrt(y.variance[[1]] + y.variance[[2]])
z
}
HPD <- function(obj, prob = 0.95, ...)
{
obj <- as.matrix(obj)
vals <- apply(obj, 2, sort)
if (!is.matrix(vals)) stop("obj must have nsamp > 1")
nsamp <- nrow(vals)
npar <- ncol(vals)
gap <- max(1, min(nsamp - 1, round(nsamp * prob)))
init <- 1:(nsamp - gap)
inds <- apply(vals[init + gap, ,drop=FALSE] - vals[init, ,drop=FALSE],
2, which.min)
ans <- cbind(vals[cbind(inds, 1:npar)],
vals[cbind(inds + gap, 1:npar)])
ans
}
res<-NULL
stem<-'Tdata'
for (i in 1001:2000){
codafile<-paste(stem,"CODAN500I",i,".txt",sep='')
if (file.exists(codafile)){
codasamp<-read.table(codafile)
par<-matrix(c(codasamp[,2]),ncol=7)
gew.max<- max(geweke(par))
m.par<-apply(par,2,mean)
sd.par<-apply(par,2,sd)
p025.par<-apply(par,2,quantile,.025)
p975.par<-apply(par,2,quantile,.975)
hpd.ci<-c(t(HPD(par)))
par.i<-c(m.par,sd.par,p025.par,p975.par,hpd.ci, gew.max,i)
res<-rbind(res,par.i)
}
file.res<-paste('res-',stem,'.txt', sep='')
}
write.table(res, file.res)
==== Normal model results ====
## process the results
## 1) convergence diagnostic
## geweke function
geweke<-function(x, frac1 = 0.2, frac2 = 0.5){
xstart <- c(start(x), end(x) - frac2 * (end(x) - start(x)))[c(1,3)]
xend <- c(start(x) + frac1 * (end(x) - start(x)), end(x))[c(1,3)]
y.variance <- y.mean <- vector("list", 2)
for (i in 1:2) {
y <- window(x, start = xstart[i], end = xend[i])
y.mean[[i]] <- apply(as.matrix(y), 2, mean)
y.variance[[i]] <- apply(as.matrix(y), 2, var)
}
z <- (y.mean[[1]] - y.mean[[2]])/sqrt(y.variance[[1]] + y.variance[[2]])
z
}
HPD <- function(obj, prob = 0.95, ...)
{
obj <- as.matrix(obj)
vals <- apply(obj, 2, sort)
if (!is.matrix(vals)) stop("obj must have nsamp > 1")
nsamp <- nrow(vals)
npar <- ncol(vals)
gap <- max(1, min(nsamp - 1, round(nsamp * prob)))
init <- 1:(nsamp - gap)
inds <- apply(vals[init + gap, ,drop=FALSE] - vals[init, ,drop=FALSE],
2, which.min)
ans <- cbind(vals[cbind(inds, 1:npar)],
vals[cbind(inds + gap, 1:npar)])
ans
}
res<-NULL
stem<-'Tdata'
for (i in 1001:2000){
codafile<-paste("/pscratch/zzhang4/Private/student-grm/CODA/Tdata/TdataCODAN400I",i,".txt",sep=''
)
if (file.exists(codafile)){
codasamp<-read.table(codafile)
par<-matrix(c(codasamp[,2]),ncol=6)
gew.max<- max(geweke(par))
m.par<-apply(par,2,mean)
sd.par<-apply(par,2,sd)
p025.par<-apply(par,2,quantile,.025)
p975.par<-apply(par,2,quantile,.975)
hpd.ci<-c(t(HPD(par)))
par.i<-c(m.par,sd.par,p025.par,p975.par,hpd.ci, gew.max,i)
res<-rbind(res,par.i)
}
file.res<-paste('res-Tdata-N-N-400-Uniform.txt', sep='')
}
write.table(res, file.res)