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PostPercentile <- function(object) {
### object - an object of class "rvalues"
if(object$aux$prior=="conjugate") {
if(object$aux$family=="gaussian") {
XX <- object$aux$unsorted$MLE
samp.var <- object$aux$unsorted$SE^2
prior.mean <- object$aux$hypers[1]
prior.var <- object$aux$hypers[2]
denom <- sqrt((samp.var + prior.var)*(prior.var^2 + 2*samp.var*prior.var))
num <- (prior.var + 2*samp.var)*prior.mean + prior.var*XX
pep <- pnorm(num/denom,lower.tail=FALSE)
}
else if(object$aux$family=="poisson") {
XX <- object$aux$unsorted$xx
eta <- object$aux$unsorted$eta
### For poisson-gamma, the hyperparameters correspond
### to the rate parameter version of the gamma
prior.shape <- object$aux$hypers[1]
prior.rate <- object$aux$hypers[2]
num <- prior.shape*(eta + prior.rate)
den <- prior.rate*(prior.shape + XX)
pep <- pf(num/den,df1=2*(prior.shape + XX),df2=2*prior.shape)
}
else if(object$aux$family=="binomial") {
ff <- function(u,xx,nn,alpha,beta) {
ans <- pbeta(u,shape1=alpha+xx,shape2=beta + nn - xx)*dbeta(u,shape1=alpha,shape2=beta)
return(ans)
}
x <- object$aux$unsorted$xx
m <- object$aux$unsorted$nn
a <- object$aux$hypers[1]
b <- object$aux$hypers[2]
pep <- rep(0,length(x))
for(i in seq_len(length(x))) {
pep[i] <- integrate(ff,lower=0,upper=1,xx=x[i],nn=m[i],alpha=a,beta=b)$value
}
#pep <- 1 - (object$aux$V%*%delta.alpha)
}
}
else if(object$aux$prior=="nonparametric") {
## object$aux$prior is null, for example if using the Valpha() function
### get distances between points on the alpha grid.
delta.alpha <- c(object$aux$alpha.grid[1],diff(object$aux$alpha.grid))
### Compute estimate posterior percentiles.
### This relies on the fact that
### pep = 1 - \int_0^1 V_\alpha(D_i) d\alpha
pep <- 1 - (object$aux$V%*%delta.alpha)
}
return(pep)
}
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