PostSummaries {rvalues}

R-values from posterior summary quantities

Description

Computes r-values assuming that, for each parameter of interest, the user supplies a value for the posterior mean and the posterior standard deviation. The assumption here is that the posterior distributions are Normal.

Usage

PostSummaries(post.means, post.sds, hypers = NULL, qtheta = NULL, alpha.grid = NULL, 
               ngrid = NULL, smooth = 0)

Arguments

post.means

a vector of posterior means

post.sds

a vector of posterior standard deviations

hypers

a list with two elements: mean and sd. These represent the parameters in the (Normal) prior which was used to generate the posterior means and sds. If hypers is not supplied then one must supply the quantile function qtheta.

qtheta

a function which returns the quantiles (for upper tail probs.) of theta. If this is not supplied, the hyperparameter must be supplied.

alpha.grid

grid of values in (0,1); used for the discrete approximation approach for computing r-values.

ngrid

number of grid points for alpha.grid; only relevant when alpha.grid = NULL

smooth

either smooth="none" or smooth takes a value between 0 and 10; this determines the level of smoothing applied to the estimate of \lambda(\alpha); if smooth is given a number, the number is used as the bass argument in supsmu.

Value

An object of class "rvals"

Author(s)

Nicholas Henderson and Michael Newton

See Also

rvalues

Examples

n <- 500
theta <- rnorm(n)
sig_sq <- rgamma(n,shape=1,scale=1)
X <- theta + sqrt(sig_sq)*rnorm(n)

pm <- X/(sig_sq + 1)
psd <- sqrt(sig_sq/(sig_sq + 1))

rvs <- PostSummaries(post.means=pm,post.sds=psd,hypers=list(mean=0,sd=1))
hist(rvs$rvalues)