PostSummaries: R-values from posterior summary quantities
In rvalues: R-Values for Ranking in High-Dimensional Settings

Description Usage Arguments Value Author(s) See Also Examples

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.

1 2	PostSummaries(post.means, post.sds, hypers = NULL, qtheta = NULL, alpha.grid = NULL, ngrid = NULL, smooth = 0)

`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 λ(α); if `smooth` is given a number, the number is used as the `bass` argument in `supsmu`.

An object of class "rvals"

Nicholas Henderson and Michael Newton

rvalues

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)