# R-values from MCMC output. Returns r-values from an array of MCMC output. ```r rvaluesMCMC(output, qtheta, alpha.grid = NULL, ngrid = NULL, smooth = "none") ``` ## Arguments - `output`: a matrix contatining mcmc ouput. The ith row should represent a sample from the posterior of the ith parameter of interest. - `qtheta`: either a function which returns the quantiles (for upper tail probs.) of theta or a vector of theta-quantiles. - `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`. ## Returns An object of class "rvals" which is a list containing at least the following components: - **main**: a data frame containing the r-values, the r-value rankings along with the rankings from several other common procedures - **aux**: a list containing other extraneous information - **rvalues**: a vector of r-values ## References Henderson, N.C. and Newton, M.A. (2016). **Making the cut: improved ranking and selection for large-scale inference**. J. Royal Statist. Soc. B., 78(4), 781-804. tools:::Rd_expr_doi("10.1111/rssb.12131") [https://arxiv.org/abs/1312.5776](https://arxiv.org/abs/1312.5776) ## Author(s) Nicholas Henderson and Michael Newton ## See Also `rvalues`, `PostSummaries` ## Examples ```r data(MCMCtest) ### For the MCMC output in MCMC_test, the prior assumed for the effect sizes of ### interest was a mixture of two t-distributions. The function qthetaTMix ### computes the quantiles for this prior. qthetaTMix <- function(p) { ### function to compute quantiles (for upper tail probabilities) for a ### mixture of two t-distributions mu <- c(.35,-.12) sig <- c(.2,.08) mix.prop <- c(.25,.75) ff <- function(x,pp) { prob_less <- 0 for(k in 1:2) { prob_less <- prob_less + pt((x - mu[k])/sig[k],df=4,lower.tail=FALSE)*mix.prop[k] } return(prob_less - pp) } nn <- length(p) ans <- numeric(nn) for(i in 1:nn) { ans[i] <- uniroot(ff,interval=c(-5,5),tol=1e-6,pp=p[i])$root } return(ans) } rvs <- rvaluesMCMC(MCMCtest, qtheta = qthetaTMix) ```

rvaluesMCMC function