comp_postprob

data


compute the posterior prob that each observation came
    from each component of the mixture m,output a k by n vector of
    probabilities computed by weighting the component densities by
    pi and then normalizing


The R package 'ashr' implements an Empirical Bayes approach for large-scale hypothesis testing and false discovery rate (FDR) estimation based on the methods proposed in M. Stephens, 2016, "False discovery rates: a new deal", <DOI:10.1093/biostatistics/kxw041>. These methods can be applied whenever two sets of summary statistics---estimated effects and standard errors---are available, just as 'qvalue' can be applied to previously computed p-values. Two main interfaces are provided: ash(), which is more user-friendly; and ash.workhorse(), which has more options and is geared toward advanced users.

comp_postprob: comp_postprob

Description

Usage

Arguments