comp_postmean

output posterior mean for beta for each component of
 prior mixture m,given data

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. The
ash() and ash.workhorse() also provides a flexible modeling
interface that can accommodate a variety of likelihoods (e.g.,
normal, Poisson) and mixture priors (e.g., uniform, normal).

Peter Carbonetto

ashr

Methods for Adaptive Shrinkage, using Empirical Bayes

Matthew Stephens

Chaoxing Dai

David Gerard

Mengyin Lu

Lei Sun

Jason Willwerscheid

Nan Xiao

Mazon Zeng

comp_postmean function

<dl><dt>m</dt>
<dd>mixture distribution with k components</dd>
<dt>data</dt>
<dd>details depend on the model</dd></dl>

Arguments

comp_postmean — comp_postmean

<dl>

<dt>m</dt>
<dd>mixture distribution with k components</dd>


<dt>data</dt>
<dd>details depend on the model</dd>

</dl>

comp_postmean: comp_postmean

Description

Usage

Arguments