prune

prunes out mixture components with low weight

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

prune function

<dl><dt>m</dt>
<dd>What is this argument?</dd>
<dt>thresh</dt>
<dd>the threshold below which components are removed</dd></dl>

Arguments

prune — prune

<dl>

<dt>m</dt>
<dd>What is this argument?</dd>


<dt>thresh</dt>
<dd>the threshold below which components are removed</dd>

</dl>

prune: prune

Description

Usage

Arguments