comp_sd

Generic function to extract the standard deviations of
 components of the mixture

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_sd function

<dl><dt>m</dt>
<dd>a mixture of k components generated by normalmix() or
unimix() or igmix()</dd></dl>

Arguments

Generic function to extract the standard deviations of
    components of the mixture — comp_sd

<dl>

<dt>m</dt>
<dd>a mixture of k components generated by normalmix() or
unimix() or igmix()</dd>

</dl>

Generic function to extract the standard deviations of
    components of the mixture

comp_sd: Generic function to extract the standard deviations of components of the mixture

Description

Usage

Value

Arguments