comp_mean

Generic function of calculating the first moment 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_mean function

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

Arguments

Generic function of calculating the first moment of components of
    the mixture — comp_mean

<dl>

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

</dl>

Generic function of calculating the first moment of components of
    the mixture

comp_mean: Generic function of calculating the first moment of components of the mixture

Description

Usage

Value

Arguments