comp_dens

Generic function of calculating the component densities 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_dens function

<dl><dt>m</dt>
<dd>mixture of k components generated by normalmix() or
unimix() or igmix()</dd>
<dt>y</dt>
<dd>is an n-vector of location</dd>
<dt>log</dt>
<dd>whether to use log-scale on densities</dd></dl>

Arguments

Generic function of calculating the component densities of the
    mixture — comp_dens

<dl>

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


<dt>y</dt>
<dd>is an n-vector of location</dd>


<dt>log</dt>
<dd>whether to use log-scale on densities</dd>

</dl>

Generic function of calculating the component densities of the
    mixture

comp_dens: Generic function of calculating the component densities of the mixture

Description

Usage

Value

Arguments