log_comp_dens_conv

compute the log density of the components of the
 mixture m when convoluted with a normal with standard deviation
 s or a scaled (se) student.t with df v, the density is
 evaluated at x

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

log_comp_dens_conv function

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

Arguments

log_comp_dens_conv — log_comp_dens_conv

<dl>

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


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

</dl>

The Learning Leader's Guide to AI Literacy

log_comp_dens_conv: log_comp_dens_conv

Description

Usage

Value

Arguments