post_sample

returns random samples from the posterior, given a prior distribution
 m and n observed datapoints.

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

post_sample function

<dl><dt>m</dt>
<dd>prior distribution (eg of type normalmix)</dd>
<dt>data</dt>
<dd>a list with components x and s, each vectors of length n, to be interpreted as a 
normally-distributed observations and corresponding standard errors</dd>
<dt>nsamp</dt>
<dd>number of random samples to return for each observation</dd></dl>

Arguments

post_sample — post_sample

<dl>

<dt>m</dt>
<dd>prior distribution (eg of type normalmix)</dd>


<dt>data</dt>
<dd>a list with components x and s, each vectors of length n, to be interpreted as a 
normally-distributed observations and corresponding standard errors</dd>


<dt>nsamp</dt>
<dd>number of random samples to return for each observation</dd>

</dl>

post_sample: post_sample

Description

Usage

Value

Arguments

Details