posterior_mean

This is an internal (non-exported) function. This help
 page provides additional documentation mainly intended for
 developers and expert users.

internal

Implements the multivariate adaptive shrinkage (mash)
method of Urbut et al (2019) <DOI:10.1038/s41588-018-0268-8> for
estimating and testing large numbers of effects in many conditions
(or many outcomes). Mash takes an empirical Bayes approach to
testing and effect estimation; it estimates patterns of similarity
among conditions, then exploits these patterns to improve accuracy
of the effect estimates. The core linear algebra is implemented in
C++ for fast model fitting and posterior computation.

Peter Carbonetto

mashr

Multivariate Adaptive Shrinkage

Matthew Stephens

Sarah Urbut

Gao Wang

Yuxin Zou

Yunqi Yang

Sam Roweis

David Hogg

Jo Bovy

posterior_mean function

<dl><dt>bhat</dt>
<dd>R vector of observation</dd>
<dt>Vinv</dt>
<dd>R x R inverse covariance matrix for the likelihood</dd>
<dt>U1</dt>
<dd>R x R posterior covariance matrix, computed using posterior_cov</dd></dl>

Arguments

posterior_mean — posterior_mean

<dl>

<dt>bhat</dt>
<dd>R vector of observation</dd>


<dt>Vinv</dt>
<dd>R x R inverse covariance matrix for the likelihood</dd>


<dt>U1</dt>
<dd>R x R posterior covariance matrix, computed using posterior_cov</dd>

</dl>

posterior_mean: posterior_mean

Description

Usage

Value

Arguments

Details