calc_lik_vector

Computes vector of likelihoods for bhat for each of P
 prior covariances.
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

calc_lik_vector function

<dl><dt>bhat</dt>
<dd>bhat vector (length R)</dd>
<dt>V</dt>
<dd>R x R covariance matrix for likelihood.</dd>
<dt>Ulist</dt>
<dd>list of prior covariance matrices.</dd>
<dt>log</dt>
<dd>If <code>TRUE</code>, the return value is a matrix of
log-likelihoods.</dd></dl>

Arguments

Compute conditional likelihoods for bhat vector. — calc_lik_vector

<dl>

<dt>bhat</dt>
<dd>bhat vector (length R)</dd>


<dt>V</dt>
<dd>R x R covariance matrix for likelihood.</dd>


<dt>Ulist</dt>
<dd>list of prior covariance matrices.</dd>


<dt>log</dt>
<dd>If <code>TRUE</code>, the return value is a matrix of
log-likelihoods.</dd>

</dl>

Compute conditional likelihoods for bhat vector.

calc_lik_vector: Compute conditional likelihoods for bhat vector.

Description

Usage

Value

Arguments