calc_relative_lik_matrix

Computes matrix of relative likelihoods for each of J
 rows of 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_relative_lik_matrix function

<dl><dt>data</dt>
<dd>A <code>mash</code> data object; e.g., created by
<code>mash_set_data</code>.</dd>
<dt>Ulist</dt>
<dd>List containing the prior covariance matrices.</dd>
<dt>algorithm.version</dt>
<dd>indicates R or Rcpp</dd></dl>

Arguments

Calculate matrix of relative likelihoods. — calc_relative_lik_matrix

<dl>

<dt>data</dt>
<dd>A <code>mash</code> data object; e.g., created by
<code>mash_set_data</code>.</dd>


<dt>Ulist</dt>
<dd>List containing the prior covariance matrices.</dd>


<dt>algorithm.version</dt>
<dd>indicates R or Rcpp</dd>

</dl>

Calculate matrix of relative likelihoods.

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calc_relative_lik_matrix: Calculate matrix of relative likelihoods.

Description

Usage

Value

Arguments