compute_loglik_from_matrix_and_pi

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

compute_loglik_from_matrix_and_pi function

<dl><dt>pi_s</dt>
<dd>the vector of mixture proportions</dd>
<dt>lm</dt>
<dd>the results of a likelihood matrix calculation from
<code>calc_relative_lik_matrix</code></dd>
<dt>Shat_alpha</dt>
<dd>matrix of Shat^alpha</dd></dl>

Arguments

Compute the total loglikelihood from a matrix of
  log-likelihoods and fitted pi — compute_loglik_from_matrix_and_pi

<dl>

<dt>pi_s</dt>
<dd>the vector of mixture proportions</dd>


<dt>lm</dt>
<dd>the results of a likelihood matrix calculation from
<code>calc_relative_lik_matrix</code></dd>


<dt>Shat_alpha</dt>
<dd>matrix of Shat^alpha</dd>

</dl>

Compute the total loglikelihood from a matrix of
  log-likelihoods and fitted pi

compute_loglik_from_matrix_and_pi: Compute the total loglikelihood from a matrix of log-likelihoods and fitted pi

Description

Usage

Arguments