cov_flash

Perform Empirical Bayes Matrix Factorization using flashier, and
 return a list of candidate covariance matrices

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

cov_flash function

<dl><dt>data</dt>
<dd>A &#8220;mash&#8221; data object.</dd>
<dt>factors</dt>
<dd>If <code>factors = "default"</code>, the factors and
loadings are both unconstrained. If <code>factors = "nonneg"</code>, the
factors are constrained to be non-negative, and the loadings are
unconstrained.</dd>
<dt>subset</dt>
<dd>Data samples (rows) used to estimate the
covariances. Sset to <code>NULL</code> to use all the data.</dd>
<dt>remove_singleton</dt>
<dd>If <code>remove_singleton = TRUE</code>, factors
corresponding to singleton matrices will be removed from the output.</dd>
<dt>tag</dt>
<dd>How to name the covariance matrices.</dd>
<dt>output_model</dt>
<dd>The fitted flash model will be saved to this file
(using <code><a href="/link/saveRDS?package=mashr&version=0.2.79" data-mini-rdoc="mashr::saveRDS">saveRDS</a></code>).</dd>
<dt>greedy_args</dt>
<dd>List containing additional parameters passed to
<code>flashier::flash_greedy</code>.</dd>
<dt>backfit_args</dt>
<dd>List containing additional parameters passed
to <code>flashier::flash_backfit</code>.</dd></dl>

Arguments

Perform Empirical Bayes Matrix Factorization using flashier, and
  return a list of candidate covariance matrices — cov_flash

<dl>

<dt>data</dt>
<dd>A &#8220;mash&#8221; data object.</dd>


<dt>factors</dt>
<dd>If <code>factors = "default"</code>, the factors and
loadings are both unconstrained. If <code>factors = "nonneg"</code>, the
factors are constrained to be non-negative, and the loadings are
unconstrained.</dd>


<dt>subset</dt>
<dd>Data samples (rows) used to estimate the
covariances. Sset to <code>NULL</code> to use all the data.</dd>


<dt>remove_singleton</dt>
<dd>If <code>remove_singleton = TRUE</code>, factors
corresponding to singleton matrices will be removed from the output.</dd>


<dt>tag</dt>
<dd>How to name the covariance matrices.</dd>


<dt>output_model</dt>
<dd>The fitted flash model will be saved to this file
(using <code><a href='https://rdrr.io/r/base/readRDS.html'>saveRDS</a></code>).</dd>


<dt>greedy_args</dt>
<dd>List containing additional parameters passed to
<code>flashier::flash_greedy</code>.</dd>


<dt>backfit_args</dt>
<dd>List containing additional parameters passed
to <code>flashier::flash_backfit</code>.</dd>

</dl>

Perform Empirical Bayes Matrix Factorization using flashier, and
  return a list of candidate covariance matrices

cov_flash: Perform Empirical Bayes Matrix Factorization using flashier, and return a list of candidate covariance matrices

Description

Usage

Value

Arguments

Examples