cov_from_factors

produce list of rank-1 covariance matrices corresponding to rows of f

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

cov_from_factors function

<dl><dt>f</dt>
<dd>a matrix of factors (each row is a factor)</dd>
<dt>name</dt>
<dd>a string indicating the name to use</dd></dl>

Arguments

produce list of rank-1 covariance matrices corresponding to rows of f — cov_from_factors

<dl>

<dt>f</dt>
<dd>a matrix of factors (each row is a factor)</dd>


<dt>name</dt>
<dd>a string indicating the name to use</dd>

</dl>

produce list of rank-1 covariance matrices corresponding to rows of f

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cov_from_factors: produce list of rank-1 covariance matrices corresponding to rows of f

Description

Usage

Value

Arguments