contrast_matrix

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

contrast_matrix function

<dl><dt>R</dt>
<dd>the number of column for the contrast matrix</dd>
<dt>ref</dt>
<dd>the reference group. It could be a number between 1,...,
R, R is number of conditions, or the name of reference group. If
there is no reference group, it can be the string 'mean'.</dd>
<dt>name</dt>
<dd>a length R vector contains the name for conditions</dd></dl>

Arguments

Create contrast matrix — contrast_matrix

<dl>

<dt>R</dt>
<dd>the number of column for the contrast matrix</dd>


<dt>ref</dt>
<dd>the reference group. It could be a number between 1,...,
R, R is number of conditions, or the name of reference group. If
there is no reference group, it can be the string 'mean'.</dd>


<dt>name</dt>
<dd>a length R vector contains the name for conditions</dd>

</dl>

Create contrast matrix

contrast_matrix: Create contrast matrix

Description

Usage

Arguments

Examples