mash_update_data

This function can update two parts of the mash
data. The first one is setting the reference group, so the mash
data can be used for commonbaseline analysis. The other one is
updating the null correlation 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

mash_update_data function

<dl><dt>mashdata</dt>
<dd>mash data object ontaining the Bhat matrix,
standard errors, V; created using <code>mash_set_data</code></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>V</dt>
<dd>an R by R matrix / [R x R x N] array of correlation matrix
of error correlations</dd></dl>

Arguments

Update the data object for mash analysis. — mash_update_data

<dl>

<dt>mashdata</dt>
<dd>mash data object ontaining the Bhat matrix,
standard errors, V; created using <code>mash_set_data</code></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>V</dt>
<dd>an R by R matrix / [R x R x N] array of correlation matrix
of error correlations</dd>

</dl>

Update the data object for mash analysis.

mash_update_data: Update the data object for mash analysis.

Description

Usage

Value

Arguments

Examples