cov_udi

Compute a list of covariance matrices corresponding to the
"Unassociated", "Directly associated" and "Indirectly associated"
models

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_udi function

<dl><dt>data</dt>
<dd>a mash data object, eg as created by <code>mash_set_data</code></dd>
<dt>model</dt>
<dd>a model matrix with R columns, where R is the number
of conditions in the data; each row should be a vector of length R
with elements "U","D" and "I" indicating whether each effect is
Unassociated, Directly associated or Indirectly associated</dd></dl>

Arguments

Compute a list of covariance matrices corresponding to the
"Unassociated", "Directly associated" and "Indirectly associated"
models — cov_udi

<dl>

<dt>data</dt>
<dd>a mash data object, eg as created by <code>mash_set_data</code></dd>


<dt>model</dt>
<dd>a model matrix with R columns, where R is the number
of conditions in the data; each row should be a vector of length R
with elements "U","D" and "I" indicating whether each effect is
Unassociated, Directly associated or Indirectly associated</dd>

</dl>

Compute a list of covariance matrices corresponding to the
"Unassociated", "Directly associated" and "Indirectly associated"
models

cov_udi: Compute a list of covariance matrices corresponding to the "Unassociated", "Directly associated" and "Indirectly associated" models

Description

Usage

Value

Arguments

Details

Examples