teem_wrapper

This is an internal (non-exported) function. This help
 page provides additional documentation mainly intended for
 developers and expert users.

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

teem_wrapper function

<dl><dt>data</dt>
<dd>mash data object</dd>
<dt>Ulist_init</dt>
<dd>a list of covariance matrices to initialize to</dd>
<dt>subset</dt>
<dd>the indices of the observations to be used (defaults
to all of them)</dd></dl>

Arguments

Fit extreme deconvolution to mash data using TEEM method
developed by Y. Yang and M Stephens — teem_wrapper

<dl>

<dt>data</dt>
<dd>mash data object</dd>


<dt>Ulist_init</dt>
<dd>a list of covariance matrices to initialize to</dd>


<dt>subset</dt>
<dd>the indices of the observations to be used (defaults
to all of them)</dd>

</dl>

Fit extreme deconvolution to mash data using TEEM method
developed by Y. Yang and M Stephens

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teem_wrapper: Fit extreme deconvolution to mash data using TEEM method developed by Y. Yang and M Stephens

Description

Usage

Value

Arguments