sim_contrast1

Create simplest simulation, cj = mu 1 data used for contrast
analysis

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

sim_contrast1 function

<dl><dt>nsamp</dt>
<dd>number of samples of each type</dd>
<dt>ncond</dt>
<dd>number of conditions</dd>
<dt>err_sd</dt>
<dd>the standard deviation of the errors</dd></dl>

Arguments

Create simplest simulation, cj = mu 1 data used for contrast
analysis — sim_contrast1

<dl>

<dt>nsamp</dt>
<dd>number of samples of each type</dd>


<dt>ncond</dt>
<dd>number of conditions</dd>


<dt>err_sd</dt>
<dd>the standard deviation of the errors</dd>

</dl>

Create simplest simulation, cj = mu 1 data used for contrast
analysis

sim_contrast1: Create simplest simulation, cj = mu 1 data used for contrast analysis

Description

Usage

Arguments

Details

Examples