compute_ss

This is a synonym for <code>compute_suff_stat</code>
 included for historical reasons (deprecated).

Implements methods for variable selection in linear
regression based on the "Sum of Single Effects" (SuSiE) model, as
described in Wang et al (2020) <DOI:10.1101/501114> and Zou et al
(2021) <DOI:10.1101/2021.11.03.467167>. These methods provide
simple summaries, called "Credible Sets", for accurately
quantifying uncertainty in which variables should be selected.
The methods are motivated by genetic fine-mapping applications,
and are particularly well-suited to settings where variables are
highly correlated and detectable effects are sparse. The fitting
algorithm, a Bayesian analogue of stepwise selection methods
called "Iterative Bayesian Stepwise Selection" (IBSS), is simple
and fast, allowing the SuSiE model be fit to large data sets
(thousands of samples and hundreds of thousands of variables).

Peter Carbonetto

susieR

Sum of Single Effects Linear Regression

Gao Wang

Yuxin Zou

Kaiqian Zhang

Matthew Stephens

compute_ss function

<dl><dt>X</dt>
<dd>An n by p matrix of covariates.</dd>
<dt>y</dt>
<dd>An n vector.</dd>
<dt>standardize</dt>
<dd>Logical flag indicating whether to standardize
columns of X to unit variance prior to computing summary data.</dd></dl>

Arguments

Compute sufficient statistics for input to susie_suff_stat — compute_ss

<dl>

<dt>X</dt>
<dd>An n by p matrix of covariates.</dd>


<dt>y</dt>
<dd>An n vector.</dd>


<dt>standardize</dt>
<dd>Logical flag indicating whether to standardize
columns of X to unit variance prior to computing summary data.</dd>

</dl>

compute_ss: Compute sufficient statistics for input to `susie_suff_stat`

Description

Usage

Value

Arguments

Examples