summary.susie

print.summary.susie

<code>summary</code> method for the &#8220;susie&#8221; class.

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

summary.susie function

<dl><dt>object</dt>
<dd>A susie fit.</dd>
<dt>...</dt>
<dd>Additional arguments passed to the generic <code>summary</code>
or <code>print.summary</code> method.</dd>
<dt>x</dt>
<dd>A susie summary.</dd></dl>

Arguments

Summarize Susie Fit. — summary.susie

<dl>

<dt>object</dt>
<dd>A susie fit.</dd>


<dt>...</dt>
<dd>Additional arguments passed to the generic <code>summary</code>
or <code>print.summary</code> method.</dd>


<dt>x</dt>
<dd>A susie summary.</dd>

</dl>

summary.susie: Summarize Susie Fit.

Description

Usage

Value

Arguments