print.GxESelection

Print a summary of a GxESelection object

Recently, multiple marginal variable selection methods have been developed and shown to be effective in Gene-Environment interactions studies. We propose a novel marginal Bayesian variable selection method for Gene-Environment interactions studies. In particular, our marginal Bayesian method is robust to data contamination and outliers in the outcome variables. With the incorporation of spike-and-slab priors, we have implemented the Gibbs sampler based on Markov Chain Monte Carlo. The core algorithms of the package have been developed in 'C++'.

Xi Lu

marble

Robust Marginal Bayesian Variable Selection for Gene-Environment
Interactions

Cen Wu

print.GxESelection function

<dl><dt>x</dt>
<dd>GxESelection object.</dd>
<dt>digits</dt>
<dd>significant digits in printout.</dd>
<dt>...</dt>
<dd>other print arguments.</dd></dl>

Arguments

print a GxESelection object — print.GxESelection

<dl>

<dt>x</dt>
<dd>GxESelection object.</dd>


<dt>digits</dt>
<dd>significant digits in printout.</dd>


<dt>...</dt>
<dd>other print arguments.</dd>

</dl>

print.GxESelection: print a GxESelection object

Description

Usage

Value

Arguments

See Also