print.Bayenet.pred

Print a summary of a predict.Bayenet object

As heavy-tailed error distribution and outliers in the response variable widely exist, models which are robust to data contamination are highly demanded. Here, we develop a novel robust Bayesian variable selection method with elastic net penalty. In particular, the spike-and-slab priors have been incorporated to impose sparsity. An efficient Gibbs sampler has been developed to facilitate computation.The core modules of the package have been developed in 'C++' and R.

Xi Lu

Bayenet

Robust Bayesian Elastic Net

Cen Wu

print.Bayenet.pred function

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

Arguments

print a predict.Bayenet object — print.Bayenet.pred

<dl>

<dt>x</dt>
<dd>predict.Bayenet object.</dd>


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


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

</dl>

print.Bayenet.pred: print a predict.Bayenet object

Description

Usage

Value

Arguments

See Also