print.emBayes

Print a summary of an 'emBayes' result

Variable selection methods have been extensively developed for analyzing highdimensional omics data within both the frequentist and Bayesian frameworks. This package provides implementations of the spike-and-slab quantile (group) LASSO which have been developed along the line of Bayesian hierarchical models but deeply rooted in frequentist regularization methods by utilizing Expectation–Maximization (EM) algorithm. The spike-and-slab quantile LASSO can handle data irregularity in terms of skewness and outliers in response variables, compared to its non-robust alternative, the spike-and-slab LASSO, which has also been implemented in the package. In addition, procedures for fitting the spike-and-slab quantile group LASSO and its non-robust counterpart have been implemented in the form of quantile/least-square varying coefficient mixed effect models for high-dimensional longitudinal data. The core module of this package is developed in 'C++'.

Yuwen Liu

emBayes

Robust Bayesian Variable Selection via Expectation-Maximization

Cen Wu

print.emBayes function

<dl><dt>x</dt>
<dd>emBayes result</dd>
<dt>digits</dt>
<dd>significant digits in printout.</dd>
<dt>...</dt>
<dd>other print arguments</dd></dl>

Arguments

print an emBayes result — print.emBayes

<dl>

<dt>x</dt>
<dd>emBayes result</dd>


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


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

</dl>

print.emBayes: print an emBayes result

Description

Usage

Value

Arguments

See Also