print.cv.regnet

Print a summary of a cv.regnet object

Network-based regularization has achieved success in variable selection for
high-dimensional biological data due to its ability to incorporate correlations among
genomic features. This package provides procedures of network-based variable selection
for generalized linear models (Ren et al. (2017) <doi:10.1186/s12863-017-0495-5> and
Ren et al.(2019) <doi:10.1002/gepi.22194>). Continuous, binary, and survival response
are supported. Robust network-based methods are available for continuous and survival
responses.

Jie Ren

regnet

Network-Based Regularization for Generalized Linear Models

Yinhao Du

Cen Wu

Yu Jiang

Junhao Liu

print.cv.regnet function

<dl><dt>x</dt>
<dd>a cv.regnet object.</dd>
<dt>digits</dt>
<dd>significant digits in the printout.</dd>
<dt>...</dt>
<dd>other print arguments</dd></dl>

Arguments

print a cv.regnet object — print.cv.regnet

<dl>

<dt>x</dt>
<dd>a cv.regnet object.</dd>


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


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

</dl>

print.cv.regnet: print a cv.regnet object

Description

Usage

Arguments

See Also