print.cv.galasso

<code>print.cv.galasso</code> print the fit and returns it invisibly.

Penalized regression methods, such as lasso and elastic net, are used in
many biomedical applications when simultaneous regression coefficient
estimation and variable selection is desired. However, missing data
complicates the implementation of these methods, particularly when
missingness is handled using multiple imputation. Applying a variable
selection algorithm on each imputed dataset will likely lead
to different sets of selected predictors, making it difficult
to ascertain a final active set without resorting to ad hoc
combination rules. 'miselect' presents Stacked Adaptive Elastic Net (saenet)
and Grouped Adaptive LASSO (galasso) for continuous and binary outcomes,
developed by Du et al (2022) <doi:10.1080/10618600.2022.2035739>. They,
by construction, force selection of the same variables across multiply
imputed data. 'miselect' also provides cross validated variants of these
methods.

Michael Kleinsasser

miselect

Variable Selection for Multiply Imputed Data

Alexander Rix

Jiacong Du

print.cv.galasso function

<dl><dt>x</dt>
<dd>An object of type "cv.galasso" to print</dd>
<dt>...</dt>
<dd>Further arguments passed to or from other methods</dd></dl>

Arguments

Print cv.galasso Objects — print.cv.galasso

<dl>

<dt>x</dt>
<dd>An object of type "cv.galasso" to print</dd>


<dt>...</dt>
<dd>Further arguments passed to or from other methods</dd>

</dl>

print.cv.galasso: Print cv.galasso Objects

Description

Usage

Arguments