coef.cv.saenet

Extract Coefficients From a "cv.saenet" Object

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

coef.cv.saenet function

<dl><dt>object</dt>
<dd>A "cv.saenet" fit</dd>
<dt>lambda</dt>
<dd>Chosen value of lambda. Must be between "min(lambda)" and
"max(lambda)". Default is "lambda.min"</dd>
<dt>alpha</dt>
<dd>Chosen value of alpha. Must be between "min(alpha)" and
"max(alpha)". Default is "alpha.min"</dd>
<dt>...</dt>
<dd>Additional unused arguments</dd></dl>

Arguments

Extract Coefficients From a "cv.saenet" Object — coef.cv.saenet

<dl>

<dt>object</dt>
<dd>A "cv.saenet" fit</dd>


<dt>lambda</dt>
<dd>Chosen value of lambda. Must be between "min(lambda)" and
"max(lambda)". Default is "lambda.min"</dd>


<dt>alpha</dt>
<dd>Chosen value of alpha. Must be between "min(alpha)" and
"max(alpha)". Default is "alpha.min"</dd>


<dt>...</dt>
<dd>Additional unused arguments</dd>

</dl>

coef.cv.saenet: Extract Coefficients From a "cv.saenet" Object

Description

Usage

Value

Arguments