miselect.df

This synthetic data is taken from the first simulation case from the miselect
paper

datasets

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

miselect.df function

A data.frame with 500 observations on 21 variables:<dl>
 <dt>Y</dt>
<dd>Binary response.</dd> <dt>X1-X20</dt>
<dd>Covariates with missing data.</dd>
</dl>

Format

Synthetic Example Data For "miselect" — miselect.df

A data.frame with 500 observations on 21 variables:<dl>
 <dt>Y</dt>
<dd>Binary response.</dd>

 <dt>X1-X20</dt>
<dd>Covariates with missing data.</dd>


</dl>

miselect.df: Synthetic Example Data For "miselect"

Description

Usage

Arguments

Format