generate_cw

internal

A flexible framework combining
variable screening and random projection techniques for fitting ensembles of
predictive generalized linear models to high-dimensional data.
Designed for extensibility, the package implements
key techniques as S3 classes with user-friendly constructors,
enabling easy integration and development of new procedures for
high-dimensional applications. For more details see
Parzer et al (2024a) <doi:10.48550/arXiv.2312.00130> and
Parzer et al (2024b) <doi:10.48550/arXiv.2410.00971>.

Laura Vana-Gür

spareg

Sparse Projected Averaged Regression

Roman Parzer

Peter Filzmoser

generate_cw function

<dl><dt>m</dt>
<dd>goal dimension, which will be randomly sampled in the SPAR algorithm</dd>
<dt>included_vector</dt>
<dd>integer vector of column indices for the variables to be
included in the random projection. These indices are produced in the
screening step of the SPAR algorithm.</dd>
<dt>x</dt>
<dd>matrix of predictors</dd>
<dt>y</dt>
<dd>vector of response variable</dd></dl>

Arguments

Sparse Embedding Matrix — generate_cw

<dl>

<dt>m</dt>
<dd>goal dimension, which will be randomly sampled in the SPAR algorithm</dd>


<dt>included_vector</dt>
<dd>integer vector of column indices for the variables to be
included in the random projection. These indices are produced in the
screening step of the SPAR algorithm.</dd>


<dt>x</dt>
<dd>matrix of predictors</dd>


<dt>y</dt>
<dd>vector of response variable</dd>

</dl>

generate_cw: Sparse Embedding Matrix

Description

Usage

Value

Arguments