calc_xscale

Calculating Centered and Scaled Matrices

internal

A scalable implementation of the highly adaptive lasso algorithm,
including routines for constructing sparse matrices of basis functions of the
observed data, as well as a custom implementation of Lasso regression tailored
to enhance efficiency when the matrix of predictors is composed exclusively of
indicator functions. For ease of use and increased flexibility, the Lasso
fitting routines invoke code from the 'glmnet' package by default. The highly
adaptive lasso was first formulated and described by MJ van der Laan (2017)
<doi:10.1515/ijb-2015-0097>, with practical demonstrations of its performance
given by Benkeser and van der Laan (2016) <doi:10.1109/DSAA.2016.93>. This
implementation of the highly adaptive lasso algorithm was described by Hejazi,
Coyle, and van der Laan (2020) <doi:10.21105/joss.02526>.

Jeremy Coyle

hal9001

The Scalable Highly Adaptive Lasso

Nima Hejazi

Rachael Phillips

Lars van der Laan

David Benkeser

Oleg Sofrygin

Weixin Cai

Mark van der Laan

calc_xscale function

<dl><dt>X</dt>
<dd>A sparse matrix, to be centered.</dd>
<dt>xcenter</dt>
<dd>A vector of column means to be used for centering X.</dd></dl>

Arguments

Calculating Centered and Scaled Matrices — calc_xscale

<dl>

<dt>X</dt>
<dd>A sparse matrix, to be centered.</dd>


<dt>xcenter</dt>
<dd>A vector of column means to be used for centering X.</dd>

</dl>

Calculating Centered and Scaled Matrices

calc_xscale: Calculating Centered and Scaled Matrices

Description

Usage

Arguments