meets_basis

Computes and returns the indicator value for the basis described by
cols and cutoffs for a given row of X

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

meets_basis function

<dl><dt>X</dt>
<dd>The design matrix, containing the original data.</dd>
<dt>row_num</dt>
<dd>Numeri for a row index over which to evaluate.</dd>
<dt>cols</dt>
<dd>Numeric for the column indices of the basis function.</dd>
<dt>cutoffs</dt>
<dd>Numeric providing thresholds.</dd>
<dt>orders</dt>
<dd>Numeric providing smoothness orders</dd></dl>

Arguments

Compute Values of Basis Functions — meets_basis

<dl>

<dt>X</dt>
<dd>The design matrix, containing the original data.</dd>


<dt>row_num</dt>
<dd>Numeri for a row index over which to evaluate.</dd>


<dt>cols</dt>
<dd>Numeric for the column indices of the basis function.</dd>


<dt>cutoffs</dt>
<dd>Numeric providing thresholds.</dd>


<dt>orders</dt>
<dd>Numeric providing smoothness orders</dd>

</dl>

meets_basis: Compute Values of Basis Functions

Description

Usage

Arguments