smooth.construct.gKRLS.smooth.spec

internal

Kernel regularized least squares, also known as kernel ridge regression,
is a flexible machine learning method. This package implements this method by
providing a smooth term for use with 'mgcv' and uses random sketching to
facilitate scalable estimation on large datasets. It provides additional
functions for calculating marginal effects after estimation and for use with
ensembles ('SuperLearning'), double/debiased machine learning ('DoubleML'),
and robust/clustered standard errors ('sandwich'). Chang and Goplerud (2024)
<doi:10.1017/pan.2023.27> provide further details.

Max Goplerud

gKRLS

Generalized Kernel Regularized Least Squares

Qing Chang

smooth.construct.gKRLS.smooth.spec function

<dl><dt>object</dt>
<dd>a smooth object; see documentation for other methods in
<code>mgcv</code>.</dd>
<dt>data</dt>
<dd>a data.frame; see documentation for other methods in
<code>mgcv</code>.</dd>
<dt>knots</dt>
<dd>not used</dd></dl>

Arguments

Constructor for gKRLS smooth — smooth.construct.gKRLS.smooth.spec

<dl>

<dt>object</dt>
<dd>a smooth object; see documentation for other methods in
<code>mgcv</code>.</dd>


<dt>data</dt>
<dd>a data.frame; see documentation for other methods in
<code>mgcv</code>.</dd>


<dt>knots</dt>
<dd>not used</dd>

</dl>

smooth.construct.gKRLS.smooth.spec: Constructor for gKRLS smooth

Description

Usage

Arguments

Details