create_sketched_kernel

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

create_sketched_kernel function

<dl><dt>X_test</dt>
<dd>Test data</dd>
<dt>X_train</dt>
<dd>Train data</dd>
<dt>S</dt>
<dd>Sketch matrix</dd>
<dt>bandwidth</dt>
<dd>Kernel bandwidth</dd></dl>

Arguments

Create the sketched kernel — create_sketched_kernel

<dl>

<dt>X_test</dt>
<dd>Test data</dd>


<dt>X_train</dt>
<dd>Train data</dd>


<dt>S</dt>
<dd>Sketch matrix</dd>


<dt>bandwidth</dt>
<dd>Kernel bandwidth</dd>

</dl>

create_sketched_kernel: Create the sketched kernel

Description

Usage

Arguments