Computes the weighted least squares estimate of the coefficients.

Performs valid statistical inference on predicted data (IPD) using recent methods, where for a subset of the data, the outcomes have been predicted by an algorithm. Provides a wrapper function with specified defaults for the type of model and method to be used for estimation and inference. Further provides methods for tidying and summarizing results. Salerno et al., (2025) <doi:10.1093/bioinformatics/btaf055>.

Stephen Salerno

Inference on Predicted Data

Jiacheng Miao

Awan Afiaz

Kentaro Hoffman

Jesse Gronsbell

Jianhui Gao

David Cheng

Anna Neufeld

Qiongshi Lu

Tyler H McCormick

Jeffrey T Leek

wls function

<dl><dt>X</dt>
<dd>(matrix): n x p matrix of covariates.</dd>
<dt>Y</dt>
<dd>(vector): p-vector of outcome values.</dd>
<dt>w</dt>
<dd>(vector, optional): n-vector of sample weights.</dd>
<dt>return_se</dt>
<dd>(bool, optional): Whether to return the standard errors of
the coefficients.</dd></dl>

Arguments

Weighted Least Squares — wls

<dl>

<dt>X</dt>
<dd>(matrix): n x p matrix of covariates.</dd>


<dt>Y</dt>
<dd>(vector): p-vector of outcome values.</dd>


<dt>w</dt>
<dd>(vector, optional): n-vector of sample weights.</dd>


<dt>return_se</dt>
<dd>(bool, optional): Whether to return the standard errors of
the coefficients.</dd>

</dl>

Weighted Least Squares

wls: Weighted Least Squares

Description

Usage

Value

Arguments