adjust_Rsq

Estimate shrinkage for regression models

R functions for criterion profile analysis, Davison and Davenport (2002) <doi:10.1037/1082-989X.7.4.468> and meta-analytic criterion profile analysis, Wiernik, Wilmot, Davison, and Ones (2020) <doi:10.1037/met0000305>. Sensitivity analyses to aid in interpreting criterion profile analysis results are also included.

Brenton M. Wiernik

configural

Multivariate Profile Analysis

adjust_Rsq function

<dl><dt>Rsq</dt>
<dd>Observed model R-squared</dd>
<dt>n</dt>
<dd>Sample size</dd>
<dt>p</dt>
<dd>Number of predictors</dd>
<dt>adjust</dt>
<dd>Which adjustment to apply. Options are "fisher" for the Adjusted R-squared method used in <code><a href="/link/stats%3A%3Alm()?package=configural&version=0.1.5" data-mini-rdoc="configural::stats::lm()">stats::lm()</a></code>, "pop" for the positive-part Pratt estimator of the population R-squared, and "cv" for the Browne/positive-part Pratt estimator of the cross-validity R-squared. Based on Shieh (2008), these are the estimators for the population and cross-validity R-squared values that show the least bias with a minimal increase in computational complexity.</dd></dl>

Arguments

Adjust a regression model R-squared for overfitting — adjust_Rsq

<dl>

<dt>Rsq</dt>
<dd>Observed model R-squared</dd>


<dt>n</dt>
<dd>Sample size</dd>


<dt>p</dt>
<dd>Number of predictors</dd>


<dt>adjust</dt>
<dd>Which adjustment to apply. Options are "fisher" for the Adjusted R-squared method used in <code><a href='https://rdrr.io/r/stats/lm.html'>stats::lm()</a></code>, "pop" for the positive-part Pratt estimator of the population R-squared, and "cv" for the Browne/positive-part Pratt estimator of the cross-validity R-squared. Based on Shieh (2008), these are the estimators for the population and cross-validity R-squared values that show the least bias with a minimal increase in computational complexity.</dd>

</dl>

adjust_Rsq: Adjust a regression model R-squared for overfitting

Description

Usage

Value

Arguments

References

Examples