print.svem_significance_test

Prints the median p-value from an object of class <code>svem_significance_test</code>.

Tools for fitting self-validated ensemble models (SVEM; Lemkus et al. (2021) <doi:10.1016/j.chemolab.2021.104439>) in small-sample design-of-experiments and related workflows, using elastic net and relaxed elastic net regression via 'glmnet' (Friedman et al. (2010) <doi:10.18637/jss.v033.i01>). Fractional random-weight bootstraps with anti-correlated validation copies are used to tune penalty paths by validation-weighted AIC/BIC. Supports Gaussian and binomial responses, deterministic expansion helpers for shared factor spaces, prediction with bootstrap uncertainty, and a random-search optimizer that respects mixture constraints and combines multiple responses via desirability functions. Also includes a permutation-based whole-model test for Gaussian SVEM fits (Karl (2024) <doi:10.1016/j.chemolab.2024.105122>). Package code was drafted with assistance from generative AI tools.

Andrew T Karl

SVEMnet

Self-Validated Ensemble Models with Lasso and Relaxed Elastic
Net Regression

Andrew T. Karl

print.svem_significance_test function

<dl><dt>x</dt>
<dd>An object of class <code>svem_significance_test</code>.</dd>
<dt>...</dt>
<dd>Additional arguments (unused).</dd></dl>

Arguments

Print Method for SVEM Significance Test — print.svem_significance_test

<dl>

<dt>x</dt>
<dd>An object of class <code>svem_significance_test</code>.</dd>


<dt>...</dt>
<dd>Additional arguments (unused).</dd>

</dl>

print.svem_significance_test: Print Method for SVEM Significance Test

Description

Usage

Arguments