var_direction

Determines directionality of final predictors for binary or regression
models, using the sign of the t-statistic or correlation coefficient
respectively for each variable compared to the outcomes.

Implements nested k*l-fold cross-validation for lasso and elastic-net regularised linear models via the 'glmnet' package and other machine learning models via the 'caret' package <doi:10.1093/bioadv/vbad048>. Cross-validation of 'glmnet' alpha mixing parameter and embedded fast filter functions for feature selection are provided. Described as double cross-validation by Stone (1977) <doi:10.1111/j.2517-6161.1977.tb01603.x>. Also implemented is a method using outer CV to measure unbiased model performance metrics when fitting Bayesian linear and logistic regression shrinkage models using the horseshoe prior over parameters to encourage a sparse model as described by Piironen & Vehtari (2017) <doi:10.1214/17-EJS1337SI>.

Myles Lewis

nestedcv

Nested Cross-Validation with 'glmnet' and 'caret'

Athina Spiliopoulou

Cankut Cubuk

David Rios Santini

Katriona Goldmann

Ryan C. Thompson

var_direction function

<dl><dt>object</dt>
<dd>a <code>nestcv.glmnet</code> or <code>nestcv.train</code> fitted model</dd></dl>

Arguments

Variable directionality — var_direction

<dl>

<dt>object</dt>
<dd>a <code>nestcv.glmnet</code> or <code>nestcv.train</code> fitted model</dd>

</dl>

var_direction: Variable directionality

Description

Usage

Value

Arguments

Details