glmnet_coefs

Convenience function for retrieving coefficients from a glmnet::cv.glmnet
model at a specified lambda. Sparsity is removed and non-intercept
coefficients are ranked by absolute value.

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

glmnet_coefs function

<dl><dt>fit</dt>
<dd>A glmnet::cv.glmnet fitted model object.</dd>
<dt>s</dt>
<dd>Value of lambda. See glmnet::coef.glmnet and
glmnet::predict.cv.glmnet</dd>
<dt>...</dt>
<dd>Other arguments passed to glmnet::coef.glmnet</dd></dl>

Arguments

glmnet coefficients — glmnet_coefs

<dl>

<dt>fit</dt>
<dd>A glmnet::cv.glmnet fitted model object.</dd>


<dt>s</dt>
<dd>Value of lambda. See glmnet::coef.glmnet and
glmnet::predict.cv.glmnet</dd>


<dt>...</dt>
<dd>Other arguments passed to glmnet::coef.glmnet</dd>

</dl>

glmnet_coefs: glmnet coefficients

Description

Usage

Value

Arguments