train_roc

Build ROC (receiver operating characteristic) curve from outer training
folds. Object can be plotted using <code><a href="/link/plot()?package=nestedcv&version=0.9.0" data-mini-rdoc="nestedcv::plot()">plot()</a></code> or passed to functions
<code><a href="/link/pROC%3A%3Aauc()?package=nestedcv&version=0.9.0" data-mini-rdoc="nestedcv::pROC::auc()">pROC::auc()</a></code> etc.

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

train_roc function

<dl><dt>x</dt>
<dd>a <code>nestcv.glmnet</code>, <code>nestcv.train</code> or <code>outercv</code> object</dd>
<dt>direction</dt>
<dd>Set ROC directionality <a href="/link/pROC%3A%3Aroc?package=nestedcv&version=0.9.0" data-mini-rdoc="nestedcv::pROC::roc">pROC::roc</a></dd>
<dt>...</dt>
<dd>Other arguments passed to <a href="/link/pROC%3A%3Aroc?package=nestedcv&version=0.9.0" data-mini-rdoc="nestedcv::pROC::roc">pROC::roc</a></dd></dl>

Arguments

Build ROC (receiver operating characteristic) curve from outer training
folds. Object can be plotted using <code><a href='https://rdrr.io/r/graphics/plot.default.html'>plot()</a></code> or passed to functions
<code><a href='https://rdrr.io/pkg/pROC/man/auc.html'>pROC::auc()</a></code> etc.

Build ROC curve from outer CV training folds — train_roc

<dl>

<dt>x</dt>
<dd>a <code>nestcv.glmnet</code>, <code>nestcv.train</code> or <code>outercv</code> object</dd>


<dt>direction</dt>
<dd>Set ROC directionality <a href='https://rdrr.io/pkg/pROC/man/roc.html'>pROC::roc</a></dd>


<dt>...</dt>
<dd>Other arguments passed to <a href='https://rdrr.io/pkg/pROC/man/roc.html'>pROC::roc</a></dd>

</dl>

train_roc: Build ROC curve from outer CV training folds

Description

Usage

Value

Arguments

Details