lines.prc

Adds a precision-recall curve to a base graphics plot. It accepts an S3
object of class 'prc', see <code>prc()</code>.

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

lines.prc function

<dl><dt>x</dt>
<dd>An object of class 'prc'</dd>
<dt>...</dt>
<dd>Optional graphical arguments passed to <code><a href="/link/lines()?package=nestedcv&version=0.9.0" data-mini-rdoc="nestedcv::lines()">lines()</a></code></dd></dl>

Arguments

Add precision-recall curve to a plot — lines.prc

<dl>

<dt>x</dt>
<dd>An object of class 'prc'</dd>


<dt>...</dt>
<dd>Optional graphical arguments passed to <code><a href='https://rdrr.io/r/graphics/lines.html'>lines()</a></code></dd>

</dl>

lines.prc: Add precision-recall curve to a plot

Description

Usage

Value

Arguments

See Also