plot_shap_bar

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

plot_shap_bar function

<dl><dt>shap</dt>
<dd>a matrix of SHAP values, or the <code>shapr</code> object returned by
<code>nestcv.explain()</code>/<code>shapr::explain()</code></dd>
<dt>x</dt>
<dd>a matrix or dataframe of feature values containing only features
values from the training data. The rows must match rows in <code>shap</code>. If a
dataframe is supplied it is converted to a numeric matrix using
<code><a href="/link/data.matrix()?package=nestedcv&version=0.9.0" data-mini-rdoc="nestedcv::data.matrix()">data.matrix()</a></code>.</dd>
<dt>sort</dt>
<dd>Logical whether to sort predictors by mean absolute SHAP value</dd>
<dt>labels</dt>
<dd>Character vector of labels for directionality</dd>
<dt>top</dt>
<dd>Sets a limit on the number of variables plotted or <code>NULL</code> to plot
all variables. If <code>top</code> is set then variables are sorted and <code>sort</code> is
overrode.</dd></dl>

Arguments

SHAP importance bar plot — plot_shap_bar

<dl>

<dt>shap</dt>
<dd>a matrix of SHAP values, or the <code>shapr</code> object returned by
<code>nestcv.explain()</code>/<code>shapr::explain()</code></dd>


<dt>x</dt>
<dd>a matrix or dataframe of feature values containing only features
values from the training data. The rows must match rows in <code>shap</code>. If a
dataframe is supplied it is converted to a numeric matrix using
<code><a href='https://rdrr.io/r/base/data.matrix.html'>data.matrix()</a></code>.</dd>


<dt>sort</dt>
<dd>Logical whether to sort predictors by mean absolute SHAP value</dd>


<dt>labels</dt>
<dd>Character vector of labels for directionality</dd>


<dt>top</dt>
<dd>Sets a limit on the number of variables plotted or <code>NULL</code> to plot
all variables. If <code>top</code> is set then variables are sorted and <code>sort</code> is
overrode.</dd>

</dl>

SHAP importance bar plot

plot_shap_bar: SHAP importance bar plot

Description

Usage

Value

Arguments