Deploy commit: harmonize with upstream 560b8c3

12-spatial-cv.md

@@ -23,10 +23,12 @@ library(lgr)                # logging framework for R
 library(mlr3)               # unified interface to machine learning algorithms
 library(mlr3learners)       # most important machine learning algorithms
 library(mlr3extralearners)  # access to even more learning algorithms
-library(mlr3spatiotempcv)   # spatiotemporal resampling strategies
+library(mlr3proba)          # make probabilistic predictions, here only needed for mlr3extralearners::list_learners()
+library(mlr3spatiotempcv)   # spatio-temporal resampling strategies
 library(mlr3tuning)         # hyperparameter tuning
 library(mlr3viz)            # plotting functions for mlr3 objects
 library(progressr)          # report progress updates
+library(pROC)               # compute roc values
 ```
 
 Required data will be attached in due course.

spatial-cv.html

@@ -124,10 +124,12 @@ <h2>Prerequisites<a class="anchor" aria-label="anchor" href="#prerequisites-10">
 <span><span class="kw"><a href="https://rdrr.io/r/base/library.html">library</a></span><span class="op">(</span><span class="va"><a href="https://mlr3.mlr-org.com">mlr3</a></span><span class="op">)</span>               <span class="co"># unified interface to machine learning algorithms</span></span>
 <span><span class="kw"><a href="https://rdrr.io/r/base/library.html">library</a></span><span class="op">(</span><span class="va"><a href="https://mlr3learners.mlr-org.com">mlr3learners</a></span><span class="op">)</span>       <span class="co"># most important machine learning algorithms</span></span>
 <span><span class="kw"><a href="https://rdrr.io/r/base/library.html">library</a></span><span class="op">(</span><span class="va">mlr3extralearners</span><span class="op">)</span>  <span class="co"># access to even more learning algorithms</span></span>
-<span><span class="kw"><a href="https://rdrr.io/r/base/library.html">library</a></span><span class="op">(</span><span class="va"><a href="https://mlr3spatiotempcv.mlr-org.com/">mlr3spatiotempcv</a></span><span class="op">)</span>   <span class="co"># spatiotemporal resampling strategies</span></span>
+<span><span class="kw"><a href="https://rdrr.io/r/base/library.html">library</a></span><span class="op">(</span><span class="va"><a href="https://mlr3proba.mlr-org.com">mlr3proba</a></span><span class="op">)</span>          <span class="co"># make probabilistic predictions, here only needed for mlr3extralearners::list_learners()</span></span>
+<span><span class="kw"><a href="https://rdrr.io/r/base/library.html">library</a></span><span class="op">(</span><span class="va"><a href="https://mlr3spatiotempcv.mlr-org.com/">mlr3spatiotempcv</a></span><span class="op">)</span>   <span class="co"># spatio-temporal resampling strategies</span></span>
 <span><span class="kw"><a href="https://rdrr.io/r/base/library.html">library</a></span><span class="op">(</span><span class="va"><a href="https://mlr3tuning.mlr-org.com">mlr3tuning</a></span><span class="op">)</span>         <span class="co"># hyperparameter tuning</span></span>
 <span><span class="kw"><a href="https://rdrr.io/r/base/library.html">library</a></span><span class="op">(</span><span class="va"><a href="https://mlr3viz.mlr-org.com">mlr3viz</a></span><span class="op">)</span>            <span class="co"># plotting functions for mlr3 objects</span></span>
-<span><span class="kw"><a href="https://rdrr.io/r/base/library.html">library</a></span><span class="op">(</span><span class="va"><a href="https://progressr.futureverse.org">progressr</a></span><span class="op">)</span>          <span class="co"># report progress updates</span></span></code></pre></div>
+<span><span class="kw"><a href="https://rdrr.io/r/base/library.html">library</a></span><span class="op">(</span><span class="va"><a href="https://progressr.futureverse.org">progressr</a></span><span class="op">)</span>          <span class="co"># report progress updates</span></span>
+<span><span class="kw"><a href="https://rdrr.io/r/base/library.html">library</a></span><span class="op">(</span><span class="va"><a href="https://xrobin.github.io/pROC/">pROC</a></span><span class="op">)</span>               <span class="co"># compute roc values</span></span></code></pre></div>
 <p>Required data will be attached in due course.</p>
 </div>
 <div id="intro-cv1" class="section level2" number="12.1">
@@ -395,8 +397,8 @@ <h2>
 One of the most popular measures to assess the predictive performance of a binomial model is the Area Under the Receiver Operator Characteristic Curve (AUROC).
 This is a value between 0.5 and 1.0, with 0.5 indicating a model that is no better than random and 1.0 indicating perfect prediction of the two classes.
 Thus, the higher the AUROC, the better the model’s predictive power.
-The following code chunk computes the AUROC value of the model with <code>roc()</code>, which takes the response and the predicted values as inputs.
-<code>auc()</code> returns the area under the curve.</p>
+The following code chunk computes the AUROC value of the model with <code><a href="https://rdrr.io/pkg/pROC/man/roc.html">roc()</a></code>, which takes the response and the predicted values as inputs.
+<code><a href="https://rdrr.io/pkg/pROC/man/auc.html">auc()</a></code> returns the area under the curve.</p>
 <div class="sourceCode" id="cb421"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu">pROC</span><span class="fu">::</span><span class="fu"><a href="https://rdrr.io/pkg/pROC/man/auc.html">auc</a></span><span class="op">(</span><span class="fu">pROC</span><span class="fu">::</span><span class="fu"><a href="https://rdrr.io/pkg/pROC/man/roc.html">roc</a></span><span class="op">(</span><span class="va">lsl</span><span class="op">$</span><span class="va">lslpts</span>, <span class="fu"><a href="https://rdrr.io/r/stats/fitted.values.html">fitted</a></span><span class="op">(</span><span class="va">fit</span><span class="op">)</span><span class="op">)</span><span class="op">)</span></span>
 <span><span class="co">#&gt; Area under the curve: 0.8216</span></span></code></pre></div>