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@ -29,7 +29,7 @@
<a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
<small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9123</small>
<small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9125</small>
<button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@ -93,7 +93,7 @@ question about the AMR package.</p>
understanding resistance patterns is crucial for managing effective
treatments. The <code>AMR</code> R package provides robust tools for
analysing AMR data, including convenient antibiotic selector functions
like <code><a href="../reference/antibiotic_class_selectors.html">aminoglycosides()</a></code> and <code><a href="../reference/antibiotic_class_selectors.html">betalactams()</a></code>. In
like <code><a href="../reference/antimicrobial_class_selectors.html">aminoglycosides()</a></code> and <code><a href="../reference/antimicrobial_class_selectors.html">betalactams()</a></code>. In
this post, we will explore how to use the <code>tidymodels</code>
framework to predict resistance patterns in the
<code>example_isolates</code> dataset.</p>
@ -110,7 +110,7 @@ antibiotic classes: aminoglycosides and beta-lactams.</p>
microorganism based on microbial data. We will:</p>
<ol style="list-style-type: decimal">
<li>Preprocess data using the selector functions
<code><a href="../reference/antibiotic_class_selectors.html">aminoglycosides()</a></code> and <code><a href="../reference/antibiotic_class_selectors.html">betalactams()</a></code>.</li>
<code><a href="../reference/antimicrobial_class_selectors.html">aminoglycosides()</a></code> and <code><a href="../reference/antimicrobial_class_selectors.html">betalactams()</a></code>.</li>
<li>Define a logistic regression model for prediction.</li>
<li>Use a structured <code>tidymodels</code> workflow to preprocess,
train, and evaluate the model.</li>
@ -140,7 +140,7 @@ package.</p>
<span><span class="co">#&gt; <span style="color: #BB0000;">✖</span> <span style="color: #0000BB;">dplyr</span>::<span style="color: #00BB00;">filter()</span> masks <span style="color: #0000BB;">stats</span>::filter()</span></span>
<span><span class="co">#&gt; <span style="color: #BB0000;">✖</span> <span style="color: #0000BB;">dplyr</span>::<span style="color: #00BB00;">lag()</span> masks <span style="color: #0000BB;">stats</span>::lag()</span></span>
<span><span class="co">#&gt; <span style="color: #BB0000;">✖</span> <span style="color: #0000BB;">recipes</span>::<span style="color: #00BB00;">step()</span> masks <span style="color: #0000BB;">stats</span>::step()</span></span>
<span><span class="co">#&gt; <span style="color: #0000BB;">•</span> Learn how to get started at <span style="color: #00BB00;">https://www.tidymodels.org/start/</span></span></span>
<span><span class="co">#&gt; <span style="color: #0000BB;">•</span> Dig deeper into tidy modeling with R at <span style="color: #00BB00;">https://www.tmwr.org</span></span></span>
<span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://msberends.github.io/AMR/">AMR</a></span><span class="op">)</span> <span class="co"># For AMR data analysis</span></span>
<span></span>
<span><span class="co"># Load the example_isolates dataset</span></span>
@ -149,7 +149,7 @@ package.</p>
<span><span class="co"># Select relevant columns for prediction</span></span>
<span><span class="va">data</span> <span class="op">&lt;-</span> <span class="va">example_isolates</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span></span>
<span> <span class="co"># select AB results dynamically</span></span>
<span> <span class="fu"><a href="https://dplyr.tidyverse.org/reference/select.html" class="external-link">select</a></span><span class="op">(</span><span class="va">mo</span>, <span class="fu"><a href="../reference/antibiotic_class_selectors.html">aminoglycosides</a></span><span class="op">(</span><span class="op">)</span>, <span class="fu"><a href="../reference/antibiotic_class_selectors.html">betalactams</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span></span>
<span> <span class="fu"><a href="https://dplyr.tidyverse.org/reference/select.html" class="external-link">select</a></span><span class="op">(</span><span class="va">mo</span>, <span class="fu"><a href="../reference/antimicrobial_class_selectors.html">aminoglycosides</a></span><span class="op">(</span><span class="op">)</span>, <span class="fu"><a href="../reference/antimicrobial_class_selectors.html">betalactams</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span></span>
<span> <span class="co"># replace NAs with NI (not-interpretable)</span></span>
<span> <span class="fu"><a href="https://dplyr.tidyverse.org/reference/mutate.html" class="external-link">mutate</a></span><span class="op">(</span><span class="fu"><a href="https://dplyr.tidyverse.org/reference/across.html" class="external-link">across</a></span><span class="op">(</span><span class="fu"><a href="https://tidyselect.r-lib.org/reference/where.html" class="external-link">where</a></span><span class="op">(</span><span class="va">is.sir</span><span class="op">)</span>,</span>
<span> <span class="op">~</span><span class="fu">replace_na</span><span class="op">(</span><span class="va">.x</span>, <span class="st">"NI"</span><span class="op">)</span><span class="op">)</span>,</span>
@ -171,7 +171,7 @@ package.</p>
<p><strong>Explanation:</strong></p>
<ul>
<li>
<code><a href="../reference/antibiotic_class_selectors.html">aminoglycosides()</a></code> and <code><a href="../reference/antibiotic_class_selectors.html">betalactams()</a></code>
<code><a href="../reference/antimicrobial_class_selectors.html">aminoglycosides()</a></code> and <code><a href="../reference/antimicrobial_class_selectors.html">betalactams()</a></code>
dynamically select columns for antibiotics in these classes.</li>
<li>
<code>drop_na()</code> ensures the model receives complete cases for
@ -191,7 +191,7 @@ three steps: preprocessing, model specification, and fitting.</p>
<div class="sourceCode" id="cb2"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span><span class="co"># Define the recipe for data preprocessing</span></span>
<span><span class="va">resistance_recipe</span> <span class="op">&lt;-</span> <span class="fu">recipe</span><span class="op">(</span><span class="va">mo</span> <span class="op">~</span> <span class="va">.</span>, data <span class="op">=</span> <span class="va">data</span><span class="op">)</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span></span>
<span> <span class="fu">step_corr</span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fu"><a href="../reference/antibiotic_class_selectors.html">aminoglycosides</a></span><span class="op">(</span><span class="op">)</span>, <span class="fu"><a href="../reference/antibiotic_class_selectors.html">betalactams</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span>, threshold <span class="op">=</span> <span class="fl">0.9</span><span class="op">)</span></span>
<span> <span class="fu">step_corr</span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fu"><a href="../reference/antimicrobial_class_selectors.html">aminoglycosides</a></span><span class="op">(</span><span class="op">)</span>, <span class="fu"><a href="../reference/antimicrobial_class_selectors.html">betalactams</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span>, threshold <span class="op">=</span> <span class="fl">0.9</span><span class="op">)</span></span>
<span><span class="va">resistance_recipe</span></span>
<span><span class="co">#&gt; </span></span>
<span><span class="co">#&gt; <span style="color: #00BBBB;">──</span> <span style="font-weight: bold;">Recipe</span> <span style="color: #00BBBB;">──────────────────────────────────────────────────────────────────────</span></span></span>
@ -353,7 +353,7 @@ antibiotics. The ROC curve looks like this:</p>
<p>In this post, we demonstrated how to build a machine learning
pipeline with the <code>tidymodels</code> framework and the
<code>AMR</code> package. By combining selector functions like
<code><a href="../reference/antibiotic_class_selectors.html">aminoglycosides()</a></code> and <code><a href="../reference/antibiotic_class_selectors.html">betalactams()</a></code> with
<code><a href="../reference/antimicrobial_class_selectors.html">aminoglycosides()</a></code> and <code><a href="../reference/antimicrobial_class_selectors.html">betalactams()</a></code> with
<code>tidymodels</code>, we efficiently prepared data, trained a model,
and evaluated its performance.</p>
<p>This workflow is extensible to other antibiotic classes and