https://github.com/cran/cutpointr
Tip revision: 4408233eb8624dea85ecf18e86d50c296165c3f2 authored by Christian Thiele on 13 April 2022, 17:12:29 UTC
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<h1 class="title toc-ignore">ROC curves with cutpointr</h1>
<h4 class="author">Christian Thiele</h4>
<h4 class="date">2022-04-13</h4>
<div id="calculating-only-the-roc-curve" class="section level2">
<h2>Calculating only the ROC curve</h2>
<p>When running <code>cutpointr</code>, a ROC curve is by default returned in the column <code>roc_curve</code>. This ROC curve can be plotted using <code>plot_roc</code>. Alternatively, if only the ROC curve is desired and no cutpoint needs to be calculated, the ROC curve can be created using <code>roc()</code> and plotted using <code>plot_cutpointr</code>. The <code>roc</code> function, unlike <code>cutpointr</code>, does not determine <code>direction</code>, <code>pos_class</code> or <code>neg_class</code> automatically.</p>
<div class="sourceCode" id="cb1"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb1-1"><a href="#cb1-1"></a><span class="kw">library</span>(cutpointr)</span>
<span id="cb1-2"><a href="#cb1-2"></a>roc_curve <-<span class="st"> </span><span class="kw">roc</span>(<span class="dt">data =</span> suicide, <span class="dt">x =</span> dsi, <span class="dt">class =</span> suicide,</span>
<span id="cb1-3"><a href="#cb1-3"></a> <span class="dt">pos_class =</span> <span class="st">"yes"</span>, <span class="dt">neg_class =</span> <span class="st">"no"</span>, <span class="dt">direction =</span> <span class="st">">="</span>)</span>
<span id="cb1-4"><a href="#cb1-4"></a><span class="kw">auc</span>(roc_curve)</span></code></pre></div>
<pre><code>## [1] 0.9237791</code></pre>
<div class="sourceCode" id="cb3"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb3-1"><a href="#cb3-1"></a><span class="kw">head</span>(roc_curve)</span></code></pre></div>
<pre><code>## # A tibble: 6 x 9
## x.sorted tp fp tn fn tpr tnr fpr fnr
## <dbl> <dbl> <dbl> <int> <int> <dbl> <dbl> <dbl> <dbl>
## 1 Inf 0 0 496 36 0 1 0 1
## 2 11 1 0 496 35 0.0278 1 0 0.972
## 3 10 2 1 495 34 0.0556 0.998 0.00202 0.944
## 4 9 3 1 495 33 0.0833 0.998 0.00202 0.917
## 5 8 4 1 495 32 0.111 0.998 0.00202 0.889
## 6 7 7 1 495 29 0.194 0.998 0.00202 0.806</code></pre>
<div class="sourceCode" id="cb5"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb5-1"><a href="#cb5-1"></a><span class="kw">plot_roc</span>(roc_curve)</span></code></pre></div>
<p><img src="data:image/png;base64,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" style="display: block; margin: auto;" /></p>
</div>
<div id="roc-curve-and-optimal-cutpoint-for-multiple-variables" class="section level2">
<h2>ROC curve and optimal cutpoint for multiple variables</h2>
<p>Alternatively, we can map the standard evaluation version <code>cutpointr</code> to the column names. If <code>direction</code> and / or <code>pos_class</code> and <code>neg_class</code> are unspecified, these parameters will automatically be determined by <strong>cutpointr</strong> so that the AUC values for all variables will be <span class="math inline">\(> 0.5\)</span>.</p>
<p>We could do this manually, e.g. using <code>purrr::map</code>, but to make this task more convenient <code>multi_cutpointr</code> can be used to achieve the same result. It maps multiple predictor columns to <code>cutpointr</code>, by default all numeric columns except for the class column.</p>
<div class="sourceCode" id="cb6"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb6-1"><a href="#cb6-1"></a>mcp <-<span class="st"> </span><span class="kw">multi_cutpointr</span>(suicide, <span class="dt">class =</span> suicide, <span class="dt">pos_class =</span> <span class="st">"yes"</span>, </span>
<span id="cb6-2"><a href="#cb6-2"></a> <span class="dt">use_midpoints =</span> <span class="ot">TRUE</span>, <span class="dt">silent =</span> <span class="ot">TRUE</span>) </span>
<span id="cb6-3"><a href="#cb6-3"></a><span class="kw">summary</span>(mcp)</span></code></pre></div>
<pre><code>## Method: maximize_metric
## Predictor: age, dsi
## Outcome: suicide
##
## Predictor: age
## --------------------------------------------------------------------------------
## direction AUC n n_pos n_neg
## <= 0.5257 532 36 496
##
## optimal_cutpoint sum_sens_spec acc sensitivity specificity tp fn fp tn
## 55.5 1.1154 0.1992 0.9722 0.1431 35 1 425 71
##
## Predictor summary:
## Data Min. 5% 1st Qu. Median Mean 3rd Qu. 95% Max. SD NAs
## Overall 18 19 24 28.0 34.1259 41.25 65.00 83 15.0542 0
## no 18 19 24 28.0 34.2218 41.25 65.50 83 15.1857 0
## yes 18 18 22 27.5 32.8056 41.25 54.25 69 13.2273 0
##
## Predictor: dsi
## --------------------------------------------------------------------------------
## direction AUC n n_pos n_neg
## >= 0.9238 532 36 496
##
## optimal_cutpoint sum_sens_spec acc sensitivity specificity tp fn fp tn
## 1.5 1.7518 0.8647 0.8889 0.8629 32 4 68 428
##
## Predictor summary:
## Data Min. 5% 1st Qu. Median Mean 3rd Qu. 95% Max. SD NAs
## Overall 0 0.00 0 0 0.9211 1 5.00 11 1.8527 0
## no 0 0.00 0 0 0.6331 0 4.00 10 1.4122 0
## yes 0 0.75 4 5 4.8889 6 9.25 11 2.5498 0</code></pre>
</div>
<div id="accessing-data-roc_curve-and-boot" class="section level2">
<h2>Accessing <code>data</code>, <code>roc_curve</code>, and <code>boot</code></h2>
<p>The object returned by <code>cutpointr</code> is of the classes <code>cutpointr</code>, <code>tbl_df</code>, <code>tbl</code>, and <code>data.frame</code>. Thus, it can be handled like a usual data frame. The columns <code>data</code>, <code>roc_curve</code>, and <code>boot</code> consist of nested data frames, which means that these are list columns whose elements are data frames. They can either be accessed using <code>[</code> or by using functions from the tidyverse. If subgroups were given, the output contains one row per subgroup and the function that accesses the data should be mapped to every row or the data should be grouped by subgroup.</p>
<div class="sourceCode" id="cb8"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb8-1"><a href="#cb8-1"></a><span class="kw">set.seed</span>(<span class="dv">123</span>)</span>
<span id="cb8-2"><a href="#cb8-2"></a>opt_cut_b_g <-<span class="st"> </span><span class="kw">cutpointr</span>(suicide, dsi, suicide, gender, <span class="dt">boot_runs =</span> <span class="dv">500</span>)</span></code></pre></div>
<div class="sourceCode" id="cb9"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb9-1"><a href="#cb9-1"></a><span class="kw">library</span>(dplyr)</span>
<span id="cb9-2"><a href="#cb9-2"></a><span class="kw">library</span>(tidyr)</span>
<span id="cb9-3"><a href="#cb9-3"></a>opt_cut_b_g <span class="op">|</span><span class="er">></span><span class="st"> </span></span>
<span id="cb9-4"><a href="#cb9-4"></a><span class="st"> </span><span class="kw">group_by</span>(subgroup) <span class="op">|</span><span class="er">></span><span class="st"> </span></span>
<span id="cb9-5"><a href="#cb9-5"></a><span class="st"> </span><span class="kw">select</span>(subgroup, boot) <span class="op">|</span><span class="er">></span><span class="st"> </span></span>
<span id="cb9-6"><a href="#cb9-6"></a><span class="st"> </span><span class="kw">unnest</span>(<span class="dt">cols =</span> boot) <span class="op">|</span><span class="er">></span><span class="st"> </span></span>
<span id="cb9-7"><a href="#cb9-7"></a><span class="st"> </span><span class="kw">summarise</span>(<span class="dt">sd_oc_boot =</span> <span class="kw">sd</span>(optimal_cutpoint),</span>
<span id="cb9-8"><a href="#cb9-8"></a> <span class="dt">m_oc_boot =</span> <span class="kw">mean</span>(optimal_cutpoint),</span>
<span id="cb9-9"><a href="#cb9-9"></a> <span class="dt">m_acc_oob =</span> <span class="kw">mean</span>(acc_oob))</span></code></pre></div>
<pre><code>## # A tibble: 2 x 4
## subgroup sd_oc_boot m_oc_boot m_acc_oob
## <chr> <dbl> <dbl> <dbl>
## 1 female 0.766 2.17 0.880
## 2 male 1.51 2.92 0.806</code></pre>
</div>
<div id="adding-metrics-to-the-result-of-cutpointr-or-roc" class="section level2">
<h2>Adding metrics to the result of cutpointr() or roc()</h2>
<p>By default, the output of <code>cutpointr</code> includes the optimized metric and several other metrics. The <code>add_metric</code> function adds further metrics. Here, we’re adding the negative predictive value (NPV) and the positive predictive value (PPV) at the optimal cutpoint per subgroup:</p>
<div class="sourceCode" id="cb11"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb11-1"><a href="#cb11-1"></a><span class="kw">cutpointr</span>(suicide, dsi, suicide, gender, <span class="dt">metric =</span> youden, <span class="dt">silent =</span> <span class="ot">TRUE</span>) <span class="op">|</span><span class="er">></span><span class="st"> </span></span>
<span id="cb11-2"><a href="#cb11-2"></a><span class="st"> </span><span class="kw">add_metric</span>(<span class="kw">list</span>(ppv, npv)) <span class="op">|</span><span class="er">></span><span class="st"> </span></span>
<span id="cb11-3"><a href="#cb11-3"></a><span class="st"> </span><span class="kw">select</span>(subgroup, optimal_cutpoint, youden, ppv, npv)</span></code></pre></div>
<pre><code>## # A tibble: 2 x 5
## subgroup optimal_cutpoint youden ppv npv
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 female 2 0.808118 0.367647 0.993827
## 2 male 3 0.625106 0.259259 0.982301</code></pre>
<p>In the same fashion, additional metric columns can be added to a <code>roc_cutpointr</code> object:</p>
<div class="sourceCode" id="cb13"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb13-1"><a href="#cb13-1"></a><span class="kw">roc</span>(<span class="dt">data =</span> suicide, <span class="dt">x =</span> dsi, <span class="dt">class =</span> suicide, <span class="dt">pos_class =</span> <span class="st">"yes"</span>,</span>
<span id="cb13-2"><a href="#cb13-2"></a> <span class="dt">neg_class =</span> <span class="st">"no"</span>, <span class="dt">direction =</span> <span class="st">">="</span>) <span class="op">|</span><span class="er">></span><span class="st"> </span></span>
<span id="cb13-3"><a href="#cb13-3"></a><span class="st"> </span><span class="kw">add_metric</span>(<span class="kw">list</span>(cohens_kappa, F1_score)) <span class="op">|</span><span class="er">></span><span class="st"> </span></span>
<span id="cb13-4"><a href="#cb13-4"></a><span class="st"> </span><span class="kw">select</span>(x.sorted, tp, fp, tn, fn, cohens_kappa, F1_score) <span class="op">|</span><span class="er">></span><span class="st"> </span></span>
<span id="cb13-5"><a href="#cb13-5"></a><span class="st"> </span><span class="kw">head</span>()</span></code></pre></div>
<pre><code>## # A tibble: 6 x 7
## x.sorted tp fp tn fn cohens_kappa F1_score
## <dbl> <dbl> <dbl> <int> <int> <dbl> <dbl>
## 1 Inf 0 0 496 36 0 0
## 2 11 1 0 496 35 0.0506 0.0541
## 3 10 2 1 495 34 0.0931 0.103
## 4 9 3 1 495 33 0.138 0.15
## 5 8 4 1 495 32 0.182 0.195
## 6 7 7 1 495 29 0.301 0.318</code></pre>
</div>
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