https://github.com/cran/cutpointr
Tip revision: 4408233eb8624dea85ecf18e86d50c296165c3f2 authored by Christian Thiele on 13 April 2022, 17:12:29 UTC
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<body>
<h1 class="title toc-ignore">Plotting with cutpointr</h1>
<h4 class="author">Christian Thiele</h4>
<h4 class="date">2022-04-13</h4>
<p><strong>cutpointr</strong> includes several convenience functions for plotting data from a <code>cutpointr</code> object. These include:</p>
<ul>
<li><code>plot_cutpointr</code>: General purpose plotting function for cutpointr or roc_cutpointr objects</li>
<li><code>plot_cut_boot</code>: Plot the bootstrapped distribution of optimal cutpoints</li>
<li><code>plot_metric</code>: If <code>maximize_metric</code> or <code>minimize_metric</code> was used this function plots all possible cutoffs on the x-axis vs. the respective metric values on the y-axis. If bootstrapping was run, a confidence interval based on the bootstrapped distribution of metric values at each cutpoint can be displayed. To display no confidence interval set <code>conf_lvl = 0</code>.</li>
<li><code>plot_metric_boot</code>: Plot the distribution of out-of-bag metric values</li>
<li><code>plot_precision_recall</code>: Plot the precision recall curve</li>
<li><code>plot_sensitivity_specificity</code>: Plot all cutpoints vs. sensitivity and specificity</li>
<li><code>plot_roc</code>: Plot the ROC curve</li>
<li><code>plot_x</code>: Plot the distribution of the predictor variable</li>
</ul>
<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></span>
<span id="cb1-3"><a href="#cb1-3"></a><span class="kw">set.seed</span>(<span class="dv">123</span>)</span>
<span id="cb1-4"><a href="#cb1-4"></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>
<pre><code>## Assuming the positive class is yes</code></pre>
<pre><code>## Assuming the positive class has higher x values</code></pre>
<pre><code>## Running bootstrap...</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_cut_boot</span>(opt_cut_b_g)</span></code></pre></div>
<pre><code>## Warning: Removed 1 rows containing non-finite values (stat_density).</code></pre>
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" style="display: block; margin: auto;" /></p>
<div class="sourceCode" id="cb7"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb7-1"><a href="#cb7-1"></a><span class="kw">plot_metric</span>(opt_cut_b_g, <span class="dt">conf_lvl =</span> <span class="fl">0.9</span>)</span></code></pre></div>
<p><img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAYAAAAEgCAMAAACKBVRjAAABU1BMVEUAAAAAADoAAGYAOjoAOmYAOpAAZrYAv8Qos7YzMzM6AAA6ADo6AGY6OgA6Ojo6OmY6OpA6ZmY6ZpA6ZrY6kLY6kNtNTU1NTW5NTY5NbqtNjshmAABmADpmAGZmOgBmOjpmOmZmZjpmZmZmkJBmkNtmtrZmtttmtv9uTU1uTW5uTY5ubqtuq8huq+SOTU2OTW6OTY6ObquOyP+QOgCQOjqQZgCQZjqQZmaQkDqQkGaQkLaQtpCQttuQ27aQ2/+rbk2rbm6rbo6rjk2ryKur5P+2ZgC2kDq2tpC2ttu225C229u22/+2/9u2//+84uO+0M/C6OnGhX7Ijk3I///L3dzM8vPbkDrbkGbbtmbbtpDb27bb29vb/7bb/9vb///kq27k///r6+vu1NLz2df4dm3+5OL/tmb/yI7/25D/27b/29v/5Kv//7b//8j//9v//+T///87YnuoAAAACXBIWXMAAA7DAAAOwwHHb6hkAAAYyElEQVR4nO2d/X/ctn3HKTs+eidlXZspXas0XdtlyprZ66xui5dW6bZeuzbzaXSsW7IlTuRR5/Msy/f//zQ8PwMECPBAyvi8XrYkggTB75t4IPAFUG2LsqrKnYA3XQVAZhUAmVUAZFYBkFkFQGYVAJlVAGRWfwCL7/yK/Pbljw/FgM1fPiK/XZ/cedoZj9dJ26ujmXfKeAKcd7Kd5p+oFIoAUO2dol8280oCsLqVGQBPgPNOttP8E5VCEQD23sZ2X92eWwB4aXcAAk6bBIBbH6NEXp/8DAF48X5Vfecz8GdVVYfXJ7NVdeuP6DG+mFdvnZKL8EMja37xdlXt/RV+Vvy84CIeD/jzH8EJf00uBJesQTzg+Lo6Rn9T6CR6FgVOAAhf0/vCGOGV5E6/fxveF58WnKjkigHwh334fJuDP0AAoCAC2julAG7Pqztfw0dY4+P4os0cWg/acFUhHavPSuPBFqqQtbfQPLfn+DjOC2sa45qdrgD4Ljl/u54Lp5BYq2MBQECi0isGwH+cwAdYzDYAwPUJfFm/nM/wCwUe75C+RyDdX9BaAj/U4taj65Nbn8EHmynPyuPZzN95ik5Aujqq/vzp9e+qGboa/EeKCBa9YC6YAHD+DJ8PkgJiXFX4LuCv2VPw10woggISlV4xAB5BK1wdHUMAG1wPwIfCAODTwUfYKBUEe4e3//P7f5pX6rPyeDbz2z/7I7vu6gifAaKFryorgVj0GgBEaAHjQXdbwMwJ7wQThlLA6wD/RPW2llVRAGA5sIaPeIhLApxNMQCceAjgWLwKn3tMc7X2rEI8C/jze6TgJRanplrLZdrWAGBGzidnrCkA6bTgRCVXFABolcVsGwIAHlrgIuK7//zvXx25nvVLUPNVxEoCAJr3kJIACElUakUBgBUxeDxcBDE7qwCkIgiY5NdHM2o4Wtzich0aTeH13/9AamFsUFyArKsPWBuIR7+QyhZeZKlFkAFAYKKSKg7Aeu8n4H9SCYMP4xfQQCuhTUIq4c0Ra1Zv5j/FmR3UhaBxR0yyqFDDcCbEswa17vb6d+StAy8nuAmqhME5f8reRR49i4KUU7TSVithDoC/0N6J6m0tq+IAwLYGeQtJNj2EzwqboRQACWCvEHgiXD/j88mzopPeen8mxEPOIORIsxKbYFVxU7DoeRQr1Ay9Tc9XmqEcQMW+4L0TlV5xAMBLckyLgQ0osW/D76ar96vZ1xwA/FJi31PbLW4Ckq+jX6G2Hzzpv+bV975G3zwsHvQh9g7JOfjD6s9wlbwRG4QsehYFTMBX6MMNV+Eb+UOMAoCnPQ1NVHJNsjd0XXWWySFdF1k1RQAv3u/uqCkABhOodzwK4wJgMIGa8p3uswqAIj8VAJlVAGRWAZBZBUBmFQCZVQBkVgGQWb0BbPZ7jE6EXATPfTGII8KotNscEAigF+SJqQDIrJgiaLP/yZwPtaABbTqwfbjFgYcbdMZm/yEaWoEWhaMeZDxlc/BxBfve8QESAbI6i/2QxXtDFQVgDmxKR1ax3ebHV0fHxKsEBlb4jM0c2nkGT8KDUbg/GQ2s0AM0Ag4A5QB6ONHzjk5xAI55qbI5wCD+76kYSM5gP/dP13ikGxkUHaYHaAQqgIMBBmJHpLgi6FQo1hcVHtZDA6h7QiA9Exp5/3QlDK+iw+wAiUABwOO9mUoBYEUMenVUQV+hvVOZjgJAGM3CAPgBFIEKgByOecgxK10OgAI2XkN7rtUcgIqgA2jcteDdhILFAwSSAoAVWTdRyQCgopwYeDPXAAiVMHT4wEZHwfQAjQD6wEFvH/TrMYs30fOOTulywJr47oEye+/X/EUmZz5EJTlthtK5NehaeoBGANudH7yHYl9UM3b4hmo3H2I3+A2OVQGQWQVAZpXu6MwqADKrAMisAiCzCoDMKgAyqzeACybhV02OsJ6XDRClKSiljZ0qAAqAdGEFwA6sVQAQ5bJWAUCUy1oFAFEuaxUARLmsVQAQ5bJWAUAU+NhtKmsVAERhj90+a1UEBQDSbgC0z4AUBAUA0k4649pLrLbdxd2mpR3kgBa9/1hCLig5AGl4AIL5JQTaZS2nUwB0y/exFfszBDUXsj3KJ1Y4/S3Z87KUNnZqaAC6/aGdgdWX50uspuEoCgB/+T22yf7A0GdNAwE0UGeCKIECoFtej9ZemqyPpNoeAyCFUAHQLZ9Ha59dmq0P9UQzP88CBUC3PB4NlD+XFuvbABACBUC3Oh8NNWsuuflVUxcASIMBwNUvBVDrBb4ZACZQAHSr49FI8+eS2V8jYAMA6+ECoFvu9NPm5yW3vycAlAUKgG4508+a/whAfRYCABIoALrlTL8EANjf1Oq3A3jWFgDdcqZfBMDqX4WAFQDIAgVAt5zpFwDU3O4yATsA1iORwpI9L0tpY6eGBlCLVpcIuABcOggUAFiu9PMuOOXzSyTgAODMAgUAliv9FEBbq1YWCLgAPHEQKACwXOlvqf11K3MCbgDPrATeJAAvf/45/PHNu1AP0M+/+JyEudJPnCBqk5UZAScARyH0BgF4zs29ff6Db7ePHwiBrvTD8cUWlf8GKzfMyC4AdgJvDoDH3/+3n1MAr/7ut9vXv/mtEOpIPx7hrW1WbgoAQT5FENA3PwQQ7qOCCOhPgByXIQD1E6sa2/FGDKnfDCciPwAwA2xf/o2YCxwvELK/6zVvjEFChxEMs9XDb2QOgDUAFqsHHOnn9reVM40HAFsh9EYCeHyPHvICwL+/bAV9Iwc1TOJlRgLQjaiHkacNABc8MBu8/hePZijPAPaalhtb+k26TCuEauRPZEdwIwHAf6gKQN8D32cNoQQAGn2YQG4hiVkAGR/qfLm0IZg8gOuTQ7KHerdSADAcli8jBJjxCQAbgskDWOC9Sr0IuADwLrhQAIQAA4C8GLnxGQAzgqkDIKtzrr1WSY0EcGa2vwLgrK6bpSLqU2pAUABYARis7Wihyi0kGwAdwdQB4JXQr468ts21p19ohWJLgpd9ufT1C9I/0horABXB5AHgjYv9ti32BYCsj9zQ/QAYPtIaOwAZwfQBBMgBgBdBzPoBANSPtA4AIoICQALQ4EkANgL+AGQCGgCOYPoAVlV1vOreLRmqAwB59yVr+XpFNFqYSMAAgCKYPIDFna+OjqO/A1o0AcZkLU+vCB2ASMAIACOYOgDQDGULxnfKDcBsLV+3lMYwktkFACIoADoASATCAFijVHPBlAFsV7AIiv0OgK1Qq7U83VKMnRTdAM4dCCYBIMl3AKiDHeWFp1uKg4ATQK+e0hEBCFBPAJxAMABCoANAj57SNxPA3bt3HQCMhdCZ3rI1AQjuKR0RALSnl9+GRXYAtbvJ0lD7OwhEAgjsKR0PgJDOOKva+klz7hLyPUEA7J4rFt8Vd8SS6qiH2J3Sd0d35QCUB+525QBzFoAxe+UAmAmmlwN2BWDZ3MUIXACsBHwB+HdVjwYA3l8t8jsAfAZ09BvUNe3ztyKwAzhrvAHImWAKAK6OKqruTGAF0DoB4BFe1udvIYDHcSwE/AGImWAKAILUCwAbX2ddzgIBYZQYh1mrAW8AQiYYBECC7bnSA6hNXZfoxRfdG1iP511ie9SBKjtmmQE8sRMwF3gTAoB64zy3jg0CgNfDEu3Cu5wbYdhMmTtgJBAIgGaCSQBYzOCnwCpqPAAC4MY+5+aXvXuWsMcTv/hyxkgPYNk1WBMAQNv3/mEFd22H1efeJwePNgcfV3DfWLZzqXRSJ4ArPBoT1wwFVQAxN1sJjvyqmEV48XmQ4hlnch4KB9A1WOMPQNv3fn7nKTQZbDleHd16tJnPsGcb2btX2svXCwCMKQ0AbHCpDrBbSyXwhNnftMyNlYC9hVonAqDue482qt7HeyGvIIBj8h21FrYGJyd1A7g+OYRfAouYIqjlOcBtEuWzdWAAwWM1lqdW970/QH03eBwd7xgObI/+4Nst0/3cuwHAEm62XUQNyqNWqFIH+FhLJpAewPLcTiCoEpb3vU8MIEROAD4msXwiSJ5xKQHYM0FgK0je954XQbTUQf0JWhF0YCnXzQCuftT3Sxi1QvsAwDX1UvhKJgx0AFYCbgBa/1w4AG3fe7UShjanlTA/ybcSHgGAWgOgEYgAYCEQkAO0fe8f4koBNkMfEgBb0gzVTtoRgGYZB4B3U5gJxAAwF0OJ+oLWlsrT/bU2AQD6OkP9ARgzQTwAWOxbS5nJAGCNJ9UzTndptBDwAeA5WuljdkFwLNdWyuwUgNIICgOwJOg010TNoy4GgJ4JRtgdnRfAUvOMUz3q4gComWC8AOg8YbJOzav777IZ8zYAciMoGADJAo5lhshgTRQAJROMEAAWW64GT49//ekDtGgH1pAA9PlMqQF0jlYOa3VBjvEAtlwNWaHj1S8+F9ZPsQGQ6uBwAPjicweBNAC6RivzAFDHA4i5yTo1Lz/6lsyZty9XAwCcn9f+3jsmhx7mO+TwEwpwEErpOdRaFB4Tk3M8gAAg69TAtSLIogVbVw6oHe+dR1CNwvRxAGW4MjYHLHkx5J8D2v81KiEAeTyAFziwHuA5YFAA8Po3FIA2HiAD8KgD1FZofwB2AukALB3j9VkAaOMBxNxknZrXn97ragWpjaBeAJa1GYC0jMHNBKCKLldD1qnp/g5IA2BZwzAbATpYkwCAw2FiHABcsgCQW6FjB2B3mDA/9A6KID+/RBeAuvuxO61FuoSMBG4wAN+1mvIBEGfRGwiEA0AEUgG4PrHPr/MakrQOHO8cwLmDwDgAgK8vDYDVyL4Ark8mAIAv8ZcEwNLis9UBAH3/KgDgsOQp7sq5/uXDqjpcoxmnsFw/RgCMbp/y/AA/t1ArAO0zoDcAey3ACCQCYPHZsgHQuyHkHLA43K6IY9x8Bo9cvXeKHFZYmAMAnR/QuxLWGkH9ASypg4QVgIHADgA4cgA199WPHl3/8nQL/8E/t6h3n4c5AATJBsC4wl5PAKbP4eQAlsYlJjoA2OoA9A7vnUoAFvCV5mFTAeDIAtZaeIcATK0gaGT8hgsAYMsGF0HmYcak6wUlB2AgwOf2pQJg9BbqCQCW8+s7TwUA8CBxYsRhLgCx6wUlBWAmIE5vta9omQ8AagqJOQC+1G+9f8zDHACil6tJC8BIYAgAJr/dPgB6KTUAZRpAYgB374ozvJMBMOSBLABi1wvSM0AcAIUANjz4vyETjG8egMj1goYBgAgIBQ9exgD97VhUN+R254ZqIBOAAO0CAJ3WLc2oZ+tIgKzQI0pzUAFgDCOTzXTnXFoLmxdW7wFAd1c0amgAkfOEhwGwNKy2yL/FJAQRALTFVYyqLUoFIGSesEGwEZTAX0fw3IGCv6huQo3gHlSnuaeXEev/NCoVgNh5wnorNE0dsNRWW5R7I3zmZHanpPbJAYMDiJonrJdAsQCYnACWdIrf1AHEzhMeEIC63KXeIYc2WAqKUg2qswOInCdsqIPTAVCWuzT2iOqz8cNSUmcHEKJoAKHWajoBwC3GgqJUgkYEoNcMGUMd7BiHqrcOAp0AzoyDMudsYSI9bp+8WN8YAF1LFeDlSwJf18YAwLS3g2lxnEwAXH4SQwBgxUp9wbe9M5sfXWYtMczWavwAGBfH8auNam8A4A4jBoCeo67Njy2tIWYhYLGW5Jo4BADvHIBuoQIIcEYZBACvg4mJjQ1DZRU9MwF/AMbdTaD9VQSJAOjdEAIAf2eU3QBAm0Aq5tdmZhkJ2KzV+AGg9xoAgDsHeDujDAFArIOFMNEM0vxcxi0AgLiMgaEMUi8L9tP2BmCsAwKcUYYFoGytRhHYpkcHtRmbEABiJkgMwNQKCnBG6QLgIX8A2MTaEg1CDAE1pgagY4OlsLW7kgHockaxAIiaH6A0gmQrG5aJEd2hAj6g+Sx6PQtYGr0ZAHQ5o5gBxM0PYAD6LJnt32ThnUKeAOjc7+EB9FK6+QGmEsh/sUj/scVwACgTTAFA3PyAOAAX/mOLdgL2y+p6CgDi5gdQAL23TfBbYqIfAGdf9XgAxFTCbTSAC68245LPog8CcG5HMBoA1yeKTxadF//yQ7hYB102aDAAnIAfgDN1qkzXGE9nz1NmAGolTNcLggtEwAU78LJBVgCGRlDois0ejXYdgNc2k8T3pKPjoxPAsG4pSiXM1gt6DtcnePyALBtkBWCw/zAArGWQl6tRDIABJNcBSve1sFgHyAVk2aCteb2glvjnxO3f5efj0yj+QU3I8kEuP6KotPeTc5IeBwAX6iDLBpEj6qtsrAJCcwDNAp45gK+j1bnNpBhm/+7OnQNUMQCv7t8jh1g9YAEQt30aJdBlyUYFgPfw9nW0sLqUjhXAyw9Z9Ts0gAsvDys9C5x1bLKnjxWY1tjPDMBWBBH7k2WDzADanQIwZAGYCUJcjYy7TGQGgEXGc6DIekGw/Q+rX7JsEJYGwFQF9ABwYRlK7gbg3GJsQgCcvddcZgA+qwJ3Bbl7zlwAnjR2BFMC0KcrYscAzAQcC6ubRsvGCsDPOVSxpLkK6AXgwsfL1gbgzJYJzIM14wJAKmHX+A2XEUDsPtb+ACgBDUDQqrojAxCkAQFcuDrvFQDawuq2TGCOUmlzTRmAsQTqCcDhNeoDIGQtIbnNlRtA/0l6ljp4QACmMoiveu+/kEc9JgD9N/PEANyuDwEAfFzXDVlA3P3Kew5xPR4AEZP0UgPwcF13A9AygWO8eEwA+k7Ss9TBQwIwlEHK3j8mz2kjgbEAiJikhwB47hDlBcBOQAWg7u1gyQRTABAxSQ/tI79jAHoW0Lee8RovPmf3yg0gRAoAcwkUAcBKQAfAVpEDAOgorZYJnH5B9F7TBuC7S6AngO7ZM8QpohGHx58RMQg0E7gds+qJA2h3DkA0etu22OqXzyQRBp3DldxjpQCQwowEiNHRCcjuaA1VIwDKoGu4EnusTBzAcgAAGgFkeeE6bHdof4xAB0AKI/dwJclTUwaAliwOtHIgAPbai9e13M4QgREAgdDtnFtPC4AktHtYkpiUeKkrj8X5rL0UJP2hqe5cV6jO7hcUJDkHWKqAyBxwUQtvvvG6VnzP29aWA2Dx1F6Ypg8LOUC6VUobO5UMgP9u4QEAuqY2yQCAke0IAAAcoX1KYC0QSGljp0YOoCtMAeDIBZfPWnKNxmD6AFpbCTQ4AJkAqoQtBCAcdpVcGBUASQFYMgEMa8UruV8KayBNtg6AixQEWzINAIkAbYaaEGgAkOpaygzCXXalGwnAhAAXT+YYGQbh2K6UBoCtDs4GQEdAiidHjFKDN6WNnUoEwGL/HQAQCchfwloLyUbAdLeUNnYqDQBbBsgKQM4EBUCMkV1hdgASAlZB+90tpY2dSgGgzQrgQjeyCQEL0wlMHkDd2OyfHwCrCoQWks/dUtrYqSQArHXwTgBwAubuaHWsoADoY2RXGBsTs40HoBGb1krgBgDoY8l0AJDY5poWBA4CBUCqMBTUmlAof7f6ZWpUu9LNA8AlkGhdWWD6AFJYK0WYLUjAYCIwdQDt6AHgMMJAJzBeAHSm/Kv77/7gW/YDi6fVUQKNCsAFygo0G7RKkHr6ruQCQNcLev3pg+03P6Q/iFhSHY3Q0QG44AxgoeS4bHDDUzkAsPWCXv0CTZgnP0goTWlduwiMD8AFY8AIjBUAXyvio2/hekHkx1ZeLyh2xag8Is2j3MnY+gGAq3QAy5MfJIy+KhPMAUiTzgFQLKkTqwOYxl8HMADOOmA31ho0ytEDgOuVoVbQPVMrqACIUicA+M/9HVAAxCjlfsI7sVYBQJTLWgUAUS5rFQBEuaxVAGjSFtEd9LJd325oFQCZVQBkVgGQWWlmSRb1VgGQWQVAZhUAmVUAZFYsAKmH1F/Phf2YQvT6U2EBd3+9/LDf7XagSACyp4S3YB93j8vQ/hHPw4G/ug9S2ec92YEiAcijZEHqcxm8XZ9bSYOp41IkgIhH65MDXn70r32KoBsMQPaUCNDLD/uV5Q+QNQOFiqA+99uB8uWAPpf1vR2ohP/2NzcSQEQdIO+M6Hm7v+/Pu1/1MbiiW0H3+hTmvUuux/2KIHCrXo2uHSjTd4C0IVPY7fo06MFnx0hboeVLOLcKgMwqADKrAMisAiCzCoDMGhuAlbqT3IvPtHM2+6eu4GlpXACuT+D2HauK720vGtugjuAJaFwAyPYpK76LSgGwS10dsVcfWXaz/8m8qg43+w+rCjK5PqmqGQrDIccbGJwzxfEaFQDhfSYATuHPzfzWI7jBGf0HD88BkNWtRyUHJNXmgO1gJgM4Rr+jDc7WyOr0UAGQVLYcAH8HxRPa6hsfE4OnrVEBoHXA1XunBUAWkeYPLdylIujgEdzmjxZBBcAg4t8BcFPL65O9U5gpTJUwASC0myaqcQEAXwL0Sxi2MD947xQcmKFmKNzgUmyGYgAwOHeS4zQ2ACZNv5xxqADIrAIgs6YA4EarAMisAiCzCoDMKgAyqwDIrAIgs/4faYKTWpt1zOgAAAAASUVORK5CYII=" style="display: block; margin: auto;" /></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">plot_metric_boot</span>(opt_cut_b_g)</span></code></pre></div>
<pre><code>## Warning: Removed 8 rows containing non-finite values (stat_density).</code></pre>
<p><img src="data:image/png;base64,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" style="display: block; margin: auto;" /></p>
<div class="sourceCode" id="cb10"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb10-1"><a href="#cb10-1"></a><span class="kw">plot_precision_recall</span>(opt_cut_b_g)</span></code></pre></div>
<p><img src="data:image/png;base64,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" style="display: block; margin: auto;" /></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">plot_sensitivity_specificity</span>(opt_cut_b_g)</span></code></pre></div>
<p><img src="data:image/png;base64,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" style="display: block; margin: auto;" /></p>
<div class="sourceCode" id="cb12"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb12-1"><a href="#cb12-1"></a><span class="kw">plot_roc</span>(opt_cut_b_g)</span></code></pre></div>
<p><img src="data:image/png;base64,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" style="display: block; margin: auto;" /></p>
<p>All plot functions, except for the standard plot method that returns a composed plot, return <code>ggplot</code> objects than can be further modified. For example, changing labels, title, and the theme can be achieved this way:</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">library</span>(ggplot2)</span>
<span id="cb13-2"><a href="#cb13-2"></a>p <-<span class="st"> </span><span class="kw">plot_x</span>(opt_cut_b_g)</span>
<span id="cb13-3"><a href="#cb13-3"></a>p <span class="op">+</span><span class="st"> </span><span class="kw">ggtitle</span>(<span class="st">"Distribution of dsi"</span>) <span class="op">+</span><span class="st"> </span></span>
<span id="cb13-4"><a href="#cb13-4"></a><span class="st"> </span><span class="kw">theme_minimal</span>() <span class="op">+</span><span class="st"> </span></span>
<span id="cb13-5"><a href="#cb13-5"></a><span class="st"> </span><span class="kw">xlab</span>(<span class="st">"Depression score"</span>)</span></code></pre></div>
<p><img src="data:image/png;base64,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" style="display: block; margin: auto;" /></p>
<div id="flexible-plotting-function" class="section level2">
<h2>Flexible plotting function</h2>
<p>Using <code>plot_cutpointr</code> any metric can be chosen to be plotted on the x- or y-axis and results of <code>cutpointr()</code> as well as <code>roc()</code> can be plotted. If a <code>cutpointr</code> object is to be plotted, it is thus irrelevant which <code>metric</code> function was chosen for cutpoint estimation. Any metric that can be calculated based on the ROC curve can be subsequently plotted as only the true / false positives / negatives over all cutpoints are needed. That way, not only the above plots can be produced, but also any combination of two metrics (or metric functions) and / or cutpoints. The built-in metric functions as well as user-defined functions or anonymous functions can be supplied to <code>xvar</code> and <code>yvar</code>. If bootstrapping was run, confidence intervals can be plotted around the y-variable. This is especially useful if the cutpoints, available in the <code>cutpoints</code> function, are placed on the x-axis. Note that confidence intervals can only be correctly plotted if the values of <code>xvar</code> are constant across bootstrap samples. For example, confidence intervals for TPR by FPR (a ROC curve) cannot be plotted easily, as the values of the false positive rate vary per bootstrap sample.</p>
<div class="sourceCode" id="cb14"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb14-1"><a href="#cb14-1"></a><span class="kw">set.seed</span>(<span class="dv">1234</span>)</span>
<span id="cb14-2"><a href="#cb14-2"></a>opt_cut_b <-<span class="st"> </span><span class="kw">cutpointr</span>(suicide, dsi, suicide, <span class="dt">boot_runs =</span> <span class="dv">500</span>)</span></code></pre></div>
<pre><code>## Assuming the positive class is yes</code></pre>
<pre><code>## Assuming the positive class has higher x values</code></pre>
<pre><code>## Running bootstrap...</code></pre>
<div class="sourceCode" id="cb18"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb18-1"><a href="#cb18-1"></a><span class="kw">plot_cutpointr</span>(opt_cut_b, <span class="dt">xvar =</span> cutpoints, <span class="dt">yvar =</span> sum_sens_spec, <span class="dt">conf_lvl =</span> <span class="fl">0.9</span>)</span></code></pre></div>
<p><img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAYAAAAEgCAMAAACKBVRjAAABIFBMVEUAAAAAADoAAGYAOjoAOmYAOpAAZrYKCgozMzM6AAA6ADo6AGY6OgA6Ojo6OmY6OpA6ZmY6ZpA6ZrY6kLY6kNtNTU1NTW5NTY5NbqtNjshmAABmADpmAGZmOgBmOmZmkJBmkNtmtrZmtttmtv9uTU1uTW5uTY5ubo5ubqtuq8huq+SOTU2OTW6OTY6Obk2ObquOyP+QOgCQZgCQZjqQkDqQkGaQtpCQttuQ27aQ2/+rbk2rbm6rjk2ryKur5OSr5P+2ZgC2kDq2tpC229u22/+2///GxsbIjk3I///W1tbbkDrbkGbbtmbbtpDb27bb/7bb/9vb///kq27k///r6+v/tmb/yI7/25D/27b/29v/5Kv//7b//8j//9v//+T///90IyxrAAAACXBIWXMAAA7DAAAOwwHHb6hkAAASN0lEQVR4nO2di3vbthHA6aaVJnuvZnaausu21OmWNesab2s3td2ibolae+k6J5aVWXb5//8XA0CCL7wOLwKQcN+XRCFPB+h+PAAkeEBRZgkqRegK7LpkAIElAwgsGUBgyQACSwYQWDKAwJIBBJZUAWyOJ2Dd9a9ecI/fnr5zAVDzK7sA4PwtEACRml/ZZQBmao7FAMDtn4ti70N6Ad2eTsr17PDlbO835WpWvHvBUy3LNw+K4qfPyZf++eMC6XZPMvqb40Nk6+1595tl+ZIeKgkArIGOr4oT8v/D+kyt1dTu9rQoisOhxberulw09anUeJXyK/oASE0L9Ku7AH4+Q/X+HP1VTHiqSAN/2JvTQ+Tr9CSrvznGBrF++03k6foDls3xnVqjioUVPbFqChoAaCyuZh2Vpj6VGqdSnkUfwHp29wL9NekBKD4sX+ILaD3rNqsdVXxVfUc+FZOL8ryYtCc5+ptjpHX7ddH/JvLZy6K+0JHGLyqNcoGbjkVdbqPV1o60LV2LqLKoBuTqv2jrQ9Q4lfIsJgDu/Ppf+EMXwAS7BP233681qqiNwv+gn3h7it2Fr9rmJEef2CKubb9Zf6ql0iDWViRkDqmJ+gMDoLVI3LvYm1cAaH1qAEylPItBH7DAQfru834fQLvFRW9gQVVXVZwX1Y+u/UJPcvTrLvZ8b95+cz3rNgy1x8/rNqhpgRotBgC1WGusKICeGq9SfsVkFPQd6sQKfDErAVBVLgB6kqOvBQBf1k2xtgB4lfIqhsPQ//wJxf2ibU1EAGrV1nldANQOq982MO03h03QpKybIBRfD5sxUKu16LUtXYv9JmgAgF8pf6IPYIV6v/L2a3TpLQoyehMDaFRR1/hXNPw77YZNa4fVb7vY7jeR6vq4to80UEdLOmGk85PGSqvV1K5up1qL/U64BYDUOJXyLMbDUFRv0jy8/UAMYKBa9AYn7UlWvx5kVpd3/c36U31t1sPK6qI977QZjVZTO3QaD0Nbi/1haAugHYYyYexPTG/E7uIq/hvdeP1X1ge0qmvUtN7p3b11TjL61W1W1RfSb5JbrOYeqbqx+lnVW/bGjY0WrV25eVBMvh9Y7NyI0fpgtQtOpfxKnI8idB40lPRm2KXF8WQbALx5oG4ydghA9eyg+9hAX1/HXah7LQ4BWhmAhr6Ou1C/eVettUMAsmhJBhBYMoDAkgEElgwgsGQAgSUDCCwZQGAxmJLcN3hWq/MlrPtmxDmpsDJSBGgCMIKcpmQAgcWoCVrv41eA2rnCon3357CsTh6uicZ6/zMyvYE9iic76omT9cFfyKRydaA2QLzeWD9s7G63mAHAb//QKdTKb7OTzfFJ/foIPllUGusZ9vMEK1WzTtWDYzKDQg9QAy0AEgH0sNvfG50YAjhpW5X1QQXifxfdk7VG8+/+fFVNkhOHksP0ADUwBHAQ4lXN0cWwCZp3mvUFfR9xVb0+0pykmtjJ+/PzZnK3Pt0cqA0MALR2t1qsAJzXDt0ck3d59uZ9OgMAnWmrCkB7gBgYAqgPO/iRMYuDCMCCfLzC/lwNI4A0QQfYuavO/Aw53T1QQxoAaJqsLRZ7AKQprx28njEAOp0wfj2lcjo5TQ9QA/hlN/xaD/l40th1+3ujEwcRsKpnE1Gbvfe39kKuNT8jLTkdhtYXffVdeoAawOPOhx8Q64ti0hzebvF8I7b9V7CtZACBJQMILPlxdGDJAAJLBhBYMoDAkgEElgwgsOgDuOzL8P8igeq5V4zTYAYQ2GAGENhgBhDYYAYQ2GAGENhgBhDYYAYQ2GAGENhgBhDYYFgASywGtbYvORqDIQEsW9GstW3JERkMB2A5FEODuwDg+rffVv8+OnrvWzcAGPczEOL0l3ODEABXtdtv/vCsfHX/tQMAAvf3IMTpL+cGAQC+uff3KgKuH78ubz6hIWBea6n7Gwhx+su5QZ0mqI2AHyGRfUEmZ/ZiWnTMAuoDbp4cNS2QYQQszwDXPxGZotGFmH4EXH/0rLx6z6YJUvgVDKBHYHcAXKHLHzdDpgDUfgUDMBkupQ/ALgJAfgUD6BDYEQD4z9XR0T0aAJoAoH4FA2gJ7AAArujURsOvcEVdP+wuAD2/whU1/ZABuAaw1PNDBuAcwFLLDxkA69fpdGoFYKnjhwxg4NfK+0IEsFDR8EMG0PNr63gBAmBblQEoC+H4ddD0cBFAO4sMQFXI0K+8hp9zDNxbZwCKQoauFvhxeAI+XHLgBiPFBAGgC13i1z4CjfGqvRuMFFMDUDUzUr92EejcMFi7wUgxLQDUtwq/tp2B1h2brRuMFBMB0HcrwK+1rt4ts6UbjBQDA4DK2Rlq+PVE+wtbME/sMwIM+lYUBboPjayuQyPFZJogo8HNtC9qAEoCOwtgynWXUvqKEhyN4hj+cm4wFQB9EcTUCP5ybtA7gKUPAD2bHUX//nJukAvg9vSw3eg0JQBSAgkBWFR7k8oJAAvxA2ApumX27S/nBnkA6kU6V/LFUoGFLI2fsclPt2YzAGkhvgAsRffWfv3l3CAPQLUg+qbZndoTgPq8KYCl6KGRV385N8gFUG2CrNgZE1bIsAvgPLlxDUCEICUAEIEVwgLgKpkAWIqfr2q7wUgxPQBiPSMAS8ljU003GCl6BHBeFCfnit2RYYU0/lfpmQCojPMV9dxgpOjxPuCd749P3NwHYB/BKmMCYCmZY9Nyg5Giz2Fos278eAC4769YAEgk8dgzgKUOgD4C4MSBTBHuBiNFj/cBuAnq3AfQJL0fvtBN0NAE0EUAvGOTz9xolKyvONp9AE3ULr95SvLEdABMhxeiqta6AJayF10SSL3nA+hJk6jdJmlrAdCttVMABqlM0QFok/Qef1U3QeBE7bOpyXS55hT+VkzXM/cB3d0DKYBHT8lyBZVAKLNdAPCy0YiA5ZlswrgTAwlFwPBh3HVnrQitPGFjAHop9SoCepk04QEwj6PpWhGfagNg+mB4rXWeW4MIpA8Aj4I0myAmAAxq7QKATiJHeADVNmuDJgj/uXmiuWCTEwCtNSEAEIF0AGyOCyqSm2FIIW4BtGYZABAC6QCACaAQTh/sptYsAAABaCJHBgBQZAGoCUATOSIAQJ7GqXaQBRTC8b+rlni7ASwm+Fbg3Ho+YFQASgLQTJrwADbVbIz942iPAHiPTRUEkgKAx6BxA+A9NpUTgKYyhQdwe3qI7wQWtk0Qrw92OBpnAcgJpAMA76g8KRfWk/I8/wcG4DSbL/phqGcAvIkDZSaNm5L19CwAbH5pcyfsG8AlC0BGoNJzU7KW3vYC4C2EKU4n20kAnmrdlMACEAcBNJtyawBwA8DxM0keAMUCRI5K1tDzBEApZvPBuoXoTxN7r5ORJBoBosljXhBA84lTiQBlIaMAuBTN3rMEoPnE2wTAV617xQgmj5kgSBGATJSFjARAPHuvSI6yLjny+QB+CzQmAOESXI5Kjnw+YDQApRCAWUZ9eABu5gPGAyB7caUTBEkBcDAfwO8CvACQvjrEzSd2UnLk8wGxAOCuVuek5LjnAwQtkB8AirfnONmULkqOexg6LgAVASaXzEXJGUBHUQ6Ak0vmoGSvTZDivUQQAH+15igqCUzTAaBcqylGAOodKgc3xvYl+xyG2gMQtED+AKhfY+8TsC/ZYwTYAxB1AfEAsF9YyF8foLgFAwLwWGu+ogrAEhgCwQHQ/AAmQwanaj+NF4CCAJNLZlvyaMPQqzYv5tVRzADkBJhUJtuSxwLQJGqjUPjdH7cIgIhABAAG6wU1a0V8+Y+qCVInao8xIc8RvaTuEDUUiXS9IArg1cfQPkAYAH4jQBoDnGQ+u5L9Po4+YdNUrx+/Th0AqBeIFsCrIywfRw1AuQ4mgEBwAML1guDD0IAAhAQSAjBcN5QmasMBiO6DxwAgQsBN5rMpOebH0cIAGAWAdCHSnQAgboHGASBdiFRJIDwA6/eCwgOQLESaAADr94IiAMASEKSzmpfscxhq+V6QuA8eDwCDICkAlu8FiQNgTADClWAVBIIDsH8vKBIAopVgYwdg/V6QpAsYGQBvTQklgfAAQCIpJCIAgpVgp4kAMEzQiAoAL6NeHgLpA5D4PwAA7lK8shDIAJwrKgAYrqyZAcAVGQCyEEgegKwLCASAzaaUzI0lAkAsZ1ODL3kXRTJ36Oq5jABZAASLACaZb5jFqm8w2iYoUgDDZD4mlVvboIsaygHIRFxIrACGT+dEBMIDsMsPkPbBUQNY6hp0oMgDYJkfsJx6r7WRYsk8oBYQCA7AMj9AGgBhAVyqABAEwQFY5gekA0BAIDgAy/yAmAEAQgC+0UCsnXDUACAEoOvce2yCFHs5SwHI++AkADheY3rkTjhyAAAC0FW+I+2E5S1QeACXEABOV9g16AMOLDrhtADwCEBXePXYBCl38EkZgJIAdIHRSJ8FybuAGACoGiHo+pZxAlD0wfEBYAlAF5eLswlStEBRAFCEAHRtM88RsPlgTj/SDJnrR0fKPOEkAMgJQJfW8t0EreircTRRG2/kef3RMwUAi8r4VTQC4P3JugxA3QQ1idpX75dkQ88tACAlwCxE6rOGEgDty6Ftkl69na0kUTvKCXmOgBdbH2nOntMJ7zF9AE7So1mqgghQdQGxRIAsBERL8XqpIQ8AIw2AmyeN/1MHMCAgWGJXymB8AHhTeRUAm8r4VRzq9V3bISDfy9txDbkAhkl6dKmCrv+3DUAnCNSbqTusIRfAMEmvTtSuliqQj4ISAsCk8VECoN3sXdWQB8AmSS8lACwBzhK7EvEJwDRJT9kHRw2gDgIoACfbs/IAWCTpKQMgKgA8AswSu1IAS+ud4XgALJL0EgPAW9FgqgkAwGDEx9FbAECyGaIIgIpBBiDW4zoWSGAQKWY1dAtA3QfHBoC/rAosCNimyqCGjgGYumEMRQ0Ayv3pBQAEDDIAmZ7AsYAggD60ywDkeiLHKglAH9plAHI9oWNVQaAYrsJq6BRAMdbbTEaKQj2xY+UEoA/txgMACIAYAQhXdlIEAeyGTVHDDOBSBgAjUGxSDxA/ADiSynwwI4oVv4mo542t5pR3OgKEa5sNQmEKmzsWhQq/Vi4BFA7dNSqAS9HSWlIKugC4DFwCgPg/WgCCpbWEFDQeW0vf9HIJANICRQzgkjKAOZZtkjIAB3pwANWeKCAIGYCWnu6EjDoUxgKQeB/QKuoBaCiEBgDyfxIALgE7Uw79upSEQgZgYlAfQEMhEABQF5AOgEvA3ogCGYZCBmBs0AxAS0G9mbpDAFvWBNViDKCDYRwAMP8nB6ARMwAthQzAlUF9AGcNBXZniwzAXNHkWdB02FO6AwDrg7cJANHTBJAjwIdBKACuEQgAmqJ08+To/usMgK8nByA2AgBAE7XxPoav3hcAAPp/ewFg4XlfYRAAoEnUvvnk207G6sBQBkCFbe6tJ+Xb/YTVidpZzAQC4Oo+BYDF7WUzgmKcBnUAtBGQATgzqANA2gdkAGaKOgDwQhHCUVAGYKYIBoD/SO8Dxqx1HCWPB4ArIWsdR8kZQOCSM4DAJQcGMBDnN2Y7ZjADCGwwAwhsMAMIbNBJhkwWc8kAAksGEFgygMCSAQQWOwC9p3Qu5Oqonod2JT98cXTvmUuD14/c1tAKQH+23oHgR69ODeI1x69cXiM3T9BPdmnQCkB/psaRODWIa+hU+rODDsQKgPPaYHEbUo+/ctsExQWgP1vvRK4fufXXo6fEZ86ENEEuqxhfBDg16L6GqBP+/ZcODcbXB7R7dTiQm099XCIuOxbLUdDHbpts923aN66bIFQ7pz85svuAV0eOh+2ohm5vLNCditOfnO+EA0sGEFgygMCSAQSWDCCwZACBZWsAvHnOHFrvz2Wn45BtAdB1tv7pgJIBBJZkAdyeFsWk9ux6//NZURyu9z8rCrwDTnuuOnOyxqfxFjnoc+iKDyRVAHjDs83xCQUwx/+uZ2+9wMfpH3x4hoCcv/WCnCaqs8gIpAqAtil9ACfkM9kIbUW8Tg+R0wfS/dECSaoA6G5zPQD4MwoLsiV4dax7ulyQhiky2SUAZMdk+TaB40uqAMRN0MELvB0gbYL6AAifgLXmSKoAaEeLN7+8Pd2bkw6Z0wnXAPBpEjTRjUdTBUCHmmRs+fCDOWrgJ2QYOin7w9AKAD5drnr7tUciyQLgSHRXN0QygMCSAQSWbQKQpGQAgSUDCCwZQGDJAAJLBhBYMoDA8n/voeGKyKxUgAAAAABJRU5ErkJggg==" style="display: block; margin: auto;" /></p>
<div class="sourceCode" id="cb19"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb19-1"><a href="#cb19-1"></a><span class="kw">plot_cutpointr</span>(opt_cut_b, <span class="dt">xvar =</span> fpr, <span class="dt">yvar =</span> tpr, <span class="dt">aspect_ratio =</span> <span class="dv">1</span>, <span class="dt">conf_lvl =</span> <span class="dv">0</span>)</span></code></pre></div>
<p><img src="data:image/png;base64,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" style="display: block; margin: auto;" /></p>
<div class="sourceCode" id="cb20"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb20-1"><a href="#cb20-1"></a><span class="kw">plot_cutpointr</span>(opt_cut_b, <span class="dt">xvar =</span> cutpoint, <span class="dt">yvar =</span> tp, <span class="dt">conf_lvl =</span> <span class="fl">0.9</span>) <span class="op">+</span><span class="st"> </span></span>
<span id="cb20-2"><a href="#cb20-2"></a><span class="st"> </span><span class="kw">geom_point</span>()</span></code></pre></div>
<p><img src="data:image/png;base64,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" style="display: block; margin: auto;" /></p>
</div>
<div id="manual-plotting" class="section level2">
<h2>Manual plotting</h2>
<p>Since <code>cutpointr</code> returns a <code>data.frame</code> with the original data, bootstrap results, and the ROC curve in nested tibbles, these data can be conveniently extracted and plotted manually. The relevant nested tibbles are in the columns <code>data</code>, <code>roc_curve</code> and <code>boot</code>. The following is an example of accessing and plotting the grouped data.</p>
<div class="sourceCode" id="cb21"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb21-1"><a href="#cb21-1"></a><span class="kw">library</span>(dplyr)</span>
<span id="cb21-2"><a href="#cb21-2"></a><span class="kw">library</span>(tidyr)</span>
<span id="cb21-3"><a href="#cb21-3"></a>opt_cut_b_g <span class="op">|</span><span class="er">></span><span class="st"> </span></span>
<span id="cb21-4"><a href="#cb21-4"></a><span class="st"> </span><span class="kw">select</span>(data, subgroup) <span class="op">|</span><span class="er">></span><span class="st"> </span></span>
<span id="cb21-5"><a href="#cb21-5"></a><span class="st"> </span><span class="kw">unnest</span>(<span class="dt">cols =</span> data) <span class="op">|</span><span class="er">></span><span class="st"> </span></span>
<span id="cb21-6"><a href="#cb21-6"></a><span class="st"> </span><span class="kw">ggplot</span>(<span class="kw">aes</span>(<span class="dt">x =</span> suicide, <span class="dt">y =</span> dsi)) <span class="op">+</span><span class="st"> </span></span>
<span id="cb21-7"><a href="#cb21-7"></a><span class="st"> </span><span class="kw">geom_boxplot</span>(<span class="dt">alpha =</span> <span class="fl">0.3</span>) <span class="op">+</span><span class="st"> </span></span>
<span id="cb21-8"><a href="#cb21-8"></a><span class="st"> </span><span class="kw">facet_grid</span>(<span class="op">~</span>subgroup)</span></code></pre></div>
<p><img src="data:image/png;base64,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" style="display: block; margin: auto;" /></p>
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