title: Get optimal threshold with at least 75% sensitivity with pROC in R
body: <p>I have a dataframe with two columns : <code>score1</code> which is <code>numeric</code> and <code>truth1</code> which is <code>boolean</code>.
  I want to predict <code>truth1</code> using <code>score1</code>. To do that, I want a simple linear model, and then ask for a good threshold, i.e., a threshold which gives me 75% of <em>sensitivity</em> in my ROC curve. Hence, I do :</p>
  
  <pre><code>roc_curve = roc(truth1 ~ score1 , data = my_data)
  coords(roc=roc_curve, x = 0.75, input='sensitivity', ret='threshold')
  </code></pre>
  
  <p>My problem is that coords return 'NA', because the sensitivty of 0.75 does not appear in the ROC curve. So here is my question: how can I get the threshold which gives me a sensitivity of at least 0.75, with max specificity?</p>