title: Logistic regression for numerical predictor?
body: <p>I'm working on a data set and want to use some of following variables to predict &quot;Operatieduur&quot;. All the predictors have been factorized.</p>
  <pre><code>LogicFit &lt;- train(Operatieduur ~ Anesthesioloog + Aorta_chirurgie + Benadering +
                      Chirurg + Operatietype, data = TrainData,
                    method=&quot;glm&quot;, family=&quot;binomial&quot;)
  </code></pre>
  <p>Here I use &quot;train&quot; function from caret package to make a logistic fitting with glm. When I ran this code I got the error message:</p>
  <pre><code>1: model fit failed for Resample01: parameter=none Error in eval(family$initialize) : y values must be 0 &lt;= y &lt;= 1
  </code></pre>
  <p>I googled it and found that the reason is that the resopnse &quot;Operatieduur&quot; is a continuous numerical value(it's a duration time). So how should I modify the function to use the predictors(they are all categorical values) to predict a continuous numerical value? Can logistic function do that?</p>