title: Calculating AUC for a glm model on data imputed using the MICE package
body: <p>On a regular dataset, the calls will go like this:</p>
  
  <pre><code>library(ROCR)
  
  mymodelFit1 &lt;- glm(data = myData, Outcome~ predictor1+ 
  predictor2+ predictor3,
               family = binomial(link = "logit")))
  
  prob &lt;- predict(mymodelFit1, newdata=myData, type="response")
  pred &lt;- prediction(prob, myData$Outcome)
  perf &lt;- performance(pred, measure = "tpr", x.measure = "fpr")
  plot(perf)
  auc &lt;- performance(pred, measure = "auc")
  auc &lt;- auc@y.values[[1]]
  auc
  </code></pre>
  
  <p>Now, I have used the mice package to impute missing data:</p>
  
  <pre><code>library(mice)
  
  impData &lt;- mice(analysis_set,m=5,maxit=50,meth='pmm',
  seed=500, printFlag = FALSE)
  </code></pre>
  
  <p>And generated a logistic model using the imputed dataset:</p>
  
  <pre><code>mymodelFit2 &lt;- with(data = impData, exp = glm(Outcome~ predictor1+ 
  predictor2 + predictor3,
               family = binomial(link = "logit")))
  </code></pre>
  
  <p><strong>The question is:</strong> how can I calculate the AUC for the new model (mymodelFit2)?</p>
  
  <p>"mice" generates x number of sets for missing values (5 in this case, specified in the m= parameter of mice()). The model has x number (5 in this case) of estimates.</p>
  
  <p>Is there a function or an easy way to calculate AUC for these models? And how can I get an AUC for the <strong>pooled model</strong>?</p>