title: How to deal with multiple class ROC analysis in R (pROC package)?
body: <p>When I use multiclass.roc function in R (pROC package), for instance, I trained a data set by random forest, here is my code:</p>
  
  <pre><code># randomForest &amp; pROC packages should be installed:
  # install.packages(c('randomForest', 'pROC'))
  data(iris)
  library(randomForest)
  library(pROC)
  set.seed(1000)
  # 3-class in response variable
  rf = randomForest(Species~., data = iris, ntree = 100)
  # predict(.., type = 'prob') returns a probability matrix
  multiclass.roc(iris$Species, predict(rf, iris, type = 'prob'))
  </code></pre>
  
  <p>And the result is:</p>
  
  <pre><code>Call:
  multiclass.roc.default(response = iris$Species, predictor = predict(rf,     
  iris, type = "prob"))
  Data: predict(rf, iris, type = "prob") with 3 levels of iris$Species: setosa,   
  versicolor, virginica.
  Multi-class area under the curve: 0.5142
  </code></pre>
  
  <p>Is this right? Thanks!!!</p>
  
  <p>"pROC" reference: <a href="http://www.inside-r.org/packages/cran/pROC/docs/multiclass.roc" rel="nofollow noreferrer">http://www.inside-r.org/packages/cran/pROC/docs/multiclass.roc</a></p>