title: Variable importance using the caret package (error); RandomForest algorithm
body: <p>I am trying to obtain the variable importance of a rf model in any way. This is the approach I have tried so far, but alternate suggestions are very welcome.</p>
  
  <p>I have trained a model in R:</p>
  
  <pre><code>require(caret)
  require(randomForest)
  myControl = trainControl(method='cv',number=5,repeats=2,returnResamp='none')
  model2 = train(increaseInAssessedLevel~., data=trainData, method = 'rf', trControl=myControl)
  </code></pre>
  
  <p>The dataset is fairly large, but the model runs fine. I can access its parts and run commands such as:</p>
  
  <pre><code>&gt; model2[3]
  $results
    mtry      RMSE  Rsquared      RMSESD RsquaredSD
  1    2 0.1901304 0.3342449 0.004586902 0.05089500
  2   61 0.1080164 0.6984240 0.006195397 0.04428158
  3  120 0.1084201 0.6954841 0.007119253 0.04362755
  </code></pre>
  
  <p>But I get the following error:</p>
  
  <pre><code>&gt; varImp(model2)
  Error in varImp[, "%IncMSE"] : subscript out of bounds
  </code></pre>
  
  <p>Apparently there is supposed to be a wrapper, but that does not seem to be the case: (cf:<a href="http://www.inside-r.org/packages/cran/caret/docs/varImp" rel="noreferrer">http://www.inside-r.org/packages/cran/caret/docs/varImp</a>)</p>
  
  <pre><code>varImp.randomForest(model2)
  Error: could not find function "varImp.randomForest"
  </code></pre>
  
  <p>But this is particularly odd:</p>
  
  <pre><code>&gt; traceback()
  No traceback available 
  
  &gt; sessionInfo()
  R version 3.0.1 (2013-05-16)
  Platform: x86_64-redhat-linux-gnu (64-bit)
  
  locale:
   [1] LC_CTYPE=en_GB.UTF-8       LC_NUMERIC=C              
   [3] LC_TIME=en_GB.UTF-8        LC_COLLATE=en_GB.UTF-8    
   [5] LC_MONETARY=en_GB.UTF-8    LC_MESSAGES=en_GB.UTF-8   
   [7] LC_PAPER=C                 LC_NAME=C                 
   [9] LC_ADDRESS=C               LC_TELEPHONE=C            
  [11] LC_MEASUREMENT=en_GB.UTF-8 LC_IDENTIFICATION=C       
  
  attached base packages:
  [1] parallel  stats     graphics  grDevices utils     datasets  methods  
  [8] base     
  
  other attached packages:
   [1] elasticnet_1.1     lars_1.2           klaR_0.6-9         MASS_7.3-26       
   [5] kernlab_0.9-18     nnet_7.3-6         randomForest_4.6-7 doMC_1.3.0        
   [9] iterators_1.0.6    caret_5.17-7       reshape2_1.2.2     plyr_1.8          
  [13] lattice_0.20-15    foreach_1.4.1      cluster_1.14.4    
  
  loaded via a namespace (and not attached):
  [1] codetools_0.2-8 compiler_3.0.1  grid_3.0.1      stringr_0.6.2  
  [5] tools_3.0.1  
  </code></pre>