title: randomForest does not work when training set has more different factor levels than test set
body: <p>When trying to test my trained model on new test data that has fewer factor levels than my training data, <code>predict()</code> returns the following:</p>
  
  <blockquote>
    <p>Type of predictors in new data do not match that of the training data.</p>
  </blockquote>
  
  <p>My training data has a variable with 7 factor levels and my test data has that same variable with 6 factor levels (all 6 ARE in the training data).</p>
  
  <p>When I add an observation containing the "missing" 7th factor, the model runs, so I'm not sure why this happens or even the logic behind it.</p>
  
  <p>I could see if the test set had more/different factor levels, then randomForest would choke, but why in the case where training set has "more" data?</p>