title: caret::train pass extra parameters rpart
body: <p>I'm building a decision tree with rpart via the caret::train function. What I'm trying to do is to set the minsplit parameter of rpart equal to 1, in order to prune it afterwards with the cp. What I get from <a href="https://stackoverflow.com/questions/23366174/carettrain-specify-further-non-tuning-parameters-for-mlpweightdecay-rsnns-pa">here</a> is that the parameters should be passed in the ... of the train function. But this doesn't work. A minimal reproducible example:</p>
  
  <pre><code>mod1 &lt;- train(Species ~ ., iris, method = "rpart", tuneGrid = expand.grid(cp = 0), minsplit=1)
  mod2 &lt;- rpart(Species ~ ., iris, cp=0, minsplit=1)
  </code></pre>
  
  <p>What I get is that mod1$finalModel and mod2 are quite different. I would like that mod1$finalModel was like mod2 (i.e., totally overfitted). I cannot pass the parameter either on the tuneGrid since it only accepts a cp column.</p>
  
  <p>So my question is: is there anyway in caret to pass the argument minsplit=1 in the train function and then cross validate over the cp parameter?</p>