title: Error with train() caret function
body: <p>I'm trying to use caret to do a basic model selection for a radial basis function network, but when i use the train() function from caret, the following error appears : </p>
  
  <pre><code>Error in UseMethod("train") : 
  no applicable method for 'train' applied to an object of class "c('matrix', 
  'double', 'numeric')"
  </code></pre>
  
  <p>I have no idea of what I'm doing wrong here, i hope you can help me with thtat one.
  Here's the code : </p>
  
  <pre><code>    Data1&lt;-as.matrix(runif(1000))
      Data2&lt;-as.matrix(runif(1000))
      Data3&lt;-as.matrix(runif(1000))
      Data4&lt;-as.matrix(runif(1000))
      Data5&lt;-as.matrix(runif(1000))
      Data6&lt;-as.matrix(runif(1000))
  
      data&lt;-cbind(Data1,Data2,Data3,Data4,Data5,Data6)
      colnames(data)&lt;-c("Feature1","Feature2","Feature3","Feture4","Feature5","Feature6")
  
      targetfunction&lt;-function(xi){
        error&lt;-rnorm(1,0,0.1)
        return (sin(2*xi[1])*xi[2]+0.5*(xi[3]-0.5)^2+xi[4]+error)
      }
      target&lt;-as.matrix(rep(0,times=1000))
      for (i in 1:1000){
        target[i]&lt;-as.matrix(targetfunction(data[i,]))
      }
  
      library(mRMRe)
  
      #Binding data and target
      DM = cbind(data, target)
      DM = mRMR.data(as.data.frame(DM))
  
  
      s1 = mRMR.classic(data = DM, feature_count = 1, target_indices = c(7))
      s2 = mRMR.classic(data = DM, feature_count = 2, target_indices = c(7))
      s3 = mRMR.classic(data = DM, feature_count = 3, target_indices = c(7))
      s4 = mRMR.classic(data = DM, feature_count = 4, target_indices = c(7))
      s5 = mRMR.classic(data = DM, feature_count = 5, target_indices = c(7))
      s6 = mRMR.classic(data = DM, feature_count = 6, target_indices = c(7))
      #Optimal solutions for feature selection (Mutual information)
      solutions(s1)
      solutions(s2)
      solutions(s3)
      o = solutions(s4)
      solutions(s5)
      solutions(s6)
  
      #for reproducibility 
      o = c(4,2,1,5)
      #########################################################################################
      #Model selection 
      #########################################################################################
      library(caret)
      library(RSNNS)
  
      #Splitting data
      prepValues = data[,o]
  
      trainSet = prepValues[1:750,]
      testset = prepValues[751:1000,]
      colnames(trainSet) = c("x1","x2","x3","x4")
      colnames(target) = "targ"
      test = cbind(target[1:750], trainSet)
  
      #Training model 
      rbf = train(trainSet, target[1:750], method = "rbf")
  </code></pre>