[R] lm-step: use train-data model to validate test-data

[Amoy Yang]

 I have model-data named as: model that is split as model.T(train) and model.V(test or validation). The least square model (from lm to step) is built withmodel.T and I like to see how model.T is robust by comparing predicted model.V toactual model.V. How do I get score for model.V based on model built on model.T? The code highlighted below does not get what I expected.Please advise! Thanks!
  # score the model 
  score.T <- data.frame(predict(step, model.T))  # get predicted score for train data
  score.V <- data.frame(predict(step, model.V))  # for test data but seems incorrect
  # get the actual values 
  actual.T <- data.frame(model.T$sales) 
  actual.V <- data.frame(model.V$sales)   # comparison for model.T
  comp.T=cbind(actual.T,round(score.T,digit=2))
  plot(comp.T)  # comparison for model.V (use Model.T to predict Model.V for true validation
  comp.V=cbind(actual.V,round(score.V,digit=2))
  plot(comp.V)
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