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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