I would like to do model selection using backward stepwise procedure and cross validation. https://www.otexts.org/fpp/5/3
I have used
MASS package for predictors selection and I would like to see whether cross validation is a better criteria. In other words, I am trying to use cross-validation as a criterion in backward selection procedure instead of AIC.
The procedure should start with a full model, drop one predictor at a time, keep the variable if it improves CV and continue until no improvements can be made.
I think that the
rfe function in the
caret package may be able to do the job. I would also like to do bootstrapping if possible. If so, how should I specify it? Or are there any functions that can do that? I have tried writing my own function, but it seems to be beyond my ability. Many thanks.