Hi, I know how to use LASSO for model selection based on the Cp criterion. I heard that we can also use cross validation as a criterion too. I used cv.lars to give me the lowest predicted error & fraction. But I'm short of a step to arrive at the number of variables to be included in the final model. How do we do that? Is it the predict.lars function? i tried > logprostate.plars.cv=predict.lars(logprostate.lars.cv, M, type = "fit", mode="fraction") but it gives me error message: Error in dim(data) <- dim : attempt to set an attribute on NULL. Please help!
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