Im using the caret and glmnet package for variable selection. I only want to find the best model and the coefficients and use them for a different model. Please help me understand the differences between caret and glmnet. Here's an example:

using state data:

statedata <- data.frame(state.x77,row.names=state.abb,check.names=T)
statedata <- scale(statedata)

in caret:

fitControl <- trainControl(## 10-fold CV
  method = "repeatedcv",
  number = 5,
  repeats = 5)

lassoFit1 <- train(Life.Exp ~ . , data = statedata,
             method = "glmnet",
             trControl = fitControl)

I get alpha=1 and lambda=0.1 as best values. But how do I get the final best model? I tried


but I get a whole list of models.


both return NULL.

Here's how I do it in glmnet:

cvfit <- cv.glmnet(x=statedata[,c(1:3,5,6,7,8)],y=statedata[,4])

When I type

coef(cvfit, s="lambda.min")

I get the coefficients. Is this the best model? It looks like glmnet does test more lambdas in the cross validation, is that true? Does caret or glmnet lead to a better model?

How do I manage to extrage the best final model from caret and glmnet and plug it in a cox hazard model for example?


  • 1
    $\begingroup$ you may want to try predictors(lassoFit1), which lists predictors used in the final model. $\endgroup$ – David Ren Feb 12 '16 at 0:32

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