Linda,
Thanks for the example.
I did this to make it more reproducible:
set.seed(1)
X<-matrix(rnorm(50*100),nrow=50)
y<-rnorm(50*1)
dimnames(X)
colnames(X) <- paste("V", 1:nrow(X))
# Applying caret package
set.seed(2)
con<-trainControl(method="cv",number=10)
data<-NULL
data<- train(X,y, "lasso", metric="RMSE",tuneLength = 10, trControl = con)
I see your point here, but this code gives the same results:
fit2 <- enet(X, y, lambda = 0)
predict(fit2, mode = "fraction", s = data$bestTune$.fraction, type =
"coefficient")$coef
(at least train() names the predictors).
To me, it looks like enet is doing some filtering:
> dim(X)
[1] 50 100
> length(fit2$meanx)
[1] 56
This appears to be independent of caret. I would contact the package
maintainer off-list and ask.
Max
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