glmnet - variable importance?

Question

I´m using the glmnet package to perform a LASSO regression. Is there a way to get the importance of the individual variables that were selected? I thought about ranking the coef

Answer 1

This is how it is done in caret package.

To summarize, you can take the absolute value of the final coefficients and rank them. The ranked coefficients are your variable importance.

To view the source code, you can type

caret::getModelInfo("glmnet")$glmnet$varImp

If you don't want to use caret package, you can run the following lines from the package, and it should work.

varImp <- function(object, lambda = NULL, ...) {

  ## skipping a few lines

  beta <- predict(object, s = lambda, type = "coef")
  if(is.list(beta)) {
    out <- do.call("cbind", lapply(beta, function(x) x[,1]))
    out <- as.data.frame(out, stringsAsFactors = TRUE)
  } else out <- data.frame(Overall = beta[,1])
  out <- abs(out[rownames(out) != "(Intercept)",,drop = FALSE])
  out
}

Finally, call the function with your fit.

varImp(cvfit, lambda = cvfit$lambda.min)