Hi Patrick, Thanks for the reply. I am referring to using the cv.glmnet() function with 10-fold cross validation and letting glmnet determine the lambda sequence. The optimal lambda that it is returning fluctuates between different runs of cv.glmnet. Sometimes the model that is return deviates from like including anywhere from 3-25 predictor variables (I am doing LASSO and I originally had 235 predictor variables). I will try the foldid option.
I was also thinking of a bootstrapping approach where I would actually run cv.glmnet say 100 times and then take the mean/median lambda across all the cv.glmnet runs. This way I generate a confidence interval for my optimal lambda I woud use in the end. Another question that I have is I am currently using glmnet to help me fit a two-class predictor (binary logistic regression). The cv.glmnet() function has a type.measure parameter which can be set to auc. If I am understanding this correctly, for each lambda it is doing 10 cross-validation and at each fold it is calculating an AUC. Therefore, the cross-validation score for this lambda is the AVERAGE auc across all folds? Or is it they pool the predicted response values from each fold and then generate one ROC on all the predicted values? Thanks, Fong -- View this message in context: http://r.789695.n4.nabble.com/glmnet-with-binary-logistic-regression-tp3688126p3689024.html Sent from the R help mailing list archive at Nabble.com. ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
