Hi All, I have a fitted model called glm.fit which I used glm and data dat is my data frame
pred= predict(glm.fit, data = dat, type="response") to predict how it predicts on my whole data but obviously I have to do cross-validation to train the model on one part of my data and predict on the other part. So, I searched for it and I found a function cv.glm which is in package boot. So, I tired to use it as: cv.glm = (cv.glm(dat, glm.fit, cost, K=nrow(dat))$delta) but I am not sure how to do the prediction for the hold-out data. Is there any better way for cross-validation to learn a model on training data and test it on test data in R? Thanks, Andra ______________________________________________ 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.
