Hi, Thanks for the reply. What I meant is that, I would like to partition my dat data (a data frame) into training and testing data and then evaluate the performance of the model on test data. So, I thought cross validation is the natural choice to see how the prediction works on the hold-out data. Is there any example that I can take a look to see how to do cross validation and get the prediction results on my data?
Thanks a lot, Andra --- On Wed, 8/24/11, Prof Brian Ripley <rip...@stats.ox.ac.uk> wrote: > From: Prof Brian Ripley <rip...@stats.ox.ac.uk> > Subject: Re: [R] How to do cross validation with glm? > To: "Andra Isan" <andra_i...@yahoo.com> > Cc: r-help@r-project.org > Date: Wednesday, August 24, 2011, 10:11 AM > What you describe is not > cross-validation, so I am afraid we do not know what you > mean. And cv.glm does 'prediction for the hold-out > data' for you: you can read the code to see how it does so. > > I suspect you mean you want to do validation on a test set, > but that is not what you actually > claim. There are lots of examples of this > sort of thing in MASS (the book, scripts in the package). > > On Wed, 24 Aug 2011, Andra Isan wrote: > > > 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. > > > > -- Brian D. Ripley, > rip...@stats.ox.ac.uk > Professor of Applied Statistics, http://www.stats.ox.ac.uk/~ripley/ > University of Oxford, > Tel: +44 1865 272861 (self) > 1 South Parks Road, > +44 1865 > 272866 (PA) > Oxford OX1 3TG, UK > Fax: +44 1865 272595 > ______________________________________________ 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.