Hello List, I am fitting a logistic regression model for some presence/absence type data. I have numerous covariates I am fitting to explain variation, and I am using AIC to rank models. However, I would like to report how well my best model (s) do at prediction. I have looked over the archives and the web and have come up with something that gives me what I think is the mean prediction error, BUT I am not sure of that. I am sort of unfamiliar with these types of statistics. Here is my code:
metrics.global<-glm(Type~MPI+IJI+ED+PRD+class2+class3+class5, family=binomial, data=metrics)## ##Type is my binary response 0 or 1 muhat<-metrics.global$fitted.values ##assigns the fitted values a name muhat global.diag<-glm.diag(metrics.global) ##creates a the diagnostic values cv.err<-mean((metrics.global$y-muhat)^2/(1-global.diag$h)^2) ###calculates cv.err cv.err My main problem is I am unsure how to interpret what cv.err means for my model. I know that h is a leverage statistic for each observation. I would appreciate some interpretation clarification. Thank you. -- View this message in context: http://www.nabble.com/Prediction-Error-Calculation-tp26031236p26031236.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.
