I would suggest doing an F-test.A descrition is given here: http://www.graphpad.com/curvefit/2_models__1_dataset.htm. The method is valid becasue one of your models is a subset of another.
Correct use of the anova function does indeed perform this test. For example: data(airquality) lm1<-lm(Ozone~.,airquality) # full model lm2<-lm(Ozone~Solar.R+Wind +Month+Day,airquality) # reduced model anova(lm2,lm1) -----Original Message----- From: [EMAIL PROTECTED] [mailto:[EMAIL PROTECTED] Behalf Of [EMAIL PROTECTED] Sent: 14 September 2007 15:49 To: r-help@r-project.org Subject: [R] Comparing regression models Dear list, I am interested in comparing two linear regression models to see if including one extra variable improves the model significantly. I have read that one possibility is doing an F test on the goodness-of-fit values for both models, and another option that is comparing the residuals of both models using a paired test. I also know about the anova() function that compares results for two models but am not sure what it actually does compare. Can you give me any suggestions? Does the same hold if the models were logistic instead of linear? I have read that the Akaike´s AIC is also a valid option. Thanks in advance for your comments David ______________________________________________ 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. ______________________________________________ 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.
