[R] Comparing regression models

[Wayne.W.Jones at shell.com]

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: r-help-bounces at r-project.org
[mailto:r-help-bounces at r-project.org]On Behalf Of
darteta001 at ikasle.ehu.es
Sent: 14 September 2007 15:49
To: r-help at 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

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