Hi All, Thanjuvar wrote: >> model2<-lm(lavi~age+sex+age*race+diabetes+hypertension, data=tb1)
David wrote: >> in the second equation you are only including the interaction of >> age*race, >> the main effect of age, but not the main effect of race which is what >> came out significant >> in your first model. I am sorry, but this is wrong. Read up about model formulae in http://cran.r-project.org/doc/manuals/R-intro.html#Statistical-models-in-R The expression age * race expands to age + race + age:race. That is, main effects of age and race, plus the interaction between age and race [age:race]. The expansion is done automatically. Thanjavur: Model selection is a huge subject. However, once you taken in the above fact, you will see that the __only__ difference between your two models is that you have added an interaction term for age:race You have two simple, but still very effective approaches. ## 1: Test the two models by doing: anova(model1, model2) ##1: Use stepAIC (you need MASS installed) on model 2, and see what happens to the ## interaction term require(MASS) stepAIC(model2, test="Chi")$anova See: ?anova ?stepAIC HTH, Mark. David Young-18 wrote: > > Thanjavur, > > I'm new to R, so it is possible I'm interpreting you syntax > incorrectly, but it looks like in the second equation you are only > including the interaction of age*race, the main effect of age, but > not the main effect of race which is what came out significant in your > first model. > > In effect you have measured two different things and one of them is > significant. In the first regression you have measured a general > shift in the regression giving each racial group a different > intercept. In the second, you are measuring whether there should be > two different slopes for the line relating to age. One for european > ages and one for non-european ages, which did not turn out to be > significant. > > Based on the information you have presented you should not include the > interaction, but should include the main effect for race. HOWEVER, as > a general rule, you should include the main effects along with your > test for interactions between them. age,race,age*race > When you do this it is possible that the interaction will then also be > significant. > > Hope that helps. > > Dave > > Tuesday, January 22, 2008, 11:20:01 AM, you wrote: > > > TB> Hi, > > TB> I am trying a linear regression model where the dependent variable is > the size of the heart corrected for the patient's height and weight. This > is labelled as LAVI. The independent variables are > TB> race (european or non-eurpoean), age, sex (male or female) of the > patient and whether they have diabetes and high blood pressure. sample > size 2000 patients selected from a community. > > TB> when I model > TB> model1<-lm(lavi~age+sex+race+diabetes+hypertension, data=tb1) > TB> and > TB> model2<-lm(lavi~age+sex+age*race+diabetes+hypertension, data=tb1) > > TB> in the first model race comes out as a significant predictor (p<0.005) > where as in the second model race is not a significant predictor of lavi > (p=.076) > > TB> in my dataset mean age is 55.2 years in the non-europeans and 56.7 > years in the europeans (p <0.0001 by t.test). > > TB> should I or should I not include the interaction (age*race) in the > model. Is it an acceptable rule to put in interactions if there is a > significant relation between the indepenedent variables in > TB> univariate analyses. > > TB> Many thanks > > TB> bragadeesh > TB> _________________________________________________________________ > TB> Helping your favorite cause is as easy as instant messaging. You IM, > we give. > > TB> [[alternative HTML version deleted]] > > > > > -- > Best regards, > > David Young > mailto:[EMAIL PROTECTED] > > ______________________________________________ > 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. > > -- View this message in context: http://www.nabble.com/Help---linear-regression-tp15016515p15093097.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.