R help - Jan 2008 - Help - linear regression

Hi,
 
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 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.
 
when I model
model1<-lm(lavi~age+sex+race+diabetes+hypertension, data=tb1)
 and 
model2<-lm(lavi~age+sex+age*race+diabetes+hypertension, data=tb1)
 
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)
 
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).
 
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 univariate analyses.
 
Many thanks
 
bragadeesh
_________________________________________________________________
Helping your favorite cause is as easy as instant messaging. You IM, we give.

	[[alternative HTML version deleted]]

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:dyoung at telefonica.net

a*b is not an interaction term. An interaction term looks like a:b.

y~age*race means
y~age+race+age:race.

For
TB> model1<-lm(lavi~age+sex+race+diabetes+hypertension, data=tb1)
TB> model2<-lm(lavi~age+sex+age*race+diabetes+hypertension, data=tb1)

both main effects are present in model 2 (below) as well as the
interaction. You should be able to see this in anova(model2), which will
also show you the order in which terms are added to the model.

If the * was just an email typo, though, forget it.



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