Using the same data, if I ran
fit2
<-glm(formula=AR~Age+LumA+LumB+HER2+Basal+Normal,family=binomial,data=RacComp1)summary(fit2)exp(coef(fit2))
I obtained:
> exp(coef(fit2))(Intercept) Age LumA LumB HER2
> Basal Normal 0.24866935 1.00433781 0.10639937 0.31614001
> 0.08220685 20.25180956 NA
while if I ran
fit2
<-glm(formula=AR~Age+LumA+LumB+Basal+Normal+HER2,family=binomial,data=RacComp1)summary(fit2)exp(coef(fit2))
I obtained:
> exp(coef(fit2)) (Intercept) Age LumA LumB
> Basal Normal HER2 0.02044232 1.00433781 1.29428846
> 3.84566516 246.35185956 12.16443690 NA
Essentially they're the same model - I just moved HER2 to the last. But the OR
changed significantly. Can someone explain?
For the latter result, I don't even know how to interpret as all factors have
OR>1 (except Intercept), how could that possible? Can I eliminate the effect of
intercept?
Also, I cannot obtain OR for the last factor due to collinearity. However, I
know others obtained OR for all factors for the same dataset. Can someone tell
me how to obtain OR for all factors? All factors are categorical variables
(i.e., 0 or 1).
Thanks!
Peter
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