[R] Why the order of parameters in a logistic regression affects results significantly?

[Qinghua He]

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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