Since you didn't provide a reproducible example, here are a couple of
possibilities to check, but I have utterly no idea if they're
applicable to your problem or not:
* does costdis1 consist of 0's and 1's?
* is costdis1 a factor?
In the first model, you treat costdis1 as a pure quadratic and in the
second model, it is a linear term. The two models are not nested.
Modeling a term as a pure quadratic is a very strong assumption - the
more usual practice is to fit both a linear and quadratic term in
costdis1 to allow more flexibility in the fitted surface, but that
would require costdis1 to be numeric.
HTH,
Dennis
On Mon, Nov 7, 2011 at 7:58 AM, Sally Ann Sims <sallys...@earthlink.net> wrote:
> Hello,
>
> I am working on fitting a logistic regression model to my dataset. I removed
> the squared term in the second version of the model, but my model output is
> exactly the same.
>
> Model version 1:
> GRP_GLM<-glm(HB_NHB~elev+costdis1^2,data=glm_1,family=binomial(link=logit))
> summary(GRP_GLM)
>
>
> Model version 2:
> QM_1<-glm(HB_NHB~elev+costdis1,data=glm_2,family=binomial(link=logit))
> summary(QM_1)
>
>
> The call in version 2 has changed:
> Call:
> glm(formula = HB_NHB ~ elev + costdis1, family = binomial(link = logit),
> data = glm_2)
> But I’m getting the exact same results as I did in the model where costdis1
> is squared.
>
> Any ideas what I might do to correct this? Thank you.
>
> Sally
> [[alternative HTML version deleted]]
>
>
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>
>
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