Are you sure your variables are categorical or numeric? Of course, glm
differentiates these two kinds of variables. For example, I ran the
same variable with different modes, the results are very different.
> dat<-data.frame(y=rpois(100,5),xf=as.factor(sample(1:4,100,replace=T)))
> glm(y~xf,data=dat,family=poisson)
Call: glm(formula = y ~ xf, family = poisson, data = dat)
Coefficients:
(Intercept) xf2 xf3 xf4
1.60944 -0.12783 -0.11878 -0.09746
...> glm(y~as.numeric(xf),data=dat,family=poisson)
Call: glm(formula = y ~ as.numeric(xf), family = poisson, data = dat)
Coefficients:
(Intercept) as.numeric(xf)
1.59047 -0.02673
Weidong Gu
On Fri, Oct 28, 2011 at 4:21 PM, CES <smit4155 at umn.edu>
wrote:> Hey all,
>
> ?I am attempting to replicate my results achieved in another program within
> R (so I can expand my options for methods). I am trying to run a GLM
(Family
> = Poisson) for count data in R. Some of my variables are factors and I am
> under the impression that the function glm() cannot run a model with
Poisson
> dist and factors??
>
> Here is why I think this, if I run two models using glm(), one treating a
> variable (e.g., Harvest.Factor) as a Factor and the other treating a
> variable as continuous (e.g., Harvest.Cont) I get the same results in R.
>
> Is there a package that will allow me to run a GLM using a Poisson dist and
> factor variables?
>
> Thanks for any help!
> -Chris
>
>
>
>
>
> --
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>
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