# Re: [R] Question on glm for Poisson distribution.

From: Spencer Graves <spencer.graves_at_pdf.com>
Date: Thu 28 Jul 2005 - 14:51:40 EST

I can't help you much, because you did not include a simple example that is sufficiently complete for me to run it myself and play with the results.

I ran the example "glm.D93 <- glm(counts ~ outcome + treatment, family=poisson())" in the "glm" help file. Then I looked at "str(glm.D93)"; "attributes(glm.D93)" might have been a better choice in this case, but "str" is more general and will expose things hidden from "attributes". Then I looked at "glm.D93\$contrasts", and found that the contrasts used for "outcome" and "treatment" were both "contr.treatment". Then I tried "contr.treatment(outcome)" and got something useful.

If this is not adequate and you still could use help, PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html and try this list again.

spencer graves

Ghislain Vieilledent wrote:

> Good afternoon,
>
> I REALLY try to answer to my question as an autonomous student searching in
> the huge pile of papers on my desk and on the Internet but I can't find out
> the solution.
> Would you mind giving me some help? Please.
>
> #########################################
>
> I'm trying to use glm with factors:
>
>

```>>Pyr.1.glm<-glm(Pyrale~Trait,DataRav,family=poisson)
```

>
>
> If I have correctly payed attention to my cyber professor explanations I
> have, for the variable Pyrale which I suppose Poisson-distributed, the
> following mathematical expression:
>
> P(Pyrale=k)=exp(-m).[(m^k)/k!]
> with log(m)=Intercept+Trait(i) (link function is log for Poisson
> distribution)
>
> Then I test the significativity of Trait:
>
>
```>>anova(Pyr.1.glm,test="Chisq")
```

>
> Analysis of Deviance Table
>
>
> Response: Pyrale
>
> Terms added sequentially (first to last)
>
>
> Df Deviance Resid. Df Resid. Dev P(>|Chi|)
> NULL 19 49.813
> Trait 3 31.281 16 18.532 7.419e-07
>
> Which means that variable Trait is significant for determining the value of
> P(Pyrale=k).
>
> I tried to order the effects of the modalities of my variable Trait using:
>
> > summary(Pyr.1.glm)
>
> Call:
> glm(formula = Pyrale ~ Trait, family = poisson, data = DataRav)
>
> Deviance Residuals:
> Min 1Q Median 3Q Max
> -1.7117 -0.8944 -0.6237 0.6390 1.5224
>
> Coefficients:
> Estimate Std. Error z value Pr(>|z|)
> (Intercept) 1.3350 0.2294 5.819 5.92e-09 ***
> TraitIns&Fong -2.9444 1.0259 -2.870 0.00410 **
> TraitInsecticide -2.2513 0.7434 -3.028 0.00246 **
> TraitTemoin -0.2364 0.3454 -0.684 0.49372
> ---
> Signif. codes: 0 `***' 0.001 `**' 0.01 `*' 0.05 `.' 0.1 ` ' 1
>
> (Dispersion parameter for poisson family taken to be 1)
>
> Null deviance: 49.813 on 19 degrees of freedom
> Residual deviance: 18.532 on 16 degrees of freedom
> AIC: 61.85
>
> Number of Fisher Scoring iterations: 5
>
> ##############################################################
>
> I have therefore two questions:
>
> - Considering the values of estimated coefficients for Trait(i), does it
> mean that the bigger is the coefficient, the lower is the probability
> considering the mathematical expression (exp(-m))?
>
> - How can I check that coefficients are significatively different one from
> each other (as with function TukeyHSD for other models)?
>
>
> Thanks for you help.
>
> Regards
>
> Ghislain.
>
>
>
```--
Spencer Graves, PhD
Senior Development Engineer
PDF Solutions, Inc.
333 West San Carlos Street Suite 700
San Jose, CA 95110, USA

spencer.graves@pdf.com
www.pdf.com <http://www.pdf.com>
Tel:  408-938-4420
Fax: 408-280-7915

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