From: Johan Stenberg <jstenberg_at_ice.mpg.de>

Date: Wed 16 Mar 2005 - 06:26:15 EST

https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html Received on Wed Mar 16 06:35:04 2005

Date: Wed 16 Mar 2005 - 06:26:15 EST

Dear all,

I have two questions concerning GLM (logistic regression) with family=binomial.

- A measure of the departure from the binomial assumption is given by the dispersion factor (= residual deviance / residual df). The data is over-dispersed when the dispersion factor is significantly higher than 1 (Crawley, page 518). Is there any way to test if the dispersion factor is significantly higher than 1? The residual deviance should be chi2-distributed which should allow to test for the significance of the departure... See below to see how my syntax looks like.
- How do you calculate the proportion of deviance explained by the model (the equivalent of r2 in a standard regression) in R?

Kind regards

Johan Stenberg

*> y<-cbind(para,unpara)
*

> model<-glm(y~log(larvae),binomial)

> summary(model)

Call:

glm(formula = y ~ log(larvae), family = binomial)

Min 1Q Median 3Q Max -2.0633 -1.6218 -0.1871 0.7907 2.7670

Estimate Std. Error z value Pr(>|z|) (Intercept) 1.0025 0.7049 1.422 0.15499 log(larvae) -1.0640 0.3870 -2.749 0.00597 **

(Dispersion parameter for binomial family taken to be 1)

Null deviance: 35.981 on 12 degrees of freedom
Residual deviance: 27.298 on 11 degrees of freedom
**AIC: 40.949
**
Number of Fisher Scoring iterations: 4

> anova(model,test="F")

Analysis of Deviance Table

Model: binomial, link: logit

Response: y

Terms added sequentially (first to last)

Df Deviance Resid. Df Resid. Dev F Pr(>F) NULL 12 35.981 log(larvae) 1 8.683 11 27.298 8.6828 0.003212 ** ______________________________________________R-help@stat.math.ethz.ch mailing list

https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html Received on Wed Mar 16 06:35:04 2005

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