Dear all,

I am fitting a glm to count data using poison errors with the log link.  My
goal is to test for the significance of model terms by calling the anova
function on two nested models following the recommendation in Michael
Crawley's guide to Statistical Computing.

Without going into too much detail, essentially, I have a small
overdispersion problem (errors do not fit the poisson assumption) so I am
following Crawley's recommendation and setting family=quasipoisson and using
an F test (rather than a chi-square test) to test for significance.

This is working fine, but I cannot figure out how the F value in the
analysis of deviance table was obtained and what degrees of freedom were
used to obtain the P value (essentially I don't know how to report the
result).  The following example (while errors are not overdispersed)
otherwise generates a comparable analysis of deviance table to my analysis.
Any help would be much appreciated.

Jonathan

counts <- c(rpois(100,5),rpois(100,20))
sites <- rep(100,200)
fac1 <- factor(c(rep("A",100),rep("B",100)))
fac2 <- factor(c(rep("C",50),rep("D",100),rep("C",50)))
model1 <- glm(counts ~ fac1 * fac2,family=quasipoisson, offset=log(sites))
model2 <- glm(counts ~ fac1 + fac2,family=quasipoisson, offset=log(sites))
anova(model1,model2,test="F")
Analysis of Deviance Table

Model 1: counts ~ fac1 * fac2
Model 2: counts ~ fac1 + fac2
  Resid. Df Resid. Dev  Df Deviance      F Pr(>F)
1       196    218.432
2       197    219.210  -1   -0.778 0.7134 0.3993

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