Hi David,
Thanks very much, that clears it up for me. I plan to report the result as
a typical F-test with numerator and denominator d.f., the value of F and the
significance. If you have other thoughts, I would appreciate it.
Thanks again.
Jonathan
On Fri, Nov 26, 2010 at 7:57 PM, David Winsemius <dwinsem...@comcast.net>wrote:
>
> On Nov 26, 2010, at 9:30 PM, Jonathan Flowers wrote:
>
> 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
>>
>
> Numerator df are the absolute values of differences and the denominator
> df's are the starting point.
> > 1-pf(0.7134, 1, 197)
> [1] 0.3993472
>
> Or equivalently:
> > pf(0.7134, 1, 196, lower.tail=FALSE)
> [1] 0.3993472
>
>
> I don't know how to report the
>> result).
>>
>
> Ergo: Time for a statistician.
>
> 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
>>
>> [[alternative HTML version deleted]]
>>
>>
> --
>
> David Winsemius, MD
> West Hartford, CT
>
>
[[alternative HTML version deleted]]
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