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
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--
David Winsemius, MD
West Hartford, CT
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