I've originally made 48 GLM binomial models and compare the AIC values. But
dispersion was very large:
Example: Residual deviance: 8811.6 on 118 degrees of freedom
I was suggested to do a quasibinomial afterwards but found that it did not
help the dispersion factor of models and received a warning:
Residual deviance: 3005.7 on 67 degrees of freedom
AIC: NA
Number of Fisher Scoring iterations: 13
Warning message:
In summary.glm(qModel48.glm) :
observations with zero weight not used for calculating dispersion
1) What does this warning message mean?
2) Is this quasibinomial step necessary or can I just compare the AIC of 48
glm models binomial even though dispersion is very large, well over 2.
Jean
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