glm() in R, I saw a few post recommending modeling underdispersed data using the Conway–Maxwell–Poisson distribution, specifically with the R package
CompGLM, however, I'm not sure I saw anybody confirming that the quasi-poisson cannot be used. Therefore, I ask: why not use quasi-poisson in
glm for underdispersed data? After all, isn't the idea of quasi-poisson to go beyond the assumption that variance and mean are equal ? (and in the case of underdispersion, there are not equal).
Basically, I am running a
glm(y~x,family=poisson) where x is a categorical variable and i am getting
Null deviance: 67.905 on 519 degrees of freedom Residual deviance: 59.584 on 507 degrees of freedom
Which strongly suggest underdispersion and I am therefore leaning towards a quasi-poisson solution.
Thank you for your help