search for: equidispersed

It seems that MASS suggest to judge on the basis of sum(residuals(mode,type="pearson"))/df.residual(mode). My question: Is there any rule of thumb of the cutpoiont value? The paper "On the Use of Corrections for Overdispersion" suggests overdispersion exists if the deviance is at least twice the number of degrees of freedom. Are there any further hints? Thanks. -- Ronggui

...r is the value of the dispersion parameter which is adjusting for the excessive variation. With this parameterization the dispersion parameter is directly related to both mu, as well as the dispersion statistic, or Pearson Chi2/(residual DOF). A dispersion parameter of 0 is Poisson, which is equidispersed. When the dispersion parameter for other mixture models such as generalized Poisson and Poisson inverse Gaussian is zero, the models reduce to Poisson. I also provide an option so that the output is similar to glm.nb, for which the dispersion parameter is indirectly related to the mean....

The documentation states : An alternative parametrization (often used in ecology) is by the mean ?mu?, and ?size?, the dispersion parameter. However, this fails : > rnbinom(10, mu = 100, size = 0) [1] NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN Warning message: In rnbinom(10, mu = 100, size = 0) : NAs produced For dispersion set to 0, it should work like drawing from a Poisson distribution.

7 Jan 09 Hello, I am using R version 2.7.0 in a Windows XP context. I am also using the glmm.admb package (created by Dave Fournier, Hans Skaug, and Anders Nielson) to run mixed-effects negative binomial models. To the best of my knowledge and ability, I have searched and studied the R-help, R-sig-mixed models, and ADMB NBMM for R (through Otter Research Ltd) list servs; R help