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...rol=glm.control(trace=10,maxit=100)) ## does setting init.theta <<1 help? glm.nb(tadT ~ Distance,init.theta=1e-6,control=glm.control(trace=10,maxit=100)) glm.nb(tadT ~ Distance,init.theta=1e-1,control=glm.control(trace=10,maxit=100)) ## setting all zeros to 1 gives reasonable answers but??? tadT1 <- tadT tadT1[tadT1==0] <- 1 glm.nb(tadT1 ~ Distance,control=glm.control(trace=10,maxit=100)) ## quasipoisson does work ... but no likelihood/AIC available glm.q1 <- glm(tadT ~ 1,family=quasipoisson()) glm.qd <- glm(tadT ~ Distance,family=quasipoisson()) -- 318 Carr Hall...

Hi there, I have been having trouble running negative binomial regression (glm.nb) using library MASS in R v2.15.0 on Mac OSX. I am running multiple models on the variables influencing the group size of damselfish in coral reefs (count data). For total group size and two of my species, glm.nb is working great to deal with overdispersion in my count data. For two of my species, I am getting a