dave fournier <otter <at> otter-rsch.com> writes: > > According to the documentation for glmmADMB if you fit > your model with a statment like > > fit =glmm.admb(y~Base*trt+Age+Visit, ... data=epil2,family="nbinom") > > and that the parameter estimates are in > > fit$b while their estimated standard deviations are > in > > fit$stdbeta > > so presumably p values can be constructed from the > quotient > > fit$b/fit$stdbeta > > by assuming a t distribution with (somehow) the correct > degrees of freedom.
As I commented elsewhere (for the record in this group), you would do that in R via 2*pnorm(-abs(fit$b/fit$stdbeta)) for a 2-tailed test, but these values should be taken as order-of-magnitude estimates of the 'true' (???) p-value at best, because they are Wald tests (not score or likelihood, both of which are more reliable) and because they assume infinite 'denominator degrees of freedom' (i.e. Z/chi-squared test rather than t/F test equivalent). Probably reliable only for a large, well-behaved data set (e.g., >40 random-effects levels (species or nests)) ... ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.