<lcayuela <at> ugr.es> writes: [snip] > model1 <- glmer(fruitset ~ Dist*wire + (1|Site), data, binomial) > summary(model1) > > Generalized linear mixed model fit by the Laplace approximation > Formula: fruitset ~ Dist * wire + (1 | Site) > Data: data > AIC BIC logLik deviance > 68.23 70.65 -29.11 58.23 > Random effects: > Groups Name Variance Std.Dev. > Lugar (Intercept) 3.5155e-14 1.8750e-07 > Number of obs: 12, groups: Lugar, 2
[snip] > My question is, how can I check for overdispersion? In glm models you can > check this by comparing the residual deviance with the residual degrees of > freedom, but in glmer you don't get this information. > > (Ubuntu Intrepid Ibex / R 2.7.1) a few thoughts -- (1) probably better to ask this question on the R-sig-mixed-models list, which specializes in these problems (2) try lme4:::sigma (3) do you really have just 12 observations in 2 groups? In that case I would strongly recommend just treating group as a fixed factor -- you have no power to estimate variance (note your random effect has a standard deviation of 2 x 10^-7), and you will avoid lots of heartache if you just fit glm(fruitset ~ Dist*wire + Site, data, binomial) [not everyone will agree with me about this ...] (4) I'm a little puzzled that your formula has "Site" as a random effect but your summary lists "Lugar" as a random effect. Did you edit the summary? ______________________________________________ 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.
