Dear List,
I am wanting to test for overdispersion in my model and am unsure how for my specific case.
I have 2 random factors, 7 fixed factors that have multiple levels and are categorical and then i have a binary response (True or False).
model1 <- lmer(threattf~1+(1|order/family) + geophyte + seasonality + pollendispersal + breedingsystem*fruit + habit + lifeform + woodyness, family=binomial)
I would be very grateful if somebody could point me in the right direction for testing for overdispersion under such scenarios?
Please see part of the output below -
Thanks for any help, and if more data is required feel free to ask.
Chris
Generalized linear mixed model fit by the Laplace approximation
Formula: threattf ~ 1 + (1 | order/family) + geophyte + seasonality + pollendispersal + breedingsystem * fruit + habit + lifeform + woodyness
AIC BIC logLik deviance
1562 1649 -764.2 1528
Random effects:
Groups Name Variance Std.Dev.
family:order (Intercept) 0.26932 0.51896
order (Intercept) 0.00000 0.00000
Number of obs: 1242, groups: family:order, 43; order, 9
Fixed effects:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -0.10413 0.98004 -0.106 0.91538