Hi,

When looking at the R book, it suggests one should be able to check for
overdispersion when looking at the summary output of a mixed model with
proportional data, but I am confused about how to assess this since it does
not give residual deviance or degrees of freedom.  Any help on this would
be greatly appreciated!

Here is the output I receive after fitting the model:

> model<-lmer(y~ Treatment*Type + (1|Site), family=binomial)
> summary(model)
Generalized linear mixed model fit by the Laplace approximation
Formula: y ~ Treatment * Type + (1 | Site)
   AIC   BIC logLik deviance
 37.39 47.99 -9.694    19.39
Random effects:
 Groups Name        Variance  Std.Dev.
 Site   (Intercept) 0.0023005 0.047964
Number of obs: 24, groups: Site, 12

Fixed effects:
                                  Estimate Std. Error z value Pr(>|z|)
(Intercept)                         0.9483     0.2246   4.222 2.43e-05 ***
TreatmentIntensive                 -0.8282     0.3607  -2.296 0.021685 *
TreatmentSingle Tree               -1.2107     0.2943  -4.114 3.89e-05 ***
TreatmentTypical                   -1.2505     0.3200  -3.908 9.32e-05 ***
TypeTransect                       -1.3318     0.2713  -4.909 9.18e-07 ***
TreatmentIntensive:TypeTransect     0.6920     0.4343   1.593 0.111119
TreatmentSingle Tree:TypeTransect   1.0884     0.3848   2.828 0.004683 **
TreatmentTypical:TypeTransect       1.3662     0.3864   3.535 0.000407 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Correlation of Fixed Effects:
            (Intr) TrtmnI TrtmST TrtmnT TypTrn TrI:TT TST:TT
TrtmntIntns -0.623
TrtmntSnglT -0.763  0.475
TrtmntTypcl -0.702  0.437  0.536
TypeTransct -0.814  0.507  0.622  0.572
TrtmntIn:TT  0.509 -0.820 -0.388 -0.357 -0.625
TrtmntST:TT  0.574 -0.358 -0.751 -0.403 -0.705  0.440
TrtmntTy:TT  0.572 -0.356 -0.436 -0.815 -0.702  0.439  0.495

Thanks!
Kristen

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