Dear all,
I am using function glmer from package lme4 to fit a generalized linear
mixed effect model. My model is as follows:
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
Fixed effects:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -2.332132 0.856518 -2.723 0.006473 **
Dist 0.001137 0.001141 0.997 0.318902
WireControl 4.710750 1.196550 3.937 8.25e-05 ***
Dist:WireControl -0.006180 0.001769 -3.494 0.000475 ***
---
Signif. codes: 0 *** 0.001 ** 0.01 * 0.05 . 0.1 1
Correlation of Fixed Effects:
(Intr) Dist WirCnt
Dist -0.963
WireContrl -0.716 0.689
Dst:WirCntr 0.621 -0.645 -0.957
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.
Does anyone know how to get information about overdispersion in the model?
Thanks in advance for your help,
(Ubuntu Intrepid Ibex / R 2.7.1)
Luis Cayuela
Investigador Post-doctoral
Grupo de Ecología Terrestre
Departamento de Ecología
Centro Andaluz de Medio Ambiente, Universidad de Granada - Junta de Andalucía
Avda. del Mediterráneo S/N. 18006. Granada. España
email: lcayu...@ugr.es
Fax: +34 958137246
Tel: +34 958241000 (ext. 31202)
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