Hello everybody,
using the lmer function, I have fitted the following logistic mixed
regression model on an experimental data set containing one fixed factor
(Cond) and three random variables (Sito, Area, Trans):
> model<-lmer(Caul~Cond+(1|Sito)+(1|Area)+(1|Trans), data=dataset,
> family=binomial)
this is the output:
> summary(model)
Generalized linear mixed model fit by the Laplace approximation
Formula: Caul ~ Cond + (1 | Sito) + (1 | Area) + (1 | Trans)
Data: dataset
AIC BIC logLik deviance
548.7 573.7 -268.3 536.7
Random effects:
Groups Name Variance Std.Dev.
Trans (Intercept) 3.2313398 1.797593
Area (Intercept) 0.0000000 0.000000
Sito (Intercept) 0.0047151 0.068667
Number of obs: 480, groups: Trans, 48; Area, 12; Sito, 2
As you can see the residual variance is missing. Can anybody tell me why?
Does anybody know how can I get it?
Thank you for your attention, I wish somebody can help me.
Have a nice day, best regards,
Tommaso Alestra
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