[R] Hoe to get RESIDUAL VARIANCE in logistic regression using lmer

[ONKELINX, Thierry]

Dear Tommaso,

The residuals variance is fixed at 1 with the binomial family.

HTH,

Thierry


------------------------------------------------------------------------
----
ir. Thierry Onkelinx
Instituut voor natuur- en bosonderzoek / Research Institute for Nature
and Forest
Cel biometrie, methodologie en kwaliteitszorg / Section biometrics,
methodology and quality assurance
Gaverstraat 4
9500 Geraardsbergen
Belgium 
tel. + 32 54/436 185
Thierry.Onkelinx at inbo.be 
www.inbo.be 

To call in the statistician after the experiment is done may be no more
than asking him to perform a post-mortem examination: he may be able to
say what the experiment died of.
~ Sir Ronald Aylmer Fisher

The plural of anecdote is not data.
~ Roger Brinner

The combination of some data and an aching desire for an answer does not
ensure that a reasonable answer can be extracted from a given body of
data.
~ John Tukey

-----Oorspronkelijk bericht-----
Van: r-help-bounces at r-project.org [mailto:r-help-bounces at r-project.org]
Namens tomal
Verzonden: donderdag 30 april 2009 12:10
Aan: r-help at r-project.org
Onderwerp: [R] Hoe to get RESIDUAL VARIANCE in logistic regression using
lmer


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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