Thank you very much Mr Onkelinx, I appreciated your answer. I'm sorry about my lack of knowledge on this subject, but I don't understand why residual variance is fixed at 1 with the binomial family and I didn't manage to find any exhaustive explanation for that. May you kindly suggest me some reference I can consult in order to go into depth on this matter? Does anybody else can profide me furher information about this topic? Thank you very much. Best regards, Tommaso Alestra
ONKELINX, Thierry wrote:
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 -- View this message in context: http://www.nabble.com/Hoe-to-get-RESIDUAL-VARIANCE-in-logistic-regressio n-using-lmer-tp23313330p23313330.html Sent from the R help mailing list archive at Nabble.com.
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