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-regression-using-lmer-tp23313330p23313330.html Sent from the R help mailing list archive at Nabble.com. ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
