I'm convinced that I do NOT understand your question, but the most
probable answer given my prior is "no". To get a more specific answer,
please provide more detail about the problem you are trying to solve,
preferably including a simple, self-contained example, e.g., the
examples in the "lmer" and "lmer-class" help pages. If the CarCorr and
vcov functions do not answer your questions, please explain why not,
e.g. using for illustration a 'predict' example applied to lme {nlme} or
glm.
I realize this does not answer your question, but I hope it gets you
closer.
Buena Suerte,
spencer graves
Per Theilgaard wrote:
> Dear R help
> I have a question on the variance of the binomial probit model.
>
> I have fitted the following model :
>
>> lmer1<-lmer(mp ~ l + op + l*op+ us_lev + bw_lev +(1|tatu) ,
> + family = binomial(link="probit"),
> + method = 'Laplace',
> + data = matings,
> + msVerbose= True)
>> summary(lmer1)
> Generalized linear mixed model fit using Laplace
> Formula: mp ~ l + op + l * op + us_lev + bw_lev + (1 | tatu)
> Data: matings
> Family: binomial(probit link)
> AIC BIC logLik deviance
> 1455.121 1556.387 -706.5605 1413.121
> Random effects:
> Groups Name Variance Std.Dev.
> tatu (Intercept) 0.42905 0.65502
> # of obs: 918, groups: tatu, 130
>
> Estimated scale (compare to 1) 0.8521378
>
> I suppose that the dispersion parameter has been restricted to be 1.
> Then the variance would be 1 + the variance of the random effect: 1+ 0.42905
> ???
>
> Thany you very much in advance
> Per
>
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