[R-sig-ME] Interpreting the output of summary() of a glmer-object

[Hans Ekbrand]

On Tue, Sep 25, 2012 at 02:04:19AM +0000, Ben Bolker wrote:
> Hans Ekbrand <hans at ...> writes:
> 
> > First, I have a very simple question. In the summary output of a
> > glmer-object, What does the "Variance" and "Std.Dev" mean for the
> > Random effects? What is the scale for these measures?
> 
>   It's a little hard to think of a way to say this that doesn't
> seem redundant ... "Variance" is the estimated variance of the
> random effects, "Std.Dev" is the standard deviation (i.e. the
> square root of the variance -- these quantities give redundant
> information; seeing the variance can be useful because of the
> additivity of variances and the traditional presentation of
> mixed models in terms of variance decomposition, while the
> standard deviation can be useful because it is on the same scale
> as the estimated fixed-effect coefficients).  The scale is the
> same as the scale of the fixed-effect coefficients, i.e. the
> scale of the linear predictor.

Thanks alot Ben for taking the time to explain the basics, it really
helps me!

> > 
> > load(url("http://sociologi.cjb.net/temp/a.strange.df.RData"))
> > my.fit.1 <- glmer(MV744A ~ (1|MV024), 
> >    data = a.strange.df, family = "binomial")
> > summary(my.fit.1)
> > 
> > Generalized linear mixed model fit by the Laplace approximation 
> > Formula: MV744A ~ (1 | MV024) 
> >    Data: a.strange.df 
> >    AIC   BIC logLik deviance
> >  76209 76227 -38102    76205
> > Random effects:
> >  Groups Name        Variance Std.Dev.
> >  MV024  (Intercept) 0.40558  0.63685 
> > Number of obs: 73601, groups: MV024, 29
> > 
> > Fixed effects:
> >             Estimate Std. Error z value Pr(>|z|)    
> > (Intercept)  -1.4187     0.1191  -11.91   <2e-16 ***
> > ---
> > Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 


So, if I understand you - which I think I do - then 0.63685 is simply the standard deviation of ranef(my.fit.1)?

When I try to compute that manually I get a numerically close figure, but not quite the same:

sd(unlist(ranef(my.fit.1)))
[1] 0.6423346

I am on the right track?