[R-sig-ME] Interpreting the output of summary() of a glmer-object
Hans Ekbrand
hans at sociologi.cjb.net
Tue Sep 25 23:20:41 CEST 2012
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?
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