In most biometric applications, those variances are treated as
nuisance parameters. They only need to be controlled for, while the
main purpose is to get the right point estimates and standard errors
for the fixed effects. In social science multilevel modeling (of which
education is probably the he
That's by intent, by the way. The standard errors of the variance
components are only useful if the distribution is symmetric, and this is
not always true. If you were using lmer, and not lme, then you could use
the mcmcsamp function to look at the distribution of the random effects
to see if it is
rds,
> Tommy
> Research Assistant of HKIEd
>
> From: ronggui [mailto:ronggui.hu...@gmail.com]
> Sent: 17/3/2009 [Tue] 14:10
> To: WONG, Ka Yau
> Cc: r-help@r-project.org
> Subject: Re: [R] Multilevel modeling using R
>
> You can use in
-project.org
Subject: Re: [R] Multilevel modeling using R
You can use intervals to get the Confidence intervals of fixed and
random effects.
Best
2009/3/17 WONG, Ka Yau :
> Dear All,
>
> I use R to conduct multilevel modeling. However, I have a problem
> about the interpretati
You can use intervals to get the Confidence intervals of fixed and
random effects.
Best
2009/3/17 WONG, Ka Yau :
> Dear All,
>
> I use R to conduct multilevel modeling. However, I have a problem
> about the interpretation of random effect. Unlike the variables in fixed
> effects, the va
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