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
gt; Sent: Tuesday, March 17, 2009 12:05 PM
> To: r-help@r-project.org
> Subject: [R] Multilevel Modeling using R
>
> Dear experts,
>
> I use R to conduct multilevel modeling. However, I
> have a problem about the interpretation of random effect.
> U
Dear experts,
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 variables in random effects have not shown the standard error
(s.e.) and p-value, so I don't know whether the
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
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 variables in random effects have not shown the p-value, so I don't
know whether they are significant or not? I want to obta
7 matches
Mail list logo
