[R] Multilevel Modeling using R

[Doran, Harold]

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 approximately normal. But, there are other ways to check
and see if retaining the variance component is necessary or not, such as
LRT statistics. 

> -----Original Message-----
> From: r-help-bounces at r-project.org 
> [mailto:r-help-bounces at r-project.org] On Behalf Of WONG, Ka Yau
> Sent: Tuesday, March 17, 2009 12:05 PM
> To: r-help at 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. 
> 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 they are significant or not? 
> I want to obtain these figures to make the decision. Thank 
> you for your great help!
>  
> Below is the syntax and output of my program:
>  
> library(nlme)
> dataset <- read.csv("d:/dataset.csv")
> lme11 <- lme(Overall~1, random=~1|School, method="ML", data=dataset)
> summary(lme11)
>  
> Linear mixed-effects model fit by maximum likelihood
> Data: dataset
>        AIC      BIC   logLik
>   12637.06 12656.27 -6315.53
> Random effects:
> Formula: ~1 | School
>                (Intercept)  Residual
> StdDev:   0.2912031 0.9894488        (<-- No s.e. & p-value)
> Fixed effects: Overall ~ 1
>                     Value       Std.Error       DF    t-value 
>     p-value
> (Intercept) 0.7755495 0.06758038 4444 11.47596       0        
>     (<-- Have s.e. & p-value)
> Standardized Within-Group Residuals:
>     Min          Q1           Med           Q3            Max
> -3.797466473 -0.661750231 -0.007874993  0.652625939  
> 3.549169733 Number of Observations: 4464 Number of Groups: 20
>  
> Best Regards,
> Tommy
> Research Assistant of HKIEd
> 
> 
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