R help - Mar 2009 - Multilevel modeling using R

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 obtain this figure to make the
decision.  Thanks a lot!
 
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 p-value)
 
Fixed effects: Overall ~ 1 
                      Value      Std.Error      DF     t-value     p-value
(Intercept) 0.7755495 0.06758038 4444 11.47596       0            (<-- Have
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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You can use intervals to get the Confidence intervals of fixed and
random effects.

Best

2009/3/17 WONG, Ka Yau <kayau at ied.edu.hk>:
> 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 obtain this figure
to make the decision. ?Thanks a lot!
>
> 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 p-value)
>
> Fixed effects: Overall ~ 1
> ? ? ? ? ? ? ? ? ? ? ?Value ? ? ?Std.Error ? ? ?DF ? ? t-value ? ? p-value
> (Intercept) 0.7755495 0.06758038 4444 11.47596 ? ? ? 0 ? ? ? ? ? ?(<--
Have 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
>
> ? ? ? ?[[alternative HTML version deleted]]
>
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> PLEASE do read the posting guide
http://www.R-project.org/posting-guide.html
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>


-- 
HUANG Ronggui, Wincent
Tel: (00852) 3442 3832
PhD Candidate
Dept of Public and Social Administration
City University of Hong Kong
Home page: http://asrr.r-forge.r-project.org/rghuang.html

A sociologist is someone who, when a beautiful women enters the room
and everybody look at her, looks at everybody.

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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