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
[[alternative HTML version deleted]]
______________________________________________
R-help@r-project.org mailing list
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
