Hi everyone,
I need to get a between-component variance (e.g. random effects Anova),
but using lmer I don't get the same results (variance component) than
using random effects Anova. I am using a database of students, clustered
on schools (there is not the same number of students by school).
According to the ICC1 command, the interclass correlation is .44
> ICC1(anova1)
[1] 0.4414491
However, I cannot get the same ICC from the lmer output:
> anova2 <- lmer(math ~ 1 + (1|schoolid), data=nels88)
> summary(anova2 <- lmer(math ~ 1 + (1|schoolid), data=nels88))
Linear mixed model fit by REML
Formula: math ~ 1 + (1 | schoolid)
Data: nels88
AIC BIC logLik deviance REMLdev
1878 1888 -935.8 1875 1872
Random effects:
Groups Name Variance Std.Dev.
schoolid (Intercept) 34.011 5.8319
Residual 72.256 8.5003
Number of obs: 260, groups: schoolid, 10
Fixed effects:
Estimate Std. Error t value
(Intercept) 48.861 1.927 25.36
The intercept random effect is 34.011. If I compute the ICC manually I get:
> 34.011/(34.011+72.256)
[1] 0.3200523
According to my Anova analysis, the between-component variance is 59.004.
Does anyone know what the problem is? How can I get the 59.004 figure
using R?
--
Sebastián Daza
sebastian.d...@gmail.com
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