How to estimate the standard error of every single random intercept in a mixed linear model?

Dear Phillip,

Thanks for the reply. yes,?se,ranef directly calculates the square
root of the posterior variance.?


attr(ranef(fit, condVar = TRUE)[[1]], "postVar")

Do you know how the posterior variance for each level of the random
factor is computed?

Thanks

Regards,
Chen

???: Phillip Alday <Phillip.Alday at unisa.edu.au>
????: 2016?11?4? 5:29
???: Chen Chun
??: r-sig-mixed-models at r-project.org
??: Re: [R-sig-ME] How to estimate the standard error of every single
random intercept in a mixed linear model?
?
Dear Chen,

have you tried entering the following in the R command line?

library(arm)
se.ranef

That will output the source code for arm::se.ranef. It's surprisingly
simple and is basically the square root of the diagonal of variance-
covariance RE matrix.

Best,
Phillip?

On 24 Oct 2016, at 20:38, Chen Chun <talischen at hotmail.com> wrote:
?
Dear all,
?
?
I am running a mixed linear model with group (a_i) as random

intercept:

?
?
y_ij=mu + a_i + e_ij
?
?
By using lmer() function, the model outputs an estimated variance

of a_i (i.e. var_hat(a)), and it is the sum of (1) the variance of
the estimated group mean (i.e. between group variance) and (2) the
sum of variance for each estimated group mean a_i_hat,?? (i.e. sum of
within group variance).

?
?
for (1) I can compute it as var(ranef(model)$group). However, I

dont know how to compute (2), which is the SE of the estimated random
intercept for each group. I know that using se.ranef() function in
arm package can help me to extract such variance. But I would like to
know how these variance are computed? it's relations to residuals and
number of observations per group?

?
?
Thanks
?
?
Chen
?
?
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?

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