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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