[R-meta] Does clubSandwich::coef_test() handle crossed random-effects?

I don't know what "directly and immediately" means. I mean clusters where
the sampling errors (or errors of estimation), defined as the difference
between the effect size estimate and its target parameter, are correlated.

James

On Wed, Oct 6, 2021 at 9:26 PM Farzad Keyhan <f.keyhaniha at gmail.com>
wrote:

Many thanks, you mean the cluster that "directly and immediately"
contains the true and subsequently overlapping observed effects, not the
ones higher up in the hierarchy, that is the logic, correct?

Fred

On Wed, Oct 6, 2021 at 9:21 PM James Pustejovsky <jepusto at gmail.com>
wrote:

Hi Fred,
The cluster argument in impute_covariance_matrix describes sets of
effect sizes that you expect to have correlated sampling errors, which
arise if multiple effect sizes are estimated from a common sample (or from
partially overlapping samples). So in your case, use cluster = study.
James

On Wed, Oct 6, 2021 at 9:03 PM Farzad Keyhan <f.keyhaniha at gmail.com>
wrote:

Dear James,

One quick question, (recall I have 'scales' subsuming 'studies'
subsuming 'true effects'). In this case, to set up a V matrix, should I use
'study' as or 'scale' to define the 'cluster' argument in
'impute_covariance_matrix()'?

Thanks,
Fred

On Sun, Oct 3, 2021 at 9:25 PM Farzad Keyhan <f.keyhaniha at gmail.com>
wrote:

Dear James,

I explored the issue, there was a re-coding bug. One thing that I
wanted to clarify is that in addition to the 'scale > study' nesting
relationship, the same 'scale' was used to measure different 'outcomes' and
different 'scales' can be used to measure the same 'outcome' across the
studies.

Do you see any potential for crossed random-effects here?
(data attached for clarity)

Fred

dat <- read.csv("https://raw.githubusercontent.com/ilzl/i/master/j.csv
")

  study scale       yi        vi es group outcome time
1    A1    p1 1.680746 0.2081713  1     1       3    0
2    A1    p1 4.122057 0.4806029  2     2       3    0
3    A1    p1 2.600443 0.2838905  3     1       3    1
4    A1    p1 3.457194 0.3836960  4     2       3    1
5    A1    p1 1.546293 0.1998273  5     1       3    2
6    A1    p1 3.071523 0.3352741  6     2       3    2

On Sun, Oct 3, 2021 at 6:59 PM James Pustejovsky <jepusto at gmail.com>
wrote:

On Sun, Oct 3, 2021 at 1:18 PM Farzad Keyhan <f.keyhaniha at gmail.com>
wrote:

I see, I'm still exploring to see what has caused the two models in
my previous email to give slightly different fits. Still curious though,
for 'scale' and 'study' to have been crossed random effects, 'scale' should
have varied in each study?

Yes.