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

[Farzad Keyhan]

Just to distinguish V elements from correlated random effects elements,
when making a multivariate model due to say variable C_ijk (recall I have
'scales' subsuming 'studies' subsuming 'true effects'), then we can assume
Cov(u_{bjk}, u_{cjk}) to be our correlated random effects at the 'study'
level, but could also assume Cov(u_{bk}, u_{ck}) as our correlated random
effect at the 'scale' level, correct?

On Wed, Oct 6, 2021 at 10:04 PM Farzad Keyhan <f.keyhaniha using gmail.com> wrote:

> Sure, I think I meant the same thing, I meant the cluster that contains
> individual effects not higher clusters that contain aggregated effects.
>
> Thanks very much,
> Fred
>
> On Wed, Oct 6, 2021 at 9:30 PM James Pustejovsky <jepusto using gmail.com>
> wrote:
>
>> 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 using 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 using 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 using 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 using 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 using gmail.com>
>>>>>> wrote:
>>>>>>
>>>>>>> On Sun, Oct 3, 2021 at 1:18 PM Farzad Keyhan <f.keyhaniha using 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.
>>>>>>>
>>>>>>

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