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

[Farzad Keyhan]

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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