[R-meta] AICc or variance components, which one matters more?

Viechtbauer, Wolfgang (SP) wo||g@ng@v|echtb@uer @end|ng |rom m@@@tr|chtun|ver@|ty@n|
Mon Nov 15 19:07:14 CET 2021


Please post the profile likelihood plots for the three variance components of model g4.

>-----Original Message-----
>From: Luke Martinez [mailto:martinezlukerm using gmail.com]
>Sent: Monday, 15 November, 2021 18:37
>To: Viechtbauer, Wolfgang (SP)
>Cc: R meta
>Subject: Re: [R-meta] AICc or variance components, which one matters more?
>
>In g4, also, all variance components are identifiable (and larger in magnitude).
>
>But what's the thinking behind either g3 or g4 where the same grouping variable
>is both nested and crossed?
>
>On Mon, Nov 15, 2021, 11:33 AM Viechtbauer, Wolfgang (SP)
><wolfgang.viechtbauer using maastrichtuniversity.nl> wrote:
>In g3, all variance components are identifiable.
>
>What have you found out about g4?
>
>>-----Original Message-----
>>From: Luke Martinez [mailto:martinezlukerm using gmail.com]
>>Sent: Monday, 15 November, 2021 18:28
>>To: Viechtbauer, Wolfgang (SP)
>>Cc: R meta
>>Subject: Re: [R-meta] AICc or variance components, which one matters more?
>>
>>Hi Wolfgang,
>>
>>Could you possibly elaborate a bit on "in this case, yes"?
>>
>>This allows me to better justify g3 or g4 models to my co-authors.
>>
>>Thanks again,
>>Luke
>>
>>On Mon, Nov 15, 2021, 11:19 AM Viechtbauer, Wolfgang (SP)
>><wolfgang.viechtbauer using maastrichtuniversity.nl> wrote:
>>>-----Original Message-----
>>>From: Luke Martinez [mailto:martinezlukerm using gmail.com]
>>>Sent: Saturday, 13 November, 2021 21:10
>>>To: Viechtbauer, Wolfgang (SP)
>>>Cc: R meta
>>>Subject: Re: [R-meta] AICc or variance components, which one matters more?
>>>
>>>Interesting! To make sure I'm following you, your suggested g3 model
>>>both considers 'study' to be nested in the 'lab', and at the same time
>>>it considers 'study' to have its own independent crossed effect. Can
>>>we consider the same variable (e.g., study) to be both nested and
>>>crossed at the same time?
>>
>>In this case, yes.
>>
>>>If so, I can then suggest the following model as well:
>>>
>>>(g4=rma.mv(yi, vi, random = list(~1|lab/study, ~ 1 | lab), data = dd))
>>
>>I suggest you profile the variance components from that model and draw
>>appropriate conclusions.
>>
>>>Doesn't this denote that one is uncertain about whether to take a
>>>variable as nested or crossed or there are other justifications?
>>>
>>>Thank you,
>>>Luke
>>>
>>>On Sat, Nov 13, 2021 at 1:11 PM Viechtbauer, Wolfgang (SP)
>>><wolfgang.viechtbauer using maastrichtuniversity.nl> wrote:
>>>>
>>>> >-----Original Message-----
>>>> >From: Luke Martinez [mailto:martinezlukerm using gmail.com]
>>>> >Sent: Saturday, 13 November, 2021 18:44
>>>> >To: Viechtbauer, Wolfgang (SP)
>>>> >Cc: Philippe Tadger; R meta
>>>> >Subject: Re: [R-meta] AICc or variance components, which one matters more?
>>>> >
>>>> >Hi Wolfgang,
>>>> >
>>>> >I'm fully with you, however, in my data only once 2 labs (labs 1 and
>>>> >2) have collaborated on study 2. Specifically, part of study 2 has
>>>> >been carried out by lab 1 (one row) and part of it by lab 2 (one row).
>>>> >Except in this case, no such between-lab collaborations have ever
>>>> >occurred in the data.
>>>> >
>>>> >If such a between-lab collaboration didn't exist, I could directly go
>>>> >for g1 (hierarchical model). But with this collaboration, there is
>>>> >just a tiny possibility for g2 (crossed model) as well.
>>>> >
>>>> >So, do you think AICc should be the basis of the comparison between g1
>>>> >vs. g2 or the dominant data structure (ignoring the one exception)?
>>>>
>>>> Using information criteria *could* be the basis. But I might be inclined to
>>>just ignore the issue you describe above though if this only affects one study.
>>>>
>>>> Just as a note: It's not necessarily an either-or choice. This model is also
>>>possible:
>>>>
>>>> (g3=rma.mv(yi, vi, random = list(~1|lab/study, ~ 1 | study), data = dd))
>>>>
>>>> and profile(g3) suggests that all variance components are identifiable -
>>>although of course this is quite overfitted with so little data.
>>>>
>>>> >(g1=rma.mv(yi, vi, random = ~1|lab/study, data = dd))
>>>> >(g2=rma.mv(yi, vi, random = list(~1|lab, ~1|study), data = dd))
>>>> >
>>>> >Thanks,
>>>> >Luke


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