[R-meta] rma.mv: why some var components change but others don't across 2 models

[Viechtbauer, Wolfgang (SP)]

These are totally different models, so I would not read anything into this. It is purely a coincidence.

Best,
Wolfgang

>-----Original Message-----
>From: Stefanou Revesz [mailto:stefanourevesz using gmail.com]
>Sent: Saturday, 30 October, 2021 18:19
>To: Viechtbauer, Wolfgang (SP)
>Cc: R meta
>Subject: Re: rma.mv: why some var components change but others don't across 2
>models
>
>Wolfgang, you're a lifesaver! That's such a confusing coincidence!
>
>As we inch toward the last few studies, the variance component for
>'outcome' across `res` (fully crossed model), and `res2` (nested +
>crossed model) get more and more similar.
>
>Does this say anything about the data structure up to these last few
>studies vs. that of the last few studies? (I'm still in shock, and
>want to rationalize why this is happening to me)
>
>res <- rma.mv(yi, vi, random = list(~ 1 | study, ~1 | outcome, ~ 1 |
>measure), data=m, subset=study <= 54)
>res2 <- rma.mv(yi, vi, random = list(~ 1 | study/outcome, ~ 1 |
>measure), data=m, subset=study <= 54)
>
>Stefanou
>
>On Sat, Oct 30, 2021 at 11:03 AM Viechtbauer, Wolfgang (SP)
><wolfgang.viechtbauer using maastrichtuniversity.nl> wrote:
>>
>> The values are not exactly identical and it is coincidence that they end up
>looking that way when rounded to 4 decimal places. For example try:
>>
>> res <- rma.mv(yi, vi, random = list(~ 1 | study, ~1 | outcome, ~ 1 | measure),
>data=m, subset=study <= 20)
>> res2 <- rma.mv(yi, vi, random = list(~ 1 | study/outcome, ~ 1 | measure),
>data=m, subset=study <= 20)
>>
>> and they are rather different.
>>
>> Best,
>> Wolfgang
>>
>> >-----Original Message-----
>> >From: Stefanou Revesz [mailto:stefanourevesz using gmail.com]
>> >Sent: Saturday, 30 October, 2021 15:06
>> >To: Viechtbauer, Wolfgang (SP)
>> >Cc: R meta
>> >Subject: Re: rma.mv: why some var components change but others don't across 2
>> >models
>> >
>> >Dear Wolfgang,
>> >
>> >Thank you for your reply. I did check that previously. But my question is why
>> >'outcome' gives the same variance component across both res (with 4 levels)
>and
>> >res2 (with 68 levels) models?
>> >
>> >Thank you so much,
>> >Stefanou
>> >
>> >On Sat, Oct 30, 2021, 7:08 AM Viechtbauer, Wolfgang (SP)
>> ><wolfgang.viechtbauer using maastrichtuniversity.nl> wrote:
>> >Dear Stefanou,
>> >
>> >With the way you have 'outcome' coded, these two formulations are not
>equivalent.
>> >I believe this post discusses this:
>> >
>> >https://stat.ethz.ch/pipermail/r-sig-meta-analysis/2018-July/000896.html
>> >
>> >Best,
>> >Wolfgang
>> >
>> >>-----Original Message-----
>> >>From: Stefanou Revesz [mailto:stefanourevesz using gmail.com]
>> >>Sent: Friday, 29 October, 2021 17:24
>> >>To: R meta
>> >>Cc: Viechtbauer, Wolfgang (SP)
>> >>Subject: rma.mv: why some var components change but others don't across 2
>models
>> >>
>> >>Dear Wolfgang and Expert List Members,
>> >>
>> >>Why `study` with 57 levels in model `res` gives `sigma^2.1 = 0.0200`
>> >>but `study` with 57 levels in model `res2` gives `sigma^2.1  =
>> >>0.0122`?
>> >>(SAME LEVELS BUT DIFFERENT RESULTS)
>> >>
>> >>Why `outcome` with 4 levels in model `res` gives `sigma^2.2 = 0.0093`
>> >>but `outcome` with 68 levels in model `res2` gives `sigma^2.2  =
>> >>0.0093`?
>> >>(DIFFERENT LEVELS BUT SAME RESULTS)
>> >>
>> >>For reproducibility, below are my data and code.
>> >>
>> >>Many thanks to you all,
>> >>Stefanou
>> >>
>> >>m <- read.csv("https://raw.githubusercontent.com/fpqq/w/main/c.csv")
>> >>
>> >>res <- rma.mv(yi, vi, random = list(~ 1 | study, ~1|outcome, ~ 1 |
>> >>measure), data=m)
>> >>                    estim       sqrt  nlvls  fixed   factor
>> >>sigma^2.1  0.0200  0.1415     57     no    study
>> >>sigma^2.2  0.0093  0.0964      4     no  outcome
>> >>sigma^2.3  0.0506  0.2249      7     no  measure
>> >>
>> >>res2 <- rma.mv(yi, vi, random = list(~ 1 | study/outcome, ~ 1 |
>> >>measure), data=m)
>> >>                    estim       sqrt  nlvls  fixed         factor
>> >>sigma^2.1  0.0122  0.1105     57     no          study
>> >>sigma^2.2  0.0093  0.0964     68     no  study/outcome
>> >>sigma^2.3  0.0363  0.1904      7     no        measure