[R-meta] rma.mv: why some var components change but others don't across 2 models
Viechtbauer, Wolfgang (SP)
wo||g@ng@v|echtb@uer @end|ng |rom m@@@tr|chtun|ver@|ty@n|
Mon Nov 1 12:09:14 CET 2021
Sounds right.
Best,
Wolfgang
>-----Original Message-----
>From: Stefanou Revesz [mailto:stefanourevesz using gmail.com]
>Sent: Saturday, 30 October, 2021 21:10
>To: Viechtbauer, Wolfgang (SP)
>Cc: R meta
>Subject: Re: rma.mv: why some var components change but others don't across 2
>models
>
>Oops. I was referring to your linked post:
>https://stat.ethz.ch/pipermail/r-sig-meta-analysis/2018-July/000896.html
>
>study outcome measure study.outcome.measure
>1 A 1 1.A.1
>1 B 1 1.B.1
>2 A 1 2.A.1
>3 A 2 3.A.2
>3 B 1 3.B.1
>3 C 2 3.C.2
>4 B 1 4.B.1
>
>list(~ 1 | study, ~1|outcome, ~ 1 | measure) would mean that rows that
>share a study, share an outcome, and share a measure, separately can
>get their own similar random effects.
>
>list(~ 1 | study/outcome, ~ 1 | measure) would mean that rows that
>share a study, and then within each study, rows that share an outcome,
>can separately get their own similar random effects. Additionally,
>rows that share a measure can get their own similar random effects.
>
>Am I correctly describing the differences?
>
>So, when "~1|outcome" from `res` model, and "study/outcome" component
>from `res2` ONLY NUMERICALLY are similar, then that means that the
>amount of variance estimated for these two completely different types
>of random-effects is the same; completely by coincidence.
>
>Thanks very much,
>Stefanou
>
>On Sat, Oct 30, 2021 at 12:35 PM Stefanou Revesz
><stefanourevesz using gmail.com> wrote:
>>
>> Sure, to confirm differences between the two models, can we say model
>> `res` (i.e., list(~ 1 | study, ~1|outcome, ~ 1 | measure)) views the
>> random effects this way:
>>
>> res_model <- with(m, interaction(study,outcome,measure))
>>
>> But model `res2` (i.e., list(~ 1 | study/outcome, ~ 1 | measure))
>> views random effects this way:
>>
>> res2_model <- with(m, interaction(interaction(study,outcome), measure))
>>
>> Is this correct?
>>
>> Stefanou
>>
>> On Sat, Oct 30, 2021 at 11:23 AM Viechtbauer, Wolfgang (SP)
>> <wolfgang.viechtbauer using maastrichtuniversity.nl> wrote:
>> >
>> > 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
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