[R-meta] rma.mv: why some var components change but others don't across 2 models
Stefanou Revesz
@te|@noureve@z @end|ng |rom gm@||@com
Sat Oct 30 21:09:57 CEST 2021
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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