[R-meta] Time as indicator vs time as meaning

Thank you very much Wolfgang. That was exactly what I was looking for.

Thank you so much!
Stefanou

On Tue, Oct 12, 2021 at 11:44 AM Viechtbauer, Wolfgang (SP)
<wolfgang.viechtbauer at maastrichtuniversity.nl> wrote:

That's seems somewhat redundant and I suspect profile() would tell you as much. But it might also depend on how much data you have. In a large dataset, one might be able to distinguish correlation that has the structure as assumed by (C)AR from whatever correlation is not like that and that might get picked up by "UN", but "UN" is already as flexible as possible (in the sense of not assuming any structure of the correlations), so again, it's a bit redudant.

Best,
Wolfgang

-----Original Message-----
From: Stefanou Revesz [mailto:stefanourevesz at gmail.com]
Sent: Tuesday, 12 October, 2021 18:25
To: Viechtbauer, Wolfgang (SP)
Cc: R meta
Subject: Re: [R-meta] Time as indicator vs time as meaning

It does very much help!

But does the same logic apply, if I had used the categorical "time"
(measurement occasions) with struct = "AR", then would it have made
sense to add a "UN" on top of
that?

yi ~ time, random = list(~ time | study, ~ time | study), struct = c("AR", "UN")

On Tue, Oct 12, 2021 at 11:13 AM Viechtbauer, Wolfgang (SP)
<wolfgang.viechtbauer at maastrichtuniversity.nl> wrote:

I don't really know how to respond to that, except to repeat what I said in my

previous mail. Or do this:

Draw two lines with different intercepts and slopes. ~ time_wthn | study with

struct="GEN" is using random effects to account for differences in intercepts and
slopes of those lines.

Now add a bunch of points around those two lines. Those are the true effects

for those two studies. Those points could be autocorrelated -- that's captured by
phi. Also, they differ from the lines. That source of heterogeneity is captured
by gamma^2.

That is what is happening here and it is conceptually analogous to what is

happening in lme() (except that in a meta-analysis model, there is the further
differentiation between the observed and true effects and the sampling errors
could also exhibit autocorrelation, but let's leave this complication aside).

Another way to put this: The error term of a model is also a random effect.

It's not typically denoted this way, but such semantic differences can be
misleading. Or put differently: What you put in 'random' and what you put in
'correlation' in lme() is still about the same data. What these things do (i.e.,
what they account for in the data) in the context of a model is different, but
again, it's all about modeling the data.

Not sure if this helps.

Best,
Wolfgang

-----Original Message-----
From: Stefanou Revesz [mailto:stefanourevesz at gmail.com]
Sent: Tuesday, 12 October, 2021 17:51
To: Viechtbauer, Wolfgang (SP)
Cc: R meta
Subject: Re: [R-meta] Time as indicator vs time as meaning

Sure, but in the lme(), "correlation=" has to do with the structure of
V matrix (i.e., e_ij), not random-effects, no?

Say, I had used the categorical "time" (measurement occasions) with
struct = "HAR", then would it have made sense to add a "UN" on top of
that?

yi ~ time, random = list(~ time | study, ~ time | study), struct =
c("HAR", "UN")

Best of all,
Stefanou