[R-meta] Random slopes in rma.mv

-----Original Message-----
From: Francisco Tapia [mailto:francisco.ninel at hotmail.com]
Sent: Sunday, 23 May, 2021 21:25
To: Viechtbauer, Wolfgang (SP); r-sig-meta-analysis at r-project.org
Subject: RE: Random slopes in rma.mv

I thought adding 0 accounted for uncorrelated random intercept and slopes -> (~ X
+ 0 | ID2), equivalently (~ X || ID2). From what you tell me, 0 and -1 would be
analogous then.
Regarding the last question, it was purely exploratory. I'll be applying profile()
to test it out.

Thanks Wolfgang!
From: Viechtbauer, Wolfgang (SP)
Sent: Sunday, May 23, 2021 3:11 PM
To: Francisco Tapia; r-sig-meta-analysis at r-project.org
Subject: RE: Random slopes in rma.mv

-----Original Message-----
From: R-sig-meta-analysis [mailto:r-sig-meta-analysis-bounces at r-project.org] On
Behalf Of Francisco Tapia
Sent: Sunday, 23 May, 2021 20:33
To: r-sig-meta-analysis at r-project.org
Subject: Re: [R-meta] Random slopes in rma.mv

Thanks for your answers Wolfgang. I'll clarify some points:

 1.  Indeed, the structure (~X | ID2/ID1) does not work. This point connected
with the 2nd one, as I wanted to know how to add the random slopes just for ID2.
 2.  Could the following structure work in the same way?

Random = list( ~ 1 | ID2, ID1, ~ X -1 | ID2) (Using the lmer syntax)

I think you meant: list(~ 1 | ID2/ID1, ~ X -1 | ID2)

You could do that too, but this implies that the random intercepts for ID2 are
assumed to be uncorrelated with the random slopes for ID2. With

list(~ 1 | interaction(ID2,ID1), ~ X | ID2)

the random intercepts and slopes are allowed to be correlated.

 1.  So sorry it wasn't clear enough. I meant:

Level 1: Effect sizes.

Level 2: ID1

Level 3: ID2 and ID3

Which translates to:

ES / ID1 / (ID2 & ID3), therefore I would have the effect size Yi(jk), where the
i-eth outcome is crossed between ID2 and ID3, both at the same level 3.

If you want to know if you could do something like random = list(~ x | ID2, ~ x |
ID3), then yes, in principle that is possible. I don't know whether this makes
sense in the context of your data or whether the parameters of such a model are
identifiable (profile() can help to determine the latter).

Thanks again for your answers!

Francisco Tapia

From: Viechtbauer, Wolfgang
(SP)<mailto:wolfgang.viechtbauer at maastrichtuniversity.nl>
Sent: Sunday, May 23, 2021 12:45 PM
To: Francisco Tapia<mailto:Francisco.ninel at hotmail.com>; r-sig-meta-analysis at r-
project.org<mailto:r-sig-meta-analysis at r-project.org>
Subject: RE: Random slopes in rma.mv

Dear Francisco,

See below for my responses.

Best,
Wolfgang

-----Original Message-----
From: R-sig-meta-analysis [mailto:r-sig-meta-analysis-bounces at r-project.org] On
Behalf Of Francisco Tapia
Sent: Thursday, 20 May, 2021 18:29
To: r-sig-meta-analysis at r-project.org
Subject: [R-meta] Random slopes in rma.mv

Dear metanalysis community:

A couple of days ago, Wolfgang provided me the information to add random slopes

to

rma.mv, from the metafor package
(https://stats.stackexchange.com/questions/524144/random-slopes-in-rma-mv)

By changing the struct to "GEN", I can now add random to my multilevel model. As
the documentation is not up yet, I wanted to check some things regarding

syntaxis

of the code and the logic behind it:

 1.  If I have ID1 nested within ID2, my structure of random effects would be:

(

~ 1 | ID2/ID1) for random intercepts.  If I want to add random slopes to ID2,
Should I do it in another random effect? For example, If I add random slopes to

(

~ 1 | ID2/ID1), therefore -> ( ~ X | ID2/ID1), I'll be adding random slopes for
each level of ID1 within ID2, and for ID2 as well. Should I leave ( ~ 1 |
ID2/ID1) alone and create another random effect to add random slopes just for

ID2?

~ X | ID2/ID1 doesn't work anyway (you should get an error if you try, at least

if

you have the 'devel' version installed).

 2.  If I create another random effect to add random slopes to ID2, for

example,

(~ X | ID2), Would I be adding another random intercept for ID2? If so, How, an
unnecessary intercept, can affect my model? I cannot see it very clearly

Yes, you would be adding random intercepts for each level of ID2 twice. I would
avoid doing so. You could do:

random = list(~ 1 | interaction(ID2,ID1), ~ X | ID2), struct="GEN"

to add random intercepts for each ID2-ID1 combination (i.e., for ID1 nested

within

ID2) and random intercepts and slopes for each level of ID2.

 3.  If I have a crossed random effect at the same level as ID2, let's say (~ 1
|ID3) for random intercepts. Can both of them, ID2 and ID3, have different

random

slopes structure from each other, despite being in the same level?

I don't understand what you mean by ID3 being 'at the same level' as ID2.

Thanks in advance!

Francisco Tapia