[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