[R-meta] metafor::matreg() and its workflow

[Stefanou Revesz]

Thanks Wolfgang! My understanding is that:

1- res$G would tell us how slopes (for continuous moderators) or
categories (assuming having used 0 + cat_mod in the random part) vary
and covary with one another. As a further step, we can turn such a
var-covar matrix and obtain regression weights out of it by taking one
of them as the dependent variable and one or more of them as the
independent variable(s). That is, this is equivalent to SEM analysis
(i.e., regression with the latent factors).

2- vcov(res) tells us how the means of latent factors vary and covary
with one another. As a further step, we can turn such a var-covar
matrix and obtain regression weights out of it by taking one of them
as the dependent variable and one or more of them as the independent
variable(s). That is, this is equivalent to a path analysis (i.e.,
regression with mean of latent factors).

Is this why you noted "They address very different questions"?

Many thanks,
Stefanou

On Thu, Dec 16, 2021 at 9:37 AM Viechtbauer, Wolfgang (SP)
<wolfgang.viechtbauer using maastrichtuniversity.nl> wrote:
>
> Hi Stefanou,
>
> They address very different questions, so I would say neither is more useful than the other.
>
> Best,
> Wolfgang
>
> >-----Original Message-----
> >From: Stefanou Revesz [mailto:stefanourevesz using gmail.com]
> >Sent: Monday, 13 December, 2021 22:15
> >To: Viechtbauer, Wolfgang (SP)
> >Cc: R meta
> >Subject: Re: metafor::matreg() and its workflow
> >
> >Super helpful! For some reason, the devel version doesn't get
> >installed on my machine (must be an R issue; mine's a version 4.0).
> >
> >At some point, one might say which regression is more useful, the one
> >on the means from the fixed effects or the one on the true effects
> >from the random effects!
> >
> >rma.mv(yi ~ 0 + outcome*group, V, random = ~ 0 + outcome*group |
> >study, struct = "GEN")
> >
> >Thank you so very much!
> >Stefanou
> >
> >On Mon, Dec 13, 2021 at 2:59 PM Viechtbauer, Wolfgang (SP)
> ><wolfgang.viechtbauer using maastrichtuniversity.nl> wrote:
> >>
> >> Here is an example using G as input to matreg():
> >>
> >> https://www.metafor-
> >project.org/doku.php/analyses:vanhouwelingen2002#regression_of_true_log_odds
> >>
> >> (you will need the devel version of metafor for the cvvc="varcov" part).
> >>
> >> Best,
> >> Wolfgang
> >>
> >> >-----Original Message-----
> >> >From: Stefanou Revesz [mailto:stefanourevesz using gmail.com]
> >> >Sent: Monday, 13 December, 2021 21:20
> >> >To: Viechtbauer, Wolfgang (SP)
> >> >Cc: R meta
> >> >Subject: Re: metafor::matreg() and its workflow
> >> >
> >> >Thank you so much! One clarification question. matreg() is not
> >> >effect-size specific, correct? I mean you may have meta-analyzed any
> >> >type effect size (SMD, ROM, OR, ...) and then subject the vcov() or G
> >> >or H matrices of those meta-analyses to matreg(), correct?
> >> >
> >> >Thanks again,
> >> >Stefanou
> >> >
> >> >On Mon, Dec 13, 2021 at 12:53 PM Viechtbauer, Wolfgang (SP)
> >> ><wolfgang.viechtbauer using maastrichtuniversity.nl> wrote:
> >> >>
> >> >> There is an upcoming talk by Suzanne Jak on MASEM at this seminar series:
> >> >>
> >> >> https://www.srmasig.org/seminar/
> >> >>
> >> >> Might be of interest.
> >> >>
> >> >> Best,
> >> >> Wolfgang
> >> >>
> >> >> >-----Original Message-----
> >> >> >From: Stefanou Revesz [mailto:stefanourevesz using gmail.com]
> >> >> >Sent: Thursday, 09 December, 2021 23:23
> >> >> >To: Viechtbauer, Wolfgang (SP)
> >> >> >Cc: R meta
> >> >> >Subject: Re: metafor::matreg() and its workflow
> >> >> >
> >> >> >Dear Wolfgang,
> >> >> >
> >> >> >I see, so conditioning (using predict() ) is the way to go even if
> >> >> >there is a large set of conditions.
> >> >> >
> >> >> >Related to the above, if instead of vcov(), one intends to use G and H
> >> >> >matrices (latent regression), would that also require conditioning on
> >> >> >the levels of fixed effects?
> >> >> >
> >> >> >The other challenge that I expect to encounter (I'm preparing to do a
> >> >> >meta-analysis exploring anxiety and achievement) is that correlations
> >> >> >reported in each study may not reflect the same pair of variables
> >> >> >across the studies. Thus, this prevents me from having a "var1.var2"
> >> >> >like variable in my model which also means I can't proceed to mateg().
> >> >> >I believe, in that case, I can do only an exploratory study of
> >> >> >correlations (with rma.mv() ) rather than a model based one (with
> >> >> >matreg() ).
> >> >> >
> >> >> >Thank you,
> >> >> >Stefanou