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

Viechtbauer, Wolfgang (SP) wo||g@ng@v|echtb@uer @end|ng |rom m@@@tr|chtun|ver@|ty@n|
Thu Dec 16 17:52:54 CET 2021

```The main input to matreg() is a correlation or covariance matrix via argument 'R'. Where did you see an example where vcov() is used as input to the 'R' argument?

The input to 'R' might come from a meta-analysis of correlation coefficients. This is the idea behind MASEM.

The input to 'R' might also come from the correlations (or covariances) among a set of random effects. This is discussed in van Houwelingen et al. (2002).

Best,
Wolfgang

>-----Original Message-----
>From: Stefanou Revesz [mailto:stefanourevesz using gmail.com]
>Sent: Thursday, 16 December, 2021 17:37
>To: Viechtbauer, Wolfgang (SP)
>Cc: R meta
>Subject: Re: metafor::matreg() and its workflow
>
>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
```