[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 9 19:24:41 CET 2021
I don't know what it is you are really trying to accomplish. One can stick moderators into the rma.mv() model, sure. So one can 'control' for them, or in other words, the estimated correlation matrix will then be a conditional estimate given a certain set of values for the moderators. That can be further passed on to matreg(). But I don't know if this is what you want to do, so I cannot say whether this is methodologically reasonable or whether a more reasonable exists -- to accomplish what? And what exactly is the problem of moderators in the rma.mv() fit?
>From: Stefanou Revesz [mailto:stefanourevesz using gmail.com]
>Sent: Thursday, 09 December, 2021 18:40
>To: Viechtbauer, Wolfgang (SP)
>Cc: R meta
>Subject: Re: metafor::matreg() and its workflow
>But is what I have done a methodologically reasonable way to do this,
>or a more reasonable way exists.
>It's great that vcov() or random effects var-covariance matrix can be
>obtained from an rma.mv() fit and then used in a secondary SEM
>But it seems to me that moderators used in rma.mv() get in the way,
>and I often have several of them.
>So, is there any literature on this or a strategy to get around the
>problem of moderators in the rma.mv() fit?
>Thank you for your guidance,
>On Thu, Dec 9, 2021 at 11:17 AM Viechtbauer, Wolfgang (SP)
><wolfgang.viechtbauer using maastrichtuniversity.nl> wrote:
>> >-----Original Message-----
>> >From: Stefanou Revesz [mailto:stefanourevesz using gmail.com]
>> >Sent: Tuesday, 07 December, 2021 23:41
>> >To: Viechtbauer, Wolfgang (SP)
>> >Cc: R meta
>> >Subject: Re: metafor::matreg() and its workflow
>> >Hi Wolfgang,
>> >Once again, thank you for the chapter and the two useful resources.
>> >For concreteness, are the last two lines OK to use or other solutions
>> >Many thanks,
>> >dat <- dat.craft2003
>> >dat$Xwb <- rnorm(nrow(dat),rnorm(nrow(dat),9,4),2)
>> >tmp <- rcalc(ri ~ var1 + var2| study, ni=ni, data=dat)
>> >V <- tmp$V
>> >dat$var1.var2 <- tmp$dat$var1.var2
>> >dat$var1.var2 <- factor(dat$var1.var2,
>> > levels=c("acog.perf", "asom.perf",
>> >"conf.perf", "acog.asom", "acog.conf", "asom.conf"))
>> >res <- rma.mv(ri~ 0+var1.var2+sport+Xwb, V, random = ~ var1.var2 |
>> >study, struct="UN", data=dat)
>> >R <- vec2mat(coef(res)[1:6]) # Is this OK?
>> The first 6 coefficients are the estimated pooled correlations when 'sport' is
>I and when Xwb is 0. If this is what you want, then this is ok.
>> >matreg(1, 2:4, R=R, V=vcov(res)[1:6,1:6]) # Is this OK?
>> If the above is ok, then this is ok.
More information about the R-sig-meta-analysis