[R-meta] Clarification on ranef.rma.mv()

[Luke Martinez]

Dear Wolfgang,

Thank you very much. For correlated random-effect structures in
rma.mv(), is it possible to extract the equivalent of the following
lme() information:

Tlist_lme <- function(fit) rev(pdMatrix(fit$modelStruct$reStruct,
factor = TRUE))

#----- Testing:
library(nlme)
fm1 <- lme(distance ~ age, data = Orthodont)

Tlist_lme(fm1)

On Mon, Sep 13, 2021 at 2:38 AM Viechtbauer, Wolfgang (SP)
<wolfgang.viechtbauer using maastrichtuniversity.nl> wrote:
>
> Dear Luke,
>
> Sure:
>
> library(metafor)
>
> dat <- dat.berkey1998
> res.mv <- rma.mv(yi~ outcome - 1, vi, data = dat, random = ~ outcome | trial, struct = "UN")
> res.mv$rho
>
> ran.mv <- ranef.rma.mv(res.mv)
> cor(matrix(ran.mv[[1]]$intrcpt, ncol=2, byrow=FALSE))[1,2]
>
> The two correlations are not the same for the reasons explained at the link you provided.
>
> If the dataset is not so nicely balanced, one can do something similar, but the restructuring of the output from ranef() into a wide format gets a bit more tedious.
>
> For example:
>
> dat <- dat[-4,]
> res.mv <- rma.mv(yi~ outcome - 1, vi, data = dat, random = ~ outcome | trial, struct = "UN")
> res.mv$rho
>
> ran.mv <- ranef.rma.mv(res.mv)
> ran.mv
>
> ran.mv <- ran.mv[[1]][1]
> ran.mv$study <- sapply(strsplit(rownames(ran.mv), "|", fixed=TRUE), tail, 1)
> ran.mv$arm   <- sapply(strsplit(rownames(ran.mv), "|", fixed=TRUE), head, 1)
> ran.mv
>
> wide <- reshape(ran.mv, direction="wide", idvar="study", v.names="intrcpt", timevar="arm")
> rownames(wide) <- NULL
> wide
> cor(wide[2:3], use="pairwise.complete.obs")[1,2]
>
> There might be a more elegant way to do this, but this gets the job done.
>
> Best,
> Wolfgang
>
> >-----Original Message-----
> >From: R-sig-meta-analysis [mailto:r-sig-meta-analysis-bounces using r-project.org] On
> >Behalf Of Luke Martinez
> >Sent: Monday, 13 September, 2021 8:38
> >To: R meta
> >Subject: [R-meta] Clarification on ranef.rma.mv()
> >
> >Dear Wolfgang and List Members,
> >
> >In ordinary multilevel models (lmer), one can use ranef() of a model
> >to get the correlations from the conditional modes of the random
> >effects (https://stats.stackexchange.com/q/153253/124093) which may
> >reveal how much random-effects for slopes and intercepts are roughly
> >correlated.
> >
> >Similarly, for a struct = "UN" model, I was wondering if
> >"ranef.rma.mv(fitted_model)" could reveal how much random-effects for
> >outcome levels are roughly correlated?
> >
> >For example, for the "res.mv" model below where the REML rho estimate
> >is "0.775", can ranef.rma.mv(res.mv) values give a rough estimate of
> >this correlation conditional on the observed data?
> >
> >dat <- dat.berkey1998
> >res.mv <- rma.mv(yi~ outcome - 1, vi, data = dat, random = ~ outcome |
> >trial, struct = "UN")
> >
> >ran.mv <- ranef.rma.mv(res.mv)
> >
> >Thank you very much for your help,
> >Luke