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

[Viechtbauer, Wolfgang (SP)]

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