[R-meta] convert rma.mv() output to data.frame

[Simon Harmel]

Dear Wolfgang,

Many thanks for your reply. The linked post in your email provides a method
using 'capture.output()' in basr R but it only works with intercept-only
models no matter how large the random term is, it creates an appropriate
datafram to fit it in.

I just wonder how to extend that to correlated random effects models?

Why l want this? Because each time it takes me a lot of time to prepare
presentable tables out of rma.mv() models esp. after spending days figuring
out what model works well. At lease when it is a dataframe I can clean it
up. But right now, I should literally copy-paste for a good chunk of time.

Many thanks,
Simon



On Wed, Oct 6, 2021, 4:25 PM Viechtbauer, Wolfgang (SP) <
wolfgang.viechtbauer using maastrichtuniversity.nl> wrote:

> Hi Simon,
>
> Please make a note of it when your question has been raised elsewhere so
> people can see what kind of answers have already been provided:
>
>
> https://stackoverflow.com/questions/69459062/convert-part-of-a-statistical-functions-output-into-a-data-frame
>
> This aside, I don't really understand how you want to make a data frame
> out of a combination of text and numbers of varying length. How many
> 'variables' is that data frame supposed to have? How many rows? What are
> the variables / rows supposed to contain? Or are you just after something
> like this?
>
> data.frame(component=c("tau2", "rho"), value=c(res$tau2, res$rho))
>
> Best,
> Wolfgang
>
> >-----Original Message-----
> >From: Simon Harmel [mailto:sim.harmel using gmail.com]
> >Sent: Wednesday, 06 October, 2021 22:47
> >To: R meta
> >Cc: Viechtbauer, Wolfgang (SP)
> >Subject: convert rma.mv() output to data.frame
> >
> >Dear Wolfgang and list members,
> >
> >I am wondering if it is possible to convert the "Variance Components"
> >part of the output of an rma.mv() object to data.frame (example
> >below)?
> >
> >Many thanks,
> >Simon
> >
> >library(metafor)
> >dat <- dat.konstantopoulos2011
> >
> >res <- rma.mv(yi, vi, random = ~ factor(school) | district, data=dat)
> >
> >#Variance Components: Can we convert this part to a data.frame?
> >
> >#outer factor: district       (nlvls = 11)
> >#inner factor: factor(school) (nlvls = 11)
> >
> >#            estim    sqrt  fixed
> >#tau^2      0.0978  0.3127     no
> >#rho        0.6653             no
> >
> >#Test for Heterogeneity:
> >#Q(df = 55) = 578.8640, p-val < .0001
>

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