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

[Milani Chaloupka]

Hi Simon

It works well for our meta-analysis models fit using Bayesian inference (Stan).

And it also specifically handles rma model.objects amongst many model objects (I assume rma.mv objects too).

But contact the author on: d.luedecke using uke.de

Milani


> On 7 Oct 2021, at 1:20 pm, Simon Harmel <sim.harmel using gmail.com> wrote:
> 
> Hi Milani,
> 
> Much appreciated. Have you made or possibly worked with this package
> before? Do you know if it works with rma.mv()?  I think this might be
> of interest to a lot of us on this list.
> 
> Thanks,
> Simon
> 
> On Wed, Oct 6, 2021 at 10:14 PM Milani Chaloupka <m.chaloupka using uq.edu.au> wrote:
>> 
>> Simon
>> 
>> Maybe try:
>> 
>> ## also on CRAN
>> remotes::install_github(“easystats/parameters”)
>> parameters::model_parameters(“your rma.object”)
>> 
>> Milani
>> 
>>> 
>>> Message: 2
>>> Date: Wed, 6 Oct 2021 16:43:26 -0500
>>> From: Simon Harmel <sim.harmel using gmail.com>
>>> To: "Viechtbauer, Wolfgang (SP)"
>>>      <wolfgang.viechtbauer using maastrichtuniversity.nl>
>>> Cc: R meta <r-sig-meta-analysis using r-project.org>
>>> Subject: Re: [R-meta] convert rma.mv() output to data.frame
>>> 
>>> 
>>> 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
>>> 
>>> 
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