[R-meta] Using rcalc() in metafor

[Lukasz Stasielowicz]

Dear Yuhang,


1. Playing around with the imputation settings for the V matrix is one 
option, e.g.,
impute_covariance_matrix from the clubSandwich package
or
vcalc from the metafor package.

There is certainly a trade-off. The risk of getting non-positive 
definite matrices can be reduced by simplifying the covariance 
structure, but it will probably lead to less realistic assumptions. 
However, one could conduct sensitivity analyses by using different 
imputation settings for the V matrix and checking whether the main 
findings change substantially.


2. The mean effect size and its confidence interval can be transformed 
using predict:
predict(res, transf=transf.ztor)



Best,

Lukasz
-- 
Lukasz Stasielowicz
Osnabrück University
Institute for Psychology
Research methods, psychological assessment, and evaluation
Lise-Meitner-Straße 3
49076 Osnabrück (Germany)
Twitter: https://twitter.com/l_stasielowicz

On 04.08.2023 12:00, r-sig-meta-analysis-request using r-project.org wrote:
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>     1. Using rcalc() in metafor (Yuhang Hu)
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> ----------------------------------------------------------------------
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> Message: 1
> Date: Fri, 4 Aug 2023 11:12:01 +0800
> From: Yuhang Hu <yh342 using nau.edu>
> To: R meta <r-sig-meta-analysis using r-project.org>
> Subject: [R-meta] Using rcalc() in metafor
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> 	<CA+dzWjp1xS_+zU2gYv2YeR04zM4qdK3U9KXy9dBJmFRShyaO6g using mail.gmail.com>
> Content-Type: text/plain; charset="utf-8"
> 
> Hello All (re-posting this in case, it slipped through the cracks),
> 
> In my data below, I have numerous NAs for the correlations reported for a
> set of 8 variables of interest across my studies. Two questions:
> 
> 1- rma.mv() says that my V is non-positive definite. To what extent can I
> ignore this and if not ignorable, what other options do I have?
> 
> 2- How can I convert back the 'coef(res)' and 'vcov(res)' from the "rtoz"
> scale to the original "r" scale ?
> 
> Thanks,
> Yuhang
> 
> # Data and code: #
> 
> dat <- read.csv("https://raw.githubusercontent.com/ilzl/i/master/j.csv")
> 
> tmp <- rcalc(ri ~ var1 + var2 | Study, ni=N, data=dat, rtoz = TRUE)
> V <- tmp$V
> dat <- tmp$dat
> 
> res <- rma.mv(yi~ var1.var2 - 1, V,
>                 random = ~var1.var2 | Study,
>                 data=dat, control = list(nearpd=TRUE), sparse = TRUE) |>
>    robust(cluster = Study, clubSandwich = TRUE)
> 
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