[R-meta] rma.mv: why some var components change but others don't across 2 models
Dear Wolfgang, Thank you for your reply. I did check that previously. But my question is why 'outcome' gives the same variance component across both res (with 4 levels) and res2 (with 68 levels) models? Thank you so much, Stefanou On Sat, Oct 30, 2021, 7:08 AM Viechtbauer, Wolfgang (SP) <
wolfgang.viechtbauer at maastrichtuniversity.nl> wrote:
Dear Stefanou, With the way you have 'outcome' coded, these two formulations are not equivalent. I believe this post discusses this: https://stat.ethz.ch/pipermail/r-sig-meta-analysis/2018-July/000896.html Best, Wolfgang
-----Original Message----- From: Stefanou Revesz [mailto:stefanourevesz at gmail.com] Sent: Friday, 29 October, 2021 17:24 To: R meta Cc: Viechtbauer, Wolfgang (SP) Subject: rma.mv: why some var components change but others don't across
2 models
Dear Wolfgang and Expert List Members,
Why `study` with 57 levels in model `res` gives `sigma^2.1 = 0.0200`
but `study` with 57 levels in model `res2` gives `sigma^2.1 =
0.0122`?
(SAME LEVELS BUT DIFFERENT RESULTS)
Why `outcome` with 4 levels in model `res` gives `sigma^2.2 = 0.0093`
but `outcome` with 68 levels in model `res2` gives `sigma^2.2 =
0.0093`?
(DIFFERENT LEVELS BUT SAME RESULTS)
For reproducibility, below are my data and code.
Many thanks to you all,
Stefanou
m <- read.csv("https://raw.githubusercontent.com/fpqq/w/main/c.csv")
res <- rma.mv(yi, vi, random = list(~ 1 | study, ~1|outcome, ~ 1 |
measure), data=m)
estim sqrt nlvls fixed factor
sigma^2.1 0.0200 0.1415 57 no study
sigma^2.2 0.0093 0.0964 4 no outcome
sigma^2.3 0.0506 0.2249 7 no measure
res2 <- rma.mv(yi, vi, random = list(~ 1 | study/outcome, ~ 1 |
measure), data=m)
estim sqrt nlvls fixed factor
sigma^2.1 0.0122 0.1105 57 no study
sigma^2.2 0.0093 0.0964 68 no study/outcome
sigma^2.3 0.0363 0.1904 7 no measure