[R-meta] robust error is smaller than model-based error
Hi Yefeng, On point 1, I am not sure what your question is. From inspecting the source code of metafor::robust(), the function is not set up to handle models with crossed random effects. I'm not at all sure what it does if you feed it a model with crossed random effects, but I would be very cautious about interpreting the output. Perhaps Wolfgang can comment on whether robust() is meant to accommodate models with crossed random effects. On point 2, I can verify that clubSandwich does not support CRVE for models with crossed random effects. Cameron, Gelbach, and Miller (2011) describe multi-way clustered standard errors, but only for ordinary least squares models. As far as I am aware, the statistical theory for multi-way clustered standard errors has not been developed for models that have crossed random effects and the extension from Cameron, Gelbach and Miller is not obvious. So if you want to stay on solid ground in terms of statistical theory, I think your best approach might be just to do a good job of developing and checking the model, and then rely on the model-based SEs for inference. James On Thu, Feb 15, 2024 at 7:37?PM Yefeng Yang via R-sig-meta-analysis <
r-sig-meta-analysis at r-project.org> wrote:
Dear community,
I (or, more precisely, my collaborator) am helping with one meta-analysis
with dependent effect sizes. We used a multilevel model with effect size
ID, study ID, and species ID as random effects. We also used the RVE to
calculate the robust error. I have two questions.
1.
The test of model coefficient based on RVE indicates a significant effect
(p < 0.05), while the test based on model-based error (we call it
naive/original error) shows a non-significant effect (p < 0.05). I used
`robust` in `metafor`, with `CR1` correction (`clubsandwich` is not working
in my case; see below?) . Sorry, I do not have the raw data so there is no
reproducible example.
2.
How to calculate the robust error for models with non-nested
random-effects structure? This issue has troubled me for a long time.
Precisely, in my case, because effect size ID is nested within the study
ID, so it is easy to calculate robust error (either using ? ?`robust` or
?`clubsandwich` ?). However, I still have species ID as the random effect
(it is a kind of crossed random effect). In such a case `clubsandwich` is
not working. `robust` is still working, but we only can use `CR1`
correction.
Regards,
Yefeng
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