[R-meta] best subset of moderators for `robumeta` package in R

Reza Norouzian rnorouz|@n @end|ng |rom gm@||@com
Thu Nov 7 18:41:54 CET 2019

Dear Wolfgang,

Thank you so much for this truly awe-inspiring response (I really can't
stop reading your post) !! At the cost of being ignorant, is there any way
to focus on "I2" index instead of information-theoretic criteria in these
model-finding quests using the packages you mentioned?

Once again, I truly appreciate your expertise and time on this,

On Thu, Nov 7, 2019 at 5:44 AM Viechtbauer, Wolfgang (SP) <
wolfgang.viechtbauer using maastrichtuniversity.nl> wrote:

> Hi Reza,
> I haven't played around with the leaps package, but you could do this with
> glmulti or MuMIn. An example of how to do this in combination with metafor
> is given here:
> http://www.metafor-project.org/doku.php/tips:model_selection_with_glmulti_and_mumin
> One could add additional steps to the rma.glmulti() function shown there,
> such as robust() from metafor or using coef_test() from clubSandwich.
> But note that with 35 moderators, you are looking at 2^35 = 34,359,738,368
> possible models. Even if fitting a single model only takes 0.01 seconds
> (which is rather optimistic), you will wait about 11 years for this to
> finish. If you have a cluster and parallelize this, you might be able to
> get this down to weeks or months. But one could also wonder if this is a
> useful exercise in the first place.
> You could restrict your search to models with at most 'm' predictors. For
> m = 8, that's choose(35,8) = 23,535,820 models, which is still a lot but
> more feasible. glmulti() has a 'maxsize' argument for this purpose.
> dredge() from MuMIn has argument 'm.lim' for this.
> Best,
> Wolfgang
> -----Original Message-----
> From: R-sig-meta-analysis [mailto:
> r-sig-meta-analysis-bounces using r-project.org] On Behalf Of Reza Norouzian
> Sent: Thursday, 07 November, 2019 3:24
> To: R meta
> Subject: [R-meta] best subset of moderators for `robumeta` package in R
> I have a large number of "categorical" moderators (35 moderators). I am
> planning to use the best subset of these moderators that can maximally
> explain the variation in my 257 correlated effect sizes from 51 studies.
> The R package `*leaps*` does perform best possible subset analysis via
> function `*regsubsets()*` but to make that suited to `*robu()*` I think
> need to define `weights` argument in `*regsubsets()*` so I can basically
> make this suited for RVE purposes not simply OLS regression.
> Any idea regarding how I can execute my plan in R or generally how I can
> choose best subset of moderators for `*robu()*` in `robumeta` in R?
> Many thanks,
> Reza
> --
> *Reza Norouzian*

*Reza Norouzian*

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