[R-meta] multi-model prediction with rma.mv ?
kelly.gravuer at gmail.com
Tue Jan 2 19:42:32 CET 2018
Using the rma.mv() function in metafor, I'm running models with two
random effects and four moderator variables. The format of the model
m1 <- rma.mv(yi ~ mod1 + mod2 + mod3 + mod4, vi, random = list(~ 1 |
rand1, ~1 | rand2), tdist=TRUE, method="ML", data=d)
I would like to do two things: (1) understand the relationship of each
of the four moderator variables to the effect size; (2) predict an
effect size with confidence interval (CI) for some new suites of
values of the moderators.
I have found multi-model inference using glmulti() to be a very useful
approach to my first objective, using the methods described here:
I would now like to use this multi-model suite to make predictions
I realize I can run the model as specified above (including all four
moderators as main effects) and then use predict.rma() to generate
estimated effect sizes + CIs. I realize I can also calculate an
effect size estimate that is informed by the multiple model fits using
the coefficient estimates in the multi-model coefficient table.
However, I don't know how to get CIs for these effect size estimates
that are informed by the multiple model fits.
I think one way to do this would be to write a predict function that
links rma.mv with glmulti. I don't feel up to this task myself, but
wanted to inquire whether anyone on this list has written such a
function? Or alternatively, does anyone have other suggestions for
how to use the information from the multiple model fits to inform the
Thanks so much for any thoughts,
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