[R-meta] Interpreting 95%CI estimated in the multimodel inference via glmulti
Gabriele Midolo
g@briele@midolo @ending from gm@il@com
Wed Sep 5 11:11:04 CEST 2018
Dear all,
Following Wolfgang instructions on multimodel inference with glmulti (
http://www.metafor-project.org/doku.php/tips:model_selection_with_glmulti),
I came out with the following output in my analysis:
> output_SLA Estimate Uncond. variance Nb models Importance +/- (alpha=0.05)
sol 0.0029 0.0005 15 0.2776 0.0421
ai 0.0133 0.0007 16 0.3906 0.0512
ele -0.0172 0.0006 16 0.4936 0.0472
mat -0.0383 0.0007 23 0.8078 0.0524
ptw 0.0948 0.0037 23 0.8322 0.1188
ldele -0.0282 0.0002 27 0.9092 0.0289
intrcpt -0.1065 0.0021 34 1.0000 0.0888
I am struggling a bit to understand alpha value applied to the categorical
moderator I have in the meta-regression i.e. 'pt'. The 'pt' variable can
assume two values: "pt:w" and "pt:h", the latter is the intrcpt in
'output_SLA'. When I re-run the model selection by making 'ptw' the
intercept I get the value that is consistent with the 'Estimate' of
'output_SLA' (i.e. pt:w calculated as 0.0948 - 0.1065 = -0.0117). But the
alpha values of the intercpt changed (now = 0.0618):
> output_SLA2 Estimate Uncond. variance Nb models Importance +/- (alpha=0.05)
sol 0.0029 0.0005 15 0.2776 0.0421
ai 0.0133 0.0007 16 0.3906 0.0512
ele -0.0172 0.0006 16 0.4936 0.0472
mat -0.0383 0.0007 23 0.8078 0.0524
pth -0.0948 0.0037 23 0.8322 0.1188
ldele -0.0282 0.0002 27 0.9092 0.0289
intrcpt -0.0117 0.0010 34 1.0000 0.0618
My question is then how to estimate 95%CI of the mean pooled effect size
for pt:h and pt:w from both models? Should I add/subtract the alpha to the
Estimate of ptw/pth and then add it to the CI of intercept? Or should I
estimate alpha directly by adding pth/ptw to the intercept's alpha? In both
cases I think I end up with different 95%CI estimated for the two
categories depending on which one is "forced" to be the intercept?
Hope I was clear,
Thanks and cheers,
Gabri
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