[R-meta] Interpreting 95%CI estimated in the multimodel inference via glmulti
Gabriele Midolo
g@briele@midolo @ending from gm@il@com
Mon Sep 10 17:04:35 CEST 2018
Ok thanks Wolfgang,
I actually noted it is the same of simply adding/subtratting the
'alpha=0.05' term to the estimate (?).
Other predictors are scaled in the regression and thereby the intercept is
the true outcome when all predictors equal to their mean.
Cheers,
Gabri
On 10 September 2018 at 10:46, Viechtbauer, Wolfgang (SP) <
wolfgang.viechtbauer using maastrichtuniversity.nl> wrote:
> Dear Gabri,
>
> To get (approximate) 95% CIs, compute 'Estimate +- 1.96 * sqrt(Uncond.
> variance)'. So, using the intercept values, for "pt:h", this would be:
>
> -0.1065 + c(-1,1) * 1.96 * sqrt(0.0021)
>
> For "pt:w", this would be:
>
> -0.0117 + c(-1,1) * 1.96 * sqrt(0.0010)
>
> Note that the intercept is the estimated average true outcome when all
> predictors are equal to 0. Whether this is a meaningful value depends on
> how the predictors ('sol', 'ai', etc.) are coded.
>
> Best,
> Wolfgang
>
> -----Original Message-----
> From: R-sig-meta-analysis [mailto:r-sig-meta-analysis-
> bounces using r-project.org] On Behalf Of Gabriele Midolo
> Sent: Wednesday, 05 September, 2018 11:11
> To: r-sig-meta-analysis using r-project.org
> Subject: [R-meta] Interpreting 95%CI estimated in the multimodel inference
> via glmulti
>
> 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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