[R-meta] Interpreting 95%CI estimated in the multimodel inference via glmulti

[Gabriele Midolo]

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
>

	[[alternative HTML version deleted]]