[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