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Apr 2, 2020 at 16:58 history edited COOLSerdash CC BY-SA 4.0
Used LaTeX for notation.
Jul 13, 2012 at 22:47 comment added Frank Harrell I wish I had a reference for that. People often examine things without penalization then do penalization at the last minute. But I think you can also compare penalized log likelihoods.
Jul 13, 2012 at 19:33 comment added julieth @FrankHarrell Do these results apply to the penalized likelihood as well? Does the penalized likelihood have any properties that make it differ from the likelihood with respect to this variable-importance measure? Could you suggest any papers that describe this in greater detail? Thanks.
May 14, 2011 at 16:02 comment added Frank Harrell For sure. The causal chain has to be understood before modeling even commences.
Apr 30, 2011 at 1:05 comment added rolando2 would you also grant that there's a risk, in devising a purely statistical solution, of missing a possible overarching problem whereby all 3 groups of predictors could be measuring characteristics/behaviours occurring at the same time. Without an earlier-causes-later sort of basis for a causal chain, might it be impossible to definitively disentangle causal relationships in this situation--whatever our calculations might be? (I'm trying to think the way James Davis does in The Logic of Causal Order.)
Apr 28, 2011 at 1:54 comment added Frank Harrell I didn't use the best notation. I mean the likelihood ratio $\chi^2$ statistic, which is the change in -2 log likelihood upon removing the set of variables being tested.
Apr 27, 2011 at 12:57 comment added B_Miner To confirm, your approach is to compute L1 as the reduction in deviance (-2*) resulting from the inclusion of the four social variables, adjusted by the df of these four variables? And likewise in turn for L2 and L3?
Apr 27, 2011 at 11:43 history answered Frank Harrell CC BY-SA 3.0