Dear all,
Thanks a lot for the input. I will take the considerations into account.
Referring to; "2 or 3 completely pre-chosen models or you will invalidate inference and estimates if you use these comparisons to build a final model"
The aim is not use the comparisons to build a final model but to select the explanatory variable which explains most of the variance or has the best predictive ability (p247 10.8 Harrell, 2001).
One procedure that will shed light on this is to bootstrap the ranks of the chi-square statistics for competing variables. I think you will be surprised how wide the confidence intervals for the ranks are. There is an example in the Alzola & Harrell document although it is for partial chi-squares for competing variables in a single model.
-FH
I'm comparing variables, which are all related to the remotely sensed water content of vegetation, with binary fire occurrence data (1: fire / 0: no fire). The aim is to select the water related variable which has the best 'performance' (Referring to literature about logistic regression used for evaluation of fire danger indices).
e.g. a lrm model is lrm(firedata~waterrelated.variable)
Thanks a lot and best regards, Jan
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In addition to Brian's comment, AIC may be of use. You can't really use c-index (ROC area) as it is not sensitive enough for comparing two models. But whatever you use, the bad news is that you can't use the results to compare more than 2 or 3 completely pre-chosen models or you will invalidate inference and estimates if you use these comparisons to build a final model.
Frank " ***
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Frank E Harrell Jr Professor and Chair School of Medicine
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