Milicic B. Marko wrote:
The only solution I can see is fitting all possib le 2 factor models enabling
interactions and then assessing if interaction term is significant...
any more ideas?
Please don't suggest such a thing unless you do simulations to back up
its predictive performance, type I error properties, and the impact of
collinearities. You'll find this approach works as well as the U.S.
economy.
Frank Harrell
Milicic B. Marko wrote:
I have a huge data set with thousands of variable and one binary
variable. I know that most of the variables are correlated and are not
good predictors... but...
It is very hard to start modeling with such a huge dataset. What would
be your suggestion. How to make a first cut... how to eliminate most
of the variables but not to ignore potential interactions... for
example, maybe variable A is not good predictor and variable B is not
good predictor either, but maybe A and B together are good
predictor...
Any suggestion is welcomed
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Frank E Harrell Jr Professor and Chair School of Medicine
Department of Biostatistics Vanderbilt University
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