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Hello everyone,
I need to do a probit regression with 5 independent variables but I have 16. In other words, I need to do all possible regressions with 16 variables 5 at the time - something like this: Y = a + x1 + x2 + x3 + x4 + x5 y = a + x1 + x11 + x13 + x14 + x6 y = a + x10 + x2 + x9 + x8 + x7 ... In the end I need the expression with the highest R^2 and the independent variables should be independent and with the lowest AIC information. Is there any way to perform this? Thanks for the help, João Caetano |
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Check the archives of this group on how to generate combinations.
Have you thought about generating them with nested loops to create a file of variable indices then use these indices to build your command syntax? Probably SWIM will come up with a Python solution ;-) --
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In reply to this post by caetano
This sounds like a fishing expedition to me. Have you ever looked at Frank Harrell's book on regression models? Mike Babyak's nice article summarizes the main points about over-fitting. You can get it here:
http://os1.amc.nl/mediawiki/images/Babyak_-_overfitting.pdf http://www.psychosomaticmedicine.org/content/66/3/411.abstract HTH.
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