It sounds as though 'logic regression' might help. See Ruczinski I, Kooperberg C, LeBlanc ML (2003). Logic Regression, Journal of Computational and Graphical Statistics, 12, 475-511.
and the LogicReg package. Peter Ehlers Weiwei Shi wrote: > Hi there, > I have a statistics question on a classification problem: > > Suppose I have 1000 binary variables and one binary dependent variable. I > want to find a way similar to PCA, in which I can find a couple of > combinations of those variables to discriminate best according to the > dependent variable. It is not only for dimension reduction, but more > important, for finding best way to construct features. This is NOT CDA since > the explanatory variables are NOT continuous. I knew the existence of that > method since I consulted before with a professor but I forgot the name of > the method.. sigh... > > I am also wondering if R has already some function or package addressing > this kind of problem. > > Thanks > > -- > Weiwei Shi, Ph.D > > "Did you always know?" > "No, I did not. But I believed..." > ---Matrix III > > [[alternative HTML version deleted]] > > ______________________________________________ > [email protected] mailing list > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html ______________________________________________ [email protected] mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
