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From |
n j cox <n.j.cox@durham.ac.uk> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: Re: st: Cluster/PCA for predicting a binary outcome? |

Date |
Wed, 18 Jul 2007 18:11:52 +0100 |

Frank Harrell has a characteristically incisive summary of logistic vs discriminant analysis in his "Regression modeling strategies" (Springer, New York, 2001), with references. The burden is: go with logistic. Nick n.j.cox@durham.ac.uk (By the way, he ain't fond of stepwise.) E. Paul Wileyto You're looking for discriminant analysis (look up Stata help on discrim)... It's like PCA, but you can think of it as selecting the loadings to maximize the F-value if you did a 1-way ANOVA by your class variable. But, for a binary outcome, many simply use logistic regression. K Jensen wrote: > I am trying to predict a binary outcome from a set of correlated > variables. Rather than using logisitic regression to include the > variables one-by-one in the model, I was wondering if there was a way > in Stata of generating sets or functions of the variables? Maybe > something similar to PCA, except that you would be trying to explain > the variation in the binary outcome rather than the whole dataset. * * For searches and help try: * http://www.stata.com/support/faqs/res/findit.html * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/

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