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
[email protected]
(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.
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