On 15 Aug 2001 09:57:27 -0700, [EMAIL PROTECTED] (Paul R.
Swank) wrote:
PRS >"If your going to use discriminant analysis you will need a lot
of data and it does assume the predictors are multivariate normal."
- well, logistic has to assume (almost) the same thing, almost as
strongly, when it has to generate a continuous predictor equation.
Logistic is practically no different when the prediction is poor -
because there will be no 'overshoot' of what is predicted.
Logistic is superior when prediction is quite good, *except*
sometimes when prediction is too-good, at 100% or near 100%.
(A degenerate likelihood surface means: failure of asymptotic
behavior, so tests become less reliable.)
PRS > "Generalized linear models would seem best, particularly
in the event that you don't know if they are ordinal."
With 5 groups, is this something different from discriminant function?
PRS >"You can do a multinomial followed by a cummulative logit
model to see if the data are approximately ordinal."
Or you can do discriminant function and see how the
categories line up on the first function, and see whether a
second function emerges. D.F. programs give you that,
but I don't know whether logistic
--
Rich Ulrich, [EMAIL PROTECTED]
http://www.pitt.edu/~wpilib/index.html
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