Help: lda predict

Thanks for your reply. Actually I called function as below.

p1 = predict(object, newdata, dimen=1)
p2 = predict(object, newdata, dimen=1, method=debiased)
p3 = predict(object, newdata, dimen=1, method="predictive")

The MAP classification of prediction results by any method are the
same. I know what the method "plug-in" and "debiased" mean, but what
does the "vague prior" for the method "predictive" mean? what is
"vague" here?

Thank you,
Shengzhe



On 8/26/05, Prof Brian Ripley <ripley at stats.ox.ac.uk> wrote:

On Fri, 26 Aug 2005, Shengzhe Wu wrote:

I use lda (package: MASS) to obtain a lda object, then want to employ
this object to do the prediction for the new data like below:

predict(object, newdata, dimen=1, method=c("plug-in", "predictive", "debiased"))

That is not how you call it: when a character vector is given like that
those are alternatives.  Do read the help page, as we ask.

What is the exact difference among the three methods? What is the
difference of prediction results when applying different method?

This is stated on the help page.  If you are unfamiliar with the area,
note that the posting guide points out that MASS is support software for a
book and the explanations are in the book.  The help page also has
references: please do read them (before posting).

--
Brian D. Ripley,                  ripley at stats.ox.ac.uk
Professor of Applied Statistics,  http://www.stats.ox.ac.uk/~ripley/
University of Oxford,             Tel:  +44 1865 272861 (self)
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