have you looked at

CMA::prediction(X.existing, y.existing, X.new, formula, model,
geneselection,  ... )

for more details:

?CMA::prediction

I agree that this package is fantabulously useful.

sorry for the horrendously late response, my apologies if you figured it out
long ago.  Maybe someone will stumble across this exchange in the future and
solve their own problem, if so :-)



On Tue, May 18, 2010 at 11:00 AM, Mike Dewar <mike.dewar@columbia.edu>wrote:

> Hi,
>
> I've trained up a classifier using the (so-far wonderful) CMA package. It
> validated well, and gives me predictions that agree well with my labels. I'd
> now like to use the classifier on a held-out data set, but for the life of
> me I can't figure out how to apply a classifier to a new example. Does
> anyone have any ideas?
>
> For example, having loaded my `exprset` and generated a `learning_set`
> using CMA's GenerateLearningsets(), I can run the classification() function
> as follows:
>
> out = classification(
>        X = t(exprs(exprset)),
>        y = pData(exprset)$labels,
>        learningsets = learning_set,
>        classifier = rfCMA
> )
>
> the object `out` gives me loads of information about how well it did and so
> on, but I can't seem to use "out" in order to classifiy a new set. An
> alternative approach would be to run something like classification() whereby
> I give it an X_test variable or something, but this doesn't seem to be
> available.
>
> Any help would be greatly appreciated : I'm sure I'm missing something
> basic!
>
> Cheers,
>
> Mike Dewar
>
>
> - - -
> Dr Michael Dewar
> Postdoctoral Research Scientist
> Applied Mathematics
> Columbia University
> http://www.columbia.edu/~md2954/
>
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>



-- 
If people do not believe that mathematics is simple, it is only because they
do not realize how complicated life is.

John von Neumann

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