Gabriel
Thanks for your feedback. Indeed, I was not particularly clear here. The
empty model is just a very special case in a more general setting. I'd
have to work around this deficiency in my code -- sure I can do that,
but I thought a generic solution should be possible. In particular, I'm
using predict.rpart(..., type = "prob") -- this just reflects the
observed relative frequencies.
Cheers
Kirill
On 08/15/2014 06:44 PM, Gabriel Becker wrote:
Kirill,
Perhaps I'm just being obtuse, but what are you proposing rpart do in
the case of an empty model? Return a "tree" that always guesses the
most common label, or doesn't guess at all (NA)? It doesn't seem like
you'd need rpart for either of those.
~G
On Wed, Aug 13, 2014 at 3:51 AM, Kirill Müller
<kirill.muel...@ivt.baug.ethz.ch
<mailto:kirill.muel...@ivt.baug.ethz.ch>> wrote:
Dear list
For my work, it would be helpful if rpart worked seamlessly with
an empty model:
library(rpart); rpart(formula=y~0, data=data.frame(y=factor(1:10)))
Currently, an unrelated error (originating from na.rpart) is thrown.
At some point in the near future, I'd like to release a package to
CRAN which uses rpart and relies on that functionality. I have
prepared a patch (minor modifications at three places, and a test)
which I'd like to propose for inclusion in the next CRAN release
of rpart. The patch can be reviewed at
https://github.com/krlmlr/rpart/tree/empty-model, the files (based
on the current CRAN release 4.1-8) can be downloaded from
https://github.com/krlmlr/rpart/archive/empty-model.zip.
Thanks for your attention.
With kindest regards
Kirill Müller
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--
Gabriel Becker
Graduate Student
Statistics Department
University of California, Davis
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