R help - Sep 2006 - Rpart, custom penalty for an error

Hello all R-help list subscribers,

I'd like to create a regression tree of a data set with binary response
variable. Only 5% of observations are a success, so the regression tree
will not find really any variable value combinations that will yield
more than 50% of probability of success. I am however interested in
areas where the probability of success is noticeably higher than 5%, for
example 20%. I've tried rpart and the weights option, increasing the
weights of the success-observations.

It works as expected in terms of the tree creation: instead of a single
root, a tree is being built. But the tree plot() and text() are somewhat
misleading. I'm interested in the observation counts inside each leaf.
I use the "use.n = TRUE" parameter. The counts displayed are
misleading,
the numbers of successes are not the original numbers from the sample,
they seem to be cloned success-observations.

I'd like to split the tree just as weights parameter allows me to,
keeping the original number of observations in the tree plot. Is it
possible? If yes, how?

Kind regards,
Maciej

-- 
Maciej Blizi?ski <m.blizinski at wit.edu.pl>
http://automatthias.wordpress.com

On Sun, 10 Sep 2006, Maciej Blizi?ski wrote:
> Hello all R-help list subscribers,
> 
> I'd like to create a regression tree of a data set with binary response
> variable. Only 5% of observations are a success, so the regression tree
> will not find really any variable value combinations that will yield
> more than 50% of probability of success. 
This would be a misuse of a regression tree, for the exact problem for 
which classification trees were designed.
> I am however interested in areas where the probability of success is 
> noticeably higher than 5%, for example 20%. I've tried rpart and the 
> weights option, increasing the weights of the success-observations.
You are 'misleading' rpart by using 'weights', claiming to have
case
weights for cases you do not have.  You need to use 'cost' instead.

This is a standard issue, discussed in all good books on classification
(including mine).
> It works as expected in terms of the tree creation: instead of a single
> root, a tree is being built. But the tree plot() and text() are somewhat
> misleading. I'm interested in the observation counts inside each leaf.
> I use the "use.n = TRUE" parameter. The counts displayed are
misleading,
> the numbers of successes are not the original numbers from the sample,
> they seem to be cloned success-observations.
They _are_ the original numbers, for that is what 'case weights' means.
> I'd like to split the tree just as weights parameter allows me to,
> keeping the original number of observations in the tree plot. Is it
> possible? If yes, how?
> 
> Kind regards,
> Maciej
-- 
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)
1 South Parks Road,                     +44 1865 272866 (PA)
Oxford OX1 3TG, UK                Fax:  +44 1865 272595