[R] rpart memory problem

[jenniferbecq@free.fr]

Hi everyone,

I have a problem using rpart (R 2.0.1 under Unix)

Indeed, I have a large matrix (9271x7), my response variable is numeric and all
my predictor variables are categorical (from 3 to 8 levels).

Here is an example :

> mydata[1:5,]
                  distance group3 group4 group5 group6 group7 group8
pos_1    0.141836040224967      a      c      e      a      g      g
pos_501  0.153605961621317      a      a      a      a      g      g
pos_1001 0.152246705384699      a      c      e      a      g      g
pos_1501 0.145563737522463      a      c      e      a      g      g
pos_2001 0.143940027378837      a      c      e      e      g      g

When using rpart() as follow, the program runs for ages, and after a few hours,
R is abruptly killed :

library(rpart)
fit <- rpart(distance ~ ., data = mydata)

When I change the categorical variables into numeric values (e.g. a = 1, b = 2,
c = 3, etc...), the program runs normally in a few seconds. But this is not
what I want because it separates my variables according to "group7 > 4.5"
(continuous) and not "group7 = a,b,d,f" or "c,e,g" (discrete).

here is the result :
>fit
n= 9271

node), split, n, deviance, yval
      * denotes terminal node

 1) root 9271 28.43239000 0.1768883
   2) group7>=4.5 5830  4.87272700 0.1534626
     4) group5< 5.5 5783  3.29538700 0.1520110
       8) group5>=4.5 3068  0.68517040 0.1412967 *
       9) group5< 4.5 2715  1.86003600 0.1641184 *
     5) group5>=5.5 47  0.06597044 0.3320614 *
   3) group7< 4.5 3441 14.93984000 0.2165781
     6) group5< 1.5 1461  1.00414700 0.1906630 *
     7) group5>=1.5 1980 12.23050000 0.2357002
      14) group6>=2.5 1659  2.95395700 0.2090232
        28) group3>=2.5 1315  1.65184200 0.1957505 *
        29) group3< 2.5 344  0.18490260 0.2597607 *
      15) group6< 2.5 321  1.99404400 0.3735729 *


When I create a small dataframe such as the example above, e.g. :

distance = rnorm(5,0.15,0.01)
group3 = c("a","a","a","a","a")
group4 = c("c","a","c","c","c")
group5 = c("e","a","e","e","e")
group6 = c("a","a","a","a","e")
smalldata = data.frame(cbind(distance,group3,group4,group5,group6))

The program runs normally in a few seconds.

Why does it work using the large dataset whith only numeric values but not with 
categorical predictor variables ?

I have the impression that it considers my response variable also as a
categorical variable and therefore it can't handle 9271 levels, which is quite
normal. Is there a way to solve this problem ?

I thank you all for your time and help,

Jennifer Becq