R help - Dec 2009 - rpart - classification and regression trees (CART)

Hi,
I had a question regarding the rpart command in R.  I used seven continuous
predictor variables in the model and the variable called "TB122" was
chosen
for the first split.  But in looking at the output, there are 4 variables
that improve the predicted membership equally (TB122, TB139, TB144, and
TB118) - output pasted below.

Node number 1: 268 observations,    complexity param=0.6
  predicted class=0  expected loss=0.3
    class counts:   197    71
   probabilities: 0.735 0.265 
  left son=2 (188 obs) right son=3 (80 obs)
  Primary splits:
      TB122 < 80  to the left,  improve=50, (0 missing)
      TB139 < 90  to the left,  improve=50, (0 missing)
      TB144 < 90  to the left,  improve=50, (0 missing)
      TB118 < 90  to the left,  improve=50, (0 missing)
      TB129 < 100 to the left,  improve=40, (0 missing)

I need to know what methods R is using to select the best variable for the
node.  Somewhere I read that the best split = greatest improvement in
predictive accuracy = maximum homogeneity of yes/no groups resulting from
the split = reduction of impurity.  I also read that the Gini index,
Chi-square, or G-square can be used evaluate the level of impurity.

For this function in R:
1) Why exactly did R pick TB122 over the other variables despite the fact
that they all had the same level of improvement?  Was TB122 chosen to be the
first node because the groups "TB122<80" and
"TB122>80" were the most
homogeneous (ie had the least impurity)?
2) If R is using impurity to determine the best nodes, which method (the
Gini index, Chi-square, or G-square) is R using?

Thanks!
Katie
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View this message in context:
http://n4.nabble.com/rpart-classification-and-regression-trees-CART-tp962680p962680.html
Sent from the R help mailing list archive at Nabble.com.

When two variables have exactly the same figure of merit, they will be
listed in the output in the same order in which they appeared in your
model statement.  
   Terry Therneau

-- begin inclusion ---
I had a question regarding the rpart command in R.  I used seven
continuous
predictor variables in the model and the variable called "TB122" was
chosen
for the first split.  But in looking at the output, there are 4
variables
that improve the predicted membership equally (TB122, TB139, TB144, and
TB118) - output pasted below.

Node number 1: 268 observations,    complexity param=0.6
  predicted class=0  expected loss=0.3
    class counts:   197    71
   probabilities: 0.735 0.265 
  left son=2 (188 obs) right son=3 (80 obs)
  Primary splits:
      TB122 < 80  to the left,  improve=50, (0 missing)
      TB139 < 90  to the left,  improve=50, (0 missing)
      TB144 < 90  to the left,  improve=50, (0 missing)
      TB118 < 90  to the left,  improve=50, (0 missing)
      TB129 < 100 to the left,  improve=40, (0 missing)

--- end inclusion ---