R help - May 2009 - questions on rpart (tree changes when rearrange the order of covariates?!)

Greetings,

I am using rpart for classification with "class" method. The test data
is
the Indian diabetes data from package mlbench.

I fitted a classification tree firstly using the original data, and then
exchanged the order of Body mass and Plasma glucose which are the
strongest/important variables in the growing phase. The second tree is a
little different from the first one. The misclassification tables are
different too. I did not change the data, but why the results are so
different?

Does anyone know how rpart deal with ties?

Here is the codes for running the two trees.


library(mlbench)
data(PimaIndiansDiabetes2)
mydata<-PimaIndiansDiabetes2
library(rpart)
fit2<-rpart(diabetes~., data=mydata,method="class")
plot(fit2,uniform=T,main="CART for original data")
text(fit2,use.n=T,cex=0.6)
printcp(fit2)
table(predict(fit2,type="class"),mydata$diabetes)
## misclassifcation table: rows are fitted class
      neg pos
  neg 437  68
  pos  63 200
#Klimt(fit2,mydata)

pmydata<-data.frame(mydata[,c(1,6,3,4,5,2,7,8,9)])
fit3<-rpart(diabetes~., data=pmydata,method="class")
plot(fit3,uniform=T,main="CART after exchaging mass & glucose")
text(fit3,use.n=T,cex=0.6)
printcp(fit3)
table(predict(fit3,type="class"),pmydata$diabetes)
##after exchage the order of BODY mass and PLASMA glucose
      neg pos
  neg 436  64
  pos  64 204
#Klimt(fit3,pmydata)


Thanks,


--------------------------------------------------------------------------------------
Yuanyuan Huang

	[[alternative HTML version deleted]]

Yuanyuan wrote:
> Greetings,
> 
> I am using rpart for classification with "class" method. The test
data  is
> the Indian diabetes data from package mlbench.
> 
> I fitted a classification tree firstly using the original data, and then
> exchanged the order of Body mass and Plasma glucose which are the
> strongest/important variables in the growing phase. The second tree is a
> little different from the first one. The misclassification tables are
> different too. I did not change the data, but why the results are so
> different?
Well, at some splits the variable that comes first and yields in the 
same reduction of the entropy criterion as another one might be used, 
hence another result.

Uwe Ligges



> 
> Does anyone know how rpart deal with ties?
> 
> Here is the codes for running the two trees.
> 
> 
> library(mlbench)
> data(PimaIndiansDiabetes2)
> mydata<-PimaIndiansDiabetes2
> library(rpart)
> fit2<-rpart(diabetes~., data=mydata,method="class")
> plot(fit2,uniform=T,main="CART for original data")
> text(fit2,use.n=T,cex=0.6)
> printcp(fit2)
> table(predict(fit2,type="class"),mydata$diabetes)
> ## misclassifcation table: rows are fitted class
>       neg pos
>   neg 437  68
>   pos  63 200
> #Klimt(fit2,mydata)
> 
> pmydata<-data.frame(mydata[,c(1,6,3,4,5,2,7,8,9)])
> fit3<-rpart(diabetes~., data=pmydata,method="class")
> plot(fit3,uniform=T,main="CART after exchaging mass &
glucose")
> text(fit3,use.n=T,cex=0.6)
> printcp(fit3)
> table(predict(fit3,type="class"),pmydata$diabetes)
> ##after exchage the order of BODY mass and PLASMA glucose
>       neg pos
>   neg 436  64
>   pos  64 204
> #Klimt(fit3,pmydata)
> 
> 
> Thanks,
> 
> 
>
--------------------------------------------------------------------------------------
> Yuanyuan Huang
> 
> 	[[alternative HTML version deleted]]
> 
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