[R] Help in Compliling user -defined functions in Rpart

[Luwis Tapiwa Diya]

I have been trying to write my own user defined function in Rpart.I
imitated the anova splitting rule which is given as an example.In the
work I am doing ,I am calculating the concentration index(ci) ,which
is in between -1 and +1.So my deviance is given by
abs(ci)*(1-abs(ci)).Now when I run rpart incorporating this user
defined function i get the following error message:

 Error in user.split(yback[1:nback], wback[1:nback], xback[1:nback], parms,  : 
        unused argument(s) ( ...)

Now I am failing to indentify where I am going wrong (In case I am
have made some mistake).So I was wondering if there is anybody who
have written some user defined functions of theirs and maybe if there
is any documentation with regards to user defined functions and
examples.

Regards ,

Luwis Diya

#####################################################################User
defined function
#####################################################################

temp.init<-function(y,offset,parms,wt){
	if (!is.null(offset)) y<-y-offset 
	if (is.matrix(y))stop ("response must be a vector")

	list(y=y,parms=0,numy=1,numresp=1,
		   summary=function(yval,dev,wt,ylevel,digits){
		         paste("mean=",format(signif(yval,digits)),
 				   "MSE=",format(signif(dev/wt,digits)),
				    sep='')
		})
	} 


temp.eval<-function(y,wt,parms){
		n<-length(y)
		r<-wt
for (i in 1:n-1) {r[i+1]=(sum(wt[1:i])+0.5*wt[i+1])/n} #fractional rank 
		r[1]<-0.5*wt[1]/n
		wmean<-sum(y*wt)/sum(wt)
		ci<-2*sum(wt*(y-wmean)*(r-0.5))/sum(wt*y) #concentration index for
socio-economic inequality
		dev<-abs(ci)*(1-abs(ci))		  #deviance following the gini impurity approach 	
        list(label=wmean,deviance=dev)
}

         
temp.split<-function(y,wt,parms,continous){
	n<-length(y)
	r<-wt
for (i in 1:n-1) {r[i+1]=(sum(wt[1:i])+0.5*wt[i+1])/n}
	r[1]<-0.5*wt[1]/n
	wmean<-sum(y*wt)/sum(wt)
	ci<-2*sum(wt*(y-wmean)*(r-0.5))/sum(wt*y)
	devci<-abs(ci)*(1-abs(ci))

	if(continous){
	  lss<-cumsum(wt*y)[-n]
	  rss<-sum(wt*y)-lss 
	  lw<-cumsum(wt)[-n]
	  rw<-sum(wt)-lw 
	  lm<-lss/lw
	  rm<-rss/rw
	  lcss<-cumsum(wt[1:length(lm)]*(y[1:length(lm)]-lm)*(r[1:length(lm)]-0.5))
	  rcss<-sum(wt*(y-wmean)*(r-0.5))-lcss
	  lci<-2*lcss/lss				  #concentration index for left side	
	  rci<-2*rcss/rss				  #concentration index for right side
	  devlci<-abs(lci)*(1-abs(lci))			  #deviance for left side
	  devrci<-abs(rci)*(1-abs(rci))			  #deviance for right side	

	  goodness<-devci-(lw/sum(wt))*devlci-(rw/sum(wt))*devrci
	  list(goodness=goodness, direction=sign(lci))
	  }
   else {	 
	 ux<-sort(unique(x))
	 wtsum<-tapply(wt,x,sum)
	 ysum<-tapply(wt*y,x,sum)
	 means<-ysum/wtsum

	 ord<-order(means)
       n<-length(ord)  	
	 lss<-cumsum(ysum[ord])[-n]
	 rss<-sum(ysum)-lss 
	 lw<-cumsum(wtsum[ord])[-n]
	 rw<-sum(wtsum)-lw 
	 lm<-lss/lw
	 rm<-rss/rw
	 lysum<-tapply(wt*(y-lm)*(r-0.5),x,sum)
	 lcss<-cumsum(lysum[ord])[-n]
	 rcss<-sum(lysum)-lcss
	 lci<-2*lcss/lss
	 rci<-2*rcss/rss
	 devlci<-abs(lci)*(1-abs(lci))
	 devrci<-abs(rci)*(1-abs(rci))

	  goodness<-devci-0.5*(lw/sum(wt))*devlci-0.5*(rw/sum(wt))*devrci
	  list(goodness=goodness, direction=sign(lci))
	 }
}

alist<-list(eval=temp.eval,split=temp.split,init=temp.init)
tree<-rpart(u~pcares+antcare.skilled+riskintb+child.born+married+mage1+mage2,
weights=popweight,method=alist)