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6.3	0	3
2.1	3.40602894496362	4
9.1	1.02325245963371	4
15.8	-1.69897000433602	1
5.2	2.20411998265592	4
10.9	0.51851393987789	1
8.3	1.71733758272386	1
11	-0.36653154442041	4
3.2	2.66745295288995	5
6.3	-1.09691001300806	1
6.6	-0.10237290870956	2
9.5	-0.69897000433602	2
3.3	1.44185217577329	5
11	-0.92081875395238	2
4.7	1.92941892571429	1
10.4	-1	3
7.4	0.01703333929878	4
2.1	2.71683772329952	5
17.9	-2	1
6.1	1.79239168949825	1
11.9	-1.69897000433602	3
13.8	0.23044892137827	1
14.3	0.54406804435028	1
15.2	-0.31875876262441	2
10	1	4
11.9	0.20951501454263	2
6.5	2.28330122870355	4
7.5	0.39794000867204	5
10.6	-0.55284196865778	3
7.4	0.62736585659273	1
8.4	0.83250891270624	2
5.7	-0.1249387366083	2
4.9	0.55630250076729	3
3.2	1.74429298312268	5
11	-0.045757490560675	2
4.9	0.30102999566398	3
13.2	-1	2
9.7	0.6222140229663	4
12.8	0.54406804435028	1

library(rpart)
library(partykit)
par1 <- as.numeric(par1)
autoprune <- function ( tree, method='Minimum CV'){
xerr <- tree$cptable[,'xerror']
cpmin.id <- which.min(xerr)
if (method == 'Minimum CV Error plus 1 SD'){
xstd <- tree$cptable[,'xstd']
errt <- xerr[cpmin.id] + xstd[cpmin.id]
cpSE1.min <- which.min( errt < xerr )
mycp <- (tree$cptable[,'CP'])[cpSE1.min]
}
if (method == 'Minimum CV') {
mycp <- (tree$cptable[,'CP'])[cpmin.id]
}
return (mycp)
}
conf.multi.mat <- function(true, new)
{
if ( all( is.na(match( levels(true),levels(new) ) )) )
stop ( 'conflict of vector levels')
multi.t <- list()
for (mylev in levels(true) ) {
true.tmp <- true
new.tmp <- new
left.lev <- levels (true.tmp)[- match(mylev,levels(true) ) ]
levels(true.tmp) <- list ( mylev = mylev, all = left.lev )
levels(new.tmp)  <- list ( mylev = mylev, all = left.lev )
curr.t <- conf.mat ( true.tmp , new.tmp )
multi.t[[mylev]] <- curr.t
multi.t[[mylev]]$precision <-
round( curr.t$conf[1,1] / sum( curr.t$conf[1,] ), 2 )
}
return (multi.t)
}
x <- t(y)
k <- length(x[1,])
n <- length(x[,1])
x1 <- cbind(x[,par1], x[,1:k!=par1])
mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
colnames(x1) <- mycolnames #colnames(x)[par1]
m <- rpart(as.data.frame(x1))
par2
if (par2 != 'No') {
mincp <- autoprune(m,method=par2)
print(mincp)
m <- prune(m,cp=mincp)
}
m$cptable
bitmap(file='test1.png')
plot(as.party(m),tp_args=list(id=FALSE))
dev.off()
bitmap(file='test2.png')
plotcp(m)
dev.off()
cbind(y=m$y,pred=predict(m),res=residuals(m))
myr <- residuals(m)
myp <- predict(m)
bitmap(file='test4.png')
op <- par(mfrow=c(2,2))
plot(myr,ylab='residuals')
plot(density(myr),main='Residual Kernel Density')
plot(myp,myr,xlab='predicted',ylab='residuals',main='Predicted vs Residuals')
plot(density(myp),main='Prediction Kernel Density')
par(op)
dev.off()
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Model Performance',6,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'#',header=TRUE)
a<-table.element(a,'Complexity',header=TRUE)
a<-table.element(a,'split',header=TRUE)
a<-table.element(a,'relative error',header=TRUE)
a<-table.element(a,'CV error',header=TRUE)
a<-table.element(a,'CV S.D.',header=TRUE)
a<-table.row.end(a)
for (i in 1:length(m$cptable[,1])) {
a<-table.row.start(a)
a<-table.element(a,i,header=TRUE)
a<-table.element(a,round(m$cptable[i,'CP'],3))
a<-table.element(a,m$cptable[i,'nsplit'])
a<-table.element(a,round(m$cptable[i,'rel error'],3))
a<-table.element(a,round(m$cptable[i,'xerror'],3))
a<-table.element(a,round(m$cptable[i,'xstd'],3))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable.tab')