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1 1 N-
1 12 2.4 3.8 1 -3.3 N+
1 6 1.1 1.4 1 1.7 N-
1 4 0.6 0.8 1 -1 N-
1 9 1.8 2.4 1 -1.2 N-
0 5 0.5 1 0 -2.9 N-
1 19 5.9 9.6 1 -26.2 N+
1 5 0.4 0.3 1 3.2 N-
1 6 1.3 1.2 1 2.2 N-
1 4 0.5 0.6 1 -0.5 N-
1 5 0.6 1.8 1 -7.3 N-
1 6 0.9 1.3 1 -0.9 N-
1 19 0.8 2 1 -4.1 N+
1 5 0.6 1.3 1 -5.9 N+
0 6 0.4 0.7 0 -1.1 N-
0 0 N-
1 5 0.4 0.7 1 -0.2 N-
1 4 0.3 0.5 1 -0.5 N-
1 1 N+
1 10 5.9 8.6 1 -10.7 N+
1 11 1.5 2.7 1 -2.2 N+
0 13 1 1.7 0 -1.3 N+
0 0 N-
1 1 N-
1 5 1.1 1 1 1.8 N-
1 1 N-
1 3 0.5 0.7 1 0.4 N-
1 8 0.9 1.3 1 -2.7 N-
1 6 0.8 1.2 1 -0.8 N-
0 5 1.1 2 0 -1.5 N-
1 7 0.8 1.6 1 -0.2 N-
0 5 0.6 1.1 0 -0.1 N-
1 3 0.5 0.5 1 2.4 N+
1 1 N-
1 8 0.7 1.7 1 -4.3 N+
1 10 1 0.9 1 1.9 N-
1 5 0.4 0.8 1 -0.6 N-
1 1 N-
0 5 1.2 1 0 7.5 N-
0 7 0 0.8 0 -6.9 N-
0 9 1 0.9 0 2.4 N-
1 10 1.2 2 1 -4.5 N-
1 1 N+
1 17 4.7 7.8 1 -27.6 N+
1 1 N-
1 5 0.3 1.7 1 -7.6 N-
1 10 8.1 12.5 1 -27.7 N+
0 0 N+
1 1 N-
1 5 1 1.5 1 -1.7 N-
1 1 N+
1 10 1.4 2.2 1 -3.5 N-
0 5 0.8 1.4 0 0.1 N-
1 1 N-
1 5 0.7 0.5 1 1.3 N-
1 5 0.9 1.1 1 0.8 N-
1 6 0.7 3.1 1 -14.3 N+
1 21 6.4 8.7 1 -13 N+
0 7 0.7 1.7 0 -2.4 N+
0 0 N-
0 7 2.5 6.8 0 -26.8 N+
1 7 3 3.8 1 -7.5 N+
0 0 N+
1 7 0.6 1.6 1 -3.6 N-
0 0 N-
1 6 1 1.6 1 -3.4 N-
0 6 2.3 2.3 0 1.5 N-
1 4 0.9 0.6 1 4.3 N+
1 8 1.2 1.9 1 -4.5 N-
1 1 N-
1 4 0.6 1.2 1 -2.9 N+
1 5 0.7 1.2 1 -0.7 N-
1 5 0.7 0.9 1 -1.3 N-
1 5 0.7 0.9 1 -0.7 N-
0 4 0.9 0.7 0 4.4 N-
1 5 0.3 1 1 -3 N-
1 5 0.6 0.8 1 -0.7 N-
1 4 0.6 0.7 1 -0.5 N-
1 5 1 1.4 1 0.2 N-
1 5 0.5 0.7 1 -0.2 N-
1 7 0.7 1.1 1 -2.8 N+
0 0 N-
0 5 0.9 2.1 0 -2.5 N-
1 1 N-
1 5 0.4 0.9 1 -3.5 N-
1 7 0.8 1.5 1 -3.9 N+
1 5 0.9 1.4 1 1.1 N-
0 7 0.8 1.5 0 -1.7 N+
0 0 N-
1 1 N+
1 6 0.8 0.6 1 3.5 N-
1 4 0.8 1 7.7 N-
1 9 1.1 2.1 1 -2.9 N-
1 7 1.3 1.3 1 0.2 N-
1 7 1.1 1.1 1 4.3 N-
0 7 0.9 1.4 0 1.9 N-
1 3 0.5 1.5 1 -3.8 N-
1 6 0.7 1.6 1 -1.3 N-
1 11 2.4 5.5 1 -6 N+
1 1 N-
1 7 0.7 2.2 1 -7.2 N+
1 9 1.1 1.7 1 -5 N+
1 1 N-
1 7 1.8 2.4 1 0.5 N-
0 5 1 1.1 0 5.6 N-
0 6 0.8 1 0 0.4 N-
0 9 0.8 1.2 0 -2.5 N-
1 8 0.9 2.1 1 -7.9 N+
1 5 0.9 1.1 1 -0.1 N+
1 1 N-
1 1 N-
1 6 0.7 3.3 1 -13.7 N-
0 0 N-
1 5 1.1 0.6 1 4.9 N-
1 1 N-
1 1 N+
1 7 1 1.8 1 -3.9 N-
0 0 N-
1 1 N-
1 5 1 0.7 1 3 N-
1 6 0.9 1.2 1 -0.8 N-
1 11 2.3 4.3 1 -3.7 N+
0 15 6.2 14.5 0 -70.9 N+
0 6 0.6 1.2 0 -0.5 N-
0 0 N-
0 4 0.3 0.4 0 0.1 N-
1 1 N-
0 13 4.9 7.6 0 -23.1 N+
1 3 0.7 0.7 1 3.5 N-
0 0 N-
1 1 N-
0 5 1.7 1.4 0 5.1 N-
1 6 0.7 0.8 1 0.5 N-
1 7 1.6 2.3 1 -4.7 N-

par4 <- 'yes'
par3 <- '3'
par2 <- 'equal'
par1 <- '7'
library(party)
library(Hmisc)
par1 <- as.numeric(par1)
par3 <- as.numeric(par3)
x <- data.frame(t(y))
is.data.frame(x)
x <- x[!is.na(x[,par1]),]
k <- length(x[1,])
n <- length(x[,1])
colnames(x)[par1]
x[,par1]
if (par2 == 'kmeans') {
cl <- kmeans(x[,par1], par3)
print(cl)
clm <- matrix(cbind(cl$centers,1:par3),ncol=2)
clm <- clm[sort.list(clm[,1]),]
for (i in 1:par3) {
cl$cluster[cl$cluster==clm[i,2]] <- paste('C',i,sep='')
}
cl$cluster <- as.factor(cl$cluster)
print(cl$cluster)
x[,par1] <- cl$cluster
}
if (par2 == 'quantiles') {
x[,par1] <- cut2(x[,par1],g=par3)
}
if (par2 == 'hclust') {
hc <- hclust(dist(x[,par1])^2, 'cen')
print(hc)
memb <- cutree(hc, k = par3)
dum <- c(mean(x[memb==1,par1]))
for (i in 2:par3) {
dum <- c(dum, mean(x[memb==i,par1]))
}
hcm <- matrix(cbind(dum,1:par3),ncol=2)
hcm <- hcm[sort.list(hcm[,1]),]
for (i in 1:par3) {
memb[memb==hcm[i,2]] <- paste('C',i,sep='')
}
memb <- as.factor(memb)
print(memb)
x[,par1] <- memb
}
if (par2=='equal') {
ed <- cut(as.numeric(x[,par1]),par3,labels=paste('C',1:par3,sep=''))
x[,par1] <- as.factor(ed)
}
table(x[,par1])
colnames(x)
colnames(x)[par1]
x[,par1]
if (par2 == 'none') {
m <- ctree(as.formula(paste(colnames(x)[par1],' ~ .',sep='')),data = x)
}
load(file='createtable')
if (par2 != 'none') {
m <- ctree(as.formula(paste('as.factor(',colnames(x)[par1],') ~ .',sep='')),data = x)
if (par4=='yes') {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'10-Fold Cross Validation',3+2*par3,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'',1,TRUE)
a<-table.element(a,'Prediction (training)',par3+1,TRUE)
a<-table.element(a,'Prediction (testing)',par3+1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Actual',1,TRUE)
for (jjj in 1:par3) a<-table.element(a,paste('C',jjj,sep=''),1,TRUE)
a<-table.element(a,'CV',1,TRUE)
for (jjj in 1:par3) a<-table.element(a,paste('C',jjj,sep=''),1,TRUE)
a<-table.element(a,'CV',1,TRUE)
a<-table.row.end(a)
for (i in 1:10) {
ind <- sample(2, nrow(x), replace=T, prob=c(0.9,0.1))
m.ct <- ctree(as.formula(paste('as.factor(',colnames(x)[par1],') ~ .',sep='')),data =x[ind==1,])
if (i==1) {
m.ct.i.pred <- predict(m.ct, newdata=x[ind==1,])
m.ct.i.actu <- x[ind==1,par1]
m.ct.x.pred <- predict(m.ct, newdata=x[ind==2,])
m.ct.x.actu <- x[ind==2,par1]
} else {
m.ct.i.pred <- c(m.ct.i.pred,predict(m.ct, newdata=x[ind==1,]))
m.ct.i.actu <- c(m.ct.i.actu,x[ind==1,par1])
m.ct.x.pred <- c(m.ct.x.pred,predict(m.ct, newdata=x[ind==2,]))
m.ct.x.actu <- c(m.ct.x.actu,x[ind==2,par1])
}
}
print(m.ct.i.tab <- table(m.ct.i.actu,m.ct.i.pred))
numer <- 0
for (i in 1:par3) {
print(m.ct.i.tab[i,i] / sum(m.ct.i.tab[i,]))
numer <- numer + m.ct.i.tab[i,i]
}
print(m.ct.i.cp <- numer / sum(m.ct.i.tab))
print(m.ct.x.tab <- table(m.ct.x.actu,m.ct.x.pred))
numer <- 0
for (i in 1:par3) {
print(m.ct.x.tab[i,i] / sum(m.ct.x.tab[i,]))
numer <- numer + m.ct.x.tab[i,i]
}
print(m.ct.x.cp <- numer / sum(m.ct.x.tab))
for (i in 1:par3) {
a<-table.row.start(a)
a<-table.element(a,paste('C',i,sep=''),1,TRUE)
for (jjj in 1:par3) a<-table.element(a,m.ct.i.tab[i,jjj])
a<-table.element(a,round(m.ct.i.tab[i,i]/sum(m.ct.i.tab[i,]),4))
for (jjj in 1:par3) a<-table.element(a,m.ct.x.tab[i,jjj])
a<-table.element(a,round(m.ct.x.tab[i,i]/sum(m.ct.x.tab[i,]),4))
a<-table.row.end(a)
}
a<-table.row.start(a)
a<-table.element(a,'Overall',1,TRUE)
for (jjj in 1:par3) a<-table.element(a,'-')
a<-table.element(a,round(m.ct.i.cp,4))
for (jjj in 1:par3) a<-table.element(a,'-')
a<-table.element(a,round(m.ct.x.cp,4))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
}
}
print(m)
bitmap(file='test1.png')
plot(m)
dev.off()
bitmap(file='test1a.png')
plot(x[,par1] ~ as.factor(where(m)),main='Response by Terminal Node',xlab='Terminal Node',ylab='Response')
dev.off()
if (par2 == 'none') {
forec <- predict(m)
result <- as.data.frame(cbind(x[,par1],forec,x[,par1]-forec))
colnames(result) <- c('Actuals','Forecasts','Residuals')
print(result)
}
if (par2 != 'none') {
print(cbind(as.factor(x[,par1]),predict(m)))
myt <- table(as.factor(x[,par1]),predict(m))
print(myt)
}
bitmap(file='test2.png')
if(par2=='none') {
op <- par(mfrow=c(2,2))
plot(density(result$Actuals),main='Kernel Density Plot of Actuals')
plot(density(result$Residuals),main='Kernel Density Plot of Residuals')
plot(result$Forecasts,result$Actuals,main='Actuals versus Predictions',xlab='Predictions',ylab='Actuals')
plot(density(result$Forecasts),main='Kernel Density Plot of Predictions')
par(op)
}
if(par2!='none') {
plot(myt,main='Confusion Matrix',xlab='Actual',ylab='Predicted')
}
dev.off()
if (par2 == 'none') {
detcoef <- cor(result$Forecasts,result$Actuals)
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Goodness of Fit',2,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Correlation',1,TRUE)
a<-table.element(a,round(detcoef,4))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'R-squared',1,TRUE)
a<-table.element(a,round(detcoef*detcoef,4))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'RMSE',1,TRUE)
a<-table.element(a,round(sqrt(mean((result$Residuals)^2)),4))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Actuals, Predictions, and Residuals',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'#',header=TRUE)
a<-table.element(a,'Actuals',header=TRUE)
a<-table.element(a,'Forecasts',header=TRUE)
a<-table.element(a,'Residuals',header=TRUE)
a<-table.row.end(a)
for (i in 1:length(result$Actuals)) {
a<-table.row.start(a)
a<-table.element(a,i,header=TRUE)
a<-table.element(a,result$Actuals[i])
a<-table.element(a,result$Forecasts[i])
a<-table.element(a,result$Residuals[i])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable.tab')
}
if (par2 != 'none') {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Confusion Matrix (predicted in columns / actuals in rows)',par3+1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'',1,TRUE)
for (i in 1:par3) {
a<-table.element(a,paste('C',i,sep=''),1,TRUE)
}
a<-table.row.end(a)
for (i in 1:par3) {
a<-table.row.start(a)
a<-table.element(a,paste('C',i,sep=''),1,TRUE)
for (j in 1:par3) {
a<-table.element(a,myt[i,j])
}
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
}