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4,031636	0,5215052	9,166456	1,303763
3,702076	0,4248284	7,970589	1,416094
3,056176	0,4250311	7,104091	1,052458
3,280707	0,4771938	6,621064	1,312283
2,984728	0,8280212	7,529215	1,309429
3,693712	0,6156186	8,170938	1,492409
3,226317	0,366627	8,15745	1,026556
2,190349	0,4308883	7,378962	1,005406
2,599515	0,2810287	7,921496	1,334886
3,080288	0,4646245	8,15674	1,393873
2,929672	0,2693951	8,856365	1,128092
2,922548	0,5779049	8,817177	1,122787
3,234943	0,5661151	8,734347	1,213104
2,983081	0,5077584	9,345927	1,253528
3,284389	0,7507175	8,99297	1,094796
3,806511	0,6808395	10,78512	0,9129438
3,784579	0,7661091	8,886867	1,19513
2,645654	0,4561473	8,818847	0,9274994
3,092081	0,4977496	8,823744	0,9653326
3,204859	0,4193273	9,165298	1,198078
3,107225	0,6095514	8,652657	0,966362
3,466909	0,457337	8,173054	0,9736851
2,984404	0,5705478	7,563416	0,9948013
3,218072	0,3478996	7,595809	0,8262616
2,82731	0,3874993	8,381467	0,6888877
3,182049	0,5824285	7,216432	0,7813066
2,236319	0,2391033	6,540178	0,6047907
2,033218	0,2367445	6,238914	1,08624
1,644804	0,2626158	5,487288	0,7740255
1,627971	0,4240934	5,759462	1,026032
1,677559	0,365275	5,993215	0,6764351
2,330828	0,3750758	7,474726	0,830525
2,493615	0,4090056	7,348907	0,7916238
2,257172	0,3891676	7,303379	0,7523907
2,655517	0,240261	7,119314	0,6702018
2,298655	0,1589496	6,99378	0,8803359
2,600402	0,4393373	6,958153	0,9142966
3,04523	0,5094681	7,595706	0,9610421
2,790583	0,3743465	8,088153	0,9301944
3,227052	0,4339828	7,555753	0,8679657
2,967479	0,4130557	7,315433	0,9891596
2,938817	0,3288928	7,893427	0,9972879
3,277961	0,5186648	8,858794	0,7987437
3,423985	0,5486504	8,839367	0,9753785
3,072646	0,5469111	8,014733	0,9347208
2,754253	0,4963494	7,873465	0,9732341
2,910431	0,5308929	8,930377	0,8152998
3,174369	0,5957761	10,50055	0,9402092
3,068387	0,5570584	12,61144	0,794493
3,089543	0,5731325	11,41787	0,9313403
2,906654	0,5005416	11,87249	0,9220503
2,931161	0,5431269	11,06082	0,7845167
3,02566	0,5593657	12,04331	0,8220981
2,939551	0,6911693	9,776299	0,8910255
2,691019	0,4403485	9,557194	0,8073056
3,19812	0,5676662	9,20259	0,9514406
3,07639	0,5969114	10,22402	1,147907
2,863873	0,4735537	9,350807	1,172609
3,013802	0,5923935	8,300913	1,281051
3,053364	0,5975556	8,365779	1,165962
2,864753	0,6334127	8,133595	0,9789106
3,057062	0,6057115	7,66047	1,410951
2,959365	0,7046107	8,074839	1,197838
3,252258	0,4805263	7,848597	1,288368
3,602988	0,702686	7,99822	1,102253
3,497704	0,7009017	7,396895	1,197657
3,296867	0,6030854	7,900419	1,299984
3,602417	0,6980919	8,1005	1,198611
3,3001	0,597656	7,899453	1,299252
3,40193	0,8023421	7,599783	1,097604
3,502591	0,6017109	8,100929	1,39977
3,402348	0,5993127	9,002175	1,398396
3,498551	0,6025625	10,2989	1,40188
3,199823	0,7016625	10,10152	1,699717
2,700064	0,4995714	10,69915	1,39761
2,801034	0,4980918	9,69814	1,500135
2,898628	0,497569	9,800951	1,400136
2,800854	0,600183	10,90047	1,400427
2,399942	0,3339542	10,69785	1,341477
2,402724	0,274437	9,297252	1,33858
2,202331	0,3209428	10,39744	1,482977
2,102594	0,5406671	10,90072	1,163253
1,798293	0,4050209	12,90127	1,328468
1,202484	0,2885961	13,09906	1,23455
1,400201	0,3275942	11,69828	1,484741
1,200832	0,3132606	11,09987	1,336579
1,298083	0,2575562	11,30157	1,339292
1,099742	0,2138386	10,70211	1,405225
1,001377	0,1861856	10,09931	1,333491
0,8361743	0,1592713	9,591119	1,14974

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')
}
}
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')
}