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1418	210907	56	3	79	30	112285
869	120982	56	4	58	28	84786
1530	176508	54	12	60	38	83123
2172	179321	89	2	108	30	101193
901	123185	40	1	49	22	38361
463	52746	25	3	0	26	68504
3201	385534	92	0	121	25	119182
371	33170	18	0	1	18	22807
1192	101645	63	0	20	11	17140
1583	149061	44	5	43	26	116174
1439	165446	33	0	69	25	57635
1764	237213	84	0	78	38	66198
1495	173326	88	7	86	44	71701
1373	133131	55	7	44	30	57793
2187	258873	60	3	104	40	80444
1491	180083	66	9	63	34	53855
4041	324799	154	0	158	47	97668
1706	230964	53	4	102	30	133824
2152	236785	119	3	77	31	101481
1036	135473	41	0	82	23	99645
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1929	215147	58	0	101	36	99052
2242	344297	75	1	80	30	67654
1220	153935	33	5	50	25	65553
1289	132943	40	7	83	39	97500
2515	174724	92	0	123	34	69112
2147	174415	100	0	73	31	82753
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1638	223632	73	0	105	33	72654
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1812	221698	45	0	105	33	77873
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807	84853	31	4	38	30	61542
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602	43287	14	4	13	19	43750
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1559	164709	109	0	81	31	87771
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2072	125930	75	4	37	17	32387
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834	82316	32	4	27	10	21233
1105	102010	53	3	28	13	44332
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1944	243511	71	0	133	42	101338
391	22938	10	0	12	1	1168
761	41566	35	5	0	9	13497
1605	152474	65	0	106	32	65567
530	61857	25	4	23	11	25162
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449	31414	19	0	18	8	14116
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568	38214	34	0	16	8	8773
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2158	272458	65	0	100	43	93815
1111	172494	52	0	46	43	86687
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2833	328107	65	3	129	41	105547
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2922	351067	95	3	136	45	213688
1002	158015	29	0	59	31	71220
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956	85439	33	0	32	28	56926
2186	229242	247	4	63	31	91721
3604	351619	139	4	95	40	115168
1035	84207	29	11	14	30	111194
1417	120445	118	0	36	16	51009
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1701	165543	65	1	70	32	51633
1249	141722	94	0	19	27	75345
946	116048	64	0	50	20	33416
1926	250047	81	0	41	18	83305
3352	299775	95	9	91	31	98952
1641	195838	67	1	111	31	102372
2035	173260	63	3	41	21	37238
2312	254488	83	10	120	39	103772
1369	104389	45	5	135	41	123969
1577	136084	30	0	27	13	27142
2201	199476	70	2	87	32	135400
961	92499	32	0	25	18	21399
1900	224330	83	1	131	39	130115
1254	135781	31	2	45	14	24874
1335	74408	67	4	29	7	34988
1597	81240	66	0	58	17	45549
207	14688	10	0	4	0	6023
1645	181633	70	2	47	30	64466
2429	271856	103	1	109	37	54990
151	7199	5	0	7	0	1644
474	46660	20	0	12	5	6179
141	17547	5	0	0	1	3926
1639	133368	36	1	37	16	32755
872	95227	34	0	37	32	34777
1318	152601	48	2	46	24	73224
1018	98146	40	0	15	17	27114
1383	79619	43	3	42	11	20760
1314	59194	31	6	7	24	37636
1335	139942	42	0	54	22	65461
1403	118612	46	2	54	12	30080
910	72880	33	0	14	19	24094
616	65475	18	2	16	13	69008
1407	99643	55	1	33	17	54968
771	71965	35	1	32	15	46090
766	77272	59	2	21	16	27507
473	49289	19	1	15	24	10672
1376	135131	66	0	38	15	34029
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1521	89746	36	3	28	18	24760
572	44296	25	0	10	20	18779
1059	77648	47	0	31	16	21280
1544	181528	54	0	32	16	40662
1230	134019	53	0	32	18	28987
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1205	92630	40	4	27	8	18513
1255	121848	39	0	37	17	30594
613	52915	14	0	20	18	24006
721	81872	45	0	32	16	27913
1109	58981	36	7	0	23	42744
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1126	60812	44	0	26	13	22574
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592	80949	17	0	11	16	18472
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628	31774	23	1	0	17	4143
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736	52164	52	0	32	16	16734
617	51567	30	2	21	14	28207
812	70551	31	0	23	15	30143
1051	84856	29	1	29	17	41369
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705	86678	40	0	12	15	29156
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554	34662	25	0	18	6	36278
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982	99611	35	0	41	21	45097
222	19349	11	0	13	1	3895
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435	30837	19	0	8	4	2325
532	31706	13	0	26	10	25139
882	89806	42	0	27	16	27975
608	62088	38	1	13	16	14483
459	40151	29	0	16	9	13127
578	27634	20	0	2	16	5839
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509	37460	20	0	5	7	3738
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778	43410	19	0	7	1	2781
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680	62215	27	0	24	10	19546
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616	53117	22	3	3	12	32689
285	19764	12	1	10	2	5752
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888	77945	20	0	28	19	25272
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1182	91005	29	3	29	11	32073
528	40248	16	1	8	4	5444
642	64187	27	0	10	16	20154
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757	62792	35	0	28	15	30884
894	72535	14	0	17	16	19540

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')
}