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5.2	67.7	32.4	5	7.7	1019	86
7.9	111.5	94.7	6.3	8.4	1010.4	77
8.7	29.4	133.4	3.9	8.7	1022.6	77
8.9	62.3	196.5	3.8	8.1	1015	71
15.3	19	307.3	3	11.2	1019.8	66
15.4	95.5	128.7	2.8	13.5	1013.7	77
18.1	30.4	279.9	3.3	14.5	1019.2	71
19.7	44.3	230.2	2.1	16.4	1018.3	72
13	56.5	92.2	2.9	12.5	1017.2	84
12.6	80	121.3	4	12.4	1010.9	83
6.2	66.1	73.4	3.4	9.1	1013.1	92
3.5	96.7	24	4	7.2	1017.1	89
3.4	83.5	93.8	4.6	6.8	1022.4	85
0	37.7	76.2	3.1	5.7	1017.8	87
9.5	24.6	113.3	3.6	9.3	1013.3	77
8.9	69.4	194.2	3.9	7.2	1016.3	65
10.4	26.9	155.3	2.9	8.7	1021.8	70
13.2	100.3	114	3.3	10.8	1013.1	71
18.9	110.6	193	2.7	15.5	1015.8	72
19	15.7	250.7	2.3	14.9	1019.3	70
16.3	55.7	173	2.5	13.5	1015.6	74
10.6	24.4	112.9	2.6	10.8	1015.6	84
5.8	174.6	63.2	3.9	8.3	1012	89
3.6	70.4	51.3	3.1	6.9	1027.9	85
2.6	30.6	45.7	3.2	6.7	1029.6	88
5	46.1	80.6	3.2	7.6	1023.6	86
7.3	92.8	78.8	3.9	8.5	1014.6	83
9.2	56.5	124.9	3.5	8.7	1012.1	75
15.7	49.3	252.5	3.3	11.7	1017.7	67
16.8	116.7	188.3	2.9	13.9	1014.9	74
18.4	67.6	192.6	2.7	15.7	1015.9	75
18.1	180.5	165	3.2	16.3	1012.4	78
14.6	31.3	139.1	3.2	13.2	1016.6	80
7.8	81.3	106.1	3.5	9.1	1008.7	85
7.6	87.9	46.8	4.2	9.5	1013.5	89
3.8	76	67.5	3.6	7.3	1017.6	89
5.6	84.9	51.8	4.6	8.4	1021.3	86
2.2	26.9	44.4	2.6	6.5	1028.8	91
6.8	4.2	169.2	3.4	7.2	1021.5	71
11.8	32	145.3	3.5	10	1010.2	72
14.9	73.7	200	3.4	12.3	1012.9	71
16.7	66	194.1	3	13.7	1015.5	73
16.7	107.6	167.3	3.2	14.9	1015.3	79
15.9	40.1	167.8	2.7	13.7	1018.4	76
13.6	94	105.9	3.1	13.1	1010.7	85
9.2	84	89.3	3.5	10.1	1013.4	85
2.8	73.2	87.1	3.2	10	1019.9	88
2.5	170.1	15.4	4.7	8.1	1006.9	92
4.8	99.1	38.6	4.5	7.9	1012.1	90
2.8	39.3	52.2	3.2	6.7	1014.4	84
7.8	95.3	104.5	4.5	8.8	1017.3	82
9	52.8	146.9	3.9	9.1	1012.2	77
12.9	69.4	152.9	2.9	11.2	1013.8	77
16.4	55	235.8	3.3	13.3	1019.2	72
21.8	68.6	268.6	2.6	17.6	1017.2	68
17.8	81.2	203.1	2.8	15.2	1015.1	75
13.5	118.7	98.1	3.2	13.5	1013.2	86
10	60.6	128.8	3.3	10.4	1015.2	83
10.4	33.6	62.1	2.9	11.5	1019.1	90
5.5	121.5	50.2	4.3	8.7	1018.7	93
4	143.6	56.2	4.8	7.2	1014.4	88
6.8	100.8	61.7	4.6	7.5	1012.1	85
5.7	76.9	155.3	4.2	6.8	1013.4	75
9.1	51.3	85.2	3.2	9.5	1017.8	81
13.6	60.4	224.3	2.8	10.4	1016.6	67
15	75.1	161.9	2.9	13.1	1018	78
20.9	50.4	243.4	2.7	17.6	1015.5	72
20.4	21.6	271.9	3	15.7	1017.7	67
14	61.8	16.8	3.2	13.1	1012	82
13.7	6	135.8	3	13.5	1020.6	84
7.1	41.4	78.7	3.2	8.7	1017.7	85
0.8	74.1	41.8	3.1	6	1017	89
2.1	15	81.8	3.5	6.3	1013.4	83
1.3	71.8	43.4	3.9	5.7	1013.3	82
3.9	26.1	131.4	3.2	6.3	1017.8	76
10.7	6	209.3	3.1	7.5	1017.7	59
11.1	73.4	96.8	3.4	9.9	1014.2	74
16.4	22.9	239.8	2.7	12.5	1021.1	68
17.1	56	211.6	3.1	13.3	1018.1	69
17.3	231.2	188.1	2.9	14.1	1015	72
12.9	47	147.8	3	11.5	1017.3	79
10.9	45.2	126.1	3	10.7	1017.1	83
5.3	116.7	49.8	3.7	8.4	1010.5	91
0.7	33.3	45.8	3.4	5.9	1014.8	85
-0.2	2.6	74.2	2.9	5.5	1023.2	86
6.5	96.2	42.4	4.7	7.9	1018.7	83
8.6	24.3	108.8	3.1	9.2	1024.3	83
8.5	24.4	200.1	3.3	7.9	1021.5	71
13.3	90.8	229	3.3	11.2	1015.1	74
16.2	91.4	181.8	3.1	13.7	1010.1	75
17.5	74.4	197.5	2.7	15.5	1018.2	77
21.2	52.6	254	2.6	17.6	1016	71
14.8	16.5	195.7	2.6	12.9	1021.9	76
10.3	89.1	146	3.3	10.5	1017.9	80
7.3	63.5	46.5	3.7	9.2	1007.2	88
5.1	74.9	29.7	4.2	8.1	1012.5	90
4.4	73.3	56.3	4.6	7.3	1016.4	85
6.2	14.5	103.8	3	7.9	1025.5	81
7.7	84	71.4	3.9	8.4	1022.5	79
9.3	107.2	93	3.5	9.3	1003.9	80
15.6	35.5	210.1	3	12.4	1016.4	71
16.3	87.7	167.6	3.3	14.3	1015.3	77
16.6	43.8	145.5	3.3	14.5	1013.8	78
17.4	64.7	212.5	2.7	14.5	1018.4	76
15.3	139.1	107.5	3.4	14.4	1010.3	84
9.7	128.7	48.3	4	10.8	1012.4	89
3.7	87.8	79.5	3.1	7.5	1018.4	89
4.6	81.7	30.6	3.8	7.7	1019.8	90
5.4	123.9	59.6	4.3	7.9	1014.5	85
3.1	76.3	63	3.7	6.8	1017.4	87
7.9	71.9	119.1	3.5	8.4	1011.7	80
10.1	71	142.3	3.4	9.9	1013.8	79
15	40	213.2	3.1	12.4	1015.7	72
15.6	83.1	205.6	3.1	13.2	1017.9	76
19.7	34.5	257	3.1	16.3	1017.1	73
18.1	91.2	177	2.7	15.3	1013.4	76
17.7	42.5	162.9	3.1	16	1011.1	81
10.7	47	120.1	3.7	11.2	1016.1	86
6.2	32.7	62.3	3.6	8.5	1021.7	88
4.2	171.9	26.6	4.4	7.7	1010.6	92
4	45.7	54.6	3.5	7.5	1025.1	90
5.9	81.9	89.4	4	7.9	1020.3	86
7.1	56.8	72.6	3.5	8.4	1020.7	84
10.5	65.1	155.1	3.5	9.6	1007.6	76
15.1	86.2	158.9	3	13.6	1015.2	79
16.8	35.1	235	3	14.1	1020	73
15.3	133.8	93.1	2.8	15.2	1013.4	86
18.4	34.5	227.6	2.4	16	1017.8	77
16.1	69.9	127.3	2.9	15.2	1013.7	83
11.3	98.3	87.3	4.1	11.9	1011.7	88
7.9	86.7	49.5	4.7	9.6	1003.4	88
5.6	58.2	41.9	4.2	8.5	1009.1	88
3.4	83.6	68.6	4	7.2	1011.6	91
4.8	83.5	59.4	3.7	7.7	1017.8	88
6.5	112.3	46.8	3.5	8.8	1004.9	88
8.5	134.3	95.4	3.6	9.1	1012.8	82
15.1	30	259.7	3.5	12.1	1017.5	72
15.7	44.5	201.4	2.9	12.8	1017.4	73
18.7	120.1	188.8	2.9	16.7	1014.5	78
19.2	43.4	220.8	2.9	15.5	1015.5	71
12.9	199.4	68.6	3	13.3	1013.4	90
14.4	68.1	113.7	3.6	14.1	1015.4	87
6.2	99.8	70.9	2.7	8.7	1022.9	91
3.3	69.5	61.3	3.3	6.9	1026.1	91
4.6	71.3	63.5	3.9	7.6	1022.8	87
7.2	167.8	76.4	5.3	8.4	1012.1	82
7.8	66.3	124.3	3.6	8	1018.2	75
9.9	41.9	193.1	3.5	8.1	1015.9	68
13.6	57.2	178.8	3.3	12	1013.6	78
17.1	72.3	200.2	2.9	14.5	1016.5	76
17.8	96.5	169.5	3.1	15.7	1016	79
18.6	172.1	152.1	2.3	17.2	1015.9	82
14.7	25.8	135.7	2.7	12.9	1019.8	79
10.5	105.1	91	3.5	10.9	1012.3	85
8.6	92.2	57.9	3.4	10	1008	90
4.4	109.3	37.9	3.8	8	1015.2	90
2.3	101.7	54.4	4	6.7	1016.5	86
2.8	29.1	145.7	3.3	5.7	1020.9	76
8.8	34.6	185.7	3.2	8.3	1023.8	73
10.7	46.7	211.7	3.6	8.7	1016.9	67
13.9	82	187.3	3	12.1	1017	78
19.3	34.4	258.3	2.8	16.1	1016	74
19.5	72.7	235	2.7	16.3	1016	74
20.4	44.4	244.3	2.5	16.7	1017.9	72
15.3	31	208.3	2.4	12.8	1020.3	75
7.9	64	127.1	3.3	9.2	1013.4	85
8.3	65.4	63.2	3.5	9.9	1014.9	89
4.5	64.5	66.8	3.5	7.7	1018.2	89
3.2	153.8	32.9	4.1	7.1	1009.9	89
5	48.8	56.3	3.7	7.3	1020.2	82
6.6	25	136.7	3.5	7.5	1020.7	76
11.1	37.2	142.8	3.4	9.3	1012.9	72
12.8	40.8	216.3	2.7	10.4	1016.3	72
16.3	78.4	200	3	13.7	1018	75
17.4	112.4	204.8	2.8	14.8	1016.7	76
18.9	122.7	159.3	3	16.7	1012.2	78
15.8	82.9	173.5	3.4	13.7	1019.7	77
11.7	67.6	101.6	3.6	11.5	1010.5	83
6.4	78.4	60.3	2.8	8.8	1022.1	90
2.9	65.7	51.9	2.7	7.1	1019.8	92
4.7	44.9	75.3	4	7.4	1022.8	83
2.4	80.9	69.8	3.4	6.2	1020.2	82
7.2	38.8	83.5	3.2	8.4	1016.4	80
10.7	46.1	120.1	3	9.4	1014.6	74
13.4	60	190.3	3.2	10.8	1017.3	71
18.5	53.9	246.8	2.8	13.5	1019	65
18.3	123.5	175.4	2.8	15.9	1015.3	76
16.8	69.5	185	2.6	15.1	1018.1	80
16.6	74.2	189.6	2.6	14.9	1018.4	79
14.1	47	140.3	3.4	13.2	1017	83
6.1	60.9	60.4	3.3	8.9	1016.5	92
3.5	51.4	26.7	3.3	7.3	1018.1	91
1.7	18.7	97.4	3.2	6.1	1024.6	85
2.3	83.1	29.9	3.4	6.5	1015.5	88
4.5	65.3	114.1	3.6	7	1011.2	79
9.3	46	141.3	3.2	8.8	1015.8	76
14.2	115.6	135.6	3.7	12.2	1015	77
17.3	25.8	246.2	2.5	13.4	1021.3	69
23	48.1	308.7	2.8	17.3	1019.7	64
16.3	202.3	94.5	2.9	15.1	1012.7	83
18.4	9.2	160.5	2.9	12.7	1015.1	77
14.2	56.3	108.3	3.5	13.5	1013	84
9.1	71.6	79.3	3.9	10.1	1017.3	87
5.9	93	40	4	8.3	1023	89
7.2	82.3	34.6	4.6	9	1018.4	86
6.8	95.4	48.7	3.6	8.5	1009.8	86
8	61.9	144.6	3.7	8	1015.7	77
14.3	0	284.2	2.8	9.9	1021.1	62
14.6	103.4	164.2	3.5	12.2	1011.5	75
17.5	99.2	130.3	3.1	15.3	1012.5	78
17.2	96.7	178.3	3.4	14.5	1012.8	75
17.2	56.9	150.8	2.8	14.6	1015.8	76
14.1	57.6	130.5	2.7	13	1020.5	81
10.5	65.2	108.5	2.4	11.2	1023.5	87
6.8	71.7	51.3	3.2	8.9	1020.3	89
4.1	89.2	70.3	3.8	7.2	1022.2	84
6.5	70.7	41.8	4.8	8.2	1016.1	83
6.1	35.4	125.7	3.6	6.9	1025	72
6.3	140.5	68.4	4.3	7.5	1007.1	78
9.3	45.4	135	3.2	8	1011.3	69
16.4	53.9	231	3	11.3	1014.8	62
16.1	69.9	184.7	2.8	13	1017.2	73
18	101.9	181.6	2.9	14.1	1015.2	72
17.6	89.3	138.3	3.1	14.4	1012.9	75
14	70.7	157.8	3.3	11.3	1017.7	75
10.5	72.4	122.1	3.1	10.5	1016	85
6.9	67.6	39.6	3.5	8.9	1013.5	90
2.8	43.3	59.8	2.9	6.7	1018.3	91
0.7	62.9	89.1	3.5	5.6	1013.9	86
3.6	57.1	33.1	3.2	6.8	1014.5	87
6.7	68.2	150.5	3.7	7	1015	75
12.5	47.1	196.9	3.1	9	1013.6	70
14.4	43.1	196.6	3.1	11.3	1018.7	72
16.5	64.5	225.2	3.1	12.6	1016.6	69
18.7	73.1	214.2	3.1	14.2	1013.7	70
19.4	37.7	258.3	2.9	13.6	1016.9	66
15.8	29.1	156.9	3.1	13.6	1021	78
11.3	105	89.8	2.9	10.9	1017.1	83
9.7	98	48.2	5.5	9.8	1006.6	84
2.9	80.8	46	4	6.9	1007.1	89

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')
}