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1418	210907	56	396	81	3	79	30
869	120982	56	297	55	4	58	28
1530	176508	54	559	50	12	60	38
2172	179321	89	967	125	2	108	30
901	123185	40	270	40	1	49	22
463	52746	25	143	37	3	0	26
3201	385534	92	1562	63	0	121	25
371	33170	18	109	44	0	1	18
1192	101645	63	371	88	0	20	11
1583	149061	44	656	66	5	43	26
1439	165446	33	511	57	0	69	25
1764	237213	84	655	74	0	78	38
1495	173326	88	465	49	7	86	44
1373	133131	55	525	52	7	44	30
2187	258873	60	885	88	3	104	40
1491	180083	66	497	36	9	63	34
4041	324799	154	1436	108	0	158	47
1706	230964	53	612	43	4	102	30
2152	236785	119	865	75	3	77	31
1036	135473	41	385	32	0	82	23
1882	202925	61	567	44	7	115	36
1929	215147	58	639	85	0	101	36
2242	344297	75	963	86	1	80	30
1220	153935	33	398	56	5	50	25
1289	132943	40	410	50	7	83	39
2515	174724	92	966	135	0	123	34
2147	174415	100	801	63	0	73	31
2352	225548	112	892	81	5	81	31
1638	223632	73	513	52	0	105	33
1222	124817	40	469	44	0	47	25
1812	221698	45	683	113	0	105	33
1677	210767	60	643	39	3	94	35
1579	170266	62	535	73	4	44	42
1731	260561	75	625	48	1	114	43
807	84853	31	264	33	4	38	30
2452	294424	77	992	59	2	107	33
829	101011	34	238	41	0	30	13
1940	215641	46	818	69	0	71	32
2662	325107	99	937	64	0	84	36
186	7176	17	70	1	0	0	0
1499	167542	66	507	59	2	59	28
865	106408	30	260	32	1	33	14
1793	96560	76	503	129	0	42	17
2527	265769	146	927	37	2	96	32
2747	269651	67	1269	31	10	106	30
1324	149112	56	537	65	6	56	35
2702	175824	107	910	107	0	57	20
1383	152871	58	532	74	5	59	28
1179	111665	34	345	54	4	39	28
2099	116408	61	918	76	1	34	39
4308	362301	119	1635	715	2	76	34
918	78800	42	330	57	2	20	26
1831	183167	66	557	66	0	91	39
3373	277965	89	1178	106	8	115	39
1713	150629	44	740	54	3	85	33
1438	168809	66	452	32	0	76	28
496	24188	24	218	20	0	8	4
2253	329267	259	764	71	8	79	39
744	65029	17	255	21	5	21	18
1161	101097	64	454	70	3	30	14
2352	218946	41	866	112	1	76	29
2144	244052	68	574	66	5	101	44
4691	341570	168	1276	190	1	94	21
1112	103597	43	379	66	1	27	16
2694	233328	132	825	165	5	92	28
1973	256462	105	798	56	0	123	35
1769	206161	71	663	61	12	75	28
3148	311473	112	1069	53	8	128	38
2474	235800	94	921	127	8	105	23
2084	177939	82	858	63	8	55	36
1954	207176	70	711	38	8	56	32
1226	196553	57	503	50	2	41	29
1389	174184	53	382	52	0	72	25
1496	143246	103	464	42	5	67	27
2269	187559	121	717	76	8	75	36
1833	187681	62	690	67	2	114	28
1268	119016	52	462	50	5	118	23
1943	182192	52	657	53	12	77	40
893	73566	32	385	39	6	22	23
1762	194979	62	577	50	7	66	40
1403	167488	45	619	77	2	69	28
1425	143756	46	479	57	0	105	34
1857	275541	63	817	73	4	116	33
1840	243199	75	752	34	3	88	28
1502	182999	88	430	39	6	73	34
1441	135649	46	451	46	2	99	30
1420	152299	53	537	63	0	62	33
1416	120221	37	519	35	1	53	22
2970	346485	90	1000	106	0	118	38
1317	145790	63	637	43	5	30	26
1644	193339	78	465	47	2	100	35
870	80953	25	437	31	0	49	8
1654	122774	45	711	162	0	24	24
1054	130585	46	299	57	5	67	29
937	112611	41	248	36	0	46	20
3004	286468	144	1162	263	1	57	29
2008	241066	82	714	78	0	75	45
2547	148446	91	905	63	1	135	37
1885	204713	71	649	54	1	68	33
1626	182079	63	512	63	2	124	33
1468	140344	53	472	77	6	33	25
2445	220516	62	905	79	1	98	32
1964	243060	63	786	110	4	58	29
1381	162765	32	489	56	2	68	28
1369	182613	39	479	56	3	81	28
1659	232138	62	617	43	0	131	31
2888	265318	117	925	111	10	110	52
1290	85574	34	351	71	0	37	21
2845	310839	92	1144	62	9	130	24
1982	225060	93	669	56	7	93	41
1904	232317	54	707	74	0	118	33
1391	144966	144	458	60	0	39	32
602	43287	14	214	43	4	13	19
1743	155754	61	599	68	4	74	20
1559	164709	109	572	53	0	81	31
2014	201940	38	897	87	0	109	31
2143	235454	73	819	46	0	151	32
2146	220801	75	720	105	1	51	18
874	99466	50	273	32	0	28	23
1590	92661	61	508	133	1	40	17
1590	133328	55	506	79	0	56	20
1210	61361	77	451	51	0	27	12
2072	125930	75	699	207	4	37	17
1281	100750	72	407	67	0	83	30
1401	224549	50	465	47	4	54	31
834	82316	32	245	34	4	27	10
1105	102010	53	370	66	3	28	13
1272	101523	42	316	76	0	59	22
1944	243511	71	603	65	0	133	42
391	22938	10	154	9	0	12	1
761	41566	35	229	42	5	0	9
1605	152474	65	577	45	0	106	32
530	61857	25	192	25	4	23	11
1988	99923	66	617	115	0	44	25
1386	132487	41	411	97	0	71	36
2395	317394	86	975	53	1	116	31
387	21054	16	146	2	0	4	0
1742	209641	42	705	52	5	62	24
620	22648	19	184	44	0	12	13
449	31414	19	200	22	0	18	8
800	46698	45	274	35	0	14	13
1684	131698	65	502	74	0	60	19
1050	91735	35	382	103	0	7	18
2699	244749	95	964	144	2	98	33
1606	184510	49	537	60	7	64	40
1502	79863	37	438	134	1	29	22
1204	128423	64	369	89	8	32	38
1138	97839	38	417	42	2	25	24
568	38214	34	276	52	0	16	8
1459	151101	32	514	98	2	48	35
2158	272458	65	822	99	0	100	43
1111	172494	52	389	52	0	46	43
1421	108043	62	466	29	1	45	14
2833	328107	65	1255	125	3	129	41
1955	250579	83	694	106	0	130	38
2922	351067	95	1024	95	3	136	45
1002	158015	29	400	40	0	59	31
1060	98866	18	397	140	0	25	13
956	85439	33	350	43	0	32	28
2186	229242	247	719	128	4	63	31
3604	351619	139	1277	142	4	95	40
1035	84207	29	356	73	11	14	30
1417	120445	118	457	72	0	36	16
3261	324598	110	1402	128	0	113	37
1587	131069	67	600	61	4	47	30
1424	204271	42	480	73	0	92	35
1701	165543	65	595	148	1	70	32
1249	141722	94	436	64	0	19	27
946	116048	64	230	45	0	50	20
1926	250047	81	651	58	0	41	18
3352	299775	95	1367	97	9	91	31
1641	195838	67	564	50	1	111	31
2035	173260	63	716	37	3	41	21
2312	254488	83	747	50	10	120	39
1369	104389	45	467	105	5	135	41
1577	136084	30	671	69	0	27	13
2201	199476	70	861	46	2	87	32
961	92499	32	319	57	0	25	18
1900	224330	83	612	52	1	131	39
1254	135781	31	433	98	2	45	14
1335	74408	67	434	61	4	29	7
1597	81240	66	503	89	0	58	17
207	14688	10	85	0	0	4	0
1645	181633	70	564	48	2	47	30
2429	271856	103	824	91	1	109	37
151	7199	5	74	0	0	7	0
474	46660	20	259	7	0	12	5
141	17547	5	69	3	0	0	1
1639	133368	36	535	54	1	37	16
872	95227	34	239	70	0	37	32
1318	152601	48	438	36	2	46	24
1018	98146	40	459	37	0	15	17
1383	79619	43	426	123	3	42	11
1314	59194	31	288	247	6	7	24
1335	139942	42	498	46	0	54	22
1403	118612	46	454	72	2	54	12
910	72880	33	376	41	0	14	19
616	65475	18	225	24	2	16	13
1407	99643	55	555	45	1	33	17
771	71965	35	252	33	1	32	15
766	77272	59	208	27	2	21	16
473	49289	19	130	36	1	15	24
1376	135131	66	481	87	0	38	15
1232	108446	60	389	90	1	22	17
1521	89746	36	565	114	3	28	18
572	44296	25	173	31	0	10	20
1059	77648	47	278	45	0	31	16
1544	181528	54	609	69	0	32	16
1230	134019	53	422	51	0	32	18
1206	124064	40	445	34	1	43	22
1205	92630	40	387	60	4	27	8
1255	121848	39	339	45	0	37	17
613	52915	14	181	54	0	20	18
721	81872	45	245	25	0	32	16
1109	58981	36	384	38	7	0	23
740	53515	28	212	52	2	5	22
1126	60812	44	399	67	0	26	13
728	56375	30	229	74	7	10	13
689	65490	22	224	38	3	27	16
592	80949	17	203	30	0	11	16
995	76302	31	333	26	0	29	20
1613	104011	55	384	67	6	25	22
2048	98104	54	636	132	2	55	17
705	67989	21	185	42	0	23	18
301	30989	14	93	35	0	5	17
1803	135458	81	581	118	3	43	12
799	73504	35	248	68	0	23	7
861	63123	43	304	43	1	34	17
1186	61254	46	344	76	1	36	14
1451	74914	30	407	64	0	35	23
628	31774	23	170	48	1	0	17
1161	81437	38	312	64	0	37	14
1463	87186	54	507	56	0	28	15
742	50090	20	224	71	0	16	17
979	65745	53	340	75	0	26	21
675	56653	45	168	39	0	38	18
1241	158399	39	443	42	0	23	18
676	46455	20	204	39	0	22	17
1049	73624	24	367	93	0	30	17
620	38395	31	210	38	0	16	16
1081	91899	35	335	60	0	18	15
1688	139526	151	364	71	0	28	21
736	52164	52	178	52	0	32	16
617	51567	30	206	27	2	21	14
812	70551	31	279	59	0	23	15
1051	84856	29	387	40	1	29	17
1656	102538	57	490	79	1	50	15
705	86678	40	238	44	0	12	15
945	85709	44	343	65	0	21	10
554	34662	25	232	10	0	18	6
1597	150580	77	530	124	0	27	22
982	99611	35	291	81	0	41	21
222	19349	11	67	15	0	13	1
1212	99373	63	397	92	1	12	18
1143	86230	44	467	42	0	21	17
435	30837	19	178	10	0	8	4
532	31706	13	175	24	0	26	10
882	89806	42	299	64	0	27	16
608	62088	38	154	45	1	13	16
459	40151	29	106	22	0	16	9
578	27634	20	189	56	0	2	16
826	76990	27	194	94	0	42	17
509	37460	20	135	19	0	5	7
717	54157	19	201	35	0	37	15
637	49862	37	207	32	0	17	14
857	84337	26	280	35	0	38	14
830	64175	42	260	48	0	37	18
652	59382	49	227	49	0	29	12
707	119308	30	239	48	0	32	16
954	76702	49	333	62	0	35	21
1461	103425	67	428	96	1	17	19
672	70344	28	230	45	0	20	16
778	43410	19	292	63	0	7	1
1141	104838	49	350	71	1	46	16
680	62215	27	186	26	0	24	10
1090	69304	30	326	48	6	40	19
616	53117	22	155	29	3	3	12
285	19764	12	75	19	1	10	2
1145	86680	31	361	45	2	37	14
733	84105	20	261	45	0	17	17
888	77945	20	299	67	0	28	19
849	89113	39	300	30	0	19	14
1182	91005	29	450	36	3	29	11
528	40248	16	183	34	1	8	4
642	64187	27	238	36	0	10	16
947	50857	21	165	34	0	15	20
819	56613	19	234	37	1	15	12
757	62792	35	176	46	0	28	15
894	72535	14	329	44	0	17	16

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')
}