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1418	210907	79	30	94	112285	24188
869	120982	58	28	103	84786	18273
1530	176508	60	38	93	83123	14130
2172	179321	108	30	103	101193	32287
901	123185	49	22	51	38361	8654
463	52746	0	26	70	68504	9245
3201	385534	121	25	91	119182	33251
371	33170	1	18	22	22807	1271
1192	101645	20	11	38	17140	5279
1583	149061	43	26	93	116174	27101
1439	165446	69	25	60	57635	16373
1764	237213	78	38	123	66198	19716
1495	173326	86	44	148	71701	17753
1373	133131	44	30	90	57793	9028
2187	258873	104	40	124	80444	18653
1491	180083	63	34	70	53855	8828
4041	324799	158	47	168	97668	29498
1706	230964	102	30	115	133824	27563
2152	236785	77	31	71	101481	18293
1036	135473	82	23	66	99645	22530
1882	202925	115	36	134	114789	15977
1929	215147	101	36	117	99052	35082
2242	344297	80	30	108	67654	16116
1220	153935	50	25	84	65553	15849
1289	132943	83	39	156	97500	16026
2515	174724	123	34	120	69112	26569
2147	174415	73	31	114	82753	24785
2352	225548	81	31	94	85323	17569
1638	223632	105	33	120	72654	23825
1222	124817	47	25	81	30727	7869
1812	221698	105	33	110	77873	14975
1677	210767	94	35	133	117478	37791
1579	170266	44	42	122	74007	9605
1731	260561	114	43	158	90183	27295
807	84853	38	30	109	61542	2746
2452	294424	107	33	124	101494	34461
829	101011	30	13	39	27570	8098
1940	215641	71	32	92	55813	4787
2662	325107	84	36	126	79215	24919
186	7176	0	0	0	1423	603
1499	167542	59	28	70	55461	16329
865	106408	33	14	37	31081	12558
1793	96560	42	17	38	22996	7784
2527	265769	96	32	120	83122	28522
2747	269651	106	30	93	70106	22265
1324	149112	56	35	95	60578	14459
2702	175824	57	20	77	39992	14526
1383	152871	59	28	90	79892	22240
1179	111665	39	28	80	49810	11802
2099	116408	34	39	31	71570	7623
4308	362301	76	34	110	100708	11912
918	78800	20	26	66	33032	7935
1831	183167	91	39	138	82875	18220
3373	277965	115	39	133	139077	19199
1713	150629	85	33	113	71595	19918
1438	168809	76	28	100	72260	21884
496	24188	8	4	7	5950	2694
2253	329267	79	39	140	115762	15808
744	65029	21	18	61	32551	3597
1161	101097	30	14	41	31701	5296
2352	218946	76	29	96	80670	25239
2144	244052	101	44	164	143558	29801
4691	341570	94	21	78	117105	18450
1112	103597	27	16	49	23789	7132
2694	233328	92	28	102	120733	34861
1973	256462	123	35	124	105195	35940
1769	206161	75	28	99	73107	16688
3148	311473	128	38	129	132068	24683
2474	235800	105	23	62	149193	46230
2084	177939	55	36	73	46821	10387
1954	207176	56	32	114	87011	21436
1226	196553	41	29	99	95260	30546
1389	174184	72	25	70	55183	19746
1496	143246	67	27	104	106671	15977
2269	187559	75	36	116	73511	22583
1833	187681	114	28	91	92945	17274
1268	119016	118	23	74	78664	16469
1943	182192	77	40	138	70054	14251
893	73566	22	23	67	22618	3007
1762	194979	66	40	151	74011	16851
1403	167488	69	28	72	83737	21113
1425	143756	105	34	120	69094	17401
1857	275541	116	33	115	93133	23958
1840	243199	88	28	105	95536	23567
1502	182999	73	34	104	225920	13065
1441	135649	99	30	108	62133	15358
1420	152299	62	33	98	61370	14587
1416	120221	53	22	69	43836	12770
2970	346485	118	38	111	106117	24021
1317	145790	30	26	99	38692	9648
1644	193339	100	35	71	84651	20537
870	80953	49	8	27	56622	7905
1654	122774	24	24	69	15986	4527
1054	130585	67	29	107	95364	30495
937	112611	46	20	73	26706	7117
3004	286468	57	29	107	89691	17719
2008	241066	75	45	93	67267	27056
2547	148446	135	37	129	126846	33473
1885	204713	68	33	69	41140	9758
1626	182079	124	33	118	102860	21115
1468	140344	33	25	73	51715	7236
2445	220516	98	32	119	55801	13790
1964	243060	58	29	104	111813	32902
1381	162765	68	28	107	120293	25131
1369	182613	81	28	99	138599	30910
1659	232138	131	31	90	161647	35947
2888	265318	110	52	197	115929	29848
1290	85574	37	21	36	24266	6943
2845	310839	130	24	85	162901	42705
1982	225060	93	41	139	109825	31808
1904	232317	118	33	106	129838	26675
1391	144966	39	32	50	37510	8435
602	43287	13	19	64	43750	7409
1743	155754	74	20	31	40652	14993
1559	164709	81	31	63	87771	36867
2014	201940	109	31	92	85872	33835
2143	235454	151	32	106	89275	24164
2146	220801	51	18	63	44418	12607
874	99466	28	23	69	192565	22609
1590	92661	40	17	41	35232	5892
1590	133328	56	20	56	40909	17014
1210	61361	27	12	25	13294	5394
2072	125930	37	17	65	32387	9178
1281	100750	83	30	93	140867	6440
1401	224549	54	31	114	120662	21916
834	82316	27	10	38	21233	4011
1105	102010	28	13	44	44332	5818
1272	101523	59	22	87	61056	18647
1944	243511	133	42	110	101338	20556
391	22938	12	1	0	1168	238
761	41566	0	9	27	13497	70
1605	152474	106	32	83	65567	22392
530	61857	23	11	30	25162	3913
1988	99923	44	25	80	32334	12237
1386	132487	71	36	98	40735	8388
2395	317394	116	31	82	91413	22120
387	21054	4	0	0	855	338
1742	209641	62	24	60	97068	11727
620	22648	12	13	28	44339	3704
449	31414	18	8	9	14116	3988
800	46698	14	13	33	10288	3030
1684	131698	60	19	59	65622	13520
1050	91735	7	18	49	16563	1421
2699	244749	98	33	115	76643	20923
1606	184510	64	40	140	110681	20237
1502	79863	29	22	49	29011	3219
1204	128423	32	38	120	92696	3769
1138	97839	25	24	66	94785	12252
568	38214	16	8	21	8773	1888
1459	151101	48	35	124	83209	14497
2158	272458	100	43	152	93815	28864
1111	172494	46	43	139	86687	21721
1421	108043	45	14	38	34553	4821
2833	328107	129	41	144	105547	33644
1955	250579	130	38	120	103487	15923
2922	351067	136	45	160	213688	42935
1002	158015	59	31	114	71220	18864
1060	98866	25	13	39	23517	4977
956	85439	32	28	78	56926	7785
2186	229242	63	31	119	91721	17939
3604	351619	95	40	141	115168	23436
1035	84207	14	30	101	111194	325
1417	120445	36	16	56	51009	13539
3261	324598	113	37	133	135777	34538
1587	131069	47	30	83	51513	12198
1424	204271	92	35	116	74163	26924
1701	165543	70	32	90	51633	12716
1249	141722	19	27	36	75345	8172
946	116048	50	20	50	33416	10855
1926	250047	41	18	61	83305	11932
3352	299775	91	31	97	98952	14300
1641	195838	111	31	98	102372	25515
2035	173260	41	21	78	37238	2805
2312	254488	120	39	117	103772	29402
1369	104389	135	41	148	123969	16440
1577	136084	27	13	41	27142	11221
2201	199476	87	32	105	135400	28732
961	92499	25	18	55	21399	5250
1900	224330	131	39	132	130115	28608
1254	135781	45	14	44	24874	8092
1335	74408	29	7	21	34988	4473
1597	81240	58	17	50	45549	1572
207	14688	4	0	0	6023	2065
1645	181633	47	30	73	64466	14817
2429	271856	109	37	86	54990	16714
151	7199	7	0	0	1644	556
474	46660	12	5	13	6179	2089
141	17547	0	1	4	3926	2658
1639	133368	37	16	57	32755	10695
872	95227	37	32	48	34777	1669
1318	152601	46	24	46	73224	16267
1018	98146	15	17	48	27114	7768
1383	79619	42	11	32	20760	7252
1314	59194	7	24	68	37636	6387
1335	139942	54	22	87	65461	18715
1403	118612	54	12	43	30080	7936
910	72880	14	19	67	24094	8643
616	65475	16	13	46	69008	7294
1407	99643	33	17	46	54968	4570
771	71965	32	15	56	46090	7185
766	77272	21	16	48	27507	10058
473	49289	15	24	44	10672	2342
1376	135131	38	15	60	34029	8509
1232	108446	22	17	65	46300	13275
1521	89746	28	18	55	24760	6816
572	44296	10	20	38	18779	1930
1059	77648	31	16	52	21280	8086
1544	181528	32	16	60	40662	10737
1230	134019	32	18	54	28987	8033
1206	124064	43	22	86	22827	7058
1205	92630	27	8	24	18513	6782
1255	121848	37	17	52	30594	5401
613	52915	20	18	49	24006	6521
721	81872	32	16	61	27913	10856
1109	58981	0	23	61	42744	2154
740	53515	5	22	81	12934	6117
1126	60812	26	13	43	22574	5238
728	56375	10	13	40	41385	4820
689	65490	27	16	40	18653	5615
592	80949	11	16	56	18472	4272
995	76302	29	20	68	30976	8702
1613	104011	25	22	79	63339	15340
2048	98104	55	17	47	25568	8030
705	67989	23	18	57	33747	9526
301	30989	5	17	41	4154	1278
1803	135458	43	12	29	19474	4236
799	73504	23	7	3	35130	3023
861	63123	34	17	60	39067	7196
1186	61254	36	14	30	13310	3394
1451	74914	35	23	79	65892	6371
628	31774	0	17	47	4143	1574
1161	81437	37	14	40	28579	9620
1463	87186	28	15	48	51776	6978
742	50090	16	17	36	21152	4911
979	65745	26	21	42	38084	8645
675	56653	38	18	49	27717	8987
1241	158399	23	18	57	32928	5544
676	46455	22	17	12	11342	3083
1049	73624	30	17	40	19499	6909
620	38395	16	16	43	16380	3189
1081	91899	18	15	33	36874	6745
1688	139526	28	21	77	48259	16724
736	52164	32	16	43	16734	4850
617	51567	21	14	45	28207	7025
812	70551	23	15	47	30143	6047
1051	84856	29	17	43	41369	7377
1656	102538	50	15	45	45833	9078
705	86678	12	15	50	29156	4605
945	85709	21	10	35	35944	3238
554	34662	18	6	7	36278	8100
1597	150580	27	22	71	45588	9653
982	99611	41	21	67	45097	8914
222	19349	13	1	0	3895	786
1212	99373	12	18	62	28394	6700
1143	86230	21	17	54	18632	5788
435	30837	8	4	4	2325	593
532	31706	26	10	25	25139	4506
882	89806	27	16	40	27975	6382
608	62088	13	16	38	14483	5621
459	40151	16	9	19	13127	3997
578	27634	2	16	17	5839	520
826	76990	42	17	67	24069	8891
509	37460	5	7	14	3738	999
717	54157	37	15	30	18625	7067
637	49862	17	14	54	36341	4639
857	84337	38	14	35	24548	5654
830	64175	37	18	59	21792	6928
652	59382	29	12	24	26263	1514
707	119308	32	16	58	23686	9238
954	76702	35	21	42	49303	8204
1461	103425	17	19	46	25659	5926
672	70344	20	16	61	28904	5785
778	43410	7	1	3	2781	4
1141	104838	46	16	52	29236	5930
680	62215	24	10	25	19546	3710
1090	69304	40	19	40	22818	705
616	53117	3	12	32	32689	443
285	19764	10	2	4	5752	2416
1145	86680	37	14	49	22197	7747
733	84105	17	17	63	20055	5432
888	77945	28	19	67	25272	4913
849	89113	19	14	32	82206	2650
1182	91005	29	11	23	32073	2370
528	40248	8	4	7	5444	775
642	64187	10	16	54	20154	5576
947	50857	15	20	37	36944	1352
819	56613	15	12	35	8019	3080
757	62792	28	15	51	30884	10205
894	72535	17	16	39	19540	6095

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')
}