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1844	162687	94	595	115	0	48	21	82	73	20465	6200	23975	39	37
1796	201906	62	545	76	1	58	20	80	56	33629	10265	85634	46	43
192	7215	18	72	1	0	0	0	0	0	1423	603	1929	0	0
2443	146367	96	679	155	0	67	27	84	63	25629	8874	36294	54	54
3566	257045	138	1201	125	0	83	31	124	116	54002	20323	72255	93	86
6917	524450	265	1967	278	1	136	36	140	138	151036	26258	189748	198	181
1840	188294	58	595	89	1	65	23	88	71	33287	10165	61834	42	42
1739	195674	59	496	59	0	86	30	115	107	31172	8247	68167	59	59
2078	177020	44	670	87	0	62	30	109	50	28113	8683	38462	49	46
3097	325899	98	1039	130	1	71	27	108	81	57803	16957	101219	83	77
1946	121844	75	634	158	2	50	24	63	58	49830	8058	43270	49	49
2369	203938	71	743	120	0	88	30	118	91	52143	20488	76183	83	79
1880	107220	104	681	87	0	50	22	71	41	21055	7945	31476	39	37
3198	220751	120	1086	264	4	79	28	112	100	47007	13448	62157	93	92
1490	172905	62	419	51	4	56	18	63	61	28735	5389	46261	31	31
1573	156326	88	474	85	3	54	22	86	74	59147	6185	50063	29	28
1807	145178	58	442	100	0	81	37	148	147	78950	24369	64483	104	103
1309	89171	61	373	72	5	13	15	54	45	13497	70	2341	2	2
2820	172624	88	899	147	0	74	34	134	110	46154	17327	48149	46	48
756	32443	25	235	49	0	14	18	57	41	53249	3878	12743	27	25
1162	87927	62	399	40	0	31	15	59	37	10726	3149	18743	16	16
2817	241285	102	850	99	0	99	30	113	84	83700	20517	97057	108	106
1760	195820	72	642	127	1	38	25	96	67	40400	2570	17675	36	35
2315	146946	56	717	164	1	59	34	96	69	33797	5162	33106	33	33
1994	159763	89	619	41	1	54	21	78	58	36205	5299	53311	46	45
1805	207078	33	657	160	0	63	21	80	60	30165	7233	42754	65	64
2152	212394	166	691	92	0	66	25	93	88	58534	15657	59056	80	73
1457	201536	95	366	59	0	90	31	109	75	44663	15329	101621	81	78
3000	394662	121	994	89	0	72	31	115	98	92556	14881	118120	69	63
2234	217808	44	929	90	0	61	20	79	67	40078	16318	79572	69	69
1684	182286	44	490	76	0	61	28	103	84	34711	9556	42744	37	36
1625	181740	46	553	116	2	61	22	71	62	31076	10462	65931	45	41
2256	137978	106	738	92	4	53	17	66	35	74608	7192	38575	62	59
3373	255929	130	1028	361	0	118	25	100	74	58092	4362	28795	33	33
2571	236489	55	844	85	1	73	25	100	93	42009	14349	94440	77	76
1	0	1	0	0	0	0	0	0	0	0	0	0	0	0
2142	230761	64	1000	63	0	54	31	121	87	36022	10881	38229	34	27
1848	130843	53	619	138	3	53	14	51	39	23333	8022	31972	44	44
2190	157118	49	532	270	9	46	35	119	101	53349	13073	40071	43	43
2186	253254	68	811	64	0	83	34	136	135	92596	26641	132480	117	104
2532	269329	71	837	96	2	106	22	84	76	49598	14426	62797	125	120
1823	161273	61	682	62	0	44	34	136	118	44093	15604	40429	49	44
1095	107181	33	400	35	2	27	23	84	76	84205	9184	45545	76	71
2162	195891	79	804	59	1	64	24	92	65	63369	5989	57568	81	78
1365	139667	51	419	56	2	71	26	103	97	60132	11270	39019	111	106
1244	171101	98	334	41	2	44	23	85	70	37403	13958	53866	61	61
755	81407	32	216	49	1	23	35	106	63	24460	7162	38345	56	53
2417	247563	104	786	121	0	78	24	96	96	46456	13275	50210	54	51
2327	239807	90	752	113	1	60	31	124	112	66616	21224	80947	47	46
2786	172743	59	964	190	8	73	30	106	82	41554	10615	43461	55	55
658	48188	28	205	37	0	12	22	82	39	22346	2102	14812	14	14
2012	169355	70	506	52	0	104	23	87	69	30874	12396	37819	44	44
2602	315622	76	830	89	0	83	27	97	93	68701	18717	102738	115	113
2071	241518	79	694	73	0	57	30	107	76	35728	9724	54509	57	55
1911	195583	59	691	49	1	67	33	126	117	29010	9863	62956	48	46
1775	159913	57	547	77	8	44	12	43	31	23110	8374	55411	40	39
1918	220241	69	538	58	0	53	26	96	65	38844	8030	50611	51	51
1046	101694	25	329	75	1	26	26	100	78	27084	7509	26692	32	31
1178	151985	67	421	32	0	67	23	91	87	35139	14146	60056	36	36
2890	202536	99	972	59	10	36	38	136	85	57476	7768	25155	47	47
1836	173505	64	541	71	6	56	32	128	119	33277	13823	42840	51	53
2254	150518	83	836	91	0	52	21	83	65	31141	7230	39358	37	38
1389	141273	61	376	87	11	54	22	74	60	61281	10170	47241	52	52
1325	125612	38	467	48	3	57	26	96	67	25820	7573	49611	42	37
1317	166049	36	430	63	0	27	28	102	94	23284	5753	41833	11	11
1525	124197	42	483	41	0	58	33	122	100	35378	9791	48930	47	45
2335	195043	71	504	86	8	76	36	144	135	74990	19365	110600	59	59
2897	138708	65	887	152	2	93	25	90	71	29653	9422	52235	82	82
1118	116552	40	271	49	0	59	25	97	78	64622	12310	53986	49	49
340	31970	15	101	40	0	5	21	78	42	4157	1283	4105	6	6
2969	255587	114	1093	135	3	56	19	72	42	29245	6372	59331	83	81
1449	151184	78	469	83	1	42	12	45	8	50008	5413	47796	56	56
1550	135926	68	528	62	2	88	30	120	86	52338	10837	38302	114	105
1684	119629	72	475	91	1	53	21	59	41	13310	3394	14063	46	46
2728	171518	71	698	95	0	81	39	150	131	92901	12964	54414	46	46
1574	108949	45	425	82	2	35	32	117	91	10956	3495	9903	2	2
2413	183471	60	709	112	1	102	28	123	102	34241	11580	53987	51	51
2563	159966	98	824	70	0	71	29	114	91	75043	9970	88937	96	95
1079	93786	34	336	78	0	28	21	75	46	21152	4911	21928	20	18
1234	84971	71	395	105	0	34	31	114	60	42249	10138	29487	57	55
966	88797	75	228	49	0	54	26	94	69	42005	14697	35334	49	48
2246	304603	65	830	60	0	49	29	116	95	41152	8464	57596	51	48
1075	75101	29	334	49	1	30	23	86	17	14399	4204	29750	40	39
1637	145043	40	524	132	0	57	25	90	61	28263	10226	41029	40	40
1207	95827	47	393	49	0	54	22	87	55	17215	3456	12416	36	36
1865	173924	58	574	71	0	38	26	99	55	48140	8895	51158	64	60
2726	241957	237	672	102	0	63	33	132	124	62897	22557	79935	117	114
1208	115367	115	284	74	0	58	24	96	73	22883	6900	26552	40	39
1419	118408	64	450	49	7	46	24	91	73	41622	8620	25807	46	45
1609	164078	53	653	74	0	46	21	77	67	40715	7820	50620	61	59
1864	158931	41	684	59	5	51	28	104	66	65897	12112	61467	59	59
2412	184139	82	706	91	1	87	28	100	77	76542	13178	65292	94	93
1238	152856	58	417	68	0	39	25	94	83	37477	7028	55516	36	35
1462	144014	59	549	81	0	28	15	60	55	53216	6616	42006	51	47
973	62535	42	394	33	0	26	13	46	27	40911	9570	26273	39	36
2319	245196	117	730	166	0	52	36	135	115	57021	14612	90248	62	59
1890	199841	71	571	97	0	96	27	99	85	73116	11219	61476	79	79
223	19349	12	67	15	0	13	1	2	0	3895	786	9604	14	14
2526	247280	108	877	105	3	43	24	96	83	46609	11252	45108	45	42
2072	159408	83	856	61	0	42	31	109	90	29351	9289	47232	43	41
778	72128	30	306	11	0	30	4	15	4	2325	593	3439	8	8
1193	104253	25	382	45	0	59	21	68	60	31747	6562	30553	41	41
1424	151090	57	435	89	0	73	27	102	74	32665	8208	24751	25	24
1327	137382	65	336	67	1	39	23	84	52	19249	7488	34458	22	22
839	87448	42	227	27	1	36	12	46	24	15292	4574	24649	18	18
596	27676	22	194	59	0	2	16	59	17	5842	522	2342	3	1
1671	165507	50	410	127	0	102	29	116	105	33994	12840	52739	54	53
1167	132148	37	273	48	1	30	26	29	20	13018	1350	6245	6	6
0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
1105	95778	33	343	58	0	46	25	91	51	98177	10623	35381	50	49
1148	109001	67	376	57	0	25	21	76	76	37941	5322	19595	33	33
1484	158833	45	495	60	0	59	24	86	61	31032	7987	50848	54	50
1526	147690	63	448	77	1	60	21	84	70	32683	10566	39443	63	64
962	89887	63	313	71	0	36	21	65	38	34545	1900	27023	56	53
78	3616	5	14	5	0	0	0	0	0	0	0	0	0	0
0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
1184	199005	45	410	70	0	45	23	84	81	27525	10698	61022	49	48
1671	160930	92	606	76	0	79	33	114	78	66856	14884	63528	90	90
2142	177948	102	593	124	2	30	32	132	76	28549	6852	34835	51	46
1015	136061	39	312	56	0	43	23	92	89	38610	6873	37172	29	29
778	43410	19	292	63	0	7	1	3	3	2781	4	13	1	1
1856	184277	74	547	92	1	80	29	109	87	41211	9188	62548	68	64
1056	108858	43	302	58	0	32	20	81	55	22698	5141	31334	29	29
2234	141744	58	632	64	8	81	33	121	73	41194	4260	20839	27	27
731	60493	40	174	29	3	3	12	48	32	32689	443	5084	4	4
285	19764	12	75	19	1	10	2	8	4	5752	2416	9927	10	10
1872	177559	56	572	64	3	47	21	80	70	26757	9831	53229	47	47
1181	140281	35	389	79	0	35	28	107	102	22527	5953	29877	44	44
1725	164249	54	562	104	0	54	35	140	109	44810	9435	37310	53	51
256	11796	9	79	22	0	1	2	8	1	0	0	0	0	0
98	10674	9	33	7	0	0	0	0	0	0	0	0	0	0
1435	151322	59	487	37	0	46	18	56	39	100674	7642	50067	40	38
41	6836	3	11	5	0	0	1	4	0	0	0	0	0	0
1930	174712	67	664	48	6	51	21	70	45	57786	6837	47708	57	57
42	5118	3	6	1	0	5	0	0	0	0	0	0	0	0
528	40248	16	183	34	1	8	4	14	7	5444	775	6012	6	6
0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
1121	127628	50	342	53	0	38	29	104	86	28470	8191	27749	24	22
1305	88837	38	269	44	0	21	26	89	52	61849	1661	47555	34	34
81	7131	4	27	0	1	0	0	0	0	0	0	0	0	0
262	9056	15	99	18	0	0	4	12	1	2179	548	1336	10	10
1099	87957	26	305	52	1	18	19	60	49	8019	3080	11017	16	16
1290	144470	53	327	56	0	53	22	84	72	39644	13400	55184	93	93
1248	111408	20	459	50	1	17	22	88	56	23494	8181	43485	28	22

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')
}