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1565	129404	80	500	109	0	20	18	70	63	18158	5636	22622	30	28
1134	130358	46	329	68	1	38	17	68	50	30461	9079	73570	42	39
192	7215	18	72	1	0	0	0	0	0	1423	603	1929	0	0
2033	112861	85	584	146	0	49	22	68	51	25629	8874	36294	54	54
3283	219904	126	1100	124	0	76	30	120	112	48758	17988	62378	86	80
5877	402036	218	1618	267	1	104	31	120	118	129230	21325	167760	157	144
1322	117604	50	442	83	1	37	19	72	59	27376	8325	52443	36	36
1187	126737	49	321	48	0	53	25	96	90	26706	7117	57283	48	48
1463	99729	38	406	87	0	42	30	109	50	26505	7996	36614	45	42
2568	256310	86	818	129	1	62	26	104	79	49801	14218	93268	77	71
1810	113066	69	568	146	2	50	20	54	49	46580	6321	35439	49	49
1915	165392	62	595	113	0	66	30	118	91	48352	19690	72405	77	74
1443	77780	89	529	60	0	38	15	49	32	13899	5659	24044	28	27
2415	152673	84	818	240	4	48	22	88	82	39342	11370	55909	84	83
1254	134368	47	359	50	4	42	17	60	58	27465	4778	44689	31	31
1374	125769	67	419	81	3	47	19	74	65	55211	5954	49319	28	28
1504	123467	50	364	85	0	71	28	112	111	74098	22924	62075	99	98
999	56232	47	284	62	5	0	12	45	36	13497	70	2341	2	2
2222	108458	79	683	127	0	50	28	110	89	38338	14369	40551	41	43
634	22762	21	188	44	0	12	13	39	28	52505	3706	11621	25	24
849	48633	50	291	37	0	16	14	55	35	10663	3147	18741	16	16
2189	182081	83	640	94	0	77	27	102	78	74484	16801	84202	96	95
1469	140857	59	520	127	0	29	25	96	67	28895	2162	15334	23	22
1791	93773	46	532	159	1	38	30	86	61	32827	4721	28024	33	33
1743	133398	78	547	41	1	50	21	78	58	36188	5290	53306	46	45
1180	113933	23	428	153	0	33	17	64	49	28173	6446	37918	59	59
1749	153851	139	561	86	0	49	22	82	77	54926	14711	54819	72	66
1101	140711	75	266	55	0	59	28	100	71	38900	13311	89058	72	70
2391	303844	105	783	78	0	55	26	99	85	88530	13577	103354	62	56
1826	163810	38	754	84	0	42	17	67	56	35482	14634	70239	55	55
1301	123344	40	394	71	0	40	23	87	71	26730	6931	33045	27	27
1433	157640	39	482	111	2	51	20	65	58	29806	9992	63852	41	37
1893	103274	90	593	82	4	45	16	63	34	41799	6185	30905	51	48
2525	193500	105	760	254	0	73	20	80	59	54289	3445	24242	26	26
2033	178768	43	668	66	1	51	21	84	77	36805	12327	78907	65	64
1	0	1	0	0	0	0	0	0	0	0	0	0	0	0
1817	181412	55	855	58	0	46	27	105	75	33146	9898	36005	28	21
1506	92342	47	464	131	3	44	14	51	39	23333	8022	31972	44	44
1820	100023	41	418	258	9	31	29	98	83	47686	10765	35853	36	36
1649	178277	50	607	56	0	71	31	124	123	77783	22717	115301	100	89
1672	145067	58	540	90	2	61	19	75	67	36042	10090	47689	104	101
1433	114146	50	551	57	0	28	30	120	105	34541	12385	34223	35	31
864	86039	25	309	35	2	21	23	84	76	75620	8513	43431	69	65
1683	125481	66	647	53	1	42	21	82	57	60610	5508	52220	73	71
1024	95535	42	321	46	2	44	22	87	82	55041	9628	33863	106	102
1029	129221	78	262	38	2	40	21	78	64	32087	11872	46879	53	53
629	61554	26	180	45	1	15	32	97	57	16356	4186	23228	43	41
1679	168048	82	582	113	0	46	20	80	80	40161	10877	42827	49	46
1715	159121	75	544	104	1	43	26	104	94	55459	17066	65765	38	37
2093	129362	51	758	150	4	47	25	93	72	36679	9175	38167	51	51
658	48188	28	205	37	0	12	22	82	39	22346	2102	14812	14	14
1234	95461	56	317	49	0	46	19	73	60	27377	10807	32615	40	40
2059	229864	64	709	83	0	56	24	87	84	50273	13662	82188	79	77
1725	191094	68	590	67	0	47	26	95	69	32104	9224	51763	52	51
1482	158572	50	537	39	1	48	27	105	102	27016	9001	59325	44	43
1454	111388	47	443	69	6	35	10	37	28	19715	7204	48976	34	33
1620	172614	58	429	58	0	45	26	96	65	33629	6572	43384	47	47
733	63205	18	205	68	0	25	23	88	67	27084	7509	26692	32	31
894	109102	56	310	30	0	47	21	83	80	32352	12920	53279	31	31
2343	137303	74	785	54	10	28	34	124	79	51845	5438	20652	40	40
1503	125304	50	434	65	6	48	29	116	107	26591	11489	38338	42	42
1627	88620	65	602	81	0	32	19	76	60	29677	6661	36735	34	35
1119	95808	48	317	84	11	28	19	65	53	54237	7941	42764	40	40
897	83419	29	288	45	3	31	23	86	59	20284	6173	44331	35	30
855	101723	25	285	52	0	13	22	85	80	22741	5562	41354	11	11
1229	94982	37	391	36	0	38	29	107	89	34178	9492	47879	43	41
1991	143566	61	449	80	8	48	31	124	115	69551	17456	103793	53	53
2393	113325	63	715	144	2	68	21	78	59	29653	9422	52235	82	82
820	81518	32	208	45	0	32	21	83	66	38071	10913	49825	41	41
340	31970	15	101	40	0	5	21	78	42	4157	1283	4105	6	6
2443	192268	102	858	126	3	53	15	59	35	28321	6198	58687	82	81
1030	91261	55	306	75	1	33	9	33	3	40195	4501	40745	47	47
1091	80820	56	360	54	2	54	23	92	72	48158	9560	33187	108	100
1414	85829	59	424	84	1	37	18	52	38	13310	3394	14063	46	46
2192	116322	53	562	86	0	52	31	121	107	78474	9871	37407	38	38
1082	56544	32	292	62	2	0	25	92	73	6386	2419	7190	0	0
1764	116173	51	492	99	1	52	24	99	80	31588	10630	49562	45	45
2072	118781	80	690	63	0	51	22	86	69	61254	8536	76324	57	56
816	60138	23	253	76	0	16	21	75	46	21152	4911	21928	20	18
1121	73422	66	366	92	0	33	26	96	52	41272	9775	27860	56	54
809	67751	57	192	45	0	48	22	81	58	34165	11227	28078	38	37
1699	214002	53	620	57	0	33	26	104	85	37054	6916	49577	42	40
751	51185	24	221	44	0	24	20	76	13	12368	3424	28145	37	37
1309	97181	32	438	132	0	37	25	90	61	23168	8637	36241	36	36
732	45100	39	247	44	0	17	19	75	49	16380	3189	10824	34	34
1327	115801	43	388	67	0	32	22	86	47	41242	8178	46892	53	49
2246	186310	190	541	82	0	55	25	100	93	48450	16739	61264	85	82
968	71960	86	233	71	0	39	22	88	65	20790	6094	22933	36	36
1015	80105	48	333	44	5	31	21	80	64	34585	7237	20787	33	33
1100	103613	41	422	68	0	26	20	73	64	35672	7355	43978	57	55
1300	98707	33	452	54	3	37	23	88	57	52168	9734	51305	50	50
1982	136234	67	584	86	1	66	22	79	61	53933	11225	55593	71	71
1091	136781	52	366	59	0	35	21	81	71	34474	6213	51648	32	31
1107	105863	52	406	74	0	24	12	48	43	43753	4875	30552	45	42
666	42228	32	265	18	0	22	9	33	18	36456	8159	23470	33	31
1903	179997	91	606	156	0	37	32	120	103	51183	11893	77530	53	51
1608	169406	50	491	87	0	86	24	90	76	52742	10754	57299	64	64
223	19349	12	67	15	0	13	1	2	0	3895	786	9604	14	14
1807	160819	87	617	104	1	21	24	96	83	37076	9706	34684	38	37
1466	109510	53	597	54	0	32	25	86	73	24079	7796	41094	39	37
552	43803	24	240	11	0	8	4	15	4	2325	593	3439	8	8
708	47062	19	219	37	0	38	15	48	41	29354	5600	25171	38	38
1079	110845	44	349	80	0	45	21	81	57	30341	7245	23437	24	23
957	92517	52	241	66	1	24	23	84	52	18992	7360	34086	22	22
585	58660	36	136	27	0	23	12	46	24	15292	4574	24649	18	18
596	27676	22	194	59	0	2	16	59	17	5842	522	2342	3	1
980	98550	32	222	113	0	52	24	96	89	28918	10905	45571	49	48
585	43646	24	153	24	0	5	9	29	20	3738	999	3255	5	5
0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
975	75566	28	281	58	0	43	25	91	51	95352	9016	30002	47	46
750	57359	48	240	43	0	18	17	63	63	37478	5134	19360	33	33
1071	104330	36	358	45	0	44	18	68	48	26839	6608	43320	44	41
931	70369	47	302	55	0	45	21	84	70	26783	8577	35513	56	57
783	65494	56	267	66	0	29	17	54	32	33392	1543	23536	49	49
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
874	143931	37	287	67	0	32	20	75	72	25446	9803	54438	45	45
1327	117946	66	476	67	0	65	26	87	56	59847	12140	56812	78	78
1831	137332	85	519	118	1	26	27	108	66	28162	6678	33838	51	46
750	84336	33	243	51	0	24	20	80	77	33298	6420	32366	25	25
778	43410	19	292	63	0	7	1	3	3	2781	4	13	1	1
1373	136250	58	410	84	1	62	24	93	73	37121	7979	55082	62	59
807	79015	34	217	35	0	30	14	55	37	22698	5141	31334	29	29
1544	100578	45	448	58	8	49	27	99	57	27615	1311	16612	26	26
685	57586	38	160	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
1336	105757	42	412	51	2	42	16	60	55	23164	8396	47413	43	43
954	103651	25	332	64	0	23	23	88	84	20304	5462	27389	36	36
1283	113402	35	417	96	0	40	28	112	90	34409	7271	30425	43	41
256	11796	9	79	22	0	1	2	8	1	0	0	0	0	0
81	7627	9	25	7	0	0	0	0	0	0	0	0	0	0
1214	121085	49	431	34	0	29	17	52	38	92538	4423	33510	33	32
41	6836	3	11	5	0	0	1	4	0	0	0	0	0	0
1634	139563	46	564	43	5	46	17	57	36	46037	5331	40389	53	53
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
890	95079	42	295	49	0	21	25	91	75	23924	6676	22205	19	18
1203	80763	32	230	44	0	21	26	89	52	52230	1489	17231	26	26
81	7131	4	27	0	1	0	0	0	0	0	0	0	0	0
61	4194	11	14	4	0	0	0	0	0	0	0	0	0	0
849	60378	20	240	40	1	15	15	54	45	8019	3080	11017	16	16
1035	109173	44	251	52	0	47	20	77	66	34542	11409	46741	84	84
964	83484	16	347	47	0	17	19	76	48	21157	6769	39869	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')
}