• QR Code (size=2)
• QR Code (size=3)
• QR Code (size=4)
• QR Code (size=5)
• QR Code (size=6)

94	112285	24188	146283	145	56
103	84786	18273	98364	101	56
93	83123	14130	86146	98	54
103	101193	32287	96933	132	89
51	38361	8654	79234	60	40
70	68504	9245	42551	38	25
91	119182	33251	195663	144	92
22	22807	1271	6853	5	18
38	17140	5279	21529	28	63
93	116174	27101	95757	84	44
60	57635	16373	85584	79	33
123	66198	19716	143983	127	84
148	71701	17753	75851	78	88
90	57793	9028	59238	60	55
124	80444	18653	93163	131	60
70	53855	8828	96037	84	66
168	97668	29498	151511	133	154
115	133824	27563	136368	150	53
71	101481	18293	112642	91	119
66	99645	22530	94728	132	41
134	114789	15977	105499	136	61
117	99052	35082	121527	124	58
108	67654	16116	127766	118	75
84	65553	15849	98958	70	33
156	97500	16026	77900	107	40
120	69112	26569	85646	119	92
114	82753	24785	98579	89	100
94	85323	17569	130767	112	112
120	72654	23825	131741	108	73
81	30727	7869	53907	52	40
110	77873	14975	178812	112	45
133	117478	37791	146761	116	60
122	74007	9605	82036	123	62
158	90183	27295	163253	125	75
109	61542	2746	27032	27	31
124	101494	34461	171975	162	77
39	27570	8098	65990	32	34
92	55813	4787	86572	64	46
126	79215	24919	159676	92	99
0	1423	603	1929	0	17
70	55461	16329	85371	83	66
37	31081	12558	58391	41	30
38	22996	7784	31580	47	76
120	83122	28522	136815	120	146
93	70106	22265	120642	105	67
95	60578	14459	69107	79	56
77	39992	14526	50495	65	107
90	79892	22240	108016	70	58
80	49810	11802	46341	55	34
31	71570	7623	78348	39	61
110	100708	11912	79336	67	119
66	33032	7935	56968	21	42
138	82875	18220	93176	127	66
133	139077	19199	161632	152	89
113	71595	19918	87850	113	44
100	72260	21884	127969	99	66
7	5950	2694	15049	7	24
140	115762	15808	155135	141	259
61	32551	3597	25109	21	17
41	31701	5296	45824	35	64
96	80670	25239	102996	109	41
164	143558	29801	160604	133	68
78	117105	18450	158051	123	168
49	23789	7132	44547	26	43
102	120733	34861	162647	230	132
124	105195	35940	174141	166	105
99	73107	16688	60622	68	71
129	132068	24683	179566	147	112
62	149193	46230	184301	179	94
73	46821	10387	75661	61	82
114	87011	21436	96144	101	70
99	95260	30546	129847	108	57
70	55183	19746	117286	90	53
104	106671	15977	71180	114	103
116	73511	22583	109377	103	121
91	92945	17274	85298	142	62
74	78664	16469	73631	79	52
138	70054	14251	86767	88	52
67	22618	3007	23824	25	32
151	74011	16851	93487	83	62
72	83737	21113	82981	113	45
120	69094	17401	73815	118	46
115	93133	23958	94552	110	63
105	95536	23567	132190	129	75
104	225920	13065	128754	51	88
108	62133	15358	66363	93	46
98	61370	14587	67808	76	53
69	43836	12770	61724	49	37
111	106117	24021	131722	118	90
99	38692	9648	68580	38	63
71	84651	20537	106175	141	78
27	56622	7905	55792	58	25
69	15986	4527	25157	27	45
107	95364	30495	76669	91	46
73	26706	7117	57283	48	41
107	89691	17719	105805	63	144
93	67267	27056	129484	56	82
129	126846	33473	72413	144	91
69	41140	9758	87831	73	71
118	102860	21115	96971	168	63
73	51715	7236	71299	64	53
119	55801	13790	77494	97	62
104	111813	32902	120336	117	63
107	120293	25131	93913	100	32
99	138599	30910	136048	149	39
90	161647	35947	181248	187	62
197	115929	29848	146123	127	117
36	24266	6943	32036	37	34
85	162901	42705	186646	245	92
139	109825	31808	102255	87	93
106	129838	26675	168237	177	54
50	37510	8435	64219	49	144
64	43750	7409	19630	49	14
31	40652	14993	76825	73	61
63	87771	36867	115338	177	109
92	85872	33835	109427	94	38
106	89275	24164	118168	117	73
63	44418	12607	84845	60	75
69	192565	22609	153197	55	50
41	35232	5892	29877	39	61
56	40909	17014	63506	64	55
25	13294	5394	22445	26	77
65	32387	9178	47695	64	75
93	140867	6440	68370	58	72
114	120662	21916	146304	95	50
38	21233	4011	38233	25	32
44	44332	5818	42071	26	53
87	61056	18647	50517	76	42
110	101338	20556	103950	129	71
0	1168	238	5841	11	10
27	13497	70	2341	2	35
83	65567	22392	84396	101	65
30	25162	3913	24610	28	25
80	32334	12237	35753	36	66
98	40735	8388	55515	89	41
82	91413	22120	209056	193	86
0	855	338	6622	4	16
60	97068	11727	115814	84	42
28	44339	3704	11609	23	19
9	14116	3988	13155	39	19
33	10288	3030	18274	14	45
59	65622	13520	72875	78	65
49	16563	1421	10112	14	35
115	76643	20923	142775	101	95
140	110681	20237	68847	82	49
49	29011	3219	17659	24	37
120	92696	3769	20112	36	64
66	94785	12252	61023	75	38
21	8773	1888	13983	16	34
124	83209	14497	65176	55	32
152	93815	28864	132432	131	65
139	86687	21721	112494	131	52
38	34553	4821	45109	39	62
144	105547	33644	170875	144	65
120	103487	15923	180759	139	83
160	213688	42935	214921	211	95
114	71220	18864	100226	78	29
39	23517	4977	32043	50	18
78	56926	7785	54454	39	33
119	91721	17939	78876	90	247
141	115168	23436	170745	166	139
101	111194	325	6940	12	29
56	51009	13539	49025	57	118
133	135777	34538	122037	133	110
83	51513	12198	53782	69	67
116	74163	26924	127748	119	42
90	51633	12716	86839	119	65
36	75345	8172	44830	65	94
50	33416	10855	77395	61	64
61	83305	11932	89324	49	81
97	98952	14300	103300	101	95
98	102372	25515	112283	196	67
78	37238	2805	10901	15	63
117	103772	29402	120691	136	83
148	123969	16440	58106	89	45
41	27142	11221	57140	40	30
105	135400	28732	122422	123	70
55	21399	5250	25899	21	32
132	130115	28608	139296	163	83
44	24874	8092	52678	29	31
21	34988	4473	23853	35	67
50	45549	1572	17306	13	66
0	6023	2065	7953	5	10
73	64466	14817	89455	96	70
86	54990	16714	147866	151	103
0	1644	556	4245	6	5
13	6179	2089	21509	13	20
4	3926	2658	7670	3	5
57	32755	10695	66675	56	36
48	34777	1669	14336	23	34
46	73224	16267	53608	57	48
48	27114	7768	30059	14	40
32	20760	7252	29668	43	43
68	37636	6387	22097	20	31
87	65461	18715	96841	72	42
43	30080	7936	41907	87	46
67	24094	8643	27080	21	33
46	69008	7294	35885	56	18
46	54968	4570	41247	59	55
56	46090	7185	28313	82	35
48	27507	10058	36845	43	59
44	10672	2342	16548	25	19
60	34029	8509	36134	38	66
65	46300	13275	55764	25	60
55	24760	6816	28910	38	36
38	18779	1930	13339	12	25
52	21280	8086	25319	29	47
60	40662	10737	66956	47	54
54	28987	8033	47487	45	53
86	22827	7058	52785	40	40
24	18513	6782	44683	30	40
52	30594	5401	35619	41	39
49	24006	6521	21920	25	14
61	27913	10856	45608	23	45
61	42744	2154	7721	14	36
81	12934	6117	20634	16	28
43	22574	5238	29788	26	44
40	41385	4820	31931	21	30
40	18653	5615	37754	27	22
56	18472	4272	32505	9	17
68	30976	8702	40557	33	31
79	63339	15340	94238	42	55
47	25568	8030	44197	68	54
57	33747	9526	43228	32	21
41	4154	1278	4103	6	14
29	19474	4236	44144	67	81
3	35130	3023	32868	33	35
60	39067	7196	27640	77	43
30	13310	3394	14063	46	46
79	65892	6371	28990	30	30
47	4143	1574	4694	0	23
40	28579	9620	42648	36	38
48	51776	6978	64329	46	54
36	21152	4911	21928	18	20
42	38084	8645	25836	48	53
49	27717	8987	22779	29	45
57	32928	5544	40820	28	39
12	11342	3083	27530	34	20
40	19499	6909	32378	33	24
43	16380	3189	10824	34	31
33	36874	6745	39613	33	35
77	48259	16724	60865	80	151
43	16734	4850	19787	32	52
45	28207	7025	20107	30	30
47	30143	6047	36605	41	31
43	41369	7377	40961	41	29
45	45833	9078	48231	51	57
50	29156	4605	39725	18	40
35	35944	3238	21455	34	44
7	36278	8100	23430	31	25
71	45588	9653	62991	39	77
67	45097	8914	49363	54	35
0	3895	786	9604	14	11
62	28394	6700	24552	24	63
54	18632	5788	31493	24	44
4	2325	593	3439	8	19
25	25139	4506	19555	26	13
40	27975	6382	21228	19	42
38	14483	5621	23177	11	38
19	13127	3997	22094	14	29
17	5839	520	2342	1	20
67	24069	8891	38798	39	27
14	3738	999	3255	5	20
30	18625	7067	24261	37	19
54	36341	4639	18511	32	37
35	24548	5654	40798	38	26
59	21792	6928	28893	47	42
24	26263	1514	21425	47	49
58	23686	9238	50276	37	30
42	49303	8204	37643	51	49
46	25659	5926	30377	45	67
61	28904	5785	27126	21	28
3	2781	4	13	1	19
52	29236	5930	42097	42	49
25	19546	3710	24451	26	27
40	22818	705	14335	21	30
32	32689	443	5084	4	22
4	5752	2416	9927	10	12
49	22197	7747	43527	43	31
63	20055	5432	27184	34	20
67	25272	4913	21610	31	20
32	82206	2650	20484	19	39
23	32073	2370	20156	34	29
7	5444	775	6012	6	16
54	20154	5576	18475	11	27
37	36944	1352	12645	24	21
35	8019	3080	11017	16	19
51	30884	10205	37623	72	35
39	19540	6095	35873	21	14

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')
}