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1	0	149	96	18	68	86	7.5	1.8	2.1	1.5
1	0	152	75	7	55	62	2.5	1.6	1.5	1.8
1	1	139	70	31	39	70	6.0	2.1	2.0	2.1
1	0	148	88	39	32	71	6.5	2.2	2.0	2.1
1	1	158	114	46	62	108	1.0	2.3	2.1	1.9
1	1	128	69	31	33	64	1.0	2.1	2.0	1.6
1	1	224	176	67	52	119	5.5	2.7	2.3	2.1
1	0	159	114	35	62	97	8.5	2.1	2.1	2.1
1	1	105	121	52	77	129	6.5	2.4	2.1	2.2
1	1	159	110	77	76	153	4.5	2.9	2.2	1.5
1	1	167	158	37	41	78	2.0	2.2	2.1	1.9
1	1	165	116	32	48	80	5.0	2.1	2.1	2.2
1	1	159	181	36	63	99	0.5	2.2	2.1	1.6
1	1	119	77	38	30	68	5.0	2.2	2.0	1.5
1	0	176	141	69	78	147	5.0	2.7	2.3	1.9
1	0	54	35	21	19	40	2.5	1.9	1.8	0.1
0	0	91	80	26	31	57	5.0	2.0	2.0	2.2
1	1	163	152	54	66	120	5.5	2.5	2.2	1.8
1	0	124	97	36	35	71	3.5	2.2	2.0	1.6
0	1	137	99	42	42	84	3.0	2.3	2.1	2.2
1	0	121	84	23	45	68	4.0	1.9	2.0	2.1
1	1	153	68	34	21	55	0.5	2.1	1.8	1.9
1	1	148	101	112	25	137	6.5	3.5	2.2	1.6
1	0	221	107	35	44	79	4.5	2.1	2.2	1.9
1	1	188	88	47	69	116	7.5	2.3	1.7	2.2
1	1	149	112	47	54	101	5.5	2.3	2.1	1.8
1	1	244	171	37	74	111	4.0	2.2	2.3	2.4
0	1	148	137	109	80	189	7.5	3.5	2.7	2.4
0	0	92	77	24	42	66	7.0	1.9	1.9	2.5
1	1	150	66	20	61	81	4.0	1.9	2.0	1.9
1	0	153	93	22	41	63	5.5	1.9	2.0	2.1
1	0	94	105	23	46	69	2.5	1.9	1.9	1.9
1	0	156	131	32	39	71	5.5	2.1	2.0	2.1
1	1	146	89	7	63	70	0.5	1.6	2.0	1.9
1	1	132	102	30	34	64	3.5	2.0	2.0	1.5
1	1	161	161	92	51	143	2.5	3.2	2.1	1.9
1	1	105	120	43	42	85	4.5	2.3	2.0	2.1
1	1	97	127	55	31	86	4.5	2.5	1.8	1.5
1	0	151	77	16	39	55	4.5	1.8	2.0	2.1
0	1	131	108	49	20	69	6.0	2.4	2.2	2.1
1	1	166	85	71	49	120	2.5	2.8	2.2	1.8
1	0	157	168	43	53	96	5.0	2.3	2.1	2.4
1	1	111	48	29	31	60	0.0	2.0	1.8	2.1
1	1	145	152	56	39	95	5.0	2.5	1.9	1.9
1	1	162	75	46	54	100	6.5	2.3	2.1	2.1
1	1	163	107	19	49	68	5.0	1.8	2.0	1.9
0	1	59	62	23	34	57	6.0	1.9	1.9	2.4
1	0	187	121	59	46	105	4.5	2.6	2.2	2.1
1	1	109	124	30	55	85	5.5	2.0	2.0	2.2
0	1	90	72	61	42	103	1.0	2.6	2.0	2.2
1	0	105	40	7	50	57	7.5	1.6	1.7	1.8
0	1	83	58	38	13	51	6.0	2.2	2.0	2.1
0	1	116	97	32	37	69	5.0	2.1	2.2	2.4
0	1	42	88	16	25	41	1.0	1.8	1.7	2.2
1	1	148	126	19	30	49	5.0	1.8	2.0	2.1
0	1	155	104	22	28	50	6.5	1.9	2.2	1.5
1	1	125	148	48	45	93	7.0	2.4	2.0	1.9
1	1	116	146	23	35	58	4.5	1.9	1.9	1.8
0	0	128	80	26	28	54	0.0	2.0	2.0	1.8
1	1	138	97	33	41	74	8.5	2.1	2.0	1.6
0	0	49	25	9	6	15	3.5	1.7	1.6	1.2
0	1	96	99	24	45	69	7.5	1.9	2.1	1.8
1	1	164	118	34	73	107	3.5	2.1	2.1	1.5
1	0	162	58	48	17	65	6.0	2.4	2.0	2.1
1	0	99	63	18	40	58	1.5	1.8	1.9	2.4
1	1	202	139	43	64	107	9.0	2.3	2.2	2.4
1	0	186	50	33	37	70	3.5	2.1	2.1	1.5
0	1	66	60	28	25	53	3.5	2.0	1.8	1.8
1	0	183	152	71	65	136	4.0	2.8	2.3	2.1
1	1	214	142	26	100	126	6.5	2.0	2.3	2.2
1	1	188	94	67	28	95	7.5	2.7	2.2	2.1
0	0	104	66	34	35	69	6.0	2.1	2.1	1.9
1	0	177	127	80	56	136	5.0	2.9	2.2	2.1
1	0	126	67	29	29	58	5.5	2.0	1.9	1.9
0	0	76	90	16	43	59	3.5	1.8	1.8	1.6
0	1	99	75	59	59	118	7.5	2.6	2.1	2.4
1	1	157	96	58	52	110	1.0	2.5	1.8	1.9
1	0	139	128	32	50	82	6.5	2.1	2.0	1.9
1	1	78	41	47	3	50	NA	2.3	1.7	1.9
1	0	162	146	43	59	102	6.5	2.3	2.1	2.1
0	1	108	69	38	27	65	6.5	2.2	2.1	1.8
1	0	159	186	29	61	90	7.0	2.0	2.1	2.1
0	0	74	81	36	28	64	3.5	2.2	1.8	2.4
1	1	110	85	32	51	83	1.5	2.1	2.0	2.1
0	0	96	54	35	35	70	4.0	2.1	2.1	2.2
0	0	116	46	21	29	50	7.5	1.9	1.9	2.1
0	0	87	106	29	48	77	4.5	2.0	2.1	2.2
0	1	97	34	12	25	37	0.0	1.7	1.0	1.6
0	0	127	60	37	44	81	3.5	2.2	2.2	2.4
0	1	106	95	37	64	101	5.5	2.2	2.1	2.1
0	1	80	57	47	32	79	5.0	2.3	1.9	1.9
0	0	74	62	51	20	71	4.5	2.4	2.0	2.4
0	0	91	36	32	28	60	2.5	2.1	1.9	2.1
0	0	133	56	21	34	55	7.5	1.9	2.0	1.8
0	1	74	54	13	31	44	7.0	1.7	1.8	2.1
0	1	114	64	14	26	40	0.0	1.8	2.0	1.8
0	1	140	76	-2	58	56	4.5	1.5	2.0	1.9
0	0	95	98	20	23	43	3.0	1.9	2.0	1.9
0	1	98	88	24	21	45	1.5	1.9	1.8	2.4
0	0	121	35	11	21	32	3.5	1.7	2.0	1.8
0	1	126	102	23	33	56	2.5	1.9	1.1	1.8
0	1	98	61	24	16	40	5.5	1.9	1.8	2.1
0	1	95	80	14	20	34	8.0	1.8	1.8	2.1
0	1	110	49	52	37	89	1.0	2.4	2.0	2.4
0	1	70	78	15	35	50	5.0	1.8	1.9	1.9
0	0	102	90	23	33	56	4.5	1.9	2.1	1.8
0	1	86	45	19	27	46	3.0	1.8	1.6	1.8
0	1	130	55	35	41	76	3.0	2.1	2.2	2.2
0	1	96	96	24	40	64	8.0	1.9	1.9	2.4
0	0	102	43	39	35	74	2.5	2.2	2.0	1.8
0	0	100	52	29	28	57	7.0	2.0	2.1	2.4
0	0	94	60	13	32	45	0.0	1.7	1.3	1.8
0	0	52	54	8	22	30	1.0	1.7	1.8	1.9
0	0	98	51	18	44	62	3.5	1.8	1.9	2.4
0	0	118	51	24	27	51	5.5	1.9	2.1	2.1
0	1	99	38	19	17	36	5.5	1.8	1.8	1.9
1	1	48	41	23	12	34	0.5	1	0.75	2.1
1	1	50	146	16	45	61	7.5	1	1.5	2.7
1	1	150	182	33	37	70	9	4	3	2.1
1	1	154	192	32	37	69	9.5	4	2.25	2.1
0	0	109	263	37	108	145	8.5	3	3	2.1
0	1	68	35	14	10	23	7	2	1.5	2.1
1	1	194	439	52	68	120	8	4	3	2.1
1	0	158	214	75	72	147	10	4	3	2.1
1	1	159	341	72	143	215	7	4	3	2.1
1	0	67	58	15	9	24	8.5	2	0.75	2.1
1	0	147	292	29	55	84	9	4	3	2.4
1	1	39	85	13	17	30	9.5	1	2.25	1.95
1	1	100	200	40	37	77	4	3	1.5	2.1
1	1	111	158	19	27	46	6	3	1.5	2.1
1	1	138	199	24	37	61	8	4	2.25	1.95
1	1	101	297	121	58	178	5.5	3	3	2.1
0	1	131	227	93	66	160	9.5	4	3	2.4
1	1	101	108	36	21	57	7.5	3	1.5	2.1
1	1	114	86	23	19	42	7	3	2.25	2.25
1	0	165	302	85	78	163	7.5	4	2.25	2.4
1	1	114	148	41	35	75	8	3	1.5	2.25
1	1	111	178	46	48	94	7	3	2.25	2.55
1	1	75	120	18	27	45	7	2	1.5	1.95
1	1	82	207	35	43	78	6	2	2.25	2.4
1	1	121	157	17	30	47	10	3	2.25	2.1
1	1	32	128	4	25	29	2.5	1	3	2.1
1	0	150	296	28	69	97	9	4	3	2.4
1	1	117	323	44	72	116	8	3	3	2.1
0	1	71	79	10	23	32	6	2	1.5	2.1
1	1	165	70	38	13	50	8.5	4	3	2.25
1	1	154	146	57	61	118	6	4	3	2.25
1	1	126	246	23	43	66	9	4	2.25	2.4
1	0	138	145	26	22	48	8	4	1.5	2.1
1	0	149	196	36	51	86	8	4	2.25	2.1
1	0	145	199	22	67	89	9	4	2.25	2.4
1	1	120	127	40	36	76	5.5	3	3	2.1
1	0	138	91	18	21	39	5	4	0.75	1.95
1	0	109	153	31	44	75	7	3	2.25	2.1
1	0	132	299	11	45	57	5.5	4	3	2.25
1	1	172	228	38	34	72	9	4	3	2.25
1	0	169	190	24	36	60	2	4	1.5	2.4
1	1	114	180	37	72	109	8.5	3	2.25	2.25
1	1	156	212	37	39	76	9	4	3	2.25
1	0	172	269	22	43	65	8.5	4	2.25	2.1
0	1	68	130	15	25	40	9	2	1.5	2.1
0	1	89	179	2	56	58	7.5	2	2.25	2.1
1	1	167	243	43	80	123	10	4	2.25	2.7
1	0	113	190	31	40	71	9	3	1.5	2.1
0	0	115	299	29	73	102	7.5	3	2.25	2.1
0	0	78	121	45	34	80	6	2	1.5	2.25
0	0	118	137	25	72	97	10.5	3	2.25	2.7
0	1	87	305	4	42	46	8.5	2	3	2.4
1	0	173	157	31	61	93	8	4	3	2.1
1	1	2	96	-4	23	19	10	1	3	2.1
0	0	162	183	66	74	140	10.5	4	3	2.4
0	1	49	52	61	16	78	6.5	1	1.5	1.95
0	0	122	238	32	66	98	9.5	4	2.25	2.7
0	1	96	40	31	9	40	8.5	3	1.5	2.1
0	0	100	226	39	41	80	7.5	3	2.25	2.25
0	0	82	190	19	57	76	5	2	2.25	2.1
0	1	100	214	31	48	79	8	3	2.25	2.7
0	0	115	145	36	51	87	10	3	3	2.1
0	1	141	119	42	53	95	7	4	1.5	2.1
1	1	165	222	21	29	49	7.5	4	2.25	1.65
1	1	165	222	21	29	49	7.5	4	2.25	1.65
0	1	110	159	25	55	80	9.5	3	3	2.1
1	1	118	165	32	54	86	6	3	2.25	2.1
1	0	158	249	26	43	69	10	4	3	2.1
0	1	146	125	28	51	79	7	4	2.25	2.1
1	0	49	122	32	20	52	3	1	1.5	2.1
0	0	90	186	41	79	120	6	2	3	2.4
0	0	121	148	29	39	69	7	3	1.5	2.4
1	1	155	274	33	61	94	10	4	3	2.1
0	0	104	172	17	55	72	7	3	3	2.25
0	1	147	84	13	30	43	3.5	4	3	2.4
0	0	110	168	32	55	87	8	3	3	2.1
0	0	108	102	30	22	52	10	3	2.25	2.1
0	0	113	106	34	37	71	5.5	3	2.25	2.4
0	0	115	2	59	2	61	6	3	0.75	2.4
0	1	61	139	13	38	51	6.5	1	3	2.1
0	1	60	95	23	27	50	6.5	1	0.75	2.1
0	1	109	130	10	56	67	8.5	3	1.5	2.4
0	1	68	72	5	25	30	4	2	1.5	2.1
0	0	111	141	31	39	70	9.5	3	3	2.7
0	0	77	113	19	33	52	8	2	1.5	2.1
0	1	73	206	32	43	75	8.5	2	2.25	2.1
1	0	151	268	30	57	87	5.5	4	3	2.25
0	0	89	175	25	43	69	7	2	3	2.1
0	0	78	77	48	23	72	9	2	1.5	2.4
0	0	110	125	35	44	79	8	3	3	2.25
1	1	220	255	67	54	121	10	4	3	2.25
0	1	65	111	15	28	43	8	2	1.5	2.1
1	0	141	132	22	36	58	6	4	1.5	2.1
0	0	117	211	18	39	57	8	3	2.25	2.4
1	1	122	92	33	16	50	5	4	1.5	2.25
0	0	63	76	46	23	69	9	2	1.5	2.1
1	1	44	171	24	40	64	4.5	1	2.25	2.1
0	1	52	83	14	24	38	8.5	1	1.5	1.65
0	1	62	119	23	29	53	7	1	2.25	1.65
0	0	131	266	12	78	90	9.5	4	3	2.7
0	1	101	186	38	57	96	8.5	3	3	2.1
0	1	42	50	12	37	49	7.5	1	0.75	1.95
1	1	152	117	28	27	56	7.5	4	1.5	2.25
1	0	107	219	41	61	102	5	3	1.5	2.4
0	0	77	246	12	27	40	7	2	2.25	1.95
1	0	154	279	31	69	100	8	4	2.25	2.1
1	1	103	148	33	34	67	5.5	3	1.5	2.4
0	1	96	137	34	44	78	8.5	3	2.25	2.1
1	0	154	130	41	21	62	7.5	4	0.75	2.1
1	1	175	181	21	34	55	9.5	4	2.25	2.4
0	1	57	98	20	39	59	7	1	0.75	2.4
0	0	112	226	44	51	96	8	3	2.25	2.4
1	0	143	234	52	34	86	8.5	4	3	2.25
0	0	49	138	7	31	38	3.5	1	0.75	2.4
1	1	110	85	29	13	43	6.5	3	0.75	2.1
1	1	131	66	11	12	23	6.5	4	3	2.1
1	0	167	236	26	51	77	10.5	4	3	1.8
0	0	56	106	24	24	48	8.5	1	3	2.7
1	0	137	135	7	19	26	8	4	3	2.1
0	1	86	122	60	30	91	10	2	1.5	2.1
1	1	121	218	13	81	94	10	3	3	2.4
1	0	149	199	20	42	62	9.5	4	3	2.55
1	0	168	112	52	22	74	9	4	3	2.55
1	0	140	278	28	85	114	10	4	3	2.1
0	1	88	94	25	27	52	7.5	2	1.5	2.1
1	1	168	113	39	25	64	4.5	4	2.25	2.1
1	1	94	84	9	22	31	4.5	2	0.75	2.25
1	1	51	86	19	19	38	0.5	1	0.75	2.25
0	0	48	62	13	14	27	6.5	1	2.25	2.1
1	1	145	222	60	45	105	4.5	4	3	2.1
1	1	66	167	19	45	64	5.5	2	2.25	1.95
0	1	85	82	34	28	62	5	2	3	2.4
1	0	109	207	14	51	65	6	3	2.25	2.1
0	0	63	184	17	41	58	4	2	3	2.4
0	1	102	83	45	31	76	8	3	1.5	2.4
0	0	162	183	66	74	140	10.5	4	3	2.4
1	1	128	85	24	24	48	8.5	4	3	2.25
0	1	86	89	48	19	68	6.5	2	0.75	1.95
0	1	114	225	29	51	80	8	3	1.5	2.1
1	0	164	237	-2	73	71	8.5	4	3	2.1
1	1	119	102	51	24	76	5.5	3	3	2.55
1	0	126	221	2	61	63	7	4	3	2.1
1	1	132	128	24	23	46	5	4	2.25	2.1
1	1	142	91	40	14	53	3.5	4	2.25	2.1
1	0	83	198	20	54	74	5	2	3	1.95
0	1	94	204	19	51	70	9	2	1.5	2.25
0	0	81	158	16	62	78	8.5	2	2.25	2.4
1	1	166	138	20	36	56	5	4	2.25	1.95
0	0	110	226	40	59	100	9.5	3	2.25	2.1
0	1	64	44	27	24	51	3	2	0.75	2.1
1	0	93	196	25	26	52	1.5	2	2.25	1.95
0	0	104	83	49	54	102	6	3	1.5	2.1
0	1	105	79	39	39	78	0.5	3	2.25	2.1
0	1	49	52	61	16	78	6.5	1	1.5	1.95
0	0	88	105	19	36	55	7.5	2	0.75	2.1
0	1	95	116	67	31	98	4.5	2	1.5	1.95
0	1	102	83	45	31	76	8	3	1.5	2.4
0	0	99	196	30	42	73	9	3	2.25	2.4
0	1	63	153	8	39	47	7.5	2	1.5	2.4
0	0	76	157	19	25	45	8.5	2	1.5	1.95
0	0	109	75	52	31	83	7	3	3	2.7
0	1	117	106	22	38	60	9.5	3	2.25	2.1
0	1	57	58	17	31	48	6.5	1	1.5	1.95
0	0	120	75	33	17	50	9.5	3	0.75	2.1
0	1	73	74	34	22	56	6	2	2.25	1.95
0	0	91	185	22	55	77	8	2	3	2.1
0	0	108	265	30	62	91	9.5	3	3	2.25
0	1	105	131	25	51	76	8	3	1.5	2.7
1	0	117	139	38	30	68	8	3	1.5	2.1
0	0	119	196	26	49	74	9	3	2.25	2.4
0	1	31	78	13	16	29	5	1	0.75	1.35

par4 <- 'yes'
par3 <- '2'
par2 <- 'equal'
par1 <- '8'
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')
}