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Author's title

Author

*The author of this computation has been verified*

R Software Module

rwasp_regression_trees1.wasp

Title produced by software

Recursive Partitioning (Regression Trees)

Date of computation

Thu, 22 Dec 2011 17:01:51 -0500

Cite this page as follows

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2011/Dec/22/t1324591640e7nhbico2zugqis.htm/, Retrieved Fri, 03 May 2024 11:01:13 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=160029, Retrieved Fri, 03 May 2024 11:01:13 +0000

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Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords

Estimated Impact

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)

-     [Kendall tau Correlation Matrix] [] [2010-12-05 17:44:33] [b98453cac15ba1066b407e146608df68]
- RMPD  [Kendall tau Correlation Matrix] [Paper: Hall of Fa...] [2011-12-22 19:51:56] [c6bc82f3e3d78f4e6e841dae94c52ed9]
-   P     [Kendall tau Correlation Matrix] [Paper: Hall of Fa...] [2011-12-22 19:55:27] [c6bc82f3e3d78f4e6e841dae94c52ed9]
- RM D      [Recursive Partitioning (Regression Trees)] [Paper: Hall of Fa...] [2011-12-22 21:25:07] [c6bc82f3e3d78f4e6e841dae94c52ed9]
- R             [Recursive Partitioning (Regression Trees)] [Paper: Hall of Fa...] [2011-12-22 22:01:51] [d7127d50f40450f0f3837a0965e389eb] [Current]

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Dataseries X:

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Histogram

Boxplots

210907	79	30	115	94	112285	24188	146283	144	145
120982	58	28	109	103	84786	18273	98364	103	101
176508	60	38	146	93	83123	14130	86146	98	98
179321	108	30	116	103	101193	32287	96933	135	132
123185	49	22	68	51	38361	8654	79234	61	60
52746	0	26	101	70	68504	9245	42551	39	38
385534	121	25	96	91	119182	33251	195663	150	144
33170	1	18	67	22	22807	1271	6853	5	5
101645	20	11	44	38	17140	5279	21529	28	28
149061	43	26	100	93	116174	27101	95757	84	84
165446	69	25	93	60	57635	16373	85584	80	79
237213	78	38	140	123	66198	19716	143983	130	127
173326	86	44	166	148	71701	17753	75851	82	78
133131	44	30	99	90	57793	9028	59238	60	60
258873	104	40	139	124	80444	18653	93163	131	131
180083	63	34	130	70	53855	8828	96037	84	84
324799	158	47	181	168	97668	29498	151511	140	133
230964	102	30	116	115	133824	27563	136368	151	150
236785	77	31	116	71	101481	18293	112642	91	91
135473	82	23	88	66	99645	22530	94728	138	132
202925	115	36	139	134	114789	15977	105499	150	136
215147	101	36	135	117	99052	35082	121527	124	124
344297	80	30	108	108	67654	16116	127766	119	118
153935	50	25	89	84	65553	15849	98958	73	70
132943	83	39	156	156	97500	16026	77900	110	107
174724	123	34	129	120	69112	26569	85646	123	119
174415	73	31	118	114	82753	24785	98579	90	89
225548	81	31	118	94	85323	17569	130767	116	112
223632	105	33	125	120	72654	23825	131741	113	108
124817	47	25	95	81	30727	7869	53907	56	52
221698	105	33	126	110	77873	14975	178812	115	112
210767	94	35	135	133	117478	37791	146761	119	116
170266	44	42	154	122	74007	9605	82036	129	123
260561	114	43	165	158	90183	27295	163253	127	125
84853	38	30	113	109	61542	2746	27032	27	27
294424	107	33	127	124	101494	34461	171975	175	162
101011	30	13	52	39	27570	8098	65990	35	32
215641	71	32	121	92	55813	4787	86572	64	64
325107	84	36	136	126	79215	24919	159676	96	92
7176	0	0	0	0	1423	603	1929	0	0
167542	59	28	108	70	55461	16329	85371	84	83
106408	33	14	46	37	31081	12558	58391	41	41
96560	42	17	54	38	22996	7784	31580	47	47
265769	96	32	124	120	83122	28522	136815	126	120
269651	106	30	115	93	70106	22265	120642	105	105
149112	56	35	128	95	60578	14459	69107	80	79
175824	57	20	80	77	39992	14526	50495	70	65
152871	59	28	97	90	79892	22240	108016	73	70
111665	39	28	104	80	49810	11802	46341	57	55
116408	34	39	59	31	71570	7623	78348	40	39
362301	76	34	125	110	100708	11912	79336	68	67
78800	20	26	82	66	33032	7935	56968	21	21
183167	91	39	149	138	82875	18220	93176	127	127
277965	115	39	149	133	139077	19199	161632	154	152
150629	85	33	122	113	71595	19918	87850	116	113
168809	76	28	118	100	72260	21884	127969	102	99
24188	8	4	12	7	5950	2694	15049	7	7
329267	79	39	144	140	115762	15808	155135	148	141
65029	21	18	67	61	32551	3597	25109	21	21
101097	30	14	52	41	31701	5296	45824	35	35
218946	76	29	108	96	80670	25239	102996	112	109
244052	101	44	166	164	143558	29801	160604	137	133
341570	94	21	80	78	117105	18450	158051	135	123
103597	27	16	60	49	23789	7132	44547	26	26
233328	92	28	107	102	120733	34861	162647	230	230
256462	123	35	127	124	105195	35940	174141	181	166
206161	75	28	107	99	73107	16688	60622	71	68
311473	128	38	146	129	132068	24683	179566	147	147
235800	105	23	84	62	149193	46230	184301	190	179
177939	55	36	141	73	46821	10387	75661	64	61
207176	56	32	123	114	87011	21436	96144	105	101
196553	41	29	111	99	95260	30546	129847	107	108
174184	72	25	98	70	55183	19746	117286	94	90
143246	67	27	105	104	106671	15977	71180	116	114
187559	75	36	135	116	73511	22583	109377	106	103
187681	114	28	107	91	92945	17274	85298	143	142
119016	118	23	85	74	78664	16469	73631	81	79
182192	77	40	155	138	70054	14251	86767	89	88
73566	22	23	88	67	22618	3007	23824	26	25
194979	66	40	155	151	74011	16851	93487	84	83
167488	69	28	104	72	83737	21113	82981	113	113
143756	105	34	132	120	69094	17401	73815	120	118
275541	116	33	127	115	93133	23958	94552	110	110
243199	88	28	108	105	95536	23567	132190	134	129
182999	73	34	129	104	225920	13065	128754	54	51
135649	99	30	116	108	62133	15358	66363	96	93
152299	62	33	122	98	61370	14587	67808	78	76
120221	53	22	85	69	43836	12770	61724	51	49
346485	118	38	147	111	106117	24021	131722	121	118
145790	30	26	99	99	38692	9648	68580	38	38
193339	100	35	87	71	84651	20537	106175	145	141
80953	49	8	28	27	56622	7905	55792	59	58
122774	24	24	90	69	15986	4527	25157	27	27
130585	67	29	109	107	95364	30495	76669	91	91
112611	46	20	78	73	26706	7117	57283	48	48
286468	57	29	111	107	89691	17719	105805	68	63
241066	75	45	158	93	67267	27056	129484	58	56
148446	135	37	141	129	126846	33473	72413	150	144
204713	68	33	122	69	41140	9758	87831	74	73
182079	124	33	124	118	102860	21115	96971	181	168
140344	33	25	93	73	51715	7236	71299	65	64
220516	98	32	124	119	55801	13790	77494	97	97
243060	58	29	112	104	111813	32902	120336	121	117
162765	68	28	108	107	120293	25131	93913	99	100
182613	81	28	99	99	138599	30910	136048	152	149
232138	131	31	117	90	161647	35947	181248	188	187
265318	110	52	199	197	115929	29848	146123	138	127
85574	37	21	78	36	24266	6943	32036	40	37
310839	130	24	91	85	162901	42705	186646	254	245
225060	93	41	158	139	109825	31808	102255	87	87
232317	118	33	126	106	129838	26675	168237	178	177
144966	39	32	122	50	37510	8435	64219	51	49
43287	13	19	71	64	43750	7409	19630	49	49
155754	74	20	75	31	40652	14993	76825	73	73
164709	81	31	115	63	87771	36867	115338	176	177
201940	109	31	119	92	85872	33835	109427	94	94
235454	151	32	124	106	89275	24164	118168	120	117
220801	51	18	72	63	44418	12607	84845	66	60
99466	28	23	91	69	192565	22609	153197	56	55
92661	40	17	45	41	35232	5892	29877	39	39
133328	56	20	78	56	40909	17014	63506	66	64
61361	27	12	39	25	13294	5394	22445	27	26
125930	37	17	68	65	32387	9178	47695	65	64
100750	83	30	119	93	140867	6440	68370	58	58
224549	54	31	117	114	120662	21916	146304	98	95
82316	27	10	39	38	21233	4011	38233	25	25
102010	28	13	50	44	44332	5818	42071	26	26
101523	59	22	88	87	61056	18647	50517	77	76
243511	133	42	155	110	101338	20556	103950	130	129
22938	12	1	0	0	1168	238	5841	11	11
41566	0	9	36	27	13497	70	2341	2	2
152474	106	32	123	83	65567	22392	84396	101	101
61857	23	11	32	30	25162	3913	24610	31	28
99923	44	25	99	80	32334	12237	35753	36	36
132487	71	36	136	98	40735	8388	55515	120	89
317394	116	31	117	82	91413	22120	209056	195	193
21054	4	0	0	0	855	338	6622	4	4
209641	62	24	88	60	97068	11727	115814	89	84
22648	12	13	39	28	44339	3704	11609	24	23
31414	18	8	25	9	14116	3988	13155	39	39
46698	14	13	52	33	10288	3030	18274	14	14
131698	60	19	75	59	65622	13520	72875	78	78
91735	7	18	71	49	16563	1421	10112	15	14
244749	98	33	124	115	76643	20923	142775	106	101
184510	64	40	151	140	110681	20237	68847	83	82
79863	29	22	71	49	29011	3219	17659	24	24
128423	32	38	145	120	92696	3769	20112	37	36
97839	25	24	87	66	94785	12252	61023	77	75
38214	16	8	27	21	8773	1888	13983	16	16
151101	48	35	131	124	83209	14497	65176	56	55
272458	100	43	162	152	93815	28864	132432	132	131
172494	46	43	165	139	86687	21721	112494	144	131
108043	45	14	54	38	34553	4821	45109	40	39
328107	129	41	159	144	105547	33644	170875	153	144
250579	130	38	147	120	103487	15923	180759	143	139
351067	136	45	170	160	213688	42935	214921	220	211
158015	59	31	119	114	71220	18864	100226	79	78
98866	25	13	49	39	23517	4977	32043	50	50
85439	32	28	104	78	56926	7785	54454	39	39
229242	63	31	120	119	91721	17939	78876	95	90
351619	95	40	150	141	115168	23436	170745	169	166
84207	14	30	112	101	111194	325	6940	12	12
120445	36	16	59	56	51009	13539	49025	63	57
324598	113	37	136	133	135777	34538	122037	134	133
131069	47	30	107	83	51513	12198	53782	69	69
204271	92	35	130	116	74163	26924	127748	119	119
165543	70	32	115	90	51633	12716	86839	119	119
141722	19	27	107	36	75345	8172	44830	75	65
116048	50	20	75	50	33416	10855	77395	63	61
250047	41	18	71	61	83305	11932	89324	55	49
299775	91	31	120	97	98952	14300	103300	103	101
195838	111	31	116	98	102372	25515	112283	197	196
173260	41	21	79	78	37238	2805	10901	16	15
254488	120	39	150	117	103772	29402	120691	140	136
104389	135	41	156	148	123969	16440	58106	89	89
136084	27	13	51	41	27142	11221	57140	40	40
199476	87	32	118	105	135400	28732	122422	125	123
92499	25	18	71	55	21399	5250	25899	21	21
224330	131	39	144	132	130115	28608	139296	167	163
135781	45	14	47	44	24874	8092	52678	32	29
74408	29	7	28	21	34988	4473	23853	36	35
81240	58	17	68	50	45549	1572	17306	13	13
14688	4	0	0	0	6023	2065	7953	5	5
181633	47	30	110	73	64466	14817	89455	96	96
271856	109	37	147	86	54990	16714	147866	151	151
7199	7	0	0	0	1644	556	4245	6	6
46660	12	5	15	13	6179	2089	21509	13	13
17547	0	1	4	4	3926	2658	7670	3	3
133368	37	16	64	57	32755	10695	66675	57	56
95227	37	32	111	48	34777	1669	14336	23	23
152601	46	24	85	46	73224	16267	53608	61	57
98146	15	17	68	48	27114	7768	30059	21	14
79619	42	11	40	32	20760	7252	29668	43	43
59194	7	24	80	68	37636	6387	22097	20	20
139942	54	22	88	87	65461	18715	96841	82	72
118612	54	12	48	43	30080	7936	41907	90	87
72880	14	19	76	67	24094	8643	27080	25	21
65475	16	13	51	46	69008	7294	35885	60	56
99643	33	17	67	46	54968	4570	41247	61	59
71965	32	15	59	56	46090	7185	28313	85	82
77272	21	16	61	48	27507	10058	36845	43	43
49289	15	24	76	44	10672	2342	16548	25	25
135131	38	15	60	60	34029	8509	36134	41	38
108446	22	17	68	65	46300	13275	55764	26	25
89746	28	18	71	55	24760	6816	28910	38	38
44296	10	20	76	38	18779	1930	13339	12	12
77648	31	16	62	52	21280	8086	25319	29	29
181528	32	16	61	60	40662	10737	66956	49	47
134019	32	18	67	54	28987	8033	47487	46	45
124064	43	22	88	86	22827	7058	52785	41	40
92630	27	8	30	24	18513	6782	44683	31	30
121848	37	17	64	52	30594	5401	35619	41	41
52915	20	18	68	49	24006	6521	21920	26	25
81872	32	16	64	61	27913	10856	45608	23	23
58981	0	23	91	61	42744	2154	7721	14	14
53515	5	22	88	81	12934	6117	20634	16	16
60812	26	13	52	43	22574	5238	29788	25	26
56375	10	13	49	40	41385	4820	31931	21	21
65490	27	16	62	40	18653	5615	37754	32	27
80949	11	16	61	56	18472	4272	32505	9	9
76302	29	20	76	68	30976	8702	40557	35	33
104011	25	22	88	79	63339	15340	94238	42	42
98104	55	17	66	47	25568	8030	44197	68	68
67989	23	18	71	57	33747	9526	43228	32	32
30989	5	17	68	41	4154	1278	4103	6	6
135458	43	12	48	29	19474	4236	44144	68	67
73504	23	7	25	3	35130	3023	32868	33	33
63123	34	17	68	60	39067	7196	27640	84	77
61254	36	14	41	30	13310	3394	14063	46	46
74914	35	23	90	79	65892	6371	28990	30	30
31774	0	17	66	47	4143	1574	4694	0	0
81437	37	14	54	40	28579	9620	42648	36	36
87186	28	15	59	48	51776	6978	64329	47	46
50090	16	17	60	36	21152	4911	21928	20	18
65745	26	21	77	42	38084	8645	25836	50	48
56653	38	18	68	49	27717	8987	22779	30	29
158399	23	18	72	57	32928	5544	40820	30	28
46455	22	17	67	12	11342	3083	27530	34	34
73624	30	17	64	40	19499	6909	32378	33	33
38395	16	16	63	43	16380	3189	10824	34	34
91899	18	15	59	33	36874	6745	39613	37	33
139526	28	21	84	77	48259	16724	60865	83	80
52164	32	16	64	43	16734	4850	19787	32	32
51567	21	14	56	45	28207	7025	20107	30	30
70551	23	15	54	47	30143	6047	36605	43	41
84856	29	17	67	43	41369	7377	40961	41	41
102538	50	15	58	45	45833	9078	48231	51	51
86678	12	15	59	50	29156	4605	39725	19	18
85709	21	10	40	35	35944	3238	21455	37	34
34662	18	6	22	7	36278	8100	23430	33	31
150580	27	22	83	71	45588	9653	62991	41	39
99611	41	21	81	67	45097	8914	49363	54	54
19349	13	1	2	0	3895	786	9604	14	14
99373	12	18	72	62	28394	6700	24552	25	24
86230	21	17	61	54	18632	5788	31493	25	24
30837	8	4	15	4	2325	593	3439	8	8
31706	26	10	32	25	25139	4506	19555	26	26
89806	27	16	62	40	27975	6382	21228	20	19
62088	13	16	58	38	14483	5621	23177	11	11
40151	16	9	36	19	13127	3997	22094	14	14
27634	2	16	59	17	5839	520	2342	3	1
76990	42	17	68	67	24069	8891	38798	40	39
37460	5	7	21	14	3738	999	3255	5	5
54157	37	15	55	30	18625	7067	24261	38	37
49862	17	14	54	54	36341	4639	18511	32	32
84337	38	14	55	35	24548	5654	40798	41	38
64175	37	18	72	59	21792	6928	28893	46	47
59382	29	12	41	24	26263	1514	21425	47	47
119308	32	16	61	58	23686	9238	50276	37	37
76702	35	21	67	42	49303	8204	37643	51	51
103425	17	19	76	46	25659	5926	30377	49	45
70344	20	16	64	61	28904	5785	27126	21	21
43410	7	1	3	3	2781	4	13	1	1
104838	46	16	63	52	29236	5930	42097	44	42
62215	24	10	40	25	19546	3710	24451	26	26
69304	40	19	69	40	22818	705	14335	21	21
53117	3	12	48	32	32689	443	5084	4	4
19764	10	2	8	4	5752	2416	9927	10	10
86680	37	14	52	49	22197	7747	43527	43	43
84105	17	17	66	63	20055	5432	27184	34	34
77945	28	19	76	67	25272	4913	21610	32	31
89113	19	14	43	32	82206	2650	20484	20	19
91005	29	11	39	23	32073	2370	20156	34	34
40248	8	4	14	7	5444	775	6012	6	6
64187	10	16	61	54	20154	5576	18475	12	11
50857	15	20	71	37	36944	1352	12645	24	24
56613	15	12	44	35	8019	3080	11017	16	16
62792	28	15	60	51	30884	10205	37623	72	72
72535	17	16	64	39	19540	6095	35873	27	21

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	7 seconds
R Server	'Gwilym Jenkins' @ jenkins.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 7 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=160029&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]7 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ jenkins.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=160029&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=160029&T=0

As an alternative you can also use a QR Code:

The GUIDs for individual cells are displayed in the table below:

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	7 seconds
R Server	'Gwilym Jenkins' @ jenkins.wessa.net

10-Fold Cross Validation
	Prediction (training)			Prediction (testing)
Actual	C1	C2	CV	C1	C2	CV
C1	1235	59	0.9544	141	15	0.9038
C2	150	1126	0.8824	29	135	0.8232
Overall	-	-	0.9187	-	-	0.8625

\begin{tabular}{lllllllll}
\hline
10-Fold Cross Validation \tabularnewline
 & Prediction (training) & Prediction (testing) \tabularnewline
Actual & C1 & C2 & CV & C1 & C2 & CV \tabularnewline
C1 & 1235 & 59 & 0.9544 & 141 & 15 & 0.9038 \tabularnewline
C2 & 150 & 1126 & 0.8824 & 29 & 135 & 0.8232 \tabularnewline
Overall & - & - & 0.9187 & - & - & 0.8625 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=160029&T=1

[TABLE]
[ROW][C]10-Fold Cross Validation[/C][/ROW]
[ROW][C][/C][C]Prediction (training)[/C][C]Prediction (testing)[/C][/ROW]
[ROW][C]Actual[/C][C]C1[/C][C]C2[/C][C]CV[/C][C]C1[/C][C]C2[/C][C]CV[/C][/ROW]
[ROW][C]C1[/C][C]1235[/C][C]59[/C][C]0.9544[/C][C]141[/C][C]15[/C][C]0.9038[/C][/ROW]
[ROW][C]C2[/C][C]150[/C][C]1126[/C][C]0.8824[/C][C]29[/C][C]135[/C][C]0.8232[/C][/ROW]
[ROW][C]Overall[/C][C]-[/C][C]-[/C][C]0.9187[/C][C]-[/C][C]-[/C][C]0.8625[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=160029&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=160029&T=1

As an alternative you can also use a QR Code:

The GUIDs for individual cells are displayed in the table below:

10-Fold Cross Validation
	Prediction (training)			Prediction (testing)
Actual	C1	C2	CV	C1	C2	CV
C1	1235	59	0.9544	141	15	0.9038
C2	150	1126	0.8824	29	135	0.8232
Overall	-	-	0.9187	-	-	0.8625

Confusion Matrix (predicted in columns / actuals in rows)
	C1	C2
C1	143	2
C2	24	120

\begin{tabular}{lllllllll}
\hline
Confusion Matrix (predicted in columns / actuals in rows) \tabularnewline
 & C1 & C2 \tabularnewline
C1 & 143 & 2 \tabularnewline
C2 & 24 & 120 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=160029&T=2

[TABLE]
[ROW][C]Confusion Matrix (predicted in columns / actuals in rows)[/C][/ROW]
[ROW][C][/C][C]C1[/C][C]C2[/C][/ROW]
[ROW][C]C1[/C][C]143[/C][C]2[/C][/ROW]
[ROW][C]C2[/C][C]24[/C][C]120[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=160029&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=160029&T=2

As an alternative you can also use a QR Code:

The GUIDs for individual cells are displayed in the table below:

Confusion Matrix (predicted in columns / actuals in rows)
	C1	C2
C1	143	2
C2	24	120

Figure 1

PNG link

Postscript link

PDF link

Figure 2

PNG link

Postscript link

PDF link

Figure 3

PNG link

Postscript link

PDF link

Parameters (Session):

par1 = kendall ;

Parameters (R input):

par1 = 6 ; par2 = quantiles ; par3 = 2 ; par4 = yes ;

R code (references can be found in the software module):

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')
}

Free Statistics

Description of Statistical Computation

Tree of Dependent Computations

Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code