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*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

Tue, 13 Dec 2011 12:01:48 -0500

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Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2011/Dec/13/t1323795737g8wtwc0amaaebnr.htm/, Retrieved Thu, 02 May 2024 21:57:33 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=154544, Retrieved Thu, 02 May 2024 21:57:33 +0000

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Estimated Impact

142

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

-     [Recursive Partitioning (Regression Trees)] [] [2010-12-05 19:50:12] [b98453cac15ba1066b407e146608df68]
- R PD    [Recursive Partitioning (Regression Trees)] [Fold-cross ] [2011-12-13 17:01:48] [8aedcf735e397266388b06f47fe45218] [Current]

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Dataseries X:

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7	0.27	0.36	20.7	0.045	45	170	1.001	3	0.45	8.8	6
6.3	0.3	0.34	1.6	0.049	14	132	0.994	3.3	0.49	9.5	6
8.1	0.28	0.4	6.9	0.05	30	97	0.9951	3.26	0.44	10.1	6
7.2	0.23	0.32	8.5	0.058	47	186	0.9956	3.19	0.4	9.9	6
7.2	0.23	0.32	8.5	0.058	47	186	0.9956	3.19	0.4	9.9	6
8.1	0.28	0.4	6.9	0.05	30	97	0.9951	3.26	0.44	10.1	6
6.2	0.32	0.16	7	0.045	30	136	0.9949	3.18	0.47	9.6	6
7	0.27	0.36	20.7	0.045	45	170	1.001	3	0.45	8.8	6
6.3	0.3	0.34	1.6	0.049	14	132	0.994	3.3	0.49	9.5	6
8.1	0.22	0.43	1.5	0.044	28	129	0.9938	3.22	0.45	11	6
8.1	0.27	0.41	1.45	0.033	11	63	0.9908	2.99	0.56	12	5
8.6	0.23	0.4	4.2	0.035	17	109	0.9947	3.14	0.53	9.7	5
7.9	0.18	0.37	1.2	0.04	16	75	0.992	3.18	0.63	10.8	5
6.6	0.16	0.4	1.5	0.044	48	143	0.9912	3.54	0.52	12.4	7
8.3	0.42	0.62	19.25	0.04	41	172	1.0002	2.98	0.67	9.7	5
6.6	0.17	0.38	1.5	0.032	28	112	0.9914	3.25	0.55	11.4	7
6.3	0.48	0.04	1.1	0.046	30	99	0.9928	3.24	0.36	9.6	6
6.2	0.66	0.48	1.2	0.029	29	75	0.9892	3.33	0.39	12.8	8
7.4	0.34	0.42	1.1	0.033	17	171	0.9917	3.12	0.53	11.3	6
6.5	0.31	0.14	7.5	0.044	34	133	0.9955	3.22	0.5	9.5	5
6.2	0.66	0.48	1.2	0.029	29	75	0.9892	3.33	0.39	12.8	8
6.4	0.31	0.38	2.9	0.038	19	102	0.9912	3.17	0.35	11	7
6.8	0.26	0.42	1.7	0.049	41	122	0.993	3.47	0.48	10.5	8
7.6	0.67	0.14	1.5	0.074	25	168	0.9937	3.05	0.51	9.3	5
6.6	0.27	0.41	1.3	0.052	16	142	0.9951	3.42	0.47	10	6
7	0.25	0.32	9	0.046	56	245	0.9955	3.25	0.5	10.4	6
6.9	0.24	0.35	1	0.052	35	146	0.993	3.45	0.44	10	6
7	0.28	0.39	8.7	0.051	32	141	0.9961	3.38	0.53	10.5	6
7.4	0.27	0.48	1.1	0.047	17	132	0.9914	3.19	0.49	11.6	6
7.2	0.32	0.36	2	0.033	37	114	0.9906	3.1	0.71	12.3	7
8.5	0.24	0.39	10.4	0.044	20	142	0.9974	3.2	0.53	10	6
8.3	0.14	0.34	1.1	0.042	7	47	0.9934	3.47	0.4	10.2	6
7.4	0.25	0.36	2.05	0.05	31	100	0.992	3.19	0.44	10.8	6
6.2	0.12	0.34	1.5	0.045	43	117	0.9939	3.42	0.51	9	6
5.8	0.27	0.2	14.95	0.044	22	179	0.9962	3.37	0.37	10.2	5
7.3	0.28	0.43	1.7	0.08	21	123	0.9905	3.19	0.42	12.8	5
6.5	0.39	0.23	5.4	0.051	25	149	0.9934	3.24	0.35	10	5
7	0.33	0.32	1.2	0.053	38	138	0.9906	3.13	0.28	11.2	6
7.3	0.24	0.39	17.95	0.057	45	149	0.9999	3.21	0.36	8.6	5
7.3	0.24	0.39	17.95	0.057	45	149	0.9999	3.21	0.36	8.6	5
6.7	0.23	0.39	2.5	0.172	63	158	0.9937	3.11	0.36	9.4	6
6.7	0.24	0.39	2.9	0.173	63	157	0.9937	3.1	0.34	9.4	6
7	0.31	0.26	7.4	0.069	28	160	0.9954	3.13	0.46	9.8	6
6.6	0.24	0.27	1.4	0.057	33	152	0.9934	3.22	0.56	9.5	6
6.7	0.23	0.26	1.4	0.06	33	154	0.9934	3.24	0.56	9.5	6
7.4	0.18	0.31	1.4	0.058	38	167	0.9931	3.16	0.53	10	7
6.2	0.45	0.26	4.4	0.063	63	206	0.994	3.27	0.52	9.8	4
6.2	0.46	0.25	4.4	0.066	62	207	0.9939	3.25	0.52	9.8	5
7	0.31	0.26	7.4	0.069	28	160	0.9954	3.13	0.46	9.8	6
6.9	0.19	0.35	5	0.067	32	150	0.995	3.36	0.48	9.8	5
7.2	0.19	0.31	1.6	0.062	31	173	0.9917	3.35	0.44	11.7	6
6.6	0.25	0.29	1.1	0.068	39	124	0.9914	3.34	0.58	11	7
6.2	0.16	0.33	1.1	0.057	21	82	0.991	3.32	0.46	10.9	7
6.4	0.18	0.35	1	0.045	39	108	0.9911	3.31	0.35	10.9	6
6.8	0.2	0.59	0.9	0.147	38	132	0.993	3.05	0.38	9.1	6
6.9	0.25	0.35	1.3	0.039	29	191	0.9908	3.13	0.52	11	6
7.2	0.21	0.34	11.9	0.043	37	213	0.9962	3.09	0.5	9.6	6
6	0.19	0.26	12.4	0.048	50	147	0.9972	3.3	0.36	8.9	6
6.6	0.38	0.15	4.6	0.044	25	78	0.9931	3.11	0.38	10.2	6
7.4	0.2	0.36	1.2	0.038	44	111	0.9926	3.36	0.34	9.9	6
6.8	0.22	0.24	4.9	0.092	30	123	0.9951	3.03	0.46	8.6	6
6	0.19	0.26	12.4	0.048	50	147	0.9972	3.3	0.36	8.9	6
7	0.47	0.07	1.1	0.035	17	151	0.991	3.02	0.34	10.5	5
6.6	0.38	0.15	4.6	0.044	25	78	0.9931	3.11	0.38	10.2	6
7.2	0.24	0.27	1.4	0.038	31	122	0.9927	3.15	0.46	10.3	6
6.2	0.35	0.03	1.2	0.064	29	120	0.9934	3.22	0.54	9.1	5
6.4	0.26	0.24	6.4	0.04	27	124	0.9903	3.22	0.49	12.6	7
6.7	0.25	0.13	1.2	0.041	81	174	0.992	3.14	0.42	9.8	5
6.7	0.23	0.31	2.1	0.046	30	96	0.9926	3.33	0.64	10.7	8
7.4	0.24	0.29	10.1	0.05	21	105	0.9962	3.13	0.35	9.5	5
6.2	0.27	0.43	7.8	0.056	48	244	0.9956	3.1	0.51	9	6
6.8	0.3	0.23	4.6	0.061	50.5	238.5	0.9958	3.32	0.6	9.5	5
6	0.27	0.28	4.8	0.063	31	201	0.9964	3.69	0.71	10	5
8.6	0.23	0.46	1	0.054	9	72	0.9941	2.95	0.49	9.1	6
6.7	0.23	0.31	2.1	0.046	30	96	0.9926	3.33	0.64	10.7	8
7.4	0.24	0.29	10.1	0.05	21	105	0.9962	3.13	0.35	9.5	5
7.1	0.18	0.36	1.4	0.043	31	87	0.9898	3.26	0.37	12.7	7
7	0.32	0.34	1.3	0.042	20	69	0.9912	3.31	0.65	12	7
7.4	0.18	0.3	8.8	0.064	26	103	0.9961	2.94	0.56	9.3	5
6.7	0.54	0.28	5.4	0.06	21	105	0.9949	3.27	0.37	9	5
6.8	0.22	0.31	1.4	0.053	34	114	0.9929	3.39	0.77	10.6	6
7.1	0.2	0.34	16	0.05	51	166	0.9985	3.21	0.6	9.2	6
7.1	0.34	0.2	6.1	0.063	47	164	0.9946	3.17	0.42	10	5
7.3	0.22	0.3	8.2	0.047	42	207	0.9966	3.33	0.46	9.5	6
7.1	0.43	0.61	11.8	0.045	54	155	0.9974	3.11	0.45	8.7	5
7.1	0.44	0.62	11.8	0.044	52	152	0.9975	3.12	0.46	8.7	6
7.2	0.39	0.63	11	0.044	55	156	0.9974	3.09	0.44	8.7	6
6.8	0.25	0.31	13.3	0.05	69	202	0.9972	3.22	0.48	9.7	6
7.1	0.43	0.61	11.8	0.045	54	155	0.9974	3.11	0.45	8.7	5
7.1	0.44	0.62	11.8	0.044	52	152	0.9975	3.12	0.46	8.7	6
7.2	0.39	0.63	11	0.044	55	156	0.9974	3.09	0.44	8.7	6
6.1	0.27	0.43	7.5	0.049	65	243	0.9957	3.12	0.47	9	5
6.9	0.24	0.33	1.7	0.035	47	136	0.99	3.26	0.4	12.6	7
6.9	0.21	0.33	1.8	0.034	48	136	0.9899	3.25	0.41	12.6	7
7.5	0.17	0.32	1.7	0.04	51	148	0.9916	3.21	0.44	11.5	7
7.1	0.26	0.29	12.4	0.044	62	240	0.9969	3.04	0.42	9.2	6
6	0.34	0.66	15.9	0.046	26	164	0.9979	3.14	0.5	8.8	6
8.6	0.265	0.36	1.2	0.034	15	80	0.9913	2.95	0.36	11.4	7
9.8	0.36	0.46	10.5	0.038	4	83	0.9956	2.89	0.3	10.1	4
6	0.34	0.66	15.9	0.046	26	164	0.9979	3.14	0.5	8.8	6
7.4	0.25	0.37	13.5	0.06	52	192	0.9975	3	0.44	9.1	5
7.1	0.12	0.32	9.6	0.054	64	162	0.9962	3.4	0.41	9.4	5
6	0.21	0.24	12.1	0.05	55	164	0.997	3.34	0.39	9.4	5
7.5	0.305	0.4	18.9	0.059	44	170	1	2.99	0.46	9	5
7.4	0.25	0.37	13.5	0.06	52	192	0.9975	3	0.44	9.1	5
7.3	0.13	0.32	14.4	0.051	34	109	0.9974	3.2	0.35	9.2	6
7.1	0.12	0.32	9.6	0.054	64	162	0.9962	3.4	0.41	9.4	5
7.1	0.23	0.35	16.5	0.04	60	171	0.999	3.16	0.59	9.1	6
7.1	0.23	0.35	16.5	0.04	60	171	0.999	3.16	0.59	9.1	6
6.9	0.33	0.28	1.3	0.051	37	187	0.9927	3.27	0.6	10.3	5
6.5	0.17	0.54	8.5	0.082	64	163	0.9959	2.89	0.39	8.8	6
7.2	0.27	0.46	18.75	0.052	45	255	1	3.04	0.52	8.9	5
7.2	0.31	0.5	13.3	0.056	68	195	0.9982	3.01	0.47	9.2	5
6.7	0.41	0.34	9.2	0.049	29	150	0.9968	3.22	0.51	9.1	5
6.7	0.41	0.34	9.2	0.049	29	150	0.9968	3.22	0.51	9.1	5
5.5	0.485	0	1.5	0.065	8	103	0.994	3.63	0.4	9.7	4
6	0.31	0.24	3.3	0.041	25	143	0.9914	3.31	0.44	11.3	6
7	0.14	0.4	1.7	0.035	16	85	0.9911	3.19	0.42	11.8	6
7.2	0.31	0.5	13.3	0.056	68	195	0.9982	3.01	0.47	9.2	5
7.3	0.32	0.48	13.3	0.06	57	196	0.9982	3.04	0.5	9.2	5
5.9	0.36	0.04	5.7	0.046	21	87	0.9934	3.22	0.51	10.2	5
7.8	0.24	0.32	12.2	0.054	42	138	0.9984	3.01	0.54	8.8	5
7.4	0.16	0.31	6.85	0.059	31	131	0.9952	3.29	0.34	9.7	5
6.9	0.19	0.28	5	0.058	14	146	0.9952	3.29	0.36	9.1	6
6.4	0.13	0.47	1.6	0.092	40	158	0.9928	3.21	0.36	9.8	6
6.7	0.19	0.36	1.1	0.026	63	143	0.9912	3.27	0.48	11	6
7.4	0.39	0.23	7	0.033	29	126	0.994	3.14	0.42	10.5	5
6.5	0.24	0.32	7.6	0.038	48	203	0.9958	3.45	0.54	9.7	7
6.1	0.3	0.56	2.8	0.044	47	179	0.9924	3.3	0.57	10.9	7
6.1	0.3	0.56	2.7	0.046	46	184	0.9924	3.31	0.57	10.9	6
5.7	0.26	0.25	10.4	0.02	7	57	0.994	3.39	0.37	10.6	5
6.5	0.24	0.32	7.6	0.038	48	203	0.9958	3.45	0.54	9.7	7
6.5	0.425	0.4	13.1	0.038	59	241	0.9979	3.23	0.57	9	5
6.6	0.24	0.27	15.8	0.035	46	188	0.9982	3.24	0.51	9.2	5
6.8	0.27	0.22	8.1	0.034	55	203	0.9961	3.19	0.52	8.9	5
6.7	0.27	0.31	15.7	0.036	44	179	0.9979	3.26	0.56	9.6	5
8.2	0.23	0.4	1.2	0.027	36	121	0.992	3.12	0.38	10.7	6
7.1	0.37	0.67	10.5	0.045	49	155	0.9975	3.16	0.44	8.7	5
6.8	0.19	0.36	1.9	0.035	30	96	0.9917	3.15	0.54	10.8	7
8.1	0.28	0.39	1.9	0.029	18	79	0.9923	3.23	0.52	11.8	6
6.3	0.31	0.34	2.2	0.045	20	77	0.9927	3.3	0.43	10.2	5
7.1	0.37	0.67	10.5	0.045	49	155	0.9975	3.16	0.44	8.7	5
7.9	0.21	0.4	1.2	0.039	38	107	0.992	3.21	0.54	10.8	6
8.5	0.21	0.41	4.3	0.036	24	99	0.9947	3.18	0.53	9.7	6
8.1	0.2	0.4	2	0.037	19	87	0.9921	3.12	0.54	11.2	6
6.3	0.255	0.37	1.1	0.04	37	114	0.9905	3	0.39	10.9	6
5.6	0.16	0.27	1.4	0.044	53	168	0.9918	3.28	0.37	10.1	6
6.4	0.595	0.14	5.2	0.058	15	97	0.9951	3.38	0.36	9	4
6.3	0.34	0.33	4.6	0.034	19	80	0.9917	3.38	0.58	12	7
6.9	0.25	0.3	4.1	0.054	23	116	0.994	2.99	0.38	9.4	6
7.9	0.22	0.38	8	0.043	46	152	0.9934	3.12	0.32	11.5	7
7.6	0.18	0.46	10.2	0.055	58	135	0.9968	3.14	0.43	9.9	6
6.9	0.25	0.3	4.1	0.054	23	116	0.994	2.99	0.38	9.4	6
7.2	0.18	0.41	1.2	0.048	41	97	0.9919	3.14	0.45	10.4	5
8.2	0.23	0.4	7.5	0.049	12	76	0.9966	3.06	0.84	9.7	6
7.4	0.24	0.42	14	0.066	48	198	0.9979	2.89	0.42	8.9	6
7.4	0.24	0.42	14	0.066	48	198	0.9979	2.89	0.42	8.9	6
6.1	0.32	0.24	1.5	0.036	38	124	0.9898	3.29	0.42	12.4	7
5.2	0.44	0.04	1.4	0.036	43	119	0.9894	3.36	0.33	12.1	8
5.2	0.44	0.04	1.4	0.036	43	119	0.9894	3.36	0.33	12.1	8
6.1	0.32	0.24	1.5	0.036	38	124	0.9898	3.29	0.42	12.4	7
6.4	0.22	0.56	14.5	0.055	27	159	0.998	2.98	0.4	9.1	5
6.3	0.36	0.3	4.8	0.049	14	85	0.9932	3.28	0.39	10.6	5
7.4	0.24	0.42	14	0.066	48	198	0.9979	2.89	0.42	8.9	6
6.7	0.24	0.35	13.1	0.05	64	205	0.997	3.15	0.5	9.5	5
7	0.23	0.36	13	0.051	72	177	0.9972	3.16	0.49	9.8	5
8.4	0.27	0.46	8.7	0.048	39	197	0.9974	3.14	0.59	9.6	6
6.7	0.46	0.18	2.4	0.034	25	98	0.9896	3.08	0.44	12.6	7
7.5	0.29	0.31	8.95	0.055	20	151	0.9968	3.08	0.54	9.3	5
9.8	0.42	0.48	9.85	0.034	5	110	0.9958	2.87	0.29	10	5
7.1	0.3	0.46	1.5	0.066	29	133	0.9906	3.12	0.54	12.7	6
7.9	0.19	0.45	1.5	0.045	17	96	0.9917	3.13	0.39	11	6
7.6	0.48	0.37	0.8	0.037	4	100	0.9902	3.03	0.39	11.4	4
6.3	0.22	0.43	4.55	0.038	31	130	0.9918	3.35	0.33	11.5	7
7.5	0.27	0.31	17.7	0.051	33	173	0.999	3.09	0.64	10.2	5
6.9	0.23	0.4	7.5	0.04	50	151	0.9927	3.11	0.27	11.4	6
7.2	0.32	0.47	5.1	0.044	19	65	0.991	3.03	0.41	12.6	4
5.9	0.23	0.3	12.9	0.054	57	170	0.9972	3.28	0.39	9.4	5
6	0.67	0.07	1.2	0.06	9	108	0.9931	3.11	0.35	8.7	4
6.4	0.25	0.32	5.5	0.049	41	176	0.995	3.19	0.68	9.2	6
6.4	0.33	0.31	5.5	0.048	42	173	0.9951	3.19	0.66	9.3	6
7.1	0.34	0.15	1.2	0.053	61	183	0.9936	3.09	0.43	9.2	5
6.8	0.28	0.4	22	0.048	48	167	1.001	2.93	0.5	8.7	5
6.9	0.27	0.4	14	0.05	64	227	0.9979	3.18	0.58	9.6	6
6.8	0.26	0.56	11.9	0.043	64	226	0.997	3.02	0.63	9.3	5
6.8	0.29	0.56	11.9	0.043	66	230	0.9972	3.02	0.63	9.3	5
6.7	0.24	0.41	9.4	0.04	49	166	0.9954	3.12	0.61	9.9	6
5.9	0.3	0.23	4.2	0.038	42	119	0.9924	3.15	0.5	11	5
6.8	0.53	0.35	3.8	0.034	26	109	0.9906	3.26	0.57	12.7	8
6.5	0.28	0.28	8.5	0.047	54	210	0.9962	3.09	0.54	8.9	4
6.6	0.28	0.28	8.5	0.052	55	211	0.9962	3.09	0.55	8.9	6
6.8	0.28	0.4	22	0.048	48	167	1.001	2.93	0.5	8.7	5
6.8	0.28	0.36	8	0.045	28	123	0.9928	3.02	0.37	11.4	6
6.6	0.15	0.34	5.1	0.055	34	125	0.9942	3.36	0.42	9.6	5
6.4	0.29	0.44	3.6	0.2	75	181	0.9942	3.02	0.41	9.1	5
6.4	0.3	0.45	3.5	0.197	76	180	0.9942	3.02	0.39	9.1	6
6.4	0.29	0.44	3.6	0.197	75	183	0.9942	3.01	0.38	9.1	5
6.8	0.26	0.24	7.8	0.052	54	214	0.9961	3.13	0.47	8.9	5
7.1	0.32	0.24	13.1	0.05	52	204	0.998	3.1	0.49	8.8	5
6.8	0.26	0.24	7.8	0.052	54	214	0.9961	3.13	0.47	8.9	5
6.8	0.27	0.26	16.1	0.049	55	196	0.9984	3.15	0.5	9.3	5
7.1	0.32	0.24	13.1	0.05	52	204	0.998	3.1	0.49	8.8	5
6.9	0.54	0.32	13.2	0.05	53	236	0.9973	3.2	0.5	9.6	5
6.8	0.26	0.34	13.9	0.034	39	134	0.9949	3.33	0.53	12	6
5.8	0.28	0.35	2.3	0.053	36	114	0.9924	3.28	0.5	10.2	4
6.4	0.21	0.5	11.6	0.042	45	153	0.9972	3.15	0.43	8.8	5
7	0.16	0.32	8.3	0.045	38	126	0.9958	3.21	0.34	9.2	5
10.2	0.44	0.88	6.2	0.049	20	124	0.9968	2.99	0.51	9.9	4
6.8	0.57	0.29	2.2	0.04	15	77	0.9938	3.32	0.74	10.2	5
6.1	0.4	0.31	0.9	0.048	23	170	0.993	3.22	0.77	9.5	6
5.6	0.245	0.25	9.7	0.032	12	68	0.994	3.31	0.34	10.5	5
6.8	0.18	0.38	1.4	0.038	35	111	0.9918	3.32	0.59	11.2	7
7	0.16	0.32	8.3	0.045	38	126	0.9958	3.21	0.34	9.2	5
6.7	0.13	0.29	5.3	0.051	31	122	0.9944	3.44	0.37	9.7	6
6.2	0.25	0.25	1.4	0.03	35	105	0.9912	3.3	0.44	11.1	7
5.8	0.26	0.24	9.2	0.044	55	152	0.9961	3.31	0.38	9.4	5
7.5	0.27	0.36	7	0.036	45	164	0.9939	3.03	0.33	11	5
5.8	0.26	0.24	9.2	0.044	55	152	0.9961	3.31	0.38	9.4	5
5.7	0.28	0.24	17.5	0.044	60	167	0.9989	3.31	0.44	9.4	5
7.5	0.23	0.36	7	0.036	43	161	0.9938	3.04	0.32	11	5
7.5	0.27	0.36	7	0.036	45	164	0.9939	3.03	0.33	11	5
7.2	0.685	0.21	9.5	0.07	33	172	0.9971	3	0.55	9.1	6
6.2	0.25	0.25	1.4	0.03	35	105	0.9912	3.3	0.44	11.1	7
6.5	0.19	0.3	0.8	0.043	33	144	0.9936	3.42	0.39	9.1	6
6.3	0.495	0.22	1.8	0.046	31	140	0.9929	3.39	0.54	10.4	6
7.1	0.24	0.41	17.8	0.046	39	145	0.9998	3.32	0.39	8.7	5
6.4	0.17	0.32	2.4	0.048	41	200	0.9938	3.5	0.5	9.7	6
7.1	0.25	0.32	10.3	0.041	66	272	0.9969	3.17	0.52	9.1	6
6.4	0.17	0.32	2.4	0.048	41	200	0.9938	3.5	0.5	9.7	6
7.1	0.24	0.41	17.8	0.046	39	145	0.9998	3.32	0.39	8.7	5
6.8	0.64	0.08	9.7	0.062	26	142	0.9972	3.37	0.46	8.9	4
8.3	0.28	0.4	7.8	0.041	38	194	0.9976	3.34	0.51	9.6	6
8.2	0.27	0.39	7.8	0.039	49	208	0.9976	3.31	0.51	9.5	6
7.2	0.23	0.38	14.3	0.058	55	194	0.9979	3.09	0.44	9	6
7.2	0.23	0.38	14.3	0.058	55	194	0.9979	3.09	0.44	9	6
7.2	0.23	0.38	14.3	0.058	55	194	0.9979	3.09	0.44	9	6
7.2	0.23	0.38	14.3	0.058	55	194	0.9979	3.09	0.44	9	6
6.8	0.52	0.32	13.2	0.044	54	221	0.9972	3.27	0.5	9.6	6
7	0.26	0.59	1.4	0.037	40	120	0.9918	3.34	0.41	11.1	7
6.2	0.25	0.21	15.55	0.039	28	159	0.9982	3.48	0.64	9.6	6
7.3	0.32	0.23	13.7	0.05	49	197	0.9985	3.2	0.46	8.7	5
7.7	0.31	0.26	7.8	0.031	23	90	0.9944	3.13	0.5	10.4	5
7.1	0.21	0.37	2.4	0.026	23	100	0.9903	3.15	0.38	11.4	7
6.8	0.24	0.34	2.7	0.047	64.5	218.5	0.9934	3.3	0.58	9.7	6
6.9	0.4	0.56	11.2	0.043	40	142	0.9975	3.14	0.46	8.7	5
6.1	0.18	0.36	2	0.038	20	249.5	0.9923	3.37	0.79	11.3	6
6.8	0.21	0.27	2.1	0.03	26	139	0.99	3.16	0.61	12.6	7
5.8	0.2	0.27	1.4	0.031	12	77	0.9905	3.25	0.36	10.9	7
5.6	0.19	0.26	1.4	0.03	12	76	0.9905	3.25	0.37	10.9	7
6.1	0.41	0.14	10.4	0.037	18	119	0.996	3.38	0.45	10	5
5.9	0.21	0.28	4.6	0.053	40	199	0.9964	3.72	0.7	10	4
8.5	0.26	0.21	16.2	0.074	41	197	0.998	3.02	0.5	9.8	3
6.9	0.4	0.56	11.2	0.043	40	142	0.9975	3.14	0.46	8.7	5
5.8	0.24	0.44	3.5	0.029	5	109	0.9913	3.53	0.43	11.7	3
5.8	0.24	0.39	1.5	0.054	37	158	0.9932	3.21	0.52	9.3	6
6.7	0.26	0.39	1.1	0.04	45	147	0.9935	3.32	0.58	9.6	8
6.3	0.35	0.3	5.7	0.035	8	97	0.9927	3.27	0.41	11	7
6.3	0.35	0.3	5.7	0.035	8	97	0.9927	3.27	0.41	11	7
6.4	0.23	0.39	1.8	0.032	23	118	0.9912	3.32	0.5	11.8	6
5.8	0.36	0.38	0.9	0.037	3	75	0.9904	3.28	0.34	11.4	4
6.9	0.115	0.35	5.4	0.048	36	108	0.9939	3.32	0.42	10.2	6
6.9	0.29	0.4	19.45	0.043	36	156	0.9996	2.93	0.47	8.9	5
6.9	0.28	0.4	8.2	0.036	15	95	0.9944	3.17	0.33	10.2	5
7.2	0.29	0.4	13.6	0.045	66	231	0.9977	3.08	0.59	9.6	6
6.2	0.24	0.35	1.2	0.038	22	167	0.9912	3.1	0.48	10.6	6
6.9	0.29	0.4	19.45	0.043	36	156	0.9996	2.93	0.47	8.9	5
6.9	0.32	0.26	8.3	0.053	32	180	0.9965	3.25	0.51	9.2	6
5.3	0.58	0.07	6.9	0.043	34	149	0.9944	3.34	0.57	9.7	5
5.3	0.585	0.07	7.1	0.044	34	145	0.9945	3.34	0.57	9.7	6
5.4	0.59	0.07	7	0.045	36	147	0.9944	3.34	0.57	9.7	6
6.9	0.32	0.26	8.3	0.053	32	180	0.9965	3.25	0.51	9.2	6
5.2	0.6	0.07	7	0.044	33	147	0.9944	3.33	0.58	9.7	5
5.8	0.25	0.26	13.1	0.051	44	148	0.9972	3.29	0.38	9.3	5
6.6	0.58	0.3	5.1	0.057	30	123	0.9949	3.24	0.38	9	5
7	0.29	0.54	10.7	0.046	59	234	0.9966	3.05	0.61	9.5	5
6.6	0.19	0.41	8.9	0.046	51	169	0.9954	3.14	0.57	9.8	6
6.7	0.2	0.41	9.1	0.044	50	166	0.9954	3.14	0.58	9.8	6
7.7	0.26	0.4	1.1	0.042	9	60	0.9915	2.89	0.5	10.6	5
6.8	0.32	0.34	1.2	0.044	14	67	0.9919	3.05	0.47	10.6	4
7	0.3	0.49	4.7	0.036	17	105	0.9916	3.26	0.68	12.4	7
7	0.24	0.36	2.8	0.034	22	112	0.99	3.19	0.38	12.6	8
6.1	0.31	0.58	5	0.039	36	114	0.9909	3.3	0.6	12.3	8
6.8	0.44	0.37	5.1	0.047	46	201	0.9938	3.08	0.65	10.5	4
6.7	0.34	0.3	15.6	0.054	51	196	0.9982	3.19	0.49	9.3	5
7.1	0.35	0.24	15.4	0.055	46	198	0.9988	3.12	0.49	8.8	5
7.3	0.32	0.25	7.2	0.056	47	180	0.9961	3.08	0.47	8.8	5
6.5	0.28	0.33	15.7	0.053	51	190	0.9978	3.22	0.51	9.7	6
7.2	0.23	0.39	14.2	0.058	49	192	0.9979	2.98	0.48	9	7
7.2	0.23	0.39	14.2	0.058	49	192	0.9979	2.98	0.48	9	7
7.2	0.23	0.39	14.2	0.058	49	192	0.9979	2.98	0.48	9	7
7.2	0.23	0.39	14.2	0.058	49	192	0.9979	2.98	0.48	9	7
5.9	0.15	0.31	5.8	0.041	53	155	0.9945	3.52	0.46	10.5	6
7.4	0.28	0.42	19.8	0.066	53	195	1	2.96	0.44	9.1	5
6.2	0.28	0.22	7.3	0.041	26	157	0.9957	3.44	0.64	9.8	7
9.1	0.59	0.38	1.6	0.066	34	182	0.9968	3.23	0.38	8.5	3
6.3	0.33	0.27	1.2	0.046	34	175	0.9934	3.37	0.54	9.4	6
8.3	0.39	0.7	10.6	0.045	33	169	0.9976	3.09	0.57	9.4	5
7.2	0.19	0.46	3.8	0.041	82	187	0.9932	3.19	0.6	11.2	7

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	8 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 & 8 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=154544&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]8 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=154544&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=154544&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	8 seconds
R Server	'Gwilym Jenkins' @ jenkins.wessa.net

10-Fold Cross Validation
	Prediction (training)			Prediction (testing)
Actual	C1	C2	CV	C1	C2	CV
C1	2094	109	0.9505	241	16	0.9377
C2	203	278	0.578	22	17	0.4359
Overall	-	-	0.8838	-	-	0.8716

\begin{tabular}{lllllllll}
\hline
10-Fold Cross Validation \tabularnewline
 & Prediction (training) & Prediction (testing) \tabularnewline
Actual & C1 & C2 & CV & C1 & C2 & CV \tabularnewline
C1 & 2094 & 109 & 0.9505 & 241 & 16 & 0.9377 \tabularnewline
C2 & 203 & 278 & 0.578 & 22 & 17 & 0.4359 \tabularnewline
Overall & - & - & 0.8838 & - & - & 0.8716 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=154544&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]2094[/C][C]109[/C][C]0.9505[/C][C]241[/C][C]16[/C][C]0.9377[/C][/ROW]
[ROW][C]C2[/C][C]203[/C][C]278[/C][C]0.578[/C][C]22[/C][C]17[/C][C]0.4359[/C][/ROW]
[ROW][C]Overall[/C][C]-[/C][C]-[/C][C]0.8838[/C][C]-[/C][C]-[/C][C]0.8716[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=154544&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=154544&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	2094	109	0.9505	241	16	0.9377
C2	203	278	0.578	22	17	0.4359
Overall	-	-	0.8838	-	-	0.8716

Confusion Matrix (predicted in columns / actuals in rows)
	C1	C2
C1	227	19
C2	13	39

\begin{tabular}{lllllllll}
\hline
Confusion Matrix (predicted in columns / actuals in rows) \tabularnewline
 & C1 & C2 \tabularnewline
C1 & 227 & 19 \tabularnewline
C2 & 13 & 39 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=154544&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]227[/C][C]19[/C][/ROW]
[ROW][C]C2[/C][C]13[/C][C]39[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=154544&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=154544&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	227	19
C2	13	39

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 = 5 ; par2 = quantiles ; par3 = 2 ; par4 = yes ;

Parameters (R input):

par1 = 12 ; 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