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R Software Module

rwasp_regression_trees1.wasp

Title produced by software

Recursive Partitioning (Regression Trees)

Date of computation

Tue, 13 Dec 2011 12:34:21 -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/t1323797720acp0w3r2zu51srk.htm/, Retrieved Thu, 02 May 2024 19:14:45 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=154567, Retrieved Thu, 02 May 2024 19:14:45 +0000

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Estimated Impact

114

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-     [Recursive Partitioning (Regression Trees)] [] [2010-12-05 19:35:21] [b98453cac15ba1066b407e146608df68]
- R PD    [Recursive Partitioning (Regression Trees)] [Ws10 recursive pa...] [2011-12-13 17:34:21] [c98b04636162cea751932dfe577607eb] [Current]

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Dataseries X:

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7.4	0.7	0	1.9	0.076	11	34	0.9978	3.51	0.56	9.4	5
7.8	0.88	0	2.6	0.098	25	67	0.9968	3.2	0.68	9.8	5
7.8	0.76	0.04	2.3	0.092	15	54	0.997	3.26	0.65	9.8	5
11.2	0.28	0.56	1.9	0.075	17	60	0.998	3.16	0.58	9.8	6
7.4	0.7	0	1.9	0.076	11	34	0.9978	3.51	0.56	9.4	5
7.4	0.66	0	1.8	0.075	13	40	0.9978	3.51	0.56	9.4	5
7.9	0.6	0.06	1.6	0.069	15	59	0.9964	3.3	0.46	9.4	5
7.3	0.65	0	1.2	0.065	15	21	0.9946	3.39	0.47	10	7
7.8	0.58	0.02	2	0.073	9	18	0.9968	3.36	0.57	9.5	7
7.5	0.5	0.36	6.1	0.071	17	102	0.9978	3.35	0.8	10.5	5
6.7	0.58	0.08	1.8	0.097	15	65	0.9959	3.28	0.54	9.2	5
7.5	0.5	0.36	6.1	0.071	17	102	0.9978	3.35	0.8	10.5	5
5.6	0.615	0	1.6	0.089	16	59	0.9943	3.58	0.52	9.9	5
7.8	0.61	0.29	1.6	0.114	9	29	0.9974	3.26	1.56	9.1	5
8.9	0.62	0.18	3.8	0.176	52	145	0.9986	3.16	0.88	9.2	5
8.9	0.62	0.19	3.9	0.17	51	148	0.9986	3.17	0.93	9.2	5
8.5	0.28	0.56	1.8	0.092	35	103	0.9969	3.3	0.75	10.5	7
8.1	0.56	0.28	1.7	0.368	16	56	0.9968	3.11	1.28	9.3	5
7.4	0.59	0.08	4.4	0.086	6	29	0.9974	3.38	0.5	9	4
7.9	0.32	0.51	1.8	0.341	17	56	0.9969	3.04	1.08	9.2	6
8.9	0.22	0.48	1.8	0.077	29	60	0.9968	3.39	0.53	9.4	6
7.6	0.39	0.31	2.3	0.082	23	71	0.9982	3.52	0.65	9.7	5
7.9	0.43	0.21	1.6	0.106	10	37	0.9966	3.17	0.91	9.5	5
8.5	0.49	0.11	2.3	0.084	9	67	0.9968	3.17	0.53	9.4	5
6.9	0.4	0.14	2.4	0.085	21	40	0.9968	3.43	0.63	9.7	6
6.3	0.39	0.16	1.4	0.08	11	23	0.9955	3.34	0.56	9.3	5
7.6	0.41	0.24	1.8	0.08	4	11	0.9962	3.28	0.59	9.5	5
7.9	0.43	0.21	1.6	0.106	10	37	0.9966	3.17	0.91	9.5	5
7.1	0.71	0	1.9	0.08	14	35	0.9972	3.47	0.55	9.4	5
7.8	0.645	0	2	0.082	8	16	0.9964	3.38	0.59	9.8	6
6.7	0.675	0.07	2.4	0.089	17	82	0.9958	3.35	0.54	10.1	5
6.9	0.685	0	2.5	0.105	22	37	0.9966	3.46	0.57	10.6	6
8.3	0.655	0.12	2.3	0.083	15	113	0.9966	3.17	0.66	9.8	5
6.9	0.605	0.12	10.7	0.073	40	83	0.9993	3.45	0.52	9.4	6
5.2	0.32	0.25	1.8	0.103	13	50	0.9957	3.38	0.55	9.2	5
7.8	0.645	0	5.5	0.086	5	18	0.9986	3.4	0.55	9.6	6
7.8	0.6	0.14	2.4	0.086	3	15	0.9975	3.42	0.6	10.8	6
8.1	0.38	0.28	2.1	0.066	13	30	0.9968	3.23	0.73	9.7	7
5.7	1.13	0.09	1.5	0.172	7	19	0.994	3.5	0.48	9.8	4
7.3	0.45	0.36	5.9	0.074	12	87	0.9978	3.33	0.83	10.5	5
7.3	0.45	0.36	5.9	0.074	12	87	0.9978	3.33	0.83	10.5	5
8.8	0.61	0.3	2.8	0.088	17	46	0.9976	3.26	0.51	9.3	4
7.5	0.49	0.2	2.6	0.332	8	14	0.9968	3.21	0.9	10.5	6
8.1	0.66	0.22	2.2	0.069	9	23	0.9968	3.3	1.2	10.3	5
6.8	0.67	0.02	1.8	0.05	5	11	0.9962	3.48	0.52	9.5	5
4.6	0.52	0.15	2.1	0.054	8	65	0.9934	3.9	0.56	13.1	4
7.7	0.935	0.43	2.2	0.114	22	114	0.997	3.25	0.73	9.2	5
8.7	0.29	0.52	1.6	0.113	12	37	0.9969	3.25	0.58	9.5	5
6.4	0.4	0.23	1.6	0.066	5	12	0.9958	3.34	0.56	9.2	5
5.6	0.31	0.37	1.4	0.074	12	96	0.9954	3.32	0.58	9.2	5
8.8	0.66	0.26	1.7	0.074	4	23	0.9971	3.15	0.74	9.2	5
6.6	0.52	0.04	2.2	0.069	8	15	0.9956	3.4	0.63	9.4	6
6.6	0.5	0.04	2.1	0.068	6	14	0.9955	3.39	0.64	9.4	6
8.6	0.38	0.36	3	0.081	30	119	0.997	3.2	0.56	9.4	5
7.6	0.51	0.15	2.8	0.11	33	73	0.9955	3.17	0.63	10.2	6
7.7	0.62	0.04	3.8	0.084	25	45	0.9978	3.34	0.53	9.5	5
10.2	0.42	0.57	3.4	0.07	4	10	0.9971	3.04	0.63	9.6	5
7.5	0.63	0.12	5.1	0.111	50	110	0.9983	3.26	0.77	9.4	5
7.8	0.59	0.18	2.3	0.076	17	54	0.9975	3.43	0.59	10	5
7.3	0.39	0.31	2.4	0.074	9	46	0.9962	3.41	0.54	9.4	6
8.8	0.4	0.4	2.2	0.079	19	52	0.998	3.44	0.64	9.2	5
7.7	0.69	0.49	1.8	0.115	20	112	0.9968	3.21	0.71	9.3	5
7.5	0.52	0.16	1.9	0.085	12	35	0.9968	3.38	0.62	9.5	7
7	0.735	0.05	2	0.081	13	54	0.9966	3.39	0.57	9.8	5
7.2	0.725	0.05	4.65	0.086	4	11	0.9962	3.41	0.39	10.9	5
7.2	0.725	0.05	4.65	0.086	4	11	0.9962	3.41	0.39	10.9	5
7.5	0.52	0.11	1.5	0.079	11	39	0.9968	3.42	0.58	9.6	5
6.6	0.705	0.07	1.6	0.076	6	15	0.9962	3.44	0.58	10.7	5
9.3	0.32	0.57	2	0.074	27	65	0.9969	3.28	0.79	10.7	5
8	0.705	0.05	1.9	0.074	8	19	0.9962	3.34	0.95	10.5	6
7.7	0.63	0.08	1.9	0.076	15	27	0.9967	3.32	0.54	9.5	6
7.7	0.67	0.23	2.1	0.088	17	96	0.9962	3.32	0.48	9.5	5
7.7	0.69	0.22	1.9	0.084	18	94	0.9961	3.31	0.48	9.5	5
8.3	0.675	0.26	2.1	0.084	11	43	0.9976	3.31	0.53	9.2	4
9.7	0.32	0.54	2.5	0.094	28	83	0.9984	3.28	0.82	9.6	5
8.8	0.41	0.64	2.2	0.093	9	42	0.9986	3.54	0.66	10.5	5
8.8	0.41	0.64	2.2	0.093	9	42	0.9986	3.54	0.66	10.5	5
6.8	0.785	0	2.4	0.104	14	30	0.9966	3.52	0.55	10.7	6
6.7	0.75	0.12	2	0.086	12	80	0.9958	3.38	0.52	10.1	5
8.3	0.625	0.2	1.5	0.08	27	119	0.9972	3.16	1.12	9.1	4
6.2	0.45	0.2	1.6	0.069	3	15	0.9958	3.41	0.56	9.2	5
7.8	0.43	0.7	1.9	0.464	22	67	0.9974	3.13	1.28	9.4	5
7.4	0.5	0.47	2	0.086	21	73	0.997	3.36	0.57	9.1	5
7.3	0.67	0.26	1.8	0.401	16	51	0.9969	3.16	1.14	9.4	5
6.3	0.3	0.48	1.8	0.069	18	61	0.9959	3.44	0.78	10.3	6
6.9	0.55	0.15	2.2	0.076	19	40	0.9961	3.41	0.59	10.1	5
8.6	0.49	0.28	1.9	0.11	20	136	0.9972	2.93	1.95	9.9	6
7.7	0.49	0.26	1.9	0.062	9	31	0.9966	3.39	0.64	9.6	5
9.3	0.39	0.44	2.1	0.107	34	125	0.9978	3.14	1.22	9.5	5
7	0.62	0.08	1.8	0.076	8	24	0.9978	3.48	0.53	9	5
7.9	0.52	0.26	1.9	0.079	42	140	0.9964	3.23	0.54	9.5	5
8.6	0.49	0.28	1.9	0.11	20	136	0.9972	2.93	1.95	9.9	6
8.6	0.49	0.29	2	0.11	19	133	0.9972	2.93	1.98	9.8	5
7.7	0.49	0.26	1.9	0.062	9	31	0.9966	3.39	0.64	9.6	5
5	1.02	0.04	1.4	0.045	41	85	0.9938	3.75	0.48	10.5	4
4.7	0.6	0.17	2.3	0.058	17	106	0.9932	3.85	0.6	12.9	6
6.8	0.775	0	3	0.102	8	23	0.9965	3.45	0.56	10.7	5
7	0.5	0.25	2	0.07	3	22	0.9963	3.25	0.63	9.2	5
7.6	0.9	0.06	2.5	0.079	5	10	0.9967	3.39	0.56	9.8	5
8.1	0.545	0.18	1.9	0.08	13	35	0.9972	3.3	0.59	9	6
8.3	0.61	0.3	2.1	0.084	11	50	0.9972	3.4	0.61	10.2	6
7.8	0.5	0.3	1.9	0.075	8	22	0.9959	3.31	0.56	10.4	6
8.1	0.545	0.18	1.9	0.08	13	35	0.9972	3.3	0.59	9	6
8.1	0.575	0.22	2.1	0.077	12	65	0.9967	3.29	0.51	9.2	5
7.2	0.49	0.24	2.2	0.07	5	36	0.996	3.33	0.48	9.4	5
8.1	0.575	0.22	2.1	0.077	12	65	0.9967	3.29	0.51	9.2	5
7.8	0.41	0.68	1.7	0.467	18	69	0.9973	3.08	1.31	9.3	5
6.2	0.63	0.31	1.7	0.088	15	64	0.9969	3.46	0.79	9.3	5
8	0.33	0.53	2.5	0.091	18	80	0.9976	3.37	0.8	9.6	6
8.1	0.785	0.52	2	0.122	37	153	0.9969	3.21	0.69	9.3	5
7.8	0.56	0.19	1.8	0.104	12	47	0.9964	3.19	0.93	9.5	5
8.4	0.62	0.09	2.2	0.084	11	108	0.9964	3.15	0.66	9.8	5
8.4	0.6	0.1	2.2	0.085	14	111	0.9964	3.15	0.66	9.8	5
10.1	0.31	0.44	2.3	0.08	22	46	0.9988	3.32	0.67	9.7	6
7.8	0.56	0.19	1.8	0.104	12	47	0.9964	3.19	0.93	9.5	5
9.4	0.4	0.31	2.2	0.09	13	62	0.9966	3.07	0.63	10.5	6
8.3	0.54	0.28	1.9	0.077	11	40	0.9978	3.39	0.61	10	6
7.8	0.56	0.12	2	0.082	7	28	0.997	3.37	0.5	9.4	6
8.8	0.55	0.04	2.2	0.119	14	56	0.9962	3.21	0.6	10.9	6
7	0.69	0.08	1.8	0.097	22	89	0.9959	3.34	0.54	9.2	6
7.3	1.07	0.09	1.7	0.178	10	89	0.9962	3.3	0.57	9	5
8.8	0.55	0.04	2.2	0.119	14	56	0.9962	3.21	0.6	10.9	6
7.3	0.695	0	2.5	0.075	3	13	0.998	3.49	0.52	9.2	5
8	0.71	0	2.6	0.08	11	34	0.9976	3.44	0.53	9.5	5
7.8	0.5	0.17	1.6	0.082	21	102	0.996	3.39	0.48	9.5	5
9	0.62	0.04	1.9	0.146	27	90	0.9984	3.16	0.7	9.4	5
8.2	1.33	0	1.7	0.081	3	12	0.9964	3.53	0.49	10.9	5
8.1	1.33	0	1.8	0.082	3	12	0.9964	3.54	0.48	10.9	5
8	0.59	0.16	1.8	0.065	3	16	0.9962	3.42	0.92	10.5	7
6.1	0.38	0.15	1.8	0.072	6	19	0.9955	3.42	0.57	9.4	5
8	0.745	0.56	2	0.118	30	134	0.9968	3.24	0.66	9.4	5
5.6	0.5	0.09	2.3	0.049	17	99	0.9937	3.63	0.63	13	5
5.6	0.5	0.09	2.3	0.049	17	99	0.9937	3.63	0.63	13	5
6.6	0.5	0.01	1.5	0.06	17	26	0.9952	3.4	0.58	9.8	6
7.9	1.04	0.05	2.2	0.084	13	29	0.9959	3.22	0.55	9.9	6
8.4	0.745	0.11	1.9	0.09	16	63	0.9965	3.19	0.82	9.6	5
8.3	0.715	0.15	1.8	0.089	10	52	0.9968	3.23	0.77	9.5	5
7.2	0.415	0.36	2	0.081	13	45	0.9972	3.48	0.64	9.2	5
7.8	0.56	0.19	2.1	0.081	15	105	0.9962	3.33	0.54	9.5	5
7.8	0.56	0.19	2	0.081	17	108	0.9962	3.32	0.54	9.5	5
8.4	0.745	0.11	1.9	0.09	16	63	0.9965	3.19	0.82	9.6	5
8.3	0.715	0.15	1.8	0.089	10	52	0.9968	3.23	0.77	9.5	5
5.2	0.34	0	1.8	0.05	27	63	0.9916	3.68	0.79	14	6
6.3	0.39	0.08	1.7	0.066	3	20	0.9954	3.34	0.58	9.4	5
5.2	0.34	0	1.8	0.05	27	63	0.9916	3.68	0.79	14	6
8.1	0.67	0.55	1.8	0.117	32	141	0.9968	3.17	0.62	9.4	5
5.8	0.68	0.02	1.8	0.087	21	94	0.9944	3.54	0.52	10	5
7.6	0.49	0.26	1.6	0.236	10	88	0.9968	3.11	0.8	9.3	5
6.9	0.49	0.1	2.3	0.074	12	30	0.9959	3.42	0.58	10.2	6
8.2	0.4	0.44	2.8	0.089	11	43	0.9975	3.53	0.61	10.5	6
7.3	0.33	0.47	2.1	0.077	5	11	0.9958	3.33	0.53	10.3	6
9.2	0.52	1	3.4	0.61	32	69	0.9996	2.74	2	9.4	4
7.5	0.6	0.03	1.8	0.095	25	99	0.995	3.35	0.54	10.1	5
7.5	0.6	0.03	1.8	0.095	25	99	0.995	3.35	0.54	10.1	5
7.1	0.43	0.42	5.5	0.07	29	129	0.9973	3.42	0.72	10.5	5
7.1	0.43	0.42	5.5	0.071	28	128	0.9973	3.42	0.71	10.5	5
7.1	0.43	0.42	5.5	0.07	29	129	0.9973	3.42	0.72	10.5	5
7.1	0.43	0.42	5.5	0.071	28	128	0.9973	3.42	0.71	10.5	5
7.1	0.68	0	2.2	0.073	12	22	0.9969	3.48	0.5	9.3	5
6.8	0.6	0.18	1.9	0.079	18	86	0.9968	3.59	0.57	9.3	6
7.6	0.95	0.03	2	0.09	7	20	0.9959	3.2	0.56	9.6	5
7.6	0.68	0.02	1.3	0.072	9	20	0.9965	3.17	1.08	9.2	4
7.8	0.53	0.04	1.7	0.076	17	31	0.9964	3.33	0.56	10	6
7.4	0.6	0.26	7.3	0.07	36	121	0.9982	3.37	0.49	9.4	5
7.3	0.59	0.26	7.2	0.07	35	121	0.9981	3.37	0.49	9.4	5
7.8	0.63	0.48	1.7	0.1	14	96	0.9961	3.19	0.62	9.5	5
6.8	0.64	0.1	2.1	0.085	18	101	0.9956	3.34	0.52	10.2	5
7.3	0.55	0.03	1.6	0.072	17	42	0.9956	3.37	0.48	9	4
6.8	0.63	0.07	2.1	0.089	11	44	0.9953	3.47	0.55	10.4	6
7.5	0.705	0.24	1.8	0.36	15	63	0.9964	3	1.59	9.5	5
7.9	0.885	0.03	1.8	0.058	4	8	0.9972	3.36	0.33	9.1	4
8	0.42	0.17	2	0.073	6	18	0.9972	3.29	0.61	9.2	6
8	0.42	0.17	2	0.073	6	18	0.9972	3.29	0.61	9.2	6
7.4	0.62	0.05	1.9	0.068	24	42	0.9961	3.42	0.57	11.5	6
7.3	0.38	0.21	2	0.08	7	35	0.9961	3.33	0.47	9.5	5
6.9	0.5	0.04	1.5	0.085	19	49	0.9958	3.35	0.78	9.5	5
7.3	0.38	0.21	2	0.08	7	35	0.9961	3.33	0.47	9.5	5
7.5	0.52	0.42	2.3	0.087	8	38	0.9972	3.58	0.61	10.5	6
7	0.805	0	2.5	0.068	7	20	0.9969	3.48	0.56	9.6	5
8.8	0.61	0.14	2.4	0.067	10	42	0.9969	3.19	0.59	9.5	5
8.8	0.61	0.14	2.4	0.067	10	42	0.9969	3.19	0.59	9.5	5
8.9	0.61	0.49	2	0.27	23	110	0.9972	3.12	1.02	9.3	5
7.2	0.73	0.02	2.5	0.076	16	42	0.9972	3.44	0.52	9.3	5
6.8	0.61	0.2	1.8	0.077	11	65	0.9971	3.54	0.58	9.3	5
6.7	0.62	0.21	1.9	0.079	8	62	0.997	3.52	0.58	9.3	6
8.9	0.31	0.57	2	0.111	26	85	0.9971	3.26	0.53	9.7	5
7.4	0.39	0.48	2	0.082	14	67	0.9972	3.34	0.55	9.2	5
7.7	0.705	0.1	2.6	0.084	9	26	0.9976	3.39	0.49	9.7	5
7.9	0.5	0.33	2	0.084	15	143	0.9968	3.2	0.55	9.5	5
7.9	0.49	0.32	1.9	0.082	17	144	0.9968	3.2	0.55	9.5	5
8.2	0.5	0.35	2.9	0.077	21	127	0.9976	3.23	0.62	9.4	5
6.4	0.37	0.25	1.9	0.074	21	49	0.9974	3.57	0.62	9.8	6
6.8	0.63	0.12	3.8	0.099	16	126	0.9969	3.28	0.61	9.5	5
7.6	0.55	0.21	2.2	0.071	7	28	0.9964	3.28	0.55	9.7	5
7.6	0.55	0.21	2.2	0.071	7	28	0.9964	3.28	0.55	9.7	5
7.8	0.59	0.33	2	0.074	24	120	0.9968	3.25	0.54	9.4	5
7.3	0.58	0.3	2.4	0.074	15	55	0.9968	3.46	0.59	10.2	5
11.5	0.3	0.6	2	0.067	12	27	0.9981	3.11	0.97	10.1	6
5.4	0.835	0.08	1.2	0.046	13	93	0.9924	3.57	0.85	13	7
6.9	1.09	0.06	2.1	0.061	12	31	0.9948	3.51	0.43	11.4	4

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	6 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 & 6 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=154567&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]6 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=154567&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=154567&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	6 seconds
R Server	'Gwilym Jenkins' @ jenkins.wessa.net

10-Fold Cross Validation
	Prediction (training)			Prediction (testing)
Actual	C1	C2	CV	C1	C2	CV
C1	631	307	0.6727	61	41	0.598
C2	229	638	0.7359	24	69	0.7419
Overall	-	-	0.703	-	-	0.6667

\begin{tabular}{lllllllll}
\hline
10-Fold Cross Validation \tabularnewline
 & Prediction (training) & Prediction (testing) \tabularnewline
Actual & C1 & C2 & CV & C1 & C2 & CV \tabularnewline
C1 & 631 & 307 & 0.6727 & 61 & 41 & 0.598 \tabularnewline
C2 & 229 & 638 & 0.7359 & 24 & 69 & 0.7419 \tabularnewline
Overall & - & - & 0.703 & - & - & 0.6667 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=154567&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]631[/C][C]307[/C][C]0.6727[/C][C]61[/C][C]41[/C][C]0.598[/C][/ROW]
[ROW][C]C2[/C][C]229[/C][C]638[/C][C]0.7359[/C][C]24[/C][C]69[/C][C]0.7419[/C][/ROW]
[ROW][C]Overall[/C][C]-[/C][C]-[/C][C]0.703[/C][C]-[/C][C]-[/C][C]0.6667[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=154567&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=154567&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	631	307	0.6727	61	41	0.598
C2	229	638	0.7359	24	69	0.7419
Overall	-	-	0.703	-	-	0.6667

Confusion Matrix (predicted in columns / actuals in rows)
	C1	C2
C1	70	34
C2	25	71

\begin{tabular}{lllllllll}
\hline
Confusion Matrix (predicted in columns / actuals in rows) \tabularnewline
 & C1 & C2 \tabularnewline
C1 & 70 & 34 \tabularnewline
C2 & 25 & 71 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=154567&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]70[/C][C]34[/C][/ROW]
[ROW][C]C2[/C][C]25[/C][C]71[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=154567&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=154567&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	70	34
C2	25	71

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):

Parameters (R input):

par1 = 11 ; 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