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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 17:12:59 -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/t1323814446rk7bkm4jzba0xg2.htm/, Retrieved Thu, 02 May 2024 22:37:15 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=154756, Retrieved Thu, 02 May 2024 22:37:15 +0000

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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 18:59:57] [b98453cac15ba1066b407e146608df68]
- R PD  [Recursive Partitioning (Regression Trees)] [] [2011-12-13 16:53:18] [a1957df0bc37aec4aa3c994e6a08412c]
-   PD      [Recursive Partitioning (Regression Trees)] [] [2011-12-13 22:12:59] [fdaf10f0fcbe7b8f79ecbd42ec74e6ad] [Current]
-   PD        [Recursive Partitioning (Regression Trees)] [] [2011-12-14 00:35:12] [a1957df0bc37aec4aa3c994e6a08412c] 
-   P           [Recursive Partitioning (Regression Trees)] [] [2011-12-14 00:44:01] [a1957df0bc37aec4aa3c994e6a08412c] 
-   P             [Recursive Partitioning (Regression Trees)] [] [2011-12-14 00:54:43] [a1957df0bc37aec4aa3c994e6a08412c] 
-   PD        [Recursive Partitioning (Regression Trees)] [] [2011-12-19 12:24:08] [a1957df0bc37aec4aa3c994e6a08412c] 
-   P           [Recursive Partitioning (Regression Trees)] [] [2011-12-19 12:51:51] [a1957df0bc37aec4aa3c994e6a08412c] 

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2981,85	2819,19	11394,84	10539,51	10407	44,23
3080,58	2892,56	11545,71	10723,78	10463	45,85
3106,22	2866,08	11809,38	10682,06	10556	53,38
3119,31	2817,41	11395,64	10283,19	10646	53,26
3061,26	2934,75	11082,38	10377,18	10702	51,8
3097,31	3036,54	11402,75	10486,64	11353	55,3
3161,69	3139,5	11716,87	10545,38	11346	57,81
3257,16	3114,31	12204,98	10554,27	11451	63,96
3277,01	3261,3	12986,62	10532,54	11964	63,77
3295,32	3201,79	13392,79	10324,31	12574	59,15
3363,99	3264,53	14368,05	10695,25	13031	56,12
3494,17	3349,1	15650,83	10827,81	13812	57,42
3667,03	3446,17	16102,64	10872,48	14544	63,52
3813,06	3469,48	16187,64	10971,19	14931	61,71
3917,96	3507,13	16311,54	11145,65	14886	63,01
3895,51	3536,2	17232,97	11234,68	16005	68,18
3801,06	3359,05	16397,83	11333,88	17064	72,03
3570,12	3378,85	14990,31	10997,97	15168	69,75
3701,61	3449,15	15147,55	11036,89	16050	74,41
3862,27	3522,89	15786,78	11257,35	15839	74,33
3970,1	3551,04	15934,09	11533,59	15137	64,24
4138,52	3669,15	16519,44	11963,12	14954	60,03
4199,75	3602	16101,07	12185,15	15648	59,44
4290,89	3697,22	16775,08	12377,62	15305	62,5
4443,91	3760,9	17286,32	12512,89	15579	55,04
4502,64	3665,08	17741,23	12631,48	16348	58,34
4356,98	3708,8	17128,37	12268,53	15928	61,92
4591,27	3858,21	17460,53	12754,8	16171	67,65
4696,96	3933,16	17611,14	13407,75	15937	67,68
4621,4	3946,98	18001,37	13480,21	15713	70,3
4562,84	3794,29	17974,77	13673,28	15594	75,26
4202,52	3765,56	16460,95	13239,71	15683	71,44
4296,49	3820,33	16235,39	13557,69	16438	76,36
4435,23	3885,12	16903,36	13901,28	17032	81,71
4105,18	3752,67	15543,76	13200,58	17696	92,6
4116,68	3683,79	15532,18	13406,97	17745	90,6
3844,49	3240,75	13731,31	12538,12	19394	92,23
3720,98	3188,82	13547,84	12419,57	20148	94,09
3674,4	3017,98	12602,93	12193,88	20108	102,79
3857,62	3237,2	13357,7	12656,63	18584	109,65
3801,06	3182,53	13995,33	12812,48	18441	124,05
3504,37	2906,42	14084,6	12056,67	18391	132,69
3032,6	2881,35	13168,91	11322,38	19178	135,81
3047,03	2915,64	12989,35	11530,75	18079	116,07
2962,34	2635,13	12123,53	11114,08	18483	101,42
2197,82	2331,43	9117,03	9181,73	19644	75,73
2014,45	2159,04	8531,45	8614,55	19195	55,48
1862,83	NA	8460,94	8595,56	19650	43,8
1905,41	1983,48	8331,49	8396,2	20830	45,29
1810,99	1770,41	7694,78	7690,5	23595	44,01
1670,07	1815,99	7764,58	7235,47	22937	47,48
1864,44	2026,97	8767,96	7992,12	21814	51,07
2052,02	2124,81	9304,43	8398,37	21928	57,84
2029,6	2098,28	9810,31	8593	21777	69,04
2070,83	2291,39	9691,12	8679,75	21383	65,61
2293,41	2401,57	10430,35	9374,63	21467	72,87
2443,27	2453,89	10302,87	9634,97	22052	68,41
2513,17	2409,53	10066,24	9857,34	22680	73,25
2466,92	2432,45	9633,83	10238,83	24320	77,43
2502,66	2585,34	10169,02	10433,44	24977	75,28
2539,91	2478,51	10661,62	10471,24	25204	77,33
2482,6	2470,18	10175,13	10214,51	25739	74,31
2626,15	2629,16	10671,49	10677,52	26434	79,7
2656,32	2541,22	11139,77	11052,15	27525	85,47
2446,66	2397,18	10103,98	10500,19	30695	77,98
2467,38	2359,66	9786,05	10159,27	32436	75,69
2462,32	2476,2	9456,84	10222,24	30160	75,2
2504,58	2449,57	9268,24	10350,4	30236	77,21
2579,39	2482,18	9346,72	10598,07	31293	77,85
2649,24	2542,76	9455,09	11044,49	31077	83,53
2636,87	2477,63	9797,18	11198,31	32226	85,99
2613,94	2586,46	10254,46	11465,26	33865	91,77
2634,01	2654,47	10449,53	11802,37	32810	96,59
2711,94	2713,48	10622,27	12190	32242	103,57
2646,43	2582,9	9852,45	12081,48	32700	114,46
2717,79	2661,37	9644,62	12434,93	32819	122,54
2701,54	2631,87	9650,78	12579,99	33947	115,08
2572,98	2561,37	9541,53	12097,31	34148	113,93
2488,92	2510,85	9996,68	12512,33	35261	116,29
2204,91	2238,24	9072,94	11326,62	39506	110,12
2123,99	2159,7	8695,42	11175,45	41591	110,86
2149,1	2318	8733,56	11515,93	39148	108,53

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	4 seconds
R Server	'George Udny Yule' @ yule.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 & 4 seconds \tabularnewline
R Server & 'George Udny Yule' @ yule.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=154756&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]4 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'George Udny Yule' @ yule.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=154756&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=154756&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	4 seconds
R Server	'George Udny Yule' @ yule.wessa.net

Goodness of Fit
Correlation	0.977
R-squared	0.9545
RMSE	175.6045

\begin{tabular}{lllllllll}
\hline
Goodness of Fit \tabularnewline
Correlation & 0.977 \tabularnewline
R-squared & 0.9545 \tabularnewline
RMSE & 175.6045 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=154756&T=1

[TABLE]
[ROW][C]Goodness of Fit[/C][/ROW]
[ROW][C]Correlation[/C][C]0.977[/C][/ROW]
[ROW][C]R-squared[/C][C]0.9545[/C][/ROW]
[ROW][C]RMSE[/C][C]175.6045[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=154756&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=154756&T=1

As an alternative you can also use a QR Code:

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

Goodness of Fit
Correlation	0.977
R-squared	0.9545
RMSE	175.6045

Actuals, Predictions, and Residuals
#	Actuals	Forecasts	Residuals
1	2981.85	2927.40444444444	54.4455555555555
2	3080.58	2927.40444444444	153.175555555556
3	3106.22	2927.40444444444	178.815555555555
4	3119.31	2927.40444444444	191.905555555556
5	3061.26	3225.12285714286	-163.862857142857
6	3097.31	3225.12285714286	-127.812857142857
7	3161.69	3225.12285714286	-63.4328571428573
8	3257.16	3225.12285714286	32.0371428571425
9	3277.01	3225.12285714286	51.8871428571429
10	3295.32	3694.41375	-399.09375
11	3363.99	3694.41375	-330.42375
12	3494.17	3694.41375	-200.24375
13	3667.03	3694.41375	-27.38375
14	3813.06	3694.41375	118.64625
15	3917.96	3694.41375	223.54625
16	3895.51	3694.41375	201.09625
17	3801.06	3694.41375	106.64625
18	3570.12	3694.41375	-124.29375
19	3701.61	3694.41375	7.19624999999996
20	3862.27	3694.41375	167.85625
21	3970.1	4345.71	-375.61
22	4138.52	4345.71	-207.19
23	4199.75	4345.71	-145.96
24	4290.89	4345.71	-54.8199999999997
25	4443.91	4345.71	98.1999999999998
26	4502.64	4345.71	156.93
27	4356.98	4345.71	11.2699999999995
28	4591.27	4345.71	245.56
29	4696.96	4345.71	351.25
30	4621.4	4345.71	275.69
31	4562.84	4345.71	217.13
32	4202.52	4345.71	-143.19
33	4296.49	4345.71	-49.2200000000003
34	4435.23	4345.71	89.5199999999995
35	4105.18	4345.71	-240.53
36	4116.68	4345.71	-229.03
37	3844.49	3694.41375	150.07625
38	3720.98	3694.41375	26.5662499999999
39	3674.4	3225.12285714286	449.277142857143
40	3857.62	3694.41375	163.20625
41	3801.06	3694.41375	106.64625
42	3504.37	3694.41375	-190.04375
43	3032.6	2927.40444444444	105.195555555556
44	3047.03	3225.12285714286	-178.092857142857
45	2962.34	2927.40444444444	34.9355555555558
46	2197.82	2007.8025	190.0175
47	2014.45	2007.8025	6.64750000000004
48	1862.83	2373.9875	-511.1575
49	1905.41	2007.8025	-102.3925
50	1810.99	2007.8025	-196.8125
51	1670.07	2007.8025	-337.7325
52	1864.44	2007.8025	-143.3625
53	2052.02	2007.8025	44.2175
54	2029.6	2007.8025	21.7974999999999
55	2070.83	2007.8025	63.0274999999999
56	2293.41	2373.9875	-80.5775000000003
57	2443.27	2373.9875	69.2824999999998
58	2513.17	2373.9875	139.1825
59	2466.92	2373.9875	92.9324999999999
60	2502.66	2583.25642857143	-80.5964285714285
61	2539.91	2583.25642857143	-43.3464285714285
62	2482.6	2373.9875	108.6125
63	2626.15	2583.25642857143	42.8935714285717
64	2656.32	2583.25642857143	73.0635714285718
65	2446.66	2583.25642857143	-136.596428571429
66	2467.38	2373.9875	93.3925
67	2462.32	2373.9875	88.3325
68	2504.58	2583.25642857143	-78.6764285714285
69	2579.39	2583.25642857143	-3.86642857142851
70	2649.24	2583.25642857143	65.9835714285714
71	2636.87	2583.25642857143	53.6135714285715
72	2613.94	2583.25642857143	30.6835714285717
73	2634.01	2927.40444444444	-293.394444444444
74	2711.94	2927.40444444444	-215.464444444444
75	2646.43	2583.25642857143	63.1735714285714
76	2717.79	2927.40444444444	-209.614444444444
77	2701.54	2583.25642857143	118.283571428572
78	2572.98	2583.25642857143	-10.2764285714284
79	2488.92	2583.25642857143	-94.3364285714283
80	2204.91	2007.8025	197.1075
81	2123.99	2007.8025	116.1875
82	2149.1	2007.8025	141.2975

\begin{tabular}{lllllllll}
\hline
Actuals, Predictions, and Residuals \tabularnewline
# & Actuals & Forecasts & Residuals \tabularnewline
1 & 2981.85 & 2927.40444444444 & 54.4455555555555 \tabularnewline
2 & 3080.58 & 2927.40444444444 & 153.175555555556 \tabularnewline
3 & 3106.22 & 2927.40444444444 & 178.815555555555 \tabularnewline
4 & 3119.31 & 2927.40444444444 & 191.905555555556 \tabularnewline
5 & 3061.26 & 3225.12285714286 & -163.862857142857 \tabularnewline
6 & 3097.31 & 3225.12285714286 & -127.812857142857 \tabularnewline
7 & 3161.69 & 3225.12285714286 & -63.4328571428573 \tabularnewline
8 & 3257.16 & 3225.12285714286 & 32.0371428571425 \tabularnewline
9 & 3277.01 & 3225.12285714286 & 51.8871428571429 \tabularnewline
10 & 3295.32 & 3694.41375 & -399.09375 \tabularnewline
11 & 3363.99 & 3694.41375 & -330.42375 \tabularnewline
12 & 3494.17 & 3694.41375 & -200.24375 \tabularnewline
13 & 3667.03 & 3694.41375 & -27.38375 \tabularnewline
14 & 3813.06 & 3694.41375 & 118.64625 \tabularnewline
15 & 3917.96 & 3694.41375 & 223.54625 \tabularnewline
16 & 3895.51 & 3694.41375 & 201.09625 \tabularnewline
17 & 3801.06 & 3694.41375 & 106.64625 \tabularnewline
18 & 3570.12 & 3694.41375 & -124.29375 \tabularnewline
19 & 3701.61 & 3694.41375 & 7.19624999999996 \tabularnewline
20 & 3862.27 & 3694.41375 & 167.85625 \tabularnewline
21 & 3970.1 & 4345.71 & -375.61 \tabularnewline
22 & 4138.52 & 4345.71 & -207.19 \tabularnewline
23 & 4199.75 & 4345.71 & -145.96 \tabularnewline
24 & 4290.89 & 4345.71 & -54.8199999999997 \tabularnewline
25 & 4443.91 & 4345.71 & 98.1999999999998 \tabularnewline
26 & 4502.64 & 4345.71 & 156.93 \tabularnewline
27 & 4356.98 & 4345.71 & 11.2699999999995 \tabularnewline
28 & 4591.27 & 4345.71 & 245.56 \tabularnewline
29 & 4696.96 & 4345.71 & 351.25 \tabularnewline
30 & 4621.4 & 4345.71 & 275.69 \tabularnewline
31 & 4562.84 & 4345.71 & 217.13 \tabularnewline
32 & 4202.52 & 4345.71 & -143.19 \tabularnewline
33 & 4296.49 & 4345.71 & -49.2200000000003 \tabularnewline
34 & 4435.23 & 4345.71 & 89.5199999999995 \tabularnewline
35 & 4105.18 & 4345.71 & -240.53 \tabularnewline
36 & 4116.68 & 4345.71 & -229.03 \tabularnewline
37 & 3844.49 & 3694.41375 & 150.07625 \tabularnewline
38 & 3720.98 & 3694.41375 & 26.5662499999999 \tabularnewline
39 & 3674.4 & 3225.12285714286 & 449.277142857143 \tabularnewline
40 & 3857.62 & 3694.41375 & 163.20625 \tabularnewline
41 & 3801.06 & 3694.41375 & 106.64625 \tabularnewline
42 & 3504.37 & 3694.41375 & -190.04375 \tabularnewline
43 & 3032.6 & 2927.40444444444 & 105.195555555556 \tabularnewline
44 & 3047.03 & 3225.12285714286 & -178.092857142857 \tabularnewline
45 & 2962.34 & 2927.40444444444 & 34.9355555555558 \tabularnewline
46 & 2197.82 & 2007.8025 & 190.0175 \tabularnewline
47 & 2014.45 & 2007.8025 & 6.64750000000004 \tabularnewline
48 & 1862.83 & 2373.9875 & -511.1575 \tabularnewline
49 & 1905.41 & 2007.8025 & -102.3925 \tabularnewline
50 & 1810.99 & 2007.8025 & -196.8125 \tabularnewline
51 & 1670.07 & 2007.8025 & -337.7325 \tabularnewline
52 & 1864.44 & 2007.8025 & -143.3625 \tabularnewline
53 & 2052.02 & 2007.8025 & 44.2175 \tabularnewline
54 & 2029.6 & 2007.8025 & 21.7974999999999 \tabularnewline
55 & 2070.83 & 2007.8025 & 63.0274999999999 \tabularnewline
56 & 2293.41 & 2373.9875 & -80.5775000000003 \tabularnewline
57 & 2443.27 & 2373.9875 & 69.2824999999998 \tabularnewline
58 & 2513.17 & 2373.9875 & 139.1825 \tabularnewline
59 & 2466.92 & 2373.9875 & 92.9324999999999 \tabularnewline
60 & 2502.66 & 2583.25642857143 & -80.5964285714285 \tabularnewline
61 & 2539.91 & 2583.25642857143 & -43.3464285714285 \tabularnewline
62 & 2482.6 & 2373.9875 & 108.6125 \tabularnewline
63 & 2626.15 & 2583.25642857143 & 42.8935714285717 \tabularnewline
64 & 2656.32 & 2583.25642857143 & 73.0635714285718 \tabularnewline
65 & 2446.66 & 2583.25642857143 & -136.596428571429 \tabularnewline
66 & 2467.38 & 2373.9875 & 93.3925 \tabularnewline
67 & 2462.32 & 2373.9875 & 88.3325 \tabularnewline
68 & 2504.58 & 2583.25642857143 & -78.6764285714285 \tabularnewline
69 & 2579.39 & 2583.25642857143 & -3.86642857142851 \tabularnewline
70 & 2649.24 & 2583.25642857143 & 65.9835714285714 \tabularnewline
71 & 2636.87 & 2583.25642857143 & 53.6135714285715 \tabularnewline
72 & 2613.94 & 2583.25642857143 & 30.6835714285717 \tabularnewline
73 & 2634.01 & 2927.40444444444 & -293.394444444444 \tabularnewline
74 & 2711.94 & 2927.40444444444 & -215.464444444444 \tabularnewline
75 & 2646.43 & 2583.25642857143 & 63.1735714285714 \tabularnewline
76 & 2717.79 & 2927.40444444444 & -209.614444444444 \tabularnewline
77 & 2701.54 & 2583.25642857143 & 118.283571428572 \tabularnewline
78 & 2572.98 & 2583.25642857143 & -10.2764285714284 \tabularnewline
79 & 2488.92 & 2583.25642857143 & -94.3364285714283 \tabularnewline
80 & 2204.91 & 2007.8025 & 197.1075 \tabularnewline
81 & 2123.99 & 2007.8025 & 116.1875 \tabularnewline
82 & 2149.1 & 2007.8025 & 141.2975 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=154756&T=2

[TABLE]
[ROW][C]Actuals, Predictions, and Residuals[/C][/ROW]
[ROW][C]#[/C][C]Actuals[/C][C]Forecasts[/C][C]Residuals[/C][/ROW]
[ROW][C]1[/C][C]2981.85[/C][C]2927.40444444444[/C][C]54.4455555555555[/C][/ROW]
[ROW][C]2[/C][C]3080.58[/C][C]2927.40444444444[/C][C]153.175555555556[/C][/ROW]
[ROW][C]3[/C][C]3106.22[/C][C]2927.40444444444[/C][C]178.815555555555[/C][/ROW]
[ROW][C]4[/C][C]3119.31[/C][C]2927.40444444444[/C][C]191.905555555556[/C][/ROW]
[ROW][C]5[/C][C]3061.26[/C][C]3225.12285714286[/C][C]-163.862857142857[/C][/ROW]
[ROW][C]6[/C][C]3097.31[/C][C]3225.12285714286[/C][C]-127.812857142857[/C][/ROW]
[ROW][C]7[/C][C]3161.69[/C][C]3225.12285714286[/C][C]-63.4328571428573[/C][/ROW]
[ROW][C]8[/C][C]3257.16[/C][C]3225.12285714286[/C][C]32.0371428571425[/C][/ROW]
[ROW][C]9[/C][C]3277.01[/C][C]3225.12285714286[/C][C]51.8871428571429[/C][/ROW]
[ROW][C]10[/C][C]3295.32[/C][C]3694.41375[/C][C]-399.09375[/C][/ROW]
[ROW][C]11[/C][C]3363.99[/C][C]3694.41375[/C][C]-330.42375[/C][/ROW]
[ROW][C]12[/C][C]3494.17[/C][C]3694.41375[/C][C]-200.24375[/C][/ROW]
[ROW][C]13[/C][C]3667.03[/C][C]3694.41375[/C][C]-27.38375[/C][/ROW]
[ROW][C]14[/C][C]3813.06[/C][C]3694.41375[/C][C]118.64625[/C][/ROW]
[ROW][C]15[/C][C]3917.96[/C][C]3694.41375[/C][C]223.54625[/C][/ROW]
[ROW][C]16[/C][C]3895.51[/C][C]3694.41375[/C][C]201.09625[/C][/ROW]
[ROW][C]17[/C][C]3801.06[/C][C]3694.41375[/C][C]106.64625[/C][/ROW]
[ROW][C]18[/C][C]3570.12[/C][C]3694.41375[/C][C]-124.29375[/C][/ROW]
[ROW][C]19[/C][C]3701.61[/C][C]3694.41375[/C][C]7.19624999999996[/C][/ROW]
[ROW][C]20[/C][C]3862.27[/C][C]3694.41375[/C][C]167.85625[/C][/ROW]
[ROW][C]21[/C][C]3970.1[/C][C]4345.71[/C][C]-375.61[/C][/ROW]
[ROW][C]22[/C][C]4138.52[/C][C]4345.71[/C][C]-207.19[/C][/ROW]
[ROW][C]23[/C][C]4199.75[/C][C]4345.71[/C][C]-145.96[/C][/ROW]
[ROW][C]24[/C][C]4290.89[/C][C]4345.71[/C][C]-54.8199999999997[/C][/ROW]
[ROW][C]25[/C][C]4443.91[/C][C]4345.71[/C][C]98.1999999999998[/C][/ROW]
[ROW][C]26[/C][C]4502.64[/C][C]4345.71[/C][C]156.93[/C][/ROW]
[ROW][C]27[/C][C]4356.98[/C][C]4345.71[/C][C]11.2699999999995[/C][/ROW]
[ROW][C]28[/C][C]4591.27[/C][C]4345.71[/C][C]245.56[/C][/ROW]
[ROW][C]29[/C][C]4696.96[/C][C]4345.71[/C][C]351.25[/C][/ROW]
[ROW][C]30[/C][C]4621.4[/C][C]4345.71[/C][C]275.69[/C][/ROW]
[ROW][C]31[/C][C]4562.84[/C][C]4345.71[/C][C]217.13[/C][/ROW]
[ROW][C]32[/C][C]4202.52[/C][C]4345.71[/C][C]-143.19[/C][/ROW]
[ROW][C]33[/C][C]4296.49[/C][C]4345.71[/C][C]-49.2200000000003[/C][/ROW]
[ROW][C]34[/C][C]4435.23[/C][C]4345.71[/C][C]89.5199999999995[/C][/ROW]
[ROW][C]35[/C][C]4105.18[/C][C]4345.71[/C][C]-240.53[/C][/ROW]
[ROW][C]36[/C][C]4116.68[/C][C]4345.71[/C][C]-229.03[/C][/ROW]
[ROW][C]37[/C][C]3844.49[/C][C]3694.41375[/C][C]150.07625[/C][/ROW]
[ROW][C]38[/C][C]3720.98[/C][C]3694.41375[/C][C]26.5662499999999[/C][/ROW]
[ROW][C]39[/C][C]3674.4[/C][C]3225.12285714286[/C][C]449.277142857143[/C][/ROW]
[ROW][C]40[/C][C]3857.62[/C][C]3694.41375[/C][C]163.20625[/C][/ROW]
[ROW][C]41[/C][C]3801.06[/C][C]3694.41375[/C][C]106.64625[/C][/ROW]
[ROW][C]42[/C][C]3504.37[/C][C]3694.41375[/C][C]-190.04375[/C][/ROW]
[ROW][C]43[/C][C]3032.6[/C][C]2927.40444444444[/C][C]105.195555555556[/C][/ROW]
[ROW][C]44[/C][C]3047.03[/C][C]3225.12285714286[/C][C]-178.092857142857[/C][/ROW]
[ROW][C]45[/C][C]2962.34[/C][C]2927.40444444444[/C][C]34.9355555555558[/C][/ROW]
[ROW][C]46[/C][C]2197.82[/C][C]2007.8025[/C][C]190.0175[/C][/ROW]
[ROW][C]47[/C][C]2014.45[/C][C]2007.8025[/C][C]6.64750000000004[/C][/ROW]
[ROW][C]48[/C][C]1862.83[/C][C]2373.9875[/C][C]-511.1575[/C][/ROW]
[ROW][C]49[/C][C]1905.41[/C][C]2007.8025[/C][C]-102.3925[/C][/ROW]
[ROW][C]50[/C][C]1810.99[/C][C]2007.8025[/C][C]-196.8125[/C][/ROW]
[ROW][C]51[/C][C]1670.07[/C][C]2007.8025[/C][C]-337.7325[/C][/ROW]
[ROW][C]52[/C][C]1864.44[/C][C]2007.8025[/C][C]-143.3625[/C][/ROW]
[ROW][C]53[/C][C]2052.02[/C][C]2007.8025[/C][C]44.2175[/C][/ROW]
[ROW][C]54[/C][C]2029.6[/C][C]2007.8025[/C][C]21.7974999999999[/C][/ROW]
[ROW][C]55[/C][C]2070.83[/C][C]2007.8025[/C][C]63.0274999999999[/C][/ROW]
[ROW][C]56[/C][C]2293.41[/C][C]2373.9875[/C][C]-80.5775000000003[/C][/ROW]
[ROW][C]57[/C][C]2443.27[/C][C]2373.9875[/C][C]69.2824999999998[/C][/ROW]
[ROW][C]58[/C][C]2513.17[/C][C]2373.9875[/C][C]139.1825[/C][/ROW]
[ROW][C]59[/C][C]2466.92[/C][C]2373.9875[/C][C]92.9324999999999[/C][/ROW]
[ROW][C]60[/C][C]2502.66[/C][C]2583.25642857143[/C][C]-80.5964285714285[/C][/ROW]
[ROW][C]61[/C][C]2539.91[/C][C]2583.25642857143[/C][C]-43.3464285714285[/C][/ROW]
[ROW][C]62[/C][C]2482.6[/C][C]2373.9875[/C][C]108.6125[/C][/ROW]
[ROW][C]63[/C][C]2626.15[/C][C]2583.25642857143[/C][C]42.8935714285717[/C][/ROW]
[ROW][C]64[/C][C]2656.32[/C][C]2583.25642857143[/C][C]73.0635714285718[/C][/ROW]
[ROW][C]65[/C][C]2446.66[/C][C]2583.25642857143[/C][C]-136.596428571429[/C][/ROW]
[ROW][C]66[/C][C]2467.38[/C][C]2373.9875[/C][C]93.3925[/C][/ROW]
[ROW][C]67[/C][C]2462.32[/C][C]2373.9875[/C][C]88.3325[/C][/ROW]
[ROW][C]68[/C][C]2504.58[/C][C]2583.25642857143[/C][C]-78.6764285714285[/C][/ROW]
[ROW][C]69[/C][C]2579.39[/C][C]2583.25642857143[/C][C]-3.86642857142851[/C][/ROW]
[ROW][C]70[/C][C]2649.24[/C][C]2583.25642857143[/C][C]65.9835714285714[/C][/ROW]
[ROW][C]71[/C][C]2636.87[/C][C]2583.25642857143[/C][C]53.6135714285715[/C][/ROW]
[ROW][C]72[/C][C]2613.94[/C][C]2583.25642857143[/C][C]30.6835714285717[/C][/ROW]
[ROW][C]73[/C][C]2634.01[/C][C]2927.40444444444[/C][C]-293.394444444444[/C][/ROW]
[ROW][C]74[/C][C]2711.94[/C][C]2927.40444444444[/C][C]-215.464444444444[/C][/ROW]
[ROW][C]75[/C][C]2646.43[/C][C]2583.25642857143[/C][C]63.1735714285714[/C][/ROW]
[ROW][C]76[/C][C]2717.79[/C][C]2927.40444444444[/C][C]-209.614444444444[/C][/ROW]
[ROW][C]77[/C][C]2701.54[/C][C]2583.25642857143[/C][C]118.283571428572[/C][/ROW]
[ROW][C]78[/C][C]2572.98[/C][C]2583.25642857143[/C][C]-10.2764285714284[/C][/ROW]
[ROW][C]79[/C][C]2488.92[/C][C]2583.25642857143[/C][C]-94.3364285714283[/C][/ROW]
[ROW][C]80[/C][C]2204.91[/C][C]2007.8025[/C][C]197.1075[/C][/ROW]
[ROW][C]81[/C][C]2123.99[/C][C]2007.8025[/C][C]116.1875[/C][/ROW]
[ROW][C]82[/C][C]2149.1[/C][C]2007.8025[/C][C]141.2975[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=154756&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=154756&T=2

As an alternative you can also use a QR Code:

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

Actuals, Predictions, and Residuals
#	Actuals	Forecasts	Residuals
1	2981.85	2927.40444444444	54.4455555555555
2	3080.58	2927.40444444444	153.175555555556
3	3106.22	2927.40444444444	178.815555555555
4	3119.31	2927.40444444444	191.905555555556
5	3061.26	3225.12285714286	-163.862857142857
6	3097.31	3225.12285714286	-127.812857142857
7	3161.69	3225.12285714286	-63.4328571428573
8	3257.16	3225.12285714286	32.0371428571425
9	3277.01	3225.12285714286	51.8871428571429
10	3295.32	3694.41375	-399.09375
11	3363.99	3694.41375	-330.42375
12	3494.17	3694.41375	-200.24375
13	3667.03	3694.41375	-27.38375
14	3813.06	3694.41375	118.64625
15	3917.96	3694.41375	223.54625
16	3895.51	3694.41375	201.09625
17	3801.06	3694.41375	106.64625
18	3570.12	3694.41375	-124.29375
19	3701.61	3694.41375	7.19624999999996
20	3862.27	3694.41375	167.85625
21	3970.1	4345.71	-375.61
22	4138.52	4345.71	-207.19
23	4199.75	4345.71	-145.96
24	4290.89	4345.71	-54.8199999999997
25	4443.91	4345.71	98.1999999999998
26	4502.64	4345.71	156.93
27	4356.98	4345.71	11.2699999999995
28	4591.27	4345.71	245.56
29	4696.96	4345.71	351.25
30	4621.4	4345.71	275.69
31	4562.84	4345.71	217.13
32	4202.52	4345.71	-143.19
33	4296.49	4345.71	-49.2200000000003
34	4435.23	4345.71	89.5199999999995
35	4105.18	4345.71	-240.53
36	4116.68	4345.71	-229.03
37	3844.49	3694.41375	150.07625
38	3720.98	3694.41375	26.5662499999999
39	3674.4	3225.12285714286	449.277142857143
40	3857.62	3694.41375	163.20625
41	3801.06	3694.41375	106.64625
42	3504.37	3694.41375	-190.04375
43	3032.6	2927.40444444444	105.195555555556
44	3047.03	3225.12285714286	-178.092857142857
45	2962.34	2927.40444444444	34.9355555555558
46	2197.82	2007.8025	190.0175
47	2014.45	2007.8025	6.64750000000004
48	1862.83	2373.9875	-511.1575
49	1905.41	2007.8025	-102.3925
50	1810.99	2007.8025	-196.8125
51	1670.07	2007.8025	-337.7325
52	1864.44	2007.8025	-143.3625
53	2052.02	2007.8025	44.2175
54	2029.6	2007.8025	21.7974999999999
55	2070.83	2007.8025	63.0274999999999
56	2293.41	2373.9875	-80.5775000000003
57	2443.27	2373.9875	69.2824999999998
58	2513.17	2373.9875	139.1825
59	2466.92	2373.9875	92.9324999999999
60	2502.66	2583.25642857143	-80.5964285714285
61	2539.91	2583.25642857143	-43.3464285714285
62	2482.6	2373.9875	108.6125
63	2626.15	2583.25642857143	42.8935714285717
64	2656.32	2583.25642857143	73.0635714285718
65	2446.66	2583.25642857143	-136.596428571429
66	2467.38	2373.9875	93.3925
67	2462.32	2373.9875	88.3325
68	2504.58	2583.25642857143	-78.6764285714285
69	2579.39	2583.25642857143	-3.86642857142851
70	2649.24	2583.25642857143	65.9835714285714
71	2636.87	2583.25642857143	53.6135714285715
72	2613.94	2583.25642857143	30.6835714285717
73	2634.01	2927.40444444444	-293.394444444444
74	2711.94	2927.40444444444	-215.464444444444
75	2646.43	2583.25642857143	63.1735714285714
76	2717.79	2927.40444444444	-209.614444444444
77	2701.54	2583.25642857143	118.283571428572
78	2572.98	2583.25642857143	-10.2764285714284
79	2488.92	2583.25642857143	-94.3364285714283
80	2204.91	2007.8025	197.1075
81	2123.99	2007.8025	116.1875
82	2149.1	2007.8025	141.2975

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 = 1 ; par2 = none ; par3 = 3 ; par4 = no ;

Parameters (R input):

par1 = 1 ; par2 = none ; par3 = 3 ; par4 = no ;

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