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R Software Module

rwasp_regression_trees1.wasp

Title produced by software

Recursive Partitioning (Regression Trees)

Date of computation

Sun, 19 Dec 2010 20:32:45 +0000

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Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2010/Dec/19/t1292792743cbz1sa5fkwnd237.htm/, Retrieved Fri, 03 May 2024 02:55:12 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=112747, Retrieved Fri, 03 May 2024 02:55:12 +0000

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Estimated Impact

244

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]
-   PD  [Recursive Partitioning (Regression Trees)] [WS 10 Cross Valid...] [2010-12-11 14:44:18] [8081b8996d5947580de3eb171e82db4f]
-         [Recursive Partitioning (Regression Trees)] [Workshop 10, Cros...] [2010-12-11 15:06:30] [3635fb7041b1998c5a1332cf9de22bce]
-   PD        [Recursive Partitioning (Regression Trees)] [Paper Recursive P...] [2010-12-19 20:32:45] [23a9b79f355c69a75648521a893cf584] [Current]
-   PD          [Recursive Partitioning (Regression Trees)] [Paper Recursive P...] [2010-12-19 21:18:05] [3635fb7041b1998c5a1332cf9de22bce] 
-   PD            [Recursive Partitioning (Regression Trees)] [Paper recursive P...] [2010-12-21 12:10:42] [3635fb7041b1998c5a1332cf9de22bce] 
-   P               [Recursive Partitioning (Regression Trees)] [Recursive Partiti...] [2010-12-22 08:24:10] [8081b8996d5947580de3eb171e82db4f] 
-                   [Recursive Partitioning (Regression Trees)] [Paper Recursive P...] [2010-12-22 08:46:16] [d946de7cca328fbcf207448a112523ab] 
-                   [Recursive Partitioning (Regression Trees)] [Paper Recursive P...] [2010-12-22 08:46:16] [d946de7cca328fbcf207448a112523ab] 
-    D              [Recursive Partitioning (Regression Trees)] [Workshop 10 Recur...] [2011-12-10 13:51:25] [aa7c7608f809e956d7797134ec926e04] 
- R                   [Recursive Partitioning (Regression Trees)] [] [2012-12-11 12:06:09] [3ba5358ad212dca7c498c7fc6d6ebde5] 
- R                   [Recursive Partitioning (Regression Trees)] [] [2012-12-11 12:07:22] [3ba5358ad212dca7c498c7fc6d6ebde5] 
-   P               [Recursive Partitioning (Regression Trees)] [workshop 10 10-fo...] [2011-12-10 14:06:44] [aa7c7608f809e956d7797134ec926e04] 
-   PD              [Recursive Partitioning (Regression Trees)] [paper] [2011-12-17 15:01:08] [43239ed98a62e091c70785d80176537f] 
- R  D              [Recursive Partitioning (Regression Trees)] [] [2011-12-23 10:38:51] [74be16979710d4c4e7c6647856088456] 

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97,06	21.454	631.923	130.678
97,73	23.899	654.294	120.877
98	24.939	671.833	137.114
97,76	23.580	586.840	134.406
97,48	24.562	600.969	120.262
97,77	24.696	625.568	130.846
97,96	23.785	558.110	120.343
98,22	23.812	630.577	98.881
98,51	21.917	628.654	115.678
98,19	19.713	603.184	120.796
98,37	19.282	656.255	94.261
98,31	18.788	600.730	89.151
98,6	21.453	670.326	119.880
98,96	24.482	678.423	131.468
99,11	27.474	641.502	155.089
99,64	27.264	625.311	149.581
100,02	27.349	628.177	122.788
99,98	30.632	589.767	143.900
100,32	29.429	582.471	112.115
100,44	30.084	636.248	109.600
100,51	26.290	599.885	117.446
101	24.379	621.694	118.456
100,88	23.335	637.406	101.901
100,55	21.346	595.994	89.940
100,82	21.106	696.308	129.143
101,5	24.514	674.201	126.102
102,15	28.353	648.861	143.048
102,39	30.805	649.605	142.258
102,54	31.348	672.392	131.011
102,85	34.556	598.396	146.471
103,47	33.855	613.177	114.073
103,56	34.787	638.104	114.642
103,69	32.529	615.632	118.226
103,49	29.998	634.465	111.338
103,47	29.257	638.686	108.701
103,45	28.155	604.243	80.512
103,48	30.466	706.669	146.865
103,93	35.704	677.185	137.179
103,89	39.327	644.328	166.536
104,4	39.351	664.825	137.070
104,79	42.234	605.707	127.090
104,77	43.630	600.136	139.966
105,13	43.722	612.166	122.243
105,26	43.121	599.659	109.097
104,96	37.985	634.210	116.591
104,75	37.135	618.234	111.964
105,01	34.646	613.576	109.754
105,15	33.026	627.200	77.609
105,2	35.087	668.973	138.445
105,77	38.846	651.479	127.901
105,78	42.013	619.661	156.615
106,26	43.908	644.260	133.264
106,13	42.868	579.936	143.521
106,12	44.423	601.752	152.139
106,57	44.167	595.376	131.523
106,44	43.636	588.902	113.925
106,54	44.382	634.341	86.495
107,1	42.142	594.305	127.877
108,1	43.452	606.200	107.017
108,4	36.912	610.926	78.716
108,84	42.413	633.685	138.278
109,62	45.344	639.696	144.238
110,42	44.873	659.451	143.679
110,67	47.510	593.248	159.932
111,66	49.554	606.677	136.781
112,28	47.369	599.434	148.173
112,87	45.998	569.578	125.673
112,18	48.140	629.873	105.573
112,36	48.441	613.438	122.405
112,16	44.928	604.172	128.045
111,49	40.454	658.328	94.467
111,25	38.661	612.633	85.573
111,36	37.246	707.372	121.501
111,74	36.843	739.770	125.074
111,1	36.424	777.535	144.979
111,33	37.594	685.030	142.120
111,25	38.144	730.234	124.213
111,04	38.737	714.154	144.407
110,97	34.560	630.872	125.170
111,31	36.080	719.492	109.267
111,02	33.508	677.023	122.354
111,07	35.462	679.272	122.589
111,36	33.374	718.317	104.982
111,54	32.110	645.672	90.542

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' @ 72.249.127.135

\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' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=112747&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' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=112747&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=112747&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' @ 72.249.127.135

Goodness of Fit
Correlation	0.9295
R-squared	0.8639
RMSE	1.7922

\begin{tabular}{lllllllll}
\hline
Goodness of Fit \tabularnewline
Correlation & 0.9295 \tabularnewline
R-squared & 0.8639 \tabularnewline
RMSE & 1.7922 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=112747&T=1

[TABLE]
[ROW][C]Goodness of Fit[/C][/ROW]
[ROW][C]Correlation[/C][C]0.9295[/C][/ROW]
[ROW][C]R-squared[/C][C]0.8639[/C][/ROW]
[ROW][C]RMSE[/C][C]1.7922[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=112747&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=112747&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	0.9295
R-squared	0.8639
RMSE	1.7922

Actuals, Predictions, and Residuals
#	Actuals	Forecasts	Residuals
1	97.06	98.9978260869565	-1.93782608695653
2	97.73	98.9978260869565	-1.26782608695653
3	98	98.9978260869565	-0.997826086956536
4	97.76	98.9978260869565	-1.23782608695653
5	97.48	98.9978260869565	-1.51782608695653
6	97.77	98.9978260869565	-1.22782608695654
7	97.96	98.9978260869565	-1.03782608695654
8	98.22	98.9978260869565	-0.777826086956537
9	98.51	98.9978260869565	-0.487826086956531
10	98.19	98.9978260869565	-0.807826086956538
11	98.37	98.9978260869565	-0.627826086956532
12	98.31	98.9978260869565	-0.687826086956534
13	98.6	98.9978260869565	-0.397826086956542
14	98.96	98.9978260869565	-0.0378260869565423
15	99.11	98.9978260869565	0.112173913043463
16	99.64	98.9978260869565	0.642173913043464
17	100.02	98.9978260869565	1.02217391304346
18	99.98	102.171	-2.19100000000000
19	100.32	102.171	-1.85100000000001
20	100.44	102.171	-1.73100000000001
21	100.51	98.9978260869565	1.51217391304347
22	101	98.9978260869565	2.00217391304346
23	100.88	98.9978260869565	1.88217391304346
24	100.55	98.9978260869565	1.55217391304346
25	100.82	98.9978260869565	1.82217391304346
26	101.5	98.9978260869565	2.50217391304346
27	102.15	102.171	-0.0210000000000008
28	102.39	102.171	0.218999999999994
29	102.54	102.171	0.369
30	102.85	106.337272727273	-3.48727272727272
31	103.47	106.337272727273	-2.86727272727272
32	103.56	106.337272727273	-2.77727272727272
33	103.69	106.337272727273	-2.64727272727272
34	103.49	102.171	1.31899999999999
35	103.47	102.171	1.29899999999999
36	103.45	102.171	1.27900000000000
37	103.48	102.171	1.30900000000000
38	103.93	106.337272727273	-2.40727272727271
39	103.89	106.337272727273	-2.44727272727272
40	104.4	106.337272727273	-1.93727272727271
41	104.79	106.337272727273	-1.54727272727271
42	104.77	106.337272727273	-1.56727272727272
43	105.13	106.337272727273	-1.20727272727272
44	105.26	106.337272727273	-1.07727272727271
45	104.96	106.337272727273	-1.37727272727273
46	104.75	106.337272727273	-1.58727272727272
47	105.01	106.337272727273	-1.32727272727271
48	105.15	106.337272727273	-1.18727272727271
49	105.2	106.337272727273	-1.13727272727272
50	105.77	106.337272727273	-0.567272727272723
51	105.78	106.337272727273	-0.557272727272718
52	106.26	106.337272727273	-0.0772727272727138
53	106.13	106.337272727273	-0.207272727272724
54	106.12	106.337272727273	-0.217272727272714
55	106.57	106.337272727273	0.232727272727274
56	106.44	106.337272727273	0.102727272727279
57	106.54	106.337272727273	0.202727272727287
58	107.1	106.337272727273	0.762727272727275
59	108.1	106.337272727273	1.76272727272728
60	108.4	106.337272727273	2.06272727272729
61	108.84	106.337272727273	2.50272727272728
62	109.62	111.58	-1.95999999999999
63	110.42	111.58	-1.16000000000000
64	110.67	111.58	-0.909999999999997
65	111.66	111.58	0.0799999999999983
66	112.28	111.58	0.700000000000003
67	112.87	111.58	1.29000000000001
68	112.18	111.58	0.600000000000009
69	112.36	111.58	0.780000000000001
70	112.16	111.58	0.579999999999998
71	111.49	106.337272727273	5.15272727272728
72	111.25	106.337272727273	4.91272727272728
73	111.36	111.284444444444	0.0755555555555674
74	111.74	111.284444444444	0.455555555555563
75	111.1	111.284444444444	-0.184444444444438
76	111.33	111.284444444444	0.0455555555555662
77	111.25	111.284444444444	-0.0344444444444321
78	111.04	111.284444444444	-0.244444444444426
79	110.97	106.337272727273	4.63272727272728
80	111.31	111.284444444444	0.0255555555555702
81	111.02	106.337272727273	4.68272727272728
82	111.07	111.284444444444	-0.214444444444439
83	111.36	111.284444444444	0.0755555555555674
84	111.54	106.337272727273	5.20272727272729

\begin{tabular}{lllllllll}
\hline
Actuals, Predictions, and Residuals \tabularnewline
# & Actuals & Forecasts & Residuals \tabularnewline
1 & 97.06 & 98.9978260869565 & -1.93782608695653 \tabularnewline
2 & 97.73 & 98.9978260869565 & -1.26782608695653 \tabularnewline
3 & 98 & 98.9978260869565 & -0.997826086956536 \tabularnewline
4 & 97.76 & 98.9978260869565 & -1.23782608695653 \tabularnewline
5 & 97.48 & 98.9978260869565 & -1.51782608695653 \tabularnewline
6 & 97.77 & 98.9978260869565 & -1.22782608695654 \tabularnewline
7 & 97.96 & 98.9978260869565 & -1.03782608695654 \tabularnewline
8 & 98.22 & 98.9978260869565 & -0.777826086956537 \tabularnewline
9 & 98.51 & 98.9978260869565 & -0.487826086956531 \tabularnewline
10 & 98.19 & 98.9978260869565 & -0.807826086956538 \tabularnewline
11 & 98.37 & 98.9978260869565 & -0.627826086956532 \tabularnewline
12 & 98.31 & 98.9978260869565 & -0.687826086956534 \tabularnewline
13 & 98.6 & 98.9978260869565 & -0.397826086956542 \tabularnewline
14 & 98.96 & 98.9978260869565 & -0.0378260869565423 \tabularnewline
15 & 99.11 & 98.9978260869565 & 0.112173913043463 \tabularnewline
16 & 99.64 & 98.9978260869565 & 0.642173913043464 \tabularnewline
17 & 100.02 & 98.9978260869565 & 1.02217391304346 \tabularnewline
18 & 99.98 & 102.171 & -2.19100000000000 \tabularnewline
19 & 100.32 & 102.171 & -1.85100000000001 \tabularnewline
20 & 100.44 & 102.171 & -1.73100000000001 \tabularnewline
21 & 100.51 & 98.9978260869565 & 1.51217391304347 \tabularnewline
22 & 101 & 98.9978260869565 & 2.00217391304346 \tabularnewline
23 & 100.88 & 98.9978260869565 & 1.88217391304346 \tabularnewline
24 & 100.55 & 98.9978260869565 & 1.55217391304346 \tabularnewline
25 & 100.82 & 98.9978260869565 & 1.82217391304346 \tabularnewline
26 & 101.5 & 98.9978260869565 & 2.50217391304346 \tabularnewline
27 & 102.15 & 102.171 & -0.0210000000000008 \tabularnewline
28 & 102.39 & 102.171 & 0.218999999999994 \tabularnewline
29 & 102.54 & 102.171 & 0.369 \tabularnewline
30 & 102.85 & 106.337272727273 & -3.48727272727272 \tabularnewline
31 & 103.47 & 106.337272727273 & -2.86727272727272 \tabularnewline
32 & 103.56 & 106.337272727273 & -2.77727272727272 \tabularnewline
33 & 103.69 & 106.337272727273 & -2.64727272727272 \tabularnewline
34 & 103.49 & 102.171 & 1.31899999999999 \tabularnewline
35 & 103.47 & 102.171 & 1.29899999999999 \tabularnewline
36 & 103.45 & 102.171 & 1.27900000000000 \tabularnewline
37 & 103.48 & 102.171 & 1.30900000000000 \tabularnewline
38 & 103.93 & 106.337272727273 & -2.40727272727271 \tabularnewline
39 & 103.89 & 106.337272727273 & -2.44727272727272 \tabularnewline
40 & 104.4 & 106.337272727273 & -1.93727272727271 \tabularnewline
41 & 104.79 & 106.337272727273 & -1.54727272727271 \tabularnewline
42 & 104.77 & 106.337272727273 & -1.56727272727272 \tabularnewline
43 & 105.13 & 106.337272727273 & -1.20727272727272 \tabularnewline
44 & 105.26 & 106.337272727273 & -1.07727272727271 \tabularnewline
45 & 104.96 & 106.337272727273 & -1.37727272727273 \tabularnewline
46 & 104.75 & 106.337272727273 & -1.58727272727272 \tabularnewline
47 & 105.01 & 106.337272727273 & -1.32727272727271 \tabularnewline
48 & 105.15 & 106.337272727273 & -1.18727272727271 \tabularnewline
49 & 105.2 & 106.337272727273 & -1.13727272727272 \tabularnewline
50 & 105.77 & 106.337272727273 & -0.567272727272723 \tabularnewline
51 & 105.78 & 106.337272727273 & -0.557272727272718 \tabularnewline
52 & 106.26 & 106.337272727273 & -0.0772727272727138 \tabularnewline
53 & 106.13 & 106.337272727273 & -0.207272727272724 \tabularnewline
54 & 106.12 & 106.337272727273 & -0.217272727272714 \tabularnewline
55 & 106.57 & 106.337272727273 & 0.232727272727274 \tabularnewline
56 & 106.44 & 106.337272727273 & 0.102727272727279 \tabularnewline
57 & 106.54 & 106.337272727273 & 0.202727272727287 \tabularnewline
58 & 107.1 & 106.337272727273 & 0.762727272727275 \tabularnewline
59 & 108.1 & 106.337272727273 & 1.76272727272728 \tabularnewline
60 & 108.4 & 106.337272727273 & 2.06272727272729 \tabularnewline
61 & 108.84 & 106.337272727273 & 2.50272727272728 \tabularnewline
62 & 109.62 & 111.58 & -1.95999999999999 \tabularnewline
63 & 110.42 & 111.58 & -1.16000000000000 \tabularnewline
64 & 110.67 & 111.58 & -0.909999999999997 \tabularnewline
65 & 111.66 & 111.58 & 0.0799999999999983 \tabularnewline
66 & 112.28 & 111.58 & 0.700000000000003 \tabularnewline
67 & 112.87 & 111.58 & 1.29000000000001 \tabularnewline
68 & 112.18 & 111.58 & 0.600000000000009 \tabularnewline
69 & 112.36 & 111.58 & 0.780000000000001 \tabularnewline
70 & 112.16 & 111.58 & 0.579999999999998 \tabularnewline
71 & 111.49 & 106.337272727273 & 5.15272727272728 \tabularnewline
72 & 111.25 & 106.337272727273 & 4.91272727272728 \tabularnewline
73 & 111.36 & 111.284444444444 & 0.0755555555555674 \tabularnewline
74 & 111.74 & 111.284444444444 & 0.455555555555563 \tabularnewline
75 & 111.1 & 111.284444444444 & -0.184444444444438 \tabularnewline
76 & 111.33 & 111.284444444444 & 0.0455555555555662 \tabularnewline
77 & 111.25 & 111.284444444444 & -0.0344444444444321 \tabularnewline
78 & 111.04 & 111.284444444444 & -0.244444444444426 \tabularnewline
79 & 110.97 & 106.337272727273 & 4.63272727272728 \tabularnewline
80 & 111.31 & 111.284444444444 & 0.0255555555555702 \tabularnewline
81 & 111.02 & 106.337272727273 & 4.68272727272728 \tabularnewline
82 & 111.07 & 111.284444444444 & -0.214444444444439 \tabularnewline
83 & 111.36 & 111.284444444444 & 0.0755555555555674 \tabularnewline
84 & 111.54 & 106.337272727273 & 5.20272727272729 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=112747&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]97.06[/C][C]98.9978260869565[/C][C]-1.93782608695653[/C][/ROW]
[ROW][C]2[/C][C]97.73[/C][C]98.9978260869565[/C][C]-1.26782608695653[/C][/ROW]
[ROW][C]3[/C][C]98[/C][C]98.9978260869565[/C][C]-0.997826086956536[/C][/ROW]
[ROW][C]4[/C][C]97.76[/C][C]98.9978260869565[/C][C]-1.23782608695653[/C][/ROW]
[ROW][C]5[/C][C]97.48[/C][C]98.9978260869565[/C][C]-1.51782608695653[/C][/ROW]
[ROW][C]6[/C][C]97.77[/C][C]98.9978260869565[/C][C]-1.22782608695654[/C][/ROW]
[ROW][C]7[/C][C]97.96[/C][C]98.9978260869565[/C][C]-1.03782608695654[/C][/ROW]
[ROW][C]8[/C][C]98.22[/C][C]98.9978260869565[/C][C]-0.777826086956537[/C][/ROW]
[ROW][C]9[/C][C]98.51[/C][C]98.9978260869565[/C][C]-0.487826086956531[/C][/ROW]
[ROW][C]10[/C][C]98.19[/C][C]98.9978260869565[/C][C]-0.807826086956538[/C][/ROW]
[ROW][C]11[/C][C]98.37[/C][C]98.9978260869565[/C][C]-0.627826086956532[/C][/ROW]
[ROW][C]12[/C][C]98.31[/C][C]98.9978260869565[/C][C]-0.687826086956534[/C][/ROW]
[ROW][C]13[/C][C]98.6[/C][C]98.9978260869565[/C][C]-0.397826086956542[/C][/ROW]
[ROW][C]14[/C][C]98.96[/C][C]98.9978260869565[/C][C]-0.0378260869565423[/C][/ROW]
[ROW][C]15[/C][C]99.11[/C][C]98.9978260869565[/C][C]0.112173913043463[/C][/ROW]
[ROW][C]16[/C][C]99.64[/C][C]98.9978260869565[/C][C]0.642173913043464[/C][/ROW]
[ROW][C]17[/C][C]100.02[/C][C]98.9978260869565[/C][C]1.02217391304346[/C][/ROW]
[ROW][C]18[/C][C]99.98[/C][C]102.171[/C][C]-2.19100000000000[/C][/ROW]
[ROW][C]19[/C][C]100.32[/C][C]102.171[/C][C]-1.85100000000001[/C][/ROW]
[ROW][C]20[/C][C]100.44[/C][C]102.171[/C][C]-1.73100000000001[/C][/ROW]
[ROW][C]21[/C][C]100.51[/C][C]98.9978260869565[/C][C]1.51217391304347[/C][/ROW]
[ROW][C]22[/C][C]101[/C][C]98.9978260869565[/C][C]2.00217391304346[/C][/ROW]
[ROW][C]23[/C][C]100.88[/C][C]98.9978260869565[/C][C]1.88217391304346[/C][/ROW]
[ROW][C]24[/C][C]100.55[/C][C]98.9978260869565[/C][C]1.55217391304346[/C][/ROW]
[ROW][C]25[/C][C]100.82[/C][C]98.9978260869565[/C][C]1.82217391304346[/C][/ROW]
[ROW][C]26[/C][C]101.5[/C][C]98.9978260869565[/C][C]2.50217391304346[/C][/ROW]
[ROW][C]27[/C][C]102.15[/C][C]102.171[/C][C]-0.0210000000000008[/C][/ROW]
[ROW][C]28[/C][C]102.39[/C][C]102.171[/C][C]0.218999999999994[/C][/ROW]
[ROW][C]29[/C][C]102.54[/C][C]102.171[/C][C]0.369[/C][/ROW]
[ROW][C]30[/C][C]102.85[/C][C]106.337272727273[/C][C]-3.48727272727272[/C][/ROW]
[ROW][C]31[/C][C]103.47[/C][C]106.337272727273[/C][C]-2.86727272727272[/C][/ROW]
[ROW][C]32[/C][C]103.56[/C][C]106.337272727273[/C][C]-2.77727272727272[/C][/ROW]
[ROW][C]33[/C][C]103.69[/C][C]106.337272727273[/C][C]-2.64727272727272[/C][/ROW]
[ROW][C]34[/C][C]103.49[/C][C]102.171[/C][C]1.31899999999999[/C][/ROW]
[ROW][C]35[/C][C]103.47[/C][C]102.171[/C][C]1.29899999999999[/C][/ROW]
[ROW][C]36[/C][C]103.45[/C][C]102.171[/C][C]1.27900000000000[/C][/ROW]
[ROW][C]37[/C][C]103.48[/C][C]102.171[/C][C]1.30900000000000[/C][/ROW]
[ROW][C]38[/C][C]103.93[/C][C]106.337272727273[/C][C]-2.40727272727271[/C][/ROW]
[ROW][C]39[/C][C]103.89[/C][C]106.337272727273[/C][C]-2.44727272727272[/C][/ROW]
[ROW][C]40[/C][C]104.4[/C][C]106.337272727273[/C][C]-1.93727272727271[/C][/ROW]
[ROW][C]41[/C][C]104.79[/C][C]106.337272727273[/C][C]-1.54727272727271[/C][/ROW]
[ROW][C]42[/C][C]104.77[/C][C]106.337272727273[/C][C]-1.56727272727272[/C][/ROW]
[ROW][C]43[/C][C]105.13[/C][C]106.337272727273[/C][C]-1.20727272727272[/C][/ROW]
[ROW][C]44[/C][C]105.26[/C][C]106.337272727273[/C][C]-1.07727272727271[/C][/ROW]
[ROW][C]45[/C][C]104.96[/C][C]106.337272727273[/C][C]-1.37727272727273[/C][/ROW]
[ROW][C]46[/C][C]104.75[/C][C]106.337272727273[/C][C]-1.58727272727272[/C][/ROW]
[ROW][C]47[/C][C]105.01[/C][C]106.337272727273[/C][C]-1.32727272727271[/C][/ROW]
[ROW][C]48[/C][C]105.15[/C][C]106.337272727273[/C][C]-1.18727272727271[/C][/ROW]
[ROW][C]49[/C][C]105.2[/C][C]106.337272727273[/C][C]-1.13727272727272[/C][/ROW]
[ROW][C]50[/C][C]105.77[/C][C]106.337272727273[/C][C]-0.567272727272723[/C][/ROW]
[ROW][C]51[/C][C]105.78[/C][C]106.337272727273[/C][C]-0.557272727272718[/C][/ROW]
[ROW][C]52[/C][C]106.26[/C][C]106.337272727273[/C][C]-0.0772727272727138[/C][/ROW]
[ROW][C]53[/C][C]106.13[/C][C]106.337272727273[/C][C]-0.207272727272724[/C][/ROW]
[ROW][C]54[/C][C]106.12[/C][C]106.337272727273[/C][C]-0.217272727272714[/C][/ROW]
[ROW][C]55[/C][C]106.57[/C][C]106.337272727273[/C][C]0.232727272727274[/C][/ROW]
[ROW][C]56[/C][C]106.44[/C][C]106.337272727273[/C][C]0.102727272727279[/C][/ROW]
[ROW][C]57[/C][C]106.54[/C][C]106.337272727273[/C][C]0.202727272727287[/C][/ROW]
[ROW][C]58[/C][C]107.1[/C][C]106.337272727273[/C][C]0.762727272727275[/C][/ROW]
[ROW][C]59[/C][C]108.1[/C][C]106.337272727273[/C][C]1.76272727272728[/C][/ROW]
[ROW][C]60[/C][C]108.4[/C][C]106.337272727273[/C][C]2.06272727272729[/C][/ROW]
[ROW][C]61[/C][C]108.84[/C][C]106.337272727273[/C][C]2.50272727272728[/C][/ROW]
[ROW][C]62[/C][C]109.62[/C][C]111.58[/C][C]-1.95999999999999[/C][/ROW]
[ROW][C]63[/C][C]110.42[/C][C]111.58[/C][C]-1.16000000000000[/C][/ROW]
[ROW][C]64[/C][C]110.67[/C][C]111.58[/C][C]-0.909999999999997[/C][/ROW]
[ROW][C]65[/C][C]111.66[/C][C]111.58[/C][C]0.0799999999999983[/C][/ROW]
[ROW][C]66[/C][C]112.28[/C][C]111.58[/C][C]0.700000000000003[/C][/ROW]
[ROW][C]67[/C][C]112.87[/C][C]111.58[/C][C]1.29000000000001[/C][/ROW]
[ROW][C]68[/C][C]112.18[/C][C]111.58[/C][C]0.600000000000009[/C][/ROW]
[ROW][C]69[/C][C]112.36[/C][C]111.58[/C][C]0.780000000000001[/C][/ROW]
[ROW][C]70[/C][C]112.16[/C][C]111.58[/C][C]0.579999999999998[/C][/ROW]
[ROW][C]71[/C][C]111.49[/C][C]106.337272727273[/C][C]5.15272727272728[/C][/ROW]
[ROW][C]72[/C][C]111.25[/C][C]106.337272727273[/C][C]4.91272727272728[/C][/ROW]
[ROW][C]73[/C][C]111.36[/C][C]111.284444444444[/C][C]0.0755555555555674[/C][/ROW]
[ROW][C]74[/C][C]111.74[/C][C]111.284444444444[/C][C]0.455555555555563[/C][/ROW]
[ROW][C]75[/C][C]111.1[/C][C]111.284444444444[/C][C]-0.184444444444438[/C][/ROW]
[ROW][C]76[/C][C]111.33[/C][C]111.284444444444[/C][C]0.0455555555555662[/C][/ROW]
[ROW][C]77[/C][C]111.25[/C][C]111.284444444444[/C][C]-0.0344444444444321[/C][/ROW]
[ROW][C]78[/C][C]111.04[/C][C]111.284444444444[/C][C]-0.244444444444426[/C][/ROW]
[ROW][C]79[/C][C]110.97[/C][C]106.337272727273[/C][C]4.63272727272728[/C][/ROW]
[ROW][C]80[/C][C]111.31[/C][C]111.284444444444[/C][C]0.0255555555555702[/C][/ROW]
[ROW][C]81[/C][C]111.02[/C][C]106.337272727273[/C][C]4.68272727272728[/C][/ROW]
[ROW][C]82[/C][C]111.07[/C][C]111.284444444444[/C][C]-0.214444444444439[/C][/ROW]
[ROW][C]83[/C][C]111.36[/C][C]111.284444444444[/C][C]0.0755555555555674[/C][/ROW]
[ROW][C]84[/C][C]111.54[/C][C]106.337272727273[/C][C]5.20272727272729[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=112747&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=112747&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	97.06	98.9978260869565	-1.93782608695653
2	97.73	98.9978260869565	-1.26782608695653
3	98	98.9978260869565	-0.997826086956536
4	97.76	98.9978260869565	-1.23782608695653
5	97.48	98.9978260869565	-1.51782608695653
6	97.77	98.9978260869565	-1.22782608695654
7	97.96	98.9978260869565	-1.03782608695654
8	98.22	98.9978260869565	-0.777826086956537
9	98.51	98.9978260869565	-0.487826086956531
10	98.19	98.9978260869565	-0.807826086956538
11	98.37	98.9978260869565	-0.627826086956532
12	98.31	98.9978260869565	-0.687826086956534
13	98.6	98.9978260869565	-0.397826086956542
14	98.96	98.9978260869565	-0.0378260869565423
15	99.11	98.9978260869565	0.112173913043463
16	99.64	98.9978260869565	0.642173913043464
17	100.02	98.9978260869565	1.02217391304346
18	99.98	102.171	-2.19100000000000
19	100.32	102.171	-1.85100000000001
20	100.44	102.171	-1.73100000000001
21	100.51	98.9978260869565	1.51217391304347
22	101	98.9978260869565	2.00217391304346
23	100.88	98.9978260869565	1.88217391304346
24	100.55	98.9978260869565	1.55217391304346
25	100.82	98.9978260869565	1.82217391304346
26	101.5	98.9978260869565	2.50217391304346
27	102.15	102.171	-0.0210000000000008
28	102.39	102.171	0.218999999999994
29	102.54	102.171	0.369
30	102.85	106.337272727273	-3.48727272727272
31	103.47	106.337272727273	-2.86727272727272
32	103.56	106.337272727273	-2.77727272727272
33	103.69	106.337272727273	-2.64727272727272
34	103.49	102.171	1.31899999999999
35	103.47	102.171	1.29899999999999
36	103.45	102.171	1.27900000000000
37	103.48	102.171	1.30900000000000
38	103.93	106.337272727273	-2.40727272727271
39	103.89	106.337272727273	-2.44727272727272
40	104.4	106.337272727273	-1.93727272727271
41	104.79	106.337272727273	-1.54727272727271
42	104.77	106.337272727273	-1.56727272727272
43	105.13	106.337272727273	-1.20727272727272
44	105.26	106.337272727273	-1.07727272727271
45	104.96	106.337272727273	-1.37727272727273
46	104.75	106.337272727273	-1.58727272727272
47	105.01	106.337272727273	-1.32727272727271
48	105.15	106.337272727273	-1.18727272727271
49	105.2	106.337272727273	-1.13727272727272
50	105.77	106.337272727273	-0.567272727272723
51	105.78	106.337272727273	-0.557272727272718
52	106.26	106.337272727273	-0.0772727272727138
53	106.13	106.337272727273	-0.207272727272724
54	106.12	106.337272727273	-0.217272727272714
55	106.57	106.337272727273	0.232727272727274
56	106.44	106.337272727273	0.102727272727279
57	106.54	106.337272727273	0.202727272727287
58	107.1	106.337272727273	0.762727272727275
59	108.1	106.337272727273	1.76272727272728
60	108.4	106.337272727273	2.06272727272729
61	108.84	106.337272727273	2.50272727272728
62	109.62	111.58	-1.95999999999999
63	110.42	111.58	-1.16000000000000
64	110.67	111.58	-0.909999999999997
65	111.66	111.58	0.0799999999999983
66	112.28	111.58	0.700000000000003
67	112.87	111.58	1.29000000000001
68	112.18	111.58	0.600000000000009
69	112.36	111.58	0.780000000000001
70	112.16	111.58	0.579999999999998
71	111.49	106.337272727273	5.15272727272728
72	111.25	106.337272727273	4.91272727272728
73	111.36	111.284444444444	0.0755555555555674
74	111.74	111.284444444444	0.455555555555563
75	111.1	111.284444444444	-0.184444444444438
76	111.33	111.284444444444	0.0455555555555662
77	111.25	111.284444444444	-0.0344444444444321
78	111.04	111.284444444444	-0.244444444444426
79	110.97	106.337272727273	4.63272727272728
80	111.31	111.284444444444	0.0255555555555702
81	111.02	106.337272727273	4.68272727272728
82	111.07	111.284444444444	-0.214444444444439
83	111.36	111.284444444444	0.0755555555555674
84	111.54	106.337272727273	5.20272727272729

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