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Author

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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 21:18:05 +0000

Cite this page as follows

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2010/Dec/19/t12927934926ihbl0f78heho1b.htm/, Retrieved Thu, 02 May 2024 19:32:07 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=112751, Retrieved Thu, 02 May 2024 19:32:07 +0000

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Estimated Impact

204

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] [3635fb7041b1998c5a1332cf9de22bce]
-   PD          [Recursive Partitioning (Regression Trees)] [Paper Recursive P...] [2010-12-19 21:18:05] [23a9b79f355c69a75648521a893cf584] [Current]
-   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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Dataseries X:

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Histogram

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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	'Sir Ronald Aylmer Fisher' @ 193.190.124.24

\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 & 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=112751&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]'Sir Ronald Aylmer Fisher' @ 193.190.124.24[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=112751&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=112751&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	'Sir Ronald Aylmer Fisher' @ 193.190.124.24

Goodness of Fit
Correlation	0.9131
R-squared	0.8337
RMSE	3.4133

\begin{tabular}{lllllllll}
\hline
Goodness of Fit \tabularnewline
Correlation & 0.9131 \tabularnewline
R-squared & 0.8337 \tabularnewline
RMSE & 3.4133 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=112751&T=1

[TABLE]
[ROW][C]Goodness of Fit[/C][/ROW]
[ROW][C]Correlation[/C][C]0.9131[/C][/ROW]
[ROW][C]R-squared[/C][C]0.8337[/C][/ROW]
[ROW][C]RMSE[/C][C]3.4133[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=112751&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=112751&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	0.9131
R-squared	0.8337
RMSE	3.4133

Actuals, Predictions, and Residuals
#	Actuals	Forecasts	Residuals
1	21.454	24.214	-2.76
2	23.899	24.214	-0.314999999999998
3	24.939	24.214	0.725000000000001
4	23.58	24.214	-0.634
5	24.562	24.214	0.348000000000003
6	24.696	24.214	0.482000000000003
7	23.785	24.214	-0.428999999999998
8	23.812	24.214	-0.401999999999997
9	21.917	24.214	-2.29700000000000
10	19.713	24.214	-4.501
11	19.282	24.214	-4.932
12	18.788	24.214	-5.426
13	21.453	24.214	-2.761
14	24.482	24.214	0.268000000000001
15	27.474	24.214	3.26
16	27.264	24.214	3.05
17	27.349	24.214	3.135
18	30.632	24.214	6.418
19	29.429	24.214	5.215
20	30.084	24.214	5.87
21	26.29	24.214	2.076
22	24.379	24.214	0.165000000000003
23	23.335	24.214	-0.878999999999998
24	21.346	24.214	-2.868
25	21.106	24.214	-3.108
26	24.514	24.214	0.300000000000001
27	28.353	30.7547777777778	-2.40177777777778
28	30.805	30.7547777777778	0.0502222222222208
29	31.348	30.7547777777778	0.59322222222222
30	34.556	30.7547777777778	3.80122222222222
31	33.855	30.7547777777778	3.10022222222222
32	34.787	38.0655483870968	-3.27854838709678
33	32.529	38.0655483870968	-5.53654838709677
34	29.998	30.7547777777778	-0.756777777777778
35	29.257	30.7547777777778	-1.49777777777778
36	28.155	30.7547777777778	-2.59977777777778
37	30.466	30.7547777777778	-0.288777777777778
38	35.704	38.0655483870968	-2.36154838709678
39	39.327	38.0655483870968	1.26145161290322
40	39.351	38.0655483870968	1.28545161290322
41	42.234	44.0426666666667	-1.80866666666667
42	43.63	44.0426666666667	-0.412666666666667
43	43.722	44.0426666666667	-0.320666666666668
44	43.121	44.0426666666667	-0.921666666666667
45	37.985	38.0655483870968	-0.0805483870967763
46	37.135	38.0655483870968	-0.930548387096778
47	34.646	38.0655483870968	-3.41954838709677
48	33.026	38.0655483870968	-5.03954838709677
49	35.087	38.0655483870968	-2.97854838709677
50	38.846	38.0655483870968	0.780451612903221
51	42.013	38.0655483870968	3.94745161290322
52	43.908	38.0655483870968	5.84245161290323
53	42.868	44.0426666666667	-1.17466666666667
54	44.423	44.0426666666667	0.380333333333333
55	44.167	44.0426666666667	0.124333333333333
56	43.636	44.0426666666667	-0.406666666666666
57	44.382	38.0655483870968	6.31645161290322
58	42.142	44.0426666666667	-1.90066666666667
59	43.452	44.0426666666667	-0.590666666666671
60	36.912	44.0426666666667	-7.13066666666667
61	42.413	38.0655483870968	4.34745161290322
62	45.344	38.0655483870968	7.27845161290323
63	44.873	38.0655483870968	6.80745161290322
64	47.51	44.0426666666667	3.46733333333333
65	49.554	44.0426666666667	5.51133333333333
66	47.369	44.0426666666667	3.32633333333333
67	45.998	44.0426666666667	1.95533333333333
68	48.14	38.0655483870968	10.0744516129032
69	48.441	44.0426666666667	4.39833333333333
70	44.928	44.0426666666667	0.885333333333328
71	40.454	38.0655483870968	2.38845161290322
72	38.661	44.0426666666667	-5.38166666666667
73	37.246	38.0655483870968	-0.819548387096773
74	36.843	38.0655483870968	-1.22254838709677
75	36.424	38.0655483870968	-1.64154838709678
76	37.594	38.0655483870968	-0.471548387096774
77	38.144	38.0655483870968	0.0784516129032227
78	38.737	38.0655483870968	0.671451612903226
79	34.56	38.0655483870968	-3.50554838709677
80	36.08	38.0655483870968	-1.98554838709678
81	33.508	38.0655483870968	-4.55754838709677
82	35.462	38.0655483870968	-2.60354838709677
83	33.374	38.0655483870968	-4.69154838709677
84	32.11	38.0655483870968	-5.95554838709678

\begin{tabular}{lllllllll}
\hline
Actuals, Predictions, and Residuals \tabularnewline
# & Actuals & Forecasts & Residuals \tabularnewline
1 & 21.454 & 24.214 & -2.76 \tabularnewline
2 & 23.899 & 24.214 & -0.314999999999998 \tabularnewline
3 & 24.939 & 24.214 & 0.725000000000001 \tabularnewline
4 & 23.58 & 24.214 & -0.634 \tabularnewline
5 & 24.562 & 24.214 & 0.348000000000003 \tabularnewline
6 & 24.696 & 24.214 & 0.482000000000003 \tabularnewline
7 & 23.785 & 24.214 & -0.428999999999998 \tabularnewline
8 & 23.812 & 24.214 & -0.401999999999997 \tabularnewline
9 & 21.917 & 24.214 & -2.29700000000000 \tabularnewline
10 & 19.713 & 24.214 & -4.501 \tabularnewline
11 & 19.282 & 24.214 & -4.932 \tabularnewline
12 & 18.788 & 24.214 & -5.426 \tabularnewline
13 & 21.453 & 24.214 & -2.761 \tabularnewline
14 & 24.482 & 24.214 & 0.268000000000001 \tabularnewline
15 & 27.474 & 24.214 & 3.26 \tabularnewline
16 & 27.264 & 24.214 & 3.05 \tabularnewline
17 & 27.349 & 24.214 & 3.135 \tabularnewline
18 & 30.632 & 24.214 & 6.418 \tabularnewline
19 & 29.429 & 24.214 & 5.215 \tabularnewline
20 & 30.084 & 24.214 & 5.87 \tabularnewline
21 & 26.29 & 24.214 & 2.076 \tabularnewline
22 & 24.379 & 24.214 & 0.165000000000003 \tabularnewline
23 & 23.335 & 24.214 & -0.878999999999998 \tabularnewline
24 & 21.346 & 24.214 & -2.868 \tabularnewline
25 & 21.106 & 24.214 & -3.108 \tabularnewline
26 & 24.514 & 24.214 & 0.300000000000001 \tabularnewline
27 & 28.353 & 30.7547777777778 & -2.40177777777778 \tabularnewline
28 & 30.805 & 30.7547777777778 & 0.0502222222222208 \tabularnewline
29 & 31.348 & 30.7547777777778 & 0.59322222222222 \tabularnewline
30 & 34.556 & 30.7547777777778 & 3.80122222222222 \tabularnewline
31 & 33.855 & 30.7547777777778 & 3.10022222222222 \tabularnewline
32 & 34.787 & 38.0655483870968 & -3.27854838709678 \tabularnewline
33 & 32.529 & 38.0655483870968 & -5.53654838709677 \tabularnewline
34 & 29.998 & 30.7547777777778 & -0.756777777777778 \tabularnewline
35 & 29.257 & 30.7547777777778 & -1.49777777777778 \tabularnewline
36 & 28.155 & 30.7547777777778 & -2.59977777777778 \tabularnewline
37 & 30.466 & 30.7547777777778 & -0.288777777777778 \tabularnewline
38 & 35.704 & 38.0655483870968 & -2.36154838709678 \tabularnewline
39 & 39.327 & 38.0655483870968 & 1.26145161290322 \tabularnewline
40 & 39.351 & 38.0655483870968 & 1.28545161290322 \tabularnewline
41 & 42.234 & 44.0426666666667 & -1.80866666666667 \tabularnewline
42 & 43.63 & 44.0426666666667 & -0.412666666666667 \tabularnewline
43 & 43.722 & 44.0426666666667 & -0.320666666666668 \tabularnewline
44 & 43.121 & 44.0426666666667 & -0.921666666666667 \tabularnewline
45 & 37.985 & 38.0655483870968 & -0.0805483870967763 \tabularnewline
46 & 37.135 & 38.0655483870968 & -0.930548387096778 \tabularnewline
47 & 34.646 & 38.0655483870968 & -3.41954838709677 \tabularnewline
48 & 33.026 & 38.0655483870968 & -5.03954838709677 \tabularnewline
49 & 35.087 & 38.0655483870968 & -2.97854838709677 \tabularnewline
50 & 38.846 & 38.0655483870968 & 0.780451612903221 \tabularnewline
51 & 42.013 & 38.0655483870968 & 3.94745161290322 \tabularnewline
52 & 43.908 & 38.0655483870968 & 5.84245161290323 \tabularnewline
53 & 42.868 & 44.0426666666667 & -1.17466666666667 \tabularnewline
54 & 44.423 & 44.0426666666667 & 0.380333333333333 \tabularnewline
55 & 44.167 & 44.0426666666667 & 0.124333333333333 \tabularnewline
56 & 43.636 & 44.0426666666667 & -0.406666666666666 \tabularnewline
57 & 44.382 & 38.0655483870968 & 6.31645161290322 \tabularnewline
58 & 42.142 & 44.0426666666667 & -1.90066666666667 \tabularnewline
59 & 43.452 & 44.0426666666667 & -0.590666666666671 \tabularnewline
60 & 36.912 & 44.0426666666667 & -7.13066666666667 \tabularnewline
61 & 42.413 & 38.0655483870968 & 4.34745161290322 \tabularnewline
62 & 45.344 & 38.0655483870968 & 7.27845161290323 \tabularnewline
63 & 44.873 & 38.0655483870968 & 6.80745161290322 \tabularnewline
64 & 47.51 & 44.0426666666667 & 3.46733333333333 \tabularnewline
65 & 49.554 & 44.0426666666667 & 5.51133333333333 \tabularnewline
66 & 47.369 & 44.0426666666667 & 3.32633333333333 \tabularnewline
67 & 45.998 & 44.0426666666667 & 1.95533333333333 \tabularnewline
68 & 48.14 & 38.0655483870968 & 10.0744516129032 \tabularnewline
69 & 48.441 & 44.0426666666667 & 4.39833333333333 \tabularnewline
70 & 44.928 & 44.0426666666667 & 0.885333333333328 \tabularnewline
71 & 40.454 & 38.0655483870968 & 2.38845161290322 \tabularnewline
72 & 38.661 & 44.0426666666667 & -5.38166666666667 \tabularnewline
73 & 37.246 & 38.0655483870968 & -0.819548387096773 \tabularnewline
74 & 36.843 & 38.0655483870968 & -1.22254838709677 \tabularnewline
75 & 36.424 & 38.0655483870968 & -1.64154838709678 \tabularnewline
76 & 37.594 & 38.0655483870968 & -0.471548387096774 \tabularnewline
77 & 38.144 & 38.0655483870968 & 0.0784516129032227 \tabularnewline
78 & 38.737 & 38.0655483870968 & 0.671451612903226 \tabularnewline
79 & 34.56 & 38.0655483870968 & -3.50554838709677 \tabularnewline
80 & 36.08 & 38.0655483870968 & -1.98554838709678 \tabularnewline
81 & 33.508 & 38.0655483870968 & -4.55754838709677 \tabularnewline
82 & 35.462 & 38.0655483870968 & -2.60354838709677 \tabularnewline
83 & 33.374 & 38.0655483870968 & -4.69154838709677 \tabularnewline
84 & 32.11 & 38.0655483870968 & -5.95554838709678 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=112751&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]21.454[/C][C]24.214[/C][C]-2.76[/C][/ROW]
[ROW][C]2[/C][C]23.899[/C][C]24.214[/C][C]-0.314999999999998[/C][/ROW]
[ROW][C]3[/C][C]24.939[/C][C]24.214[/C][C]0.725000000000001[/C][/ROW]
[ROW][C]4[/C][C]23.58[/C][C]24.214[/C][C]-0.634[/C][/ROW]
[ROW][C]5[/C][C]24.562[/C][C]24.214[/C][C]0.348000000000003[/C][/ROW]
[ROW][C]6[/C][C]24.696[/C][C]24.214[/C][C]0.482000000000003[/C][/ROW]
[ROW][C]7[/C][C]23.785[/C][C]24.214[/C][C]-0.428999999999998[/C][/ROW]
[ROW][C]8[/C][C]23.812[/C][C]24.214[/C][C]-0.401999999999997[/C][/ROW]
[ROW][C]9[/C][C]21.917[/C][C]24.214[/C][C]-2.29700000000000[/C][/ROW]
[ROW][C]10[/C][C]19.713[/C][C]24.214[/C][C]-4.501[/C][/ROW]
[ROW][C]11[/C][C]19.282[/C][C]24.214[/C][C]-4.932[/C][/ROW]
[ROW][C]12[/C][C]18.788[/C][C]24.214[/C][C]-5.426[/C][/ROW]
[ROW][C]13[/C][C]21.453[/C][C]24.214[/C][C]-2.761[/C][/ROW]
[ROW][C]14[/C][C]24.482[/C][C]24.214[/C][C]0.268000000000001[/C][/ROW]
[ROW][C]15[/C][C]27.474[/C][C]24.214[/C][C]3.26[/C][/ROW]
[ROW][C]16[/C][C]27.264[/C][C]24.214[/C][C]3.05[/C][/ROW]
[ROW][C]17[/C][C]27.349[/C][C]24.214[/C][C]3.135[/C][/ROW]
[ROW][C]18[/C][C]30.632[/C][C]24.214[/C][C]6.418[/C][/ROW]
[ROW][C]19[/C][C]29.429[/C][C]24.214[/C][C]5.215[/C][/ROW]
[ROW][C]20[/C][C]30.084[/C][C]24.214[/C][C]5.87[/C][/ROW]
[ROW][C]21[/C][C]26.29[/C][C]24.214[/C][C]2.076[/C][/ROW]
[ROW][C]22[/C][C]24.379[/C][C]24.214[/C][C]0.165000000000003[/C][/ROW]
[ROW][C]23[/C][C]23.335[/C][C]24.214[/C][C]-0.878999999999998[/C][/ROW]
[ROW][C]24[/C][C]21.346[/C][C]24.214[/C][C]-2.868[/C][/ROW]
[ROW][C]25[/C][C]21.106[/C][C]24.214[/C][C]-3.108[/C][/ROW]
[ROW][C]26[/C][C]24.514[/C][C]24.214[/C][C]0.300000000000001[/C][/ROW]
[ROW][C]27[/C][C]28.353[/C][C]30.7547777777778[/C][C]-2.40177777777778[/C][/ROW]
[ROW][C]28[/C][C]30.805[/C][C]30.7547777777778[/C][C]0.0502222222222208[/C][/ROW]
[ROW][C]29[/C][C]31.348[/C][C]30.7547777777778[/C][C]0.59322222222222[/C][/ROW]
[ROW][C]30[/C][C]34.556[/C][C]30.7547777777778[/C][C]3.80122222222222[/C][/ROW]
[ROW][C]31[/C][C]33.855[/C][C]30.7547777777778[/C][C]3.10022222222222[/C][/ROW]
[ROW][C]32[/C][C]34.787[/C][C]38.0655483870968[/C][C]-3.27854838709678[/C][/ROW]
[ROW][C]33[/C][C]32.529[/C][C]38.0655483870968[/C][C]-5.53654838709677[/C][/ROW]
[ROW][C]34[/C][C]29.998[/C][C]30.7547777777778[/C][C]-0.756777777777778[/C][/ROW]
[ROW][C]35[/C][C]29.257[/C][C]30.7547777777778[/C][C]-1.49777777777778[/C][/ROW]
[ROW][C]36[/C][C]28.155[/C][C]30.7547777777778[/C][C]-2.59977777777778[/C][/ROW]
[ROW][C]37[/C][C]30.466[/C][C]30.7547777777778[/C][C]-0.288777777777778[/C][/ROW]
[ROW][C]38[/C][C]35.704[/C][C]38.0655483870968[/C][C]-2.36154838709678[/C][/ROW]
[ROW][C]39[/C][C]39.327[/C][C]38.0655483870968[/C][C]1.26145161290322[/C][/ROW]
[ROW][C]40[/C][C]39.351[/C][C]38.0655483870968[/C][C]1.28545161290322[/C][/ROW]
[ROW][C]41[/C][C]42.234[/C][C]44.0426666666667[/C][C]-1.80866666666667[/C][/ROW]
[ROW][C]42[/C][C]43.63[/C][C]44.0426666666667[/C][C]-0.412666666666667[/C][/ROW]
[ROW][C]43[/C][C]43.722[/C][C]44.0426666666667[/C][C]-0.320666666666668[/C][/ROW]
[ROW][C]44[/C][C]43.121[/C][C]44.0426666666667[/C][C]-0.921666666666667[/C][/ROW]
[ROW][C]45[/C][C]37.985[/C][C]38.0655483870968[/C][C]-0.0805483870967763[/C][/ROW]
[ROW][C]46[/C][C]37.135[/C][C]38.0655483870968[/C][C]-0.930548387096778[/C][/ROW]
[ROW][C]47[/C][C]34.646[/C][C]38.0655483870968[/C][C]-3.41954838709677[/C][/ROW]
[ROW][C]48[/C][C]33.026[/C][C]38.0655483870968[/C][C]-5.03954838709677[/C][/ROW]
[ROW][C]49[/C][C]35.087[/C][C]38.0655483870968[/C][C]-2.97854838709677[/C][/ROW]
[ROW][C]50[/C][C]38.846[/C][C]38.0655483870968[/C][C]0.780451612903221[/C][/ROW]
[ROW][C]51[/C][C]42.013[/C][C]38.0655483870968[/C][C]3.94745161290322[/C][/ROW]
[ROW][C]52[/C][C]43.908[/C][C]38.0655483870968[/C][C]5.84245161290323[/C][/ROW]
[ROW][C]53[/C][C]42.868[/C][C]44.0426666666667[/C][C]-1.17466666666667[/C][/ROW]
[ROW][C]54[/C][C]44.423[/C][C]44.0426666666667[/C][C]0.380333333333333[/C][/ROW]
[ROW][C]55[/C][C]44.167[/C][C]44.0426666666667[/C][C]0.124333333333333[/C][/ROW]
[ROW][C]56[/C][C]43.636[/C][C]44.0426666666667[/C][C]-0.406666666666666[/C][/ROW]
[ROW][C]57[/C][C]44.382[/C][C]38.0655483870968[/C][C]6.31645161290322[/C][/ROW]
[ROW][C]58[/C][C]42.142[/C][C]44.0426666666667[/C][C]-1.90066666666667[/C][/ROW]
[ROW][C]59[/C][C]43.452[/C][C]44.0426666666667[/C][C]-0.590666666666671[/C][/ROW]
[ROW][C]60[/C][C]36.912[/C][C]44.0426666666667[/C][C]-7.13066666666667[/C][/ROW]
[ROW][C]61[/C][C]42.413[/C][C]38.0655483870968[/C][C]4.34745161290322[/C][/ROW]
[ROW][C]62[/C][C]45.344[/C][C]38.0655483870968[/C][C]7.27845161290323[/C][/ROW]
[ROW][C]63[/C][C]44.873[/C][C]38.0655483870968[/C][C]6.80745161290322[/C][/ROW]
[ROW][C]64[/C][C]47.51[/C][C]44.0426666666667[/C][C]3.46733333333333[/C][/ROW]
[ROW][C]65[/C][C]49.554[/C][C]44.0426666666667[/C][C]5.51133333333333[/C][/ROW]
[ROW][C]66[/C][C]47.369[/C][C]44.0426666666667[/C][C]3.32633333333333[/C][/ROW]
[ROW][C]67[/C][C]45.998[/C][C]44.0426666666667[/C][C]1.95533333333333[/C][/ROW]
[ROW][C]68[/C][C]48.14[/C][C]38.0655483870968[/C][C]10.0744516129032[/C][/ROW]
[ROW][C]69[/C][C]48.441[/C][C]44.0426666666667[/C][C]4.39833333333333[/C][/ROW]
[ROW][C]70[/C][C]44.928[/C][C]44.0426666666667[/C][C]0.885333333333328[/C][/ROW]
[ROW][C]71[/C][C]40.454[/C][C]38.0655483870968[/C][C]2.38845161290322[/C][/ROW]
[ROW][C]72[/C][C]38.661[/C][C]44.0426666666667[/C][C]-5.38166666666667[/C][/ROW]
[ROW][C]73[/C][C]37.246[/C][C]38.0655483870968[/C][C]-0.819548387096773[/C][/ROW]
[ROW][C]74[/C][C]36.843[/C][C]38.0655483870968[/C][C]-1.22254838709677[/C][/ROW]
[ROW][C]75[/C][C]36.424[/C][C]38.0655483870968[/C][C]-1.64154838709678[/C][/ROW]
[ROW][C]76[/C][C]37.594[/C][C]38.0655483870968[/C][C]-0.471548387096774[/C][/ROW]
[ROW][C]77[/C][C]38.144[/C][C]38.0655483870968[/C][C]0.0784516129032227[/C][/ROW]
[ROW][C]78[/C][C]38.737[/C][C]38.0655483870968[/C][C]0.671451612903226[/C][/ROW]
[ROW][C]79[/C][C]34.56[/C][C]38.0655483870968[/C][C]-3.50554838709677[/C][/ROW]
[ROW][C]80[/C][C]36.08[/C][C]38.0655483870968[/C][C]-1.98554838709678[/C][/ROW]
[ROW][C]81[/C][C]33.508[/C][C]38.0655483870968[/C][C]-4.55754838709677[/C][/ROW]
[ROW][C]82[/C][C]35.462[/C][C]38.0655483870968[/C][C]-2.60354838709677[/C][/ROW]
[ROW][C]83[/C][C]33.374[/C][C]38.0655483870968[/C][C]-4.69154838709677[/C][/ROW]
[ROW][C]84[/C][C]32.11[/C][C]38.0655483870968[/C][C]-5.95554838709678[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=112751&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=112751&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	21.454	24.214	-2.76
2	23.899	24.214	-0.314999999999998
3	24.939	24.214	0.725000000000001
4	23.58	24.214	-0.634
5	24.562	24.214	0.348000000000003
6	24.696	24.214	0.482000000000003
7	23.785	24.214	-0.428999999999998
8	23.812	24.214	-0.401999999999997
9	21.917	24.214	-2.29700000000000
10	19.713	24.214	-4.501
11	19.282	24.214	-4.932
12	18.788	24.214	-5.426
13	21.453	24.214	-2.761
14	24.482	24.214	0.268000000000001
15	27.474	24.214	3.26
16	27.264	24.214	3.05
17	27.349	24.214	3.135
18	30.632	24.214	6.418
19	29.429	24.214	5.215
20	30.084	24.214	5.87
21	26.29	24.214	2.076
22	24.379	24.214	0.165000000000003
23	23.335	24.214	-0.878999999999998
24	21.346	24.214	-2.868
25	21.106	24.214	-3.108
26	24.514	24.214	0.300000000000001
27	28.353	30.7547777777778	-2.40177777777778
28	30.805	30.7547777777778	0.0502222222222208
29	31.348	30.7547777777778	0.59322222222222
30	34.556	30.7547777777778	3.80122222222222
31	33.855	30.7547777777778	3.10022222222222
32	34.787	38.0655483870968	-3.27854838709678
33	32.529	38.0655483870968	-5.53654838709677
34	29.998	30.7547777777778	-0.756777777777778
35	29.257	30.7547777777778	-1.49777777777778
36	28.155	30.7547777777778	-2.59977777777778
37	30.466	30.7547777777778	-0.288777777777778
38	35.704	38.0655483870968	-2.36154838709678
39	39.327	38.0655483870968	1.26145161290322
40	39.351	38.0655483870968	1.28545161290322
41	42.234	44.0426666666667	-1.80866666666667
42	43.63	44.0426666666667	-0.412666666666667
43	43.722	44.0426666666667	-0.320666666666668
44	43.121	44.0426666666667	-0.921666666666667
45	37.985	38.0655483870968	-0.0805483870967763
46	37.135	38.0655483870968	-0.930548387096778
47	34.646	38.0655483870968	-3.41954838709677
48	33.026	38.0655483870968	-5.03954838709677
49	35.087	38.0655483870968	-2.97854838709677
50	38.846	38.0655483870968	0.780451612903221
51	42.013	38.0655483870968	3.94745161290322
52	43.908	38.0655483870968	5.84245161290323
53	42.868	44.0426666666667	-1.17466666666667
54	44.423	44.0426666666667	0.380333333333333
55	44.167	44.0426666666667	0.124333333333333
56	43.636	44.0426666666667	-0.406666666666666
57	44.382	38.0655483870968	6.31645161290322
58	42.142	44.0426666666667	-1.90066666666667
59	43.452	44.0426666666667	-0.590666666666671
60	36.912	44.0426666666667	-7.13066666666667
61	42.413	38.0655483870968	4.34745161290322
62	45.344	38.0655483870968	7.27845161290323
63	44.873	38.0655483870968	6.80745161290322
64	47.51	44.0426666666667	3.46733333333333
65	49.554	44.0426666666667	5.51133333333333
66	47.369	44.0426666666667	3.32633333333333
67	45.998	44.0426666666667	1.95533333333333
68	48.14	38.0655483870968	10.0744516129032
69	48.441	44.0426666666667	4.39833333333333
70	44.928	44.0426666666667	0.885333333333328
71	40.454	38.0655483870968	2.38845161290322
72	38.661	44.0426666666667	-5.38166666666667
73	37.246	38.0655483870968	-0.819548387096773
74	36.843	38.0655483870968	-1.22254838709677
75	36.424	38.0655483870968	-1.64154838709678
76	37.594	38.0655483870968	-0.471548387096774
77	38.144	38.0655483870968	0.0784516129032227
78	38.737	38.0655483870968	0.671451612903226
79	34.56	38.0655483870968	-3.50554838709677
80	36.08	38.0655483870968	-1.98554838709678
81	33.508	38.0655483870968	-4.55754838709677
82	35.462	38.0655483870968	-2.60354838709677
83	33.374	38.0655483870968	-4.69154838709677
84	32.11	38.0655483870968	-5.95554838709678

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 = 2 ; par2 = equal ; par3 = 4 ; par4 = no ;

Parameters (R input):

par1 = 2 ; par2 = none ; par3 = 4 ; 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