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Author's title

Author

*The author of this computation has been verified*

R Software Module

rwasp_regression_trees1.wasp

Title produced by software

Recursive Partitioning (Regression Trees)

Date of computation

Tue, 11 Dec 2012 07:06:09 -0500

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/2012/Dec/11/t1355227580gtz8eyra1906ccj.htm/, Retrieved Fri, 19 Apr 2024 21:37:29 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=198458, Retrieved Fri, 19 Apr 2024 21:37:29 +0000

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Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords

Estimated Impact

141

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] [3635fb7041b1998c5a1332cf9de22bce]
-   PD          [Recursive Partitioning (Regression Trees)] [Paper recursive P...] [2010-12-21 12:10:42] [3635fb7041b1998c5a1332cf9de22bce]
-    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] [90f4fc95bc23bd40c615363dd079f863] [Current]

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Dataseries X:

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Histogram

Boxplots

631.923	21.454	97.06	130.678
654.294	23.899	97.73	120.877
671.833	24.939	98	137.114
586.840	23.580	97.76	134.406
600.969	24.562	97.48	120.262
625.568	24.696	97.77	130.846
558.110	23.785	97.96	120.343
630.577	23.812	98.22	98.881
628.654	21.917	98.51	115.678
603.184	19.713	98.19	120.796
656.255	19.282	98.37	94.261
600.730	18.788	98.31	89.151
670.326	21.453	98.6	119.880
678.423	24.482	98.96	131.468
641.502	27.474	99.11	155.089
625.311	27.264	99.64	149.581
628.177	27.349	100.02	122.788
589.767	30.632	99.98	143.900
582.471	29.429	100.32	112.115
636.248	30.084	100.44	109.600
599.885	26.290	100.51	117.446
621.694	24.379	101	118.456
637.406	23.335	100.88	101.901
595.994	21.346	100.55	89.940
696.308	21.106	100.82	129.143
674.201	24.514	101.5	126.102
648.861	28.353	102.15	143.048
649.605	30.805	102.39	142.258
672.392	31.348	102.54	131.011
598.396	34.556	102.85	146.471
613.177	33.855	103.47	114.073
638.104	34.787	103.56	114.642
615.632	32.529	103.69	118.226
634.465	29.998	103.49	111.338
638.686	29.257	103.47	108.701
604.243	28.155	103.45	80.512
706.669	30.466	103.48	146.865
677.185	35.704	103.93	137.179
644.328	39.327	103.89	166.536
664.825	39.351	104.4	137.070
605.707	42.234	104.79	127.090
600.136	43.630	104.77	139.966
612.166	43.722	105.13	122.243
599.659	43.121	105.26	109.097
634.210	37.985	104.96	116.591
618.234	37.135	104.75	111.964
613.576	34.646	105.01	109.754
627.200	33.026	105.15	77.609
668.973	35.087	105.2	138.445
651.479	38.846	105.77	127.901
619.661	42.013	105.78	156.615
644.260	43.908	106.26	133.264
579.936	42.868	106.13	143.521
601.752	44.423	106.12	152.139
595.376	44.167	106.57	131.523
588.902	43.636	106.44	113.925
634.341	44.382	106.54	86.495
594.305	42.142	107.1	127.877
606.200	43.452	108.1	107.017
610.926	36.912	108.4	78.716
633.685	42.413	108.84	138.278
639.696	45.344	109.62	144.238
659.451	44.873	110.42	143.679
593.248	47.510	110.67	159.932
606.677	49.554	111.66	136.781
599.434	47.369	112.28	148.173
569.578	45.998	112.87	125.673
629.873	48.140	112.18	105.573
613.438	48.441	112.36	122.405
604.172	44.928	112.16	128.045
658.328	40.454	111.49	94.467
612.633	38.661	111.25	85.573
707.372	37.246	111.36	121.501
739.770	36.843	111.74	125.074
777.535	36.424	111.1	144.979
685.030	37.594	111.33	142.120
730.234	38.144	111.25	124.213
714.154	38.737	111.04	144.407
630.872	34.560	110.97	125.170
719.492	36.080	111.31	109.267
677.023	33.508	111.02	122.354
679.272	35.462	111.07	122.589
718.317	33.374	111.36	104.982
645.672	32.110	111.54	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' @ jenkins.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 6 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=198458&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]6 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ jenkins.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=198458&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=198458&T=0

As an alternative you can also use a QR Code:

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

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	6 seconds
R Server	'Gwilym Jenkins' @ jenkins.wessa.net

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=198458&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=198458&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=198458&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	-2.297
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 & -2.297 \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=198458&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]-2.297[/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=198458&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=198458&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	-2.297
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 = none ; 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