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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_regression_trees1.wasp
Title produced by softwareRecursive Partitioning (Regression Trees)
Date of computationSun, 19 Dec 2010 16:23:40 +0000
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2010/Dec/19/t1292775737a8cz0cr2cj6ophd.htm/, Retrieved Sun, 05 May 2024 08:32:47 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=112571, Retrieved Sun, 05 May 2024 08:32:47 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact125

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Kendall tau Correlation Matrix] [] [2010-12-05 18:04:16] [b98453cac15ba1066b407e146608df68]
- RMPD  [Recursive Partitioning (Regression Trees)] [RECURSIVE PARTITI...] [2010-12-12 19:03:25] [c289bfbb56808c5d93a0f55b5d39f5bd]
-   PD    [Recursive Partitioning (Regression Trees)] [RP - Depressie - ...] [2010-12-19 16:08:15] [c289bfbb56808c5d93a0f55b5d39f5bd]
-             [Recursive Partitioning (Regression Trees)] [RP - Cannotdo - N...] [2010-12-19 16:23:40] [3ee4962e6ce79244b15c133e74cea133] [Current]
-               [Recursive Partitioning (Regression Trees)] [RP - Cannotdo - 2...] [2010-12-19 16:25:39] [c289bfbb56808c5d93a0f55b5d39f5bd]
-                 [Recursive Partitioning (Regression Trees)] [RP - Cannotdo - 2...] [2010-12-19 16:26:51] [c289bfbb56808c5d93a0f55b5d39f5bd]
-                 [Recursive Partitioning (Regression Trees)] [RP - Voorspellen ...] [2010-12-19 16:33:31] [c289bfbb56808c5d93a0f55b5d39f5bd]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
0	1	4	4	2
0	1	2	2	2
0	1	5	5	4
1	1	4	5	3
0	2	1	1	2
0	1	2	4	1
0	4	5	6	4
0	1	1	5	3
0	1	3	4	1
0	2	5	5	4
1	1	2	7	4
0	1	2	2	4
1	2	2	7	3
0	1	2	5	4
0	1	1	5	1
1	1	4	7	4
1	1	3	3	1
0	1	6	6	4
1	1	1	2	4
0	2	3	6	3
1	1	2	1	2
0	2	5	5	6
0	1	5	4	5
0	2	3	4	4
1	1	3	7	6
0	1	5	7	1
1	1	5	5	2
0	2	4	6	4
1	1	2	5	4
0	1	1	1	1
1	2	4	6	2
0	1	6	4	1
0	1	2	2	2
1	1	3	2	2
1	1	2	6	2
1	2	4	6	6
1	1	2	6	2
0	1	1	1	1
1	1	5	6	4
1	1	5	6	3
0	1	1	1	3
1	1	1	1	1
1	1	2	7	4
0	1	4	2	3
0	1	5	3	4
0	1	3	5	3
0	1	3	3	2
1	1	1	4	1
0	1	2	2	5
1	1	3	3	4
1	2	2	7	1
0	2	5	7	2
1	1	4	5	4
0	1	4	1	3
0	1	2	2	2
0	2	3	5	3
1	1	6	2	3
0	1	2	4	2
1	2	3	7	2
1	1	2	2	4
0	1	5	5	4
0	1	5	6	2
0	1	5	3	2
1	1	6	7	5
0	2	4	4	4
1	1	2	3	5
0	1	5	5	5
1	2	2	3	2
1	1	1	2	3
0	1	6	6	4
0	1	6	6	2
1	1	3	5	2
1	1	4	2	2
0	3	5	3	5
0	2	2	4	2
0	2	4	6	3
1	1	3	5	2
1	1	2	2	2
1	1	2	5	2
1	1	3	2	2
0	1	3	1	2
1	1	7	2	1
0	1	2	4	3
0	1	2	5	3
1	1	2	5	3
0	1	5	3	3
0	1	1	2	1
0	3	5	7	4
0	1	2	1	1
0	1	1	5	1
0	1	2	5	1
0	1	2	2	3
0	1	0	6	2
0	1	5	2	3
0	1	3	5	5
0	1	2	3	3
1	1	4	3	2
1	1	2	5	2
0	1	2	5	3
1	2	4	5	4
0	1	1	6	4
1	1	5	5	3
0	1	4	5	2
1	2	6	6	3
0	1	2	2	3
1	2	5	5	4
1	2	1	5	2
0	3	7	1	5
0	2	5	5	2
0	2	3	6	2
0	1	4	6	4
1	1	4	3	5
0	1	2	3	0
1	1	1	3	1
0	1	6	5	6
0	1	4	5	1
0	1	2	2	2
0	2	7	3	1
0	1	4	3	4
0	1	4	6	2
0	1	4	5	4
0	1	2	2	1
0	1	5	4	4
1	1	3	2	3
0	1	2	2	1
0	1	3	5	2
0	1	4	5	5
1	2	5	4	3
1	2	6	5	2
0	1	2	1	2
1	1	2	5	4
1	1	2	5	4
0	1	2	5	4
1	4	2	6	4
1	1	5	5	4
0	2	2	5	4
0	1	3	6	2
1	1	6	5	4
0	1	4	5	2
1	1	5	7	2
0	1	1	1	1
1	1	2	3	3
0	1	2	5	2
0	1	2	5	1
1	1	6	6	3
1	1	2	4	3
0	1	2	2	2
0	2	1	4	5
1	1	5	5	2
0	1	3	5	5
0	3	6	5	4
0	1	1	5	1

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time6 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=112571&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=112571&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=112571&T=0

As an alternative you can also use a QR Code:  
QR Code


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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time6 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135






Goodness of Fit
Correlation0.3238
R-squared0.1049
RMSE1.5444

\begin{tabular}{lllllllll}
\hline
Goodness of Fit \tabularnewline
Correlation & 0.3238 \tabularnewline
R-squared & 0.1049 \tabularnewline
RMSE & 1.5444 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=112571&T=1

[TABLE]
[ROW][C]Goodness of Fit[/C][/ROW]
[ROW][C]Correlation[/C][C]0.3238[/C][/ROW]
[ROW][C]R-squared[/C][C]0.1049[/C][/ROW]
[ROW][C]RMSE[/C][C]1.5444[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=112571&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=112571&T=1

As an alternative you can also use a QR Code:  
QR Code


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

Goodness of Fit
Correlation0.3238
R-squared0.1049
RMSE1.5444






Actuals, Predictions, and Residuals
#ActualsForecastsResiduals
143.1250.875
223.125-1.125
353.792452830188681.20754716981132
443.1250.875
511.72727272727273-0.727272727272727
623.125-1.125
753.792452830188681.20754716981132
813.125-2.125
933.125-0.125
1053.792452830188681.20754716981132
1123.79245283018868-1.79245283018868
1223.79245283018868-1.79245283018868
1323.125-1.125
1423.79245283018868-1.79245283018868
1513.125-2.125
1643.792452830188680.207547169811321
1733.125-0.125
1863.792452830188682.20754716981132
1913.79245283018868-2.79245283018868
2033.125-0.125
2121.727272727272730.272727272727273
2253.792452830188681.20754716981132
2353.792452830188681.20754716981132
2433.79245283018868-0.79245283018868
2533.79245283018868-0.79245283018868
2653.1251.875
2753.1251.875
2843.792452830188680.207547169811321
2923.79245283018868-1.79245283018868
3011.72727272727273-0.727272727272727
3143.1250.875
3263.1252.875
3323.125-1.125
3433.125-0.125
3523.125-1.125
3643.792452830188680.207547169811321
3723.125-1.125
3811.72727272727273-0.727272727272727
3953.792452830188681.20754716981132
4053.1251.875
4111.72727272727273-0.727272727272727
4211.72727272727273-0.727272727272727
4323.79245283018868-1.79245283018868
4443.1250.875
4553.792452830188681.20754716981132
4633.125-0.125
4733.125-0.125
4813.125-2.125
4923.79245283018868-1.79245283018868
5033.79245283018868-0.79245283018868
5123.125-1.125
5253.1251.875
5343.792452830188680.207547169811321
5441.727272727272732.27272727272727
5523.125-1.125
5633.125-0.125
5763.1252.875
5823.125-1.125
5933.125-0.125
6023.79245283018868-1.79245283018868
6153.792452830188681.20754716981132
6253.1251.875
6353.1251.875
6463.792452830188682.20754716981132
6543.792452830188680.207547169811321
6623.79245283018868-1.79245283018868
6753.792452830188681.20754716981132
6823.125-1.125
6913.125-2.125
7063.792452830188682.20754716981132
7163.1252.875
7233.125-0.125
7343.1250.875
7453.792452830188681.20754716981132
7523.125-1.125
7643.1250.875
7733.125-0.125
7823.125-1.125
7923.125-1.125
8033.125-0.125
8131.727272727272731.27272727272727
8273.1253.875
8323.125-1.125
8423.125-1.125
8523.125-1.125
8653.1251.875
8713.125-2.125
8853.792452830188681.20754716981132
8921.727272727272730.272727272727273
9013.125-2.125
9123.125-1.125
9223.125-1.125
9303.125-3.125
9453.1251.875
9533.79245283018868-0.79245283018868
9623.125-1.125
9743.1250.875
9823.125-1.125
9923.125-1.125
10043.792452830188680.207547169811321
10113.79245283018868-2.79245283018868
10253.1251.875
10343.1250.875
10463.1252.875
10523.125-1.125
10653.792452830188681.20754716981132
10713.125-2.125
10873.792452830188683.20754716981132
10953.1251.875
11033.125-0.125
11143.792452830188680.207547169811321
11243.792452830188680.207547169811321
11323.125-1.125
11413.125-2.125
11563.792452830188682.20754716981132
11643.1250.875
11723.125-1.125
11873.1253.875
11943.792452830188680.207547169811321
12043.1250.875
12143.792452830188680.207547169811321
12223.125-1.125
12353.792452830188681.20754716981132
12433.125-0.125
12523.125-1.125
12633.125-0.125
12743.792452830188680.207547169811321
12853.1251.875
12963.1252.875
13021.727272727272730.272727272727273
13123.79245283018868-1.79245283018868
13223.79245283018868-1.79245283018868
13323.79245283018868-1.79245283018868
13423.79245283018868-1.79245283018868
13553.792452830188681.20754716981132
13623.79245283018868-1.79245283018868
13733.125-0.125
13863.792452830188682.20754716981132
13943.1250.875
14053.1251.875
14111.72727272727273-0.727272727272727
14223.125-1.125
14323.125-1.125
14423.125-1.125
14563.1252.875
14623.125-1.125
14723.125-1.125
14813.79245283018868-2.79245283018868
14953.1251.875
15033.79245283018868-0.79245283018868
15163.792452830188682.20754716981132
15213.125-2.125

\begin{tabular}{lllllllll}
\hline
Actuals, Predictions, and Residuals \tabularnewline
# & Actuals & Forecasts & Residuals \tabularnewline
1 & 4 & 3.125 & 0.875 \tabularnewline
2 & 2 & 3.125 & -1.125 \tabularnewline
3 & 5 & 3.79245283018868 & 1.20754716981132 \tabularnewline
4 & 4 & 3.125 & 0.875 \tabularnewline
5 & 1 & 1.72727272727273 & -0.727272727272727 \tabularnewline
6 & 2 & 3.125 & -1.125 \tabularnewline
7 & 5 & 3.79245283018868 & 1.20754716981132 \tabularnewline
8 & 1 & 3.125 & -2.125 \tabularnewline
9 & 3 & 3.125 & -0.125 \tabularnewline
10 & 5 & 3.79245283018868 & 1.20754716981132 \tabularnewline
11 & 2 & 3.79245283018868 & -1.79245283018868 \tabularnewline
12 & 2 & 3.79245283018868 & -1.79245283018868 \tabularnewline
13 & 2 & 3.125 & -1.125 \tabularnewline
14 & 2 & 3.79245283018868 & -1.79245283018868 \tabularnewline
15 & 1 & 3.125 & -2.125 \tabularnewline
16 & 4 & 3.79245283018868 & 0.207547169811321 \tabularnewline
17 & 3 & 3.125 & -0.125 \tabularnewline
18 & 6 & 3.79245283018868 & 2.20754716981132 \tabularnewline
19 & 1 & 3.79245283018868 & -2.79245283018868 \tabularnewline
20 & 3 & 3.125 & -0.125 \tabularnewline
21 & 2 & 1.72727272727273 & 0.272727272727273 \tabularnewline
22 & 5 & 3.79245283018868 & 1.20754716981132 \tabularnewline
23 & 5 & 3.79245283018868 & 1.20754716981132 \tabularnewline
24 & 3 & 3.79245283018868 & -0.79245283018868 \tabularnewline
25 & 3 & 3.79245283018868 & -0.79245283018868 \tabularnewline
26 & 5 & 3.125 & 1.875 \tabularnewline
27 & 5 & 3.125 & 1.875 \tabularnewline
28 & 4 & 3.79245283018868 & 0.207547169811321 \tabularnewline
29 & 2 & 3.79245283018868 & -1.79245283018868 \tabularnewline
30 & 1 & 1.72727272727273 & -0.727272727272727 \tabularnewline
31 & 4 & 3.125 & 0.875 \tabularnewline
32 & 6 & 3.125 & 2.875 \tabularnewline
33 & 2 & 3.125 & -1.125 \tabularnewline
34 & 3 & 3.125 & -0.125 \tabularnewline
35 & 2 & 3.125 & -1.125 \tabularnewline
36 & 4 & 3.79245283018868 & 0.207547169811321 \tabularnewline
37 & 2 & 3.125 & -1.125 \tabularnewline
38 & 1 & 1.72727272727273 & -0.727272727272727 \tabularnewline
39 & 5 & 3.79245283018868 & 1.20754716981132 \tabularnewline
40 & 5 & 3.125 & 1.875 \tabularnewline
41 & 1 & 1.72727272727273 & -0.727272727272727 \tabularnewline
42 & 1 & 1.72727272727273 & -0.727272727272727 \tabularnewline
43 & 2 & 3.79245283018868 & -1.79245283018868 \tabularnewline
44 & 4 & 3.125 & 0.875 \tabularnewline
45 & 5 & 3.79245283018868 & 1.20754716981132 \tabularnewline
46 & 3 & 3.125 & -0.125 \tabularnewline
47 & 3 & 3.125 & -0.125 \tabularnewline
48 & 1 & 3.125 & -2.125 \tabularnewline
49 & 2 & 3.79245283018868 & -1.79245283018868 \tabularnewline
50 & 3 & 3.79245283018868 & -0.79245283018868 \tabularnewline
51 & 2 & 3.125 & -1.125 \tabularnewline
52 & 5 & 3.125 & 1.875 \tabularnewline
53 & 4 & 3.79245283018868 & 0.207547169811321 \tabularnewline
54 & 4 & 1.72727272727273 & 2.27272727272727 \tabularnewline
55 & 2 & 3.125 & -1.125 \tabularnewline
56 & 3 & 3.125 & -0.125 \tabularnewline
57 & 6 & 3.125 & 2.875 \tabularnewline
58 & 2 & 3.125 & -1.125 \tabularnewline
59 & 3 & 3.125 & -0.125 \tabularnewline
60 & 2 & 3.79245283018868 & -1.79245283018868 \tabularnewline
61 & 5 & 3.79245283018868 & 1.20754716981132 \tabularnewline
62 & 5 & 3.125 & 1.875 \tabularnewline
63 & 5 & 3.125 & 1.875 \tabularnewline
64 & 6 & 3.79245283018868 & 2.20754716981132 \tabularnewline
65 & 4 & 3.79245283018868 & 0.207547169811321 \tabularnewline
66 & 2 & 3.79245283018868 & -1.79245283018868 \tabularnewline
67 & 5 & 3.79245283018868 & 1.20754716981132 \tabularnewline
68 & 2 & 3.125 & -1.125 \tabularnewline
69 & 1 & 3.125 & -2.125 \tabularnewline
70 & 6 & 3.79245283018868 & 2.20754716981132 \tabularnewline
71 & 6 & 3.125 & 2.875 \tabularnewline
72 & 3 & 3.125 & -0.125 \tabularnewline
73 & 4 & 3.125 & 0.875 \tabularnewline
74 & 5 & 3.79245283018868 & 1.20754716981132 \tabularnewline
75 & 2 & 3.125 & -1.125 \tabularnewline
76 & 4 & 3.125 & 0.875 \tabularnewline
77 & 3 & 3.125 & -0.125 \tabularnewline
78 & 2 & 3.125 & -1.125 \tabularnewline
79 & 2 & 3.125 & -1.125 \tabularnewline
80 & 3 & 3.125 & -0.125 \tabularnewline
81 & 3 & 1.72727272727273 & 1.27272727272727 \tabularnewline
82 & 7 & 3.125 & 3.875 \tabularnewline
83 & 2 & 3.125 & -1.125 \tabularnewline
84 & 2 & 3.125 & -1.125 \tabularnewline
85 & 2 & 3.125 & -1.125 \tabularnewline
86 & 5 & 3.125 & 1.875 \tabularnewline
87 & 1 & 3.125 & -2.125 \tabularnewline
88 & 5 & 3.79245283018868 & 1.20754716981132 \tabularnewline
89 & 2 & 1.72727272727273 & 0.272727272727273 \tabularnewline
90 & 1 & 3.125 & -2.125 \tabularnewline
91 & 2 & 3.125 & -1.125 \tabularnewline
92 & 2 & 3.125 & -1.125 \tabularnewline
93 & 0 & 3.125 & -3.125 \tabularnewline
94 & 5 & 3.125 & 1.875 \tabularnewline
95 & 3 & 3.79245283018868 & -0.79245283018868 \tabularnewline
96 & 2 & 3.125 & -1.125 \tabularnewline
97 & 4 & 3.125 & 0.875 \tabularnewline
98 & 2 & 3.125 & -1.125 \tabularnewline
99 & 2 & 3.125 & -1.125 \tabularnewline
100 & 4 & 3.79245283018868 & 0.207547169811321 \tabularnewline
101 & 1 & 3.79245283018868 & -2.79245283018868 \tabularnewline
102 & 5 & 3.125 & 1.875 \tabularnewline
103 & 4 & 3.125 & 0.875 \tabularnewline
104 & 6 & 3.125 & 2.875 \tabularnewline
105 & 2 & 3.125 & -1.125 \tabularnewline
106 & 5 & 3.79245283018868 & 1.20754716981132 \tabularnewline
107 & 1 & 3.125 & -2.125 \tabularnewline
108 & 7 & 3.79245283018868 & 3.20754716981132 \tabularnewline
109 & 5 & 3.125 & 1.875 \tabularnewline
110 & 3 & 3.125 & -0.125 \tabularnewline
111 & 4 & 3.79245283018868 & 0.207547169811321 \tabularnewline
112 & 4 & 3.79245283018868 & 0.207547169811321 \tabularnewline
113 & 2 & 3.125 & -1.125 \tabularnewline
114 & 1 & 3.125 & -2.125 \tabularnewline
115 & 6 & 3.79245283018868 & 2.20754716981132 \tabularnewline
116 & 4 & 3.125 & 0.875 \tabularnewline
117 & 2 & 3.125 & -1.125 \tabularnewline
118 & 7 & 3.125 & 3.875 \tabularnewline
119 & 4 & 3.79245283018868 & 0.207547169811321 \tabularnewline
120 & 4 & 3.125 & 0.875 \tabularnewline
121 & 4 & 3.79245283018868 & 0.207547169811321 \tabularnewline
122 & 2 & 3.125 & -1.125 \tabularnewline
123 & 5 & 3.79245283018868 & 1.20754716981132 \tabularnewline
124 & 3 & 3.125 & -0.125 \tabularnewline
125 & 2 & 3.125 & -1.125 \tabularnewline
126 & 3 & 3.125 & -0.125 \tabularnewline
127 & 4 & 3.79245283018868 & 0.207547169811321 \tabularnewline
128 & 5 & 3.125 & 1.875 \tabularnewline
129 & 6 & 3.125 & 2.875 \tabularnewline
130 & 2 & 1.72727272727273 & 0.272727272727273 \tabularnewline
131 & 2 & 3.79245283018868 & -1.79245283018868 \tabularnewline
132 & 2 & 3.79245283018868 & -1.79245283018868 \tabularnewline
133 & 2 & 3.79245283018868 & -1.79245283018868 \tabularnewline
134 & 2 & 3.79245283018868 & -1.79245283018868 \tabularnewline
135 & 5 & 3.79245283018868 & 1.20754716981132 \tabularnewline
136 & 2 & 3.79245283018868 & -1.79245283018868 \tabularnewline
137 & 3 & 3.125 & -0.125 \tabularnewline
138 & 6 & 3.79245283018868 & 2.20754716981132 \tabularnewline
139 & 4 & 3.125 & 0.875 \tabularnewline
140 & 5 & 3.125 & 1.875 \tabularnewline
141 & 1 & 1.72727272727273 & -0.727272727272727 \tabularnewline
142 & 2 & 3.125 & -1.125 \tabularnewline
143 & 2 & 3.125 & -1.125 \tabularnewline
144 & 2 & 3.125 & -1.125 \tabularnewline
145 & 6 & 3.125 & 2.875 \tabularnewline
146 & 2 & 3.125 & -1.125 \tabularnewline
147 & 2 & 3.125 & -1.125 \tabularnewline
148 & 1 & 3.79245283018868 & -2.79245283018868 \tabularnewline
149 & 5 & 3.125 & 1.875 \tabularnewline
150 & 3 & 3.79245283018868 & -0.79245283018868 \tabularnewline
151 & 6 & 3.79245283018868 & 2.20754716981132 \tabularnewline
152 & 1 & 3.125 & -2.125 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=112571&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]4[/C][C]3.125[/C][C]0.875[/C][/ROW]
[ROW][C]2[/C][C]2[/C][C]3.125[/C][C]-1.125[/C][/ROW]
[ROW][C]3[/C][C]5[/C][C]3.79245283018868[/C][C]1.20754716981132[/C][/ROW]
[ROW][C]4[/C][C]4[/C][C]3.125[/C][C]0.875[/C][/ROW]
[ROW][C]5[/C][C]1[/C][C]1.72727272727273[/C][C]-0.727272727272727[/C][/ROW]
[ROW][C]6[/C][C]2[/C][C]3.125[/C][C]-1.125[/C][/ROW]
[ROW][C]7[/C][C]5[/C][C]3.79245283018868[/C][C]1.20754716981132[/C][/ROW]
[ROW][C]8[/C][C]1[/C][C]3.125[/C][C]-2.125[/C][/ROW]
[ROW][C]9[/C][C]3[/C][C]3.125[/C][C]-0.125[/C][/ROW]
[ROW][C]10[/C][C]5[/C][C]3.79245283018868[/C][C]1.20754716981132[/C][/ROW]
[ROW][C]11[/C][C]2[/C][C]3.79245283018868[/C][C]-1.79245283018868[/C][/ROW]
[ROW][C]12[/C][C]2[/C][C]3.79245283018868[/C][C]-1.79245283018868[/C][/ROW]
[ROW][C]13[/C][C]2[/C][C]3.125[/C][C]-1.125[/C][/ROW]
[ROW][C]14[/C][C]2[/C][C]3.79245283018868[/C][C]-1.79245283018868[/C][/ROW]
[ROW][C]15[/C][C]1[/C][C]3.125[/C][C]-2.125[/C][/ROW]
[ROW][C]16[/C][C]4[/C][C]3.79245283018868[/C][C]0.207547169811321[/C][/ROW]
[ROW][C]17[/C][C]3[/C][C]3.125[/C][C]-0.125[/C][/ROW]
[ROW][C]18[/C][C]6[/C][C]3.79245283018868[/C][C]2.20754716981132[/C][/ROW]
[ROW][C]19[/C][C]1[/C][C]3.79245283018868[/C][C]-2.79245283018868[/C][/ROW]
[ROW][C]20[/C][C]3[/C][C]3.125[/C][C]-0.125[/C][/ROW]
[ROW][C]21[/C][C]2[/C][C]1.72727272727273[/C][C]0.272727272727273[/C][/ROW]
[ROW][C]22[/C][C]5[/C][C]3.79245283018868[/C][C]1.20754716981132[/C][/ROW]
[ROW][C]23[/C][C]5[/C][C]3.79245283018868[/C][C]1.20754716981132[/C][/ROW]
[ROW][C]24[/C][C]3[/C][C]3.79245283018868[/C][C]-0.79245283018868[/C][/ROW]
[ROW][C]25[/C][C]3[/C][C]3.79245283018868[/C][C]-0.79245283018868[/C][/ROW]
[ROW][C]26[/C][C]5[/C][C]3.125[/C][C]1.875[/C][/ROW]
[ROW][C]27[/C][C]5[/C][C]3.125[/C][C]1.875[/C][/ROW]
[ROW][C]28[/C][C]4[/C][C]3.79245283018868[/C][C]0.207547169811321[/C][/ROW]
[ROW][C]29[/C][C]2[/C][C]3.79245283018868[/C][C]-1.79245283018868[/C][/ROW]
[ROW][C]30[/C][C]1[/C][C]1.72727272727273[/C][C]-0.727272727272727[/C][/ROW]
[ROW][C]31[/C][C]4[/C][C]3.125[/C][C]0.875[/C][/ROW]
[ROW][C]32[/C][C]6[/C][C]3.125[/C][C]2.875[/C][/ROW]
[ROW][C]33[/C][C]2[/C][C]3.125[/C][C]-1.125[/C][/ROW]
[ROW][C]34[/C][C]3[/C][C]3.125[/C][C]-0.125[/C][/ROW]
[ROW][C]35[/C][C]2[/C][C]3.125[/C][C]-1.125[/C][/ROW]
[ROW][C]36[/C][C]4[/C][C]3.79245283018868[/C][C]0.207547169811321[/C][/ROW]
[ROW][C]37[/C][C]2[/C][C]3.125[/C][C]-1.125[/C][/ROW]
[ROW][C]38[/C][C]1[/C][C]1.72727272727273[/C][C]-0.727272727272727[/C][/ROW]
[ROW][C]39[/C][C]5[/C][C]3.79245283018868[/C][C]1.20754716981132[/C][/ROW]
[ROW][C]40[/C][C]5[/C][C]3.125[/C][C]1.875[/C][/ROW]
[ROW][C]41[/C][C]1[/C][C]1.72727272727273[/C][C]-0.727272727272727[/C][/ROW]
[ROW][C]42[/C][C]1[/C][C]1.72727272727273[/C][C]-0.727272727272727[/C][/ROW]
[ROW][C]43[/C][C]2[/C][C]3.79245283018868[/C][C]-1.79245283018868[/C][/ROW]
[ROW][C]44[/C][C]4[/C][C]3.125[/C][C]0.875[/C][/ROW]
[ROW][C]45[/C][C]5[/C][C]3.79245283018868[/C][C]1.20754716981132[/C][/ROW]
[ROW][C]46[/C][C]3[/C][C]3.125[/C][C]-0.125[/C][/ROW]
[ROW][C]47[/C][C]3[/C][C]3.125[/C][C]-0.125[/C][/ROW]
[ROW][C]48[/C][C]1[/C][C]3.125[/C][C]-2.125[/C][/ROW]
[ROW][C]49[/C][C]2[/C][C]3.79245283018868[/C][C]-1.79245283018868[/C][/ROW]
[ROW][C]50[/C][C]3[/C][C]3.79245283018868[/C][C]-0.79245283018868[/C][/ROW]
[ROW][C]51[/C][C]2[/C][C]3.125[/C][C]-1.125[/C][/ROW]
[ROW][C]52[/C][C]5[/C][C]3.125[/C][C]1.875[/C][/ROW]
[ROW][C]53[/C][C]4[/C][C]3.79245283018868[/C][C]0.207547169811321[/C][/ROW]
[ROW][C]54[/C][C]4[/C][C]1.72727272727273[/C][C]2.27272727272727[/C][/ROW]
[ROW][C]55[/C][C]2[/C][C]3.125[/C][C]-1.125[/C][/ROW]
[ROW][C]56[/C][C]3[/C][C]3.125[/C][C]-0.125[/C][/ROW]
[ROW][C]57[/C][C]6[/C][C]3.125[/C][C]2.875[/C][/ROW]
[ROW][C]58[/C][C]2[/C][C]3.125[/C][C]-1.125[/C][/ROW]
[ROW][C]59[/C][C]3[/C][C]3.125[/C][C]-0.125[/C][/ROW]
[ROW][C]60[/C][C]2[/C][C]3.79245283018868[/C][C]-1.79245283018868[/C][/ROW]
[ROW][C]61[/C][C]5[/C][C]3.79245283018868[/C][C]1.20754716981132[/C][/ROW]
[ROW][C]62[/C][C]5[/C][C]3.125[/C][C]1.875[/C][/ROW]
[ROW][C]63[/C][C]5[/C][C]3.125[/C][C]1.875[/C][/ROW]
[ROW][C]64[/C][C]6[/C][C]3.79245283018868[/C][C]2.20754716981132[/C][/ROW]
[ROW][C]65[/C][C]4[/C][C]3.79245283018868[/C][C]0.207547169811321[/C][/ROW]
[ROW][C]66[/C][C]2[/C][C]3.79245283018868[/C][C]-1.79245283018868[/C][/ROW]
[ROW][C]67[/C][C]5[/C][C]3.79245283018868[/C][C]1.20754716981132[/C][/ROW]
[ROW][C]68[/C][C]2[/C][C]3.125[/C][C]-1.125[/C][/ROW]
[ROW][C]69[/C][C]1[/C][C]3.125[/C][C]-2.125[/C][/ROW]
[ROW][C]70[/C][C]6[/C][C]3.79245283018868[/C][C]2.20754716981132[/C][/ROW]
[ROW][C]71[/C][C]6[/C][C]3.125[/C][C]2.875[/C][/ROW]
[ROW][C]72[/C][C]3[/C][C]3.125[/C][C]-0.125[/C][/ROW]
[ROW][C]73[/C][C]4[/C][C]3.125[/C][C]0.875[/C][/ROW]
[ROW][C]74[/C][C]5[/C][C]3.79245283018868[/C][C]1.20754716981132[/C][/ROW]
[ROW][C]75[/C][C]2[/C][C]3.125[/C][C]-1.125[/C][/ROW]
[ROW][C]76[/C][C]4[/C][C]3.125[/C][C]0.875[/C][/ROW]
[ROW][C]77[/C][C]3[/C][C]3.125[/C][C]-0.125[/C][/ROW]
[ROW][C]78[/C][C]2[/C][C]3.125[/C][C]-1.125[/C][/ROW]
[ROW][C]79[/C][C]2[/C][C]3.125[/C][C]-1.125[/C][/ROW]
[ROW][C]80[/C][C]3[/C][C]3.125[/C][C]-0.125[/C][/ROW]
[ROW][C]81[/C][C]3[/C][C]1.72727272727273[/C][C]1.27272727272727[/C][/ROW]
[ROW][C]82[/C][C]7[/C][C]3.125[/C][C]3.875[/C][/ROW]
[ROW][C]83[/C][C]2[/C][C]3.125[/C][C]-1.125[/C][/ROW]
[ROW][C]84[/C][C]2[/C][C]3.125[/C][C]-1.125[/C][/ROW]
[ROW][C]85[/C][C]2[/C][C]3.125[/C][C]-1.125[/C][/ROW]
[ROW][C]86[/C][C]5[/C][C]3.125[/C][C]1.875[/C][/ROW]
[ROW][C]87[/C][C]1[/C][C]3.125[/C][C]-2.125[/C][/ROW]
[ROW][C]88[/C][C]5[/C][C]3.79245283018868[/C][C]1.20754716981132[/C][/ROW]
[ROW][C]89[/C][C]2[/C][C]1.72727272727273[/C][C]0.272727272727273[/C][/ROW]
[ROW][C]90[/C][C]1[/C][C]3.125[/C][C]-2.125[/C][/ROW]
[ROW][C]91[/C][C]2[/C][C]3.125[/C][C]-1.125[/C][/ROW]
[ROW][C]92[/C][C]2[/C][C]3.125[/C][C]-1.125[/C][/ROW]
[ROW][C]93[/C][C]0[/C][C]3.125[/C][C]-3.125[/C][/ROW]
[ROW][C]94[/C][C]5[/C][C]3.125[/C][C]1.875[/C][/ROW]
[ROW][C]95[/C][C]3[/C][C]3.79245283018868[/C][C]-0.79245283018868[/C][/ROW]
[ROW][C]96[/C][C]2[/C][C]3.125[/C][C]-1.125[/C][/ROW]
[ROW][C]97[/C][C]4[/C][C]3.125[/C][C]0.875[/C][/ROW]
[ROW][C]98[/C][C]2[/C][C]3.125[/C][C]-1.125[/C][/ROW]
[ROW][C]99[/C][C]2[/C][C]3.125[/C][C]-1.125[/C][/ROW]
[ROW][C]100[/C][C]4[/C][C]3.79245283018868[/C][C]0.207547169811321[/C][/ROW]
[ROW][C]101[/C][C]1[/C][C]3.79245283018868[/C][C]-2.79245283018868[/C][/ROW]
[ROW][C]102[/C][C]5[/C][C]3.125[/C][C]1.875[/C][/ROW]
[ROW][C]103[/C][C]4[/C][C]3.125[/C][C]0.875[/C][/ROW]
[ROW][C]104[/C][C]6[/C][C]3.125[/C][C]2.875[/C][/ROW]
[ROW][C]105[/C][C]2[/C][C]3.125[/C][C]-1.125[/C][/ROW]
[ROW][C]106[/C][C]5[/C][C]3.79245283018868[/C][C]1.20754716981132[/C][/ROW]
[ROW][C]107[/C][C]1[/C][C]3.125[/C][C]-2.125[/C][/ROW]
[ROW][C]108[/C][C]7[/C][C]3.79245283018868[/C][C]3.20754716981132[/C][/ROW]
[ROW][C]109[/C][C]5[/C][C]3.125[/C][C]1.875[/C][/ROW]
[ROW][C]110[/C][C]3[/C][C]3.125[/C][C]-0.125[/C][/ROW]
[ROW][C]111[/C][C]4[/C][C]3.79245283018868[/C][C]0.207547169811321[/C][/ROW]
[ROW][C]112[/C][C]4[/C][C]3.79245283018868[/C][C]0.207547169811321[/C][/ROW]
[ROW][C]113[/C][C]2[/C][C]3.125[/C][C]-1.125[/C][/ROW]
[ROW][C]114[/C][C]1[/C][C]3.125[/C][C]-2.125[/C][/ROW]
[ROW][C]115[/C][C]6[/C][C]3.79245283018868[/C][C]2.20754716981132[/C][/ROW]
[ROW][C]116[/C][C]4[/C][C]3.125[/C][C]0.875[/C][/ROW]
[ROW][C]117[/C][C]2[/C][C]3.125[/C][C]-1.125[/C][/ROW]
[ROW][C]118[/C][C]7[/C][C]3.125[/C][C]3.875[/C][/ROW]
[ROW][C]119[/C][C]4[/C][C]3.79245283018868[/C][C]0.207547169811321[/C][/ROW]
[ROW][C]120[/C][C]4[/C][C]3.125[/C][C]0.875[/C][/ROW]
[ROW][C]121[/C][C]4[/C][C]3.79245283018868[/C][C]0.207547169811321[/C][/ROW]
[ROW][C]122[/C][C]2[/C][C]3.125[/C][C]-1.125[/C][/ROW]
[ROW][C]123[/C][C]5[/C][C]3.79245283018868[/C][C]1.20754716981132[/C][/ROW]
[ROW][C]124[/C][C]3[/C][C]3.125[/C][C]-0.125[/C][/ROW]
[ROW][C]125[/C][C]2[/C][C]3.125[/C][C]-1.125[/C][/ROW]
[ROW][C]126[/C][C]3[/C][C]3.125[/C][C]-0.125[/C][/ROW]
[ROW][C]127[/C][C]4[/C][C]3.79245283018868[/C][C]0.207547169811321[/C][/ROW]
[ROW][C]128[/C][C]5[/C][C]3.125[/C][C]1.875[/C][/ROW]
[ROW][C]129[/C][C]6[/C][C]3.125[/C][C]2.875[/C][/ROW]
[ROW][C]130[/C][C]2[/C][C]1.72727272727273[/C][C]0.272727272727273[/C][/ROW]
[ROW][C]131[/C][C]2[/C][C]3.79245283018868[/C][C]-1.79245283018868[/C][/ROW]
[ROW][C]132[/C][C]2[/C][C]3.79245283018868[/C][C]-1.79245283018868[/C][/ROW]
[ROW][C]133[/C][C]2[/C][C]3.79245283018868[/C][C]-1.79245283018868[/C][/ROW]
[ROW][C]134[/C][C]2[/C][C]3.79245283018868[/C][C]-1.79245283018868[/C][/ROW]
[ROW][C]135[/C][C]5[/C][C]3.79245283018868[/C][C]1.20754716981132[/C][/ROW]
[ROW][C]136[/C][C]2[/C][C]3.79245283018868[/C][C]-1.79245283018868[/C][/ROW]
[ROW][C]137[/C][C]3[/C][C]3.125[/C][C]-0.125[/C][/ROW]
[ROW][C]138[/C][C]6[/C][C]3.79245283018868[/C][C]2.20754716981132[/C][/ROW]
[ROW][C]139[/C][C]4[/C][C]3.125[/C][C]0.875[/C][/ROW]
[ROW][C]140[/C][C]5[/C][C]3.125[/C][C]1.875[/C][/ROW]
[ROW][C]141[/C][C]1[/C][C]1.72727272727273[/C][C]-0.727272727272727[/C][/ROW]
[ROW][C]142[/C][C]2[/C][C]3.125[/C][C]-1.125[/C][/ROW]
[ROW][C]143[/C][C]2[/C][C]3.125[/C][C]-1.125[/C][/ROW]
[ROW][C]144[/C][C]2[/C][C]3.125[/C][C]-1.125[/C][/ROW]
[ROW][C]145[/C][C]6[/C][C]3.125[/C][C]2.875[/C][/ROW]
[ROW][C]146[/C][C]2[/C][C]3.125[/C][C]-1.125[/C][/ROW]
[ROW][C]147[/C][C]2[/C][C]3.125[/C][C]-1.125[/C][/ROW]
[ROW][C]148[/C][C]1[/C][C]3.79245283018868[/C][C]-2.79245283018868[/C][/ROW]
[ROW][C]149[/C][C]5[/C][C]3.125[/C][C]1.875[/C][/ROW]
[ROW][C]150[/C][C]3[/C][C]3.79245283018868[/C][C]-0.79245283018868[/C][/ROW]
[ROW][C]151[/C][C]6[/C][C]3.79245283018868[/C][C]2.20754716981132[/C][/ROW]
[ROW][C]152[/C][C]1[/C][C]3.125[/C][C]-2.125[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=112571&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=112571&T=2

As an alternative you can also use a QR Code:  
QR Code


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

Actuals, Predictions, and Residuals
#ActualsForecastsResiduals
143.1250.875
223.125-1.125
353.792452830188681.20754716981132
443.1250.875
511.72727272727273-0.727272727272727
623.125-1.125
753.792452830188681.20754716981132
813.125-2.125
933.125-0.125
1053.792452830188681.20754716981132
1123.79245283018868-1.79245283018868
1223.79245283018868-1.79245283018868
1323.125-1.125
1423.79245283018868-1.79245283018868
1513.125-2.125
1643.792452830188680.207547169811321
1733.125-0.125
1863.792452830188682.20754716981132
1913.79245283018868-2.79245283018868
2033.125-0.125
2121.727272727272730.272727272727273
2253.792452830188681.20754716981132
2353.792452830188681.20754716981132
2433.79245283018868-0.79245283018868
2533.79245283018868-0.79245283018868
2653.1251.875
2753.1251.875
2843.792452830188680.207547169811321
2923.79245283018868-1.79245283018868
3011.72727272727273-0.727272727272727
3143.1250.875
3263.1252.875
3323.125-1.125
3433.125-0.125
3523.125-1.125
3643.792452830188680.207547169811321
3723.125-1.125
3811.72727272727273-0.727272727272727
3953.792452830188681.20754716981132
4053.1251.875
4111.72727272727273-0.727272727272727
4211.72727272727273-0.727272727272727
4323.79245283018868-1.79245283018868
4443.1250.875
4553.792452830188681.20754716981132
4633.125-0.125
4733.125-0.125
4813.125-2.125
4923.79245283018868-1.79245283018868
5033.79245283018868-0.79245283018868
5123.125-1.125
5253.1251.875
5343.792452830188680.207547169811321
5441.727272727272732.27272727272727
5523.125-1.125
5633.125-0.125
5763.1252.875
5823.125-1.125
5933.125-0.125
6023.79245283018868-1.79245283018868
6153.792452830188681.20754716981132
6253.1251.875
6353.1251.875
6463.792452830188682.20754716981132
6543.792452830188680.207547169811321
6623.79245283018868-1.79245283018868
6753.792452830188681.20754716981132
6823.125-1.125
6913.125-2.125
7063.792452830188682.20754716981132
7163.1252.875
7233.125-0.125
7343.1250.875
7453.792452830188681.20754716981132
7523.125-1.125
7643.1250.875
7733.125-0.125
7823.125-1.125
7923.125-1.125
8033.125-0.125
8131.727272727272731.27272727272727
8273.1253.875
8323.125-1.125
8423.125-1.125
8523.125-1.125
8653.1251.875
8713.125-2.125
8853.792452830188681.20754716981132
8921.727272727272730.272727272727273
9013.125-2.125
9123.125-1.125
9223.125-1.125
9303.125-3.125
9453.1251.875
9533.79245283018868-0.79245283018868
9623.125-1.125
9743.1250.875
9823.125-1.125
9923.125-1.125
10043.792452830188680.207547169811321
10113.79245283018868-2.79245283018868
10253.1251.875
10343.1250.875
10463.1252.875
10523.125-1.125
10653.792452830188681.20754716981132
10713.125-2.125
10873.792452830188683.20754716981132
10953.1251.875
11033.125-0.125
11143.792452830188680.207547169811321
11243.792452830188680.207547169811321
11323.125-1.125
11413.125-2.125
11563.792452830188682.20754716981132
11643.1250.875
11723.125-1.125
11873.1253.875
11943.792452830188680.207547169811321
12043.1250.875
12143.792452830188680.207547169811321
12223.125-1.125
12353.792452830188681.20754716981132
12433.125-0.125
12523.125-1.125
12633.125-0.125
12743.792452830188680.207547169811321
12853.1251.875
12963.1252.875
13021.727272727272730.272727272727273
13123.79245283018868-1.79245283018868
13223.79245283018868-1.79245283018868
13323.79245283018868-1.79245283018868
13423.79245283018868-1.79245283018868
13553.792452830188681.20754716981132
13623.79245283018868-1.79245283018868
13733.125-0.125
13863.792452830188682.20754716981132
13943.1250.875
14053.1251.875
14111.72727272727273-0.727272727272727
14223.125-1.125
14323.125-1.125
14423.125-1.125
14563.1252.875
14623.125-1.125
14723.125-1.125
14813.79245283018868-2.79245283018868
14953.1251.875
15033.79245283018868-0.79245283018868
15163.792452830188682.20754716981132
15213.125-2.125


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = 5 ; par2 = quantiles ; par3 = 2 ; par4 = no ;
Parameters (R input):
par1 = 3 ; 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')
}