Free Statistics

of Irreproducible Research!

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 computationFri, 07 Dec 2012 05:39:31 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2012/Dec/07/t13548767910lw619vm5oa4au1.htm/, Retrieved Fri, 29 Mar 2024 12:31:33 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=197281, Retrieved Fri, 29 Mar 2024 12:31:33 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact130
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Recursive Partitioning (Regression Trees)] [] [2010-12-05 18:59:57] [b98453cac15ba1066b407e146608df68]
- R PD    [Recursive Partitioning (Regression Trees)] [ws10.3] [2012-12-07 10:39:31] [e5ad38085056e4424dc3e3ce5946aa62] [Current]
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Dataseries X:
1	1	4	0	2
1	1	0	0	2
0	1	4	1	1.5
0	0	0	0	0
1	1	0	1	1
1	1	0	1	2
1	1	0	1	2
0	1	0	1	1
0	1	4	1	2
1	1	1	0	2
0	0	4	0	2
0	1	0	1	0
0	1	2	1	0
0	1	0	0	2
0	0	0	NA	NA
1	1	0	1	2
1	1	1	0	2
1	1	0	1	0.5
0	1	0	1	2
0	0	2	1	0
1	1	2	1	2
1	1	1	0	0
0	0	2	NA	NA
1	0	0	NA	NA
1	1	3	1	2
1	0	0	1	0
1	1	0	NA	NA
0	0	0	NA	NA
0	0	1	0	2
1	1	0	1	1
1	0	0	0	0.5
1	1	4	0	2
0	0	0	1	0.5
0	0	1	NA	NA
0	0	0	1	0.5
1	1	0	NA	NA
1	1	4	0	2
0	1	1	1	0
0	1	0	1	1
1	1	4	1	2
1	1	0	1	1
1	1	4	1	2
1	1	0	0	0
1	1	0	1	0.5
0	0	0	1	0
0	1	4	1	2
0	1	0	0	0
1	1	0	0	1
1	1	4	1	2
0	0	4	0	0.5
0	1	0	1	2
1	1	1	1	2
0	1	0	1	2
0	0	4	NA	NA
0	1	0	0	0
0	1	2	1	0
0	1	0	1	0.5
0	1	4	NA	NA
0	0	4	0	2
0	0	0	NA	NA
0	1	0	1	0
1	1	4	1	2
1	1	0	1	1
1	0	0	1	0
0	0	2	1	2
0	1	0	0	1
0	1	0	1	2
0	0	0	0	0
1	1	4	1	1
1	1	4	1	2
0	1	2	0	0
0	1	0	0	0
0	1	0	0	0
0	1	4	0	0
1	1	0	1	2
1	0	0	1	2
0	0	1	1	2
1	1	2	1	2
1	0	0	1	2
1	1	2	1	2
0	0	0	1	2
0	0	4	1	2
0	0	4	1	2
1	0	0	1	2
0	0	0	NA	NA
0	0	4	1	2
1	0	0	NA	NA
1	1	4	1	2
0	0	2	1	2
0	0	2	NA	NA
1	1	0	0	0
1	1	0	1	2
1	1	4	NA	NA
0	1	0	1	2
1	1	0	1	2
1	1	0	1	2
1	1	4	1	2
1	1	4	1	2
0	0	0	NA	NA
0	0	0	0	0
1	1	2	0	0
0	0	1	1	2
0	0	0	0	0
0	0	2	1	2
0	1	1	0	0




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time4 seconds
R Server'Herman Ole Andreas Wold' @ wold.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 4 seconds \tabularnewline
R Server & 'Herman Ole Andreas Wold' @ wold.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=197281&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]4 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Herman Ole Andreas Wold' @ wold.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=197281&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=197281&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 Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time4 seconds
R Server'Herman Ole Andreas Wold' @ wold.wessa.net







Goodness of Fit
Correlation0.5048
R-squared0.2549
RMSE0.4306

\begin{tabular}{lllllllll}
\hline
Goodness of Fit \tabularnewline
Correlation & 0.5048 \tabularnewline
R-squared & 0.2549 \tabularnewline
RMSE & 0.4306 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=197281&T=1

[TABLE]
[ROW][C]Goodness of Fit[/C][/ROW]
[ROW][C]Correlation[/C][C]0.5048[/C][/ROW]
[ROW][C]R-squared[/C][C]0.2549[/C][/ROW]
[ROW][C]RMSE[/C][C]0.4306[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=197281&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=197281&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
Correlation0.5048
R-squared0.2549
RMSE0.4306







Actuals, Predictions, and Residuals
#ActualsForecastsResiduals
110.7254901960784310.274509803921569
210.7254901960784310.274509803921569
300.725490196078431-0.725490196078431
400.210526315789474-0.210526315789474
510.7254901960784310.274509803921569
610.7254901960784310.274509803921569
710.7254901960784310.274509803921569
800.725490196078431-0.725490196078431
900.725490196078431-0.725490196078431
1010.7254901960784310.274509803921569
1100.210526315789474-0.210526315789474
1200.25-0.25
1300.25-0.25
1400.725490196078431-0.725490196078431
1500.210526315789474-0.210526315789474
1610.7254901960784310.274509803921569
1710.7254901960784310.274509803921569
1810.7254901960784310.274509803921569
1900.725490196078431-0.725490196078431
2000.210526315789474-0.210526315789474
2110.7254901960784310.274509803921569
2210.250.75
2300.210526315789474-0.210526315789474
2410.2105263157894740.789473684210526
2510.7254901960784310.274509803921569
2610.2105263157894740.789473684210526
2710.7254901960784310.274509803921569
2800.210526315789474-0.210526315789474
2900.210526315789474-0.210526315789474
3010.7254901960784310.274509803921569
3110.2105263157894740.789473684210526
3210.7254901960784310.274509803921569
3300.210526315789474-0.210526315789474
3400.210526315789474-0.210526315789474
3500.210526315789474-0.210526315789474
3610.7254901960784310.274509803921569
3710.7254901960784310.274509803921569
3800.25-0.25
3900.725490196078431-0.725490196078431
4010.7254901960784310.274509803921569
4110.7254901960784310.274509803921569
4210.7254901960784310.274509803921569
4310.250.75
4410.7254901960784310.274509803921569
4500.210526315789474-0.210526315789474
4600.725490196078431-0.725490196078431
4700.25-0.25
4810.7254901960784310.274509803921569
4910.7254901960784310.274509803921569
5000.210526315789474-0.210526315789474
5100.725490196078431-0.725490196078431
5210.7254901960784310.274509803921569
5300.725490196078431-0.725490196078431
5400.210526315789474-0.210526315789474
5500.25-0.25
5600.25-0.25
5700.725490196078431-0.725490196078431
5800.725490196078431-0.725490196078431
5900.210526315789474-0.210526315789474
6000.210526315789474-0.210526315789474
6100.25-0.25
6210.7254901960784310.274509803921569
6310.7254901960784310.274509803921569
6410.2105263157894740.789473684210526
6500.210526315789474-0.210526315789474
6600.725490196078431-0.725490196078431
6700.725490196078431-0.725490196078431
6800.210526315789474-0.210526315789474
6910.7254901960784310.274509803921569
7010.7254901960784310.274509803921569
7100.25-0.25
7200.25-0.25
7300.25-0.25
7400.25-0.25
7510.7254901960784310.274509803921569
7610.2105263157894740.789473684210526
7700.210526315789474-0.210526315789474
7810.7254901960784310.274509803921569
7910.2105263157894740.789473684210526
8010.7254901960784310.274509803921569
8100.210526315789474-0.210526315789474
8200.210526315789474-0.210526315789474
8300.210526315789474-0.210526315789474
8410.2105263157894740.789473684210526
8500.210526315789474-0.210526315789474
8600.210526315789474-0.210526315789474
8710.2105263157894740.789473684210526
8810.7254901960784310.274509803921569
8900.210526315789474-0.210526315789474
9000.210526315789474-0.210526315789474
9110.250.75
9210.7254901960784310.274509803921569
9310.7254901960784310.274509803921569
9400.725490196078431-0.725490196078431
9510.7254901960784310.274509803921569
9610.7254901960784310.274509803921569
9710.7254901960784310.274509803921569
9810.7254901960784310.274509803921569
9900.210526315789474-0.210526315789474
10000.210526315789474-0.210526315789474
10110.250.75
10200.210526315789474-0.210526315789474
10300.210526315789474-0.210526315789474
10400.210526315789474-0.210526315789474
10500.25-0.25

\begin{tabular}{lllllllll}
\hline
Actuals, Predictions, and Residuals \tabularnewline
# & Actuals & Forecasts & Residuals \tabularnewline
1 & 1 & 0.725490196078431 & 0.274509803921569 \tabularnewline
2 & 1 & 0.725490196078431 & 0.274509803921569 \tabularnewline
3 & 0 & 0.725490196078431 & -0.725490196078431 \tabularnewline
4 & 0 & 0.210526315789474 & -0.210526315789474 \tabularnewline
5 & 1 & 0.725490196078431 & 0.274509803921569 \tabularnewline
6 & 1 & 0.725490196078431 & 0.274509803921569 \tabularnewline
7 & 1 & 0.725490196078431 & 0.274509803921569 \tabularnewline
8 & 0 & 0.725490196078431 & -0.725490196078431 \tabularnewline
9 & 0 & 0.725490196078431 & -0.725490196078431 \tabularnewline
10 & 1 & 0.725490196078431 & 0.274509803921569 \tabularnewline
11 & 0 & 0.210526315789474 & -0.210526315789474 \tabularnewline
12 & 0 & 0.25 & -0.25 \tabularnewline
13 & 0 & 0.25 & -0.25 \tabularnewline
14 & 0 & 0.725490196078431 & -0.725490196078431 \tabularnewline
15 & 0 & 0.210526315789474 & -0.210526315789474 \tabularnewline
16 & 1 & 0.725490196078431 & 0.274509803921569 \tabularnewline
17 & 1 & 0.725490196078431 & 0.274509803921569 \tabularnewline
18 & 1 & 0.725490196078431 & 0.274509803921569 \tabularnewline
19 & 0 & 0.725490196078431 & -0.725490196078431 \tabularnewline
20 & 0 & 0.210526315789474 & -0.210526315789474 \tabularnewline
21 & 1 & 0.725490196078431 & 0.274509803921569 \tabularnewline
22 & 1 & 0.25 & 0.75 \tabularnewline
23 & 0 & 0.210526315789474 & -0.210526315789474 \tabularnewline
24 & 1 & 0.210526315789474 & 0.789473684210526 \tabularnewline
25 & 1 & 0.725490196078431 & 0.274509803921569 \tabularnewline
26 & 1 & 0.210526315789474 & 0.789473684210526 \tabularnewline
27 & 1 & 0.725490196078431 & 0.274509803921569 \tabularnewline
28 & 0 & 0.210526315789474 & -0.210526315789474 \tabularnewline
29 & 0 & 0.210526315789474 & -0.210526315789474 \tabularnewline
30 & 1 & 0.725490196078431 & 0.274509803921569 \tabularnewline
31 & 1 & 0.210526315789474 & 0.789473684210526 \tabularnewline
32 & 1 & 0.725490196078431 & 0.274509803921569 \tabularnewline
33 & 0 & 0.210526315789474 & -0.210526315789474 \tabularnewline
34 & 0 & 0.210526315789474 & -0.210526315789474 \tabularnewline
35 & 0 & 0.210526315789474 & -0.210526315789474 \tabularnewline
36 & 1 & 0.725490196078431 & 0.274509803921569 \tabularnewline
37 & 1 & 0.725490196078431 & 0.274509803921569 \tabularnewline
38 & 0 & 0.25 & -0.25 \tabularnewline
39 & 0 & 0.725490196078431 & -0.725490196078431 \tabularnewline
40 & 1 & 0.725490196078431 & 0.274509803921569 \tabularnewline
41 & 1 & 0.725490196078431 & 0.274509803921569 \tabularnewline
42 & 1 & 0.725490196078431 & 0.274509803921569 \tabularnewline
43 & 1 & 0.25 & 0.75 \tabularnewline
44 & 1 & 0.725490196078431 & 0.274509803921569 \tabularnewline
45 & 0 & 0.210526315789474 & -0.210526315789474 \tabularnewline
46 & 0 & 0.725490196078431 & -0.725490196078431 \tabularnewline
47 & 0 & 0.25 & -0.25 \tabularnewline
48 & 1 & 0.725490196078431 & 0.274509803921569 \tabularnewline
49 & 1 & 0.725490196078431 & 0.274509803921569 \tabularnewline
50 & 0 & 0.210526315789474 & -0.210526315789474 \tabularnewline
51 & 0 & 0.725490196078431 & -0.725490196078431 \tabularnewline
52 & 1 & 0.725490196078431 & 0.274509803921569 \tabularnewline
53 & 0 & 0.725490196078431 & -0.725490196078431 \tabularnewline
54 & 0 & 0.210526315789474 & -0.210526315789474 \tabularnewline
55 & 0 & 0.25 & -0.25 \tabularnewline
56 & 0 & 0.25 & -0.25 \tabularnewline
57 & 0 & 0.725490196078431 & -0.725490196078431 \tabularnewline
58 & 0 & 0.725490196078431 & -0.725490196078431 \tabularnewline
59 & 0 & 0.210526315789474 & -0.210526315789474 \tabularnewline
60 & 0 & 0.210526315789474 & -0.210526315789474 \tabularnewline
61 & 0 & 0.25 & -0.25 \tabularnewline
62 & 1 & 0.725490196078431 & 0.274509803921569 \tabularnewline
63 & 1 & 0.725490196078431 & 0.274509803921569 \tabularnewline
64 & 1 & 0.210526315789474 & 0.789473684210526 \tabularnewline
65 & 0 & 0.210526315789474 & -0.210526315789474 \tabularnewline
66 & 0 & 0.725490196078431 & -0.725490196078431 \tabularnewline
67 & 0 & 0.725490196078431 & -0.725490196078431 \tabularnewline
68 & 0 & 0.210526315789474 & -0.210526315789474 \tabularnewline
69 & 1 & 0.725490196078431 & 0.274509803921569 \tabularnewline
70 & 1 & 0.725490196078431 & 0.274509803921569 \tabularnewline
71 & 0 & 0.25 & -0.25 \tabularnewline
72 & 0 & 0.25 & -0.25 \tabularnewline
73 & 0 & 0.25 & -0.25 \tabularnewline
74 & 0 & 0.25 & -0.25 \tabularnewline
75 & 1 & 0.725490196078431 & 0.274509803921569 \tabularnewline
76 & 1 & 0.210526315789474 & 0.789473684210526 \tabularnewline
77 & 0 & 0.210526315789474 & -0.210526315789474 \tabularnewline
78 & 1 & 0.725490196078431 & 0.274509803921569 \tabularnewline
79 & 1 & 0.210526315789474 & 0.789473684210526 \tabularnewline
80 & 1 & 0.725490196078431 & 0.274509803921569 \tabularnewline
81 & 0 & 0.210526315789474 & -0.210526315789474 \tabularnewline
82 & 0 & 0.210526315789474 & -0.210526315789474 \tabularnewline
83 & 0 & 0.210526315789474 & -0.210526315789474 \tabularnewline
84 & 1 & 0.210526315789474 & 0.789473684210526 \tabularnewline
85 & 0 & 0.210526315789474 & -0.210526315789474 \tabularnewline
86 & 0 & 0.210526315789474 & -0.210526315789474 \tabularnewline
87 & 1 & 0.210526315789474 & 0.789473684210526 \tabularnewline
88 & 1 & 0.725490196078431 & 0.274509803921569 \tabularnewline
89 & 0 & 0.210526315789474 & -0.210526315789474 \tabularnewline
90 & 0 & 0.210526315789474 & -0.210526315789474 \tabularnewline
91 & 1 & 0.25 & 0.75 \tabularnewline
92 & 1 & 0.725490196078431 & 0.274509803921569 \tabularnewline
93 & 1 & 0.725490196078431 & 0.274509803921569 \tabularnewline
94 & 0 & 0.725490196078431 & -0.725490196078431 \tabularnewline
95 & 1 & 0.725490196078431 & 0.274509803921569 \tabularnewline
96 & 1 & 0.725490196078431 & 0.274509803921569 \tabularnewline
97 & 1 & 0.725490196078431 & 0.274509803921569 \tabularnewline
98 & 1 & 0.725490196078431 & 0.274509803921569 \tabularnewline
99 & 0 & 0.210526315789474 & -0.210526315789474 \tabularnewline
100 & 0 & 0.210526315789474 & -0.210526315789474 \tabularnewline
101 & 1 & 0.25 & 0.75 \tabularnewline
102 & 0 & 0.210526315789474 & -0.210526315789474 \tabularnewline
103 & 0 & 0.210526315789474 & -0.210526315789474 \tabularnewline
104 & 0 & 0.210526315789474 & -0.210526315789474 \tabularnewline
105 & 0 & 0.25 & -0.25 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=197281&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]1[/C][C]0.725490196078431[/C][C]0.274509803921569[/C][/ROW]
[ROW][C]2[/C][C]1[/C][C]0.725490196078431[/C][C]0.274509803921569[/C][/ROW]
[ROW][C]3[/C][C]0[/C][C]0.725490196078431[/C][C]-0.725490196078431[/C][/ROW]
[ROW][C]4[/C][C]0[/C][C]0.210526315789474[/C][C]-0.210526315789474[/C][/ROW]
[ROW][C]5[/C][C]1[/C][C]0.725490196078431[/C][C]0.274509803921569[/C][/ROW]
[ROW][C]6[/C][C]1[/C][C]0.725490196078431[/C][C]0.274509803921569[/C][/ROW]
[ROW][C]7[/C][C]1[/C][C]0.725490196078431[/C][C]0.274509803921569[/C][/ROW]
[ROW][C]8[/C][C]0[/C][C]0.725490196078431[/C][C]-0.725490196078431[/C][/ROW]
[ROW][C]9[/C][C]0[/C][C]0.725490196078431[/C][C]-0.725490196078431[/C][/ROW]
[ROW][C]10[/C][C]1[/C][C]0.725490196078431[/C][C]0.274509803921569[/C][/ROW]
[ROW][C]11[/C][C]0[/C][C]0.210526315789474[/C][C]-0.210526315789474[/C][/ROW]
[ROW][C]12[/C][C]0[/C][C]0.25[/C][C]-0.25[/C][/ROW]
[ROW][C]13[/C][C]0[/C][C]0.25[/C][C]-0.25[/C][/ROW]
[ROW][C]14[/C][C]0[/C][C]0.725490196078431[/C][C]-0.725490196078431[/C][/ROW]
[ROW][C]15[/C][C]0[/C][C]0.210526315789474[/C][C]-0.210526315789474[/C][/ROW]
[ROW][C]16[/C][C]1[/C][C]0.725490196078431[/C][C]0.274509803921569[/C][/ROW]
[ROW][C]17[/C][C]1[/C][C]0.725490196078431[/C][C]0.274509803921569[/C][/ROW]
[ROW][C]18[/C][C]1[/C][C]0.725490196078431[/C][C]0.274509803921569[/C][/ROW]
[ROW][C]19[/C][C]0[/C][C]0.725490196078431[/C][C]-0.725490196078431[/C][/ROW]
[ROW][C]20[/C][C]0[/C][C]0.210526315789474[/C][C]-0.210526315789474[/C][/ROW]
[ROW][C]21[/C][C]1[/C][C]0.725490196078431[/C][C]0.274509803921569[/C][/ROW]
[ROW][C]22[/C][C]1[/C][C]0.25[/C][C]0.75[/C][/ROW]
[ROW][C]23[/C][C]0[/C][C]0.210526315789474[/C][C]-0.210526315789474[/C][/ROW]
[ROW][C]24[/C][C]1[/C][C]0.210526315789474[/C][C]0.789473684210526[/C][/ROW]
[ROW][C]25[/C][C]1[/C][C]0.725490196078431[/C][C]0.274509803921569[/C][/ROW]
[ROW][C]26[/C][C]1[/C][C]0.210526315789474[/C][C]0.789473684210526[/C][/ROW]
[ROW][C]27[/C][C]1[/C][C]0.725490196078431[/C][C]0.274509803921569[/C][/ROW]
[ROW][C]28[/C][C]0[/C][C]0.210526315789474[/C][C]-0.210526315789474[/C][/ROW]
[ROW][C]29[/C][C]0[/C][C]0.210526315789474[/C][C]-0.210526315789474[/C][/ROW]
[ROW][C]30[/C][C]1[/C][C]0.725490196078431[/C][C]0.274509803921569[/C][/ROW]
[ROW][C]31[/C][C]1[/C][C]0.210526315789474[/C][C]0.789473684210526[/C][/ROW]
[ROW][C]32[/C][C]1[/C][C]0.725490196078431[/C][C]0.274509803921569[/C][/ROW]
[ROW][C]33[/C][C]0[/C][C]0.210526315789474[/C][C]-0.210526315789474[/C][/ROW]
[ROW][C]34[/C][C]0[/C][C]0.210526315789474[/C][C]-0.210526315789474[/C][/ROW]
[ROW][C]35[/C][C]0[/C][C]0.210526315789474[/C][C]-0.210526315789474[/C][/ROW]
[ROW][C]36[/C][C]1[/C][C]0.725490196078431[/C][C]0.274509803921569[/C][/ROW]
[ROW][C]37[/C][C]1[/C][C]0.725490196078431[/C][C]0.274509803921569[/C][/ROW]
[ROW][C]38[/C][C]0[/C][C]0.25[/C][C]-0.25[/C][/ROW]
[ROW][C]39[/C][C]0[/C][C]0.725490196078431[/C][C]-0.725490196078431[/C][/ROW]
[ROW][C]40[/C][C]1[/C][C]0.725490196078431[/C][C]0.274509803921569[/C][/ROW]
[ROW][C]41[/C][C]1[/C][C]0.725490196078431[/C][C]0.274509803921569[/C][/ROW]
[ROW][C]42[/C][C]1[/C][C]0.725490196078431[/C][C]0.274509803921569[/C][/ROW]
[ROW][C]43[/C][C]1[/C][C]0.25[/C][C]0.75[/C][/ROW]
[ROW][C]44[/C][C]1[/C][C]0.725490196078431[/C][C]0.274509803921569[/C][/ROW]
[ROW][C]45[/C][C]0[/C][C]0.210526315789474[/C][C]-0.210526315789474[/C][/ROW]
[ROW][C]46[/C][C]0[/C][C]0.725490196078431[/C][C]-0.725490196078431[/C][/ROW]
[ROW][C]47[/C][C]0[/C][C]0.25[/C][C]-0.25[/C][/ROW]
[ROW][C]48[/C][C]1[/C][C]0.725490196078431[/C][C]0.274509803921569[/C][/ROW]
[ROW][C]49[/C][C]1[/C][C]0.725490196078431[/C][C]0.274509803921569[/C][/ROW]
[ROW][C]50[/C][C]0[/C][C]0.210526315789474[/C][C]-0.210526315789474[/C][/ROW]
[ROW][C]51[/C][C]0[/C][C]0.725490196078431[/C][C]-0.725490196078431[/C][/ROW]
[ROW][C]52[/C][C]1[/C][C]0.725490196078431[/C][C]0.274509803921569[/C][/ROW]
[ROW][C]53[/C][C]0[/C][C]0.725490196078431[/C][C]-0.725490196078431[/C][/ROW]
[ROW][C]54[/C][C]0[/C][C]0.210526315789474[/C][C]-0.210526315789474[/C][/ROW]
[ROW][C]55[/C][C]0[/C][C]0.25[/C][C]-0.25[/C][/ROW]
[ROW][C]56[/C][C]0[/C][C]0.25[/C][C]-0.25[/C][/ROW]
[ROW][C]57[/C][C]0[/C][C]0.725490196078431[/C][C]-0.725490196078431[/C][/ROW]
[ROW][C]58[/C][C]0[/C][C]0.725490196078431[/C][C]-0.725490196078431[/C][/ROW]
[ROW][C]59[/C][C]0[/C][C]0.210526315789474[/C][C]-0.210526315789474[/C][/ROW]
[ROW][C]60[/C][C]0[/C][C]0.210526315789474[/C][C]-0.210526315789474[/C][/ROW]
[ROW][C]61[/C][C]0[/C][C]0.25[/C][C]-0.25[/C][/ROW]
[ROW][C]62[/C][C]1[/C][C]0.725490196078431[/C][C]0.274509803921569[/C][/ROW]
[ROW][C]63[/C][C]1[/C][C]0.725490196078431[/C][C]0.274509803921569[/C][/ROW]
[ROW][C]64[/C][C]1[/C][C]0.210526315789474[/C][C]0.789473684210526[/C][/ROW]
[ROW][C]65[/C][C]0[/C][C]0.210526315789474[/C][C]-0.210526315789474[/C][/ROW]
[ROW][C]66[/C][C]0[/C][C]0.725490196078431[/C][C]-0.725490196078431[/C][/ROW]
[ROW][C]67[/C][C]0[/C][C]0.725490196078431[/C][C]-0.725490196078431[/C][/ROW]
[ROW][C]68[/C][C]0[/C][C]0.210526315789474[/C][C]-0.210526315789474[/C][/ROW]
[ROW][C]69[/C][C]1[/C][C]0.725490196078431[/C][C]0.274509803921569[/C][/ROW]
[ROW][C]70[/C][C]1[/C][C]0.725490196078431[/C][C]0.274509803921569[/C][/ROW]
[ROW][C]71[/C][C]0[/C][C]0.25[/C][C]-0.25[/C][/ROW]
[ROW][C]72[/C][C]0[/C][C]0.25[/C][C]-0.25[/C][/ROW]
[ROW][C]73[/C][C]0[/C][C]0.25[/C][C]-0.25[/C][/ROW]
[ROW][C]74[/C][C]0[/C][C]0.25[/C][C]-0.25[/C][/ROW]
[ROW][C]75[/C][C]1[/C][C]0.725490196078431[/C][C]0.274509803921569[/C][/ROW]
[ROW][C]76[/C][C]1[/C][C]0.210526315789474[/C][C]0.789473684210526[/C][/ROW]
[ROW][C]77[/C][C]0[/C][C]0.210526315789474[/C][C]-0.210526315789474[/C][/ROW]
[ROW][C]78[/C][C]1[/C][C]0.725490196078431[/C][C]0.274509803921569[/C][/ROW]
[ROW][C]79[/C][C]1[/C][C]0.210526315789474[/C][C]0.789473684210526[/C][/ROW]
[ROW][C]80[/C][C]1[/C][C]0.725490196078431[/C][C]0.274509803921569[/C][/ROW]
[ROW][C]81[/C][C]0[/C][C]0.210526315789474[/C][C]-0.210526315789474[/C][/ROW]
[ROW][C]82[/C][C]0[/C][C]0.210526315789474[/C][C]-0.210526315789474[/C][/ROW]
[ROW][C]83[/C][C]0[/C][C]0.210526315789474[/C][C]-0.210526315789474[/C][/ROW]
[ROW][C]84[/C][C]1[/C][C]0.210526315789474[/C][C]0.789473684210526[/C][/ROW]
[ROW][C]85[/C][C]0[/C][C]0.210526315789474[/C][C]-0.210526315789474[/C][/ROW]
[ROW][C]86[/C][C]0[/C][C]0.210526315789474[/C][C]-0.210526315789474[/C][/ROW]
[ROW][C]87[/C][C]1[/C][C]0.210526315789474[/C][C]0.789473684210526[/C][/ROW]
[ROW][C]88[/C][C]1[/C][C]0.725490196078431[/C][C]0.274509803921569[/C][/ROW]
[ROW][C]89[/C][C]0[/C][C]0.210526315789474[/C][C]-0.210526315789474[/C][/ROW]
[ROW][C]90[/C][C]0[/C][C]0.210526315789474[/C][C]-0.210526315789474[/C][/ROW]
[ROW][C]91[/C][C]1[/C][C]0.25[/C][C]0.75[/C][/ROW]
[ROW][C]92[/C][C]1[/C][C]0.725490196078431[/C][C]0.274509803921569[/C][/ROW]
[ROW][C]93[/C][C]1[/C][C]0.725490196078431[/C][C]0.274509803921569[/C][/ROW]
[ROW][C]94[/C][C]0[/C][C]0.725490196078431[/C][C]-0.725490196078431[/C][/ROW]
[ROW][C]95[/C][C]1[/C][C]0.725490196078431[/C][C]0.274509803921569[/C][/ROW]
[ROW][C]96[/C][C]1[/C][C]0.725490196078431[/C][C]0.274509803921569[/C][/ROW]
[ROW][C]97[/C][C]1[/C][C]0.725490196078431[/C][C]0.274509803921569[/C][/ROW]
[ROW][C]98[/C][C]1[/C][C]0.725490196078431[/C][C]0.274509803921569[/C][/ROW]
[ROW][C]99[/C][C]0[/C][C]0.210526315789474[/C][C]-0.210526315789474[/C][/ROW]
[ROW][C]100[/C][C]0[/C][C]0.210526315789474[/C][C]-0.210526315789474[/C][/ROW]
[ROW][C]101[/C][C]1[/C][C]0.25[/C][C]0.75[/C][/ROW]
[ROW][C]102[/C][C]0[/C][C]0.210526315789474[/C][C]-0.210526315789474[/C][/ROW]
[ROW][C]103[/C][C]0[/C][C]0.210526315789474[/C][C]-0.210526315789474[/C][/ROW]
[ROW][C]104[/C][C]0[/C][C]0.210526315789474[/C][C]-0.210526315789474[/C][/ROW]
[ROW][C]105[/C][C]0[/C][C]0.25[/C][C]-0.25[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=197281&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=197281&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
#ActualsForecastsResiduals
110.7254901960784310.274509803921569
210.7254901960784310.274509803921569
300.725490196078431-0.725490196078431
400.210526315789474-0.210526315789474
510.7254901960784310.274509803921569
610.7254901960784310.274509803921569
710.7254901960784310.274509803921569
800.725490196078431-0.725490196078431
900.725490196078431-0.725490196078431
1010.7254901960784310.274509803921569
1100.210526315789474-0.210526315789474
1200.25-0.25
1300.25-0.25
1400.725490196078431-0.725490196078431
1500.210526315789474-0.210526315789474
1610.7254901960784310.274509803921569
1710.7254901960784310.274509803921569
1810.7254901960784310.274509803921569
1900.725490196078431-0.725490196078431
2000.210526315789474-0.210526315789474
2110.7254901960784310.274509803921569
2210.250.75
2300.210526315789474-0.210526315789474
2410.2105263157894740.789473684210526
2510.7254901960784310.274509803921569
2610.2105263157894740.789473684210526
2710.7254901960784310.274509803921569
2800.210526315789474-0.210526315789474
2900.210526315789474-0.210526315789474
3010.7254901960784310.274509803921569
3110.2105263157894740.789473684210526
3210.7254901960784310.274509803921569
3300.210526315789474-0.210526315789474
3400.210526315789474-0.210526315789474
3500.210526315789474-0.210526315789474
3610.7254901960784310.274509803921569
3710.7254901960784310.274509803921569
3800.25-0.25
3900.725490196078431-0.725490196078431
4010.7254901960784310.274509803921569
4110.7254901960784310.274509803921569
4210.7254901960784310.274509803921569
4310.250.75
4410.7254901960784310.274509803921569
4500.210526315789474-0.210526315789474
4600.725490196078431-0.725490196078431
4700.25-0.25
4810.7254901960784310.274509803921569
4910.7254901960784310.274509803921569
5000.210526315789474-0.210526315789474
5100.725490196078431-0.725490196078431
5210.7254901960784310.274509803921569
5300.725490196078431-0.725490196078431
5400.210526315789474-0.210526315789474
5500.25-0.25
5600.25-0.25
5700.725490196078431-0.725490196078431
5800.725490196078431-0.725490196078431
5900.210526315789474-0.210526315789474
6000.210526315789474-0.210526315789474
6100.25-0.25
6210.7254901960784310.274509803921569
6310.7254901960784310.274509803921569
6410.2105263157894740.789473684210526
6500.210526315789474-0.210526315789474
6600.725490196078431-0.725490196078431
6700.725490196078431-0.725490196078431
6800.210526315789474-0.210526315789474
6910.7254901960784310.274509803921569
7010.7254901960784310.274509803921569
7100.25-0.25
7200.25-0.25
7300.25-0.25
7400.25-0.25
7510.7254901960784310.274509803921569
7610.2105263157894740.789473684210526
7700.210526315789474-0.210526315789474
7810.7254901960784310.274509803921569
7910.2105263157894740.789473684210526
8010.7254901960784310.274509803921569
8100.210526315789474-0.210526315789474
8200.210526315789474-0.210526315789474
8300.210526315789474-0.210526315789474
8410.2105263157894740.789473684210526
8500.210526315789474-0.210526315789474
8600.210526315789474-0.210526315789474
8710.2105263157894740.789473684210526
8810.7254901960784310.274509803921569
8900.210526315789474-0.210526315789474
9000.210526315789474-0.210526315789474
9110.250.75
9210.7254901960784310.274509803921569
9310.7254901960784310.274509803921569
9400.725490196078431-0.725490196078431
9510.7254901960784310.274509803921569
9610.7254901960784310.274509803921569
9710.7254901960784310.274509803921569
9810.7254901960784310.274509803921569
9900.210526315789474-0.210526315789474
10000.210526315789474-0.210526315789474
10110.250.75
10200.210526315789474-0.210526315789474
10300.210526315789474-0.210526315789474
10400.210526315789474-0.210526315789474
10500.25-0.25



Parameters (Session):
par1 = 1 ; par2 = none ; par3 = 3 ; par4 = no ;
Parameters (R input):
par1 = 1 ; par2 = none ; par3 = 3 ; par4 = no ;
R code (references can be found in the software module):
library(party)
library(Hmisc)
par1 <- as.numeric(par1)
par3 <- as.numeric(par3)
x <- data.frame(t(y))
is.data.frame(x)
x <- x[!is.na(x[,par1]),]
k <- length(x[1,])
n <- length(x[,1])
colnames(x)[par1]
x[,par1]
if (par2 == 'kmeans') {
cl <- kmeans(x[,par1], par3)
print(cl)
clm <- matrix(cbind(cl$centers,1:par3),ncol=2)
clm <- clm[sort.list(clm[,1]),]
for (i in 1:par3) {
cl$cluster[cl$cluster==clm[i,2]] <- paste('C',i,sep='')
}
cl$cluster <- as.factor(cl$cluster)
print(cl$cluster)
x[,par1] <- cl$cluster
}
if (par2 == 'quantiles') {
x[,par1] <- cut2(x[,par1],g=par3)
}
if (par2 == 'hclust') {
hc <- hclust(dist(x[,par1])^2, 'cen')
print(hc)
memb <- cutree(hc, k = par3)
dum <- c(mean(x[memb==1,par1]))
for (i in 2:par3) {
dum <- c(dum, mean(x[memb==i,par1]))
}
hcm <- matrix(cbind(dum,1:par3),ncol=2)
hcm <- hcm[sort.list(hcm[,1]),]
for (i in 1:par3) {
memb[memb==hcm[i,2]] <- paste('C',i,sep='')
}
memb <- as.factor(memb)
print(memb)
x[,par1] <- memb
}
if (par2=='equal') {
ed <- cut(as.numeric(x[,par1]),par3,labels=paste('C',1:par3,sep=''))
x[,par1] <- as.factor(ed)
}
table(x[,par1])
colnames(x)
colnames(x)[par1]
x[,par1]
if (par2 == 'none') {
m <- ctree(as.formula(paste(colnames(x)[par1],' ~ .',sep='')),data = x)
}
load(file='createtable')
if (par2 != 'none') {
m <- ctree(as.formula(paste('as.factor(',colnames(x)[par1],') ~ .',sep='')),data = x)
if (par4=='yes') {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'10-Fold Cross Validation',3+2*par3,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'',1,TRUE)
a<-table.element(a,'Prediction (training)',par3+1,TRUE)
a<-table.element(a,'Prediction (testing)',par3+1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Actual',1,TRUE)
for (jjj in 1:par3) a<-table.element(a,paste('C',jjj,sep=''),1,TRUE)
a<-table.element(a,'CV',1,TRUE)
for (jjj in 1:par3) a<-table.element(a,paste('C',jjj,sep=''),1,TRUE)
a<-table.element(a,'CV',1,TRUE)
a<-table.row.end(a)
for (i in 1:10) {
ind <- sample(2, nrow(x), replace=T, prob=c(0.9,0.1))
m.ct <- ctree(as.formula(paste('as.factor(',colnames(x)[par1],') ~ .',sep='')),data =x[ind==1,])
if (i==1) {
m.ct.i.pred <- predict(m.ct, newdata=x[ind==1,])
m.ct.i.actu <- x[ind==1,par1]
m.ct.x.pred <- predict(m.ct, newdata=x[ind==2,])
m.ct.x.actu <- x[ind==2,par1]
} else {
m.ct.i.pred <- c(m.ct.i.pred,predict(m.ct, newdata=x[ind==1,]))
m.ct.i.actu <- c(m.ct.i.actu,x[ind==1,par1])
m.ct.x.pred <- c(m.ct.x.pred,predict(m.ct, newdata=x[ind==2,]))
m.ct.x.actu <- c(m.ct.x.actu,x[ind==2,par1])
}
}
print(m.ct.i.tab <- table(m.ct.i.actu,m.ct.i.pred))
numer <- 0
for (i in 1:par3) {
print(m.ct.i.tab[i,i] / sum(m.ct.i.tab[i,]))
numer <- numer + m.ct.i.tab[i,i]
}
print(m.ct.i.cp <- numer / sum(m.ct.i.tab))
print(m.ct.x.tab <- table(m.ct.x.actu,m.ct.x.pred))
numer <- 0
for (i in 1:par3) {
print(m.ct.x.tab[i,i] / sum(m.ct.x.tab[i,]))
numer <- numer + m.ct.x.tab[i,i]
}
print(m.ct.x.cp <- numer / sum(m.ct.x.tab))
for (i in 1:par3) {
a<-table.row.start(a)
a<-table.element(a,paste('C',i,sep=''),1,TRUE)
for (jjj in 1:par3) a<-table.element(a,m.ct.i.tab[i,jjj])
a<-table.element(a,round(m.ct.i.tab[i,i]/sum(m.ct.i.tab[i,]),4))
for (jjj in 1:par3) a<-table.element(a,m.ct.x.tab[i,jjj])
a<-table.element(a,round(m.ct.x.tab[i,i]/sum(m.ct.x.tab[i,]),4))
a<-table.row.end(a)
}
a<-table.row.start(a)
a<-table.element(a,'Overall',1,TRUE)
for (jjj in 1:par3) a<-table.element(a,'-')
a<-table.element(a,round(m.ct.i.cp,4))
for (jjj in 1:par3) a<-table.element(a,'-')
a<-table.element(a,round(m.ct.x.cp,4))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
}
}
m
bitmap(file='test1.png')
plot(m)
dev.off()
bitmap(file='test1a.png')
plot(x[,par1] ~ as.factor(where(m)),main='Response by Terminal Node',xlab='Terminal Node',ylab='Response')
dev.off()
if (par2 == 'none') {
forec <- predict(m)
result <- as.data.frame(cbind(x[,par1],forec,x[,par1]-forec))
colnames(result) <- c('Actuals','Forecasts','Residuals')
print(result)
}
if (par2 != 'none') {
print(cbind(as.factor(x[,par1]),predict(m)))
myt <- table(as.factor(x[,par1]),predict(m))
print(myt)
}
bitmap(file='test2.png')
if(par2=='none') {
op <- par(mfrow=c(2,2))
plot(density(result$Actuals),main='Kernel Density Plot of Actuals')
plot(density(result$Residuals),main='Kernel Density Plot of Residuals')
plot(result$Forecasts,result$Actuals,main='Actuals versus Predictions',xlab='Predictions',ylab='Actuals')
plot(density(result$Forecasts),main='Kernel Density Plot of Predictions')
par(op)
}
if(par2!='none') {
plot(myt,main='Confusion Matrix',xlab='Actual',ylab='Predicted')
}
dev.off()
if (par2 == 'none') {
detcoef <- cor(result$Forecasts,result$Actuals)
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Goodness of Fit',2,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Correlation',1,TRUE)
a<-table.element(a,round(detcoef,4))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'R-squared',1,TRUE)
a<-table.element(a,round(detcoef*detcoef,4))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'RMSE',1,TRUE)
a<-table.element(a,round(sqrt(mean((result$Residuals)^2)),4))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Actuals, Predictions, and Residuals',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'#',header=TRUE)
a<-table.element(a,'Actuals',header=TRUE)
a<-table.element(a,'Forecasts',header=TRUE)
a<-table.element(a,'Residuals',header=TRUE)
a<-table.row.end(a)
for (i in 1:length(result$Actuals)) {
a<-table.row.start(a)
a<-table.element(a,i,header=TRUE)
a<-table.element(a,result$Actuals[i])
a<-table.element(a,result$Forecasts[i])
a<-table.element(a,result$Residuals[i])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable.tab')
}
if (par2 != 'none') {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Confusion Matrix (predicted in columns / actuals in rows)',par3+1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'',1,TRUE)
for (i in 1:par3) {
a<-table.element(a,paste('C',i,sep=''),1,TRUE)
}
a<-table.row.end(a)
for (i in 1:par3) {
a<-table.row.start(a)
a<-table.element(a,paste('C',i,sep=''),1,TRUE)
for (j in 1:par3) {
a<-table.element(a,myt[i,j])
}
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
}