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R Software Module

rwasp_regression_trees1.wasp

Title produced by software

Recursive Partitioning (Regression Trees)

Date of computation

Tue, 14 Dec 2010 17:18:06 +0000

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Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2010/Dec/14/t12923469627jptnugg3ro2nfi.htm/, Retrieved Thu, 02 May 2024 18:42:34 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=109913, Retrieved Thu, 02 May 2024 18:42:34 +0000

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No (this computation is public)

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Estimated Impact

109

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]
-   PD    [Recursive Partitioning (Regression Trees)] [Blog 4] [2010-12-14 17:18:06] [47bfda5353cd53c1cf7ea7aa9038654a] [Current]

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Dataseries X:

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3,18	0,22	6,62	3,64
3,14	0,22	6,56	3,62
3,02	0,23	6,59	3,61
3,02	0,24	6,56	3,6
3,03	0,25	6,57	3,6
3,04	0,25	6,62	3,63
3,09	0,24	6,69	3,59
3,06	0,24	6,69	3,55
3,06	0,22	6,64	3,54
3,09	0,21	6,6	3,53
3,11	0,21	6,66	3,53
3,1	0,21	6,62	3,53
3,09	0,2	6,64	3,52
3,19	0,2	6,64	3,52
3,22	0,2	6,73	3,48
3,22	0,2	6,73	3,49
3,25	0,2	6,69	3,47
3,25	0,2	6,78	3,46
3,27	0,2	6,77	3,4
3,28	0,2	6,8	3,36
3,24	0,2	6,8	3,3
3,23	0,2	6,74	3,28
3,2	0,2	6,84	3,28
3,19	0,2	6,83	3,24
3,23	0,2	6,89	3,23
3,19	0,2	6,9	3,2
3,16	0,2	6,86	3,15
3,11	0,2	6,78	3,1
3,11	0,2	6,82	3,07
3,07	0,2	6,81	3,03
3,05	0,21	6,81	2,96
3	0,2	6,78	2,88
2,95	0,2	6,79	2,83
2,9	0,19	6,83	2,8
2,88	0,18	6,9	2,8
2,9	0,18	6,79	2,79
2,89	0,17	6,88	2,79
2,89	0,17	6,89	2,78
2,91	0,17	6,91	2,79
2,9	0,17	6,93	2,78
2,9	0,17	6,89	2,78
2,88	0,16	7	2,74
2,83	0,16	7,01	2,71
2,8	0,16	7,15	2,69
2,77	0,16	7,25	2,68
2,78	0,16	7,33	2,68
2,75	0,16	7,39	2,68
2,74	0,15	7,38	2,69
2,73	0,15	7,38	2,68
2,69	0,15	7,35	2,69
2,67	0,15	7,38	2,68
2,66	0,15	7,34	2,68
2,67	0,16	7,25	2,63
2,65	0,15	7,07	2,58
2,64	0,15	6,73	2,52
2,63	0,15	6,56	2,5

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	5 seconds
R Server	'Sir Ronald Aylmer Fisher' @ 193.190.124.24

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 5 seconds \tabularnewline
R Server & 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=109913&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]5 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Sir Ronald Aylmer Fisher' @ 193.190.124.24[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=109913&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=109913&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	5 seconds
R Server	'Sir Ronald Aylmer Fisher' @ 193.190.124.24

Goodness of Fit
Correlation	0.9573
R-squared	0.9164
RMSE	0.0566

\begin{tabular}{lllllllll}
\hline
Goodness of Fit \tabularnewline
Correlation & 0.9573 \tabularnewline
R-squared & 0.9164 \tabularnewline
RMSE & 0.0566 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=109913&T=1

[TABLE]
[ROW][C]Goodness of Fit[/C][/ROW]
[ROW][C]Correlation[/C][C]0.9573[/C][/ROW]
[ROW][C]R-squared[/C][C]0.9164[/C][/ROW]
[ROW][C]RMSE[/C][C]0.0566[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=109913&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=109913&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	0.9573
R-squared	0.9164
RMSE	0.0566

Actuals, Predictions, and Residuals
#	Actuals	Forecasts	Residuals
1	3.18	3.07615384615385	0.103846153846154
2	3.14	3.07615384615385	0.0638461538461539
3	3.02	3.07615384615385	-0.0561538461538462
4	3.02	3.07615384615385	-0.0561538461538462
5	3.03	3.07615384615385	-0.0461538461538464
6	3.04	3.07615384615385	-0.0361538461538462
7	3.09	3.07615384615385	0.0138461538461536
8	3.06	3.07615384615385	-0.0161538461538462
9	3.06	3.07615384615385	-0.0161538461538462
10	3.09	3.07615384615385	0.0138461538461536
11	3.11	3.07615384615385	0.0338461538461536
12	3.1	3.07615384615385	0.0238461538461539
13	3.09	3.18421052631579	-0.0942105263157895
14	3.19	3.18421052631579	0.00578947368421057
15	3.22	3.18421052631579	0.0357894736842108
16	3.22	3.18421052631579	0.0357894736842108
17	3.25	3.18421052631579	0.0657894736842106
18	3.25	3.18421052631579	0.0657894736842106
19	3.27	3.18421052631579	0.0857894736842106
20	3.28	3.18421052631579	0.0957894736842104
21	3.24	3.18421052631579	0.0557894736842108
22	3.23	3.18421052631579	0.0457894736842106
23	3.2	3.18421052631579	0.0157894736842108
24	3.19	3.18421052631579	0.00578947368421057
25	3.23	3.18421052631579	0.0457894736842106
26	3.19	3.18421052631579	0.00578947368421057
27	3.16	3.18421052631579	-0.0242105263157892
28	3.11	3.18421052631579	-0.0742105263157895
29	3.11	3.18421052631579	-0.0742105263157895
30	3.07	3.18421052631579	-0.114210526315790
31	3.05	3.07615384615385	-0.0261538461538464
32	3	3.18421052631579	-0.184210526315789
33	2.95	2.89363636363636	0.0563636363636366
34	2.9	2.89363636363636	0.00636363636363635
35	2.88	2.89363636363636	-0.0136363636363637
36	2.9	2.89363636363636	0.00636363636363635
37	2.89	2.89363636363636	-0.00363636363636344
38	2.89	2.89363636363636	-0.00363636363636344
39	2.91	2.89363636363636	0.0163636363636366
40	2.9	2.89363636363636	0.00636363636363635
41	2.9	2.89363636363636	0.00636363636363635
42	2.88	2.89363636363636	-0.0136363636363637
43	2.83	2.89363636363636	-0.0636363636363635
44	2.8	2.70615384615385	0.0938461538461537
45	2.77	2.70615384615385	0.0638461538461539
46	2.78	2.70615384615385	0.0738461538461537
47	2.75	2.70615384615385	0.0438461538461539
48	2.74	2.70615384615385	0.0338461538461541
49	2.73	2.70615384615385	0.0238461538461539
50	2.69	2.70615384615385	-0.0161538461538462
51	2.67	2.70615384615385	-0.0361538461538462
52	2.66	2.70615384615385	-0.046153846153846
53	2.67	2.70615384615385	-0.0361538461538462
54	2.65	2.70615384615385	-0.0561538461538462
55	2.64	2.70615384615385	-0.066153846153846
56	2.63	2.70615384615385	-0.0761538461538462

\begin{tabular}{lllllllll}
\hline
Actuals, Predictions, and Residuals \tabularnewline
# & Actuals & Forecasts & Residuals \tabularnewline
1 & 3.18 & 3.07615384615385 & 0.103846153846154 \tabularnewline
2 & 3.14 & 3.07615384615385 & 0.0638461538461539 \tabularnewline
3 & 3.02 & 3.07615384615385 & -0.0561538461538462 \tabularnewline
4 & 3.02 & 3.07615384615385 & -0.0561538461538462 \tabularnewline
5 & 3.03 & 3.07615384615385 & -0.0461538461538464 \tabularnewline
6 & 3.04 & 3.07615384615385 & -0.0361538461538462 \tabularnewline
7 & 3.09 & 3.07615384615385 & 0.0138461538461536 \tabularnewline
8 & 3.06 & 3.07615384615385 & -0.0161538461538462 \tabularnewline
9 & 3.06 & 3.07615384615385 & -0.0161538461538462 \tabularnewline
10 & 3.09 & 3.07615384615385 & 0.0138461538461536 \tabularnewline
11 & 3.11 & 3.07615384615385 & 0.0338461538461536 \tabularnewline
12 & 3.1 & 3.07615384615385 & 0.0238461538461539 \tabularnewline
13 & 3.09 & 3.18421052631579 & -0.0942105263157895 \tabularnewline
14 & 3.19 & 3.18421052631579 & 0.00578947368421057 \tabularnewline
15 & 3.22 & 3.18421052631579 & 0.0357894736842108 \tabularnewline
16 & 3.22 & 3.18421052631579 & 0.0357894736842108 \tabularnewline
17 & 3.25 & 3.18421052631579 & 0.0657894736842106 \tabularnewline
18 & 3.25 & 3.18421052631579 & 0.0657894736842106 \tabularnewline
19 & 3.27 & 3.18421052631579 & 0.0857894736842106 \tabularnewline
20 & 3.28 & 3.18421052631579 & 0.0957894736842104 \tabularnewline
21 & 3.24 & 3.18421052631579 & 0.0557894736842108 \tabularnewline
22 & 3.23 & 3.18421052631579 & 0.0457894736842106 \tabularnewline
23 & 3.2 & 3.18421052631579 & 0.0157894736842108 \tabularnewline
24 & 3.19 & 3.18421052631579 & 0.00578947368421057 \tabularnewline
25 & 3.23 & 3.18421052631579 & 0.0457894736842106 \tabularnewline
26 & 3.19 & 3.18421052631579 & 0.00578947368421057 \tabularnewline
27 & 3.16 & 3.18421052631579 & -0.0242105263157892 \tabularnewline
28 & 3.11 & 3.18421052631579 & -0.0742105263157895 \tabularnewline
29 & 3.11 & 3.18421052631579 & -0.0742105263157895 \tabularnewline
30 & 3.07 & 3.18421052631579 & -0.114210526315790 \tabularnewline
31 & 3.05 & 3.07615384615385 & -0.0261538461538464 \tabularnewline
32 & 3 & 3.18421052631579 & -0.184210526315789 \tabularnewline
33 & 2.95 & 2.89363636363636 & 0.0563636363636366 \tabularnewline
34 & 2.9 & 2.89363636363636 & 0.00636363636363635 \tabularnewline
35 & 2.88 & 2.89363636363636 & -0.0136363636363637 \tabularnewline
36 & 2.9 & 2.89363636363636 & 0.00636363636363635 \tabularnewline
37 & 2.89 & 2.89363636363636 & -0.00363636363636344 \tabularnewline
38 & 2.89 & 2.89363636363636 & -0.00363636363636344 \tabularnewline
39 & 2.91 & 2.89363636363636 & 0.0163636363636366 \tabularnewline
40 & 2.9 & 2.89363636363636 & 0.00636363636363635 \tabularnewline
41 & 2.9 & 2.89363636363636 & 0.00636363636363635 \tabularnewline
42 & 2.88 & 2.89363636363636 & -0.0136363636363637 \tabularnewline
43 & 2.83 & 2.89363636363636 & -0.0636363636363635 \tabularnewline
44 & 2.8 & 2.70615384615385 & 0.0938461538461537 \tabularnewline
45 & 2.77 & 2.70615384615385 & 0.0638461538461539 \tabularnewline
46 & 2.78 & 2.70615384615385 & 0.0738461538461537 \tabularnewline
47 & 2.75 & 2.70615384615385 & 0.0438461538461539 \tabularnewline
48 & 2.74 & 2.70615384615385 & 0.0338461538461541 \tabularnewline
49 & 2.73 & 2.70615384615385 & 0.0238461538461539 \tabularnewline
50 & 2.69 & 2.70615384615385 & -0.0161538461538462 \tabularnewline
51 & 2.67 & 2.70615384615385 & -0.0361538461538462 \tabularnewline
52 & 2.66 & 2.70615384615385 & -0.046153846153846 \tabularnewline
53 & 2.67 & 2.70615384615385 & -0.0361538461538462 \tabularnewline
54 & 2.65 & 2.70615384615385 & -0.0561538461538462 \tabularnewline
55 & 2.64 & 2.70615384615385 & -0.066153846153846 \tabularnewline
56 & 2.63 & 2.70615384615385 & -0.0761538461538462 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=109913&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]3.18[/C][C]3.07615384615385[/C][C]0.103846153846154[/C][/ROW]
[ROW][C]2[/C][C]3.14[/C][C]3.07615384615385[/C][C]0.0638461538461539[/C][/ROW]
[ROW][C]3[/C][C]3.02[/C][C]3.07615384615385[/C][C]-0.0561538461538462[/C][/ROW]
[ROW][C]4[/C][C]3.02[/C][C]3.07615384615385[/C][C]-0.0561538461538462[/C][/ROW]
[ROW][C]5[/C][C]3.03[/C][C]3.07615384615385[/C][C]-0.0461538461538464[/C][/ROW]
[ROW][C]6[/C][C]3.04[/C][C]3.07615384615385[/C][C]-0.0361538461538462[/C][/ROW]
[ROW][C]7[/C][C]3.09[/C][C]3.07615384615385[/C][C]0.0138461538461536[/C][/ROW]
[ROW][C]8[/C][C]3.06[/C][C]3.07615384615385[/C][C]-0.0161538461538462[/C][/ROW]
[ROW][C]9[/C][C]3.06[/C][C]3.07615384615385[/C][C]-0.0161538461538462[/C][/ROW]
[ROW][C]10[/C][C]3.09[/C][C]3.07615384615385[/C][C]0.0138461538461536[/C][/ROW]
[ROW][C]11[/C][C]3.11[/C][C]3.07615384615385[/C][C]0.0338461538461536[/C][/ROW]
[ROW][C]12[/C][C]3.1[/C][C]3.07615384615385[/C][C]0.0238461538461539[/C][/ROW]
[ROW][C]13[/C][C]3.09[/C][C]3.18421052631579[/C][C]-0.0942105263157895[/C][/ROW]
[ROW][C]14[/C][C]3.19[/C][C]3.18421052631579[/C][C]0.00578947368421057[/C][/ROW]
[ROW][C]15[/C][C]3.22[/C][C]3.18421052631579[/C][C]0.0357894736842108[/C][/ROW]
[ROW][C]16[/C][C]3.22[/C][C]3.18421052631579[/C][C]0.0357894736842108[/C][/ROW]
[ROW][C]17[/C][C]3.25[/C][C]3.18421052631579[/C][C]0.0657894736842106[/C][/ROW]
[ROW][C]18[/C][C]3.25[/C][C]3.18421052631579[/C][C]0.0657894736842106[/C][/ROW]
[ROW][C]19[/C][C]3.27[/C][C]3.18421052631579[/C][C]0.0857894736842106[/C][/ROW]
[ROW][C]20[/C][C]3.28[/C][C]3.18421052631579[/C][C]0.0957894736842104[/C][/ROW]
[ROW][C]21[/C][C]3.24[/C][C]3.18421052631579[/C][C]0.0557894736842108[/C][/ROW]
[ROW][C]22[/C][C]3.23[/C][C]3.18421052631579[/C][C]0.0457894736842106[/C][/ROW]
[ROW][C]23[/C][C]3.2[/C][C]3.18421052631579[/C][C]0.0157894736842108[/C][/ROW]
[ROW][C]24[/C][C]3.19[/C][C]3.18421052631579[/C][C]0.00578947368421057[/C][/ROW]
[ROW][C]25[/C][C]3.23[/C][C]3.18421052631579[/C][C]0.0457894736842106[/C][/ROW]
[ROW][C]26[/C][C]3.19[/C][C]3.18421052631579[/C][C]0.00578947368421057[/C][/ROW]
[ROW][C]27[/C][C]3.16[/C][C]3.18421052631579[/C][C]-0.0242105263157892[/C][/ROW]
[ROW][C]28[/C][C]3.11[/C][C]3.18421052631579[/C][C]-0.0742105263157895[/C][/ROW]
[ROW][C]29[/C][C]3.11[/C][C]3.18421052631579[/C][C]-0.0742105263157895[/C][/ROW]
[ROW][C]30[/C][C]3.07[/C][C]3.18421052631579[/C][C]-0.114210526315790[/C][/ROW]
[ROW][C]31[/C][C]3.05[/C][C]3.07615384615385[/C][C]-0.0261538461538464[/C][/ROW]
[ROW][C]32[/C][C]3[/C][C]3.18421052631579[/C][C]-0.184210526315789[/C][/ROW]
[ROW][C]33[/C][C]2.95[/C][C]2.89363636363636[/C][C]0.0563636363636366[/C][/ROW]
[ROW][C]34[/C][C]2.9[/C][C]2.89363636363636[/C][C]0.00636363636363635[/C][/ROW]
[ROW][C]35[/C][C]2.88[/C][C]2.89363636363636[/C][C]-0.0136363636363637[/C][/ROW]
[ROW][C]36[/C][C]2.9[/C][C]2.89363636363636[/C][C]0.00636363636363635[/C][/ROW]
[ROW][C]37[/C][C]2.89[/C][C]2.89363636363636[/C][C]-0.00363636363636344[/C][/ROW]
[ROW][C]38[/C][C]2.89[/C][C]2.89363636363636[/C][C]-0.00363636363636344[/C][/ROW]
[ROW][C]39[/C][C]2.91[/C][C]2.89363636363636[/C][C]0.0163636363636366[/C][/ROW]
[ROW][C]40[/C][C]2.9[/C][C]2.89363636363636[/C][C]0.00636363636363635[/C][/ROW]
[ROW][C]41[/C][C]2.9[/C][C]2.89363636363636[/C][C]0.00636363636363635[/C][/ROW]
[ROW][C]42[/C][C]2.88[/C][C]2.89363636363636[/C][C]-0.0136363636363637[/C][/ROW]
[ROW][C]43[/C][C]2.83[/C][C]2.89363636363636[/C][C]-0.0636363636363635[/C][/ROW]
[ROW][C]44[/C][C]2.8[/C][C]2.70615384615385[/C][C]0.0938461538461537[/C][/ROW]
[ROW][C]45[/C][C]2.77[/C][C]2.70615384615385[/C][C]0.0638461538461539[/C][/ROW]
[ROW][C]46[/C][C]2.78[/C][C]2.70615384615385[/C][C]0.0738461538461537[/C][/ROW]
[ROW][C]47[/C][C]2.75[/C][C]2.70615384615385[/C][C]0.0438461538461539[/C][/ROW]
[ROW][C]48[/C][C]2.74[/C][C]2.70615384615385[/C][C]0.0338461538461541[/C][/ROW]
[ROW][C]49[/C][C]2.73[/C][C]2.70615384615385[/C][C]0.0238461538461539[/C][/ROW]
[ROW][C]50[/C][C]2.69[/C][C]2.70615384615385[/C][C]-0.0161538461538462[/C][/ROW]
[ROW][C]51[/C][C]2.67[/C][C]2.70615384615385[/C][C]-0.0361538461538462[/C][/ROW]
[ROW][C]52[/C][C]2.66[/C][C]2.70615384615385[/C][C]-0.046153846153846[/C][/ROW]
[ROW][C]53[/C][C]2.67[/C][C]2.70615384615385[/C][C]-0.0361538461538462[/C][/ROW]
[ROW][C]54[/C][C]2.65[/C][C]2.70615384615385[/C][C]-0.0561538461538462[/C][/ROW]
[ROW][C]55[/C][C]2.64[/C][C]2.70615384615385[/C][C]-0.066153846153846[/C][/ROW]
[ROW][C]56[/C][C]2.63[/C][C]2.70615384615385[/C][C]-0.0761538461538462[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=109913&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=109913&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	3.18	3.07615384615385	0.103846153846154
2	3.14	3.07615384615385	0.0638461538461539
3	3.02	3.07615384615385	-0.0561538461538462
4	3.02	3.07615384615385	-0.0561538461538462
5	3.03	3.07615384615385	-0.0461538461538464
6	3.04	3.07615384615385	-0.0361538461538462
7	3.09	3.07615384615385	0.0138461538461536
8	3.06	3.07615384615385	-0.0161538461538462
9	3.06	3.07615384615385	-0.0161538461538462
10	3.09	3.07615384615385	0.0138461538461536
11	3.11	3.07615384615385	0.0338461538461536
12	3.1	3.07615384615385	0.0238461538461539
13	3.09	3.18421052631579	-0.0942105263157895
14	3.19	3.18421052631579	0.00578947368421057
15	3.22	3.18421052631579	0.0357894736842108
16	3.22	3.18421052631579	0.0357894736842108
17	3.25	3.18421052631579	0.0657894736842106
18	3.25	3.18421052631579	0.0657894736842106
19	3.27	3.18421052631579	0.0857894736842106
20	3.28	3.18421052631579	0.0957894736842104
21	3.24	3.18421052631579	0.0557894736842108
22	3.23	3.18421052631579	0.0457894736842106
23	3.2	3.18421052631579	0.0157894736842108
24	3.19	3.18421052631579	0.00578947368421057
25	3.23	3.18421052631579	0.0457894736842106
26	3.19	3.18421052631579	0.00578947368421057
27	3.16	3.18421052631579	-0.0242105263157892
28	3.11	3.18421052631579	-0.0742105263157895
29	3.11	3.18421052631579	-0.0742105263157895
30	3.07	3.18421052631579	-0.114210526315790
31	3.05	3.07615384615385	-0.0261538461538464
32	3	3.18421052631579	-0.184210526315789
33	2.95	2.89363636363636	0.0563636363636366
34	2.9	2.89363636363636	0.00636363636363635
35	2.88	2.89363636363636	-0.0136363636363637
36	2.9	2.89363636363636	0.00636363636363635
37	2.89	2.89363636363636	-0.00363636363636344
38	2.89	2.89363636363636	-0.00363636363636344
39	2.91	2.89363636363636	0.0163636363636366
40	2.9	2.89363636363636	0.00636363636363635
41	2.9	2.89363636363636	0.00636363636363635
42	2.88	2.89363636363636	-0.0136363636363637
43	2.83	2.89363636363636	-0.0636363636363635
44	2.8	2.70615384615385	0.0938461538461537
45	2.77	2.70615384615385	0.0638461538461539
46	2.78	2.70615384615385	0.0738461538461537
47	2.75	2.70615384615385	0.0438461538461539
48	2.74	2.70615384615385	0.0338461538461541
49	2.73	2.70615384615385	0.0238461538461539
50	2.69	2.70615384615385	-0.0161538461538462
51	2.67	2.70615384615385	-0.0361538461538462
52	2.66	2.70615384615385	-0.046153846153846
53	2.67	2.70615384615385	-0.0361538461538462
54	2.65	2.70615384615385	-0.0561538461538462
55	2.64	2.70615384615385	-0.066153846153846
56	2.63	2.70615384615385	-0.0761538461538462

Figure 1

PNG link

Postscript link

PDF link

Figure 2

PNG link

Postscript link

PDF link

Figure 3

PNG link

Postscript link

PDF link

Parameters (Session):

par1 = 1 ; par2 = none ; par3 = 4 ; par4 = no ;

Parameters (R input):

par1 = 1 ; 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