Repository of Reproducible Computations

Free Statistics

of Irreproducible Research!

Author's title

Author

*The author of this computation has been verified*

R Software Module

rwasp_regression_trees1.wasp

Title produced by software

Recursive Partitioning (Regression Trees)

Date of computation

Tue, 11 Dec 2012 16:06:19 -0500

Cite this page as follows

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2012/Dec/11/t1355260034uwpm7qt73ce6mbu.htm/, Retrieved Thu, 25 Apr 2024 07:58:47 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=198706, Retrieved Thu, 25 Apr 2024 07:58:47 +0000

QR Codes:

Paste this QR Code to cite your computation.

Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords

Estimated Impact

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)] [WS10.4] [2012-12-11 20:38:27] [516331a1326ffbbf54349d9c9d5f2d94]
-   PD    [Recursive Partitioning (Regression Trees)] [WS10.5] [2012-12-11 20:51:39] [516331a1326ffbbf54349d9c9d5f2d94]
-   P       [Recursive Partitioning (Regression Trees)] [WS10.6] [2012-12-11 20:58:50] [516331a1326ffbbf54349d9c9d5f2d94]
-   P         [Recursive Partitioning (Regression Trees)] [WS10.6] [2012-12-11 21:03:34] [516331a1326ffbbf54349d9c9d5f2d94]
-                 [Recursive Partitioning (Regression Trees)] [WS10.7] [2012-12-11 21:06:19] [6144fd9dab7e8876ce9100c6a2ac91c2] [Current]

Feedback Forum

Post a new message

Dataseries X:

Download CSV

Histogram

Boxplots

6.8	225	0.442	0.672	9.2
6.3	180	0.435	0.797	11.7
6.4	190	0.456	0.761	15.8
6.2	180	0.416	0.651	8.6
6.9	205	0.449	0.9	23.2
6.4	225	0.431	0.78	27.4
6.3	185	0.487	0.771	9.3
6.8	235	0.469	0.75	16
6.9	235	0.435	0.818	4.7
6.7	210	0.48	0.825	12.5
6.9	245	0.516	0.632	20.1
6.9	245	0.493	0.757	9.1
6.3	185	0.374	0.709	8.1
6.1	185	0.424	0.782	8.6
6.2	180	0.441	0.775	20.3
6.8	220	0.503	0.88	25
6.5	194	0.503	0.833	19.2
7.6	225	0.425	0.571	3.3
6.3	210	0.371	0.816	11.2
7.1	240	0.504	0.714	10.5
6.8	225	0.4	0.765	10.1
7.3	263	0.482	0.655	7.2
6.4	210	0.475	0.244	13.6
6.8	235	0.428	0.728	9
7.2	230	0.559	0.721	24.6
6.4	190	0.441	0.757	12.6
6.6	220	0.492	0.747	5.6
6.8	210	0.402	0.739	8.7
6.1	180	0.415	0.713	7.7
6.5	235	0.492	0.742	24.1
6.4	185	0.484	0.861	11.7
6	175	0.387	0.721	7.7
6	192	0.436	0.785	9.6
7.3	263	0.482	0.655	7.2
6.1	180	0.34	0.821	12.3
6.7	240	0.516	0.728	8.9
6.4	210	0.475	0.846	13.6
5.8	160	0.412	0.813	11.2
6.9	230	0.411	0.595	2.8
7	245	0.407	0.573	3.2
7.3	228	0.445	0.726	9.4
5.9	155	0.291	0.707	11.9
6.2	200	0.449	0.804	15.4
6.8	235	0.546	0.784	7.4
7	235	0.48	0.744	18.9
5.9	105	0.359	0.839	7.9
6.1	180	0.528	0.79	12.2
5.7	185	0.352	0.701	11
7.1	245	0.414	0.778	2.8
5.8	180	0.425	0.872	11.8
7.4	240	0.599	0.713	17.1
6.8	225	0.482	0.701	11.6
6.8	215	0.457	0.734	5.8
7	230	0.435	0.764	8.3

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	4 seconds
R Server	'Sir Maurice George Kendall' @ kendall.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 & 'Sir Maurice George Kendall' @ kendall.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=198706&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]'Sir Maurice George Kendall' @ kendall.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=198706&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=198706&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	4 seconds
R Server	'Sir Maurice George Kendall' @ kendall.wessa.net

Goodness of Fit
Correlation	NA
R-squared	NA
RMSE	0.0992

\begin{tabular}{lllllllll}
\hline
Goodness of Fit \tabularnewline
Correlation & NA \tabularnewline
R-squared & NA \tabularnewline
RMSE & 0.0992 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=198706&T=1

[TABLE]
[ROW][C]Goodness of Fit[/C][/ROW]
[ROW][C]Correlation[/C][C]NA[/C][/ROW]
[ROW][C]R-squared[/C][C]NA[/C][/ROW]
[ROW][C]RMSE[/C][C]0.0992[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=198706&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=198706&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	NA
R-squared	NA
RMSE	0.0992

Actuals, Predictions, and Residuals
#	Actuals	Forecasts	Residuals
1	0.672	0.741851851851852	-0.0698518518518518
2	0.797	0.741851851851852	0.0551481481481482
3	0.761	0.741851851851852	0.0191481481481481
4	0.651	0.741851851851852	-0.0908518518518519
5	0.9	0.741851851851852	0.158148148148148
6	0.78	0.741851851851852	0.0381481481481482
7	0.771	0.741851851851852	0.0291481481481481
8	0.75	0.741851851851852	0.00814814814814813
9	0.818	0.741851851851852	0.0761481481481481
10	0.825	0.741851851851852	0.0831481481481481
11	0.632	0.741851851851852	-0.109851851851852
12	0.757	0.741851851851852	0.0151481481481481
13	0.709	0.741851851851852	-0.0328518518518519
14	0.782	0.741851851851852	0.0401481481481482
15	0.775	0.741851851851852	0.0331481481481481
16	0.88	0.741851851851852	0.138148148148148
17	0.833	0.741851851851852	0.0911481481481481
18	0.571	0.741851851851852	-0.170851851851852
19	0.816	0.741851851851852	0.0741481481481481
20	0.714	0.741851851851852	-0.0278518518518519
21	0.765	0.741851851851852	0.0231481481481481
22	0.655	0.741851851851852	-0.0868518518518518
23	0.244	0.741851851851852	-0.497851851851852
24	0.728	0.741851851851852	-0.0138518518518519
25	0.721	0.741851851851852	-0.0208518518518519
26	0.757	0.741851851851852	0.0151481481481481
27	0.747	0.741851851851852	0.00514814814814812
28	0.739	0.741851851851852	-0.00285185185185188
29	0.713	0.741851851851852	-0.0288518518518519
30	0.742	0.741851851851852	0.000148148148148119
31	0.861	0.741851851851852	0.119148148148148
32	0.721	0.741851851851852	-0.0208518518518519
33	0.785	0.741851851851852	0.0431481481481482
34	0.655	0.741851851851852	-0.0868518518518518
35	0.821	0.741851851851852	0.0791481481481481
36	0.728	0.741851851851852	-0.0138518518518519
37	0.846	0.741851851851852	0.104148148148148
38	0.813	0.741851851851852	0.0711481481481481
39	0.595	0.741851851851852	-0.146851851851852
40	0.573	0.741851851851852	-0.168851851851852
41	0.726	0.741851851851852	-0.0158518518518519
42	0.707	0.741851851851852	-0.0348518518518519
43	0.804	0.741851851851852	0.0621481481481482
44	0.784	0.741851851851852	0.0421481481481482
45	0.744	0.741851851851852	0.00214814814814812
46	0.839	0.741851851851852	0.0971481481481481
47	0.79	0.741851851851852	0.0481481481481482
48	0.701	0.741851851851852	-0.0408518518518519
49	0.778	0.741851851851852	0.0361481481481482
50	0.872	0.741851851851852	0.130148148148148
51	0.713	0.741851851851852	-0.0288518518518519
52	0.701	0.741851851851852	-0.0408518518518519
53	0.734	0.741851851851852	-0.00785185185185189
54	0.764	0.741851851851852	0.0221481481481481

\begin{tabular}{lllllllll}
\hline
Actuals, Predictions, and Residuals \tabularnewline
# & Actuals & Forecasts & Residuals \tabularnewline
1 & 0.672 & 0.741851851851852 & -0.0698518518518518 \tabularnewline
2 & 0.797 & 0.741851851851852 & 0.0551481481481482 \tabularnewline
3 & 0.761 & 0.741851851851852 & 0.0191481481481481 \tabularnewline
4 & 0.651 & 0.741851851851852 & -0.0908518518518519 \tabularnewline
5 & 0.9 & 0.741851851851852 & 0.158148148148148 \tabularnewline
6 & 0.78 & 0.741851851851852 & 0.0381481481481482 \tabularnewline
7 & 0.771 & 0.741851851851852 & 0.0291481481481481 \tabularnewline
8 & 0.75 & 0.741851851851852 & 0.00814814814814813 \tabularnewline
9 & 0.818 & 0.741851851851852 & 0.0761481481481481 \tabularnewline
10 & 0.825 & 0.741851851851852 & 0.0831481481481481 \tabularnewline
11 & 0.632 & 0.741851851851852 & -0.109851851851852 \tabularnewline
12 & 0.757 & 0.741851851851852 & 0.0151481481481481 \tabularnewline
13 & 0.709 & 0.741851851851852 & -0.0328518518518519 \tabularnewline
14 & 0.782 & 0.741851851851852 & 0.0401481481481482 \tabularnewline
15 & 0.775 & 0.741851851851852 & 0.0331481481481481 \tabularnewline
16 & 0.88 & 0.741851851851852 & 0.138148148148148 \tabularnewline
17 & 0.833 & 0.741851851851852 & 0.0911481481481481 \tabularnewline
18 & 0.571 & 0.741851851851852 & -0.170851851851852 \tabularnewline
19 & 0.816 & 0.741851851851852 & 0.0741481481481481 \tabularnewline
20 & 0.714 & 0.741851851851852 & -0.0278518518518519 \tabularnewline
21 & 0.765 & 0.741851851851852 & 0.0231481481481481 \tabularnewline
22 & 0.655 & 0.741851851851852 & -0.0868518518518518 \tabularnewline
23 & 0.244 & 0.741851851851852 & -0.497851851851852 \tabularnewline
24 & 0.728 & 0.741851851851852 & -0.0138518518518519 \tabularnewline
25 & 0.721 & 0.741851851851852 & -0.0208518518518519 \tabularnewline
26 & 0.757 & 0.741851851851852 & 0.0151481481481481 \tabularnewline
27 & 0.747 & 0.741851851851852 & 0.00514814814814812 \tabularnewline
28 & 0.739 & 0.741851851851852 & -0.00285185185185188 \tabularnewline
29 & 0.713 & 0.741851851851852 & -0.0288518518518519 \tabularnewline
30 & 0.742 & 0.741851851851852 & 0.000148148148148119 \tabularnewline
31 & 0.861 & 0.741851851851852 & 0.119148148148148 \tabularnewline
32 & 0.721 & 0.741851851851852 & -0.0208518518518519 \tabularnewline
33 & 0.785 & 0.741851851851852 & 0.0431481481481482 \tabularnewline
34 & 0.655 & 0.741851851851852 & -0.0868518518518518 \tabularnewline
35 & 0.821 & 0.741851851851852 & 0.0791481481481481 \tabularnewline
36 & 0.728 & 0.741851851851852 & -0.0138518518518519 \tabularnewline
37 & 0.846 & 0.741851851851852 & 0.104148148148148 \tabularnewline
38 & 0.813 & 0.741851851851852 & 0.0711481481481481 \tabularnewline
39 & 0.595 & 0.741851851851852 & -0.146851851851852 \tabularnewline
40 & 0.573 & 0.741851851851852 & -0.168851851851852 \tabularnewline
41 & 0.726 & 0.741851851851852 & -0.0158518518518519 \tabularnewline
42 & 0.707 & 0.741851851851852 & -0.0348518518518519 \tabularnewline
43 & 0.804 & 0.741851851851852 & 0.0621481481481482 \tabularnewline
44 & 0.784 & 0.741851851851852 & 0.0421481481481482 \tabularnewline
45 & 0.744 & 0.741851851851852 & 0.00214814814814812 \tabularnewline
46 & 0.839 & 0.741851851851852 & 0.0971481481481481 \tabularnewline
47 & 0.79 & 0.741851851851852 & 0.0481481481481482 \tabularnewline
48 & 0.701 & 0.741851851851852 & -0.0408518518518519 \tabularnewline
49 & 0.778 & 0.741851851851852 & 0.0361481481481482 \tabularnewline
50 & 0.872 & 0.741851851851852 & 0.130148148148148 \tabularnewline
51 & 0.713 & 0.741851851851852 & -0.0288518518518519 \tabularnewline
52 & 0.701 & 0.741851851851852 & -0.0408518518518519 \tabularnewline
53 & 0.734 & 0.741851851851852 & -0.00785185185185189 \tabularnewline
54 & 0.764 & 0.741851851851852 & 0.0221481481481481 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=198706&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]0.672[/C][C]0.741851851851852[/C][C]-0.0698518518518518[/C][/ROW]
[ROW][C]2[/C][C]0.797[/C][C]0.741851851851852[/C][C]0.0551481481481482[/C][/ROW]
[ROW][C]3[/C][C]0.761[/C][C]0.741851851851852[/C][C]0.0191481481481481[/C][/ROW]
[ROW][C]4[/C][C]0.651[/C][C]0.741851851851852[/C][C]-0.0908518518518519[/C][/ROW]
[ROW][C]5[/C][C]0.9[/C][C]0.741851851851852[/C][C]0.158148148148148[/C][/ROW]
[ROW][C]6[/C][C]0.78[/C][C]0.741851851851852[/C][C]0.0381481481481482[/C][/ROW]
[ROW][C]7[/C][C]0.771[/C][C]0.741851851851852[/C][C]0.0291481481481481[/C][/ROW]
[ROW][C]8[/C][C]0.75[/C][C]0.741851851851852[/C][C]0.00814814814814813[/C][/ROW]
[ROW][C]9[/C][C]0.818[/C][C]0.741851851851852[/C][C]0.0761481481481481[/C][/ROW]
[ROW][C]10[/C][C]0.825[/C][C]0.741851851851852[/C][C]0.0831481481481481[/C][/ROW]
[ROW][C]11[/C][C]0.632[/C][C]0.741851851851852[/C][C]-0.109851851851852[/C][/ROW]
[ROW][C]12[/C][C]0.757[/C][C]0.741851851851852[/C][C]0.0151481481481481[/C][/ROW]
[ROW][C]13[/C][C]0.709[/C][C]0.741851851851852[/C][C]-0.0328518518518519[/C][/ROW]
[ROW][C]14[/C][C]0.782[/C][C]0.741851851851852[/C][C]0.0401481481481482[/C][/ROW]
[ROW][C]15[/C][C]0.775[/C][C]0.741851851851852[/C][C]0.0331481481481481[/C][/ROW]
[ROW][C]16[/C][C]0.88[/C][C]0.741851851851852[/C][C]0.138148148148148[/C][/ROW]
[ROW][C]17[/C][C]0.833[/C][C]0.741851851851852[/C][C]0.0911481481481481[/C][/ROW]
[ROW][C]18[/C][C]0.571[/C][C]0.741851851851852[/C][C]-0.170851851851852[/C][/ROW]
[ROW][C]19[/C][C]0.816[/C][C]0.741851851851852[/C][C]0.0741481481481481[/C][/ROW]
[ROW][C]20[/C][C]0.714[/C][C]0.741851851851852[/C][C]-0.0278518518518519[/C][/ROW]
[ROW][C]21[/C][C]0.765[/C][C]0.741851851851852[/C][C]0.0231481481481481[/C][/ROW]
[ROW][C]22[/C][C]0.655[/C][C]0.741851851851852[/C][C]-0.0868518518518518[/C][/ROW]
[ROW][C]23[/C][C]0.244[/C][C]0.741851851851852[/C][C]-0.497851851851852[/C][/ROW]
[ROW][C]24[/C][C]0.728[/C][C]0.741851851851852[/C][C]-0.0138518518518519[/C][/ROW]
[ROW][C]25[/C][C]0.721[/C][C]0.741851851851852[/C][C]-0.0208518518518519[/C][/ROW]
[ROW][C]26[/C][C]0.757[/C][C]0.741851851851852[/C][C]0.0151481481481481[/C][/ROW]
[ROW][C]27[/C][C]0.747[/C][C]0.741851851851852[/C][C]0.00514814814814812[/C][/ROW]
[ROW][C]28[/C][C]0.739[/C][C]0.741851851851852[/C][C]-0.00285185185185188[/C][/ROW]
[ROW][C]29[/C][C]0.713[/C][C]0.741851851851852[/C][C]-0.0288518518518519[/C][/ROW]
[ROW][C]30[/C][C]0.742[/C][C]0.741851851851852[/C][C]0.000148148148148119[/C][/ROW]
[ROW][C]31[/C][C]0.861[/C][C]0.741851851851852[/C][C]0.119148148148148[/C][/ROW]
[ROW][C]32[/C][C]0.721[/C][C]0.741851851851852[/C][C]-0.0208518518518519[/C][/ROW]
[ROW][C]33[/C][C]0.785[/C][C]0.741851851851852[/C][C]0.0431481481481482[/C][/ROW]
[ROW][C]34[/C][C]0.655[/C][C]0.741851851851852[/C][C]-0.0868518518518518[/C][/ROW]
[ROW][C]35[/C][C]0.821[/C][C]0.741851851851852[/C][C]0.0791481481481481[/C][/ROW]
[ROW][C]36[/C][C]0.728[/C][C]0.741851851851852[/C][C]-0.0138518518518519[/C][/ROW]
[ROW][C]37[/C][C]0.846[/C][C]0.741851851851852[/C][C]0.104148148148148[/C][/ROW]
[ROW][C]38[/C][C]0.813[/C][C]0.741851851851852[/C][C]0.0711481481481481[/C][/ROW]
[ROW][C]39[/C][C]0.595[/C][C]0.741851851851852[/C][C]-0.146851851851852[/C][/ROW]
[ROW][C]40[/C][C]0.573[/C][C]0.741851851851852[/C][C]-0.168851851851852[/C][/ROW]
[ROW][C]41[/C][C]0.726[/C][C]0.741851851851852[/C][C]-0.0158518518518519[/C][/ROW]
[ROW][C]42[/C][C]0.707[/C][C]0.741851851851852[/C][C]-0.0348518518518519[/C][/ROW]
[ROW][C]43[/C][C]0.804[/C][C]0.741851851851852[/C][C]0.0621481481481482[/C][/ROW]
[ROW][C]44[/C][C]0.784[/C][C]0.741851851851852[/C][C]0.0421481481481482[/C][/ROW]
[ROW][C]45[/C][C]0.744[/C][C]0.741851851851852[/C][C]0.00214814814814812[/C][/ROW]
[ROW][C]46[/C][C]0.839[/C][C]0.741851851851852[/C][C]0.0971481481481481[/C][/ROW]
[ROW][C]47[/C][C]0.79[/C][C]0.741851851851852[/C][C]0.0481481481481482[/C][/ROW]
[ROW][C]48[/C][C]0.701[/C][C]0.741851851851852[/C][C]-0.0408518518518519[/C][/ROW]
[ROW][C]49[/C][C]0.778[/C][C]0.741851851851852[/C][C]0.0361481481481482[/C][/ROW]
[ROW][C]50[/C][C]0.872[/C][C]0.741851851851852[/C][C]0.130148148148148[/C][/ROW]
[ROW][C]51[/C][C]0.713[/C][C]0.741851851851852[/C][C]-0.0288518518518519[/C][/ROW]
[ROW][C]52[/C][C]0.701[/C][C]0.741851851851852[/C][C]-0.0408518518518519[/C][/ROW]
[ROW][C]53[/C][C]0.734[/C][C]0.741851851851852[/C][C]-0.00785185185185189[/C][/ROW]
[ROW][C]54[/C][C]0.764[/C][C]0.741851851851852[/C][C]0.0221481481481481[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=198706&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=198706&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	0.672	0.741851851851852	-0.0698518518518518
2	0.797	0.741851851851852	0.0551481481481482
3	0.761	0.741851851851852	0.0191481481481481
4	0.651	0.741851851851852	-0.0908518518518519
5	0.9	0.741851851851852	0.158148148148148
6	0.78	0.741851851851852	0.0381481481481482
7	0.771	0.741851851851852	0.0291481481481481
8	0.75	0.741851851851852	0.00814814814814813
9	0.818	0.741851851851852	0.0761481481481481
10	0.825	0.741851851851852	0.0831481481481481
11	0.632	0.741851851851852	-0.109851851851852
12	0.757	0.741851851851852	0.0151481481481481
13	0.709	0.741851851851852	-0.0328518518518519
14	0.782	0.741851851851852	0.0401481481481482
15	0.775	0.741851851851852	0.0331481481481481
16	0.88	0.741851851851852	0.138148148148148
17	0.833	0.741851851851852	0.0911481481481481
18	0.571	0.741851851851852	-0.170851851851852
19	0.816	0.741851851851852	0.0741481481481481
20	0.714	0.741851851851852	-0.0278518518518519
21	0.765	0.741851851851852	0.0231481481481481
22	0.655	0.741851851851852	-0.0868518518518518
23	0.244	0.741851851851852	-0.497851851851852
24	0.728	0.741851851851852	-0.0138518518518519
25	0.721	0.741851851851852	-0.0208518518518519
26	0.757	0.741851851851852	0.0151481481481481
27	0.747	0.741851851851852	0.00514814814814812
28	0.739	0.741851851851852	-0.00285185185185188
29	0.713	0.741851851851852	-0.0288518518518519
30	0.742	0.741851851851852	0.000148148148148119
31	0.861	0.741851851851852	0.119148148148148
32	0.721	0.741851851851852	-0.0208518518518519
33	0.785	0.741851851851852	0.0431481481481482
34	0.655	0.741851851851852	-0.0868518518518518
35	0.821	0.741851851851852	0.0791481481481481
36	0.728	0.741851851851852	-0.0138518518518519
37	0.846	0.741851851851852	0.104148148148148
38	0.813	0.741851851851852	0.0711481481481481
39	0.595	0.741851851851852	-0.146851851851852
40	0.573	0.741851851851852	-0.168851851851852
41	0.726	0.741851851851852	-0.0158518518518519
42	0.707	0.741851851851852	-0.0348518518518519
43	0.804	0.741851851851852	0.0621481481481482
44	0.784	0.741851851851852	0.0421481481481482
45	0.744	0.741851851851852	0.00214814814814812
46	0.839	0.741851851851852	0.0971481481481481
47	0.79	0.741851851851852	0.0481481481481482
48	0.701	0.741851851851852	-0.0408518518518519
49	0.778	0.741851851851852	0.0361481481481482
50	0.872	0.741851851851852	0.130148148148148
51	0.713	0.741851851851852	-0.0288518518518519
52	0.701	0.741851851851852	-0.0408518518518519
53	0.734	0.741851851851852	-0.00785185185185189
54	0.764	0.741851851851852	0.0221481481481481

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 = 3 ; par2 = none ; par3 = 4 ; par4 = no ;

Parameters (R input):

par1 = 4 ; 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