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 computationTue, 11 Dec 2012 15:38:27 -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/11/t1355258347m4eamo6suah6mks.htm/, Retrieved Thu, 28 Mar 2024 23:36:14 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=198685, Retrieved Thu, 28 Mar 2024 23:36:14 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact84
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] [6144fd9dab7e8876ce9100c6a2ac91c2] [Current]
-   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] [516331a1326ffbbf54349d9c9d5f2d94]
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Dataseries X:
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 Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time4 seconds
R Server'Sir Ronald Aylmer Fisher' @ fisher.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 Ronald Aylmer Fisher' @ fisher.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=198685&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 Ronald Aylmer Fisher' @ fisher.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=198685&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=198685&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'Sir Ronald Aylmer Fisher' @ fisher.wessa.net







Goodness of Fit
CorrelationNA
R-squaredNA
RMSE5.8444

\begin{tabular}{lllllllll}
\hline
Goodness of Fit \tabularnewline
Correlation & NA \tabularnewline
R-squared & NA \tabularnewline
RMSE & 5.8444 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=198685&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]5.8444[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=198685&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=198685&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
CorrelationNA
R-squaredNA
RMSE5.8444







Actuals, Predictions, and Residuals
#ActualsForecastsResiduals
19.211.7907407407407-2.59074074074074
211.711.7907407407407-0.0907407407407419
315.811.79074074074074.00925925925926
48.611.7907407407407-3.19074074074074
523.211.790740740740711.4092592592593
627.411.790740740740715.6092592592593
79.311.7907407407407-2.49074074074074
81611.79074074074074.20925925925926
94.711.7907407407407-7.09074074074074
1012.511.79074074074070.709259259259259
1120.111.79074074074078.30925925925926
129.111.7907407407407-2.69074074074074
138.111.7907407407407-3.69074074074074
148.611.7907407407407-3.19074074074074
1520.311.79074074074078.50925925925926
162511.790740740740713.2092592592593
1719.211.79074074074077.40925925925926
183.311.7907407407407-8.49074074074074
1911.211.7907407407407-0.590740740740742
2010.511.7907407407407-1.29074074074074
2110.111.7907407407407-1.69074074074074
227.211.7907407407407-4.59074074074074
2313.611.79074074074071.80925925925926
24911.7907407407407-2.79074074074074
2524.611.790740740740712.8092592592593
2612.611.79074074074070.809259259259258
275.611.7907407407407-6.19074074074074
288.711.7907407407407-3.09074074074074
297.711.7907407407407-4.09074074074074
3024.111.790740740740712.3092592592593
3111.711.7907407407407-0.0907407407407419
327.711.7907407407407-4.09074074074074
339.611.7907407407407-2.19074074074074
347.211.7907407407407-4.59074074074074
3512.311.79074074074070.50925925925926
368.911.7907407407407-2.89074074074074
3713.611.79074074074071.80925925925926
3811.211.7907407407407-0.590740740740742
392.811.7907407407407-8.99074074074074
403.211.7907407407407-8.59074074074074
419.411.7907407407407-2.39074074074074
4211.911.79074074074070.109259259259259
4315.411.79074074074073.60925925925926
447.411.7907407407407-4.39074074074074
4518.911.79074074074077.10925925925926
467.911.7907407407407-3.89074074074074
4712.211.79074074074070.409259259259258
481111.7907407407407-0.790740740740741
492.811.7907407407407-8.99074074074074
5011.811.79074074074070.00925925925925952
5117.111.79074074074075.30925925925926
5211.611.7907407407407-0.190740740740742
535.811.7907407407407-5.99074074074074
548.311.7907407407407-3.49074074074074

\begin{tabular}{lllllllll}
\hline
Actuals, Predictions, and Residuals \tabularnewline
# & Actuals & Forecasts & Residuals \tabularnewline
1 & 9.2 & 11.7907407407407 & -2.59074074074074 \tabularnewline
2 & 11.7 & 11.7907407407407 & -0.0907407407407419 \tabularnewline
3 & 15.8 & 11.7907407407407 & 4.00925925925926 \tabularnewline
4 & 8.6 & 11.7907407407407 & -3.19074074074074 \tabularnewline
5 & 23.2 & 11.7907407407407 & 11.4092592592593 \tabularnewline
6 & 27.4 & 11.7907407407407 & 15.6092592592593 \tabularnewline
7 & 9.3 & 11.7907407407407 & -2.49074074074074 \tabularnewline
8 & 16 & 11.7907407407407 & 4.20925925925926 \tabularnewline
9 & 4.7 & 11.7907407407407 & -7.09074074074074 \tabularnewline
10 & 12.5 & 11.7907407407407 & 0.709259259259259 \tabularnewline
11 & 20.1 & 11.7907407407407 & 8.30925925925926 \tabularnewline
12 & 9.1 & 11.7907407407407 & -2.69074074074074 \tabularnewline
13 & 8.1 & 11.7907407407407 & -3.69074074074074 \tabularnewline
14 & 8.6 & 11.7907407407407 & -3.19074074074074 \tabularnewline
15 & 20.3 & 11.7907407407407 & 8.50925925925926 \tabularnewline
16 & 25 & 11.7907407407407 & 13.2092592592593 \tabularnewline
17 & 19.2 & 11.7907407407407 & 7.40925925925926 \tabularnewline
18 & 3.3 & 11.7907407407407 & -8.49074074074074 \tabularnewline
19 & 11.2 & 11.7907407407407 & -0.590740740740742 \tabularnewline
20 & 10.5 & 11.7907407407407 & -1.29074074074074 \tabularnewline
21 & 10.1 & 11.7907407407407 & -1.69074074074074 \tabularnewline
22 & 7.2 & 11.7907407407407 & -4.59074074074074 \tabularnewline
23 & 13.6 & 11.7907407407407 & 1.80925925925926 \tabularnewline
24 & 9 & 11.7907407407407 & -2.79074074074074 \tabularnewline
25 & 24.6 & 11.7907407407407 & 12.8092592592593 \tabularnewline
26 & 12.6 & 11.7907407407407 & 0.809259259259258 \tabularnewline
27 & 5.6 & 11.7907407407407 & -6.19074074074074 \tabularnewline
28 & 8.7 & 11.7907407407407 & -3.09074074074074 \tabularnewline
29 & 7.7 & 11.7907407407407 & -4.09074074074074 \tabularnewline
30 & 24.1 & 11.7907407407407 & 12.3092592592593 \tabularnewline
31 & 11.7 & 11.7907407407407 & -0.0907407407407419 \tabularnewline
32 & 7.7 & 11.7907407407407 & -4.09074074074074 \tabularnewline
33 & 9.6 & 11.7907407407407 & -2.19074074074074 \tabularnewline
34 & 7.2 & 11.7907407407407 & -4.59074074074074 \tabularnewline
35 & 12.3 & 11.7907407407407 & 0.50925925925926 \tabularnewline
36 & 8.9 & 11.7907407407407 & -2.89074074074074 \tabularnewline
37 & 13.6 & 11.7907407407407 & 1.80925925925926 \tabularnewline
38 & 11.2 & 11.7907407407407 & -0.590740740740742 \tabularnewline
39 & 2.8 & 11.7907407407407 & -8.99074074074074 \tabularnewline
40 & 3.2 & 11.7907407407407 & -8.59074074074074 \tabularnewline
41 & 9.4 & 11.7907407407407 & -2.39074074074074 \tabularnewline
42 & 11.9 & 11.7907407407407 & 0.109259259259259 \tabularnewline
43 & 15.4 & 11.7907407407407 & 3.60925925925926 \tabularnewline
44 & 7.4 & 11.7907407407407 & -4.39074074074074 \tabularnewline
45 & 18.9 & 11.7907407407407 & 7.10925925925926 \tabularnewline
46 & 7.9 & 11.7907407407407 & -3.89074074074074 \tabularnewline
47 & 12.2 & 11.7907407407407 & 0.409259259259258 \tabularnewline
48 & 11 & 11.7907407407407 & -0.790740740740741 \tabularnewline
49 & 2.8 & 11.7907407407407 & -8.99074074074074 \tabularnewline
50 & 11.8 & 11.7907407407407 & 0.00925925925925952 \tabularnewline
51 & 17.1 & 11.7907407407407 & 5.30925925925926 \tabularnewline
52 & 11.6 & 11.7907407407407 & -0.190740740740742 \tabularnewline
53 & 5.8 & 11.7907407407407 & -5.99074074074074 \tabularnewline
54 & 8.3 & 11.7907407407407 & -3.49074074074074 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=198685&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]9.2[/C][C]11.7907407407407[/C][C]-2.59074074074074[/C][/ROW]
[ROW][C]2[/C][C]11.7[/C][C]11.7907407407407[/C][C]-0.0907407407407419[/C][/ROW]
[ROW][C]3[/C][C]15.8[/C][C]11.7907407407407[/C][C]4.00925925925926[/C][/ROW]
[ROW][C]4[/C][C]8.6[/C][C]11.7907407407407[/C][C]-3.19074074074074[/C][/ROW]
[ROW][C]5[/C][C]23.2[/C][C]11.7907407407407[/C][C]11.4092592592593[/C][/ROW]
[ROW][C]6[/C][C]27.4[/C][C]11.7907407407407[/C][C]15.6092592592593[/C][/ROW]
[ROW][C]7[/C][C]9.3[/C][C]11.7907407407407[/C][C]-2.49074074074074[/C][/ROW]
[ROW][C]8[/C][C]16[/C][C]11.7907407407407[/C][C]4.20925925925926[/C][/ROW]
[ROW][C]9[/C][C]4.7[/C][C]11.7907407407407[/C][C]-7.09074074074074[/C][/ROW]
[ROW][C]10[/C][C]12.5[/C][C]11.7907407407407[/C][C]0.709259259259259[/C][/ROW]
[ROW][C]11[/C][C]20.1[/C][C]11.7907407407407[/C][C]8.30925925925926[/C][/ROW]
[ROW][C]12[/C][C]9.1[/C][C]11.7907407407407[/C][C]-2.69074074074074[/C][/ROW]
[ROW][C]13[/C][C]8.1[/C][C]11.7907407407407[/C][C]-3.69074074074074[/C][/ROW]
[ROW][C]14[/C][C]8.6[/C][C]11.7907407407407[/C][C]-3.19074074074074[/C][/ROW]
[ROW][C]15[/C][C]20.3[/C][C]11.7907407407407[/C][C]8.50925925925926[/C][/ROW]
[ROW][C]16[/C][C]25[/C][C]11.7907407407407[/C][C]13.2092592592593[/C][/ROW]
[ROW][C]17[/C][C]19.2[/C][C]11.7907407407407[/C][C]7.40925925925926[/C][/ROW]
[ROW][C]18[/C][C]3.3[/C][C]11.7907407407407[/C][C]-8.49074074074074[/C][/ROW]
[ROW][C]19[/C][C]11.2[/C][C]11.7907407407407[/C][C]-0.590740740740742[/C][/ROW]
[ROW][C]20[/C][C]10.5[/C][C]11.7907407407407[/C][C]-1.29074074074074[/C][/ROW]
[ROW][C]21[/C][C]10.1[/C][C]11.7907407407407[/C][C]-1.69074074074074[/C][/ROW]
[ROW][C]22[/C][C]7.2[/C][C]11.7907407407407[/C][C]-4.59074074074074[/C][/ROW]
[ROW][C]23[/C][C]13.6[/C][C]11.7907407407407[/C][C]1.80925925925926[/C][/ROW]
[ROW][C]24[/C][C]9[/C][C]11.7907407407407[/C][C]-2.79074074074074[/C][/ROW]
[ROW][C]25[/C][C]24.6[/C][C]11.7907407407407[/C][C]12.8092592592593[/C][/ROW]
[ROW][C]26[/C][C]12.6[/C][C]11.7907407407407[/C][C]0.809259259259258[/C][/ROW]
[ROW][C]27[/C][C]5.6[/C][C]11.7907407407407[/C][C]-6.19074074074074[/C][/ROW]
[ROW][C]28[/C][C]8.7[/C][C]11.7907407407407[/C][C]-3.09074074074074[/C][/ROW]
[ROW][C]29[/C][C]7.7[/C][C]11.7907407407407[/C][C]-4.09074074074074[/C][/ROW]
[ROW][C]30[/C][C]24.1[/C][C]11.7907407407407[/C][C]12.3092592592593[/C][/ROW]
[ROW][C]31[/C][C]11.7[/C][C]11.7907407407407[/C][C]-0.0907407407407419[/C][/ROW]
[ROW][C]32[/C][C]7.7[/C][C]11.7907407407407[/C][C]-4.09074074074074[/C][/ROW]
[ROW][C]33[/C][C]9.6[/C][C]11.7907407407407[/C][C]-2.19074074074074[/C][/ROW]
[ROW][C]34[/C][C]7.2[/C][C]11.7907407407407[/C][C]-4.59074074074074[/C][/ROW]
[ROW][C]35[/C][C]12.3[/C][C]11.7907407407407[/C][C]0.50925925925926[/C][/ROW]
[ROW][C]36[/C][C]8.9[/C][C]11.7907407407407[/C][C]-2.89074074074074[/C][/ROW]
[ROW][C]37[/C][C]13.6[/C][C]11.7907407407407[/C][C]1.80925925925926[/C][/ROW]
[ROW][C]38[/C][C]11.2[/C][C]11.7907407407407[/C][C]-0.590740740740742[/C][/ROW]
[ROW][C]39[/C][C]2.8[/C][C]11.7907407407407[/C][C]-8.99074074074074[/C][/ROW]
[ROW][C]40[/C][C]3.2[/C][C]11.7907407407407[/C][C]-8.59074074074074[/C][/ROW]
[ROW][C]41[/C][C]9.4[/C][C]11.7907407407407[/C][C]-2.39074074074074[/C][/ROW]
[ROW][C]42[/C][C]11.9[/C][C]11.7907407407407[/C][C]0.109259259259259[/C][/ROW]
[ROW][C]43[/C][C]15.4[/C][C]11.7907407407407[/C][C]3.60925925925926[/C][/ROW]
[ROW][C]44[/C][C]7.4[/C][C]11.7907407407407[/C][C]-4.39074074074074[/C][/ROW]
[ROW][C]45[/C][C]18.9[/C][C]11.7907407407407[/C][C]7.10925925925926[/C][/ROW]
[ROW][C]46[/C][C]7.9[/C][C]11.7907407407407[/C][C]-3.89074074074074[/C][/ROW]
[ROW][C]47[/C][C]12.2[/C][C]11.7907407407407[/C][C]0.409259259259258[/C][/ROW]
[ROW][C]48[/C][C]11[/C][C]11.7907407407407[/C][C]-0.790740740740741[/C][/ROW]
[ROW][C]49[/C][C]2.8[/C][C]11.7907407407407[/C][C]-8.99074074074074[/C][/ROW]
[ROW][C]50[/C][C]11.8[/C][C]11.7907407407407[/C][C]0.00925925925925952[/C][/ROW]
[ROW][C]51[/C][C]17.1[/C][C]11.7907407407407[/C][C]5.30925925925926[/C][/ROW]
[ROW][C]52[/C][C]11.6[/C][C]11.7907407407407[/C][C]-0.190740740740742[/C][/ROW]
[ROW][C]53[/C][C]5.8[/C][C]11.7907407407407[/C][C]-5.99074074074074[/C][/ROW]
[ROW][C]54[/C][C]8.3[/C][C]11.7907407407407[/C][C]-3.49074074074074[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=198685&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=198685&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
19.211.7907407407407-2.59074074074074
211.711.7907407407407-0.0907407407407419
315.811.79074074074074.00925925925926
48.611.7907407407407-3.19074074074074
523.211.790740740740711.4092592592593
627.411.790740740740715.6092592592593
79.311.7907407407407-2.49074074074074
81611.79074074074074.20925925925926
94.711.7907407407407-7.09074074074074
1012.511.79074074074070.709259259259259
1120.111.79074074074078.30925925925926
129.111.7907407407407-2.69074074074074
138.111.7907407407407-3.69074074074074
148.611.7907407407407-3.19074074074074
1520.311.79074074074078.50925925925926
162511.790740740740713.2092592592593
1719.211.79074074074077.40925925925926
183.311.7907407407407-8.49074074074074
1911.211.7907407407407-0.590740740740742
2010.511.7907407407407-1.29074074074074
2110.111.7907407407407-1.69074074074074
227.211.7907407407407-4.59074074074074
2313.611.79074074074071.80925925925926
24911.7907407407407-2.79074074074074
2524.611.790740740740712.8092592592593
2612.611.79074074074070.809259259259258
275.611.7907407407407-6.19074074074074
288.711.7907407407407-3.09074074074074
297.711.7907407407407-4.09074074074074
3024.111.790740740740712.3092592592593
3111.711.7907407407407-0.0907407407407419
327.711.7907407407407-4.09074074074074
339.611.7907407407407-2.19074074074074
347.211.7907407407407-4.59074074074074
3512.311.79074074074070.50925925925926
368.911.7907407407407-2.89074074074074
3713.611.79074074074071.80925925925926
3811.211.7907407407407-0.590740740740742
392.811.7907407407407-8.99074074074074
403.211.7907407407407-8.59074074074074
419.411.7907407407407-2.39074074074074
4211.911.79074074074070.109259259259259
4315.411.79074074074073.60925925925926
447.411.7907407407407-4.39074074074074
4518.911.79074074074077.10925925925926
467.911.7907407407407-3.89074074074074
4712.211.79074074074070.409259259259258
481111.7907407407407-0.790740740740741
492.811.7907407407407-8.99074074074074
5011.811.79074074074070.00925925925925952
5117.111.79074074074075.30925925925926
5211.611.7907407407407-0.190740740740742
535.811.7907407407407-5.99074074074074
548.311.7907407407407-3.49074074074074



Parameters (Session):
par1 = 5 ; par2 = none ; par3 = 4 ; par4 = no ;
Parameters (R input):
par1 = 5 ; 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')
}