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Description of Statistical Computation

Author's title

Author*Unverified author*
R Software Modulerwasp_regression_trees1.wasp
Title produced by softwareRecursive Partitioning (Regression Trees)
Date of computationWed, 08 Dec 2010 21:59:07 +0000
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2010/Dec/08/t12918454841ma7hbx6m171x9y.htm/, Retrieved Fri, 03 May 2024 14:28:02 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=107140, Retrieved Fri, 03 May 2024 14:28:02 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact103

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Recursive Partitioning (Regression Trees)] [Puntenanalyse] [2010-12-08 21:59:07] [d41d8cd98f00b204e9800998ecf8427e] [Current]
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Dataset

Dataseries X:
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Histogram
Boxplots 
5	8	8	10	11	8	8	10	13	16	10	12	10	1	2
0	14	13	12	0	0	0	0	13	0	13	10	12	2	4
0	12	0	0	0	0	0	0	0	0	0	0	0	2	2
7	14	14	11	12	15	9	13	10	10	11	12	14	2	1
11	12	13	11	11	14	11	6	12	13	13	12	11	1	2
13	14	16	17	14	12	11	15	16	16	17	13	13	2	1
11	13	13	10	9	12	10	10	11	11	12	9	14	1	1
0	13	8	13	13	8	0	7	0	13	11	11	9	1	1
10	12	8	10	13	0	11	8	12	12	10	12	9	1	2
0	0	13	9	10	0	12	6	11	0	11	14	14	1	1
16	13	15	15	13	17	13	15	16	19	12	11	15	2	2
0	11	0	0	0	0	0	0	0	0	0	12	0	1	2
0	10	0	11	0	0	0	0	0	0	11	0	0	1	1
12	12	11	0	11	0	11	10	10	14	11	12	12	2	2
4	14	0	10	0	10	10	7	5	14	13	10	11	1	3
5	11	10	9	0	5	13	10	10	11	11	10	11	1	4
0	15	0	10	0	0	0	0	0	0	0	12	13	2	3
0	11	0	15	13	0	10	12	5	11	0	0	0	1	1
9	12	7	8	8	13	10	10	10	11	10	14	13	1	2
3	11	15	10	10	17	13	7	4	1	1	0	10	1	2
11	13	15	12	11	16	14	16	13	15	12	13	12	2	2
0	0	0	0	0	0	0	0	0	0	0	0	0	1	3
15	13	16	13	11	11	15	16	16	16	11	14	14	2	2
12	13	16	13	9	10	13	12	16	16	12	12	13	2	2
14	9	15	12	12	13	13	16	15	15	15	11	11	1	1
9	11	11	11	10	6	12	9	10	15	11	12	11	2	2
11	11	11	8	10	0	0	0	6	0	0	0	0	1	1
0	13	13	12	8	8	15	0	14	0	16	11	12	2	2
0	0	0	0	0	0	0	0	0	0	0	0	0	1	2
14	13	17	12	12	10	14	15	15	11	17	14	11	1	1
8	13	11	10	11	8	15	7	14	14	11	10	13	1	2
9	12	16	12	13	16	10	11	12	15	11	13	11	2	2
0	10	0	0	0	0	0	0	0	0	0	0	0	1	2
0	0	10	10	10	0	10	7	9	0	11	12	10	1	2
11	11	16	14	12	12	13	12	13	12	14	12	12	1	2
11	12	14	10	13	14	14	10	12	14	17	12	11	1	1
13	11	12	14	14	8	14	14	12	12	14	12	13	1	2
14	14	15	9	12	13	12	12	16	16	14	15	13	1	1
9	0	11	11	0	6	13	5	8	0	11	12	10	1	2
6	13	13	7	10	11	12	6	11	11	10	11	11	1	2
7	13	10	13	10	5	11	7	9	11	10	14	11	2	2
0	0	0	0	0	0	0	0	0	0	0	0	0	1	1
11	12	13	10	8	11	12	10	10	16	10	13	11	2	1
0	11	10	0	11	0	0	0	0	0	0	0	10	1	3
16	15	16	15	14	11	14	16	18	16	17	14	15	1	1
10	10	11	13	12	9	15	12	15	16	15	11	11	1	1
6	12	12	15	9	5	12	9	13	14	11	13	10	1	2
10	11	13	11	10	9	11	10	10	13	12	16	13	1	2
5	10	12	10	12	12	12	6	13	13	10	11	12	1	2
10	0	6	10	10	0	0	7	12	0	0	0	0	1	1
10	9	13	10	11	13	13	12	13	12	10	12	14	2	2
17	10	15	12	12	14	13	16	16	12	13	13	13	1	2
11	11	15	11	11	10	14	11	12	13	10	11	11	1	1
6	10	14	13	10	12	14	11	13	10	10	14	12	2	2
5	13	13	13	12	10	12	12	10	14	11	13	13	2	2
7	12	12	9	7	8	10	10	10	11	9	11	12	1	2
8	13	8	12	7	17	11	12	12	10	12	15	14	2	2
14	11	16	11	12	16	13	13	13	13	12	15	14	1	2
0	0	0	0	0	0	0	0	0	0	0	0	0	1	3
0	0	0	0	0	0	0	0	0	0	11	0	0	1	3
15	10	12	11	12	0	17	11	15	16	12	18	14	2	1
10	12	13	10	11	10	13	11	11	9	11	11	11	1	1
0	0	0	0	0	0	0	0	0	0	0	0	0	1	2
0	11	12	13	0	6	10	8	0	11	12	12	12	1	3
0	0	0	0	0	0	0	0	0	0	0	0	0	1	2
0	13	7	10	11	11	15	14	4	5	9	15	11	1	1
16	13	13	10	10	12	14	13	15	14	8	14	13	1	1
0	0	0	0	0	0	0	0	0	0	0	0	0	1	1
0	0	0	0	0	0	0	0	0	0	0	0	0	1	2
0	10	0	0	0	0	0	0	0	0	0	0	0	2	3
10	12	13	17	9	10	16	14	12	14	12	12	12	2	1
0	12	0	9	0	0	12	0	0	0	0	0	0	1	3
10	10	8	10	12	13	14	10	11	13	14	10	12	2	2
14	11	15	11	11	17	17	17	13	17	17	14	14	1	1
0	12	12	14	11	6	15	0	13	0	0	14	10	1	1
0	0	0	0	0	0	0	0	0	0	0	0	0	1	3
9	11	7	9	13	10	15	15	13	10	13	13	10	1	2
11	12	14	14	1	11	14	11	12	10	14	11	12	1	2
12	13	10	16	12	13	15	11	15	16	11	15	14	1	1
17	15	17	17	13	16	17	18	16	16	15	14	14	2	1
0	0	0	10	11	0	0	0	0	0	0	0	0	2	3
12	12	14	14	12	9	16	11	16	15	13	14	13	2	1
0	0	0	0	0	0	0	0	0	0	0	0	0	1	3
11	10	10	13	9	13	13	14	15	10	12	15	13	1	2
0	0	0	0	4	0	0	0	0	0	0	10	0	1	2
13	10	12	11	11	9	13	10	10	10	11	13	13	1	1
16	12	14	10	12	14	16	13	16	12	14	16	16	1	2
14	13	12	11	9	12	16	16	12	13	15	16	12	2	1
0	12	0	11	4	0	0	0	0	0	0	0	0	2	3
12	12	14	17	11	17	17	16	16	16	15	15	15	2	1
0	0	0	0	4	0	0	0	0	0	0	0	0	1	1
0	12	0	0	10	0	0	10	0	0	13	10	0	2	1
0	0	0	0	4	0	0	0	0	0	0	0	0	1	3
12	16	16	13	14	13	14	15	16	19	14	15	15	2	1
0	10	8	9	6	5	11	0	0	9	11	11	10	1	4
0	0	0	0	0	0	0	0	0	0	0	0	0	1	3
0	0	0	0	0	0	0	0	0	0	0	0	0	1	3
0	0	0	0	0	0	0	0	0	0	0	0	0	1	3
0	10	0	0	0	0	10	0	0	10	0	0	0	1	2
9	12	11	10	10	2	9	11	10	12	12	0	13	2	1
18	15	14	13	11	17	16	16	11	14	14	12	16	1	1
10	12	11	10	11	8	13	12	13	13	12	13	14	2	1
0	12	10	0	11	10	14	10	12	13	15	11	15	1	1
5	8	10	12	7	8	15	0	14	14	12	12	13	1	2
13	12	12	11	13	10	15	10	11	13	12	9	15	1	2
0	0	0	0	0	0	0	0	0	0	0	0	0	1	1
10	12	7	10	7	13	15	11	12	11	13	13	14	1	2
0	0	10	12	10	0	14	2	0	0	0	10	12	1	1
0	14	0	10	0	0	0	0	0	0	0	0	12	2	2
3	12	0	12	0	0	11	0	4	11	9	11	12	1	3
13	15	12	13	14	15	18	13	14	17	16	15	15	2	1
14	13	13	11	14	11	15	13	15	17	13	12	11	1	1
0	0	0	0	0	0	0	0	0	0	0	0	10	2	3
0	12	13	14	10	11	15	0	10	14	11	11	15	1	1
4	11	8	10	7	8	14	11	10	10	11	12	13	1	1
9	15	10	13	9	14	15	12	13	17	12	13	15	2	1
15	15	16	15	15	15	18	15	15	18	18	16	17	2	1
4	11	11	0	12	4	7	8	11	8	10	0	12	2	2
0	0	0	0	0	0	0	0	0	0	0	0	0	1	3
2	14	10	10	12	0	10	1	0	9	0	10	15	2	1
10	13	13	10	13	14	11	11	12	11	16	11	15	1	2
17	12	10	12	14	11	14	15	12	13	17	13	14	1	1
0	10	11	10	10	0	0	8	0	10	0	0	0	2	2
0	0	0	7	10	0	0	0	0	10	11	11	11	1	1
10	14	12	11	12	12	15	10	11	13	15	12	14	2	2
0	13	0	0	0	0	0	0	0	0	13	11	12	1	1
8	12	7	0	7	6	12	10	10	10	14	11	11	2	3
10	11	12	10	8	15	14	10	10	11	11	14	14	1	2
11	14	12	12	14	12	12	10	13	17	13	15	17	1	1
0	12	10	11	8	6	13	9	0	12	12	15	13	1	2
10	0	7	10	11	0	11	7	7	10	10	0	10	1	1
0	0	0	0	0	0	0	0	0	10	0	0	0	2	2
0	0	0	15	0	0	14	0	0	11	0	0	12	1	2
11	14	13	12	0	14	14	0	0	16	10	14	13	1	2
11	10	8	10	7	15	11	11	7	10	10	12	13	1	1
8	16	8	14	13	17	14	14	13	14	12	16	16	2	1
0	0	0	10	0	0	10	0	0	0	0	0	0	1	3
6	11	9	12	8	10	13	10	10	14	10	14	12	1	2
0	0	0	0	0	0	0	0	0	10	0	0	10	1	2
0	0	0	15	0	0	10	0	0	17	0	0	13	2	2
12	12	9	10	9	12	15	12	12	16	14	10	12	1	2
10	10	11	11	9	13	16	13	15	14	12	11	14	1	2
6	12	9	13	10	16	13	11	11	14	10	15	14	1	2
0	0	7	6	10	0	10	0	0	0	0	10	15	1	2
0	0	0	0	0	0	0	0	0	0	0	0	0	1	3
5	0	5	10	0	0	10	8	0	0	10	10	11	1	2
9	13	12	12	13	14	14	10	12	14	15	16	15	2	1
0	0	0	11	0	0	11	0	0	13	0	0	12	1	2
0	0	11	10	10	1	0	3	6	16	11	12	13	2	4
10	15	13	15	10	18	11	13	12	16	13	16	14	2	4
0	12	6	6	10	6	11	7	7	14	9	0	13	1	2
12	15	7	12	10	16	11	11	10	13	13	15	15	2	2
3	12	9	12	13	13	14	10	17	18	16	15	15	2	2
10	12	10	10	11	11	15	10	0	17	10	11	16	1	2
0	0	0	10	0	0	0	0	0	14	0	0	13	1	2
13	11	11	12	11	5	15	11	11	13	15	10	13	1	1
0	14	0	12	0	0	13	0	0	11	0	0	15	2	2
5	0	10	5	6	8	0	3	11	12	3	0	10	1	2
0	0	0	0	0	0	0	0	0	0	0	0	0	1	2
5	13	11	15	10	12	14	10	12	13	12	14	14	2	2
7	13	8	15	10	6	16	12	10	13	12	15	13	2	1
7	0	11	12	0	0	14	0	10	10	0	0	12	1	1
8	0	10	11	9	11	10	0	5	8	0	13	14	1	1
12	12	10	13	9	16	12	15	11	15	15	13	14	1	1
0	13	10	17	0	0	15	0	14	11	15	16	14	2	1
0	0	0	11	0	0	0	0	0	0	0	0	0	1	3
13	12	13	14	13	17	15	13	11	19	12	15	13	1	1
11	15	12	17	10	12	13	10	10	16	10	14	14	1	2
13	13	10	12	8	13	15	14	13	16	15	14	13	1	1
0	0	0	0	0	0	10	0	0	0	0	0	0	1	3
4	12	6	6	9	11	10	3	5	0	9	13	12	1	2
0	11	11	12	13	8	14	7	10	15	11	16	11	1	1
11	16	12	15	13	14	15	13	12	17	12	16	16	2	1
0	0	0	0	0	0	0	0	0	0	0	0	0	2	2
0	0	0	0	0	0	0	0	0	0	0	0	0	1	3
0	0	0	0	0	0	0	0	0	14	0	0	0	1	3
5	0	5	10	0	0	0	5	12	0	0	0	11	1	1
9	15	13	15	10	7	15	12	11	17	13	13	16	2	1
0	0	11	11	0	0	0	0	0	0	0	0	11	1	1
12	13	10	15	12	14	14	12	11	15	10	11	15	1	1
0	0	11	0	0	0	0	0	0	0	0	0	12	1	2
0	0	0	0	0	0	0	0	0	0	0	0	0	1	3
0	0	0	0	0	0	0	0	0	0	0	0	0	2	3
0	10	10	10	0	0	12	0	0	11	0	0	10	2	2
0	0	0	0	0	0	0	0	0	14	0	0	0	2	2
0	0	10	0	10	0	10	0	10	14	0	0	12	1	2
10	13	11	13	10	14	12	10	11	18	11	12	14	1	3
0	0	0	13	0	0	0	0	0	0	0	0	0	1	2
0	0	0	10	0	0	10	0	0	0	0	0	0	2	2
9	14	13	15	11	13	14	11	10	16	10	16	16	2	1
7	16	12	12	11	10	15	11	10	17	11	14	14	2	2
3	0	8	0	0	0	1	1	3	0	0	0	11	2	2
6	13	10	9	10	9	10	11	8	12	15	14	13	1	2
0	0	0	10	0	0	0	0	0	0	0	0	11	1	3
4	12	13	14	10	2	10	2	7	12	11	0	12	1	1
11	16	14	16	15	12	12	15	14	17	18	14	14	2	1
11	13	10	17	11	11	12	12	9	15	10	15	13	2	1
0	14	0	0	0	0	0	0	0	0	0	0	14	2	2
10	11	10	13	9	0	13	11	0	12	12	10	10	1	2
0	0	0	0	0	0	0	0	0	0	0	0	0	1	2
12	15	11	15	11	15	11	11	9	14	12	14	16	2	2
0	0	0	12	0	0	0	0	6	0	3	0	0	1	2
10	14	7	10	10	15	10	11	7	12	12	13	14	2	2
11	15	13	16	10	7	16	11	11	12	15	14	13	1	1
15	13	10	12	9	12	16	12	14	11	14	14	12	1	1
12	15	8	12	10	13	14	13	10	15	11	12	14	2	1
11	13	13	11	12	12	15	10	10	12	9	13	11	1	1
13	16	14	12	13	17	16	14	13	17	12	15	16	2	1
14	12	11	8	11	12	15	14	10	12	12	13	11	1	1
14	15	14	11	11	15	14	13	8	14	12	12	15	2	2
13	13	10	11	12	15	15	15	11	10	12	14	12	1	2
0	14	0	12	0	0	12	0	0	12	0	0	13	2	3
3	15	6	11	10	11	13	11	11	14	11	14	14	2	2
10	10	10	14	9	10	12	7	10	12	6	7	14	1	2
11	15	9	13	11	15	16	13	13	14	13	14	12	2	2
0	0	0	12	0	0	12	0	0	0	0	0	13	1	2

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time13 seconds
R Server'George Udny Yule' @ 72.249.76.132

\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 & 13 seconds \tabularnewline
R Server & 'George Udny Yule' @ 72.249.76.132 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=107140&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]13 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'George Udny Yule' @ 72.249.76.132[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=107140&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=107140&T=0

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


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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time13 seconds
R Server'George Udny Yule' @ 72.249.76.132






10-Fold Cross Validation
Prediction (training)Prediction (testing)
ActualC1C2CVC1C2CV
C16371040.859685140.8586
C26611270.944771200.9449
Overall--0.9121--0.9071

\begin{tabular}{lllllllll}
\hline
10-Fold Cross Validation \tabularnewline
 & Prediction (training) & Prediction (testing) \tabularnewline
Actual & C1 & C2 & CV & C1 & C2 & CV \tabularnewline
C1 & 637 & 104 & 0.8596 & 85 & 14 & 0.8586 \tabularnewline
C2 & 66 & 1127 & 0.9447 & 7 & 120 & 0.9449 \tabularnewline
Overall & - & - & 0.9121 & - & - & 0.9071 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=107140&T=1

[TABLE]
[ROW][C]10-Fold Cross Validation[/C][/ROW]
[ROW][C][/C][C]Prediction (training)[/C][C]Prediction (testing)[/C][/ROW]
[ROW][C]Actual[/C][C]C1[/C][C]C2[/C][C]CV[/C][C]C1[/C][C]C2[/C][C]CV[/C][/ROW]
[ROW][C]C1[/C][C]637[/C][C]104[/C][C]0.8596[/C][C]85[/C][C]14[/C][C]0.8586[/C][/ROW]
[ROW][C]C2[/C][C]66[/C][C]1127[/C][C]0.9447[/C][C]7[/C][C]120[/C][C]0.9449[/C][/ROW]
[ROW][C]Overall[/C][C]-[/C][C]-[/C][C]0.9121[/C][C]-[/C][C]-[/C][C]0.9071[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=107140&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=107140&T=1

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


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

10-Fold Cross Validation
Prediction (training)Prediction (testing)
ActualC1C2CVC1C2CV
C16371040.859685140.8586
C26611270.944771200.9449
Overall--0.9121--0.9071






Confusion Matrix (predicted in columns / actuals in rows)
C1C2
C16915
C25127

\begin{tabular}{lllllllll}
\hline
Confusion Matrix (predicted in columns / actuals in rows) \tabularnewline
 & C1 & C2 \tabularnewline
C1 & 69 & 15 \tabularnewline
C2 & 5 & 127 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=107140&T=2

[TABLE]
[ROW][C]Confusion Matrix (predicted in columns / actuals in rows)[/C][/ROW]
[ROW][C][/C][C]C1[/C][C]C2[/C][/ROW]
[ROW][C]C1[/C][C]69[/C][C]15[/C][/ROW]
[ROW][C]C2[/C][C]5[/C][C]127[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=107140&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=107140&T=2

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


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

Confusion Matrix (predicted in columns / actuals in rows)
C1C2
C16915
C25127


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = 8 ; par2 = hclust ; par3 = 2 ; par4 = yes ;
Parameters (R input):
par1 = 8 ; par2 = hclust ; par3 = 2 ; par4 = yes ;
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')
}