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Description of Statistical Computation

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 computationSun, 12 Dec 2010 10:40:43 +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/12/t1292150372eywajanshkrkmno.htm/, Retrieved Thu, 20 Feb 2025 19:16:09 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=108363, Retrieved Thu, 20 Feb 2025 19:16:09 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact213

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)] [] [2010-12-05 18:59:57] [b98453cac15ba1066b407e146608df68]
-   PD  [Recursive Partitioning (Regression Trees)] [Recursive Partiti...] [2010-12-11 15:32:45] [2960375a246cc0628590c95c4038a43c]
-    D      [Recursive Partitioning (Regression Trees)] [Recursive Partiti...] [2010-12-12 10:40:43] [4b5105369ca2b03f8f7589f5d63124c0] [Current]
-   PD        [Recursive Partitioning (Regression Trees)] [Recursive Partiti...] [2010-12-12 16:48:09] [2960375a246cc0628590c95c4038a43c]
-   PD        [Recursive Partitioning (Regression Trees)] [Recursive Partiti...] [2010-12-12 16:48:09] [2960375a246cc0628590c95c4038a43c]
-   PD        [Recursive Partitioning (Regression Trees)] [Recursive Partiti...] [2010-12-12 16:48:09] [2960375a246cc0628590c95c4038a43c]
-   P           [Recursive Partitioning (Regression Trees)] [Recursive Partiti...] [2010-12-12 16:54:09] [2960375a246cc0628590c95c4038a43c]
-   P             [Recursive Partitioning (Regression Trees)] [Recursive Partiti...] [2010-12-12 17:19:52] [2960375a246cc0628590c95c4038a43c]
- RMPD            [Kendall tau Correlation Matrix] [Pearson Correlatie] [2010-12-12 17:46:50] [2960375a246cc0628590c95c4038a43c]
- RMPD            [Kendall tau Correlation Matrix] [Kendall tau Corre...] [2010-12-12 17:47:54] [2960375a246cc0628590c95c4038a43c]
- RMPD            [Kendall tau Correlation Matrix] [Kendall tau Corre...] [2010-12-12 17:47:54] [2960375a246cc0628590c95c4038a43c]
- RMPD            [Kendall tau Correlation Matrix] [Kendall tau Corre...] [2010-12-12 17:47:54] [2960375a246cc0628590c95c4038a43c]
- RMPD            [Multiple Regression] [Meervoudige regre...] [2010-12-12 17:55:36] [2960375a246cc0628590c95c4038a43c]
- RMPD            [Multiple Regression] [Meervoudige Regre...] [2010-12-12 18:04:55] [2960375a246cc0628590c95c4038a43c]
-                   [Multiple Regression] [MR met log Wb] [2010-12-14 09:31:50] [62f7c80c4d96454bbd2b2b026ea9aad9]
- RMPD            [Multiple Regression] [Meervoudige Regre...] [2010-12-12 18:08:28] [74be16979710d4c4e7c6647856088456]
- RMPD            [Multiple Regression] [Meervoudige Regre...] [2010-12-12 18:08:28] [2960375a246cc0628590c95c4038a43c]
-                   [Multiple Regression] [Meervoudig regres...] [2010-12-14 09:37:23] [62f7c80c4d96454bbd2b2b026ea9aad9]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
16198,9	16896,2	0
16554,2	16698	0
19554,2	19691,6	0
15903,8	15930,7	0
18003,8	17444,6	0
18329,6	17699,4	0
16260,7	15189,8	0
14851,9	15672,7	0
18174,1	17180,8	0
18406,6	17664,9	0
18466,5	17862,9	0
16016,5	16162,3	0
17428,5	17463,6	0
17167,2	16772,1	0
19630	19106,9	0
17183,6	16721,3	0
18344,7	18161,3	0
19301,4	18509,9	0
18147,5	17802,7	0
16192,9	16409,9	0
18374,4	17967,7	0
20515,2	20286,6	0
18957,2	19537,3	0
16471,5	18021,9	0
18746,8	20194,3	0
19009,5	19049,6	0
19211,2	20244,7	0
20547,7	21473,3	0
19325,8	19673,6	0
20605,5	21053,2	0
20056,9	20159,5	0
16141,4	18203,6	0
20359,8	21289,5	0
19711,6	20432,3	1
15638,6	17180,4	1
14384,5	15816,8	1
13855,6	15071,8	1
14308,3	14521,1	1
15290,6	15668,8	1
14423,8	14346,9	1
13779,7	13881	1
15686,3	15465,9	1
14733,8	14238,2	1
12522,5	13557,7	1
16189,4	16127,6	1
16059,1	16793,9	1
16007,1	16014	1
15806,8	16867,9	1
15160	16014,6	0
15692,1	15878,6	0
18908,9	18664,9	0
16969,9	17962,5	0
16997,5	17332,7	0
19858,9	19542,1	0
17681,2	17203,6	0

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time17 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 & 17 seconds \tabularnewline
R Server & 'George Udny Yule' @ 72.249.76.132 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=108363&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]17 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=108363&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=108363&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 time17 seconds
R Server'George Udny Yule' @ 72.249.76.132






Goodness of Fit
Correlation0.9345
R-squared0.8733
RMSE712.1775

\begin{tabular}{lllllllll}
\hline
Goodness of Fit \tabularnewline
Correlation & 0.9345 \tabularnewline
R-squared & 0.8733 \tabularnewline
RMSE & 712.1775 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=108363&T=1

[TABLE]
[ROW][C]Goodness of Fit[/C][/ROW]
[ROW][C]Correlation[/C][C]0.9345[/C][/ROW]
[ROW][C]R-squared[/C][C]0.8733[/C][/ROW]
[ROW][C]RMSE[/C][C]712.1775[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=108363&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=108363&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:

Goodness of Fit
Correlation0.9345
R-squared0.8733
RMSE712.1775






Actuals, Predictions, and Residuals
#ActualsForecastsResiduals
116198.916126.442857142972.4571428571435
216554.216126.4428571429427.757142857145
319554.219643.7875-89.5875000000015
415903.816126.4428571429-222.642857142857
518003.817709.8294
618329.617709.8619.799999999999
716260.714554.33636363641706.36363636364
814851.914554.3363636364297.563636363637
918174.117709.8464.299999999999
1018406.617709.8696.799999999999
1118466.517709.8756.7
1216016.516126.4428571429-109.942857142856
1317428.517709.8-281.299999999999
1417167.216126.44285714291040.75714285714
151963019643.7875-13.7875000000022
1617183.616126.44285714291057.15714285714
1718344.717709.8634.900000000001
1819301.419643.7875-342.387500000001
1918147.517709.8437.700000000001
2016192.916126.442857142966.4571428571435
2118374.417709.8664.600000000002
2220515.219643.7875871.412499999999
2318957.219643.7875-686.587500000001
2416471.517709.8-1238.3
2518746.819643.7875-896.987500000003
2619009.519643.7875-634.287500000002
2719211.219643.7875-432.587500000001
2820547.719643.7875903.912499999999
2919325.819643.7875-317.987500000003
3020605.519643.7875961.712499999998
3120056.919643.7875413.112499999999
3216141.417709.8-1568.4
3320359.819643.7875716.012499999997
3419711.619643.787567.8124999999964
3515638.616126.4428571429-487.842857142856
3614384.514554.3363636364-169.836363636363
3713855.614554.3363636364-698.736363636363
3814308.314554.3363636364-246.036363636364
3915290.614554.3363636364736.263636363637
4014423.814554.3363636364-130.536363636364
4113779.714554.3363636364-774.636363636362
4215686.314554.33636363641131.96363636364
4314733.814554.3363636364179.463636363636
4412522.514554.3363636364-2031.83636363636
4516189.416126.442857142962.9571428571435
4616059.116126.4428571429-67.3428571428558
4716007.116126.4428571429-119.342857142856
4815806.816126.4428571429-319.642857142857
491516016126.4428571429-966.442857142856
5015692.116126.4428571429-434.342857142856
5118908.919643.7875-734.887500000001
5216969.917709.8-739.899999999998
5316997.517709.8-712.300
5419858.919643.7875215.112499999999
5517681.217709.8-28.5999999999985

\begin{tabular}{lllllllll}
\hline
Actuals, Predictions, and Residuals \tabularnewline
# & Actuals & Forecasts & Residuals \tabularnewline
1 & 16198.9 & 16126.4428571429 & 72.4571428571435 \tabularnewline
2 & 16554.2 & 16126.4428571429 & 427.757142857145 \tabularnewline
3 & 19554.2 & 19643.7875 & -89.5875000000015 \tabularnewline
4 & 15903.8 & 16126.4428571429 & -222.642857142857 \tabularnewline
5 & 18003.8 & 17709.8 & 294 \tabularnewline
6 & 18329.6 & 17709.8 & 619.799999999999 \tabularnewline
7 & 16260.7 & 14554.3363636364 & 1706.36363636364 \tabularnewline
8 & 14851.9 & 14554.3363636364 & 297.563636363637 \tabularnewline
9 & 18174.1 & 17709.8 & 464.299999999999 \tabularnewline
10 & 18406.6 & 17709.8 & 696.799999999999 \tabularnewline
11 & 18466.5 & 17709.8 & 756.7 \tabularnewline
12 & 16016.5 & 16126.4428571429 & -109.942857142856 \tabularnewline
13 & 17428.5 & 17709.8 & -281.299999999999 \tabularnewline
14 & 17167.2 & 16126.4428571429 & 1040.75714285714 \tabularnewline
15 & 19630 & 19643.7875 & -13.7875000000022 \tabularnewline
16 & 17183.6 & 16126.4428571429 & 1057.15714285714 \tabularnewline
17 & 18344.7 & 17709.8 & 634.900000000001 \tabularnewline
18 & 19301.4 & 19643.7875 & -342.387500000001 \tabularnewline
19 & 18147.5 & 17709.8 & 437.700000000001 \tabularnewline
20 & 16192.9 & 16126.4428571429 & 66.4571428571435 \tabularnewline
21 & 18374.4 & 17709.8 & 664.600000000002 \tabularnewline
22 & 20515.2 & 19643.7875 & 871.412499999999 \tabularnewline
23 & 18957.2 & 19643.7875 & -686.587500000001 \tabularnewline
24 & 16471.5 & 17709.8 & -1238.3 \tabularnewline
25 & 18746.8 & 19643.7875 & -896.987500000003 \tabularnewline
26 & 19009.5 & 19643.7875 & -634.287500000002 \tabularnewline
27 & 19211.2 & 19643.7875 & -432.587500000001 \tabularnewline
28 & 20547.7 & 19643.7875 & 903.912499999999 \tabularnewline
29 & 19325.8 & 19643.7875 & -317.987500000003 \tabularnewline
30 & 20605.5 & 19643.7875 & 961.712499999998 \tabularnewline
31 & 20056.9 & 19643.7875 & 413.112499999999 \tabularnewline
32 & 16141.4 & 17709.8 & -1568.4 \tabularnewline
33 & 20359.8 & 19643.7875 & 716.012499999997 \tabularnewline
34 & 19711.6 & 19643.7875 & 67.8124999999964 \tabularnewline
35 & 15638.6 & 16126.4428571429 & -487.842857142856 \tabularnewline
36 & 14384.5 & 14554.3363636364 & -169.836363636363 \tabularnewline
37 & 13855.6 & 14554.3363636364 & -698.736363636363 \tabularnewline
38 & 14308.3 & 14554.3363636364 & -246.036363636364 \tabularnewline
39 & 15290.6 & 14554.3363636364 & 736.263636363637 \tabularnewline
40 & 14423.8 & 14554.3363636364 & -130.536363636364 \tabularnewline
41 & 13779.7 & 14554.3363636364 & -774.636363636362 \tabularnewline
42 & 15686.3 & 14554.3363636364 & 1131.96363636364 \tabularnewline
43 & 14733.8 & 14554.3363636364 & 179.463636363636 \tabularnewline
44 & 12522.5 & 14554.3363636364 & -2031.83636363636 \tabularnewline
45 & 16189.4 & 16126.4428571429 & 62.9571428571435 \tabularnewline
46 & 16059.1 & 16126.4428571429 & -67.3428571428558 \tabularnewline
47 & 16007.1 & 16126.4428571429 & -119.342857142856 \tabularnewline
48 & 15806.8 & 16126.4428571429 & -319.642857142857 \tabularnewline
49 & 15160 & 16126.4428571429 & -966.442857142856 \tabularnewline
50 & 15692.1 & 16126.4428571429 & -434.342857142856 \tabularnewline
51 & 18908.9 & 19643.7875 & -734.887500000001 \tabularnewline
52 & 16969.9 & 17709.8 & -739.899999999998 \tabularnewline
53 & 16997.5 & 17709.8 & -712.300 \tabularnewline
54 & 19858.9 & 19643.7875 & 215.112499999999 \tabularnewline
55 & 17681.2 & 17709.8 & -28.5999999999985 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=108363&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]16198.9[/C][C]16126.4428571429[/C][C]72.4571428571435[/C][/ROW]
[ROW][C]2[/C][C]16554.2[/C][C]16126.4428571429[/C][C]427.757142857145[/C][/ROW]
[ROW][C]3[/C][C]19554.2[/C][C]19643.7875[/C][C]-89.5875000000015[/C][/ROW]
[ROW][C]4[/C][C]15903.8[/C][C]16126.4428571429[/C][C]-222.642857142857[/C][/ROW]
[ROW][C]5[/C][C]18003.8[/C][C]17709.8[/C][C]294[/C][/ROW]
[ROW][C]6[/C][C]18329.6[/C][C]17709.8[/C][C]619.799999999999[/C][/ROW]
[ROW][C]7[/C][C]16260.7[/C][C]14554.3363636364[/C][C]1706.36363636364[/C][/ROW]
[ROW][C]8[/C][C]14851.9[/C][C]14554.3363636364[/C][C]297.563636363637[/C][/ROW]
[ROW][C]9[/C][C]18174.1[/C][C]17709.8[/C][C]464.299999999999[/C][/ROW]
[ROW][C]10[/C][C]18406.6[/C][C]17709.8[/C][C]696.799999999999[/C][/ROW]
[ROW][C]11[/C][C]18466.5[/C][C]17709.8[/C][C]756.7[/C][/ROW]
[ROW][C]12[/C][C]16016.5[/C][C]16126.4428571429[/C][C]-109.942857142856[/C][/ROW]
[ROW][C]13[/C][C]17428.5[/C][C]17709.8[/C][C]-281.299999999999[/C][/ROW]
[ROW][C]14[/C][C]17167.2[/C][C]16126.4428571429[/C][C]1040.75714285714[/C][/ROW]
[ROW][C]15[/C][C]19630[/C][C]19643.7875[/C][C]-13.7875000000022[/C][/ROW]
[ROW][C]16[/C][C]17183.6[/C][C]16126.4428571429[/C][C]1057.15714285714[/C][/ROW]
[ROW][C]17[/C][C]18344.7[/C][C]17709.8[/C][C]634.900000000001[/C][/ROW]
[ROW][C]18[/C][C]19301.4[/C][C]19643.7875[/C][C]-342.387500000001[/C][/ROW]
[ROW][C]19[/C][C]18147.5[/C][C]17709.8[/C][C]437.700000000001[/C][/ROW]
[ROW][C]20[/C][C]16192.9[/C][C]16126.4428571429[/C][C]66.4571428571435[/C][/ROW]
[ROW][C]21[/C][C]18374.4[/C][C]17709.8[/C][C]664.600000000002[/C][/ROW]
[ROW][C]22[/C][C]20515.2[/C][C]19643.7875[/C][C]871.412499999999[/C][/ROW]
[ROW][C]23[/C][C]18957.2[/C][C]19643.7875[/C][C]-686.587500000001[/C][/ROW]
[ROW][C]24[/C][C]16471.5[/C][C]17709.8[/C][C]-1238.3[/C][/ROW]
[ROW][C]25[/C][C]18746.8[/C][C]19643.7875[/C][C]-896.987500000003[/C][/ROW]
[ROW][C]26[/C][C]19009.5[/C][C]19643.7875[/C][C]-634.287500000002[/C][/ROW]
[ROW][C]27[/C][C]19211.2[/C][C]19643.7875[/C][C]-432.587500000001[/C][/ROW]
[ROW][C]28[/C][C]20547.7[/C][C]19643.7875[/C][C]903.912499999999[/C][/ROW]
[ROW][C]29[/C][C]19325.8[/C][C]19643.7875[/C][C]-317.987500000003[/C][/ROW]
[ROW][C]30[/C][C]20605.5[/C][C]19643.7875[/C][C]961.712499999998[/C][/ROW]
[ROW][C]31[/C][C]20056.9[/C][C]19643.7875[/C][C]413.112499999999[/C][/ROW]
[ROW][C]32[/C][C]16141.4[/C][C]17709.8[/C][C]-1568.4[/C][/ROW]
[ROW][C]33[/C][C]20359.8[/C][C]19643.7875[/C][C]716.012499999997[/C][/ROW]
[ROW][C]34[/C][C]19711.6[/C][C]19643.7875[/C][C]67.8124999999964[/C][/ROW]
[ROW][C]35[/C][C]15638.6[/C][C]16126.4428571429[/C][C]-487.842857142856[/C][/ROW]
[ROW][C]36[/C][C]14384.5[/C][C]14554.3363636364[/C][C]-169.836363636363[/C][/ROW]
[ROW][C]37[/C][C]13855.6[/C][C]14554.3363636364[/C][C]-698.736363636363[/C][/ROW]
[ROW][C]38[/C][C]14308.3[/C][C]14554.3363636364[/C][C]-246.036363636364[/C][/ROW]
[ROW][C]39[/C][C]15290.6[/C][C]14554.3363636364[/C][C]736.263636363637[/C][/ROW]
[ROW][C]40[/C][C]14423.8[/C][C]14554.3363636364[/C][C]-130.536363636364[/C][/ROW]
[ROW][C]41[/C][C]13779.7[/C][C]14554.3363636364[/C][C]-774.636363636362[/C][/ROW]
[ROW][C]42[/C][C]15686.3[/C][C]14554.3363636364[/C][C]1131.96363636364[/C][/ROW]
[ROW][C]43[/C][C]14733.8[/C][C]14554.3363636364[/C][C]179.463636363636[/C][/ROW]
[ROW][C]44[/C][C]12522.5[/C][C]14554.3363636364[/C][C]-2031.83636363636[/C][/ROW]
[ROW][C]45[/C][C]16189.4[/C][C]16126.4428571429[/C][C]62.9571428571435[/C][/ROW]
[ROW][C]46[/C][C]16059.1[/C][C]16126.4428571429[/C][C]-67.3428571428558[/C][/ROW]
[ROW][C]47[/C][C]16007.1[/C][C]16126.4428571429[/C][C]-119.342857142856[/C][/ROW]
[ROW][C]48[/C][C]15806.8[/C][C]16126.4428571429[/C][C]-319.642857142857[/C][/ROW]
[ROW][C]49[/C][C]15160[/C][C]16126.4428571429[/C][C]-966.442857142856[/C][/ROW]
[ROW][C]50[/C][C]15692.1[/C][C]16126.4428571429[/C][C]-434.342857142856[/C][/ROW]
[ROW][C]51[/C][C]18908.9[/C][C]19643.7875[/C][C]-734.887500000001[/C][/ROW]
[ROW][C]52[/C][C]16969.9[/C][C]17709.8[/C][C]-739.899999999998[/C][/ROW]
[ROW][C]53[/C][C]16997.5[/C][C]17709.8[/C][C]-712.300[/C][/ROW]
[ROW][C]54[/C][C]19858.9[/C][C]19643.7875[/C][C]215.112499999999[/C][/ROW]
[ROW][C]55[/C][C]17681.2[/C][C]17709.8[/C][C]-28.5999999999985[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=108363&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=108363&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:

Actuals, Predictions, and Residuals
#ActualsForecastsResiduals
116198.916126.442857142972.4571428571435
216554.216126.4428571429427.757142857145
319554.219643.7875-89.5875000000015
415903.816126.4428571429-222.642857142857
518003.817709.8294
618329.617709.8619.799999999999
716260.714554.33636363641706.36363636364
814851.914554.3363636364297.563636363637
918174.117709.8464.299999999999
1018406.617709.8696.799999999999
1118466.517709.8756.7
1216016.516126.4428571429-109.942857142856
1317428.517709.8-281.299999999999
1417167.216126.44285714291040.75714285714
151963019643.7875-13.7875000000022
1617183.616126.44285714291057.15714285714
1718344.717709.8634.900000000001
1819301.419643.7875-342.387500000001
1918147.517709.8437.700000000001
2016192.916126.442857142966.4571428571435
2118374.417709.8664.600000000002
2220515.219643.7875871.412499999999
2318957.219643.7875-686.587500000001
2416471.517709.8-1238.3
2518746.819643.7875-896.987500000003
2619009.519643.7875-634.287500000002
2719211.219643.7875-432.587500000001
2820547.719643.7875903.912499999999
2919325.819643.7875-317.987500000003
3020605.519643.7875961.712499999998
3120056.919643.7875413.112499999999
3216141.417709.8-1568.4
3320359.819643.7875716.012499999997
3419711.619643.787567.8124999999964
3515638.616126.4428571429-487.842857142856
3614384.514554.3363636364-169.836363636363
3713855.614554.3363636364-698.736363636363
3814308.314554.3363636364-246.036363636364
3915290.614554.3363636364736.263636363637
4014423.814554.3363636364-130.536363636364
4113779.714554.3363636364-774.636363636362
4215686.314554.33636363641131.96363636364
4314733.814554.3363636364179.463636363636
4412522.514554.3363636364-2031.83636363636
4516189.416126.442857142962.9571428571435
4616059.116126.4428571429-67.3428571428558
4716007.116126.4428571429-119.342857142856
4815806.816126.4428571429-319.642857142857
491516016126.4428571429-966.442857142856
5015692.116126.4428571429-434.342857142856
5118908.919643.7875-734.887500000001
5216969.917709.8-739.899999999998
5316997.517709.8-712.300
5419858.919643.7875215.112499999999
5517681.217709.8-28.5999999999985


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = 1 ; par2 = none ; par4 = no ;
Parameters (R input):
par1 = 1 ; par2 = none ; par3 = ; 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')
}