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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 computationThu, 09 Dec 2010 09:47:05 +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/09/t1291888072hv00pb4iujn1mm2.htm/, Retrieved Mon, 29 Apr 2024 00:41:19 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=107152, Retrieved Mon, 29 Apr 2024 00:41:19 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact278

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Bivariate Data Series] [Bivariate dataset] [2008-01-05 23:51:08] [74be16979710d4c4e7c6647856088456]
- RMPD  [Bivariate Explorative Data Analysis] [Ws4 part 1.1 s090...] [2009-10-27 21:56:53] [e0fc65a5811681d807296d590d5b45de]
-  M D    [Bivariate Explorative Data Analysis] [Paper; bivariate ...] [2009-12-19 19:10:37] [e0fc65a5811681d807296d590d5b45de]
- RMPD      [Cross Correlation Function] [cross correlation...] [2010-12-08 19:50:23] [74be16979710d4c4e7c6647856088456]
- RMPD          [Recursive Partitioning (Regression Trees)] [] [2010-12-09 09:47:05] [d76b387543b13b5e3afd8ff9e5fdc89f] [Current]
-    D            [Recursive Partitioning (Regression Trees)] [Recursive Partiti...] [2010-12-10 09:15:12] [aeb27d5c05332f2e597ad139ee63fbe4]
-    D            [Recursive Partitioning (Regression Trees)] [workshop 10] [2010-12-12 20:51:14] [717f3d787904f94c39256c5c1fc72d4c]
- R                 [Recursive Partitioning (Regression Trees)] [Verbetering Peer] [2010-12-17 17:29:25] [d6a5e6c1b0014d57cedb2bdfb4a7099f]
-    D            [Recursive Partitioning (Regression Trees)] [] [2010-12-22 20:22:32] [de55ccbf69577500a5f46ed42a101114]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
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-0.0520908486	-0.0127524528	-0.0162178014	0.0227588167	0.0523775174	-0.0328847346	0.0681769863	0.0379810835	0.0004433607	0.0885041253	0.0595552648
0.0666010623	-0.0822608913	-0.0127524528	-0.0162178014	0.0227588167	0.0523775174	-0.0520908486	0.0681769863	0.0379810835	0.0004433607	0.0885041253
0.0677173945	0.0221140649	-0.0822608913	-0.0127524528	-0.0162178014	0.0227588167	0.0666010623	-0.0520908486	0.0681769863	0.0379810835	0.0004433607
0.1251127483	0.0317810643	0.0221140649	-0.0822608913	-0.0127524528	-0.0162178014	0.0677173945	0.0666010623	-0.0520908486	0.0681769863	0.0379810835
-0.0218349286	-0.0773298837	0.0317810643	0.0221140649	-0.0822608913	-0.0127524528	0.1251127483	0.0677173945	0.0666010623	-0.0520908486	0.0681769863
0.0178312442	0.0027974224	-0.0773298837	0.0317810643	0.0221140649	-0.0822608913	-0.0218349286	0.1251127483	0.0677173945	0.0666010623	-0.0520908486
0.0199663138	-0.0684060589	0.0027974224	-0.0773298837	0.0317810643	0.0221140649	0.0178312442	-0.0218349286	0.1251127483	0.0677173945	0.0666010623
0.088436301	-0.0326538799	-0.0684060589	0.0027974224	-0.0773298837	0.0317810643	0.0199663138	0.0178312442	-0.0218349286	0.1251127483	0.0677173945
0.0646054028	-0.0125972204	-0.0326538799	-0.0684060589	0.0027974224	-0.0773298837	0.088436301	0.0199663138	0.0178312442	-0.0218349286	0.1251127483
0.1233912353	0.048660559	-0.0125972204	-0.0326538799	-0.0684060589	0.0027974224	0.0646054028	0.088436301	0.0199663138	0.0178312442	-0.0218349286
0.0673308704	-0.0147704378	0.048660559	-0.0125972204	-0.0326538799	-0.0684060589	0.1233912353	0.0646054028	0.088436301	0.0199663138	0.0178312442
0.0232412696	-0.0812692141	-0.0147704378	0.048660559	-0.0125972204	-0.0326538799	0.0673308704	0.1233912353	0.0646054028	0.088436301	0.0199663138
-0.1570633927	-0.1445904237	-0.0812692141	-0.0147704378	0.048660559	-0.0125972204	0.0232412696	0.0673308704	0.1233912353	0.0646054028	0.088436301
-0.134923147	0.0047470083	-0.1445904237	-0.0812692141	-0.0147704378	0.048660559	-0.1570633927	0.0232412696	0.0673308704	0.1233912353	0.0646054028
-0.2920959272	-0.0281878491	0.0047470083	-0.1445904237	-0.0812692141	-0.0147704378	-0.134923147	-0.1570633927	0.0232412696	0.0673308704	0.1233912353
-0.3111517877	-0.2985135366	-0.0281878491	0.0047470083	-0.1445904237	-0.0812692141	-0.2920959272	-0.134923147	-0.1570633927	0.0232412696	0.0673308704
-0.2363887781	-0.0871197547	-0.2985135366	-0.0281878491	0.0047470083	-0.1445904237	-0.3111517877	-0.2920959272	-0.134923147	-0.1570633927	0.0232412696
0.0334524402	-0.0782493685	-0.0871197547	-0.2985135366	-0.0281878491	0.0047470083	-0.2363887781	-0.3111517877	-0.2920959272	-0.134923147	-0.1570633927

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time6 seconds
R Server'Sir Ronald Aylmer Fisher' @ 193.190.124.24

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 6 seconds \tabularnewline
R Server & 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=107152&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]6 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Sir Ronald Aylmer Fisher' @ 193.190.124.24[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=107152&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=107152&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 time6 seconds
R Server'Sir Ronald Aylmer Fisher' @ 193.190.124.24






Goodness of Fit
Correlation0.4006
R-squared0.1604
RMSE0.0835

\begin{tabular}{lllllllll}
\hline
Goodness of Fit \tabularnewline
Correlation & 0.4006 \tabularnewline
R-squared & 0.1604 \tabularnewline
RMSE & 0.0835 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=107152&T=1

[TABLE]
[ROW][C]Goodness of Fit[/C][/ROW]
[ROW][C]Correlation[/C][C]0.4006[/C][/ROW]
[ROW][C]R-squared[/C][C]0.1604[/C][/ROW]
[ROW][C]RMSE[/C][C]0.0835[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=107152&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=107152&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
Correlation0.4006
R-squared0.1604
RMSE0.0835






Actuals, Predictions, and Residuals
#ActualsForecastsResiduals
1-0.03264333820.0139979511135417-0.0466412893135417
20.0440720340.01399795111354170.0300740828864583
30.08823611690.01399795111354170.0742381657864583
4-0.03550668850.0139979511135417-0.0495046396135417
50.02782733880.01399795111354170.0138293876864583
6-0.20043089140.0139979511135417-0.214428842513542
7-0.02634242790.0139979511135417-0.0403403790135417
80.06550517180.01399795111354170.0515072206864583
9-0.07093575140.0139979511135417-0.0849337025135417
10-0.0129185590.0139979511135417-0.0269165101135417
110.1183957540.01399795111354170.104397802886458
12-0.03309321730.0139979511135417-0.0470911684135417
13-0.08609086930.0139979511135417-0.100088820413542
140.02106983910.01399795111354170.00707188798645833
150.014945670.01399795111354170.000947718886458332
16-0.190034757-0.131116500914286-0.0589182560857143
17-0.12424360270.0139979511135417-0.138241553813542
180.00623054980.0139979511135417-0.00776740131354167
190.02555070010.01399795111354170.0115527489864583
200.02688066280.01399795111354170.0128827116864583
210.17731559660.01399795111354170.163317645486458
220.06605423730.01399795111354170.0520562861864583
23-0.01395912550.0139979511135417-0.0279570766135417
24-0.03739496970.0139979511135417-0.0513929208135417
250.0366137196-0.1311165009142860.167730220514286
260.019350449-0.1311165009142860.150466949914286
270.08378014970.01399795111354170.0697821985864583
28-0.03698141750.0139979511135417-0.0509793686135417
29-0.1113117010.0139979511135417-0.125309652113542
300.11569127010.01399795111354170.101693318986458
310.09610440850.01399795111354170.0821064573864583
320.06716078290.01399795111354170.0531628317864583
33-0.0791409595-0.1311165009142860.0519755414142857
34-0.18319237440.0139979511135417-0.197190325513542
350.02423157290.01399795111354170.0102336217864583
360.06826000230.01399795111354170.0542620511864583
370.03458557960.01399795111354170.0205876284864583
380.04635904470.01399795111354170.0323610935864583
39-0.09311515990.0139979511135417-0.107113111013542
400.07606735530.01399795111354170.0620694041864583
41-0.01106511980.0139979511135417-0.0250630709135417
420.02845093360.01399795111354170.0144529824864583
430.03682618820.01399795111354170.0228282370864583
44-0.00588044820.0139979511135417-0.0198783993135417
450.06807501920.01399795111354170.0540770680864583
460.01579140010.01399795111354170.00179344898645833
470.11217664250.01399795111354170.0981786913864583
48-0.04376644550.0139979511135417-0.0577643966135417
490.0603266530.01399795111354170.0463287018864583
500.10286734410.01399795111354170.0888693929864583
510.02595097270.01399795111354170.0119530215864583
520.13486957460.01399795111354170.120871623486458
53-0.0967153180.0139979511135417-0.110713269113542
54-0.10359366480.0139979511135417-0.117591615913542
550.09503890750.01399795111354170.0810409563864583
560.03597190680.01399795111354170.0219739556864583
570.15206094530.01399795111354170.138062994186458
58-0.00225056360.0139979511135417-0.0162485147135417
59-0.02779543110.0139979511135417-0.0417933822135417
600.06538275930.01399795111354170.0513848081864583
610.04438886260.01399795111354170.0303909114864583
620.10109611690.01399795111354170.0870981657864583
63-0.00297502760.0139979511135417-0.0169729787135417
64-0.07520626990.0139979511135417-0.0892042210135417
65-0.05258433520.0139979511135417-0.0665822863135417
660.02290041950.01399795111354170.00890246838645833
670.10096214220.01399795111354170.0869641910864583
68-0.02890882460.0139979511135417-0.0429067757135417
690.02084745160.01399795111354170.00684950048645833
700.07885782280.01399795111354170.0648598716864583
710.05493143220.01399795111354170.0409334810864583
72-0.03216527820.0139979511135417-0.0461632293135417
730.06467292070.01399795111354170.0506749695864583
74-0.00107570270.0139979511135417-0.0150736538135417
75-0.14588856910.0139979511135417-0.159886520213542
76-0.06778163240.0139979511135417-0.0817795835135417
77-0.00987703690.0139979511135417-0.0238749880135417
780.05019915630.01399795111354170.0362012051864583
79-0.12710636310.0139979511135417-0.141104314213542
800.05822777080.01399795111354170.0442298196864583
810.05955526480.01399795111354170.0455573136864583
820.08850412530.01399795111354170.0745061741864583
830.00044336070.0139979511135417-0.0135545904135417
840.03798108350.01399795111354170.0239831323864583
850.06817698630.01399795111354170.0541790351864583
86-0.05209084860.0139979511135417-0.0660887997135417
870.06660106230.01399795111354170.0526031111864583
880.06771739450.01399795111354170.0537194433864583
890.12511274830.01399795111354170.111114797186458
90-0.02183492860.0139979511135417-0.0358328797135417
910.01783124420.01399795111354170.00383329308645833
920.01996631380.01399795111354170.00596836268645833
930.0884363010.01399795111354170.0744383498864583
940.06460540280.01399795111354170.0506074516864583
950.12339123530.01399795111354170.109393284186458
960.06733087040.01399795111354170.0533329192864583
970.02324126960.01399795111354170.00924331848645833
98-0.1570633927-0.131116500914286-0.0259468917857143
99-0.1349231470.0139979511135417-0.148921098113542
100-0.29209592720.0139979511135417-0.306093878313542
101-0.3111517877-0.131116500914286-0.180035286785714
102-0.2363887781-0.131116500914286-0.105272277185714
1030.03345244020.01399795111354170.0194544890864583

\begin{tabular}{lllllllll}
\hline
Actuals, Predictions, and Residuals \tabularnewline
# & Actuals & Forecasts & Residuals \tabularnewline
1 & -0.0326433382 & 0.0139979511135417 & -0.0466412893135417 \tabularnewline
2 & 0.044072034 & 0.0139979511135417 & 0.0300740828864583 \tabularnewline
3 & 0.0882361169 & 0.0139979511135417 & 0.0742381657864583 \tabularnewline
4 & -0.0355066885 & 0.0139979511135417 & -0.0495046396135417 \tabularnewline
5 & 0.0278273388 & 0.0139979511135417 & 0.0138293876864583 \tabularnewline
6 & -0.2004308914 & 0.0139979511135417 & -0.214428842513542 \tabularnewline
7 & -0.0263424279 & 0.0139979511135417 & -0.0403403790135417 \tabularnewline
8 & 0.0655051718 & 0.0139979511135417 & 0.0515072206864583 \tabularnewline
9 & -0.0709357514 & 0.0139979511135417 & -0.0849337025135417 \tabularnewline
10 & -0.012918559 & 0.0139979511135417 & -0.0269165101135417 \tabularnewline
11 & 0.118395754 & 0.0139979511135417 & 0.104397802886458 \tabularnewline
12 & -0.0330932173 & 0.0139979511135417 & -0.0470911684135417 \tabularnewline
13 & -0.0860908693 & 0.0139979511135417 & -0.100088820413542 \tabularnewline
14 & 0.0210698391 & 0.0139979511135417 & 0.00707188798645833 \tabularnewline
15 & 0.01494567 & 0.0139979511135417 & 0.000947718886458332 \tabularnewline
16 & -0.190034757 & -0.131116500914286 & -0.0589182560857143 \tabularnewline
17 & -0.1242436027 & 0.0139979511135417 & -0.138241553813542 \tabularnewline
18 & 0.0062305498 & 0.0139979511135417 & -0.00776740131354167 \tabularnewline
19 & 0.0255507001 & 0.0139979511135417 & 0.0115527489864583 \tabularnewline
20 & 0.0268806628 & 0.0139979511135417 & 0.0128827116864583 \tabularnewline
21 & 0.1773155966 & 0.0139979511135417 & 0.163317645486458 \tabularnewline
22 & 0.0660542373 & 0.0139979511135417 & 0.0520562861864583 \tabularnewline
23 & -0.0139591255 & 0.0139979511135417 & -0.0279570766135417 \tabularnewline
24 & -0.0373949697 & 0.0139979511135417 & -0.0513929208135417 \tabularnewline
25 & 0.0366137196 & -0.131116500914286 & 0.167730220514286 \tabularnewline
26 & 0.019350449 & -0.131116500914286 & 0.150466949914286 \tabularnewline
27 & 0.0837801497 & 0.0139979511135417 & 0.0697821985864583 \tabularnewline
28 & -0.0369814175 & 0.0139979511135417 & -0.0509793686135417 \tabularnewline
29 & -0.111311701 & 0.0139979511135417 & -0.125309652113542 \tabularnewline
30 & 0.1156912701 & 0.0139979511135417 & 0.101693318986458 \tabularnewline
31 & 0.0961044085 & 0.0139979511135417 & 0.0821064573864583 \tabularnewline
32 & 0.0671607829 & 0.0139979511135417 & 0.0531628317864583 \tabularnewline
33 & -0.0791409595 & -0.131116500914286 & 0.0519755414142857 \tabularnewline
34 & -0.1831923744 & 0.0139979511135417 & -0.197190325513542 \tabularnewline
35 & 0.0242315729 & 0.0139979511135417 & 0.0102336217864583 \tabularnewline
36 & 0.0682600023 & 0.0139979511135417 & 0.0542620511864583 \tabularnewline
37 & 0.0345855796 & 0.0139979511135417 & 0.0205876284864583 \tabularnewline
38 & 0.0463590447 & 0.0139979511135417 & 0.0323610935864583 \tabularnewline
39 & -0.0931151599 & 0.0139979511135417 & -0.107113111013542 \tabularnewline
40 & 0.0760673553 & 0.0139979511135417 & 0.0620694041864583 \tabularnewline
41 & -0.0110651198 & 0.0139979511135417 & -0.0250630709135417 \tabularnewline
42 & 0.0284509336 & 0.0139979511135417 & 0.0144529824864583 \tabularnewline
43 & 0.0368261882 & 0.0139979511135417 & 0.0228282370864583 \tabularnewline
44 & -0.0058804482 & 0.0139979511135417 & -0.0198783993135417 \tabularnewline
45 & 0.0680750192 & 0.0139979511135417 & 0.0540770680864583 \tabularnewline
46 & 0.0157914001 & 0.0139979511135417 & 0.00179344898645833 \tabularnewline
47 & 0.1121766425 & 0.0139979511135417 & 0.0981786913864583 \tabularnewline
48 & -0.0437664455 & 0.0139979511135417 & -0.0577643966135417 \tabularnewline
49 & 0.060326653 & 0.0139979511135417 & 0.0463287018864583 \tabularnewline
50 & 0.1028673441 & 0.0139979511135417 & 0.0888693929864583 \tabularnewline
51 & 0.0259509727 & 0.0139979511135417 & 0.0119530215864583 \tabularnewline
52 & 0.1348695746 & 0.0139979511135417 & 0.120871623486458 \tabularnewline
53 & -0.096715318 & 0.0139979511135417 & -0.110713269113542 \tabularnewline
54 & -0.1035936648 & 0.0139979511135417 & -0.117591615913542 \tabularnewline
55 & 0.0950389075 & 0.0139979511135417 & 0.0810409563864583 \tabularnewline
56 & 0.0359719068 & 0.0139979511135417 & 0.0219739556864583 \tabularnewline
57 & 0.1520609453 & 0.0139979511135417 & 0.138062994186458 \tabularnewline
58 & -0.0022505636 & 0.0139979511135417 & -0.0162485147135417 \tabularnewline
59 & -0.0277954311 & 0.0139979511135417 & -0.0417933822135417 \tabularnewline
60 & 0.0653827593 & 0.0139979511135417 & 0.0513848081864583 \tabularnewline
61 & 0.0443888626 & 0.0139979511135417 & 0.0303909114864583 \tabularnewline
62 & 0.1010961169 & 0.0139979511135417 & 0.0870981657864583 \tabularnewline
63 & -0.0029750276 & 0.0139979511135417 & -0.0169729787135417 \tabularnewline
64 & -0.0752062699 & 0.0139979511135417 & -0.0892042210135417 \tabularnewline
65 & -0.0525843352 & 0.0139979511135417 & -0.0665822863135417 \tabularnewline
66 & 0.0229004195 & 0.0139979511135417 & 0.00890246838645833 \tabularnewline
67 & 0.1009621422 & 0.0139979511135417 & 0.0869641910864583 \tabularnewline
68 & -0.0289088246 & 0.0139979511135417 & -0.0429067757135417 \tabularnewline
69 & 0.0208474516 & 0.0139979511135417 & 0.00684950048645833 \tabularnewline
70 & 0.0788578228 & 0.0139979511135417 & 0.0648598716864583 \tabularnewline
71 & 0.0549314322 & 0.0139979511135417 & 0.0409334810864583 \tabularnewline
72 & -0.0321652782 & 0.0139979511135417 & -0.0461632293135417 \tabularnewline
73 & 0.0646729207 & 0.0139979511135417 & 0.0506749695864583 \tabularnewline
74 & -0.0010757027 & 0.0139979511135417 & -0.0150736538135417 \tabularnewline
75 & -0.1458885691 & 0.0139979511135417 & -0.159886520213542 \tabularnewline
76 & -0.0677816324 & 0.0139979511135417 & -0.0817795835135417 \tabularnewline
77 & -0.0098770369 & 0.0139979511135417 & -0.0238749880135417 \tabularnewline
78 & 0.0501991563 & 0.0139979511135417 & 0.0362012051864583 \tabularnewline
79 & -0.1271063631 & 0.0139979511135417 & -0.141104314213542 \tabularnewline
80 & 0.0582277708 & 0.0139979511135417 & 0.0442298196864583 \tabularnewline
81 & 0.0595552648 & 0.0139979511135417 & 0.0455573136864583 \tabularnewline
82 & 0.0885041253 & 0.0139979511135417 & 0.0745061741864583 \tabularnewline
83 & 0.0004433607 & 0.0139979511135417 & -0.0135545904135417 \tabularnewline
84 & 0.0379810835 & 0.0139979511135417 & 0.0239831323864583 \tabularnewline
85 & 0.0681769863 & 0.0139979511135417 & 0.0541790351864583 \tabularnewline
86 & -0.0520908486 & 0.0139979511135417 & -0.0660887997135417 \tabularnewline
87 & 0.0666010623 & 0.0139979511135417 & 0.0526031111864583 \tabularnewline
88 & 0.0677173945 & 0.0139979511135417 & 0.0537194433864583 \tabularnewline
89 & 0.1251127483 & 0.0139979511135417 & 0.111114797186458 \tabularnewline
90 & -0.0218349286 & 0.0139979511135417 & -0.0358328797135417 \tabularnewline
91 & 0.0178312442 & 0.0139979511135417 & 0.00383329308645833 \tabularnewline
92 & 0.0199663138 & 0.0139979511135417 & 0.00596836268645833 \tabularnewline
93 & 0.088436301 & 0.0139979511135417 & 0.0744383498864583 \tabularnewline
94 & 0.0646054028 & 0.0139979511135417 & 0.0506074516864583 \tabularnewline
95 & 0.1233912353 & 0.0139979511135417 & 0.109393284186458 \tabularnewline
96 & 0.0673308704 & 0.0139979511135417 & 0.0533329192864583 \tabularnewline
97 & 0.0232412696 & 0.0139979511135417 & 0.00924331848645833 \tabularnewline
98 & -0.1570633927 & -0.131116500914286 & -0.0259468917857143 \tabularnewline
99 & -0.134923147 & 0.0139979511135417 & -0.148921098113542 \tabularnewline
100 & -0.2920959272 & 0.0139979511135417 & -0.306093878313542 \tabularnewline
101 & -0.3111517877 & -0.131116500914286 & -0.180035286785714 \tabularnewline
102 & -0.2363887781 & -0.131116500914286 & -0.105272277185714 \tabularnewline
103 & 0.0334524402 & 0.0139979511135417 & 0.0194544890864583 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=107152&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]-0.0326433382[/C][C]0.0139979511135417[/C][C]-0.0466412893135417[/C][/ROW]
[ROW][C]2[/C][C]0.044072034[/C][C]0.0139979511135417[/C][C]0.0300740828864583[/C][/ROW]
[ROW][C]3[/C][C]0.0882361169[/C][C]0.0139979511135417[/C][C]0.0742381657864583[/C][/ROW]
[ROW][C]4[/C][C]-0.0355066885[/C][C]0.0139979511135417[/C][C]-0.0495046396135417[/C][/ROW]
[ROW][C]5[/C][C]0.0278273388[/C][C]0.0139979511135417[/C][C]0.0138293876864583[/C][/ROW]
[ROW][C]6[/C][C]-0.2004308914[/C][C]0.0139979511135417[/C][C]-0.214428842513542[/C][/ROW]
[ROW][C]7[/C][C]-0.0263424279[/C][C]0.0139979511135417[/C][C]-0.0403403790135417[/C][/ROW]
[ROW][C]8[/C][C]0.0655051718[/C][C]0.0139979511135417[/C][C]0.0515072206864583[/C][/ROW]
[ROW][C]9[/C][C]-0.0709357514[/C][C]0.0139979511135417[/C][C]-0.0849337025135417[/C][/ROW]
[ROW][C]10[/C][C]-0.012918559[/C][C]0.0139979511135417[/C][C]-0.0269165101135417[/C][/ROW]
[ROW][C]11[/C][C]0.118395754[/C][C]0.0139979511135417[/C][C]0.104397802886458[/C][/ROW]
[ROW][C]12[/C][C]-0.0330932173[/C][C]0.0139979511135417[/C][C]-0.0470911684135417[/C][/ROW]
[ROW][C]13[/C][C]-0.0860908693[/C][C]0.0139979511135417[/C][C]-0.100088820413542[/C][/ROW]
[ROW][C]14[/C][C]0.0210698391[/C][C]0.0139979511135417[/C][C]0.00707188798645833[/C][/ROW]
[ROW][C]15[/C][C]0.01494567[/C][C]0.0139979511135417[/C][C]0.000947718886458332[/C][/ROW]
[ROW][C]16[/C][C]-0.190034757[/C][C]-0.131116500914286[/C][C]-0.0589182560857143[/C][/ROW]
[ROW][C]17[/C][C]-0.1242436027[/C][C]0.0139979511135417[/C][C]-0.138241553813542[/C][/ROW]
[ROW][C]18[/C][C]0.0062305498[/C][C]0.0139979511135417[/C][C]-0.00776740131354167[/C][/ROW]
[ROW][C]19[/C][C]0.0255507001[/C][C]0.0139979511135417[/C][C]0.0115527489864583[/C][/ROW]
[ROW][C]20[/C][C]0.0268806628[/C][C]0.0139979511135417[/C][C]0.0128827116864583[/C][/ROW]
[ROW][C]21[/C][C]0.1773155966[/C][C]0.0139979511135417[/C][C]0.163317645486458[/C][/ROW]
[ROW][C]22[/C][C]0.0660542373[/C][C]0.0139979511135417[/C][C]0.0520562861864583[/C][/ROW]
[ROW][C]23[/C][C]-0.0139591255[/C][C]0.0139979511135417[/C][C]-0.0279570766135417[/C][/ROW]
[ROW][C]24[/C][C]-0.0373949697[/C][C]0.0139979511135417[/C][C]-0.0513929208135417[/C][/ROW]
[ROW][C]25[/C][C]0.0366137196[/C][C]-0.131116500914286[/C][C]0.167730220514286[/C][/ROW]
[ROW][C]26[/C][C]0.019350449[/C][C]-0.131116500914286[/C][C]0.150466949914286[/C][/ROW]
[ROW][C]27[/C][C]0.0837801497[/C][C]0.0139979511135417[/C][C]0.0697821985864583[/C][/ROW]
[ROW][C]28[/C][C]-0.0369814175[/C][C]0.0139979511135417[/C][C]-0.0509793686135417[/C][/ROW]
[ROW][C]29[/C][C]-0.111311701[/C][C]0.0139979511135417[/C][C]-0.125309652113542[/C][/ROW]
[ROW][C]30[/C][C]0.1156912701[/C][C]0.0139979511135417[/C][C]0.101693318986458[/C][/ROW]
[ROW][C]31[/C][C]0.0961044085[/C][C]0.0139979511135417[/C][C]0.0821064573864583[/C][/ROW]
[ROW][C]32[/C][C]0.0671607829[/C][C]0.0139979511135417[/C][C]0.0531628317864583[/C][/ROW]
[ROW][C]33[/C][C]-0.0791409595[/C][C]-0.131116500914286[/C][C]0.0519755414142857[/C][/ROW]
[ROW][C]34[/C][C]-0.1831923744[/C][C]0.0139979511135417[/C][C]-0.197190325513542[/C][/ROW]
[ROW][C]35[/C][C]0.0242315729[/C][C]0.0139979511135417[/C][C]0.0102336217864583[/C][/ROW]
[ROW][C]36[/C][C]0.0682600023[/C][C]0.0139979511135417[/C][C]0.0542620511864583[/C][/ROW]
[ROW][C]37[/C][C]0.0345855796[/C][C]0.0139979511135417[/C][C]0.0205876284864583[/C][/ROW]
[ROW][C]38[/C][C]0.0463590447[/C][C]0.0139979511135417[/C][C]0.0323610935864583[/C][/ROW]
[ROW][C]39[/C][C]-0.0931151599[/C][C]0.0139979511135417[/C][C]-0.107113111013542[/C][/ROW]
[ROW][C]40[/C][C]0.0760673553[/C][C]0.0139979511135417[/C][C]0.0620694041864583[/C][/ROW]
[ROW][C]41[/C][C]-0.0110651198[/C][C]0.0139979511135417[/C][C]-0.0250630709135417[/C][/ROW]
[ROW][C]42[/C][C]0.0284509336[/C][C]0.0139979511135417[/C][C]0.0144529824864583[/C][/ROW]
[ROW][C]43[/C][C]0.0368261882[/C][C]0.0139979511135417[/C][C]0.0228282370864583[/C][/ROW]
[ROW][C]44[/C][C]-0.0058804482[/C][C]0.0139979511135417[/C][C]-0.0198783993135417[/C][/ROW]
[ROW][C]45[/C][C]0.0680750192[/C][C]0.0139979511135417[/C][C]0.0540770680864583[/C][/ROW]
[ROW][C]46[/C][C]0.0157914001[/C][C]0.0139979511135417[/C][C]0.00179344898645833[/C][/ROW]
[ROW][C]47[/C][C]0.1121766425[/C][C]0.0139979511135417[/C][C]0.0981786913864583[/C][/ROW]
[ROW][C]48[/C][C]-0.0437664455[/C][C]0.0139979511135417[/C][C]-0.0577643966135417[/C][/ROW]
[ROW][C]49[/C][C]0.060326653[/C][C]0.0139979511135417[/C][C]0.0463287018864583[/C][/ROW]
[ROW][C]50[/C][C]0.1028673441[/C][C]0.0139979511135417[/C][C]0.0888693929864583[/C][/ROW]
[ROW][C]51[/C][C]0.0259509727[/C][C]0.0139979511135417[/C][C]0.0119530215864583[/C][/ROW]
[ROW][C]52[/C][C]0.1348695746[/C][C]0.0139979511135417[/C][C]0.120871623486458[/C][/ROW]
[ROW][C]53[/C][C]-0.096715318[/C][C]0.0139979511135417[/C][C]-0.110713269113542[/C][/ROW]
[ROW][C]54[/C][C]-0.1035936648[/C][C]0.0139979511135417[/C][C]-0.117591615913542[/C][/ROW]
[ROW][C]55[/C][C]0.0950389075[/C][C]0.0139979511135417[/C][C]0.0810409563864583[/C][/ROW]
[ROW][C]56[/C][C]0.0359719068[/C][C]0.0139979511135417[/C][C]0.0219739556864583[/C][/ROW]
[ROW][C]57[/C][C]0.1520609453[/C][C]0.0139979511135417[/C][C]0.138062994186458[/C][/ROW]
[ROW][C]58[/C][C]-0.0022505636[/C][C]0.0139979511135417[/C][C]-0.0162485147135417[/C][/ROW]
[ROW][C]59[/C][C]-0.0277954311[/C][C]0.0139979511135417[/C][C]-0.0417933822135417[/C][/ROW]
[ROW][C]60[/C][C]0.0653827593[/C][C]0.0139979511135417[/C][C]0.0513848081864583[/C][/ROW]
[ROW][C]61[/C][C]0.0443888626[/C][C]0.0139979511135417[/C][C]0.0303909114864583[/C][/ROW]
[ROW][C]62[/C][C]0.1010961169[/C][C]0.0139979511135417[/C][C]0.0870981657864583[/C][/ROW]
[ROW][C]63[/C][C]-0.0029750276[/C][C]0.0139979511135417[/C][C]-0.0169729787135417[/C][/ROW]
[ROW][C]64[/C][C]-0.0752062699[/C][C]0.0139979511135417[/C][C]-0.0892042210135417[/C][/ROW]
[ROW][C]65[/C][C]-0.0525843352[/C][C]0.0139979511135417[/C][C]-0.0665822863135417[/C][/ROW]
[ROW][C]66[/C][C]0.0229004195[/C][C]0.0139979511135417[/C][C]0.00890246838645833[/C][/ROW]
[ROW][C]67[/C][C]0.1009621422[/C][C]0.0139979511135417[/C][C]0.0869641910864583[/C][/ROW]
[ROW][C]68[/C][C]-0.0289088246[/C][C]0.0139979511135417[/C][C]-0.0429067757135417[/C][/ROW]
[ROW][C]69[/C][C]0.0208474516[/C][C]0.0139979511135417[/C][C]0.00684950048645833[/C][/ROW]
[ROW][C]70[/C][C]0.0788578228[/C][C]0.0139979511135417[/C][C]0.0648598716864583[/C][/ROW]
[ROW][C]71[/C][C]0.0549314322[/C][C]0.0139979511135417[/C][C]0.0409334810864583[/C][/ROW]
[ROW][C]72[/C][C]-0.0321652782[/C][C]0.0139979511135417[/C][C]-0.0461632293135417[/C][/ROW]
[ROW][C]73[/C][C]0.0646729207[/C][C]0.0139979511135417[/C][C]0.0506749695864583[/C][/ROW]
[ROW][C]74[/C][C]-0.0010757027[/C][C]0.0139979511135417[/C][C]-0.0150736538135417[/C][/ROW]
[ROW][C]75[/C][C]-0.1458885691[/C][C]0.0139979511135417[/C][C]-0.159886520213542[/C][/ROW]
[ROW][C]76[/C][C]-0.0677816324[/C][C]0.0139979511135417[/C][C]-0.0817795835135417[/C][/ROW]
[ROW][C]77[/C][C]-0.0098770369[/C][C]0.0139979511135417[/C][C]-0.0238749880135417[/C][/ROW]
[ROW][C]78[/C][C]0.0501991563[/C][C]0.0139979511135417[/C][C]0.0362012051864583[/C][/ROW]
[ROW][C]79[/C][C]-0.1271063631[/C][C]0.0139979511135417[/C][C]-0.141104314213542[/C][/ROW]
[ROW][C]80[/C][C]0.0582277708[/C][C]0.0139979511135417[/C][C]0.0442298196864583[/C][/ROW]
[ROW][C]81[/C][C]0.0595552648[/C][C]0.0139979511135417[/C][C]0.0455573136864583[/C][/ROW]
[ROW][C]82[/C][C]0.0885041253[/C][C]0.0139979511135417[/C][C]0.0745061741864583[/C][/ROW]
[ROW][C]83[/C][C]0.0004433607[/C][C]0.0139979511135417[/C][C]-0.0135545904135417[/C][/ROW]
[ROW][C]84[/C][C]0.0379810835[/C][C]0.0139979511135417[/C][C]0.0239831323864583[/C][/ROW]
[ROW][C]85[/C][C]0.0681769863[/C][C]0.0139979511135417[/C][C]0.0541790351864583[/C][/ROW]
[ROW][C]86[/C][C]-0.0520908486[/C][C]0.0139979511135417[/C][C]-0.0660887997135417[/C][/ROW]
[ROW][C]87[/C][C]0.0666010623[/C][C]0.0139979511135417[/C][C]0.0526031111864583[/C][/ROW]
[ROW][C]88[/C][C]0.0677173945[/C][C]0.0139979511135417[/C][C]0.0537194433864583[/C][/ROW]
[ROW][C]89[/C][C]0.1251127483[/C][C]0.0139979511135417[/C][C]0.111114797186458[/C][/ROW]
[ROW][C]90[/C][C]-0.0218349286[/C][C]0.0139979511135417[/C][C]-0.0358328797135417[/C][/ROW]
[ROW][C]91[/C][C]0.0178312442[/C][C]0.0139979511135417[/C][C]0.00383329308645833[/C][/ROW]
[ROW][C]92[/C][C]0.0199663138[/C][C]0.0139979511135417[/C][C]0.00596836268645833[/C][/ROW]
[ROW][C]93[/C][C]0.088436301[/C][C]0.0139979511135417[/C][C]0.0744383498864583[/C][/ROW]
[ROW][C]94[/C][C]0.0646054028[/C][C]0.0139979511135417[/C][C]0.0506074516864583[/C][/ROW]
[ROW][C]95[/C][C]0.1233912353[/C][C]0.0139979511135417[/C][C]0.109393284186458[/C][/ROW]
[ROW][C]96[/C][C]0.0673308704[/C][C]0.0139979511135417[/C][C]0.0533329192864583[/C][/ROW]
[ROW][C]97[/C][C]0.0232412696[/C][C]0.0139979511135417[/C][C]0.00924331848645833[/C][/ROW]
[ROW][C]98[/C][C]-0.1570633927[/C][C]-0.131116500914286[/C][C]-0.0259468917857143[/C][/ROW]
[ROW][C]99[/C][C]-0.134923147[/C][C]0.0139979511135417[/C][C]-0.148921098113542[/C][/ROW]
[ROW][C]100[/C][C]-0.2920959272[/C][C]0.0139979511135417[/C][C]-0.306093878313542[/C][/ROW]
[ROW][C]101[/C][C]-0.3111517877[/C][C]-0.131116500914286[/C][C]-0.180035286785714[/C][/ROW]
[ROW][C]102[/C][C]-0.2363887781[/C][C]-0.131116500914286[/C][C]-0.105272277185714[/C][/ROW]
[ROW][C]103[/C][C]0.0334524402[/C][C]0.0139979511135417[/C][C]0.0194544890864583[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=107152&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=107152&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
1-0.03264333820.0139979511135417-0.0466412893135417
20.0440720340.01399795111354170.0300740828864583
30.08823611690.01399795111354170.0742381657864583
4-0.03550668850.0139979511135417-0.0495046396135417
50.02782733880.01399795111354170.0138293876864583
6-0.20043089140.0139979511135417-0.214428842513542
7-0.02634242790.0139979511135417-0.0403403790135417
80.06550517180.01399795111354170.0515072206864583
9-0.07093575140.0139979511135417-0.0849337025135417
10-0.0129185590.0139979511135417-0.0269165101135417
110.1183957540.01399795111354170.104397802886458
12-0.03309321730.0139979511135417-0.0470911684135417
13-0.08609086930.0139979511135417-0.100088820413542
140.02106983910.01399795111354170.00707188798645833
150.014945670.01399795111354170.000947718886458332
16-0.190034757-0.131116500914286-0.0589182560857143
17-0.12424360270.0139979511135417-0.138241553813542
180.00623054980.0139979511135417-0.00776740131354167
190.02555070010.01399795111354170.0115527489864583
200.02688066280.01399795111354170.0128827116864583
210.17731559660.01399795111354170.163317645486458
220.06605423730.01399795111354170.0520562861864583
23-0.01395912550.0139979511135417-0.0279570766135417
24-0.03739496970.0139979511135417-0.0513929208135417
250.0366137196-0.1311165009142860.167730220514286
260.019350449-0.1311165009142860.150466949914286
270.08378014970.01399795111354170.0697821985864583
28-0.03698141750.0139979511135417-0.0509793686135417
29-0.1113117010.0139979511135417-0.125309652113542
300.11569127010.01399795111354170.101693318986458
310.09610440850.01399795111354170.0821064573864583
320.06716078290.01399795111354170.0531628317864583
33-0.0791409595-0.1311165009142860.0519755414142857
34-0.18319237440.0139979511135417-0.197190325513542
350.02423157290.01399795111354170.0102336217864583
360.06826000230.01399795111354170.0542620511864583
370.03458557960.01399795111354170.0205876284864583
380.04635904470.01399795111354170.0323610935864583
39-0.09311515990.0139979511135417-0.107113111013542
400.07606735530.01399795111354170.0620694041864583
41-0.01106511980.0139979511135417-0.0250630709135417
420.02845093360.01399795111354170.0144529824864583
430.03682618820.01399795111354170.0228282370864583
44-0.00588044820.0139979511135417-0.0198783993135417
450.06807501920.01399795111354170.0540770680864583
460.01579140010.01399795111354170.00179344898645833
470.11217664250.01399795111354170.0981786913864583
48-0.04376644550.0139979511135417-0.0577643966135417
490.0603266530.01399795111354170.0463287018864583
500.10286734410.01399795111354170.0888693929864583
510.02595097270.01399795111354170.0119530215864583
520.13486957460.01399795111354170.120871623486458
53-0.0967153180.0139979511135417-0.110713269113542
54-0.10359366480.0139979511135417-0.117591615913542
550.09503890750.01399795111354170.0810409563864583
560.03597190680.01399795111354170.0219739556864583
570.15206094530.01399795111354170.138062994186458
58-0.00225056360.0139979511135417-0.0162485147135417
59-0.02779543110.0139979511135417-0.0417933822135417
600.06538275930.01399795111354170.0513848081864583
610.04438886260.01399795111354170.0303909114864583
620.10109611690.01399795111354170.0870981657864583
63-0.00297502760.0139979511135417-0.0169729787135417
64-0.07520626990.0139979511135417-0.0892042210135417
65-0.05258433520.0139979511135417-0.0665822863135417
660.02290041950.01399795111354170.00890246838645833
670.10096214220.01399795111354170.0869641910864583
68-0.02890882460.0139979511135417-0.0429067757135417
690.02084745160.01399795111354170.00684950048645833
700.07885782280.01399795111354170.0648598716864583
710.05493143220.01399795111354170.0409334810864583
72-0.03216527820.0139979511135417-0.0461632293135417
730.06467292070.01399795111354170.0506749695864583
74-0.00107570270.0139979511135417-0.0150736538135417
75-0.14588856910.0139979511135417-0.159886520213542
76-0.06778163240.0139979511135417-0.0817795835135417
77-0.00987703690.0139979511135417-0.0238749880135417
780.05019915630.01399795111354170.0362012051864583
79-0.12710636310.0139979511135417-0.141104314213542
800.05822777080.01399795111354170.0442298196864583
810.05955526480.01399795111354170.0455573136864583
820.08850412530.01399795111354170.0745061741864583
830.00044336070.0139979511135417-0.0135545904135417
840.03798108350.01399795111354170.0239831323864583
850.06817698630.01399795111354170.0541790351864583
86-0.05209084860.0139979511135417-0.0660887997135417
870.06660106230.01399795111354170.0526031111864583
880.06771739450.01399795111354170.0537194433864583
890.12511274830.01399795111354170.111114797186458
90-0.02183492860.0139979511135417-0.0358328797135417
910.01783124420.01399795111354170.00383329308645833
920.01996631380.01399795111354170.00596836268645833
930.0884363010.01399795111354170.0744383498864583
940.06460540280.01399795111354170.0506074516864583
950.12339123530.01399795111354170.109393284186458
960.06733087040.01399795111354170.0533329192864583
970.02324126960.01399795111354170.00924331848645833
98-0.1570633927-0.131116500914286-0.0259468917857143
99-0.1349231470.0139979511135417-0.148921098113542
100-0.29209592720.0139979511135417-0.306093878313542
101-0.3111517877-0.131116500914286-0.180035286785714
102-0.2363887781-0.131116500914286-0.105272277185714
1030.03345244020.01399795111354170.0194544890864583


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = 1 ; par2 = none ; par3 = 3 ; par4 = no ;
Parameters (R input):
par1 = 1 ; par2 = none ; par3 = 3 ; 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')
}