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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 computationTue, 21 Dec 2010 12:10:42 +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/21/t1292933445loh24oqomfmgwf7.htm/, Retrieved Thu, 02 May 2024 21:47:57 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=113354, Retrieved Thu, 02 May 2024 21:47:57 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact254

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 19:50:12] [b98453cac15ba1066b407e146608df68]
-   PD  [Recursive Partitioning (Regression Trees)] [WS 10 Cross Valid...] [2010-12-11 14:44:18] [8081b8996d5947580de3eb171e82db4f]
-         [Recursive Partitioning (Regression Trees)] [Workshop 10, Cros...] [2010-12-11 15:06:30] [3635fb7041b1998c5a1332cf9de22bce]
-   PD      [Recursive Partitioning (Regression Trees)] [Paper Recursive P...] [2010-12-19 20:32:45] [3635fb7041b1998c5a1332cf9de22bce]
-   PD        [Recursive Partitioning (Regression Trees)] [Paper Recursive P...] [2010-12-19 21:18:05] [3635fb7041b1998c5a1332cf9de22bce]
-   PD            [Recursive Partitioning (Regression Trees)] [Paper recursive P...] [2010-12-21 12:10:42] [23a9b79f355c69a75648521a893cf584] [Current]
-   P               [Recursive Partitioning (Regression Trees)] [Recursive Partiti...] [2010-12-22 08:24:10] [8081b8996d5947580de3eb171e82db4f]
-                   [Recursive Partitioning (Regression Trees)] [Paper Recursive P...] [2010-12-22 08:46:16] [d946de7cca328fbcf207448a112523ab]
-                   [Recursive Partitioning (Regression Trees)] [Paper Recursive P...] [2010-12-22 08:46:16] [d946de7cca328fbcf207448a112523ab]
-    D              [Recursive Partitioning (Regression Trees)] [Workshop 10 Recur...] [2011-12-10 13:51:25] [aa7c7608f809e956d7797134ec926e04]
- R                   [Recursive Partitioning (Regression Trees)] [] [2012-12-11 12:06:09] [3ba5358ad212dca7c498c7fc6d6ebde5]
- R                   [Recursive Partitioning (Regression Trees)] [] [2012-12-11 12:07:22] [3ba5358ad212dca7c498c7fc6d6ebde5]
-   P               [Recursive Partitioning (Regression Trees)] [workshop 10 10-fo...] [2011-12-10 14:06:44] [aa7c7608f809e956d7797134ec926e04]
-   PD              [Recursive Partitioning (Regression Trees)] [paper] [2011-12-17 15:01:08] [43239ed98a62e091c70785d80176537f]
- R  D              [Recursive Partitioning (Regression Trees)] [] [2011-12-23 10:38:51] [74be16979710d4c4e7c6647856088456]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
97.06	21454	631923	130678
97.73	23899	654294	120877
98	24939	671833	137114
97.76	23580	586840	134406
97.48	24562	600969	120262
97.77	24696	625568	130846
97.96	23785	558110	120343
98.22	23812	630577	98881
98.51	21917	628654	115678
98.19	19713	603184	120796
98.37	19282	656255	94261
98.31	18788	600730	89151
98.6	21453	670326	119880
98.96	24482	678423	131468
99.11	27474	641502	155089
99.64	27264	625311	149581
100.02	27349	628177	122788
99.98	30632	589767	143900
100.32	29429	582471	112115
100.44	30084	636248	109600
100.51	26290	599885	117446
101	24379	621694	118456
100.88	23335	637406	101901
100.55	21346	595994	89940
100.82	21106	696308	129143
101.5	24514	674201	126102
102.15	28353	648861	143048
102.39	30805	649605	142258
102.54	31348	672392	131011
102.85	34556	598396	146471
103.47	33855	613177	114073
103.56	34787	638104	114642
103.69	32529	615632	118226
103.49	29998	634465	111338
103.47	29257	638686	108701
103.45	28155	604243	80512
103.48	30466	706669	146865
103.93	35704	677185	137179
103.89	39327	644328	166536
104.4	39351	664825	137070
104.79	42234	605707	127090
104.77	43630	600136	139966
105.13	43722	612166	122243
105.26	43121	599659	109097
104.96	37985	634210	116591
104.75	37135	618234	111964
105.01	34646	613576	109754
105.15	33026	627200	77609
105.2	35087	668973	138445
105.77	38846	651479	127901
105.78	42013	619661	156615
106.26	43908	644260	133264
106.13	42868	579936	143521
106.12	44423	601752	152139
106.57	44167	595376	131523
106.44	43636	588902	113925
106.54	44382	634341	86495
107.1	42142	594305	127877
108.1	43452	606200	107017
108.4	36912	610926	78716
108.84	42413	633685	138278
109.62	45344	639696	144238
110.42	44873	659451	143679
110.67	47510	593248	159932
111.66	49554	606677	136781
112.28	47369	599434	148173
112.87	45998	569578	125673
112.18	48140	629873	105573
112.36	48441	613438	122405
112.16	44928	604172	128045
111.49	40454	658328	94467
111.25	38661	612633	85573
111.36	37246	707372	121501
111.74	36843	739770	125074
111.1	36424	777535	144979
111.33	37594	685030	142120
111.25	38144	730234	124213
111.04	38737	714154	144407
110.97	34560	630872	125170
111.31	36080	719492	109267
111.02	33508	677023	122354
111.07	35462	679272	122589
111.36	33374	718317	104982
111.54	32110	645672	90542

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time4 seconds
R Server'RServer@AstonUniversity' @ vre.aston.ac.uk

\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 & 'RServer@AstonUniversity' @ vre.aston.ac.uk \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=113354&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]'RServer@AstonUniversity' @ vre.aston.ac.uk[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=113354&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=113354&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 time4 seconds
R Server'RServer@AstonUniversity' @ vre.aston.ac.uk






Goodness of Fit
Correlation0.9131
R-squared0.8337
RMSE3413.2969

\begin{tabular}{lllllllll}
\hline
Goodness of Fit \tabularnewline
Correlation & 0.9131 \tabularnewline
R-squared & 0.8337 \tabularnewline
RMSE & 3413.2969 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=113354&T=1

[TABLE]
[ROW][C]Goodness of Fit[/C][/ROW]
[ROW][C]Correlation[/C][C]0.9131[/C][/ROW]
[ROW][C]R-squared[/C][C]0.8337[/C][/ROW]
[ROW][C]RMSE[/C][C]3413.2969[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=113354&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=113354&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
Correlation0.9131
R-squared0.8337
RMSE3413.2969






Actuals, Predictions, and Residuals
#ActualsForecastsResiduals
12145424214-2760
22389924214-315
32493924214725
42358024214-634
52456224214348
62469624214482
72378524214-429
82381224214-402
92191724214-2297
101971324214-4501
111928224214-4932
121878824214-5426
132145324214-2761
142448224214268
1527474242143260
1627264242143050
1727349242143135
1830632242146418
1929429242145215
2030084242145870
2126290242142076
222437924214165
232333524214-879
242134624214-2868
252110624214-3108
262451424214300
272835330754.7777777778-2401.77777777778
283080530754.777777777850.2222222222226
293134830754.7777777778593.222222222223
303455630754.77777777783801.22222222222
313385530754.77777777783100.22222222222
323478738065.5483870968-3278.54838709677
333252938065.5483870968-5536.54838709677
342999830754.7777777778-756.777777777777
352925730754.7777777778-1497.77777777778
362815530754.7777777778-2599.77777777778
373046630754.7777777778-288.777777777777
383570438065.5483870968-2361.54838709677
393932738065.54838709681261.45161290323
403935138065.54838709681285.45161290323
414223444042.6666666667-1808.66666666666
424363044042.6666666667-412.666666666664
434372244042.6666666667-320.666666666664
444312144042.6666666667-921.666666666664
453798538065.5483870968-80.5483870967728
463713538065.5483870968-930.548387096773
473464638065.5483870968-3419.54838709677
483302638065.5483870968-5039.54838709677
493508738065.5483870968-2978.54838709677
503884638065.5483870968780.451612903227
514201338065.54838709683947.45161290323
524390838065.54838709685842.45161290323
534286844042.6666666667-1174.66666666666
544442344042.6666666667380.333333333336
554416744042.6666666667124.333333333336
564363644042.6666666667-406.666666666664
574438238065.54838709686316.45161290323
584214244042.6666666667-1900.66666666666
594345244042.6666666667-590.666666666664
603691244042.6666666667-7130.66666666666
614241338065.54838709684347.45161290323
624534438065.54838709687278.45161290323
634487338065.54838709686807.45161290323
644751044042.66666666673467.33333333334
654955444042.66666666675511.33333333334
664736944042.66666666673326.33333333334
674599844042.66666666671955.33333333334
684814038065.548387096810074.4516129032
694844144042.66666666674398.33333333334
704492844042.6666666667885.333333333336
714045438065.54838709682388.45161290323
723866144042.6666666667-5381.66666666666
733724638065.5483870968-819.548387096773
743684338065.5483870968-1222.54838709677
753642438065.5483870968-1641.54838709677
763759438065.5483870968-471.548387096773
773814438065.548387096878.4516129032272
783873738065.5483870968671.451612903227
793456038065.5483870968-3505.54838709677
803608038065.5483870968-1985.54838709677
813350838065.5483870968-4557.54838709677
823546238065.5483870968-2603.54838709677
833337438065.5483870968-4691.54838709677
843211038065.5483870968-5955.54838709677

\begin{tabular}{lllllllll}
\hline
Actuals, Predictions, and Residuals \tabularnewline
# & Actuals & Forecasts & Residuals \tabularnewline
1 & 21454 & 24214 & -2760 \tabularnewline
2 & 23899 & 24214 & -315 \tabularnewline
3 & 24939 & 24214 & 725 \tabularnewline
4 & 23580 & 24214 & -634 \tabularnewline
5 & 24562 & 24214 & 348 \tabularnewline
6 & 24696 & 24214 & 482 \tabularnewline
7 & 23785 & 24214 & -429 \tabularnewline
8 & 23812 & 24214 & -402 \tabularnewline
9 & 21917 & 24214 & -2297 \tabularnewline
10 & 19713 & 24214 & -4501 \tabularnewline
11 & 19282 & 24214 & -4932 \tabularnewline
12 & 18788 & 24214 & -5426 \tabularnewline
13 & 21453 & 24214 & -2761 \tabularnewline
14 & 24482 & 24214 & 268 \tabularnewline
15 & 27474 & 24214 & 3260 \tabularnewline
16 & 27264 & 24214 & 3050 \tabularnewline
17 & 27349 & 24214 & 3135 \tabularnewline
18 & 30632 & 24214 & 6418 \tabularnewline
19 & 29429 & 24214 & 5215 \tabularnewline
20 & 30084 & 24214 & 5870 \tabularnewline
21 & 26290 & 24214 & 2076 \tabularnewline
22 & 24379 & 24214 & 165 \tabularnewline
23 & 23335 & 24214 & -879 \tabularnewline
24 & 21346 & 24214 & -2868 \tabularnewline
25 & 21106 & 24214 & -3108 \tabularnewline
26 & 24514 & 24214 & 300 \tabularnewline
27 & 28353 & 30754.7777777778 & -2401.77777777778 \tabularnewline
28 & 30805 & 30754.7777777778 & 50.2222222222226 \tabularnewline
29 & 31348 & 30754.7777777778 & 593.222222222223 \tabularnewline
30 & 34556 & 30754.7777777778 & 3801.22222222222 \tabularnewline
31 & 33855 & 30754.7777777778 & 3100.22222222222 \tabularnewline
32 & 34787 & 38065.5483870968 & -3278.54838709677 \tabularnewline
33 & 32529 & 38065.5483870968 & -5536.54838709677 \tabularnewline
34 & 29998 & 30754.7777777778 & -756.777777777777 \tabularnewline
35 & 29257 & 30754.7777777778 & -1497.77777777778 \tabularnewline
36 & 28155 & 30754.7777777778 & -2599.77777777778 \tabularnewline
37 & 30466 & 30754.7777777778 & -288.777777777777 \tabularnewline
38 & 35704 & 38065.5483870968 & -2361.54838709677 \tabularnewline
39 & 39327 & 38065.5483870968 & 1261.45161290323 \tabularnewline
40 & 39351 & 38065.5483870968 & 1285.45161290323 \tabularnewline
41 & 42234 & 44042.6666666667 & -1808.66666666666 \tabularnewline
42 & 43630 & 44042.6666666667 & -412.666666666664 \tabularnewline
43 & 43722 & 44042.6666666667 & -320.666666666664 \tabularnewline
44 & 43121 & 44042.6666666667 & -921.666666666664 \tabularnewline
45 & 37985 & 38065.5483870968 & -80.5483870967728 \tabularnewline
46 & 37135 & 38065.5483870968 & -930.548387096773 \tabularnewline
47 & 34646 & 38065.5483870968 & -3419.54838709677 \tabularnewline
48 & 33026 & 38065.5483870968 & -5039.54838709677 \tabularnewline
49 & 35087 & 38065.5483870968 & -2978.54838709677 \tabularnewline
50 & 38846 & 38065.5483870968 & 780.451612903227 \tabularnewline
51 & 42013 & 38065.5483870968 & 3947.45161290323 \tabularnewline
52 & 43908 & 38065.5483870968 & 5842.45161290323 \tabularnewline
53 & 42868 & 44042.6666666667 & -1174.66666666666 \tabularnewline
54 & 44423 & 44042.6666666667 & 380.333333333336 \tabularnewline
55 & 44167 & 44042.6666666667 & 124.333333333336 \tabularnewline
56 & 43636 & 44042.6666666667 & -406.666666666664 \tabularnewline
57 & 44382 & 38065.5483870968 & 6316.45161290323 \tabularnewline
58 & 42142 & 44042.6666666667 & -1900.66666666666 \tabularnewline
59 & 43452 & 44042.6666666667 & -590.666666666664 \tabularnewline
60 & 36912 & 44042.6666666667 & -7130.66666666666 \tabularnewline
61 & 42413 & 38065.5483870968 & 4347.45161290323 \tabularnewline
62 & 45344 & 38065.5483870968 & 7278.45161290323 \tabularnewline
63 & 44873 & 38065.5483870968 & 6807.45161290323 \tabularnewline
64 & 47510 & 44042.6666666667 & 3467.33333333334 \tabularnewline
65 & 49554 & 44042.6666666667 & 5511.33333333334 \tabularnewline
66 & 47369 & 44042.6666666667 & 3326.33333333334 \tabularnewline
67 & 45998 & 44042.6666666667 & 1955.33333333334 \tabularnewline
68 & 48140 & 38065.5483870968 & 10074.4516129032 \tabularnewline
69 & 48441 & 44042.6666666667 & 4398.33333333334 \tabularnewline
70 & 44928 & 44042.6666666667 & 885.333333333336 \tabularnewline
71 & 40454 & 38065.5483870968 & 2388.45161290323 \tabularnewline
72 & 38661 & 44042.6666666667 & -5381.66666666666 \tabularnewline
73 & 37246 & 38065.5483870968 & -819.548387096773 \tabularnewline
74 & 36843 & 38065.5483870968 & -1222.54838709677 \tabularnewline
75 & 36424 & 38065.5483870968 & -1641.54838709677 \tabularnewline
76 & 37594 & 38065.5483870968 & -471.548387096773 \tabularnewline
77 & 38144 & 38065.5483870968 & 78.4516129032272 \tabularnewline
78 & 38737 & 38065.5483870968 & 671.451612903227 \tabularnewline
79 & 34560 & 38065.5483870968 & -3505.54838709677 \tabularnewline
80 & 36080 & 38065.5483870968 & -1985.54838709677 \tabularnewline
81 & 33508 & 38065.5483870968 & -4557.54838709677 \tabularnewline
82 & 35462 & 38065.5483870968 & -2603.54838709677 \tabularnewline
83 & 33374 & 38065.5483870968 & -4691.54838709677 \tabularnewline
84 & 32110 & 38065.5483870968 & -5955.54838709677 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=113354&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]21454[/C][C]24214[/C][C]-2760[/C][/ROW]
[ROW][C]2[/C][C]23899[/C][C]24214[/C][C]-315[/C][/ROW]
[ROW][C]3[/C][C]24939[/C][C]24214[/C][C]725[/C][/ROW]
[ROW][C]4[/C][C]23580[/C][C]24214[/C][C]-634[/C][/ROW]
[ROW][C]5[/C][C]24562[/C][C]24214[/C][C]348[/C][/ROW]
[ROW][C]6[/C][C]24696[/C][C]24214[/C][C]482[/C][/ROW]
[ROW][C]7[/C][C]23785[/C][C]24214[/C][C]-429[/C][/ROW]
[ROW][C]8[/C][C]23812[/C][C]24214[/C][C]-402[/C][/ROW]
[ROW][C]9[/C][C]21917[/C][C]24214[/C][C]-2297[/C][/ROW]
[ROW][C]10[/C][C]19713[/C][C]24214[/C][C]-4501[/C][/ROW]
[ROW][C]11[/C][C]19282[/C][C]24214[/C][C]-4932[/C][/ROW]
[ROW][C]12[/C][C]18788[/C][C]24214[/C][C]-5426[/C][/ROW]
[ROW][C]13[/C][C]21453[/C][C]24214[/C][C]-2761[/C][/ROW]
[ROW][C]14[/C][C]24482[/C][C]24214[/C][C]268[/C][/ROW]
[ROW][C]15[/C][C]27474[/C][C]24214[/C][C]3260[/C][/ROW]
[ROW][C]16[/C][C]27264[/C][C]24214[/C][C]3050[/C][/ROW]
[ROW][C]17[/C][C]27349[/C][C]24214[/C][C]3135[/C][/ROW]
[ROW][C]18[/C][C]30632[/C][C]24214[/C][C]6418[/C][/ROW]
[ROW][C]19[/C][C]29429[/C][C]24214[/C][C]5215[/C][/ROW]
[ROW][C]20[/C][C]30084[/C][C]24214[/C][C]5870[/C][/ROW]
[ROW][C]21[/C][C]26290[/C][C]24214[/C][C]2076[/C][/ROW]
[ROW][C]22[/C][C]24379[/C][C]24214[/C][C]165[/C][/ROW]
[ROW][C]23[/C][C]23335[/C][C]24214[/C][C]-879[/C][/ROW]
[ROW][C]24[/C][C]21346[/C][C]24214[/C][C]-2868[/C][/ROW]
[ROW][C]25[/C][C]21106[/C][C]24214[/C][C]-3108[/C][/ROW]
[ROW][C]26[/C][C]24514[/C][C]24214[/C][C]300[/C][/ROW]
[ROW][C]27[/C][C]28353[/C][C]30754.7777777778[/C][C]-2401.77777777778[/C][/ROW]
[ROW][C]28[/C][C]30805[/C][C]30754.7777777778[/C][C]50.2222222222226[/C][/ROW]
[ROW][C]29[/C][C]31348[/C][C]30754.7777777778[/C][C]593.222222222223[/C][/ROW]
[ROW][C]30[/C][C]34556[/C][C]30754.7777777778[/C][C]3801.22222222222[/C][/ROW]
[ROW][C]31[/C][C]33855[/C][C]30754.7777777778[/C][C]3100.22222222222[/C][/ROW]
[ROW][C]32[/C][C]34787[/C][C]38065.5483870968[/C][C]-3278.54838709677[/C][/ROW]
[ROW][C]33[/C][C]32529[/C][C]38065.5483870968[/C][C]-5536.54838709677[/C][/ROW]
[ROW][C]34[/C][C]29998[/C][C]30754.7777777778[/C][C]-756.777777777777[/C][/ROW]
[ROW][C]35[/C][C]29257[/C][C]30754.7777777778[/C][C]-1497.77777777778[/C][/ROW]
[ROW][C]36[/C][C]28155[/C][C]30754.7777777778[/C][C]-2599.77777777778[/C][/ROW]
[ROW][C]37[/C][C]30466[/C][C]30754.7777777778[/C][C]-288.777777777777[/C][/ROW]
[ROW][C]38[/C][C]35704[/C][C]38065.5483870968[/C][C]-2361.54838709677[/C][/ROW]
[ROW][C]39[/C][C]39327[/C][C]38065.5483870968[/C][C]1261.45161290323[/C][/ROW]
[ROW][C]40[/C][C]39351[/C][C]38065.5483870968[/C][C]1285.45161290323[/C][/ROW]
[ROW][C]41[/C][C]42234[/C][C]44042.6666666667[/C][C]-1808.66666666666[/C][/ROW]
[ROW][C]42[/C][C]43630[/C][C]44042.6666666667[/C][C]-412.666666666664[/C][/ROW]
[ROW][C]43[/C][C]43722[/C][C]44042.6666666667[/C][C]-320.666666666664[/C][/ROW]
[ROW][C]44[/C][C]43121[/C][C]44042.6666666667[/C][C]-921.666666666664[/C][/ROW]
[ROW][C]45[/C][C]37985[/C][C]38065.5483870968[/C][C]-80.5483870967728[/C][/ROW]
[ROW][C]46[/C][C]37135[/C][C]38065.5483870968[/C][C]-930.548387096773[/C][/ROW]
[ROW][C]47[/C][C]34646[/C][C]38065.5483870968[/C][C]-3419.54838709677[/C][/ROW]
[ROW][C]48[/C][C]33026[/C][C]38065.5483870968[/C][C]-5039.54838709677[/C][/ROW]
[ROW][C]49[/C][C]35087[/C][C]38065.5483870968[/C][C]-2978.54838709677[/C][/ROW]
[ROW][C]50[/C][C]38846[/C][C]38065.5483870968[/C][C]780.451612903227[/C][/ROW]
[ROW][C]51[/C][C]42013[/C][C]38065.5483870968[/C][C]3947.45161290323[/C][/ROW]
[ROW][C]52[/C][C]43908[/C][C]38065.5483870968[/C][C]5842.45161290323[/C][/ROW]
[ROW][C]53[/C][C]42868[/C][C]44042.6666666667[/C][C]-1174.66666666666[/C][/ROW]
[ROW][C]54[/C][C]44423[/C][C]44042.6666666667[/C][C]380.333333333336[/C][/ROW]
[ROW][C]55[/C][C]44167[/C][C]44042.6666666667[/C][C]124.333333333336[/C][/ROW]
[ROW][C]56[/C][C]43636[/C][C]44042.6666666667[/C][C]-406.666666666664[/C][/ROW]
[ROW][C]57[/C][C]44382[/C][C]38065.5483870968[/C][C]6316.45161290323[/C][/ROW]
[ROW][C]58[/C][C]42142[/C][C]44042.6666666667[/C][C]-1900.66666666666[/C][/ROW]
[ROW][C]59[/C][C]43452[/C][C]44042.6666666667[/C][C]-590.666666666664[/C][/ROW]
[ROW][C]60[/C][C]36912[/C][C]44042.6666666667[/C][C]-7130.66666666666[/C][/ROW]
[ROW][C]61[/C][C]42413[/C][C]38065.5483870968[/C][C]4347.45161290323[/C][/ROW]
[ROW][C]62[/C][C]45344[/C][C]38065.5483870968[/C][C]7278.45161290323[/C][/ROW]
[ROW][C]63[/C][C]44873[/C][C]38065.5483870968[/C][C]6807.45161290323[/C][/ROW]
[ROW][C]64[/C][C]47510[/C][C]44042.6666666667[/C][C]3467.33333333334[/C][/ROW]
[ROW][C]65[/C][C]49554[/C][C]44042.6666666667[/C][C]5511.33333333334[/C][/ROW]
[ROW][C]66[/C][C]47369[/C][C]44042.6666666667[/C][C]3326.33333333334[/C][/ROW]
[ROW][C]67[/C][C]45998[/C][C]44042.6666666667[/C][C]1955.33333333334[/C][/ROW]
[ROW][C]68[/C][C]48140[/C][C]38065.5483870968[/C][C]10074.4516129032[/C][/ROW]
[ROW][C]69[/C][C]48441[/C][C]44042.6666666667[/C][C]4398.33333333334[/C][/ROW]
[ROW][C]70[/C][C]44928[/C][C]44042.6666666667[/C][C]885.333333333336[/C][/ROW]
[ROW][C]71[/C][C]40454[/C][C]38065.5483870968[/C][C]2388.45161290323[/C][/ROW]
[ROW][C]72[/C][C]38661[/C][C]44042.6666666667[/C][C]-5381.66666666666[/C][/ROW]
[ROW][C]73[/C][C]37246[/C][C]38065.5483870968[/C][C]-819.548387096773[/C][/ROW]
[ROW][C]74[/C][C]36843[/C][C]38065.5483870968[/C][C]-1222.54838709677[/C][/ROW]
[ROW][C]75[/C][C]36424[/C][C]38065.5483870968[/C][C]-1641.54838709677[/C][/ROW]
[ROW][C]76[/C][C]37594[/C][C]38065.5483870968[/C][C]-471.548387096773[/C][/ROW]
[ROW][C]77[/C][C]38144[/C][C]38065.5483870968[/C][C]78.4516129032272[/C][/ROW]
[ROW][C]78[/C][C]38737[/C][C]38065.5483870968[/C][C]671.451612903227[/C][/ROW]
[ROW][C]79[/C][C]34560[/C][C]38065.5483870968[/C][C]-3505.54838709677[/C][/ROW]
[ROW][C]80[/C][C]36080[/C][C]38065.5483870968[/C][C]-1985.54838709677[/C][/ROW]
[ROW][C]81[/C][C]33508[/C][C]38065.5483870968[/C][C]-4557.54838709677[/C][/ROW]
[ROW][C]82[/C][C]35462[/C][C]38065.5483870968[/C][C]-2603.54838709677[/C][/ROW]
[ROW][C]83[/C][C]33374[/C][C]38065.5483870968[/C][C]-4691.54838709677[/C][/ROW]
[ROW][C]84[/C][C]32110[/C][C]38065.5483870968[/C][C]-5955.54838709677[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=113354&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=113354&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
12145424214-2760
22389924214-315
32493924214725
42358024214-634
52456224214348
62469624214482
72378524214-429
82381224214-402
92191724214-2297
101971324214-4501
111928224214-4932
121878824214-5426
132145324214-2761
142448224214268
1527474242143260
1627264242143050
1727349242143135
1830632242146418
1929429242145215
2030084242145870
2126290242142076
222437924214165
232333524214-879
242134624214-2868
252110624214-3108
262451424214300
272835330754.7777777778-2401.77777777778
283080530754.777777777850.2222222222226
293134830754.7777777778593.222222222223
303455630754.77777777783801.22222222222
313385530754.77777777783100.22222222222
323478738065.5483870968-3278.54838709677
333252938065.5483870968-5536.54838709677
342999830754.7777777778-756.777777777777
352925730754.7777777778-1497.77777777778
362815530754.7777777778-2599.77777777778
373046630754.7777777778-288.777777777777
383570438065.5483870968-2361.54838709677
393932738065.54838709681261.45161290323
403935138065.54838709681285.45161290323
414223444042.6666666667-1808.66666666666
424363044042.6666666667-412.666666666664
434372244042.6666666667-320.666666666664
444312144042.6666666667-921.666666666664
453798538065.5483870968-80.5483870967728
463713538065.5483870968-930.548387096773
473464638065.5483870968-3419.54838709677
483302638065.5483870968-5039.54838709677
493508738065.5483870968-2978.54838709677
503884638065.5483870968780.451612903227
514201338065.54838709683947.45161290323
524390838065.54838709685842.45161290323
534286844042.6666666667-1174.66666666666
544442344042.6666666667380.333333333336
554416744042.6666666667124.333333333336
564363644042.6666666667-406.666666666664
574438238065.54838709686316.45161290323
584214244042.6666666667-1900.66666666666
594345244042.6666666667-590.666666666664
603691244042.6666666667-7130.66666666666
614241338065.54838709684347.45161290323
624534438065.54838709687278.45161290323
634487338065.54838709686807.45161290323
644751044042.66666666673467.33333333334
654955444042.66666666675511.33333333334
664736944042.66666666673326.33333333334
674599844042.66666666671955.33333333334
684814038065.548387096810074.4516129032
694844144042.66666666674398.33333333334
704492844042.6666666667885.333333333336
714045438065.54838709682388.45161290323
723866144042.6666666667-5381.66666666666
733724638065.5483870968-819.548387096773
743684338065.5483870968-1222.54838709677
753642438065.5483870968-1641.54838709677
763759438065.5483870968-471.548387096773
773814438065.548387096878.4516129032272
783873738065.5483870968671.451612903227
793456038065.5483870968-3505.54838709677
803608038065.5483870968-1985.54838709677
813350838065.5483870968-4557.54838709677
823546238065.5483870968-2603.54838709677
833337438065.5483870968-4691.54838709677
843211038065.5483870968-5955.54838709677


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = 2 ; par2 = none ; par3 = 4 ; par4 = no ;
Parameters (R input):
par1 = 2 ; 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')
}