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R Software Module

rwasp_regression_trees1.wasp

Title produced by software

Recursive Partitioning (Regression Trees)

Date of computation

Tue, 21 Dec 2010 15:30:20 +0000

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Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2010/Dec/21/t1292945279hl7gwg07b28jwcz.htm/, Retrieved Fri, 17 May 2024 11:10:02 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=113676, Retrieved Fri, 17 May 2024 11:10:02 +0000

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No (this computation is public)

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Estimated Impact

111

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)] [] [2010-12-14 17:41:08] [8ef75e99f9f5061c72c54640f2f1c3e7]
-   PD      [Recursive Partitioning (Regression Trees)] [] [2010-12-21 15:30:20] [e26438ba7029caa0090c95690001dbf5] [Current]

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Dataseries X:

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4,031636	0,5215052	9,166456	1,303763
3,702076	0,4248284	7,970589	1,416094
3,056176	0,4250311	7,104091	1,052458
3,280707	0,4771938	6,621064	1,312283
2,984728	0,8280212	7,529215	1,309429
3,693712	0,6156186	8,170938	1,492409
3,226317	0,366627	8,15745	1,026556
2,190349	0,4308883	7,378962	1,005406
2,599515	0,2810287	7,921496	1,334886
3,080288	0,4646245	8,15674	1,393873
2,929672	0,2693951	8,856365	1,128092
2,922548	0,5779049	8,817177	1,122787
3,234943	0,5661151	8,734347	1,213104
2,983081	0,5077584	9,345927	1,253528
3,284389	0,7507175	8,99297	1,094796
3,806511	0,6808395	10,78512	0,9129438
3,784579	0,7661091	8,886867	1,19513
2,645654	0,4561473	8,818847	0,9274994
3,092081	0,4977496	8,823744	0,9653326
3,204859	0,4193273	9,165298	1,198078
3,107225	0,6095514	8,652657	0,966362
3,466909	0,457337	8,173054	0,9736851
2,984404	0,5705478	7,563416	0,9948013
3,218072	0,3478996	7,595809	0,8262616
2,82731	0,3874993	8,381467	0,6888877
3,182049	0,5824285	7,216432	0,7813066
2,236319	0,2391033	6,540178	0,6047907
2,033218	0,2367445	6,238914	1,08624
1,644804	0,2626158	5,487288	0,7740255
1,627971	0,4240934	5,759462	1,026032
1,677559	0,365275	5,993215	0,6764351
2,330828	0,3750758	7,474726	0,830525
2,493615	0,4090056	7,348907	0,7916238
2,257172	0,3891676	7,303379	0,7523907
2,655517	0,240261	7,119314	0,6702018
2,298655	0,1589496	6,99378	0,8803359
2,600402	0,4393373	6,958153	0,9142966
3,04523	0,5094681	7,595706	0,9610421
2,790583	0,3743465	8,088153	0,9301944
3,227052	0,4339828	7,555753	0,8679657
2,967479	0,4130557	7,315433	0,9891596
2,938817	0,3288928	7,893427	0,9972879
3,277961	0,5186648	8,858794	0,7987437
3,423985	0,5486504	8,839367	0,9753785
3,072646	0,5469111	8,014733	0,9347208
2,754253	0,4963494	7,873465	0,9732341
2,910431	0,5308929	8,930377	0,8152998
3,174369	0,5957761	10,50055	0,9402092
3,068387	0,5570584	12,61144	0,794493
3,089543	0,5731325	11,41787	0,9313403
2,906654	0,5005416	11,87249	0,9220503
2,931161	0,5431269	11,06082	0,7845167
3,02566	0,5593657	12,04331	0,8220981
2,939551	0,6911693	9,776299	0,8910255
2,691019	0,4403485	9,557194	0,8073056
3,19812	0,5676662	9,20259	0,9514406
3,07639	0,5969114	10,22402	1,147907
2,863873	0,4735537	9,350807	1,172609
3,013802	0,5923935	8,300913	1,281051
3,053364	0,5975556	8,365779	1,165962
2,864753	0,6334127	8,133595	0,9789106
3,057062	0,6057115	7,66047	1,410951
2,959365	0,7046107	8,074839	1,197838
3,252258	0,4805263	7,848597	1,288368
3,602988	0,702686	7,99822	1,102253
3,497704	0,7009017	7,396895	1,197657
3,296867	0,6030854	7,900419	1,299984
3,602417	0,6980919	8,1005	1,198611
3,3001	0,597656	7,899453	1,299252
3,40193	0,8023421	7,599783	1,097604
3,502591	0,6017109	8,100929	1,39977
3,402348	0,5993127	9,002175	1,398396
3,498551	0,6025625	10,2989	1,40188
3,199823	0,7016625	10,10152	1,699717
2,700064	0,4995714	10,69915	1,39761
2,801034	0,4980918	9,69814	1,500135
2,898628	0,497569	9,800951	1,400136
2,800854	0,600183	10,90047	1,400427
2,399942	0,3339542	10,69785	1,341477
2,402724	0,274437	9,297252	1,33858
2,202331	0,3209428	10,39744	1,482977
2,102594	0,5406671	10,90072	1,163253
1,798293	0,4050209	12,90127	1,328468
1,202484	0,2885961	13,09906	1,23455
1,400201	0,3275942	11,69828	1,484741
1,200832	0,3132606	11,09987	1,336579
1,298083	0,2575562	11,30157	1,339292
1,099742	0,2138386	10,70211	1,405225
1,001377	0,1861856	10,09931	1,333491
0,8361743	0,1592713	9,591119	1,14974

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	5 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 & 5 seconds \tabularnewline
R Server & 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=113676&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]5 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=113676&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=113676&T=0

As an alternative you can also use a QR Code:

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

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	5 seconds
R Server	'Sir Ronald Aylmer Fisher' @ 193.190.124.24

Goodness of Fit
Correlation	0.7435
R-squared	0.5528
RMSE	0.4527

\begin{tabular}{lllllllll}
\hline
Goodness of Fit \tabularnewline
Correlation & 0.7435 \tabularnewline
R-squared & 0.5528 \tabularnewline
RMSE & 0.4527 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=113676&T=1

[TABLE]
[ROW][C]Goodness of Fit[/C][/ROW]
[ROW][C]Correlation[/C][C]0.7435[/C][/ROW]
[ROW][C]R-squared[/C][C]0.5528[/C][/ROW]
[ROW][C]RMSE[/C][C]0.4527[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=113676&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=113676&T=1

As an alternative you can also use a QR Code:

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

Goodness of Fit
Correlation	0.7435
R-squared	0.5528
RMSE	0.4527

Actuals, Predictions, and Residuals
#	Actuals	Forecasts	Residuals
1	4.031636	3.16013398113208	0.871502018867924
2	3.702076	2.66054976190476	1.04152623809524
3	3.056176	2.66054976190476	0.395626238095238
4	3.280707	3.16013398113208	0.120573018867924
5	2.984728	3.16013398113208	-0.175405981132076
6	3.693712	3.16013398113208	0.533578018867924
7	3.226317	2.66054976190476	0.565767238095238
8	2.190349	2.66054976190476	-0.470200761904762
9	2.599515	1.81510301875	0.78441198125
10	3.080288	3.16013398113208	-0.0798459811320757
11	2.929672	1.81510301875	1.11456898125
12	2.922548	3.16013398113208	-0.237585981132076
13	3.234943	3.16013398113208	0.0748090188679242
14	2.983081	3.16013398113208	-0.177052981132076
15	3.284389	3.16013398113208	0.124255018867924
16	3.806511	3.16013398113208	0.646377018867924
17	3.784579	3.16013398113208	0.624445018867924
18	2.645654	2.66054976190476	-0.0148957619047621
19	3.092081	3.16013398113208	-0.0680529811320758
20	3.204859	2.66054976190476	0.544309238095238
21	3.107225	3.16013398113208	-0.0529089811320755
22	3.466909	3.16013398113208	0.306775018867924
23	2.984404	3.16013398113208	-0.175729981132076
24	3.218072	2.66054976190476	0.557522238095238
25	2.82731	2.66054976190476	0.166760238095238
26	3.182049	3.16013398113208	0.0219150188679245
27	2.236319	1.81510301875	0.42121598125
28	2.033218	1.81510301875	0.21811498125
29	1.644804	1.81510301875	-0.170299018750000
30	1.627971	2.66054976190476	-1.03257876190476
31	1.677559	2.66054976190476	-0.982990761904762
32	2.330828	2.66054976190476	-0.329721761904762
33	2.493615	2.66054976190476	-0.166934761904762
34	2.257172	2.66054976190476	-0.403377761904762
35	2.655517	1.81510301875	0.84041398125
36	2.298655	1.81510301875	0.48355198125
37	2.600402	2.66054976190476	-0.0601477619047621
38	3.04523	3.16013398113208	-0.114903981132076
39	2.790583	2.66054976190476	0.130033238095238
40	3.227052	2.66054976190476	0.566502238095238
41	2.967479	2.66054976190476	0.306929238095238
42	2.938817	2.66054976190476	0.278267238095238
43	3.277961	3.16013398113208	0.117827018867924
44	3.423985	3.16013398113208	0.263851018867924
45	3.072646	3.16013398113208	-0.0874879811320755
46	2.754253	3.16013398113208	-0.405880981132076
47	2.910431	3.16013398113208	-0.249702981132076
48	3.174369	3.16013398113208	0.0142350188679243
49	3.068387	3.16013398113208	-0.0917469811320757
50	3.089543	3.16013398113208	-0.0705909811320757
51	2.906654	3.16013398113208	-0.253479981132076
52	2.931161	3.16013398113208	-0.228972981132076
53	3.02566	3.16013398113208	-0.134473981132076
54	2.939551	3.16013398113208	-0.220582981132076
55	2.691019	2.66054976190476	0.0304692380952378
56	3.19812	3.16013398113208	0.0379860188679242
57	3.07639	3.16013398113208	-0.0837439811320757
58	2.863873	3.16013398113208	-0.296260981132076
59	3.013802	3.16013398113208	-0.146331981132076
60	3.053364	3.16013398113208	-0.106769981132075
61	2.864753	3.16013398113208	-0.295380981132076
62	3.057062	3.16013398113208	-0.103071981132075
63	2.959365	3.16013398113208	-0.200768981132076
64	3.252258	3.16013398113208	0.0921240188679242
65	3.602988	3.16013398113208	0.442854018867924
66	3.497704	3.16013398113208	0.337570018867924
67	3.296867	3.16013398113208	0.136733018867925
68	3.602417	3.16013398113208	0.442283018867924
69	3.3001	3.16013398113208	0.139966018867924
70	3.40193	3.16013398113208	0.241796018867924
71	3.502591	3.16013398113208	0.342457018867924
72	3.402348	3.16013398113208	0.242214018867924
73	3.498551	3.16013398113208	0.338417018867924
74	3.199823	3.16013398113208	0.0396890188679242
75	2.700064	3.16013398113208	-0.460069981132076
76	2.801034	3.16013398113208	-0.359099981132076
77	2.898628	3.16013398113208	-0.261505981132076
78	2.800854	3.16013398113208	-0.359279981132075
79	2.399942	2.66054976190476	-0.260607761904762
80	2.402724	1.81510301875	0.58762098125
81	2.202331	1.81510301875	0.38722798125
82	2.102594	3.16013398113208	-1.05753998113208
83	1.798293	2.66054976190476	-0.862256761904762
84	1.202484	1.81510301875	-0.61261901875
85	1.400201	1.81510301875	-0.41490201875
86	1.200832	1.81510301875	-0.61427101875
87	1.298083	1.81510301875	-0.51702001875
88	1.099742	1.81510301875	-0.71536101875
89	1.001377	1.81510301875	-0.81372601875
90	0.8361743	1.81510301875	-0.97892871875

\begin{tabular}{lllllllll}
\hline
Actuals, Predictions, and Residuals \tabularnewline
# & Actuals & Forecasts & Residuals \tabularnewline
1 & 4.031636 & 3.16013398113208 & 0.871502018867924 \tabularnewline
2 & 3.702076 & 2.66054976190476 & 1.04152623809524 \tabularnewline
3 & 3.056176 & 2.66054976190476 & 0.395626238095238 \tabularnewline
4 & 3.280707 & 3.16013398113208 & 0.120573018867924 \tabularnewline
5 & 2.984728 & 3.16013398113208 & -0.175405981132076 \tabularnewline
6 & 3.693712 & 3.16013398113208 & 0.533578018867924 \tabularnewline
7 & 3.226317 & 2.66054976190476 & 0.565767238095238 \tabularnewline
8 & 2.190349 & 2.66054976190476 & -0.470200761904762 \tabularnewline
9 & 2.599515 & 1.81510301875 & 0.78441198125 \tabularnewline
10 & 3.080288 & 3.16013398113208 & -0.0798459811320757 \tabularnewline
11 & 2.929672 & 1.81510301875 & 1.11456898125 \tabularnewline
12 & 2.922548 & 3.16013398113208 & -0.237585981132076 \tabularnewline
13 & 3.234943 & 3.16013398113208 & 0.0748090188679242 \tabularnewline
14 & 2.983081 & 3.16013398113208 & -0.177052981132076 \tabularnewline
15 & 3.284389 & 3.16013398113208 & 0.124255018867924 \tabularnewline
16 & 3.806511 & 3.16013398113208 & 0.646377018867924 \tabularnewline
17 & 3.784579 & 3.16013398113208 & 0.624445018867924 \tabularnewline
18 & 2.645654 & 2.66054976190476 & -0.0148957619047621 \tabularnewline
19 & 3.092081 & 3.16013398113208 & -0.0680529811320758 \tabularnewline
20 & 3.204859 & 2.66054976190476 & 0.544309238095238 \tabularnewline
21 & 3.107225 & 3.16013398113208 & -0.0529089811320755 \tabularnewline
22 & 3.466909 & 3.16013398113208 & 0.306775018867924 \tabularnewline
23 & 2.984404 & 3.16013398113208 & -0.175729981132076 \tabularnewline
24 & 3.218072 & 2.66054976190476 & 0.557522238095238 \tabularnewline
25 & 2.82731 & 2.66054976190476 & 0.166760238095238 \tabularnewline
26 & 3.182049 & 3.16013398113208 & 0.0219150188679245 \tabularnewline
27 & 2.236319 & 1.81510301875 & 0.42121598125 \tabularnewline
28 & 2.033218 & 1.81510301875 & 0.21811498125 \tabularnewline
29 & 1.644804 & 1.81510301875 & -0.170299018750000 \tabularnewline
30 & 1.627971 & 2.66054976190476 & -1.03257876190476 \tabularnewline
31 & 1.677559 & 2.66054976190476 & -0.982990761904762 \tabularnewline
32 & 2.330828 & 2.66054976190476 & -0.329721761904762 \tabularnewline
33 & 2.493615 & 2.66054976190476 & -0.166934761904762 \tabularnewline
34 & 2.257172 & 2.66054976190476 & -0.403377761904762 \tabularnewline
35 & 2.655517 & 1.81510301875 & 0.84041398125 \tabularnewline
36 & 2.298655 & 1.81510301875 & 0.48355198125 \tabularnewline
37 & 2.600402 & 2.66054976190476 & -0.0601477619047621 \tabularnewline
38 & 3.04523 & 3.16013398113208 & -0.114903981132076 \tabularnewline
39 & 2.790583 & 2.66054976190476 & 0.130033238095238 \tabularnewline
40 & 3.227052 & 2.66054976190476 & 0.566502238095238 \tabularnewline
41 & 2.967479 & 2.66054976190476 & 0.306929238095238 \tabularnewline
42 & 2.938817 & 2.66054976190476 & 0.278267238095238 \tabularnewline
43 & 3.277961 & 3.16013398113208 & 0.117827018867924 \tabularnewline
44 & 3.423985 & 3.16013398113208 & 0.263851018867924 \tabularnewline
45 & 3.072646 & 3.16013398113208 & -0.0874879811320755 \tabularnewline
46 & 2.754253 & 3.16013398113208 & -0.405880981132076 \tabularnewline
47 & 2.910431 & 3.16013398113208 & -0.249702981132076 \tabularnewline
48 & 3.174369 & 3.16013398113208 & 0.0142350188679243 \tabularnewline
49 & 3.068387 & 3.16013398113208 & -0.0917469811320757 \tabularnewline
50 & 3.089543 & 3.16013398113208 & -0.0705909811320757 \tabularnewline
51 & 2.906654 & 3.16013398113208 & -0.253479981132076 \tabularnewline
52 & 2.931161 & 3.16013398113208 & -0.228972981132076 \tabularnewline
53 & 3.02566 & 3.16013398113208 & -0.134473981132076 \tabularnewline
54 & 2.939551 & 3.16013398113208 & -0.220582981132076 \tabularnewline
55 & 2.691019 & 2.66054976190476 & 0.0304692380952378 \tabularnewline
56 & 3.19812 & 3.16013398113208 & 0.0379860188679242 \tabularnewline
57 & 3.07639 & 3.16013398113208 & -0.0837439811320757 \tabularnewline
58 & 2.863873 & 3.16013398113208 & -0.296260981132076 \tabularnewline
59 & 3.013802 & 3.16013398113208 & -0.146331981132076 \tabularnewline
60 & 3.053364 & 3.16013398113208 & -0.106769981132075 \tabularnewline
61 & 2.864753 & 3.16013398113208 & -0.295380981132076 \tabularnewline
62 & 3.057062 & 3.16013398113208 & -0.103071981132075 \tabularnewline
63 & 2.959365 & 3.16013398113208 & -0.200768981132076 \tabularnewline
64 & 3.252258 & 3.16013398113208 & 0.0921240188679242 \tabularnewline
65 & 3.602988 & 3.16013398113208 & 0.442854018867924 \tabularnewline
66 & 3.497704 & 3.16013398113208 & 0.337570018867924 \tabularnewline
67 & 3.296867 & 3.16013398113208 & 0.136733018867925 \tabularnewline
68 & 3.602417 & 3.16013398113208 & 0.442283018867924 \tabularnewline
69 & 3.3001 & 3.16013398113208 & 0.139966018867924 \tabularnewline
70 & 3.40193 & 3.16013398113208 & 0.241796018867924 \tabularnewline
71 & 3.502591 & 3.16013398113208 & 0.342457018867924 \tabularnewline
72 & 3.402348 & 3.16013398113208 & 0.242214018867924 \tabularnewline
73 & 3.498551 & 3.16013398113208 & 0.338417018867924 \tabularnewline
74 & 3.199823 & 3.16013398113208 & 0.0396890188679242 \tabularnewline
75 & 2.700064 & 3.16013398113208 & -0.460069981132076 \tabularnewline
76 & 2.801034 & 3.16013398113208 & -0.359099981132076 \tabularnewline
77 & 2.898628 & 3.16013398113208 & -0.261505981132076 \tabularnewline
78 & 2.800854 & 3.16013398113208 & -0.359279981132075 \tabularnewline
79 & 2.399942 & 2.66054976190476 & -0.260607761904762 \tabularnewline
80 & 2.402724 & 1.81510301875 & 0.58762098125 \tabularnewline
81 & 2.202331 & 1.81510301875 & 0.38722798125 \tabularnewline
82 & 2.102594 & 3.16013398113208 & -1.05753998113208 \tabularnewline
83 & 1.798293 & 2.66054976190476 & -0.862256761904762 \tabularnewline
84 & 1.202484 & 1.81510301875 & -0.61261901875 \tabularnewline
85 & 1.400201 & 1.81510301875 & -0.41490201875 \tabularnewline
86 & 1.200832 & 1.81510301875 & -0.61427101875 \tabularnewline
87 & 1.298083 & 1.81510301875 & -0.51702001875 \tabularnewline
88 & 1.099742 & 1.81510301875 & -0.71536101875 \tabularnewline
89 & 1.001377 & 1.81510301875 & -0.81372601875 \tabularnewline
90 & 0.8361743 & 1.81510301875 & -0.97892871875 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=113676&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]4.031636[/C][C]3.16013398113208[/C][C]0.871502018867924[/C][/ROW]
[ROW][C]2[/C][C]3.702076[/C][C]2.66054976190476[/C][C]1.04152623809524[/C][/ROW]
[ROW][C]3[/C][C]3.056176[/C][C]2.66054976190476[/C][C]0.395626238095238[/C][/ROW]
[ROW][C]4[/C][C]3.280707[/C][C]3.16013398113208[/C][C]0.120573018867924[/C][/ROW]
[ROW][C]5[/C][C]2.984728[/C][C]3.16013398113208[/C][C]-0.175405981132076[/C][/ROW]
[ROW][C]6[/C][C]3.693712[/C][C]3.16013398113208[/C][C]0.533578018867924[/C][/ROW]
[ROW][C]7[/C][C]3.226317[/C][C]2.66054976190476[/C][C]0.565767238095238[/C][/ROW]
[ROW][C]8[/C][C]2.190349[/C][C]2.66054976190476[/C][C]-0.470200761904762[/C][/ROW]
[ROW][C]9[/C][C]2.599515[/C][C]1.81510301875[/C][C]0.78441198125[/C][/ROW]
[ROW][C]10[/C][C]3.080288[/C][C]3.16013398113208[/C][C]-0.0798459811320757[/C][/ROW]
[ROW][C]11[/C][C]2.929672[/C][C]1.81510301875[/C][C]1.11456898125[/C][/ROW]
[ROW][C]12[/C][C]2.922548[/C][C]3.16013398113208[/C][C]-0.237585981132076[/C][/ROW]
[ROW][C]13[/C][C]3.234943[/C][C]3.16013398113208[/C][C]0.0748090188679242[/C][/ROW]
[ROW][C]14[/C][C]2.983081[/C][C]3.16013398113208[/C][C]-0.177052981132076[/C][/ROW]
[ROW][C]15[/C][C]3.284389[/C][C]3.16013398113208[/C][C]0.124255018867924[/C][/ROW]
[ROW][C]16[/C][C]3.806511[/C][C]3.16013398113208[/C][C]0.646377018867924[/C][/ROW]
[ROW][C]17[/C][C]3.784579[/C][C]3.16013398113208[/C][C]0.624445018867924[/C][/ROW]
[ROW][C]18[/C][C]2.645654[/C][C]2.66054976190476[/C][C]-0.0148957619047621[/C][/ROW]
[ROW][C]19[/C][C]3.092081[/C][C]3.16013398113208[/C][C]-0.0680529811320758[/C][/ROW]
[ROW][C]20[/C][C]3.204859[/C][C]2.66054976190476[/C][C]0.544309238095238[/C][/ROW]
[ROW][C]21[/C][C]3.107225[/C][C]3.16013398113208[/C][C]-0.0529089811320755[/C][/ROW]
[ROW][C]22[/C][C]3.466909[/C][C]3.16013398113208[/C][C]0.306775018867924[/C][/ROW]
[ROW][C]23[/C][C]2.984404[/C][C]3.16013398113208[/C][C]-0.175729981132076[/C][/ROW]
[ROW][C]24[/C][C]3.218072[/C][C]2.66054976190476[/C][C]0.557522238095238[/C][/ROW]
[ROW][C]25[/C][C]2.82731[/C][C]2.66054976190476[/C][C]0.166760238095238[/C][/ROW]
[ROW][C]26[/C][C]3.182049[/C][C]3.16013398113208[/C][C]0.0219150188679245[/C][/ROW]
[ROW][C]27[/C][C]2.236319[/C][C]1.81510301875[/C][C]0.42121598125[/C][/ROW]
[ROW][C]28[/C][C]2.033218[/C][C]1.81510301875[/C][C]0.21811498125[/C][/ROW]
[ROW][C]29[/C][C]1.644804[/C][C]1.81510301875[/C][C]-0.170299018750000[/C][/ROW]
[ROW][C]30[/C][C]1.627971[/C][C]2.66054976190476[/C][C]-1.03257876190476[/C][/ROW]
[ROW][C]31[/C][C]1.677559[/C][C]2.66054976190476[/C][C]-0.982990761904762[/C][/ROW]
[ROW][C]32[/C][C]2.330828[/C][C]2.66054976190476[/C][C]-0.329721761904762[/C][/ROW]
[ROW][C]33[/C][C]2.493615[/C][C]2.66054976190476[/C][C]-0.166934761904762[/C][/ROW]
[ROW][C]34[/C][C]2.257172[/C][C]2.66054976190476[/C][C]-0.403377761904762[/C][/ROW]
[ROW][C]35[/C][C]2.655517[/C][C]1.81510301875[/C][C]0.84041398125[/C][/ROW]
[ROW][C]36[/C][C]2.298655[/C][C]1.81510301875[/C][C]0.48355198125[/C][/ROW]
[ROW][C]37[/C][C]2.600402[/C][C]2.66054976190476[/C][C]-0.0601477619047621[/C][/ROW]
[ROW][C]38[/C][C]3.04523[/C][C]3.16013398113208[/C][C]-0.114903981132076[/C][/ROW]
[ROW][C]39[/C][C]2.790583[/C][C]2.66054976190476[/C][C]0.130033238095238[/C][/ROW]
[ROW][C]40[/C][C]3.227052[/C][C]2.66054976190476[/C][C]0.566502238095238[/C][/ROW]
[ROW][C]41[/C][C]2.967479[/C][C]2.66054976190476[/C][C]0.306929238095238[/C][/ROW]
[ROW][C]42[/C][C]2.938817[/C][C]2.66054976190476[/C][C]0.278267238095238[/C][/ROW]
[ROW][C]43[/C][C]3.277961[/C][C]3.16013398113208[/C][C]0.117827018867924[/C][/ROW]
[ROW][C]44[/C][C]3.423985[/C][C]3.16013398113208[/C][C]0.263851018867924[/C][/ROW]
[ROW][C]45[/C][C]3.072646[/C][C]3.16013398113208[/C][C]-0.0874879811320755[/C][/ROW]
[ROW][C]46[/C][C]2.754253[/C][C]3.16013398113208[/C][C]-0.405880981132076[/C][/ROW]
[ROW][C]47[/C][C]2.910431[/C][C]3.16013398113208[/C][C]-0.249702981132076[/C][/ROW]
[ROW][C]48[/C][C]3.174369[/C][C]3.16013398113208[/C][C]0.0142350188679243[/C][/ROW]
[ROW][C]49[/C][C]3.068387[/C][C]3.16013398113208[/C][C]-0.0917469811320757[/C][/ROW]
[ROW][C]50[/C][C]3.089543[/C][C]3.16013398113208[/C][C]-0.0705909811320757[/C][/ROW]
[ROW][C]51[/C][C]2.906654[/C][C]3.16013398113208[/C][C]-0.253479981132076[/C][/ROW]
[ROW][C]52[/C][C]2.931161[/C][C]3.16013398113208[/C][C]-0.228972981132076[/C][/ROW]
[ROW][C]53[/C][C]3.02566[/C][C]3.16013398113208[/C][C]-0.134473981132076[/C][/ROW]
[ROW][C]54[/C][C]2.939551[/C][C]3.16013398113208[/C][C]-0.220582981132076[/C][/ROW]
[ROW][C]55[/C][C]2.691019[/C][C]2.66054976190476[/C][C]0.0304692380952378[/C][/ROW]
[ROW][C]56[/C][C]3.19812[/C][C]3.16013398113208[/C][C]0.0379860188679242[/C][/ROW]
[ROW][C]57[/C][C]3.07639[/C][C]3.16013398113208[/C][C]-0.0837439811320757[/C][/ROW]
[ROW][C]58[/C][C]2.863873[/C][C]3.16013398113208[/C][C]-0.296260981132076[/C][/ROW]
[ROW][C]59[/C][C]3.013802[/C][C]3.16013398113208[/C][C]-0.146331981132076[/C][/ROW]
[ROW][C]60[/C][C]3.053364[/C][C]3.16013398113208[/C][C]-0.106769981132075[/C][/ROW]
[ROW][C]61[/C][C]2.864753[/C][C]3.16013398113208[/C][C]-0.295380981132076[/C][/ROW]
[ROW][C]62[/C][C]3.057062[/C][C]3.16013398113208[/C][C]-0.103071981132075[/C][/ROW]
[ROW][C]63[/C][C]2.959365[/C][C]3.16013398113208[/C][C]-0.200768981132076[/C][/ROW]
[ROW][C]64[/C][C]3.252258[/C][C]3.16013398113208[/C][C]0.0921240188679242[/C][/ROW]
[ROW][C]65[/C][C]3.602988[/C][C]3.16013398113208[/C][C]0.442854018867924[/C][/ROW]
[ROW][C]66[/C][C]3.497704[/C][C]3.16013398113208[/C][C]0.337570018867924[/C][/ROW]
[ROW][C]67[/C][C]3.296867[/C][C]3.16013398113208[/C][C]0.136733018867925[/C][/ROW]
[ROW][C]68[/C][C]3.602417[/C][C]3.16013398113208[/C][C]0.442283018867924[/C][/ROW]
[ROW][C]69[/C][C]3.3001[/C][C]3.16013398113208[/C][C]0.139966018867924[/C][/ROW]
[ROW][C]70[/C][C]3.40193[/C][C]3.16013398113208[/C][C]0.241796018867924[/C][/ROW]
[ROW][C]71[/C][C]3.502591[/C][C]3.16013398113208[/C][C]0.342457018867924[/C][/ROW]
[ROW][C]72[/C][C]3.402348[/C][C]3.16013398113208[/C][C]0.242214018867924[/C][/ROW]
[ROW][C]73[/C][C]3.498551[/C][C]3.16013398113208[/C][C]0.338417018867924[/C][/ROW]
[ROW][C]74[/C][C]3.199823[/C][C]3.16013398113208[/C][C]0.0396890188679242[/C][/ROW]
[ROW][C]75[/C][C]2.700064[/C][C]3.16013398113208[/C][C]-0.460069981132076[/C][/ROW]
[ROW][C]76[/C][C]2.801034[/C][C]3.16013398113208[/C][C]-0.359099981132076[/C][/ROW]
[ROW][C]77[/C][C]2.898628[/C][C]3.16013398113208[/C][C]-0.261505981132076[/C][/ROW]
[ROW][C]78[/C][C]2.800854[/C][C]3.16013398113208[/C][C]-0.359279981132075[/C][/ROW]
[ROW][C]79[/C][C]2.399942[/C][C]2.66054976190476[/C][C]-0.260607761904762[/C][/ROW]
[ROW][C]80[/C][C]2.402724[/C][C]1.81510301875[/C][C]0.58762098125[/C][/ROW]
[ROW][C]81[/C][C]2.202331[/C][C]1.81510301875[/C][C]0.38722798125[/C][/ROW]
[ROW][C]82[/C][C]2.102594[/C][C]3.16013398113208[/C][C]-1.05753998113208[/C][/ROW]
[ROW][C]83[/C][C]1.798293[/C][C]2.66054976190476[/C][C]-0.862256761904762[/C][/ROW]
[ROW][C]84[/C][C]1.202484[/C][C]1.81510301875[/C][C]-0.61261901875[/C][/ROW]
[ROW][C]85[/C][C]1.400201[/C][C]1.81510301875[/C][C]-0.41490201875[/C][/ROW]
[ROW][C]86[/C][C]1.200832[/C][C]1.81510301875[/C][C]-0.61427101875[/C][/ROW]
[ROW][C]87[/C][C]1.298083[/C][C]1.81510301875[/C][C]-0.51702001875[/C][/ROW]
[ROW][C]88[/C][C]1.099742[/C][C]1.81510301875[/C][C]-0.71536101875[/C][/ROW]
[ROW][C]89[/C][C]1.001377[/C][C]1.81510301875[/C][C]-0.81372601875[/C][/ROW]
[ROW][C]90[/C][C]0.8361743[/C][C]1.81510301875[/C][C]-0.97892871875[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=113676&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=113676&T=2

As an alternative you can also use a QR Code:

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

Actuals, Predictions, and Residuals
#	Actuals	Forecasts	Residuals
1	4.031636	3.16013398113208	0.871502018867924
2	3.702076	2.66054976190476	1.04152623809524
3	3.056176	2.66054976190476	0.395626238095238
4	3.280707	3.16013398113208	0.120573018867924
5	2.984728	3.16013398113208	-0.175405981132076
6	3.693712	3.16013398113208	0.533578018867924
7	3.226317	2.66054976190476	0.565767238095238
8	2.190349	2.66054976190476	-0.470200761904762
9	2.599515	1.81510301875	0.78441198125
10	3.080288	3.16013398113208	-0.0798459811320757
11	2.929672	1.81510301875	1.11456898125
12	2.922548	3.16013398113208	-0.237585981132076
13	3.234943	3.16013398113208	0.0748090188679242
14	2.983081	3.16013398113208	-0.177052981132076
15	3.284389	3.16013398113208	0.124255018867924
16	3.806511	3.16013398113208	0.646377018867924
17	3.784579	3.16013398113208	0.624445018867924
18	2.645654	2.66054976190476	-0.0148957619047621
19	3.092081	3.16013398113208	-0.0680529811320758
20	3.204859	2.66054976190476	0.544309238095238
21	3.107225	3.16013398113208	-0.0529089811320755
22	3.466909	3.16013398113208	0.306775018867924
23	2.984404	3.16013398113208	-0.175729981132076
24	3.218072	2.66054976190476	0.557522238095238
25	2.82731	2.66054976190476	0.166760238095238
26	3.182049	3.16013398113208	0.0219150188679245
27	2.236319	1.81510301875	0.42121598125
28	2.033218	1.81510301875	0.21811498125
29	1.644804	1.81510301875	-0.170299018750000
30	1.627971	2.66054976190476	-1.03257876190476
31	1.677559	2.66054976190476	-0.982990761904762
32	2.330828	2.66054976190476	-0.329721761904762
33	2.493615	2.66054976190476	-0.166934761904762
34	2.257172	2.66054976190476	-0.403377761904762
35	2.655517	1.81510301875	0.84041398125
36	2.298655	1.81510301875	0.48355198125
37	2.600402	2.66054976190476	-0.0601477619047621
38	3.04523	3.16013398113208	-0.114903981132076
39	2.790583	2.66054976190476	0.130033238095238
40	3.227052	2.66054976190476	0.566502238095238
41	2.967479	2.66054976190476	0.306929238095238
42	2.938817	2.66054976190476	0.278267238095238
43	3.277961	3.16013398113208	0.117827018867924
44	3.423985	3.16013398113208	0.263851018867924
45	3.072646	3.16013398113208	-0.0874879811320755
46	2.754253	3.16013398113208	-0.405880981132076
47	2.910431	3.16013398113208	-0.249702981132076
48	3.174369	3.16013398113208	0.0142350188679243
49	3.068387	3.16013398113208	-0.0917469811320757
50	3.089543	3.16013398113208	-0.0705909811320757
51	2.906654	3.16013398113208	-0.253479981132076
52	2.931161	3.16013398113208	-0.228972981132076
53	3.02566	3.16013398113208	-0.134473981132076
54	2.939551	3.16013398113208	-0.220582981132076
55	2.691019	2.66054976190476	0.0304692380952378
56	3.19812	3.16013398113208	0.0379860188679242
57	3.07639	3.16013398113208	-0.0837439811320757
58	2.863873	3.16013398113208	-0.296260981132076
59	3.013802	3.16013398113208	-0.146331981132076
60	3.053364	3.16013398113208	-0.106769981132075
61	2.864753	3.16013398113208	-0.295380981132076
62	3.057062	3.16013398113208	-0.103071981132075
63	2.959365	3.16013398113208	-0.200768981132076
64	3.252258	3.16013398113208	0.0921240188679242
65	3.602988	3.16013398113208	0.442854018867924
66	3.497704	3.16013398113208	0.337570018867924
67	3.296867	3.16013398113208	0.136733018867925
68	3.602417	3.16013398113208	0.442283018867924
69	3.3001	3.16013398113208	0.139966018867924
70	3.40193	3.16013398113208	0.241796018867924
71	3.502591	3.16013398113208	0.342457018867924
72	3.402348	3.16013398113208	0.242214018867924
73	3.498551	3.16013398113208	0.338417018867924
74	3.199823	3.16013398113208	0.0396890188679242
75	2.700064	3.16013398113208	-0.460069981132076
76	2.801034	3.16013398113208	-0.359099981132076
77	2.898628	3.16013398113208	-0.261505981132076
78	2.800854	3.16013398113208	-0.359279981132075
79	2.399942	2.66054976190476	-0.260607761904762
80	2.402724	1.81510301875	0.58762098125
81	2.202331	1.81510301875	0.38722798125
82	2.102594	3.16013398113208	-1.05753998113208
83	1.798293	2.66054976190476	-0.862256761904762
84	1.202484	1.81510301875	-0.61261901875
85	1.400201	1.81510301875	-0.41490201875
86	1.200832	1.81510301875	-0.61427101875
87	1.298083	1.81510301875	-0.51702001875
88	1.099742	1.81510301875	-0.71536101875
89	1.001377	1.81510301875	-0.81372601875
90	0.8361743	1.81510301875	-0.97892871875

Figure 1

PNG link

Postscript link

PDF link

Figure 2

PNG link

Postscript link

PDF link

Figure 3

PNG link

Postscript link

PDF link

Parameters (Session):

par1 = 1 ; par2 = none ; par3 = 4 ; par4 = no ;

Parameters (R input):

par1 = 1 ; 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')
}

Free Statistics

Description of Statistical Computation

Tree of Dependent Computations

Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code