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Author's title

Author

*The author of this computation has been verified*

R Software Module

rwasp_regression_trees1.wasp

Title produced by software

Recursive Partitioning (Regression Trees)

Date of computation

Fri, 10 Dec 2010 09:15:12 +0000

Cite this page as follows

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2010/Dec/10/t1291972442mqr6gn04bbt43v6.htm/, Retrieved Mon, 29 Apr 2024 10:54:01 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=107451, Retrieved Mon, 29 Apr 2024 10:54:01 +0000

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Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords

Estimated Impact

176

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] [b98453cac15ba1066b407e146608df68]
-    D            [Recursive Partitioning (Regression Trees)] [Recursive Partiti...] [2010-12-10 09:15:12] [18ef3d986e8801a4b28404e69e5bf56b] [Current]

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Dataseries X:

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Histogram

Boxplots

40399	44164	44496	43110	43880	195722	198563	229139	229527	211868
36763	40399	44164	44496	43110	202196	195722	198563	229139	229527
37903	36763	40399	44164	44496	205816	202196	195722	198563	229139
35532	37903	36763	40399	44164	212588	205816	202196	195722	198563
35533	35532	37903	36763	40399	214320	212588	205816	202196	195722
32110	35533	35532	37903	36763	220375	214320	212588	205816	202196
33374	32110	35533	35532	37903	204442	220375	214320	212588	205816
35462	33374	32110	35533	35532	206903	204442	220375	214320	212588
33508	35462	33374	32110	35533	214126	206903	204442	220375	214320
36080	33508	35462	33374	32110	226899	214126	206903	204442	220375
34560	36080	33508	35462	33374	223532	226899	214126	206903	204442
38737	34560	36080	33508	35462	195309	223532	226899	214126	206903
38144	38737	34560	36080	33508	186005	195309	223532	226899	214126
37594	38144	38737	34560	36080	188906	186005	195309	223532	226899
36424	37594	38144	38737	34560	191563	188906	186005	195309	223532
36843	36424	37594	38144	38737	189226	191563	188906	186005	195309
37246	36843	36424	37594	38144	186413	189226	191563	188906	186005
38661	37246	36843	36424	37594	178037	186413	189226	191563	188906
40454	38661	37246	36843	36424	166827	178037	186413	189226	191563
44928	40454	38661	37246	36843	169362	166827	178037	186413	189226
48441	44928	40454	38661	37246	174330	169362	166827	178037	186413
48140	48441	44928	40454	38661	187069	174330	169362	166827	178037
45998	48140	48441	44928	40454	186530	187069	174330	169362	166827
47369	45998	48140	48441	44928	158114	186530	187069	174330	169362
49554	47369	45998	48140	48441	151001	158114	186530	187069	174330
47510	49554	47369	45998	48140	159612	151001	158114	186530	187069
44873	47510	49554	47369	45998	161914	159612	151001	158114	186530
45344	44873	47510	49554	47369	164182	161914	159612	151001	158114
42413	45344	44873	47510	49554	169701	164182	161914	159612	151001
36912	42413	45344	44873	47510	171297	169701	164182	161914	159612
43452	36912	42413	45344	44873	166444	171297	169701	164182	161914
42142	43452	36912	42413	45344	173476	166444	171297	169701	164182
44382	42142	43452	36912	42413	182516	173476	166444	171297	169701
43636	44382	42142	43452	36912	202388	182516	173476	166444	171297
44167	43636	44382	42142	43452	202300	202388	182516	173476	166444
44423	44167	43636	44382	42142	168053	202300	202388	182516	173476
42868	44423	44167	43636	44382	167302	168053	202300	202388	182516
43908	42868	44423	44167	43636	172608	167302	168053	202300	202388
42013	43908	42868	44423	44167	178106	172608	167302	168053	202300
38846	42013	43908	42868	44423	185686	178106	172608	167302	168053
35087	38846	42013	43908	42868	194581	185686	178106	172608	167302
33026	35087	38846	42013	43908	194596	194581	185686	178106	172608
34646	33026	35087	38846	42013	197922	194596	194581	185686	178106
37135	34646	33026	35087	38846	208795	197922	194596	194581	185686
37985	37135	34646	33026	35087	230580	208795	197922	194596	194581
43121	37985	37135	34646	33026	240636	230580	208795	197922	194596
43722	43121	37985	37135	34646	240048	240636	230580	208795	197922
43630	43722	43121	37985	37135	211457	240048	240636	230580	208795
42234	43630	43722	43121	37985	211142	211457	240048	240636	230580
39351	42234	43630	43722	43121	214771	211142	211457	240048	240636
39327	39351	42234	43630	43722	212610	214771	211142	211457	240048
35704	39327	39351	42234	43630	219313	212610	214771	211142	211457
30466	35704	39327	39351	42234	219277	219313	212610	214771	211142
28155	30466	35704	39327	39351	231805	219277	219313	212610	214771
29257	28155	30466	35704	39327	229245	231805	219277	219313	212610
29998	29257	28155	30466	35704	241114	229245	231805	219277	219313
32529	29998	29257	28155	30466	248624	241114	229245	231805	219277
34787	32529	29998	29257	28155	265845	248624	241114	229245	231805
33855	34787	32529	29998	29257	256446	265845	248624	241114	229245
34556	33855	34787	32529	29998	219452	256446	265845	248624	241114
31348	34556	33855	34787	32529	217142	219452	256446	265845	248624
30805	31348	34556	33855	34787	221678	217142	219452	256446	265845
28353	30805	31348	34556	33855	227184	221678	217142	219452	256446
24514	28353	30805	31348	34556	230354	227184	221678	217142	219452
21106	24514	28353	30805	31348	235243	230354	227184	221678	217142
21346	21106	24514	28353	30805	237217	235243	230354	227184	221678
23335	21346	21106	24514	28353	233575	237217	235243	230354	227184
24379	23335	21346	21106	24514	244460	233575	237217	235243	230354
26290	24379	23335	21346	21106	243324	244460	233575	237217	235243
30084	26290	24379	23335	21346	260307	243324	244460	233575	237217
29429	30084	26290	24379	23335	241476	260307	243324	244460	233575
30632	29429	30084	26290	24379	203666	241476	260307	243324	244460
27349	30632	29429	30084	26290	200237	203666	241476	260307	243324
27264	27349	30632	29429	30084	204045	200237	203666	241476	260307
27474	27264	27349	30632	29429	209465	204045	200237	203666	241476
24482	27474	27264	27349	30632	213586	209465	204045	200237	203666
21453	24482	27474	27264	27349	216234	213586	209465	204045	200237
18788	21453	24482	27474	27264	213188	216234	213586	209465	204045
19282	18788	21453	24482	27474	208679	213188	216234	213586	209465
19713	19282	18788	21453	24482	217859	208679	213188	216234	213586
21917	19713	19282	18788	21453	227247	217859	208679	213188	216234
23812	21917	19713	19282	18788	243477	227247	217859	208679	213188
23785	23812	21917	19713	19282	232571	243477	227247	217859	208679
24696	23785	23812	21917	19713	191531	232571	243477	227247	217859
24562	24696	23785	23812	21917	186029	191531	232571	243477	227247
23580	24562	24696	23785	23812	189733	186029	191531	232571	243477
24939	23580	24562	24696	23785	190420	189733	186029	191531	232571
23899	24939	23580	24562	24696	194163	190420	189733	186029	191531
21454	23899	24939	23580	24562	198770	194163	190420	189733	186029
19761	21454	23899	24939	23580	195198	198770	194163	190420	189733
19815	19761	21454	23899	24939	193111	195198	198770	194163	190420
20780	19815	19761	21454	23899	195411	193111	195198	198770	194163
23462	20780	19815	19761	21454	202108	195411	193111	195198	198770
25005	23462	20780	19815	19761	215706	202108	195411	193111	195198
24725	25005	23462	20780	19815	206348	215706	202108	195411	193111
26198	24725	25005	23462	20780	166972	206348	215706	202108	195411
27543	26198	24725	25005	23462	166070	166972	206348	215706	202108
26471	27543	26198	24725	25005	169292	166070	166972	206348	215706
26558	26471	27543	26198	24725	175041	169292	166070	166972	206348
25317	26558	26471	27543	26198	177876	175041	169292	166070	166972
22896	25317	26558	26471	27543	181140	177876	175041	169292	166070
22248	22896	25317	26558	26471	179566	181140	177876	175041	169292
23406	22248	22896	25317	26558	175335	179566	181140	177876	175041
25073	23406	22248	22896	25317	184128	175335	179566	181140	177876
27691	25073	23406	22248	22896	189917	184128	175335	179566	181140
30599	27691	25073	23406	22248	194690	189917	184128	175335	179566
31948	30599	27691	25073	23406	179612	194690	189917	184128	175335
32946	31948	30599	27691	25073	150605	179612	194690	189917	184128
34012	32946	31948	30599	27691	150569	150605	179612	194690	189917
32936	34012	32946	31948	30599	153745	150569	150605	179612	194690
32974	32936	34012	32946	31948	155511	153745	150569	150605	179612
30951	32974	32936	34012	32946	159044	155511	153745	150569	150605
29812	30951	32974	32936	34012	163095	159044	155511	153745	150569
29010	29812	30951	32974	32936	159585	163095	159044	155511	153745
31068	29010	29812	30951	32974	158644	159585	163095	159044	155511
32447	31068	29010	29812	30951	166618	158644	159585	163095	159044
34844	32447	31068	29010	29812	176512	166618	158644	159585	163095
35676	34844	32447	31068	29010	200765	176512	166618	158644	159585
35387	35676	34844	32447	31068	182698	200765	176512	166618	158644
36488	35387	35676	34844	32447	153730	182698	200765	176512	166618
35652	36488	35387	35676	34844	156145	153730	182698	200765	176512
33488	35652	36488	35387	35676	161570	156145	153730	182698	200765
32914	33488	35652	36488	35387	165688	161570	156145	153730	182698
29781	32914	33488	35652	36488	173666	165688	161570	156145	153730
27951	29781	32914	33488	35652	180144	173666	165688	161570	156145

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	7 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 & 7 seconds \tabularnewline
R Server & 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=107451&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]7 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=107451&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=107451&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	7 seconds
R Server	'Sir Ronald Aylmer Fisher' @ 193.190.124.24

Goodness of Fit
Correlation	0.9643
R-squared	0.9299
RMSE	2061.7021

\begin{tabular}{lllllllll}
\hline
Goodness of Fit \tabularnewline
Correlation & 0.9643 \tabularnewline
R-squared & 0.9299 \tabularnewline
RMSE & 2061.7021 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=107451&T=1

[TABLE]
[ROW][C]Goodness of Fit[/C][/ROW]
[ROW][C]Correlation[/C][C]0.9643[/C][/ROW]
[ROW][C]R-squared[/C][C]0.9299[/C][/ROW]
[ROW][C]RMSE[/C][C]2061.7021[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=107451&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=107451&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	0.9643
R-squared	0.9299
RMSE	2061.7021

Actuals, Predictions, and Residuals
#	Actuals	Forecasts	Residuals
1	40399	42347.5625	-1948.5625
2	36763	37993.6470588235	-1230.64705882353
3	37903	37993.6470588235	-90.6470588235316
4	35532	37993.6470588235	-2461.64705882353
5	35533	33985	1548
6	32110	33985	-1875
7	33374	33985	-611
8	35462	33985	1477
9	33508	33985	-477
10	36080	33985	2095
11	34560	33985	575
12	38737	33985	4752
13	38144	37993.6470588235	150.352941176468
14	37594	37993.6470588235	-399.647058823532
15	36424	37993.6470588235	-1569.64705882353
16	36843	37993.6470588235	-1150.64705882353
17	37246	37993.6470588235	-747.647058823532
18	38661	37993.6470588235	667.352941176468
19	40454	37993.6470588235	2460.35294117647
20	44928	42347.5625	2580.4375
21	48441	46626.8888888889	1814.11111111111
22	48140	46626.8888888889	1513.11111111111
23	45998	46626.8888888889	-628.88888888889
24	47369	46626.8888888889	742.11111111111
25	49554	46626.8888888889	2927.11111111111
26	47510	46626.8888888889	883.11111111111
27	44873	46626.8888888889	-1753.88888888889
28	45344	46626.8888888889	-1282.88888888889
29	42413	46626.8888888889	-4213.88888888889
30	36912	42347.5625	-5435.5625
31	43452	37993.6470588235	5458.35294117647
32	42142	42347.5625	-205.5625
33	44382	42347.5625	2034.4375
34	43636	42347.5625	1288.4375
35	44167	42347.5625	1819.4375
36	44423	42347.5625	2075.4375
37	42868	42347.5625	520.4375
38	43908	42347.5625	1560.4375
39	42013	42347.5625	-334.5625
40	38846	42347.5625	-3501.5625
41	35087	37993.6470588235	-2906.64705882353
42	33026	33985	-959
43	34646	33985	661
44	37135	33985	3150
45	37985	37993.6470588235	-8.64705882353155
46	43121	37993.6470588235	5127.35294117647
47	43722	42347.5625	1374.4375
48	43630	42347.5625	1282.4375
49	42234	42347.5625	-113.5625
50	39351	42347.5625	-2996.5625
51	39327	37993.6470588235	1333.35294117647
52	35704	37993.6470588235	-2289.64705882353
53	30466	33985	-3519
54	28155	28761.96	-606.959999999999
55	29257	28761.96	495.040000000001
56	29998	28761.96	1236.04
57	32529	28761.96	3767.04
58	34787	33985	802
59	33855	33985	-130
60	34556	33985	571
61	31348	33985	-2637
62	30805	28761.96	2043.04
63	28353	28761.96	-408.959999999999
64	24514	28761.96	-4247.96
65	21106	24059.85	-2953.85
66	21346	20819.9	526.099999999999
67	23335	20819.9	2515.1
68	24379	24059.85	319.150000000001
69	26290	24059.85	2230.15
70	30084	28761.96	1322.04
71	29429	28761.96	667.040000000001
72	30632	28761.96	1870.04
73	27349	28761.96	-1412.96
74	27264	28761.96	-1497.96
75	27474	28761.96	-1287.96
76	24482	28761.96	-4279.96
77	21453	24059.85	-2606.85
78	18788	20819.9	-2031.9
79	19282	20819.9	-1537.9
80	19713	20819.9	-1106.90000000000
81	21917	20819.9	1097.10000000000
82	23812	24059.85	-247.849999999999
83	23785	24059.85	-274.849999999999
84	24696	24059.85	636.150000000001
85	24562	24059.85	502.150000000001
86	23580	24059.85	-479.849999999999
87	24939	24059.85	879.150000000001
88	23899	24059.85	-160.849999999999
89	21454	24059.85	-2605.85
90	19761	20819.9	-1058.90000000000
91	19815	20819.9	-1004.90000000000
92	20780	20819.9	-39.9000000000015
93	23462	20819.9	2642.1
94	25005	24059.85	945.150000000001
95	24725	24059.85	665.150000000001
96	26198	24059.85	2138.15
97	27543	28761.96	-1218.96
98	26471	28761.96	-2290.96
99	26558	28761.96	-2203.96
100	25317	28761.96	-3444.96
101	22896	24059.85	-1163.85000000000
102	22248	24059.85	-1811.85
103	23406	24059.85	-653.849999999999
104	25073	24059.85	1013.15000000000
105	27691	24059.85	3631.15
106	30599	28761.96	1837.04
107	31948	28761.96	3186.04
108	32946	33985	-1039
109	34012	33985	27
110	32936	33985	-1049
111	32974	33985	-1011
112	30951	33985	-3034
113	29812	28761.96	1050.04
114	29010	28761.96	248.040000000001
115	31068	28761.96	2306.04
116	32447	28761.96	3685.04
117	34844	33985	859
118	35676	33985	1691
119	35387	33985	1402
120	36488	33985	2503
121	35652	37993.6470588235	-2341.64705882353
122	33488	33985	-497
123	32914	33985	-1071
124	29781	33985	-4204
125	27951	28761.96	-810.959999999999

\begin{tabular}{lllllllll}
\hline
Actuals, Predictions, and Residuals \tabularnewline
# & Actuals & Forecasts & Residuals \tabularnewline
1 & 40399 & 42347.5625 & -1948.5625 \tabularnewline
2 & 36763 & 37993.6470588235 & -1230.64705882353 \tabularnewline
3 & 37903 & 37993.6470588235 & -90.6470588235316 \tabularnewline
4 & 35532 & 37993.6470588235 & -2461.64705882353 \tabularnewline
5 & 35533 & 33985 & 1548 \tabularnewline
6 & 32110 & 33985 & -1875 \tabularnewline
7 & 33374 & 33985 & -611 \tabularnewline
8 & 35462 & 33985 & 1477 \tabularnewline
9 & 33508 & 33985 & -477 \tabularnewline
10 & 36080 & 33985 & 2095 \tabularnewline
11 & 34560 & 33985 & 575 \tabularnewline
12 & 38737 & 33985 & 4752 \tabularnewline
13 & 38144 & 37993.6470588235 & 150.352941176468 \tabularnewline
14 & 37594 & 37993.6470588235 & -399.647058823532 \tabularnewline
15 & 36424 & 37993.6470588235 & -1569.64705882353 \tabularnewline
16 & 36843 & 37993.6470588235 & -1150.64705882353 \tabularnewline
17 & 37246 & 37993.6470588235 & -747.647058823532 \tabularnewline
18 & 38661 & 37993.6470588235 & 667.352941176468 \tabularnewline
19 & 40454 & 37993.6470588235 & 2460.35294117647 \tabularnewline
20 & 44928 & 42347.5625 & 2580.4375 \tabularnewline
21 & 48441 & 46626.8888888889 & 1814.11111111111 \tabularnewline
22 & 48140 & 46626.8888888889 & 1513.11111111111 \tabularnewline
23 & 45998 & 46626.8888888889 & -628.88888888889 \tabularnewline
24 & 47369 & 46626.8888888889 & 742.11111111111 \tabularnewline
25 & 49554 & 46626.8888888889 & 2927.11111111111 \tabularnewline
26 & 47510 & 46626.8888888889 & 883.11111111111 \tabularnewline
27 & 44873 & 46626.8888888889 & -1753.88888888889 \tabularnewline
28 & 45344 & 46626.8888888889 & -1282.88888888889 \tabularnewline
29 & 42413 & 46626.8888888889 & -4213.88888888889 \tabularnewline
30 & 36912 & 42347.5625 & -5435.5625 \tabularnewline
31 & 43452 & 37993.6470588235 & 5458.35294117647 \tabularnewline
32 & 42142 & 42347.5625 & -205.5625 \tabularnewline
33 & 44382 & 42347.5625 & 2034.4375 \tabularnewline
34 & 43636 & 42347.5625 & 1288.4375 \tabularnewline
35 & 44167 & 42347.5625 & 1819.4375 \tabularnewline
36 & 44423 & 42347.5625 & 2075.4375 \tabularnewline
37 & 42868 & 42347.5625 & 520.4375 \tabularnewline
38 & 43908 & 42347.5625 & 1560.4375 \tabularnewline
39 & 42013 & 42347.5625 & -334.5625 \tabularnewline
40 & 38846 & 42347.5625 & -3501.5625 \tabularnewline
41 & 35087 & 37993.6470588235 & -2906.64705882353 \tabularnewline
42 & 33026 & 33985 & -959 \tabularnewline
43 & 34646 & 33985 & 661 \tabularnewline
44 & 37135 & 33985 & 3150 \tabularnewline
45 & 37985 & 37993.6470588235 & -8.64705882353155 \tabularnewline
46 & 43121 & 37993.6470588235 & 5127.35294117647 \tabularnewline
47 & 43722 & 42347.5625 & 1374.4375 \tabularnewline
48 & 43630 & 42347.5625 & 1282.4375 \tabularnewline
49 & 42234 & 42347.5625 & -113.5625 \tabularnewline
50 & 39351 & 42347.5625 & -2996.5625 \tabularnewline
51 & 39327 & 37993.6470588235 & 1333.35294117647 \tabularnewline
52 & 35704 & 37993.6470588235 & -2289.64705882353 \tabularnewline
53 & 30466 & 33985 & -3519 \tabularnewline
54 & 28155 & 28761.96 & -606.959999999999 \tabularnewline
55 & 29257 & 28761.96 & 495.040000000001 \tabularnewline
56 & 29998 & 28761.96 & 1236.04 \tabularnewline
57 & 32529 & 28761.96 & 3767.04 \tabularnewline
58 & 34787 & 33985 & 802 \tabularnewline
59 & 33855 & 33985 & -130 \tabularnewline
60 & 34556 & 33985 & 571 \tabularnewline
61 & 31348 & 33985 & -2637 \tabularnewline
62 & 30805 & 28761.96 & 2043.04 \tabularnewline
63 & 28353 & 28761.96 & -408.959999999999 \tabularnewline
64 & 24514 & 28761.96 & -4247.96 \tabularnewline
65 & 21106 & 24059.85 & -2953.85 \tabularnewline
66 & 21346 & 20819.9 & 526.099999999999 \tabularnewline
67 & 23335 & 20819.9 & 2515.1 \tabularnewline
68 & 24379 & 24059.85 & 319.150000000001 \tabularnewline
69 & 26290 & 24059.85 & 2230.15 \tabularnewline
70 & 30084 & 28761.96 & 1322.04 \tabularnewline
71 & 29429 & 28761.96 & 667.040000000001 \tabularnewline
72 & 30632 & 28761.96 & 1870.04 \tabularnewline
73 & 27349 & 28761.96 & -1412.96 \tabularnewline
74 & 27264 & 28761.96 & -1497.96 \tabularnewline
75 & 27474 & 28761.96 & -1287.96 \tabularnewline
76 & 24482 & 28761.96 & -4279.96 \tabularnewline
77 & 21453 & 24059.85 & -2606.85 \tabularnewline
78 & 18788 & 20819.9 & -2031.9 \tabularnewline
79 & 19282 & 20819.9 & -1537.9 \tabularnewline
80 & 19713 & 20819.9 & -1106.90000000000 \tabularnewline
81 & 21917 & 20819.9 & 1097.10000000000 \tabularnewline
82 & 23812 & 24059.85 & -247.849999999999 \tabularnewline
83 & 23785 & 24059.85 & -274.849999999999 \tabularnewline
84 & 24696 & 24059.85 & 636.150000000001 \tabularnewline
85 & 24562 & 24059.85 & 502.150000000001 \tabularnewline
86 & 23580 & 24059.85 & -479.849999999999 \tabularnewline
87 & 24939 & 24059.85 & 879.150000000001 \tabularnewline
88 & 23899 & 24059.85 & -160.849999999999 \tabularnewline
89 & 21454 & 24059.85 & -2605.85 \tabularnewline
90 & 19761 & 20819.9 & -1058.90000000000 \tabularnewline
91 & 19815 & 20819.9 & -1004.90000000000 \tabularnewline
92 & 20780 & 20819.9 & -39.9000000000015 \tabularnewline
93 & 23462 & 20819.9 & 2642.1 \tabularnewline
94 & 25005 & 24059.85 & 945.150000000001 \tabularnewline
95 & 24725 & 24059.85 & 665.150000000001 \tabularnewline
96 & 26198 & 24059.85 & 2138.15 \tabularnewline
97 & 27543 & 28761.96 & -1218.96 \tabularnewline
98 & 26471 & 28761.96 & -2290.96 \tabularnewline
99 & 26558 & 28761.96 & -2203.96 \tabularnewline
100 & 25317 & 28761.96 & -3444.96 \tabularnewline
101 & 22896 & 24059.85 & -1163.85000000000 \tabularnewline
102 & 22248 & 24059.85 & -1811.85 \tabularnewline
103 & 23406 & 24059.85 & -653.849999999999 \tabularnewline
104 & 25073 & 24059.85 & 1013.15000000000 \tabularnewline
105 & 27691 & 24059.85 & 3631.15 \tabularnewline
106 & 30599 & 28761.96 & 1837.04 \tabularnewline
107 & 31948 & 28761.96 & 3186.04 \tabularnewline
108 & 32946 & 33985 & -1039 \tabularnewline
109 & 34012 & 33985 & 27 \tabularnewline
110 & 32936 & 33985 & -1049 \tabularnewline
111 & 32974 & 33985 & -1011 \tabularnewline
112 & 30951 & 33985 & -3034 \tabularnewline
113 & 29812 & 28761.96 & 1050.04 \tabularnewline
114 & 29010 & 28761.96 & 248.040000000001 \tabularnewline
115 & 31068 & 28761.96 & 2306.04 \tabularnewline
116 & 32447 & 28761.96 & 3685.04 \tabularnewline
117 & 34844 & 33985 & 859 \tabularnewline
118 & 35676 & 33985 & 1691 \tabularnewline
119 & 35387 & 33985 & 1402 \tabularnewline
120 & 36488 & 33985 & 2503 \tabularnewline
121 & 35652 & 37993.6470588235 & -2341.64705882353 \tabularnewline
122 & 33488 & 33985 & -497 \tabularnewline
123 & 32914 & 33985 & -1071 \tabularnewline
124 & 29781 & 33985 & -4204 \tabularnewline
125 & 27951 & 28761.96 & -810.959999999999 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=107451&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]40399[/C][C]42347.5625[/C][C]-1948.5625[/C][/ROW]
[ROW][C]2[/C][C]36763[/C][C]37993.6470588235[/C][C]-1230.64705882353[/C][/ROW]
[ROW][C]3[/C][C]37903[/C][C]37993.6470588235[/C][C]-90.6470588235316[/C][/ROW]
[ROW][C]4[/C][C]35532[/C][C]37993.6470588235[/C][C]-2461.64705882353[/C][/ROW]
[ROW][C]5[/C][C]35533[/C][C]33985[/C][C]1548[/C][/ROW]
[ROW][C]6[/C][C]32110[/C][C]33985[/C][C]-1875[/C][/ROW]
[ROW][C]7[/C][C]33374[/C][C]33985[/C][C]-611[/C][/ROW]
[ROW][C]8[/C][C]35462[/C][C]33985[/C][C]1477[/C][/ROW]
[ROW][C]9[/C][C]33508[/C][C]33985[/C][C]-477[/C][/ROW]
[ROW][C]10[/C][C]36080[/C][C]33985[/C][C]2095[/C][/ROW]
[ROW][C]11[/C][C]34560[/C][C]33985[/C][C]575[/C][/ROW]
[ROW][C]12[/C][C]38737[/C][C]33985[/C][C]4752[/C][/ROW]
[ROW][C]13[/C][C]38144[/C][C]37993.6470588235[/C][C]150.352941176468[/C][/ROW]
[ROW][C]14[/C][C]37594[/C][C]37993.6470588235[/C][C]-399.647058823532[/C][/ROW]
[ROW][C]15[/C][C]36424[/C][C]37993.6470588235[/C][C]-1569.64705882353[/C][/ROW]
[ROW][C]16[/C][C]36843[/C][C]37993.6470588235[/C][C]-1150.64705882353[/C][/ROW]
[ROW][C]17[/C][C]37246[/C][C]37993.6470588235[/C][C]-747.647058823532[/C][/ROW]
[ROW][C]18[/C][C]38661[/C][C]37993.6470588235[/C][C]667.352941176468[/C][/ROW]
[ROW][C]19[/C][C]40454[/C][C]37993.6470588235[/C][C]2460.35294117647[/C][/ROW]
[ROW][C]20[/C][C]44928[/C][C]42347.5625[/C][C]2580.4375[/C][/ROW]
[ROW][C]21[/C][C]48441[/C][C]46626.8888888889[/C][C]1814.11111111111[/C][/ROW]
[ROW][C]22[/C][C]48140[/C][C]46626.8888888889[/C][C]1513.11111111111[/C][/ROW]
[ROW][C]23[/C][C]45998[/C][C]46626.8888888889[/C][C]-628.88888888889[/C][/ROW]
[ROW][C]24[/C][C]47369[/C][C]46626.8888888889[/C][C]742.11111111111[/C][/ROW]
[ROW][C]25[/C][C]49554[/C][C]46626.8888888889[/C][C]2927.11111111111[/C][/ROW]
[ROW][C]26[/C][C]47510[/C][C]46626.8888888889[/C][C]883.11111111111[/C][/ROW]
[ROW][C]27[/C][C]44873[/C][C]46626.8888888889[/C][C]-1753.88888888889[/C][/ROW]
[ROW][C]28[/C][C]45344[/C][C]46626.8888888889[/C][C]-1282.88888888889[/C][/ROW]
[ROW][C]29[/C][C]42413[/C][C]46626.8888888889[/C][C]-4213.88888888889[/C][/ROW]
[ROW][C]30[/C][C]36912[/C][C]42347.5625[/C][C]-5435.5625[/C][/ROW]
[ROW][C]31[/C][C]43452[/C][C]37993.6470588235[/C][C]5458.35294117647[/C][/ROW]
[ROW][C]32[/C][C]42142[/C][C]42347.5625[/C][C]-205.5625[/C][/ROW]
[ROW][C]33[/C][C]44382[/C][C]42347.5625[/C][C]2034.4375[/C][/ROW]
[ROW][C]34[/C][C]43636[/C][C]42347.5625[/C][C]1288.4375[/C][/ROW]
[ROW][C]35[/C][C]44167[/C][C]42347.5625[/C][C]1819.4375[/C][/ROW]
[ROW][C]36[/C][C]44423[/C][C]42347.5625[/C][C]2075.4375[/C][/ROW]
[ROW][C]37[/C][C]42868[/C][C]42347.5625[/C][C]520.4375[/C][/ROW]
[ROW][C]38[/C][C]43908[/C][C]42347.5625[/C][C]1560.4375[/C][/ROW]
[ROW][C]39[/C][C]42013[/C][C]42347.5625[/C][C]-334.5625[/C][/ROW]
[ROW][C]40[/C][C]38846[/C][C]42347.5625[/C][C]-3501.5625[/C][/ROW]
[ROW][C]41[/C][C]35087[/C][C]37993.6470588235[/C][C]-2906.64705882353[/C][/ROW]
[ROW][C]42[/C][C]33026[/C][C]33985[/C][C]-959[/C][/ROW]
[ROW][C]43[/C][C]34646[/C][C]33985[/C][C]661[/C][/ROW]
[ROW][C]44[/C][C]37135[/C][C]33985[/C][C]3150[/C][/ROW]
[ROW][C]45[/C][C]37985[/C][C]37993.6470588235[/C][C]-8.64705882353155[/C][/ROW]
[ROW][C]46[/C][C]43121[/C][C]37993.6470588235[/C][C]5127.35294117647[/C][/ROW]
[ROW][C]47[/C][C]43722[/C][C]42347.5625[/C][C]1374.4375[/C][/ROW]
[ROW][C]48[/C][C]43630[/C][C]42347.5625[/C][C]1282.4375[/C][/ROW]
[ROW][C]49[/C][C]42234[/C][C]42347.5625[/C][C]-113.5625[/C][/ROW]
[ROW][C]50[/C][C]39351[/C][C]42347.5625[/C][C]-2996.5625[/C][/ROW]
[ROW][C]51[/C][C]39327[/C][C]37993.6470588235[/C][C]1333.35294117647[/C][/ROW]
[ROW][C]52[/C][C]35704[/C][C]37993.6470588235[/C][C]-2289.64705882353[/C][/ROW]
[ROW][C]53[/C][C]30466[/C][C]33985[/C][C]-3519[/C][/ROW]
[ROW][C]54[/C][C]28155[/C][C]28761.96[/C][C]-606.959999999999[/C][/ROW]
[ROW][C]55[/C][C]29257[/C][C]28761.96[/C][C]495.040000000001[/C][/ROW]
[ROW][C]56[/C][C]29998[/C][C]28761.96[/C][C]1236.04[/C][/ROW]
[ROW][C]57[/C][C]32529[/C][C]28761.96[/C][C]3767.04[/C][/ROW]
[ROW][C]58[/C][C]34787[/C][C]33985[/C][C]802[/C][/ROW]
[ROW][C]59[/C][C]33855[/C][C]33985[/C][C]-130[/C][/ROW]
[ROW][C]60[/C][C]34556[/C][C]33985[/C][C]571[/C][/ROW]
[ROW][C]61[/C][C]31348[/C][C]33985[/C][C]-2637[/C][/ROW]
[ROW][C]62[/C][C]30805[/C][C]28761.96[/C][C]2043.04[/C][/ROW]
[ROW][C]63[/C][C]28353[/C][C]28761.96[/C][C]-408.959999999999[/C][/ROW]
[ROW][C]64[/C][C]24514[/C][C]28761.96[/C][C]-4247.96[/C][/ROW]
[ROW][C]65[/C][C]21106[/C][C]24059.85[/C][C]-2953.85[/C][/ROW]
[ROW][C]66[/C][C]21346[/C][C]20819.9[/C][C]526.099999999999[/C][/ROW]
[ROW][C]67[/C][C]23335[/C][C]20819.9[/C][C]2515.1[/C][/ROW]
[ROW][C]68[/C][C]24379[/C][C]24059.85[/C][C]319.150000000001[/C][/ROW]
[ROW][C]69[/C][C]26290[/C][C]24059.85[/C][C]2230.15[/C][/ROW]
[ROW][C]70[/C][C]30084[/C][C]28761.96[/C][C]1322.04[/C][/ROW]
[ROW][C]71[/C][C]29429[/C][C]28761.96[/C][C]667.040000000001[/C][/ROW]
[ROW][C]72[/C][C]30632[/C][C]28761.96[/C][C]1870.04[/C][/ROW]
[ROW][C]73[/C][C]27349[/C][C]28761.96[/C][C]-1412.96[/C][/ROW]
[ROW][C]74[/C][C]27264[/C][C]28761.96[/C][C]-1497.96[/C][/ROW]
[ROW][C]75[/C][C]27474[/C][C]28761.96[/C][C]-1287.96[/C][/ROW]
[ROW][C]76[/C][C]24482[/C][C]28761.96[/C][C]-4279.96[/C][/ROW]
[ROW][C]77[/C][C]21453[/C][C]24059.85[/C][C]-2606.85[/C][/ROW]
[ROW][C]78[/C][C]18788[/C][C]20819.9[/C][C]-2031.9[/C][/ROW]
[ROW][C]79[/C][C]19282[/C][C]20819.9[/C][C]-1537.9[/C][/ROW]
[ROW][C]80[/C][C]19713[/C][C]20819.9[/C][C]-1106.90000000000[/C][/ROW]
[ROW][C]81[/C][C]21917[/C][C]20819.9[/C][C]1097.10000000000[/C][/ROW]
[ROW][C]82[/C][C]23812[/C][C]24059.85[/C][C]-247.849999999999[/C][/ROW]
[ROW][C]83[/C][C]23785[/C][C]24059.85[/C][C]-274.849999999999[/C][/ROW]
[ROW][C]84[/C][C]24696[/C][C]24059.85[/C][C]636.150000000001[/C][/ROW]
[ROW][C]85[/C][C]24562[/C][C]24059.85[/C][C]502.150000000001[/C][/ROW]
[ROW][C]86[/C][C]23580[/C][C]24059.85[/C][C]-479.849999999999[/C][/ROW]
[ROW][C]87[/C][C]24939[/C][C]24059.85[/C][C]879.150000000001[/C][/ROW]
[ROW][C]88[/C][C]23899[/C][C]24059.85[/C][C]-160.849999999999[/C][/ROW]
[ROW][C]89[/C][C]21454[/C][C]24059.85[/C][C]-2605.85[/C][/ROW]
[ROW][C]90[/C][C]19761[/C][C]20819.9[/C][C]-1058.90000000000[/C][/ROW]
[ROW][C]91[/C][C]19815[/C][C]20819.9[/C][C]-1004.90000000000[/C][/ROW]
[ROW][C]92[/C][C]20780[/C][C]20819.9[/C][C]-39.9000000000015[/C][/ROW]
[ROW][C]93[/C][C]23462[/C][C]20819.9[/C][C]2642.1[/C][/ROW]
[ROW][C]94[/C][C]25005[/C][C]24059.85[/C][C]945.150000000001[/C][/ROW]
[ROW][C]95[/C][C]24725[/C][C]24059.85[/C][C]665.150000000001[/C][/ROW]
[ROW][C]96[/C][C]26198[/C][C]24059.85[/C][C]2138.15[/C][/ROW]
[ROW][C]97[/C][C]27543[/C][C]28761.96[/C][C]-1218.96[/C][/ROW]
[ROW][C]98[/C][C]26471[/C][C]28761.96[/C][C]-2290.96[/C][/ROW]
[ROW][C]99[/C][C]26558[/C][C]28761.96[/C][C]-2203.96[/C][/ROW]
[ROW][C]100[/C][C]25317[/C][C]28761.96[/C][C]-3444.96[/C][/ROW]
[ROW][C]101[/C][C]22896[/C][C]24059.85[/C][C]-1163.85000000000[/C][/ROW]
[ROW][C]102[/C][C]22248[/C][C]24059.85[/C][C]-1811.85[/C][/ROW]
[ROW][C]103[/C][C]23406[/C][C]24059.85[/C][C]-653.849999999999[/C][/ROW]
[ROW][C]104[/C][C]25073[/C][C]24059.85[/C][C]1013.15000000000[/C][/ROW]
[ROW][C]105[/C][C]27691[/C][C]24059.85[/C][C]3631.15[/C][/ROW]
[ROW][C]106[/C][C]30599[/C][C]28761.96[/C][C]1837.04[/C][/ROW]
[ROW][C]107[/C][C]31948[/C][C]28761.96[/C][C]3186.04[/C][/ROW]
[ROW][C]108[/C][C]32946[/C][C]33985[/C][C]-1039[/C][/ROW]
[ROW][C]109[/C][C]34012[/C][C]33985[/C][C]27[/C][/ROW]
[ROW][C]110[/C][C]32936[/C][C]33985[/C][C]-1049[/C][/ROW]
[ROW][C]111[/C][C]32974[/C][C]33985[/C][C]-1011[/C][/ROW]
[ROW][C]112[/C][C]30951[/C][C]33985[/C][C]-3034[/C][/ROW]
[ROW][C]113[/C][C]29812[/C][C]28761.96[/C][C]1050.04[/C][/ROW]
[ROW][C]114[/C][C]29010[/C][C]28761.96[/C][C]248.040000000001[/C][/ROW]
[ROW][C]115[/C][C]31068[/C][C]28761.96[/C][C]2306.04[/C][/ROW]
[ROW][C]116[/C][C]32447[/C][C]28761.96[/C][C]3685.04[/C][/ROW]
[ROW][C]117[/C][C]34844[/C][C]33985[/C][C]859[/C][/ROW]
[ROW][C]118[/C][C]35676[/C][C]33985[/C][C]1691[/C][/ROW]
[ROW][C]119[/C][C]35387[/C][C]33985[/C][C]1402[/C][/ROW]
[ROW][C]120[/C][C]36488[/C][C]33985[/C][C]2503[/C][/ROW]
[ROW][C]121[/C][C]35652[/C][C]37993.6470588235[/C][C]-2341.64705882353[/C][/ROW]
[ROW][C]122[/C][C]33488[/C][C]33985[/C][C]-497[/C][/ROW]
[ROW][C]123[/C][C]32914[/C][C]33985[/C][C]-1071[/C][/ROW]
[ROW][C]124[/C][C]29781[/C][C]33985[/C][C]-4204[/C][/ROW]
[ROW][C]125[/C][C]27951[/C][C]28761.96[/C][C]-810.959999999999[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=107451&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=107451&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	40399	42347.5625	-1948.5625
2	36763	37993.6470588235	-1230.64705882353
3	37903	37993.6470588235	-90.6470588235316
4	35532	37993.6470588235	-2461.64705882353
5	35533	33985	1548
6	32110	33985	-1875
7	33374	33985	-611
8	35462	33985	1477
9	33508	33985	-477
10	36080	33985	2095
11	34560	33985	575
12	38737	33985	4752
13	38144	37993.6470588235	150.352941176468
14	37594	37993.6470588235	-399.647058823532
15	36424	37993.6470588235	-1569.64705882353
16	36843	37993.6470588235	-1150.64705882353
17	37246	37993.6470588235	-747.647058823532
18	38661	37993.6470588235	667.352941176468
19	40454	37993.6470588235	2460.35294117647
20	44928	42347.5625	2580.4375
21	48441	46626.8888888889	1814.11111111111
22	48140	46626.8888888889	1513.11111111111
23	45998	46626.8888888889	-628.88888888889
24	47369	46626.8888888889	742.11111111111
25	49554	46626.8888888889	2927.11111111111
26	47510	46626.8888888889	883.11111111111
27	44873	46626.8888888889	-1753.88888888889
28	45344	46626.8888888889	-1282.88888888889
29	42413	46626.8888888889	-4213.88888888889
30	36912	42347.5625	-5435.5625
31	43452	37993.6470588235	5458.35294117647
32	42142	42347.5625	-205.5625
33	44382	42347.5625	2034.4375
34	43636	42347.5625	1288.4375
35	44167	42347.5625	1819.4375
36	44423	42347.5625	2075.4375
37	42868	42347.5625	520.4375
38	43908	42347.5625	1560.4375
39	42013	42347.5625	-334.5625
40	38846	42347.5625	-3501.5625
41	35087	37993.6470588235	-2906.64705882353
42	33026	33985	-959
43	34646	33985	661
44	37135	33985	3150
45	37985	37993.6470588235	-8.64705882353155
46	43121	37993.6470588235	5127.35294117647
47	43722	42347.5625	1374.4375
48	43630	42347.5625	1282.4375
49	42234	42347.5625	-113.5625
50	39351	42347.5625	-2996.5625
51	39327	37993.6470588235	1333.35294117647
52	35704	37993.6470588235	-2289.64705882353
53	30466	33985	-3519
54	28155	28761.96	-606.959999999999
55	29257	28761.96	495.040000000001
56	29998	28761.96	1236.04
57	32529	28761.96	3767.04
58	34787	33985	802
59	33855	33985	-130
60	34556	33985	571
61	31348	33985	-2637
62	30805	28761.96	2043.04
63	28353	28761.96	-408.959999999999
64	24514	28761.96	-4247.96
65	21106	24059.85	-2953.85
66	21346	20819.9	526.099999999999
67	23335	20819.9	2515.1
68	24379	24059.85	319.150000000001
69	26290	24059.85	2230.15
70	30084	28761.96	1322.04
71	29429	28761.96	667.040000000001
72	30632	28761.96	1870.04
73	27349	28761.96	-1412.96
74	27264	28761.96	-1497.96
75	27474	28761.96	-1287.96
76	24482	28761.96	-4279.96
77	21453	24059.85	-2606.85
78	18788	20819.9	-2031.9
79	19282	20819.9	-1537.9
80	19713	20819.9	-1106.90000000000
81	21917	20819.9	1097.10000000000
82	23812	24059.85	-247.849999999999
83	23785	24059.85	-274.849999999999
84	24696	24059.85	636.150000000001
85	24562	24059.85	502.150000000001
86	23580	24059.85	-479.849999999999
87	24939	24059.85	879.150000000001
88	23899	24059.85	-160.849999999999
89	21454	24059.85	-2605.85
90	19761	20819.9	-1058.90000000000
91	19815	20819.9	-1004.90000000000
92	20780	20819.9	-39.9000000000015
93	23462	20819.9	2642.1
94	25005	24059.85	945.150000000001
95	24725	24059.85	665.150000000001
96	26198	24059.85	2138.15
97	27543	28761.96	-1218.96
98	26471	28761.96	-2290.96
99	26558	28761.96	-2203.96
100	25317	28761.96	-3444.96
101	22896	24059.85	-1163.85000000000
102	22248	24059.85	-1811.85
103	23406	24059.85	-653.849999999999
104	25073	24059.85	1013.15000000000
105	27691	24059.85	3631.15
106	30599	28761.96	1837.04
107	31948	28761.96	3186.04
108	32946	33985	-1039
109	34012	33985	27
110	32936	33985	-1049
111	32974	33985	-1011
112	30951	33985	-3034
113	29812	28761.96	1050.04
114	29010	28761.96	248.040000000001
115	31068	28761.96	2306.04
116	32447	28761.96	3685.04
117	34844	33985	859
118	35676	33985	1691
119	35387	33985	1402
120	36488	33985	2503
121	35652	37993.6470588235	-2341.64705882353
122	33488	33985	-497
123	32914	33985	-1071
124	29781	33985	-4204
125	27951	28761.96	-810.959999999999

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 = 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')
}

Free Statistics

Description of Statistical Computation

Tree of Dependent Computations

Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code