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R Software Module

rwasp_regression_trees1.wasp

Title produced by software

Recursive Partitioning (Regression Trees)

Date of computation

Tue, 14 Dec 2010 18:38:26 +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/14/t1292351793erdu495p94fy8vy.htm/, Retrieved Thu, 02 May 2024 22:14:13 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=109999, Retrieved Thu, 02 May 2024 22:14:13 +0000

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IsPrivate?

No (this computation is public)

User-defined keywords

Estimated Impact

114

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)] [ws 10] [2010-12-14 18:38:26] [09489ba95453d3f5c9e6f2eaeda915af] [Current]

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14544,5	94,6	-3,0	14097,8
15116,3	95,9	-3,7	14776,8
17413,2	104,7	-4,7	16833,3
16181,5	102,8	-6,4	15385,5
15607,4	98,1	-7,5	15172,6
17160,9	113,9	-7,8	16858,9
14915,8	80,9	-7,7	14143,5
13768	95,7	-6,6	14731,8
17487,5	113,2	-4,2	16471,6
16198,1	105,9	-2,0	15214
17535,2	108,8	-0,7	17637,4
16571,8	102,3	0,1	17972,4
16198,9	99	0,9	16896,2
16554,2	100,7	2,1	16698
19554,2	115,5	3,5	19691,6
15903,8	100,7	4,9	15930,7
18003,8	109,9	5,7	17444,6
18329,6	114,6	6,2	17699,4
16260,7	85,4	6,5	15189,8
14851,9	100,5	6,5	15672,7
18174,1	114,8	6,3	17180,8
18406,6	116,5	6,2	17664,9
18466,5	112,9	6,4	17862,9
16016,5	102	6,3	16162,3
17428,5	106	5,8	17463,6
17167,2	105,3	5,1	16772,1
19630	118,8	5,1	19106,9
17183,6	106,1	5,8	16721,3
18344,7	109,3	6,7	18161,3
19301,4	117,2	7,1	18509,9
18147,5	92,5	6,7	17802,7
16192,9	104,2	5,5	16409,9
18374,4	112,5	4,2	17967,7
20515,2	122,4	3,0	20286,6
18957,2	113,3	2,2	19537,3
16471,5	100	2,0	18021,9
18746,8	110,7	1,8	20194,3
19009,5	112,8	1,8	19049,6
19211,2	109,8	1,5	20244,7
20547,7	117,3	0,4	21473,3
19325,8	109,1	-0,9	19673,6
20605,5	115,9	-1,7	21053,2
20056,9	96	-2,6	20159,5
16141,4	99,8	-4,4	18203,6
20359,8	116,8	-8,3	21289,5
19711,6	115,7	-14,4	20432,3
15638,6	99,4	-21,3	17180,4
14384,5	94,3	-26,5	15816,8
13855,6	91	-29,2	15071,8
14308,3	93,2	-30,8	14521,1
15290,6	103,1	-30,9	15668,8
14423,8	94,1	-29,5	14346,9
13779,7	91,8	-27,1	13881
15686,3	102,7	-24,4	15465,9
14733,8	82,6	-21,9	14238,2
12522,5	89,1	-19,3	13557,7
16189,4	104,5	-17,0	16127,6
16059,1	105,1	-13,8	16793,9
16007,1	95,1	-9,9	16014
15806,8	88,7	-7,9	16867,9
15160	86,3	-7,2	16014,6
15692,1	91,8	-6,2	15878,6
18908,9	111,5	-4,5	18664,9
16969,9	99,7	-3,9	17962,5
16997,5	97,5	-5,0	17332,7
19858,9	111,7	-6,2	19542,1
17681,2	86,2	-6,1	17203,6
16006,9	95,4	-5,0	16579

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	4 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=109999&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=109999&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=109999&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	4 seconds
R Server	'RServer@AstonUniversity' @ vre.aston.ac.uk

Goodness of Fit
Correlation	0.7989
R-squared	0.6383
RMSE	5.9402

\begin{tabular}{lllllllll}
\hline
Goodness of Fit \tabularnewline
Correlation & 0.7989 \tabularnewline
R-squared & 0.6383 \tabularnewline
RMSE & 5.9402 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=109999&T=1

[TABLE]
[ROW][C]Goodness of Fit[/C][/ROW]
[ROW][C]Correlation[/C][C]0.7989[/C][/ROW]
[ROW][C]R-squared[/C][C]0.6383[/C][/ROW]
[ROW][C]RMSE[/C][C]5.9402[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=109999&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=109999&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	0.7989
R-squared	0.6383
RMSE	5.9402

Actuals, Predictions, and Residuals
#	Actuals	Forecasts	Residuals
1	94.6	91.5384615384615	3.06153846153846
2	95.9	91.5384615384615	4.36153846153847
3	104.7	100.924242424242	3.77575757575758
4	102.8	100.924242424242	1.87575757575758
5	98.1	100.924242424242	-2.82424242424243
6	113.9	100.924242424242	12.9757575757576
7	80.9	91.5384615384615	-10.6384615384615
8	95.7	91.5384615384615	4.16153846153847
9	113.2	100.924242424242	12.2757575757576
10	105.9	100.924242424242	4.97575757575758
11	108.8	100.924242424242	7.87575757575758
12	102.3	100.924242424242	1.37575757575758
13	99	100.924242424242	-1.92424242424242
14	100.7	100.924242424242	-0.224242424242419
15	115.5	113.413636363636	2.08636363636364
16	100.7	100.924242424242	-0.224242424242419
17	109.9	100.924242424242	8.97575757575758
18	114.6	113.413636363636	1.18636363636364
19	85.4	100.924242424242	-15.5242424242424
20	100.5	91.5384615384615	8.96153846153847
21	114.8	113.413636363636	1.38636363636364
22	116.5	113.413636363636	3.08636363636364
23	112.9	113.413636363636	-0.513636363636351
24	102	100.924242424242	1.07575757575758
25	106	100.924242424242	5.07575757575758
26	105.3	100.924242424242	4.37575757575758
27	118.8	113.413636363636	5.38636363636364
28	106.1	100.924242424242	5.17575757575757
29	109.3	113.413636363636	-4.11363636363636
30	117.2	113.413636363636	3.78636363636365
31	92.5	100.924242424242	-8.42424242424242
32	104.2	100.924242424242	3.27575757575758
33	112.5	113.413636363636	-0.913636363636357
34	122.4	113.413636363636	8.98636363636365
35	113.3	113.413636363636	-0.11363636363636
36	100	100.924242424242	-0.924242424242422
37	110.7	113.413636363636	-2.71363636363635
38	112.8	113.413636363636	-0.61363636363636
39	109.8	113.413636363636	-3.61363636363636
40	117.3	113.413636363636	3.88636363636364
41	109.1	113.413636363636	-4.31363636363636
42	115.9	113.413636363636	2.48636363636365
43	96	113.413636363636	-17.4136363636364
44	99.8	100.924242424242	-1.12424242424242
45	116.8	113.413636363636	3.38636363636364
46	115.7	113.413636363636	2.28636363636365
47	99.4	100.924242424242	-1.52424242424242
48	94.3	91.5384615384615	2.76153846153846
49	91	91.5384615384615	-0.538461538461533
50	93.2	91.5384615384615	1.66153846153847
51	103.1	100.924242424242	2.17575757575757
52	94.1	91.5384615384615	2.56153846153846
53	91.8	91.5384615384615	0.261538461538464
54	102.7	100.924242424242	1.77575757575758
55	82.6	91.5384615384615	-8.93846153846154
56	89.1	91.5384615384615	-2.43846153846154
57	104.5	100.924242424242	3.57575757575758
58	105.1	100.924242424242	4.17575757575757
59	95.1	100.924242424242	-5.82424242424243
60	88.7	100.924242424242	-12.2242424242424
61	86.3	91.5384615384615	-5.23846153846154
62	91.8	100.924242424242	-9.12424242424242
63	111.5	113.413636363636	-1.91363636363636
64	99.7	100.924242424242	-1.22424242424242
65	97.5	100.924242424242	-3.42424242424242
66	111.7	113.413636363636	-1.71363636363635
67	86.2	100.924242424242	-14.7242424242424
68	95.4	100.924242424242	-5.52424242424242

\begin{tabular}{lllllllll}
\hline
Actuals, Predictions, and Residuals \tabularnewline
# & Actuals & Forecasts & Residuals \tabularnewline
1 & 94.6 & 91.5384615384615 & 3.06153846153846 \tabularnewline
2 & 95.9 & 91.5384615384615 & 4.36153846153847 \tabularnewline
3 & 104.7 & 100.924242424242 & 3.77575757575758 \tabularnewline
4 & 102.8 & 100.924242424242 & 1.87575757575758 \tabularnewline
5 & 98.1 & 100.924242424242 & -2.82424242424243 \tabularnewline
6 & 113.9 & 100.924242424242 & 12.9757575757576 \tabularnewline
7 & 80.9 & 91.5384615384615 & -10.6384615384615 \tabularnewline
8 & 95.7 & 91.5384615384615 & 4.16153846153847 \tabularnewline
9 & 113.2 & 100.924242424242 & 12.2757575757576 \tabularnewline
10 & 105.9 & 100.924242424242 & 4.97575757575758 \tabularnewline
11 & 108.8 & 100.924242424242 & 7.87575757575758 \tabularnewline
12 & 102.3 & 100.924242424242 & 1.37575757575758 \tabularnewline
13 & 99 & 100.924242424242 & -1.92424242424242 \tabularnewline
14 & 100.7 & 100.924242424242 & -0.224242424242419 \tabularnewline
15 & 115.5 & 113.413636363636 & 2.08636363636364 \tabularnewline
16 & 100.7 & 100.924242424242 & -0.224242424242419 \tabularnewline
17 & 109.9 & 100.924242424242 & 8.97575757575758 \tabularnewline
18 & 114.6 & 113.413636363636 & 1.18636363636364 \tabularnewline
19 & 85.4 & 100.924242424242 & -15.5242424242424 \tabularnewline
20 & 100.5 & 91.5384615384615 & 8.96153846153847 \tabularnewline
21 & 114.8 & 113.413636363636 & 1.38636363636364 \tabularnewline
22 & 116.5 & 113.413636363636 & 3.08636363636364 \tabularnewline
23 & 112.9 & 113.413636363636 & -0.513636363636351 \tabularnewline
24 & 102 & 100.924242424242 & 1.07575757575758 \tabularnewline
25 & 106 & 100.924242424242 & 5.07575757575758 \tabularnewline
26 & 105.3 & 100.924242424242 & 4.37575757575758 \tabularnewline
27 & 118.8 & 113.413636363636 & 5.38636363636364 \tabularnewline
28 & 106.1 & 100.924242424242 & 5.17575757575757 \tabularnewline
29 & 109.3 & 113.413636363636 & -4.11363636363636 \tabularnewline
30 & 117.2 & 113.413636363636 & 3.78636363636365 \tabularnewline
31 & 92.5 & 100.924242424242 & -8.42424242424242 \tabularnewline
32 & 104.2 & 100.924242424242 & 3.27575757575758 \tabularnewline
33 & 112.5 & 113.413636363636 & -0.913636363636357 \tabularnewline
34 & 122.4 & 113.413636363636 & 8.98636363636365 \tabularnewline
35 & 113.3 & 113.413636363636 & -0.11363636363636 \tabularnewline
36 & 100 & 100.924242424242 & -0.924242424242422 \tabularnewline
37 & 110.7 & 113.413636363636 & -2.71363636363635 \tabularnewline
38 & 112.8 & 113.413636363636 & -0.61363636363636 \tabularnewline
39 & 109.8 & 113.413636363636 & -3.61363636363636 \tabularnewline
40 & 117.3 & 113.413636363636 & 3.88636363636364 \tabularnewline
41 & 109.1 & 113.413636363636 & -4.31363636363636 \tabularnewline
42 & 115.9 & 113.413636363636 & 2.48636363636365 \tabularnewline
43 & 96 & 113.413636363636 & -17.4136363636364 \tabularnewline
44 & 99.8 & 100.924242424242 & -1.12424242424242 \tabularnewline
45 & 116.8 & 113.413636363636 & 3.38636363636364 \tabularnewline
46 & 115.7 & 113.413636363636 & 2.28636363636365 \tabularnewline
47 & 99.4 & 100.924242424242 & -1.52424242424242 \tabularnewline
48 & 94.3 & 91.5384615384615 & 2.76153846153846 \tabularnewline
49 & 91 & 91.5384615384615 & -0.538461538461533 \tabularnewline
50 & 93.2 & 91.5384615384615 & 1.66153846153847 \tabularnewline
51 & 103.1 & 100.924242424242 & 2.17575757575757 \tabularnewline
52 & 94.1 & 91.5384615384615 & 2.56153846153846 \tabularnewline
53 & 91.8 & 91.5384615384615 & 0.261538461538464 \tabularnewline
54 & 102.7 & 100.924242424242 & 1.77575757575758 \tabularnewline
55 & 82.6 & 91.5384615384615 & -8.93846153846154 \tabularnewline
56 & 89.1 & 91.5384615384615 & -2.43846153846154 \tabularnewline
57 & 104.5 & 100.924242424242 & 3.57575757575758 \tabularnewline
58 & 105.1 & 100.924242424242 & 4.17575757575757 \tabularnewline
59 & 95.1 & 100.924242424242 & -5.82424242424243 \tabularnewline
60 & 88.7 & 100.924242424242 & -12.2242424242424 \tabularnewline
61 & 86.3 & 91.5384615384615 & -5.23846153846154 \tabularnewline
62 & 91.8 & 100.924242424242 & -9.12424242424242 \tabularnewline
63 & 111.5 & 113.413636363636 & -1.91363636363636 \tabularnewline
64 & 99.7 & 100.924242424242 & -1.22424242424242 \tabularnewline
65 & 97.5 & 100.924242424242 & -3.42424242424242 \tabularnewline
66 & 111.7 & 113.413636363636 & -1.71363636363635 \tabularnewline
67 & 86.2 & 100.924242424242 & -14.7242424242424 \tabularnewline
68 & 95.4 & 100.924242424242 & -5.52424242424242 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=109999&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]94.6[/C][C]91.5384615384615[/C][C]3.06153846153846[/C][/ROW]
[ROW][C]2[/C][C]95.9[/C][C]91.5384615384615[/C][C]4.36153846153847[/C][/ROW]
[ROW][C]3[/C][C]104.7[/C][C]100.924242424242[/C][C]3.77575757575758[/C][/ROW]
[ROW][C]4[/C][C]102.8[/C][C]100.924242424242[/C][C]1.87575757575758[/C][/ROW]
[ROW][C]5[/C][C]98.1[/C][C]100.924242424242[/C][C]-2.82424242424243[/C][/ROW]
[ROW][C]6[/C][C]113.9[/C][C]100.924242424242[/C][C]12.9757575757576[/C][/ROW]
[ROW][C]7[/C][C]80.9[/C][C]91.5384615384615[/C][C]-10.6384615384615[/C][/ROW]
[ROW][C]8[/C][C]95.7[/C][C]91.5384615384615[/C][C]4.16153846153847[/C][/ROW]
[ROW][C]9[/C][C]113.2[/C][C]100.924242424242[/C][C]12.2757575757576[/C][/ROW]
[ROW][C]10[/C][C]105.9[/C][C]100.924242424242[/C][C]4.97575757575758[/C][/ROW]
[ROW][C]11[/C][C]108.8[/C][C]100.924242424242[/C][C]7.87575757575758[/C][/ROW]
[ROW][C]12[/C][C]102.3[/C][C]100.924242424242[/C][C]1.37575757575758[/C][/ROW]
[ROW][C]13[/C][C]99[/C][C]100.924242424242[/C][C]-1.92424242424242[/C][/ROW]
[ROW][C]14[/C][C]100.7[/C][C]100.924242424242[/C][C]-0.224242424242419[/C][/ROW]
[ROW][C]15[/C][C]115.5[/C][C]113.413636363636[/C][C]2.08636363636364[/C][/ROW]
[ROW][C]16[/C][C]100.7[/C][C]100.924242424242[/C][C]-0.224242424242419[/C][/ROW]
[ROW][C]17[/C][C]109.9[/C][C]100.924242424242[/C][C]8.97575757575758[/C][/ROW]
[ROW][C]18[/C][C]114.6[/C][C]113.413636363636[/C][C]1.18636363636364[/C][/ROW]
[ROW][C]19[/C][C]85.4[/C][C]100.924242424242[/C][C]-15.5242424242424[/C][/ROW]
[ROW][C]20[/C][C]100.5[/C][C]91.5384615384615[/C][C]8.96153846153847[/C][/ROW]
[ROW][C]21[/C][C]114.8[/C][C]113.413636363636[/C][C]1.38636363636364[/C][/ROW]
[ROW][C]22[/C][C]116.5[/C][C]113.413636363636[/C][C]3.08636363636364[/C][/ROW]
[ROW][C]23[/C][C]112.9[/C][C]113.413636363636[/C][C]-0.513636363636351[/C][/ROW]
[ROW][C]24[/C][C]102[/C][C]100.924242424242[/C][C]1.07575757575758[/C][/ROW]
[ROW][C]25[/C][C]106[/C][C]100.924242424242[/C][C]5.07575757575758[/C][/ROW]
[ROW][C]26[/C][C]105.3[/C][C]100.924242424242[/C][C]4.37575757575758[/C][/ROW]
[ROW][C]27[/C][C]118.8[/C][C]113.413636363636[/C][C]5.38636363636364[/C][/ROW]
[ROW][C]28[/C][C]106.1[/C][C]100.924242424242[/C][C]5.17575757575757[/C][/ROW]
[ROW][C]29[/C][C]109.3[/C][C]113.413636363636[/C][C]-4.11363636363636[/C][/ROW]
[ROW][C]30[/C][C]117.2[/C][C]113.413636363636[/C][C]3.78636363636365[/C][/ROW]
[ROW][C]31[/C][C]92.5[/C][C]100.924242424242[/C][C]-8.42424242424242[/C][/ROW]
[ROW][C]32[/C][C]104.2[/C][C]100.924242424242[/C][C]3.27575757575758[/C][/ROW]
[ROW][C]33[/C][C]112.5[/C][C]113.413636363636[/C][C]-0.913636363636357[/C][/ROW]
[ROW][C]34[/C][C]122.4[/C][C]113.413636363636[/C][C]8.98636363636365[/C][/ROW]
[ROW][C]35[/C][C]113.3[/C][C]113.413636363636[/C][C]-0.11363636363636[/C][/ROW]
[ROW][C]36[/C][C]100[/C][C]100.924242424242[/C][C]-0.924242424242422[/C][/ROW]
[ROW][C]37[/C][C]110.7[/C][C]113.413636363636[/C][C]-2.71363636363635[/C][/ROW]
[ROW][C]38[/C][C]112.8[/C][C]113.413636363636[/C][C]-0.61363636363636[/C][/ROW]
[ROW][C]39[/C][C]109.8[/C][C]113.413636363636[/C][C]-3.61363636363636[/C][/ROW]
[ROW][C]40[/C][C]117.3[/C][C]113.413636363636[/C][C]3.88636363636364[/C][/ROW]
[ROW][C]41[/C][C]109.1[/C][C]113.413636363636[/C][C]-4.31363636363636[/C][/ROW]
[ROW][C]42[/C][C]115.9[/C][C]113.413636363636[/C][C]2.48636363636365[/C][/ROW]
[ROW][C]43[/C][C]96[/C][C]113.413636363636[/C][C]-17.4136363636364[/C][/ROW]
[ROW][C]44[/C][C]99.8[/C][C]100.924242424242[/C][C]-1.12424242424242[/C][/ROW]
[ROW][C]45[/C][C]116.8[/C][C]113.413636363636[/C][C]3.38636363636364[/C][/ROW]
[ROW][C]46[/C][C]115.7[/C][C]113.413636363636[/C][C]2.28636363636365[/C][/ROW]
[ROW][C]47[/C][C]99.4[/C][C]100.924242424242[/C][C]-1.52424242424242[/C][/ROW]
[ROW][C]48[/C][C]94.3[/C][C]91.5384615384615[/C][C]2.76153846153846[/C][/ROW]
[ROW][C]49[/C][C]91[/C][C]91.5384615384615[/C][C]-0.538461538461533[/C][/ROW]
[ROW][C]50[/C][C]93.2[/C][C]91.5384615384615[/C][C]1.66153846153847[/C][/ROW]
[ROW][C]51[/C][C]103.1[/C][C]100.924242424242[/C][C]2.17575757575757[/C][/ROW]
[ROW][C]52[/C][C]94.1[/C][C]91.5384615384615[/C][C]2.56153846153846[/C][/ROW]
[ROW][C]53[/C][C]91.8[/C][C]91.5384615384615[/C][C]0.261538461538464[/C][/ROW]
[ROW][C]54[/C][C]102.7[/C][C]100.924242424242[/C][C]1.77575757575758[/C][/ROW]
[ROW][C]55[/C][C]82.6[/C][C]91.5384615384615[/C][C]-8.93846153846154[/C][/ROW]
[ROW][C]56[/C][C]89.1[/C][C]91.5384615384615[/C][C]-2.43846153846154[/C][/ROW]
[ROW][C]57[/C][C]104.5[/C][C]100.924242424242[/C][C]3.57575757575758[/C][/ROW]
[ROW][C]58[/C][C]105.1[/C][C]100.924242424242[/C][C]4.17575757575757[/C][/ROW]
[ROW][C]59[/C][C]95.1[/C][C]100.924242424242[/C][C]-5.82424242424243[/C][/ROW]
[ROW][C]60[/C][C]88.7[/C][C]100.924242424242[/C][C]-12.2242424242424[/C][/ROW]
[ROW][C]61[/C][C]86.3[/C][C]91.5384615384615[/C][C]-5.23846153846154[/C][/ROW]
[ROW][C]62[/C][C]91.8[/C][C]100.924242424242[/C][C]-9.12424242424242[/C][/ROW]
[ROW][C]63[/C][C]111.5[/C][C]113.413636363636[/C][C]-1.91363636363636[/C][/ROW]
[ROW][C]64[/C][C]99.7[/C][C]100.924242424242[/C][C]-1.22424242424242[/C][/ROW]
[ROW][C]65[/C][C]97.5[/C][C]100.924242424242[/C][C]-3.42424242424242[/C][/ROW]
[ROW][C]66[/C][C]111.7[/C][C]113.413636363636[/C][C]-1.71363636363635[/C][/ROW]
[ROW][C]67[/C][C]86.2[/C][C]100.924242424242[/C][C]-14.7242424242424[/C][/ROW]
[ROW][C]68[/C][C]95.4[/C][C]100.924242424242[/C][C]-5.52424242424242[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=109999&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=109999&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	94.6	91.5384615384615	3.06153846153846
2	95.9	91.5384615384615	4.36153846153847
3	104.7	100.924242424242	3.77575757575758
4	102.8	100.924242424242	1.87575757575758
5	98.1	100.924242424242	-2.82424242424243
6	113.9	100.924242424242	12.9757575757576
7	80.9	91.5384615384615	-10.6384615384615
8	95.7	91.5384615384615	4.16153846153847
9	113.2	100.924242424242	12.2757575757576
10	105.9	100.924242424242	4.97575757575758
11	108.8	100.924242424242	7.87575757575758
12	102.3	100.924242424242	1.37575757575758
13	99	100.924242424242	-1.92424242424242
14	100.7	100.924242424242	-0.224242424242419
15	115.5	113.413636363636	2.08636363636364
16	100.7	100.924242424242	-0.224242424242419
17	109.9	100.924242424242	8.97575757575758
18	114.6	113.413636363636	1.18636363636364
19	85.4	100.924242424242	-15.5242424242424
20	100.5	91.5384615384615	8.96153846153847
21	114.8	113.413636363636	1.38636363636364
22	116.5	113.413636363636	3.08636363636364
23	112.9	113.413636363636	-0.513636363636351
24	102	100.924242424242	1.07575757575758
25	106	100.924242424242	5.07575757575758
26	105.3	100.924242424242	4.37575757575758
27	118.8	113.413636363636	5.38636363636364
28	106.1	100.924242424242	5.17575757575757
29	109.3	113.413636363636	-4.11363636363636
30	117.2	113.413636363636	3.78636363636365
31	92.5	100.924242424242	-8.42424242424242
32	104.2	100.924242424242	3.27575757575758
33	112.5	113.413636363636	-0.913636363636357
34	122.4	113.413636363636	8.98636363636365
35	113.3	113.413636363636	-0.11363636363636
36	100	100.924242424242	-0.924242424242422
37	110.7	113.413636363636	-2.71363636363635
38	112.8	113.413636363636	-0.61363636363636
39	109.8	113.413636363636	-3.61363636363636
40	117.3	113.413636363636	3.88636363636364
41	109.1	113.413636363636	-4.31363636363636
42	115.9	113.413636363636	2.48636363636365
43	96	113.413636363636	-17.4136363636364
44	99.8	100.924242424242	-1.12424242424242
45	116.8	113.413636363636	3.38636363636364
46	115.7	113.413636363636	2.28636363636365
47	99.4	100.924242424242	-1.52424242424242
48	94.3	91.5384615384615	2.76153846153846
49	91	91.5384615384615	-0.538461538461533
50	93.2	91.5384615384615	1.66153846153847
51	103.1	100.924242424242	2.17575757575757
52	94.1	91.5384615384615	2.56153846153846
53	91.8	91.5384615384615	0.261538461538464
54	102.7	100.924242424242	1.77575757575758
55	82.6	91.5384615384615	-8.93846153846154
56	89.1	91.5384615384615	-2.43846153846154
57	104.5	100.924242424242	3.57575757575758
58	105.1	100.924242424242	4.17575757575757
59	95.1	100.924242424242	-5.82424242424243
60	88.7	100.924242424242	-12.2242424242424
61	86.3	91.5384615384615	-5.23846153846154
62	91.8	100.924242424242	-9.12424242424242
63	111.5	113.413636363636	-1.91363636363636
64	99.7	100.924242424242	-1.22424242424242
65	97.5	100.924242424242	-3.42424242424242
66	111.7	113.413636363636	-1.71363636363635
67	86.2	100.924242424242	-14.7242424242424
68	95.4	100.924242424242	-5.52424242424242

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 = 2 ; par2 = none ; par3 = 3 ; par4 = no ;

Parameters (R input):

par1 = 2 ; 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