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R Software Module

rwasp_regression_trees1.wasp

Title produced by software

Recursive Partitioning (Regression Trees)

Date of computation

Mon, 12 Dec 2011 16:34:38 -0500

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/2011/Dec/12/t13237257128wgplq0ed8236oo.htm/, Retrieved Fri, 03 May 2024 10:59:32 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=154233, Retrieved Fri, 03 May 2024 10:59:32 +0000

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User-defined keywords

Estimated Impact

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)

F     [Kendall tau Correlation Matrix] [Correlation Matri...] [2010-12-10 11:09:34] [1429a1a14191a86916b95357f6de790b]
- R  D  [Kendall tau Correlation Matrix] [WS10 - Correlatio...] [2011-12-12 20:58:56] [805a2cd4f7b6665cd8870eed4006f53c]
- RMPD      [Recursive Partitioning (Regression Trees)] [WS10 - Recursive ...] [2011-12-12 21:34:38] [c18e83883fa784c15a15b4fbc0636edd] [Current]
- R P         [Recursive Partitioning (Regression Trees)] [WS10 - Recursive ...] [2011-12-12 21:47:26] [805a2cd4f7b6665cd8870eed4006f53c] 
-   P           [Recursive Partitioning (Regression Trees)] [WS 10 - Quantiles ] [2011-12-12 21:52:11] [805a2cd4f7b6665cd8870eed4006f53c] 

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Dataseries X:

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Boxplots

26	NA	21	NA	21	NA	23	NA	17	NA	23	NA
20	NA	16	NA	15	NA	24	NA	17	NA	20	NA
19	NA	19	NA	18	NA	22	NA	18	NA	20	NA
NA	19	NA	18	NA	11	NA	20	NA	21	NA	21
20	NA	16	NA	8	NA	24	NA	20	NA	24	NA
25	NA	23	NA	19	NA	27	NA	28	NA	22	NA
NA	25	NA	17	NA	4	NA	28	NA	19	NA	23
22	NA	12	NA	20	NA	27	NA	22	NA	20	NA
26	NA	19	NA	16	NA	24	NA	16	NA	25	NA
22	NA	16	NA	14	NA	23	NA	18	NA	23	NA
NA	17	NA	19	NA	10	NA	24	NA	25	NA	27
NA	22	NA	20	NA	13	NA	27	NA	17	NA	27
19	NA	13	NA	14	NA	27	NA	14	NA	22	NA
24	NA	20	NA	8	NA	28	NA	11	NA	24	NA
26	NA	27	NA	23	NA	27	NA	27	NA	25	NA
NA	21	NA	17	NA	11	NA	23	NA	20	NA	22
13	NA	8	NA	9	NA	24	NA	22	NA	28	NA
NA	26	NA	25	NA	24	NA	28	NA	22	NA	28
NA	20	NA	26	NA	5	NA	27	NA	21	NA	27
22	NA	13	NA	15	NA	25	NA	23	NA	25	NA
NA	14	NA	19	NA	5	NA	19	NA	17	NA	16
21	NA	15	NA	19	NA	24	NA	24	NA	28	NA
7	NA	5	NA	6	NA	20	NA	14	NA	21	NA
NA	23	NA	16	NA	13	NA	28	NA	17	NA	24
17	NA	14	NA	11	NA	26	NA	23	NA	27	NA
25	NA	24	NA	17	NA	23	NA	24	NA	14	NA
25	NA	24	NA	17	NA	23	NA	24	NA	14	NA
19	NA	9	NA	5	NA	20	NA	8	NA	27	NA
NA	20	NA	19	NA	9	NA	11	NA	22	NA	20
23	NA	19	NA	15	NA	24	NA	23	NA	21	NA
NA	22	NA	25	NA	17	NA	25	NA	25	NA	22
22	NA	19	NA	17	NA	23	NA	21	NA	21	NA
21	NA	18	NA	20	NA	18	NA	24	NA	12	NA
NA	15	NA	15	NA	12	NA	20	NA	15	NA	20
NA	20	NA	12	NA	7	NA	20	NA	22	NA	24
NA	22	NA	21	NA	16	NA	24	NA	21	NA	19
18	NA	12	NA	7	NA	23	NA	25	NA	28	NA
NA	20	NA	15	NA	14	NA	25	NA	16	NA	23
NA	28	NA	28	NA	24	NA	28	NA	28	NA	27
22	NA	25	NA	15	NA	26	NA	23	NA	22	NA
18	NA	19	NA	15	NA	26	NA	21	NA	27	NA
23	NA	20	NA	10	NA	23	NA	21	NA	26	NA
20	NA	24	NA	14	NA	22	NA	26	NA	22	NA
NA	25	NA	26	NA	18	NA	24	NA	22	NA	21
NA	26	NA	25	NA	12	NA	21	NA	21	NA	19
15	NA	12	NA	9	NA	20	NA	18	NA	24	NA
NA	17	NA	12	NA	9	NA	22	NA	12	NA	19
NA	23	NA	15	NA	8	NA	20	NA	25	NA	26
21	NA	17	NA	18	NA	25	NA	17	NA	22	NA
NA	13	NA	14	NA	10	NA	20	NA	24	NA	28
18	NA	16	NA	17	NA	22	NA	15	NA	21	NA
19	NA	11	NA	14	NA	23	NA	13	NA	23	NA
22	NA	20	NA	16	NA	25	NA	26	NA	28	NA
16	NA	11	NA	10	NA	23	NA	16	NA	10	NA
NA	24	NA	22	NA	19	NA	23	NA	24	NA	24
18	NA	20	NA	10	NA	22	NA	21	NA	21	NA
20	NA	19	NA	14	NA	24	NA	20	NA	21	NA
24	NA	17	NA	10	NA	25	NA	14	NA	24	NA
NA	14	NA	21	NA	4	NA	21	NA	25	NA	24
NA	22	NA	23	NA	19	NA	12	NA	25	NA	25
24	NA	18	NA	9	NA	17	NA	20	NA	25	NA
18	NA	17	NA	12	NA	20	NA	22	NA	23	NA
21	NA	27	NA	16	NA	23	NA	20	NA	21	NA
NA	23	NA	25	NA	11	NA	23	NA	26	NA	16
17	NA	19	NA	18	NA	20	NA	18	NA	17	NA
NA	22	NA	22	NA	11	NA	28	NA	22	NA	25
NA	24	NA	24	NA	24	NA	24	NA	24	NA	24
NA	21	NA	20	NA	17	NA	24	NA	17	NA	23
22	NA	19	NA	18	NA	24	NA	24	NA	25	NA
16	NA	11	NA	9	NA	24	NA	20	NA	23	NA
21	NA	22	NA	19	NA	28	NA	19	NA	28	NA
NA	23	NA	22	NA	18	NA	25	NA	20	NA	26
NA	22	NA	16	NA	12	NA	21	NA	15	NA	22
24	NA	20	NA	23	NA	25	NA	23	NA	19	NA
24	NA	24	NA	22	NA	25	NA	26	NA	26	NA
16	NA	16	NA	14	NA	18	NA	22	NA	18	NA
16	NA	16	NA	14	NA	17	NA	20	NA	18	NA
NA	21	NA	22	NA	16	NA	26	NA	24	NA	25
NA	26	NA	24	NA	23	NA	28	NA	26	NA	27
NA	15	NA	16	NA	7	NA	21	NA	21	NA	12
NA	25	NA	27	NA	10	NA	27	NA	25	NA	15
18	NA	11	NA	12	NA	22	NA	13	NA	21	NA
NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
20	NA	20	NA	12	NA	25	NA	22	NA	22	NA
NA	17	NA	20	NA	17	NA	22	NA	23	NA	21
NA	25	NA	27	NA	21	NA	23	NA	28	NA	24
24	NA	20	NA	16	NA	26	NA	22	NA	27	NA
17	NA	12	NA	11	NA	19	NA	20	NA	22	NA
19	NA	8	NA	14	NA	25	NA	6	NA	28	NA
20	NA	21	NA	13	NA	21	NA	21	NA	26	NA
15	NA	18	NA	9	NA	13	NA	20	NA	10	NA
NA	27	NA	24	NA	19	NA	24	NA	18	NA	19
22	NA	16	NA	13	NA	25	NA	23	NA	22	NA
23	NA	18	NA	19	NA	26	NA	20	NA	21	NA
16	NA	20	NA	13	NA	25	NA	24	NA	24	NA
19	NA	20	NA	13	NA	25	NA	22	NA	25	NA
NA	25	NA	19	NA	13	NA	22	NA	21	NA	21
19	NA	17	NA	14	NA	21	NA	18	NA	20	NA
NA	19	NA	16	NA	12	NA	23	NA	21	NA	21
NA	26	NA	26	NA	22	NA	25	NA	23	NA	24
21	NA	15	NA	11	NA	24	NA	23	NA	23	NA
NA	20	NA	22	NA	5	NA	21	NA	15	NA	18
24	NA	17	NA	18	NA	21	NA	21	NA	24	NA
22	NA	23	NA	19	NA	25	NA	24	NA	24	NA
NA	20	NA	21	NA	14	NA	22	NA	23	NA	19
18	NA	19	NA	15	NA	20	NA	21	NA	20	NA
NA	18	NA	14	NA	12	NA	20	NA	21	NA	18
24	NA	17	NA	19	NA	23	NA	20	NA	20	NA
24	NA	12	NA	15	NA	28	NA	11	NA	27	NA
22	NA	24	NA	17	NA	23	NA	22	NA	23	NA
23	NA	18	NA	8	NA	28	NA	27	NA	26	NA
22	NA	20	NA	10	NA	24	NA	25	NA	23	NA
20	NA	16	NA	12	NA	18	NA	18	NA	17	NA
18	NA	20	NA	12	NA	20	NA	20	NA	21	NA
25	NA	22	NA	20	NA	28	NA	24	NA	25	NA
NA	18	NA	12	NA	12	NA	21	NA	10	NA	23
16	NA	16	NA	12	NA	21	NA	27	NA	27	NA
20	NA	17	NA	14	NA	25	NA	21	NA	24	NA
NA	19	NA	22	NA	6	NA	19	NA	21	NA	20
15	NA	12	NA	10	NA	18	NA	18	NA	27	NA
19	NA	14	NA	18	NA	21	NA	15	NA	21	NA
19	NA	23	NA	18	NA	22	NA	24	NA	24	NA
16	NA	15	NA	7	NA	24	NA	22	NA	21	NA
17	NA	17	NA	18	NA	15	NA	14	NA	15	NA
28	NA	28	NA	9	NA	28	NA	28	NA	25	NA
NA	23	NA	20	NA	17	NA	26	NA	18	NA	25
25	NA	23	NA	22	NA	23	NA	26	NA	22	NA
20	NA	13	NA	11	NA	26	NA	17	NA	24	NA
NA	17	NA	18	NA	15	NA	20	NA	19	NA	21
NA	23	NA	23	NA	17	NA	22	NA	22	NA	22
16	NA	19	NA	15	NA	20	NA	18	NA	23	NA
NA	23	NA	23	NA	22	NA	23	NA	24	NA	22
NA	11	NA	12	NA	9	NA	22	NA	15	NA	20
NA	18	NA	16	NA	13	NA	24	NA	18	NA	23
NA	24	NA	23	NA	20	NA	23	NA	26	NA	25
23	NA	13	NA	14	NA	22	NA	11	NA	23	NA
21	NA	22	NA	14	NA	26	NA	26	NA	22	NA
NA	16	NA	18	NA	12	NA	23	NA	21	NA	25
NA	24	NA	23	NA	20	NA	27	NA	23	NA	26
23	NA	20	NA	20	NA	23	NA	23	NA	22	NA
18	NA	10	NA	8	NA	21	NA	15	NA	24	NA
20	NA	17	NA	17	NA	26	NA	22	NA	24	NA
9	NA	18	NA	9	NA	23	NA	26	NA	25	NA
NA	24	NA	15	NA	18	NA	21	NA	16	NA	20
25	NA	23	NA	22	NA	27	NA	20	NA	26	NA
20	NA	17	NA	10	NA	19	NA	18	NA	21	NA
NA	21	NA	17	NA	13	NA	23	NA	22	NA	26
NA	25	NA	22	NA	15	NA	25	NA	16	NA	21
NA	22	NA	20	NA	18	NA	23	NA	19	NA	22
NA	21	NA	20	NA	18	NA	22	NA	20	NA	16
21	NA	19	NA	12	NA	22	NA	19	NA	26	NA
22	NA	18	NA	12	NA	25	NA	23	NA	28	NA
27	NA	22	NA	20	NA	25	NA	24	NA	18	NA
NA	24	NA	20	NA	12	NA	28	NA	25	NA	25
NA	24	NA	22	NA	16	NA	28	NA	21	NA	23
NA	21	NA	18	NA	16	NA	20	NA	21	NA	21
18	NA	16	NA	18	NA	25	NA	23	NA	20	NA
16	NA	16	NA	16	NA	19	NA	27	NA	25	NA
22	NA	16	NA	13	NA	25	NA	23	NA	22	NA
20	NA	16	NA	17	NA	22	NA	18	NA	21	NA
NA	18	NA	17	NA	13	NA	18	NA	16	NA	16
20	NA	18	NA	17	NA	20	NA	16	NA	18	NA

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	'Herman Ole Andreas Wold' @ wold.wessa.net

\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 & 'Herman Ole Andreas Wold' @ wold.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=154233&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]'Herman Ole Andreas Wold' @ wold.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=154233&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=154233&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	'Herman Ole Andreas Wold' @ wold.wessa.net

Goodness of Fit
Correlation	0.6185
R-squared	0.3826
RMSE	2.822

\begin{tabular}{lllllllll}
\hline
Goodness of Fit \tabularnewline
Correlation & 0.6185 \tabularnewline
R-squared & 0.3826 \tabularnewline
RMSE & 2.822 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=154233&T=1

[TABLE]
[ROW][C]Goodness of Fit[/C][/ROW]
[ROW][C]Correlation[/C][C]0.6185[/C][/ROW]
[ROW][C]R-squared[/C][C]0.3826[/C][/ROW]
[ROW][C]RMSE[/C][C]2.822[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=154233&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=154233&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	0.6185
R-squared	0.3826
RMSE	2.822

Actuals, Predictions, and Residuals
#	Actuals	Forecasts	Residuals
1	26	23.45	2.55
2	20	20.6862745098039	-0.686274509803923
3	19	20.6862745098039	-1.68627450980392
4	20	20.6862745098039	-0.686274509803923
5	25	23.45	1.55
6	22	20.6862745098039	1.31372549019608
7	26	20.6862745098039	5.31372549019608
8	22	20.6862745098039	1.31372549019608
9	19	20.6862745098039	-1.68627450980392
10	24	20.6862745098039	3.31372549019608
11	26	23.45	2.55
12	13	16.1111111111111	-3.11111111111111
13	22	20.6862745098039	1.31372549019608
14	21	20.6862745098039	0.313725490196077
15	7	16.1111111111111	-9.11111111111111
16	17	20.6862745098039	-3.68627450980392
17	25	23.45	1.55
18	25	23.45	1.55
19	19	16.1111111111111	2.88888888888889
20	23	20.6862745098039	2.31372549019608
21	22	20.6862745098039	1.31372549019608
22	21	17.7222222222222	3.27777777777778
23	18	20.6862745098039	-2.68627450980392
24	22	23.45	-1.45
25	18	20.6862745098039	-2.68627450980392
26	23	20.6862745098039	2.31372549019608
27	20	23.45	-3.45
28	15	17.7222222222222	-2.72222222222222
29	21	20.6862745098039	0.313725490196077
30	18	20.6862745098039	-2.68627450980392
31	19	16.1111111111111	2.88888888888889
32	22	20.6862745098039	1.31372549019608
33	16	16.1111111111111	-0.111111111111111
34	18	20.6862745098039	-2.68627450980392
35	20	20.6862745098039	-0.686274509803923
36	24	20.6862745098039	3.31372549019608
37	24	17.7222222222222	6.27777777777778
38	18	17.7222222222222	0.277777777777779
39	21	23.45	-2.45
40	17	17.7222222222222	-0.722222222222221
41	22	20.6862745098039	1.31372549019608
42	16	16.1111111111111	-0.111111111111111
43	21	23.45	-2.45
44	24	20.6862745098039	3.31372549019608
45	24	23.45	0.550000000000001
46	16	17.7222222222222	-1.72222222222222
47	16	17.7222222222222	-1.72222222222222
48	18	16.1111111111111	1.88888888888889
49	20	20.6862745098039	-0.686274509803923
50	24	20.6862745098039	3.31372549019608
51	17	17.7222222222222	-0.722222222222221
52	19	16.1111111111111	2.88888888888889
53	20	23.45	-3.45
54	15	17.7222222222222	-2.72222222222222
55	22	20.6862745098039	1.31372549019608
56	23	20.6862745098039	2.31372549019608
57	16	20.6862745098039	-4.68627450980392
58	19	20.6862745098039	-1.68627450980392
59	19	20.6862745098039	-1.68627450980392
60	21	20.6862745098039	0.313725490196077
61	24	20.6862745098039	3.31372549019608
62	22	23.45	-1.45
63	18	17.7222222222222	0.277777777777779
64	24	20.6862745098039	3.31372549019608
65	24	20.6862745098039	3.31372549019608
66	22	23.45	-1.45
67	23	20.6862745098039	2.31372549019608
68	22	20.6862745098039	1.31372549019608
69	20	17.7222222222222	2.27777777777778
70	18	17.7222222222222	0.277777777777779
71	25	23.45	1.55
72	16	20.6862745098039	-4.68627450980392
73	20	20.6862745098039	-0.686274509803923
74	15	17.7222222222222	-2.72222222222222
75	19	20.6862745098039	-1.68627450980392
76	19	23.45	-4.45
77	16	20.6862745098039	-4.68627450980392
78	17	17.7222222222222	-0.722222222222221
79	28	23.45	4.55
80	25	23.45	1.55
81	20	20.6862745098039	-0.686274509803923
82	16	17.7222222222222	-1.72222222222222
83	23	20.6862745098039	2.31372549019608
84	21	23.45	-2.45
85	23	20.6862745098039	2.31372549019608
86	18	16.1111111111111	1.88888888888889
87	20	20.6862745098039	-0.686274509803923
88	9	20.6862745098039	-11.6862745098039
89	25	23.45	1.55
90	20	17.7222222222222	2.27777777777778
91	21	20.6862745098039	0.313725490196077
92	22	20.6862745098039	1.31372549019608
93	27	23.45	3.55
94	18	20.6862745098039	-2.68627450980392
95	16	17.7222222222222	-1.72222222222222
96	22	20.6862745098039	1.31372549019608
97	20	20.6862745098039	-0.686274509803923
98	20	17.7222222222222	2.27777777777778

\begin{tabular}{lllllllll}
\hline
Actuals, Predictions, and Residuals \tabularnewline
# & Actuals & Forecasts & Residuals \tabularnewline
1 & 26 & 23.45 & 2.55 \tabularnewline
2 & 20 & 20.6862745098039 & -0.686274509803923 \tabularnewline
3 & 19 & 20.6862745098039 & -1.68627450980392 \tabularnewline
4 & 20 & 20.6862745098039 & -0.686274509803923 \tabularnewline
5 & 25 & 23.45 & 1.55 \tabularnewline
6 & 22 & 20.6862745098039 & 1.31372549019608 \tabularnewline
7 & 26 & 20.6862745098039 & 5.31372549019608 \tabularnewline
8 & 22 & 20.6862745098039 & 1.31372549019608 \tabularnewline
9 & 19 & 20.6862745098039 & -1.68627450980392 \tabularnewline
10 & 24 & 20.6862745098039 & 3.31372549019608 \tabularnewline
11 & 26 & 23.45 & 2.55 \tabularnewline
12 & 13 & 16.1111111111111 & -3.11111111111111 \tabularnewline
13 & 22 & 20.6862745098039 & 1.31372549019608 \tabularnewline
14 & 21 & 20.6862745098039 & 0.313725490196077 \tabularnewline
15 & 7 & 16.1111111111111 & -9.11111111111111 \tabularnewline
16 & 17 & 20.6862745098039 & -3.68627450980392 \tabularnewline
17 & 25 & 23.45 & 1.55 \tabularnewline
18 & 25 & 23.45 & 1.55 \tabularnewline
19 & 19 & 16.1111111111111 & 2.88888888888889 \tabularnewline
20 & 23 & 20.6862745098039 & 2.31372549019608 \tabularnewline
21 & 22 & 20.6862745098039 & 1.31372549019608 \tabularnewline
22 & 21 & 17.7222222222222 & 3.27777777777778 \tabularnewline
23 & 18 & 20.6862745098039 & -2.68627450980392 \tabularnewline
24 & 22 & 23.45 & -1.45 \tabularnewline
25 & 18 & 20.6862745098039 & -2.68627450980392 \tabularnewline
26 & 23 & 20.6862745098039 & 2.31372549019608 \tabularnewline
27 & 20 & 23.45 & -3.45 \tabularnewline
28 & 15 & 17.7222222222222 & -2.72222222222222 \tabularnewline
29 & 21 & 20.6862745098039 & 0.313725490196077 \tabularnewline
30 & 18 & 20.6862745098039 & -2.68627450980392 \tabularnewline
31 & 19 & 16.1111111111111 & 2.88888888888889 \tabularnewline
32 & 22 & 20.6862745098039 & 1.31372549019608 \tabularnewline
33 & 16 & 16.1111111111111 & -0.111111111111111 \tabularnewline
34 & 18 & 20.6862745098039 & -2.68627450980392 \tabularnewline
35 & 20 & 20.6862745098039 & -0.686274509803923 \tabularnewline
36 & 24 & 20.6862745098039 & 3.31372549019608 \tabularnewline
37 & 24 & 17.7222222222222 & 6.27777777777778 \tabularnewline
38 & 18 & 17.7222222222222 & 0.277777777777779 \tabularnewline
39 & 21 & 23.45 & -2.45 \tabularnewline
40 & 17 & 17.7222222222222 & -0.722222222222221 \tabularnewline
41 & 22 & 20.6862745098039 & 1.31372549019608 \tabularnewline
42 & 16 & 16.1111111111111 & -0.111111111111111 \tabularnewline
43 & 21 & 23.45 & -2.45 \tabularnewline
44 & 24 & 20.6862745098039 & 3.31372549019608 \tabularnewline
45 & 24 & 23.45 & 0.550000000000001 \tabularnewline
46 & 16 & 17.7222222222222 & -1.72222222222222 \tabularnewline
47 & 16 & 17.7222222222222 & -1.72222222222222 \tabularnewline
48 & 18 & 16.1111111111111 & 1.88888888888889 \tabularnewline
49 & 20 & 20.6862745098039 & -0.686274509803923 \tabularnewline
50 & 24 & 20.6862745098039 & 3.31372549019608 \tabularnewline
51 & 17 & 17.7222222222222 & -0.722222222222221 \tabularnewline
52 & 19 & 16.1111111111111 & 2.88888888888889 \tabularnewline
53 & 20 & 23.45 & -3.45 \tabularnewline
54 & 15 & 17.7222222222222 & -2.72222222222222 \tabularnewline
55 & 22 & 20.6862745098039 & 1.31372549019608 \tabularnewline
56 & 23 & 20.6862745098039 & 2.31372549019608 \tabularnewline
57 & 16 & 20.6862745098039 & -4.68627450980392 \tabularnewline
58 & 19 & 20.6862745098039 & -1.68627450980392 \tabularnewline
59 & 19 & 20.6862745098039 & -1.68627450980392 \tabularnewline
60 & 21 & 20.6862745098039 & 0.313725490196077 \tabularnewline
61 & 24 & 20.6862745098039 & 3.31372549019608 \tabularnewline
62 & 22 & 23.45 & -1.45 \tabularnewline
63 & 18 & 17.7222222222222 & 0.277777777777779 \tabularnewline
64 & 24 & 20.6862745098039 & 3.31372549019608 \tabularnewline
65 & 24 & 20.6862745098039 & 3.31372549019608 \tabularnewline
66 & 22 & 23.45 & -1.45 \tabularnewline
67 & 23 & 20.6862745098039 & 2.31372549019608 \tabularnewline
68 & 22 & 20.6862745098039 & 1.31372549019608 \tabularnewline
69 & 20 & 17.7222222222222 & 2.27777777777778 \tabularnewline
70 & 18 & 17.7222222222222 & 0.277777777777779 \tabularnewline
71 & 25 & 23.45 & 1.55 \tabularnewline
72 & 16 & 20.6862745098039 & -4.68627450980392 \tabularnewline
73 & 20 & 20.6862745098039 & -0.686274509803923 \tabularnewline
74 & 15 & 17.7222222222222 & -2.72222222222222 \tabularnewline
75 & 19 & 20.6862745098039 & -1.68627450980392 \tabularnewline
76 & 19 & 23.45 & -4.45 \tabularnewline
77 & 16 & 20.6862745098039 & -4.68627450980392 \tabularnewline
78 & 17 & 17.7222222222222 & -0.722222222222221 \tabularnewline
79 & 28 & 23.45 & 4.55 \tabularnewline
80 & 25 & 23.45 & 1.55 \tabularnewline
81 & 20 & 20.6862745098039 & -0.686274509803923 \tabularnewline
82 & 16 & 17.7222222222222 & -1.72222222222222 \tabularnewline
83 & 23 & 20.6862745098039 & 2.31372549019608 \tabularnewline
84 & 21 & 23.45 & -2.45 \tabularnewline
85 & 23 & 20.6862745098039 & 2.31372549019608 \tabularnewline
86 & 18 & 16.1111111111111 & 1.88888888888889 \tabularnewline
87 & 20 & 20.6862745098039 & -0.686274509803923 \tabularnewline
88 & 9 & 20.6862745098039 & -11.6862745098039 \tabularnewline
89 & 25 & 23.45 & 1.55 \tabularnewline
90 & 20 & 17.7222222222222 & 2.27777777777778 \tabularnewline
91 & 21 & 20.6862745098039 & 0.313725490196077 \tabularnewline
92 & 22 & 20.6862745098039 & 1.31372549019608 \tabularnewline
93 & 27 & 23.45 & 3.55 \tabularnewline
94 & 18 & 20.6862745098039 & -2.68627450980392 \tabularnewline
95 & 16 & 17.7222222222222 & -1.72222222222222 \tabularnewline
96 & 22 & 20.6862745098039 & 1.31372549019608 \tabularnewline
97 & 20 & 20.6862745098039 & -0.686274509803923 \tabularnewline
98 & 20 & 17.7222222222222 & 2.27777777777778 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=154233&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]26[/C][C]23.45[/C][C]2.55[/C][/ROW]
[ROW][C]2[/C][C]20[/C][C]20.6862745098039[/C][C]-0.686274509803923[/C][/ROW]
[ROW][C]3[/C][C]19[/C][C]20.6862745098039[/C][C]-1.68627450980392[/C][/ROW]
[ROW][C]4[/C][C]20[/C][C]20.6862745098039[/C][C]-0.686274509803923[/C][/ROW]
[ROW][C]5[/C][C]25[/C][C]23.45[/C][C]1.55[/C][/ROW]
[ROW][C]6[/C][C]22[/C][C]20.6862745098039[/C][C]1.31372549019608[/C][/ROW]
[ROW][C]7[/C][C]26[/C][C]20.6862745098039[/C][C]5.31372549019608[/C][/ROW]
[ROW][C]8[/C][C]22[/C][C]20.6862745098039[/C][C]1.31372549019608[/C][/ROW]
[ROW][C]9[/C][C]19[/C][C]20.6862745098039[/C][C]-1.68627450980392[/C][/ROW]
[ROW][C]10[/C][C]24[/C][C]20.6862745098039[/C][C]3.31372549019608[/C][/ROW]
[ROW][C]11[/C][C]26[/C][C]23.45[/C][C]2.55[/C][/ROW]
[ROW][C]12[/C][C]13[/C][C]16.1111111111111[/C][C]-3.11111111111111[/C][/ROW]
[ROW][C]13[/C][C]22[/C][C]20.6862745098039[/C][C]1.31372549019608[/C][/ROW]
[ROW][C]14[/C][C]21[/C][C]20.6862745098039[/C][C]0.313725490196077[/C][/ROW]
[ROW][C]15[/C][C]7[/C][C]16.1111111111111[/C][C]-9.11111111111111[/C][/ROW]
[ROW][C]16[/C][C]17[/C][C]20.6862745098039[/C][C]-3.68627450980392[/C][/ROW]
[ROW][C]17[/C][C]25[/C][C]23.45[/C][C]1.55[/C][/ROW]
[ROW][C]18[/C][C]25[/C][C]23.45[/C][C]1.55[/C][/ROW]
[ROW][C]19[/C][C]19[/C][C]16.1111111111111[/C][C]2.88888888888889[/C][/ROW]
[ROW][C]20[/C][C]23[/C][C]20.6862745098039[/C][C]2.31372549019608[/C][/ROW]
[ROW][C]21[/C][C]22[/C][C]20.6862745098039[/C][C]1.31372549019608[/C][/ROW]
[ROW][C]22[/C][C]21[/C][C]17.7222222222222[/C][C]3.27777777777778[/C][/ROW]
[ROW][C]23[/C][C]18[/C][C]20.6862745098039[/C][C]-2.68627450980392[/C][/ROW]
[ROW][C]24[/C][C]22[/C][C]23.45[/C][C]-1.45[/C][/ROW]
[ROW][C]25[/C][C]18[/C][C]20.6862745098039[/C][C]-2.68627450980392[/C][/ROW]
[ROW][C]26[/C][C]23[/C][C]20.6862745098039[/C][C]2.31372549019608[/C][/ROW]
[ROW][C]27[/C][C]20[/C][C]23.45[/C][C]-3.45[/C][/ROW]
[ROW][C]28[/C][C]15[/C][C]17.7222222222222[/C][C]-2.72222222222222[/C][/ROW]
[ROW][C]29[/C][C]21[/C][C]20.6862745098039[/C][C]0.313725490196077[/C][/ROW]
[ROW][C]30[/C][C]18[/C][C]20.6862745098039[/C][C]-2.68627450980392[/C][/ROW]
[ROW][C]31[/C][C]19[/C][C]16.1111111111111[/C][C]2.88888888888889[/C][/ROW]
[ROW][C]32[/C][C]22[/C][C]20.6862745098039[/C][C]1.31372549019608[/C][/ROW]
[ROW][C]33[/C][C]16[/C][C]16.1111111111111[/C][C]-0.111111111111111[/C][/ROW]
[ROW][C]34[/C][C]18[/C][C]20.6862745098039[/C][C]-2.68627450980392[/C][/ROW]
[ROW][C]35[/C][C]20[/C][C]20.6862745098039[/C][C]-0.686274509803923[/C][/ROW]
[ROW][C]36[/C][C]24[/C][C]20.6862745098039[/C][C]3.31372549019608[/C][/ROW]
[ROW][C]37[/C][C]24[/C][C]17.7222222222222[/C][C]6.27777777777778[/C][/ROW]
[ROW][C]38[/C][C]18[/C][C]17.7222222222222[/C][C]0.277777777777779[/C][/ROW]
[ROW][C]39[/C][C]21[/C][C]23.45[/C][C]-2.45[/C][/ROW]
[ROW][C]40[/C][C]17[/C][C]17.7222222222222[/C][C]-0.722222222222221[/C][/ROW]
[ROW][C]41[/C][C]22[/C][C]20.6862745098039[/C][C]1.31372549019608[/C][/ROW]
[ROW][C]42[/C][C]16[/C][C]16.1111111111111[/C][C]-0.111111111111111[/C][/ROW]
[ROW][C]43[/C][C]21[/C][C]23.45[/C][C]-2.45[/C][/ROW]
[ROW][C]44[/C][C]24[/C][C]20.6862745098039[/C][C]3.31372549019608[/C][/ROW]
[ROW][C]45[/C][C]24[/C][C]23.45[/C][C]0.550000000000001[/C][/ROW]
[ROW][C]46[/C][C]16[/C][C]17.7222222222222[/C][C]-1.72222222222222[/C][/ROW]
[ROW][C]47[/C][C]16[/C][C]17.7222222222222[/C][C]-1.72222222222222[/C][/ROW]
[ROW][C]48[/C][C]18[/C][C]16.1111111111111[/C][C]1.88888888888889[/C][/ROW]
[ROW][C]49[/C][C]20[/C][C]20.6862745098039[/C][C]-0.686274509803923[/C][/ROW]
[ROW][C]50[/C][C]24[/C][C]20.6862745098039[/C][C]3.31372549019608[/C][/ROW]
[ROW][C]51[/C][C]17[/C][C]17.7222222222222[/C][C]-0.722222222222221[/C][/ROW]
[ROW][C]52[/C][C]19[/C][C]16.1111111111111[/C][C]2.88888888888889[/C][/ROW]
[ROW][C]53[/C][C]20[/C][C]23.45[/C][C]-3.45[/C][/ROW]
[ROW][C]54[/C][C]15[/C][C]17.7222222222222[/C][C]-2.72222222222222[/C][/ROW]
[ROW][C]55[/C][C]22[/C][C]20.6862745098039[/C][C]1.31372549019608[/C][/ROW]
[ROW][C]56[/C][C]23[/C][C]20.6862745098039[/C][C]2.31372549019608[/C][/ROW]
[ROW][C]57[/C][C]16[/C][C]20.6862745098039[/C][C]-4.68627450980392[/C][/ROW]
[ROW][C]58[/C][C]19[/C][C]20.6862745098039[/C][C]-1.68627450980392[/C][/ROW]
[ROW][C]59[/C][C]19[/C][C]20.6862745098039[/C][C]-1.68627450980392[/C][/ROW]
[ROW][C]60[/C][C]21[/C][C]20.6862745098039[/C][C]0.313725490196077[/C][/ROW]
[ROW][C]61[/C][C]24[/C][C]20.6862745098039[/C][C]3.31372549019608[/C][/ROW]
[ROW][C]62[/C][C]22[/C][C]23.45[/C][C]-1.45[/C][/ROW]
[ROW][C]63[/C][C]18[/C][C]17.7222222222222[/C][C]0.277777777777779[/C][/ROW]
[ROW][C]64[/C][C]24[/C][C]20.6862745098039[/C][C]3.31372549019608[/C][/ROW]
[ROW][C]65[/C][C]24[/C][C]20.6862745098039[/C][C]3.31372549019608[/C][/ROW]
[ROW][C]66[/C][C]22[/C][C]23.45[/C][C]-1.45[/C][/ROW]
[ROW][C]67[/C][C]23[/C][C]20.6862745098039[/C][C]2.31372549019608[/C][/ROW]
[ROW][C]68[/C][C]22[/C][C]20.6862745098039[/C][C]1.31372549019608[/C][/ROW]
[ROW][C]69[/C][C]20[/C][C]17.7222222222222[/C][C]2.27777777777778[/C][/ROW]
[ROW][C]70[/C][C]18[/C][C]17.7222222222222[/C][C]0.277777777777779[/C][/ROW]
[ROW][C]71[/C][C]25[/C][C]23.45[/C][C]1.55[/C][/ROW]
[ROW][C]72[/C][C]16[/C][C]20.6862745098039[/C][C]-4.68627450980392[/C][/ROW]
[ROW][C]73[/C][C]20[/C][C]20.6862745098039[/C][C]-0.686274509803923[/C][/ROW]
[ROW][C]74[/C][C]15[/C][C]17.7222222222222[/C][C]-2.72222222222222[/C][/ROW]
[ROW][C]75[/C][C]19[/C][C]20.6862745098039[/C][C]-1.68627450980392[/C][/ROW]
[ROW][C]76[/C][C]19[/C][C]23.45[/C][C]-4.45[/C][/ROW]
[ROW][C]77[/C][C]16[/C][C]20.6862745098039[/C][C]-4.68627450980392[/C][/ROW]
[ROW][C]78[/C][C]17[/C][C]17.7222222222222[/C][C]-0.722222222222221[/C][/ROW]
[ROW][C]79[/C][C]28[/C][C]23.45[/C][C]4.55[/C][/ROW]
[ROW][C]80[/C][C]25[/C][C]23.45[/C][C]1.55[/C][/ROW]
[ROW][C]81[/C][C]20[/C][C]20.6862745098039[/C][C]-0.686274509803923[/C][/ROW]
[ROW][C]82[/C][C]16[/C][C]17.7222222222222[/C][C]-1.72222222222222[/C][/ROW]
[ROW][C]83[/C][C]23[/C][C]20.6862745098039[/C][C]2.31372549019608[/C][/ROW]
[ROW][C]84[/C][C]21[/C][C]23.45[/C][C]-2.45[/C][/ROW]
[ROW][C]85[/C][C]23[/C][C]20.6862745098039[/C][C]2.31372549019608[/C][/ROW]
[ROW][C]86[/C][C]18[/C][C]16.1111111111111[/C][C]1.88888888888889[/C][/ROW]
[ROW][C]87[/C][C]20[/C][C]20.6862745098039[/C][C]-0.686274509803923[/C][/ROW]
[ROW][C]88[/C][C]9[/C][C]20.6862745098039[/C][C]-11.6862745098039[/C][/ROW]
[ROW][C]89[/C][C]25[/C][C]23.45[/C][C]1.55[/C][/ROW]
[ROW][C]90[/C][C]20[/C][C]17.7222222222222[/C][C]2.27777777777778[/C][/ROW]
[ROW][C]91[/C][C]21[/C][C]20.6862745098039[/C][C]0.313725490196077[/C][/ROW]
[ROW][C]92[/C][C]22[/C][C]20.6862745098039[/C][C]1.31372549019608[/C][/ROW]
[ROW][C]93[/C][C]27[/C][C]23.45[/C][C]3.55[/C][/ROW]
[ROW][C]94[/C][C]18[/C][C]20.6862745098039[/C][C]-2.68627450980392[/C][/ROW]
[ROW][C]95[/C][C]16[/C][C]17.7222222222222[/C][C]-1.72222222222222[/C][/ROW]
[ROW][C]96[/C][C]22[/C][C]20.6862745098039[/C][C]1.31372549019608[/C][/ROW]
[ROW][C]97[/C][C]20[/C][C]20.6862745098039[/C][C]-0.686274509803923[/C][/ROW]
[ROW][C]98[/C][C]20[/C][C]17.7222222222222[/C][C]2.27777777777778[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=154233&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=154233&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	26	23.45	2.55
2	20	20.6862745098039	-0.686274509803923
3	19	20.6862745098039	-1.68627450980392
4	20	20.6862745098039	-0.686274509803923
5	25	23.45	1.55
6	22	20.6862745098039	1.31372549019608
7	26	20.6862745098039	5.31372549019608
8	22	20.6862745098039	1.31372549019608
9	19	20.6862745098039	-1.68627450980392
10	24	20.6862745098039	3.31372549019608
11	26	23.45	2.55
12	13	16.1111111111111	-3.11111111111111
13	22	20.6862745098039	1.31372549019608
14	21	20.6862745098039	0.313725490196077
15	7	16.1111111111111	-9.11111111111111
16	17	20.6862745098039	-3.68627450980392
17	25	23.45	1.55
18	25	23.45	1.55
19	19	16.1111111111111	2.88888888888889
20	23	20.6862745098039	2.31372549019608
21	22	20.6862745098039	1.31372549019608
22	21	17.7222222222222	3.27777777777778
23	18	20.6862745098039	-2.68627450980392
24	22	23.45	-1.45
25	18	20.6862745098039	-2.68627450980392
26	23	20.6862745098039	2.31372549019608
27	20	23.45	-3.45
28	15	17.7222222222222	-2.72222222222222
29	21	20.6862745098039	0.313725490196077
30	18	20.6862745098039	-2.68627450980392
31	19	16.1111111111111	2.88888888888889
32	22	20.6862745098039	1.31372549019608
33	16	16.1111111111111	-0.111111111111111
34	18	20.6862745098039	-2.68627450980392
35	20	20.6862745098039	-0.686274509803923
36	24	20.6862745098039	3.31372549019608
37	24	17.7222222222222	6.27777777777778
38	18	17.7222222222222	0.277777777777779
39	21	23.45	-2.45
40	17	17.7222222222222	-0.722222222222221
41	22	20.6862745098039	1.31372549019608
42	16	16.1111111111111	-0.111111111111111
43	21	23.45	-2.45
44	24	20.6862745098039	3.31372549019608
45	24	23.45	0.550000000000001
46	16	17.7222222222222	-1.72222222222222
47	16	17.7222222222222	-1.72222222222222
48	18	16.1111111111111	1.88888888888889
49	20	20.6862745098039	-0.686274509803923
50	24	20.6862745098039	3.31372549019608
51	17	17.7222222222222	-0.722222222222221
52	19	16.1111111111111	2.88888888888889
53	20	23.45	-3.45
54	15	17.7222222222222	-2.72222222222222
55	22	20.6862745098039	1.31372549019608
56	23	20.6862745098039	2.31372549019608
57	16	20.6862745098039	-4.68627450980392
58	19	20.6862745098039	-1.68627450980392
59	19	20.6862745098039	-1.68627450980392
60	21	20.6862745098039	0.313725490196077
61	24	20.6862745098039	3.31372549019608
62	22	23.45	-1.45
63	18	17.7222222222222	0.277777777777779
64	24	20.6862745098039	3.31372549019608
65	24	20.6862745098039	3.31372549019608
66	22	23.45	-1.45
67	23	20.6862745098039	2.31372549019608
68	22	20.6862745098039	1.31372549019608
69	20	17.7222222222222	2.27777777777778
70	18	17.7222222222222	0.277777777777779
71	25	23.45	1.55
72	16	20.6862745098039	-4.68627450980392
73	20	20.6862745098039	-0.686274509803923
74	15	17.7222222222222	-2.72222222222222
75	19	20.6862745098039	-1.68627450980392
76	19	23.45	-4.45
77	16	20.6862745098039	-4.68627450980392
78	17	17.7222222222222	-0.722222222222221
79	28	23.45	4.55
80	25	23.45	1.55
81	20	20.6862745098039	-0.686274509803923
82	16	17.7222222222222	-1.72222222222222
83	23	20.6862745098039	2.31372549019608
84	21	23.45	-2.45
85	23	20.6862745098039	2.31372549019608
86	18	16.1111111111111	1.88888888888889
87	20	20.6862745098039	-0.686274509803923
88	9	20.6862745098039	-11.6862745098039
89	25	23.45	1.55
90	20	17.7222222222222	2.27777777777778
91	21	20.6862745098039	0.313725490196077
92	22	20.6862745098039	1.31372549019608
93	27	23.45	3.55
94	18	20.6862745098039	-2.68627450980392
95	16	17.7222222222222	-1.72222222222222
96	22	20.6862745098039	1.31372549019608
97	20	20.6862745098039	-0.686274509803923
98	20	17.7222222222222	2.27777777777778

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