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Author's title

Author

*Unverified author*

R Software Module

rwasp_regression_trees1.wasp

Title produced by software

Recursive Partitioning (Regression Trees)

Date of computation

Sat, 29 Jul 2017 10:41:32 +0200

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/2017/Jul/29/t1501317883o04cymk4damusd7.htm/, Retrieved Tue, 14 May 2024 05:33:40 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=306784, Retrieved Tue, 14 May 2024 05:33:40 +0000

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Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords

Estimated Impact

125

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

-       [Recursive Partitioning (Regression Trees)] [] [2017-07-29 08:41:32] [d41d8cd98f00b204e9800998ecf8427e] [Current]

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Dataseries X:

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Histogram

Boxplots

1	12	2,4	3,8	0,12
0	6	1,1	1,4	-0,14
0	4	0,6	0,8	0,14
0	9	1,8	2,4	0,06
0	5	0,5	1	0,53
1	19	5,9	9,6	0,41
0	5	0,4	0,3	-0,58
0	4	0,6	1,8	0,97
0	6	0,9	1,3	0,09
1	19	0,8	2	0,42
1	4	0,6	1,3	0,96
0	6	0,4	0,7	0,25
0	5	0,4	0,7	0,05
0	4	0,3	0,5	0,13
1	10	5,9	8,6	0,16
1	9	1,5	2,7	0,12
1	11	1	1,7	0,11
0	16	4,8	6,6	0,23
0	3	1,1	1	-0,16
0	3	0,5	0,7	-0,07
0	8	0,9	1,3	0,28
0	6	0,8	1,2	0,08
0	5	1,1	2	0,11
0	7	0,8	1,6	0,02
0	5	0,6	1,1	0,01
1	3	0,5	0,5	-0,39
1	8	0,7	1,7	0,5
0	10	1	0,9	-0,18
0	5	0,4	0,8	0,12
0	5	1,2	1	-0,51
0	9	1	0,9	-0,21
0	9	1,2	2	0,34
1	17	4,7	7,8	0,58
0	5	0,3	1,7	1,8
1	10	8,1	12,5	0,32
0	7	1,6	3,5	0,36
0	5	1	1,5	0,15
0	10	1,4	2,2	0,23
0	5	0,7	0,5	-0,2
0	5	0,9	1,1	-0,08
1	6	0,7	3,1	1,61
1	21	6,4	8,7	0,19
0	7	2,4	2,2	-0,19
1	7	0,7	1,7	0,28
1	6	2,5	6,8	0,91
0	7	0,6	1,6	0,46
0	6	1	1,6	0,31
0	6	2,3	2,3	-0,06
1	4	0,9	0,6	-0,43
0	8	1,2	1,9	0,34
1	4	0,6	1,2	0,43
0	4	0,7	1,2	0,08
0	5	0,7	0,9	0,17
0	5	0,7	0,9	0,09
0	4	0,9	0,7	-0,41
0	8	0,8	1,6	0,8
0	5	0,3	1	0,69
0	5	1	1,4	-0,02
0	5	0,5	0,7	0,04
1	4	0,7	1,1	0,38
0	5	0,9	2,1	0,21
0	5	0,4	0,9	0,73
1	5	0,8	1,5	0,42
0	5	0,9	1,4	-0,09
1	6	0,8	1,5	0,17
0	6	0,8	0,6	-0,38
0	9	1,1	2,1	0,21
0	7	1,3	1,3	-0,02
0	7	1,1	1,1	-0,32
0	5	0,9	1,4	-0,16
0	3	0,5	1,5	0,6
0	6	0,7	1,6	0,14
0	8	1,2	1,6	0,21
1	11	2,4	5,5	0,19
1	7	0,7	2,2	0,78
1	9	1,1	1,7	0,44
0	6	1,8	2,4	-0,02
0	4	1	1,1	-0,42
0	6	0,8	1	-0,05
0	9	0,8	1,2	0,29
1	8	0,9	2,1	0,75
1	5	0,9	1,1	0,01
0	6	0,7	3,3	1,49
0	12	9	15,4	0,46
0	5	1,4	2,6	0,14
0	5	1,1	0,6	-0,44
0	7	1	1,8	0,34
0	5	1	0,7	-0,31
0	6	0,9	1,2	0,08
1	11	2,3	4,3	0,13
1	15	6,2	14,5	1,08
0	6	0,6	1,2	0,06
0	4	0,3	0,4	-0,04
1	13	4,9	7,6	0,46
0	3	0,7	0,7	-0,41
0	5	1,7	1,4	-0,28
0	6	0,7	0,8	-0,06
0	7	1,6	2,3	0,28

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	5 seconds
R Server	Big Analytics Cloud Computing Center

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input view raw input (R code)  \tabularnewline
Raw Outputview raw output of R engine  \tabularnewline
Computing time5 seconds \tabularnewline
R ServerBig Analytics Cloud Computing Center \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=306784&T=0

[TABLE]
[ROW]

Summary of computational transaction[/C][/ROW] [ROW]

Raw Input[/C]

view raw input (R code) [/C][/ROW] [ROW]

Raw Output[/C]

view raw output of R engine [/C][/ROW] [ROW]

Computing time[/C]

5 seconds[/C][/ROW] [ROW]

R Server[/C]

Big Analytics Cloud Computing Center[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=306784&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=306784&T=0

As an alternative you can also use a QR Code:

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

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	5 seconds
R Server	Big Analytics Cloud Computing Center

Goodness of Fit
Correlation	0.5846
R-squared	0.3417
RMSE	0.3665

\begin{tabular}{lllllllll}
\hline
Goodness of Fit \tabularnewline
Correlation & 0.5846 \tabularnewline
R-squared & 0.3417 \tabularnewline
RMSE & 0.3665 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=306784&T=1

[TABLE]
[ROW][C]Goodness of Fit[/C][/ROW]
[ROW][C]Correlation[/C][C]0.5846[/C][/ROW]
[ROW][C]R-squared[/C][C]0.3417[/C][/ROW]
[ROW][C]RMSE[/C][C]0.3665[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=306784&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=306784&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	0.5846
R-squared	0.3417
RMSE	0.3665

Actuals, Predictions, and Residuals
#	Actuals	Forecasts	Residuals
1	1	0.75	0.25
2	0	0.0952380952380952	-0.0952380952380952
3	0	0.0952380952380952	-0.0952380952380952
4	0	0.0952380952380952	-0.0952380952380952
5	0	0.526315789473684	-0.526315789473684
6	1	0.75	0.25
7	0	0.0952380952380952	-0.0952380952380952
8	0	0.526315789473684	-0.526315789473684
9	0	0.0952380952380952	-0.0952380952380952
10	1	0.75	0.25
11	1	0.526315789473684	0.473684210526316
12	0	0.0952380952380952	-0.0952380952380952
13	0	0.0952380952380952	-0.0952380952380952
14	0	0.0952380952380952	-0.0952380952380952
15	1	0.75	0.25
16	1	0.0952380952380952	0.904761904761905
17	1	0.75	0.25
18	0	0.75	-0.75
19	0	0.0952380952380952	-0.0952380952380952
20	0	0.0952380952380952	-0.0952380952380952
21	0	0.0952380952380952	-0.0952380952380952
22	0	0.0952380952380952	-0.0952380952380952
23	0	0.0952380952380952	-0.0952380952380952
24	0	0.0952380952380952	-0.0952380952380952
25	0	0.0952380952380952	-0.0952380952380952
26	1	0.0952380952380952	0.904761904761905
27	1	0.526315789473684	0.473684210526316
28	0	0.75	-0.75
29	0	0.0952380952380952	-0.0952380952380952
30	0	0.0952380952380952	-0.0952380952380952
31	0	0.0952380952380952	-0.0952380952380952
32	0	0.0952380952380952	-0.0952380952380952
33	1	0.75	0.25
34	0	0.526315789473684	-0.526315789473684
35	1	0.75	0.25
36	0	0.0952380952380952	-0.0952380952380952
37	0	0.0952380952380952	-0.0952380952380952
38	0	0.75	-0.75
39	0	0.0952380952380952	-0.0952380952380952
40	0	0.0952380952380952	-0.0952380952380952
41	1	0.526315789473684	0.473684210526316
42	1	0.75	0.25
43	0	0.0952380952380952	-0.0952380952380952
44	1	0.0952380952380952	0.904761904761905
45	1	0.526315789473684	0.473684210526316
46	0	0.526315789473684	-0.526315789473684
47	0	0.0952380952380952	-0.0952380952380952
48	0	0.0952380952380952	-0.0952380952380952
49	1	0.0952380952380952	0.904761904761905
50	0	0.0952380952380952	-0.0952380952380952
51	1	0.526315789473684	0.473684210526316
52	0	0.0952380952380952	-0.0952380952380952
53	0	0.0952380952380952	-0.0952380952380952
54	0	0.0952380952380952	-0.0952380952380952
55	0	0.0952380952380952	-0.0952380952380952
56	0	0.526315789473684	-0.526315789473684
57	0	0.526315789473684	-0.526315789473684
58	0	0.0952380952380952	-0.0952380952380952
59	0	0.0952380952380952	-0.0952380952380952
60	1	0.526315789473684	0.473684210526316
61	0	0.0952380952380952	-0.0952380952380952
62	0	0.526315789473684	-0.526315789473684
63	1	0.526315789473684	0.473684210526316
64	0	0.0952380952380952	-0.0952380952380952
65	1	0.0952380952380952	0.904761904761905
66	0	0.0952380952380952	-0.0952380952380952
67	0	0.0952380952380952	-0.0952380952380952
68	0	0.0952380952380952	-0.0952380952380952
69	0	0.0952380952380952	-0.0952380952380952
70	0	0.0952380952380952	-0.0952380952380952
71	0	0.526315789473684	-0.526315789473684
72	0	0.0952380952380952	-0.0952380952380952
73	0	0.0952380952380952	-0.0952380952380952
74	1	0.75	0.25
75	1	0.526315789473684	0.473684210526316
76	1	0.526315789473684	0.473684210526316
77	0	0.0952380952380952	-0.0952380952380952
78	0	0.0952380952380952	-0.0952380952380952
79	0	0.0952380952380952	-0.0952380952380952
80	0	0.0952380952380952	-0.0952380952380952
81	1	0.526315789473684	0.473684210526316
82	1	0.0952380952380952	0.904761904761905
83	0	0.526315789473684	-0.526315789473684
84	0	0.75	-0.75
85	0	0.0952380952380952	-0.0952380952380952
86	0	0.0952380952380952	-0.0952380952380952
87	0	0.0952380952380952	-0.0952380952380952
88	0	0.0952380952380952	-0.0952380952380952
89	0	0.0952380952380952	-0.0952380952380952
90	1	0.75	0.25
91	1	0.75	0.25
92	0	0.0952380952380952	-0.0952380952380952
93	0	0.0952380952380952	-0.0952380952380952
94	1	0.75	0.25
95	0	0.0952380952380952	-0.0952380952380952
96	0	0.0952380952380952	-0.0952380952380952
97	0	0.0952380952380952	-0.0952380952380952
98	0	0.0952380952380952	-0.0952380952380952

\begin{tabular}{lllllllll}
\hline
Actuals, Predictions, and Residuals \tabularnewline
# & Actuals & Forecasts & Residuals \tabularnewline
1 & 1 & 0.75 & 0.25 \tabularnewline
2 & 0 & 0.0952380952380952 & -0.0952380952380952 \tabularnewline
3 & 0 & 0.0952380952380952 & -0.0952380952380952 \tabularnewline
4 & 0 & 0.0952380952380952 & -0.0952380952380952 \tabularnewline
5 & 0 & 0.526315789473684 & -0.526315789473684 \tabularnewline
6 & 1 & 0.75 & 0.25 \tabularnewline
7 & 0 & 0.0952380952380952 & -0.0952380952380952 \tabularnewline
8 & 0 & 0.526315789473684 & -0.526315789473684 \tabularnewline
9 & 0 & 0.0952380952380952 & -0.0952380952380952 \tabularnewline
10 & 1 & 0.75 & 0.25 \tabularnewline
11 & 1 & 0.526315789473684 & 0.473684210526316 \tabularnewline
12 & 0 & 0.0952380952380952 & -0.0952380952380952 \tabularnewline
13 & 0 & 0.0952380952380952 & -0.0952380952380952 \tabularnewline
14 & 0 & 0.0952380952380952 & -0.0952380952380952 \tabularnewline
15 & 1 & 0.75 & 0.25 \tabularnewline
16 & 1 & 0.0952380952380952 & 0.904761904761905 \tabularnewline
17 & 1 & 0.75 & 0.25 \tabularnewline
18 & 0 & 0.75 & -0.75 \tabularnewline
19 & 0 & 0.0952380952380952 & -0.0952380952380952 \tabularnewline
20 & 0 & 0.0952380952380952 & -0.0952380952380952 \tabularnewline
21 & 0 & 0.0952380952380952 & -0.0952380952380952 \tabularnewline
22 & 0 & 0.0952380952380952 & -0.0952380952380952 \tabularnewline
23 & 0 & 0.0952380952380952 & -0.0952380952380952 \tabularnewline
24 & 0 & 0.0952380952380952 & -0.0952380952380952 \tabularnewline
25 & 0 & 0.0952380952380952 & -0.0952380952380952 \tabularnewline
26 & 1 & 0.0952380952380952 & 0.904761904761905 \tabularnewline
27 & 1 & 0.526315789473684 & 0.473684210526316 \tabularnewline
28 & 0 & 0.75 & -0.75 \tabularnewline
29 & 0 & 0.0952380952380952 & -0.0952380952380952 \tabularnewline
30 & 0 & 0.0952380952380952 & -0.0952380952380952 \tabularnewline
31 & 0 & 0.0952380952380952 & -0.0952380952380952 \tabularnewline
32 & 0 & 0.0952380952380952 & -0.0952380952380952 \tabularnewline
33 & 1 & 0.75 & 0.25 \tabularnewline
34 & 0 & 0.526315789473684 & -0.526315789473684 \tabularnewline
35 & 1 & 0.75 & 0.25 \tabularnewline
36 & 0 & 0.0952380952380952 & -0.0952380952380952 \tabularnewline
37 & 0 & 0.0952380952380952 & -0.0952380952380952 \tabularnewline
38 & 0 & 0.75 & -0.75 \tabularnewline
39 & 0 & 0.0952380952380952 & -0.0952380952380952 \tabularnewline
40 & 0 & 0.0952380952380952 & -0.0952380952380952 \tabularnewline
41 & 1 & 0.526315789473684 & 0.473684210526316 \tabularnewline
42 & 1 & 0.75 & 0.25 \tabularnewline
43 & 0 & 0.0952380952380952 & -0.0952380952380952 \tabularnewline
44 & 1 & 0.0952380952380952 & 0.904761904761905 \tabularnewline
45 & 1 & 0.526315789473684 & 0.473684210526316 \tabularnewline
46 & 0 & 0.526315789473684 & -0.526315789473684 \tabularnewline
47 & 0 & 0.0952380952380952 & -0.0952380952380952 \tabularnewline
48 & 0 & 0.0952380952380952 & -0.0952380952380952 \tabularnewline
49 & 1 & 0.0952380952380952 & 0.904761904761905 \tabularnewline
50 & 0 & 0.0952380952380952 & -0.0952380952380952 \tabularnewline
51 & 1 & 0.526315789473684 & 0.473684210526316 \tabularnewline
52 & 0 & 0.0952380952380952 & -0.0952380952380952 \tabularnewline
53 & 0 & 0.0952380952380952 & -0.0952380952380952 \tabularnewline
54 & 0 & 0.0952380952380952 & -0.0952380952380952 \tabularnewline
55 & 0 & 0.0952380952380952 & -0.0952380952380952 \tabularnewline
56 & 0 & 0.526315789473684 & -0.526315789473684 \tabularnewline
57 & 0 & 0.526315789473684 & -0.526315789473684 \tabularnewline
58 & 0 & 0.0952380952380952 & -0.0952380952380952 \tabularnewline
59 & 0 & 0.0952380952380952 & -0.0952380952380952 \tabularnewline
60 & 1 & 0.526315789473684 & 0.473684210526316 \tabularnewline
61 & 0 & 0.0952380952380952 & -0.0952380952380952 \tabularnewline
62 & 0 & 0.526315789473684 & -0.526315789473684 \tabularnewline
63 & 1 & 0.526315789473684 & 0.473684210526316 \tabularnewline
64 & 0 & 0.0952380952380952 & -0.0952380952380952 \tabularnewline
65 & 1 & 0.0952380952380952 & 0.904761904761905 \tabularnewline
66 & 0 & 0.0952380952380952 & -0.0952380952380952 \tabularnewline
67 & 0 & 0.0952380952380952 & -0.0952380952380952 \tabularnewline
68 & 0 & 0.0952380952380952 & -0.0952380952380952 \tabularnewline
69 & 0 & 0.0952380952380952 & -0.0952380952380952 \tabularnewline
70 & 0 & 0.0952380952380952 & -0.0952380952380952 \tabularnewline
71 & 0 & 0.526315789473684 & -0.526315789473684 \tabularnewline
72 & 0 & 0.0952380952380952 & -0.0952380952380952 \tabularnewline
73 & 0 & 0.0952380952380952 & -0.0952380952380952 \tabularnewline
74 & 1 & 0.75 & 0.25 \tabularnewline
75 & 1 & 0.526315789473684 & 0.473684210526316 \tabularnewline
76 & 1 & 0.526315789473684 & 0.473684210526316 \tabularnewline
77 & 0 & 0.0952380952380952 & -0.0952380952380952 \tabularnewline
78 & 0 & 0.0952380952380952 & -0.0952380952380952 \tabularnewline
79 & 0 & 0.0952380952380952 & -0.0952380952380952 \tabularnewline
80 & 0 & 0.0952380952380952 & -0.0952380952380952 \tabularnewline
81 & 1 & 0.526315789473684 & 0.473684210526316 \tabularnewline
82 & 1 & 0.0952380952380952 & 0.904761904761905 \tabularnewline
83 & 0 & 0.526315789473684 & -0.526315789473684 \tabularnewline
84 & 0 & 0.75 & -0.75 \tabularnewline
85 & 0 & 0.0952380952380952 & -0.0952380952380952 \tabularnewline
86 & 0 & 0.0952380952380952 & -0.0952380952380952 \tabularnewline
87 & 0 & 0.0952380952380952 & -0.0952380952380952 \tabularnewline
88 & 0 & 0.0952380952380952 & -0.0952380952380952 \tabularnewline
89 & 0 & 0.0952380952380952 & -0.0952380952380952 \tabularnewline
90 & 1 & 0.75 & 0.25 \tabularnewline
91 & 1 & 0.75 & 0.25 \tabularnewline
92 & 0 & 0.0952380952380952 & -0.0952380952380952 \tabularnewline
93 & 0 & 0.0952380952380952 & -0.0952380952380952 \tabularnewline
94 & 1 & 0.75 & 0.25 \tabularnewline
95 & 0 & 0.0952380952380952 & -0.0952380952380952 \tabularnewline
96 & 0 & 0.0952380952380952 & -0.0952380952380952 \tabularnewline
97 & 0 & 0.0952380952380952 & -0.0952380952380952 \tabularnewline
98 & 0 & 0.0952380952380952 & -0.0952380952380952 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=306784&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]1[/C][C]0.75[/C][C]0.25[/C][/ROW]
[ROW][C]2[/C][C]0[/C][C]0.0952380952380952[/C][C]-0.0952380952380952[/C][/ROW]
[ROW][C]3[/C][C]0[/C][C]0.0952380952380952[/C][C]-0.0952380952380952[/C][/ROW]
[ROW][C]4[/C][C]0[/C][C]0.0952380952380952[/C][C]-0.0952380952380952[/C][/ROW]
[ROW][C]5[/C][C]0[/C][C]0.526315789473684[/C][C]-0.526315789473684[/C][/ROW]
[ROW][C]6[/C][C]1[/C][C]0.75[/C][C]0.25[/C][/ROW]
[ROW][C]7[/C][C]0[/C][C]0.0952380952380952[/C][C]-0.0952380952380952[/C][/ROW]
[ROW][C]8[/C][C]0[/C][C]0.526315789473684[/C][C]-0.526315789473684[/C][/ROW]
[ROW][C]9[/C][C]0[/C][C]0.0952380952380952[/C][C]-0.0952380952380952[/C][/ROW]
[ROW][C]10[/C][C]1[/C][C]0.75[/C][C]0.25[/C][/ROW]
[ROW][C]11[/C][C]1[/C][C]0.526315789473684[/C][C]0.473684210526316[/C][/ROW]
[ROW][C]12[/C][C]0[/C][C]0.0952380952380952[/C][C]-0.0952380952380952[/C][/ROW]
[ROW][C]13[/C][C]0[/C][C]0.0952380952380952[/C][C]-0.0952380952380952[/C][/ROW]
[ROW][C]14[/C][C]0[/C][C]0.0952380952380952[/C][C]-0.0952380952380952[/C][/ROW]
[ROW][C]15[/C][C]1[/C][C]0.75[/C][C]0.25[/C][/ROW]
[ROW][C]16[/C][C]1[/C][C]0.0952380952380952[/C][C]0.904761904761905[/C][/ROW]
[ROW][C]17[/C][C]1[/C][C]0.75[/C][C]0.25[/C][/ROW]
[ROW][C]18[/C][C]0[/C][C]0.75[/C][C]-0.75[/C][/ROW]
[ROW][C]19[/C][C]0[/C][C]0.0952380952380952[/C][C]-0.0952380952380952[/C][/ROW]
[ROW][C]20[/C][C]0[/C][C]0.0952380952380952[/C][C]-0.0952380952380952[/C][/ROW]
[ROW][C]21[/C][C]0[/C][C]0.0952380952380952[/C][C]-0.0952380952380952[/C][/ROW]
[ROW][C]22[/C][C]0[/C][C]0.0952380952380952[/C][C]-0.0952380952380952[/C][/ROW]
[ROW][C]23[/C][C]0[/C][C]0.0952380952380952[/C][C]-0.0952380952380952[/C][/ROW]
[ROW][C]24[/C][C]0[/C][C]0.0952380952380952[/C][C]-0.0952380952380952[/C][/ROW]
[ROW][C]25[/C][C]0[/C][C]0.0952380952380952[/C][C]-0.0952380952380952[/C][/ROW]
[ROW][C]26[/C][C]1[/C][C]0.0952380952380952[/C][C]0.904761904761905[/C][/ROW]
[ROW][C]27[/C][C]1[/C][C]0.526315789473684[/C][C]0.473684210526316[/C][/ROW]
[ROW][C]28[/C][C]0[/C][C]0.75[/C][C]-0.75[/C][/ROW]
[ROW][C]29[/C][C]0[/C][C]0.0952380952380952[/C][C]-0.0952380952380952[/C][/ROW]
[ROW][C]30[/C][C]0[/C][C]0.0952380952380952[/C][C]-0.0952380952380952[/C][/ROW]
[ROW][C]31[/C][C]0[/C][C]0.0952380952380952[/C][C]-0.0952380952380952[/C][/ROW]
[ROW][C]32[/C][C]0[/C][C]0.0952380952380952[/C][C]-0.0952380952380952[/C][/ROW]
[ROW][C]33[/C][C]1[/C][C]0.75[/C][C]0.25[/C][/ROW]
[ROW][C]34[/C][C]0[/C][C]0.526315789473684[/C][C]-0.526315789473684[/C][/ROW]
[ROW][C]35[/C][C]1[/C][C]0.75[/C][C]0.25[/C][/ROW]
[ROW][C]36[/C][C]0[/C][C]0.0952380952380952[/C][C]-0.0952380952380952[/C][/ROW]
[ROW][C]37[/C][C]0[/C][C]0.0952380952380952[/C][C]-0.0952380952380952[/C][/ROW]
[ROW][C]38[/C][C]0[/C][C]0.75[/C][C]-0.75[/C][/ROW]
[ROW][C]39[/C][C]0[/C][C]0.0952380952380952[/C][C]-0.0952380952380952[/C][/ROW]
[ROW][C]40[/C][C]0[/C][C]0.0952380952380952[/C][C]-0.0952380952380952[/C][/ROW]
[ROW][C]41[/C][C]1[/C][C]0.526315789473684[/C][C]0.473684210526316[/C][/ROW]
[ROW][C]42[/C][C]1[/C][C]0.75[/C][C]0.25[/C][/ROW]
[ROW][C]43[/C][C]0[/C][C]0.0952380952380952[/C][C]-0.0952380952380952[/C][/ROW]
[ROW][C]44[/C][C]1[/C][C]0.0952380952380952[/C][C]0.904761904761905[/C][/ROW]
[ROW][C]45[/C][C]1[/C][C]0.526315789473684[/C][C]0.473684210526316[/C][/ROW]
[ROW][C]46[/C][C]0[/C][C]0.526315789473684[/C][C]-0.526315789473684[/C][/ROW]
[ROW][C]47[/C][C]0[/C][C]0.0952380952380952[/C][C]-0.0952380952380952[/C][/ROW]
[ROW][C]48[/C][C]0[/C][C]0.0952380952380952[/C][C]-0.0952380952380952[/C][/ROW]
[ROW][C]49[/C][C]1[/C][C]0.0952380952380952[/C][C]0.904761904761905[/C][/ROW]
[ROW][C]50[/C][C]0[/C][C]0.0952380952380952[/C][C]-0.0952380952380952[/C][/ROW]
[ROW][C]51[/C][C]1[/C][C]0.526315789473684[/C][C]0.473684210526316[/C][/ROW]
[ROW][C]52[/C][C]0[/C][C]0.0952380952380952[/C][C]-0.0952380952380952[/C][/ROW]
[ROW][C]53[/C][C]0[/C][C]0.0952380952380952[/C][C]-0.0952380952380952[/C][/ROW]
[ROW][C]54[/C][C]0[/C][C]0.0952380952380952[/C][C]-0.0952380952380952[/C][/ROW]
[ROW][C]55[/C][C]0[/C][C]0.0952380952380952[/C][C]-0.0952380952380952[/C][/ROW]
[ROW][C]56[/C][C]0[/C][C]0.526315789473684[/C][C]-0.526315789473684[/C][/ROW]
[ROW][C]57[/C][C]0[/C][C]0.526315789473684[/C][C]-0.526315789473684[/C][/ROW]
[ROW][C]58[/C][C]0[/C][C]0.0952380952380952[/C][C]-0.0952380952380952[/C][/ROW]
[ROW][C]59[/C][C]0[/C][C]0.0952380952380952[/C][C]-0.0952380952380952[/C][/ROW]
[ROW][C]60[/C][C]1[/C][C]0.526315789473684[/C][C]0.473684210526316[/C][/ROW]
[ROW][C]61[/C][C]0[/C][C]0.0952380952380952[/C][C]-0.0952380952380952[/C][/ROW]
[ROW][C]62[/C][C]0[/C][C]0.526315789473684[/C][C]-0.526315789473684[/C][/ROW]
[ROW][C]63[/C][C]1[/C][C]0.526315789473684[/C][C]0.473684210526316[/C][/ROW]
[ROW][C]64[/C][C]0[/C][C]0.0952380952380952[/C][C]-0.0952380952380952[/C][/ROW]
[ROW][C]65[/C][C]1[/C][C]0.0952380952380952[/C][C]0.904761904761905[/C][/ROW]
[ROW][C]66[/C][C]0[/C][C]0.0952380952380952[/C][C]-0.0952380952380952[/C][/ROW]
[ROW][C]67[/C][C]0[/C][C]0.0952380952380952[/C][C]-0.0952380952380952[/C][/ROW]
[ROW][C]68[/C][C]0[/C][C]0.0952380952380952[/C][C]-0.0952380952380952[/C][/ROW]
[ROW][C]69[/C][C]0[/C][C]0.0952380952380952[/C][C]-0.0952380952380952[/C][/ROW]
[ROW][C]70[/C][C]0[/C][C]0.0952380952380952[/C][C]-0.0952380952380952[/C][/ROW]
[ROW][C]71[/C][C]0[/C][C]0.526315789473684[/C][C]-0.526315789473684[/C][/ROW]
[ROW][C]72[/C][C]0[/C][C]0.0952380952380952[/C][C]-0.0952380952380952[/C][/ROW]
[ROW][C]73[/C][C]0[/C][C]0.0952380952380952[/C][C]-0.0952380952380952[/C][/ROW]
[ROW][C]74[/C][C]1[/C][C]0.75[/C][C]0.25[/C][/ROW]
[ROW][C]75[/C][C]1[/C][C]0.526315789473684[/C][C]0.473684210526316[/C][/ROW]
[ROW][C]76[/C][C]1[/C][C]0.526315789473684[/C][C]0.473684210526316[/C][/ROW]
[ROW][C]77[/C][C]0[/C][C]0.0952380952380952[/C][C]-0.0952380952380952[/C][/ROW]
[ROW][C]78[/C][C]0[/C][C]0.0952380952380952[/C][C]-0.0952380952380952[/C][/ROW]
[ROW][C]79[/C][C]0[/C][C]0.0952380952380952[/C][C]-0.0952380952380952[/C][/ROW]
[ROW][C]80[/C][C]0[/C][C]0.0952380952380952[/C][C]-0.0952380952380952[/C][/ROW]
[ROW][C]81[/C][C]1[/C][C]0.526315789473684[/C][C]0.473684210526316[/C][/ROW]
[ROW][C]82[/C][C]1[/C][C]0.0952380952380952[/C][C]0.904761904761905[/C][/ROW]
[ROW][C]83[/C][C]0[/C][C]0.526315789473684[/C][C]-0.526315789473684[/C][/ROW]
[ROW][C]84[/C][C]0[/C][C]0.75[/C][C]-0.75[/C][/ROW]
[ROW][C]85[/C][C]0[/C][C]0.0952380952380952[/C][C]-0.0952380952380952[/C][/ROW]
[ROW][C]86[/C][C]0[/C][C]0.0952380952380952[/C][C]-0.0952380952380952[/C][/ROW]
[ROW][C]87[/C][C]0[/C][C]0.0952380952380952[/C][C]-0.0952380952380952[/C][/ROW]
[ROW][C]88[/C][C]0[/C][C]0.0952380952380952[/C][C]-0.0952380952380952[/C][/ROW]
[ROW][C]89[/C][C]0[/C][C]0.0952380952380952[/C][C]-0.0952380952380952[/C][/ROW]
[ROW][C]90[/C][C]1[/C][C]0.75[/C][C]0.25[/C][/ROW]
[ROW][C]91[/C][C]1[/C][C]0.75[/C][C]0.25[/C][/ROW]
[ROW][C]92[/C][C]0[/C][C]0.0952380952380952[/C][C]-0.0952380952380952[/C][/ROW]
[ROW][C]93[/C][C]0[/C][C]0.0952380952380952[/C][C]-0.0952380952380952[/C][/ROW]
[ROW][C]94[/C][C]1[/C][C]0.75[/C][C]0.25[/C][/ROW]
[ROW][C]95[/C][C]0[/C][C]0.0952380952380952[/C][C]-0.0952380952380952[/C][/ROW]
[ROW][C]96[/C][C]0[/C][C]0.0952380952380952[/C][C]-0.0952380952380952[/C][/ROW]
[ROW][C]97[/C][C]0[/C][C]0.0952380952380952[/C][C]-0.0952380952380952[/C][/ROW]
[ROW][C]98[/C][C]0[/C][C]0.0952380952380952[/C][C]-0.0952380952380952[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=306784&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=306784&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	1	0.75	0.25
2	0	0.0952380952380952	-0.0952380952380952
3	0	0.0952380952380952	-0.0952380952380952
4	0	0.0952380952380952	-0.0952380952380952
5	0	0.526315789473684	-0.526315789473684
6	1	0.75	0.25
7	0	0.0952380952380952	-0.0952380952380952
8	0	0.526315789473684	-0.526315789473684
9	0	0.0952380952380952	-0.0952380952380952
10	1	0.75	0.25
11	1	0.526315789473684	0.473684210526316
12	0	0.0952380952380952	-0.0952380952380952
13	0	0.0952380952380952	-0.0952380952380952
14	0	0.0952380952380952	-0.0952380952380952
15	1	0.75	0.25
16	1	0.0952380952380952	0.904761904761905
17	1	0.75	0.25
18	0	0.75	-0.75
19	0	0.0952380952380952	-0.0952380952380952
20	0	0.0952380952380952	-0.0952380952380952
21	0	0.0952380952380952	-0.0952380952380952
22	0	0.0952380952380952	-0.0952380952380952
23	0	0.0952380952380952	-0.0952380952380952
24	0	0.0952380952380952	-0.0952380952380952
25	0	0.0952380952380952	-0.0952380952380952
26	1	0.0952380952380952	0.904761904761905
27	1	0.526315789473684	0.473684210526316
28	0	0.75	-0.75
29	0	0.0952380952380952	-0.0952380952380952
30	0	0.0952380952380952	-0.0952380952380952
31	0	0.0952380952380952	-0.0952380952380952
32	0	0.0952380952380952	-0.0952380952380952
33	1	0.75	0.25
34	0	0.526315789473684	-0.526315789473684
35	1	0.75	0.25
36	0	0.0952380952380952	-0.0952380952380952
37	0	0.0952380952380952	-0.0952380952380952
38	0	0.75	-0.75
39	0	0.0952380952380952	-0.0952380952380952
40	0	0.0952380952380952	-0.0952380952380952
41	1	0.526315789473684	0.473684210526316
42	1	0.75	0.25
43	0	0.0952380952380952	-0.0952380952380952
44	1	0.0952380952380952	0.904761904761905
45	1	0.526315789473684	0.473684210526316
46	0	0.526315789473684	-0.526315789473684
47	0	0.0952380952380952	-0.0952380952380952
48	0	0.0952380952380952	-0.0952380952380952
49	1	0.0952380952380952	0.904761904761905
50	0	0.0952380952380952	-0.0952380952380952
51	1	0.526315789473684	0.473684210526316
52	0	0.0952380952380952	-0.0952380952380952
53	0	0.0952380952380952	-0.0952380952380952
54	0	0.0952380952380952	-0.0952380952380952
55	0	0.0952380952380952	-0.0952380952380952
56	0	0.526315789473684	-0.526315789473684
57	0	0.526315789473684	-0.526315789473684
58	0	0.0952380952380952	-0.0952380952380952
59	0	0.0952380952380952	-0.0952380952380952
60	1	0.526315789473684	0.473684210526316
61	0	0.0952380952380952	-0.0952380952380952
62	0	0.526315789473684	-0.526315789473684
63	1	0.526315789473684	0.473684210526316
64	0	0.0952380952380952	-0.0952380952380952
65	1	0.0952380952380952	0.904761904761905
66	0	0.0952380952380952	-0.0952380952380952
67	0	0.0952380952380952	-0.0952380952380952
68	0	0.0952380952380952	-0.0952380952380952
69	0	0.0952380952380952	-0.0952380952380952
70	0	0.0952380952380952	-0.0952380952380952
71	0	0.526315789473684	-0.526315789473684
72	0	0.0952380952380952	-0.0952380952380952
73	0	0.0952380952380952	-0.0952380952380952
74	1	0.75	0.25
75	1	0.526315789473684	0.473684210526316
76	1	0.526315789473684	0.473684210526316
77	0	0.0952380952380952	-0.0952380952380952
78	0	0.0952380952380952	-0.0952380952380952
79	0	0.0952380952380952	-0.0952380952380952
80	0	0.0952380952380952	-0.0952380952380952
81	1	0.526315789473684	0.473684210526316
82	1	0.0952380952380952	0.904761904761905
83	0	0.526315789473684	-0.526315789473684
84	0	0.75	-0.75
85	0	0.0952380952380952	-0.0952380952380952
86	0	0.0952380952380952	-0.0952380952380952
87	0	0.0952380952380952	-0.0952380952380952
88	0	0.0952380952380952	-0.0952380952380952
89	0	0.0952380952380952	-0.0952380952380952
90	1	0.75	0.25
91	1	0.75	0.25
92	0	0.0952380952380952	-0.0952380952380952
93	0	0.0952380952380952	-0.0952380952380952
94	1	0.75	0.25
95	0	0.0952380952380952	-0.0952380952380952
96	0	0.0952380952380952	-0.0952380952380952
97	0	0.0952380952380952	-0.0952380952380952
98	0	0.0952380952380952	-0.0952380952380952

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')
}
}
print(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