Repository of Reproducible Computations

Free Statistics

of Irreproducible Research!

Author's title

Author

*The author of this computation has been verified*

R Software Module

rwasp_regression_trees1.wasp

Title produced by software

Recursive Partitioning (Regression Trees)

Date of computation

Thu, 06 Dec 2012 11:38:41 -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/2012/Dec/06/t1354811987jwxp7w9st18tq3m.htm/, Retrieved Tue, 16 Apr 2024 18:34:04 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=197171, Retrieved Tue, 16 Apr 2024 18:34:04 +0000

QR Codes:

Paste this QR Code to cite your computation.

Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords

Estimated Impact

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

-       [Recursive Partitioning (Regression Trees)] [WS10 Recursive Pa...] [2012-12-06 16:38:41] [b126d3b292555ea554033ae826bcef2a] [Current]

Feedback Forum

Post a new message

Dataseries X:

Download CSV

Histogram

Boxplots

1	1	4	0	2
1	1	0	0	2
0	1	4	1	1.5
0	0	0	0	0
1	1	0	1	1
1	1	0	1	2
1	1	0	1	2
0	1	0	1	1
0	1	4	1	2
1	1	1	0	2
0	0	4	0	2
0	1	0	1	0
0	1	2	1	0
0	1	0	0	2
0	0	0	NA	NA
1	1	0	1	2
1	1	1	0	2
1	1	0	1	0.5
0	1	0	1	2
0	0	2	1	0
1	1	2	1	2
1	1	1	0	0
0	0	2	NA	NA
1	0	0	NA	NA
1	1	3	1	2
1	0	0	1	0
1	1	0	NA	NA
0	0	0	NA	NA
0	0	1	0	2
1	1	0	1	1
1	0	0	0	0.5
1	1	4	0	2
0	0	0	1	0.5
0	0	1	NA	NA
0	0	0	1	0.5
1	1	0	NA	NA
1	1	4	0	2
0	1	1	1	0
0	1	0	1	1
1	1	4	1	2
1	1	0	1	1
1	1	4	1	2
1	1	0	0	0
1	1	0	1	0.5
0	0	0	1	0
0	1	4	1	2
0	1	0	0	0
1	1	0	0	1
1	1	4	1	2
0	0	4	0	0.5
0	1	0	1	2
1	1	1	1	2
0	1	0	1	2
0	0	4	NA	NA
0	1	0	0	0
0	1	2	1	0
0	1	0	1	0.5
0	1	4	NA	NA
0	0	4	0	2
0	0	0	NA	NA
0	1	0	1	0
1	1	4	1	2
1	1	0	1	1
1	0	0	1	0
0	0	2	1	2
0	1	0	0	1
0	1	0	1	2
0	0	0	0	0
1	1	4	1	1
1	1	4	1	2
0	1	2	0	0
0	1	0	0	0
0	1	0	0	0
0	1	4	0	0
1	1	0	1	2
1	0	0	1	2
0	0	1	1	2
1	1	2	1	2
1	0	0	1	2
1	1	2	1	2
0	0	0	1	2
0	0	4	1	2
0	0	4	1	2
1	0	0	1	2
0	0	0	NA	NA
0	0	4	1	2
1	0	0	NA	NA
1	1	4	1	2
0	0	2	1	2
0	0	2	NA	NA
1	1	0	0	0
1	1	0	1	2
1	1	4	NA	NA
0	1	0	1	2
1	1	0	1	2
1	1	0	1	2
1	1	4	1	2
1	1	4	1	2
0	0	0	NA	NA
0	0	0	0	0
1	1	2	0	0
0	0	1	1	2
0	0	0	0	0
0	0	2	1	2
0	1	1	0	0

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	6 seconds
R Server	'Sir Ronald Aylmer Fisher' @ fisher.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 & 6 seconds \tabularnewline
R Server & 'Sir Ronald Aylmer Fisher' @ fisher.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=197171&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]6 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Sir Ronald Aylmer Fisher' @ fisher.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=197171&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=197171&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	6 seconds
R Server	'Sir Ronald Aylmer Fisher' @ fisher.wessa.net

Goodness of Fit
Correlation	0.433
R-squared	0.1875
RMSE	0.7928

\begin{tabular}{lllllllll}
\hline
Goodness of Fit \tabularnewline
Correlation & 0.433 \tabularnewline
R-squared & 0.1875 \tabularnewline
RMSE & 0.7928 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=197171&T=1

[TABLE]
[ROW][C]Goodness of Fit[/C][/ROW]
[ROW][C]Correlation[/C][C]0.433[/C][/ROW]
[ROW][C]R-squared[/C][C]0.1875[/C][/ROW]
[ROW][C]RMSE[/C][C]0.7928[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=197171&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=197171&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	0.433
R-squared	0.1875
RMSE	0.7928

Actuals, Predictions, and Residuals
#	Actuals	Forecasts	Residuals
1	2	0.793103448275862	1.20689655172414
2	2	0.793103448275862	1.20689655172414
3	1.5	1.90625	-0.40625
4	0	0.793103448275862	-0.793103448275862
5	1	1.3	-0.3
6	2	1.3	0.7
7	2	1.3	0.7
8	1	1.3	-0.3
9	2	1.90625	0.09375
10	2	0.793103448275862	1.20689655172414
11	2	0.793103448275862	1.20689655172414
12	0	1.3	-1.3
13	0	1.3	-1.3
14	2	0.793103448275862	1.20689655172414
15	2	1.3	0.7
16	2	0.793103448275862	1.20689655172414
17	0.5	1.3	-0.8
18	2	1.3	0.7
19	0	1.3	-1.3
20	2	1.3	0.7
21	0	0.793103448275862	-0.793103448275862
22	2	1.90625	0.09375
23	0	1.3	-1.3
24	2	0.793103448275862	1.20689655172414
25	1	1.3	-0.3
26	0.5	0.793103448275862	-0.293103448275862
27	2	0.793103448275862	1.20689655172414
28	0.5	1.3	-0.8
29	0.5	1.3	-0.8
30	2	0.793103448275862	1.20689655172414
31	0	1.3	-1.3
32	1	1.3	-0.3
33	2	1.90625	0.09375
34	1	1.3	-0.3
35	2	1.90625	0.09375
36	0	0.793103448275862	-0.793103448275862
37	0.5	1.3	-0.8
38	0	1.3	-1.3
39	2	1.90625	0.09375
40	0	0.793103448275862	-0.793103448275862
41	1	0.793103448275862	0.206896551724138
42	2	1.90625	0.09375
43	0.5	0.793103448275862	-0.293103448275862
44	2	1.3	0.7
45	2	1.3	0.7
46	2	1.3	0.7
47	0	0.793103448275862	-0.793103448275862
48	0	1.3	-1.3
49	0.5	1.3	-0.8
50	2	0.793103448275862	1.20689655172414
51	0	1.3	-1.3
52	2	1.90625	0.09375
53	1	1.3	-0.3
54	0	1.3	-1.3
55	2	1.3	0.7
56	1	0.793103448275862	0.206896551724138
57	2	1.3	0.7
58	0	0.793103448275862	-0.793103448275862
59	1	1.90625	-0.90625
60	2	1.90625	0.09375
61	0	0.793103448275862	-0.793103448275862
62	0	0.793103448275862	-0.793103448275862
63	0	0.793103448275862	-0.793103448275862
64	0	0.793103448275862	-0.793103448275862
65	2	1.3	0.7
66	2	1.3	0.7
67	2	1.3	0.7
68	2	1.3	0.7
69	2	1.3	0.7
70	2	1.3	0.7
71	2	1.3	0.7
72	2	1.90625	0.09375
73	2	1.90625	0.09375
74	2	1.3	0.7
75	2	1.90625	0.09375
76	2	1.90625	0.09375
77	2	1.3	0.7
78	0	0.793103448275862	-0.793103448275862
79	2	1.3	0.7
80	2	1.3	0.7
81	2	1.3	0.7
82	2	1.3	0.7
83	2	1.90625	0.09375
84	2	1.90625	0.09375
85	0	0.793103448275862	-0.793103448275862
86	0	0.793103448275862	-0.793103448275862
87	2	1.3	0.7
88	0	0.793103448275862	-0.793103448275862
89	2	1.3	0.7
90	0	0.793103448275862	-0.793103448275862

\begin{tabular}{lllllllll}
\hline
Actuals, Predictions, and Residuals \tabularnewline
# & Actuals & Forecasts & Residuals \tabularnewline
1 & 2 & 0.793103448275862 & 1.20689655172414 \tabularnewline
2 & 2 & 0.793103448275862 & 1.20689655172414 \tabularnewline
3 & 1.5 & 1.90625 & -0.40625 \tabularnewline
4 & 0 & 0.793103448275862 & -0.793103448275862 \tabularnewline
5 & 1 & 1.3 & -0.3 \tabularnewline
6 & 2 & 1.3 & 0.7 \tabularnewline
7 & 2 & 1.3 & 0.7 \tabularnewline
8 & 1 & 1.3 & -0.3 \tabularnewline
9 & 2 & 1.90625 & 0.09375 \tabularnewline
10 & 2 & 0.793103448275862 & 1.20689655172414 \tabularnewline
11 & 2 & 0.793103448275862 & 1.20689655172414 \tabularnewline
12 & 0 & 1.3 & -1.3 \tabularnewline
13 & 0 & 1.3 & -1.3 \tabularnewline
14 & 2 & 0.793103448275862 & 1.20689655172414 \tabularnewline
15 & 2 & 1.3 & 0.7 \tabularnewline
16 & 2 & 0.793103448275862 & 1.20689655172414 \tabularnewline
17 & 0.5 & 1.3 & -0.8 \tabularnewline
18 & 2 & 1.3 & 0.7 \tabularnewline
19 & 0 & 1.3 & -1.3 \tabularnewline
20 & 2 & 1.3 & 0.7 \tabularnewline
21 & 0 & 0.793103448275862 & -0.793103448275862 \tabularnewline
22 & 2 & 1.90625 & 0.09375 \tabularnewline
23 & 0 & 1.3 & -1.3 \tabularnewline
24 & 2 & 0.793103448275862 & 1.20689655172414 \tabularnewline
25 & 1 & 1.3 & -0.3 \tabularnewline
26 & 0.5 & 0.793103448275862 & -0.293103448275862 \tabularnewline
27 & 2 & 0.793103448275862 & 1.20689655172414 \tabularnewline
28 & 0.5 & 1.3 & -0.8 \tabularnewline
29 & 0.5 & 1.3 & -0.8 \tabularnewline
30 & 2 & 0.793103448275862 & 1.20689655172414 \tabularnewline
31 & 0 & 1.3 & -1.3 \tabularnewline
32 & 1 & 1.3 & -0.3 \tabularnewline
33 & 2 & 1.90625 & 0.09375 \tabularnewline
34 & 1 & 1.3 & -0.3 \tabularnewline
35 & 2 & 1.90625 & 0.09375 \tabularnewline
36 & 0 & 0.793103448275862 & -0.793103448275862 \tabularnewline
37 & 0.5 & 1.3 & -0.8 \tabularnewline
38 & 0 & 1.3 & -1.3 \tabularnewline
39 & 2 & 1.90625 & 0.09375 \tabularnewline
40 & 0 & 0.793103448275862 & -0.793103448275862 \tabularnewline
41 & 1 & 0.793103448275862 & 0.206896551724138 \tabularnewline
42 & 2 & 1.90625 & 0.09375 \tabularnewline
43 & 0.5 & 0.793103448275862 & -0.293103448275862 \tabularnewline
44 & 2 & 1.3 & 0.7 \tabularnewline
45 & 2 & 1.3 & 0.7 \tabularnewline
46 & 2 & 1.3 & 0.7 \tabularnewline
47 & 0 & 0.793103448275862 & -0.793103448275862 \tabularnewline
48 & 0 & 1.3 & -1.3 \tabularnewline
49 & 0.5 & 1.3 & -0.8 \tabularnewline
50 & 2 & 0.793103448275862 & 1.20689655172414 \tabularnewline
51 & 0 & 1.3 & -1.3 \tabularnewline
52 & 2 & 1.90625 & 0.09375 \tabularnewline
53 & 1 & 1.3 & -0.3 \tabularnewline
54 & 0 & 1.3 & -1.3 \tabularnewline
55 & 2 & 1.3 & 0.7 \tabularnewline
56 & 1 & 0.793103448275862 & 0.206896551724138 \tabularnewline
57 & 2 & 1.3 & 0.7 \tabularnewline
58 & 0 & 0.793103448275862 & -0.793103448275862 \tabularnewline
59 & 1 & 1.90625 & -0.90625 \tabularnewline
60 & 2 & 1.90625 & 0.09375 \tabularnewline
61 & 0 & 0.793103448275862 & -0.793103448275862 \tabularnewline
62 & 0 & 0.793103448275862 & -0.793103448275862 \tabularnewline
63 & 0 & 0.793103448275862 & -0.793103448275862 \tabularnewline
64 & 0 & 0.793103448275862 & -0.793103448275862 \tabularnewline
65 & 2 & 1.3 & 0.7 \tabularnewline
66 & 2 & 1.3 & 0.7 \tabularnewline
67 & 2 & 1.3 & 0.7 \tabularnewline
68 & 2 & 1.3 & 0.7 \tabularnewline
69 & 2 & 1.3 & 0.7 \tabularnewline
70 & 2 & 1.3 & 0.7 \tabularnewline
71 & 2 & 1.3 & 0.7 \tabularnewline
72 & 2 & 1.90625 & 0.09375 \tabularnewline
73 & 2 & 1.90625 & 0.09375 \tabularnewline
74 & 2 & 1.3 & 0.7 \tabularnewline
75 & 2 & 1.90625 & 0.09375 \tabularnewline
76 & 2 & 1.90625 & 0.09375 \tabularnewline
77 & 2 & 1.3 & 0.7 \tabularnewline
78 & 0 & 0.793103448275862 & -0.793103448275862 \tabularnewline
79 & 2 & 1.3 & 0.7 \tabularnewline
80 & 2 & 1.3 & 0.7 \tabularnewline
81 & 2 & 1.3 & 0.7 \tabularnewline
82 & 2 & 1.3 & 0.7 \tabularnewline
83 & 2 & 1.90625 & 0.09375 \tabularnewline
84 & 2 & 1.90625 & 0.09375 \tabularnewline
85 & 0 & 0.793103448275862 & -0.793103448275862 \tabularnewline
86 & 0 & 0.793103448275862 & -0.793103448275862 \tabularnewline
87 & 2 & 1.3 & 0.7 \tabularnewline
88 & 0 & 0.793103448275862 & -0.793103448275862 \tabularnewline
89 & 2 & 1.3 & 0.7 \tabularnewline
90 & 0 & 0.793103448275862 & -0.793103448275862 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=197171&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]2[/C][C]0.793103448275862[/C][C]1.20689655172414[/C][/ROW]
[ROW][C]2[/C][C]2[/C][C]0.793103448275862[/C][C]1.20689655172414[/C][/ROW]
[ROW][C]3[/C][C]1.5[/C][C]1.90625[/C][C]-0.40625[/C][/ROW]
[ROW][C]4[/C][C]0[/C][C]0.793103448275862[/C][C]-0.793103448275862[/C][/ROW]
[ROW][C]5[/C][C]1[/C][C]1.3[/C][C]-0.3[/C][/ROW]
[ROW][C]6[/C][C]2[/C][C]1.3[/C][C]0.7[/C][/ROW]
[ROW][C]7[/C][C]2[/C][C]1.3[/C][C]0.7[/C][/ROW]
[ROW][C]8[/C][C]1[/C][C]1.3[/C][C]-0.3[/C][/ROW]
[ROW][C]9[/C][C]2[/C][C]1.90625[/C][C]0.09375[/C][/ROW]
[ROW][C]10[/C][C]2[/C][C]0.793103448275862[/C][C]1.20689655172414[/C][/ROW]
[ROW][C]11[/C][C]2[/C][C]0.793103448275862[/C][C]1.20689655172414[/C][/ROW]
[ROW][C]12[/C][C]0[/C][C]1.3[/C][C]-1.3[/C][/ROW]
[ROW][C]13[/C][C]0[/C][C]1.3[/C][C]-1.3[/C][/ROW]
[ROW][C]14[/C][C]2[/C][C]0.793103448275862[/C][C]1.20689655172414[/C][/ROW]
[ROW][C]15[/C][C]2[/C][C]1.3[/C][C]0.7[/C][/ROW]
[ROW][C]16[/C][C]2[/C][C]0.793103448275862[/C][C]1.20689655172414[/C][/ROW]
[ROW][C]17[/C][C]0.5[/C][C]1.3[/C][C]-0.8[/C][/ROW]
[ROW][C]18[/C][C]2[/C][C]1.3[/C][C]0.7[/C][/ROW]
[ROW][C]19[/C][C]0[/C][C]1.3[/C][C]-1.3[/C][/ROW]
[ROW][C]20[/C][C]2[/C][C]1.3[/C][C]0.7[/C][/ROW]
[ROW][C]21[/C][C]0[/C][C]0.793103448275862[/C][C]-0.793103448275862[/C][/ROW]
[ROW][C]22[/C][C]2[/C][C]1.90625[/C][C]0.09375[/C][/ROW]
[ROW][C]23[/C][C]0[/C][C]1.3[/C][C]-1.3[/C][/ROW]
[ROW][C]24[/C][C]2[/C][C]0.793103448275862[/C][C]1.20689655172414[/C][/ROW]
[ROW][C]25[/C][C]1[/C][C]1.3[/C][C]-0.3[/C][/ROW]
[ROW][C]26[/C][C]0.5[/C][C]0.793103448275862[/C][C]-0.293103448275862[/C][/ROW]
[ROW][C]27[/C][C]2[/C][C]0.793103448275862[/C][C]1.20689655172414[/C][/ROW]
[ROW][C]28[/C][C]0.5[/C][C]1.3[/C][C]-0.8[/C][/ROW]
[ROW][C]29[/C][C]0.5[/C][C]1.3[/C][C]-0.8[/C][/ROW]
[ROW][C]30[/C][C]2[/C][C]0.793103448275862[/C][C]1.20689655172414[/C][/ROW]
[ROW][C]31[/C][C]0[/C][C]1.3[/C][C]-1.3[/C][/ROW]
[ROW][C]32[/C][C]1[/C][C]1.3[/C][C]-0.3[/C][/ROW]
[ROW][C]33[/C][C]2[/C][C]1.90625[/C][C]0.09375[/C][/ROW]
[ROW][C]34[/C][C]1[/C][C]1.3[/C][C]-0.3[/C][/ROW]
[ROW][C]35[/C][C]2[/C][C]1.90625[/C][C]0.09375[/C][/ROW]
[ROW][C]36[/C][C]0[/C][C]0.793103448275862[/C][C]-0.793103448275862[/C][/ROW]
[ROW][C]37[/C][C]0.5[/C][C]1.3[/C][C]-0.8[/C][/ROW]
[ROW][C]38[/C][C]0[/C][C]1.3[/C][C]-1.3[/C][/ROW]
[ROW][C]39[/C][C]2[/C][C]1.90625[/C][C]0.09375[/C][/ROW]
[ROW][C]40[/C][C]0[/C][C]0.793103448275862[/C][C]-0.793103448275862[/C][/ROW]
[ROW][C]41[/C][C]1[/C][C]0.793103448275862[/C][C]0.206896551724138[/C][/ROW]
[ROW][C]42[/C][C]2[/C][C]1.90625[/C][C]0.09375[/C][/ROW]
[ROW][C]43[/C][C]0.5[/C][C]0.793103448275862[/C][C]-0.293103448275862[/C][/ROW]
[ROW][C]44[/C][C]2[/C][C]1.3[/C][C]0.7[/C][/ROW]
[ROW][C]45[/C][C]2[/C][C]1.3[/C][C]0.7[/C][/ROW]
[ROW][C]46[/C][C]2[/C][C]1.3[/C][C]0.7[/C][/ROW]
[ROW][C]47[/C][C]0[/C][C]0.793103448275862[/C][C]-0.793103448275862[/C][/ROW]
[ROW][C]48[/C][C]0[/C][C]1.3[/C][C]-1.3[/C][/ROW]
[ROW][C]49[/C][C]0.5[/C][C]1.3[/C][C]-0.8[/C][/ROW]
[ROW][C]50[/C][C]2[/C][C]0.793103448275862[/C][C]1.20689655172414[/C][/ROW]
[ROW][C]51[/C][C]0[/C][C]1.3[/C][C]-1.3[/C][/ROW]
[ROW][C]52[/C][C]2[/C][C]1.90625[/C][C]0.09375[/C][/ROW]
[ROW][C]53[/C][C]1[/C][C]1.3[/C][C]-0.3[/C][/ROW]
[ROW][C]54[/C][C]0[/C][C]1.3[/C][C]-1.3[/C][/ROW]
[ROW][C]55[/C][C]2[/C][C]1.3[/C][C]0.7[/C][/ROW]
[ROW][C]56[/C][C]1[/C][C]0.793103448275862[/C][C]0.206896551724138[/C][/ROW]
[ROW][C]57[/C][C]2[/C][C]1.3[/C][C]0.7[/C][/ROW]
[ROW][C]58[/C][C]0[/C][C]0.793103448275862[/C][C]-0.793103448275862[/C][/ROW]
[ROW][C]59[/C][C]1[/C][C]1.90625[/C][C]-0.90625[/C][/ROW]
[ROW][C]60[/C][C]2[/C][C]1.90625[/C][C]0.09375[/C][/ROW]
[ROW][C]61[/C][C]0[/C][C]0.793103448275862[/C][C]-0.793103448275862[/C][/ROW]
[ROW][C]62[/C][C]0[/C][C]0.793103448275862[/C][C]-0.793103448275862[/C][/ROW]
[ROW][C]63[/C][C]0[/C][C]0.793103448275862[/C][C]-0.793103448275862[/C][/ROW]
[ROW][C]64[/C][C]0[/C][C]0.793103448275862[/C][C]-0.793103448275862[/C][/ROW]
[ROW][C]65[/C][C]2[/C][C]1.3[/C][C]0.7[/C][/ROW]
[ROW][C]66[/C][C]2[/C][C]1.3[/C][C]0.7[/C][/ROW]
[ROW][C]67[/C][C]2[/C][C]1.3[/C][C]0.7[/C][/ROW]
[ROW][C]68[/C][C]2[/C][C]1.3[/C][C]0.7[/C][/ROW]
[ROW][C]69[/C][C]2[/C][C]1.3[/C][C]0.7[/C][/ROW]
[ROW][C]70[/C][C]2[/C][C]1.3[/C][C]0.7[/C][/ROW]
[ROW][C]71[/C][C]2[/C][C]1.3[/C][C]0.7[/C][/ROW]
[ROW][C]72[/C][C]2[/C][C]1.90625[/C][C]0.09375[/C][/ROW]
[ROW][C]73[/C][C]2[/C][C]1.90625[/C][C]0.09375[/C][/ROW]
[ROW][C]74[/C][C]2[/C][C]1.3[/C][C]0.7[/C][/ROW]
[ROW][C]75[/C][C]2[/C][C]1.90625[/C][C]0.09375[/C][/ROW]
[ROW][C]76[/C][C]2[/C][C]1.90625[/C][C]0.09375[/C][/ROW]
[ROW][C]77[/C][C]2[/C][C]1.3[/C][C]0.7[/C][/ROW]
[ROW][C]78[/C][C]0[/C][C]0.793103448275862[/C][C]-0.793103448275862[/C][/ROW]
[ROW][C]79[/C][C]2[/C][C]1.3[/C][C]0.7[/C][/ROW]
[ROW][C]80[/C][C]2[/C][C]1.3[/C][C]0.7[/C][/ROW]
[ROW][C]81[/C][C]2[/C][C]1.3[/C][C]0.7[/C][/ROW]
[ROW][C]82[/C][C]2[/C][C]1.3[/C][C]0.7[/C][/ROW]
[ROW][C]83[/C][C]2[/C][C]1.90625[/C][C]0.09375[/C][/ROW]
[ROW][C]84[/C][C]2[/C][C]1.90625[/C][C]0.09375[/C][/ROW]
[ROW][C]85[/C][C]0[/C][C]0.793103448275862[/C][C]-0.793103448275862[/C][/ROW]
[ROW][C]86[/C][C]0[/C][C]0.793103448275862[/C][C]-0.793103448275862[/C][/ROW]
[ROW][C]87[/C][C]2[/C][C]1.3[/C][C]0.7[/C][/ROW]
[ROW][C]88[/C][C]0[/C][C]0.793103448275862[/C][C]-0.793103448275862[/C][/ROW]
[ROW][C]89[/C][C]2[/C][C]1.3[/C][C]0.7[/C][/ROW]
[ROW][C]90[/C][C]0[/C][C]0.793103448275862[/C][C]-0.793103448275862[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=197171&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=197171&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	2	0.793103448275862	1.20689655172414
2	2	0.793103448275862	1.20689655172414
3	1.5	1.90625	-0.40625
4	0	0.793103448275862	-0.793103448275862
5	1	1.3	-0.3
6	2	1.3	0.7
7	2	1.3	0.7
8	1	1.3	-0.3
9	2	1.90625	0.09375
10	2	0.793103448275862	1.20689655172414
11	2	0.793103448275862	1.20689655172414
12	0	1.3	-1.3
13	0	1.3	-1.3
14	2	0.793103448275862	1.20689655172414
15	2	1.3	0.7
16	2	0.793103448275862	1.20689655172414
17	0.5	1.3	-0.8
18	2	1.3	0.7
19	0	1.3	-1.3
20	2	1.3	0.7
21	0	0.793103448275862	-0.793103448275862
22	2	1.90625	0.09375
23	0	1.3	-1.3
24	2	0.793103448275862	1.20689655172414
25	1	1.3	-0.3
26	0.5	0.793103448275862	-0.293103448275862
27	2	0.793103448275862	1.20689655172414
28	0.5	1.3	-0.8
29	0.5	1.3	-0.8
30	2	0.793103448275862	1.20689655172414
31	0	1.3	-1.3
32	1	1.3	-0.3
33	2	1.90625	0.09375
34	1	1.3	-0.3
35	2	1.90625	0.09375
36	0	0.793103448275862	-0.793103448275862
37	0.5	1.3	-0.8
38	0	1.3	-1.3
39	2	1.90625	0.09375
40	0	0.793103448275862	-0.793103448275862
41	1	0.793103448275862	0.206896551724138
42	2	1.90625	0.09375
43	0.5	0.793103448275862	-0.293103448275862
44	2	1.3	0.7
45	2	1.3	0.7
46	2	1.3	0.7
47	0	0.793103448275862	-0.793103448275862
48	0	1.3	-1.3
49	0.5	1.3	-0.8
50	2	0.793103448275862	1.20689655172414
51	0	1.3	-1.3
52	2	1.90625	0.09375
53	1	1.3	-0.3
54	0	1.3	-1.3
55	2	1.3	0.7
56	1	0.793103448275862	0.206896551724138
57	2	1.3	0.7
58	0	0.793103448275862	-0.793103448275862
59	1	1.90625	-0.90625
60	2	1.90625	0.09375
61	0	0.793103448275862	-0.793103448275862
62	0	0.793103448275862	-0.793103448275862
63	0	0.793103448275862	-0.793103448275862
64	0	0.793103448275862	-0.793103448275862
65	2	1.3	0.7
66	2	1.3	0.7
67	2	1.3	0.7
68	2	1.3	0.7
69	2	1.3	0.7
70	2	1.3	0.7
71	2	1.3	0.7
72	2	1.90625	0.09375
73	2	1.90625	0.09375
74	2	1.3	0.7
75	2	1.90625	0.09375
76	2	1.90625	0.09375
77	2	1.3	0.7
78	0	0.793103448275862	-0.793103448275862
79	2	1.3	0.7
80	2	1.3	0.7
81	2	1.3	0.7
82	2	1.3	0.7
83	2	1.90625	0.09375
84	2	1.90625	0.09375
85	0	0.793103448275862	-0.793103448275862
86	0	0.793103448275862	-0.793103448275862
87	2	1.3	0.7
88	0	0.793103448275862	-0.793103448275862
89	2	1.3	0.7
90	0	0.793103448275862	-0.793103448275862

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 = 5 ; par2 = none ; par4 = no ;

Parameters (R input):

par1 = 5 ; par2 = none ; par3 = ; 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