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

Tue, 14 Dec 2010 17:43:47 +0000

Cite this page as follows

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2010/Dec/14/t1292348518825lcpoydlke21q.htm/, Retrieved Thu, 02 May 2024 17:08:11 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=109943, Retrieved Thu, 02 May 2024 17:08:11 +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

198

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

-     [Kendall tau Correlation Matrix] [] [2010-12-05 17:44:33] [b98453cac15ba1066b407e146608df68]
- RMPD    [Recursive Partitioning (Regression Trees)] [] [2010-12-14 17:43:47] [5842cf9dd57f9603e676e11284d3404a] [Current]
-    D      [Recursive Partitioning (Regression Trees)] [WS10RP] [2010-12-14 18:47:01] [3fb95cad3bbcce10c72dbbcc5bec5662] 
-   P       [Recursive Partitioning (Regression Trees)] [WS10 - Review] [2010-12-16 18:47:05] [4a7069087cf9e0eda253aeed7d8c30d6] 
-             [Recursive Partitioning (Regression Trees)] [WS10 - Review] [2010-12-16 18:49:10] [4a7069087cf9e0eda253aeed7d8c30d6] 
-   P       [Recursive Partitioning (Regression Trees)] [] [2010-12-21 11:01:46] [049b50ae610f671f7417ed8e2d1295c1] 

Feedback Forum

Post a new message

Dataseries X:

Download CSV

Histogram

Boxplots

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	4 seconds
R Server	'RServer@AstonUniversity' @ vre.aston.ac.uk

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 4 seconds \tabularnewline
R Server & 'RServer@AstonUniversity' @ vre.aston.ac.uk \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=109943&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]4 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'RServer@AstonUniversity' @ vre.aston.ac.uk[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=109943&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=109943&T=0

As an alternative you can also use a QR Code:

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

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	4 seconds
R Server	'RServer@AstonUniversity' @ vre.aston.ac.uk

Goodness of Fit
Correlation	0.8399
R-squared	0.7054
RMSE	115.6339

\begin{tabular}{lllllllll}
\hline
Goodness of Fit \tabularnewline
Correlation & 0.8399 \tabularnewline
R-squared & 0.7054 \tabularnewline
RMSE & 115.6339 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=109943&T=1

[TABLE]
[ROW][C]Goodness of Fit[/C][/ROW]
[ROW][C]Correlation[/C][C]0.8399[/C][/ROW]
[ROW][C]R-squared[/C][C]0.7054[/C][/ROW]
[ROW][C]RMSE[/C][C]115.6339[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=109943&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=109943&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	0.8399
R-squared	0.7054
RMSE	115.6339

Actuals, Predictions, and Residuals
#	Actuals	Forecasts	Residuals
1	1280	1280	0
2	1024	1129.525	-105.525
3	1120	1129.525	-9.5250000000001
4	1024	1129.525	-105.525
5	1280	1276.28846153846	3.71153846153857
6	1280	1280	0
7	1280	1276.28846153846	3.71153846153857
8	1024	1129.525	-105.525
9	1280	1276.28846153846	3.71153846153857
10	1280	1280	0
11	1280	1276.28846153846	3.71153846153857
12	1280	1276.28846153846	3.71153846153857
13	1280	1280	0
14	1688	1479.44827586207	208.551724137931
15	1440	1479.44827586207	-39.4482758620691
16	1600	1840	-240
17	1280	1276.28846153846	3.71153846153857
18	1280	1276.28846153846	3.71153846153857
19	1280	1129.525	150.475
20	1176	1129.525	46.4749999999999
21	1280	1276.28846153846	3.71153846153857
22	1503	1276.28846153846	226.711538461539
23	1440	1479.44827586207	-39.4482758620691
24	1366	1129.525	236.475
25	1280	1129.525	150.475
26	1024	1129.525	-105.525
27	1280	1276.28846153846	3.71153846153857
28	2560	1840	720
29	1280	1129.525	150.475
30	1024	1129.525	-105.525
31	1280	1280	0
32	1280	1276.28846153846	3.71153846153857
33	1440	1479.44827586207	-39.4482758620691
34	1280	1276.28846153846	3.71153846153857
35	1440	1479.44827586207	-39.4482758620691
36	1024	1129.525	-105.525
37	1440	1479.44827586207	-39.4482758620691
38	1143	1276.28846153846	-133.288461538461
39	1280	1276.28846153846	3.71153846153857
40	1440	1479.44827586207	-39.4482758620691
41	1280	1276.28846153846	3.71153846153857
42	1366	1129.525	236.475
43	1024	1129.525	-105.525
44	1408	1479.44827586207	-71.4482758620691
45	1366	1129.525	236.475
46	1176	1129.525	46.4749999999999
47	1920	1840	80
48	1257	1276.28846153846	-19.2884615384614
49	1280	1276.28846153846	3.71153846153857
50	1280	1276.28846153846	3.71153846153857
51	1440	1479.44827586207	-39.4482758620691
52	1680	1840	-160
53	1440	1479.44827586207	-39.4482758620691
54	1024	1129.525	-105.525
55	1140	1129.525	10.4749999999999
56	1280	1280	0
57	1280	1276.28846153846	3.71153846153857
58	1280	1276.28846153846	3.71153846153857
59	1280	1276.28846153846	3.71153846153857
60	1280	1276.28846153846	3.71153846153857
61	1440	1479.44827586207	-39.4482758620691
62	1280	1276.28846153846	3.71153846153857
63	1152	1276.28846153846	-124.288461538461
64	1280	1280	0
65	1280	1276.28846153846	3.71153846153857
66	1440	1479.44827586207	-39.4482758620691
67	1280	1276.28846153846	3.71153846153857
68	1280	1280	0
69	1440	1479.44827586207	-39.4482758620691
70	1280	1276.28846153846	3.71153846153857
71	1280	1276.28846153846	3.71153846153857
72	1440	1479.44827586207	-39.4482758620691
73	1280	1276.28846153846	3.71153846153857
74	1280	1280	0
75	1600	1479.44827586207	120.551724137931
76	1024	1129.525	-105.525
77	1366	1129.525	236.475
78	1280	1276.28846153846	3.71153846153857
79	1280	1276.28846153846	3.71153846153857
80	1440	1479.44827586207	-39.4482758620691
81	1366	1129.525	236.475
82	1280	1276.28846153846	3.71153846153857
83	1024	1129.525	-105.525
84	1280	1276.28846153846	3.71153846153857
85	1440	1479.44827586207	-39.4482758620691
86	1280	1276.28846153846	3.71153846153857
87	1280	1276.28846153846	3.71153846153857
88	1408	1479.44827586207	-71.4482758620691
89	1280	1276.28846153846	3.71153846153857
90	1600	1479.44827586207	120.551724137931
91	1600	1479.44827586207	120.551724137931
92	1680	1840	-160
93	1440	1479.44827586207	-39.4482758620691
94	1440	1479.44827586207	-39.4482758620691
95	917	1129.525	-212.525
96	1280	1276.28846153846	3.71153846153857
97	1760	1479.44827586207	280.551724137931
98	1280	1276.28846153846	3.71153846153857
99	1280	1276.28846153846	3.71153846153857
100	1280	1276.28846153846	3.71153846153857
101	1024	1129.525	-105.525
102	1366	1129.525	236.475
103	1440	1479.44827586207	-39.4482758620691
104	1280	1276.28846153846	3.71153846153857
105	1280	1280	0
106	1920	1840	80
107	1024	1129.525	-105.525
108	1024	1129.525	-105.525
109	1600	1479.44827586207	120.551724137931
110	1117	1129.525	-12.5250000000001
111	1440	1479.44827586207	-39.4482758620691
112	983	1129.525	-146.525
113	1024	1129.525	-105.525
114	1024	1129.525	-105.525
115	1280	1276.28846153846	3.71153846153857
116	1440	1479.44827586207	-39.4482758620691
117	1280	1276.28846153846	3.71153846153857
118	1280	1276.28846153846	3.71153846153857
119	1280	1276.28846153846	3.71153846153857
120	1440	1479.44827586207	-39.4482758620691
121	1280	1276.28846153846	3.71153846153857
122	1024	1129.525	-105.525
123	1024	1129.525	-105.525
124	1152	1276.28846153846	-124.288461538461
125	1280	1129.525	150.475
126	1024	1129.525	-105.525
127	1366	1129.525	236.475
128	1680	1840	-160
129	1680	1840	-160
130	1280	1276.28846153846	3.71153846153857
131	1366	1129.525	236.475
132	1024	1129.525	-105.525
133	1440	1479.44827586207	-39.4482758620691
134	1024	1129.525	-105.525
135	1280	1276.28846153846	3.71153846153857
136	1280	1276.28846153846	3.71153846153857
137	1280	1276.28846153846	3.71153846153857
138	1024	1129.525	-105.525
139	1280	1276.28846153846	3.71153846153857

\begin{tabular}{lllllllll}
\hline
Actuals, Predictions, and Residuals \tabularnewline
# & Actuals & Forecasts & Residuals \tabularnewline
1 & 1280 & 1280 & 0 \tabularnewline
2 & 1024 & 1129.525 & -105.525 \tabularnewline
3 & 1120 & 1129.525 & -9.5250000000001 \tabularnewline
4 & 1024 & 1129.525 & -105.525 \tabularnewline
5 & 1280 & 1276.28846153846 & 3.71153846153857 \tabularnewline
6 & 1280 & 1280 & 0 \tabularnewline
7 & 1280 & 1276.28846153846 & 3.71153846153857 \tabularnewline
8 & 1024 & 1129.525 & -105.525 \tabularnewline
9 & 1280 & 1276.28846153846 & 3.71153846153857 \tabularnewline
10 & 1280 & 1280 & 0 \tabularnewline
11 & 1280 & 1276.28846153846 & 3.71153846153857 \tabularnewline
12 & 1280 & 1276.28846153846 & 3.71153846153857 \tabularnewline
13 & 1280 & 1280 & 0 \tabularnewline
14 & 1688 & 1479.44827586207 & 208.551724137931 \tabularnewline
15 & 1440 & 1479.44827586207 & -39.4482758620691 \tabularnewline
16 & 1600 & 1840 & -240 \tabularnewline
17 & 1280 & 1276.28846153846 & 3.71153846153857 \tabularnewline
18 & 1280 & 1276.28846153846 & 3.71153846153857 \tabularnewline
19 & 1280 & 1129.525 & 150.475 \tabularnewline
20 & 1176 & 1129.525 & 46.4749999999999 \tabularnewline
21 & 1280 & 1276.28846153846 & 3.71153846153857 \tabularnewline
22 & 1503 & 1276.28846153846 & 226.711538461539 \tabularnewline
23 & 1440 & 1479.44827586207 & -39.4482758620691 \tabularnewline
24 & 1366 & 1129.525 & 236.475 \tabularnewline
25 & 1280 & 1129.525 & 150.475 \tabularnewline
26 & 1024 & 1129.525 & -105.525 \tabularnewline
27 & 1280 & 1276.28846153846 & 3.71153846153857 \tabularnewline
28 & 2560 & 1840 & 720 \tabularnewline
29 & 1280 & 1129.525 & 150.475 \tabularnewline
30 & 1024 & 1129.525 & -105.525 \tabularnewline
31 & 1280 & 1280 & 0 \tabularnewline
32 & 1280 & 1276.28846153846 & 3.71153846153857 \tabularnewline
33 & 1440 & 1479.44827586207 & -39.4482758620691 \tabularnewline
34 & 1280 & 1276.28846153846 & 3.71153846153857 \tabularnewline
35 & 1440 & 1479.44827586207 & -39.4482758620691 \tabularnewline
36 & 1024 & 1129.525 & -105.525 \tabularnewline
37 & 1440 & 1479.44827586207 & -39.4482758620691 \tabularnewline
38 & 1143 & 1276.28846153846 & -133.288461538461 \tabularnewline
39 & 1280 & 1276.28846153846 & 3.71153846153857 \tabularnewline
40 & 1440 & 1479.44827586207 & -39.4482758620691 \tabularnewline
41 & 1280 & 1276.28846153846 & 3.71153846153857 \tabularnewline
42 & 1366 & 1129.525 & 236.475 \tabularnewline
43 & 1024 & 1129.525 & -105.525 \tabularnewline
44 & 1408 & 1479.44827586207 & -71.4482758620691 \tabularnewline
45 & 1366 & 1129.525 & 236.475 \tabularnewline
46 & 1176 & 1129.525 & 46.4749999999999 \tabularnewline
47 & 1920 & 1840 & 80 \tabularnewline
48 & 1257 & 1276.28846153846 & -19.2884615384614 \tabularnewline
49 & 1280 & 1276.28846153846 & 3.71153846153857 \tabularnewline
50 & 1280 & 1276.28846153846 & 3.71153846153857 \tabularnewline
51 & 1440 & 1479.44827586207 & -39.4482758620691 \tabularnewline
52 & 1680 & 1840 & -160 \tabularnewline
53 & 1440 & 1479.44827586207 & -39.4482758620691 \tabularnewline
54 & 1024 & 1129.525 & -105.525 \tabularnewline
55 & 1140 & 1129.525 & 10.4749999999999 \tabularnewline
56 & 1280 & 1280 & 0 \tabularnewline
57 & 1280 & 1276.28846153846 & 3.71153846153857 \tabularnewline
58 & 1280 & 1276.28846153846 & 3.71153846153857 \tabularnewline
59 & 1280 & 1276.28846153846 & 3.71153846153857 \tabularnewline
60 & 1280 & 1276.28846153846 & 3.71153846153857 \tabularnewline
61 & 1440 & 1479.44827586207 & -39.4482758620691 \tabularnewline
62 & 1280 & 1276.28846153846 & 3.71153846153857 \tabularnewline
63 & 1152 & 1276.28846153846 & -124.288461538461 \tabularnewline
64 & 1280 & 1280 & 0 \tabularnewline
65 & 1280 & 1276.28846153846 & 3.71153846153857 \tabularnewline
66 & 1440 & 1479.44827586207 & -39.4482758620691 \tabularnewline
67 & 1280 & 1276.28846153846 & 3.71153846153857 \tabularnewline
68 & 1280 & 1280 & 0 \tabularnewline
69 & 1440 & 1479.44827586207 & -39.4482758620691 \tabularnewline
70 & 1280 & 1276.28846153846 & 3.71153846153857 \tabularnewline
71 & 1280 & 1276.28846153846 & 3.71153846153857 \tabularnewline
72 & 1440 & 1479.44827586207 & -39.4482758620691 \tabularnewline
73 & 1280 & 1276.28846153846 & 3.71153846153857 \tabularnewline
74 & 1280 & 1280 & 0 \tabularnewline
75 & 1600 & 1479.44827586207 & 120.551724137931 \tabularnewline
76 & 1024 & 1129.525 & -105.525 \tabularnewline
77 & 1366 & 1129.525 & 236.475 \tabularnewline
78 & 1280 & 1276.28846153846 & 3.71153846153857 \tabularnewline
79 & 1280 & 1276.28846153846 & 3.71153846153857 \tabularnewline
80 & 1440 & 1479.44827586207 & -39.4482758620691 \tabularnewline
81 & 1366 & 1129.525 & 236.475 \tabularnewline
82 & 1280 & 1276.28846153846 & 3.71153846153857 \tabularnewline
83 & 1024 & 1129.525 & -105.525 \tabularnewline
84 & 1280 & 1276.28846153846 & 3.71153846153857 \tabularnewline
85 & 1440 & 1479.44827586207 & -39.4482758620691 \tabularnewline
86 & 1280 & 1276.28846153846 & 3.71153846153857 \tabularnewline
87 & 1280 & 1276.28846153846 & 3.71153846153857 \tabularnewline
88 & 1408 & 1479.44827586207 & -71.4482758620691 \tabularnewline
89 & 1280 & 1276.28846153846 & 3.71153846153857 \tabularnewline
90 & 1600 & 1479.44827586207 & 120.551724137931 \tabularnewline
91 & 1600 & 1479.44827586207 & 120.551724137931 \tabularnewline
92 & 1680 & 1840 & -160 \tabularnewline
93 & 1440 & 1479.44827586207 & -39.4482758620691 \tabularnewline
94 & 1440 & 1479.44827586207 & -39.4482758620691 \tabularnewline
95 & 917 & 1129.525 & -212.525 \tabularnewline
96 & 1280 & 1276.28846153846 & 3.71153846153857 \tabularnewline
97 & 1760 & 1479.44827586207 & 280.551724137931 \tabularnewline
98 & 1280 & 1276.28846153846 & 3.71153846153857 \tabularnewline
99 & 1280 & 1276.28846153846 & 3.71153846153857 \tabularnewline
100 & 1280 & 1276.28846153846 & 3.71153846153857 \tabularnewline
101 & 1024 & 1129.525 & -105.525 \tabularnewline
102 & 1366 & 1129.525 & 236.475 \tabularnewline
103 & 1440 & 1479.44827586207 & -39.4482758620691 \tabularnewline
104 & 1280 & 1276.28846153846 & 3.71153846153857 \tabularnewline
105 & 1280 & 1280 & 0 \tabularnewline
106 & 1920 & 1840 & 80 \tabularnewline
107 & 1024 & 1129.525 & -105.525 \tabularnewline
108 & 1024 & 1129.525 & -105.525 \tabularnewline
109 & 1600 & 1479.44827586207 & 120.551724137931 \tabularnewline
110 & 1117 & 1129.525 & -12.5250000000001 \tabularnewline
111 & 1440 & 1479.44827586207 & -39.4482758620691 \tabularnewline
112 & 983 & 1129.525 & -146.525 \tabularnewline
113 & 1024 & 1129.525 & -105.525 \tabularnewline
114 & 1024 & 1129.525 & -105.525 \tabularnewline
115 & 1280 & 1276.28846153846 & 3.71153846153857 \tabularnewline
116 & 1440 & 1479.44827586207 & -39.4482758620691 \tabularnewline
117 & 1280 & 1276.28846153846 & 3.71153846153857 \tabularnewline
118 & 1280 & 1276.28846153846 & 3.71153846153857 \tabularnewline
119 & 1280 & 1276.28846153846 & 3.71153846153857 \tabularnewline
120 & 1440 & 1479.44827586207 & -39.4482758620691 \tabularnewline
121 & 1280 & 1276.28846153846 & 3.71153846153857 \tabularnewline
122 & 1024 & 1129.525 & -105.525 \tabularnewline
123 & 1024 & 1129.525 & -105.525 \tabularnewline
124 & 1152 & 1276.28846153846 & -124.288461538461 \tabularnewline
125 & 1280 & 1129.525 & 150.475 \tabularnewline
126 & 1024 & 1129.525 & -105.525 \tabularnewline
127 & 1366 & 1129.525 & 236.475 \tabularnewline
128 & 1680 & 1840 & -160 \tabularnewline
129 & 1680 & 1840 & -160 \tabularnewline
130 & 1280 & 1276.28846153846 & 3.71153846153857 \tabularnewline
131 & 1366 & 1129.525 & 236.475 \tabularnewline
132 & 1024 & 1129.525 & -105.525 \tabularnewline
133 & 1440 & 1479.44827586207 & -39.4482758620691 \tabularnewline
134 & 1024 & 1129.525 & -105.525 \tabularnewline
135 & 1280 & 1276.28846153846 & 3.71153846153857 \tabularnewline
136 & 1280 & 1276.28846153846 & 3.71153846153857 \tabularnewline
137 & 1280 & 1276.28846153846 & 3.71153846153857 \tabularnewline
138 & 1024 & 1129.525 & -105.525 \tabularnewline
139 & 1280 & 1276.28846153846 & 3.71153846153857 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=109943&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]1280[/C][C]1280[/C][C]0[/C][/ROW]
[ROW][C]2[/C][C]1024[/C][C]1129.525[/C][C]-105.525[/C][/ROW]
[ROW][C]3[/C][C]1120[/C][C]1129.525[/C][C]-9.5250000000001[/C][/ROW]
[ROW][C]4[/C][C]1024[/C][C]1129.525[/C][C]-105.525[/C][/ROW]
[ROW][C]5[/C][C]1280[/C][C]1276.28846153846[/C][C]3.71153846153857[/C][/ROW]
[ROW][C]6[/C][C]1280[/C][C]1280[/C][C]0[/C][/ROW]
[ROW][C]7[/C][C]1280[/C][C]1276.28846153846[/C][C]3.71153846153857[/C][/ROW]
[ROW][C]8[/C][C]1024[/C][C]1129.525[/C][C]-105.525[/C][/ROW]
[ROW][C]9[/C][C]1280[/C][C]1276.28846153846[/C][C]3.71153846153857[/C][/ROW]
[ROW][C]10[/C][C]1280[/C][C]1280[/C][C]0[/C][/ROW]
[ROW][C]11[/C][C]1280[/C][C]1276.28846153846[/C][C]3.71153846153857[/C][/ROW]
[ROW][C]12[/C][C]1280[/C][C]1276.28846153846[/C][C]3.71153846153857[/C][/ROW]
[ROW][C]13[/C][C]1280[/C][C]1280[/C][C]0[/C][/ROW]
[ROW][C]14[/C][C]1688[/C][C]1479.44827586207[/C][C]208.551724137931[/C][/ROW]
[ROW][C]15[/C][C]1440[/C][C]1479.44827586207[/C][C]-39.4482758620691[/C][/ROW]
[ROW][C]16[/C][C]1600[/C][C]1840[/C][C]-240[/C][/ROW]
[ROW][C]17[/C][C]1280[/C][C]1276.28846153846[/C][C]3.71153846153857[/C][/ROW]
[ROW][C]18[/C][C]1280[/C][C]1276.28846153846[/C][C]3.71153846153857[/C][/ROW]
[ROW][C]19[/C][C]1280[/C][C]1129.525[/C][C]150.475[/C][/ROW]
[ROW][C]20[/C][C]1176[/C][C]1129.525[/C][C]46.4749999999999[/C][/ROW]
[ROW][C]21[/C][C]1280[/C][C]1276.28846153846[/C][C]3.71153846153857[/C][/ROW]
[ROW][C]22[/C][C]1503[/C][C]1276.28846153846[/C][C]226.711538461539[/C][/ROW]
[ROW][C]23[/C][C]1440[/C][C]1479.44827586207[/C][C]-39.4482758620691[/C][/ROW]
[ROW][C]24[/C][C]1366[/C][C]1129.525[/C][C]236.475[/C][/ROW]
[ROW][C]25[/C][C]1280[/C][C]1129.525[/C][C]150.475[/C][/ROW]
[ROW][C]26[/C][C]1024[/C][C]1129.525[/C][C]-105.525[/C][/ROW]
[ROW][C]27[/C][C]1280[/C][C]1276.28846153846[/C][C]3.71153846153857[/C][/ROW]
[ROW][C]28[/C][C]2560[/C][C]1840[/C][C]720[/C][/ROW]
[ROW][C]29[/C][C]1280[/C][C]1129.525[/C][C]150.475[/C][/ROW]
[ROW][C]30[/C][C]1024[/C][C]1129.525[/C][C]-105.525[/C][/ROW]
[ROW][C]31[/C][C]1280[/C][C]1280[/C][C]0[/C][/ROW]
[ROW][C]32[/C][C]1280[/C][C]1276.28846153846[/C][C]3.71153846153857[/C][/ROW]
[ROW][C]33[/C][C]1440[/C][C]1479.44827586207[/C][C]-39.4482758620691[/C][/ROW]
[ROW][C]34[/C][C]1280[/C][C]1276.28846153846[/C][C]3.71153846153857[/C][/ROW]
[ROW][C]35[/C][C]1440[/C][C]1479.44827586207[/C][C]-39.4482758620691[/C][/ROW]
[ROW][C]36[/C][C]1024[/C][C]1129.525[/C][C]-105.525[/C][/ROW]
[ROW][C]37[/C][C]1440[/C][C]1479.44827586207[/C][C]-39.4482758620691[/C][/ROW]
[ROW][C]38[/C][C]1143[/C][C]1276.28846153846[/C][C]-133.288461538461[/C][/ROW]
[ROW][C]39[/C][C]1280[/C][C]1276.28846153846[/C][C]3.71153846153857[/C][/ROW]
[ROW][C]40[/C][C]1440[/C][C]1479.44827586207[/C][C]-39.4482758620691[/C][/ROW]
[ROW][C]41[/C][C]1280[/C][C]1276.28846153846[/C][C]3.71153846153857[/C][/ROW]
[ROW][C]42[/C][C]1366[/C][C]1129.525[/C][C]236.475[/C][/ROW]
[ROW][C]43[/C][C]1024[/C][C]1129.525[/C][C]-105.525[/C][/ROW]
[ROW][C]44[/C][C]1408[/C][C]1479.44827586207[/C][C]-71.4482758620691[/C][/ROW]
[ROW][C]45[/C][C]1366[/C][C]1129.525[/C][C]236.475[/C][/ROW]
[ROW][C]46[/C][C]1176[/C][C]1129.525[/C][C]46.4749999999999[/C][/ROW]
[ROW][C]47[/C][C]1920[/C][C]1840[/C][C]80[/C][/ROW]
[ROW][C]48[/C][C]1257[/C][C]1276.28846153846[/C][C]-19.2884615384614[/C][/ROW]
[ROW][C]49[/C][C]1280[/C][C]1276.28846153846[/C][C]3.71153846153857[/C][/ROW]
[ROW][C]50[/C][C]1280[/C][C]1276.28846153846[/C][C]3.71153846153857[/C][/ROW]
[ROW][C]51[/C][C]1440[/C][C]1479.44827586207[/C][C]-39.4482758620691[/C][/ROW]
[ROW][C]52[/C][C]1680[/C][C]1840[/C][C]-160[/C][/ROW]
[ROW][C]53[/C][C]1440[/C][C]1479.44827586207[/C][C]-39.4482758620691[/C][/ROW]
[ROW][C]54[/C][C]1024[/C][C]1129.525[/C][C]-105.525[/C][/ROW]
[ROW][C]55[/C][C]1140[/C][C]1129.525[/C][C]10.4749999999999[/C][/ROW]
[ROW][C]56[/C][C]1280[/C][C]1280[/C][C]0[/C][/ROW]
[ROW][C]57[/C][C]1280[/C][C]1276.28846153846[/C][C]3.71153846153857[/C][/ROW]
[ROW][C]58[/C][C]1280[/C][C]1276.28846153846[/C][C]3.71153846153857[/C][/ROW]
[ROW][C]59[/C][C]1280[/C][C]1276.28846153846[/C][C]3.71153846153857[/C][/ROW]
[ROW][C]60[/C][C]1280[/C][C]1276.28846153846[/C][C]3.71153846153857[/C][/ROW]
[ROW][C]61[/C][C]1440[/C][C]1479.44827586207[/C][C]-39.4482758620691[/C][/ROW]
[ROW][C]62[/C][C]1280[/C][C]1276.28846153846[/C][C]3.71153846153857[/C][/ROW]
[ROW][C]63[/C][C]1152[/C][C]1276.28846153846[/C][C]-124.288461538461[/C][/ROW]
[ROW][C]64[/C][C]1280[/C][C]1280[/C][C]0[/C][/ROW]
[ROW][C]65[/C][C]1280[/C][C]1276.28846153846[/C][C]3.71153846153857[/C][/ROW]
[ROW][C]66[/C][C]1440[/C][C]1479.44827586207[/C][C]-39.4482758620691[/C][/ROW]
[ROW][C]67[/C][C]1280[/C][C]1276.28846153846[/C][C]3.71153846153857[/C][/ROW]
[ROW][C]68[/C][C]1280[/C][C]1280[/C][C]0[/C][/ROW]
[ROW][C]69[/C][C]1440[/C][C]1479.44827586207[/C][C]-39.4482758620691[/C][/ROW]
[ROW][C]70[/C][C]1280[/C][C]1276.28846153846[/C][C]3.71153846153857[/C][/ROW]
[ROW][C]71[/C][C]1280[/C][C]1276.28846153846[/C][C]3.71153846153857[/C][/ROW]
[ROW][C]72[/C][C]1440[/C][C]1479.44827586207[/C][C]-39.4482758620691[/C][/ROW]
[ROW][C]73[/C][C]1280[/C][C]1276.28846153846[/C][C]3.71153846153857[/C][/ROW]
[ROW][C]74[/C][C]1280[/C][C]1280[/C][C]0[/C][/ROW]
[ROW][C]75[/C][C]1600[/C][C]1479.44827586207[/C][C]120.551724137931[/C][/ROW]
[ROW][C]76[/C][C]1024[/C][C]1129.525[/C][C]-105.525[/C][/ROW]
[ROW][C]77[/C][C]1366[/C][C]1129.525[/C][C]236.475[/C][/ROW]
[ROW][C]78[/C][C]1280[/C][C]1276.28846153846[/C][C]3.71153846153857[/C][/ROW]
[ROW][C]79[/C][C]1280[/C][C]1276.28846153846[/C][C]3.71153846153857[/C][/ROW]
[ROW][C]80[/C][C]1440[/C][C]1479.44827586207[/C][C]-39.4482758620691[/C][/ROW]
[ROW][C]81[/C][C]1366[/C][C]1129.525[/C][C]236.475[/C][/ROW]
[ROW][C]82[/C][C]1280[/C][C]1276.28846153846[/C][C]3.71153846153857[/C][/ROW]
[ROW][C]83[/C][C]1024[/C][C]1129.525[/C][C]-105.525[/C][/ROW]
[ROW][C]84[/C][C]1280[/C][C]1276.28846153846[/C][C]3.71153846153857[/C][/ROW]
[ROW][C]85[/C][C]1440[/C][C]1479.44827586207[/C][C]-39.4482758620691[/C][/ROW]
[ROW][C]86[/C][C]1280[/C][C]1276.28846153846[/C][C]3.71153846153857[/C][/ROW]
[ROW][C]87[/C][C]1280[/C][C]1276.28846153846[/C][C]3.71153846153857[/C][/ROW]
[ROW][C]88[/C][C]1408[/C][C]1479.44827586207[/C][C]-71.4482758620691[/C][/ROW]
[ROW][C]89[/C][C]1280[/C][C]1276.28846153846[/C][C]3.71153846153857[/C][/ROW]
[ROW][C]90[/C][C]1600[/C][C]1479.44827586207[/C][C]120.551724137931[/C][/ROW]
[ROW][C]91[/C][C]1600[/C][C]1479.44827586207[/C][C]120.551724137931[/C][/ROW]
[ROW][C]92[/C][C]1680[/C][C]1840[/C][C]-160[/C][/ROW]
[ROW][C]93[/C][C]1440[/C][C]1479.44827586207[/C][C]-39.4482758620691[/C][/ROW]
[ROW][C]94[/C][C]1440[/C][C]1479.44827586207[/C][C]-39.4482758620691[/C][/ROW]
[ROW][C]95[/C][C]917[/C][C]1129.525[/C][C]-212.525[/C][/ROW]
[ROW][C]96[/C][C]1280[/C][C]1276.28846153846[/C][C]3.71153846153857[/C][/ROW]
[ROW][C]97[/C][C]1760[/C][C]1479.44827586207[/C][C]280.551724137931[/C][/ROW]
[ROW][C]98[/C][C]1280[/C][C]1276.28846153846[/C][C]3.71153846153857[/C][/ROW]
[ROW][C]99[/C][C]1280[/C][C]1276.28846153846[/C][C]3.71153846153857[/C][/ROW]
[ROW][C]100[/C][C]1280[/C][C]1276.28846153846[/C][C]3.71153846153857[/C][/ROW]
[ROW][C]101[/C][C]1024[/C][C]1129.525[/C][C]-105.525[/C][/ROW]
[ROW][C]102[/C][C]1366[/C][C]1129.525[/C][C]236.475[/C][/ROW]
[ROW][C]103[/C][C]1440[/C][C]1479.44827586207[/C][C]-39.4482758620691[/C][/ROW]
[ROW][C]104[/C][C]1280[/C][C]1276.28846153846[/C][C]3.71153846153857[/C][/ROW]
[ROW][C]105[/C][C]1280[/C][C]1280[/C][C]0[/C][/ROW]
[ROW][C]106[/C][C]1920[/C][C]1840[/C][C]80[/C][/ROW]
[ROW][C]107[/C][C]1024[/C][C]1129.525[/C][C]-105.525[/C][/ROW]
[ROW][C]108[/C][C]1024[/C][C]1129.525[/C][C]-105.525[/C][/ROW]
[ROW][C]109[/C][C]1600[/C][C]1479.44827586207[/C][C]120.551724137931[/C][/ROW]
[ROW][C]110[/C][C]1117[/C][C]1129.525[/C][C]-12.5250000000001[/C][/ROW]
[ROW][C]111[/C][C]1440[/C][C]1479.44827586207[/C][C]-39.4482758620691[/C][/ROW]
[ROW][C]112[/C][C]983[/C][C]1129.525[/C][C]-146.525[/C][/ROW]
[ROW][C]113[/C][C]1024[/C][C]1129.525[/C][C]-105.525[/C][/ROW]
[ROW][C]114[/C][C]1024[/C][C]1129.525[/C][C]-105.525[/C][/ROW]
[ROW][C]115[/C][C]1280[/C][C]1276.28846153846[/C][C]3.71153846153857[/C][/ROW]
[ROW][C]116[/C][C]1440[/C][C]1479.44827586207[/C][C]-39.4482758620691[/C][/ROW]
[ROW][C]117[/C][C]1280[/C][C]1276.28846153846[/C][C]3.71153846153857[/C][/ROW]
[ROW][C]118[/C][C]1280[/C][C]1276.28846153846[/C][C]3.71153846153857[/C][/ROW]
[ROW][C]119[/C][C]1280[/C][C]1276.28846153846[/C][C]3.71153846153857[/C][/ROW]
[ROW][C]120[/C][C]1440[/C][C]1479.44827586207[/C][C]-39.4482758620691[/C][/ROW]
[ROW][C]121[/C][C]1280[/C][C]1276.28846153846[/C][C]3.71153846153857[/C][/ROW]
[ROW][C]122[/C][C]1024[/C][C]1129.525[/C][C]-105.525[/C][/ROW]
[ROW][C]123[/C][C]1024[/C][C]1129.525[/C][C]-105.525[/C][/ROW]
[ROW][C]124[/C][C]1152[/C][C]1276.28846153846[/C][C]-124.288461538461[/C][/ROW]
[ROW][C]125[/C][C]1280[/C][C]1129.525[/C][C]150.475[/C][/ROW]
[ROW][C]126[/C][C]1024[/C][C]1129.525[/C][C]-105.525[/C][/ROW]
[ROW][C]127[/C][C]1366[/C][C]1129.525[/C][C]236.475[/C][/ROW]
[ROW][C]128[/C][C]1680[/C][C]1840[/C][C]-160[/C][/ROW]
[ROW][C]129[/C][C]1680[/C][C]1840[/C][C]-160[/C][/ROW]
[ROW][C]130[/C][C]1280[/C][C]1276.28846153846[/C][C]3.71153846153857[/C][/ROW]
[ROW][C]131[/C][C]1366[/C][C]1129.525[/C][C]236.475[/C][/ROW]
[ROW][C]132[/C][C]1024[/C][C]1129.525[/C][C]-105.525[/C][/ROW]
[ROW][C]133[/C][C]1440[/C][C]1479.44827586207[/C][C]-39.4482758620691[/C][/ROW]
[ROW][C]134[/C][C]1024[/C][C]1129.525[/C][C]-105.525[/C][/ROW]
[ROW][C]135[/C][C]1280[/C][C]1276.28846153846[/C][C]3.71153846153857[/C][/ROW]
[ROW][C]136[/C][C]1280[/C][C]1276.28846153846[/C][C]3.71153846153857[/C][/ROW]
[ROW][C]137[/C][C]1280[/C][C]1276.28846153846[/C][C]3.71153846153857[/C][/ROW]
[ROW][C]138[/C][C]1024[/C][C]1129.525[/C][C]-105.525[/C][/ROW]
[ROW][C]139[/C][C]1280[/C][C]1276.28846153846[/C][C]3.71153846153857[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=109943&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=109943&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	1280	1280	0
2	1024	1129.525	-105.525
3	1120	1129.525	-9.5250000000001
4	1024	1129.525	-105.525
5	1280	1276.28846153846	3.71153846153857
6	1280	1280	0
7	1280	1276.28846153846	3.71153846153857
8	1024	1129.525	-105.525
9	1280	1276.28846153846	3.71153846153857
10	1280	1280	0
11	1280	1276.28846153846	3.71153846153857
12	1280	1276.28846153846	3.71153846153857
13	1280	1280	0
14	1688	1479.44827586207	208.551724137931
15	1440	1479.44827586207	-39.4482758620691
16	1600	1840	-240
17	1280	1276.28846153846	3.71153846153857
18	1280	1276.28846153846	3.71153846153857
19	1280	1129.525	150.475
20	1176	1129.525	46.4749999999999
21	1280	1276.28846153846	3.71153846153857
22	1503	1276.28846153846	226.711538461539
23	1440	1479.44827586207	-39.4482758620691
24	1366	1129.525	236.475
25	1280	1129.525	150.475
26	1024	1129.525	-105.525
27	1280	1276.28846153846	3.71153846153857
28	2560	1840	720
29	1280	1129.525	150.475
30	1024	1129.525	-105.525
31	1280	1280	0
32	1280	1276.28846153846	3.71153846153857
33	1440	1479.44827586207	-39.4482758620691
34	1280	1276.28846153846	3.71153846153857
35	1440	1479.44827586207	-39.4482758620691
36	1024	1129.525	-105.525
37	1440	1479.44827586207	-39.4482758620691
38	1143	1276.28846153846	-133.288461538461
39	1280	1276.28846153846	3.71153846153857
40	1440	1479.44827586207	-39.4482758620691
41	1280	1276.28846153846	3.71153846153857
42	1366	1129.525	236.475
43	1024	1129.525	-105.525
44	1408	1479.44827586207	-71.4482758620691
45	1366	1129.525	236.475
46	1176	1129.525	46.4749999999999
47	1920	1840	80
48	1257	1276.28846153846	-19.2884615384614
49	1280	1276.28846153846	3.71153846153857
50	1280	1276.28846153846	3.71153846153857
51	1440	1479.44827586207	-39.4482758620691
52	1680	1840	-160
53	1440	1479.44827586207	-39.4482758620691
54	1024	1129.525	-105.525
55	1140	1129.525	10.4749999999999
56	1280	1280	0
57	1280	1276.28846153846	3.71153846153857
58	1280	1276.28846153846	3.71153846153857
59	1280	1276.28846153846	3.71153846153857
60	1280	1276.28846153846	3.71153846153857
61	1440	1479.44827586207	-39.4482758620691
62	1280	1276.28846153846	3.71153846153857
63	1152	1276.28846153846	-124.288461538461
64	1280	1280	0
65	1280	1276.28846153846	3.71153846153857
66	1440	1479.44827586207	-39.4482758620691
67	1280	1276.28846153846	3.71153846153857
68	1280	1280	0
69	1440	1479.44827586207	-39.4482758620691
70	1280	1276.28846153846	3.71153846153857
71	1280	1276.28846153846	3.71153846153857
72	1440	1479.44827586207	-39.4482758620691
73	1280	1276.28846153846	3.71153846153857
74	1280	1280	0
75	1600	1479.44827586207	120.551724137931
76	1024	1129.525	-105.525
77	1366	1129.525	236.475
78	1280	1276.28846153846	3.71153846153857
79	1280	1276.28846153846	3.71153846153857
80	1440	1479.44827586207	-39.4482758620691
81	1366	1129.525	236.475
82	1280	1276.28846153846	3.71153846153857
83	1024	1129.525	-105.525
84	1280	1276.28846153846	3.71153846153857
85	1440	1479.44827586207	-39.4482758620691
86	1280	1276.28846153846	3.71153846153857
87	1280	1276.28846153846	3.71153846153857
88	1408	1479.44827586207	-71.4482758620691
89	1280	1276.28846153846	3.71153846153857
90	1600	1479.44827586207	120.551724137931
91	1600	1479.44827586207	120.551724137931
92	1680	1840	-160
93	1440	1479.44827586207	-39.4482758620691
94	1440	1479.44827586207	-39.4482758620691
95	917	1129.525	-212.525
96	1280	1276.28846153846	3.71153846153857
97	1760	1479.44827586207	280.551724137931
98	1280	1276.28846153846	3.71153846153857
99	1280	1276.28846153846	3.71153846153857
100	1280	1276.28846153846	3.71153846153857
101	1024	1129.525	-105.525
102	1366	1129.525	236.475
103	1440	1479.44827586207	-39.4482758620691
104	1280	1276.28846153846	3.71153846153857
105	1280	1280	0
106	1920	1840	80
107	1024	1129.525	-105.525
108	1024	1129.525	-105.525
109	1600	1479.44827586207	120.551724137931
110	1117	1129.525	-12.5250000000001
111	1440	1479.44827586207	-39.4482758620691
112	983	1129.525	-146.525
113	1024	1129.525	-105.525
114	1024	1129.525	-105.525
115	1280	1276.28846153846	3.71153846153857
116	1440	1479.44827586207	-39.4482758620691
117	1280	1276.28846153846	3.71153846153857
118	1280	1276.28846153846	3.71153846153857
119	1280	1276.28846153846	3.71153846153857
120	1440	1479.44827586207	-39.4482758620691
121	1280	1276.28846153846	3.71153846153857
122	1024	1129.525	-105.525
123	1024	1129.525	-105.525
124	1152	1276.28846153846	-124.288461538461
125	1280	1129.525	150.475
126	1024	1129.525	-105.525
127	1366	1129.525	236.475
128	1680	1840	-160
129	1680	1840	-160
130	1280	1276.28846153846	3.71153846153857
131	1366	1129.525	236.475
132	1024	1129.525	-105.525
133	1440	1479.44827586207	-39.4482758620691
134	1024	1129.525	-105.525
135	1280	1276.28846153846	3.71153846153857
136	1280	1276.28846153846	3.71153846153857
137	1280	1276.28846153846	3.71153846153857
138	1024	1129.525	-105.525
139	1280	1276.28846153846	3.71153846153857

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 = 2 ; par4 = no ;

Parameters (R input):

par1 = 1 ; par2 = none ; par3 = 2 ; 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