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Author

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R Software Module

rwasp_regression_trees1.wasp

Title produced by software

Recursive Partitioning (Regression Trees)

Date of computation

Thu, 27 Dec 2012 07:58:39 -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/27/t1356613377q5yoqzxbahvede9.htm/, Retrieved Thu, 18 Apr 2024 16:28:06 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=204762, Retrieved Thu, 18 Apr 2024 16:28:06 +0000

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Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords

Estimated Impact

129

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

-     [Recursive Partitioning (Regression Trees)] [Regressieboom Lon...] [2012-12-16 18:29:13] [ae5ad0a586ac3fd0e6f11dab2358eb50]
- R  D    [Recursive Partitioning (Regression Trees)] [Zonder NA] [2012-12-27 12:58:39] [ae7a5a1cf44a58c30c60b8a64f459c03] [Current]

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Dataseries X:

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0	3289,5	0,66	0,814	0,526	1
6	25299,2	0,384	0,743	0,587	2
1	32642,4	0,681	0,813	0,612	0
64	17096,2	0,952	0,895	0,779	0
4	7670,5	0,709	0,875	0,793	0
0	492,9	0,602	0,825	0,754	1
0	105256	0,253	0,623	0,277	0
4	9949	0,764	0,884	0,791	0
0	190,2	0,615	0,828	0,54	0
0	4773,1	0,203	0,453	0,342	0
0	6658,5	0,604	0,612	0,476	0
0	1382,4	0,497	0,696	0,607	0
0	12180,8	0,346	0,525	0,428	2
47	27700,9	0,875	0,904	0,796	0
0	2934,8	0,219	0,455	0,3	1
0	13187,8	0,677	0,847	0,592	0
1	33203,3	0,39	0,762	0,582	1
0	3070,2	0,548	0,879	0,587	0
93	10570,3	0,69	0,859	0,523	2
0	766,7	0,626	0,892	0,747	0
11	5141	0,774	0,866	0,79	0
0	7194,7	0,499	0,751	0,513	0
0	10260,6	0,486	0,771	0,557	0
0	56843,3	0,376	0,663	0,493	1
0	5332,8	0,386	0,726	0,513	1
6	1567,6	0,716	0,78	0,661	1
0	728,4	0,674	0,718	0,503	1
11	4986,4	0,74	0,871	0,776	0
66	56708,3	0,666	0,895	0,787	0
0	929	0,484	0,652	0,702	1
235	79098,1	0,721	0,874	0,796	0
1	14793,4	0,406	0,581	0,31	1
43	57214,5	0,688	0,877	0,781	0
10	10160,5	0,671	0,9	0,742	0
0	8923,1	0,296	0,666	0,499	1
0	724,9	0,541	0,646	0,335	1
0	7124,9	0,294	0,554	0,384	2
0	4889,3	0,405	0,731	0,457	0
86	10376,3	0,661	0,778	0,683	0
0	254,8	0,727	0,917	0,79	0
0	873785,4	0,318	0,605	0,359	1
15	184345,9	0,39	0,664	0,43	1
7	3531,2	0,76	0,863	0,729	0
3	4499,9	0,784	0,891	0,739	0
48	56832,3	0,636	0,897	0,781	0
7	2364,9	0,592	0,8	0,571	0
42	122251,2	0,761	0,931	0,798	0
0	3415,6	0,526	0,795	0,493	1
20	23447,2	0,408	0,621	0,375	1
82	42980,4	0,738	0,816	0,678	0
0	2087,7	0,514	0,828	0,848	1
5	2663,9	0,652	0,772	0,661	1
0	1639,2	0,431	0,623	0,388	1
0	381,2	0,656	0,868	0,86	0
0	9380,9	0,239	0,428	0,242	2
1	18208,6	0,534	0,789	0,595	1
0	8672,9	0,082	0,382	0,27	0
0	367,5	0,686	0,872	0,713	0
0	1995,5	0,194	0,568	0,4	2
2	84306,6	0,518	0,802	0,657	1
2	2192,6	0,58	0,639	0,426	0
8	24781,1	0,254	0,696	0,466	1
0	13547,1	0,116	0,367	0,189	1
4	1414,8	0,524	0,644	0,53	0
0	19081,1	0,257	0,536	0,287	0
29	14891,7	0,814	0,899	0,797	0
18	3398	0,873	0,871	0,745	0
0	7788,2	0,079	0,338	0,27	1
19	4241,5	0,82	0,891	0,823	0
0	1868,1	0,71	0,798	0,718	1
1	111844,7	0,241	0,642	0,411	1
0	2415,9	0,599	0,825	0,581	1
0	4157,7	0,222	0,561	0,4	0
0	4243,9	0,466	0,757	0,532	1
140	1145195,2	0,437	0,78	0,345	2
1	61628,7	0,578	0,712	0,452	1
0	9925,5	0,568	0,856	0,729	0
40	23206,7	0,705	0,779	0,624	1
0	7109,5	0,217	0,202	0,286	2
0	16139,1	0,57	0,767	0,762	2
0	7241,6	0,247	0,524	0,375	1
0	3981,6	0,182	0,296	0,258	2
4	36793,9	0,572	0,655	0,622	1
81	38889,2	0,619	0,9	0,756	0
0	17337	0,59	0,781	0,43	1
0	107,4	0,665	0,777	0,536	0
0	26494,2	0,163	0,513	0,315	2
0	862,9	0,451	0,622	0,517	1
23	8558,8	0,759	0,909	0,786	0
5	6673,7	0,762	0,909	0,836	0
0	12324,1	0,425	0,806	0,48	2
1	57072,1	0,417	0,828	0,526	1
0	966,2	0,179	0,522	0,34	0
0	3665,5	0,312	0,521	0,306	2
0	1215,5	0,609	0,771	0,659	0
0	8215,1	0,396	0,767	0,524	1
16	54130,3	0,41	0,679	0,622	1
0	17699,7	0,262	0,431	0,235	2
0	1808,6	0,462	0,817	0,871	1
290	253339,1	0,917	0,871	0,825	0
0	3109,1	0,64	0,828	0,609	0
0	19685,2	0,476	0,805	0,651	1
0	7860,1	0,406	0,434	0,346	0
0	10469,2	0,451	0,64	0,266	1

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	'Gwilym Jenkins' @ jenkins.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 & 5 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=204762&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]5 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ jenkins.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=204762&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=204762&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	'Gwilym Jenkins' @ jenkins.wessa.net

Goodness of Fit
Correlation	0.6582
R-squared	0.4332
RMSE	31.7846

\begin{tabular}{lllllllll}
\hline
Goodness of Fit \tabularnewline
Correlation & 0.6582 \tabularnewline
R-squared & 0.4332 \tabularnewline
RMSE & 31.7846 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=204762&T=1

[TABLE]
[ROW][C]Goodness of Fit[/C][/ROW]
[ROW][C]Correlation[/C][C]0.6582[/C][/ROW]
[ROW][C]R-squared[/C][C]0.4332[/C][/ROW]
[ROW][C]RMSE[/C][C]31.7846[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=204762&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=204762&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	0.6582
R-squared	0.4332
RMSE	31.7846

Actuals, Predictions, and Residuals
#	Actuals	Forecasts	Residuals
1	0	2.29411764705882	-2.29411764705882
2	6	2.29411764705882	3.70588235294118
3	1	2.29411764705882	-1.29411764705882
4	64	96.5	-32.5
5	4	8.53846153846154	-4.53846153846154
6	0	2.29411764705882	-2.29411764705882
7	0	28.5384615384615	-28.5384615384615
8	4	8.53846153846154	-4.53846153846154
9	0	2.29411764705882	-2.29411764705882
10	0	2.29411764705882	-2.29411764705882
11	0	2.29411764705882	-2.29411764705882
12	0	2.29411764705882	-2.29411764705882
13	0	2.29411764705882	-2.29411764705882
14	47	96.5	-49.5
15	0	2.29411764705882	-2.29411764705882
16	0	2.29411764705882	-2.29411764705882
17	1	2.29411764705882	-1.29411764705882
18	0	2.29411764705882	-2.29411764705882
19	93	96.5	-3.5
20	0	2.29411764705882	-2.29411764705882
21	11	8.53846153846154	2.46153846153846
22	0	2.29411764705882	-2.29411764705882
23	0	2.29411764705882	-2.29411764705882
24	0	28.5384615384615	-28.5384615384615
25	0	2.29411764705882	-2.29411764705882
26	6	8.53846153846154	-2.53846153846154
27	0	2.29411764705882	-2.29411764705882
28	11	8.53846153846154	2.46153846153846
29	66	28.5384615384615	37.4615384615385
30	0	2.29411764705882	-2.29411764705882
31	235	96.5	138.5
32	1	2.29411764705882	-1.29411764705882
33	43	96.5	-53.5
34	10	2.29411764705882	7.70588235294118
35	0	2.29411764705882	-2.29411764705882
36	0	2.29411764705882	-2.29411764705882
37	0	2.29411764705882	-2.29411764705882
38	0	2.29411764705882	-2.29411764705882
39	86	2.29411764705882	83.7058823529412
40	0	8.53846153846154	-8.53846153846154
41	0	28.5384615384615	-28.5384615384615
42	15	28.5384615384615	-13.5384615384615
43	7	8.53846153846154	-1.53846153846154
44	3	8.53846153846154	-5.53846153846154
45	48	28.5384615384615	19.4615384615385
46	7	2.29411764705882	4.70588235294118
47	42	96.5	-54.5
48	0	2.29411764705882	-2.29411764705882
49	20	2.29411764705882	17.7058823529412
50	82	96.5	-14.5
51	0	2.29411764705882	-2.29411764705882
52	5	2.29411764705882	2.70588235294118
53	0	2.29411764705882	-2.29411764705882
54	0	2.29411764705882	-2.29411764705882
55	0	2.29411764705882	-2.29411764705882
56	1	2.29411764705882	-1.29411764705882
57	0	2.29411764705882	-2.29411764705882
58	0	2.29411764705882	-2.29411764705882
59	0	2.29411764705882	-2.29411764705882
60	2	28.5384615384615	-26.5384615384615
61	2	2.29411764705882	-0.294117647058823
62	8	2.29411764705882	5.70588235294118
63	0	2.29411764705882	-2.29411764705882
64	4	2.29411764705882	1.70588235294118
65	0	2.29411764705882	-2.29411764705882
66	29	96.5	-67.5
67	18	8.53846153846154	9.46153846153846
68	0	2.29411764705882	-2.29411764705882
69	19	8.53846153846154	10.4615384615385
70	0	8.53846153846154	-8.53846153846154
71	1	28.5384615384615	-27.5384615384615
72	0	2.29411764705882	-2.29411764705882
73	0	2.29411764705882	-2.29411764705882
74	0	2.29411764705882	-2.29411764705882
75	140	28.5384615384615	111.461538461538
76	1	28.5384615384615	-27.5384615384615
77	0	2.29411764705882	-2.29411764705882
78	40	96.5	-56.5
79	0	2.29411764705882	-2.29411764705882
80	0	2.29411764705882	-2.29411764705882
81	0	2.29411764705882	-2.29411764705882
82	0	2.29411764705882	-2.29411764705882
83	4	2.29411764705882	1.70588235294118
84	81	28.5384615384615	52.4615384615385
85	0	2.29411764705882	-2.29411764705882
86	0	2.29411764705882	-2.29411764705882
87	0	2.29411764705882	-2.29411764705882
88	0	2.29411764705882	-2.29411764705882
89	23	8.53846153846154	14.4615384615385
90	5	8.53846153846154	-3.53846153846154
91	0	2.29411764705882	-2.29411764705882
92	1	28.5384615384615	-27.5384615384615
93	0	2.29411764705882	-2.29411764705882
94	0	2.29411764705882	-2.29411764705882
95	0	2.29411764705882	-2.29411764705882
96	0	2.29411764705882	-2.29411764705882
97	16	28.5384615384615	-12.5384615384615
98	0	2.29411764705882	-2.29411764705882
99	0	2.29411764705882	-2.29411764705882
100	290	96.5	193.5
101	0	2.29411764705882	-2.29411764705882
102	0	2.29411764705882	-2.29411764705882
103	0	2.29411764705882	-2.29411764705882
104	0	2.29411764705882	-2.29411764705882

\begin{tabular}{lllllllll}
\hline
Actuals, Predictions, and Residuals \tabularnewline
# & Actuals & Forecasts & Residuals \tabularnewline
1 & 0 & 2.29411764705882 & -2.29411764705882 \tabularnewline
2 & 6 & 2.29411764705882 & 3.70588235294118 \tabularnewline
3 & 1 & 2.29411764705882 & -1.29411764705882 \tabularnewline
4 & 64 & 96.5 & -32.5 \tabularnewline
5 & 4 & 8.53846153846154 & -4.53846153846154 \tabularnewline
6 & 0 & 2.29411764705882 & -2.29411764705882 \tabularnewline
7 & 0 & 28.5384615384615 & -28.5384615384615 \tabularnewline
8 & 4 & 8.53846153846154 & -4.53846153846154 \tabularnewline
9 & 0 & 2.29411764705882 & -2.29411764705882 \tabularnewline
10 & 0 & 2.29411764705882 & -2.29411764705882 \tabularnewline
11 & 0 & 2.29411764705882 & -2.29411764705882 \tabularnewline
12 & 0 & 2.29411764705882 & -2.29411764705882 \tabularnewline
13 & 0 & 2.29411764705882 & -2.29411764705882 \tabularnewline
14 & 47 & 96.5 & -49.5 \tabularnewline
15 & 0 & 2.29411764705882 & -2.29411764705882 \tabularnewline
16 & 0 & 2.29411764705882 & -2.29411764705882 \tabularnewline
17 & 1 & 2.29411764705882 & -1.29411764705882 \tabularnewline
18 & 0 & 2.29411764705882 & -2.29411764705882 \tabularnewline
19 & 93 & 96.5 & -3.5 \tabularnewline
20 & 0 & 2.29411764705882 & -2.29411764705882 \tabularnewline
21 & 11 & 8.53846153846154 & 2.46153846153846 \tabularnewline
22 & 0 & 2.29411764705882 & -2.29411764705882 \tabularnewline
23 & 0 & 2.29411764705882 & -2.29411764705882 \tabularnewline
24 & 0 & 28.5384615384615 & -28.5384615384615 \tabularnewline
25 & 0 & 2.29411764705882 & -2.29411764705882 \tabularnewline
26 & 6 & 8.53846153846154 & -2.53846153846154 \tabularnewline
27 & 0 & 2.29411764705882 & -2.29411764705882 \tabularnewline
28 & 11 & 8.53846153846154 & 2.46153846153846 \tabularnewline
29 & 66 & 28.5384615384615 & 37.4615384615385 \tabularnewline
30 & 0 & 2.29411764705882 & -2.29411764705882 \tabularnewline
31 & 235 & 96.5 & 138.5 \tabularnewline
32 & 1 & 2.29411764705882 & -1.29411764705882 \tabularnewline
33 & 43 & 96.5 & -53.5 \tabularnewline
34 & 10 & 2.29411764705882 & 7.70588235294118 \tabularnewline
35 & 0 & 2.29411764705882 & -2.29411764705882 \tabularnewline
36 & 0 & 2.29411764705882 & -2.29411764705882 \tabularnewline
37 & 0 & 2.29411764705882 & -2.29411764705882 \tabularnewline
38 & 0 & 2.29411764705882 & -2.29411764705882 \tabularnewline
39 & 86 & 2.29411764705882 & 83.7058823529412 \tabularnewline
40 & 0 & 8.53846153846154 & -8.53846153846154 \tabularnewline
41 & 0 & 28.5384615384615 & -28.5384615384615 \tabularnewline
42 & 15 & 28.5384615384615 & -13.5384615384615 \tabularnewline
43 & 7 & 8.53846153846154 & -1.53846153846154 \tabularnewline
44 & 3 & 8.53846153846154 & -5.53846153846154 \tabularnewline
45 & 48 & 28.5384615384615 & 19.4615384615385 \tabularnewline
46 & 7 & 2.29411764705882 & 4.70588235294118 \tabularnewline
47 & 42 & 96.5 & -54.5 \tabularnewline
48 & 0 & 2.29411764705882 & -2.29411764705882 \tabularnewline
49 & 20 & 2.29411764705882 & 17.7058823529412 \tabularnewline
50 & 82 & 96.5 & -14.5 \tabularnewline
51 & 0 & 2.29411764705882 & -2.29411764705882 \tabularnewline
52 & 5 & 2.29411764705882 & 2.70588235294118 \tabularnewline
53 & 0 & 2.29411764705882 & -2.29411764705882 \tabularnewline
54 & 0 & 2.29411764705882 & -2.29411764705882 \tabularnewline
55 & 0 & 2.29411764705882 & -2.29411764705882 \tabularnewline
56 & 1 & 2.29411764705882 & -1.29411764705882 \tabularnewline
57 & 0 & 2.29411764705882 & -2.29411764705882 \tabularnewline
58 & 0 & 2.29411764705882 & -2.29411764705882 \tabularnewline
59 & 0 & 2.29411764705882 & -2.29411764705882 \tabularnewline
60 & 2 & 28.5384615384615 & -26.5384615384615 \tabularnewline
61 & 2 & 2.29411764705882 & -0.294117647058823 \tabularnewline
62 & 8 & 2.29411764705882 & 5.70588235294118 \tabularnewline
63 & 0 & 2.29411764705882 & -2.29411764705882 \tabularnewline
64 & 4 & 2.29411764705882 & 1.70588235294118 \tabularnewline
65 & 0 & 2.29411764705882 & -2.29411764705882 \tabularnewline
66 & 29 & 96.5 & -67.5 \tabularnewline
67 & 18 & 8.53846153846154 & 9.46153846153846 \tabularnewline
68 & 0 & 2.29411764705882 & -2.29411764705882 \tabularnewline
69 & 19 & 8.53846153846154 & 10.4615384615385 \tabularnewline
70 & 0 & 8.53846153846154 & -8.53846153846154 \tabularnewline
71 & 1 & 28.5384615384615 & -27.5384615384615 \tabularnewline
72 & 0 & 2.29411764705882 & -2.29411764705882 \tabularnewline
73 & 0 & 2.29411764705882 & -2.29411764705882 \tabularnewline
74 & 0 & 2.29411764705882 & -2.29411764705882 \tabularnewline
75 & 140 & 28.5384615384615 & 111.461538461538 \tabularnewline
76 & 1 & 28.5384615384615 & -27.5384615384615 \tabularnewline
77 & 0 & 2.29411764705882 & -2.29411764705882 \tabularnewline
78 & 40 & 96.5 & -56.5 \tabularnewline
79 & 0 & 2.29411764705882 & -2.29411764705882 \tabularnewline
80 & 0 & 2.29411764705882 & -2.29411764705882 \tabularnewline
81 & 0 & 2.29411764705882 & -2.29411764705882 \tabularnewline
82 & 0 & 2.29411764705882 & -2.29411764705882 \tabularnewline
83 & 4 & 2.29411764705882 & 1.70588235294118 \tabularnewline
84 & 81 & 28.5384615384615 & 52.4615384615385 \tabularnewline
85 & 0 & 2.29411764705882 & -2.29411764705882 \tabularnewline
86 & 0 & 2.29411764705882 & -2.29411764705882 \tabularnewline
87 & 0 & 2.29411764705882 & -2.29411764705882 \tabularnewline
88 & 0 & 2.29411764705882 & -2.29411764705882 \tabularnewline
89 & 23 & 8.53846153846154 & 14.4615384615385 \tabularnewline
90 & 5 & 8.53846153846154 & -3.53846153846154 \tabularnewline
91 & 0 & 2.29411764705882 & -2.29411764705882 \tabularnewline
92 & 1 & 28.5384615384615 & -27.5384615384615 \tabularnewline
93 & 0 & 2.29411764705882 & -2.29411764705882 \tabularnewline
94 & 0 & 2.29411764705882 & -2.29411764705882 \tabularnewline
95 & 0 & 2.29411764705882 & -2.29411764705882 \tabularnewline
96 & 0 & 2.29411764705882 & -2.29411764705882 \tabularnewline
97 & 16 & 28.5384615384615 & -12.5384615384615 \tabularnewline
98 & 0 & 2.29411764705882 & -2.29411764705882 \tabularnewline
99 & 0 & 2.29411764705882 & -2.29411764705882 \tabularnewline
100 & 290 & 96.5 & 193.5 \tabularnewline
101 & 0 & 2.29411764705882 & -2.29411764705882 \tabularnewline
102 & 0 & 2.29411764705882 & -2.29411764705882 \tabularnewline
103 & 0 & 2.29411764705882 & -2.29411764705882 \tabularnewline
104 & 0 & 2.29411764705882 & -2.29411764705882 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=204762&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]0[/C][C]2.29411764705882[/C][C]-2.29411764705882[/C][/ROW]
[ROW][C]2[/C][C]6[/C][C]2.29411764705882[/C][C]3.70588235294118[/C][/ROW]
[ROW][C]3[/C][C]1[/C][C]2.29411764705882[/C][C]-1.29411764705882[/C][/ROW]
[ROW][C]4[/C][C]64[/C][C]96.5[/C][C]-32.5[/C][/ROW]
[ROW][C]5[/C][C]4[/C][C]8.53846153846154[/C][C]-4.53846153846154[/C][/ROW]
[ROW][C]6[/C][C]0[/C][C]2.29411764705882[/C][C]-2.29411764705882[/C][/ROW]
[ROW][C]7[/C][C]0[/C][C]28.5384615384615[/C][C]-28.5384615384615[/C][/ROW]
[ROW][C]8[/C][C]4[/C][C]8.53846153846154[/C][C]-4.53846153846154[/C][/ROW]
[ROW][C]9[/C][C]0[/C][C]2.29411764705882[/C][C]-2.29411764705882[/C][/ROW]
[ROW][C]10[/C][C]0[/C][C]2.29411764705882[/C][C]-2.29411764705882[/C][/ROW]
[ROW][C]11[/C][C]0[/C][C]2.29411764705882[/C][C]-2.29411764705882[/C][/ROW]
[ROW][C]12[/C][C]0[/C][C]2.29411764705882[/C][C]-2.29411764705882[/C][/ROW]
[ROW][C]13[/C][C]0[/C][C]2.29411764705882[/C][C]-2.29411764705882[/C][/ROW]
[ROW][C]14[/C][C]47[/C][C]96.5[/C][C]-49.5[/C][/ROW]
[ROW][C]15[/C][C]0[/C][C]2.29411764705882[/C][C]-2.29411764705882[/C][/ROW]
[ROW][C]16[/C][C]0[/C][C]2.29411764705882[/C][C]-2.29411764705882[/C][/ROW]
[ROW][C]17[/C][C]1[/C][C]2.29411764705882[/C][C]-1.29411764705882[/C][/ROW]
[ROW][C]18[/C][C]0[/C][C]2.29411764705882[/C][C]-2.29411764705882[/C][/ROW]
[ROW][C]19[/C][C]93[/C][C]96.5[/C][C]-3.5[/C][/ROW]
[ROW][C]20[/C][C]0[/C][C]2.29411764705882[/C][C]-2.29411764705882[/C][/ROW]
[ROW][C]21[/C][C]11[/C][C]8.53846153846154[/C][C]2.46153846153846[/C][/ROW]
[ROW][C]22[/C][C]0[/C][C]2.29411764705882[/C][C]-2.29411764705882[/C][/ROW]
[ROW][C]23[/C][C]0[/C][C]2.29411764705882[/C][C]-2.29411764705882[/C][/ROW]
[ROW][C]24[/C][C]0[/C][C]28.5384615384615[/C][C]-28.5384615384615[/C][/ROW]
[ROW][C]25[/C][C]0[/C][C]2.29411764705882[/C][C]-2.29411764705882[/C][/ROW]
[ROW][C]26[/C][C]6[/C][C]8.53846153846154[/C][C]-2.53846153846154[/C][/ROW]
[ROW][C]27[/C][C]0[/C][C]2.29411764705882[/C][C]-2.29411764705882[/C][/ROW]
[ROW][C]28[/C][C]11[/C][C]8.53846153846154[/C][C]2.46153846153846[/C][/ROW]
[ROW][C]29[/C][C]66[/C][C]28.5384615384615[/C][C]37.4615384615385[/C][/ROW]
[ROW][C]30[/C][C]0[/C][C]2.29411764705882[/C][C]-2.29411764705882[/C][/ROW]
[ROW][C]31[/C][C]235[/C][C]96.5[/C][C]138.5[/C][/ROW]
[ROW][C]32[/C][C]1[/C][C]2.29411764705882[/C][C]-1.29411764705882[/C][/ROW]
[ROW][C]33[/C][C]43[/C][C]96.5[/C][C]-53.5[/C][/ROW]
[ROW][C]34[/C][C]10[/C][C]2.29411764705882[/C][C]7.70588235294118[/C][/ROW]
[ROW][C]35[/C][C]0[/C][C]2.29411764705882[/C][C]-2.29411764705882[/C][/ROW]
[ROW][C]36[/C][C]0[/C][C]2.29411764705882[/C][C]-2.29411764705882[/C][/ROW]
[ROW][C]37[/C][C]0[/C][C]2.29411764705882[/C][C]-2.29411764705882[/C][/ROW]
[ROW][C]38[/C][C]0[/C][C]2.29411764705882[/C][C]-2.29411764705882[/C][/ROW]
[ROW][C]39[/C][C]86[/C][C]2.29411764705882[/C][C]83.7058823529412[/C][/ROW]
[ROW][C]40[/C][C]0[/C][C]8.53846153846154[/C][C]-8.53846153846154[/C][/ROW]
[ROW][C]41[/C][C]0[/C][C]28.5384615384615[/C][C]-28.5384615384615[/C][/ROW]
[ROW][C]42[/C][C]15[/C][C]28.5384615384615[/C][C]-13.5384615384615[/C][/ROW]
[ROW][C]43[/C][C]7[/C][C]8.53846153846154[/C][C]-1.53846153846154[/C][/ROW]
[ROW][C]44[/C][C]3[/C][C]8.53846153846154[/C][C]-5.53846153846154[/C][/ROW]
[ROW][C]45[/C][C]48[/C][C]28.5384615384615[/C][C]19.4615384615385[/C][/ROW]
[ROW][C]46[/C][C]7[/C][C]2.29411764705882[/C][C]4.70588235294118[/C][/ROW]
[ROW][C]47[/C][C]42[/C][C]96.5[/C][C]-54.5[/C][/ROW]
[ROW][C]48[/C][C]0[/C][C]2.29411764705882[/C][C]-2.29411764705882[/C][/ROW]
[ROW][C]49[/C][C]20[/C][C]2.29411764705882[/C][C]17.7058823529412[/C][/ROW]
[ROW][C]50[/C][C]82[/C][C]96.5[/C][C]-14.5[/C][/ROW]
[ROW][C]51[/C][C]0[/C][C]2.29411764705882[/C][C]-2.29411764705882[/C][/ROW]
[ROW][C]52[/C][C]5[/C][C]2.29411764705882[/C][C]2.70588235294118[/C][/ROW]
[ROW][C]53[/C][C]0[/C][C]2.29411764705882[/C][C]-2.29411764705882[/C][/ROW]
[ROW][C]54[/C][C]0[/C][C]2.29411764705882[/C][C]-2.29411764705882[/C][/ROW]
[ROW][C]55[/C][C]0[/C][C]2.29411764705882[/C][C]-2.29411764705882[/C][/ROW]
[ROW][C]56[/C][C]1[/C][C]2.29411764705882[/C][C]-1.29411764705882[/C][/ROW]
[ROW][C]57[/C][C]0[/C][C]2.29411764705882[/C][C]-2.29411764705882[/C][/ROW]
[ROW][C]58[/C][C]0[/C][C]2.29411764705882[/C][C]-2.29411764705882[/C][/ROW]
[ROW][C]59[/C][C]0[/C][C]2.29411764705882[/C][C]-2.29411764705882[/C][/ROW]
[ROW][C]60[/C][C]2[/C][C]28.5384615384615[/C][C]-26.5384615384615[/C][/ROW]
[ROW][C]61[/C][C]2[/C][C]2.29411764705882[/C][C]-0.294117647058823[/C][/ROW]
[ROW][C]62[/C][C]8[/C][C]2.29411764705882[/C][C]5.70588235294118[/C][/ROW]
[ROW][C]63[/C][C]0[/C][C]2.29411764705882[/C][C]-2.29411764705882[/C][/ROW]
[ROW][C]64[/C][C]4[/C][C]2.29411764705882[/C][C]1.70588235294118[/C][/ROW]
[ROW][C]65[/C][C]0[/C][C]2.29411764705882[/C][C]-2.29411764705882[/C][/ROW]
[ROW][C]66[/C][C]29[/C][C]96.5[/C][C]-67.5[/C][/ROW]
[ROW][C]67[/C][C]18[/C][C]8.53846153846154[/C][C]9.46153846153846[/C][/ROW]
[ROW][C]68[/C][C]0[/C][C]2.29411764705882[/C][C]-2.29411764705882[/C][/ROW]
[ROW][C]69[/C][C]19[/C][C]8.53846153846154[/C][C]10.4615384615385[/C][/ROW]
[ROW][C]70[/C][C]0[/C][C]8.53846153846154[/C][C]-8.53846153846154[/C][/ROW]
[ROW][C]71[/C][C]1[/C][C]28.5384615384615[/C][C]-27.5384615384615[/C][/ROW]
[ROW][C]72[/C][C]0[/C][C]2.29411764705882[/C][C]-2.29411764705882[/C][/ROW]
[ROW][C]73[/C][C]0[/C][C]2.29411764705882[/C][C]-2.29411764705882[/C][/ROW]
[ROW][C]74[/C][C]0[/C][C]2.29411764705882[/C][C]-2.29411764705882[/C][/ROW]
[ROW][C]75[/C][C]140[/C][C]28.5384615384615[/C][C]111.461538461538[/C][/ROW]
[ROW][C]76[/C][C]1[/C][C]28.5384615384615[/C][C]-27.5384615384615[/C][/ROW]
[ROW][C]77[/C][C]0[/C][C]2.29411764705882[/C][C]-2.29411764705882[/C][/ROW]
[ROW][C]78[/C][C]40[/C][C]96.5[/C][C]-56.5[/C][/ROW]
[ROW][C]79[/C][C]0[/C][C]2.29411764705882[/C][C]-2.29411764705882[/C][/ROW]
[ROW][C]80[/C][C]0[/C][C]2.29411764705882[/C][C]-2.29411764705882[/C][/ROW]
[ROW][C]81[/C][C]0[/C][C]2.29411764705882[/C][C]-2.29411764705882[/C][/ROW]
[ROW][C]82[/C][C]0[/C][C]2.29411764705882[/C][C]-2.29411764705882[/C][/ROW]
[ROW][C]83[/C][C]4[/C][C]2.29411764705882[/C][C]1.70588235294118[/C][/ROW]
[ROW][C]84[/C][C]81[/C][C]28.5384615384615[/C][C]52.4615384615385[/C][/ROW]
[ROW][C]85[/C][C]0[/C][C]2.29411764705882[/C][C]-2.29411764705882[/C][/ROW]
[ROW][C]86[/C][C]0[/C][C]2.29411764705882[/C][C]-2.29411764705882[/C][/ROW]
[ROW][C]87[/C][C]0[/C][C]2.29411764705882[/C][C]-2.29411764705882[/C][/ROW]
[ROW][C]88[/C][C]0[/C][C]2.29411764705882[/C][C]-2.29411764705882[/C][/ROW]
[ROW][C]89[/C][C]23[/C][C]8.53846153846154[/C][C]14.4615384615385[/C][/ROW]
[ROW][C]90[/C][C]5[/C][C]8.53846153846154[/C][C]-3.53846153846154[/C][/ROW]
[ROW][C]91[/C][C]0[/C][C]2.29411764705882[/C][C]-2.29411764705882[/C][/ROW]
[ROW][C]92[/C][C]1[/C][C]28.5384615384615[/C][C]-27.5384615384615[/C][/ROW]
[ROW][C]93[/C][C]0[/C][C]2.29411764705882[/C][C]-2.29411764705882[/C][/ROW]
[ROW][C]94[/C][C]0[/C][C]2.29411764705882[/C][C]-2.29411764705882[/C][/ROW]
[ROW][C]95[/C][C]0[/C][C]2.29411764705882[/C][C]-2.29411764705882[/C][/ROW]
[ROW][C]96[/C][C]0[/C][C]2.29411764705882[/C][C]-2.29411764705882[/C][/ROW]
[ROW][C]97[/C][C]16[/C][C]28.5384615384615[/C][C]-12.5384615384615[/C][/ROW]
[ROW][C]98[/C][C]0[/C][C]2.29411764705882[/C][C]-2.29411764705882[/C][/ROW]
[ROW][C]99[/C][C]0[/C][C]2.29411764705882[/C][C]-2.29411764705882[/C][/ROW]
[ROW][C]100[/C][C]290[/C][C]96.5[/C][C]193.5[/C][/ROW]
[ROW][C]101[/C][C]0[/C][C]2.29411764705882[/C][C]-2.29411764705882[/C][/ROW]
[ROW][C]102[/C][C]0[/C][C]2.29411764705882[/C][C]-2.29411764705882[/C][/ROW]
[ROW][C]103[/C][C]0[/C][C]2.29411764705882[/C][C]-2.29411764705882[/C][/ROW]
[ROW][C]104[/C][C]0[/C][C]2.29411764705882[/C][C]-2.29411764705882[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=204762&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=204762&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	0	2.29411764705882	-2.29411764705882
2	6	2.29411764705882	3.70588235294118
3	1	2.29411764705882	-1.29411764705882
4	64	96.5	-32.5
5	4	8.53846153846154	-4.53846153846154
6	0	2.29411764705882	-2.29411764705882
7	0	28.5384615384615	-28.5384615384615
8	4	8.53846153846154	-4.53846153846154
9	0	2.29411764705882	-2.29411764705882
10	0	2.29411764705882	-2.29411764705882
11	0	2.29411764705882	-2.29411764705882
12	0	2.29411764705882	-2.29411764705882
13	0	2.29411764705882	-2.29411764705882
14	47	96.5	-49.5
15	0	2.29411764705882	-2.29411764705882
16	0	2.29411764705882	-2.29411764705882
17	1	2.29411764705882	-1.29411764705882
18	0	2.29411764705882	-2.29411764705882
19	93	96.5	-3.5
20	0	2.29411764705882	-2.29411764705882
21	11	8.53846153846154	2.46153846153846
22	0	2.29411764705882	-2.29411764705882
23	0	2.29411764705882	-2.29411764705882
24	0	28.5384615384615	-28.5384615384615
25	0	2.29411764705882	-2.29411764705882
26	6	8.53846153846154	-2.53846153846154
27	0	2.29411764705882	-2.29411764705882
28	11	8.53846153846154	2.46153846153846
29	66	28.5384615384615	37.4615384615385
30	0	2.29411764705882	-2.29411764705882
31	235	96.5	138.5
32	1	2.29411764705882	-1.29411764705882
33	43	96.5	-53.5
34	10	2.29411764705882	7.70588235294118
35	0	2.29411764705882	-2.29411764705882
36	0	2.29411764705882	-2.29411764705882
37	0	2.29411764705882	-2.29411764705882
38	0	2.29411764705882	-2.29411764705882
39	86	2.29411764705882	83.7058823529412
40	0	8.53846153846154	-8.53846153846154
41	0	28.5384615384615	-28.5384615384615
42	15	28.5384615384615	-13.5384615384615
43	7	8.53846153846154	-1.53846153846154
44	3	8.53846153846154	-5.53846153846154
45	48	28.5384615384615	19.4615384615385
46	7	2.29411764705882	4.70588235294118
47	42	96.5	-54.5
48	0	2.29411764705882	-2.29411764705882
49	20	2.29411764705882	17.7058823529412
50	82	96.5	-14.5
51	0	2.29411764705882	-2.29411764705882
52	5	2.29411764705882	2.70588235294118
53	0	2.29411764705882	-2.29411764705882
54	0	2.29411764705882	-2.29411764705882
55	0	2.29411764705882	-2.29411764705882
56	1	2.29411764705882	-1.29411764705882
57	0	2.29411764705882	-2.29411764705882
58	0	2.29411764705882	-2.29411764705882
59	0	2.29411764705882	-2.29411764705882
60	2	28.5384615384615	-26.5384615384615
61	2	2.29411764705882	-0.294117647058823
62	8	2.29411764705882	5.70588235294118
63	0	2.29411764705882	-2.29411764705882
64	4	2.29411764705882	1.70588235294118
65	0	2.29411764705882	-2.29411764705882
66	29	96.5	-67.5
67	18	8.53846153846154	9.46153846153846
68	0	2.29411764705882	-2.29411764705882
69	19	8.53846153846154	10.4615384615385
70	0	8.53846153846154	-8.53846153846154
71	1	28.5384615384615	-27.5384615384615
72	0	2.29411764705882	-2.29411764705882
73	0	2.29411764705882	-2.29411764705882
74	0	2.29411764705882	-2.29411764705882
75	140	28.5384615384615	111.461538461538
76	1	28.5384615384615	-27.5384615384615
77	0	2.29411764705882	-2.29411764705882
78	40	96.5	-56.5
79	0	2.29411764705882	-2.29411764705882
80	0	2.29411764705882	-2.29411764705882
81	0	2.29411764705882	-2.29411764705882
82	0	2.29411764705882	-2.29411764705882
83	4	2.29411764705882	1.70588235294118
84	81	28.5384615384615	52.4615384615385
85	0	2.29411764705882	-2.29411764705882
86	0	2.29411764705882	-2.29411764705882
87	0	2.29411764705882	-2.29411764705882
88	0	2.29411764705882	-2.29411764705882
89	23	8.53846153846154	14.4615384615385
90	5	8.53846153846154	-3.53846153846154
91	0	2.29411764705882	-2.29411764705882
92	1	28.5384615384615	-27.5384615384615
93	0	2.29411764705882	-2.29411764705882
94	0	2.29411764705882	-2.29411764705882
95	0	2.29411764705882	-2.29411764705882
96	0	2.29411764705882	-2.29411764705882
97	16	28.5384615384615	-12.5384615384615
98	0	2.29411764705882	-2.29411764705882
99	0	2.29411764705882	-2.29411764705882
100	290	96.5	193.5
101	0	2.29411764705882	-2.29411764705882
102	0	2.29411764705882	-2.29411764705882
103	0	2.29411764705882	-2.29411764705882
104	0	2.29411764705882	-2.29411764705882

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):

par4 <- 'no'
par3 <- '3'
par2 <- 'none'
par1 <- '1'
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