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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 10:29:52 +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/t1292322618icaa2o9emw2164c.htm/, Retrieved Thu, 02 May 2024 16:02:51 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=109361, Retrieved Thu, 02 May 2024 16:02:51 +0000

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IsPrivate?

No (this computation is public)

User-defined keywords

Estimated Impact

123

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

-     [Kendall tau Correlation Matrix] [Correlation scien...] [2010-12-13 18:47:35] [6bc4f9343b7ea3ef5a59412d1f72bb2b]
- RMPD  [Multiple Regression] [Multiple regressi...] [2010-12-14 10:04:25] [6bc4f9343b7ea3ef5a59412d1f72bb2b]
- RMPD      [Recursive Partitioning (Regression Trees)] [SWS Tree no categ...] [2010-12-14 10:29:52] [b6992a7b26e556359948e164e4227eba] [Current]

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6,300000	0,301030	1,504077	0,000000	0,819544	1,623249	3,000000	1,000000	3,000000
2,100000	0,255273	4,234107	3,406029	3,663041	2,795185	3,000000	5,000000	4,000000
9,100000	-0,154902	3,295837	1,023252	2,254064	2,255273	4,000000	4,000000	4,000000
15,800000	0,591065	2,944439	-1,638272	-0,522879	1,544068	1,000000	1,000000	1,000000
5,200000	0,000000	3,414443	2,204120	2,227887	2,593286	4,000000	5,000000	4,000000
10,900000	0,556303	3,332205	0,518514	1,408240	1,799341	1,000000	2,000000	1,000000
8,300000	0,146128	3,912023	1,717338	2,643453	2,361728	1,000000	1,000000	1,000000
11,000000	0,176091	1,945910	-0,371611	0,806180	2,049218	5,000000	4,000000	4,000000
3,200000	-0,154902	3,401197	2,667453	2,626340	2,448706	5,000000	5,000000	5,000000
6,300000	0,322219	1,252763	-1,124939	0,079181	1,623249	1,000000	1,000000	1,000000
6,600000	0,612784	1,791759	-0,105130	0,544068	1,623249	2,000000	2,000000	2,000000
9,500000	0,079181	2,341806	-0,698970	0,698970	2,079181	2,000000	2,000000	2,000000
3,300000	-0,301030	2,995732	1,441852	2,060698	2,170262	5,000000	5,000000	5,000000
11,000000	0,531479	1,360977	-0,920819	0,000000	1,204120	3,000000	1,000000	2,000000
4,700000	0,176091	3,713572	1,929419	2,511883	2,491362	1,000000	3,000000	1,000000
10,400000	0,531479	2,197225	-0,995679	0,602060	1,447158	5,000000	1,000000	3,000000
7,400000	-0,096910	2,028148	0,017033	0,740363	1,832509	5,000000	3,000000	4,000000
2,100000	-0,096910	3,828641	2,716838	2,816241	2,526339	5,000000	5,000000	5,000000
17,900000	0,301030	3,178054	-2,000000	-0,602060	1,698970	1,000000	1,000000	1,000000
6,100000	0,278754	4,605170	1,792392	3,120574	2,426511	1,000000	1,000000	1,000000
11,900000	0,113943	1,163151	-1,638272	-0,397940	1,278754	4,000000	1,000000	3,000000
13,800000	0,748188	1,609438	0,230449	0,799341	1,079181	2,000000	1,000000	1,000000
14,300000	0,491362	1,871802	0,544068	1,033424	2,079181	2,000000	1,000000	1,000000
15,200000	0,255273	2,484907	-0,318759	1,190332	2,146128	2,000000	2,000000	2,000000
10,000000	-0,045757	3,005683	1,000000	2,060698	2,230449	4,000000	4,000000	4,000000
11,900000	0,255273	2,564949	0,209515	1,056905	1,230449	2,000000	1,000000	2,000000
6,500000	0,278754	3,295837	2,283301	2,255273	2,060698	4,000000	4,000000	4,000000
7,500000	-0,045757	2,890372	0,397940	1,082785	1,491362	5,000000	5,000000	5,000000
10,600000	0,414973	1,547563	-0,552842	0,278754	1,322219	3,000000	1,000000	3,000000
7,400000	0,380211	2,282382	0,626853	1,702431	1,716003	1,000000	1,000000	1,000000
8,400000	0,079181	3,367296	0,832509	2,252853	2,214844	2,000000	3,000000	2,000000
5,700000	-0,045757	1,945910	-0,124939	1,089905	2,352183	2,000000	2,000000	2,000000
4,900000	-0,301030	1,791759	0,556303	1,322219	2,352183	3,000000	2,000000	3,000000
3,200000	-0,221849	2,995732	1,744293	2,243038	2,178977	5,000000	5,000000	5,000000
11,000000	0,361728	1,504077	-0,045757	0,414973	1,778151	2,000000	1,000000	2,000000
4,900000	-0,301030	2,014903	0,301030	1,089905	2,301030	3,000000	1,000000	3,000000
13,200000	0,414973	0,832909	-0,982967	0,397940	1,662758	3,000000	2,000000	2,000000
9,700000	-0,221849	3,178054	0,622214	1,763428	2,322219	4,000000	3,000000	4,000000
12,800000	0,819544	1,098612	0,544068	0,591065	1,146128	2,000000	1,000000	1,000000

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	7 seconds
R Server	'Gwilym Jenkins' @ 72.249.127.135

\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 & 7 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=109361&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]7 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=109361&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=109361&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	7 seconds
R Server	'Gwilym Jenkins' @ 72.249.127.135

Goodness of Fit
Correlation	0.7499
R-squared	0.5624
RMSE	2.5911

\begin{tabular}{lllllllll}
\hline
Goodness of Fit \tabularnewline
Correlation & 0.7499 \tabularnewline
R-squared & 0.5624 \tabularnewline
RMSE & 2.5911 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=109361&T=1

[TABLE]
[ROW][C]Goodness of Fit[/C][/ROW]
[ROW][C]Correlation[/C][C]0.7499[/C][/ROW]
[ROW][C]R-squared[/C][C]0.5624[/C][/ROW]
[ROW][C]RMSE[/C][C]2.5911[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=109361&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=109361&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	0.7499
R-squared	0.5624
RMSE	2.5911

Actuals, Predictions, and Residuals
#	Actuals	Forecasts	Residuals
1	6.3	11.4894736842105	-5.18947368421053
2	2.1	4.47	-2.37
3	9.1	7.71	1.39
4	15.8	11.4894736842105	4.31052631578947
5	5.2	4.47	0.73
6	10.9	11.4894736842105	-0.589473684210526
7	8.3	4.47	3.83
8	11	11.4894736842105	-0.489473684210527
9	3.2	4.47	-1.27
10	6.3	11.4894736842105	-5.18947368421053
11	6.6	11.4894736842105	-4.88947368421053
12	9.5	7.71	1.79
13	3.3	4.47	-1.17
14	11	11.4894736842105	-0.489473684210527
15	4.7	4.47	0.230000000000000
16	10.4	11.4894736842105	-1.08947368421053
17	7.4	7.71	-0.310000000000000
18	2.1	4.47	-2.37
19	17.9	11.4894736842105	6.41052631578947
20	6.1	4.47	1.63
21	11.9	11.4894736842105	0.410526315789474
22	13.8	11.4894736842105	2.31052631578947
23	14.3	11.4894736842105	2.81052631578947
24	15.2	11.4894736842105	3.71052631578947
25	10	7.71	2.29
26	11.9	11.4894736842105	0.410526315789474
27	6.5	4.47	2.03
28	7.5	7.71	-0.210000000000001
29	10.6	11.4894736842105	-0.889473684210527
30	7.4	11.4894736842105	-4.08947368421053
31	8.4	7.71	0.69
32	5.7	7.71	-2.01
33	4.9	7.71	-2.81
34	3.2	4.47	-1.27
35	11	11.4894736842105	-0.489473684210527
36	4.9	7.71	-2.81
37	13.2	11.4894736842105	1.71052631578947
38	9.7	7.71	1.99
39	12.8	11.4894736842105	1.31052631578947

\begin{tabular}{lllllllll}
\hline
Actuals, Predictions, and Residuals \tabularnewline
# & Actuals & Forecasts & Residuals \tabularnewline
1 & 6.3 & 11.4894736842105 & -5.18947368421053 \tabularnewline
2 & 2.1 & 4.47 & -2.37 \tabularnewline
3 & 9.1 & 7.71 & 1.39 \tabularnewline
4 & 15.8 & 11.4894736842105 & 4.31052631578947 \tabularnewline
5 & 5.2 & 4.47 & 0.73 \tabularnewline
6 & 10.9 & 11.4894736842105 & -0.589473684210526 \tabularnewline
7 & 8.3 & 4.47 & 3.83 \tabularnewline
8 & 11 & 11.4894736842105 & -0.489473684210527 \tabularnewline
9 & 3.2 & 4.47 & -1.27 \tabularnewline
10 & 6.3 & 11.4894736842105 & -5.18947368421053 \tabularnewline
11 & 6.6 & 11.4894736842105 & -4.88947368421053 \tabularnewline
12 & 9.5 & 7.71 & 1.79 \tabularnewline
13 & 3.3 & 4.47 & -1.17 \tabularnewline
14 & 11 & 11.4894736842105 & -0.489473684210527 \tabularnewline
15 & 4.7 & 4.47 & 0.230000000000000 \tabularnewline
16 & 10.4 & 11.4894736842105 & -1.08947368421053 \tabularnewline
17 & 7.4 & 7.71 & -0.310000000000000 \tabularnewline
18 & 2.1 & 4.47 & -2.37 \tabularnewline
19 & 17.9 & 11.4894736842105 & 6.41052631578947 \tabularnewline
20 & 6.1 & 4.47 & 1.63 \tabularnewline
21 & 11.9 & 11.4894736842105 & 0.410526315789474 \tabularnewline
22 & 13.8 & 11.4894736842105 & 2.31052631578947 \tabularnewline
23 & 14.3 & 11.4894736842105 & 2.81052631578947 \tabularnewline
24 & 15.2 & 11.4894736842105 & 3.71052631578947 \tabularnewline
25 & 10 & 7.71 & 2.29 \tabularnewline
26 & 11.9 & 11.4894736842105 & 0.410526315789474 \tabularnewline
27 & 6.5 & 4.47 & 2.03 \tabularnewline
28 & 7.5 & 7.71 & -0.210000000000001 \tabularnewline
29 & 10.6 & 11.4894736842105 & -0.889473684210527 \tabularnewline
30 & 7.4 & 11.4894736842105 & -4.08947368421053 \tabularnewline
31 & 8.4 & 7.71 & 0.69 \tabularnewline
32 & 5.7 & 7.71 & -2.01 \tabularnewline
33 & 4.9 & 7.71 & -2.81 \tabularnewline
34 & 3.2 & 4.47 & -1.27 \tabularnewline
35 & 11 & 11.4894736842105 & -0.489473684210527 \tabularnewline
36 & 4.9 & 7.71 & -2.81 \tabularnewline
37 & 13.2 & 11.4894736842105 & 1.71052631578947 \tabularnewline
38 & 9.7 & 7.71 & 1.99 \tabularnewline
39 & 12.8 & 11.4894736842105 & 1.31052631578947 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=109361&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]6.3[/C][C]11.4894736842105[/C][C]-5.18947368421053[/C][/ROW]
[ROW][C]2[/C][C]2.1[/C][C]4.47[/C][C]-2.37[/C][/ROW]
[ROW][C]3[/C][C]9.1[/C][C]7.71[/C][C]1.39[/C][/ROW]
[ROW][C]4[/C][C]15.8[/C][C]11.4894736842105[/C][C]4.31052631578947[/C][/ROW]
[ROW][C]5[/C][C]5.2[/C][C]4.47[/C][C]0.73[/C][/ROW]
[ROW][C]6[/C][C]10.9[/C][C]11.4894736842105[/C][C]-0.589473684210526[/C][/ROW]
[ROW][C]7[/C][C]8.3[/C][C]4.47[/C][C]3.83[/C][/ROW]
[ROW][C]8[/C][C]11[/C][C]11.4894736842105[/C][C]-0.489473684210527[/C][/ROW]
[ROW][C]9[/C][C]3.2[/C][C]4.47[/C][C]-1.27[/C][/ROW]
[ROW][C]10[/C][C]6.3[/C][C]11.4894736842105[/C][C]-5.18947368421053[/C][/ROW]
[ROW][C]11[/C][C]6.6[/C][C]11.4894736842105[/C][C]-4.88947368421053[/C][/ROW]
[ROW][C]12[/C][C]9.5[/C][C]7.71[/C][C]1.79[/C][/ROW]
[ROW][C]13[/C][C]3.3[/C][C]4.47[/C][C]-1.17[/C][/ROW]
[ROW][C]14[/C][C]11[/C][C]11.4894736842105[/C][C]-0.489473684210527[/C][/ROW]
[ROW][C]15[/C][C]4.7[/C][C]4.47[/C][C]0.230000000000000[/C][/ROW]
[ROW][C]16[/C][C]10.4[/C][C]11.4894736842105[/C][C]-1.08947368421053[/C][/ROW]
[ROW][C]17[/C][C]7.4[/C][C]7.71[/C][C]-0.310000000000000[/C][/ROW]
[ROW][C]18[/C][C]2.1[/C][C]4.47[/C][C]-2.37[/C][/ROW]
[ROW][C]19[/C][C]17.9[/C][C]11.4894736842105[/C][C]6.41052631578947[/C][/ROW]
[ROW][C]20[/C][C]6.1[/C][C]4.47[/C][C]1.63[/C][/ROW]
[ROW][C]21[/C][C]11.9[/C][C]11.4894736842105[/C][C]0.410526315789474[/C][/ROW]
[ROW][C]22[/C][C]13.8[/C][C]11.4894736842105[/C][C]2.31052631578947[/C][/ROW]
[ROW][C]23[/C][C]14.3[/C][C]11.4894736842105[/C][C]2.81052631578947[/C][/ROW]
[ROW][C]24[/C][C]15.2[/C][C]11.4894736842105[/C][C]3.71052631578947[/C][/ROW]
[ROW][C]25[/C][C]10[/C][C]7.71[/C][C]2.29[/C][/ROW]
[ROW][C]26[/C][C]11.9[/C][C]11.4894736842105[/C][C]0.410526315789474[/C][/ROW]
[ROW][C]27[/C][C]6.5[/C][C]4.47[/C][C]2.03[/C][/ROW]
[ROW][C]28[/C][C]7.5[/C][C]7.71[/C][C]-0.210000000000001[/C][/ROW]
[ROW][C]29[/C][C]10.6[/C][C]11.4894736842105[/C][C]-0.889473684210527[/C][/ROW]
[ROW][C]30[/C][C]7.4[/C][C]11.4894736842105[/C][C]-4.08947368421053[/C][/ROW]
[ROW][C]31[/C][C]8.4[/C][C]7.71[/C][C]0.69[/C][/ROW]
[ROW][C]32[/C][C]5.7[/C][C]7.71[/C][C]-2.01[/C][/ROW]
[ROW][C]33[/C][C]4.9[/C][C]7.71[/C][C]-2.81[/C][/ROW]
[ROW][C]34[/C][C]3.2[/C][C]4.47[/C][C]-1.27[/C][/ROW]
[ROW][C]35[/C][C]11[/C][C]11.4894736842105[/C][C]-0.489473684210527[/C][/ROW]
[ROW][C]36[/C][C]4.9[/C][C]7.71[/C][C]-2.81[/C][/ROW]
[ROW][C]37[/C][C]13.2[/C][C]11.4894736842105[/C][C]1.71052631578947[/C][/ROW]
[ROW][C]38[/C][C]9.7[/C][C]7.71[/C][C]1.99[/C][/ROW]
[ROW][C]39[/C][C]12.8[/C][C]11.4894736842105[/C][C]1.31052631578947[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=109361&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=109361&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	6.3	11.4894736842105	-5.18947368421053
2	2.1	4.47	-2.37
3	9.1	7.71	1.39
4	15.8	11.4894736842105	4.31052631578947
5	5.2	4.47	0.73
6	10.9	11.4894736842105	-0.589473684210526
7	8.3	4.47	3.83
8	11	11.4894736842105	-0.489473684210527
9	3.2	4.47	-1.27
10	6.3	11.4894736842105	-5.18947368421053
11	6.6	11.4894736842105	-4.88947368421053
12	9.5	7.71	1.79
13	3.3	4.47	-1.17
14	11	11.4894736842105	-0.489473684210527
15	4.7	4.47	0.230000000000000
16	10.4	11.4894736842105	-1.08947368421053
17	7.4	7.71	-0.310000000000000
18	2.1	4.47	-2.37
19	17.9	11.4894736842105	6.41052631578947
20	6.1	4.47	1.63
21	11.9	11.4894736842105	0.410526315789474
22	13.8	11.4894736842105	2.31052631578947
23	14.3	11.4894736842105	2.81052631578947
24	15.2	11.4894736842105	3.71052631578947
25	10	7.71	2.29
26	11.9	11.4894736842105	0.410526315789474
27	6.5	4.47	2.03
28	7.5	7.71	-0.210000000000001
29	10.6	11.4894736842105	-0.889473684210527
30	7.4	11.4894736842105	-4.08947368421053
31	8.4	7.71	0.69
32	5.7	7.71	-2.01
33	4.9	7.71	-2.81
34	3.2	4.47	-1.27
35	11	11.4894736842105	-0.489473684210527
36	4.9	7.71	-2.81
37	13.2	11.4894736842105	1.71052631578947
38	9.7	7.71	1.99
39	12.8	11.4894736842105	1.31052631578947

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 = quantiles ; par3 = 4 ; par4 = no ;

Parameters (R input):

par1 = 1 ; par2 = none ; par3 = 3 ; par4 = no ;

R code (references can be found in the software module):

library(party)
library(Hmisc)
par1 <- as.numeric(par1)
par3 <- as.numeric(par3)
x <- data.frame(t(y))
is.data.frame(x)
x <- x[!is.na(x[,par1]),]
k <- length(x[1,])
n <- length(x[,1])
colnames(x)[par1]
x[,par1]
if (par2 == 'kmeans') {
cl <- kmeans(x[,par1], par3)
print(cl)
clm <- matrix(cbind(cl$centers,1:par3),ncol=2)
clm <- clm[sort.list(clm[,1]),]
for (i in 1:par3) {
cl$cluster[cl$cluster==clm[i,2]] <- paste('C',i,sep='')
}
cl$cluster <- as.factor(cl$cluster)
print(cl$cluster)
x[,par1] <- cl$cluster
}
if (par2 == 'quantiles') {
x[,par1] <- cut2(x[,par1],g=par3)
}
if (par2 == 'hclust') {
hc <- hclust(dist(x[,par1])^2, 'cen')
print(hc)
memb <- cutree(hc, k = par3)
dum <- c(mean(x[memb==1,par1]))
for (i in 2:par3) {
dum <- c(dum, mean(x[memb==i,par1]))
}
hcm <- matrix(cbind(dum,1:par3),ncol=2)
hcm <- hcm[sort.list(hcm[,1]),]
for (i in 1:par3) {
memb[memb==hcm[i,2]] <- paste('C',i,sep='')
}
memb <- as.factor(memb)
print(memb)
x[,par1] <- memb
}
if (par2=='equal') {
ed <- cut(as.numeric(x[,par1]),par3,labels=paste('C',1:par3,sep=''))
x[,par1] <- as.factor(ed)
}
table(x[,par1])
colnames(x)
colnames(x)[par1]
x[,par1]
if (par2 == 'none') {
m <- ctree(as.formula(paste(colnames(x)[par1],' ~ .',sep='')),data = x)
}
load(file='createtable')
if (par2 != 'none') {
m <- ctree(as.formula(paste('as.factor(',colnames(x)[par1],') ~ .',sep='')),data = x)
if (par4=='yes') {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'10-Fold Cross Validation',3+2*par3,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'',1,TRUE)
a<-table.element(a,'Prediction (training)',par3+1,TRUE)
a<-table.element(a,'Prediction (testing)',par3+1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Actual',1,TRUE)
for (jjj in 1:par3) a<-table.element(a,paste('C',jjj,sep=''),1,TRUE)
a<-table.element(a,'CV',1,TRUE)
for (jjj in 1:par3) a<-table.element(a,paste('C',jjj,sep=''),1,TRUE)
a<-table.element(a,'CV',1,TRUE)
a<-table.row.end(a)
for (i in 1:10) {
ind <- sample(2, nrow(x), replace=T, prob=c(0.9,0.1))
m.ct <- ctree(as.formula(paste('as.factor(',colnames(x)[par1],') ~ .',sep='')),data =x[ind==1,])
if (i==1) {
m.ct.i.pred <- predict(m.ct, newdata=x[ind==1,])
m.ct.i.actu <- x[ind==1,par1]
m.ct.x.pred <- predict(m.ct, newdata=x[ind==2,])
m.ct.x.actu <- x[ind==2,par1]
} else {
m.ct.i.pred <- c(m.ct.i.pred,predict(m.ct, newdata=x[ind==1,]))
m.ct.i.actu <- c(m.ct.i.actu,x[ind==1,par1])
m.ct.x.pred <- c(m.ct.x.pred,predict(m.ct, newdata=x[ind==2,]))
m.ct.x.actu <- c(m.ct.x.actu,x[ind==2,par1])
}
}
print(m.ct.i.tab <- table(m.ct.i.actu,m.ct.i.pred))
numer <- 0
for (i in 1:par3) {
print(m.ct.i.tab[i,i] / sum(m.ct.i.tab[i,]))
numer <- numer + m.ct.i.tab[i,i]
}
print(m.ct.i.cp <- numer / sum(m.ct.i.tab))
print(m.ct.x.tab <- table(m.ct.x.actu,m.ct.x.pred))
numer <- 0
for (i in 1:par3) {
print(m.ct.x.tab[i,i] / sum(m.ct.x.tab[i,]))
numer <- numer + m.ct.x.tab[i,i]
}
print(m.ct.x.cp <- numer / sum(m.ct.x.tab))
for (i in 1:par3) {
a<-table.row.start(a)
a<-table.element(a,paste('C',i,sep=''),1,TRUE)
for (jjj in 1:par3) a<-table.element(a,m.ct.i.tab[i,jjj])
a<-table.element(a,round(m.ct.i.tab[i,i]/sum(m.ct.i.tab[i,]),4))
for (jjj in 1:par3) a<-table.element(a,m.ct.x.tab[i,jjj])
a<-table.element(a,round(m.ct.x.tab[i,i]/sum(m.ct.x.tab[i,]),4))
a<-table.row.end(a)
}
a<-table.row.start(a)
a<-table.element(a,'Overall',1,TRUE)
for (jjj in 1:par3) a<-table.element(a,'-')
a<-table.element(a,round(m.ct.i.cp,4))
for (jjj in 1:par3) a<-table.element(a,'-')
a<-table.element(a,round(m.ct.x.cp,4))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
}
}
m
bitmap(file='test1.png')
plot(m)
dev.off()
bitmap(file='test1a.png')
plot(x[,par1] ~ as.factor(where(m)),main='Response by Terminal Node',xlab='Terminal Node',ylab='Response')
dev.off()
if (par2 == 'none') {
forec <- predict(m)
result <- as.data.frame(cbind(x[,par1],forec,x[,par1]-forec))
colnames(result) <- c('Actuals','Forecasts','Residuals')
print(result)
}
if (par2 != 'none') {
print(cbind(as.factor(x[,par1]),predict(m)))
myt <- table(as.factor(x[,par1]),predict(m))
print(myt)
}
bitmap(file='test2.png')
if(par2=='none') {
op <- par(mfrow=c(2,2))
plot(density(result$Actuals),main='Kernel Density Plot of Actuals')
plot(density(result$Residuals),main='Kernel Density Plot of Residuals')
plot(result$Forecasts,result$Actuals,main='Actuals versus Predictions',xlab='Predictions',ylab='Actuals')
plot(density(result$Forecasts),main='Kernel Density Plot of Predictions')
par(op)
}
if(par2!='none') {
plot(myt,main='Confusion Matrix',xlab='Actual',ylab='Predicted')
}
dev.off()
if (par2 == 'none') {
detcoef <- cor(result$Forecasts,result$Actuals)
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Goodness of Fit',2,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Correlation',1,TRUE)
a<-table.element(a,round(detcoef,4))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'R-squared',1,TRUE)
a<-table.element(a,round(detcoef*detcoef,4))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'RMSE',1,TRUE)
a<-table.element(a,round(sqrt(mean((result$Residuals)^2)),4))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Actuals, Predictions, and Residuals',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'#',header=TRUE)
a<-table.element(a,'Actuals',header=TRUE)
a<-table.element(a,'Forecasts',header=TRUE)
a<-table.element(a,'Residuals',header=TRUE)
a<-table.row.end(a)
for (i in 1:length(result$Actuals)) {
a<-table.row.start(a)
a<-table.element(a,i,header=TRUE)
a<-table.element(a,result$Actuals[i])
a<-table.element(a,result$Forecasts[i])
a<-table.element(a,result$Residuals[i])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable.tab')
}
if (par2 != 'none') {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Confusion Matrix (predicted in columns / actuals in rows)',par3+1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'',1,TRUE)
for (i in 1:par3) {
a<-table.element(a,paste('C',i,sep=''),1,TRUE)
}
a<-table.row.end(a)
for (i in 1:par3) {
a<-table.row.start(a)
a<-table.element(a,paste('C',i,sep=''),1,TRUE)
for (j in 1:par3) {
a<-table.element(a,myt[i,j])
}
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
}

Free Statistics

Description of Statistical Computation

Tree of Dependent Computations

Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code