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R Software Module

rwasp_regression_trees1.wasp

Title produced by software

Recursive Partitioning (Regression Trees)

Date of computation

Tue, 11 Dec 2012 15:58:50 -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/11/t1355259556h7zx14sglgorat3.htm/, Retrieved Sat, 20 Apr 2024 12:52:26 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=198701, Retrieved Sat, 20 Apr 2024 12:52:26 +0000

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Estimated Impact

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

-     [Recursive Partitioning (Regression Trees)] [] [2010-12-05 18:59:57] [b98453cac15ba1066b407e146608df68]
- R PD  [Recursive Partitioning (Regression Trees)] [WS10.4] [2012-12-11 20:38:27] [516331a1326ffbbf54349d9c9d5f2d94]
-   PD    [Recursive Partitioning (Regression Trees)] [WS10.5] [2012-12-11 20:51:39] [516331a1326ffbbf54349d9c9d5f2d94]
-   P         [Recursive Partitioning (Regression Trees)] [WS10.6] [2012-12-11 20:58:50] [6144fd9dab7e8876ce9100c6a2ac91c2] [Current]
-   P           [Recursive Partitioning (Regression Trees)] [WS10.6] [2012-12-11 21:03:34] [516331a1326ffbbf54349d9c9d5f2d94] 
-                 [Recursive Partitioning (Regression Trees)] [WS10.7] [2012-12-11 21:06:19] [516331a1326ffbbf54349d9c9d5f2d94] 

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Dataseries X:

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Histogram

Boxplots

6.8	225	0.442	0.672	9.2
6.3	180	0.435	0.797	11.7
6.4	190	0.456	0.761	15.8
6.2	180	0.416	0.651	8.6
6.9	205	0.449	0.9	23.2
6.4	225	0.431	0.78	27.4
6.3	185	0.487	0.771	9.3
6.8	235	0.469	0.75	16
6.9	235	0.435	0.818	4.7
6.7	210	0.48	0.825	12.5
6.9	245	0.516	0.632	20.1
6.9	245	0.493	0.757	9.1
6.3	185	0.374	0.709	8.1
6.1	185	0.424	0.782	8.6
6.2	180	0.441	0.775	20.3
6.8	220	0.503	0.88	25
6.5	194	0.503	0.833	19.2
7.6	225	0.425	0.571	3.3
6.3	210	0.371	0.816	11.2
7.1	240	0.504	0.714	10.5
6.8	225	0.4	0.765	10.1
7.3	263	0.482	0.655	7.2
6.4	210	0.475	0.244	13.6
6.8	235	0.428	0.728	9
7.2	230	0.559	0.721	24.6
6.4	190	0.441	0.757	12.6
6.6	220	0.492	0.747	5.6
6.8	210	0.402	0.739	8.7
6.1	180	0.415	0.713	7.7
6.5	235	0.492	0.742	24.1
6.4	185	0.484	0.861	11.7
6	175	0.387	0.721	7.7
6	192	0.436	0.785	9.6
7.3	263	0.482	0.655	7.2
6.1	180	0.34	0.821	12.3
6.7	240	0.516	0.728	8.9
6.4	210	0.475	0.846	13.6
5.8	160	0.412	0.813	11.2
6.9	230	0.411	0.595	2.8
7	245	0.407	0.573	3.2
7.3	228	0.445	0.726	9.4
5.9	155	0.291	0.707	11.9
6.2	200	0.449	0.804	15.4
6.8	235	0.546	0.784	7.4
7	235	0.48	0.744	18.9
5.9	105	0.359	0.839	7.9
6.1	180	0.528	0.79	12.2
5.7	185	0.352	0.701	11
7.1	245	0.414	0.778	2.8
5.8	180	0.425	0.872	11.8
7.4	240	0.599	0.713	17.1
6.8	225	0.482	0.701	11.6
6.8	215	0.457	0.734	5.8
7	230	0.435	0.764	8.3

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	'Gertrude Mary Cox' @ cox.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 & 4 seconds \tabularnewline
R Server & 'Gertrude Mary Cox' @ cox.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=198701&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]'Gertrude Mary Cox' @ cox.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=198701&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=198701&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	'Gertrude Mary Cox' @ cox.wessa.net

Goodness of Fit
Correlation	0.8466
R-squared	0.7167
RMSE	15.9591

\begin{tabular}{lllllllll}
\hline
Goodness of Fit \tabularnewline
Correlation & 0.8466 \tabularnewline
R-squared & 0.7167 \tabularnewline
RMSE & 15.9591 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=198701&T=1

[TABLE]
[ROW][C]Goodness of Fit[/C][/ROW]
[ROW][C]Correlation[/C][C]0.8466[/C][/ROW]
[ROW][C]R-squared[/C][C]0.7167[/C][/ROW]
[ROW][C]RMSE[/C][C]15.9591[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=198701&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=198701&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	0.8466
R-squared	0.7167
RMSE	15.9591

Actuals, Predictions, and Residuals
#	Actuals	Forecasts	Residuals
1	225	238.142857142857	-13.1428571428571
2	180	191.470588235294	-11.4705882352941
3	190	191.470588235294	-1.47058823529412
4	180	191.470588235294	-11.4705882352941
5	205	224.625	-19.625
6	225	191.470588235294	33.5294117647059
7	185	191.470588235294	-6.47058823529412
8	235	224.625	10.375
9	235	224.625	10.375
10	210	224.625	-14.625
11	245	238.142857142857	6.85714285714286
12	245	224.625	20.375
13	185	191.470588235294	-6.47058823529412
14	185	191.470588235294	-6.47058823529412
15	180	191.470588235294	-11.4705882352941
16	220	224.625	-4.625
17	194	224.625	-30.625
18	225	238.142857142857	-13.1428571428571
19	210	191.470588235294	18.5294117647059
20	240	238.142857142857	1.85714285714286
21	225	224.625	0.375
22	263	238.142857142857	24.8571428571429
23	210	191.470588235294	18.5294117647059
24	235	238.142857142857	-3.14285714285714
25	230	238.142857142857	-8.14285714285714
26	190	191.470588235294	-1.47058823529412
27	220	224.625	-4.625
28	210	224.625	-14.625
29	180	191.470588235294	-11.4705882352941
30	235	224.625	10.375
31	185	191.470588235294	-6.47058823529412
32	175	164.571428571429	10.4285714285714
33	192	164.571428571429	27.4285714285714
34	263	238.142857142857	24.8571428571429
35	180	191.470588235294	-11.4705882352941
36	240	238.142857142857	1.85714285714286
37	210	191.470588235294	18.5294117647059
38	160	164.571428571429	-4.57142857142858
39	230	238.142857142857	-8.14285714285714
40	245	238.142857142857	6.85714285714286
41	228	238.142857142857	-10.1428571428571
42	155	164.571428571429	-9.57142857142858
43	200	191.470588235294	8.52941176470588
44	235	224.625	10.375
45	235	224.625	10.375
46	105	164.571428571429	-59.5714285714286
47	180	191.470588235294	-11.4705882352941
48	185	164.571428571429	20.4285714285714
49	245	224.625	20.375
50	180	164.571428571429	15.4285714285714
51	240	238.142857142857	1.85714285714286
52	225	238.142857142857	-13.1428571428571
53	215	224.625	-9.625
54	230	224.625	5.375

\begin{tabular}{lllllllll}
\hline
Actuals, Predictions, and Residuals \tabularnewline
# & Actuals & Forecasts & Residuals \tabularnewline
1 & 225 & 238.142857142857 & -13.1428571428571 \tabularnewline
2 & 180 & 191.470588235294 & -11.4705882352941 \tabularnewline
3 & 190 & 191.470588235294 & -1.47058823529412 \tabularnewline
4 & 180 & 191.470588235294 & -11.4705882352941 \tabularnewline
5 & 205 & 224.625 & -19.625 \tabularnewline
6 & 225 & 191.470588235294 & 33.5294117647059 \tabularnewline
7 & 185 & 191.470588235294 & -6.47058823529412 \tabularnewline
8 & 235 & 224.625 & 10.375 \tabularnewline
9 & 235 & 224.625 & 10.375 \tabularnewline
10 & 210 & 224.625 & -14.625 \tabularnewline
11 & 245 & 238.142857142857 & 6.85714285714286 \tabularnewline
12 & 245 & 224.625 & 20.375 \tabularnewline
13 & 185 & 191.470588235294 & -6.47058823529412 \tabularnewline
14 & 185 & 191.470588235294 & -6.47058823529412 \tabularnewline
15 & 180 & 191.470588235294 & -11.4705882352941 \tabularnewline
16 & 220 & 224.625 & -4.625 \tabularnewline
17 & 194 & 224.625 & -30.625 \tabularnewline
18 & 225 & 238.142857142857 & -13.1428571428571 \tabularnewline
19 & 210 & 191.470588235294 & 18.5294117647059 \tabularnewline
20 & 240 & 238.142857142857 & 1.85714285714286 \tabularnewline
21 & 225 & 224.625 & 0.375 \tabularnewline
22 & 263 & 238.142857142857 & 24.8571428571429 \tabularnewline
23 & 210 & 191.470588235294 & 18.5294117647059 \tabularnewline
24 & 235 & 238.142857142857 & -3.14285714285714 \tabularnewline
25 & 230 & 238.142857142857 & -8.14285714285714 \tabularnewline
26 & 190 & 191.470588235294 & -1.47058823529412 \tabularnewline
27 & 220 & 224.625 & -4.625 \tabularnewline
28 & 210 & 224.625 & -14.625 \tabularnewline
29 & 180 & 191.470588235294 & -11.4705882352941 \tabularnewline
30 & 235 & 224.625 & 10.375 \tabularnewline
31 & 185 & 191.470588235294 & -6.47058823529412 \tabularnewline
32 & 175 & 164.571428571429 & 10.4285714285714 \tabularnewline
33 & 192 & 164.571428571429 & 27.4285714285714 \tabularnewline
34 & 263 & 238.142857142857 & 24.8571428571429 \tabularnewline
35 & 180 & 191.470588235294 & -11.4705882352941 \tabularnewline
36 & 240 & 238.142857142857 & 1.85714285714286 \tabularnewline
37 & 210 & 191.470588235294 & 18.5294117647059 \tabularnewline
38 & 160 & 164.571428571429 & -4.57142857142858 \tabularnewline
39 & 230 & 238.142857142857 & -8.14285714285714 \tabularnewline
40 & 245 & 238.142857142857 & 6.85714285714286 \tabularnewline
41 & 228 & 238.142857142857 & -10.1428571428571 \tabularnewline
42 & 155 & 164.571428571429 & -9.57142857142858 \tabularnewline
43 & 200 & 191.470588235294 & 8.52941176470588 \tabularnewline
44 & 235 & 224.625 & 10.375 \tabularnewline
45 & 235 & 224.625 & 10.375 \tabularnewline
46 & 105 & 164.571428571429 & -59.5714285714286 \tabularnewline
47 & 180 & 191.470588235294 & -11.4705882352941 \tabularnewline
48 & 185 & 164.571428571429 & 20.4285714285714 \tabularnewline
49 & 245 & 224.625 & 20.375 \tabularnewline
50 & 180 & 164.571428571429 & 15.4285714285714 \tabularnewline
51 & 240 & 238.142857142857 & 1.85714285714286 \tabularnewline
52 & 225 & 238.142857142857 & -13.1428571428571 \tabularnewline
53 & 215 & 224.625 & -9.625 \tabularnewline
54 & 230 & 224.625 & 5.375 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=198701&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]225[/C][C]238.142857142857[/C][C]-13.1428571428571[/C][/ROW]
[ROW][C]2[/C][C]180[/C][C]191.470588235294[/C][C]-11.4705882352941[/C][/ROW]
[ROW][C]3[/C][C]190[/C][C]191.470588235294[/C][C]-1.47058823529412[/C][/ROW]
[ROW][C]4[/C][C]180[/C][C]191.470588235294[/C][C]-11.4705882352941[/C][/ROW]
[ROW][C]5[/C][C]205[/C][C]224.625[/C][C]-19.625[/C][/ROW]
[ROW][C]6[/C][C]225[/C][C]191.470588235294[/C][C]33.5294117647059[/C][/ROW]
[ROW][C]7[/C][C]185[/C][C]191.470588235294[/C][C]-6.47058823529412[/C][/ROW]
[ROW][C]8[/C][C]235[/C][C]224.625[/C][C]10.375[/C][/ROW]
[ROW][C]9[/C][C]235[/C][C]224.625[/C][C]10.375[/C][/ROW]
[ROW][C]10[/C][C]210[/C][C]224.625[/C][C]-14.625[/C][/ROW]
[ROW][C]11[/C][C]245[/C][C]238.142857142857[/C][C]6.85714285714286[/C][/ROW]
[ROW][C]12[/C][C]245[/C][C]224.625[/C][C]20.375[/C][/ROW]
[ROW][C]13[/C][C]185[/C][C]191.470588235294[/C][C]-6.47058823529412[/C][/ROW]
[ROW][C]14[/C][C]185[/C][C]191.470588235294[/C][C]-6.47058823529412[/C][/ROW]
[ROW][C]15[/C][C]180[/C][C]191.470588235294[/C][C]-11.4705882352941[/C][/ROW]
[ROW][C]16[/C][C]220[/C][C]224.625[/C][C]-4.625[/C][/ROW]
[ROW][C]17[/C][C]194[/C][C]224.625[/C][C]-30.625[/C][/ROW]
[ROW][C]18[/C][C]225[/C][C]238.142857142857[/C][C]-13.1428571428571[/C][/ROW]
[ROW][C]19[/C][C]210[/C][C]191.470588235294[/C][C]18.5294117647059[/C][/ROW]
[ROW][C]20[/C][C]240[/C][C]238.142857142857[/C][C]1.85714285714286[/C][/ROW]
[ROW][C]21[/C][C]225[/C][C]224.625[/C][C]0.375[/C][/ROW]
[ROW][C]22[/C][C]263[/C][C]238.142857142857[/C][C]24.8571428571429[/C][/ROW]
[ROW][C]23[/C][C]210[/C][C]191.470588235294[/C][C]18.5294117647059[/C][/ROW]
[ROW][C]24[/C][C]235[/C][C]238.142857142857[/C][C]-3.14285714285714[/C][/ROW]
[ROW][C]25[/C][C]230[/C][C]238.142857142857[/C][C]-8.14285714285714[/C][/ROW]
[ROW][C]26[/C][C]190[/C][C]191.470588235294[/C][C]-1.47058823529412[/C][/ROW]
[ROW][C]27[/C][C]220[/C][C]224.625[/C][C]-4.625[/C][/ROW]
[ROW][C]28[/C][C]210[/C][C]224.625[/C][C]-14.625[/C][/ROW]
[ROW][C]29[/C][C]180[/C][C]191.470588235294[/C][C]-11.4705882352941[/C][/ROW]
[ROW][C]30[/C][C]235[/C][C]224.625[/C][C]10.375[/C][/ROW]
[ROW][C]31[/C][C]185[/C][C]191.470588235294[/C][C]-6.47058823529412[/C][/ROW]
[ROW][C]32[/C][C]175[/C][C]164.571428571429[/C][C]10.4285714285714[/C][/ROW]
[ROW][C]33[/C][C]192[/C][C]164.571428571429[/C][C]27.4285714285714[/C][/ROW]
[ROW][C]34[/C][C]263[/C][C]238.142857142857[/C][C]24.8571428571429[/C][/ROW]
[ROW][C]35[/C][C]180[/C][C]191.470588235294[/C][C]-11.4705882352941[/C][/ROW]
[ROW][C]36[/C][C]240[/C][C]238.142857142857[/C][C]1.85714285714286[/C][/ROW]
[ROW][C]37[/C][C]210[/C][C]191.470588235294[/C][C]18.5294117647059[/C][/ROW]
[ROW][C]38[/C][C]160[/C][C]164.571428571429[/C][C]-4.57142857142858[/C][/ROW]
[ROW][C]39[/C][C]230[/C][C]238.142857142857[/C][C]-8.14285714285714[/C][/ROW]
[ROW][C]40[/C][C]245[/C][C]238.142857142857[/C][C]6.85714285714286[/C][/ROW]
[ROW][C]41[/C][C]228[/C][C]238.142857142857[/C][C]-10.1428571428571[/C][/ROW]
[ROW][C]42[/C][C]155[/C][C]164.571428571429[/C][C]-9.57142857142858[/C][/ROW]
[ROW][C]43[/C][C]200[/C][C]191.470588235294[/C][C]8.52941176470588[/C][/ROW]
[ROW][C]44[/C][C]235[/C][C]224.625[/C][C]10.375[/C][/ROW]
[ROW][C]45[/C][C]235[/C][C]224.625[/C][C]10.375[/C][/ROW]
[ROW][C]46[/C][C]105[/C][C]164.571428571429[/C][C]-59.5714285714286[/C][/ROW]
[ROW][C]47[/C][C]180[/C][C]191.470588235294[/C][C]-11.4705882352941[/C][/ROW]
[ROW][C]48[/C][C]185[/C][C]164.571428571429[/C][C]20.4285714285714[/C][/ROW]
[ROW][C]49[/C][C]245[/C][C]224.625[/C][C]20.375[/C][/ROW]
[ROW][C]50[/C][C]180[/C][C]164.571428571429[/C][C]15.4285714285714[/C][/ROW]
[ROW][C]51[/C][C]240[/C][C]238.142857142857[/C][C]1.85714285714286[/C][/ROW]
[ROW][C]52[/C][C]225[/C][C]238.142857142857[/C][C]-13.1428571428571[/C][/ROW]
[ROW][C]53[/C][C]215[/C][C]224.625[/C][C]-9.625[/C][/ROW]
[ROW][C]54[/C][C]230[/C][C]224.625[/C][C]5.375[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=198701&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=198701&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	225	238.142857142857	-13.1428571428571
2	180	191.470588235294	-11.4705882352941
3	190	191.470588235294	-1.47058823529412
4	180	191.470588235294	-11.4705882352941
5	205	224.625	-19.625
6	225	191.470588235294	33.5294117647059
7	185	191.470588235294	-6.47058823529412
8	235	224.625	10.375
9	235	224.625	10.375
10	210	224.625	-14.625
11	245	238.142857142857	6.85714285714286
12	245	224.625	20.375
13	185	191.470588235294	-6.47058823529412
14	185	191.470588235294	-6.47058823529412
15	180	191.470588235294	-11.4705882352941
16	220	224.625	-4.625
17	194	224.625	-30.625
18	225	238.142857142857	-13.1428571428571
19	210	191.470588235294	18.5294117647059
20	240	238.142857142857	1.85714285714286
21	225	224.625	0.375
22	263	238.142857142857	24.8571428571429
23	210	191.470588235294	18.5294117647059
24	235	238.142857142857	-3.14285714285714
25	230	238.142857142857	-8.14285714285714
26	190	191.470588235294	-1.47058823529412
27	220	224.625	-4.625
28	210	224.625	-14.625
29	180	191.470588235294	-11.4705882352941
30	235	224.625	10.375
31	185	191.470588235294	-6.47058823529412
32	175	164.571428571429	10.4285714285714
33	192	164.571428571429	27.4285714285714
34	263	238.142857142857	24.8571428571429
35	180	191.470588235294	-11.4705882352941
36	240	238.142857142857	1.85714285714286
37	210	191.470588235294	18.5294117647059
38	160	164.571428571429	-4.57142857142858
39	230	238.142857142857	-8.14285714285714
40	245	238.142857142857	6.85714285714286
41	228	238.142857142857	-10.1428571428571
42	155	164.571428571429	-9.57142857142858
43	200	191.470588235294	8.52941176470588
44	235	224.625	10.375
45	235	224.625	10.375
46	105	164.571428571429	-59.5714285714286
47	180	191.470588235294	-11.4705882352941
48	185	164.571428571429	20.4285714285714
49	245	224.625	20.375
50	180	164.571428571429	15.4285714285714
51	240	238.142857142857	1.85714285714286
52	225	238.142857142857	-13.1428571428571
53	215	224.625	-9.625
54	230	224.625	5.375

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 = 2 ; par2 = none ; par3 = 4 ; par4 = no ;

Parameters (R input):

par1 = 2 ; par2 = none ; par3 = 4 ; 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