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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

Tue, 14 Dec 2010 21:03:33 +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/t12923609863te3v8td0m3yitt.htm/, Retrieved Fri, 03 May 2024 01:23:22 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=110203, Retrieved Fri, 03 May 2024 01:23:22 +0000

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Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords

Estimated Impact

120

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]
-   PD    [Recursive Partitioning (Regression Trees)] [recursive partiti...] [2010-12-14 21:03:33] [b47314d83d48c7bf812ec2bcd743b159] [Current]

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Dataseries X:

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Histogram

Boxplots

113	14.3	15.89
110	14.2	16.93
107	15.9	20.28
103	15.3	22.52
98	15.5	23.51
98	15.1	22.59
137	15	23.51
148	12.1	24.76
147	15.8	26.08
139	16.9	25.29
130	15.1	23.38
128	13.7	25.29
127	14.8	28.42
123	14.7	31.85
118	16	30.1
114	15.4	25.45
108	15	24.95
111	15.5	26.84
151	15.1	27.52
159	11.7	27.94
158	16.3	25.23
148	16.7	26.53
138	15	27.21
137	14.9	28.53
136	14.6	30.35
133	15.3	31.21
126	17.9	32.86
120	16.4	33.2
114	15.4	35.73
116	17.9	34.53
153	15.9	36.54
162	13.9	40.1
161	17.8	40.56
149	17.9	46.14
139	17.4	42.85
135	16.7	38.22
130	16	40.18
127	16.6	42.19
122	19.1	47.56
117	17.8	47.26
112	17.2	44.03
113	18.6	49.83
149	16.3	53.35
157	15.1	58.9
157	19.2	59.64
147	17.7	56.99
137	19.1	53.2
132	18	53.24
125	17.5	57.85
123	17.8	55.69
117	21.1	55.64
114	17.2	62.52
111	19.4	64.4
112	19.8	64.65
144	17.6	67.71
150	16.2	67.21
149	19.5	59.37
134	19.9	53.26
123	20	52.42
116	17.3	55.03

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	6 seconds
R Server	'George Udny Yule' @ 72.249.76.132

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 6 seconds \tabularnewline
R Server & 'George Udny Yule' @ 72.249.76.132 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=110203&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]6 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'George Udny Yule' @ 72.249.76.132[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=110203&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=110203&T=0

As an alternative you can also use a QR Code:

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

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	6 seconds
R Server	'George Udny Yule' @ 72.249.76.132

Goodness of Fit
Correlation	NA
R-squared	NA
RMSE	17.1063

\begin{tabular}{lllllllll}
\hline
Goodness of Fit \tabularnewline
Correlation & NA \tabularnewline
R-squared & NA \tabularnewline
RMSE & 17.1063 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=110203&T=1

[TABLE]
[ROW][C]Goodness of Fit[/C][/ROW]
[ROW][C]Correlation[/C][C]NA[/C][/ROW]
[ROW][C]R-squared[/C][C]NA[/C][/ROW]
[ROW][C]RMSE[/C][C]17.1063[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=110203&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=110203&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	NA
R-squared	NA
RMSE	17.1063

Actuals, Predictions, and Residuals
#	Actuals	Forecasts	Residuals
1	113	130.2	-17.2
2	110	130.2	-20.2
3	107	130.2	-23.2
4	103	130.2	-27.2
5	98	130.2	-32.2
6	98	130.2	-32.2
7	137	130.2	6.80000000000001
8	148	130.2	17.8
9	147	130.2	16.8
10	139	130.2	8.80000000000001
11	130	130.2	-0.199999999999989
12	128	130.2	-2.19999999999999
13	127	130.2	-3.19999999999999
14	123	130.2	-7.19999999999999
15	118	130.2	-12.2
16	114	130.2	-16.2
17	108	130.2	-22.2
18	111	130.2	-19.2
19	151	130.2	20.8
20	159	130.2	28.8
21	158	130.2	27.8
22	148	130.2	17.8
23	138	130.2	7.80000000000001
24	137	130.2	6.80000000000001
25	136	130.2	5.80000000000001
26	133	130.2	2.80000000000001
27	126	130.2	-4.19999999999999
28	120	130.2	-10.2000000000000
29	114	130.2	-16.2
30	116	130.2	-14.2
31	153	130.2	22.8
32	162	130.2	31.8
33	161	130.2	30.8
34	149	130.2	18.8
35	139	130.2	8.80000000000001
36	135	130.2	4.80000000000001
37	130	130.2	-0.199999999999989
38	127	130.2	-3.19999999999999
39	122	130.2	-8.19999999999999
40	117	130.2	-13.2
41	112	130.2	-18.2
42	113	130.2	-17.2
43	149	130.2	18.8
44	157	130.2	26.8
45	157	130.2	26.8
46	147	130.2	16.8
47	137	130.2	6.80000000000001
48	132	130.2	1.80000000000001
49	125	130.2	-5.19999999999999
50	123	130.2	-7.19999999999999
51	117	130.2	-13.2
52	114	130.2	-16.2
53	111	130.2	-19.2
54	112	130.2	-18.2
55	144	130.2	13.8
56	150	130.2	19.8
57	149	130.2	18.8
58	134	130.2	3.80000000000001
59	123	130.2	-7.19999999999999
60	116	130.2	-14.2

\begin{tabular}{lllllllll}
\hline
Actuals, Predictions, and Residuals \tabularnewline
# & Actuals & Forecasts & Residuals \tabularnewline
1 & 113 & 130.2 & -17.2 \tabularnewline
2 & 110 & 130.2 & -20.2 \tabularnewline
3 & 107 & 130.2 & -23.2 \tabularnewline
4 & 103 & 130.2 & -27.2 \tabularnewline
5 & 98 & 130.2 & -32.2 \tabularnewline
6 & 98 & 130.2 & -32.2 \tabularnewline
7 & 137 & 130.2 & 6.80000000000001 \tabularnewline
8 & 148 & 130.2 & 17.8 \tabularnewline
9 & 147 & 130.2 & 16.8 \tabularnewline
10 & 139 & 130.2 & 8.80000000000001 \tabularnewline
11 & 130 & 130.2 & -0.199999999999989 \tabularnewline
12 & 128 & 130.2 & -2.19999999999999 \tabularnewline
13 & 127 & 130.2 & -3.19999999999999 \tabularnewline
14 & 123 & 130.2 & -7.19999999999999 \tabularnewline
15 & 118 & 130.2 & -12.2 \tabularnewline
16 & 114 & 130.2 & -16.2 \tabularnewline
17 & 108 & 130.2 & -22.2 \tabularnewline
18 & 111 & 130.2 & -19.2 \tabularnewline
19 & 151 & 130.2 & 20.8 \tabularnewline
20 & 159 & 130.2 & 28.8 \tabularnewline
21 & 158 & 130.2 & 27.8 \tabularnewline
22 & 148 & 130.2 & 17.8 \tabularnewline
23 & 138 & 130.2 & 7.80000000000001 \tabularnewline
24 & 137 & 130.2 & 6.80000000000001 \tabularnewline
25 & 136 & 130.2 & 5.80000000000001 \tabularnewline
26 & 133 & 130.2 & 2.80000000000001 \tabularnewline
27 & 126 & 130.2 & -4.19999999999999 \tabularnewline
28 & 120 & 130.2 & -10.2000000000000 \tabularnewline
29 & 114 & 130.2 & -16.2 \tabularnewline
30 & 116 & 130.2 & -14.2 \tabularnewline
31 & 153 & 130.2 & 22.8 \tabularnewline
32 & 162 & 130.2 & 31.8 \tabularnewline
33 & 161 & 130.2 & 30.8 \tabularnewline
34 & 149 & 130.2 & 18.8 \tabularnewline
35 & 139 & 130.2 & 8.80000000000001 \tabularnewline
36 & 135 & 130.2 & 4.80000000000001 \tabularnewline
37 & 130 & 130.2 & -0.199999999999989 \tabularnewline
38 & 127 & 130.2 & -3.19999999999999 \tabularnewline
39 & 122 & 130.2 & -8.19999999999999 \tabularnewline
40 & 117 & 130.2 & -13.2 \tabularnewline
41 & 112 & 130.2 & -18.2 \tabularnewline
42 & 113 & 130.2 & -17.2 \tabularnewline
43 & 149 & 130.2 & 18.8 \tabularnewline
44 & 157 & 130.2 & 26.8 \tabularnewline
45 & 157 & 130.2 & 26.8 \tabularnewline
46 & 147 & 130.2 & 16.8 \tabularnewline
47 & 137 & 130.2 & 6.80000000000001 \tabularnewline
48 & 132 & 130.2 & 1.80000000000001 \tabularnewline
49 & 125 & 130.2 & -5.19999999999999 \tabularnewline
50 & 123 & 130.2 & -7.19999999999999 \tabularnewline
51 & 117 & 130.2 & -13.2 \tabularnewline
52 & 114 & 130.2 & -16.2 \tabularnewline
53 & 111 & 130.2 & -19.2 \tabularnewline
54 & 112 & 130.2 & -18.2 \tabularnewline
55 & 144 & 130.2 & 13.8 \tabularnewline
56 & 150 & 130.2 & 19.8 \tabularnewline
57 & 149 & 130.2 & 18.8 \tabularnewline
58 & 134 & 130.2 & 3.80000000000001 \tabularnewline
59 & 123 & 130.2 & -7.19999999999999 \tabularnewline
60 & 116 & 130.2 & -14.2 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=110203&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]113[/C][C]130.2[/C][C]-17.2[/C][/ROW]
[ROW][C]2[/C][C]110[/C][C]130.2[/C][C]-20.2[/C][/ROW]
[ROW][C]3[/C][C]107[/C][C]130.2[/C][C]-23.2[/C][/ROW]
[ROW][C]4[/C][C]103[/C][C]130.2[/C][C]-27.2[/C][/ROW]
[ROW][C]5[/C][C]98[/C][C]130.2[/C][C]-32.2[/C][/ROW]
[ROW][C]6[/C][C]98[/C][C]130.2[/C][C]-32.2[/C][/ROW]
[ROW][C]7[/C][C]137[/C][C]130.2[/C][C]6.80000000000001[/C][/ROW]
[ROW][C]8[/C][C]148[/C][C]130.2[/C][C]17.8[/C][/ROW]
[ROW][C]9[/C][C]147[/C][C]130.2[/C][C]16.8[/C][/ROW]
[ROW][C]10[/C][C]139[/C][C]130.2[/C][C]8.80000000000001[/C][/ROW]
[ROW][C]11[/C][C]130[/C][C]130.2[/C][C]-0.199999999999989[/C][/ROW]
[ROW][C]12[/C][C]128[/C][C]130.2[/C][C]-2.19999999999999[/C][/ROW]
[ROW][C]13[/C][C]127[/C][C]130.2[/C][C]-3.19999999999999[/C][/ROW]
[ROW][C]14[/C][C]123[/C][C]130.2[/C][C]-7.19999999999999[/C][/ROW]
[ROW][C]15[/C][C]118[/C][C]130.2[/C][C]-12.2[/C][/ROW]
[ROW][C]16[/C][C]114[/C][C]130.2[/C][C]-16.2[/C][/ROW]
[ROW][C]17[/C][C]108[/C][C]130.2[/C][C]-22.2[/C][/ROW]
[ROW][C]18[/C][C]111[/C][C]130.2[/C][C]-19.2[/C][/ROW]
[ROW][C]19[/C][C]151[/C][C]130.2[/C][C]20.8[/C][/ROW]
[ROW][C]20[/C][C]159[/C][C]130.2[/C][C]28.8[/C][/ROW]
[ROW][C]21[/C][C]158[/C][C]130.2[/C][C]27.8[/C][/ROW]
[ROW][C]22[/C][C]148[/C][C]130.2[/C][C]17.8[/C][/ROW]
[ROW][C]23[/C][C]138[/C][C]130.2[/C][C]7.80000000000001[/C][/ROW]
[ROW][C]24[/C][C]137[/C][C]130.2[/C][C]6.80000000000001[/C][/ROW]
[ROW][C]25[/C][C]136[/C][C]130.2[/C][C]5.80000000000001[/C][/ROW]
[ROW][C]26[/C][C]133[/C][C]130.2[/C][C]2.80000000000001[/C][/ROW]
[ROW][C]27[/C][C]126[/C][C]130.2[/C][C]-4.19999999999999[/C][/ROW]
[ROW][C]28[/C][C]120[/C][C]130.2[/C][C]-10.2000000000000[/C][/ROW]
[ROW][C]29[/C][C]114[/C][C]130.2[/C][C]-16.2[/C][/ROW]
[ROW][C]30[/C][C]116[/C][C]130.2[/C][C]-14.2[/C][/ROW]
[ROW][C]31[/C][C]153[/C][C]130.2[/C][C]22.8[/C][/ROW]
[ROW][C]32[/C][C]162[/C][C]130.2[/C][C]31.8[/C][/ROW]
[ROW][C]33[/C][C]161[/C][C]130.2[/C][C]30.8[/C][/ROW]
[ROW][C]34[/C][C]149[/C][C]130.2[/C][C]18.8[/C][/ROW]
[ROW][C]35[/C][C]139[/C][C]130.2[/C][C]8.80000000000001[/C][/ROW]
[ROW][C]36[/C][C]135[/C][C]130.2[/C][C]4.80000000000001[/C][/ROW]
[ROW][C]37[/C][C]130[/C][C]130.2[/C][C]-0.199999999999989[/C][/ROW]
[ROW][C]38[/C][C]127[/C][C]130.2[/C][C]-3.19999999999999[/C][/ROW]
[ROW][C]39[/C][C]122[/C][C]130.2[/C][C]-8.19999999999999[/C][/ROW]
[ROW][C]40[/C][C]117[/C][C]130.2[/C][C]-13.2[/C][/ROW]
[ROW][C]41[/C][C]112[/C][C]130.2[/C][C]-18.2[/C][/ROW]
[ROW][C]42[/C][C]113[/C][C]130.2[/C][C]-17.2[/C][/ROW]
[ROW][C]43[/C][C]149[/C][C]130.2[/C][C]18.8[/C][/ROW]
[ROW][C]44[/C][C]157[/C][C]130.2[/C][C]26.8[/C][/ROW]
[ROW][C]45[/C][C]157[/C][C]130.2[/C][C]26.8[/C][/ROW]
[ROW][C]46[/C][C]147[/C][C]130.2[/C][C]16.8[/C][/ROW]
[ROW][C]47[/C][C]137[/C][C]130.2[/C][C]6.80000000000001[/C][/ROW]
[ROW][C]48[/C][C]132[/C][C]130.2[/C][C]1.80000000000001[/C][/ROW]
[ROW][C]49[/C][C]125[/C][C]130.2[/C][C]-5.19999999999999[/C][/ROW]
[ROW][C]50[/C][C]123[/C][C]130.2[/C][C]-7.19999999999999[/C][/ROW]
[ROW][C]51[/C][C]117[/C][C]130.2[/C][C]-13.2[/C][/ROW]
[ROW][C]52[/C][C]114[/C][C]130.2[/C][C]-16.2[/C][/ROW]
[ROW][C]53[/C][C]111[/C][C]130.2[/C][C]-19.2[/C][/ROW]
[ROW][C]54[/C][C]112[/C][C]130.2[/C][C]-18.2[/C][/ROW]
[ROW][C]55[/C][C]144[/C][C]130.2[/C][C]13.8[/C][/ROW]
[ROW][C]56[/C][C]150[/C][C]130.2[/C][C]19.8[/C][/ROW]
[ROW][C]57[/C][C]149[/C][C]130.2[/C][C]18.8[/C][/ROW]
[ROW][C]58[/C][C]134[/C][C]130.2[/C][C]3.80000000000001[/C][/ROW]
[ROW][C]59[/C][C]123[/C][C]130.2[/C][C]-7.19999999999999[/C][/ROW]
[ROW][C]60[/C][C]116[/C][C]130.2[/C][C]-14.2[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=110203&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=110203&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	113	130.2	-17.2
2	110	130.2	-20.2
3	107	130.2	-23.2
4	103	130.2	-27.2
5	98	130.2	-32.2
6	98	130.2	-32.2
7	137	130.2	6.80000000000001
8	148	130.2	17.8
9	147	130.2	16.8
10	139	130.2	8.80000000000001
11	130	130.2	-0.199999999999989
12	128	130.2	-2.19999999999999
13	127	130.2	-3.19999999999999
14	123	130.2	-7.19999999999999
15	118	130.2	-12.2
16	114	130.2	-16.2
17	108	130.2	-22.2
18	111	130.2	-19.2
19	151	130.2	20.8
20	159	130.2	28.8
21	158	130.2	27.8
22	148	130.2	17.8
23	138	130.2	7.80000000000001
24	137	130.2	6.80000000000001
25	136	130.2	5.80000000000001
26	133	130.2	2.80000000000001
27	126	130.2	-4.19999999999999
28	120	130.2	-10.2000000000000
29	114	130.2	-16.2
30	116	130.2	-14.2
31	153	130.2	22.8
32	162	130.2	31.8
33	161	130.2	30.8
34	149	130.2	18.8
35	139	130.2	8.80000000000001
36	135	130.2	4.80000000000001
37	130	130.2	-0.199999999999989
38	127	130.2	-3.19999999999999
39	122	130.2	-8.19999999999999
40	117	130.2	-13.2
41	112	130.2	-18.2
42	113	130.2	-17.2
43	149	130.2	18.8
44	157	130.2	26.8
45	157	130.2	26.8
46	147	130.2	16.8
47	137	130.2	6.80000000000001
48	132	130.2	1.80000000000001
49	125	130.2	-5.19999999999999
50	123	130.2	-7.19999999999999
51	117	130.2	-13.2
52	114	130.2	-16.2
53	111	130.2	-19.2
54	112	130.2	-18.2
55	144	130.2	13.8
56	150	130.2	19.8
57	149	130.2	18.8
58	134	130.2	3.80000000000001
59	123	130.2	-7.19999999999999
60	116	130.2	-14.2

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 = Do not include Seasonal Dummies ; par3 = No Linear Trend ; par4 = 0 ; par5 = 0 ; par6 = 0 ;

Parameters (R input):

par1 = 1 ; par2 = none ; par3 = 0 ; 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