Repository of Reproducible Computations

Free Statistics

of Irreproducible Research!

Author's title

Author

*The author of this computation has been verified*

R Software Module

rwasp_regression_trees1.wasp

Title produced by software

Recursive Partitioning (Regression Trees)

Date of computation

Tue, 13 Dec 2011 13:07:37 -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/2011/Dec/13/t1323799862w8xoz0lj6ibv7y1.htm/, Retrieved Fri, 03 May 2024 01:17:24 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=154599, Retrieved Fri, 03 May 2024 01:17:24 +0000

QR Codes:

Paste this QR Code to cite your computation.

Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords

Estimated Impact

108

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

-       [Recursive Partitioning (Regression Trees)] [Regression tree] [2011-12-13 18:07:37] [8432dc408001a08517818ba7ac24bdb0] [Current]

Feedback Forum

Post a new message

Dataseries X:

Download CSV

Histogram

Boxplots

-1	1	1	0	0	1	1	0	3
-1	-1	-1	1	1	1	1	-1	0
0	1	1	-1	1	1	-1	-1	1
0	0	0	0	0	0	0	0	0
-1	-1	1	1	0	1	1	1	3
-1	1	-1	-1	0	0	1	-1	-2
0	0	-1	1	1	-1	1	1	2
-1	1	-1	1	1	1	1	-1	2
-1	-1	1	-1	1	-1	-1	-1	-4
-1	1	-1	0	1	1	1	1	3
1	-1	1	-1	1	1	1	1	4
-1	-1	-1	-1	0	1	1	-1	-3
-1	1	1	-1	1	1	1	-1	2
0	1	1	0	1	0	1	0	4
-1	1	1	0	1	1	1	-1	3
-1	1	-1	0	0	1	1	-1	0
-1	-1	-1	1	0	-1	0	-1	-4
-1	-1	0	-1	-1	-1	1	0	-4
-1	1	0	1	1	0	-1	1	2
0	0	0	0	0	0	0	0	0
-1	1	-1	1	1	1	1	-1	2
-1	1	-1	-1	-1	0	1	-1	-3
-1	1	-1	1	0	-1	0	1	0
-1	1	0	0	0	1	1	1	3
-1	-1	1	-1	-1	1	-1	-1	-4
0	1	1	1	-1	1	-1	1	3
-1	1	-1	1	1	1	-1	-1	0
-1	1	1	1	1	1	1	-1	4
-1	1	1	1	1	1	1	1	6
-1	1	1	0	-1	1	1	0	2
1	-1	1	-1	-1	1	1	1	2
-1	-1	1	-1	-1	-1	1	-1	-4
-1	-1	-1	0	-1	0	1	-1	-4
-1	-1	1	1	1	1	1	-1	2
1	-1	1	1	-1	1	1	-1	2
-1	-1	1	0	1	0	-1	-1	-2
-1	1	-1	-1	-1	0	1	-1	-3
-1	1	0	-1	1	1	1	1	3
-1	-1	-1	-1	1	1	1	-1	-2
-1	-1	1	-1	-1	1	1	-1	-2
-1	0	1	-1	1	1	1	0	2
-1	1	-1	1	1	1	1	-1	2
-1	-1	0	1	1	1	-1	-1	-1
-1	-1	-1	1	0	0	1	1	0
-1	-1	-1	1	1	0	1	1	1
1	-1	-1	1	1	1	1	-1	2
-1	0	-1	0	1	0	1	-1	-1
1	1	1	-1	1	0	1	1	5
0	0	0	0	0	0	0	0	0
-1	0	-1	-1	1	1	0	1	0
-1	1	-1	1	1	-1	0	-1	-1
-1	-1	-1	1	0	0	1	1	0
-1	1	1	0	1	-1	1	0	2
-1	-1	-1	1	1	1	1	-1	0
-1	1	-1	0	1	0	1	-1	0
-1	1	1	1	1	1	1	-1	4
-1	1	-1	-1	1	1	-1	-1	-2
-1	-1	-1	-1	1	0	1	-1	-3
-1	-1	-1	1	-1	0	1	-1	-3

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	3 seconds
R Server	'AstonUniversity' @ aston.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 & 3 seconds \tabularnewline
R Server & 'AstonUniversity' @ aston.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=154599&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]3 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'AstonUniversity' @ aston.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=154599&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=154599&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	3 seconds
R Server	'AstonUniversity' @ aston.wessa.net

Goodness of Fit
Correlation	0.7326
R-squared	0.5367
RMSE	1.764

\begin{tabular}{lllllllll}
\hline
Goodness of Fit \tabularnewline
Correlation & 0.7326 \tabularnewline
R-squared & 0.5367 \tabularnewline
RMSE & 1.764 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=154599&T=1

[TABLE]
[ROW][C]Goodness of Fit[/C][/ROW]
[ROW][C]Correlation[/C][C]0.7326[/C][/ROW]
[ROW][C]R-squared[/C][C]0.5367[/C][/ROW]
[ROW][C]RMSE[/C][C]1.764[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=154599&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=154599&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	0.7326
R-squared	0.5367
RMSE	1.764

Actuals, Predictions, and Residuals
#	Actuals	Forecasts	Residuals
1	3	1.84	1.16
2	0	1.57142857142857	-1.57142857142857
3	1	-1.42857142857143	2.42857142857143
4	0	1.84	-1.84
5	3	1.84	1.16
6	-2	-2.61538461538462	0.615384615384615
7	2	1.84	0.16
8	2	1.57142857142857	0.428571428571429
9	-4	-2.61538461538462	-1.38461538461538
10	3	1.84	1.16
11	4	1.84	2.16
12	-3	-1.42857142857143	-1.57142857142857
13	2	-1.42857142857143	3.42857142857143
14	4	1.84	2.16
15	3	1.57142857142857	1.42857142857143
16	0	1.57142857142857	-1.57142857142857
17	-4	-2.61538461538462	-1.38461538461538
18	-4	1.84	-5.84
19	2	1.84	0.16
20	0	1.84	-1.84
21	2	1.57142857142857	0.428571428571429
22	-3	-2.61538461538462	-0.384615384615385
23	0	1.84	-1.84
24	3	1.84	1.16
25	-4	-1.42857142857143	-2.57142857142857
26	3	1.84	1.16
27	0	1.57142857142857	-1.57142857142857
28	4	1.57142857142857	2.42857142857143
29	6	1.84	4.16
30	2	1.84	0.16
31	2	1.84	0.16
32	-4	-2.61538461538462	-1.38461538461538
33	-4	-2.61538461538462	-1.38461538461538
34	2	1.57142857142857	0.428571428571429
35	2	1.57142857142857	0.428571428571429
36	-2	-2.61538461538462	0.615384615384615
37	-3	-2.61538461538462	-0.384615384615385
38	3	1.84	1.16
39	-2	-1.42857142857143	-0.571428571428571
40	-2	-1.42857142857143	-0.571428571428571
41	2	1.84	0.16
42	2	1.57142857142857	0.428571428571429
43	-1	1.57142857142857	-2.57142857142857
44	0	1.84	-1.84
45	1	1.84	-0.84
46	2	1.57142857142857	0.428571428571429
47	-1	-2.61538461538462	1.61538461538462
48	5	1.84	3.16
49	0	1.84	-1.84
50	0	1.84	-1.84
51	-1	-2.61538461538462	1.61538461538462
52	0	1.84	-1.84
53	2	1.84	0.16
54	0	1.57142857142857	-1.57142857142857
55	0	-2.61538461538462	2.61538461538462
56	4	1.57142857142857	2.42857142857143
57	-2	-1.42857142857143	-0.571428571428571
58	-3	-2.61538461538462	-0.384615384615385
59	-3	-2.61538461538462	-0.384615384615385

\begin{tabular}{lllllllll}
\hline
Actuals, Predictions, and Residuals \tabularnewline
# & Actuals & Forecasts & Residuals \tabularnewline
1 & 3 & 1.84 & 1.16 \tabularnewline
2 & 0 & 1.57142857142857 & -1.57142857142857 \tabularnewline
3 & 1 & -1.42857142857143 & 2.42857142857143 \tabularnewline
4 & 0 & 1.84 & -1.84 \tabularnewline
5 & 3 & 1.84 & 1.16 \tabularnewline
6 & -2 & -2.61538461538462 & 0.615384615384615 \tabularnewline
7 & 2 & 1.84 & 0.16 \tabularnewline
8 & 2 & 1.57142857142857 & 0.428571428571429 \tabularnewline
9 & -4 & -2.61538461538462 & -1.38461538461538 \tabularnewline
10 & 3 & 1.84 & 1.16 \tabularnewline
11 & 4 & 1.84 & 2.16 \tabularnewline
12 & -3 & -1.42857142857143 & -1.57142857142857 \tabularnewline
13 & 2 & -1.42857142857143 & 3.42857142857143 \tabularnewline
14 & 4 & 1.84 & 2.16 \tabularnewline
15 & 3 & 1.57142857142857 & 1.42857142857143 \tabularnewline
16 & 0 & 1.57142857142857 & -1.57142857142857 \tabularnewline
17 & -4 & -2.61538461538462 & -1.38461538461538 \tabularnewline
18 & -4 & 1.84 & -5.84 \tabularnewline
19 & 2 & 1.84 & 0.16 \tabularnewline
20 & 0 & 1.84 & -1.84 \tabularnewline
21 & 2 & 1.57142857142857 & 0.428571428571429 \tabularnewline
22 & -3 & -2.61538461538462 & -0.384615384615385 \tabularnewline
23 & 0 & 1.84 & -1.84 \tabularnewline
24 & 3 & 1.84 & 1.16 \tabularnewline
25 & -4 & -1.42857142857143 & -2.57142857142857 \tabularnewline
26 & 3 & 1.84 & 1.16 \tabularnewline
27 & 0 & 1.57142857142857 & -1.57142857142857 \tabularnewline
28 & 4 & 1.57142857142857 & 2.42857142857143 \tabularnewline
29 & 6 & 1.84 & 4.16 \tabularnewline
30 & 2 & 1.84 & 0.16 \tabularnewline
31 & 2 & 1.84 & 0.16 \tabularnewline
32 & -4 & -2.61538461538462 & -1.38461538461538 \tabularnewline
33 & -4 & -2.61538461538462 & -1.38461538461538 \tabularnewline
34 & 2 & 1.57142857142857 & 0.428571428571429 \tabularnewline
35 & 2 & 1.57142857142857 & 0.428571428571429 \tabularnewline
36 & -2 & -2.61538461538462 & 0.615384615384615 \tabularnewline
37 & -3 & -2.61538461538462 & -0.384615384615385 \tabularnewline
38 & 3 & 1.84 & 1.16 \tabularnewline
39 & -2 & -1.42857142857143 & -0.571428571428571 \tabularnewline
40 & -2 & -1.42857142857143 & -0.571428571428571 \tabularnewline
41 & 2 & 1.84 & 0.16 \tabularnewline
42 & 2 & 1.57142857142857 & 0.428571428571429 \tabularnewline
43 & -1 & 1.57142857142857 & -2.57142857142857 \tabularnewline
44 & 0 & 1.84 & -1.84 \tabularnewline
45 & 1 & 1.84 & -0.84 \tabularnewline
46 & 2 & 1.57142857142857 & 0.428571428571429 \tabularnewline
47 & -1 & -2.61538461538462 & 1.61538461538462 \tabularnewline
48 & 5 & 1.84 & 3.16 \tabularnewline
49 & 0 & 1.84 & -1.84 \tabularnewline
50 & 0 & 1.84 & -1.84 \tabularnewline
51 & -1 & -2.61538461538462 & 1.61538461538462 \tabularnewline
52 & 0 & 1.84 & -1.84 \tabularnewline
53 & 2 & 1.84 & 0.16 \tabularnewline
54 & 0 & 1.57142857142857 & -1.57142857142857 \tabularnewline
55 & 0 & -2.61538461538462 & 2.61538461538462 \tabularnewline
56 & 4 & 1.57142857142857 & 2.42857142857143 \tabularnewline
57 & -2 & -1.42857142857143 & -0.571428571428571 \tabularnewline
58 & -3 & -2.61538461538462 & -0.384615384615385 \tabularnewline
59 & -3 & -2.61538461538462 & -0.384615384615385 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=154599&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]3[/C][C]1.84[/C][C]1.16[/C][/ROW]
[ROW][C]2[/C][C]0[/C][C]1.57142857142857[/C][C]-1.57142857142857[/C][/ROW]
[ROW][C]3[/C][C]1[/C][C]-1.42857142857143[/C][C]2.42857142857143[/C][/ROW]
[ROW][C]4[/C][C]0[/C][C]1.84[/C][C]-1.84[/C][/ROW]
[ROW][C]5[/C][C]3[/C][C]1.84[/C][C]1.16[/C][/ROW]
[ROW][C]6[/C][C]-2[/C][C]-2.61538461538462[/C][C]0.615384615384615[/C][/ROW]
[ROW][C]7[/C][C]2[/C][C]1.84[/C][C]0.16[/C][/ROW]
[ROW][C]8[/C][C]2[/C][C]1.57142857142857[/C][C]0.428571428571429[/C][/ROW]
[ROW][C]9[/C][C]-4[/C][C]-2.61538461538462[/C][C]-1.38461538461538[/C][/ROW]
[ROW][C]10[/C][C]3[/C][C]1.84[/C][C]1.16[/C][/ROW]
[ROW][C]11[/C][C]4[/C][C]1.84[/C][C]2.16[/C][/ROW]
[ROW][C]12[/C][C]-3[/C][C]-1.42857142857143[/C][C]-1.57142857142857[/C][/ROW]
[ROW][C]13[/C][C]2[/C][C]-1.42857142857143[/C][C]3.42857142857143[/C][/ROW]
[ROW][C]14[/C][C]4[/C][C]1.84[/C][C]2.16[/C][/ROW]
[ROW][C]15[/C][C]3[/C][C]1.57142857142857[/C][C]1.42857142857143[/C][/ROW]
[ROW][C]16[/C][C]0[/C][C]1.57142857142857[/C][C]-1.57142857142857[/C][/ROW]
[ROW][C]17[/C][C]-4[/C][C]-2.61538461538462[/C][C]-1.38461538461538[/C][/ROW]
[ROW][C]18[/C][C]-4[/C][C]1.84[/C][C]-5.84[/C][/ROW]
[ROW][C]19[/C][C]2[/C][C]1.84[/C][C]0.16[/C][/ROW]
[ROW][C]20[/C][C]0[/C][C]1.84[/C][C]-1.84[/C][/ROW]
[ROW][C]21[/C][C]2[/C][C]1.57142857142857[/C][C]0.428571428571429[/C][/ROW]
[ROW][C]22[/C][C]-3[/C][C]-2.61538461538462[/C][C]-0.384615384615385[/C][/ROW]
[ROW][C]23[/C][C]0[/C][C]1.84[/C][C]-1.84[/C][/ROW]
[ROW][C]24[/C][C]3[/C][C]1.84[/C][C]1.16[/C][/ROW]
[ROW][C]25[/C][C]-4[/C][C]-1.42857142857143[/C][C]-2.57142857142857[/C][/ROW]
[ROW][C]26[/C][C]3[/C][C]1.84[/C][C]1.16[/C][/ROW]
[ROW][C]27[/C][C]0[/C][C]1.57142857142857[/C][C]-1.57142857142857[/C][/ROW]
[ROW][C]28[/C][C]4[/C][C]1.57142857142857[/C][C]2.42857142857143[/C][/ROW]
[ROW][C]29[/C][C]6[/C][C]1.84[/C][C]4.16[/C][/ROW]
[ROW][C]30[/C][C]2[/C][C]1.84[/C][C]0.16[/C][/ROW]
[ROW][C]31[/C][C]2[/C][C]1.84[/C][C]0.16[/C][/ROW]
[ROW][C]32[/C][C]-4[/C][C]-2.61538461538462[/C][C]-1.38461538461538[/C][/ROW]
[ROW][C]33[/C][C]-4[/C][C]-2.61538461538462[/C][C]-1.38461538461538[/C][/ROW]
[ROW][C]34[/C][C]2[/C][C]1.57142857142857[/C][C]0.428571428571429[/C][/ROW]
[ROW][C]35[/C][C]2[/C][C]1.57142857142857[/C][C]0.428571428571429[/C][/ROW]
[ROW][C]36[/C][C]-2[/C][C]-2.61538461538462[/C][C]0.615384615384615[/C][/ROW]
[ROW][C]37[/C][C]-3[/C][C]-2.61538461538462[/C][C]-0.384615384615385[/C][/ROW]
[ROW][C]38[/C][C]3[/C][C]1.84[/C][C]1.16[/C][/ROW]
[ROW][C]39[/C][C]-2[/C][C]-1.42857142857143[/C][C]-0.571428571428571[/C][/ROW]
[ROW][C]40[/C][C]-2[/C][C]-1.42857142857143[/C][C]-0.571428571428571[/C][/ROW]
[ROW][C]41[/C][C]2[/C][C]1.84[/C][C]0.16[/C][/ROW]
[ROW][C]42[/C][C]2[/C][C]1.57142857142857[/C][C]0.428571428571429[/C][/ROW]
[ROW][C]43[/C][C]-1[/C][C]1.57142857142857[/C][C]-2.57142857142857[/C][/ROW]
[ROW][C]44[/C][C]0[/C][C]1.84[/C][C]-1.84[/C][/ROW]
[ROW][C]45[/C][C]1[/C][C]1.84[/C][C]-0.84[/C][/ROW]
[ROW][C]46[/C][C]2[/C][C]1.57142857142857[/C][C]0.428571428571429[/C][/ROW]
[ROW][C]47[/C][C]-1[/C][C]-2.61538461538462[/C][C]1.61538461538462[/C][/ROW]
[ROW][C]48[/C][C]5[/C][C]1.84[/C][C]3.16[/C][/ROW]
[ROW][C]49[/C][C]0[/C][C]1.84[/C][C]-1.84[/C][/ROW]
[ROW][C]50[/C][C]0[/C][C]1.84[/C][C]-1.84[/C][/ROW]
[ROW][C]51[/C][C]-1[/C][C]-2.61538461538462[/C][C]1.61538461538462[/C][/ROW]
[ROW][C]52[/C][C]0[/C][C]1.84[/C][C]-1.84[/C][/ROW]
[ROW][C]53[/C][C]2[/C][C]1.84[/C][C]0.16[/C][/ROW]
[ROW][C]54[/C][C]0[/C][C]1.57142857142857[/C][C]-1.57142857142857[/C][/ROW]
[ROW][C]55[/C][C]0[/C][C]-2.61538461538462[/C][C]2.61538461538462[/C][/ROW]
[ROW][C]56[/C][C]4[/C][C]1.57142857142857[/C][C]2.42857142857143[/C][/ROW]
[ROW][C]57[/C][C]-2[/C][C]-1.42857142857143[/C][C]-0.571428571428571[/C][/ROW]
[ROW][C]58[/C][C]-3[/C][C]-2.61538461538462[/C][C]-0.384615384615385[/C][/ROW]
[ROW][C]59[/C][C]-3[/C][C]-2.61538461538462[/C][C]-0.384615384615385[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=154599&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=154599&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	3	1.84	1.16
2	0	1.57142857142857	-1.57142857142857
3	1	-1.42857142857143	2.42857142857143
4	0	1.84	-1.84
5	3	1.84	1.16
6	-2	-2.61538461538462	0.615384615384615
7	2	1.84	0.16
8	2	1.57142857142857	0.428571428571429
9	-4	-2.61538461538462	-1.38461538461538
10	3	1.84	1.16
11	4	1.84	2.16
12	-3	-1.42857142857143	-1.57142857142857
13	2	-1.42857142857143	3.42857142857143
14	4	1.84	2.16
15	3	1.57142857142857	1.42857142857143
16	0	1.57142857142857	-1.57142857142857
17	-4	-2.61538461538462	-1.38461538461538
18	-4	1.84	-5.84
19	2	1.84	0.16
20	0	1.84	-1.84
21	2	1.57142857142857	0.428571428571429
22	-3	-2.61538461538462	-0.384615384615385
23	0	1.84	-1.84
24	3	1.84	1.16
25	-4	-1.42857142857143	-2.57142857142857
26	3	1.84	1.16
27	0	1.57142857142857	-1.57142857142857
28	4	1.57142857142857	2.42857142857143
29	6	1.84	4.16
30	2	1.84	0.16
31	2	1.84	0.16
32	-4	-2.61538461538462	-1.38461538461538
33	-4	-2.61538461538462	-1.38461538461538
34	2	1.57142857142857	0.428571428571429
35	2	1.57142857142857	0.428571428571429
36	-2	-2.61538461538462	0.615384615384615
37	-3	-2.61538461538462	-0.384615384615385
38	3	1.84	1.16
39	-2	-1.42857142857143	-0.571428571428571
40	-2	-1.42857142857143	-0.571428571428571
41	2	1.84	0.16
42	2	1.57142857142857	0.428571428571429
43	-1	1.57142857142857	-2.57142857142857
44	0	1.84	-1.84
45	1	1.84	-0.84
46	2	1.57142857142857	0.428571428571429
47	-1	-2.61538461538462	1.61538461538462
48	5	1.84	3.16
49	0	1.84	-1.84
50	0	1.84	-1.84
51	-1	-2.61538461538462	1.61538461538462
52	0	1.84	-1.84
53	2	1.84	0.16
54	0	1.57142857142857	-1.57142857142857
55	0	-2.61538461538462	2.61538461538462
56	4	1.57142857142857	2.42857142857143
57	-2	-1.42857142857143	-0.571428571428571
58	-3	-2.61538461538462	-0.384615384615385
59	-3	-2.61538461538462	-0.384615384615385

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 = 9 ; par2 = none ; par3 = 3 ; par4 = no ;

Parameters (R input):

par1 = 9 ; 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