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

Fri, 17 Dec 2010 14:38:35 +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/17/t1292596643f7rwsqlx8hfx9fs.htm/, Retrieved Sat, 27 Apr 2024 02:29:18 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=111494, Retrieved Sat, 27 Apr 2024 02:29:18 +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

147

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

-     [Kendall tau Correlation Matrix] [Sleep in Mammals ...] [2010-03-20 10:44:59] [b98453cac15ba1066b407e146608df68]
-   PD  [Kendall tau Correlation Matrix] [Review of Sleep A...] [2010-05-01 13:16:27] [b98453cac15ba1066b407e146608df68]
- RMPD      [Recursive Partitioning (Regression Trees)] [] [2010-12-17 14:38:35] [39ab8462d2190635c809d7a35eacc961] [Current]

Feedback Forum

Post a new message

Dataseries X:

Download CSV

Histogram

Boxplots

30.00	1	1	2	0	0
5.00	1	1	1	1	2
5.00	0	1	2	1	2
30.00	0	1	1	1	2
25.00	0	1	2	1	2
50.00	1	1	1	0	0
65.00	1	1	2	1	0
40.00	0	1	1	0	0
55.00	1	1	2	2	0
75.00	0	1	1	0	0
50.00	0	1	1	1	0
20.00	1	1	2	1	0
25.00	1	1	2	0	0
30.00	1	1	2	0	0
80.00	1	1	1	0	0
NA	0	1	1	0	0
25.00	0	1	1	0	1
50.00	1	1	2	0	1
35.00	1	1	2	0	0
40.00	0	1	1	0	0
40.00	1	1	1	1	1
50.00	1	1	2	1	1
30.00	1	1	1	0	0
45.00	1	1	2	0	0
30.00	1	1	2	0	0
25.00	0	1	1	0	0
25.00	1	1	1	1	0
20.00	1	1	2	1	0
35.00	1	1	2	0	0
30.00	1	1	1	0	0
45.00	0	1	1	0	0
40.00	1	1	1	0	0
30.00	1	1	2	0	0
60.00	1	1	2	0	0
45.00	0	1	1	0	0
45.00	1	1	2	0	0
25.00	1	1	2	0	1
NA	1	1	1	0	0
45.00	0	1	1	0	0
50.00	0	1	1	0	0
NA	0	1	1	0	0
45.00	1	1	2	0	0
60.00	1	1	2	2	0
60.00	1	1	2	0	0
45.00	1	1	2	0	0
45.00	0	1	1	0	0
NA	0	1	1	0	0
45.00	1	1	1	0	0
55.00	1	1	2	0	0
40.00	1	1	1	0	1
60.00	1	1	2	0	1
35.00	0	1	1	0	0
30.00	0	1	1	0	0
20.00	0	1	1	0	0
15.00	1	1	1	1	2
15.00	1	1	2	1	2
40.00	1	1	1	1	2
40.00	1	1	2	1	2
50.00	0	1	1	0	0
NA	1	1	2	0	0
40.00	0	1	1	1	0
80.00	1	1	2	1	0
50.00	0	1	1	1	0
50.00	1	1	2	1	0
30.00	1	1	1	1	0
35.00	1	1	2	1	0
40.00	1	1	2	0	0
30.00	1	1	2	0	0
45.00	1	1	1	0	0
NA	1	1	2	0	1
NA	0	1	1	0	0
30.00	0	1	1	1	0
30.00	1	1	2	1	0
25.00	1	1	2	0	0
NA	0	1	1	0	0
50.00	0	1	1	0	0
40.00	1	1	2	1	1
40.00	0	1	1	1	1
65.00	1	1	2	0	2
55.00	1	1	2	2	0
NA	0	1	1	0	0
70.00	0	1	1	1	1
40.00	1	1	2	0	2
65.00	1	1	2	1	1
40.00	0	1	1	1	0
40.00	1	1	2	1	0
55.00	1	1	1	0	0
30.00	1	1	1	0	0
35.00	1	1	2	0	0
35.00	0	1	1	1	0
30.00	1	1	2	1	2
35.00	1	1	1	1	0
20.00	1	1	2	1	0
55.00	1	1	1	1	1
5.00	1	1	1	0	2
55.00	1	1	2	1	1
50.00	0	1	1	1	0
30.00	1	1	2	1	1
50.00	1	1	2	1	0
50.00	1	1	2	1	0
50.00	1	1	1	1	0
40.00	0	1	1	0	0
25.00	1	1	2	0	1
40.00	1	1	2	0	0
55.00	1	1	2	1	0
40.00	0	1	2	0	0
NA	0	1	1	0	0
NA	1	1	2	0	0
NA	1	1	2	0	0
40.00	1	1	1	0	0
30.00	0	1	1	0	0
25.00	1	1	2	3	2
30.00	1	1	2	3	2
25.00	1	1	2	3	2
20.00	0	1	1	3	2
65.00	0	1	1	1	4
60.00	1	1	2	1	4
30.00	1	1	2	0	0
0.00	0	1	1	0	0
50.00	1	1	1	2	0
45.00	1	1	1	1	0
NA	1	1	2	1	0
25.00	1	1	2	0	2
60.00	1	1	1	1	1
50.00	1	1	2	1	1
NA	0	1	1	0	0
40.00	0	1	1	1	0
35.00	1	1	2	1	0
50.00	0	1	1	0	0
35.00	1	1	2	0	0
25.00	1	1	2	0	1
45.00	1	1	2	0	1
25.00	0	1	1	0	0
50.00	1	1	1	1	0
NA	1	1	2	1	0
75.00	0	1	1	0	0
55.00	1	1	1	0	0
20.00	1	1	2	0	0
40.00	0	1	1	0	1
60.00	1	1	2	0	1
25.00	1	1	1	0	1
45.00	1	1	2	0	1
50.00	0	1	1	0	0
25.00	1	1	1	1	0
25.00	1	1	2	1	0
50.00	1	1	1	1	0
50.00	1	1	2	1	0
NA	0	1	1	0	0
45.00	0	1	1	1	0
35.00	1	1	2	1	0
40.00	0	1	1	0	0
30.00	1	1	1	0	0
NA	1	1	1	0	0
25.00	1	1	2	0	0
55.00	0	1	1	1	1
55.00	1	1	2	1	1
45.00	0	1	1	0	0
NA	0	1	1	0	0
55.00	0	1	1	0	0
20.00	1	1	2	0	1
45.00	1	1	2	0	1
55.00	1	1	2	1	0
45.00	0	1	1	1	0
35.00	1	1	2	1	0
35.00	1	1	1	0	0
40.00	1	1	1	1	0
NA	0	1	1	0	0
35.00	1	1	2	1	0
40.00	1	1	2	0	0
50.00	0	1	2	0	0
50.00	1	1	1	0	0
50.00	0	1	1	0	0
50.00	0	1	1	0	0
35.00	0	1	1	0	0
60.00	0	1	1	0	0
45.00	0	1	1	1	0
NA	1	1	2	1	0
45.00	1	1	1	1	0
45.00	1	1	2	1	0
NA	0	1	1	0	0
40.00	1	1	2	0	0
50.00	1	1	2	0	0
30.00	1	1	2	0	0
35.00	1	1	1	0	0
NA	0	1	1	0	0
45.00	0	1	1	0	0
55.00	1	1	2	0	0
20.00	1	1	2	0	1
55.00	1	1	2	0	1
30.00	0	1	1	0	0
25.00	1	1	2	0	0
30.00	0	1	1	0	0
60.00	1	1	2	0	0
15.00	1	1	2	0	1
30.00	0	1	1	0	0
20.00	1	1	2	0	0
NA	1	1	1	0	0
20.00	0	1	1	1	0
20.00	1	1	2	1	0
25.00	1	1	2	0	0
50.00	0	1	1	0	0
55.00	0	1	1	0	0
40.00	1	1	1	0	0
30.00	0	1	1	1	0
NA	1	1	2	1	0
65.00	0	1	1	0	0
45.00	0	1	1	2	0
35.00	1	1	2	1	0
40.00	1	1	2	1	0
30.00	1	1	1	1	0
55.00	0	1	1	0	0
50.00	0	1	1	0	0
40.00	0	1	1	0	1
35.00	1	1	2	1	0
25.00	1	1	2	1	0
60.00	0	1	1	0	2
20.00	1	1	2	0	2
65.00	0	1	1	0	0
40.00	1	1	2	0	0
NA	1	1	1	0	0
25.00	1	1	2	0	1
65.00	0	1	1	0	1
30.00	0	1	1	0	0
NA	0	1	1	0	0
50.00	0	1	1	0	0
25.00	0	1	1	0	0
30.00	0	1	1	1	0
25.00	1	1	2	1	0
20.00	0	1	1	1	0
20.00	1	1	2	1	0
30.00	1	1	2	0	0
55.00	1	1	1	0	0
50.00	0	1	1	0	0
60.00	1	1	2	0	1
40.00	0	1	1	0	0
NA	1	1	1	0	0
25.00	0	1	1	0	0
NA	0	1	1	0	0
45.00	1	1	2	0	1
35.00	0	1	1	0	0
45.00	1	1	1	0	0
NA	0	1	1	0	0
35.00	1	1	2	0	0
50.00	0	1	1	0	0
40.00	0	1	1	0	0
35.00	1	1	2	0	0
55.00	0	1	1	1	0
55.00	1	1	2	1	0
35.00	0	1	1	0	0
15.00	1	1	1	2	2
20.00	1	1	2	2	2
25.00	1	1	2	2	2
65.00	0	1	1	1	3
50.00	1	1	2	1	3
NA	1	1	1	0	0
25.00	1	1	2	0	0
NA	1	1	1	0	0
35.00	1	1	2	1	0
30.00	1	1	2	0	0
35.00	1	1	1	0	0
40.00	1	1	2	0	0
35.00	1	1	1	0	0
50.00	0	1	1	1	0
40.00	1	1	2	1	0
60.00	1	1	1	0	0
30.00	1	1	1	0	0
60.00	0	1	1	0	0
60.00	0	1	1	0	0
25.00	0	1	1	1	0
NA	0	1	1	0	0
20.00	1	1	2	1	0
25.00	1	1	1	1	0
25.00	1	1	2	1	0
10.00	1	1	1	0	2
45.00	1	1	1	1	1
40.00	1	1	2	1	1
60.00	0	1	1	1	0
NA	1	1	2	1	0
35.00	1	1	1	0	0
65.00	0	1	1	0	0
55.00	1	1	1	1	0
45.00	1	1	2	1	0
55.00	1	1	2	1	0
NA	0	1	1	0	0
65.00	1	1	2	0	0
70.00	0	1	1	1	0
65.00	0	1	2	1	0
65.00	0	1	1	0	0
50.00	1	1	2	0	0
20.00	1	1	2	0	2
55.00	0	1	1	1	1
40.00	1	1	2	1	1
50.00	1	1	1	1	0
NA	1	1	2	1	0
50.00	0	1	1	1	1
20.00	1	1	1	0	2
40.00	1	1	2	1	1
NA	1	1	2	0	0
35.00	1	1	1	0	0
40.00	0	1	1	0	0
65.00	0	1	1	0	0
50.00	0	1	1	0	0
35.00	1	1	2	0	0
65.00	0	1	1	1	0
60.00	1	1	2	1	0
60.00	0	1	1	0	0
55.00	0	1	1	0	1
25.00	0	1	1	0	1
55.00	1	1	2	0	0
35.00	1	1	2	0	2
60.00	0	1	1	1	1
45.00	1	1	2	1	1
50.00	0	1	1	1	1
30.00	0	1	1	0	2
50.00	1	1	2	1	1
25.00	1	1	2	0	0
55.00	0	1	1	0	1
25.00	1	1	1	0	1
NA	0	1	1	0	0
35.00	1	1	2	0	0
NA	1	1	1	0	0
65.00	0	1	1	0	0
40.00	1	1	2	0	0
30.00	0	2	1	1	0
30.00	1	2	2	1	0
30.00	0	2	1	0	0
20.00	0	2	1	0	0
25.00	0	2	1	0	0
35.00	0	2	1	1	0
40.00	1	2	2	1	0
60.00	0	2	1	0	0
20.00	0	2	1	0	0
25.00	0	2	1	0	0
40.00	1	2	2	0	0
30.00	0	2	1	0	0
55.00	0	2	1	0	0
35.00	1	2	1	1	0
20.00	1	2	2	1	0
30.00	0	2	1	0	0
5.00	1	2	1	2	1
5.00	1	2	2	2	1
15.00	1	2	2	2	1
40.00	1	2	2	0	3
35.00	1	2	1	0	0
20.00	1	2	2	0	0
25.00	0	2	1	0	0
30.00	0	2	1	0	0
45.00	0	2	1	0	0
30.00	0	2	1	0	0
25.00	1	2	2	0	0
15.00	1	2	2	0	2
60.00	0	2	1	1	1
40.00	1	2	2	1	1
20.00	1	2	2	1	0
25.00	0	2	1	1	0
40.00	1	2	2	0	0
25.00	0	2	1	0	0
45.00	0	2	1	0	0
45.00	1	2	2	0	0
5.00	1	2	1	0	2
30.00	1	2	1	1	1
25.00	1	2	2	1	1
35.00	1	2	2	0	0
NA	0	2	1	0	0
20.00	0	2	1	0	0
45.00	0	2	2	1	0
55.00	0	2	1	1	0
55.00	0	2	1	0	0
40.00	0	2	1	1	0
30.00	0	2	2	1	0
25.00	1	2	2	1	1
45.00	1	2	2	0	2
30.00	0	2	1	1	0
30.00	1	2	2	1	0
30.00	0	2	1	0	0
30.00	0	2	1	0	0
25.00	1	2	1	0	0
10.00	1	2	2	0	2
35.00	0	2	1	1	1
35.00	1	2	2	1	1
25.00	1	2	2	0	0
30.00	0	2	2	0	0
NA	0	2	2	0	0
25.00	0	2	1	0	0
NA	0	2	1	0	0
10.00	1	2	1	1	1
20.00	0	2	1	0	0
50.00	1	2	2	0	2
30.00	1	2	2	0	0
35.00	0	2	1	0	0
20.00	0	2	1	0	0
30.00	0	2	1	1	0
25.00	1	2	2	1	0
25.00	0	2	1	0	0
20.00	0	2	1	0	0
20.00	1	2	2	0	1
35.00	1	2	2	0	1
55.00	0	2	1	0	0
10.00	1	2	1	0	2
45.00	0	2	1	1	1
35.00	1	2	2	1	1
30.00	1	2	2	1	0
30.00	1	2	2	1	0
25.00	0	2	1	0	0
25.00	0	2	1	0	0
20.00	0	2	1	0	0
40.00	0	2	1	1	0
30.00	1	2	2	1	0
20.00	0	2	1	0	0
40.00	0	2	1	0	0
NA	0	2	1	0	0
40.00	0	2	2	0	0
35.00	0	2	1	0	0
40.00	0	2	1	1	0
35.00	0	2	1	1	0
35.00	1	2	2	0	0
20.00	0	2	1	0	0
30.00	0	2	1	0	0
50.00	0	2	1	0	0
25.00	0	2	1	1	0
25.00	0	2	1	1	0
25.00	0	2	1	0	0
25.00	0	2	1	0	0
35.00	0	2	1	0	0
30.00	0	2	1	0	0
55.00	0	2	1	0	0
30.00	0	2	1	0	0
5.00	1	2	1	1	1
25.00	1	2	2	0	2
45.00	0	2	1	0	0
10.00	1	2	2	0	1
30.00	0	2	1	0	1
65.00	1	2	1	0	0
30.00	0	2	1	0	0
10.00	1	2	2	0	2
45.00	0	2	1	1	1
45.00	1	2	2	1	1
25.00	1	2	2	1	2
25.00	1	2	2	1	2
50.00	0	2	1	1	2
50.00	1	2	2	1	2
55.00	1	2	2	0	0
25.00	0	2	1	2	0
35.00	0	2	1	2	0
25.00	0	2	1	2	0
20.00	0	2	2	1	1
20.00	1	2	2	2	1
25.00	0	2	1	2	1
40.00	0	2	1	0	0
55.00	1	2	2	1	3
50.00	0	2	1	0	0
45.00	0	2	1	1	0
30.00	1	2	2	1	0
25.00	0	2	1	0	0
45.00	1	2	1	0	0
65.00	0	2	1	1	0
60.00	0	2	2	1	0
35.00	0	2	1	0	0
20.00	1	2	2	0	0
45.00	0	2	1	1	0
25.00	1	2	2	2	1
50.00	0	2	1	0	0
25.00	0	2	1	2	0
25.00	0	2	1	2	0
35.00	0	2	1	0	0
25.00	1	2	2	0	0
35.00	0	2	1	1	0
25.00	1	2	2	1	0
25.00	0	2	2	0	0
NA	1	2	2	0	0
35.00	0	2	1	0	0
45.00	1	2	2	0	0
60.00	0	2	1	0	0
NA	0	2	1	0	0
35.00	0	2	1	0	0
30.00	0	2	2	1	1
30.00	0	2	1	1	1
NA	0	2	1	0	0
5.00	1	2	2	1	2
5.00	1	2	2	1	2
25.00	0	2	1	1	2
25.00	1	2	2	1	2
20.00	1	2	2	0	0
NA	1	2	2	0	0
35.00	1	2	2	0	0
40.00	0	2	1	0	0
25.00	0	2	1	0	0
65.00	0	2	1	0	0
50.00	0	2	1	1	0
45.00	1	2	2	1	0
60.00	0	2	2	0	0
NA	0	2	1	0	0
5.00	1	2	1	0	2
35.00	0	2	1	1	1
25.00	1	2	2	1	1
NA	0	2	1	0	0
30.00	0	2	1	0	0
40.00	0	2	1	0	0
35.00	0	2	1	0	0
30.00	0	2	1	0	0
50.00	0	2	1	0	0
15.00	1	2	2	0	1
45.00	1	2	2	0	1
20.00	1	2	1	0	0
40.00	0	2	1	0	0
50.00	0	2	1	0	0
70.00	0	2	1	0	0
30.00	0	2	1	0	0
55.00	0	2	1	0	0
40.00	0	2	1	0	0
20.00	0	2	1	0	0
65.00	0	2	1	0	0
35.00	0	2	1	1	0
15.00	1	2	2	1	0
5.00	1	2	1	1	1
5.00	1	2	1	1	1
40.00	0	2	1	0	2
30.00	0	2	1	0	0
20.00	0	2	1	1	1
30.00	0	2	1	0	0
20.00	1	2	1	0	0
30.00	1	2	2	0	0
40.00	0	2	1	0	0
25.00	1	2	1	0	0
NA	1	2	1	0	0
25.00	0	2	1	0	0
30.00	1	2	1	0	0
30.00	0	2	1	0	0
NA	0	2	1	0	0
50.00	1	2	2	0	1
20.00	0	2	1	0	0
NA	0	2	1	0	0
45.00	0	2	1	0	0
25.00	1	2	2	0	1
20.00	1	2	2	0	0
45.00	0	2	1	0	1
35.00	1	2	2	0	0
35.00	0	2	1	0	0
30.00	1	2	1	0	0
30.00	0	2	1	0	0
5.00	1	2	2	1	1
10.00	1	2	2	1	1
35.00	1	2	2	0	2
40.00	0	2	1	0	0
35.00	0	2	1	1	0
30.00	1	2	2	3	0
30.00	1	2	2	0	0
25.00	0	2	1	0	0
5.00	1	2	1	1	1
5.00	1	2	1	1	1
25.00	1	2	2	2	3
50.00	1	2	2	0	0
20.00	0	2	1	0	0
25.00	1	2	2	0	0
30.00	0	2	1	0	0
25.00	0	2	1	0	0
30.00	0	2	1	0	0
25.00	1	2	2	0	1
20.00	1	2	2	0	2
20.00	1	2	2	0	0
30.00	1	2	2	0	0
60.00	0	2	1	0	0
30.00	1	2	2	0	0
35.00	0	2	1	0	0
40.00	1	2	2	0	0
25.00	0	2	1	0	0
45.00	0	2	1	0	0
25.00	0	2	1	0	0
20.00	0	2	1	0	0
15.00	0	2	1	0	0
50.00	1	2	2	0	0
25.00	0	2	1	0	0
30.00	1	2	2	0	0
30.00	1	2	2	0	0
30.00	0	2	1	1	0
30.00	0	2	2	1	0
40.00	0	2	1	0	0
35.00	1	2	2	0	0
30.00	0	2	1	1	0
25.00	0	2	1	1	0
35.00	1	2	2	0	0
NA	0	2	1	0	0
15.00	1	2	2	0	0
40.00	1	2	2	0	0
35.00	1	2	2	0	0
30.00	0	2	1	1	0
30.00	1	2	2	1	0
5.00	1	2	1	1	1
5.00	1	2	2	1	1
30.00	1	2	2	0	2
5.00	1	2	2	1	2
5.00	1	2	2	1	2
40.00	0	2	1	1	2
35.00	1	2	2	1	2
70.00	0	2	1	0	0
NA	0	2	1	0	0
35.00	1	2	1	0	0
NA	1	2	1	0	0
30.00	1	2	2	0	0
25.00	0	2	2	0	0
45.00	0	3	1	0	0
15.00	0	3	1	0	2
20.00	0	3	1	1	1
35.00	1	3	2	1	1
20.00	1	3	2	0	0
25.00	1	3	1	0	0
20.00	1	3	1	0	0
20.00	1	3	2	0	0
30.00	0	3	1	0	0
30.00	0	3	1	0	0
40.00	0	3	2	1	0
5.00	1	3	1	0	1
20.00	1	3	2	0	1
30.00	1	3	1	0	0
30.00	0	3	1	0	0
20.00	0	3	1	0	0
25.00	0	3	1	0	0
25.00	0	3	1	0	0
35.00	0	3	1	0	0
20.00	0	3	1	0	0
35.00	0	3	1	0	0
20.00	1	3	2	1	0
5.00	0	3	1	4	2
10.00	0	3	2	4	2
5.00	0	3	2	4	2
20.00	1	3	2	4	2
40.00	0	3	2	4	2
10.00	0	3	2	4	2
15.00	0	3	2	4	2
40.00	0	3	1	1	5
30.00	1	3	1	0	0
30.00	0	3	1	0	0
40.00	0	3	2	1	5
20.00	0	3	1	0	0
30.00	0	3	1	0	0
25.00	0	3	1	1	0
20.00	0	3	2	1	0
25.00	0	3	1	0	0
35.00	0	3	1	0	0
5.00	0	3	1	4	2
10.00	0	3	1	4	2
5.00	1	3	1	4	2
15.00	0	3	1	4	2
5.00	1	3	2	4	2
40.00	0	3	1	1	5
25.00	1	3	1	0	0
40.00	1	3	2	1	5
45.00	1	3	2	0	0
25.00	0	3	1	0	0
25.00	0	3	1	0	0
20.00	0	3	2	0	0
30.00	0	3	1	0	0
25.00	0	3	1	0	0
15.00	1	3	2	0	0
20.00	0	3	1	0	0
35.00	0	3	1	1	0
35.00	1	3	2	3	0
5.00	1	3	2	2	1
5.00	1	3	2	2	1
5.00	1	3	2	2	1
25.00	1	3	2	0	3
20.00	1	3	2	0	0
40.00	0	3	1	0	0
30.00	0	3	1	0	0
20.00	1	3	1	0	0
20.00	0	3	2	0	1
45.00	0	3	2	0	1
30.00	0	3	2	0	0
25.00	0	3	1	0	0
20.00	0	3	1	0	0
30.00	0	3	1	0	0
25.00	0	3	1	0	0
NA	0	3	1	0	0
20.00	0	3	1	0	0
35.00	1	3	1	0	0
25.00	0	3	1	0	0
20.00	0	3	1	0	0
30.00	0	3	1	0	0
10.00	0	3	1	1	1
10.00	0	3	2	1	1
NA	0	3	1	0	0
NA	0	3	2	0	2
NA	0	3	2	0	2
40.00	0	3	1	1	1
35.00	0	3	2	1	1
25.00	0	3	1	0	0
25.00	1	3	2	0	0
20.00	0	3	2	0	0
30.00	0	3	1	1	0
25.00	0	3	1	1	0
25.00	0	3	1	0	0
35.00	0	3	1	0	0
20.00	0	3	2	0	0
25.00	1	3	1	0	0
20.00	0	3	1	0	0
20.00	0	3	2	0	0
25.00	0	3	2	0	0
30.00	0	3	2	0	0
20.00	0	3	1	0	0
40.00	0	3	1	0	0
20.00	0	3	1	0	0
20.00	0	3	1	0	0
25.00	0	3	2	0	0
25.00	0	3	1	0	0
25.00	0	3	1	0	0
NA	0	3	1	1	0
NA	0	3	2	1	0
30.00	0	3	1	0	0
25.00	0	3	1	0	0
20.00	1	3	2	0	0
40.00	0	3	2	0	0
30.00	0	3	1	0	0
25.00	0	3	1	0	0
25.00	0	3	1	0	0
35.00	1	3	1	0	0
30.00	0	3	1	0	0
30.00	0	3	1	1	0
20.00	0	3	1	1	0
20.00	0	3	1	0	0
20.00	1	3	1	0	0
25.00	0	3	1	0	0
40.00	0	3	1	0	0
25.00	0	3	1	0	0
35.00	0	3	1	0	0
35.00	0	3	1	0	0
25.00	1	3	2	0	0
30.00	0	3	2	0	0
75.00	0	3	1	0	0
45.00	0	3	1	0	0
35.00	0	3	1	0	0
30.00	0	3	1	0	0
20.00	0	3	1	0	0
30.00	0	3	1	0	0
10.00	1	3	1	1	1
5.00	1	3	1	1	1
40.00	1	3	2	0	2
60.00	0	3	1	0	0
20.00	0	3	1	0	0
20.00	1	3	2	0	1
45.00	0	3	1	0	1
20.00	0	3	1	0	0
25.00	0	3	1	0	0
45.00	1	3	1	0	0
25.00	0	3	2	0	0
20.00	0	3	1	0	0
30.00	1	3	2	0	0
30.00	1	3	1	0	0
5.00	0	3	1	0	2
35.00	0	3	1	1	1
30.00	0	3	2	1	1
30.00	0	3	1	0	0
25.00	0	3	1	0	0
25.00	0	3	1	2	0
25.00	0	3	1	0	0
25.00	0	3	1	2	0
20.00	0	3	1	2	0
NA	0	3	1	1	0
NA	1	3	2	1	0
40.00	1	3	1	1	0
40.00	1	3	2	1	0
30.00	1	3	1	0	0
20.00	0	3	1	0	0
5.00	1	3	1	1	2
5.00	1	3	2	1	2
30.00	0	3	1	1	2
35.00	1	3	2	1	2
25.00	0	3	1	0	0
NA	0	3	1	0	0
NA	0	3	1	0	0
25.00	0	3	1	0	0
40.00	0	3	1	0	0
20.00	1	3	2	0	0
20.00	0	3	1	0	0
45.00	0	3	1	0	0
45.00	0	3	1	0	0
NA	0	3	1	0	0
35.00	0	3	1	0	0
20.00	1	3	1	0	0
30.00	1	3	2	0	0
25.00	0	3	2	0	0
25.00	1	3	2	0	0
35.00	0	3	1	0	0
65.00	0	3	1	0	0
25.00	1	3	1	0	0
25.00	0	3	1	1	0
25.00	1	3	2	1	0
20.00	0	3	1	0	0
20.00	0	3	1	0	0
45.00	0	3	1	0	0
NA	0	3	1	0	0
40.00	0	3	1	0	2
20.00	0	3	1	1	1
15.00	0	3	1	1	1
50.00	0	3	1	0	0
5.00	1	3	2	0	0
NA	0	3	1	0	0
45.00	0	3	1	0	0
45.00	0	3	1	0	0
NA	1	3	1	0	0
20.00	0	3	1	0	0
NA	0	3	2	0	0
NA	0	3	1	0	0
NA	0	3	1	0	0
30.00	0	3	1	0	0
NA	0	3	1	0	0
NA	1	3	1	0	0
25.00	0	3	2	2	2
10.00	0	3	2	2	2
NA	0	3	1	0	0
20.00	0	3	1	2	2
20.00	0	3	1	1	3
50.00	0	3	2	1	3
NA	0	3	1	0	0
NA	0	3	1	0	0
25.00	0	3	1	0	0
NA	0	3	1	0	0
NA	0	3	1	0	0
25.00	0	3	1	0	0
20.00	1	3	2	0	0
NA	1	3	2	0	0
10.00	1	3	1	0	2
35.00	0	3	1	1	1
45.00	0	3	1	0	0
35.00	1	3	2	1	1
40.00	0	3	1	0	0
10.00	0	3	1	5	2
5.00	0	3	1	5	2
15.00	0	3	1	5	2
10.00	0	3	2	5	2
20.00	0	3	2	5	2
15.00	0	3	1	5	2
40.00	0	3	1	1	6
45.00	0	3	2	1	6
55.00	0	3	1	0	0
35.00	0	3	1	0	0
NA	0	3	1	0	0
20.00	0	3	1	0	0
40.00	0	3	1	2	0
30.00	0	3	1	2	0
20.00	0	3	1	0	0
25.00	0	3	2	0	0
20.00	0	3	2	0	0
NA	0	3	1	1	0
NA	0	3	1	1	0
30.00	0	3	1	1	0
25.00	1	3	2	1	0
20.00	0	3	1	0	0
25.00	0	3	1	0	0
45.00	0	3	1	2	0
30.00	0	3	1	1	0
25.00	0	3	1	0	0
45.00	1	3	2	1	0
NA	0	3	2	0	0
25.00	0	3	1	0	0
NA	0	3	1	0	0
15.00	0	3	1	0	0
NA	1	3	2	0	0
30.00	1	3	1	0	0
NA	1	3	1	0	0
20.00	0	3	2	0	0
30.00	1	3	2	0	0
25.00	0	3	2	0	0
25.00	1	3	2	0	0
30.00	0	3	1	0	0
30.00	0	3	2	0	0
NA	0	3	2	0	0
5.00	1	3	2	0	1
25.00	1	3	2	1	1
45.00	0	3	1	0	0
30.00	0	3	1	0	0
30.00	1	3	2	0	0
NA	0	3	1	0	0
NA	1	3	2	0	0
45.00	0	3	1	0	0
NA	1	3	1	0	0
30.00	0	3	1	0	0
NA	0	3	1	0	0
30.00	0	3	2	1	0
25.00	0	3	2	1	0
NA	0	3	1	0	0
30.00	1	3	1	0	0
25.00	1	3	1	0	0
NA	0	3	1	0	0
20.00	0	3	1	0	0
50.00	0	3	1	0	0
20.00	0	3	1	1	0
NA	1	3	2	0	0
NA	1	3	1	0	0
35.00	0	3	1	0	0
30.00	1	3	1	0	0
25.00	0	3	1	0	0
35.00	0	3	1	0	0
35.00	0	3	1	0	0
30.00	0	3	1	0	0
5.00	1	3	1	1	1
5.00	1	3	2	1	1
50.00	0	3	1	0	0
35.00	0	3	1	0	0
20.00	0	3	1	0	0
30.00	1	3	2	0	2
NA	0	3	1	1	2
NA	0	3	2	1	2
NA	0	3	1	1	2
NA	0	3	2	1	2
25.00	0	3	1	0	0
35.00	1	3	1	0	0
30.00	0	3	1	0	0
20.00	0	3	2	1	0
25.00	0	3	2	1	0
35.00	1	3	1	0	0
20.00	0	3	1	0	0
25.00	0	3	1	0	0
30.00	0	3	1	0	0
25.00	1	3	1	0	0
35.00	0	3	1	0	0
25.00	0	3	1	0	0
5.00	1	3	2	0	1
40.00	1	3	1	0	1
NA	0	3	1	0	0
20.00	0	3	1	0	0
NA	0	3	1	0	0
NA	0	3	1	0	0
NA	1	3	2	0	0
NA	1	3	2	0	0
35.00	0	3	1	0	0
45.00	0	3	1	0	0
NA	1	3	1	0	0
NA	0	3	1	1	0
NA	0	3	2	1	0
NA	0	3	1	1	0
NA	0	3	1	1	0
NA	0	3	1	0	0
25.00	0	3	2	2	0
30.00	0	3	1	2	0
5.00	1	3	2	0	2
30.00	1	3	1	3	1
30.00	1	3	2	1	1
5.00	0	3	2	1	1
20.00	0	3	1	1	1
40.00	0	3	2	0	2
NA	0	3	1	0	0
25.00	1	3	1	0	0
NA	0	3	1	0	0
40.00	0	3	2	0	0
NA	0	3	1	0	0
NA	1	3	1	0	0
NA	0	3	1	0	0
25.00	1	3	2	0	0
NA	0	3	1	0	0
30.00	1	3	1	0	0
30.00	0	3	1	0	0
30.00	0	3	1	0	0
25.00	0	3	1	0	0
25.00	1	3	1	0	0
NA	0	3	1	3	1
NA	0	3	2	3	1
NA	0	3	2	3	1
NA	0	3	2	3	1
NA	0	3	2	0	4
35.00	0	3	1	0	0
35.00	0	3	1	0	0
NA	0	3	2	1	0
NA	0	3	1	1	0
40.00	0	3	1	0	0
40.00	0	3	1	0	0
25.00	0	3	1	0	0
25.00	0	3	2	0	0
45.00	0	3	2	0	0
40.00	0	3	1	1	0
30.00	0	3	2	1	0
20.00	1	3	1	1	0
NA	0	3	1	0	0
30.00	0	3	1	0	0
NA	0	3	1	0	0
30.00	0	3	1	1	0
30.00	0	3	2	1	0
NA	0	3	1	0	0
25.00	0	3	1	0	0
30.00	1	3	1	0	0
55.00	0	3	1	0	0
25.00	1	3	2	0	0
35.00	1	3	1	0	0
NA	0	3	1	0	0
NA	0	3	1	0	0
NA	1	3	2	0	0
25.00	1	3	1	0	0
25.00	0	3	1	0	0
NA	0	3	2	0	0
NA	0	3	1	0	0
NA	0	3	1	0	0
30.00	0	3	1	0	0
NA	1	3	1	0	0
35.00	0	3	2	0	0
NA	1	3	2	0	0
NA	0	3	1	0	0
35.00	0	3	1	0	0
35.00	0	3	1	0	0
NA	1	3	2	0	0
NA	0	3	1	0	0
NA	1	3	2	0	0
NA	1	3	1	0	0
NA	1	3	2	2	0
NA	1	3	2	2	0
NA	1	3	1	2	0
NA	1	3	2	0	0
NA	0	3	1	0	0
NA	1	3	2	0	0
15.00	1	3	2	0	0
35.00	0	3	2	0	0
NA	0	3	1	0	0
25.00	0	3	1	1	0
20.00	0	3	2	1	0
NA	0	3	2	0	0
NA	0	3	2	0	0
NA	0	3	2	0	0
60.00	0	3	1	0	0
NA	0	3	1	0	0
25.00	1	3	1	0	0
NA	0	3	1	0	0
25.00	0	3	1	0	0
25.00	0	3	1	0	0
30.00	0	3	1	0	0
NA	0	3	1	0	0
NA	1	3	2	0	0
25.00	0	3	1	0	0
10.00	1	3	1	0	1
30.00	1	3	2	0	1
NA	0	3	1	0	0
NA	1	3	2	1	0
NA	0	3	1	1	0
NA	0	3	1	0	0
35.00	0	3	1	0	0
NA	0	3	1	0	0
NA	1	3	1	0	0
NA	1	3	1	1	1
NA	1	3	1	1	1
NA	1	3	2	0	2
NA	1	3	2	0	0
NA	0	3	1	0	0
NA	1	3	2	0	0
25.00	1	3	2	0	0
NA	0	3	1	0	0
NA	1	3	2	1	0
NA	1	3	2	1	0
NA	1	3	2	0	0
20.00	0	3	1	0	0
25.00	0	3	1	0	0
15.00	1	3	2	0	0
5.00	1	3	2	0	2
20.00	1	3	1	1	1
20.00	1	3	2	1	1
35.00	0	3	1	0	0
NA	0	3	1	0	0
NA	0	3	1	0	0
NA	0	3	2	0	0
NA	0	3	1	0	0
15.00	1	3	1	1	0
15.00	1	3	2	1	0
30.00	0	3	2	0	0
30.00	0	3	1	0	0
20.00	1	3	2	0	0
30.00	1	3	2	0	0
25.00	0	3	1	0	0
45.00	0	3	1	0	0
40.00	1	3	1	0	0
25.00	0	3	1	0	0
30.00	0	3	1	0	0
25.00	1	3	2	0	0
65.00	0	3	1	0	0
NA	0	3	1	1	0
NA	0	3	1	0	0
NA	1	3	2	1	0
NA	0	3	1	0	0
NA	0	3	1	0	0
NA	0	3	1	0	0
25.00	0	3	1	0	0
NA	0	3	2	0	0
NA	1	3	2	0	0
NA	1	3	2	0	0
25.00	1	3	2	0	0
NA	1	3	1	0	0
NA	1	3	2	0	0
10.00	1	3	1	0	1
30.00	0	3	1	0	0
45.00	0	3	1	0	1
NA	0	3	1	0	0
35.00	0	3	2	0	0
30.00	0	3	1	0	0
35.00	1	3	1	0	0
20.00	0	3	1	0	0
25.00	0	3	2	0	0
20.00	0	3	2	0	0
20.00	0	3	1	0	0
20.00	0	3	1	0	0
35.00	1	3	2	0	0
0.00	0	3	2	0	0
5.00	0	3	1	3	1
10.00	0	3	1	3	1
5.00	0	3	2	3	1
10.00	0	3	2	3	1
30.00	0	3	2	0	4
5.00	0	3	1	4	1
10.00	0	3	1	4	1
5.00	0	3	1	4	1
20.00	0	3	1	4	1
15.00	0	3	1	4	1
45.00	0	3	2	0	5
25.00	0	3	1	0	0
20.00	0	3	1	0	0
NA	0	3	1	0	0
35.00	0	3	1	0	0
5.00	0	3	1	1	1
5.00	0	3	2	1	1
30.00	0	3	2	0	2
NA	0	3	1	0	0
NA	0	3	1	0	0
NA	0	3	1	0	0
25.00	0	3	1	0	0
25.00	0	3	1	0	0
25.00	0	3	1	0	0
25.00	1	3	1	1	0
NA	1	3	1	1	1
NA	1	3	2	1	1
NA	1	3	2	0	2
NA	0	3	2	0	0
25.00	0	3	1	0	0
30.00	0	3	2	0	0
20.00	0	3	1	0	0
0.00	0	3	1	0	0
25.00	0	3	1	1	0
20.00	0	3	2	0	0
35.00	1	3	1	0	0
NA	0	3	1	0	0
20.00	0	3	1	0	0
25.00	0	3	1	0	0
NA	0	3	1	0	0
NA	0	3	2	0	0
NA	0	3	1	0	0
NA	0	3	1	0	0
40.00	0	3	1	0	0
25.00	0	3	1	0	0
10.00	0	3	1	4	1
5.00	0	3	1	4	1
10.00	0	3	1	4	1
5.00	0	3	1	4	1
10.00	0	3	1	4	1
40.00	0	3	2	0	5
25.00	0	3	2	0	0
35.00	0	3	1	0	0
NA	1	3	2	0	0
NA	0	3	1	0	0
NA	0	3	2	0	0
50.00	0	3	1	1	0
50.00	0	3	2	1	0
NA	0	3	1	0	0
NA	0	3	1	0	0
5.00	0	3	2	1	1
20.00	0	3	1	1	1
45.00	0	3	2	0	2
NA	1	3	2	0	0
50.00	0	3	1	0	0
20.00	0	3	1	0	0
NA	1	3	1	0	0
NA	0	3	1	0	0
NA	0	3	1	0	0
25.00	0	3	1	0	0
NA	0	3	1	0	0
NA	0	3	1	0	0
NA	0	3	1	0	0
40.00	0	3	1	0	0
NA	0	3	1	8	2
15.00	0	3	1	8	2
NA	0	3	2	8	2
NA	0	3	2	8	2
NA	0	3	2	8	2
NA	0	3	2	8	2
NA	0	3	1	8	2
NA	0	3	1	8	2
NA	0	3	1	8	2
NA	0	3	1	1	9
NA	0	3	2	1	9
25.00	0	3	1	0	0
25.00	1	3	2	0	0
40.00	0	3	1	0	0
NA	0	3	1	2	0
NA	0	3	1	2	0
NA	0	3	1	2	0
5.00	1	3	2	1	1
25.00	1	3	2	0	2
5.00	1	3	2	1	1
25.00	1	3	1	0	0
20.00	0	3	1	0	0
25.00	0	3	1	0	0
NA	0	3	1	0	0
NA	0	3	1	0	0
NA	0	3	1	0	0
30.00	1	3	1	0	0
NA	0	3	1	0	0
NA	1	3	2	0	0
NA	0	3	1	0	0
NA	0	3	1	0	0
25.00	0	3	1	0	0
NA	0	3	1	0	0
40.00	0	3	1	0	0
25.00	0	3	1	0	0
20.00	1	3	2	0	0
5.00	0	3	1	3	2
10.00	0	3	1	3	2
10.00	0	3	2	3	2
5.00	0	3	2	3	2
40.00	0	3	1	1	4
45.00	0	3	2	1	4
NA	0	3	1	0	0
NA	0	3	1	0	0
NA	0	3	1	0	0
NA	0	3	1	0	0
NA	1	3	2	0	0
20.00	0	3	1	0	0
30.00	0	3	1	0	0
NA	0	3	1	0	0
35.00	0	3	1	0	0
NA	0	3	1	0	0
35.00	0	3	1	0	0
25.00	1	3	2	0	0
25.00	0	3	1	0	0
65.00	0	3	1	0	0
20.00	0	3	1	0	0
25.00	0	3	2	0	0
35.00	1	3	1	0	0
30.00	0	3	1	0	0
5.00	0	3	2	0	1
30.00	0	3	2	1	1
20.00	1	3	1	0	0
45.00	1	3	1	0	0
25.00	0	3	1	0	0
75.00	0	3	1	0	0
15.00	1	3	1	0	0
25.00	0	3	1	0	0
25.00	1	3	1	0	0
35.00	0	3	1	0	0
5.00	1	3	1	0	1
NA	0	3	1	1	0
NA	0	3	1	0	0
NA	0	3	1	0	0
20.00	1	3	2	1	1
NA	0	3	1	0	0
NA	0	3	1	1	0
NA	1	3	2	1	0
35.00	0	3	1	0	0
NA	0	3	1	0	0
NA	0	3	1	0	0
35.00	0	3	1	0	0
45.00	0	3	1	0	0
0.00	0	3	1	0	0
25.00	1	3	1	0	0
NA	0	3	1	0	0
10.00	1	3	1	1	1
10.00	1	3	2	1	1
30.00	1	3	2	0	2
40.00	0	3	1	0	0
20.00	1	3	2	0	0
65.00	1	3	2	0	0
NA	0	3	1	1	1
15.00	0	3	1	1	1
45.00	0	3	1	0	2
10.00	0	3	2	0	2
40.00	0	3	1	1	1
30.00	0	3	2	1	1
NA	0	3	1	0	0
35.00	0	3	1	0	0
30.00	0	3	1	0	0
30.00	0	3	1	0	0
50.00	0	3	1	0	0
20.00	0	3	2	2	0
35.00	0	3	1	3	0
20.00	0	3	1	2	0
35.00	0	3	2	1	0
25.00	1	3	1	0	0
20.00	0	3	1	0	0
15.00	0	3	2	0	0
25.00	0	3	1	0	0
25.00	0	3	1	0	0
NA	0	3	1	0	0
NA	0	3	1	0	0
NA	0	3	1	0	0
35.00	0	3	1	0	0
40.00	1	3	2	0	0
55.00	0	3	1	0	0
20.00	0	3	1	1	0
25.00	0	3	1	1	0
50.00	1	3	2	1	0
NA	0	3	1	0	0
NA	0	3	1	0	0
NA	0	3	1	0	0
30.00	0	3	1	0	0
25.00	0	3	1	0	0
30.00	0	3	1	0	0
0.00	0	3	1	0	0
40.00	0	3	1	0	0
30.00	0	3	1	1	0
15.00	1	3	2	1	0
50.00	0	3	1	0	0
NA	0	3	1	0	0
NA	0	3	1	0	0
15.00	0	3	2	1	0
NA	0	3	2	1	0
30.00	0	3	1	0	0
30.00	0	3	1	0	0
NA	0	3	1	0	0

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	30 seconds
R Server	'Gwilym Jenkins' @ 72.249.127.135
R Framework error message	The field 'Names of X columns' contains a hard return which cannot be interpreted. Please, resubmit your request without hard returns in the 'Names of X columns'.

\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 & 30 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
R Framework error message & The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'. \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=111494&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]30 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[ROW][C]R Framework error message[/C][C]The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=111494&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=111494&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	30 seconds
R Server	'Gwilym Jenkins' @ 72.249.127.135
R Framework error message	The field 'Names of X columns' contains a hard return which cannot be interpreted. Please, resubmit your request without hard returns in the 'Names of X columns'.

Confusion Matrix (predicted in columns / actuals in rows)
	C1	C2	C3
C1	344	57	47
C2	148	71	61
C3	89	56	177

\begin{tabular}{lllllllll}
\hline
Confusion Matrix (predicted in columns / actuals in rows) \tabularnewline
 & C1 & C2 & C3 \tabularnewline
C1 & 344 & 57 & 47 \tabularnewline
C2 & 148 & 71 & 61 \tabularnewline
C3 & 89 & 56 & 177 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=111494&T=1

[TABLE]
[ROW][C]Confusion Matrix (predicted in columns / actuals in rows)[/C][/ROW]
[ROW][C][/C][C]C1[/C][C]C2[/C][C]C3[/C][/ROW]
[ROW][C]C1[/C][C]344[/C][C]57[/C][C]47[/C][/ROW]
[ROW][C]C2[/C][C]148[/C][C]71[/C][C]61[/C][/ROW]
[ROW][C]C3[/C][C]89[/C][C]56[/C][C]177[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=111494&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=111494&T=1

As an alternative you can also use a QR Code:

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

Confusion Matrix (predicted in columns / actuals in rows)
	C1	C2	C3
C1	344	57	47
C2	148	71	61
C3	89	56	177

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):

Parameters (R input):

par1 = 1 ; par2 = quantiles ; 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