Free Statistics

of Irreproducible Research!

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_regression_trees1.wasp
Title produced by softwareRecursive Partitioning (Regression Trees)
Date of computationWed, 14 Dec 2011 13:12:14 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2011/Dec/14/t1323886394po81r7jxlno4yhe.htm/, Retrieved Thu, 31 Oct 2024 22:53:27 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=155173, Retrieved Thu, 31 Oct 2024 22:53:27 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact125
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 20:06:20] [b98453cac15ba1066b407e146608df68]
-   PD    [Recursive Partitioning (Regression Trees)] [workshop 10: corr...] [2011-12-14 18:12:14] [d7127d50f40450f0f3837a0965e389eb] [Current]
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Dataseries X:
2050	2650	13	7	1	0	1639
2150	2664	6	5	1	0	1193
2150	2921	3	6	1	0	1635
1999	2580	4	4	1	0	1732
1900	2580	4	4	1	0	1534
1800	2774	2	4	1	0	1765
1560	1920	1	5	1	0	1161
1449	1710	1	3	1	0	1010
1375	1837	4	5	1	0	1191
1270	1880	8	6	1	0	930
1250	2150	15	3	1	0	984
1235	1894	14	5	1	0	1112
1170	1928	18	8	1	0	600
1155	1767	16	4	1	0	794
1110	1630	15	3	1	1	867
1139	1680	17	4	1	1	750
995	1500	15	4	1	0	743
900	1400	16	2	1	1	731
960	1573	17	6	1	0	768
1695	2931	28	3	1	1	1142
1553	2200	28	4	1	0	1035
1020	1478	53	3	1	1	626
1020	1713	30	4	1	1	600
850	1190	41	1	1	0	600
720	1121	46	4	1	0	398
749	1733	43	6	1	0	656
2150	2848	4	6	1	0	1487
1350	2253	23	4	1	0	939
1299	2743	25	5	1	1	1232
1250	2180	17	4	1	1	1141
1239	1706	14	4	1	0	810
1125	1710	16	4	1	0	800
1080	2200	26	4	1	0	1076
1050	1680	13	4	1	0	875
1049	1900	34	3	1	0	690
934	1543	20	3	1	0	820
875	1173	6	4	1	0	456
805	1258	7	4	1	1	821
759	997	4	4	1	0	461
729	1007	19	6	1	0	513
710	1083	22	4	1	0	504
690	1348	15	2	1	0	
975	1500	7	3	0	1	700
939	1428	40	2	0	0	701
2100	2116	25	3	0	0	1209
580	1051	15	2	0	0	426
1844	2250	40	6	0	0	915
699	1400	45	1	0	1	481
1160	1720	5	4	0	0	867
1109	1740	4	3	0	0	816
1129	1700	6	4	0	0	725
1050	1620	6	4	0	0	800
1045	1630	6	4	0	0	750
1050	1920	8	4	0	0	944
1020	1606	5	4	0	0	811
1000	1535	7	5	0	1	668
1030	1540	6	2	0	1	826
975	1739	13	3	0	0	880
940	1305	5	3	0	0	647
920	1415	7	4	0	0	866
945	1580	9	3	0	0	810
874	1236	3	4	0	0	707
872	1229	6	3	0	0	721
870	1273	4	4	0	0	638
869	1165	7	4	0	0	694
766	1200	7	4	0	1	634
739	970	4	4	0	1	541




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time3 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 & 3 seconds \tabularnewline
R Server & 'Gertrude Mary Cox' @ cox.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=155173&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]'Gertrude Mary Cox' @ cox.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=155173&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=155173&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 Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time3 seconds
R Server'Gertrude Mary Cox' @ cox.wessa.net







Goodness of Fit
Correlation0.5596
R-squared0.3131
RMSE303.2469

\begin{tabular}{lllllllll}
\hline
Goodness of Fit \tabularnewline
Correlation & 0.5596 \tabularnewline
R-squared & 0.3131 \tabularnewline
RMSE & 303.2469 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=155173&T=1

[TABLE]
[ROW][C]Goodness of Fit[/C][/ROW]
[ROW][C]Correlation[/C][C]0.5596[/C][/ROW]
[ROW][C]R-squared[/C][C]0.3131[/C][/ROW]
[ROW][C]RMSE[/C][C]303.2469[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=155173&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=155173&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
Correlation0.5596
R-squared0.3131
RMSE303.2469







Actuals, Predictions, and Residuals
#ActualsForecastsResiduals
116391307.57894736842331.421052631579
211931307.57894736842-114.578947368421
316351307.57894736842327.421052631579
417321307.57894736842424.421052631579
515341307.57894736842226.421052631579
617651307.57894736842457.421052631579
711611307.57894736842-146.578947368421
810101307.57894736842-297.578947368421
911911307.57894736842-116.578947368421
10930831.74285714285798.2571428571429
11984831.742857142857152.257142857143
121112831.742857142857280.257142857143
13600831.742857142857-231.742857142857
14794831.742857142857-37.7428571428571
15867831.74285714285735.2571428571429
16750831.742857142857-81.7428571428571
17743831.742857142857-88.7428571428571
18731831.742857142857-100.742857142857
19768831.742857142857-63.7428571428571
2011421307.57894736842-165.578947368421
2110351307.57894736842-272.578947368421
22626831.742857142857-205.742857142857
23600831.742857142857-231.742857142857
24600831.742857142857-231.742857142857
25398831.742857142857-433.742857142857
26656831.742857142857-175.742857142857
2714871307.57894736842179.421052631579
289391307.57894736842-368.578947368421
2912321307.57894736842-75.578947368421
301141831.742857142857309.257142857143
31810831.742857142857-21.7428571428571
32800831.742857142857-31.7428571428571
331076831.742857142857244.257142857143
34875831.74285714285743.2571428571429
35690831.742857142857-141.742857142857
36820831.742857142857-11.7428571428571
37456831.742857142857-375.742857142857
38821831.742857142857-10.7428571428571
39461831.742857142857-370.742857142857
40513831.742857142857-318.742857142857
41504831.742857142857-327.742857142857
42975831.742857142857143.257142857143
439391307.57894736842-368.578947368421
4421001307.57894736842792.421052631579
45580955.076923076923-375.076923076923
461844831.7428571428571012.25714285714
47699955.076923076923-256.076923076923
4811601307.57894736842-147.578947368421
491109955.076923076923153.923076923077
501129955.076923076923173.923076923077
511050955.07692307692394.9230769230769
521045955.07692307692389.9230769230769
531050955.07692307692394.9230769230769
541020955.07692307692364.9230769230769
551000955.07692307692344.9230769230769
5610301307.57894736842-277.578947368421
57975955.07692307692319.9230769230769
58940955.076923076923-15.0769230769231
599201307.57894736842-387.578947368421
60945955.076923076923-10.0769230769231
61874955.076923076923-81.0769230769231
62872831.74285714285740.2571428571429
63870831.74285714285738.2571428571429
64869831.74285714285737.2571428571429
65766831.742857142857-65.7428571428571
66739831.742857142857-92.7428571428571
672050831.7428571428571218.25714285714

\begin{tabular}{lllllllll}
\hline
Actuals, Predictions, and Residuals \tabularnewline
# & Actuals & Forecasts & Residuals \tabularnewline
1 & 1639 & 1307.57894736842 & 331.421052631579 \tabularnewline
2 & 1193 & 1307.57894736842 & -114.578947368421 \tabularnewline
3 & 1635 & 1307.57894736842 & 327.421052631579 \tabularnewline
4 & 1732 & 1307.57894736842 & 424.421052631579 \tabularnewline
5 & 1534 & 1307.57894736842 & 226.421052631579 \tabularnewline
6 & 1765 & 1307.57894736842 & 457.421052631579 \tabularnewline
7 & 1161 & 1307.57894736842 & -146.578947368421 \tabularnewline
8 & 1010 & 1307.57894736842 & -297.578947368421 \tabularnewline
9 & 1191 & 1307.57894736842 & -116.578947368421 \tabularnewline
10 & 930 & 831.742857142857 & 98.2571428571429 \tabularnewline
11 & 984 & 831.742857142857 & 152.257142857143 \tabularnewline
12 & 1112 & 831.742857142857 & 280.257142857143 \tabularnewline
13 & 600 & 831.742857142857 & -231.742857142857 \tabularnewline
14 & 794 & 831.742857142857 & -37.7428571428571 \tabularnewline
15 & 867 & 831.742857142857 & 35.2571428571429 \tabularnewline
16 & 750 & 831.742857142857 & -81.7428571428571 \tabularnewline
17 & 743 & 831.742857142857 & -88.7428571428571 \tabularnewline
18 & 731 & 831.742857142857 & -100.742857142857 \tabularnewline
19 & 768 & 831.742857142857 & -63.7428571428571 \tabularnewline
20 & 1142 & 1307.57894736842 & -165.578947368421 \tabularnewline
21 & 1035 & 1307.57894736842 & -272.578947368421 \tabularnewline
22 & 626 & 831.742857142857 & -205.742857142857 \tabularnewline
23 & 600 & 831.742857142857 & -231.742857142857 \tabularnewline
24 & 600 & 831.742857142857 & -231.742857142857 \tabularnewline
25 & 398 & 831.742857142857 & -433.742857142857 \tabularnewline
26 & 656 & 831.742857142857 & -175.742857142857 \tabularnewline
27 & 1487 & 1307.57894736842 & 179.421052631579 \tabularnewline
28 & 939 & 1307.57894736842 & -368.578947368421 \tabularnewline
29 & 1232 & 1307.57894736842 & -75.578947368421 \tabularnewline
30 & 1141 & 831.742857142857 & 309.257142857143 \tabularnewline
31 & 810 & 831.742857142857 & -21.7428571428571 \tabularnewline
32 & 800 & 831.742857142857 & -31.7428571428571 \tabularnewline
33 & 1076 & 831.742857142857 & 244.257142857143 \tabularnewline
34 & 875 & 831.742857142857 & 43.2571428571429 \tabularnewline
35 & 690 & 831.742857142857 & -141.742857142857 \tabularnewline
36 & 820 & 831.742857142857 & -11.7428571428571 \tabularnewline
37 & 456 & 831.742857142857 & -375.742857142857 \tabularnewline
38 & 821 & 831.742857142857 & -10.7428571428571 \tabularnewline
39 & 461 & 831.742857142857 & -370.742857142857 \tabularnewline
40 & 513 & 831.742857142857 & -318.742857142857 \tabularnewline
41 & 504 & 831.742857142857 & -327.742857142857 \tabularnewline
42 & 975 & 831.742857142857 & 143.257142857143 \tabularnewline
43 & 939 & 1307.57894736842 & -368.578947368421 \tabularnewline
44 & 2100 & 1307.57894736842 & 792.421052631579 \tabularnewline
45 & 580 & 955.076923076923 & -375.076923076923 \tabularnewline
46 & 1844 & 831.742857142857 & 1012.25714285714 \tabularnewline
47 & 699 & 955.076923076923 & -256.076923076923 \tabularnewline
48 & 1160 & 1307.57894736842 & -147.578947368421 \tabularnewline
49 & 1109 & 955.076923076923 & 153.923076923077 \tabularnewline
50 & 1129 & 955.076923076923 & 173.923076923077 \tabularnewline
51 & 1050 & 955.076923076923 & 94.9230769230769 \tabularnewline
52 & 1045 & 955.076923076923 & 89.9230769230769 \tabularnewline
53 & 1050 & 955.076923076923 & 94.9230769230769 \tabularnewline
54 & 1020 & 955.076923076923 & 64.9230769230769 \tabularnewline
55 & 1000 & 955.076923076923 & 44.9230769230769 \tabularnewline
56 & 1030 & 1307.57894736842 & -277.578947368421 \tabularnewline
57 & 975 & 955.076923076923 & 19.9230769230769 \tabularnewline
58 & 940 & 955.076923076923 & -15.0769230769231 \tabularnewline
59 & 920 & 1307.57894736842 & -387.578947368421 \tabularnewline
60 & 945 & 955.076923076923 & -10.0769230769231 \tabularnewline
61 & 874 & 955.076923076923 & -81.0769230769231 \tabularnewline
62 & 872 & 831.742857142857 & 40.2571428571429 \tabularnewline
63 & 870 & 831.742857142857 & 38.2571428571429 \tabularnewline
64 & 869 & 831.742857142857 & 37.2571428571429 \tabularnewline
65 & 766 & 831.742857142857 & -65.7428571428571 \tabularnewline
66 & 739 & 831.742857142857 & -92.7428571428571 \tabularnewline
67 & 2050 & 831.742857142857 & 1218.25714285714 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=155173&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]1639[/C][C]1307.57894736842[/C][C]331.421052631579[/C][/ROW]
[ROW][C]2[/C][C]1193[/C][C]1307.57894736842[/C][C]-114.578947368421[/C][/ROW]
[ROW][C]3[/C][C]1635[/C][C]1307.57894736842[/C][C]327.421052631579[/C][/ROW]
[ROW][C]4[/C][C]1732[/C][C]1307.57894736842[/C][C]424.421052631579[/C][/ROW]
[ROW][C]5[/C][C]1534[/C][C]1307.57894736842[/C][C]226.421052631579[/C][/ROW]
[ROW][C]6[/C][C]1765[/C][C]1307.57894736842[/C][C]457.421052631579[/C][/ROW]
[ROW][C]7[/C][C]1161[/C][C]1307.57894736842[/C][C]-146.578947368421[/C][/ROW]
[ROW][C]8[/C][C]1010[/C][C]1307.57894736842[/C][C]-297.578947368421[/C][/ROW]
[ROW][C]9[/C][C]1191[/C][C]1307.57894736842[/C][C]-116.578947368421[/C][/ROW]
[ROW][C]10[/C][C]930[/C][C]831.742857142857[/C][C]98.2571428571429[/C][/ROW]
[ROW][C]11[/C][C]984[/C][C]831.742857142857[/C][C]152.257142857143[/C][/ROW]
[ROW][C]12[/C][C]1112[/C][C]831.742857142857[/C][C]280.257142857143[/C][/ROW]
[ROW][C]13[/C][C]600[/C][C]831.742857142857[/C][C]-231.742857142857[/C][/ROW]
[ROW][C]14[/C][C]794[/C][C]831.742857142857[/C][C]-37.7428571428571[/C][/ROW]
[ROW][C]15[/C][C]867[/C][C]831.742857142857[/C][C]35.2571428571429[/C][/ROW]
[ROW][C]16[/C][C]750[/C][C]831.742857142857[/C][C]-81.7428571428571[/C][/ROW]
[ROW][C]17[/C][C]743[/C][C]831.742857142857[/C][C]-88.7428571428571[/C][/ROW]
[ROW][C]18[/C][C]731[/C][C]831.742857142857[/C][C]-100.742857142857[/C][/ROW]
[ROW][C]19[/C][C]768[/C][C]831.742857142857[/C][C]-63.7428571428571[/C][/ROW]
[ROW][C]20[/C][C]1142[/C][C]1307.57894736842[/C][C]-165.578947368421[/C][/ROW]
[ROW][C]21[/C][C]1035[/C][C]1307.57894736842[/C][C]-272.578947368421[/C][/ROW]
[ROW][C]22[/C][C]626[/C][C]831.742857142857[/C][C]-205.742857142857[/C][/ROW]
[ROW][C]23[/C][C]600[/C][C]831.742857142857[/C][C]-231.742857142857[/C][/ROW]
[ROW][C]24[/C][C]600[/C][C]831.742857142857[/C][C]-231.742857142857[/C][/ROW]
[ROW][C]25[/C][C]398[/C][C]831.742857142857[/C][C]-433.742857142857[/C][/ROW]
[ROW][C]26[/C][C]656[/C][C]831.742857142857[/C][C]-175.742857142857[/C][/ROW]
[ROW][C]27[/C][C]1487[/C][C]1307.57894736842[/C][C]179.421052631579[/C][/ROW]
[ROW][C]28[/C][C]939[/C][C]1307.57894736842[/C][C]-368.578947368421[/C][/ROW]
[ROW][C]29[/C][C]1232[/C][C]1307.57894736842[/C][C]-75.578947368421[/C][/ROW]
[ROW][C]30[/C][C]1141[/C][C]831.742857142857[/C][C]309.257142857143[/C][/ROW]
[ROW][C]31[/C][C]810[/C][C]831.742857142857[/C][C]-21.7428571428571[/C][/ROW]
[ROW][C]32[/C][C]800[/C][C]831.742857142857[/C][C]-31.7428571428571[/C][/ROW]
[ROW][C]33[/C][C]1076[/C][C]831.742857142857[/C][C]244.257142857143[/C][/ROW]
[ROW][C]34[/C][C]875[/C][C]831.742857142857[/C][C]43.2571428571429[/C][/ROW]
[ROW][C]35[/C][C]690[/C][C]831.742857142857[/C][C]-141.742857142857[/C][/ROW]
[ROW][C]36[/C][C]820[/C][C]831.742857142857[/C][C]-11.7428571428571[/C][/ROW]
[ROW][C]37[/C][C]456[/C][C]831.742857142857[/C][C]-375.742857142857[/C][/ROW]
[ROW][C]38[/C][C]821[/C][C]831.742857142857[/C][C]-10.7428571428571[/C][/ROW]
[ROW][C]39[/C][C]461[/C][C]831.742857142857[/C][C]-370.742857142857[/C][/ROW]
[ROW][C]40[/C][C]513[/C][C]831.742857142857[/C][C]-318.742857142857[/C][/ROW]
[ROW][C]41[/C][C]504[/C][C]831.742857142857[/C][C]-327.742857142857[/C][/ROW]
[ROW][C]42[/C][C]975[/C][C]831.742857142857[/C][C]143.257142857143[/C][/ROW]
[ROW][C]43[/C][C]939[/C][C]1307.57894736842[/C][C]-368.578947368421[/C][/ROW]
[ROW][C]44[/C][C]2100[/C][C]1307.57894736842[/C][C]792.421052631579[/C][/ROW]
[ROW][C]45[/C][C]580[/C][C]955.076923076923[/C][C]-375.076923076923[/C][/ROW]
[ROW][C]46[/C][C]1844[/C][C]831.742857142857[/C][C]1012.25714285714[/C][/ROW]
[ROW][C]47[/C][C]699[/C][C]955.076923076923[/C][C]-256.076923076923[/C][/ROW]
[ROW][C]48[/C][C]1160[/C][C]1307.57894736842[/C][C]-147.578947368421[/C][/ROW]
[ROW][C]49[/C][C]1109[/C][C]955.076923076923[/C][C]153.923076923077[/C][/ROW]
[ROW][C]50[/C][C]1129[/C][C]955.076923076923[/C][C]173.923076923077[/C][/ROW]
[ROW][C]51[/C][C]1050[/C][C]955.076923076923[/C][C]94.9230769230769[/C][/ROW]
[ROW][C]52[/C][C]1045[/C][C]955.076923076923[/C][C]89.9230769230769[/C][/ROW]
[ROW][C]53[/C][C]1050[/C][C]955.076923076923[/C][C]94.9230769230769[/C][/ROW]
[ROW][C]54[/C][C]1020[/C][C]955.076923076923[/C][C]64.9230769230769[/C][/ROW]
[ROW][C]55[/C][C]1000[/C][C]955.076923076923[/C][C]44.9230769230769[/C][/ROW]
[ROW][C]56[/C][C]1030[/C][C]1307.57894736842[/C][C]-277.578947368421[/C][/ROW]
[ROW][C]57[/C][C]975[/C][C]955.076923076923[/C][C]19.9230769230769[/C][/ROW]
[ROW][C]58[/C][C]940[/C][C]955.076923076923[/C][C]-15.0769230769231[/C][/ROW]
[ROW][C]59[/C][C]920[/C][C]1307.57894736842[/C][C]-387.578947368421[/C][/ROW]
[ROW][C]60[/C][C]945[/C][C]955.076923076923[/C][C]-10.0769230769231[/C][/ROW]
[ROW][C]61[/C][C]874[/C][C]955.076923076923[/C][C]-81.0769230769231[/C][/ROW]
[ROW][C]62[/C][C]872[/C][C]831.742857142857[/C][C]40.2571428571429[/C][/ROW]
[ROW][C]63[/C][C]870[/C][C]831.742857142857[/C][C]38.2571428571429[/C][/ROW]
[ROW][C]64[/C][C]869[/C][C]831.742857142857[/C][C]37.2571428571429[/C][/ROW]
[ROW][C]65[/C][C]766[/C][C]831.742857142857[/C][C]-65.7428571428571[/C][/ROW]
[ROW][C]66[/C][C]739[/C][C]831.742857142857[/C][C]-92.7428571428571[/C][/ROW]
[ROW][C]67[/C][C]2050[/C][C]831.742857142857[/C][C]1218.25714285714[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=155173&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=155173&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
#ActualsForecastsResiduals
116391307.57894736842331.421052631579
211931307.57894736842-114.578947368421
316351307.57894736842327.421052631579
417321307.57894736842424.421052631579
515341307.57894736842226.421052631579
617651307.57894736842457.421052631579
711611307.57894736842-146.578947368421
810101307.57894736842-297.578947368421
911911307.57894736842-116.578947368421
10930831.74285714285798.2571428571429
11984831.742857142857152.257142857143
121112831.742857142857280.257142857143
13600831.742857142857-231.742857142857
14794831.742857142857-37.7428571428571
15867831.74285714285735.2571428571429
16750831.742857142857-81.7428571428571
17743831.742857142857-88.7428571428571
18731831.742857142857-100.742857142857
19768831.742857142857-63.7428571428571
2011421307.57894736842-165.578947368421
2110351307.57894736842-272.578947368421
22626831.742857142857-205.742857142857
23600831.742857142857-231.742857142857
24600831.742857142857-231.742857142857
25398831.742857142857-433.742857142857
26656831.742857142857-175.742857142857
2714871307.57894736842179.421052631579
289391307.57894736842-368.578947368421
2912321307.57894736842-75.578947368421
301141831.742857142857309.257142857143
31810831.742857142857-21.7428571428571
32800831.742857142857-31.7428571428571
331076831.742857142857244.257142857143
34875831.74285714285743.2571428571429
35690831.742857142857-141.742857142857
36820831.742857142857-11.7428571428571
37456831.742857142857-375.742857142857
38821831.742857142857-10.7428571428571
39461831.742857142857-370.742857142857
40513831.742857142857-318.742857142857
41504831.742857142857-327.742857142857
42975831.742857142857143.257142857143
439391307.57894736842-368.578947368421
4421001307.57894736842792.421052631579
45580955.076923076923-375.076923076923
461844831.7428571428571012.25714285714
47699955.076923076923-256.076923076923
4811601307.57894736842-147.578947368421
491109955.076923076923153.923076923077
501129955.076923076923173.923076923077
511050955.07692307692394.9230769230769
521045955.07692307692389.9230769230769
531050955.07692307692394.9230769230769
541020955.07692307692364.9230769230769
551000955.07692307692344.9230769230769
5610301307.57894736842-277.578947368421
57975955.07692307692319.9230769230769
58940955.076923076923-15.0769230769231
599201307.57894736842-387.578947368421
60945955.076923076923-10.0769230769231
61874955.076923076923-81.0769230769231
62872831.74285714285740.2571428571429
63870831.74285714285738.2571428571429
64869831.74285714285737.2571428571429
65766831.742857142857-65.7428571428571
66739831.742857142857-92.7428571428571
672050831.7428571428571218.25714285714



Parameters (Session):
Parameters (R input):
par1 = 7 ; par2 = none ; par3 = 2 ; 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')
}