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 computationThu, 15 Dec 2011 08:57:46 -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/15/t13239575029co1c01wqck58w0.htm/, Retrieved Thu, 31 Oct 2024 22:48:54 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=155414, Retrieved Thu, 31 Oct 2024 22:48:54 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact124
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Recursive Partitioning (Regression Trees)] [workshop 10: corr...] [2011-12-15 13:57:46] [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=155414&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=155414&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=155414&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
Correlation0.7182
R-squared0.5159
RMSE8.311

\begin{tabular}{lllllllll}
\hline
Goodness of Fit \tabularnewline
Correlation & 0.7182 \tabularnewline
R-squared & 0.5159 \tabularnewline
RMSE & 8.311 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=155414&T=1

[TABLE]
[ROW][C]Goodness of Fit[/C][/ROW]
[ROW][C]Correlation[/C][C]0.7182[/C][/ROW]
[ROW][C]R-squared[/C][C]0.5159[/C][/ROW]
[ROW][C]RMSE[/C][C]8.311[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=155414&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=155414&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
Correlation0.7182
R-squared0.5159
RMSE8.311







Actuals, Predictions, and Residuals
#ActualsForecastsResiduals
11313.1290322580645-0.129032258064516
2613.1290322580645-7.12903225806452
3313.1290322580645-10.1290322580645
4413.1290322580645-9.12903225806452
5413.1290322580645-9.12903225806452
6213.1290322580645-11.1290322580645
7113.1290322580645-12.1290322580645
8113.1290322580645-12.1290322580645
9413.1290322580645-9.12903225806452
10813.1290322580645-5.12903225806452
111513.12903225806451.87096774193548
121413.12903225806450.870967741935484
131828.7272727272727-10.7272727272727
141613.12903225806452.87096774193548
151513.12903225806451.87096774193548
161713.12903225806453.87096774193548
171513.12903225806451.87096774193548
181613.12903225806452.87096774193548
191713.12903225806453.87096774193548
202813.129032258064514.8709677419355
212813.129032258064514.8709677419355
225328.727272727272724.2727272727273
233028.72727272727271.27272727272727
244128.727272727272712.2727272727273
254628.727272727272717.2727272727273
264328.727272727272714.2727272727273
27413.1290322580645-9.12903225806452
282313.12903225806459.87096774193548
292513.129032258064511.8709677419355
301713.12903225806453.87096774193548
311413.12903225806450.870967741935484
321613.12903225806452.87096774193548
332613.129032258064512.8709677419355
341313.1290322580645-0.129032258064516
353428.72727272727275.27272727272727
362013.12903225806456.87096774193548
37628.7272727272727-22.7272727272727
38713.1290322580645-6.12903225806452
39428.7272727272727-24.7272727272727
401928.7272727272727-9.72727272727273
412228.7272727272727-6.72727272727273
421513.12903225806451.87096774193548
4333.48-0.48
4423.48-1.48
4533.48-0.48
4623.48-1.48
4763.482.52
4813.48-2.48
4943.480.52
5033.48-0.48
5143.480.52
5243.480.52
5343.480.52
5443.480.52
5543.480.52
5653.481.52
5723.48-1.48
5833.48-0.48
5933.48-0.48
6043.480.52
6133.48-0.48
6243.480.52
6333.48-0.48
6443.480.52
6543.480.52
6643.480.52
6743.480.52

\begin{tabular}{lllllllll}
\hline
Actuals, Predictions, and Residuals \tabularnewline
# & Actuals & Forecasts & Residuals \tabularnewline
1 & 13 & 13.1290322580645 & -0.129032258064516 \tabularnewline
2 & 6 & 13.1290322580645 & -7.12903225806452 \tabularnewline
3 & 3 & 13.1290322580645 & -10.1290322580645 \tabularnewline
4 & 4 & 13.1290322580645 & -9.12903225806452 \tabularnewline
5 & 4 & 13.1290322580645 & -9.12903225806452 \tabularnewline
6 & 2 & 13.1290322580645 & -11.1290322580645 \tabularnewline
7 & 1 & 13.1290322580645 & -12.1290322580645 \tabularnewline
8 & 1 & 13.1290322580645 & -12.1290322580645 \tabularnewline
9 & 4 & 13.1290322580645 & -9.12903225806452 \tabularnewline
10 & 8 & 13.1290322580645 & -5.12903225806452 \tabularnewline
11 & 15 & 13.1290322580645 & 1.87096774193548 \tabularnewline
12 & 14 & 13.1290322580645 & 0.870967741935484 \tabularnewline
13 & 18 & 28.7272727272727 & -10.7272727272727 \tabularnewline
14 & 16 & 13.1290322580645 & 2.87096774193548 \tabularnewline
15 & 15 & 13.1290322580645 & 1.87096774193548 \tabularnewline
16 & 17 & 13.1290322580645 & 3.87096774193548 \tabularnewline
17 & 15 & 13.1290322580645 & 1.87096774193548 \tabularnewline
18 & 16 & 13.1290322580645 & 2.87096774193548 \tabularnewline
19 & 17 & 13.1290322580645 & 3.87096774193548 \tabularnewline
20 & 28 & 13.1290322580645 & 14.8709677419355 \tabularnewline
21 & 28 & 13.1290322580645 & 14.8709677419355 \tabularnewline
22 & 53 & 28.7272727272727 & 24.2727272727273 \tabularnewline
23 & 30 & 28.7272727272727 & 1.27272727272727 \tabularnewline
24 & 41 & 28.7272727272727 & 12.2727272727273 \tabularnewline
25 & 46 & 28.7272727272727 & 17.2727272727273 \tabularnewline
26 & 43 & 28.7272727272727 & 14.2727272727273 \tabularnewline
27 & 4 & 13.1290322580645 & -9.12903225806452 \tabularnewline
28 & 23 & 13.1290322580645 & 9.87096774193548 \tabularnewline
29 & 25 & 13.1290322580645 & 11.8709677419355 \tabularnewline
30 & 17 & 13.1290322580645 & 3.87096774193548 \tabularnewline
31 & 14 & 13.1290322580645 & 0.870967741935484 \tabularnewline
32 & 16 & 13.1290322580645 & 2.87096774193548 \tabularnewline
33 & 26 & 13.1290322580645 & 12.8709677419355 \tabularnewline
34 & 13 & 13.1290322580645 & -0.129032258064516 \tabularnewline
35 & 34 & 28.7272727272727 & 5.27272727272727 \tabularnewline
36 & 20 & 13.1290322580645 & 6.87096774193548 \tabularnewline
37 & 6 & 28.7272727272727 & -22.7272727272727 \tabularnewline
38 & 7 & 13.1290322580645 & -6.12903225806452 \tabularnewline
39 & 4 & 28.7272727272727 & -24.7272727272727 \tabularnewline
40 & 19 & 28.7272727272727 & -9.72727272727273 \tabularnewline
41 & 22 & 28.7272727272727 & -6.72727272727273 \tabularnewline
42 & 15 & 13.1290322580645 & 1.87096774193548 \tabularnewline
43 & 3 & 3.48 & -0.48 \tabularnewline
44 & 2 & 3.48 & -1.48 \tabularnewline
45 & 3 & 3.48 & -0.48 \tabularnewline
46 & 2 & 3.48 & -1.48 \tabularnewline
47 & 6 & 3.48 & 2.52 \tabularnewline
48 & 1 & 3.48 & -2.48 \tabularnewline
49 & 4 & 3.48 & 0.52 \tabularnewline
50 & 3 & 3.48 & -0.48 \tabularnewline
51 & 4 & 3.48 & 0.52 \tabularnewline
52 & 4 & 3.48 & 0.52 \tabularnewline
53 & 4 & 3.48 & 0.52 \tabularnewline
54 & 4 & 3.48 & 0.52 \tabularnewline
55 & 4 & 3.48 & 0.52 \tabularnewline
56 & 5 & 3.48 & 1.52 \tabularnewline
57 & 2 & 3.48 & -1.48 \tabularnewline
58 & 3 & 3.48 & -0.48 \tabularnewline
59 & 3 & 3.48 & -0.48 \tabularnewline
60 & 4 & 3.48 & 0.52 \tabularnewline
61 & 3 & 3.48 & -0.48 \tabularnewline
62 & 4 & 3.48 & 0.52 \tabularnewline
63 & 3 & 3.48 & -0.48 \tabularnewline
64 & 4 & 3.48 & 0.52 \tabularnewline
65 & 4 & 3.48 & 0.52 \tabularnewline
66 & 4 & 3.48 & 0.52 \tabularnewline
67 & 4 & 3.48 & 0.52 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=155414&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]13[/C][C]13.1290322580645[/C][C]-0.129032258064516[/C][/ROW]
[ROW][C]2[/C][C]6[/C][C]13.1290322580645[/C][C]-7.12903225806452[/C][/ROW]
[ROW][C]3[/C][C]3[/C][C]13.1290322580645[/C][C]-10.1290322580645[/C][/ROW]
[ROW][C]4[/C][C]4[/C][C]13.1290322580645[/C][C]-9.12903225806452[/C][/ROW]
[ROW][C]5[/C][C]4[/C][C]13.1290322580645[/C][C]-9.12903225806452[/C][/ROW]
[ROW][C]6[/C][C]2[/C][C]13.1290322580645[/C][C]-11.1290322580645[/C][/ROW]
[ROW][C]7[/C][C]1[/C][C]13.1290322580645[/C][C]-12.1290322580645[/C][/ROW]
[ROW][C]8[/C][C]1[/C][C]13.1290322580645[/C][C]-12.1290322580645[/C][/ROW]
[ROW][C]9[/C][C]4[/C][C]13.1290322580645[/C][C]-9.12903225806452[/C][/ROW]
[ROW][C]10[/C][C]8[/C][C]13.1290322580645[/C][C]-5.12903225806452[/C][/ROW]
[ROW][C]11[/C][C]15[/C][C]13.1290322580645[/C][C]1.87096774193548[/C][/ROW]
[ROW][C]12[/C][C]14[/C][C]13.1290322580645[/C][C]0.870967741935484[/C][/ROW]
[ROW][C]13[/C][C]18[/C][C]28.7272727272727[/C][C]-10.7272727272727[/C][/ROW]
[ROW][C]14[/C][C]16[/C][C]13.1290322580645[/C][C]2.87096774193548[/C][/ROW]
[ROW][C]15[/C][C]15[/C][C]13.1290322580645[/C][C]1.87096774193548[/C][/ROW]
[ROW][C]16[/C][C]17[/C][C]13.1290322580645[/C][C]3.87096774193548[/C][/ROW]
[ROW][C]17[/C][C]15[/C][C]13.1290322580645[/C][C]1.87096774193548[/C][/ROW]
[ROW][C]18[/C][C]16[/C][C]13.1290322580645[/C][C]2.87096774193548[/C][/ROW]
[ROW][C]19[/C][C]17[/C][C]13.1290322580645[/C][C]3.87096774193548[/C][/ROW]
[ROW][C]20[/C][C]28[/C][C]13.1290322580645[/C][C]14.8709677419355[/C][/ROW]
[ROW][C]21[/C][C]28[/C][C]13.1290322580645[/C][C]14.8709677419355[/C][/ROW]
[ROW][C]22[/C][C]53[/C][C]28.7272727272727[/C][C]24.2727272727273[/C][/ROW]
[ROW][C]23[/C][C]30[/C][C]28.7272727272727[/C][C]1.27272727272727[/C][/ROW]
[ROW][C]24[/C][C]41[/C][C]28.7272727272727[/C][C]12.2727272727273[/C][/ROW]
[ROW][C]25[/C][C]46[/C][C]28.7272727272727[/C][C]17.2727272727273[/C][/ROW]
[ROW][C]26[/C][C]43[/C][C]28.7272727272727[/C][C]14.2727272727273[/C][/ROW]
[ROW][C]27[/C][C]4[/C][C]13.1290322580645[/C][C]-9.12903225806452[/C][/ROW]
[ROW][C]28[/C][C]23[/C][C]13.1290322580645[/C][C]9.87096774193548[/C][/ROW]
[ROW][C]29[/C][C]25[/C][C]13.1290322580645[/C][C]11.8709677419355[/C][/ROW]
[ROW][C]30[/C][C]17[/C][C]13.1290322580645[/C][C]3.87096774193548[/C][/ROW]
[ROW][C]31[/C][C]14[/C][C]13.1290322580645[/C][C]0.870967741935484[/C][/ROW]
[ROW][C]32[/C][C]16[/C][C]13.1290322580645[/C][C]2.87096774193548[/C][/ROW]
[ROW][C]33[/C][C]26[/C][C]13.1290322580645[/C][C]12.8709677419355[/C][/ROW]
[ROW][C]34[/C][C]13[/C][C]13.1290322580645[/C][C]-0.129032258064516[/C][/ROW]
[ROW][C]35[/C][C]34[/C][C]28.7272727272727[/C][C]5.27272727272727[/C][/ROW]
[ROW][C]36[/C][C]20[/C][C]13.1290322580645[/C][C]6.87096774193548[/C][/ROW]
[ROW][C]37[/C][C]6[/C][C]28.7272727272727[/C][C]-22.7272727272727[/C][/ROW]
[ROW][C]38[/C][C]7[/C][C]13.1290322580645[/C][C]-6.12903225806452[/C][/ROW]
[ROW][C]39[/C][C]4[/C][C]28.7272727272727[/C][C]-24.7272727272727[/C][/ROW]
[ROW][C]40[/C][C]19[/C][C]28.7272727272727[/C][C]-9.72727272727273[/C][/ROW]
[ROW][C]41[/C][C]22[/C][C]28.7272727272727[/C][C]-6.72727272727273[/C][/ROW]
[ROW][C]42[/C][C]15[/C][C]13.1290322580645[/C][C]1.87096774193548[/C][/ROW]
[ROW][C]43[/C][C]3[/C][C]3.48[/C][C]-0.48[/C][/ROW]
[ROW][C]44[/C][C]2[/C][C]3.48[/C][C]-1.48[/C][/ROW]
[ROW][C]45[/C][C]3[/C][C]3.48[/C][C]-0.48[/C][/ROW]
[ROW][C]46[/C][C]2[/C][C]3.48[/C][C]-1.48[/C][/ROW]
[ROW][C]47[/C][C]6[/C][C]3.48[/C][C]2.52[/C][/ROW]
[ROW][C]48[/C][C]1[/C][C]3.48[/C][C]-2.48[/C][/ROW]
[ROW][C]49[/C][C]4[/C][C]3.48[/C][C]0.52[/C][/ROW]
[ROW][C]50[/C][C]3[/C][C]3.48[/C][C]-0.48[/C][/ROW]
[ROW][C]51[/C][C]4[/C][C]3.48[/C][C]0.52[/C][/ROW]
[ROW][C]52[/C][C]4[/C][C]3.48[/C][C]0.52[/C][/ROW]
[ROW][C]53[/C][C]4[/C][C]3.48[/C][C]0.52[/C][/ROW]
[ROW][C]54[/C][C]4[/C][C]3.48[/C][C]0.52[/C][/ROW]
[ROW][C]55[/C][C]4[/C][C]3.48[/C][C]0.52[/C][/ROW]
[ROW][C]56[/C][C]5[/C][C]3.48[/C][C]1.52[/C][/ROW]
[ROW][C]57[/C][C]2[/C][C]3.48[/C][C]-1.48[/C][/ROW]
[ROW][C]58[/C][C]3[/C][C]3.48[/C][C]-0.48[/C][/ROW]
[ROW][C]59[/C][C]3[/C][C]3.48[/C][C]-0.48[/C][/ROW]
[ROW][C]60[/C][C]4[/C][C]3.48[/C][C]0.52[/C][/ROW]
[ROW][C]61[/C][C]3[/C][C]3.48[/C][C]-0.48[/C][/ROW]
[ROW][C]62[/C][C]4[/C][C]3.48[/C][C]0.52[/C][/ROW]
[ROW][C]63[/C][C]3[/C][C]3.48[/C][C]-0.48[/C][/ROW]
[ROW][C]64[/C][C]4[/C][C]3.48[/C][C]0.52[/C][/ROW]
[ROW][C]65[/C][C]4[/C][C]3.48[/C][C]0.52[/C][/ROW]
[ROW][C]66[/C][C]4[/C][C]3.48[/C][C]0.52[/C][/ROW]
[ROW][C]67[/C][C]4[/C][C]3.48[/C][C]0.52[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=155414&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=155414&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
11313.1290322580645-0.129032258064516
2613.1290322580645-7.12903225806452
3313.1290322580645-10.1290322580645
4413.1290322580645-9.12903225806452
5413.1290322580645-9.12903225806452
6213.1290322580645-11.1290322580645
7113.1290322580645-12.1290322580645
8113.1290322580645-12.1290322580645
9413.1290322580645-9.12903225806452
10813.1290322580645-5.12903225806452
111513.12903225806451.87096774193548
121413.12903225806450.870967741935484
131828.7272727272727-10.7272727272727
141613.12903225806452.87096774193548
151513.12903225806451.87096774193548
161713.12903225806453.87096774193548
171513.12903225806451.87096774193548
181613.12903225806452.87096774193548
191713.12903225806453.87096774193548
202813.129032258064514.8709677419355
212813.129032258064514.8709677419355
225328.727272727272724.2727272727273
233028.72727272727271.27272727272727
244128.727272727272712.2727272727273
254628.727272727272717.2727272727273
264328.727272727272714.2727272727273
27413.1290322580645-9.12903225806452
282313.12903225806459.87096774193548
292513.129032258064511.8709677419355
301713.12903225806453.87096774193548
311413.12903225806450.870967741935484
321613.12903225806452.87096774193548
332613.129032258064512.8709677419355
341313.1290322580645-0.129032258064516
353428.72727272727275.27272727272727
362013.12903225806456.87096774193548
37628.7272727272727-22.7272727272727
38713.1290322580645-6.12903225806452
39428.7272727272727-24.7272727272727
401928.7272727272727-9.72727272727273
412228.7272727272727-6.72727272727273
421513.12903225806451.87096774193548
4333.48-0.48
4423.48-1.48
4533.48-0.48
4623.48-1.48
4763.482.52
4813.48-2.48
4943.480.52
5033.48-0.48
5143.480.52
5243.480.52
5343.480.52
5443.480.52
5543.480.52
5653.481.52
5723.48-1.48
5833.48-0.48
5933.48-0.48
6043.480.52
6133.48-0.48
6243.480.52
6333.48-0.48
6443.480.52
6543.480.52
6643.480.52
6743.480.52



Parameters (Session):
par1 = 3 ; par2 = none ; par3 = 2 ; par4 = no ;
Parameters (R input):
par1 = 3 ; 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')
}