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*The author of this computation has been verified*
R Software Module: /rwasp_regression_trees1.wasp (opens new window with default values)
Title produced by software: Recursive Partitioning (Regression Trees)
Date of computation: Wed, 22 Dec 2010 16:47:45 +0000
 
Cite this page as follows:
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2010/Dec/22/t1293036335a0asu3frhyri27a.htm/, Retrieved Wed, 22 Dec 2010 17:45:35 +0100
 
BibTeX entries for LaTeX users:
@Manual{KEY,
    author = {{YOUR NAME}},
    publisher = {Office for Research Development and Education},
    title = {Statistical Computations at FreeStatistics.org, URL http://www.freestatistics.org/blog/date/2010/Dec/22/t1293036335a0asu3frhyri27a.htm/},
    year = {2010},
}
@Manual{R,
    title = {R: A Language and Environment for Statistical Computing},
    author = {{R Development Core Team}},
    organization = {R Foundation for Statistical Computing},
    address = {Vienna, Austria},
    year = {2010},
    note = {{ISBN} 3-900051-07-0},
    url = {http://www.R-project.org},
}
 
Original text written by user:
 
IsPrivate?
No (this computation is public)
 
User-defined keywords:
 
Dataseries X:
» Textbox « » Textfile « » CSV «
3.567 2.217 1.061 3.536 2.176 1.041 3.538 2.146 1.021 3.638 2.297 1.000 3.635 2.332 0.929 3.646 2.352 1.000 3.552 2.230 1.000 3.557 2.204 0.903 3.489 2.352 1.000 3.375 1.978 1.176 3.443 1.987 1.190 3.264 1.663 1.312 3.427 1.940 1.243 3.376 1.978 1.243 3.349 2.053 1.097 3.664 2.332 1.146 3.641 2.301 1.176 3.675 2.286 1.267 3.328 1.944 1.161 3.348 1.978 1.146 3.522 2.000 1.190 3.517 2.000 1.190 3.624 2.217 1.079 3.612 2.176 1.114 3.676 2.230 1.079 3.711 2.243 1.079 3.382 1.857 1.279 3.516 2.000 1.176 3.346 1.934 1.146 3.327 1.954 1.146 3.315 1.881 1.161 3.249 1.813 1.279 3.263 1.778 1.279 3.291 1.845 1.312 3.328 1.903 1.230 3.348 1.934 1.217 3.631 2.217 1.079 3.616 2.176 1.130 3.616 2.185 1.114 3.666 2.318 1.041 3.653 2.190 1.130 3.646 2.279 1.097 3.367 1.987 1.130 3.613 2.114 1.146 3.633 2.146 1.204 3.467 2.049 1.161 3.400 1.881 1.255 3.379 1.934 1.204 3.399 1.987 1. etc...
 
Output produced by software:

Enter (or paste) a matrix (table) containing all data (time) series. Every column represents a different variable and must be delimited by a space or Tab. Every row represents a period in time (or category) and must be delimited by hard returns. The easiest way to enter data is to copy and paste a block of spreadsheet cells. Please, do not use commas or spaces to seperate groups of digits!


Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time17 seconds
R Server'Sir Ronald Aylmer Fisher' @ 193.190.124.24
R Framework
error message
Warning: there are blank lines in the 'Data X' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values.


Goodness of Fit
Correlation0.8401
R-squared0.7057
RMSE0.042


Actuals, Predictions, and Residuals
#ActualsForecastsResiduals
11.0611.046565217391300.0144347826086957
21.0411.07995454545455-0.0389545454545455
31.0211.07995454545455-0.0589545454545455
410.9794444444444440.0205555555555555
50.9290.979444444444444-0.0504444444444444
611.09741176470588-0.0974117647058823
711.04656521739130-0.0465652173913043
80.9031.04656521739130-0.143565217391304
910.9794444444444440.0205555555555555
101.1761.18881818181818-0.0128181818181818
111.191.189916666666678.33333333332131e-05
121.3121.32894444444444-0.0169444444444444
131.2431.210514705882350.0324852941176472
141.2431.188818181818180.0541818181818183
151.0971.12896-0.03196
161.1461.097411764705880.0485882352941176
171.1761.097411764705880.0785882352941176
181.2671.097411764705880.169588235294118
191.1611.21051470588235-0.0495147058823528
201.1461.18881818181818-0.0428181818181819
211.191.19414285714286-0.00414285714285723
221.191.19414285714286-0.00414285714285723
231.0791.046565217391300.0324347826086957
241.1141.12152-0.00751999999999997
251.0791.09741176470588-0.0184117647058823
261.0791.09741176470588-0.0184117647058823
271.2791.210514705882350.068485294117647
281.1761.19414285714286-0.0181428571428572
291.1461.21051470588235-0.064514705882353
301.1461.18881818181818-0.0428181818181819
311.1611.21051470588235-0.0495147058823528
321.2791.210514705882350.068485294117647
331.2791.24250.0365
341.3121.210514705882350.101485294117647
351.231.210514705882350.0194852941176471
361.2171.210514705882350.0064852941176472
371.0791.046565217391300.0324347826086957
381.131.121520.00847999999999982
391.1141.12152-0.00751999999999997
401.0411.09741176470588-0.0564117647058824
411.131.14836363636364-0.0183636363636364
421.0971.09741176470588-0.000411764705882334
431.131.16553333333333-0.0355333333333334
441.1461.16803571428571-0.0220357142857144
451.2041.168035714285710.0359642857142857
461.1611.18991666666667-0.0289166666666667
471.2551.210514705882350.044485294117647
481.2041.21051470588235-0.00651470588235292
491.1611.16553333333333-0.00453333333333328
501.1761.21051470588235-0.0345147058823529
511.1141.046565217391300.0674347826086958
521.0611.07995454545455-0.0189545454545454
531.1611.16803571428571-0.00703571428571426
541.0971.079954545454550.0170454545454546
551.0791.14836363636364-0.0693636363636363
561.1141.14836363636364-0.0343636363636362
571.0411.09741176470588-0.0564117647058824
581.0411.09741176470588-0.0564117647058824
591.2171.194142857142860.0228571428571429
601.2551.194142857142860.0608571428571427
611.2171.24097368421053-0.0239736842105263
621.2041.188818181818180.0151818181818182
631.1461.14836363636364-0.00236363636363635
641.0971.09741176470588-0.000411764705882334
651.1761.18991666666667-0.0139166666666668
661.291.210514705882350.0794852941176472
671.2171.188818181818180.0281818181818183
681.2671.210514705882350.056485294117647
691.1461.133769230769230.0122307692307690
701.1141.12152-0.00751999999999997
710.9780.979444444444444-0.00144444444444447
721.191.21051470588235-0.0205147058823529
731.1461.18881818181818-0.0428181818181819
741.0411.07995454545455-0.0389545454545455
751.1461.133769230769230.0122307692307690
761.0411.04656521739130-0.00556521739130433
771.2171.24097368421053-0.0239736842105263
781.2041.189916666666670.0140833333333332
791.2171.210514705882350.0064852941176472
801.3221.210514705882350.111485294117647
811.231.25714285714286-0.0271428571428571
821.2551.25714285714286-0.00214285714285722
831.1461.16803571428571-0.0220357142857144
841.1611.148363636363640.0126363636363638
851.2041.168035714285710.0359642857142857
861.191.148363636363640.0416363636363637
871.191.21051470588235-0.0205147058823529
881.1611.21051470588235-0.0495147058823528
891.2791.32894444444444-0.0499444444444446
901.1611.21051470588235-0.0495147058823528
911.1461.21051470588235-0.064514705882353
921.1761.165533333333330.0104666666666666
931.2041.21051470588235-0.00651470588235292
941.2041.24097368421053-0.0369736842105264
951.291.32169230769231-0.0316923076923077
961.0611.09741176470588-0.0364117647058824
971.1461.14836363636364-0.00236363636363635
981.131.14836363636364-0.0183636363636364
991.3221.257142857142860.064857142857143
1001.2791.257142857142860.0218571428571428
1011.2791.257142857142860.0218571428571428
1021.131.19414285714286-0.0641428571428573
1031.0791.12896-0.04996
1041.231.210514705882350.0194852941176471
1051.2041.189916666666670.0140833333333332
1061.131.16553333333333-0.0355333333333334
1071.2171.210514705882350.0064852941176472
1081.1611.19414285714286-0.0331428571428571
1091.1761.18881818181818-0.0128181818181818
1101.231.24097368421053-0.0109736842105264
1111.131.128960.00103999999999993
1121.2431.24250.000500000000000167
1131.2281.210514705882350.0174852941176471
1141.1731.18881818181818-0.0158181818181817
1151.1851.21051470588235-0.0255147058823528
1161.1141.16803571428571-0.0540357142857142
1171.1431.16803571428571-0.0250357142857143
1181.1071.12152-0.0145200000000001
1191.1611.19414285714286-0.0331428571428571
1201.2461.240973684210530.00502631578947366
1211.3461.328944444444440.0170555555555556
1221.3441.328944444444440.0150555555555556
1231.2481.25714285714286-0.00914285714285712
1241.211.25714285714286-0.0471428571428572
1251.251.240973684210530.00902631578947366
1261.231.210514705882350.0194852941176471
1271.2151.210514705882350.0044852941176472
1281.1961.194142857142860.00185714285714278
1291.1211.12152-0.000520000000000076
1301.2231.168035714285710.0549642857142858
1311.0831.09741176470588-0.0144117647058823
1321.1761.168035714285710.00796428571428565
1331.1461.079954545454550.0660454545454545
1341.171.21051470588235-0.0405147058823530
1351.271.24250.0275000000000001
1361.2251.210514705882350.0144852941176472
1371.0971.12152-0.0245200000000001
1381.1371.121520.0154799999999999
1391.2281.25714285714286-0.0291428571428571
1401.2481.25714285714286-0.00914285714285712
1411.0451.04656521739130-0.00156521739130433
1421.0571.046565217391300.0104347826086957
1431.1611.148363636363640.0126363636363638
1441.1611.21051470588235-0.0495147058823528
1451.261.210514705882350.0494852941176471
1461.1991.21051470588235-0.0115147058823528
1471.2011.21051470588235-0.00951470588235281
1481.2151.210514705882350.0044852941176472
1491.1611.18991666666667-0.0289166666666667
1501.1071.13376923076923-0.0267692307692309
1511.3321.328944444444440.00305555555555559
1521.1581.21051470588235-0.052514705882353
1531.271.210514705882350.0594852941176471
1541.1211.079954545454550.0410454545454546
1551.1071.079954545454550.0270454545454546
1561.261.240973684210530.0190263157894737
1571.1991.24097368421053-0.0419736842105263
1581.2361.25714285714286-0.0211428571428571
1591.2361.24097368421053-0.00497368421052635
1601.2231.24097368421053-0.0179736842105263
1611.2721.257142857142860.0148571428571429
1621.1211.079954545454550.0410454545454546
1631.1271.046565217391300.0804347826086957
1641.1371.16803571428571-0.0310357142857143
1651.2171.210514705882350.0064852941176472
1661.1671.165533333333330.00146666666666673
1671.1611.21051470588235-0.0495147058823528
1681.2461.210514705882350.0354852941176471
1691.2011.189916666666670.0110833333333333
1701.1341.128960.00503999999999993
1711.1991.128960.0700400000000001
1721.1731.21051470588235-0.0375147058823528
1731.221.210514705882350.0094852941176471
1741.261.240973684210530.0190263157894737
1751.2381.210514705882350.0274852941176471
1761.221.194142857142860.0258571428571428
1771.1881.168035714285710.0199642857142857
1781.1211.16803571428571-0.0470357142857143
1791.1821.168035714285710.0139642857142857
1801.1551.121520.0334800000000000
1811.1761.128960.04704
1821.1461.21051470588235-0.064514705882353
1831.1821.21051470588235-0.0285147058823529
1841.1761.21051470588235-0.0345147058823529
1851.3941.321692307692310.0723076923076922
1861.3461.257142857142860.088857142857143
1871.1731.21051470588235-0.0375147058823528
1881.2831.210514705882350.072485294117647
1891.2041.188818181818180.0151818181818182
1901.0531.12896-0.07596
1911.1211.18881818181818-0.0678181818181818
1921.1671.21051470588235-0.0435147058823528
1931.191.21051470588235-0.0205147058823529
1941.2151.210514705882350.0044852941176472
1951.2581.210514705882350.0474852941176471
1961.3031.240973684210530.0620263157894736
1971.1991.21051470588235-0.0115147058823528
1981.191.188818181818180.00118181818181817
1991.1761.18881818181818-0.0128181818181818
2001.1821.21051470588235-0.0285147058823529
2011.1581.18991666666667-0.0319166666666668
2021.2831.210514705882350.072485294117647
2031.2991.32169230769231-0.0226923076923078
2041.141.21051470588235-0.070514705882353
2051.1851.21051470588235-0.0255147058823528
2061.1791.21051470588235-0.0315147058823528
2071.1961.21051470588235-0.0145147058823529
2081.2151.210514705882350.0044852941176472
2091.11.13376923076923-0.0337692307692308
2101.1111.21051470588235-0.0995147058823529
2111.2151.210514705882350.0044852941176472
2121.2071.2425-0.0354999999999999
2131.2881.210514705882350.0774852941176472
2141.2381.210514705882350.0274852941176471
2151.1731.21051470588235-0.0375147058823528
2161.211.21051470588235-0.000514705882352917
2171.1521.21051470588235-0.058514705882353
2181.171.133769230769230.0362307692307691
2191.311.32169230769231-0.0116923076923077
2201.141.128960.0110399999999999
2211.1991.194142857142860.00485714285714289
2221.2331.24097368421053-0.00797368421052624
2231.221.25714285714286-0.0371428571428571
2241.271.210514705882350.0594852941176471
2251.2551.210514705882350.044485294117647
2261.2041.21051470588235-0.00651470588235292
2271.2551.188818181818180.0661818181818181
2281.1851.21051470588235-0.0255147058823528
2291.2461.210514705882350.0354852941176471
2301.1671.21051470588235-0.0435147058823528
2311.1611.21051470588235-0.0495147058823528
2321.1611.21051470588235-0.0495147058823528
2331.1961.21051470588235-0.0145147058823529
2341.2151.189916666666670.0250833333333333
2351.231.24097368421053-0.0109736842105264
2361.1431.16553333333333-0.0225333333333333
2371.2381.24097368421053-0.00297368421052635
2381.1931.21051470588235-0.0175147058823528
2391.0641.21051470588235-0.146514705882353
2401.271.210514705882350.0594852941176471
2411.2551.210514705882350.044485294117647
2421.231.2425-0.0125000000000000
2431.231.2425-0.0125000000000000
2441.2041.2425-0.0385
2451.2791.210514705882350.068485294117647
2461.2551.24250.0125000000000000
2471.231.2425-0.0125000000000000
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2491.2041.2425-0.0385
2501.0791.21051470588235-0.131514705882353
2511.2791.24250.0365
2521.2791.24250.0365
2531.3221.32894444444444-0.00694444444444442
2541.3221.210514705882350.111485294117647
2551.1761.21051470588235-0.0345147058823529
2561.1461.21051470588235-0.064514705882353
2571.3011.32894444444444-0.0279444444444445
2581.2041.21051470588235-0.00651470588235292
2591.1461.21051470588235-0.064514705882353
2601.1461.21051470588235-0.064514705882353
2611.1461.21051470588235-0.064514705882353
2621.1461.21051470588235-0.064514705882353
2631.1761.21051470588235-0.0345147058823529
2641.231.210514705882350.0194852941176471
2651.3221.32894444444444-0.00694444444444442
2661.2791.210514705882350.068485294117647
2671.3221.210514705882350.111485294117647
2681.2041.21051470588235-0.00651470588235292
2691.1761.21051470588235-0.0345147058823529
2701.2041.21051470588235-0.00651470588235292
2711.2791.32894444444444-0.0499444444444446
2721.2551.210514705882350.044485294117647
2731.1761.21051470588235-0.0345147058823529
2741.2791.210514705882350.068485294117647
2751.1761.21051470588235-0.0345147058823529
2761.3421.328944444444440.0130555555555556
2771.2041.21051470588235-0.00651470588235292
2781.2791.32894444444444-0.0499444444444446
2791.2041.21051470588235-0.00651470588235292
2801.1761.21051470588235-0.0345147058823529
2811.231.210514705882350.0194852941176471
2821.2041.21051470588235-0.00651470588235292
2831.2041.21051470588235-0.00651470588235292
2841.2791.32894444444444-0.0499444444444446
2851.2041.21051470588235-0.00651470588235292
2861.2791.210514705882350.068485294117647
2871.2551.210514705882350.044485294117647
2881.3421.328944444444440.0130555555555556
2891.2041.21051470588235-0.00651470588235292
2901.2791.210514705882350.068485294117647
2911.231.210514705882350.0194852941176471
2921.231.210514705882350.0194852941176471
2931.1761.21051470588235-0.0345147058823529
2941.2551.210514705882350.044485294117647
2951.2791.210514705882350.068485294117647
2961.3011.210514705882350.090485294117647
2971.2041.21051470588235-0.00651470588235292
2981.3421.328944444444440.0130555555555556
2991.231.210514705882350.0194852941176471
3001.2041.21051470588235-0.00651470588235292
3011.2551.210514705882350.044485294117647
3021.2791.210514705882350.068485294117647
3031.2041.21051470588235-0.00651470588235292
3041.1461.18881818181818-0.0428181818181819
3051.1461.18881818181818-0.0428181818181819
3061.1761.21051470588235-0.0345147058823529
3071.1761.21051470588235-0.0345147058823529
3081.231.210514705882350.0194852941176471
3091.231.210514705882350.0194852941176471
3101.1761.21051470588235-0.0345147058823529
3111.3981.328944444444440.0690555555555554
3121.1761.21051470588235-0.0345147058823529
3131.1761.21051470588235-0.0345147058823529
3141.231.210514705882350.0194852941176471
3151.1761.21051470588235-0.0345147058823529
3161.2551.210514705882350.044485294117647
3171.1761.21051470588235-0.0345147058823529
3181.1761.21051470588235-0.0345147058823529
3191.231.210514705882350.0194852941176471
3201.2041.21051470588235-0.00651470588235292
3211.2041.21051470588235-0.00651470588235292
3221.1761.21051470588235-0.0345147058823529
3231.1761.21051470588235-0.0345147058823529
3241.3421.328944444444440.0130555555555556
3251.2041.21051470588235-0.00651470588235292
3261.2041.21051470588235-0.00651470588235292
3271.2551.210514705882350.044485294117647
3281.1461.21051470588235-0.064514705882353
3291.1461.21051470588235-0.064514705882353
3301.1141.21051470588235-0.0965147058823528
3311.1761.21051470588235-0.0345147058823529
3321.1761.21051470588235-0.0345147058823529
3331.1461.21051470588235-0.064514705882353
3341.1761.21051470588235-0.0345147058823529
3351.231.210514705882350.0194852941176471
3361.1461.21051470588235-0.064514705882353
3371.231.210514705882350.0194852941176471
3381.231.210514705882350.0194852941176471
3391.231.210514705882350.0194852941176471
3401.1461.18881818181818-0.0428181818181819
3411.1761.21051470588235-0.0345147058823529
3421.1141.12896-0.0149599999999999
3431.231.210514705882350.0194852941176471
3441.1461.21051470588235-0.064514705882353
3451.381.328944444444440.0510555555555554
3461.2551.210514705882350.044485294117647
3471.2041.188818181818180.0151818181818182
3481.2041.188818181818180.0151818181818182
3491.2551.210514705882350.044485294117647
3501.1761.21051470588235-0.0345147058823529
3511.2041.188818181818180.0151818181818182
3521.2791.210514705882350.068485294117647
3531.231.188818181818180.0411818181818182
3541.231.210514705882350.0194852941176471
3551.0791.21051470588235-0.131514705882353
3561.2041.165533333333330.0384666666666666
3571.1761.21051470588235-0.0345147058823529
3581.1761.165533333333330.0104666666666666
3591.2791.210514705882350.068485294117647
3601.1461.16553333333333-0.0195333333333334
3611.381.328944444444440.0510555555555554
3621.231.210514705882350.0194852941176471
3631.1141.21051470588235-0.0965147058823528
3641.1761.18881818181818-0.0128181818181818
3651.2551.188818181818180.0661818181818181
3661.231.188818181818180.0411818181818182
3671.3221.210514705882350.111485294117647
3681.1141.21051470588235-0.0965147058823528
3691.2041.188818181818180.0151818181818182
3701.2041.21051470588235-0.00651470588235292
3711.2551.210514705882350.044485294117647
3721.3011.210514705882350.090485294117647
3731.1761.165533333333330.0104666666666666
3741.2791.210514705882350.068485294117647
3751.3011.188818181818180.112181818181818
3761.1141.13376923076923-0.0197692307692308
3771.1761.18881818181818-0.0128181818181818
3781.1761.18881818181818-0.0128181818181818
3791.231.210514705882350.0194852941176471
3801.2041.21051470588235-0.00651470588235292
3811.1461.133769230769230.0122307692307690
3821.1761.165533333333330.0104666666666666
3831.2041.21051470588235-0.00651470588235292
3841.1761.21051470588235-0.0345147058823529
3851.1761.165533333333330.0104666666666666
3861.2551.210514705882350.044485294117647
3871.1761.18881818181818-0.0128181818181818
3881.2041.21051470588235-0.00651470588235292
3891.231.210514705882350.0194852941176471
3901.2551.210514705882350.044485294117647
3911.231.165533333333330.0644666666666667
3921.1141.18881818181818-0.0748181818181817
3931.1461.18881818181818-0.0428181818181819
3941.1461.21051470588235-0.064514705882353
3951.1761.18881818181818-0.0128181818181818
3961.2041.21051470588235-0.00651470588235292
3971.1461.18881818181818-0.0428181818181819
3981.1761.18881818181818-0.0128181818181818
3991.2041.21051470588235-0.00651470588235292
4001.2791.210514705882350.068485294117647
4011.0411.12896-0.08796
4021.1141.13376923076923-0.0197692307692308
4031.3011.210514705882350.090485294117647
4041.1761.133769230769230.0422307692307691
4051.1461.18881818181818-0.0428181818181819
4061.2791.210514705882350.068485294117647
4071.1141.13376923076923-0.0197692307692308
4081.2041.21051470588235-0.00651470588235292
4091.2551.210514705882350.044485294117647
4101.231.210514705882350.0194852941176471
4111.2791.210514705882350.068485294117647
4121.1761.18881818181818-0.0128181818181818
4131.1461.133769230769230.0122307692307690
4141.1461.16553333333333-0.0195333333333334
4151.1761.21051470588235-0.0345147058823529
4161.2041.188818181818180.0151818181818182
4171.1461.128960.0170399999999999
4181.2551.210514705882350.044485294117647
4191.231.188818181818180.0411818181818182
4201.1761.18881818181818-0.0128181818181818
4211.1141.12896-0.0149599999999999
4221.1461.16553333333333-0.0195333333333334
4231.2041.188818181818180.0151818181818182
4241.1761.21051470588235-0.0345147058823529
4251.2791.210514705882350.068485294117647
4261.1461.133769230769230.0122307692307690
4271.1141.13376923076923-0.0197692307692308
4281.2551.188818181818180.0661818181818181
4291.2041.21051470588235-0.00651470588235292
4301.231.189916666666670.0400833333333332
4311.2041.21051470588235-0.00651470588235292
4321.2041.189916666666670.0140833333333332
4331.1761.18991666666667-0.0139166666666668
4341.2041.21051470588235-0.00651470588235292
4351.2041.128960.07504
4361.1461.18991666666667-0.0439166666666668
4371.1461.128960.0170399999999999
4381.1761.18991666666667-0.0139166666666668
4391.2041.189916666666670.0140833333333332
4401.2041.188818181818180.0151818181818182
4411.1761.18991666666667-0.0139166666666668
4421.2551.210514705882350.044485294117647
4431.2041.188818181818180.0151818181818182
4441.2041.189916666666670.0140833333333332
4451.2551.210514705882350.044485294117647
4461.231.210514705882350.0194852941176471
4471.1141.12896-0.0149599999999999
4481.2041.189916666666670.0140833333333332
4491.2041.188818181818180.0151818181818182
4501.0411.12896-0.08796
4511.2041.189916666666670.0140833333333332
4521.2041.189916666666670.0140833333333332
4531.1461.128960.0170399999999999
4541.1761.18991666666667-0.0139166666666668
4551.2041.189916666666670.0140833333333332
4561.2041.189916666666670.0140833333333332
4571.1761.18991666666667-0.0139166666666668
4581.231.24097368421053-0.0109736842105264
4591.3011.32169230769231-0.0206923076923078
4601.231.24097368421053-0.0109736842105264
4611.1461.19414285714286-0.0481428571428573
4621.2041.24097368421053-0.0369736842105264
4631.3011.240973684210530.0600263157894736
4641.1761.19414285714286-0.0181428571428572
4651.2551.240973684210530.0140263157894736
4661.3011.240973684210530.0600263157894736
4671.2551.240973684210530.0140263157894736
4681.231.24097368421053-0.0109736842105264
4691.231.24097368421053-0.0109736842105264
4701.1761.19414285714286-0.0181428571428572
4711.231.24097368421053-0.0109736842105264
4721.231.24097368421053-0.0109736842105264
4731.2551.240973684210530.0140263157894736
47410.9794444444444440.0205555555555555
4751.231.24097368421053-0.0109736842105264
4761.231.194142857142860.0358571428571428
4771.1761.24097368421053-0.0649736842105264
4781.1461.128960.0170399999999999
4791.2041.194142857142860.00985714285714279
4801.2551.240973684210530.0140263157894736
4811.3011.32169230769231-0.0206923076923078
4821.3011.32169230769231-0.0206923076923078
4831.0791.12896-0.04996
4841.3981.321692307692310.0763076923076922
4851.2551.240973684210530.0140263157894736
4861.0411.07995454545455-0.0389545454545455
4871.231.24097368421053-0.0109736842105264
4881.231.24097368421053-0.0109736842105264
4891.1461.19414285714286-0.0481428571428573
4901.3011.32169230769231-0.0206923076923078
4911.1761.19414285714286-0.0181428571428572
4921.1761.128960.04704
4931.3421.321692307692310.0203076923076924
4941.2041.24097368421053-0.0369736842105264
4951.2551.240973684210530.0140263157894736
4961.3421.240973684210530.101026315789474
4971.2551.194142857142860.0608571428571427
4981.1761.19414285714286-0.0181428571428572
4991.2041.194142857142860.00985714285714279
5001.2041.24097368421053-0.0369736842105264
5011.2041.194142857142860.00985714285714279
5021.231.194142857142860.0358571428571428
5031.1761.19414285714286-0.0181428571428572
5041.231.194142857142860.0358571428571428
5051.2041.194142857142860.00985714285714279
5061.2041.194142857142860.00985714285714279
5071.2791.240973684210530.0380263157894736
5081.0411.07995454545455-0.0389545454545455
5091.1761.128960.04704
5101.2041.128960.07504
5111.2041.194142857142860.00985714285714279
5121.3421.257142857142860.084857142857143
5131.231.25714285714286-0.0271428571428571
5141.1141.079954545454550.0340454545454547
5151.231.25714285714286-0.0271428571428571
5161.3221.321692307692310.000307692307692342
5171.0791.07995454545455-0.00095454545454543
5181.0411.07995454545455-0.0389545454545455
5191.2041.25714285714286-0.0531428571428572
5201.1141.046565217391300.0674347826086958
5211.0411.07995454545455-0.0389545454545455
5221.2041.25714285714286-0.0531428571428572
5231.231.25714285714286-0.0271428571428571
5241.0791.12896-0.04996
5251.2041.25714285714286-0.0531428571428572
5261.2791.257142857142860.0218571428571428
5271.3011.32169230769231-0.0206923076923078
5281.2791.257142857142860.0218571428571428
52911.04656521739130-0.0465652173913043
5301.1141.079954545454550.0340454545454547
5311.3221.321692307692310.000307692307692342
5321.1761.128960.04704
5330.9031.04656521739130-0.143565217391304
5341.231.25714285714286-0.0271428571428571
5351.2791.257142857142860.0218571428571428
5361.2551.25714285714286-0.00214285714285722
5371.2551.25714285714286-0.00214285714285722
5381.2041.25714285714286-0.0531428571428572
5391.2551.25714285714286-0.00214285714285722
5401.0411.04656521739130-0.00556521739130433
5411.0791.07995454545455-0.00095454545454543
5421.0791.07995454545455-0.00095454545454543
5431.0791.046565217391300.0324347826086957
5441.1141.12896-0.0149599999999999
5451.2791.257142857142860.0218571428571428
5461.2791.257142857142860.0218571428571428
5471.1141.079954545454550.0340454545454547
5481.1461.079954545454550.0660454545454545
54911.07995454545455-0.0799545454545454
5501.1141.079954545454550.0340454545454547
5511.231.25714285714286-0.0271428571428571
5521.2791.257142857142860.0218571428571428
5531.231.168035714285710.0619642857142857
5541.0411.04656521739130-0.00556521739130433
5551.1761.168035714285710.00796428571428565
5561.1761.168035714285710.00796428571428565
5570.9541.04656521739130-0.0925652173913043
5581.1761.168035714285710.00796428571428565
5591.1141.12152-0.00751999999999997
5601.1141.12152-0.00751999999999997
5611.2791.257142857142860.0218571428571428
5621.231.168035714285710.0619642857142857
5631.3221.257142857142860.064857142857143
5641.1141.12152-0.00751999999999997
5651.1141.12152-0.00751999999999997
5661.1141.16803571428571-0.0540357142857142
5671.1461.16803571428571-0.0220357142857144
5681.1141.12152-0.00751999999999997
5691.1761.168035714285710.00796428571428565
5701.1461.121520.0244799999999998
5711.0791.12152-0.0425200000000001
5721.2791.257142857142860.0218571428571428
5731.1461.121520.0244799999999998
5741.1461.16803571428571-0.0220357142857144
5751.1141.12152-0.00751999999999997
5761.1141.12152-0.00751999999999997
5771.1461.16803571428571-0.0220357142857144
5781.1141.046565217391300.0674347826086958
5791.1141.12152-0.00751999999999997
5801.1461.121520.0244799999999998
5811.1461.121520.0244799999999998
5821.1461.16803571428571-0.0220357142857144
5831.1461.121520.0244799999999998
5841.0411.04656521739130-0.00556521739130433
5851.1141.12152-0.00751999999999997
5861.0791.046565217391300.0324347826086957
5871.1141.16803571428571-0.0540357142857142
5881.1141.12152-0.00751999999999997
5891.0411.04656521739130-0.00556521739130433
5901.1761.148363636363640.0276363636363637
5911.2041.148363636363640.0556363636363637
5921.0791.046565217391300.0324347826086957
59310.9794444444444440.0205555555555555
5941.2041.168035714285710.0359642857142857
5951.1761.168035714285710.00796428571428565
5960.9540.979444444444444-0.0254444444444445
5971.0791.046565217391300.0324347826086957
5981.1761.148363636363640.0276363636363637
59910.9794444444444440.0205555555555555
6000.9540.979444444444444-0.0254444444444445
6011.1761.148363636363640.0276363636363637
6021.1141.14836363636364-0.0343636363636362
6031.1761.097411764705880.0785882352941176
6041.0791.09741176470588-0.0184117647058823
6051.1461.097411764705880.0485882352941176
6061.0791.09741176470588-0.0184117647058823
6071.1141.097411764705880.0165882352941178
60811.09741176470588-0.0974117647058823
6091.1461.14836363636364-0.00236363636363635
6101.1461.097411764705880.0485882352941176
6111.1461.14836363636364-0.00236363636363635
6121.0791.09741176470588-0.0184117647058823
6131.0791.14836363636364-0.0693636363636363
6141.1761.148363636363640.0276363636363637
6151.1141.097411764705880.0165882352941178
6161.1461.14836363636364-0.00236363636363635
6171.1461.097411764705880.0485882352941176
6181.0411.09741176470588-0.0564117647058824
6191.2041.168035714285710.0359642857142857
6201.1141.097411764705880.0165882352941178
6211.1461.14836363636364-0.00236363636363635
6221.0791.09741176470588-0.0184117647058823
6231.1761.148363636363640.0276363636363637
6241.2791.097411764705880.181588235294118
6251.0411.09741176470588-0.0564117647058824
6261.0791.09741176470588-0.0184117647058823
6271.1141.097411764705880.0165882352941178
6281.0411.09741176470588-0.0564117647058824
6291.0791.09741176470588-0.0184117647058823
6301.0791.09741176470588-0.0184117647058823
6311.1461.14836363636364-0.00236363636363635
6321.0791.09741176470588-0.0184117647058823
 
Charts produced by software:
http://www.freestatistics.org/blog/date/2010/Dec/22/t1293036335a0asu3frhyri27a/2n2xk1293036445.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/22/t1293036335a0asu3frhyri27a/2n2xk1293036445.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/22/t1293036335a0asu3frhyri27a/3gbwn1293036445.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/22/t1293036335a0asu3frhyri27a/3gbwn1293036445.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/22/t1293036335a0asu3frhyri27a/4q3wq1293036445.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/22/t1293036335a0asu3frhyri27a/4q3wq1293036445.ps (open in new window)


 
Parameters (Session):
par1 = 5 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
 
Parameters (R input):
par1 = 3 ; 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')
}
 





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Software written by Ed van Stee & Patrick Wessa


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