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WS10 - PR

*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: Fri, 10 Dec 2010 14:56:00 +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/10/t1291992835arvf93eds1aqwjw.htm/, Retrieved Fri, 10 Dec 2010 15:53:55 +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/10/t1291992835arvf93eds1aqwjw.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 «
3.66356 7.74414 -4.4 4.2 0 18 19 116 3.04452 8.03398 -5.7 4.8 -0.3 69.1 9 506 3.71357 4.70048 -13.5 4.3 0.2 80 3 95 2.94444 7.5251 1.4 3 0.1 177 22 161 4.06044 7.7626 4.1 5.6 1.1 287 7 80 3.68888 7.88683 5.8 2.3 -0.1 200 9 33 3.3322 7.81521 2.7 1.9 0.4 228 7 129 3.3673 7.77779 7.1 8.9 0.2 220 15 155 2.07944 6.89163 4.1 2 0.1 183 9 132 1.94591 7.6774 1.1 5.2 0.1 43.1 10 480 3.3322 5.71373 -9 3.4 0 80 6 98 3.21888 8.33471 1.6 4.4 -0.3 78.1 16 558 1.09861 4.65396 -0.4 5.7 0.8 267 3 121 5.3845 7.38895 -4.8 6 0 81 20 122 3.93183 7.81763 -0.9 1.4 -0.3 236 15 51 3.3673 4.96981 -0.5 0.6 1.7 341.1 4 552 2.83321 7.80384 -1 1.8 0 16 7 150 3.04452 7.62168 -2.3 5.2 -0.1 66.1 12 428 2.99573 5.21494 5.8 2.6 -0.2 68.8 3 582 4.21951 7.64204 5.1 2.2 1.1 199 18 538 3.04452 7.77149 10.8 2.4 -1.5 329.8 13 579 3.7612 8.31385 12.2 4 -2.8 230.4 17 572 3.58352 7.63964 -2.8 2.1 -1.6 210.2 11 512 2.48491 7.05531 11.2 3.3 2.3 302.8 23 604 2.48491 7.38275 13.6 0.6 -0.8 159.8 11 602 3.3322 7.56786 -12.3 3.7 -0.2 7 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 time20 seconds
R Server'Sir Ronald Aylmer Fisher' @ 193.190.124.24


Goodness of Fit
Correlation0.4739
R-squared0.2245
RMSE0.7801


Actuals, Predictions, and Residuals
#ActualsForecastsResiduals
13.663563.523634723127040.139925276872964
23.044523.52363472312704-0.479114723127036
33.713572.767722881355930.945847118644068
42.944443.52363472312704-0.579194723127036
54.060443.523634723127040.536805276872964
63.688883.523634723127040.165245276872964
73.33223.52363472312704-0.191434723127036
83.36733.52363472312704-0.156334723127036
92.079442.76772288135593-0.688282881355932
101.945913.52363472312704-1.57772472312704
113.33222.767722881355930.564477118644068
123.218883.52363472312704-0.304754723127036
131.098612.10102692307692-1.00241692307692
145.38453.523634723127041.86086527687296
153.931833.523634723127040.408195276872964
163.36733.52021857142857-0.152918571428571
172.833213.52363472312704-0.690424723127036
183.044523.52363472312704-0.479114723127036
192.995732.767722881355930.228007118644068
204.219513.523634723127040.695875276872964
213.044523.52363472312704-0.479114723127036
223.76123.523634723127040.237565276872964
233.583523.523634723127040.0598852768729641
242.484912.76772288135593-0.282812881355932
252.484913.52363472312704-1.03872472312704
263.33223.52363472312704-0.191434723127036
273.135493.52363472312704-0.388144723127036
282.079443.52363472312704-1.44419472312704
293.688883.523634723127040.165245276872964
302.995732.767722881355930.228007118644068
313.988983.523634723127040.465345276872964
323.044522.101026923076920.943493076923077
333.555353.523634723127040.0317152768729638
341.791763.52363472312704-1.73187472312704
353.044522.767722881355930.276797118644068
362.564953.52363472312704-0.958684723127036
374.369453.523634723127040.845815276872964
383.465743.52363472312704-0.0578947231270361
394.605173.523634723127041.08153527687296
403.091043.52363472312704-0.432594723127036
414.158883.523634723127040.635245276872964
421.791763.52363472312704-1.73187472312704
432.484913.52021857142857-1.03530857142857
443.218883.52363472312704-0.304754723127036
453.737672.767722881355930.969947118644068
464.24852.767722881355931.48077711864407
473.87123.520218571428570.350981428571429
484.007333.523634723127040.483695276872964
492.944442.767722881355930.176717118644068
503.091042.767722881355930.323317118644068
511.945912.76772288135593-0.821812881355932
523.526363.523634723127040.00272527687296398
533.87123.523634723127040.347565276872964
542.995733.52363472312704-0.527904723127036
554.077543.523634723127040.553905276872964
563.218883.52363472312704-0.304754723127036
573.496513.52363472312704-0.0271247231270362
583.25812.767722881355930.490377118644068
592.995733.52363472312704-0.527904723127036
602.708052.76772288135593-0.0596728813559322
613.091043.52021857142857-0.429178571428571
624.060443.523634723127040.536805276872964
633.610923.523634723127040.0872852768729642
642.564952.76772288135593-0.202772881355932
651.609442.10102692307692-0.491586923076923
665.08763.523634723127041.56396527687296
672.302593.52363472312704-1.22104472312704
683.25813.52363472312704-0.265534723127036
693.663562.767722881355930.895837118644068
703.044523.52363472312704-0.479114723127036
713.40123.52363472312704-0.122434723127036
723.044523.52021857142857-0.475698571428572
733.40123.52363472312704-0.122434723127036
743.555353.520218571428570.0351314285714284
753.970293.523634723127040.446655276872964
764.727393.523634723127041.20375527687296
774.304073.523634723127040.780435276872964
782.564953.52363472312704-0.958684723127036
793.784193.523634723127040.260555276872964
804.955833.520218571428571.43561142857143
813.663563.523634723127040.139925276872964
825.267863.523634723127041.74422527687296
833.465742.767722881355930.698017118644068
842.079442.76772288135593-0.688282881355932
853.36733.52363472312704-0.156334723127036
863.295843.52363472312704-0.227794723127036
872.197223.52363472312704-1.32641472312704
883.433993.52363472312704-0.0896447231270359
893.25813.52021857142857-0.262118571428571
902.833213.52363472312704-0.690424723127036
913.637593.523634723127040.113955276872964
922.708053.52363472312704-0.815584723127036
932.995733.52363472312704-0.527904723127036
944.007333.523634723127040.483695276872964
953.433992.767722881355930.666267118644068
960.693152.76772288135593-2.07457288135593
973.526363.523634723127040.00272527687296398
982.639063.52363472312704-0.884574723127036
991.945913.52363472312704-1.57772472312704
1003.713573.523634723127040.189935276872964
1012.833212.767722881355930.0654871186440675
1023.433993.52363472312704-0.0896447231270359
1033.295843.52363472312704-0.227794723127036
1043.178053.52363472312704-0.345584723127036
1054.812183.523634723127041.28854527687296
1064.634733.523634723127041.11109527687296
1073.044522.767722881355930.276797118644068
1083.87123.520218571428570.350981428571429
1093.688883.523634723127040.165245276872964
1104.330733.523634723127040.807095276872964
1114.615123.523634723127041.09148527687296
1124.430823.523634723127040.907185276872964
1133.40122.767722881355930.633477118644068
1142.484913.52363472312704-1.03872472312704
1153.295843.52363472312704-0.227794723127036
1162.944443.52363472312704-0.579194723127036
1173.36733.52363472312704-0.156334723127036
1182.890373.52363472312704-0.633264723127036
1194.905273.523634723127041.38163527687296
1202.639062.76772288135593-0.128662881355932
1213.295843.52363472312704-0.227794723127036
1223.713572.767722881355930.945847118644068
1234.394453.523634723127040.870815276872964
1243.828643.523634723127040.305005276872964
1252.564952.76772288135593-0.202772881355932
1263.33223.52363472312704-0.191434723127036
1273.135492.767722881355930.367767118644068
1280.693152.10102692307692-1.40787692307692
1294.007333.523634723127040.483695276872964
1303.25813.52363472312704-0.265534723127036
1313.25812.767722881355930.490377118644068
1324.844193.523634723127041.32055527687296
1332.639062.76772288135593-0.128662881355932
1341.386292.10102692307692-0.714736923076923
1353.25812.767722881355930.490377118644068
1362.302593.52363472312704-1.22104472312704
1373.091042.767722881355930.323317118644068
1382.944443.52363472312704-0.579194723127036
1393.40122.767722881355930.633477118644068
1402.995732.101026923076920.894703076923077
1413.828643.523634723127040.305005276872964
1422.302593.52363472312704-1.22104472312704
1434.290463.523634723127040.766825276872964
1444.418843.523634723127040.895205276872964
1453.36733.52363472312704-0.156334723127036
1462.302592.101026923076920.201563076923077
1473.988983.523634723127040.465345276872964
1484.127132.767722881355931.35940711864407
1493.688883.520218571428570.168661428571429
1502.302592.101026923076920.201563076923077
1513.295843.52363472312704-0.227794723127036
1523.135492.767722881355930.367767118644068
1532.079442.76772288135593-0.688282881355932
1545.252273.523634723127041.72863527687296
1553.784193.523634723127040.260555276872964
1563.970293.520218571428570.450071428571428
1572.995732.767722881355930.228007118644068
1582.564953.52021857142857-0.955268571428571
1593.218882.767722881355930.451157118644068
1601.386293.52363472312704-2.13734472312704
1613.135493.52363472312704-0.388144723127036
1624.060443.523634723127040.536805276872964
1634.276673.523634723127040.753035276872964
1643.496513.52363472312704-0.0271247231270362
1653.218883.52363472312704-0.304754723127036
1664.077543.523634723127040.553905276872964
1674.204693.523634723127040.681055276872964
1684.189653.520218571428570.669431428571429
1692.484912.76772288135593-0.282812881355932
1701.098612.76772288135593-1.66911288135593
1712.484913.52363472312704-1.03872472312704
1722.484913.52363472312704-1.03872472312704
1730.693153.52363472312704-2.83048472312704
1744.343813.523634723127040.820175276872964
1752.639062.76772288135593-0.128662881355932
1763.295843.52363472312704-0.227794723127036
1774.174393.523634723127040.650755276872964
1783.713573.523634723127040.189935276872964
1794.025353.523634723127040.501715276872964
1804.852033.523634723127041.32839527687296
1814.644393.520218571428571.12417142857143
1823.637593.523634723127040.113955276872964
1833.40123.52363472312704-0.122434723127036
1841.791762.76772288135593-0.975962881355932
1854.110873.523634723127040.587235276872964
1863.36733.52363472312704-0.156334723127036
1874.442653.523634723127040.919015276872964
1882.772593.52363472312704-0.751044723127036
1893.713572.767722881355930.945847118644068
1905.170483.523634723127041.64684527687296
1913.091043.52363472312704-0.432594723127036
1922.708053.52363472312704-0.815584723127036
1934.110873.523634723127040.587235276872964
1944.795793.523634723127041.27215527687296
1952.39793.52021857142857-1.12231857142857
1962.995732.767722881355930.228007118644068
1973.135493.52363472312704-0.388144723127036
1983.433993.52363472312704-0.0896447231270359
1993.40123.52363472312704-0.122434723127036
2003.091043.52363472312704-0.432594723127036
2014.454353.523634723127040.930715276872964
2024.189653.523634723127040.666015276872964
2033.737673.523634723127040.214035276872964
2044.204693.520218571428570.684471428571429
2052.564952.76772288135593-0.202772881355932
2062.564953.52363472312704-0.958684723127036
2074.859813.523634723127041.33617527687296
2085.252273.523634723127041.72863527687296
2092.639062.76772288135593-0.128662881355932
2103.135492.767722881355930.367767118644068
2111.945912.76772288135593-0.821812881355932
2123.40123.52021857142857-0.119018571428572
2134.727393.523634723127041.20375527687296
2143.36732.767722881355930.599577118644068
2153.25813.52363472312704-0.265534723127036
2163.737673.523634723127040.214035276872964
2171.791763.52363472312704-1.73187472312704
2184.060443.523634723127040.536805276872964
2193.912023.523634723127040.388385276872964
2200.693152.76772288135593-2.07457288135593
2211.609442.76772288135593-1.15828288135593
2224.465913.523634723127040.942275276872964
2233.178053.52363472312704-0.345584723127036
2242.890373.52021857142857-0.629848571428572
2254.174393.523634723127040.650755276872964
2263.737673.523634723127040.214035276872964
2274.007333.523634723127040.483695276872964
2283.044523.52363472312704-0.479114723127036
2292.639063.52363472312704-0.884574723127036
2304.110873.523634723127040.587235276872964
2314.477343.523634723127040.953705276872964
2323.178053.52363472312704-0.345584723127036
2333.33223.52363472312704-0.191434723127036
2340.693152.76772288135593-2.07457288135593
2354.584973.523634723127041.06133527687296
2360.693152.10102692307692-1.40787692307692
2373.40123.52363472312704-0.122434723127036
2382.079443.52363472312704-1.44419472312704
2394.553883.523634723127041.03024527687296
2403.218882.767722881355930.451157118644068
2412.772592.101026923076920.671563076923077
2423.295842.767722881355930.528117118644068
2433.33223.52363472312704-0.191434723127036
2444.025353.523634723127040.501715276872964
2454.382033.523634723127040.858395276872964
2462.995733.52021857142857-0.524488571428571
2473.555353.523634723127040.0317152768729638
2483.25813.52363472312704-0.265534723127036
2494.477343.523634723127040.953705276872964
2504.060443.520218571428570.540221428571428
2513.218883.52363472312704-0.304754723127036
2524.465913.520218571428570.945691428571429
2533.135492.767722881355930.367767118644068
2542.302592.101026923076920.201563076923077
2553.610923.520218571428570.0907014285714287
2563.135493.52021857142857-0.384728571428572
2573.891823.523634723127040.368185276872964
2583.218883.52363472312704-0.304754723127036
2593.178052.767722881355930.410327118644068
2603.737673.523634723127040.214035276872964
2612.39793.52363472312704-1.12573472312704
2623.688883.523634723127040.165245276872964
2634.262683.523634723127040.739045276872964
2644.941643.523634723127041.41800527687296
2653.295843.52363472312704-0.227794723127036
2662.079442.76772288135593-0.688282881355932
2672.197223.52363472312704-1.32641472312704
2682.079443.52021857142857-1.44077857142857
2694.543293.523634723127041.01965527687296
2702.079443.52363472312704-1.44419472312704
2714.369453.523634723127040.845815276872964
2723.970293.523634723127040.446655276872964
2733.988983.520218571428570.468761428571429
2742.995733.52363472312704-0.527904723127036
2753.526362.767722881355930.758637118644068
2762.995733.52363472312704-0.527904723127036
2775.393633.523634723127041.86999527687296
2783.637593.523634723127040.113955276872964
2792.302592.76772288135593-0.465132881355932
2804.927253.523634723127041.40361527687296
2812.944443.52363472312704-0.579194723127036
2822.708053.52363472312704-0.815584723127036
2832.079443.52363472312704-1.44419472312704
2843.40123.52363472312704-0.122434723127036
2855.056253.523634723127041.53261527687296
2864.934473.523634723127041.41083527687296
2872.564953.52363472312704-0.958684723127036
2883.044523.52363472312704-0.479114723127036
2891.386292.76772288135593-1.38143288135593
2902.772593.52363472312704-0.751044723127036
2913.912023.523634723127040.388385276872964
2924.356713.523634723127040.833075276872964
2933.40123.52363472312704-0.122434723127036
2944.025353.523634723127040.501715276872964
2953.737673.523634723127040.214035276872964
2962.944443.52363472312704-0.579194723127036
2974.094343.520218571428570.574121428571428
2983.044523.52021857142857-0.475698571428572
2993.178053.52363472312704-0.345584723127036
3002.564953.52363472312704-0.958684723127036
3012.639062.101026923076920.538033076923077
3023.091043.52363472312704-0.432594723127036
3033.40123.52363472312704-0.122434723127036
3042.197222.76772288135593-0.570502881355932
3053.76123.520218571428570.240981428571429
3062.772592.767722881355930.00486711864406786
3073.40122.767722881355930.633477118644068
3083.610922.767722881355930.843197118644068
3093.36732.767722881355930.599577118644068
3100.693152.10102692307692-1.40787692307692
3112.944442.767722881355930.176717118644068
3124.174393.523634723127040.650755276872964
3132.833213.52363472312704-0.690424723127036
3143.044522.101026923076920.943493076923077
3153.25813.52363472312704-0.265534723127036
3163.218883.52363472312704-0.304754723127036
3174.234113.523634723127040.710475276872964
3184.812183.523634723127041.28854527687296
3194.394453.523634723127040.870815276872964
3202.639062.76772288135593-0.128662881355932
3213.25813.52363472312704-0.265534723127036
3221.945913.52021857142857-1.57430857142857
3232.944443.52363472312704-0.579194723127036
3242.564953.52363472312704-0.958684723127036
3253.218882.767722881355930.451157118644068
3262.772593.52363472312704-0.751044723127036
3274.615123.523634723127041.09148527687296
3281.609442.10102692307692-0.491586923076923
3292.079442.10102692307692-0.021586923076923
3303.87123.520218571428570.350981428571429
3313.178053.52363472312704-0.345584723127036
3323.583523.523634723127040.0598852768729641
3334.682133.523634723127041.15849527687296
3342.564953.52363472312704-0.958684723127036
3352.302592.76772288135593-0.465132881355932
3363.555353.523634723127040.0317152768729638
3373.465743.52021857142857-0.0544785714285716
3382.484913.52363472312704-1.03872472312704
3394.219513.520218571428570.699291428571428
3402.302593.52363472312704-1.22104472312704
3412.708053.52021857142857-0.812168571428571
3422.995733.52363472312704-0.527904723127036
3432.484912.76772288135593-0.282812881355932
3443.135493.52363472312704-0.388144723127036
3454.158883.523634723127040.635245276872964
3461.791762.76772288135593-0.975962881355932
3473.295843.52363472312704-0.227794723127036
3482.833213.52021857142857-0.687008571428572
3493.526362.101026923076921.42533307692308
3503.25812.767722881355930.490377118644068
3513.988982.767722881355931.22125711864407
3521.609442.76772288135593-1.15828288135593
3533.496513.52363472312704-0.0271247231270362
3543.178053.52363472312704-0.345584723127036
3554.672833.523634723127041.14919527687296
3563.433992.767722881355930.666267118644068
3572.079443.52363472312704-1.44419472312704
3583.295843.52363472312704-0.227794723127036
3592.302593.52363472312704-1.22104472312704
3602.079442.76772288135593-0.688282881355932
3610.693152.10102692307692-1.40787692307692
3623.135493.52363472312704-0.388144723127036
3633.828643.523634723127040.305005276872964
3643.87123.523634723127040.347565276872964
3652.995732.767722881355930.228007118644068
3663.25813.52363472312704-0.265534723127036
3673.33223.52021857142857-0.188018571428572
3683.496513.52363472312704-0.0271247231270362
3691.945912.76772288135593-0.821812881355932
3704.574713.523634723127041.05107527687296
3713.912023.523634723127040.388385276872964
3723.806662.767722881355931.03893711864407
3733.931833.520218571428570.411611428571429
3742.484912.76772288135593-0.282812881355932
3753.135493.52363472312704-0.388144723127036
3762.944442.767722881355930.176717118644068
3773.40123.52363472312704-0.122434723127036
3781.945912.76772288135593-0.821812881355932
3793.178052.767722881355930.410327118644068
3803.218883.52363472312704-0.304754723127036
3812.639062.101026923076920.538033076923077
3823.33223.52363472312704-0.191434723127036
3832.708052.76772288135593-0.0596728813559322
3843.784193.520218571428570.263971428571429
3853.784193.523634723127040.260555276872964
3861.609443.52363472312704-1.91419472312704
3873.36733.52363472312704-0.156334723127036
3883.044522.101026923076920.943493076923077
3894.454353.523634723127040.930715276872964
3901.945912.76772288135593-0.821812881355932
3912.639062.101026923076920.538033076923077
3923.33223.52363472312704-0.191434723127036
3933.433992.767722881355930.666267118644068
3943.891823.523634723127040.368185276872964
3952.197223.52363472312704-1.32641472312704
3964.595123.523634723127041.07148527687296
3973.496513.52021857142857-0.0237085714285716
3982.833213.52363472312704-0.690424723127036
3992.197222.76772288135593-0.570502881355932
4002.890372.767722881355930.122647118644068
4013.40123.52021857142857-0.119018571428572
4022.995732.767722881355930.228007118644068
4033.737673.523634723127040.214035276872964
4042.944442.767722881355930.176717118644068
4053.583523.523634723127040.0598852768729641
4063.295843.52363472312704-0.227794723127036
4074.87523.523634723127041.35156527687296
4084.330733.523634723127040.807095276872964
4093.295843.52021857142857-0.224378571428571
4103.784193.523634723127040.260555276872964
4113.610922.767722881355930.843197118644068
4122.833212.767722881355930.0654871186440675
4133.76123.520218571428570.240981428571429
4143.583522.767722881355930.815797118644068
4153.178052.767722881355930.410327118644068
4163.526363.520218571428570.00614142857142852
4174.219513.523634723127040.695875276872964
4183.931833.523634723127040.408195276872964
4192.302592.76772288135593-0.465132881355932
4203.25812.767722881355930.490377118644068
4212.708052.76772288135593-0.0596728813559322
4223.555352.767722881355930.787627118644068
4231.945912.76772288135593-0.821812881355932
4244.369453.520218571428570.849231428571428
4254.700483.523634723127041.17684527687296
4262.484912.101026923076920.383883076923077
4271.609443.52363472312704-1.91419472312704
4283.784193.523634723127040.260555276872964
4291.945912.76772288135593-0.821812881355932
4303.091043.52363472312704-0.432594723127036
4313.218883.52363472312704-0.304754723127036
4322.484913.52363472312704-1.03872472312704
4332.197222.76772288135593-0.570502881355932
4344.934473.520218571428571.41425142857143
4354.043053.523634723127040.519415276872964
4364.510863.523634723127040.987225276872964
4371.945912.10102692307692-0.155116923076923
4383.091043.52363472312704-0.432594723127036
4394.605173.523634723127041.08153527687296
4403.637593.523634723127040.113955276872964
4413.465743.52363472312704-0.0578947231270361
4423.87123.523634723127040.347565276872964
4435.153293.523634723127041.62965527687296
4443.044523.52363472312704-0.479114723127036
4453.496513.52363472312704-0.0271247231270362
4462.564952.76772288135593-0.202772881355932
4473.850153.523634723127040.326515276872964
4482.484912.76772288135593-0.282812881355932
4492.079442.76772288135593-0.688282881355932
4502.995733.52363472312704-0.527904723127036
4511.386292.76772288135593-1.38143288135593
4523.178053.52363472312704-0.345584723127036
4532.197222.76772288135593-0.570502881355932
4543.555353.523634723127040.0317152768729638
4553.87123.523634723127040.347565276872964
4563.40123.52363472312704-0.122434723127036
4573.044523.52363472312704-0.479114723127036
4584.343813.520218571428570.823591428571429
4592.39793.52363472312704-1.12573472312704
4602.639063.52363472312704-0.884574723127036
4612.564952.76772288135593-0.202772881355932
4623.33222.767722881355930.564477118644068
4632.564953.52363472312704-0.958684723127036
4643.688883.523634723127040.165245276872964
4652.39793.52363472312704-1.12573472312704
4662.39793.52021857142857-1.12231857142857
4674.682133.523634723127041.15849527687296
4683.044523.52363472312704-0.479114723127036
4693.044523.52021857142857-0.475698571428572
4702.197222.101026923076920.0961930769230772
4713.178053.52363472312704-0.345584723127036
4724.488643.523634723127040.965005276872964
4732.302593.52363472312704-1.22104472312704
4743.295843.52363472312704-0.227794723127036
4753.178053.52363472312704-0.345584723127036
4764.060442.767722881355931.29271711864407
4774.158882.767722881355931.39115711864407
4783.091042.101026923076920.990013076923077
4794.418843.523634723127040.895205276872964
4802.890372.767722881355930.122647118644068
4811.098612.10102692307692-1.00241692307692
4823.433993.52363472312704-0.0896447231270359
4833.526363.523634723127040.00272527687296398
4843.135493.52363472312704-0.388144723127036
4852.197223.52363472312704-1.32641472312704
4863.135493.52363472312704-0.388144723127036
4872.079442.76772288135593-0.688282881355932
4883.637592.767722881355930.869867118644068
4893.737673.520218571428570.217451428571429
4902.484912.76772288135593-0.282812881355932
4911.945913.52363472312704-1.57772472312704
4921.945912.76772288135593-0.821812881355932
4932.772592.767722881355930.00486711864406786
4942.708052.76772288135593-0.0596728813559322
4951.791762.76772288135593-0.975962881355932
4962.302592.76772288135593-0.465132881355932
4974.110873.523634723127040.587235276872964
4983.40123.52021857142857-0.119018571428572
4993.688883.523634723127040.165245276872964
5004.174393.523634723127040.650755276872964
 
Charts produced by software:
http://www.freestatistics.org/blog/date/2010/Dec/10/t1291992835arvf93eds1aqwjw/2ta3i1291992932.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/10/t1291992835arvf93eds1aqwjw/2ta3i1291992932.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/10/t1291992835arvf93eds1aqwjw/3m1331291992932.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/10/t1291992835arvf93eds1aqwjw/3m1331291992932.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/10/t1291992835arvf93eds1aqwjw/4wa261291992932.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/10/t1291992835arvf93eds1aqwjw/4wa261291992932.ps (open in new window)


 
Parameters (Session):
par1 = 1 ; par2 = none ; par4 = no ;
 
Parameters (R input):
par1 = 1 ; par2 = none ; 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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