R version 3.0.2 (2013-09-25) -- "Frisbee Sailing"
Copyright (C) 2013 The R Foundation for Statistical Computing
Platform: i686-pc-linux-gnu (32-bit)

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> x <- array(list(119.992
+ ,157.302
+ ,74.997
+ ,0.00784
+ ,0.00007
+ ,0.0037
+ ,0.00554
+ ,0.04374
+ ,0.426
+ ,0.02971
+ ,122.4
+ ,148.65
+ ,113.819
+ ,0.00968
+ ,0.00008
+ ,0.00465
+ ,0.00696
+ ,0.06134
+ ,0.626
+ ,0.04368
+ ,116.682
+ ,131.111
+ ,111.555
+ ,0.0105
+ ,0.00009
+ ,0.00544
+ ,0.00781
+ ,0.05233
+ ,0.482
+ ,0.0359
+ ,116.676
+ ,137.871
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+ ,0.00997
+ ,0.00009
+ ,0.00502
+ ,0.00698
+ ,0.05492
+ ,0.517
+ ,0.03772
+ ,116.014
+ ,141.781
+ ,110.655
+ ,0.01284
+ ,0.00011
+ ,0.00655
+ ,0.00908
+ ,0.06425
+ ,0.584
+ ,0.04465
+ ,120.552
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+ ,113.787
+ ,0.00968
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+ ,0.04701
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+ ,0.01608
+ ,0.14
+ ,0.01351
+ ,107.332
+ ,113.84
+ ,104.315
+ ,0.0029
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+ ,0.00144
+ ,0.00182
+ ,0.01567
+ ,0.134
+ ,0.01256
+ ,95.73
+ ,132.068
+ ,91.754
+ ,0.00551
+ ,0.00006
+ ,0.00293
+ ,0.00332
+ ,0.02093
+ ,0.191
+ ,0.01717
+ ,95.056
+ ,120.103
+ ,91.226
+ ,0.00532
+ ,0.00006
+ ,0.00268
+ ,0.00332
+ ,0.02838
+ ,0.255
+ ,0.02444
+ ,88.333
+ ,112.24
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+ ,0.00505
+ ,0.00006
+ ,0.00254
+ ,0.0033
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+ ,0.197
+ ,0.01892
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+ ,115.871
+ ,86.292
+ ,0.0054
+ ,0.00006
+ ,0.00281
+ ,0.00336
+ ,0.02752
+ ,0.249
+ ,0.02214
+ ,136.926
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+ ,131.276
+ ,0.00293
+ ,0.00002
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+ ,0.0114
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+ ,179.139
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+ ,0.00003
+ ,0.00165
+ ,0.00208
+ ,0.01642
+ ,0.154
+ ,0.01797
+ ,152.845
+ ,163.305
+ ,75.836
+ ,0.00294
+ ,0.00002
+ ,0.00121
+ ,0.00149
+ ,0.01828
+ ,0.158
+ ,0.01246
+ ,142.167
+ ,217.455
+ ,83.159
+ ,0.00369
+ ,0.00003
+ ,0.00157
+ ,0.00203
+ ,0.01503
+ ,0.126
+ ,0.01359
+ ,144.188
+ ,349.259
+ ,82.764
+ ,0.00544
+ ,0.00004
+ ,0.00211
+ ,0.00292
+ ,0.02047
+ ,0.192
+ ,0.02074
+ ,168.778
+ ,232.181
+ ,75.603
+ ,0.00718
+ ,0.00004
+ ,0.00284
+ ,0.00387
+ ,0.03327
+ ,0.348
+ ,0.0343
+ ,153.046
+ ,175.829
+ ,68.623
+ ,0.00742
+ ,0.00005
+ ,0.00364
+ ,0.00432
+ ,0.05517
+ ,0.542
+ ,0.05767
+ ,156.405
+ ,189.398
+ ,142.822
+ ,0.00768
+ ,0.00005
+ ,0.00372
+ ,0.00399
+ ,0.03995
+ ,0.348
+ ,0.0431
+ ,153.848
+ ,165.738
+ ,65.782
+ ,0.0084
+ ,0.00005
+ ,0.00428
+ ,0.0045
+ ,0.0381
+ ,0.328
+ ,0.04055
+ ,153.88
+ ,172.86
+ ,78.128
+ ,0.0048
+ ,0.00003
+ ,0.00232
+ ,0.00267
+ ,0.04137
+ ,0.37
+ ,0.04525
+ ,167.93
+ ,193.221
+ ,79.068
+ ,0.00442
+ ,0.00003
+ ,0.0022
+ ,0.00247
+ ,0.04351
+ ,0.377
+ ,0.04246
+ ,173.917
+ ,192.735
+ ,86.18
+ ,0.00476
+ ,0.00003
+ ,0.00221
+ ,0.00258
+ ,0.04192
+ ,0.364
+ ,0.03772
+ ,163.656
+ ,200.841
+ ,76.779
+ ,0.00742
+ ,0.00005
+ ,0.0038
+ ,0.0039
+ ,0.01659
+ ,0.164
+ ,0.01497
+ ,104.4
+ ,206.002
+ ,77.968
+ ,0.00633
+ ,0.00006
+ ,0.00316
+ ,0.00375
+ ,0.03767
+ ,0.381
+ ,0.0378
+ ,171.041
+ ,208.313
+ ,75.501
+ ,0.00455
+ ,0.00003
+ ,0.0025
+ ,0.00234
+ ,0.01966
+ ,0.186
+ ,0.01872
+ ,146.845
+ ,208.701
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+ ,0.01919
+ ,0.198
+ ,0.01826
+ ,155.358
+ ,227.383
+ ,80.055
+ ,0.0031
+ ,0.00002
+ ,0.00159
+ ,0.00176
+ ,0.01718
+ ,0.161
+ ,0.01661
+ ,162.568
+ ,198.346
+ ,77.63
+ ,0.00502
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+ ,0.0028
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+ ,0.01791
+ ,0.168
+ ,0.01799
+ ,197.076
+ ,206.896
+ ,192.055
+ ,0.00289
+ ,0.00001
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+ ,0.00168
+ ,0.01098
+ ,0.097
+ ,0.00802
+ ,199.228
+ ,209.512
+ ,192.091
+ ,0.00241
+ ,0.00001
+ ,0.00134
+ ,0.00138
+ ,0.01015
+ ,0.089
+ ,0.00762
+ ,198.383
+ ,215.203
+ ,193.104
+ ,0.00212
+ ,0.00001
+ ,0.00113
+ ,0.00135
+ ,0.01263
+ ,0.111
+ ,0.00951
+ ,202.266
+ ,211.604
+ ,197.079
+ ,0.0018
+ ,0.000009
+ ,0.00093
+ ,0.00107
+ ,0.00954
+ ,0.085
+ ,0.00719
+ ,203.184
+ ,211.526
+ ,196.16
+ ,0.00178
+ ,0.000009
+ ,0.00094
+ ,0.00106
+ ,0.00958
+ ,0.085
+ ,0.00726
+ ,201.464
+ ,210.565
+ ,195.708
+ ,0.00198
+ ,0.00001
+ ,0.00105
+ ,0.00115
+ ,0.01194
+ ,0.107
+ ,0.00957
+ ,177.876
+ ,192.921
+ ,168.013
+ ,0.00411
+ ,0.00002
+ ,0.00233
+ ,0.00241
+ ,0.02126
+ ,0.189
+ ,0.01612
+ ,176.17
+ ,185.604
+ ,163.564
+ ,0.00369
+ ,0.00002
+ ,0.00205
+ ,0.00218
+ ,0.01851
+ ,0.168
+ ,0.01491
+ ,180.198
+ ,201.249
+ ,175.456
+ ,0.00284
+ ,0.00002
+ ,0.00153
+ ,0.00166
+ ,0.01444
+ ,0.131
+ ,0.0119
+ ,187.733
+ ,202.324
+ ,173.015
+ ,0.00316
+ ,0.00002
+ ,0.00168
+ ,0.00182
+ ,0.01663
+ ,0.151
+ ,0.01366
+ ,186.163
+ ,197.724
+ ,177.584
+ ,0.00298
+ ,0.00002
+ ,0.00165
+ ,0.00175
+ ,0.01495
+ ,0.135
+ ,0.01233
+ ,184.055
+ ,196.537
+ ,166.977
+ ,0.00258
+ ,0.00001
+ ,0.00134
+ ,0.00147
+ ,0.01463
+ ,0.132
+ ,0.01234
+ ,237.226
+ ,247.326
+ ,225.227
+ ,0.00298
+ ,0.00001
+ ,0.00169
+ ,0.00182
+ ,0.01752
+ ,0.164
+ ,0.01133
+ ,241.404
+ ,248.834
+ ,232.483
+ ,0.00281
+ ,0.00001
+ ,0.00157
+ ,0.00173
+ ,0.0176
+ ,0.154
+ ,0.01251
+ ,243.439
+ ,250.912
+ ,232.435
+ ,0.0021
+ ,0.000009
+ ,0.00109
+ ,0.00137
+ ,0.01419
+ ,0.126
+ ,0.01033
+ ,242.852
+ ,255.034
+ ,227.911
+ ,0.00225
+ ,0.000009
+ ,0.00117
+ ,0.00139
+ ,0.01494
+ ,0.134
+ ,0.01014
+ ,245.51
+ ,262.09
+ ,231.848
+ ,0.00235
+ ,0.00001
+ ,0.00127
+ ,0.00148
+ ,0.01608
+ ,0.141
+ ,0.01149
+ ,252.455
+ ,261.487
+ ,182.786
+ ,0.00185
+ ,0.000007
+ ,0.00092
+ ,0.00113
+ ,0.01152
+ ,0.103
+ ,0.0086
+ ,122.188
+ ,128.611
+ ,115.765
+ ,0.00524
+ ,0.00004
+ ,0.00169
+ ,0.00203
+ ,0.01613
+ ,0.143
+ ,0.01433
+ ,122.964
+ ,130.049
+ ,114.676
+ ,0.00428
+ ,0.00003
+ ,0.00124
+ ,0.00155
+ ,0.01681
+ ,0.154
+ ,0.014
+ ,124.445
+ ,135.069
+ ,117.495
+ ,0.00431
+ ,0.00003
+ ,0.00141
+ ,0.00167
+ ,0.02184
+ ,0.197
+ ,0.01685
+ ,126.344
+ ,134.231
+ ,112.773
+ ,0.00448
+ ,0.00004
+ ,0.00131
+ ,0.00169
+ ,0.02033
+ ,0.185
+ ,0.01614
+ ,128.001
+ ,138.052
+ ,122.08
+ ,0.00436
+ ,0.00003
+ ,0.00137
+ ,0.00166
+ ,0.02297
+ ,0.21
+ ,0.01677
+ ,129.336
+ ,139.867
+ ,118.604
+ ,0.0049
+ ,0.00004
+ ,0.00165
+ ,0.00183
+ ,0.02498
+ ,0.228
+ ,0.01947
+ ,108.807
+ ,134.656
+ ,102.874
+ ,0.00761
+ ,0.00007
+ ,0.00349
+ ,0.00486
+ ,0.02719
+ ,0.255
+ ,0.02067
+ ,109.86
+ ,126.358
+ ,104.437
+ ,0.00874
+ ,0.00008
+ ,0.00398
+ ,0.00539
+ ,0.03209
+ ,0.307
+ ,0.02454
+ ,110.417
+ ,131.067
+ ,103.37
+ ,0.00784
+ ,0.00007
+ ,0.00352
+ ,0.00514
+ ,0.03715
+ ,0.334
+ ,0.02802
+ ,117.274
+ ,129.916
+ ,110.402
+ ,0.00752
+ ,0.00006
+ ,0.00299
+ ,0.00469
+ ,0.02293
+ ,0.221
+ ,0.01948
+ ,116.879
+ ,131.897
+ ,108.153
+ ,0.00788
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+ ,0.02519
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+ ,0.00507
+ ,0.00463
+ ,0.03111
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+ ,0.01101
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+ ,162.824
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+ ,0.00762
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+ ,0.00001
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+ ,0.001
+ ,0.0103
+ ,0.094
+ ,0.00871
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+ ,0.00128
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+ ,0.01075
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+ ,0.00005
+ ,0.00241
+ ,0.00314
+ ,0.02008
+ ,0.221
+ ,0.01734
+ ,176.824
+ ,215.293
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+ ,0.0046
+ ,0.00003
+ ,0.00209
+ ,0.00221
+ ,0.01169
+ ,0.117
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+ ,0.00704
+ ,0.00005
+ ,0.00406
+ ,0.00398
+ ,0.04479
+ ,0.441
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+ ,197.173
+ ,79.187
+ ,0.00842
+ ,0.00005
+ ,0.00506
+ ,0.00449
+ ,0.02503
+ ,0.231
+ ,0.01931
+ ,156.239
+ ,195.107
+ ,79.82
+ ,0.00694
+ ,0.00004
+ ,0.00403
+ ,0.00395
+ ,0.02343
+ ,0.224
+ ,0.0172
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+ ,80.637
+ ,0.00733
+ ,0.00005
+ ,0.00414
+ ,0.00422
+ ,0.02362
+ ,0.233
+ ,0.01944
+ ,138.145
+ ,197.238
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+ ,0.00544
+ ,0.00004
+ ,0.00294
+ ,0.00327
+ ,0.02791
+ ,0.246
+ ,0.02259
+ ,166.888
+ ,198.966
+ ,79.512
+ ,0.00638
+ ,0.00004
+ ,0.00368
+ ,0.00351
+ ,0.02857
+ ,0.257
+ ,0.02301
+ ,119.031
+ ,127.533
+ ,109.216
+ ,0.0044
+ ,0.00004
+ ,0.00214
+ ,0.00192
+ ,0.01033
+ ,0.098
+ ,0.00811
+ ,120.078
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+ ,105.667
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+ ,0.00002
+ ,0.00116
+ ,0.00135
+ ,0.01022
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+ ,0.00903
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+ ,100.209
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+ ,0.00004
+ ,0.00269
+ ,0.00238
+ ,0.01412
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+ ,0.00003
+ ,0.00224
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+ ,0.01516
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+ ,0.0131
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+ ,0.00346
+ ,0.00003
+ ,0.00169
+ ,0.0017
+ ,0.01201
+ ,0.106
+ ,0.00915
+ ,118.747
+ ,123.723
+ ,109.836
+ ,0.00331
+ ,0.00003
+ ,0.00168
+ ,0.00171
+ ,0.01043
+ ,0.099
+ ,0.00903
+ ,106.516
+ ,112.777
+ ,93.105
+ ,0.00589
+ ,0.00006
+ ,0.00291
+ ,0.00319
+ ,0.04932
+ ,0.441
+ ,0.03651
+ ,110.453
+ ,127.611
+ ,105.554
+ ,0.00494
+ ,0.00004
+ ,0.00244
+ ,0.00315
+ ,0.04128
+ ,0.379
+ ,0.03316
+ ,113.4
+ ,133.344
+ ,107.816
+ ,0.00451
+ ,0.00004
+ ,0.00219
+ ,0.00283
+ ,0.04879
+ ,0.431
+ ,0.0437
+ ,113.166
+ ,130.27
+ ,100.673
+ ,0.00502
+ ,0.00004
+ ,0.00257
+ ,0.00312
+ ,0.05279
+ ,0.476
+ ,0.04134
+ ,112.239
+ ,126.609
+ ,104.095
+ ,0.00472
+ ,0.00004
+ ,0.00238
+ ,0.0029
+ ,0.05643
+ ,0.517
+ ,0.04451
+ ,116.15
+ ,131.731
+ ,109.815
+ ,0.00381
+ ,0.00003
+ ,0.00181
+ ,0.00232
+ ,0.03026
+ ,0.267
+ ,0.0277
+ ,170.368
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+ ,0.00571
+ ,0.00003
+ ,0.00232
+ ,0.00269
+ ,0.03273
+ ,0.281
+ ,0.02824
+ ,208.083
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+ ,0.00004
+ ,0.00428
+ ,0.00428
+ ,0.06725
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+ ,198.458
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+ ,0.00002
+ ,0.00182
+ ,0.00215
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+ ,0.0253
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+ ,0.00211
+ ,0.01997
+ ,0.18
+ ,0.01506
+ ,202.544
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+ ,0.00254
+ ,0.00001
+ ,0.001
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+ ,0.02662
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+ ,0.00819
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+ ,0.00009
+ ,0.00996
+ ,0.01027
+ ,0.07118
+ ,0.722
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+ ,0.00008
+ ,0.00919
+ ,0.00963
+ ,0.0717
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+ ,149.442
+ ,0.01872
+ ,0.0001
+ ,0.01075
+ ,0.01154
+ ,0.0583
+ ,0.784
+ ,0.06259
+ ,192.818
+ ,224.429
+ ,168.793
+ ,0.03107
+ ,0.00016
+ ,0.018
+ ,0.01958
+ ,0.11908
+ ,1.302
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+ ,0.02714
+ ,0.00014
+ ,0.01568
+ ,0.01699
+ ,0.08684
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+ ,0.00332
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+ ,0.003
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+ ,0.01574
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+ ,0.00003
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+ ,0.00231
+ ,0.0145
+ ,0.131
+ ,0.01263
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+ ,0.00396
+ ,0.00003
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+ ,0.00233
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+ ,0.237
+ ,0.02148
+ ,148.462
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+ ,0.00003
+ ,0.00202
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+ ,149.818
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+ ,106.656
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+ ,0.0027
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+ ,217.552
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+ ,0.00005
+ ,0.0026
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+ ,0.01795
+ ,0.163
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+ ,177.291
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+ ,0.00314
+ ,0.00003
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+ ,0.00192
+ ,0.01564
+ ,0.136
+ ,0.01691
+ ,116.556
+ ,592.03
+ ,86.228
+ ,0.00496
+ ,0.00004
+ ,0.00254
+ ,0.00263
+ ,0.0166
+ ,0.154
+ ,0.01491
+ ,116.342
+ ,581.289
+ ,94.246
+ ,0.00267
+ ,0.00002
+ ,0.00115
+ ,0.00148
+ ,0.013
+ ,0.117
+ ,0.01144
+ ,114.563
+ ,119.167
+ ,86.647
+ ,0.00327
+ ,0.00003
+ ,0.00146
+ ,0.00184
+ ,0.01185
+ ,0.106
+ ,0.01095
+ ,201.774
+ ,262.707
+ ,78.228
+ ,0.00694
+ ,0.00003
+ ,0.00412
+ ,0.00396
+ ,0.02574
+ ,0.255
+ ,0.01758
+ ,174.188
+ ,230.978
+ ,94.261
+ ,0.00459
+ ,0.00003
+ ,0.00263
+ ,0.00259
+ ,0.04087
+ ,0.405
+ ,0.02745
+ ,209.516
+ ,253.017
+ ,89.488
+ ,0.00564
+ ,0.00003
+ ,0.00331
+ ,0.00292
+ ,0.02751
+ ,0.263
+ ,0.01879
+ ,174.688
+ ,240.005
+ ,74.287
+ ,0.0136
+ ,0.00008
+ ,0.00624
+ ,0.00564
+ ,0.02308
+ ,0.256
+ ,0.01667
+ ,198.764
+ ,396.961
+ ,74.904
+ ,0.0074
+ ,0.00004
+ ,0.0037
+ ,0.0039
+ ,0.02296
+ ,0.241
+ ,0.01588
+ ,214.289
+ ,260.277
+ ,77.973
+ ,0.00567
+ ,0.00003
+ ,0.00295
+ ,0.00317
+ ,0.01884
+ ,0.19
+ ,0.01373)
+ ,dim=c(10
+ ,195)
+ ,dimnames=list(c('MDVP:Fo(Hz)'
+ ,'MDVP:Fhi(Hz)'
+ ,'MDVP:Flo(Hz)'
+ ,'MDVP:Jitter(%)'
+ ,'MDVP:Jitter(Abs)'
+ ,'MDVP:RAP'
+ ,'MDVP:PPQ'
+ ,'MDVP:Shimmer'
+ ,'MDVP:Shimmer(dB)'
+ ,'MDVP:APQ')
+ ,1:195))
>  y <- array(NA,dim=c(10,195),dimnames=list(c('MDVP:Fo(Hz)','MDVP:Fhi(Hz)','MDVP:Flo(Hz)','MDVP:Jitter(%)','MDVP:Jitter(Abs)','MDVP:RAP','MDVP:PPQ','MDVP:Shimmer','MDVP:Shimmer(dB)','MDVP:APQ'),1:195))
>  for (i in 1:dim(x)[1])
+  {
+  	for (j in 1:dim(x)[2])
+  	{
+  		y[i,j] <- as.numeric(x[i,j])
+  	}
+  }
> par4 = 'no'
> par3 = ''
> par2 = 'none'
> par1 = '1'
> par4 <- 'no'
> par3 <- ''
> par2 <- 'none'
> par1 <- '1'
> #'GNU S' R Code compiled by R2WASP v. 1.2.291 ()
> #Author: root
> #To cite this work: Wessa P., 2012, Recursive Partitioning (Regression Trees) (v1.0.3) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_regression_trees.wasp/
> #Source of accompanying publication: 
> #
> library(party)
Loading required package: survival
Loading required package: splines
Loading required package: grid
Loading required package: modeltools
Loading required package: stats4
Loading required package: coin
Loading required package: mvtnorm
Loading required package: zoo

Attaching package: 'zoo'

The following objects are masked from 'package:base':

    as.Date, as.Date.numeric

Loading required package: sandwich
Loading required package: strucchange
Loading required package: vcd
Loading required package: MASS
Loading required package: colorspace
> library(Hmisc)
Hmisc library by Frank E Harrell Jr

Type library(help='Hmisc'), ?Overview, or ?Hmisc.Overview')
to see overall documentation.

NOTE:Hmisc no longer redefines [.factor to drop unused levels when
subsetting.  To get the old behavior of Hmisc type dropUnusedLevels().


Attaching package: 'Hmisc'

The following object is masked from 'package:survival':

    untangle.specials

The following objects are masked from 'package:base':

    format.pval, round.POSIXt, trunc.POSIXt, units

> par1 <- as.numeric(par1)
> par3 <- as.numeric(par3)
> x <- data.frame(t(y))
> is.data.frame(x)
[1] TRUE
> x <- x[!is.na(x[,par1]),]
> k <- length(x[1,])
> n <- length(x[,1])
> colnames(x)[par1]
[1] "MDVP.Fo.Hz."
> x[,par1]
  [1] 119.992 122.400 116.682 116.676 116.014 120.552 120.267 107.332  95.730
 [10]  95.056  88.333  91.904 136.926 139.173 152.845 142.167 144.188 168.778
 [19] 153.046 156.405 153.848 153.880 167.930 173.917 163.656 104.400 171.041
 [28] 146.845 155.358 162.568 197.076 199.228 198.383 202.266 203.184 201.464
 [37] 177.876 176.170 180.198 187.733 186.163 184.055 237.226 241.404 243.439
 [46] 242.852 245.510 252.455 122.188 122.964 124.445 126.344 128.001 129.336
 [55] 108.807 109.860 110.417 117.274 116.879 114.847 209.144 223.365 222.236
 [64] 228.832 229.401 228.969 140.341 136.969 143.533 148.090 142.729 136.358
 [73] 120.080 112.014 110.793 110.707 112.876 110.568  95.385 100.770  96.106
 [82]  95.605 100.960  98.804 176.858 180.978 178.222 176.281 173.898 179.711
 [91] 166.605 151.955 148.272 152.125 157.821 157.447 159.116 125.036 125.791
[100] 126.512 125.641 128.451 139.224 150.258 154.003 149.689 155.078 151.884
[109] 151.989 193.030 200.714 208.519 204.664 210.141 206.327 151.872 158.219
[118] 170.756 178.285 217.116 128.940 176.824 138.190 182.018 156.239 145.174
[127] 138.145 166.888 119.031 120.078 120.289 120.256 119.056 118.747 106.516
[136] 110.453 113.400 113.166 112.239 116.150 170.368 208.083 198.458 202.805
[145] 202.544 223.361 169.774 183.520 188.620 202.632 186.695 192.818 198.116
[154] 121.345 119.100 117.870 122.336 117.963 126.144 127.930 114.238 115.322
[163] 114.554 112.150 102.273 236.200 237.323 260.105 197.569 240.301 244.990
[172] 112.547 110.739 113.715 117.004 115.380 116.388 151.737 148.790 148.143
[181] 150.440 148.462 149.818 117.226 116.848 116.286 116.556 116.342 114.563
[190] 201.774 174.188 209.516 174.688 198.764 214.289
> 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])

 88.333  91.904  95.056  95.385  95.605   95.73  96.106  98.804  100.77  100.96 
      1       1       1       1       1       1       1       1       1       1 
102.273   104.4 106.516 107.332 108.807  109.86 110.417 110.453 110.568 110.707 
      1       1       1       1       1       1       1       1       1       1 
110.739 110.793 112.014  112.15 112.239 112.547 112.876 113.166   113.4 113.715 
      1       1       1       1       1       1       1       1       1       1 
114.238 114.554 114.563 114.847 115.322  115.38 116.014  116.15 116.286 116.342 
      1       1       1       1       1       1       1       1       1       1 
116.388 116.556 116.676 116.682 116.848 116.879 117.004 117.226 117.274  117.87 
      1       1       1       1       1       1       1       1       1       1 
117.963 118.747 119.031 119.056   119.1 119.992 120.078  120.08 120.256 120.267 
      1       1       1       1       1       1       1       1       1       1 
120.289 120.552 121.345 122.188 122.336   122.4 122.964 124.445 125.036 125.641 
      1       1       1       1       1       1       1       1       1       1 
125.791 126.144 126.344 126.512  127.93 128.001 128.451  128.94 129.336 136.358 
      1       1       1       1       1       1       1       1       1       1 
136.926 136.969 138.145  138.19 139.173 139.224 140.341 142.167 142.729 143.533 
      1       1       1       1       1       1       1       1       1       1 
144.188 145.174 146.845  148.09 148.143 148.272 148.462  148.79 149.689 149.818 
      1       1       1       1       1       1       1       1       1       1 
150.258  150.44 151.737 151.872 151.884 151.955 151.989 152.125 152.845 153.046 
      1       1       1       1       1       1       1       1       1       1 
153.848  153.88 154.003 155.078 155.358 156.239 156.405 157.447 157.821 158.219 
      1       1       1       1       1       1       1       1       1       1 
159.116 162.568 163.656 166.605 166.888  167.93 168.778 169.774 170.368 170.756 
      1       1       1       1       1       1       1       1       1       1 
171.041 173.898 173.917 174.188 174.688  176.17 176.281 176.824 176.858 177.876 
      1       1       1       1       1       1       1       1       1       1 
178.222 178.285 179.711 180.198 180.978 182.018  183.52 184.055 186.163 186.695 
      1       1       1       1       1       1       1       1       1       1 
187.733  188.62 192.818  193.03 197.076 197.569 198.116 198.383 198.458 198.764 
      1       1       1       1       1       1       1       1       1       1 
199.228 200.714 201.464 201.774 202.266 202.544 202.632 202.805 203.184 204.664 
      1       1       1       1       1       1       1       1       1       1 
206.327 208.083 208.519 209.144 209.516 210.141 214.289 217.116 222.236 223.361 
      1       1       1       1       1       1       1       1       1       1 
223.365 228.832 228.969 229.401   236.2 237.226 237.323 240.301 241.404 242.852 
      1       1       1       1       1       1       1       1       1       1 
243.439  244.99  245.51 252.455 260.105 
      1       1       1       1       1 
> colnames(x)
 [1] "MDVP.Fo.Hz."      "MDVP.Fhi.Hz."     "MDVP.Flo.Hz."     "MDVP.Jitter..."  
 [5] "MDVP.Jitter.Abs." "MDVP.RAP"         "MDVP.PPQ"         "MDVP.Shimmer"    
 [9] "MDVP.Shimmer.dB." "MDVP.APQ"        
> colnames(x)[par1]
[1] "MDVP.Fo.Hz."
> x[,par1]
  [1] 119.992 122.400 116.682 116.676 116.014 120.552 120.267 107.332  95.730
 [10]  95.056  88.333  91.904 136.926 139.173 152.845 142.167 144.188 168.778
 [19] 153.046 156.405 153.848 153.880 167.930 173.917 163.656 104.400 171.041
 [28] 146.845 155.358 162.568 197.076 199.228 198.383 202.266 203.184 201.464
 [37] 177.876 176.170 180.198 187.733 186.163 184.055 237.226 241.404 243.439
 [46] 242.852 245.510 252.455 122.188 122.964 124.445 126.344 128.001 129.336
 [55] 108.807 109.860 110.417 117.274 116.879 114.847 209.144 223.365 222.236
 [64] 228.832 229.401 228.969 140.341 136.969 143.533 148.090 142.729 136.358
 [73] 120.080 112.014 110.793 110.707 112.876 110.568  95.385 100.770  96.106
 [82]  95.605 100.960  98.804 176.858 180.978 178.222 176.281 173.898 179.711
 [91] 166.605 151.955 148.272 152.125 157.821 157.447 159.116 125.036 125.791
[100] 126.512 125.641 128.451 139.224 150.258 154.003 149.689 155.078 151.884
[109] 151.989 193.030 200.714 208.519 204.664 210.141 206.327 151.872 158.219
[118] 170.756 178.285 217.116 128.940 176.824 138.190 182.018 156.239 145.174
[127] 138.145 166.888 119.031 120.078 120.289 120.256 119.056 118.747 106.516
[136] 110.453 113.400 113.166 112.239 116.150 170.368 208.083 198.458 202.805
[145] 202.544 223.361 169.774 183.520 188.620 202.632 186.695 192.818 198.116
[154] 121.345 119.100 117.870 122.336 117.963 126.144 127.930 114.238 115.322
[163] 114.554 112.150 102.273 236.200 237.323 260.105 197.569 240.301 244.990
[172] 112.547 110.739 113.715 117.004 115.380 116.388 151.737 148.790 148.143
[181] 150.440 148.462 149.818 117.226 116.848 116.286 116.556 116.342 114.563
[190] 201.774 174.188 209.516 174.688 198.764 214.289
> if (par2 == 'none') {
+ m <- ctree(as.formula(paste(colnames(x)[par1],' ~ .',sep='')),data = x)
+ }
> 
> #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/wessaorg/rcomp/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="/var/wessaorg/rcomp/tmp/1igcu1386767922.tab") 
+ }
+ }
> m

	 Conditional inference tree with 8 terminal nodes

Response:  MDVP.Fo.Hz. 
Inputs:  MDVP.Fhi.Hz., MDVP.Flo.Hz., MDVP.Jitter..., MDVP.Jitter.Abs., MDVP.RAP, MDVP.PPQ, MDVP.Shimmer, MDVP.Shimmer.dB., MDVP.APQ 
Number of observations:  195 

1) MDVP.Flo.Hz. <= 147.226; criterion = 1, statistic = 69.038
  2) MDVP.Fhi.Hz. <= 154.284; criterion = 1, statistic = 21.311
    3) MDVP.Fhi.Hz. <= 120.103; criterion = 1, statistic = 34.439
      4)*  weights = 14 
    3) MDVP.Fhi.Hz. > 120.103
      5) MDVP.Fhi.Hz. <= 134.656; criterion = 0.96, statistic = 8.068
        6)*  weights = 35 
      5) MDVP.Fhi.Hz. > 134.656
        7)*  weights = 21 
  2) MDVP.Fhi.Hz. > 154.284
    8) MDVP.Jitter.Abs. <= 4e-05; criterion = 0.984, statistic = 9.81
      9)*  weights = 64 
    8) MDVP.Jitter.Abs. > 4e-05
      10)*  weights = 22 
1) MDVP.Flo.Hz. > 147.226
  11) MDVP.Flo.Hz. <= 199.02; criterion = 1, statistic = 29.559
    12) MDVP.Flo.Hz. <= 177.584; criterion = 0.972, statistic = 8.724
      13)*  weights = 17 
    12) MDVP.Flo.Hz. > 177.584
      14)*  weights = 10 
  11) MDVP.Flo.Hz. > 199.02
    15)*  weights = 12 
> postscript(file="/var/wessaorg/rcomp/tmp/272ci1386767922.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) 
> plot(m)
> dev.off()
null device 
          1 
> postscript(file="/var/wessaorg/rcomp/tmp/36k7i1386767922.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) 
> plot(x[,par1] ~ as.factor(where(m)),main='Response by Terminal Node',xlab='Terminal Node',ylab='Response')
> dev.off()
null device 
          1 
> 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)
+ }
    Actuals Forecasts    Residuals
1   119.992  141.0537 -21.06172727
2   122.400  121.6133   0.78666667
3   116.682  115.4299   1.25211429
4   116.676  121.6133  -4.93733333
5   116.014  121.6133  -5.59933333
6   120.552  115.4299   5.12211429
7   120.267  121.6133  -1.34633333
8   107.332  101.1277   6.20428571
9    95.730  115.4299 -19.69988571
10   95.056  101.1277  -6.07171429
11   88.333  101.1277 -12.79471429
12   91.904  101.1277  -9.22371429
13  136.926  169.6390 -32.71295313
14  139.173  169.6390 -30.46595312
15  152.845  169.6390 -16.79395313
16  142.167  169.6390 -27.47195312
17  144.188  169.6390 -25.45095313
18  168.778  169.6390  -0.86095313
19  153.046  141.0537  11.99227273
20  156.405  141.0537  15.35127273
21  153.848  141.0537  12.79427273
22  153.880  169.6390 -15.75895313
23  167.930  169.6390  -1.70895312
24  173.917  169.6390   4.27804688
25  163.656  141.0537  22.60227273
26  104.400  141.0537 -36.65372727
27  171.041  169.6390   1.40204687
28  146.845  169.6390 -22.79395313
29  155.358  169.6390 -14.28095312
30  162.568  169.6390  -7.07095312
31  197.076  207.7380 -10.66200000
32  199.228  207.7380  -8.51000000
33  198.383  207.7380  -9.35500000
34  202.266  207.7380  -5.47200000
35  203.184  207.7380  -4.55400000
36  201.464  207.7380  -6.27400000
37  177.876  185.5175  -7.64147059
38  176.170  185.5175  -9.34747059
39  180.198  185.5175  -5.31947059
40  187.733  185.5175   2.21552941
41  186.163  185.5175   0.64552941
42  184.055  185.5175  -1.46247059
43  237.226  239.4683  -2.24225000
44  241.404  239.4683   1.93575000
45  243.439  239.4683   3.97075000
46  242.852  239.4683   3.38375000
47  245.510  239.4683   6.04175000
48  252.455  207.7380  44.71700000
49  122.188  115.4299   6.75811429
50  122.964  115.4299   7.53411429
51  124.445  121.6133   2.83166667
52  126.344  115.4299  10.91411429
53  128.001  121.6133   6.38766667
54  129.336  121.6133   7.72266667
55  108.807  115.4299  -6.62288571
56  109.860  115.4299  -5.56988571
57  110.417  115.4299  -5.01288571
58  117.274  115.4299   1.84411429
59  116.879  115.4299   1.44911429
60  114.847  141.0537 -26.20672727
61  209.144  169.6390  39.50504688
62  223.365  169.6390  53.72604688
63  222.236  239.4683 -17.23225000
64  228.832  239.4683 -10.63625000
65  229.401  239.4683 -10.06725000
66  228.969  169.6390  59.33004687
67  140.341  141.0537  -0.71272727
68  136.969  141.0537  -4.08472727
69  143.533  141.0537   2.47927273
70  148.090  141.0537   7.03627273
71  142.729  141.0537   1.67527273
72  136.358  141.0537  -4.69572727
73  120.080  121.6133  -1.53333333
74  112.014  141.0537 -29.03972727
75  110.793  115.4299  -4.63688571
76  110.707  115.4299  -4.72288571
77  112.876  121.6133  -8.73733333
78  110.568  115.4299  -4.86188571
79   95.385  101.1277  -5.74271429
80  100.770  101.1277  -0.35771429
81   96.106  101.1277  -5.02171429
82   95.605  101.1277  -5.52271429
83  100.960  101.1277  -0.16771429
84   98.804  101.1277  -2.32371429
85  176.858  169.6390   7.21904688
86  180.978  185.5175  -4.53947059
87  178.222  169.6390   8.58304688
88  176.281  169.6390   6.64204688
89  173.898  169.6390   4.25904687
90  179.711  169.6390  10.07204688
91  166.605  169.6390  -3.03395313
92  151.955  169.6390 -17.68395312
93  148.272  169.6390 -21.36695313
94  152.125  169.6390 -17.51395313
95  157.821  169.6390 -11.81795313
96  157.447  185.5175 -28.07047059
97  159.116  169.6390 -10.52295312
98  125.036  121.6133   3.42266667
99  125.791  121.6133   4.17766667
100 126.512  121.6133   4.89866667
101 125.641  121.6133   4.02766667
102 128.451  121.6133   6.83766667
103 139.224  141.0537  -1.82972727
104 150.258  169.6390 -19.38095312
105 154.003  169.6390 -15.63595313
106 149.689  169.6390 -19.94995313
107 155.078  169.6390 -14.56095312
108 151.884  169.6390 -17.75495313
109 151.989  169.6390 -17.64995312
110 193.030  169.6390  23.39104688
111 200.714  169.6390  31.07504687
112 208.519  207.7380   0.78100000
113 204.664  207.7380  -3.07400000
114 210.141  207.7380   2.40300000
115 206.327  169.6390  36.68804687
116 151.872  141.0537  10.81827273
117 158.219  169.6390 -11.41995313
118 170.756  169.6390   1.11704687
119 178.285  169.6390   8.64604687
120 217.116  169.6390  47.47704688
121 128.940  141.0537 -12.11372727
122 176.824  169.6390   7.18504688
123 138.190  141.0537  -2.86372727
124 182.018  141.0537  40.96427273
125 156.239  169.6390 -13.39995312
126 145.174  141.0537   4.12027273
127 138.145  169.6390 -31.49395312
128 166.888  169.6390  -2.75095312
129 119.031  115.4299   3.60111429
130 120.078  115.4299   4.64811429
131 120.289  115.4299   4.85911429
132 120.256  115.4299   4.82611429
133 119.056  115.4299   3.62611429
134 118.747  115.4299   3.31711429
135 106.516  101.1277   5.38828571
136 110.453  115.4299  -4.97688571
137 113.400  115.4299  -2.02988571
138 113.166  115.4299  -2.26388571
139 112.239  115.4299  -3.19088571
140 116.150  115.4299   0.72011429
141 170.368  169.6390   0.72904687
142 208.083  169.6390  38.44404687
143 198.458  185.5175  12.94052941
144 202.805  169.6390  33.16604688
145 202.544  185.5175  17.02652941
146 223.361  169.6390  53.72204687
147 169.774  185.5175 -15.74347059
148 183.520  185.5175  -1.99747059
149 188.620  185.5175   3.10252941
150 202.632  185.5175  17.11452941
151 186.695  185.5175   1.17752941
152 192.818  185.5175   7.30052941
153 198.116  185.5175  12.59852941
154 121.345  121.6133  -0.26833333
155 119.100  115.4299   3.67011429
156 117.870  115.4299   2.44011429
157 122.336  121.6133   0.72266667
158 117.963  115.4299   2.53311429
159 126.144  121.6133   4.53066667
160 127.930  121.6133   6.31666667
161 114.238  115.4299  -1.19188571
162 115.322  121.6133  -6.29133333
163 114.554  115.4299  -0.87588571
164 112.150  115.4299  -3.27988571
165 102.273  121.6133 -19.34033333
166 236.200  169.6390  66.56104687
167 237.323  239.4683  -2.14525000
168 260.105  239.4683  20.63675000
169 197.569  169.6390  27.93004687
170 240.301  239.4683   0.83275000
171 244.990  239.4683   5.52175000
172 112.547  115.4299  -2.88288571
173 110.739  101.1277   9.61128571
174 113.715  101.1277  12.58728571
175 117.004  121.6133  -4.60933333
176 115.380  115.4299  -0.04988571
177 116.388  115.4299   0.95811429
178 151.737  169.6390 -17.90195313
179 148.790  169.6390 -20.84895313
180 148.143  169.6390 -21.49595312
181 150.440  169.6390 -19.19895313
182 148.462  169.6390 -21.17695313
183 149.818  169.6390 -19.82095312
184 117.226  115.4299   1.79611429
185 116.848  141.0537 -24.20572727
186 116.286  169.6390 -53.35295312
187 116.556  169.6390 -53.08295313
188 116.342  169.6390 -53.29695313
189 114.563  101.1277  13.43528571
190 201.774  169.6390  32.13504688
191 174.188  169.6390   4.54904687
192 209.516  169.6390  39.87704687
193 174.688  141.0537  33.63427273
194 198.764  169.6390  29.12504688
195 214.289  169.6390  44.65004687
> if (par2 != 'none') {
+ print(cbind(as.factor(x[,par1]),predict(m)))
+ myt <- table(as.factor(x[,par1]),predict(m))
+ print(myt)
+ }
> postscript(file="/var/wessaorg/rcomp/tmp/4jug61386767922.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) 
> 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$Forecasts),main='Kernel Density Plot of Predictions')
+ par(op)
+ }
> if(par2!='none') {
+ plot(myt,main='Confusion Matrix',xlab='Actual',ylab='Predicted')
+ }
> dev.off()
null device 
          1 
> if (par2 == 'none') {
+ detcoef <- cor(result$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="/var/wessaorg/rcomp/tmp/56esv1386767922.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="/var/wessaorg/rcomp/tmp/6o2zv1386767922.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="/var/wessaorg/rcomp/tmp/7h57x1386767922.tab") 
+ }
> 
> try(system("convert tmp/272ci1386767922.ps tmp/272ci1386767922.png",intern=TRUE))
character(0)
> try(system("convert tmp/36k7i1386767922.ps tmp/36k7i1386767922.png",intern=TRUE))
character(0)
> try(system("convert tmp/4jug61386767922.ps tmp/4jug61386767922.png",intern=TRUE))
character(0)
> 
> 
> proc.time()
   user  system elapsed 
 10.129   1.848  11.954