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.
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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
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+ ,0.04374
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+ ,0.06134
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+ ,0.00004
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+ ,0.02047
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+ ,68.623
+ ,0.00742
+ ,0.00005
+ ,0.00364
+ ,0.00432
+ ,0.05517
+ ,0.542
+ ,0.05767
+ ,156.405
+ ,189.398
+ ,142.822
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+ ,0.00372
+ ,0.00399
+ ,0.03995
+ ,0.348
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+ ,0.00428
+ ,0.0045
+ ,0.0381
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+ ,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
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+ ,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
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+ ,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
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+ ,0.186
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+ ,192.055
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+ ,0.01098
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+ ,192.091
+ ,0.00241
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+ ,0.00138
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+ ,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
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+ ,0.00106
+ ,0.00958
+ ,0.085
+ ,0.00726
+ ,201.464
+ ,210.565
+ ,195.708
+ ,0.00198
+ ,0.00001
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+ ,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
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+ ,0.00002
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+ ,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
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+ ,0.00203
+ ,0.01613
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+ ,0.01433
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+ ,130.049
+ ,114.676
+ ,0.00428
+ ,0.00003
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+ ,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
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+ ,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
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+ ,0.334
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+ ,117.274
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+ ,149.689
+ ,160.368
+ ,133.608
+ ,0.00257
+ ,0.00002
+ ,0.00116
+ ,0.00134
+ ,0.01346
+ ,0.126
+ ,0.01059
+ ,155.078
+ ,163.736
+ ,144.148
+ ,0.00168
+ ,0.00001
+ ,0.00068
+ ,0.00092
+ ,0.01064
+ ,0.097
+ ,0.00928
+ ,151.884
+ ,157.765
+ ,133.751
+ ,0.00258
+ ,0.00002
+ ,0.00115
+ ,0.00122
+ ,0.0145
+ ,0.137
+ ,0.01267
+ ,151.989
+ ,157.339
+ ,132.857
+ ,0.00174
+ ,0.00001
+ ,0.00075
+ ,0.00096
+ ,0.01024
+ ,0.093
+ ,0.00993
+ ,193.03
+ ,208.9
+ ,80.297
+ ,0.00766
+ ,0.00004
+ ,0.0045
+ ,0.00389
+ ,0.03044
+ ,0.275
+ ,0.02084
+ ,200.714
+ ,223.982
+ ,89.686
+ ,0.00621
+ ,0.00003
+ ,0.00371
+ ,0.00337
+ ,0.02286
+ ,0.207
+ ,0.01852
+ ,208.519
+ ,220.315
+ ,199.02
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+ ,0.00003
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+ ,0.00339
+ ,0.01761
+ ,0.155
+ ,0.01307
+ ,204.664
+ ,221.3
+ ,189.621
+ ,0.00841
+ ,0.00004
+ ,0.00502
+ ,0.00485
+ ,0.02378
+ ,0.21
+ ,0.01767
+ ,210.141
+ ,232.706
+ ,185.258
+ ,0.00534
+ ,0.00003
+ ,0.00321
+ ,0.0028
+ ,0.0168
+ ,0.149
+ ,0.01301
+ ,206.327
+ ,226.355
+ ,92.02
+ ,0.00495
+ ,0.00002
+ ,0.00302
+ ,0.00246
+ ,0.02105
+ ,0.209
+ ,0.01604
+ ,151.872
+ ,492.892
+ ,69.085
+ ,0.00856
+ ,0.00006
+ ,0.00404
+ ,0.00385
+ ,0.01843
+ ,0.235
+ ,0.01271
+ ,158.219
+ ,442.557
+ ,71.948
+ ,0.00476
+ ,0.00003
+ ,0.00214
+ ,0.00207
+ ,0.01458
+ ,0.148
+ ,0.01312
+ ,170.756
+ ,450.247
+ ,79.032
+ ,0.00555
+ ,0.00003
+ ,0.00244
+ ,0.00261
+ ,0.01725
+ ,0.175
+ ,0.01652
+ ,178.285
+ ,442.824
+ ,82.063
+ ,0.00462
+ ,0.00003
+ ,0.00157
+ ,0.00194
+ ,0.01279
+ ,0.129
+ ,0.01151
+ ,217.116
+ ,233.481
+ ,93.978
+ ,0.00404
+ ,0.00002
+ ,0.00127
+ ,0.00128
+ ,0.01299
+ ,0.124
+ ,0.01075
+ ,128.94
+ ,479.697
+ ,88.251
+ ,0.00581
+ ,0.00005
+ ,0.00241
+ ,0.00314
+ ,0.02008
+ ,0.221
+ ,0.01734
+ ,176.824
+ ,215.293
+ ,83.961
+ ,0.0046
+ ,0.00003
+ ,0.00209
+ ,0.00221
+ ,0.01169
+ ,0.117
+ ,0.01104
+ ,138.19
+ ,203.522
+ ,83.34
+ ,0.00704
+ ,0.00005
+ ,0.00406
+ ,0.00398
+ ,0.04479
+ ,0.441
+ ,0.0322
+ ,182.018
+ ,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
+ ,145.174
+ ,198.109
+ ,80.637
+ ,0.00733
+ ,0.00005
+ ,0.00414
+ ,0.00422
+ ,0.02362
+ ,0.233
+ ,0.01944
+ ,138.145
+ ,197.238
+ ,81.114
+ ,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
+ ,126.632
+ ,105.667
+ ,0.0027
+ ,0.00002
+ ,0.00116
+ ,0.00135
+ ,0.01022
+ ,0.09
+ ,0.00903
+ ,120.289
+ ,128.143
+ ,100.209
+ ,0.00492
+ ,0.00004
+ ,0.00269
+ ,0.00238
+ ,0.01412
+ ,0.125
+ ,0.01194
+ ,120.256
+ ,125.306
+ ,104.773
+ ,0.00407
+ ,0.00003
+ ,0.00224
+ ,0.00205
+ ,0.01516
+ ,0.138
+ ,0.0131
+ ,119.056
+ ,125.213
+ ,86.795
+ ,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
+ ,268.796
+ ,79.543
+ ,0.00571
+ ,0.00003
+ ,0.00232
+ ,0.00269
+ ,0.03273
+ ,0.281
+ ,0.02824
+ ,208.083
+ ,253.792
+ ,91.802
+ ,0.00757
+ ,0.00004
+ ,0.00428
+ ,0.00428
+ ,0.06725
+ ,0.571
+ ,0.04464
+ ,198.458
+ ,219.29
+ ,148.691
+ ,0.00376
+ ,0.00002
+ ,0.00182
+ ,0.00215
+ ,0.03527
+ ,0.297
+ ,0.0253
+ ,202.805
+ ,231.508
+ ,86.232
+ ,0.0037
+ ,0.00002
+ ,0.00189
+ ,0.00211
+ ,0.01997
+ ,0.18
+ ,0.01506
+ ,202.544
+ ,241.35
+ ,164.168
+ ,0.00254
+ ,0.00001
+ ,0.001
+ ,0.00133
+ ,0.02662
+ ,0.228
+ ,0.02006
+ ,223.361
+ ,263.872
+ ,87.638
+ ,0.00352
+ ,0.00002
+ ,0.00169
+ ,0.00188
+ ,0.02536
+ ,0.225
+ ,0.01909
+ ,169.774
+ ,191.759
+ ,151.451
+ ,0.01568
+ ,0.00009
+ ,0.00863
+ ,0.00946
+ ,0.08143
+ ,0.821
+ ,0.08808
+ ,183.52
+ ,216.814
+ ,161.34
+ ,0.01466
+ ,0.00008
+ ,0.00849
+ ,0.00819
+ ,0.0605
+ ,0.618
+ ,0.06359
+ ,188.62
+ ,216.302
+ ,165.982
+ ,0.01719
+ ,0.00009
+ ,0.00996
+ ,0.01027
+ ,0.07118
+ ,0.722
+ ,0.06824
+ ,202.632
+ ,565.74
+ ,177.258
+ ,0.01627
+ ,0.00008
+ ,0.00919
+ ,0.00963
+ ,0.0717
+ ,0.833
+ ,0.0646
+ ,186.695
+ ,211.961
+ ,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
+ ,0.13778
+ ,198.116
+ ,233.099
+ ,174.478
+ ,0.02714
+ ,0.00014
+ ,0.01568
+ ,0.01699
+ ,0.08684
+ ,1.018
+ ,0.08318
+ ,121.345
+ ,139.644
+ ,98.25
+ ,0.00684
+ ,0.00006
+ ,0.00388
+ ,0.00332
+ ,0.02534
+ ,0.241
+ ,0.02056
+ ,119.1
+ ,128.442
+ ,88.833
+ ,0.00692
+ ,0.00006
+ ,0.00393
+ ,0.003
+ ,0.02682
+ ,0.236
+ ,0.02018
+ ,117.87
+ ,127.349
+ ,95.654
+ ,0.00647
+ ,0.00005
+ ,0.00356
+ ,0.003
+ ,0.03087
+ ,0.276
+ ,0.02402
+ ,122.336
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+ ,0.00727
+ ,0.00006
+ ,0.00415
+ ,0.00339
+ ,0.02293
+ ,0.223
+ ,0.01771
+ ,117.963
+ ,134.209
+ ,100.757
+ ,0.01813
+ ,0.00015
+ ,0.01117
+ ,0.00718
+ ,0.04912
+ ,0.438
+ ,0.02916
+ ,126.144
+ ,154.284
+ ,97.543
+ ,0.00975
+ ,0.00008
+ ,0.00593
+ ,0.00454
+ ,0.02852
+ ,0.266
+ ,0.02157
+ ,127.93
+ ,138.752
+ ,112.173
+ ,0.00605
+ ,0.00005
+ ,0.00321
+ ,0.00318
+ ,0.03235
+ ,0.339
+ ,0.03105
+ ,114.238
+ ,124.393
+ ,77.022
+ ,0.00581
+ ,0.00005
+ ,0.00299
+ ,0.00316
+ ,0.04009
+ ,0.406
+ ,0.04114
+ ,115.322
+ ,135.738
+ ,107.802
+ ,0.00619
+ ,0.00005
+ ,0.00352
+ ,0.00329
+ ,0.03273
+ ,0.325
+ ,0.02931
+ ,114.554
+ ,126.778
+ ,91.121
+ ,0.00651
+ ,0.00006
+ ,0.00366
+ ,0.0034
+ ,0.03658
+ ,0.369
+ ,0.03091
+ ,112.15
+ ,131.669
+ ,97.527
+ ,0.00519
+ ,0.00005
+ ,0.00291
+ ,0.00284
+ ,0.01756
+ ,0.155
+ ,0.01363
+ ,102.273
+ ,142.83
+ ,85.902
+ ,0.00907
+ ,0.00009
+ ,0.00493
+ ,0.00461
+ ,0.02814
+ ,0.272
+ ,0.02073
+ ,236.2
+ ,244.663
+ ,102.137
+ ,0.00277
+ ,0.00001
+ ,0.00154
+ ,0.00153
+ ,0.02448
+ ,0.217
+ ,0.01621
+ ,237.323
+ ,243.709
+ ,229.256
+ ,0.00303
+ ,0.00001
+ ,0.00173
+ ,0.00159
+ ,0.01242
+ ,0.116
+ ,0.00882
+ ,260.105
+ ,264.919
+ ,237.303
+ ,0.00339
+ ,0.00001
+ ,0.00205
+ ,0.00186
+ ,0.0203
+ ,0.197
+ ,0.01367
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+ ,217.627
+ ,90.794
+ ,0.00803
+ ,0.00004
+ ,0.0049
+ ,0.00448
+ ,0.02177
+ ,0.189
+ ,0.01439
+ ,240.301
+ ,245.135
+ ,219.783
+ ,0.00517
+ ,0.00002
+ ,0.00316
+ ,0.00283
+ ,0.02018
+ ,0.212
+ ,0.01344
+ ,244.99
+ ,272.21
+ ,239.17
+ ,0.00451
+ ,0.00002
+ ,0.00279
+ ,0.00237
+ ,0.01897
+ ,0.181
+ ,0.01255
+ ,112.547
+ ,133.374
+ ,105.715
+ ,0.00355
+ ,0.00003
+ ,0.00166
+ ,0.0019
+ ,0.01358
+ ,0.129
+ ,0.0114
+ ,110.739
+ ,113.597
+ ,100.139
+ ,0.00356
+ ,0.00003
+ ,0.0017
+ ,0.002
+ ,0.01484
+ ,0.133
+ ,0.01285
+ ,113.715
+ ,116.443
+ ,96.913
+ ,0.00349
+ ,0.00003
+ ,0.00171
+ ,0.00203
+ ,0.01472
+ ,0.133
+ ,0.01148
+ ,117.004
+ ,144.466
+ ,99.923
+ ,0.00353
+ ,0.00003
+ ,0.00176
+ ,0.00218
+ ,0.01657
+ ,0.145
+ ,0.01318
+ ,115.38
+ ,123.109
+ ,108.634
+ ,0.00332
+ ,0.00003
+ ,0.0016
+ ,0.00199
+ ,0.01503
+ ,0.137
+ ,0.01133
+ ,116.388
+ ,129.038
+ ,108.97
+ ,0.00346
+ ,0.00003
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+ ,0.00213
+ ,0.01725
+ ,0.155
+ ,0.01331
+ ,151.737
+ ,190.204
+ ,129.859
+ ,0.00314
+ ,0.00002
+ ,0.00135
+ ,0.00162
+ ,0.01469
+ ,0.132
+ ,0.0123
+ ,148.79
+ ,158.359
+ ,138.99
+ ,0.00309
+ ,0.00002
+ ,0.00152
+ ,0.00186
+ ,0.01574
+ ,0.142
+ ,0.01309
+ ,148.143
+ ,155.982
+ ,135.041
+ ,0.00392
+ ,0.00003
+ ,0.00204
+ ,0.00231
+ ,0.0145
+ ,0.131
+ ,0.01263
+ ,150.44
+ ,163.441
+ ,144.736
+ ,0.00396
+ ,0.00003
+ ,0.00206
+ ,0.00233
+ ,0.02551
+ ,0.237
+ ,0.02148
+ ,148.462
+ ,161.078
+ ,141.998
+ ,0.00397
+ ,0.00003
+ ,0.00202
+ ,0.00235
+ ,0.01831
+ ,0.163
+ ,0.01559
+ ,149.818
+ ,163.417
+ ,144.786
+ ,0.00336
+ ,0.00002
+ ,0.00174
+ ,0.00198
+ ,0.02145
+ ,0.198
+ ,0.01666
+ ,117.226
+ ,123.925
+ ,106.656
+ ,0.00417
+ ,0.00004
+ ,0.00186
+ ,0.0027
+ ,0.01909
+ ,0.171
+ ,0.01949
+ ,116.848
+ ,217.552
+ ,99.503
+ ,0.00531
+ ,0.00005
+ ,0.0026
+ ,0.00346
+ ,0.01795
+ ,0.163
+ ,0.01756
+ ,116.286
+ ,177.291
+ ,96.983
+ ,0.00314
+ ,0.00003
+ ,0.00134
+ ,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])
+ }
+ }
> par3 = 'No Linear Trend'
> par2 = 'Do not include Seasonal Dummies'
> par1 = '1'
> par3 <- 'No Linear Trend'
> par2 <- 'Do not include Seasonal Dummies'
> par1 <- '1'
> #'GNU S' R Code compiled by R2WASP v. 1.2.327 ()
> #Author: root
> #To cite this work: Wessa P., (2013), Multiple Regression (v1.0.29) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_multipleregression.wasp/
> #Source of accompanying publication: Office for Research, Development, and Education
> #
> library(lattice)
> library(lmtest)
Loading required package: zoo
Attaching package: 'zoo'
The following objects are masked from 'package:base':
as.Date, as.Date.numeric
> n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
> par1 <- as.numeric(par1)
> x <- t(y)
> k <- length(x[1,])
> n <- length(x[,1])
> x1 <- cbind(x[,par1], x[,1:k!=par1])
> mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
> colnames(x1) <- mycolnames #colnames(x)[par1]
> x <- x1
> if (par3 == 'First Differences'){
+ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
+ for (i in 1:n-1) {
+ for (j in 1:k) {
+ x2[i,j] <- x[i+1,j] - x[i,j]
+ }
+ }
+ x <- x2
+ }
> if (par2 == 'Include Monthly Dummies'){
+ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
+ for (i in 1:11){
+ x2[seq(i,n,12),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> if (par2 == 'Include Quarterly Dummies'){
+ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
+ for (i in 1:3){
+ x2[seq(i,n,4),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> k <- length(x[1,])
> if (par3 == 'Linear Trend'){
+ x <- cbind(x, c(1:n))
+ colnames(x)[k+1] <- 't'
+ }
> x
MDVP:Fo(Hz) MDVP:Fhi(Hz) MDVP:Flo(Hz) MDVP:Jitter(%) MDVP:Jitter(Abs)
1 119.992 157.302 74.997 0.00784 7.0e-05
2 122.400 148.650 113.819 0.00968 8.0e-05
3 116.682 131.111 111.555 0.01050 9.0e-05
4 116.676 137.871 111.366 0.00997 9.0e-05
5 116.014 141.781 110.655 0.01284 1.1e-04
6 120.552 131.162 113.787 0.00968 8.0e-05
7 120.267 137.244 114.820 0.00333 3.0e-05
8 107.332 113.840 104.315 0.00290 3.0e-05
9 95.730 132.068 91.754 0.00551 6.0e-05
10 95.056 120.103 91.226 0.00532 6.0e-05
11 88.333 112.240 84.072 0.00505 6.0e-05
12 91.904 115.871 86.292 0.00540 6.0e-05
13 136.926 159.866 131.276 0.00293 2.0e-05
14 139.173 179.139 76.556 0.00390 3.0e-05
15 152.845 163.305 75.836 0.00294 2.0e-05
16 142.167 217.455 83.159 0.00369 3.0e-05
17 144.188 349.259 82.764 0.00544 4.0e-05
18 168.778 232.181 75.603 0.00718 4.0e-05
19 153.046 175.829 68.623 0.00742 5.0e-05
20 156.405 189.398 142.822 0.00768 5.0e-05
21 153.848 165.738 65.782 0.00840 5.0e-05
22 153.880 172.860 78.128 0.00480 3.0e-05
23 167.930 193.221 79.068 0.00442 3.0e-05
24 173.917 192.735 86.180 0.00476 3.0e-05
25 163.656 200.841 76.779 0.00742 5.0e-05
26 104.400 206.002 77.968 0.00633 6.0e-05
27 171.041 208.313 75.501 0.00455 3.0e-05
28 146.845 208.701 81.737 0.00496 3.0e-05
29 155.358 227.383 80.055 0.00310 2.0e-05
30 162.568 198.346 77.630 0.00502 3.0e-05
31 197.076 206.896 192.055 0.00289 1.0e-05
32 199.228 209.512 192.091 0.00241 1.0e-05
33 198.383 215.203 193.104 0.00212 1.0e-05
34 202.266 211.604 197.079 0.00180 9.0e-06
35 203.184 211.526 196.160 0.00178 9.0e-06
36 201.464 210.565 195.708 0.00198 1.0e-05
37 177.876 192.921 168.013 0.00411 2.0e-05
38 176.170 185.604 163.564 0.00369 2.0e-05
39 180.198 201.249 175.456 0.00284 2.0e-05
40 187.733 202.324 173.015 0.00316 2.0e-05
41 186.163 197.724 177.584 0.00298 2.0e-05
42 184.055 196.537 166.977 0.00258 1.0e-05
43 237.226 247.326 225.227 0.00298 1.0e-05
44 241.404 248.834 232.483 0.00281 1.0e-05
45 243.439 250.912 232.435 0.00210 9.0e-06
46 242.852 255.034 227.911 0.00225 9.0e-06
47 245.510 262.090 231.848 0.00235 1.0e-05
48 252.455 261.487 182.786 0.00185 7.0e-06
49 122.188 128.611 115.765 0.00524 4.0e-05
50 122.964 130.049 114.676 0.00428 3.0e-05
51 124.445 135.069 117.495 0.00431 3.0e-05
52 126.344 134.231 112.773 0.00448 4.0e-05
53 128.001 138.052 122.080 0.00436 3.0e-05
54 129.336 139.867 118.604 0.00490 4.0e-05
55 108.807 134.656 102.874 0.00761 7.0e-05
56 109.860 126.358 104.437 0.00874 8.0e-05
57 110.417 131.067 103.370 0.00784 7.0e-05
58 117.274 129.916 110.402 0.00752 6.0e-05
59 116.879 131.897 108.153 0.00788 7.0e-05
60 114.847 271.314 104.680 0.00867 8.0e-05
61 209.144 237.494 109.379 0.00282 1.0e-05
62 223.365 238.987 98.664 0.00264 1.0e-05
63 222.236 231.345 205.495 0.00266 1.0e-05
64 228.832 234.619 223.634 0.00296 1.0e-05
65 229.401 252.221 221.156 0.00205 9.0e-06
66 228.969 239.541 113.201 0.00238 1.0e-05
67 140.341 159.774 67.021 0.00817 6.0e-05
68 136.969 166.607 66.004 0.00923 7.0e-05
69 143.533 162.215 65.809 0.01101 8.0e-05
70 148.090 162.824 67.343 0.00762 5.0e-05
71 142.729 162.408 65.476 0.00831 6.0e-05
72 136.358 176.595 65.750 0.00971 7.0e-05
73 120.080 139.710 111.208 0.00405 3.0e-05
74 112.014 588.518 107.024 0.00533 5.0e-05
75 110.793 128.101 107.316 0.00494 4.0e-05
76 110.707 122.611 105.007 0.00516 5.0e-05
77 112.876 148.826 106.981 0.00500 4.0e-05
78 110.568 125.394 106.821 0.00462 4.0e-05
79 95.385 102.145 90.264 0.00608 6.0e-05
80 100.770 115.697 85.545 0.01038 1.0e-04
81 96.106 108.664 84.510 0.00694 7.0e-05
82 95.605 107.715 87.549 0.00702 7.0e-05
83 100.960 110.019 95.628 0.00606 6.0e-05
84 98.804 102.305 87.804 0.00432 4.0e-05
85 176.858 205.560 75.344 0.00747 4.0e-05
86 180.978 200.125 155.495 0.00406 2.0e-05
87 178.222 202.450 141.047 0.00321 2.0e-05
88 176.281 227.381 125.610 0.00520 3.0e-05
89 173.898 211.350 74.677 0.00448 3.0e-05
90 179.711 225.930 144.878 0.00709 4.0e-05
91 166.605 206.008 78.032 0.00742 4.0e-05
92 151.955 163.335 147.226 0.00419 3.0e-05
93 148.272 164.989 142.299 0.00459 3.0e-05
94 152.125 161.469 76.596 0.00382 3.0e-05
95 157.821 172.975 68.401 0.00358 2.0e-05
96 157.447 163.267 149.605 0.00369 2.0e-05
97 159.116 168.913 144.811 0.00342 2.0e-05
98 125.036 143.946 116.187 0.01280 1.0e-04
99 125.791 140.557 96.206 0.01378 1.1e-04
100 126.512 141.756 99.770 0.01936 1.5e-04
101 125.641 141.068 116.346 0.03316 2.6e-04
102 128.451 150.449 75.632 0.01551 1.2e-04
103 139.224 586.567 66.157 0.03011 2.2e-04
104 150.258 154.609 75.349 0.00248 2.0e-05
105 154.003 160.267 128.621 0.00183 1.0e-05
106 149.689 160.368 133.608 0.00257 2.0e-05
107 155.078 163.736 144.148 0.00168 1.0e-05
108 151.884 157.765 133.751 0.00258 2.0e-05
109 151.989 157.339 132.857 0.00174 1.0e-05
110 193.030 208.900 80.297 0.00766 4.0e-05
111 200.714 223.982 89.686 0.00621 3.0e-05
112 208.519 220.315 199.020 0.00609 3.0e-05
113 204.664 221.300 189.621 0.00841 4.0e-05
114 210.141 232.706 185.258 0.00534 3.0e-05
115 206.327 226.355 92.020 0.00495 2.0e-05
116 151.872 492.892 69.085 0.00856 6.0e-05
117 158.219 442.557 71.948 0.00476 3.0e-05
118 170.756 450.247 79.032 0.00555 3.0e-05
119 178.285 442.824 82.063 0.00462 3.0e-05
120 217.116 233.481 93.978 0.00404 2.0e-05
121 128.940 479.697 88.251 0.00581 5.0e-05
122 176.824 215.293 83.961 0.00460 3.0e-05
123 138.190 203.522 83.340 0.00704 5.0e-05
124 182.018 197.173 79.187 0.00842 5.0e-05
125 156.239 195.107 79.820 0.00694 4.0e-05
126 145.174 198.109 80.637 0.00733 5.0e-05
127 138.145 197.238 81.114 0.00544 4.0e-05
128 166.888 198.966 79.512 0.00638 4.0e-05
129 119.031 127.533 109.216 0.00440 4.0e-05
130 120.078 126.632 105.667 0.00270 2.0e-05
131 120.289 128.143 100.209 0.00492 4.0e-05
132 120.256 125.306 104.773 0.00407 3.0e-05
133 119.056 125.213 86.795 0.00346 3.0e-05
134 118.747 123.723 109.836 0.00331 3.0e-05
135 106.516 112.777 93.105 0.00589 6.0e-05
136 110.453 127.611 105.554 0.00494 4.0e-05
137 113.400 133.344 107.816 0.00451 4.0e-05
138 113.166 130.270 100.673 0.00502 4.0e-05
139 112.239 126.609 104.095 0.00472 4.0e-05
140 116.150 131.731 109.815 0.00381 3.0e-05
141 170.368 268.796 79.543 0.00571 3.0e-05
142 208.083 253.792 91.802 0.00757 4.0e-05
143 198.458 219.290 148.691 0.00376 2.0e-05
144 202.805 231.508 86.232 0.00370 2.0e-05
145 202.544 241.350 164.168 0.00254 1.0e-05
146 223.361 263.872 87.638 0.00352 2.0e-05
147 169.774 191.759 151.451 0.01568 9.0e-05
148 183.520 216.814 161.340 0.01466 8.0e-05
149 188.620 216.302 165.982 0.01719 9.0e-05
150 202.632 565.740 177.258 0.01627 8.0e-05
151 186.695 211.961 149.442 0.01872 1.0e-04
152 192.818 224.429 168.793 0.03107 1.6e-04
153 198.116 233.099 174.478 0.02714 1.4e-04
154 121.345 139.644 98.250 0.00684 6.0e-05
155 119.100 128.442 88.833 0.00692 6.0e-05
156 117.870 127.349 95.654 0.00647 5.0e-05
157 122.336 142.369 94.794 0.00727 6.0e-05
158 117.963 134.209 100.757 0.01813 1.5e-04
159 126.144 154.284 97.543 0.00975 8.0e-05
160 127.930 138.752 112.173 0.00605 5.0e-05
161 114.238 124.393 77.022 0.00581 5.0e-05
162 115.322 135.738 107.802 0.00619 5.0e-05
163 114.554 126.778 91.121 0.00651 6.0e-05
164 112.150 131.669 97.527 0.00519 5.0e-05
165 102.273 142.830 85.902 0.00907 9.0e-05
166 236.200 244.663 102.137 0.00277 1.0e-05
167 237.323 243.709 229.256 0.00303 1.0e-05
168 260.105 264.919 237.303 0.00339 1.0e-05
169 197.569 217.627 90.794 0.00803 4.0e-05
170 240.301 245.135 219.783 0.00517 2.0e-05
171 244.990 272.210 239.170 0.00451 2.0e-05
172 112.547 133.374 105.715 0.00355 3.0e-05
173 110.739 113.597 100.139 0.00356 3.0e-05
174 113.715 116.443 96.913 0.00349 3.0e-05
175 117.004 144.466 99.923 0.00353 3.0e-05
176 115.380 123.109 108.634 0.00332 3.0e-05
177 116.388 129.038 108.970 0.00346 3.0e-05
178 151.737 190.204 129.859 0.00314 2.0e-05
179 148.790 158.359 138.990 0.00309 2.0e-05
180 148.143 155.982 135.041 0.00392 3.0e-05
181 150.440 163.441 144.736 0.00396 3.0e-05
182 148.462 161.078 141.998 0.00397 3.0e-05
183 149.818 163.417 144.786 0.00336 2.0e-05
184 117.226 123.925 106.656 0.00417 4.0e-05
185 116.848 217.552 99.503 0.00531 5.0e-05
186 116.286 177.291 96.983 0.00314 3.0e-05
187 116.556 592.030 86.228 0.00496 4.0e-05
188 116.342 581.289 94.246 0.00267 2.0e-05
189 114.563 119.167 86.647 0.00327 3.0e-05
190 201.774 262.707 78.228 0.00694 3.0e-05
191 174.188 230.978 94.261 0.00459 3.0e-05
192 209.516 253.017 89.488 0.00564 3.0e-05
193 174.688 240.005 74.287 0.01360 8.0e-05
194 198.764 396.961 74.904 0.00740 4.0e-05
195 214.289 260.277 77.973 0.00567 3.0e-05
MDVP:RAP MDVP:PPQ MDVP:Shimmer MDVP:Shimmer(dB) MDVP:APQ
1 0.00370 0.00554 0.04374 0.426 0.02971
2 0.00465 0.00696 0.06134 0.626 0.04368
3 0.00544 0.00781 0.05233 0.482 0.03590
4 0.00502 0.00698 0.05492 0.517 0.03772
5 0.00655 0.00908 0.06425 0.584 0.04465
6 0.00463 0.00750 0.04701 0.456 0.03243
7 0.00155 0.00202 0.01608 0.140 0.01351
8 0.00144 0.00182 0.01567 0.134 0.01256
9 0.00293 0.00332 0.02093 0.191 0.01717
10 0.00268 0.00332 0.02838 0.255 0.02444
11 0.00254 0.00330 0.02143 0.197 0.01892
12 0.00281 0.00336 0.02752 0.249 0.02214
13 0.00118 0.00153 0.01259 0.112 0.01140
14 0.00165 0.00208 0.01642 0.154 0.01797
15 0.00121 0.00149 0.01828 0.158 0.01246
16 0.00157 0.00203 0.01503 0.126 0.01359
17 0.00211 0.00292 0.02047 0.192 0.02074
18 0.00284 0.00387 0.03327 0.348 0.03430
19 0.00364 0.00432 0.05517 0.542 0.05767
20 0.00372 0.00399 0.03995 0.348 0.04310
21 0.00428 0.00450 0.03810 0.328 0.04055
22 0.00232 0.00267 0.04137 0.370 0.04525
23 0.00220 0.00247 0.04351 0.377 0.04246
24 0.00221 0.00258 0.04192 0.364 0.03772
25 0.00380 0.00390 0.01659 0.164 0.01497
26 0.00316 0.00375 0.03767 0.381 0.03780
27 0.00250 0.00234 0.01966 0.186 0.01872
28 0.00250 0.00275 0.01919 0.198 0.01826
29 0.00159 0.00176 0.01718 0.161 0.01661
30 0.00280 0.00253 0.01791 0.168 0.01799
31 0.00166 0.00168 0.01098 0.097 0.00802
32 0.00134 0.00138 0.01015 0.089 0.00762
33 0.00113 0.00135 0.01263 0.111 0.00951
34 0.00093 0.00107 0.00954 0.085 0.00719
35 0.00094 0.00106 0.00958 0.085 0.00726
36 0.00105 0.00115 0.01194 0.107 0.00957
37 0.00233 0.00241 0.02126 0.189 0.01612
38 0.00205 0.00218 0.01851 0.168 0.01491
39 0.00153 0.00166 0.01444 0.131 0.01190
40 0.00168 0.00182 0.01663 0.151 0.01366
41 0.00165 0.00175 0.01495 0.135 0.01233
42 0.00134 0.00147 0.01463 0.132 0.01234
43 0.00169 0.00182 0.01752 0.164 0.01133
44 0.00157 0.00173 0.01760 0.154 0.01251
45 0.00109 0.00137 0.01419 0.126 0.01033
46 0.00117 0.00139 0.01494 0.134 0.01014
47 0.00127 0.00148 0.01608 0.141 0.01149
48 0.00092 0.00113 0.01152 0.103 0.00860
49 0.00169 0.00203 0.01613 0.143 0.01433
50 0.00124 0.00155 0.01681 0.154 0.01400
51 0.00141 0.00167 0.02184 0.197 0.01685
52 0.00131 0.00169 0.02033 0.185 0.01614
53 0.00137 0.00166 0.02297 0.210 0.01677
54 0.00165 0.00183 0.02498 0.228 0.01947
55 0.00349 0.00486 0.02719 0.255 0.02067
56 0.00398 0.00539 0.03209 0.307 0.02454
57 0.00352 0.00514 0.03715 0.334 0.02802
58 0.00299 0.00469 0.02293 0.221 0.01948
59 0.00334 0.00493 0.02645 0.265 0.02137
60 0.00373 0.00520 0.03225 0.350 0.02519
61 0.00147 0.00152 0.01861 0.170 0.01382
62 0.00154 0.00151 0.01906 0.165 0.01340
63 0.00152 0.00144 0.01643 0.145 0.01200
64 0.00175 0.00155 0.01644 0.145 0.01179
65 0.00114 0.00113 0.01457 0.129 0.01016
66 0.00136 0.00140 0.01745 0.154 0.01234
67 0.00430 0.00440 0.03198 0.313 0.02428
68 0.00507 0.00463 0.03111 0.308 0.02603
69 0.00647 0.00467 0.05384 0.478 0.03392
70 0.00467 0.00354 0.05428 0.497 0.03635
71 0.00469 0.00419 0.03485 0.365 0.02949
72 0.00534 0.00478 0.04978 0.483 0.03736
73 0.00180 0.00220 0.01706 0.152 0.01345
74 0.00268 0.00329 0.02448 0.226 0.01956
75 0.00260 0.00283 0.02442 0.216 0.01831
76 0.00277 0.00289 0.02215 0.206 0.01715
77 0.00270 0.00289 0.03999 0.350 0.02704
78 0.00226 0.00280 0.02199 0.197 0.01636
79 0.00331 0.00332 0.03202 0.263 0.02455
80 0.00622 0.00576 0.03121 0.361 0.02139
81 0.00389 0.00415 0.04024 0.364 0.02876
82 0.00428 0.00371 0.03156 0.296 0.02190
83 0.00351 0.00348 0.02427 0.216 0.01751
84 0.00247 0.00258 0.02223 0.202 0.01552
85 0.00418 0.00420 0.04795 0.435 0.03510
86 0.00220 0.00244 0.03852 0.331 0.02877
87 0.00163 0.00194 0.03759 0.327 0.02784
88 0.00287 0.00312 0.06511 0.580 0.04683
89 0.00237 0.00254 0.06727 0.650 0.04802
90 0.00391 0.00419 0.04313 0.442 0.03455
91 0.00387 0.00453 0.06640 0.634 0.05114
92 0.00224 0.00227 0.07959 0.772 0.05690
93 0.00250 0.00256 0.04190 0.383 0.03051
94 0.00191 0.00226 0.05925 0.637 0.04398
95 0.00196 0.00196 0.03716 0.307 0.02764
96 0.00201 0.00197 0.03272 0.283 0.02571
97 0.00178 0.00184 0.03381 0.307 0.02809
98 0.00743 0.00623 0.03886 0.342 0.03088
99 0.00826 0.00655 0.04689 0.422 0.03908
100 0.01159 0.00990 0.06734 0.659 0.05783
101 0.02144 0.01522 0.09178 0.891 0.06196
102 0.00905 0.00909 0.06170 0.584 0.05174
103 0.01854 0.01628 0.09419 0.930 0.06023
104 0.00105 0.00136 0.01131 0.107 0.01009
105 0.00076 0.00100 0.01030 0.094 0.00871
106 0.00116 0.00134 0.01346 0.126 0.01059
107 0.00068 0.00092 0.01064 0.097 0.00928
108 0.00115 0.00122 0.01450 0.137 0.01267
109 0.00075 0.00096 0.01024 0.093 0.00993
110 0.00450 0.00389 0.03044 0.275 0.02084
111 0.00371 0.00337 0.02286 0.207 0.01852
112 0.00368 0.00339 0.01761 0.155 0.01307
113 0.00502 0.00485 0.02378 0.210 0.01767
114 0.00321 0.00280 0.01680 0.149 0.01301
115 0.00302 0.00246 0.02105 0.209 0.01604
116 0.00404 0.00385 0.01843 0.235 0.01271
117 0.00214 0.00207 0.01458 0.148 0.01312
118 0.00244 0.00261 0.01725 0.175 0.01652
119 0.00157 0.00194 0.01279 0.129 0.01151
120 0.00127 0.00128 0.01299 0.124 0.01075
121 0.00241 0.00314 0.02008 0.221 0.01734
122 0.00209 0.00221 0.01169 0.117 0.01104
123 0.00406 0.00398 0.04479 0.441 0.03220
124 0.00506 0.00449 0.02503 0.231 0.01931
125 0.00403 0.00395 0.02343 0.224 0.01720
126 0.00414 0.00422 0.02362 0.233 0.01944
127 0.00294 0.00327 0.02791 0.246 0.02259
128 0.00368 0.00351 0.02857 0.257 0.02301
129 0.00214 0.00192 0.01033 0.098 0.00811
130 0.00116 0.00135 0.01022 0.090 0.00903
131 0.00269 0.00238 0.01412 0.125 0.01194
132 0.00224 0.00205 0.01516 0.138 0.01310
133 0.00169 0.00170 0.01201 0.106 0.00915
134 0.00168 0.00171 0.01043 0.099 0.00903
135 0.00291 0.00319 0.04932 0.441 0.03651
136 0.00244 0.00315 0.04128 0.379 0.03316
137 0.00219 0.00283 0.04879 0.431 0.04370
138 0.00257 0.00312 0.05279 0.476 0.04134
139 0.00238 0.00290 0.05643 0.517 0.04451
140 0.00181 0.00232 0.03026 0.267 0.02770
141 0.00232 0.00269 0.03273 0.281 0.02824
142 0.00428 0.00428 0.06725 0.571 0.04464
143 0.00182 0.00215 0.03527 0.297 0.02530
144 0.00189 0.00211 0.01997 0.180 0.01506
145 0.00100 0.00133 0.02662 0.228 0.02006
146 0.00169 0.00188 0.02536 0.225 0.01909
147 0.00863 0.00946 0.08143 0.821 0.08808
148 0.00849 0.00819 0.06050 0.618 0.06359
149 0.00996 0.01027 0.07118 0.722 0.06824
150 0.00919 0.00963 0.07170 0.833 0.06460
151 0.01075 0.01154 0.05830 0.784 0.06259
152 0.01800 0.01958 0.11908 1.302 0.13778
153 0.01568 0.01699 0.08684 1.018 0.08318
154 0.00388 0.00332 0.02534 0.241 0.02056
155 0.00393 0.00300 0.02682 0.236 0.02018
156 0.00356 0.00300 0.03087 0.276 0.02402
157 0.00415 0.00339 0.02293 0.223 0.01771
158 0.01117 0.00718 0.04912 0.438 0.02916
159 0.00593 0.00454 0.02852 0.266 0.02157
160 0.00321 0.00318 0.03235 0.339 0.03105
161 0.00299 0.00316 0.04009 0.406 0.04114
162 0.00352 0.00329 0.03273 0.325 0.02931
163 0.00366 0.00340 0.03658 0.369 0.03091
164 0.00291 0.00284 0.01756 0.155 0.01363
165 0.00493 0.00461 0.02814 0.272 0.02073
166 0.00154 0.00153 0.02448 0.217 0.01621
167 0.00173 0.00159 0.01242 0.116 0.00882
168 0.00205 0.00186 0.02030 0.197 0.01367
169 0.00490 0.00448 0.02177 0.189 0.01439
170 0.00316 0.00283 0.02018 0.212 0.01344
171 0.00279 0.00237 0.01897 0.181 0.01255
172 0.00166 0.00190 0.01358 0.129 0.01140
173 0.00170 0.00200 0.01484 0.133 0.01285
174 0.00171 0.00203 0.01472 0.133 0.01148
175 0.00176 0.00218 0.01657 0.145 0.01318
176 0.00160 0.00199 0.01503 0.137 0.01133
177 0.00169 0.00213 0.01725 0.155 0.01331
178 0.00135 0.00162 0.01469 0.132 0.01230
179 0.00152 0.00186 0.01574 0.142 0.01309
180 0.00204 0.00231 0.01450 0.131 0.01263
181 0.00206 0.00233 0.02551 0.237 0.02148
182 0.00202 0.00235 0.01831 0.163 0.01559
183 0.00174 0.00198 0.02145 0.198 0.01666
184 0.00186 0.00270 0.01909 0.171 0.01949
185 0.00260 0.00346 0.01795 0.163 0.01756
186 0.00134 0.00192 0.01564 0.136 0.01691
187 0.00254 0.00263 0.01660 0.154 0.01491
188 0.00115 0.00148 0.01300 0.117 0.01144
189 0.00146 0.00184 0.01185 0.106 0.01095
190 0.00412 0.00396 0.02574 0.255 0.01758
191 0.00263 0.00259 0.04087 0.405 0.02745
192 0.00331 0.00292 0.02751 0.263 0.01879
193 0.00624 0.00564 0.02308 0.256 0.01667
194 0.00370 0.00390 0.02296 0.241 0.01588
195 0.00295 0.00317 0.01884 0.190 0.01373
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) `MDVP:Fhi(Hz)` `MDVP:Flo(Hz)` `MDVP:Jitter(%)`
1.190e+02 6.479e-02 2.787e-01 1.305e+04
`MDVP:Jitter(Abs)` `MDVP:RAP` `MDVP:PPQ` `MDVP:Shimmer`
-2.403e+06 8.285e+03 -3.238e+03 2.140e+03
`MDVP:Shimmer(dB)` `MDVP:APQ`
-9.430e+01 -1.610e+03
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-56.524 -13.604 0.697 11.913 54.672
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.190e+02 7.595e+00 15.673 < 2e-16 ***
`MDVP:Fhi(Hz)` 6.479e-02 1.727e-02 3.752 0.000235 ***
`MDVP:Flo(Hz)` 2.787e-01 3.849e-02 7.240 1.17e-11 ***
`MDVP:Jitter(%)` 1.305e+04 3.331e+03 3.919 0.000125 ***
`MDVP:Jitter(Abs)` -2.403e+06 1.569e+05 -15.320 < 2e-16 ***
`MDVP:RAP` 8.285e+03 3.874e+03 2.139 0.033772 *
`MDVP:PPQ` -3.238e+03 2.744e+03 -1.180 0.239649
`MDVP:Shimmer` 2.140e+03 5.727e+02 3.737 0.000248 ***
`MDVP:Shimmer(dB)` -9.430e+01 6.230e+01 -1.514 0.131795
`MDVP:APQ` -1.610e+03 3.391e+02 -4.747 4.13e-06 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 19.37 on 185 degrees of freedom
Multiple R-squared: 0.7912, Adjusted R-squared: 0.781
F-statistic: 77.89 on 9 and 185 DF, p-value: < 2.2e-16
> if (n > n25) {
+ kp3 <- k + 3
+ nmkm3 <- n - k - 3
+ gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
+ numgqtests <- 0
+ numsignificant1 <- 0
+ numsignificant5 <- 0
+ numsignificant10 <- 0
+ for (mypoint in kp3:nmkm3) {
+ j <- 0
+ numgqtests <- numgqtests + 1
+ for (myalt in c('greater', 'two.sided', 'less')) {
+ j <- j + 1
+ gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
+ }
+ if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
+ }
+ gqarr
+ }
[,1] [,2] [,3]
[1,] 3.406400e-05 6.812800e-05 0.9999659
[2,] 6.894285e-04 1.378857e-03 0.9993106
[3,] 1.630041e-03 3.260082e-03 0.9983700
[4,] 3.874891e-04 7.749782e-04 0.9996125
[5,] 4.070032e-04 8.140065e-04 0.9995930
[6,] 1.491189e-04 2.982378e-04 0.9998509
[7,] 4.513241e-05 9.026481e-05 0.9999549
[8,] 1.184505e-05 2.369010e-05 0.9999882
[9,] 3.661754e-05 7.323509e-05 0.9999634
[10,] 1.087431e-05 2.174861e-05 0.9999891
[11,] 8.024362e-06 1.604872e-05 0.9999920
[12,] 2.757551e-06 5.515102e-06 0.9999972
[13,] 6.221919e-05 1.244384e-04 0.9999378
[14,] 3.930086e-05 7.860173e-05 0.9999607
[15,] 3.940125e-05 7.880249e-05 0.9999606
[16,] 5.289411e-05 1.057882e-04 0.9999471
[17,] 2.105657e-05 4.211315e-05 0.9999789
[18,] 8.241891e-06 1.648378e-05 0.9999918
[19,] 2.359085e-05 4.718171e-05 0.9999764
[20,] 4.302839e-05 8.605677e-05 0.9999570
[21,] 4.455925e-05 8.911851e-05 0.9999554
[22,] 7.610534e-05 1.522107e-04 0.9999239
[23,] 8.305016e-05 1.661003e-04 0.9999169
[24,] 5.402515e-05 1.080503e-04 0.9999460
[25,] 1.727032e-04 3.454064e-04 0.9998273
[26,] 1.223617e-04 2.447235e-04 0.9998776
[27,] 6.698403e-05 1.339681e-04 0.9999330
[28,] 3.913536e-05 7.827072e-05 0.9999609
[29,] 2.287716e-05 4.575433e-05 0.9999771
[30,] 1.379188e-05 2.758375e-05 0.9999862
[31,] 9.584723e-06 1.916945e-05 0.9999904
[32,] 9.467963e-06 1.893593e-05 0.9999905
[33,] 3.893353e-05 7.786705e-05 0.9999611
[34,] 5.639212e-05 1.127842e-04 0.9999436
[35,] 6.940684e-05 1.388137e-04 0.9999306
[36,] 2.015071e-03 4.030141e-03 0.9979849
[37,] 1.370050e-03 2.740101e-03 0.9986299
[38,] 1.048579e-03 2.097158e-03 0.9989514
[39,] 1.029085e-03 2.058171e-03 0.9989709
[40,] 8.984484e-04 1.796897e-03 0.9991016
[41,] 9.217516e-04 1.843503e-03 0.9990782
[42,] 6.128231e-04 1.225646e-03 0.9993872
[43,] 5.041244e-04 1.008249e-03 0.9994959
[44,] 6.219278e-04 1.243856e-03 0.9993781
[45,] 4.305045e-04 8.610089e-04 0.9995695
[46,] 3.137209e-04 6.274418e-04 0.9996863
[47,] 3.987635e-04 7.975269e-04 0.9996012
[48,] 5.214784e-04 1.042957e-03 0.9994785
[49,] 5.545439e-04 1.109088e-03 0.9994455
[50,] 1.607932e-03 3.215864e-03 0.9983921
[51,] 1.149847e-03 2.299695e-03 0.9988502
[52,] 8.159541e-04 1.631908e-03 0.9991840
[53,] 7.188232e-04 1.437646e-03 0.9992812
[54,] 3.246073e-03 6.492146e-03 0.9967539
[55,] 2.407586e-03 4.815173e-03 0.9975924
[56,] 1.835011e-03 3.670021e-03 0.9981650
[57,] 1.953690e-03 3.907381e-03 0.9980463
[58,] 3.100081e-03 6.200162e-03 0.9968999
[59,] 2.436521e-03 4.873043e-03 0.9975635
[60,] 1.760764e-03 3.521528e-03 0.9982392
[61,] 2.490263e-03 4.980526e-03 0.9975097
[62,] 1.341888e-01 2.683777e-01 0.8658112
[63,] 1.819360e-01 3.638720e-01 0.8180640
[64,] 1.563735e-01 3.127471e-01 0.8436265
[65,] 2.384109e-01 4.768218e-01 0.7615891
[66,] 2.439916e-01 4.879832e-01 0.7560084
[67,] 2.110121e-01 4.220242e-01 0.7889879
[68,] 2.290136e-01 4.580271e-01 0.7709864
[69,] 2.048098e-01 4.096196e-01 0.7951902
[70,] 1.756809e-01 3.513617e-01 0.8243191
[71,] 1.503176e-01 3.006351e-01 0.8496824
[72,] 1.724753e-01 3.449505e-01 0.8275247
[73,] 1.527619e-01 3.055238e-01 0.8472381
[74,] 1.461748e-01 2.923496e-01 0.8538252
[75,] 1.244484e-01 2.488968e-01 0.8755516
[76,] 1.095705e-01 2.191411e-01 0.8904295
[77,] 9.966805e-02 1.993361e-01 0.9003320
[78,] 1.164721e-01 2.329441e-01 0.8835279
[79,] 1.098302e-01 2.196603e-01 0.8901698
[80,] 1.123422e-01 2.246844e-01 0.8876578
[81,] 1.294357e-01 2.588713e-01 0.8705643
[82,] 1.152932e-01 2.305863e-01 0.8847068
[83,] 9.959584e-02 1.991917e-01 0.9004042
[84,] 1.201772e-01 2.403544e-01 0.8798228
[85,] 1.164076e-01 2.328151e-01 0.8835924
[86,] 9.709764e-02 1.941953e-01 0.9029024
[87,] 8.437267e-02 1.687453e-01 0.9156273
[88,] 8.637525e-02 1.727505e-01 0.9136248
[89,] 7.240318e-02 1.448064e-01 0.9275968
[90,] 1.061702e-01 2.123403e-01 0.8938298
[91,] 1.536178e-01 3.072357e-01 0.8463822
[92,] 1.422685e-01 2.845369e-01 0.8577315
[93,] 1.365513e-01 2.731026e-01 0.8634487
[94,] 1.179986e-01 2.359971e-01 0.8820014
[95,] 1.138273e-01 2.276545e-01 0.8861727
[96,] 9.597688e-02 1.919538e-01 0.9040231
[97,] 9.384978e-02 1.876996e-01 0.9061502
[98,] 7.954880e-02 1.590976e-01 0.9204512
[99,] 6.735422e-02 1.347084e-01 0.9326458
[100,] 6.093057e-02 1.218611e-01 0.9390694
[101,] 7.583510e-02 1.516702e-01 0.9241649
[102,] 6.472835e-02 1.294567e-01 0.9352716
[103,] 5.370832e-02 1.074166e-01 0.9462917
[104,] 4.331029e-02 8.662057e-02 0.9566897
[105,] 3.551921e-02 7.103842e-02 0.9644808
[106,] 2.915423e-02 5.830847e-02 0.9708458
[107,] 3.021827e-02 6.043654e-02 0.9697817
[108,] 9.248220e-02 1.849644e-01 0.9075178
[109,] 9.016112e-02 1.803222e-01 0.9098389
[110,] 1.084214e-01 2.168428e-01 0.8915786
[111,] 9.937944e-02 1.987589e-01 0.9006206
[112,] 8.342917e-02 1.668583e-01 0.9165708
[113,] 8.044675e-02 1.608935e-01 0.9195533
[114,] 6.768403e-02 1.353681e-01 0.9323160
[115,] 5.490700e-02 1.098140e-01 0.9450930
[116,] 4.434252e-02 8.868505e-02 0.9556575
[117,] 3.624989e-02 7.249978e-02 0.9637501
[118,] 4.892000e-02 9.784000e-02 0.9510800
[119,] 4.579782e-02 9.159565e-02 0.9542022
[120,] 6.712293e-02 1.342459e-01 0.9328771
[121,] 6.119733e-02 1.223947e-01 0.9388027
[122,] 5.835253e-02 1.167051e-01 0.9416475
[123,] 5.630972e-02 1.126194e-01 0.9436903
[124,] 5.668267e-02 1.133653e-01 0.9433173
[125,] 4.685233e-02 9.370466e-02 0.9531477
[126,] 4.929810e-02 9.859620e-02 0.9507019
[127,] 5.135615e-02 1.027123e-01 0.9486438
[128,] 5.886598e-02 1.177320e-01 0.9411340
[129,] 4.712126e-02 9.424251e-02 0.9528787
[130,] 4.528069e-02 9.056138e-02 0.9547193
[131,] 3.669240e-02 7.338479e-02 0.9633076
[132,] 5.697366e-02 1.139473e-01 0.9430263
[133,] 4.491140e-02 8.982279e-02 0.9550886
[134,] 2.036324e-01 4.072648e-01 0.7963676
[135,] 2.258030e-01 4.516060e-01 0.7741970
[136,] 2.146864e-01 4.293728e-01 0.7853136
[137,] 2.399610e-01 4.799220e-01 0.7600390
[138,] 3.678118e-01 7.356236e-01 0.6321882
[139,] 3.343161e-01 6.686322e-01 0.6656839
[140,] 3.549157e-01 7.098313e-01 0.6450843
[141,] 6.771886e-01 6.456228e-01 0.3228114
[142,] 6.333450e-01 7.333101e-01 0.3666550
[143,] 5.936495e-01 8.127009e-01 0.4063505
[144,] 5.975124e-01 8.049752e-01 0.4024876
[145,] 5.428332e-01 9.143335e-01 0.4571668
[146,] 5.002288e-01 9.995425e-01 0.4997712
[147,] 4.405807e-01 8.811613e-01 0.5594193
[148,] 3.841378e-01 7.682756e-01 0.6158622
[149,] 3.743645e-01 7.487289e-01 0.6256355
[150,] 3.489717e-01 6.979435e-01 0.6510283
[151,] 2.928627e-01 5.857253e-01 0.7071373
[152,] 2.438741e-01 4.877483e-01 0.7561259
[153,] 4.110429e-01 8.220859e-01 0.5889571
[154,] 7.913271e-01 4.173459e-01 0.2086729
[155,] 7.825221e-01 4.349557e-01 0.2174779
[156,] 8.215165e-01 3.569670e-01 0.1784835
[157,] 7.835824e-01 4.328351e-01 0.2164176
[158,] 7.922360e-01 4.155280e-01 0.2077640
[159,] 8.098105e-01 3.803791e-01 0.1901895
[160,] 7.867484e-01 4.265032e-01 0.2132516
[161,] 7.676193e-01 4.647613e-01 0.2323807
[162,] 7.222494e-01 5.555012e-01 0.2777506
[163,] 6.483506e-01 7.032988e-01 0.3516494
[164,] 5.825974e-01 8.348053e-01 0.4174026
[165,] 5.665075e-01 8.669851e-01 0.4334925
[166,] 4.729929e-01 9.459858e-01 0.5270071
[167,] 3.671468e-01 7.342935e-01 0.6328532
[168,] 2.600664e-01 5.201329e-01 0.7399336
[169,] 1.887534e-01 3.775067e-01 0.8112466
[170,] 1.270702e-01 2.541405e-01 0.8729298
> postscript(file="/var/fisher/rcomp/tmp/13o651386770074.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
> points(x[,1]-mysum$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/2ibau1386770074.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/3874z1386770074.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/48cuy1386770074.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/5xpst1386770074.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
> qqline(mysum$resid)
> grid()
> dev.off()
null device
1
> (myerror <- as.ts(mysum$resid))
Time Series:
Start = 1
End = 195
Frequency = 1
1 2 3 4 5 6
17.4002349 9.9656023 8.7299544 16.7367004 18.2431633 7.7044015
7 8 9 10 11 12
-16.8024046 -20.6337906 2.1555930 8.7479229 9.6683441 2.8256863
13 14 15 16 17 18
-22.0928084 9.7495760 2.4102315 4.9469875 4.2220693 21.8492470
19 20 21 22 23 24
36.4806307 3.9851216 10.0120527 19.9160406 29.2804350 23.6988214
25 26 27 28 29 30
18.7052555 13.9557599 22.6253144 -5.9613072 4.5505886 7.0720243
31 32 33 34 35 36
-12.3383166 -2.0415914 1.6997552 7.6602774 9.0123596 7.3947419
37 38 39 40 41 42
-19.3036124 -10.2796657 3.5183123 6.7935404 6.5660233 -9.2387915
43 44 45 46 47 48
12.6257073 18.3911996 31.1307510 27.8244837 29.8819235 53.1477010
49 50 51 52 53 54
-15.1189571 -24.4071192 -27.5720863 -0.6398269 -27.1623109 -8.0219188
55 56 57 58 59 60
7.2003643 15.9373149 4.5233340 -1.3914012 15.5786111 18.5933318
61 62 63 64 65 66
22.6469265 39.3838378 10.1455562 5.6491777 18.8085017 46.1324325
67 68 69 70 71 72
8.5844909 13.8198865 -9.8714510 -17.6052686 12.6412885 -0.6048456
73 74 75 76 77 78
-28.0926969 -35.5490101 -29.6128922 -6.7060098 -36.8334093 -22.3568883
79 80 81 82 83 84
-11.4920511 24.0008414 -0.1958509 -6.0592311 -7.9768243 -27.2555260
85 86 87 88 89 90
-4.0228151 -14.5419626 0.8940107 -11.2363653 17.1709849 -4.6107132
91 92 93 94 95 96
-5.6680611 -18.4607403 -25.8080616 19.0525125 -6.1390902 -26.1987456
97 98 99 100 101 102
-14.7886970 -5.0869439 10.4103304 25.3611288 15.9415053 25.2460011
103 104 105 106 107 108
-18.4708298 13.7262272 -13.3360263 -7.6080176 -13.9782293 -3.5607832
109 110 111 112 113 114
-13.2145451 3.8521502 13.7784773 -9.2084728 -23.7524492 8.2918151
115 116 117 118 119 120
13.8453117 -3.7391327 -6.7416921 -5.4227459 16.0653901 46.9057140
121 122 123 124 125 126
-5.4822061 26.1110552 -12.1991592 10.2897704 -14.0894846 -2.6256294
127 128 129 130 131 132
-5.1092064 6.6439707 -11.3421202 -27.8822932 -16.8699920 -27.3992550
133 134 135 136 137 138
-14.8301842 -16.8618505 2.5414144 -23.8794115 -9.4894831 -24.5179616
139 140 141 142 143 144
-20.2049350 -22.1483424 0.6971975 4.4877471 7.8823872 34.1487575
145 146 147 148 149 150
5.8322261 54.6722628 11.7456454 -6.3235104 -22.4916221 -38.4822121
151 152 153 154 155 156
6.4194124 -4.9342219 -33.3741054 0.9692749 -4.6712537 -21.5378814
157 158 159 160 161 162
-6.0037481 -0.2384529 -0.7662520 6.2351413 14.1545775 -13.9340108
163 164 165 166 167 168
8.0619123 -6.1301482 20.7963569 47.0770438 12.4545704 23.7224696
169 170 171 172 173 174
-1.2692994 6.1227959 12.0805089 -24.9889357 -24.0853326 -21.4151826
175 176 177 178 179 180
-21.3230720 -20.9752341 -22.4319753 -13.8711667 -17.3109640 -5.4821091
181 182 183 184 185 186
-6.3168075 -8.1619825 -24.3787941 2.1009962 1.7078745 -8.4730498
187 188 189 190 191 192
-43.0260651 -56.5236496 -10.8109358 1.3550942 7.5930995 25.8151273
193 194 195
2.2522205 13.7783604 40.2552121
> postscript(file="/var/fisher/rcomp/tmp/65q781386770074.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> dum <- cbind(lag(myerror,k=1),myerror)
> dum
Time Series:
Start = 0
End = 195
Frequency = 1
lag(myerror, k = 1) myerror
0 17.4002349 NA
1 9.9656023 17.4002349
2 8.7299544 9.9656023
3 16.7367004 8.7299544
4 18.2431633 16.7367004
5 7.7044015 18.2431633
6 -16.8024046 7.7044015
7 -20.6337906 -16.8024046
8 2.1555930 -20.6337906
9 8.7479229 2.1555930
10 9.6683441 8.7479229
11 2.8256863 9.6683441
12 -22.0928084 2.8256863
13 9.7495760 -22.0928084
14 2.4102315 9.7495760
15 4.9469875 2.4102315
16 4.2220693 4.9469875
17 21.8492470 4.2220693
18 36.4806307 21.8492470
19 3.9851216 36.4806307
20 10.0120527 3.9851216
21 19.9160406 10.0120527
22 29.2804350 19.9160406
23 23.6988214 29.2804350
24 18.7052555 23.6988214
25 13.9557599 18.7052555
26 22.6253144 13.9557599
27 -5.9613072 22.6253144
28 4.5505886 -5.9613072
29 7.0720243 4.5505886
30 -12.3383166 7.0720243
31 -2.0415914 -12.3383166
32 1.6997552 -2.0415914
33 7.6602774 1.6997552
34 9.0123596 7.6602774
35 7.3947419 9.0123596
36 -19.3036124 7.3947419
37 -10.2796657 -19.3036124
38 3.5183123 -10.2796657
39 6.7935404 3.5183123
40 6.5660233 6.7935404
41 -9.2387915 6.5660233
42 12.6257073 -9.2387915
43 18.3911996 12.6257073
44 31.1307510 18.3911996
45 27.8244837 31.1307510
46 29.8819235 27.8244837
47 53.1477010 29.8819235
48 -15.1189571 53.1477010
49 -24.4071192 -15.1189571
50 -27.5720863 -24.4071192
51 -0.6398269 -27.5720863
52 -27.1623109 -0.6398269
53 -8.0219188 -27.1623109
54 7.2003643 -8.0219188
55 15.9373149 7.2003643
56 4.5233340 15.9373149
57 -1.3914012 4.5233340
58 15.5786111 -1.3914012
59 18.5933318 15.5786111
60 22.6469265 18.5933318
61 39.3838378 22.6469265
62 10.1455562 39.3838378
63 5.6491777 10.1455562
64 18.8085017 5.6491777
65 46.1324325 18.8085017
66 8.5844909 46.1324325
67 13.8198865 8.5844909
68 -9.8714510 13.8198865
69 -17.6052686 -9.8714510
70 12.6412885 -17.6052686
71 -0.6048456 12.6412885
72 -28.0926969 -0.6048456
73 -35.5490101 -28.0926969
74 -29.6128922 -35.5490101
75 -6.7060098 -29.6128922
76 -36.8334093 -6.7060098
77 -22.3568883 -36.8334093
78 -11.4920511 -22.3568883
79 24.0008414 -11.4920511
80 -0.1958509 24.0008414
81 -6.0592311 -0.1958509
82 -7.9768243 -6.0592311
83 -27.2555260 -7.9768243
84 -4.0228151 -27.2555260
85 -14.5419626 -4.0228151
86 0.8940107 -14.5419626
87 -11.2363653 0.8940107
88 17.1709849 -11.2363653
89 -4.6107132 17.1709849
90 -5.6680611 -4.6107132
91 -18.4607403 -5.6680611
92 -25.8080616 -18.4607403
93 19.0525125 -25.8080616
94 -6.1390902 19.0525125
95 -26.1987456 -6.1390902
96 -14.7886970 -26.1987456
97 -5.0869439 -14.7886970
98 10.4103304 -5.0869439
99 25.3611288 10.4103304
100 15.9415053 25.3611288
101 25.2460011 15.9415053
102 -18.4708298 25.2460011
103 13.7262272 -18.4708298
104 -13.3360263 13.7262272
105 -7.6080176 -13.3360263
106 -13.9782293 -7.6080176
107 -3.5607832 -13.9782293
108 -13.2145451 -3.5607832
109 3.8521502 -13.2145451
110 13.7784773 3.8521502
111 -9.2084728 13.7784773
112 -23.7524492 -9.2084728
113 8.2918151 -23.7524492
114 13.8453117 8.2918151
115 -3.7391327 13.8453117
116 -6.7416921 -3.7391327
117 -5.4227459 -6.7416921
118 16.0653901 -5.4227459
119 46.9057140 16.0653901
120 -5.4822061 46.9057140
121 26.1110552 -5.4822061
122 -12.1991592 26.1110552
123 10.2897704 -12.1991592
124 -14.0894846 10.2897704
125 -2.6256294 -14.0894846
126 -5.1092064 -2.6256294
127 6.6439707 -5.1092064
128 -11.3421202 6.6439707
129 -27.8822932 -11.3421202
130 -16.8699920 -27.8822932
131 -27.3992550 -16.8699920
132 -14.8301842 -27.3992550
133 -16.8618505 -14.8301842
134 2.5414144 -16.8618505
135 -23.8794115 2.5414144
136 -9.4894831 -23.8794115
137 -24.5179616 -9.4894831
138 -20.2049350 -24.5179616
139 -22.1483424 -20.2049350
140 0.6971975 -22.1483424
141 4.4877471 0.6971975
142 7.8823872 4.4877471
143 34.1487575 7.8823872
144 5.8322261 34.1487575
145 54.6722628 5.8322261
146 11.7456454 54.6722628
147 -6.3235104 11.7456454
148 -22.4916221 -6.3235104
149 -38.4822121 -22.4916221
150 6.4194124 -38.4822121
151 -4.9342219 6.4194124
152 -33.3741054 -4.9342219
153 0.9692749 -33.3741054
154 -4.6712537 0.9692749
155 -21.5378814 -4.6712537
156 -6.0037481 -21.5378814
157 -0.2384529 -6.0037481
158 -0.7662520 -0.2384529
159 6.2351413 -0.7662520
160 14.1545775 6.2351413
161 -13.9340108 14.1545775
162 8.0619123 -13.9340108
163 -6.1301482 8.0619123
164 20.7963569 -6.1301482
165 47.0770438 20.7963569
166 12.4545704 47.0770438
167 23.7224696 12.4545704
168 -1.2692994 23.7224696
169 6.1227959 -1.2692994
170 12.0805089 6.1227959
171 -24.9889357 12.0805089
172 -24.0853326 -24.9889357
173 -21.4151826 -24.0853326
174 -21.3230720 -21.4151826
175 -20.9752341 -21.3230720
176 -22.4319753 -20.9752341
177 -13.8711667 -22.4319753
178 -17.3109640 -13.8711667
179 -5.4821091 -17.3109640
180 -6.3168075 -5.4821091
181 -8.1619825 -6.3168075
182 -24.3787941 -8.1619825
183 2.1009962 -24.3787941
184 1.7078745 2.1009962
185 -8.4730498 1.7078745
186 -43.0260651 -8.4730498
187 -56.5236496 -43.0260651
188 -10.8109358 -56.5236496
189 1.3550942 -10.8109358
190 7.5930995 1.3550942
191 25.8151273 7.5930995
192 2.2522205 25.8151273
193 13.7783604 2.2522205
194 40.2552121 13.7783604
195 NA 40.2552121
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 9.9656023 17.4002349
[2,] 8.7299544 9.9656023
[3,] 16.7367004 8.7299544
[4,] 18.2431633 16.7367004
[5,] 7.7044015 18.2431633
[6,] -16.8024046 7.7044015
[7,] -20.6337906 -16.8024046
[8,] 2.1555930 -20.6337906
[9,] 8.7479229 2.1555930
[10,] 9.6683441 8.7479229
[11,] 2.8256863 9.6683441
[12,] -22.0928084 2.8256863
[13,] 9.7495760 -22.0928084
[14,] 2.4102315 9.7495760
[15,] 4.9469875 2.4102315
[16,] 4.2220693 4.9469875
[17,] 21.8492470 4.2220693
[18,] 36.4806307 21.8492470
[19,] 3.9851216 36.4806307
[20,] 10.0120527 3.9851216
[21,] 19.9160406 10.0120527
[22,] 29.2804350 19.9160406
[23,] 23.6988214 29.2804350
[24,] 18.7052555 23.6988214
[25,] 13.9557599 18.7052555
[26,] 22.6253144 13.9557599
[27,] -5.9613072 22.6253144
[28,] 4.5505886 -5.9613072
[29,] 7.0720243 4.5505886
[30,] -12.3383166 7.0720243
[31,] -2.0415914 -12.3383166
[32,] 1.6997552 -2.0415914
[33,] 7.6602774 1.6997552
[34,] 9.0123596 7.6602774
[35,] 7.3947419 9.0123596
[36,] -19.3036124 7.3947419
[37,] -10.2796657 -19.3036124
[38,] 3.5183123 -10.2796657
[39,] 6.7935404 3.5183123
[40,] 6.5660233 6.7935404
[41,] -9.2387915 6.5660233
[42,] 12.6257073 -9.2387915
[43,] 18.3911996 12.6257073
[44,] 31.1307510 18.3911996
[45,] 27.8244837 31.1307510
[46,] 29.8819235 27.8244837
[47,] 53.1477010 29.8819235
[48,] -15.1189571 53.1477010
[49,] -24.4071192 -15.1189571
[50,] -27.5720863 -24.4071192
[51,] -0.6398269 -27.5720863
[52,] -27.1623109 -0.6398269
[53,] -8.0219188 -27.1623109
[54,] 7.2003643 -8.0219188
[55,] 15.9373149 7.2003643
[56,] 4.5233340 15.9373149
[57,] -1.3914012 4.5233340
[58,] 15.5786111 -1.3914012
[59,] 18.5933318 15.5786111
[60,] 22.6469265 18.5933318
[61,] 39.3838378 22.6469265
[62,] 10.1455562 39.3838378
[63,] 5.6491777 10.1455562
[64,] 18.8085017 5.6491777
[65,] 46.1324325 18.8085017
[66,] 8.5844909 46.1324325
[67,] 13.8198865 8.5844909
[68,] -9.8714510 13.8198865
[69,] -17.6052686 -9.8714510
[70,] 12.6412885 -17.6052686
[71,] -0.6048456 12.6412885
[72,] -28.0926969 -0.6048456
[73,] -35.5490101 -28.0926969
[74,] -29.6128922 -35.5490101
[75,] -6.7060098 -29.6128922
[76,] -36.8334093 -6.7060098
[77,] -22.3568883 -36.8334093
[78,] -11.4920511 -22.3568883
[79,] 24.0008414 -11.4920511
[80,] -0.1958509 24.0008414
[81,] -6.0592311 -0.1958509
[82,] -7.9768243 -6.0592311
[83,] -27.2555260 -7.9768243
[84,] -4.0228151 -27.2555260
[85,] -14.5419626 -4.0228151
[86,] 0.8940107 -14.5419626
[87,] -11.2363653 0.8940107
[88,] 17.1709849 -11.2363653
[89,] -4.6107132 17.1709849
[90,] -5.6680611 -4.6107132
[91,] -18.4607403 -5.6680611
[92,] -25.8080616 -18.4607403
[93,] 19.0525125 -25.8080616
[94,] -6.1390902 19.0525125
[95,] -26.1987456 -6.1390902
[96,] -14.7886970 -26.1987456
[97,] -5.0869439 -14.7886970
[98,] 10.4103304 -5.0869439
[99,] 25.3611288 10.4103304
[100,] 15.9415053 25.3611288
[101,] 25.2460011 15.9415053
[102,] -18.4708298 25.2460011
[103,] 13.7262272 -18.4708298
[104,] -13.3360263 13.7262272
[105,] -7.6080176 -13.3360263
[106,] -13.9782293 -7.6080176
[107,] -3.5607832 -13.9782293
[108,] -13.2145451 -3.5607832
[109,] 3.8521502 -13.2145451
[110,] 13.7784773 3.8521502
[111,] -9.2084728 13.7784773
[112,] -23.7524492 -9.2084728
[113,] 8.2918151 -23.7524492
[114,] 13.8453117 8.2918151
[115,] -3.7391327 13.8453117
[116,] -6.7416921 -3.7391327
[117,] -5.4227459 -6.7416921
[118,] 16.0653901 -5.4227459
[119,] 46.9057140 16.0653901
[120,] -5.4822061 46.9057140
[121,] 26.1110552 -5.4822061
[122,] -12.1991592 26.1110552
[123,] 10.2897704 -12.1991592
[124,] -14.0894846 10.2897704
[125,] -2.6256294 -14.0894846
[126,] -5.1092064 -2.6256294
[127,] 6.6439707 -5.1092064
[128,] -11.3421202 6.6439707
[129,] -27.8822932 -11.3421202
[130,] -16.8699920 -27.8822932
[131,] -27.3992550 -16.8699920
[132,] -14.8301842 -27.3992550
[133,] -16.8618505 -14.8301842
[134,] 2.5414144 -16.8618505
[135,] -23.8794115 2.5414144
[136,] -9.4894831 -23.8794115
[137,] -24.5179616 -9.4894831
[138,] -20.2049350 -24.5179616
[139,] -22.1483424 -20.2049350
[140,] 0.6971975 -22.1483424
[141,] 4.4877471 0.6971975
[142,] 7.8823872 4.4877471
[143,] 34.1487575 7.8823872
[144,] 5.8322261 34.1487575
[145,] 54.6722628 5.8322261
[146,] 11.7456454 54.6722628
[147,] -6.3235104 11.7456454
[148,] -22.4916221 -6.3235104
[149,] -38.4822121 -22.4916221
[150,] 6.4194124 -38.4822121
[151,] -4.9342219 6.4194124
[152,] -33.3741054 -4.9342219
[153,] 0.9692749 -33.3741054
[154,] -4.6712537 0.9692749
[155,] -21.5378814 -4.6712537
[156,] -6.0037481 -21.5378814
[157,] -0.2384529 -6.0037481
[158,] -0.7662520 -0.2384529
[159,] 6.2351413 -0.7662520
[160,] 14.1545775 6.2351413
[161,] -13.9340108 14.1545775
[162,] 8.0619123 -13.9340108
[163,] -6.1301482 8.0619123
[164,] 20.7963569 -6.1301482
[165,] 47.0770438 20.7963569
[166,] 12.4545704 47.0770438
[167,] 23.7224696 12.4545704
[168,] -1.2692994 23.7224696
[169,] 6.1227959 -1.2692994
[170,] 12.0805089 6.1227959
[171,] -24.9889357 12.0805089
[172,] -24.0853326 -24.9889357
[173,] -21.4151826 -24.0853326
[174,] -21.3230720 -21.4151826
[175,] -20.9752341 -21.3230720
[176,] -22.4319753 -20.9752341
[177,] -13.8711667 -22.4319753
[178,] -17.3109640 -13.8711667
[179,] -5.4821091 -17.3109640
[180,] -6.3168075 -5.4821091
[181,] -8.1619825 -6.3168075
[182,] -24.3787941 -8.1619825
[183,] 2.1009962 -24.3787941
[184,] 1.7078745 2.1009962
[185,] -8.4730498 1.7078745
[186,] -43.0260651 -8.4730498
[187,] -56.5236496 -43.0260651
[188,] -10.8109358 -56.5236496
[189,] 1.3550942 -10.8109358
[190,] 7.5930995 1.3550942
[191,] 25.8151273 7.5930995
[192,] 2.2522205 25.8151273
[193,] 13.7783604 2.2522205
[194,] 40.2552121 13.7783604
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 9.9656023 17.4002349
2 8.7299544 9.9656023
3 16.7367004 8.7299544
4 18.2431633 16.7367004
5 7.7044015 18.2431633
6 -16.8024046 7.7044015
7 -20.6337906 -16.8024046
8 2.1555930 -20.6337906
9 8.7479229 2.1555930
10 9.6683441 8.7479229
11 2.8256863 9.6683441
12 -22.0928084 2.8256863
13 9.7495760 -22.0928084
14 2.4102315 9.7495760
15 4.9469875 2.4102315
16 4.2220693 4.9469875
17 21.8492470 4.2220693
18 36.4806307 21.8492470
19 3.9851216 36.4806307
20 10.0120527 3.9851216
21 19.9160406 10.0120527
22 29.2804350 19.9160406
23 23.6988214 29.2804350
24 18.7052555 23.6988214
25 13.9557599 18.7052555
26 22.6253144 13.9557599
27 -5.9613072 22.6253144
28 4.5505886 -5.9613072
29 7.0720243 4.5505886
30 -12.3383166 7.0720243
31 -2.0415914 -12.3383166
32 1.6997552 -2.0415914
33 7.6602774 1.6997552
34 9.0123596 7.6602774
35 7.3947419 9.0123596
36 -19.3036124 7.3947419
37 -10.2796657 -19.3036124
38 3.5183123 -10.2796657
39 6.7935404 3.5183123
40 6.5660233 6.7935404
41 -9.2387915 6.5660233
42 12.6257073 -9.2387915
43 18.3911996 12.6257073
44 31.1307510 18.3911996
45 27.8244837 31.1307510
46 29.8819235 27.8244837
47 53.1477010 29.8819235
48 -15.1189571 53.1477010
49 -24.4071192 -15.1189571
50 -27.5720863 -24.4071192
51 -0.6398269 -27.5720863
52 -27.1623109 -0.6398269
53 -8.0219188 -27.1623109
54 7.2003643 -8.0219188
55 15.9373149 7.2003643
56 4.5233340 15.9373149
57 -1.3914012 4.5233340
58 15.5786111 -1.3914012
59 18.5933318 15.5786111
60 22.6469265 18.5933318
61 39.3838378 22.6469265
62 10.1455562 39.3838378
63 5.6491777 10.1455562
64 18.8085017 5.6491777
65 46.1324325 18.8085017
66 8.5844909 46.1324325
67 13.8198865 8.5844909
68 -9.8714510 13.8198865
69 -17.6052686 -9.8714510
70 12.6412885 -17.6052686
71 -0.6048456 12.6412885
72 -28.0926969 -0.6048456
73 -35.5490101 -28.0926969
74 -29.6128922 -35.5490101
75 -6.7060098 -29.6128922
76 -36.8334093 -6.7060098
77 -22.3568883 -36.8334093
78 -11.4920511 -22.3568883
79 24.0008414 -11.4920511
80 -0.1958509 24.0008414
81 -6.0592311 -0.1958509
82 -7.9768243 -6.0592311
83 -27.2555260 -7.9768243
84 -4.0228151 -27.2555260
85 -14.5419626 -4.0228151
86 0.8940107 -14.5419626
87 -11.2363653 0.8940107
88 17.1709849 -11.2363653
89 -4.6107132 17.1709849
90 -5.6680611 -4.6107132
91 -18.4607403 -5.6680611
92 -25.8080616 -18.4607403
93 19.0525125 -25.8080616
94 -6.1390902 19.0525125
95 -26.1987456 -6.1390902
96 -14.7886970 -26.1987456
97 -5.0869439 -14.7886970
98 10.4103304 -5.0869439
99 25.3611288 10.4103304
100 15.9415053 25.3611288
101 25.2460011 15.9415053
102 -18.4708298 25.2460011
103 13.7262272 -18.4708298
104 -13.3360263 13.7262272
105 -7.6080176 -13.3360263
106 -13.9782293 -7.6080176
107 -3.5607832 -13.9782293
108 -13.2145451 -3.5607832
109 3.8521502 -13.2145451
110 13.7784773 3.8521502
111 -9.2084728 13.7784773
112 -23.7524492 -9.2084728
113 8.2918151 -23.7524492
114 13.8453117 8.2918151
115 -3.7391327 13.8453117
116 -6.7416921 -3.7391327
117 -5.4227459 -6.7416921
118 16.0653901 -5.4227459
119 46.9057140 16.0653901
120 -5.4822061 46.9057140
121 26.1110552 -5.4822061
122 -12.1991592 26.1110552
123 10.2897704 -12.1991592
124 -14.0894846 10.2897704
125 -2.6256294 -14.0894846
126 -5.1092064 -2.6256294
127 6.6439707 -5.1092064
128 -11.3421202 6.6439707
129 -27.8822932 -11.3421202
130 -16.8699920 -27.8822932
131 -27.3992550 -16.8699920
132 -14.8301842 -27.3992550
133 -16.8618505 -14.8301842
134 2.5414144 -16.8618505
135 -23.8794115 2.5414144
136 -9.4894831 -23.8794115
137 -24.5179616 -9.4894831
138 -20.2049350 -24.5179616
139 -22.1483424 -20.2049350
140 0.6971975 -22.1483424
141 4.4877471 0.6971975
142 7.8823872 4.4877471
143 34.1487575 7.8823872
144 5.8322261 34.1487575
145 54.6722628 5.8322261
146 11.7456454 54.6722628
147 -6.3235104 11.7456454
148 -22.4916221 -6.3235104
149 -38.4822121 -22.4916221
150 6.4194124 -38.4822121
151 -4.9342219 6.4194124
152 -33.3741054 -4.9342219
153 0.9692749 -33.3741054
154 -4.6712537 0.9692749
155 -21.5378814 -4.6712537
156 -6.0037481 -21.5378814
157 -0.2384529 -6.0037481
158 -0.7662520 -0.2384529
159 6.2351413 -0.7662520
160 14.1545775 6.2351413
161 -13.9340108 14.1545775
162 8.0619123 -13.9340108
163 -6.1301482 8.0619123
164 20.7963569 -6.1301482
165 47.0770438 20.7963569
166 12.4545704 47.0770438
167 23.7224696 12.4545704
168 -1.2692994 23.7224696
169 6.1227959 -1.2692994
170 12.0805089 6.1227959
171 -24.9889357 12.0805089
172 -24.0853326 -24.9889357
173 -21.4151826 -24.0853326
174 -21.3230720 -21.4151826
175 -20.9752341 -21.3230720
176 -22.4319753 -20.9752341
177 -13.8711667 -22.4319753
178 -17.3109640 -13.8711667
179 -5.4821091 -17.3109640
180 -6.3168075 -5.4821091
181 -8.1619825 -6.3168075
182 -24.3787941 -8.1619825
183 2.1009962 -24.3787941
184 1.7078745 2.1009962
185 -8.4730498 1.7078745
186 -43.0260651 -8.4730498
187 -56.5236496 -43.0260651
188 -10.8109358 -56.5236496
189 1.3550942 -10.8109358
190 7.5930995 1.3550942
191 25.8151273 7.5930995
192 2.2522205 25.8151273
193 13.7783604 2.2522205
194 40.2552121 13.7783604
> plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
> lines(lowess(z))
> abline(lm(z))
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/74sgq1386770074.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/89cmo1386770074.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/99b6w1386770074.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/fisher/rcomp/tmp/109wno1386770074.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/fisher/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/fisher/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
+ myeq <- paste(myeq, signif(mysum$coefficients[i,1],6), sep=' ')
+ if (rownames(mysum$coefficients)[i] != '(Intercept)') {
+ myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
+ if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/fisher/rcomp/tmp/113zzm1386770074.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
+ a<-table.element(a,signif(mysum$coefficients[i,1],6))
+ a<-table.element(a, signif(mysum$coefficients[i,2],6))
+ a<-table.element(a, signif(mysum$coefficients[i,3],4))
+ a<-table.element(a, signif(mysum$coefficients[i,4],6))
+ a<-table.element(a, signif(mysum$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/fisher/rcomp/tmp/1229x71386770075.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, signif(sqrt(mysum$r.squared),6))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'R-squared',1,TRUE)
> a<-table.element(a, signif(mysum$r.squared,6))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Adjusted R-squared',1,TRUE)
> a<-table.element(a, signif(mysum$adj.r.squared,6))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (value)',1,TRUE)
> a<-table.element(a, signif(mysum$fstatistic[1],6))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
> a<-table.element(a, signif(mysum$fstatistic[2],6))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
> a<-table.element(a, signif(mysum$fstatistic[3],6))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'p-value',1,TRUE)
> a<-table.element(a, signif(1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]),6))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
> a<-table.element(a, signif(mysum$sigma,6))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, signif(sum(myerror*myerror),6))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/fisher/rcomp/tmp/132eed1386770075.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,signif(x[i],6))
+ a<-table.element(a,signif(x[i]-mysum$resid[i],6))
+ a<-table.element(a,signif(mysum$resid[i],6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/fisher/rcomp/tmp/1427761386770075.tab")
> if (n > n25) {
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'p-values',header=TRUE)
+ a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'breakpoint index',header=TRUE)
+ a<-table.element(a,'greater',header=TRUE)
+ a<-table.element(a,'2-sided',header=TRUE)
+ a<-table.element(a,'less',header=TRUE)
+ a<-table.row.end(a)
+ for (mypoint in kp3:nmkm3) {
+ a<-table.row.start(a)
+ a<-table.element(a,mypoint,header=TRUE)
+ a<-table.element(a,signif(gqarr[mypoint-kp3+1,1],6))
+ a<-table.element(a,signif(gqarr[mypoint-kp3+1,2],6))
+ a<-table.element(a,signif(gqarr[mypoint-kp3+1,3],6))
+ a<-table.row.end(a)
+ }
+ a<-table.end(a)
+ table.save(a,file="/var/fisher/rcomp/tmp/157gso1386770075.tab")
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'Description',header=TRUE)
+ a<-table.element(a,'# significant tests',header=TRUE)
+ a<-table.element(a,'% significant tests',header=TRUE)
+ a<-table.element(a,'OK/NOK',header=TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'1% type I error level',header=TRUE)
+ a<-table.element(a,signif(numsignificant1,6))
+ a<-table.element(a,signif(numsignificant1/numgqtests,6))
+ if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'5% type I error level',header=TRUE)
+ a<-table.element(a,signif(numsignificant5,6))
+ a<-table.element(a,signif(numsignificant5/numgqtests,6))
+ if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'10% type I error level',header=TRUE)
+ a<-table.element(a,signif(numsignificant10,6))
+ a<-table.element(a,signif(numsignificant10/numgqtests,6))
+ if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.end(a)
+ table.save(a,file="/var/fisher/rcomp/tmp/16tyqp1386770075.tab")
+ }
>
> try(system("convert tmp/13o651386770074.ps tmp/13o651386770074.png",intern=TRUE))
character(0)
> try(system("convert tmp/2ibau1386770074.ps tmp/2ibau1386770074.png",intern=TRUE))
character(0)
> try(system("convert tmp/3874z1386770074.ps tmp/3874z1386770074.png",intern=TRUE))
character(0)
> try(system("convert tmp/48cuy1386770074.ps tmp/48cuy1386770074.png",intern=TRUE))
character(0)
> try(system("convert tmp/5xpst1386770074.ps tmp/5xpst1386770074.png",intern=TRUE))
character(0)
> try(system("convert tmp/65q781386770074.ps tmp/65q781386770074.png",intern=TRUE))
character(0)
> try(system("convert tmp/74sgq1386770074.ps tmp/74sgq1386770074.png",intern=TRUE))
character(0)
> try(system("convert tmp/89cmo1386770074.ps tmp/89cmo1386770074.png",intern=TRUE))
character(0)
> try(system("convert tmp/99b6w1386770074.ps tmp/99b6w1386770074.png",intern=TRUE))
character(0)
> try(system("convert tmp/109wno1386770074.ps tmp/109wno1386770074.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
19.623 3.544 23.390