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(1
+ ,0.00968
+ ,0.01394
+ ,0.03134
+ ,0.01929
+ ,19.085
+ ,0.458359
+ ,0.819521
+ ,-4.075192
+ ,2.486855
+ ,0.368674
+ ,1
+ ,0.0105
+ ,0.01633
+ ,0.02757
+ ,0.01309
+ ,20.651
+ ,0.429895
+ ,0.825288
+ ,-4.443179
+ ,2.342259
+ ,0.332634
+ ,1
+ ,0.00997
+ ,0.01505
+ ,0.02924
+ ,0.01353
+ ,20.644
+ ,0.434969
+ ,0.819235
+ ,-4.117501
+ ,2.405554
+ ,0.368975
+ ,1
+ ,0.01284
+ ,0.01966
+ ,0.0349
+ ,0.01767
+ ,19.649
+ ,0.417356
+ ,0.823484
+ ,-3.747787
+ ,2.33218
+ ,0.410335
+ ,1
+ ,0.00968
+ ,0.01388
+ ,0.02328
+ ,0.01222
+ ,21.378
+ ,0.415564
+ ,0.825069
+ ,-4.242867
+ ,2.18756
+ ,0.357775
+ ,1
+ ,0.00333
+ ,0.00466
+ ,0.00779
+ ,0.00607
+ ,24.886
+ ,0.59604
+ ,0.764112
+ ,-5.634322
+ ,1.854785
+ ,0.211756
+ ,1
+ ,0.0029
+ ,0.00431
+ ,0.00829
+ ,0.00344
+ ,26.892
+ ,0.63742
+ ,0.763262
+ ,-6.167603
+ ,2.064693
+ ,0.163755
+ ,1
+ ,0.00551
+ ,0.0088
+ ,0.01073
+ ,0.0107
+ ,21.812
+ ,0.615551
+ ,0.773587
+ ,-5.498678
+ ,2.322511
+ ,0.231571
+ ,1
+ ,0.00532
+ ,0.00803
+ ,0.01441
+ ,0.01022
+ ,21.862
+ ,0.547037
+ ,0.798463
+ ,-5.011879
+ ,2.432792
+ ,0.271362
+ ,1
+ ,0.00505
+ ,0.00763
+ ,0.01079
+ ,0.01166
+ ,21.118
+ ,0.611137
+ ,0.776156
+ ,-5.24977
+ ,2.407313
+ ,0.24974
+ ,1
+ ,0.0054
+ ,0.00844
+ ,0.01424
+ ,0.01141
+ ,21.414
+ ,0.58339
+ ,0.79252
+ ,-4.960234
+ ,2.642476
+ ,0.275931
+ ,1
+ ,0.00293
+ ,0.00355
+ ,0.00656
+ ,0.00581
+ ,25.703
+ ,0.4606
+ ,0.646846
+ ,-6.547148
+ ,2.041277
+ ,0.138512
+ ,1
+ ,0.0039
+ ,0.00496
+ ,0.00728
+ ,0.01041
+ ,24.889
+ ,0.430166
+ ,0.665833
+ ,-5.660217
+ ,2.519422
+ ,0.199889
+ ,1
+ ,0.00294
+ ,0.00364
+ ,0.01064
+ ,0.00609
+ ,24.922
+ ,0.474791
+ ,0.654027
+ ,-6.105098
+ ,2.125618
+ ,0.1701
+ ,1
+ ,0.00369
+ ,0.00471
+ ,0.00772
+ ,0.00839
+ ,25.175
+ ,0.565924
+ ,0.658245
+ ,-5.340115
+ ,2.205546
+ ,0.234589
+ ,1
+ ,0.00544
+ ,0.00632
+ ,0.00969
+ ,0.01859
+ ,22.333
+ ,0.56738
+ ,0.644692
+ ,-5.44004
+ ,2.264501
+ ,0.218164
+ ,1
+ ,0.00718
+ ,0.00853
+ ,0.01441
+ ,0.02919
+ ,20.376
+ ,0.631099
+ ,0.605417
+ ,-2.93107
+ ,3.007463
+ ,0.430788
+ ,1
+ ,0.00742
+ ,0.01092
+ ,0.02471
+ ,0.0316
+ ,17.28
+ ,0.665318
+ ,0.719467
+ ,-3.949079
+ ,3.10901
+ ,0.377429
+ ,1
+ ,0.00768
+ ,0.01116
+ ,0.01721
+ ,0.03365
+ ,17.153
+ ,0.649554
+ ,0.68608
+ ,-4.554466
+ ,2.856676
+ ,0.322111
+ ,1
+ ,0.0084
+ ,0.01285
+ ,0.01667
+ ,0.03871
+ ,17.536
+ ,0.660125
+ ,0.704087
+ ,-4.095442
+ ,2.73971
+ ,0.365391
+ ,1
+ ,0.0048
+ ,0.00696
+ ,0.02021
+ ,0.01849
+ ,19.493
+ ,0.629017
+ ,0.698951
+ ,-5.18696
+ ,2.557536
+ ,0.259765
+ ,1
+ ,0.00442
+ ,0.00661
+ ,0.02228
+ ,0.0128
+ ,22.468
+ ,0.61906
+ ,0.679834
+ ,-4.330956
+ ,2.916777
+ ,0.285695
+ ,1
+ ,0.00476
+ ,0.00663
+ ,0.02187
+ ,0.0184
+ ,20.422
+ ,0.537264
+ ,0.686894
+ ,-5.248776
+ ,2.547508
+ ,0.253556
+ ,1
+ ,0.00742
+ ,0.0114
+ ,0.00738
+ ,0.01778
+ ,23.831
+ ,0.397937
+ ,0.732479
+ ,-5.557447
+ ,2.692176
+ ,0.215961
+ ,1
+ ,0.00633
+ ,0.00948
+ ,0.01732
+ ,0.02887
+ ,22.066
+ ,0.522746
+ ,0.737948
+ ,-5.571843
+ ,2.846369
+ ,0.219514
+ ,1
+ ,0.00455
+ ,0.0075
+ ,0.00889
+ ,0.01095
+ ,25.908
+ ,0.418622
+ ,0.720916
+ ,-6.18359
+ ,2.589702
+ ,0.147403
+ ,1
+ ,0.00496
+ ,0.00749
+ ,0.00883
+ ,0.01328
+ ,25.119
+ ,0.358773
+ ,0.726652
+ ,-6.27169
+ ,2.314209
+ ,0.162999
+ ,1
+ ,0.0031
+ ,0.00476
+ ,0.00769
+ ,0.00677
+ ,25.97
+ ,0.470478
+ ,0.676258
+ ,-7.120925
+ ,2.241742
+ ,0.108514
+ ,1
+ ,0.00502
+ ,0.00841
+ ,0.00793
+ ,0.0117
+ ,25.678
+ ,0.427785
+ ,0.723797
+ ,-6.635729
+ ,1.957961
+ ,0.135242
+ ,0
+ ,0.00289
+ ,0.00498
+ ,0.00563
+ ,0.00339
+ ,26.775
+ ,0.422229
+ ,0.741367
+ ,-7.3483
+ ,1.743867
+ ,0.085569
+ ,0
+ ,0.00241
+ ,0.00402
+ ,0.00504
+ ,0.00167
+ ,30.94
+ ,0.432439
+ ,0.742055
+ ,-7.682587
+ ,2.103106
+ ,0.068501
+ ,0
+ ,0.00212
+ ,0.00339
+ ,0.0064
+ ,0.00119
+ ,30.775
+ ,0.465946
+ ,0.738703
+ ,-7.067931
+ ,1.512275
+ ,0.09632
+ ,0
+ ,0.0018
+ ,0.00278
+ ,0.00469
+ ,0.00072
+ ,32.684
+ ,0.368535
+ ,0.742133
+ ,-7.695734
+ ,1.544609
+ ,0.056141
+ ,0
+ ,0.00178
+ ,0.00283
+ ,0.00468
+ ,0.00065
+ ,33.047
+ ,0.340068
+ ,0.741899
+ ,-7.964984
+ ,1.423287
+ ,0.044539
+ ,0
+ ,0.00198
+ ,0.00314
+ ,0.00586
+ ,0.00135
+ ,31.732
+ ,0.344252
+ ,0.742737
+ ,-7.777685
+ ,2.447064
+ ,0.05761
+ ,1
+ ,0.00411
+ ,0.007
+ ,0.01154
+ ,0.00586
+ ,23.216
+ ,0.360148
+ ,0.778834
+ ,-6.149653
+ ,2.477082
+ ,0.165827
+ ,1
+ ,0.00369
+ ,0.00616
+ ,0.00938
+ ,0.0034
+ ,24.951
+ ,0.341435
+ ,0.783626
+ ,-6.006414
+ ,2.536527
+ ,0.173218
+ ,1
+ ,0.00284
+ ,0.00459
+ ,0.00726
+ ,0.00231
+ ,26.738
+ ,0.403884
+ ,0.766209
+ ,-6.452058
+ ,2.269398
+ ,0.141929
+ ,1
+ ,0.00316
+ ,0.00504
+ ,0.00829
+ ,0.00265
+ ,26.31
+ ,0.396793
+ ,0.758324
+ ,-6.006647
+ ,2.382544
+ ,0.160691
+ ,1
+ ,0.00298
+ ,0.00496
+ ,0.00774
+ ,0.00231
+ ,26.822
+ ,0.32648
+ ,0.765623
+ ,-6.647379
+ ,2.374073
+ ,0.130554
+ ,1
+ ,0.00258
+ ,0.00403
+ ,0.00742
+ ,0.00257
+ ,26.453
+ ,0.306443
+ ,0.759203
+ ,-7.044105
+ ,2.361532
+ ,0.11573
+ ,0
+ ,0.00298
+ ,0.00507
+ ,0.01035
+ ,0.0074
+ ,22.736
+ ,0.305062
+ ,0.654172
+ ,-7.31055
+ ,2.416838
+ ,0.095032
+ ,0
+ ,0.00281
+ ,0.0047
+ ,0.01006
+ ,0.00675
+ ,23.145
+ ,0.457702
+ ,0.634267
+ ,-6.793547
+ ,2.256699
+ ,0.117399
+ ,0
+ ,0.0021
+ ,0.00327
+ ,0.00777
+ ,0.00454
+ ,25.368
+ ,0.438296
+ ,0.635285
+ ,-7.057869
+ ,2.330716
+ ,0.09147
+ ,0
+ ,0.00225
+ ,0.0035
+ ,0.00847
+ ,0.00476
+ ,25.032
+ ,0.431285
+ ,0.638928
+ ,-6.99582
+ ,2.3658
+ ,0.102706
+ ,0
+ ,0.00235
+ ,0.0038
+ ,0.00906
+ ,0.00476
+ ,24.602
+ ,0.467489
+ ,0.631653
+ ,-7.156076
+ ,2.392122
+ ,0.097336
+ ,0
+ ,0.00185
+ ,0.00276
+ ,0.00614
+ ,0.00432
+ ,26.805
+ ,0.610367
+ ,0.635204
+ ,-7.31951
+ ,2.028612
+ ,0.086398
+ ,0
+ ,0.00524
+ ,0.00507
+ ,0.00855
+ ,0.00839
+ ,23.162
+ ,0.579597
+ ,0.733659
+ ,-6.439398
+ ,2.079922
+ ,0.133867
+ ,0
+ ,0.00428
+ ,0.00373
+ ,0.0093
+ ,0.00462
+ ,24.971
+ ,0.538688
+ ,0.754073
+ ,-6.482096
+ ,2.054419
+ ,0.128872
+ ,0
+ ,0.00431
+ ,0.00422
+ ,0.01241
+ ,0.00479
+ ,25.135
+ ,0.553134
+ ,0.775933
+ ,-6.650471
+ ,1.840198
+ ,0.103561
+ ,0
+ ,0.00448
+ ,0.00393
+ ,0.01143
+ ,0.00474
+ ,25.03
+ ,0.507504
+ ,0.760361
+ ,-6.689151
+ ,2.431854
+ ,0.105993
+ ,0
+ ,0.00436
+ ,0.00411
+ ,0.01323
+ ,0.00481
+ ,24.692
+ ,0.459766
+ ,0.766204
+ ,-7.072419
+ ,1.972297
+ ,0.119308
+ ,0
+ ,0.0049
+ ,0.00495
+ ,0.01396
+ ,0.00484
+ ,25.429
+ ,0.420383
+ ,0.785714
+ ,-6.836811
+ ,2.223719
+ ,0.147491
+ ,1
+ ,0.00761
+ ,0.01046
+ ,0.01483
+ ,0.01036
+ ,21.028
+ ,0.536009
+ ,0.819032
+ ,-4.649573
+ ,1.986899
+ ,0.3167
+ ,1
+ ,0.00874
+ ,0.01193
+ ,0.01789
+ ,0.0118
+ ,20.767
+ ,0.558586
+ ,0.811843
+ ,-4.333543
+ ,2.014606
+ ,0.344834
+ ,1
+ ,0.00784
+ ,0.01056
+ ,0.02032
+ ,0.00969
+ ,21.422
+ ,0.541781
+ ,0.821364
+ ,-4.438453
+ ,1.92294
+ ,0.335041
+ ,1
+ ,0.00752
+ ,0.00898
+ ,0.01189
+ ,0.00681
+ ,22.817
+ ,0.530529
+ ,0.817756
+ ,-4.60826
+ ,2.021591
+ ,0.314464
+ ,1
+ ,0.00788
+ ,0.01003
+ ,0.01394
+ ,0.00786
+ ,22.603
+ ,0.540049
+ ,0.813432
+ ,-4.476755
+ ,1.827012
+ ,0.326197
+ ,1
+ ,0.00867
+ ,0.0112
+ ,0.01805
+ ,0.01143
+ ,21.66
+ ,0.547975
+ ,0.817396
+ ,-4.609161
+ ,1.831691
+ ,0.316395
+ ,0
+ ,0.00282
+ ,0.00442
+ ,0.00975
+ ,0.00871
+ ,25.554
+ ,0.341788
+ ,0.678874
+ ,-7.040508
+ ,2.460791
+ ,0.101516
+ ,0
+ ,0.00264
+ ,0.00461
+ ,0.01013
+ ,0.00301
+ ,26.138
+ ,0.447979
+ ,0.686264
+ ,-7.293801
+ ,2.32156
+ ,0.098555
+ ,0
+ ,0.00266
+ ,0.00457
+ ,0.00867
+ ,0.0034
+ ,25.856
+ ,0.364867
+ ,0.694399
+ ,-6.966321
+ ,2.278687
+ ,0.103224
+ ,0
+ ,0.00296
+ ,0.00526
+ ,0.00882
+ ,0.00351
+ ,25.964
+ ,0.25657
+ ,0.683296
+ ,-7.24562
+ ,2.498224
+ ,0.093534
+ ,0
+ ,0.00205
+ ,0.00342
+ ,0.00769
+ ,0.003
+ ,26.415
+ ,0.27685
+ ,0.673636
+ ,-7.496264
+ ,2.003032
+ ,0.073581
+ ,0
+ ,0.00238
+ ,0.00408
+ ,0.00942
+ ,0.0042
+ ,24.547
+ ,0.305429
+ ,0.681811
+ ,-7.314237
+ ,2.118596
+ ,0.091546
+ ,1
+ ,0.00817
+ ,0.01289
+ ,0.0183
+ ,0.02183
+ ,19.56
+ ,0.460139
+ ,0.720908
+ ,-5.409423
+ ,2.359973
+ ,0.226156
+ ,1
+ ,0.00923
+ ,0.0152
+ ,0.01638
+ ,0.02659
+ ,19.979
+ ,0.498133
+ ,0.729067
+ ,-5.324574
+ ,2.291558
+ ,0.226247
+ ,1
+ ,0.01101
+ ,0.01941
+ ,0.03152
+ ,0.04882
+ ,20.338
+ ,0.513237
+ ,0.731444
+ ,-5.86975
+ ,2.118496
+ ,0.18558
+ ,1
+ ,0.00762
+ ,0.014
+ ,0.03357
+ ,0.02431
+ ,21.718
+ ,0.487407
+ ,0.727313
+ ,-6.261141
+ ,2.137075
+ ,0.141958
+ ,1
+ ,0.00831
+ ,0.01407
+ ,0.01868
+ ,0.02599
+ ,20.264
+ ,0.489345
+ ,0.730387
+ ,-5.720868
+ ,2.277927
+ ,0.180828
+ ,1
+ ,0.00971
+ ,0.01601
+ ,0.02749
+ ,0.03361
+ ,18.57
+ ,0.543299
+ ,0.733232
+ ,-5.207985
+ ,2.642276
+ ,0.242981
+ ,1
+ ,0.00405
+ ,0.0054
+ ,0.00974
+ ,0.00442
+ ,25.742
+ ,0.495954
+ ,0.762959
+ ,-5.79182
+ ,2.205024
+ ,0.18818
+ ,1
+ ,0.00533
+ ,0.00805
+ ,0.01373
+ ,0.00623
+ ,24.178
+ ,0.509127
+ ,0.789532
+ ,-5.389129
+ ,1.928708
+ ,0.225461
+ ,1
+ ,0.00494
+ ,0.0078
+ ,0.01432
+ ,0.00479
+ ,25.438
+ ,0.437031
+ ,0.815908
+ ,-5.31336
+ ,2.225815
+ ,0.244512
+ ,1
+ ,0.00516
+ ,0.00831
+ ,0.01284
+ ,0.00472
+ ,25.197
+ ,0.463514
+ ,0.807217
+ ,-5.477592
+ ,1.862092
+ ,0.228624
+ ,1
+ ,0.005
+ ,0.0081
+ ,0.02413
+ ,0.00905
+ ,23.37
+ ,0.489538
+ ,0.789977
+ ,-5.775966
+ ,2.007923
+ ,0.193918
+ ,1
+ ,0.00462
+ ,0.00677
+ ,0.01284
+ ,0.0042
+ ,25.82
+ ,0.429484
+ ,0.81634
+ ,-5.391029
+ ,1.777901
+ ,0.232744
+ ,1
+ ,0.00608
+ ,0.00994
+ ,0.01803
+ ,0.01062
+ ,21.875
+ ,0.644954
+ ,0.779612
+ ,-5.115212
+ ,2.017753
+ ,0.260015
+ ,1
+ ,0.01038
+ ,0.01865
+ ,0.01773
+ ,0.0222
+ ,19.2
+ ,0.594387
+ ,0.790117
+ ,-4.913885
+ ,2.398422
+ ,0.277948
+ ,1
+ ,0.00694
+ ,0.01168
+ ,0.02266
+ ,0.01823
+ ,19.055
+ ,0.544805
+ ,0.770466
+ ,-4.441519
+ ,2.645959
+ ,0.327978
+ ,1
+ ,0.00702
+ ,0.01283
+ ,0.01792
+ ,0.01825
+ ,19.659
+ ,0.576084
+ ,0.778747
+ ,-5.132032
+ ,2.232576
+ ,0.260633
+ ,1
+ ,0.00606
+ ,0.01053
+ ,0.01371
+ ,0.01237
+ ,20.536
+ ,0.55461
+ ,0.787896
+ ,-5.022288
+ ,2.428306
+ ,0.264666
+ ,1
+ ,0.00432
+ ,0.00742
+ ,0.01277
+ ,0.00882
+ ,22.244
+ ,0.576644
+ ,0.772416
+ ,-6.025367
+ ,2.053601
+ ,0.177275
+ ,1
+ ,0.00747
+ ,0.01254
+ ,0.02679
+ ,0.0547
+ ,13.893
+ ,0.556494
+ ,0.729586
+ ,-5.288912
+ ,3.099301
+ ,0.242119
+ ,1
+ ,0.00406
+ ,0.00659
+ ,0.02107
+ ,0.02782
+ ,16.176
+ ,0.583574
+ ,0.727747
+ ,-5.657899
+ ,3.098256
+ ,0.200423
+ ,1
+ ,0.00321
+ ,0.00488
+ ,0.02073
+ ,0.03151
+ ,15.924
+ ,0.598714
+ ,0.712199
+ ,-6.366916
+ ,2.654271
+ ,0.144614
+ ,1
+ ,0.0052
+ ,0.00862
+ ,0.03671
+ ,0.04824
+ ,13.922
+ ,0.602874
+ ,0.740837
+ ,-5.515071
+ ,3.13655
+ ,0.220968
+ ,1
+ ,0.00448
+ ,0.0071
+ ,0.03788
+ ,0.04214
+ ,14.739
+ ,0.599371
+ ,0.743937
+ ,-5.783272
+ ,3.007096
+ ,0.194052
+ ,1
+ ,0.00709
+ ,0.01172
+ ,0.02297
+ ,0.07223
+ ,11.866
+ ,0.590951
+ ,0.745526
+ ,-4.379411
+ ,3.671155
+ ,0.332086
+ ,1
+ ,0.00742
+ ,0.01161
+ ,0.0365
+ ,0.08725
+ ,11.744
+ ,0.65341
+ ,0.733165
+ ,-4.508984
+ ,3.317586
+ ,0.301952
+ ,1
+ ,0.00419
+ ,0.00672
+ ,0.04421
+ ,0.01658
+ ,19.664
+ ,0.501037
+ ,0.71436
+ ,-6.411497
+ ,2.344876
+ ,0.13412
+ ,1
+ ,0.00459
+ ,0.0075
+ ,0.02383
+ ,0.01914
+ ,18.78
+ ,0.454444
+ ,0.734504
+ ,-5.952058
+ ,2.344336
+ ,0.186489
+ ,1
+ ,0.00382
+ ,0.00574
+ ,0.03341
+ ,0.01211
+ ,20.969
+ ,0.447456
+ ,0.69779
+ ,-6.152551
+ ,2.080121
+ ,0.160809
+ ,1
+ ,0.00358
+ ,0.00587
+ ,0.02062
+ ,0.0085
+ ,22.219
+ ,0.50238
+ ,0.71217
+ ,-6.251425
+ ,2.143851
+ ,0.160812
+ ,1
+ ,0.00369
+ ,0.00602
+ ,0.01813
+ ,0.01018
+ ,21.693
+ ,0.447285
+ ,0.705658
+ ,-6.247076
+ ,2.344348
+ ,0.164916
+ ,1
+ ,0.00342
+ ,0.00535
+ ,0.01806
+ ,0.00852
+ ,22.663
+ ,0.366329
+ ,0.693429
+ ,-6.41744
+ ,2.473239
+ ,0.151709
+ ,1
+ ,0.0128
+ ,0.02228
+ ,0.02135
+ ,0.08151
+ ,15.338
+ ,0.629574
+ ,0.714485
+ ,-4.020042
+ ,2.671825
+ ,0.340623
+ ,1
+ ,0.01378
+ ,0.02478
+ ,0.02542
+ ,0.10323
+ ,15.433
+ ,0.57101
+ ,0.690892
+ ,-5.159169
+ ,2.441612
+ ,0.260375
+ ,1
+ ,0.01936
+ ,0.03476
+ ,0.03611
+ ,0.16744
+ ,12.435
+ ,0.638545
+ ,0.674953
+ ,-3.760348
+ ,2.634633
+ ,0.378483
+ ,1
+ ,0.03316
+ ,0.06433
+ ,0.05358
+ ,0.31482
+ ,8.867
+ ,0.671299
+ ,0.656846
+ ,-3.700544
+ ,2.991063
+ ,0.370961
+ ,1
+ ,0.01551
+ ,0.02716
+ ,0.03223
+ ,0.11843
+ ,15.06
+ ,0.639808
+ ,0.643327
+ ,-4.20273
+ ,2.638279
+ ,0.356881
+ ,1
+ ,0.03011
+ ,0.05563
+ ,0.05551
+ ,0.2593
+ ,10.489
+ ,0.596362
+ ,0.641418
+ ,-3.269487
+ ,2.690917
+ ,0.444774
+ ,1
+ ,0.00248
+ ,0.00315
+ ,0.00522
+ ,0.00495
+ ,26.759
+ ,0.296888
+ ,0.722356
+ ,-6.878393
+ ,2.004055
+ ,0.113942
+ ,1
+ ,0.00183
+ ,0.00229
+ ,0.00469
+ ,0.00243
+ ,28.409
+ ,0.263654
+ ,0.691483
+ ,-7.111576
+ ,2.065477
+ ,0.093193
+ ,1
+ ,0.00257
+ ,0.00349
+ ,0.0066
+ ,0.00578
+ ,27.421
+ ,0.365488
+ ,0.719974
+ ,-6.997403
+ ,1.994387
+ ,0.112878
+ ,1
+ ,0.00168
+ ,0.00204
+ ,0.00522
+ ,0.00233
+ ,29.746
+ ,0.334171
+ ,0.67793
+ ,-6.981201
+ ,2.129924
+ ,0.106802
+ ,1
+ ,0.00258
+ ,0.00346
+ ,0.00633
+ ,0.00659
+ ,26.833
+ ,0.393563
+ ,0.700246
+ ,-6.600023
+ ,2.499148
+ ,0.105306
+ ,1
+ ,0.00174
+ ,0.00225
+ ,0.00455
+ ,0.00238
+ ,29.928
+ ,0.311369
+ ,0.676066
+ ,-6.739151
+ ,2.296873
+ ,0.11513
+ ,1
+ ,0.00766
+ ,0.01351
+ ,0.01771
+ ,0.00947
+ ,21.934
+ ,0.497554
+ ,0.740539
+ ,-5.845099
+ ,2.608749
+ ,0.185668
+ ,1
+ ,0.00621
+ ,0.01112
+ ,0.01192
+ ,0.00704
+ ,23.239
+ ,0.436084
+ ,0.727863
+ ,-5.25832
+ ,2.550961
+ ,0.23252
+ ,1
+ ,0.00609
+ ,0.01105
+ ,0.00952
+ ,0.0083
+ ,22.407
+ ,0.338097
+ ,0.712466
+ ,-6.471427
+ ,2.502336
+ ,0.13639
+ ,1
+ ,0.00841
+ ,0.01506
+ ,0.01277
+ ,0.01316
+ ,21.305
+ ,0.498877
+ ,0.722085
+ ,-4.876336
+ ,2.376749
+ ,0.268144
+ ,1
+ ,0.00534
+ ,0.00964
+ ,0.00861
+ ,0.0062
+ ,23.671
+ ,0.441097
+ ,0.722254
+ ,-5.96304
+ ,2.489191
+ ,0.177807
+ ,1
+ ,0.00495
+ ,0.00905
+ ,0.01107
+ ,0.01048
+ ,21.864
+ ,0.331508
+ ,0.715121
+ ,-6.729713
+ ,2.938114
+ ,0.115515
+ ,1
+ ,0.00856
+ ,0.01211
+ ,0.00796
+ ,0.06051
+ ,23.693
+ ,0.407701
+ ,0.662668
+ ,-4.673241
+ ,2.702355
+ ,0.274407
+ ,1
+ ,0.00476
+ ,0.00642
+ ,0.00606
+ ,0.01554
+ ,26.356
+ ,0.450798
+ ,0.653823
+ ,-6.051233
+ ,2.640798
+ ,0.170106
+ ,1
+ ,0.00555
+ ,0.00731
+ ,0.00757
+ ,0.01802
+ ,25.69
+ ,0.486738
+ ,0.676023
+ ,-4.597834
+ ,2.975889
+ ,0.28278
+ ,1
+ ,0.00462
+ ,0.00472
+ ,0.00617
+ ,0.00856
+ ,25.02
+ ,0.470422
+ ,0.655239
+ ,-4.913137
+ ,2.816781
+ ,0.251972
+ ,1
+ ,0.00404
+ ,0.00381
+ ,0.00679
+ ,0.00681
+ ,24.581
+ ,0.462516
+ ,0.58271
+ ,-5.517173
+ ,2.925862
+ ,0.220657
+ ,1
+ ,0.00581
+ ,0.00723
+ ,0.00849
+ ,0.0235
+ ,24.743
+ ,0.487756
+ ,0.68413
+ ,-6.186128
+ ,2.68624
+ ,0.152428
+ ,1
+ ,0.0046
+ ,0.00628
+ ,0.00534
+ ,0.01161
+ ,27.166
+ ,0.400088
+ ,0.656182
+ ,-4.711007
+ ,2.655744
+ ,0.234809
+ ,1
+ ,0.00704
+ ,0.01218
+ ,0.02587
+ ,0.01968
+ ,18.305
+ ,0.538016
+ ,0.74148
+ ,-5.418787
+ ,2.090438
+ ,0.229892
+ ,1
+ ,0.00842
+ ,0.01517
+ ,0.01372
+ ,0.01813
+ ,18.784
+ ,0.589956
+ ,0.732903
+ ,-5.44514
+ ,2.174306
+ ,0.215558
+ ,1
+ ,0.00694
+ ,0.01209
+ ,0.01289
+ ,0.0202
+ ,19.196
+ ,0.618663
+ ,0.728421
+ ,-5.944191
+ ,1.929715
+ ,0.181988
+ ,1
+ ,0.00733
+ ,0.01242
+ ,0.01235
+ ,0.01874
+ ,18.857
+ ,0.637518
+ ,0.735546
+ ,-5.594275
+ ,1.765957
+ ,0.222716
+ ,1
+ ,0.00544
+ ,0.00883
+ ,0.01484
+ ,0.01794
+ ,18.178
+ ,0.623209
+ ,0.738245
+ ,-5.540351
+ ,1.821297
+ ,0.214075
+ ,1
+ ,0.00638
+ ,0.01104
+ ,0.01547
+ ,0.01796
+ ,18.33
+ ,0.585169
+ ,0.736964
+ ,-5.825257
+ ,1.996146
+ ,0.196535
+ ,1
+ ,0.0044
+ ,0.00641
+ ,0.00538
+ ,0.01724
+ ,26.842
+ ,0.457541
+ ,0.699787
+ ,-6.890021
+ ,2.328513
+ ,0.112856
+ ,1
+ ,0.0027
+ ,0.00349
+ ,0.00476
+ ,0.00487
+ ,26.369
+ ,0.491345
+ ,0.718839
+ ,-5.892061
+ ,2.108873
+ ,0.183572
+ ,1
+ ,0.00492
+ ,0.00808
+ ,0.00703
+ ,0.0161
+ ,23.949
+ ,0.46716
+ ,0.724045
+ ,-6.135296
+ ,2.539724
+ ,0.169923
+ ,1
+ ,0.00407
+ ,0.00671
+ ,0.00721
+ ,0.01015
+ ,26.017
+ ,0.468621
+ ,0.735136
+ ,-6.112667
+ ,2.527742
+ ,0.170633
+ ,1
+ ,0.00346
+ ,0.00508
+ ,0.00633
+ ,0.00903
+ ,23.389
+ ,0.470972
+ ,0.721308
+ ,-5.436135
+ ,2.51632
+ ,0.232209
+ ,1
+ ,0.00331
+ ,0.00504
+ ,0.0049
+ ,0.00504
+ ,25.619
+ ,0.482296
+ ,0.723096
+ ,-6.448134
+ ,2.034827
+ ,0.141422
+ ,1
+ ,0.00589
+ ,0.00873
+ ,0.02683
+ ,0.03031
+ ,17.06
+ ,0.637814
+ ,0.744064
+ ,-5.301321
+ ,2.375138
+ ,0.24308
+ ,1
+ ,0.00494
+ ,0.00731
+ ,0.02229
+ ,0.02529
+ ,17.707
+ ,0.653427
+ ,0.706687
+ ,-5.333619
+ ,2.631793
+ ,0.228319
+ ,1
+ ,0.00451
+ ,0.00658
+ ,0.02385
+ ,0.02278
+ ,19.013
+ ,0.6479
+ ,0.708144
+ ,-4.378916
+ ,2.445502
+ ,0.259451
+ ,1
+ ,0.00502
+ ,0.00772
+ ,0.02896
+ ,0.0369
+ ,16.747
+ ,0.625362
+ ,0.708617
+ ,-4.654894
+ ,2.672362
+ ,0.274387
+ ,1
+ ,0.00472
+ ,0.00715
+ ,0.0307
+ ,0.02629
+ ,17.366
+ ,0.640945
+ ,0.701404
+ ,-5.634576
+ ,2.419253
+ ,0.209191
+ ,1
+ ,0.00381
+ ,0.00542
+ ,0.01514
+ ,0.01827
+ ,18.801
+ ,0.624811
+ ,0.696049
+ ,-5.866357
+ ,2.445646
+ ,0.184985
+ ,1
+ ,0.00571
+ ,0.00696
+ ,0.01713
+ ,0.02485
+ ,18.54
+ ,0.677131
+ ,0.685057
+ ,-4.796845
+ ,2.963799
+ ,0.277227
+ ,1
+ ,0.00757
+ ,0.01285
+ ,0.04016
+ ,0.04238
+ ,15.648
+ ,0.606344
+ ,0.665945
+ ,-5.410336
+ ,2.665133
+ ,0.231723
+ ,1
+ ,0.00376
+ ,0.00546
+ ,0.02055
+ ,0.01728
+ ,18.702
+ ,0.606273
+ ,0.661735
+ ,-5.585259
+ ,2.465528
+ ,0.209863
+ ,1
+ ,0.0037
+ ,0.00568
+ ,0.01117
+ ,0.0201
+ ,18.687
+ ,0.536102
+ ,0.632631
+ ,-5.898673
+ ,2.470746
+ ,0.189032
+ ,1
+ ,0.00254
+ ,0.00301
+ ,0.01475
+ ,0.01049
+ ,20.68
+ ,0.49748
+ ,0.630409
+ ,-6.132663
+ ,2.576563
+ ,0.159777
+ ,1
+ ,0.00352
+ ,0.00506
+ ,0.01379
+ ,0.01493
+ ,20.366
+ ,0.566849
+ ,0.574282
+ ,-5.456811
+ ,2.840556
+ ,0.232861
+ ,1
+ ,0.01568
+ ,0.02589
+ ,0.03804
+ ,0.0753
+ ,12.359
+ ,0.56161
+ ,0.793509
+ ,-3.297668
+ ,3.413649
+ ,0.457533
+ ,1
+ ,0.01466
+ ,0.02546
+ ,0.02865
+ ,0.06057
+ ,14.367
+ ,0.478024
+ ,0.768974
+ ,-4.276605
+ ,3.142364
+ ,0.336085
+ ,1
+ ,0.01719
+ ,0.02987
+ ,0.03474
+ ,0.08069
+ ,12.298
+ ,0.55287
+ ,0.764036
+ ,-3.377325
+ ,3.274865
+ ,0.418646
+ ,1
+ ,0.01627
+ ,0.02756
+ ,0.03515
+ ,0.07889
+ ,14.989
+ ,0.427627
+ ,0.775708
+ ,-4.892495
+ ,2.910213
+ ,0.270173
+ ,1
+ ,0.01872
+ ,0.03225
+ ,0.02699
+ ,0.10952
+ ,12.529
+ ,0.507826
+ ,0.762726
+ ,-4.484303
+ ,2.958815
+ ,0.301487
+ ,1
+ ,0.03107
+ ,0.05401
+ ,0.05647
+ ,0.21713
+ ,8.441
+ ,0.625866
+ ,0.76832
+ ,-2.434031
+ ,3.079221
+ ,0.527367
+ ,1
+ ,0.02714
+ ,0.04705
+ ,0.04284
+ ,0.16265
+ ,9.449
+ ,0.584164
+ ,0.754449
+ ,-2.839756
+ ,3.184027
+ ,0.454721
+ ,1
+ ,0.00684
+ ,0.01164
+ ,0.0134
+ ,0.04179
+ ,21.52
+ ,0.566867
+ ,0.670475
+ ,-4.865194
+ ,2.01353
+ ,0.168581
+ ,1
+ ,0.00692
+ ,0.01179
+ ,0.01484
+ ,0.04611
+ ,21.824
+ ,0.65168
+ ,0.659333
+ ,-4.239028
+ ,2.45113
+ ,0.247455
+ ,1
+ ,0.00647
+ ,0.01067
+ ,0.01659
+ ,0.02631
+ ,22.431
+ ,0.6283
+ ,0.652025
+ ,-3.583722
+ ,2.439597
+ ,0.206256
+ ,1
+ ,0.00727
+ ,0.01246
+ ,0.01205
+ ,0.03191
+ ,22.953
+ ,0.611679
+ ,0.623731
+ ,-5.4351
+ ,2.699645
+ ,0.220546
+ ,1
+ ,0.01813
+ ,0.03351
+ ,0.0261
+ ,0.10748
+ ,19.075
+ ,0.630547
+ ,0.646786
+ ,-3.444478
+ ,2.964568
+ ,0.261305
+ ,1
+ ,0.00975
+ ,0.01778
+ ,0.015
+ ,0.03828
+ ,21.534
+ ,0.635015
+ ,0.627337
+ ,-5.070096
+ ,2.8923
+ ,0.249703
+ ,1
+ ,0.00605
+ ,0.00962
+ ,0.0136
+ ,0.02663
+ ,19.651
+ ,0.654945
+ ,0.675865
+ ,-5.498456
+ ,2.103014
+ ,0.216638
+ ,1
+ ,0.00581
+ ,0.00896
+ ,0.01579
+ ,0.02073
+ ,20.437
+ ,0.653139
+ ,0.694571
+ ,-5.185987
+ ,2.151121
+ ,0.244948
+ ,1
+ ,0.00619
+ ,0.01057
+ ,0.01644
+ ,0.0281
+ ,19.388
+ ,0.577802
+ ,0.684373
+ ,-5.283009
+ ,2.442906
+ ,0.238281
+ ,1
+ ,0.00651
+ ,0.01097
+ ,0.01864
+ ,0.02707
+ ,18.954
+ ,0.685151
+ ,0.719576
+ ,-5.529833
+ ,2.408689
+ ,0.22052
+ ,1
+ ,0.00519
+ ,0.00873
+ ,0.00967
+ ,0.01435
+ ,21.219
+ ,0.557045
+ ,0.673086
+ ,-5.617124
+ ,1.871871
+ ,0.212386
+ ,1
+ ,0.00907
+ ,0.0148
+ ,0.01579
+ ,0.03882
+ ,18.447
+ ,0.671378
+ ,0.674562
+ ,-2.929379
+ ,2.560422
+ ,0.367233
+ ,0
+ ,0.00277
+ ,0.00462
+ ,0.0141
+ ,0.0062
+ ,24.078
+ ,0.469928
+ ,0.628232
+ ,-6.816086
+ ,2.235197
+ ,0.119652
+ ,0
+ ,0.00303
+ ,0.00519
+ ,0.00696
+ ,0.00533
+ ,24.679
+ ,0.384868
+ ,0.62671
+ ,-7.018057
+ ,1.852402
+ ,0.091604
+ ,0
+ ,0.00339
+ ,0.00616
+ ,0.01186
+ ,0.0091
+ ,21.083
+ ,0.440988
+ ,0.628058
+ ,-7.517934
+ ,1.881767
+ ,0.075587
+ ,0
+ ,0.00803
+ ,0.0147
+ ,0.01279
+ ,0.01337
+ ,19.269
+ ,0.372222
+ ,0.725216
+ ,-5.736781
+ ,2.88245
+ ,0.202879
+ ,0
+ ,0.00517
+ ,0.00949
+ ,0.01176
+ ,0.00965
+ ,21.02
+ ,0.371837
+ ,0.646167
+ ,-7.169701
+ ,2.266432
+ ,0.100881
+ ,0
+ ,0.00451
+ ,0.00837
+ ,0.01084
+ ,0.01049
+ ,21.528
+ ,0.522812
+ ,0.646818
+ ,-7.3045
+ ,2.095237
+ ,0.09622
+ ,0
+ ,0.00355
+ ,0.00499
+ ,0.00664
+ ,0.00435
+ ,26.436
+ ,0.413295
+ ,0.7567
+ ,-6.323531
+ ,2.193412
+ ,0.160376
+ ,0
+ ,0.00356
+ ,0.0051
+ ,0.00754
+ ,0.0043
+ ,26.55
+ ,0.36909
+ ,0.776158
+ ,-6.085567
+ ,1.889002
+ ,0.174152
+ ,0
+ ,0.00349
+ ,0.00514
+ ,0.00748
+ ,0.00478
+ ,26.547
+ ,0.380253
+ ,0.7667
+ ,-5.943501
+ ,1.852542
+ ,0.179677
+ ,0
+ ,0.00353
+ ,0.00528
+ ,0.00881
+ ,0.0059
+ ,25.445
+ ,0.387482
+ ,0.756482
+ ,-6.012559
+ ,1.872946
+ ,0.163118
+ ,0
+ ,0.00332
+ ,0.0048
+ ,0.00812
+ ,0.00401
+ ,26.005
+ ,0.405991
+ ,0.761255
+ ,-5.966779
+ ,1.974857
+ ,0.184067
+ ,0
+ ,0.00346
+ ,0.00507
+ ,0.00874
+ ,0.00415
+ ,26.143
+ ,0.361232
+ ,0.763242
+ ,-6.016891
+ ,2.004719
+ ,0.174429
+ ,1
+ ,0.00314
+ ,0.00406
+ ,0.00728
+ ,0.0057
+ ,24.151
+ ,0.39661
+ ,0.745957
+ ,-6.486822
+ ,2.449763
+ ,0.132703
+ ,1
+ ,0.00309
+ ,0.00456
+ ,0.00839
+ ,0.00488
+ ,24.412
+ ,0.402591
+ ,0.762508
+ ,-6.311987
+ ,2.251553
+ ,0.160306
+ ,1
+ ,0.00392
+ ,0.00612
+ ,0.00725
+ ,0.0054
+ ,23.683
+ ,0.398499
+ ,0.778349
+ ,-5.711205
+ ,2.845109
+ ,0.19273
+ ,1
+ ,0.00396
+ ,0.00619
+ ,0.01321
+ ,0.00611
+ ,23.133
+ ,0.352396
+ ,0.75932
+ ,-6.261446
+ ,2.264226
+ ,0.144105
+ ,1
+ ,0.00397
+ ,0.00605
+ ,0.0095
+ ,0.00639
+ ,22.866
+ ,0.408598
+ ,0.768845
+ ,-5.704053
+ ,2.679185
+ ,0.19771
+ ,1
+ ,0.00336
+ ,0.00521
+ ,0.01155
+ ,0.00595
+ ,23.008
+ ,0.329577
+ ,0.75718
+ ,-6.27717
+ ,2.209021
+ ,0.156368
+ ,0
+ ,0.00417
+ ,0.00558
+ ,0.00864
+ ,0.00955
+ ,23.079
+ ,0.603515
+ ,0.669565
+ ,-5.61907
+ ,2.027228
+ ,0.215724
+ ,0
+ ,0.00531
+ ,0.0078
+ ,0.0081
+ ,0.01179
+ ,22.085
+ ,0.663842
+ ,0.656516
+ ,-5.198864
+ ,2.120412
+ ,0.252404
+ ,0
+ ,0.00314
+ ,0.00403
+ ,0.00667
+ ,0.00737
+ ,24.199
+ ,0.598515
+ ,0.654331
+ ,-5.592584
+ ,2.058658
+ ,0.214346
+ ,0
+ ,0.00496
+ ,0.00762
+ ,0.0082
+ ,0.01397
+ ,23.958
+ ,0.566424
+ ,0.667654
+ ,-6.431119
+ ,2.161936
+ ,0.120605
+ ,0
+ ,0.00267
+ ,0.00345
+ ,0.00631
+ ,0.0068
+ ,25.023
+ ,0.528485
+ ,0.663884
+ ,-6.359018
+ ,2.152083
+ ,0.138868
+ ,0
+ ,0.00327
+ ,0.00439
+ ,0.00557
+ ,0.00703
+ ,24.775
+ ,0.555303
+ ,0.659132
+ ,-6.710219
+ ,1.91399
+ ,0.121777
+ ,0
+ ,0.00694
+ ,0.01235
+ ,0.01454
+ ,0.04441
+ ,19.368
+ ,0.508479
+ ,0.683761
+ ,-6.934474
+ ,2.316346
+ ,0.112838
+ ,0
+ ,0.00459
+ ,0.0079
+ ,0.02336
+ ,0.02764
+ ,19.517
+ ,0.448439
+ ,0.657899
+ ,-6.538586
+ ,2.657476
+ ,0.13305
+ ,0
+ ,0.00564
+ ,0.00994
+ ,0.01604
+ ,0.0181
+ ,19.147
+ ,0.431674
+ ,0.683244
+ ,-6.195325
+ ,2.784312
+ ,0.168895
+ ,0
+ ,0.0136
+ ,0.01873
+ ,0.01268
+ ,0.10715
+ ,17.883
+ ,0.407567
+ ,0.655683
+ ,-6.787197
+ ,2.679772
+ ,0.131728
+ ,0
+ ,0.0074
+ ,0.01109
+ ,0.01265
+ ,0.07223
+ ,19.02
+ ,0.451221
+ ,0.643956
+ ,-6.744577
+ ,2.138608
+ ,0.123306
+ ,0
+ ,0.00567
+ ,0.00885
+ ,0.01026
+ ,0.04398
+ ,21.209
+ ,0.462803
+ ,0.664357
+ ,-5.724056
+ ,2.555477
+ ,0.148569)
+ ,dim=c(11
+ ,194)
+ ,dimnames=list(c('status'
+ ,'MDVP:Jitter(%)'
+ ,'Jitter:DDP'
+ ,'Shimmer:APQ3'
+ ,'NHR'
+ ,'HNR'
+ ,'RPDE'
+ ,'DFA'
+ ,'spread1'
+ ,'D2'
+ ,'PPE')
+ ,1:194))
> y <- array(NA,dim=c(11,194),dimnames=list(c('status','MDVP:Jitter(%)','Jitter:DDP','Shimmer:APQ3','NHR','HNR','RPDE','DFA','spread1','D2','PPE'),1:194))
> 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
status MDVP:Jitter(%) Jitter:DDP Shimmer:APQ3 NHR HNR RPDE
1 1 0.00968 0.01394 0.03134 0.01929 19.085 0.458359
2 1 0.01050 0.01633 0.02757 0.01309 20.651 0.429895
3 1 0.00997 0.01505 0.02924 0.01353 20.644 0.434969
4 1 0.01284 0.01966 0.03490 0.01767 19.649 0.417356
5 1 0.00968 0.01388 0.02328 0.01222 21.378 0.415564
6 1 0.00333 0.00466 0.00779 0.00607 24.886 0.596040
7 1 0.00290 0.00431 0.00829 0.00344 26.892 0.637420
8 1 0.00551 0.00880 0.01073 0.01070 21.812 0.615551
9 1 0.00532 0.00803 0.01441 0.01022 21.862 0.547037
10 1 0.00505 0.00763 0.01079 0.01166 21.118 0.611137
11 1 0.00540 0.00844 0.01424 0.01141 21.414 0.583390
12 1 0.00293 0.00355 0.00656 0.00581 25.703 0.460600
13 1 0.00390 0.00496 0.00728 0.01041 24.889 0.430166
14 1 0.00294 0.00364 0.01064 0.00609 24.922 0.474791
15 1 0.00369 0.00471 0.00772 0.00839 25.175 0.565924
16 1 0.00544 0.00632 0.00969 0.01859 22.333 0.567380
17 1 0.00718 0.00853 0.01441 0.02919 20.376 0.631099
18 1 0.00742 0.01092 0.02471 0.03160 17.280 0.665318
19 1 0.00768 0.01116 0.01721 0.03365 17.153 0.649554
20 1 0.00840 0.01285 0.01667 0.03871 17.536 0.660125
21 1 0.00480 0.00696 0.02021 0.01849 19.493 0.629017
22 1 0.00442 0.00661 0.02228 0.01280 22.468 0.619060
23 1 0.00476 0.00663 0.02187 0.01840 20.422 0.537264
24 1 0.00742 0.01140 0.00738 0.01778 23.831 0.397937
25 1 0.00633 0.00948 0.01732 0.02887 22.066 0.522746
26 1 0.00455 0.00750 0.00889 0.01095 25.908 0.418622
27 1 0.00496 0.00749 0.00883 0.01328 25.119 0.358773
28 1 0.00310 0.00476 0.00769 0.00677 25.970 0.470478
29 1 0.00502 0.00841 0.00793 0.01170 25.678 0.427785
30 0 0.00289 0.00498 0.00563 0.00339 26.775 0.422229
31 0 0.00241 0.00402 0.00504 0.00167 30.940 0.432439
32 0 0.00212 0.00339 0.00640 0.00119 30.775 0.465946
33 0 0.00180 0.00278 0.00469 0.00072 32.684 0.368535
34 0 0.00178 0.00283 0.00468 0.00065 33.047 0.340068
35 0 0.00198 0.00314 0.00586 0.00135 31.732 0.344252
36 1 0.00411 0.00700 0.01154 0.00586 23.216 0.360148
37 1 0.00369 0.00616 0.00938 0.00340 24.951 0.341435
38 1 0.00284 0.00459 0.00726 0.00231 26.738 0.403884
39 1 0.00316 0.00504 0.00829 0.00265 26.310 0.396793
40 1 0.00298 0.00496 0.00774 0.00231 26.822 0.326480
41 1 0.00258 0.00403 0.00742 0.00257 26.453 0.306443
42 0 0.00298 0.00507 0.01035 0.00740 22.736 0.305062
43 0 0.00281 0.00470 0.01006 0.00675 23.145 0.457702
44 0 0.00210 0.00327 0.00777 0.00454 25.368 0.438296
45 0 0.00225 0.00350 0.00847 0.00476 25.032 0.431285
46 0 0.00235 0.00380 0.00906 0.00476 24.602 0.467489
47 0 0.00185 0.00276 0.00614 0.00432 26.805 0.610367
48 0 0.00524 0.00507 0.00855 0.00839 23.162 0.579597
49 0 0.00428 0.00373 0.00930 0.00462 24.971 0.538688
50 0 0.00431 0.00422 0.01241 0.00479 25.135 0.553134
51 0 0.00448 0.00393 0.01143 0.00474 25.030 0.507504
52 0 0.00436 0.00411 0.01323 0.00481 24.692 0.459766
53 0 0.00490 0.00495 0.01396 0.00484 25.429 0.420383
54 1 0.00761 0.01046 0.01483 0.01036 21.028 0.536009
55 1 0.00874 0.01193 0.01789 0.01180 20.767 0.558586
56 1 0.00784 0.01056 0.02032 0.00969 21.422 0.541781
57 1 0.00752 0.00898 0.01189 0.00681 22.817 0.530529
58 1 0.00788 0.01003 0.01394 0.00786 22.603 0.540049
59 1 0.00867 0.01120 0.01805 0.01143 21.660 0.547975
60 0 0.00282 0.00442 0.00975 0.00871 25.554 0.341788
61 0 0.00264 0.00461 0.01013 0.00301 26.138 0.447979
62 0 0.00266 0.00457 0.00867 0.00340 25.856 0.364867
63 0 0.00296 0.00526 0.00882 0.00351 25.964 0.256570
64 0 0.00205 0.00342 0.00769 0.00300 26.415 0.276850
65 0 0.00238 0.00408 0.00942 0.00420 24.547 0.305429
66 1 0.00817 0.01289 0.01830 0.02183 19.560 0.460139
67 1 0.00923 0.01520 0.01638 0.02659 19.979 0.498133
68 1 0.01101 0.01941 0.03152 0.04882 20.338 0.513237
69 1 0.00762 0.01400 0.03357 0.02431 21.718 0.487407
70 1 0.00831 0.01407 0.01868 0.02599 20.264 0.489345
71 1 0.00971 0.01601 0.02749 0.03361 18.570 0.543299
72 1 0.00405 0.00540 0.00974 0.00442 25.742 0.495954
73 1 0.00533 0.00805 0.01373 0.00623 24.178 0.509127
74 1 0.00494 0.00780 0.01432 0.00479 25.438 0.437031
75 1 0.00516 0.00831 0.01284 0.00472 25.197 0.463514
76 1 0.00500 0.00810 0.02413 0.00905 23.370 0.489538
77 1 0.00462 0.00677 0.01284 0.00420 25.820 0.429484
78 1 0.00608 0.00994 0.01803 0.01062 21.875 0.644954
79 1 0.01038 0.01865 0.01773 0.02220 19.200 0.594387
80 1 0.00694 0.01168 0.02266 0.01823 19.055 0.544805
81 1 0.00702 0.01283 0.01792 0.01825 19.659 0.576084
82 1 0.00606 0.01053 0.01371 0.01237 20.536 0.554610
83 1 0.00432 0.00742 0.01277 0.00882 22.244 0.576644
84 1 0.00747 0.01254 0.02679 0.05470 13.893 0.556494
85 1 0.00406 0.00659 0.02107 0.02782 16.176 0.583574
86 1 0.00321 0.00488 0.02073 0.03151 15.924 0.598714
87 1 0.00520 0.00862 0.03671 0.04824 13.922 0.602874
88 1 0.00448 0.00710 0.03788 0.04214 14.739 0.599371
89 1 0.00709 0.01172 0.02297 0.07223 11.866 0.590951
90 1 0.00742 0.01161 0.03650 0.08725 11.744 0.653410
91 1 0.00419 0.00672 0.04421 0.01658 19.664 0.501037
92 1 0.00459 0.00750 0.02383 0.01914 18.780 0.454444
93 1 0.00382 0.00574 0.03341 0.01211 20.969 0.447456
94 1 0.00358 0.00587 0.02062 0.00850 22.219 0.502380
95 1 0.00369 0.00602 0.01813 0.01018 21.693 0.447285
96 1 0.00342 0.00535 0.01806 0.00852 22.663 0.366329
97 1 0.01280 0.02228 0.02135 0.08151 15.338 0.629574
98 1 0.01378 0.02478 0.02542 0.10323 15.433 0.571010
99 1 0.01936 0.03476 0.03611 0.16744 12.435 0.638545
100 1 0.03316 0.06433 0.05358 0.31482 8.867 0.671299
101 1 0.01551 0.02716 0.03223 0.11843 15.060 0.639808
102 1 0.03011 0.05563 0.05551 0.25930 10.489 0.596362
103 1 0.00248 0.00315 0.00522 0.00495 26.759 0.296888
104 1 0.00183 0.00229 0.00469 0.00243 28.409 0.263654
105 1 0.00257 0.00349 0.00660 0.00578 27.421 0.365488
106 1 0.00168 0.00204 0.00522 0.00233 29.746 0.334171
107 1 0.00258 0.00346 0.00633 0.00659 26.833 0.393563
108 1 0.00174 0.00225 0.00455 0.00238 29.928 0.311369
109 1 0.00766 0.01351 0.01771 0.00947 21.934 0.497554
110 1 0.00621 0.01112 0.01192 0.00704 23.239 0.436084
111 1 0.00609 0.01105 0.00952 0.00830 22.407 0.338097
112 1 0.00841 0.01506 0.01277 0.01316 21.305 0.498877
113 1 0.00534 0.00964 0.00861 0.00620 23.671 0.441097
114 1 0.00495 0.00905 0.01107 0.01048 21.864 0.331508
115 1 0.00856 0.01211 0.00796 0.06051 23.693 0.407701
116 1 0.00476 0.00642 0.00606 0.01554 26.356 0.450798
117 1 0.00555 0.00731 0.00757 0.01802 25.690 0.486738
118 1 0.00462 0.00472 0.00617 0.00856 25.020 0.470422
119 1 0.00404 0.00381 0.00679 0.00681 24.581 0.462516
120 1 0.00581 0.00723 0.00849 0.02350 24.743 0.487756
121 1 0.00460 0.00628 0.00534 0.01161 27.166 0.400088
122 1 0.00704 0.01218 0.02587 0.01968 18.305 0.538016
123 1 0.00842 0.01517 0.01372 0.01813 18.784 0.589956
124 1 0.00694 0.01209 0.01289 0.02020 19.196 0.618663
125 1 0.00733 0.01242 0.01235 0.01874 18.857 0.637518
126 1 0.00544 0.00883 0.01484 0.01794 18.178 0.623209
127 1 0.00638 0.01104 0.01547 0.01796 18.330 0.585169
128 1 0.00440 0.00641 0.00538 0.01724 26.842 0.457541
129 1 0.00270 0.00349 0.00476 0.00487 26.369 0.491345
130 1 0.00492 0.00808 0.00703 0.01610 23.949 0.467160
131 1 0.00407 0.00671 0.00721 0.01015 26.017 0.468621
132 1 0.00346 0.00508 0.00633 0.00903 23.389 0.470972
133 1 0.00331 0.00504 0.00490 0.00504 25.619 0.482296
134 1 0.00589 0.00873 0.02683 0.03031 17.060 0.637814
135 1 0.00494 0.00731 0.02229 0.02529 17.707 0.653427
136 1 0.00451 0.00658 0.02385 0.02278 19.013 0.647900
137 1 0.00502 0.00772 0.02896 0.03690 16.747 0.625362
138 1 0.00472 0.00715 0.03070 0.02629 17.366 0.640945
139 1 0.00381 0.00542 0.01514 0.01827 18.801 0.624811
140 1 0.00571 0.00696 0.01713 0.02485 18.540 0.677131
141 1 0.00757 0.01285 0.04016 0.04238 15.648 0.606344
142 1 0.00376 0.00546 0.02055 0.01728 18.702 0.606273
143 1 0.00370 0.00568 0.01117 0.02010 18.687 0.536102
144 1 0.00254 0.00301 0.01475 0.01049 20.680 0.497480
145 1 0.00352 0.00506 0.01379 0.01493 20.366 0.566849
146 1 0.01568 0.02589 0.03804 0.07530 12.359 0.561610
147 1 0.01466 0.02546 0.02865 0.06057 14.367 0.478024
148 1 0.01719 0.02987 0.03474 0.08069 12.298 0.552870
149 1 0.01627 0.02756 0.03515 0.07889 14.989 0.427627
150 1 0.01872 0.03225 0.02699 0.10952 12.529 0.507826
151 1 0.03107 0.05401 0.05647 0.21713 8.441 0.625866
152 1 0.02714 0.04705 0.04284 0.16265 9.449 0.584164
153 1 0.00684 0.01164 0.01340 0.04179 21.520 0.566867
154 1 0.00692 0.01179 0.01484 0.04611 21.824 0.651680
155 1 0.00647 0.01067 0.01659 0.02631 22.431 0.628300
156 1 0.00727 0.01246 0.01205 0.03191 22.953 0.611679
157 1 0.01813 0.03351 0.02610 0.10748 19.075 0.630547
158 1 0.00975 0.01778 0.01500 0.03828 21.534 0.635015
159 1 0.00605 0.00962 0.01360 0.02663 19.651 0.654945
160 1 0.00581 0.00896 0.01579 0.02073 20.437 0.653139
161 1 0.00619 0.01057 0.01644 0.02810 19.388 0.577802
162 1 0.00651 0.01097 0.01864 0.02707 18.954 0.685151
163 1 0.00519 0.00873 0.00967 0.01435 21.219 0.557045
164 1 0.00907 0.01480 0.01579 0.03882 18.447 0.671378
165 0 0.00277 0.00462 0.01410 0.00620 24.078 0.469928
166 0 0.00303 0.00519 0.00696 0.00533 24.679 0.384868
167 0 0.00339 0.00616 0.01186 0.00910 21.083 0.440988
168 0 0.00803 0.01470 0.01279 0.01337 19.269 0.372222
169 0 0.00517 0.00949 0.01176 0.00965 21.020 0.371837
170 0 0.00451 0.00837 0.01084 0.01049 21.528 0.522812
171 0 0.00355 0.00499 0.00664 0.00435 26.436 0.413295
172 0 0.00356 0.00510 0.00754 0.00430 26.550 0.369090
173 0 0.00349 0.00514 0.00748 0.00478 26.547 0.380253
174 0 0.00353 0.00528 0.00881 0.00590 25.445 0.387482
175 0 0.00332 0.00480 0.00812 0.00401 26.005 0.405991
176 0 0.00346 0.00507 0.00874 0.00415 26.143 0.361232
177 1 0.00314 0.00406 0.00728 0.00570 24.151 0.396610
178 1 0.00309 0.00456 0.00839 0.00488 24.412 0.402591
179 1 0.00392 0.00612 0.00725 0.00540 23.683 0.398499
180 1 0.00396 0.00619 0.01321 0.00611 23.133 0.352396
181 1 0.00397 0.00605 0.00950 0.00639 22.866 0.408598
182 1 0.00336 0.00521 0.01155 0.00595 23.008 0.329577
183 0 0.00417 0.00558 0.00864 0.00955 23.079 0.603515
184 0 0.00531 0.00780 0.00810 0.01179 22.085 0.663842
185 0 0.00314 0.00403 0.00667 0.00737 24.199 0.598515
186 0 0.00496 0.00762 0.00820 0.01397 23.958 0.566424
187 0 0.00267 0.00345 0.00631 0.00680 25.023 0.528485
188 0 0.00327 0.00439 0.00557 0.00703 24.775 0.555303
189 0 0.00694 0.01235 0.01454 0.04441 19.368 0.508479
190 0 0.00459 0.00790 0.02336 0.02764 19.517 0.448439
191 0 0.00564 0.00994 0.01604 0.01810 19.147 0.431674
192 0 0.01360 0.01873 0.01268 0.10715 17.883 0.407567
193 0 0.00740 0.01109 0.01265 0.07223 19.020 0.451221
194 0 0.00567 0.00885 0.01026 0.04398 21.209 0.462803
DFA spread1 D2 PPE
1 0.819521 -4.075192 2.486855 0.368674
2 0.825288 -4.443179 2.342259 0.332634
3 0.819235 -4.117501 2.405554 0.368975
4 0.823484 -3.747787 2.332180 0.410335
5 0.825069 -4.242867 2.187560 0.357775
6 0.764112 -5.634322 1.854785 0.211756
7 0.763262 -6.167603 2.064693 0.163755
8 0.773587 -5.498678 2.322511 0.231571
9 0.798463 -5.011879 2.432792 0.271362
10 0.776156 -5.249770 2.407313 0.249740
11 0.792520 -4.960234 2.642476 0.275931
12 0.646846 -6.547148 2.041277 0.138512
13 0.665833 -5.660217 2.519422 0.199889
14 0.654027 -6.105098 2.125618 0.170100
15 0.658245 -5.340115 2.205546 0.234589
16 0.644692 -5.440040 2.264501 0.218164
17 0.605417 -2.931070 3.007463 0.430788
18 0.719467 -3.949079 3.109010 0.377429
19 0.686080 -4.554466 2.856676 0.322111
20 0.704087 -4.095442 2.739710 0.365391
21 0.698951 -5.186960 2.557536 0.259765
22 0.679834 -4.330956 2.916777 0.285695
23 0.686894 -5.248776 2.547508 0.253556
24 0.732479 -5.557447 2.692176 0.215961
25 0.737948 -5.571843 2.846369 0.219514
26 0.720916 -6.183590 2.589702 0.147403
27 0.726652 -6.271690 2.314209 0.162999
28 0.676258 -7.120925 2.241742 0.108514
29 0.723797 -6.635729 1.957961 0.135242
30 0.741367 -7.348300 1.743867 0.085569
31 0.742055 -7.682587 2.103106 0.068501
32 0.738703 -7.067931 1.512275 0.096320
33 0.742133 -7.695734 1.544609 0.056141
34 0.741899 -7.964984 1.423287 0.044539
35 0.742737 -7.777685 2.447064 0.057610
36 0.778834 -6.149653 2.477082 0.165827
37 0.783626 -6.006414 2.536527 0.173218
38 0.766209 -6.452058 2.269398 0.141929
39 0.758324 -6.006647 2.382544 0.160691
40 0.765623 -6.647379 2.374073 0.130554
41 0.759203 -7.044105 2.361532 0.115730
42 0.654172 -7.310550 2.416838 0.095032
43 0.634267 -6.793547 2.256699 0.117399
44 0.635285 -7.057869 2.330716 0.091470
45 0.638928 -6.995820 2.365800 0.102706
46 0.631653 -7.156076 2.392122 0.097336
47 0.635204 -7.319510 2.028612 0.086398
48 0.733659 -6.439398 2.079922 0.133867
49 0.754073 -6.482096 2.054419 0.128872
50 0.775933 -6.650471 1.840198 0.103561
51 0.760361 -6.689151 2.431854 0.105993
52 0.766204 -7.072419 1.972297 0.119308
53 0.785714 -6.836811 2.223719 0.147491
54 0.819032 -4.649573 1.986899 0.316700
55 0.811843 -4.333543 2.014606 0.344834
56 0.821364 -4.438453 1.922940 0.335041
57 0.817756 -4.608260 2.021591 0.314464
58 0.813432 -4.476755 1.827012 0.326197
59 0.817396 -4.609161 1.831691 0.316395
60 0.678874 -7.040508 2.460791 0.101516
61 0.686264 -7.293801 2.321560 0.098555
62 0.694399 -6.966321 2.278687 0.103224
63 0.683296 -7.245620 2.498224 0.093534
64 0.673636 -7.496264 2.003032 0.073581
65 0.681811 -7.314237 2.118596 0.091546
66 0.720908 -5.409423 2.359973 0.226156
67 0.729067 -5.324574 2.291558 0.226247
68 0.731444 -5.869750 2.118496 0.185580
69 0.727313 -6.261141 2.137075 0.141958
70 0.730387 -5.720868 2.277927 0.180828
71 0.733232 -5.207985 2.642276 0.242981
72 0.762959 -5.791820 2.205024 0.188180
73 0.789532 -5.389129 1.928708 0.225461
74 0.815908 -5.313360 2.225815 0.244512
75 0.807217 -5.477592 1.862092 0.228624
76 0.789977 -5.775966 2.007923 0.193918
77 0.816340 -5.391029 1.777901 0.232744
78 0.779612 -5.115212 2.017753 0.260015
79 0.790117 -4.913885 2.398422 0.277948
80 0.770466 -4.441519 2.645959 0.327978
81 0.778747 -5.132032 2.232576 0.260633
82 0.787896 -5.022288 2.428306 0.264666
83 0.772416 -6.025367 2.053601 0.177275
84 0.729586 -5.288912 3.099301 0.242119
85 0.727747 -5.657899 3.098256 0.200423
86 0.712199 -6.366916 2.654271 0.144614
87 0.740837 -5.515071 3.136550 0.220968
88 0.743937 -5.783272 3.007096 0.194052
89 0.745526 -4.379411 3.671155 0.332086
90 0.733165 -4.508984 3.317586 0.301952
91 0.714360 -6.411497 2.344876 0.134120
92 0.734504 -5.952058 2.344336 0.186489
93 0.697790 -6.152551 2.080121 0.160809
94 0.712170 -6.251425 2.143851 0.160812
95 0.705658 -6.247076 2.344348 0.164916
96 0.693429 -6.417440 2.473239 0.151709
97 0.714485 -4.020042 2.671825 0.340623
98 0.690892 -5.159169 2.441612 0.260375
99 0.674953 -3.760348 2.634633 0.378483
100 0.656846 -3.700544 2.991063 0.370961
101 0.643327 -4.202730 2.638279 0.356881
102 0.641418 -3.269487 2.690917 0.444774
103 0.722356 -6.878393 2.004055 0.113942
104 0.691483 -7.111576 2.065477 0.093193
105 0.719974 -6.997403 1.994387 0.112878
106 0.677930 -6.981201 2.129924 0.106802
107 0.700246 -6.600023 2.499148 0.105306
108 0.676066 -6.739151 2.296873 0.115130
109 0.740539 -5.845099 2.608749 0.185668
110 0.727863 -5.258320 2.550961 0.232520
111 0.712466 -6.471427 2.502336 0.136390
112 0.722085 -4.876336 2.376749 0.268144
113 0.722254 -5.963040 2.489191 0.177807
114 0.715121 -6.729713 2.938114 0.115515
115 0.662668 -4.673241 2.702355 0.274407
116 0.653823 -6.051233 2.640798 0.170106
117 0.676023 -4.597834 2.975889 0.282780
118 0.655239 -4.913137 2.816781 0.251972
119 0.582710 -5.517173 2.925862 0.220657
120 0.684130 -6.186128 2.686240 0.152428
121 0.656182 -4.711007 2.655744 0.234809
122 0.741480 -5.418787 2.090438 0.229892
123 0.732903 -5.445140 2.174306 0.215558
124 0.728421 -5.944191 1.929715 0.181988
125 0.735546 -5.594275 1.765957 0.222716
126 0.738245 -5.540351 1.821297 0.214075
127 0.736964 -5.825257 1.996146 0.196535
128 0.699787 -6.890021 2.328513 0.112856
129 0.718839 -5.892061 2.108873 0.183572
130 0.724045 -6.135296 2.539724 0.169923
131 0.735136 -6.112667 2.527742 0.170633
132 0.721308 -5.436135 2.516320 0.232209
133 0.723096 -6.448134 2.034827 0.141422
134 0.744064 -5.301321 2.375138 0.243080
135 0.706687 -5.333619 2.631793 0.228319
136 0.708144 -4.378916 2.445502 0.259451
137 0.708617 -4.654894 2.672362 0.274387
138 0.701404 -5.634576 2.419253 0.209191
139 0.696049 -5.866357 2.445646 0.184985
140 0.685057 -4.796845 2.963799 0.277227
141 0.665945 -5.410336 2.665133 0.231723
142 0.661735 -5.585259 2.465528 0.209863
143 0.632631 -5.898673 2.470746 0.189032
144 0.630409 -6.132663 2.576563 0.159777
145 0.574282 -5.456811 2.840556 0.232861
146 0.793509 -3.297668 3.413649 0.457533
147 0.768974 -4.276605 3.142364 0.336085
148 0.764036 -3.377325 3.274865 0.418646
149 0.775708 -4.892495 2.910213 0.270173
150 0.762726 -4.484303 2.958815 0.301487
151 0.768320 -2.434031 3.079221 0.527367
152 0.754449 -2.839756 3.184027 0.454721
153 0.670475 -4.865194 2.013530 0.168581
154 0.659333 -4.239028 2.451130 0.247455
155 0.652025 -3.583722 2.439597 0.206256
156 0.623731 -5.435100 2.699645 0.220546
157 0.646786 -3.444478 2.964568 0.261305
158 0.627337 -5.070096 2.892300 0.249703
159 0.675865 -5.498456 2.103014 0.216638
160 0.694571 -5.185987 2.151121 0.244948
161 0.684373 -5.283009 2.442906 0.238281
162 0.719576 -5.529833 2.408689 0.220520
163 0.673086 -5.617124 1.871871 0.212386
164 0.674562 -2.929379 2.560422 0.367233
165 0.628232 -6.816086 2.235197 0.119652
166 0.626710 -7.018057 1.852402 0.091604
167 0.628058 -7.517934 1.881767 0.075587
168 0.725216 -5.736781 2.882450 0.202879
169 0.646167 -7.169701 2.266432 0.100881
170 0.646818 -7.304500 2.095237 0.096220
171 0.756700 -6.323531 2.193412 0.160376
172 0.776158 -6.085567 1.889002 0.174152
173 0.766700 -5.943501 1.852542 0.179677
174 0.756482 -6.012559 1.872946 0.163118
175 0.761255 -5.966779 1.974857 0.184067
176 0.763242 -6.016891 2.004719 0.174429
177 0.745957 -6.486822 2.449763 0.132703
178 0.762508 -6.311987 2.251553 0.160306
179 0.778349 -5.711205 2.845109 0.192730
180 0.759320 -6.261446 2.264226 0.144105
181 0.768845 -5.704053 2.679185 0.197710
182 0.757180 -6.277170 2.209021 0.156368
183 0.669565 -5.619070 2.027228 0.215724
184 0.656516 -5.198864 2.120412 0.252404
185 0.654331 -5.592584 2.058658 0.214346
186 0.667654 -6.431119 2.161936 0.120605
187 0.663884 -6.359018 2.152083 0.138868
188 0.659132 -6.710219 1.913990 0.121777
189 0.683761 -6.934474 2.316346 0.112838
190 0.657899 -6.538586 2.657476 0.133050
191 0.683244 -6.195325 2.784312 0.168895
192 0.655683 -6.787197 2.679772 0.131728
193 0.643956 -6.744577 2.138608 0.123306
194 0.664357 -5.724056 2.555477 0.148569
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) `MDVP:Jitter(%)` `Jitter:DDP` `Shimmer:APQ3`
1.34068 -134.46053 58.44675 4.34436
NHR HNR RPDE DFA
-0.15760 0.00299 -0.02044 1.43089
spread1 D2 PPE
0.31225 0.16732 -0.48774
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.85332 -0.25971 0.08751 0.26075 0.63127
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.34068 1.09812 1.221 0.223701
`MDVP:Jitter(%)` -134.46053 45.27396 -2.970 0.003378 **
`Jitter:DDP` 58.44675 23.45324 2.492 0.013590 *
`Shimmer:APQ3` 4.34436 4.88767 0.889 0.375256
NHR -0.15760 1.92417 -0.082 0.934813
HNR 0.00299 0.01337 0.224 0.823301
RPDE -0.02043 0.37459 -0.055 0.956553
DFA 1.43089 0.59184 2.418 0.016599 *
spread1 0.31225 0.09112 3.427 0.000754 ***
D2 0.16732 0.09332 1.793 0.074629 .
PPE -0.48774 1.17483 -0.415 0.678512
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.3428 on 183 degrees of freedom
Multiple R-squared: 0.4046, Adjusted R-squared: 0.3721
F-statistic: 12.44 on 10 and 183 DF, p-value: 2.298e-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,] 3.133036e-48 6.266072e-48 1.0000000000
[2,] 7.868538e-63 1.573708e-62 1.0000000000
[3,] 0.000000e+00 0.000000e+00 1.0000000000
[4,] 6.848492e-100 1.369698e-99 1.0000000000
[5,] 4.367692e-106 8.735383e-106 1.0000000000
[6,] 1.387778e-121 2.775557e-121 1.0000000000
[7,] 3.471389e-145 6.942778e-145 1.0000000000
[8,] 6.568853e-174 1.313771e-173 1.0000000000
[9,] 7.724629e-166 1.544926e-165 1.0000000000
[10,] 8.781128e-184 1.756226e-183 1.0000000000
[11,] 5.060632e-196 1.012126e-195 1.0000000000
[12,] 2.226779e-219 4.453558e-219 1.0000000000
[13,] 2.665894e-256 5.331788e-256 1.0000000000
[14,] 1.784933e-249 3.569866e-249 1.0000000000
[15,] 1.086937e-256 2.173874e-256 1.0000000000
[16,] 3.831234e-280 7.662468e-280 1.0000000000
[17,] 2.108061e-03 4.216123e-03 0.9978919387
[18,] 6.745854e-03 1.349171e-02 0.9932541460
[19,] 8.728813e-03 1.745763e-02 0.9912711872
[20,] 5.325955e-03 1.065191e-02 0.9946740446
[21,] 3.340886e-03 6.681772e-03 0.9966591141
[22,] 2.425094e-03 4.850188e-03 0.9975749060
[23,] 1.415495e-03 2.830990e-03 0.9985845050
[24,] 8.116640e-04 1.623328e-03 0.9991883360
[25,] 7.507372e-04 1.501474e-03 0.9992492628
[26,] 4.215206e-04 8.430412e-04 0.9995784794
[27,] 3.695545e-04 7.391090e-04 0.9996304455
[28,] 4.031126e-04 8.062251e-04 0.9995968874
[29,] 1.040309e-02 2.080618e-02 0.9895969101
[30,] 3.028213e-02 6.056426e-02 0.9697178720
[31,] 4.569234e-02 9.138467e-02 0.9543076643
[32,] 5.262895e-02 1.052579e-01 0.9473710486
[33,] 4.539844e-02 9.079688e-02 0.9546015580
[34,] 3.514932e-02 7.029863e-02 0.9648506848
[35,] 1.228572e-01 2.457143e-01 0.8771428361
[36,] 1.331927e-01 2.663854e-01 0.8668072807
[37,] 1.273402e-01 2.546804e-01 0.8726598013
[38,] 1.183346e-01 2.366691e-01 0.8816654495
[39,] 1.061351e-01 2.122701e-01 0.8938649439
[40,] 1.016075e-01 2.032150e-01 0.8983925214
[41,] 8.134044e-02 1.626809e-01 0.9186595632
[42,] 6.363261e-02 1.272652e-01 0.9363673857
[43,] 4.933658e-02 9.867317e-02 0.9506634174
[44,] 3.776252e-02 7.552504e-02 0.9622374815
[45,] 2.844622e-02 5.689244e-02 0.9715537811
[46,] 2.259835e-02 4.519671e-02 0.9774016473
[47,] 3.068776e-02 6.137551e-02 0.9693122450
[48,] 2.977177e-02 5.954354e-02 0.9702282285
[49,] 4.634518e-02 9.269036e-02 0.9536548199
[50,] 5.353799e-02 1.070760e-01 0.9464620094
[51,] 4.970741e-02 9.941481e-02 0.9502925934
[52,] 4.839072e-02 9.678144e-02 0.9516092810
[53,] 4.154854e-02 8.309708e-02 0.9584514585
[54,] 3.358278e-02 6.716557e-02 0.9664172168
[55,] 2.831598e-02 5.663196e-02 0.9716840213
[56,] 2.999486e-02 5.998971e-02 0.9700051441
[57,] 2.418154e-02 4.836308e-02 0.9758184599
[58,] 1.832099e-02 3.664198e-02 0.9816790106
[59,] 1.467366e-02 2.934733e-02 0.9853263360
[60,] 1.107894e-02 2.215787e-02 0.9889210635
[61,] 8.445627e-03 1.689125e-02 0.9915543730
[62,] 6.334781e-03 1.266956e-02 0.9936652190
[63,] 4.872402e-03 9.744804e-03 0.9951275982
[64,] 3.689083e-03 7.378167e-03 0.9963109166
[65,] 2.616712e-03 5.233424e-03 0.9973832882
[66,] 2.291561e-03 4.583121e-03 0.9977084393
[67,] 1.948193e-03 3.896387e-03 0.9980518065
[68,] 1.521299e-03 3.042597e-03 0.9984787014
[69,] 1.188727e-03 2.377454e-03 0.9988112731
[70,] 9.208810e-04 1.841762e-03 0.9990791190
[71,] 7.146287e-04 1.429257e-03 0.9992853713
[72,] 4.994694e-04 9.989388e-04 0.9995005306
[73,] 4.681394e-04 9.362788e-04 0.9995318606
[74,] 3.233390e-04 6.466780e-04 0.9996766610
[75,] 2.273566e-04 4.547132e-04 0.9997726434
[76,] 4.872503e-04 9.745006e-04 0.9995127497
[77,] 5.527401e-04 1.105480e-03 0.9994472599
[78,] 5.963927e-04 1.192785e-03 0.9994036073
[79,] 4.470976e-04 8.941953e-04 0.9995529024
[80,] 3.808865e-04 7.617729e-04 0.9996191135
[81,] 3.408744e-04 6.817488e-04 0.9996591256
[82,] 3.169443e-04 6.338886e-04 0.9996830557
[83,] 3.510722e-04 7.021443e-04 0.9996489278
[84,] 3.211887e-04 6.423775e-04 0.9996788113
[85,] 2.545985e-04 5.091969e-04 0.9997454015
[86,] 1.810485e-04 3.620970e-04 0.9998189515
[87,] 1.217764e-04 2.435529e-04 0.9998782236
[88,] 9.054827e-05 1.810965e-04 0.9999094517
[89,] 6.956108e-05 1.391222e-04 0.9999304389
[90,] 1.432323e-04 2.864647e-04 0.9998567677
[91,] 3.718266e-04 7.436533e-04 0.9996281734
[92,] 9.106967e-04 1.821393e-03 0.9990893033
[93,] 2.127402e-03 4.254805e-03 0.9978725977
[94,] 1.998352e-03 3.996703e-03 0.9980016484
[95,] 3.288615e-03 6.577229e-03 0.9967113855
[96,] 2.612357e-03 5.224713e-03 0.9973876433
[97,] 2.069975e-03 4.139951e-03 0.9979300246
[98,] 2.322781e-03 4.645563e-03 0.9976772186
[99,] 1.915805e-03 3.831609e-03 0.9980841953
[100,] 1.577359e-03 3.154718e-03 0.9984226411
[101,] 1.597443e-03 3.194885e-03 0.9984025574
[102,] 1.493200e-03 2.986401e-03 0.9985067997
[103,] 1.990067e-03 3.980134e-03 0.9980099329
[104,] 1.444422e-03 2.888844e-03 0.9985555782
[105,] 1.096791e-03 2.193581e-03 0.9989032094
[106,] 1.782061e-03 3.564121e-03 0.9982179395
[107,] 2.780063e-03 5.560126e-03 0.9972199372
[108,] 5.935613e-03 1.187123e-02 0.9940643871
[109,] 5.045750e-03 1.009150e-02 0.9949542503
[110,] 3.751333e-03 7.502666e-03 0.9962486670
[111,] 3.128460e-03 6.256920e-03 0.9968715400
[112,] 2.794100e-03 5.588200e-03 0.9972059001
[113,] 2.185431e-03 4.370861e-03 0.9978145693
[114,] 1.932496e-03 3.864992e-03 0.9980675041
[115,] 5.547048e-03 1.109410e-02 0.9944529517
[116,] 6.466075e-03 1.293215e-02 0.9935339250
[117,] 6.422195e-03 1.284439e-02 0.9935778055
[118,] 6.591083e-03 1.318217e-02 0.9934089167
[119,] 5.654041e-03 1.130808e-02 0.9943459591
[120,] 1.168944e-02 2.337888e-02 0.9883105587
[121,] 8.696457e-03 1.739291e-02 0.9913035431
[122,] 6.427868e-03 1.285574e-02 0.9935721320
[123,] 7.415066e-03 1.483013e-02 0.9925849344
[124,] 7.204136e-03 1.440827e-02 0.9927958636
[125,] 5.435365e-03 1.087073e-02 0.9945646349
[126,] 4.040802e-03 8.081603e-03 0.9959591985
[127,] 2.965902e-03 5.931804e-03 0.9970340982
[128,] 2.116838e-03 4.233676e-03 0.9978831619
[129,] 1.506429e-03 3.012858e-03 0.9984935708
[130,] 1.371396e-03 2.742792e-03 0.9986286039
[131,] 1.746490e-03 3.492980e-03 0.9982535099
[132,] 3.249269e-03 6.498539e-03 0.9967507307
[133,] 2.549925e-03 5.099850e-03 0.9974500752
[134,] 1.785320e-03 3.570639e-03 0.9982146805
[135,] 1.538077e-03 3.076154e-03 0.9984619230
[136,] 1.157979e-03 2.315958e-03 0.9988420210
[137,] 7.544719e-04 1.508944e-03 0.9992455281
[138,] 4.860716e-04 9.721432e-04 0.9995139284
[139,] 3.336855e-04 6.673709e-04 0.9996663145
[140,] 4.684459e-04 9.368917e-04 0.9995315541
[141,] 4.004555e-04 8.009110e-04 0.9995995445
[142,] 5.277914e-04 1.055583e-03 0.9994722086
[143,] 1.136707e-03 2.273414e-03 0.9988632932
[144,] 8.972705e-04 1.794541e-03 0.9991027295
[145,] 6.521873e-02 1.304375e-01 0.9347812701
[146,] 5.226029e-02 1.045206e-01 0.9477397128
[147,] 4.270429e-02 8.540858e-02 0.9572957079
[148,] 6.938941e-02 1.387788e-01 0.9306105867
[149,] 6.605484e-02 1.321097e-01 0.9339451644
[150,] 1.745461e-01 3.490921e-01 0.8254539467
[151,] 8.335701e-01 3.328597e-01 0.1664298556
[152,] 9.183970e-01 1.632059e-01 0.0816029691
[153,] 9.864461e-01 2.710780e-02 0.0135538989
[154,] 9.881176e-01 2.376474e-02 0.0118823710
[155,] 9.856489e-01 2.870224e-02 0.0143511178
[156,] 9.880147e-01 2.397067e-02 0.0119853341
[157,] 9.892337e-01 2.153263e-02 0.0107663153
[158,] 9.849440e-01 3.011197e-02 0.0150559832
[159,] 9.795004e-01 4.099922e-02 0.0204996112
[160,] 9.700467e-01 5.990654e-02 0.0299532724
[161,] 9.798636e-01 4.027285e-02 0.0201364234
[162,] 9.821041e-01 3.579186e-02 0.0178959324
[163,] 9.994264e-01 1.147287e-03 0.0005736434
[164,] 9.992063e-01 1.587423e-03 0.0007937117
[165,] 9.965802e-01 6.839689e-03 0.0034198444
[166,] 9.862602e-01 2.747970e-02 0.0137398481
[167,] 9.502195e-01 9.956100e-02 0.0497804987
> postscript(file="/var/fisher/rcomp/tmp/129sj1386679785.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/2malr1386679785.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3zzmg1386679785.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/4rfxz1386679785.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/5r3jj1386679785.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 = 194
Frequency = 1
1 2 3 4 5
-1.711085e-01 -7.713330e-02 -1.665448e-01 -1.604777e-01 -5.225077e-02
6 7 8 9 10
1.984958e-01 2.625949e-01 1.226895e-01 -6.211017e-02 4.437058e-02
11 12 13 14 15
-1.127865e-01 5.955781e-01 2.888217e-01 4.295955e-01 2.552337e-01
16 17 18 19 20
4.307367e-01 -2.239912e-01 -2.541524e-01 5.179023e-02 -7.637611e-02
21 22 23 24 25
8.590556e-02 -2.511186e-01 1.231397e-01 2.404379e-01 1.450897e-01
26 27 28 29 30
2.648215e-01 3.953035e-01 6.312659e-01 5.168267e-01 -3.548573e-01
31 32 33 34 35
-3.382791e-01 -4.199612e-01 -2.615626e-01 -1.697592e-01 -3.865921e-01
36 37 38 39 40
1.637810e-01 1.019033e-01 2.778548e-01 1.537162e-01 3.099012e-01
41 42 43 44 45
4.405306e-01 -3.532138e-01 -4.466426e-01 -3.999272e-01 -4.203235e-01
46 47 48 49 50
-3.715223e-01 -2.675800e-01 -3.474627e-01 -4.223708e-01 -4.158605e-01
51 52 53 54 55
-4.358723e-01 -2.755980e-01 -3.880681e-01 5.840255e-02 3.329301e-02
56 57 58 59 60
8.855297e-03 1.215841e-01 1.041191e-01 1.577576e-01 -4.654591e-01
61 62 63 64 65
-4.125232e-01 -5.063929e-01 -4.479138e-01 -2.936254e-01 -3.680950e-01
66 67 68 69 70
2.523314e-01 2.417851e-01 3.479822e-01 2.946737e-01 2.749909e-01
71 72 73 74 75
1.240755e-01 2.194477e-01 1.252234e-01 -2.245646e-02 1.018246e-01
76 77 78 79 80
1.267228e-01 9.259652e-02 3.789536e-02 -1.574539e-02 -2.299341e-01
81 82 83 84 85
-2.687667e-02 -8.537131e-02 2.167398e-01 -8.964336e-05 1.184662e-03
86 87 88 89 90
2.806663e-01 -8.148587e-02 -1.017428e-02 -3.357398e-01 -2.371007e-01
91 92 93 94 95
2.448358e-01 1.970123e-01 2.937505e-01 3.058972e-01 2.998643e-01
96 97 98 99 100
3.408940e-01 -8.268481e-02 2.760636e-01 2.840936e-02 5.829153e-02
101 102 103 104 105
1.284822e-01 1.068575e-01 5.472617e-01 6.030093e-01 5.747174e-01
106 107 108 109 110
5.671140e-01 3.974434e-01 4.680305e-01 1.883782e-01 2.014630e-02
111 112 113 114 115
3.812892e-01 2.553450e-02 2.144509e-01 3.337514e-01 2.076523e-01
116 117 118 119 120
4.257144e-01 -1.022770e-02 1.621520e-01 3.944010e-01 4.983618e-01
121 122 123 124 125
1.862685e-02 1.344157e-01 1.968615e-01 3.679575e-01 3.324323e-01
126 127 128 129 130
2.447462e-01 2.909907e-01 6.002723e-01 2.775681e-01 2.962040e-01
131 132 133 134 135
2.335260e-01 9.880834e-02 4.298479e-01 1.031138e-01 8.911148e-02
136 137 138 139 140
-1.910696e-01 -1.479587e-01 1.610347e-01 2.653162e-01 6.508751e-02
141 142 143 144 145
1.275365e-01 2.026471e-01 3.500022e-01 3.705337e-01 2.504658e-01
146 147 148 149 150
-3.781371e-01 -1.324563e-01 -3.212296e-01 1.224192e-01 1.252623e-01
151 152 153 154 155
-1.406098e-01 -1.219212e-01 9.942352e-02 -1.176661e-01 -3.380574e-01
156 157 158 159 160
2.656375e-01 -2.205850e-01 1.439237e-01 3.138847e-01 1.887809e-01
161 162 163 164 165
1.385255e-01 1.757079e-01 3.363470e-01 -3.898741e-01 -4.471533e-01
166 167 168 169 170
-3.025487e-01 -1.781961e-01 -8.533247e-01 -3.208621e-01 -2.709193e-01
171 172 173 174 175
-6.507616e-01 -7.055019e-01 -7.387103e-01 -7.189790e-01 -7.457167e-01
176 177 178 179 180
-7.435646e-01 3.623591e-01 2.891555e-01 2.294920e-02 2.716786e-01
181 182 183 184 185
6.835174e-02 2.785641e-01 -6.363103e-01 -7.161128e-01 -6.718517e-01
186 187 188 189 190
-4.627306e-01 -5.303643e-01 -3.521222e-01 -3.786546e-01 -6.110127e-01
191 192 193 194
-7.051888e-01 1.069023e-01 -1.981882e-01 -7.055433e-01
> postscript(file="/var/fisher/rcomp/tmp/685ty1386679785.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 = 194
Frequency = 1
lag(myerror, k = 1) myerror
0 -1.711085e-01 NA
1 -7.713330e-02 -1.711085e-01
2 -1.665448e-01 -7.713330e-02
3 -1.604777e-01 -1.665448e-01
4 -5.225077e-02 -1.604777e-01
5 1.984958e-01 -5.225077e-02
6 2.625949e-01 1.984958e-01
7 1.226895e-01 2.625949e-01
8 -6.211017e-02 1.226895e-01
9 4.437058e-02 -6.211017e-02
10 -1.127865e-01 4.437058e-02
11 5.955781e-01 -1.127865e-01
12 2.888217e-01 5.955781e-01
13 4.295955e-01 2.888217e-01
14 2.552337e-01 4.295955e-01
15 4.307367e-01 2.552337e-01
16 -2.239912e-01 4.307367e-01
17 -2.541524e-01 -2.239912e-01
18 5.179023e-02 -2.541524e-01
19 -7.637611e-02 5.179023e-02
20 8.590556e-02 -7.637611e-02
21 -2.511186e-01 8.590556e-02
22 1.231397e-01 -2.511186e-01
23 2.404379e-01 1.231397e-01
24 1.450897e-01 2.404379e-01
25 2.648215e-01 1.450897e-01
26 3.953035e-01 2.648215e-01
27 6.312659e-01 3.953035e-01
28 5.168267e-01 6.312659e-01
29 -3.548573e-01 5.168267e-01
30 -3.382791e-01 -3.548573e-01
31 -4.199612e-01 -3.382791e-01
32 -2.615626e-01 -4.199612e-01
33 -1.697592e-01 -2.615626e-01
34 -3.865921e-01 -1.697592e-01
35 1.637810e-01 -3.865921e-01
36 1.019033e-01 1.637810e-01
37 2.778548e-01 1.019033e-01
38 1.537162e-01 2.778548e-01
39 3.099012e-01 1.537162e-01
40 4.405306e-01 3.099012e-01
41 -3.532138e-01 4.405306e-01
42 -4.466426e-01 -3.532138e-01
43 -3.999272e-01 -4.466426e-01
44 -4.203235e-01 -3.999272e-01
45 -3.715223e-01 -4.203235e-01
46 -2.675800e-01 -3.715223e-01
47 -3.474627e-01 -2.675800e-01
48 -4.223708e-01 -3.474627e-01
49 -4.158605e-01 -4.223708e-01
50 -4.358723e-01 -4.158605e-01
51 -2.755980e-01 -4.358723e-01
52 -3.880681e-01 -2.755980e-01
53 5.840255e-02 -3.880681e-01
54 3.329301e-02 5.840255e-02
55 8.855297e-03 3.329301e-02
56 1.215841e-01 8.855297e-03
57 1.041191e-01 1.215841e-01
58 1.577576e-01 1.041191e-01
59 -4.654591e-01 1.577576e-01
60 -4.125232e-01 -4.654591e-01
61 -5.063929e-01 -4.125232e-01
62 -4.479138e-01 -5.063929e-01
63 -2.936254e-01 -4.479138e-01
64 -3.680950e-01 -2.936254e-01
65 2.523314e-01 -3.680950e-01
66 2.417851e-01 2.523314e-01
67 3.479822e-01 2.417851e-01
68 2.946737e-01 3.479822e-01
69 2.749909e-01 2.946737e-01
70 1.240755e-01 2.749909e-01
71 2.194477e-01 1.240755e-01
72 1.252234e-01 2.194477e-01
73 -2.245646e-02 1.252234e-01
74 1.018246e-01 -2.245646e-02
75 1.267228e-01 1.018246e-01
76 9.259652e-02 1.267228e-01
77 3.789536e-02 9.259652e-02
78 -1.574539e-02 3.789536e-02
79 -2.299341e-01 -1.574539e-02
80 -2.687667e-02 -2.299341e-01
81 -8.537131e-02 -2.687667e-02
82 2.167398e-01 -8.537131e-02
83 -8.964336e-05 2.167398e-01
84 1.184662e-03 -8.964336e-05
85 2.806663e-01 1.184662e-03
86 -8.148587e-02 2.806663e-01
87 -1.017428e-02 -8.148587e-02
88 -3.357398e-01 -1.017428e-02
89 -2.371007e-01 -3.357398e-01
90 2.448358e-01 -2.371007e-01
91 1.970123e-01 2.448358e-01
92 2.937505e-01 1.970123e-01
93 3.058972e-01 2.937505e-01
94 2.998643e-01 3.058972e-01
95 3.408940e-01 2.998643e-01
96 -8.268481e-02 3.408940e-01
97 2.760636e-01 -8.268481e-02
98 2.840936e-02 2.760636e-01
99 5.829153e-02 2.840936e-02
100 1.284822e-01 5.829153e-02
101 1.068575e-01 1.284822e-01
102 5.472617e-01 1.068575e-01
103 6.030093e-01 5.472617e-01
104 5.747174e-01 6.030093e-01
105 5.671140e-01 5.747174e-01
106 3.974434e-01 5.671140e-01
107 4.680305e-01 3.974434e-01
108 1.883782e-01 4.680305e-01
109 2.014630e-02 1.883782e-01
110 3.812892e-01 2.014630e-02
111 2.553450e-02 3.812892e-01
112 2.144509e-01 2.553450e-02
113 3.337514e-01 2.144509e-01
114 2.076523e-01 3.337514e-01
115 4.257144e-01 2.076523e-01
116 -1.022770e-02 4.257144e-01
117 1.621520e-01 -1.022770e-02
118 3.944010e-01 1.621520e-01
119 4.983618e-01 3.944010e-01
120 1.862685e-02 4.983618e-01
121 1.344157e-01 1.862685e-02
122 1.968615e-01 1.344157e-01
123 3.679575e-01 1.968615e-01
124 3.324323e-01 3.679575e-01
125 2.447462e-01 3.324323e-01
126 2.909907e-01 2.447462e-01
127 6.002723e-01 2.909907e-01
128 2.775681e-01 6.002723e-01
129 2.962040e-01 2.775681e-01
130 2.335260e-01 2.962040e-01
131 9.880834e-02 2.335260e-01
132 4.298479e-01 9.880834e-02
133 1.031138e-01 4.298479e-01
134 8.911148e-02 1.031138e-01
135 -1.910696e-01 8.911148e-02
136 -1.479587e-01 -1.910696e-01
137 1.610347e-01 -1.479587e-01
138 2.653162e-01 1.610347e-01
139 6.508751e-02 2.653162e-01
140 1.275365e-01 6.508751e-02
141 2.026471e-01 1.275365e-01
142 3.500022e-01 2.026471e-01
143 3.705337e-01 3.500022e-01
144 2.504658e-01 3.705337e-01
145 -3.781371e-01 2.504658e-01
146 -1.324563e-01 -3.781371e-01
147 -3.212296e-01 -1.324563e-01
148 1.224192e-01 -3.212296e-01
149 1.252623e-01 1.224192e-01
150 -1.406098e-01 1.252623e-01
151 -1.219212e-01 -1.406098e-01
152 9.942352e-02 -1.219212e-01
153 -1.176661e-01 9.942352e-02
154 -3.380574e-01 -1.176661e-01
155 2.656375e-01 -3.380574e-01
156 -2.205850e-01 2.656375e-01
157 1.439237e-01 -2.205850e-01
158 3.138847e-01 1.439237e-01
159 1.887809e-01 3.138847e-01
160 1.385255e-01 1.887809e-01
161 1.757079e-01 1.385255e-01
162 3.363470e-01 1.757079e-01
163 -3.898741e-01 3.363470e-01
164 -4.471533e-01 -3.898741e-01
165 -3.025487e-01 -4.471533e-01
166 -1.781961e-01 -3.025487e-01
167 -8.533247e-01 -1.781961e-01
168 -3.208621e-01 -8.533247e-01
169 -2.709193e-01 -3.208621e-01
170 -6.507616e-01 -2.709193e-01
171 -7.055019e-01 -6.507616e-01
172 -7.387103e-01 -7.055019e-01
173 -7.189790e-01 -7.387103e-01
174 -7.457167e-01 -7.189790e-01
175 -7.435646e-01 -7.457167e-01
176 3.623591e-01 -7.435646e-01
177 2.891555e-01 3.623591e-01
178 2.294920e-02 2.891555e-01
179 2.716786e-01 2.294920e-02
180 6.835174e-02 2.716786e-01
181 2.785641e-01 6.835174e-02
182 -6.363103e-01 2.785641e-01
183 -7.161128e-01 -6.363103e-01
184 -6.718517e-01 -7.161128e-01
185 -4.627306e-01 -6.718517e-01
186 -5.303643e-01 -4.627306e-01
187 -3.521222e-01 -5.303643e-01
188 -3.786546e-01 -3.521222e-01
189 -6.110127e-01 -3.786546e-01
190 -7.051888e-01 -6.110127e-01
191 1.069023e-01 -7.051888e-01
192 -1.981882e-01 1.069023e-01
193 -7.055433e-01 -1.981882e-01
194 NA -7.055433e-01
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -7.713330e-02 -1.711085e-01
[2,] -1.665448e-01 -7.713330e-02
[3,] -1.604777e-01 -1.665448e-01
[4,] -5.225077e-02 -1.604777e-01
[5,] 1.984958e-01 -5.225077e-02
[6,] 2.625949e-01 1.984958e-01
[7,] 1.226895e-01 2.625949e-01
[8,] -6.211017e-02 1.226895e-01
[9,] 4.437058e-02 -6.211017e-02
[10,] -1.127865e-01 4.437058e-02
[11,] 5.955781e-01 -1.127865e-01
[12,] 2.888217e-01 5.955781e-01
[13,] 4.295955e-01 2.888217e-01
[14,] 2.552337e-01 4.295955e-01
[15,] 4.307367e-01 2.552337e-01
[16,] -2.239912e-01 4.307367e-01
[17,] -2.541524e-01 -2.239912e-01
[18,] 5.179023e-02 -2.541524e-01
[19,] -7.637611e-02 5.179023e-02
[20,] 8.590556e-02 -7.637611e-02
[21,] -2.511186e-01 8.590556e-02
[22,] 1.231397e-01 -2.511186e-01
[23,] 2.404379e-01 1.231397e-01
[24,] 1.450897e-01 2.404379e-01
[25,] 2.648215e-01 1.450897e-01
[26,] 3.953035e-01 2.648215e-01
[27,] 6.312659e-01 3.953035e-01
[28,] 5.168267e-01 6.312659e-01
[29,] -3.548573e-01 5.168267e-01
[30,] -3.382791e-01 -3.548573e-01
[31,] -4.199612e-01 -3.382791e-01
[32,] -2.615626e-01 -4.199612e-01
[33,] -1.697592e-01 -2.615626e-01
[34,] -3.865921e-01 -1.697592e-01
[35,] 1.637810e-01 -3.865921e-01
[36,] 1.019033e-01 1.637810e-01
[37,] 2.778548e-01 1.019033e-01
[38,] 1.537162e-01 2.778548e-01
[39,] 3.099012e-01 1.537162e-01
[40,] 4.405306e-01 3.099012e-01
[41,] -3.532138e-01 4.405306e-01
[42,] -4.466426e-01 -3.532138e-01
[43,] -3.999272e-01 -4.466426e-01
[44,] -4.203235e-01 -3.999272e-01
[45,] -3.715223e-01 -4.203235e-01
[46,] -2.675800e-01 -3.715223e-01
[47,] -3.474627e-01 -2.675800e-01
[48,] -4.223708e-01 -3.474627e-01
[49,] -4.158605e-01 -4.223708e-01
[50,] -4.358723e-01 -4.158605e-01
[51,] -2.755980e-01 -4.358723e-01
[52,] -3.880681e-01 -2.755980e-01
[53,] 5.840255e-02 -3.880681e-01
[54,] 3.329301e-02 5.840255e-02
[55,] 8.855297e-03 3.329301e-02
[56,] 1.215841e-01 8.855297e-03
[57,] 1.041191e-01 1.215841e-01
[58,] 1.577576e-01 1.041191e-01
[59,] -4.654591e-01 1.577576e-01
[60,] -4.125232e-01 -4.654591e-01
[61,] -5.063929e-01 -4.125232e-01
[62,] -4.479138e-01 -5.063929e-01
[63,] -2.936254e-01 -4.479138e-01
[64,] -3.680950e-01 -2.936254e-01
[65,] 2.523314e-01 -3.680950e-01
[66,] 2.417851e-01 2.523314e-01
[67,] 3.479822e-01 2.417851e-01
[68,] 2.946737e-01 3.479822e-01
[69,] 2.749909e-01 2.946737e-01
[70,] 1.240755e-01 2.749909e-01
[71,] 2.194477e-01 1.240755e-01
[72,] 1.252234e-01 2.194477e-01
[73,] -2.245646e-02 1.252234e-01
[74,] 1.018246e-01 -2.245646e-02
[75,] 1.267228e-01 1.018246e-01
[76,] 9.259652e-02 1.267228e-01
[77,] 3.789536e-02 9.259652e-02
[78,] -1.574539e-02 3.789536e-02
[79,] -2.299341e-01 -1.574539e-02
[80,] -2.687667e-02 -2.299341e-01
[81,] -8.537131e-02 -2.687667e-02
[82,] 2.167398e-01 -8.537131e-02
[83,] -8.964336e-05 2.167398e-01
[84,] 1.184662e-03 -8.964336e-05
[85,] 2.806663e-01 1.184662e-03
[86,] -8.148587e-02 2.806663e-01
[87,] -1.017428e-02 -8.148587e-02
[88,] -3.357398e-01 -1.017428e-02
[89,] -2.371007e-01 -3.357398e-01
[90,] 2.448358e-01 -2.371007e-01
[91,] 1.970123e-01 2.448358e-01
[92,] 2.937505e-01 1.970123e-01
[93,] 3.058972e-01 2.937505e-01
[94,] 2.998643e-01 3.058972e-01
[95,] 3.408940e-01 2.998643e-01
[96,] -8.268481e-02 3.408940e-01
[97,] 2.760636e-01 -8.268481e-02
[98,] 2.840936e-02 2.760636e-01
[99,] 5.829153e-02 2.840936e-02
[100,] 1.284822e-01 5.829153e-02
[101,] 1.068575e-01 1.284822e-01
[102,] 5.472617e-01 1.068575e-01
[103,] 6.030093e-01 5.472617e-01
[104,] 5.747174e-01 6.030093e-01
[105,] 5.671140e-01 5.747174e-01
[106,] 3.974434e-01 5.671140e-01
[107,] 4.680305e-01 3.974434e-01
[108,] 1.883782e-01 4.680305e-01
[109,] 2.014630e-02 1.883782e-01
[110,] 3.812892e-01 2.014630e-02
[111,] 2.553450e-02 3.812892e-01
[112,] 2.144509e-01 2.553450e-02
[113,] 3.337514e-01 2.144509e-01
[114,] 2.076523e-01 3.337514e-01
[115,] 4.257144e-01 2.076523e-01
[116,] -1.022770e-02 4.257144e-01
[117,] 1.621520e-01 -1.022770e-02
[118,] 3.944010e-01 1.621520e-01
[119,] 4.983618e-01 3.944010e-01
[120,] 1.862685e-02 4.983618e-01
[121,] 1.344157e-01 1.862685e-02
[122,] 1.968615e-01 1.344157e-01
[123,] 3.679575e-01 1.968615e-01
[124,] 3.324323e-01 3.679575e-01
[125,] 2.447462e-01 3.324323e-01
[126,] 2.909907e-01 2.447462e-01
[127,] 6.002723e-01 2.909907e-01
[128,] 2.775681e-01 6.002723e-01
[129,] 2.962040e-01 2.775681e-01
[130,] 2.335260e-01 2.962040e-01
[131,] 9.880834e-02 2.335260e-01
[132,] 4.298479e-01 9.880834e-02
[133,] 1.031138e-01 4.298479e-01
[134,] 8.911148e-02 1.031138e-01
[135,] -1.910696e-01 8.911148e-02
[136,] -1.479587e-01 -1.910696e-01
[137,] 1.610347e-01 -1.479587e-01
[138,] 2.653162e-01 1.610347e-01
[139,] 6.508751e-02 2.653162e-01
[140,] 1.275365e-01 6.508751e-02
[141,] 2.026471e-01 1.275365e-01
[142,] 3.500022e-01 2.026471e-01
[143,] 3.705337e-01 3.500022e-01
[144,] 2.504658e-01 3.705337e-01
[145,] -3.781371e-01 2.504658e-01
[146,] -1.324563e-01 -3.781371e-01
[147,] -3.212296e-01 -1.324563e-01
[148,] 1.224192e-01 -3.212296e-01
[149,] 1.252623e-01 1.224192e-01
[150,] -1.406098e-01 1.252623e-01
[151,] -1.219212e-01 -1.406098e-01
[152,] 9.942352e-02 -1.219212e-01
[153,] -1.176661e-01 9.942352e-02
[154,] -3.380574e-01 -1.176661e-01
[155,] 2.656375e-01 -3.380574e-01
[156,] -2.205850e-01 2.656375e-01
[157,] 1.439237e-01 -2.205850e-01
[158,] 3.138847e-01 1.439237e-01
[159,] 1.887809e-01 3.138847e-01
[160,] 1.385255e-01 1.887809e-01
[161,] 1.757079e-01 1.385255e-01
[162,] 3.363470e-01 1.757079e-01
[163,] -3.898741e-01 3.363470e-01
[164,] -4.471533e-01 -3.898741e-01
[165,] -3.025487e-01 -4.471533e-01
[166,] -1.781961e-01 -3.025487e-01
[167,] -8.533247e-01 -1.781961e-01
[168,] -3.208621e-01 -8.533247e-01
[169,] -2.709193e-01 -3.208621e-01
[170,] -6.507616e-01 -2.709193e-01
[171,] -7.055019e-01 -6.507616e-01
[172,] -7.387103e-01 -7.055019e-01
[173,] -7.189790e-01 -7.387103e-01
[174,] -7.457167e-01 -7.189790e-01
[175,] -7.435646e-01 -7.457167e-01
[176,] 3.623591e-01 -7.435646e-01
[177,] 2.891555e-01 3.623591e-01
[178,] 2.294920e-02 2.891555e-01
[179,] 2.716786e-01 2.294920e-02
[180,] 6.835174e-02 2.716786e-01
[181,] 2.785641e-01 6.835174e-02
[182,] -6.363103e-01 2.785641e-01
[183,] -7.161128e-01 -6.363103e-01
[184,] -6.718517e-01 -7.161128e-01
[185,] -4.627306e-01 -6.718517e-01
[186,] -5.303643e-01 -4.627306e-01
[187,] -3.521222e-01 -5.303643e-01
[188,] -3.786546e-01 -3.521222e-01
[189,] -6.110127e-01 -3.786546e-01
[190,] -7.051888e-01 -6.110127e-01
[191,] 1.069023e-01 -7.051888e-01
[192,] -1.981882e-01 1.069023e-01
[193,] -7.055433e-01 -1.981882e-01
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -7.713330e-02 -1.711085e-01
2 -1.665448e-01 -7.713330e-02
3 -1.604777e-01 -1.665448e-01
4 -5.225077e-02 -1.604777e-01
5 1.984958e-01 -5.225077e-02
6 2.625949e-01 1.984958e-01
7 1.226895e-01 2.625949e-01
8 -6.211017e-02 1.226895e-01
9 4.437058e-02 -6.211017e-02
10 -1.127865e-01 4.437058e-02
11 5.955781e-01 -1.127865e-01
12 2.888217e-01 5.955781e-01
13 4.295955e-01 2.888217e-01
14 2.552337e-01 4.295955e-01
15 4.307367e-01 2.552337e-01
16 -2.239912e-01 4.307367e-01
17 -2.541524e-01 -2.239912e-01
18 5.179023e-02 -2.541524e-01
19 -7.637611e-02 5.179023e-02
20 8.590556e-02 -7.637611e-02
21 -2.511186e-01 8.590556e-02
22 1.231397e-01 -2.511186e-01
23 2.404379e-01 1.231397e-01
24 1.450897e-01 2.404379e-01
25 2.648215e-01 1.450897e-01
26 3.953035e-01 2.648215e-01
27 6.312659e-01 3.953035e-01
28 5.168267e-01 6.312659e-01
29 -3.548573e-01 5.168267e-01
30 -3.382791e-01 -3.548573e-01
31 -4.199612e-01 -3.382791e-01
32 -2.615626e-01 -4.199612e-01
33 -1.697592e-01 -2.615626e-01
34 -3.865921e-01 -1.697592e-01
35 1.637810e-01 -3.865921e-01
36 1.019033e-01 1.637810e-01
37 2.778548e-01 1.019033e-01
38 1.537162e-01 2.778548e-01
39 3.099012e-01 1.537162e-01
40 4.405306e-01 3.099012e-01
41 -3.532138e-01 4.405306e-01
42 -4.466426e-01 -3.532138e-01
43 -3.999272e-01 -4.466426e-01
44 -4.203235e-01 -3.999272e-01
45 -3.715223e-01 -4.203235e-01
46 -2.675800e-01 -3.715223e-01
47 -3.474627e-01 -2.675800e-01
48 -4.223708e-01 -3.474627e-01
49 -4.158605e-01 -4.223708e-01
50 -4.358723e-01 -4.158605e-01
51 -2.755980e-01 -4.358723e-01
52 -3.880681e-01 -2.755980e-01
53 5.840255e-02 -3.880681e-01
54 3.329301e-02 5.840255e-02
55 8.855297e-03 3.329301e-02
56 1.215841e-01 8.855297e-03
57 1.041191e-01 1.215841e-01
58 1.577576e-01 1.041191e-01
59 -4.654591e-01 1.577576e-01
60 -4.125232e-01 -4.654591e-01
61 -5.063929e-01 -4.125232e-01
62 -4.479138e-01 -5.063929e-01
63 -2.936254e-01 -4.479138e-01
64 -3.680950e-01 -2.936254e-01
65 2.523314e-01 -3.680950e-01
66 2.417851e-01 2.523314e-01
67 3.479822e-01 2.417851e-01
68 2.946737e-01 3.479822e-01
69 2.749909e-01 2.946737e-01
70 1.240755e-01 2.749909e-01
71 2.194477e-01 1.240755e-01
72 1.252234e-01 2.194477e-01
73 -2.245646e-02 1.252234e-01
74 1.018246e-01 -2.245646e-02
75 1.267228e-01 1.018246e-01
76 9.259652e-02 1.267228e-01
77 3.789536e-02 9.259652e-02
78 -1.574539e-02 3.789536e-02
79 -2.299341e-01 -1.574539e-02
80 -2.687667e-02 -2.299341e-01
81 -8.537131e-02 -2.687667e-02
82 2.167398e-01 -8.537131e-02
83 -8.964336e-05 2.167398e-01
84 1.184662e-03 -8.964336e-05
85 2.806663e-01 1.184662e-03
86 -8.148587e-02 2.806663e-01
87 -1.017428e-02 -8.148587e-02
88 -3.357398e-01 -1.017428e-02
89 -2.371007e-01 -3.357398e-01
90 2.448358e-01 -2.371007e-01
91 1.970123e-01 2.448358e-01
92 2.937505e-01 1.970123e-01
93 3.058972e-01 2.937505e-01
94 2.998643e-01 3.058972e-01
95 3.408940e-01 2.998643e-01
96 -8.268481e-02 3.408940e-01
97 2.760636e-01 -8.268481e-02
98 2.840936e-02 2.760636e-01
99 5.829153e-02 2.840936e-02
100 1.284822e-01 5.829153e-02
101 1.068575e-01 1.284822e-01
102 5.472617e-01 1.068575e-01
103 6.030093e-01 5.472617e-01
104 5.747174e-01 6.030093e-01
105 5.671140e-01 5.747174e-01
106 3.974434e-01 5.671140e-01
107 4.680305e-01 3.974434e-01
108 1.883782e-01 4.680305e-01
109 2.014630e-02 1.883782e-01
110 3.812892e-01 2.014630e-02
111 2.553450e-02 3.812892e-01
112 2.144509e-01 2.553450e-02
113 3.337514e-01 2.144509e-01
114 2.076523e-01 3.337514e-01
115 4.257144e-01 2.076523e-01
116 -1.022770e-02 4.257144e-01
117 1.621520e-01 -1.022770e-02
118 3.944010e-01 1.621520e-01
119 4.983618e-01 3.944010e-01
120 1.862685e-02 4.983618e-01
121 1.344157e-01 1.862685e-02
122 1.968615e-01 1.344157e-01
123 3.679575e-01 1.968615e-01
124 3.324323e-01 3.679575e-01
125 2.447462e-01 3.324323e-01
126 2.909907e-01 2.447462e-01
127 6.002723e-01 2.909907e-01
128 2.775681e-01 6.002723e-01
129 2.962040e-01 2.775681e-01
130 2.335260e-01 2.962040e-01
131 9.880834e-02 2.335260e-01
132 4.298479e-01 9.880834e-02
133 1.031138e-01 4.298479e-01
134 8.911148e-02 1.031138e-01
135 -1.910696e-01 8.911148e-02
136 -1.479587e-01 -1.910696e-01
137 1.610347e-01 -1.479587e-01
138 2.653162e-01 1.610347e-01
139 6.508751e-02 2.653162e-01
140 1.275365e-01 6.508751e-02
141 2.026471e-01 1.275365e-01
142 3.500022e-01 2.026471e-01
143 3.705337e-01 3.500022e-01
144 2.504658e-01 3.705337e-01
145 -3.781371e-01 2.504658e-01
146 -1.324563e-01 -3.781371e-01
147 -3.212296e-01 -1.324563e-01
148 1.224192e-01 -3.212296e-01
149 1.252623e-01 1.224192e-01
150 -1.406098e-01 1.252623e-01
151 -1.219212e-01 -1.406098e-01
152 9.942352e-02 -1.219212e-01
153 -1.176661e-01 9.942352e-02
154 -3.380574e-01 -1.176661e-01
155 2.656375e-01 -3.380574e-01
156 -2.205850e-01 2.656375e-01
157 1.439237e-01 -2.205850e-01
158 3.138847e-01 1.439237e-01
159 1.887809e-01 3.138847e-01
160 1.385255e-01 1.887809e-01
161 1.757079e-01 1.385255e-01
162 3.363470e-01 1.757079e-01
163 -3.898741e-01 3.363470e-01
164 -4.471533e-01 -3.898741e-01
165 -3.025487e-01 -4.471533e-01
166 -1.781961e-01 -3.025487e-01
167 -8.533247e-01 -1.781961e-01
168 -3.208621e-01 -8.533247e-01
169 -2.709193e-01 -3.208621e-01
170 -6.507616e-01 -2.709193e-01
171 -7.055019e-01 -6.507616e-01
172 -7.387103e-01 -7.055019e-01
173 -7.189790e-01 -7.387103e-01
174 -7.457167e-01 -7.189790e-01
175 -7.435646e-01 -7.457167e-01
176 3.623591e-01 -7.435646e-01
177 2.891555e-01 3.623591e-01
178 2.294920e-02 2.891555e-01
179 2.716786e-01 2.294920e-02
180 6.835174e-02 2.716786e-01
181 2.785641e-01 6.835174e-02
182 -6.363103e-01 2.785641e-01
183 -7.161128e-01 -6.363103e-01
184 -6.718517e-01 -7.161128e-01
185 -4.627306e-01 -6.718517e-01
186 -5.303643e-01 -4.627306e-01
187 -3.521222e-01 -5.303643e-01
188 -3.786546e-01 -3.521222e-01
189 -6.110127e-01 -3.786546e-01
190 -7.051888e-01 -6.110127e-01
191 1.069023e-01 -7.051888e-01
192 -1.981882e-01 1.069023e-01
193 -7.055433e-01 -1.981882e-01
> 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/7tsyo1386679785.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/8od4c1386679785.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/9vvem1386679785.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/fisher/rcomp/tmp/10a38n1386679785.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/fisher/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/fisher/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/113b1v1386679785.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/12d2z41386679785.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/13syyb1386679785.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/1497au1386679785.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/15u02g1386679785.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/16l5121386679785.tab")
+ }
>
> try(system("convert tmp/129sj1386679785.ps tmp/129sj1386679785.png",intern=TRUE))
character(0)
> try(system("convert tmp/2malr1386679785.ps tmp/2malr1386679785.png",intern=TRUE))
character(0)
> try(system("convert tmp/3zzmg1386679785.ps tmp/3zzmg1386679785.png",intern=TRUE))
character(0)
> try(system("convert tmp/4rfxz1386679785.ps tmp/4rfxz1386679785.png",intern=TRUE))
character(0)
> try(system("convert tmp/5r3jj1386679785.ps tmp/5r3jj1386679785.png",intern=TRUE))
character(0)
> try(system("convert tmp/685ty1386679785.ps tmp/685ty1386679785.png",intern=TRUE))
character(0)
> try(system("convert tmp/7tsyo1386679785.ps tmp/7tsyo1386679785.png",intern=TRUE))
character(0)
> try(system("convert tmp/8od4c1386679785.ps tmp/8od4c1386679785.png",intern=TRUE))
character(0)
> try(system("convert tmp/9vvem1386679785.ps tmp/9vvem1386679785.png",intern=TRUE))
character(0)
> try(system("convert tmp/10a38n1386679785.ps tmp/10a38n1386679785.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
20.010 4.093 24.140