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)
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Type 'contributors()' for more information and
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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
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+ ,0.00954
+ ,0.085
+ ,0.00719
+ ,0
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+ ,211.526
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+ ,0.00094
+ ,0.00106
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+ ,0.00105
+ ,0.00115
+ ,0.01194
+ ,0.107
+ ,0.00957
+ ,0
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+ ,192.921
+ ,168.013
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+ ,0.00241
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+ ,163.564
+ ,0.00369
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+ ,0.00205
+ ,0.00218
+ ,0.01851
+ ,0.168
+ ,0.01491
+ ,1
+ ,180.198
+ ,201.249
+ ,175.456
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+ ,0.00166
+ ,0.01444
+ ,0.131
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+ ,186.163
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+ ,0.00175
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+ ,0.01149
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+ ,0.00826
+ ,0.00655
+ ,0.04689
+ ,0.422
+ ,0.03908
+ ,1
+ ,126.512
+ ,141.756
+ ,99.77
+ ,0.01936
+ ,0.00015
+ ,0.01159
+ ,0.0099
+ ,0.06734
+ ,0.659
+ ,0.05783
+ ,1
+ ,125.641
+ ,141.068
+ ,116.346
+ ,0.03316
+ ,0.00026
+ ,0.02144
+ ,0.01522
+ ,0.09178
+ ,0.891
+ ,0.06196
+ ,1
+ ,128.451
+ ,150.449
+ ,75.632
+ ,0.01551
+ ,0.00012
+ ,0.00905
+ ,0.00909
+ ,0.0617
+ ,0.584
+ ,0.05174
+ ,1
+ ,139.224
+ ,586.567
+ ,66.157
+ ,0.03011
+ ,0.00022
+ ,0.01854
+ ,0.01628
+ ,0.09419
+ ,0.93
+ ,0.06023
+ ,1
+ ,150.258
+ ,154.609
+ ,75.349
+ ,0.00248
+ ,0.00002
+ ,0.00105
+ ,0.00136
+ ,0.01131
+ ,0.107
+ ,0.01009
+ ,1
+ ,154.003
+ ,160.267
+ ,128.621
+ ,0.00183
+ ,0.00001
+ ,0.00076
+ ,0.001
+ ,0.0103
+ ,0.094
+ ,0.00871
+ ,1
+ ,149.689
+ ,160.368
+ ,133.608
+ ,0.00257
+ ,0.00002
+ ,0.00116
+ ,0.00134
+ ,0.01346
+ ,0.126
+ ,0.01059
+ ,1
+ ,155.078
+ ,163.736
+ ,144.148
+ ,0.00168
+ ,0.00001
+ ,0.00068
+ ,0.00092
+ ,0.01064
+ ,0.097
+ ,0.00928
+ ,1
+ ,151.884
+ ,157.765
+ ,133.751
+ ,0.00258
+ ,0.00002
+ ,0.00115
+ ,0.00122
+ ,0.0145
+ ,0.137
+ ,0.01267
+ ,1
+ ,151.989
+ ,157.339
+ ,132.857
+ ,0.00174
+ ,0.00001
+ ,0.00075
+ ,0.00096
+ ,0.01024
+ ,0.093
+ ,0.00993
+ ,1
+ ,193.03
+ ,208.9
+ ,80.297
+ ,0.00766
+ ,0.00004
+ ,0.0045
+ ,0.00389
+ ,0.03044
+ ,0.275
+ ,0.02084
+ ,1
+ ,200.714
+ ,223.982
+ ,89.686
+ ,0.00621
+ ,0.00003
+ ,0.00371
+ ,0.00337
+ ,0.02286
+ ,0.207
+ ,0.01852
+ ,1
+ ,208.519
+ ,220.315
+ ,199.02
+ ,0.00609
+ ,0.00003
+ ,0.00368
+ ,0.00339
+ ,0.01761
+ ,0.155
+ ,0.01307
+ ,1
+ ,204.664
+ ,221.3
+ ,189.621
+ ,0.00841
+ ,0.00004
+ ,0.00502
+ ,0.00485
+ ,0.02378
+ ,0.21
+ ,0.01767
+ ,1
+ ,210.141
+ ,232.706
+ ,185.258
+ ,0.00534
+ ,0.00003
+ ,0.00321
+ ,0.0028
+ ,0.0168
+ ,0.149
+ ,0.01301
+ ,1
+ ,206.327
+ ,226.355
+ ,92.02
+ ,0.00495
+ ,0.00002
+ ,0.00302
+ ,0.00246
+ ,0.02105
+ ,0.209
+ ,0.01604
+ ,1
+ ,151.872
+ ,492.892
+ ,69.085
+ ,0.00856
+ ,0.00006
+ ,0.00404
+ ,0.00385
+ ,0.01843
+ ,0.235
+ ,0.01271
+ ,1
+ ,158.219
+ ,442.557
+ ,71.948
+ ,0.00476
+ ,0.00003
+ ,0.00214
+ ,0.00207
+ ,0.01458
+ ,0.148
+ ,0.01312
+ ,1
+ ,170.756
+ ,450.247
+ ,79.032
+ ,0.00555
+ ,0.00003
+ ,0.00244
+ ,0.00261
+ ,0.01725
+ ,0.175
+ ,0.01652
+ ,1
+ ,178.285
+ ,442.824
+ ,82.063
+ ,0.00462
+ ,0.00003
+ ,0.00157
+ ,0.00194
+ ,0.01279
+ ,0.129
+ ,0.01151
+ ,1
+ ,217.116
+ ,233.481
+ ,93.978
+ ,0.00404
+ ,0.00002
+ ,0.00127
+ ,0.00128
+ ,0.01299
+ ,0.124
+ ,0.01075
+ ,1
+ ,128.94
+ ,479.697
+ ,88.251
+ ,0.00581
+ ,0.00005
+ ,0.00241
+ ,0.00314
+ ,0.02008
+ ,0.221
+ ,0.01734
+ ,1
+ ,176.824
+ ,215.293
+ ,83.961
+ ,0.0046
+ ,0.00003
+ ,0.00209
+ ,0.00221
+ ,0.01169
+ ,0.117
+ ,0.01104
+ ,1
+ ,138.19
+ ,203.522
+ ,83.34
+ ,0.00704
+ ,0.00005
+ ,0.00406
+ ,0.00398
+ ,0.04479
+ ,0.441
+ ,0.0322
+ ,1
+ ,182.018
+ ,197.173
+ ,79.187
+ ,0.00842
+ ,0.00005
+ ,0.00506
+ ,0.00449
+ ,0.02503
+ ,0.231
+ ,0.01931
+ ,1
+ ,156.239
+ ,195.107
+ ,79.82
+ ,0.00694
+ ,0.00004
+ ,0.00403
+ ,0.00395
+ ,0.02343
+ ,0.224
+ ,0.0172
+ ,1
+ ,145.174
+ ,198.109
+ ,80.637
+ ,0.00733
+ ,0.00005
+ ,0.00414
+ ,0.00422
+ ,0.02362
+ ,0.233
+ ,0.01944
+ ,1
+ ,138.145
+ ,197.238
+ ,81.114
+ ,0.00544
+ ,0.00004
+ ,0.00294
+ ,0.00327
+ ,0.02791
+ ,0.246
+ ,0.02259
+ ,1
+ ,166.888
+ ,198.966
+ ,79.512
+ ,0.00638
+ ,0.00004
+ ,0.00368
+ ,0.00351
+ ,0.02857
+ ,0.257
+ ,0.02301
+ ,1
+ ,119.031
+ ,127.533
+ ,109.216
+ ,0.0044
+ ,0.00004
+ ,0.00214
+ ,0.00192
+ ,0.01033
+ ,0.098
+ ,0.00811
+ ,1
+ ,120.078
+ ,126.632
+ ,105.667
+ ,0.0027
+ ,0.00002
+ ,0.00116
+ ,0.00135
+ ,0.01022
+ ,0.09
+ ,0.00903
+ ,1
+ ,120.289
+ ,128.143
+ ,100.209
+ ,0.00492
+ ,0.00004
+ ,0.00269
+ ,0.00238
+ ,0.01412
+ ,0.125
+ ,0.01194
+ ,1
+ ,120.256
+ ,125.306
+ ,104.773
+ ,0.00407
+ ,0.00003
+ ,0.00224
+ ,0.00205
+ ,0.01516
+ ,0.138
+ ,0.0131
+ ,1
+ ,119.056
+ ,125.213
+ ,86.795
+ ,0.00346
+ ,0.00003
+ ,0.00169
+ ,0.0017
+ ,0.01201
+ ,0.106
+ ,0.00915
+ ,1
+ ,118.747
+ ,123.723
+ ,109.836
+ ,0.00331
+ ,0.00003
+ ,0.00168
+ ,0.00171
+ ,0.01043
+ ,0.099
+ ,0.00903
+ ,1
+ ,106.516
+ ,112.777
+ ,93.105
+ ,0.00589
+ ,0.00006
+ ,0.00291
+ ,0.00319
+ ,0.04932
+ ,0.441
+ ,0.03651
+ ,1
+ ,110.453
+ ,127.611
+ ,105.554
+ ,0.00494
+ ,0.00004
+ ,0.00244
+ ,0.00315
+ ,0.04128
+ ,0.379
+ ,0.03316
+ ,1
+ ,113.4
+ ,133.344
+ ,107.816
+ ,0.00451
+ ,0.00004
+ ,0.00219
+ ,0.00283
+ ,0.04879
+ ,0.431
+ ,0.0437
+ ,1
+ ,113.166
+ ,130.27
+ ,100.673
+ ,0.00502
+ ,0.00004
+ ,0.00257
+ ,0.00312
+ ,0.05279
+ ,0.476
+ ,0.04134
+ ,1
+ ,112.239
+ ,126.609
+ ,104.095
+ ,0.00472
+ ,0.00004
+ ,0.00238
+ ,0.0029
+ ,0.05643
+ ,0.517
+ ,0.04451
+ ,1
+ ,116.15
+ ,131.731
+ ,109.815
+ ,0.00381
+ ,0.00003
+ ,0.00181
+ ,0.00232
+ ,0.03026
+ ,0.267
+ ,0.0277
+ ,1
+ ,170.368
+ ,268.796
+ ,79.543
+ ,0.00571
+ ,0.00003
+ ,0.00232
+ ,0.00269
+ ,0.03273
+ ,0.281
+ ,0.02824
+ ,1
+ ,208.083
+ ,253.792
+ ,91.802
+ ,0.00757
+ ,0.00004
+ ,0.00428
+ ,0.00428
+ ,0.06725
+ ,0.571
+ ,0.04464
+ ,1
+ ,198.458
+ ,219.29
+ ,148.691
+ ,0.00376
+ ,0.00002
+ ,0.00182
+ ,0.00215
+ ,0.03527
+ ,0.297
+ ,0.0253
+ ,1
+ ,202.805
+ ,231.508
+ ,86.232
+ ,0.0037
+ ,0.00002
+ ,0.00189
+ ,0.00211
+ ,0.01997
+ ,0.18
+ ,0.01506
+ ,1
+ ,202.544
+ ,241.35
+ ,164.168
+ ,0.00254
+ ,0.00001
+ ,0.001
+ ,0.00133
+ ,0.02662
+ ,0.228
+ ,0.02006
+ ,1
+ ,223.361
+ ,263.872
+ ,87.638
+ ,0.00352
+ ,0.00002
+ ,0.00169
+ ,0.00188
+ ,0.02536
+ ,0.225
+ ,0.01909
+ ,1
+ ,169.774
+ ,191.759
+ ,151.451
+ ,0.01568
+ ,0.00009
+ ,0.00863
+ ,0.00946
+ ,0.08143
+ ,0.821
+ ,0.08808
+ ,1
+ ,183.52
+ ,216.814
+ ,161.34
+ ,0.01466
+ ,0.00008
+ ,0.00849
+ ,0.00819
+ ,0.0605
+ ,0.618
+ ,0.06359
+ ,1
+ ,188.62
+ ,216.302
+ ,165.982
+ ,0.01719
+ ,0.00009
+ ,0.00996
+ ,0.01027
+ ,0.07118
+ ,0.722
+ ,0.06824
+ ,1
+ ,202.632
+ ,565.74
+ ,177.258
+ ,0.01627
+ ,0.00008
+ ,0.00919
+ ,0.00963
+ ,0.0717
+ ,0.833
+ ,0.0646
+ ,1
+ ,186.695
+ ,211.961
+ ,149.442
+ ,0.01872
+ ,0.0001
+ ,0.01075
+ ,0.01154
+ ,0.0583
+ ,0.784
+ ,0.06259
+ ,1
+ ,192.818
+ ,224.429
+ ,168.793
+ ,0.03107
+ ,0.00016
+ ,0.018
+ ,0.01958
+ ,0.11908
+ ,1.302
+ ,0.13778
+ ,1
+ ,198.116
+ ,233.099
+ ,174.478
+ ,0.02714
+ ,0.00014
+ ,0.01568
+ ,0.01699
+ ,0.08684
+ ,1.018
+ ,0.08318
+ ,1
+ ,121.345
+ ,139.644
+ ,98.25
+ ,0.00684
+ ,0.00006
+ ,0.00388
+ ,0.00332
+ ,0.02534
+ ,0.241
+ ,0.02056
+ ,1
+ ,119.1
+ ,128.442
+ ,88.833
+ ,0.00692
+ ,0.00006
+ ,0.00393
+ ,0.003
+ ,0.02682
+ ,0.236
+ ,0.02018
+ ,1
+ ,117.87
+ ,127.349
+ ,95.654
+ ,0.00647
+ ,0.00005
+ ,0.00356
+ ,0.003
+ ,0.03087
+ ,0.276
+ ,0.02402
+ ,1
+ ,122.336
+ ,142.369
+ ,94.794
+ ,0.00727
+ ,0.00006
+ ,0.00415
+ ,0.00339
+ ,0.02293
+ ,0.223
+ ,0.01771
+ ,1
+ ,117.963
+ ,134.209
+ ,100.757
+ ,0.01813
+ ,0.00015
+ ,0.01117
+ ,0.00718
+ ,0.04912
+ ,0.438
+ ,0.02916
+ ,1
+ ,126.144
+ ,154.284
+ ,97.543
+ ,0.00975
+ ,0.00008
+ ,0.00593
+ ,0.00454
+ ,0.02852
+ ,0.266
+ ,0.02157
+ ,1
+ ,127.93
+ ,138.752
+ ,112.173
+ ,0.00605
+ ,0.00005
+ ,0.00321
+ ,0.00318
+ ,0.03235
+ ,0.339
+ ,0.03105
+ ,1
+ ,114.238
+ ,124.393
+ ,77.022
+ ,0.00581
+ ,0.00005
+ ,0.00299
+ ,0.00316
+ ,0.04009
+ ,0.406
+ ,0.04114
+ ,1
+ ,115.322
+ ,135.738
+ ,107.802
+ ,0.00619
+ ,0.00005
+ ,0.00352
+ ,0.00329
+ ,0.03273
+ ,0.325
+ ,0.02931
+ ,1
+ ,114.554
+ ,126.778
+ ,91.121
+ ,0.00651
+ ,0.00006
+ ,0.00366
+ ,0.0034
+ ,0.03658
+ ,0.369
+ ,0.03091
+ ,1
+ ,112.15
+ ,131.669
+ ,97.527
+ ,0.00519
+ ,0.00005
+ ,0.00291
+ ,0.00284
+ ,0.01756
+ ,0.155
+ ,0.01363
+ ,1
+ ,102.273
+ ,142.83
+ ,85.902
+ ,0.00907
+ ,0.00009
+ ,0.00493
+ ,0.00461
+ ,0.02814
+ ,0.272
+ ,0.02073
+ ,1
+ ,236.2
+ ,244.663
+ ,102.137
+ ,0.00277
+ ,0.00001
+ ,0.00154
+ ,0.00153
+ ,0.02448
+ ,0.217
+ ,0.01621
+ ,0
+ ,237.323
+ ,243.709
+ ,229.256
+ ,0.00303
+ ,0.00001
+ ,0.00173
+ ,0.00159
+ ,0.01242
+ ,0.116
+ ,0.00882
+ ,0
+ ,260.105
+ ,264.919
+ ,237.303
+ ,0.00339
+ ,0.00001
+ ,0.00205
+ ,0.00186
+ ,0.0203
+ ,0.197
+ ,0.01367
+ ,0
+ ,197.569
+ ,217.627
+ ,90.794
+ ,0.00803
+ ,0.00004
+ ,0.0049
+ ,0.00448
+ ,0.02177
+ ,0.189
+ ,0.01439
+ ,0
+ ,240.301
+ ,245.135
+ ,219.783
+ ,0.00517
+ ,0.00002
+ ,0.00316
+ ,0.00283
+ ,0.02018
+ ,0.212
+ ,0.01344
+ ,0
+ ,244.99
+ ,272.21
+ ,239.17
+ ,0.00451
+ ,0.00002
+ ,0.00279
+ ,0.00237
+ ,0.01897
+ ,0.181
+ ,0.01255
+ ,0
+ ,112.547
+ ,133.374
+ ,105.715
+ ,0.00355
+ ,0.00003
+ ,0.00166
+ ,0.0019
+ ,0.01358
+ ,0.129
+ ,0.0114
+ ,0
+ ,110.739
+ ,113.597
+ ,100.139
+ ,0.00356
+ ,0.00003
+ ,0.0017
+ ,0.002
+ ,0.01484
+ ,0.133
+ ,0.01285
+ ,0
+ ,113.715
+ ,116.443
+ ,96.913
+ ,0.00349
+ ,0.00003
+ ,0.00171
+ ,0.00203
+ ,0.01472
+ ,0.133
+ ,0.01148
+ ,0
+ ,117.004
+ ,144.466
+ ,99.923
+ ,0.00353
+ ,0.00003
+ ,0.00176
+ ,0.00218
+ ,0.01657
+ ,0.145
+ ,0.01318
+ ,0
+ ,115.38
+ ,123.109
+ ,108.634
+ ,0.00332
+ ,0.00003
+ ,0.0016
+ ,0.00199
+ ,0.01503
+ ,0.137
+ ,0.01133
+ ,0
+ ,116.388
+ ,129.038
+ ,108.97
+ ,0.00346
+ ,0.00003
+ ,0.00169
+ ,0.00213
+ ,0.01725
+ ,0.155
+ ,0.01331
+ ,0
+ ,151.737
+ ,190.204
+ ,129.859
+ ,0.00314
+ ,0.00002
+ ,0.00135
+ ,0.00162
+ ,0.01469
+ ,0.132
+ ,0.0123
+ ,1
+ ,148.79
+ ,158.359
+ ,138.99
+ ,0.00309
+ ,0.00002
+ ,0.00152
+ ,0.00186
+ ,0.01574
+ ,0.142
+ ,0.01309
+ ,1
+ ,148.143
+ ,155.982
+ ,135.041
+ ,0.00392
+ ,0.00003
+ ,0.00204
+ ,0.00231
+ ,0.0145
+ ,0.131
+ ,0.01263
+ ,1
+ ,150.44
+ ,163.441
+ ,144.736
+ ,0.00396
+ ,0.00003
+ ,0.00206
+ ,0.00233
+ ,0.02551
+ ,0.237
+ ,0.02148
+ ,1
+ ,148.462
+ ,161.078
+ ,141.998
+ ,0.00397
+ ,0.00003
+ ,0.00202
+ ,0.00235
+ ,0.01831
+ ,0.163
+ ,0.01559
+ ,1
+ ,149.818
+ ,163.417
+ ,144.786
+ ,0.00336
+ ,0.00002
+ ,0.00174
+ ,0.00198
+ ,0.02145
+ ,0.198
+ ,0.01666
+ ,1
+ ,117.226
+ ,123.925
+ ,106.656
+ ,0.00417
+ ,0.00004
+ ,0.00186
+ ,0.0027
+ ,0.01909
+ ,0.171
+ ,0.01949
+ ,0
+ ,116.848
+ ,217.552
+ ,99.503
+ ,0.00531
+ ,0.00005
+ ,0.0026
+ ,0.00346
+ ,0.01795
+ ,0.163
+ ,0.01756
+ ,0
+ ,116.286
+ ,177.291
+ ,96.983
+ ,0.00314
+ ,0.00003
+ ,0.00134
+ ,0.00192
+ ,0.01564
+ ,0.136
+ ,0.01691
+ ,0
+ ,116.556
+ ,592.03
+ ,86.228
+ ,0.00496
+ ,0.00004
+ ,0.00254
+ ,0.00263
+ ,0.0166
+ ,0.154
+ ,0.01491
+ ,0
+ ,116.342
+ ,581.289
+ ,94.246
+ ,0.00267
+ ,0.00002
+ ,0.00115
+ ,0.00148
+ ,0.013
+ ,0.117
+ ,0.01144
+ ,0
+ ,114.563
+ ,119.167
+ ,86.647
+ ,0.00327
+ ,0.00003
+ ,0.00146
+ ,0.00184
+ ,0.01185
+ ,0.106
+ ,0.01095
+ ,0
+ ,201.774
+ ,262.707
+ ,78.228
+ ,0.00694
+ ,0.00003
+ ,0.00412
+ ,0.00396
+ ,0.02574
+ ,0.255
+ ,0.01758
+ ,0
+ ,174.188
+ ,230.978
+ ,94.261
+ ,0.00459
+ ,0.00003
+ ,0.00263
+ ,0.00259
+ ,0.04087
+ ,0.405
+ ,0.02745
+ ,0
+ ,209.516
+ ,253.017
+ ,89.488
+ ,0.00564
+ ,0.00003
+ ,0.00331
+ ,0.00292
+ ,0.02751
+ ,0.263
+ ,0.01879
+ ,0
+ ,174.688
+ ,240.005
+ ,74.287
+ ,0.0136
+ ,0.00008
+ ,0.00624
+ ,0.00564
+ ,0.02308
+ ,0.256
+ ,0.01667
+ ,0
+ ,198.764
+ ,396.961
+ ,74.904
+ ,0.0074
+ ,0.00004
+ ,0.0037
+ ,0.0039
+ ,0.02296
+ ,0.241
+ ,0.01588
+ ,0
+ ,214.289
+ ,260.277
+ ,77.973
+ ,0.00567
+ ,0.00003
+ ,0.00295
+ ,0.00317
+ ,0.01884
+ ,0.19
+ ,0.01373
+ ,0)
+ ,dim=c(11
+ ,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'
+ ,'status')
+ ,1:195))
> y <- array(NA,dim=c(11,195),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','status'),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 = '11'
> par3 <- 'No Linear Trend'
> par2 <- 'Do not include Seasonal Dummies'
> par1 <- '11'
> #'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:Fo(Hz) MDVP:Fhi(Hz) MDVP:Flo(Hz) MDVP:Jitter(%)
1 1 119.992 157.302 74.997 0.00784
2 1 122.400 148.650 113.819 0.00968
3 1 116.682 131.111 111.555 0.01050
4 1 116.676 137.871 111.366 0.00997
5 1 116.014 141.781 110.655 0.01284
6 1 120.552 131.162 113.787 0.00968
7 1 120.267 137.244 114.820 0.00333
8 1 107.332 113.840 104.315 0.00290
9 1 95.730 132.068 91.754 0.00551
10 1 95.056 120.103 91.226 0.00532
11 1 88.333 112.240 84.072 0.00505
12 1 91.904 115.871 86.292 0.00540
13 1 136.926 159.866 131.276 0.00293
14 1 139.173 179.139 76.556 0.00390
15 1 152.845 163.305 75.836 0.00294
16 1 142.167 217.455 83.159 0.00369
17 1 144.188 349.259 82.764 0.00544
18 1 168.778 232.181 75.603 0.00718
19 1 153.046 175.829 68.623 0.00742
20 1 156.405 189.398 142.822 0.00768
21 1 153.848 165.738 65.782 0.00840
22 1 153.880 172.860 78.128 0.00480
23 1 167.930 193.221 79.068 0.00442
24 1 173.917 192.735 86.180 0.00476
25 1 163.656 200.841 76.779 0.00742
26 1 104.400 206.002 77.968 0.00633
27 1 171.041 208.313 75.501 0.00455
28 1 146.845 208.701 81.737 0.00496
29 1 155.358 227.383 80.055 0.00310
30 1 162.568 198.346 77.630 0.00502
31 0 197.076 206.896 192.055 0.00289
32 0 199.228 209.512 192.091 0.00241
33 0 198.383 215.203 193.104 0.00212
34 0 202.266 211.604 197.079 0.00180
35 0 203.184 211.526 196.160 0.00178
36 0 201.464 210.565 195.708 0.00198
37 1 177.876 192.921 168.013 0.00411
38 1 176.170 185.604 163.564 0.00369
39 1 180.198 201.249 175.456 0.00284
40 1 187.733 202.324 173.015 0.00316
41 1 186.163 197.724 177.584 0.00298
42 1 184.055 196.537 166.977 0.00258
43 0 237.226 247.326 225.227 0.00298
44 0 241.404 248.834 232.483 0.00281
45 0 243.439 250.912 232.435 0.00210
46 0 242.852 255.034 227.911 0.00225
47 0 245.510 262.090 231.848 0.00235
48 0 252.455 261.487 182.786 0.00185
49 0 122.188 128.611 115.765 0.00524
50 0 122.964 130.049 114.676 0.00428
51 0 124.445 135.069 117.495 0.00431
52 0 126.344 134.231 112.773 0.00448
53 0 128.001 138.052 122.080 0.00436
54 0 129.336 139.867 118.604 0.00490
55 1 108.807 134.656 102.874 0.00761
56 1 109.860 126.358 104.437 0.00874
57 1 110.417 131.067 103.370 0.00784
58 1 117.274 129.916 110.402 0.00752
59 1 116.879 131.897 108.153 0.00788
60 1 114.847 271.314 104.680 0.00867
61 0 209.144 237.494 109.379 0.00282
62 0 223.365 238.987 98.664 0.00264
63 0 222.236 231.345 205.495 0.00266
64 0 228.832 234.619 223.634 0.00296
65 0 229.401 252.221 221.156 0.00205
66 0 228.969 239.541 113.201 0.00238
67 1 140.341 159.774 67.021 0.00817
68 1 136.969 166.607 66.004 0.00923
69 1 143.533 162.215 65.809 0.01101
70 1 148.090 162.824 67.343 0.00762
71 1 142.729 162.408 65.476 0.00831
72 1 136.358 176.595 65.750 0.00971
73 1 120.080 139.710 111.208 0.00405
74 1 112.014 588.518 107.024 0.00533
75 1 110.793 128.101 107.316 0.00494
76 1 110.707 122.611 105.007 0.00516
77 1 112.876 148.826 106.981 0.00500
78 1 110.568 125.394 106.821 0.00462
79 1 95.385 102.145 90.264 0.00608
80 1 100.770 115.697 85.545 0.01038
81 1 96.106 108.664 84.510 0.00694
82 1 95.605 107.715 87.549 0.00702
83 1 100.960 110.019 95.628 0.00606
84 1 98.804 102.305 87.804 0.00432
85 1 176.858 205.560 75.344 0.00747
86 1 180.978 200.125 155.495 0.00406
87 1 178.222 202.450 141.047 0.00321
88 1 176.281 227.381 125.610 0.00520
89 1 173.898 211.350 74.677 0.00448
90 1 179.711 225.930 144.878 0.00709
91 1 166.605 206.008 78.032 0.00742
92 1 151.955 163.335 147.226 0.00419
93 1 148.272 164.989 142.299 0.00459
94 1 152.125 161.469 76.596 0.00382
95 1 157.821 172.975 68.401 0.00358
96 1 157.447 163.267 149.605 0.00369
97 1 159.116 168.913 144.811 0.00342
98 1 125.036 143.946 116.187 0.01280
99 1 125.791 140.557 96.206 0.01378
100 1 126.512 141.756 99.770 0.01936
101 1 125.641 141.068 116.346 0.03316
102 1 128.451 150.449 75.632 0.01551
103 1 139.224 586.567 66.157 0.03011
104 1 150.258 154.609 75.349 0.00248
105 1 154.003 160.267 128.621 0.00183
106 1 149.689 160.368 133.608 0.00257
107 1 155.078 163.736 144.148 0.00168
108 1 151.884 157.765 133.751 0.00258
109 1 151.989 157.339 132.857 0.00174
110 1 193.030 208.900 80.297 0.00766
111 1 200.714 223.982 89.686 0.00621
112 1 208.519 220.315 199.020 0.00609
113 1 204.664 221.300 189.621 0.00841
114 1 210.141 232.706 185.258 0.00534
115 1 206.327 226.355 92.020 0.00495
116 1 151.872 492.892 69.085 0.00856
117 1 158.219 442.557 71.948 0.00476
118 1 170.756 450.247 79.032 0.00555
119 1 178.285 442.824 82.063 0.00462
120 1 217.116 233.481 93.978 0.00404
121 1 128.940 479.697 88.251 0.00581
122 1 176.824 215.293 83.961 0.00460
123 1 138.190 203.522 83.340 0.00704
124 1 182.018 197.173 79.187 0.00842
125 1 156.239 195.107 79.820 0.00694
126 1 145.174 198.109 80.637 0.00733
127 1 138.145 197.238 81.114 0.00544
128 1 166.888 198.966 79.512 0.00638
129 1 119.031 127.533 109.216 0.00440
130 1 120.078 126.632 105.667 0.00270
131 1 120.289 128.143 100.209 0.00492
132 1 120.256 125.306 104.773 0.00407
133 1 119.056 125.213 86.795 0.00346
134 1 118.747 123.723 109.836 0.00331
135 1 106.516 112.777 93.105 0.00589
136 1 110.453 127.611 105.554 0.00494
137 1 113.400 133.344 107.816 0.00451
138 1 113.166 130.270 100.673 0.00502
139 1 112.239 126.609 104.095 0.00472
140 1 116.150 131.731 109.815 0.00381
141 1 170.368 268.796 79.543 0.00571
142 1 208.083 253.792 91.802 0.00757
143 1 198.458 219.290 148.691 0.00376
144 1 202.805 231.508 86.232 0.00370
145 1 202.544 241.350 164.168 0.00254
146 1 223.361 263.872 87.638 0.00352
147 1 169.774 191.759 151.451 0.01568
148 1 183.520 216.814 161.340 0.01466
149 1 188.620 216.302 165.982 0.01719
150 1 202.632 565.740 177.258 0.01627
151 1 186.695 211.961 149.442 0.01872
152 1 192.818 224.429 168.793 0.03107
153 1 198.116 233.099 174.478 0.02714
154 1 121.345 139.644 98.250 0.00684
155 1 119.100 128.442 88.833 0.00692
156 1 117.870 127.349 95.654 0.00647
157 1 122.336 142.369 94.794 0.00727
158 1 117.963 134.209 100.757 0.01813
159 1 126.144 154.284 97.543 0.00975
160 1 127.930 138.752 112.173 0.00605
161 1 114.238 124.393 77.022 0.00581
162 1 115.322 135.738 107.802 0.00619
163 1 114.554 126.778 91.121 0.00651
164 1 112.150 131.669 97.527 0.00519
165 1 102.273 142.830 85.902 0.00907
166 0 236.200 244.663 102.137 0.00277
167 0 237.323 243.709 229.256 0.00303
168 0 260.105 264.919 237.303 0.00339
169 0 197.569 217.627 90.794 0.00803
170 0 240.301 245.135 219.783 0.00517
171 0 244.990 272.210 239.170 0.00451
172 0 112.547 133.374 105.715 0.00355
173 0 110.739 113.597 100.139 0.00356
174 0 113.715 116.443 96.913 0.00349
175 0 117.004 144.466 99.923 0.00353
176 0 115.380 123.109 108.634 0.00332
177 0 116.388 129.038 108.970 0.00346
178 1 151.737 190.204 129.859 0.00314
179 1 148.790 158.359 138.990 0.00309
180 1 148.143 155.982 135.041 0.00392
181 1 150.440 163.441 144.736 0.00396
182 1 148.462 161.078 141.998 0.00397
183 1 149.818 163.417 144.786 0.00336
184 0 117.226 123.925 106.656 0.00417
185 0 116.848 217.552 99.503 0.00531
186 0 116.286 177.291 96.983 0.00314
187 0 116.556 592.030 86.228 0.00496
188 0 116.342 581.289 94.246 0.00267
189 0 114.563 119.167 86.647 0.00327
190 0 201.774 262.707 78.228 0.00694
191 0 174.188 230.978 94.261 0.00459
192 0 209.516 253.017 89.488 0.00564
193 0 174.688 240.005 74.287 0.01360
194 0 198.764 396.961 74.904 0.00740
195 0 214.289 260.277 77.973 0.00567
MDVP:Jitter(Abs) MDVP:RAP MDVP:PPQ MDVP:Shimmer MDVP:Shimmer(dB) MDVP:APQ
1 7.0e-05 0.00370 0.00554 0.04374 0.426 0.02971
2 8.0e-05 0.00465 0.00696 0.06134 0.626 0.04368
3 9.0e-05 0.00544 0.00781 0.05233 0.482 0.03590
4 9.0e-05 0.00502 0.00698 0.05492 0.517 0.03772
5 1.1e-04 0.00655 0.00908 0.06425 0.584 0.04465
6 8.0e-05 0.00463 0.00750 0.04701 0.456 0.03243
7 3.0e-05 0.00155 0.00202 0.01608 0.140 0.01351
8 3.0e-05 0.00144 0.00182 0.01567 0.134 0.01256
9 6.0e-05 0.00293 0.00332 0.02093 0.191 0.01717
10 6.0e-05 0.00268 0.00332 0.02838 0.255 0.02444
11 6.0e-05 0.00254 0.00330 0.02143 0.197 0.01892
12 6.0e-05 0.00281 0.00336 0.02752 0.249 0.02214
13 2.0e-05 0.00118 0.00153 0.01259 0.112 0.01140
14 3.0e-05 0.00165 0.00208 0.01642 0.154 0.01797
15 2.0e-05 0.00121 0.00149 0.01828 0.158 0.01246
16 3.0e-05 0.00157 0.00203 0.01503 0.126 0.01359
17 4.0e-05 0.00211 0.00292 0.02047 0.192 0.02074
18 4.0e-05 0.00284 0.00387 0.03327 0.348 0.03430
19 5.0e-05 0.00364 0.00432 0.05517 0.542 0.05767
20 5.0e-05 0.00372 0.00399 0.03995 0.348 0.04310
21 5.0e-05 0.00428 0.00450 0.03810 0.328 0.04055
22 3.0e-05 0.00232 0.00267 0.04137 0.370 0.04525
23 3.0e-05 0.00220 0.00247 0.04351 0.377 0.04246
24 3.0e-05 0.00221 0.00258 0.04192 0.364 0.03772
25 5.0e-05 0.00380 0.00390 0.01659 0.164 0.01497
26 6.0e-05 0.00316 0.00375 0.03767 0.381 0.03780
27 3.0e-05 0.00250 0.00234 0.01966 0.186 0.01872
28 3.0e-05 0.00250 0.00275 0.01919 0.198 0.01826
29 2.0e-05 0.00159 0.00176 0.01718 0.161 0.01661
30 3.0e-05 0.00280 0.00253 0.01791 0.168 0.01799
31 1.0e-05 0.00166 0.00168 0.01098 0.097 0.00802
32 1.0e-05 0.00134 0.00138 0.01015 0.089 0.00762
33 1.0e-05 0.00113 0.00135 0.01263 0.111 0.00951
34 9.0e-06 0.00093 0.00107 0.00954 0.085 0.00719
35 9.0e-06 0.00094 0.00106 0.00958 0.085 0.00726
36 1.0e-05 0.00105 0.00115 0.01194 0.107 0.00957
37 2.0e-05 0.00233 0.00241 0.02126 0.189 0.01612
38 2.0e-05 0.00205 0.00218 0.01851 0.168 0.01491
39 2.0e-05 0.00153 0.00166 0.01444 0.131 0.01190
40 2.0e-05 0.00168 0.00182 0.01663 0.151 0.01366
41 2.0e-05 0.00165 0.00175 0.01495 0.135 0.01233
42 1.0e-05 0.00134 0.00147 0.01463 0.132 0.01234
43 1.0e-05 0.00169 0.00182 0.01752 0.164 0.01133
44 1.0e-05 0.00157 0.00173 0.01760 0.154 0.01251
45 9.0e-06 0.00109 0.00137 0.01419 0.126 0.01033
46 9.0e-06 0.00117 0.00139 0.01494 0.134 0.01014
47 1.0e-05 0.00127 0.00148 0.01608 0.141 0.01149
48 7.0e-06 0.00092 0.00113 0.01152 0.103 0.00860
49 4.0e-05 0.00169 0.00203 0.01613 0.143 0.01433
50 3.0e-05 0.00124 0.00155 0.01681 0.154 0.01400
51 3.0e-05 0.00141 0.00167 0.02184 0.197 0.01685
52 4.0e-05 0.00131 0.00169 0.02033 0.185 0.01614
53 3.0e-05 0.00137 0.00166 0.02297 0.210 0.01677
54 4.0e-05 0.00165 0.00183 0.02498 0.228 0.01947
55 7.0e-05 0.00349 0.00486 0.02719 0.255 0.02067
56 8.0e-05 0.00398 0.00539 0.03209 0.307 0.02454
57 7.0e-05 0.00352 0.00514 0.03715 0.334 0.02802
58 6.0e-05 0.00299 0.00469 0.02293 0.221 0.01948
59 7.0e-05 0.00334 0.00493 0.02645 0.265 0.02137
60 8.0e-05 0.00373 0.00520 0.03225 0.350 0.02519
61 1.0e-05 0.00147 0.00152 0.01861 0.170 0.01382
62 1.0e-05 0.00154 0.00151 0.01906 0.165 0.01340
63 1.0e-05 0.00152 0.00144 0.01643 0.145 0.01200
64 1.0e-05 0.00175 0.00155 0.01644 0.145 0.01179
65 9.0e-06 0.00114 0.00113 0.01457 0.129 0.01016
66 1.0e-05 0.00136 0.00140 0.01745 0.154 0.01234
67 6.0e-05 0.00430 0.00440 0.03198 0.313 0.02428
68 7.0e-05 0.00507 0.00463 0.03111 0.308 0.02603
69 8.0e-05 0.00647 0.00467 0.05384 0.478 0.03392
70 5.0e-05 0.00467 0.00354 0.05428 0.497 0.03635
71 6.0e-05 0.00469 0.00419 0.03485 0.365 0.02949
72 7.0e-05 0.00534 0.00478 0.04978 0.483 0.03736
73 3.0e-05 0.00180 0.00220 0.01706 0.152 0.01345
74 5.0e-05 0.00268 0.00329 0.02448 0.226 0.01956
75 4.0e-05 0.00260 0.00283 0.02442 0.216 0.01831
76 5.0e-05 0.00277 0.00289 0.02215 0.206 0.01715
77 4.0e-05 0.00270 0.00289 0.03999 0.350 0.02704
78 4.0e-05 0.00226 0.00280 0.02199 0.197 0.01636
79 6.0e-05 0.00331 0.00332 0.03202 0.263 0.02455
80 1.0e-04 0.00622 0.00576 0.03121 0.361 0.02139
81 7.0e-05 0.00389 0.00415 0.04024 0.364 0.02876
82 7.0e-05 0.00428 0.00371 0.03156 0.296 0.02190
83 6.0e-05 0.00351 0.00348 0.02427 0.216 0.01751
84 4.0e-05 0.00247 0.00258 0.02223 0.202 0.01552
85 4.0e-05 0.00418 0.00420 0.04795 0.435 0.03510
86 2.0e-05 0.00220 0.00244 0.03852 0.331 0.02877
87 2.0e-05 0.00163 0.00194 0.03759 0.327 0.02784
88 3.0e-05 0.00287 0.00312 0.06511 0.580 0.04683
89 3.0e-05 0.00237 0.00254 0.06727 0.650 0.04802
90 4.0e-05 0.00391 0.00419 0.04313 0.442 0.03455
91 4.0e-05 0.00387 0.00453 0.06640 0.634 0.05114
92 3.0e-05 0.00224 0.00227 0.07959 0.772 0.05690
93 3.0e-05 0.00250 0.00256 0.04190 0.383 0.03051
94 3.0e-05 0.00191 0.00226 0.05925 0.637 0.04398
95 2.0e-05 0.00196 0.00196 0.03716 0.307 0.02764
96 2.0e-05 0.00201 0.00197 0.03272 0.283 0.02571
97 2.0e-05 0.00178 0.00184 0.03381 0.307 0.02809
98 1.0e-04 0.00743 0.00623 0.03886 0.342 0.03088
99 1.1e-04 0.00826 0.00655 0.04689 0.422 0.03908
100 1.5e-04 0.01159 0.00990 0.06734 0.659 0.05783
101 2.6e-04 0.02144 0.01522 0.09178 0.891 0.06196
102 1.2e-04 0.00905 0.00909 0.06170 0.584 0.05174
103 2.2e-04 0.01854 0.01628 0.09419 0.930 0.06023
104 2.0e-05 0.00105 0.00136 0.01131 0.107 0.01009
105 1.0e-05 0.00076 0.00100 0.01030 0.094 0.00871
106 2.0e-05 0.00116 0.00134 0.01346 0.126 0.01059
107 1.0e-05 0.00068 0.00092 0.01064 0.097 0.00928
108 2.0e-05 0.00115 0.00122 0.01450 0.137 0.01267
109 1.0e-05 0.00075 0.00096 0.01024 0.093 0.00993
110 4.0e-05 0.00450 0.00389 0.03044 0.275 0.02084
111 3.0e-05 0.00371 0.00337 0.02286 0.207 0.01852
112 3.0e-05 0.00368 0.00339 0.01761 0.155 0.01307
113 4.0e-05 0.00502 0.00485 0.02378 0.210 0.01767
114 3.0e-05 0.00321 0.00280 0.01680 0.149 0.01301
115 2.0e-05 0.00302 0.00246 0.02105 0.209 0.01604
116 6.0e-05 0.00404 0.00385 0.01843 0.235 0.01271
117 3.0e-05 0.00214 0.00207 0.01458 0.148 0.01312
118 3.0e-05 0.00244 0.00261 0.01725 0.175 0.01652
119 3.0e-05 0.00157 0.00194 0.01279 0.129 0.01151
120 2.0e-05 0.00127 0.00128 0.01299 0.124 0.01075
121 5.0e-05 0.00241 0.00314 0.02008 0.221 0.01734
122 3.0e-05 0.00209 0.00221 0.01169 0.117 0.01104
123 5.0e-05 0.00406 0.00398 0.04479 0.441 0.03220
124 5.0e-05 0.00506 0.00449 0.02503 0.231 0.01931
125 4.0e-05 0.00403 0.00395 0.02343 0.224 0.01720
126 5.0e-05 0.00414 0.00422 0.02362 0.233 0.01944
127 4.0e-05 0.00294 0.00327 0.02791 0.246 0.02259
128 4.0e-05 0.00368 0.00351 0.02857 0.257 0.02301
129 4.0e-05 0.00214 0.00192 0.01033 0.098 0.00811
130 2.0e-05 0.00116 0.00135 0.01022 0.090 0.00903
131 4.0e-05 0.00269 0.00238 0.01412 0.125 0.01194
132 3.0e-05 0.00224 0.00205 0.01516 0.138 0.01310
133 3.0e-05 0.00169 0.00170 0.01201 0.106 0.00915
134 3.0e-05 0.00168 0.00171 0.01043 0.099 0.00903
135 6.0e-05 0.00291 0.00319 0.04932 0.441 0.03651
136 4.0e-05 0.00244 0.00315 0.04128 0.379 0.03316
137 4.0e-05 0.00219 0.00283 0.04879 0.431 0.04370
138 4.0e-05 0.00257 0.00312 0.05279 0.476 0.04134
139 4.0e-05 0.00238 0.00290 0.05643 0.517 0.04451
140 3.0e-05 0.00181 0.00232 0.03026 0.267 0.02770
141 3.0e-05 0.00232 0.00269 0.03273 0.281 0.02824
142 4.0e-05 0.00428 0.00428 0.06725 0.571 0.04464
143 2.0e-05 0.00182 0.00215 0.03527 0.297 0.02530
144 2.0e-05 0.00189 0.00211 0.01997 0.180 0.01506
145 1.0e-05 0.00100 0.00133 0.02662 0.228 0.02006
146 2.0e-05 0.00169 0.00188 0.02536 0.225 0.01909
147 9.0e-05 0.00863 0.00946 0.08143 0.821 0.08808
148 8.0e-05 0.00849 0.00819 0.06050 0.618 0.06359
149 9.0e-05 0.00996 0.01027 0.07118 0.722 0.06824
150 8.0e-05 0.00919 0.00963 0.07170 0.833 0.06460
151 1.0e-04 0.01075 0.01154 0.05830 0.784 0.06259
152 1.6e-04 0.01800 0.01958 0.11908 1.302 0.13778
153 1.4e-04 0.01568 0.01699 0.08684 1.018 0.08318
154 6.0e-05 0.00388 0.00332 0.02534 0.241 0.02056
155 6.0e-05 0.00393 0.00300 0.02682 0.236 0.02018
156 5.0e-05 0.00356 0.00300 0.03087 0.276 0.02402
157 6.0e-05 0.00415 0.00339 0.02293 0.223 0.01771
158 1.5e-04 0.01117 0.00718 0.04912 0.438 0.02916
159 8.0e-05 0.00593 0.00454 0.02852 0.266 0.02157
160 5.0e-05 0.00321 0.00318 0.03235 0.339 0.03105
161 5.0e-05 0.00299 0.00316 0.04009 0.406 0.04114
162 5.0e-05 0.00352 0.00329 0.03273 0.325 0.02931
163 6.0e-05 0.00366 0.00340 0.03658 0.369 0.03091
164 5.0e-05 0.00291 0.00284 0.01756 0.155 0.01363
165 9.0e-05 0.00493 0.00461 0.02814 0.272 0.02073
166 1.0e-05 0.00154 0.00153 0.02448 0.217 0.01621
167 1.0e-05 0.00173 0.00159 0.01242 0.116 0.00882
168 1.0e-05 0.00205 0.00186 0.02030 0.197 0.01367
169 4.0e-05 0.00490 0.00448 0.02177 0.189 0.01439
170 2.0e-05 0.00316 0.00283 0.02018 0.212 0.01344
171 2.0e-05 0.00279 0.00237 0.01897 0.181 0.01255
172 3.0e-05 0.00166 0.00190 0.01358 0.129 0.01140
173 3.0e-05 0.00170 0.00200 0.01484 0.133 0.01285
174 3.0e-05 0.00171 0.00203 0.01472 0.133 0.01148
175 3.0e-05 0.00176 0.00218 0.01657 0.145 0.01318
176 3.0e-05 0.00160 0.00199 0.01503 0.137 0.01133
177 3.0e-05 0.00169 0.00213 0.01725 0.155 0.01331
178 2.0e-05 0.00135 0.00162 0.01469 0.132 0.01230
179 2.0e-05 0.00152 0.00186 0.01574 0.142 0.01309
180 3.0e-05 0.00204 0.00231 0.01450 0.131 0.01263
181 3.0e-05 0.00206 0.00233 0.02551 0.237 0.02148
182 3.0e-05 0.00202 0.00235 0.01831 0.163 0.01559
183 2.0e-05 0.00174 0.00198 0.02145 0.198 0.01666
184 4.0e-05 0.00186 0.00270 0.01909 0.171 0.01949
185 5.0e-05 0.00260 0.00346 0.01795 0.163 0.01756
186 3.0e-05 0.00134 0.00192 0.01564 0.136 0.01691
187 4.0e-05 0.00254 0.00263 0.01660 0.154 0.01491
188 2.0e-05 0.00115 0.00148 0.01300 0.117 0.01144
189 3.0e-05 0.00146 0.00184 0.01185 0.106 0.01095
190 3.0e-05 0.00412 0.00396 0.02574 0.255 0.01758
191 3.0e-05 0.00263 0.00259 0.04087 0.405 0.02745
192 3.0e-05 0.00331 0.00292 0.02751 0.263 0.01879
193 8.0e-05 0.00624 0.00564 0.02308 0.256 0.01667
194 4.0e-05 0.00370 0.00390 0.02296 0.241 0.01588
195 3.0e-05 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:Fo(Hz)` `MDVP:Fhi(Hz)` `MDVP:Flo(Hz)`
1.294768 -0.002381 -0.000185 -0.002440
`MDVP:Jitter(%)` `MDVP:Jitter(Abs)` `MDVP:RAP` `MDVP:PPQ`
-94.728998 -85.046138 116.984696 40.779554
`MDVP:Shimmer` `MDVP:Shimmer(dB)` `MDVP:APQ`
4.459670 -0.527263 9.492059
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.84206 -0.17933 0.07661 0.25092 0.61793
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.295e+00 2.215e-01 5.844 2.27e-08 ***
`MDVP:Fo(Hz)` -2.381e-03 1.406e-03 -1.694 0.09200 .
`MDVP:Fhi(Hz)` -1.850e-04 3.425e-04 -0.540 0.58980
`MDVP:Flo(Hz)` -2.440e-03 8.337e-04 -2.927 0.00386 **
`MDVP:Jitter(%)` -9.473e+01 6.627e+01 -1.429 0.15458
`MDVP:Jitter(Abs)` -8.505e+01 4.517e+03 -0.019 0.98500
`MDVP:RAP` 1.170e+02 7.498e+01 1.560 0.12043
`MDVP:PPQ` 4.078e+01 5.267e+01 0.774 0.43978
`MDVP:Shimmer` 4.460e+00 1.136e+01 0.393 0.69497
`MDVP:Shimmer(dB)` -5.273e-01 1.198e+00 -0.440 0.66049
`MDVP:APQ` 9.492e+00 6.868e+00 1.382 0.16861
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.3703 on 184 degrees of freedom
Multiple R-squared: 0.3027, Adjusted R-squared: 0.2648
F-statistic: 7.988 on 10 and 184 DF, p-value: 1.21e-10
> 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,] 4.735597e-49 9.471194e-49 1.000000e+00
[2,] 2.685020e-65 5.370040e-65 1.000000e+00
[3,] 0.000000e+00 0.000000e+00 1.000000e+00
[4,] 1.875720e-99 3.751441e-99 1.000000e+00
[5,] 7.149518e-109 1.429904e-108 1.000000e+00
[6,] 3.752717e-123 7.505433e-123 1.000000e+00
[7,] 1.161392e-144 2.322784e-144 1.000000e+00
[8,] 1.640450e-173 3.280900e-173 1.000000e+00
[9,] 4.814220e-171 9.628440e-171 1.000000e+00
[10,] 1.392806e-183 2.785613e-183 1.000000e+00
[11,] 2.518606e-201 5.037211e-201 1.000000e+00
[12,] 2.090589e-219 4.181177e-219 1.000000e+00
[13,] 2.235231e-256 4.470461e-256 1.000000e+00
[14,] 1.458472e-249 2.916943e-249 1.000000e+00
[15,] 5.728574e-261 1.145715e-260 1.000000e+00
[16,] 1.359553e-280 2.719106e-280 1.000000e+00
[17,] 6.604873e-297 1.320975e-296 1.000000e+00
[18,] 2.757952e-08 5.515905e-08 1.000000e+00
[19,] 1.675411e-08 3.350822e-08 1.000000e+00
[20,] 5.746888e-09 1.149378e-08 1.000000e+00
[21,] 1.729087e-09 3.458174e-09 1.000000e+00
[22,] 5.123662e-10 1.024732e-09 1.000000e+00
[23,] 1.476651e-10 2.953303e-10 1.000000e+00
[24,] 1.717540e-08 3.435081e-08 1.000000e+00
[25,] 3.270280e-07 6.540559e-07 9.999997e-01
[26,] 5.189316e-05 1.037863e-04 9.999481e-01
[27,] 5.439352e-04 1.087870e-03 9.994561e-01
[28,] 2.566751e-03 5.133502e-03 9.974332e-01
[29,] 3.490532e-03 6.981064e-03 9.965095e-01
[30,] 2.386868e-03 4.773735e-03 9.976131e-01
[31,] 1.503049e-03 3.006099e-03 9.984970e-01
[32,] 1.045024e-03 2.090048e-03 9.989550e-01
[33,] 6.532266e-04 1.306453e-03 9.993468e-01
[34,] 4.121505e-04 8.243010e-04 9.995878e-01
[35,] 2.613754e-04 5.227508e-04 9.997386e-01
[36,] 1.510176e-03 3.020352e-03 9.984898e-01
[37,] 2.254793e-03 4.509585e-03 9.977452e-01
[38,] 3.198231e-03 6.396462e-03 9.968018e-01
[39,] 2.893446e-03 5.786891e-03 9.971066e-01
[40,] 3.261921e-03 6.523842e-03 9.967381e-01
[41,] 3.762300e-03 7.524600e-03 9.962377e-01
[42,] 3.257746e-03 6.515491e-03 9.967423e-01
[43,] 3.625447e-03 7.250894e-03 9.963746e-01
[44,] 2.775863e-03 5.551727e-03 9.972241e-01
[45,] 2.481645e-03 4.963291e-03 9.975184e-01
[46,] 2.216013e-03 4.432026e-03 9.977840e-01
[47,] 1.853353e-03 3.706705e-03 9.981466e-01
[48,] 4.314906e-03 8.629812e-03 9.956851e-01
[49,] 6.134057e-03 1.226811e-02 9.938659e-01
[50,] 5.167719e-03 1.033544e-02 9.948323e-01
[51,] 4.220096e-03 8.440192e-03 9.957799e-01
[52,] 3.412796e-03 6.825592e-03 9.965872e-01
[53,] 3.549775e-03 7.099550e-03 9.964502e-01
[54,] 2.813974e-03 5.627949e-03 9.971860e-01
[55,] 1.976172e-03 3.952343e-03 9.980238e-01
[56,] 2.486305e-03 4.972610e-03 9.975137e-01
[57,] 1.762660e-03 3.525320e-03 9.982373e-01
[58,] 1.253944e-03 2.507888e-03 9.987461e-01
[59,] 8.419767e-04 1.683953e-03 9.991580e-01
[60,] 6.274025e-04 1.254805e-03 9.993726e-01
[61,] 1.029393e-03 2.058787e-03 9.989706e-01
[62,] 7.148303e-04 1.429661e-03 9.992852e-01
[63,] 4.955619e-04 9.911238e-04 9.995044e-01
[64,] 3.428242e-04 6.856484e-04 9.996572e-01
[65,] 2.374044e-04 4.748088e-04 9.997626e-01
[66,] 1.570010e-04 3.140020e-04 9.998430e-01
[67,] 1.428930e-04 2.857859e-04 9.998571e-01
[68,] 9.552306e-05 1.910461e-04 9.999045e-01
[69,] 6.526466e-05 1.305293e-04 9.999347e-01
[70,] 4.660398e-05 9.320796e-05 9.999534e-01
[71,] 3.363946e-05 6.727893e-05 9.999664e-01
[72,] 2.116151e-05 4.232301e-05 9.999788e-01
[73,] 2.579975e-05 5.159950e-05 9.999742e-01
[74,] 4.342930e-05 8.685859e-05 9.999566e-01
[75,] 3.392274e-05 6.784548e-05 9.999661e-01
[76,] 2.823220e-05 5.646439e-05 9.999718e-01
[77,] 1.885613e-05 3.771226e-05 9.999811e-01
[78,] 1.305343e-05 2.610685e-05 9.999869e-01
[79,] 1.083292e-05 2.166585e-05 9.999892e-01
[80,] 7.210949e-06 1.442190e-05 9.999928e-01
[81,] 4.460644e-06 8.921288e-06 9.999955e-01
[82,] 2.700907e-06 5.401814e-06 9.999973e-01
[83,] 1.869532e-06 3.739064e-06 9.999981e-01
[84,] 1.295212e-06 2.590424e-06 9.999987e-01
[85,] 7.730405e-07 1.546081e-06 9.999992e-01
[86,] 4.549990e-07 9.099980e-07 9.999995e-01
[87,] 4.163953e-07 8.327905e-07 9.999996e-01
[88,] 3.357894e-07 6.715788e-07 9.999997e-01
[89,] 4.648212e-07 9.296424e-07 9.999995e-01
[90,] 9.152294e-07 1.830459e-06 9.999991e-01
[91,] 7.364721e-07 1.472944e-06 9.999993e-01
[92,] 6.408968e-07 1.281794e-06 9.999994e-01
[93,] 6.499355e-07 1.299871e-06 9.999994e-01
[94,] 6.122305e-07 1.224461e-06 9.999994e-01
[95,] 5.959952e-07 1.191990e-06 9.999994e-01
[96,] 4.701560e-07 9.403120e-07 9.999995e-01
[97,] 3.649475e-07 7.298950e-07 9.999996e-01
[98,] 2.860659e-07 5.721318e-07 9.999997e-01
[99,] 6.176278e-07 1.235256e-06 9.999994e-01
[100,] 9.182165e-07 1.836433e-06 9.999991e-01
[101,] 2.918175e-06 5.836350e-06 9.999971e-01
[102,] 2.367038e-06 4.734075e-06 9.999976e-01
[103,] 3.314612e-06 6.629223e-06 9.999967e-01
[104,] 2.956128e-06 5.912257e-06 9.999970e-01
[105,] 2.945259e-06 5.890519e-06 9.999971e-01
[106,] 7.806247e-06 1.561249e-05 9.999922e-01
[107,] 5.050261e-05 1.010052e-04 9.999495e-01
[108,] 8.205901e-05 1.641180e-04 9.999179e-01
[109,] 1.174210e-04 2.348420e-04 9.998826e-01
[110,] 8.049709e-05 1.609942e-04 9.999195e-01
[111,] 7.476310e-05 1.495262e-04 9.999252e-01
[112,] 7.860117e-05 1.572023e-04 9.999214e-01
[113,] 8.938965e-05 1.787793e-04 9.999106e-01
[114,] 8.920478e-05 1.784096e-04 9.999108e-01
[115,] 8.940896e-05 1.788179e-04 9.999106e-01
[116,] 8.616397e-05 1.723279e-04 9.999138e-01
[117,] 8.108473e-05 1.621695e-04 9.999189e-01
[118,] 7.684012e-05 1.536802e-04 9.999232e-01
[119,] 7.308888e-05 1.461778e-04 9.999269e-01
[120,] 8.619025e-05 1.723805e-04 9.999138e-01
[121,] 1.095404e-04 2.190807e-04 9.998905e-01
[122,] 8.060559e-05 1.612112e-04 9.999194e-01
[123,] 5.365430e-05 1.073086e-04 9.999463e-01
[124,] 3.515507e-05 7.031015e-05 9.999648e-01
[125,] 2.334220e-05 4.668439e-05 9.999767e-01
[126,] 1.971968e-05 3.943936e-05 9.999803e-01
[127,] 1.389377e-05 2.778755e-05 9.999861e-01
[128,] 1.734527e-05 3.469054e-05 9.999827e-01
[129,] 1.167890e-05 2.335780e-05 9.999883e-01
[130,] 1.647673e-05 3.295346e-05 9.999835e-01
[131,] 6.345003e-05 1.269001e-04 9.999365e-01
[132,] 1.726108e-04 3.452216e-04 9.998274e-01
[133,] 1.342250e-03 2.684500e-03 9.986578e-01
[134,] 1.022095e-03 2.044191e-03 9.989779e-01
[135,] 6.819280e-04 1.363856e-03 9.993181e-01
[136,] 5.315424e-04 1.063085e-03 9.994685e-01
[137,] 3.973074e-04 7.946148e-04 9.996027e-01
[138,] 2.681048e-04 5.362096e-04 9.997319e-01
[139,] 7.823537e-04 1.564707e-03 9.992176e-01
[140,] 5.003840e-04 1.000768e-03 9.994996e-01
[141,] 3.947428e-04 7.894855e-04 9.996053e-01
[142,] 2.964300e-04 5.928600e-04 9.997036e-01
[143,] 2.285443e-04 4.570887e-04 9.997715e-01
[144,] 2.263404e-04 4.526808e-04 9.997737e-01
[145,] 4.433537e-04 8.867074e-04 9.995566e-01
[146,] 2.839830e-04 5.679660e-04 9.997160e-01
[147,] 1.954089e-04 3.908179e-04 9.998046e-01
[148,] 1.199163e-04 2.398325e-04 9.998801e-01
[149,] 6.794475e-05 1.358895e-04 9.999321e-01
[150,] 3.986115e-05 7.972231e-05 9.999601e-01
[151,] 6.804058e-05 1.360812e-04 9.999320e-01
[152,] 3.833379e-03 7.666758e-03 9.961666e-01
[153,] 4.357822e-03 8.715644e-03 9.956422e-01
[154,] 4.461329e-03 8.922658e-03 9.955387e-01
[155,] 1.159212e-02 2.318423e-02 9.884079e-01
[156,] 1.670534e-02 3.341067e-02 9.832947e-01
[157,] 1.248862e-02 2.497725e-02 9.875114e-01
[158,] 9.970805e-01 5.839030e-03 2.919515e-03
[159,] 9.992438e-01 1.512368e-03 7.561838e-04
[160,] 9.988294e-01 2.341248e-03 1.170624e-03
[161,] 9.978189e-01 4.362159e-03 2.181080e-03
[162,] 9.978372e-01 4.325696e-03 2.162848e-03
[163,] 9.986612e-01 2.677630e-03 1.338815e-03
[164,] 9.989561e-01 2.087834e-03 1.043917e-03
[165,] 9.999054e-01 1.892602e-04 9.463012e-05
[166,] 9.994165e-01 1.166943e-03 5.834717e-04
[167,] 9.987920e-01 2.416037e-03 1.208018e-03
[168,] 9.931867e-01 1.362663e-02 6.813314e-03
> postscript(file="/var/wessaorg/rcomp/tmp/1p5uq1386320470.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/2w7ae1386320470.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/3y31l1386320470.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/4g1iq1386320470.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/57wdh1386320470.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
0.040434571 0.039765405 0.006933691 0.030111174 -0.035589164 0.093428814
7 8 9 10 11 12
0.225311742 0.152524067 0.074777456 0.012431443 0.021939299 0.005346817
13 14 15 16 17 18
0.354669045 0.188088733 0.245812998 0.242933403 0.280996607 0.237556600
19 20 21 22 23 24
-0.132918021 0.191259051 -0.003387485 -0.047727439 -0.001387602 0.101912431
25 26 27 28 29 30
0.293342198 -0.061601405 0.213625757 0.206213659 0.200733089 0.203725133
31 32 33 34 35 36
-0.380663260 -0.367488364 -0.385056999 -0.340268116 -0.343838775 -0.367574046
37 38 39 40 41 42
0.446419901 0.445174044 0.515407772 0.517904118 0.525457320 0.503028165
43 44 45 46 47 48
-0.222596416 -0.209889957 -0.180149967 -0.185112295 -0.187889758 -0.255852972
49 50 51 52 53 54
-0.610833159 -0.625050113 -0.662467802 -0.634640104 -0.629863355 -0.647616769
55 56 57 58 59 60
0.177600499 0.180175980 0.116308817 0.283667894 0.251388772 0.265611993
61 62 63 64 65 66
-0.576479212 -0.593978381 -0.315838063 -0.256291068 -0.240151458 -0.532705798
67 68 69 70 71 72
0.120994171 0.098186323 0.029958840 -0.037374688 0.064763690 0.007190620
73 74 75 76 77 78
0.250653582 0.227757718 0.137567753 0.145931369 0.055432958 0.165336803
79 80 81 82 83 84
-0.007220431 0.052681114 -0.061085475 -0.007169479 0.065387791 0.052177399
85 86 87 88 89 90
0.081302625 0.311663924 0.287694738 0.076610874 -0.026391891 0.287939187
91 92 93 94 95 96
-0.048003937 0.013349223 0.201988380 -0.012807495 0.045303006 0.270393749
97 98 99 100 101 102
0.255547274 0.124765639 -0.010746702 -0.138507582 -0.178763982 -0.184853397
103 104 105 106 107 108
-0.156532135 0.243989612 0.380874003 0.378015968 0.415029897 0.371538552
109 110 111 112 113 114
0.368308884 0.254799675 0.294183129 0.617930197 0.548143085 0.599517746
115 116 117 118 119 120
0.343968292 0.434026911 0.346555833 0.382889533 0.491030672 0.583732668
121 122 123 124 125 126
0.323348380 0.379392472 0.036280263 0.222018112 0.186740853 0.159496461
127 128 129 130 131 132
0.100820002 0.157225397 0.298920551 0.255275611 0.207188702 0.193657831
133 134 135 136 137 138
0.202413883 0.248672582 -0.034546643 0.007832907 -0.083128876 -0.081363569
139 140 141 142 143 144
-0.097823249 0.074128640 0.251183541 0.094324068 0.397591768 0.349294833
145 146 147 148 149 150
0.514047472 0.384816941 -0.154187302 0.096767659 0.066936636 0.311394139
151 152 153 154 155 156
0.166529464 -0.482011888 0.061419617 0.142389955 0.121142607 0.101041089
157 158 159 160 161 162
0.171423010 0.122554652 0.132004826 0.121117565 -0.091060365 0.059826139
163 164 165 166 167 168
0.016778940 0.148285214 0.108041296 -0.565821607 -0.182506232 -0.157517561
169 170 171 172 173 174
-0.688424310 -0.240394620 -0.179894009 -0.777759279 -0.824412604 -0.820157220
175 176 177 178 179 180
-0.826033966 -0.785813113 -0.803676079 0.381060756 0.349113804 0.341886673
181 182 183 184 185 186
0.295810668 0.337794434 0.331762108 -0.841727415 -0.832294470 -0.842056067
187 188 189 190 191 192
-0.762813588 -0.725386889 -0.822936681 -0.705571997 -0.812510092 -0.662595878
193 194 195
-0.444220679 -0.578853524 -0.595020096
> postscript(file="/var/wessaorg/rcomp/tmp/68w4i1386320470.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 0.040434571 NA
1 0.039765405 0.040434571
2 0.006933691 0.039765405
3 0.030111174 0.006933691
4 -0.035589164 0.030111174
5 0.093428814 -0.035589164
6 0.225311742 0.093428814
7 0.152524067 0.225311742
8 0.074777456 0.152524067
9 0.012431443 0.074777456
10 0.021939299 0.012431443
11 0.005346817 0.021939299
12 0.354669045 0.005346817
13 0.188088733 0.354669045
14 0.245812998 0.188088733
15 0.242933403 0.245812998
16 0.280996607 0.242933403
17 0.237556600 0.280996607
18 -0.132918021 0.237556600
19 0.191259051 -0.132918021
20 -0.003387485 0.191259051
21 -0.047727439 -0.003387485
22 -0.001387602 -0.047727439
23 0.101912431 -0.001387602
24 0.293342198 0.101912431
25 -0.061601405 0.293342198
26 0.213625757 -0.061601405
27 0.206213659 0.213625757
28 0.200733089 0.206213659
29 0.203725133 0.200733089
30 -0.380663260 0.203725133
31 -0.367488364 -0.380663260
32 -0.385056999 -0.367488364
33 -0.340268116 -0.385056999
34 -0.343838775 -0.340268116
35 -0.367574046 -0.343838775
36 0.446419901 -0.367574046
37 0.445174044 0.446419901
38 0.515407772 0.445174044
39 0.517904118 0.515407772
40 0.525457320 0.517904118
41 0.503028165 0.525457320
42 -0.222596416 0.503028165
43 -0.209889957 -0.222596416
44 -0.180149967 -0.209889957
45 -0.185112295 -0.180149967
46 -0.187889758 -0.185112295
47 -0.255852972 -0.187889758
48 -0.610833159 -0.255852972
49 -0.625050113 -0.610833159
50 -0.662467802 -0.625050113
51 -0.634640104 -0.662467802
52 -0.629863355 -0.634640104
53 -0.647616769 -0.629863355
54 0.177600499 -0.647616769
55 0.180175980 0.177600499
56 0.116308817 0.180175980
57 0.283667894 0.116308817
58 0.251388772 0.283667894
59 0.265611993 0.251388772
60 -0.576479212 0.265611993
61 -0.593978381 -0.576479212
62 -0.315838063 -0.593978381
63 -0.256291068 -0.315838063
64 -0.240151458 -0.256291068
65 -0.532705798 -0.240151458
66 0.120994171 -0.532705798
67 0.098186323 0.120994171
68 0.029958840 0.098186323
69 -0.037374688 0.029958840
70 0.064763690 -0.037374688
71 0.007190620 0.064763690
72 0.250653582 0.007190620
73 0.227757718 0.250653582
74 0.137567753 0.227757718
75 0.145931369 0.137567753
76 0.055432958 0.145931369
77 0.165336803 0.055432958
78 -0.007220431 0.165336803
79 0.052681114 -0.007220431
80 -0.061085475 0.052681114
81 -0.007169479 -0.061085475
82 0.065387791 -0.007169479
83 0.052177399 0.065387791
84 0.081302625 0.052177399
85 0.311663924 0.081302625
86 0.287694738 0.311663924
87 0.076610874 0.287694738
88 -0.026391891 0.076610874
89 0.287939187 -0.026391891
90 -0.048003937 0.287939187
91 0.013349223 -0.048003937
92 0.201988380 0.013349223
93 -0.012807495 0.201988380
94 0.045303006 -0.012807495
95 0.270393749 0.045303006
96 0.255547274 0.270393749
97 0.124765639 0.255547274
98 -0.010746702 0.124765639
99 -0.138507582 -0.010746702
100 -0.178763982 -0.138507582
101 -0.184853397 -0.178763982
102 -0.156532135 -0.184853397
103 0.243989612 -0.156532135
104 0.380874003 0.243989612
105 0.378015968 0.380874003
106 0.415029897 0.378015968
107 0.371538552 0.415029897
108 0.368308884 0.371538552
109 0.254799675 0.368308884
110 0.294183129 0.254799675
111 0.617930197 0.294183129
112 0.548143085 0.617930197
113 0.599517746 0.548143085
114 0.343968292 0.599517746
115 0.434026911 0.343968292
116 0.346555833 0.434026911
117 0.382889533 0.346555833
118 0.491030672 0.382889533
119 0.583732668 0.491030672
120 0.323348380 0.583732668
121 0.379392472 0.323348380
122 0.036280263 0.379392472
123 0.222018112 0.036280263
124 0.186740853 0.222018112
125 0.159496461 0.186740853
126 0.100820002 0.159496461
127 0.157225397 0.100820002
128 0.298920551 0.157225397
129 0.255275611 0.298920551
130 0.207188702 0.255275611
131 0.193657831 0.207188702
132 0.202413883 0.193657831
133 0.248672582 0.202413883
134 -0.034546643 0.248672582
135 0.007832907 -0.034546643
136 -0.083128876 0.007832907
137 -0.081363569 -0.083128876
138 -0.097823249 -0.081363569
139 0.074128640 -0.097823249
140 0.251183541 0.074128640
141 0.094324068 0.251183541
142 0.397591768 0.094324068
143 0.349294833 0.397591768
144 0.514047472 0.349294833
145 0.384816941 0.514047472
146 -0.154187302 0.384816941
147 0.096767659 -0.154187302
148 0.066936636 0.096767659
149 0.311394139 0.066936636
150 0.166529464 0.311394139
151 -0.482011888 0.166529464
152 0.061419617 -0.482011888
153 0.142389955 0.061419617
154 0.121142607 0.142389955
155 0.101041089 0.121142607
156 0.171423010 0.101041089
157 0.122554652 0.171423010
158 0.132004826 0.122554652
159 0.121117565 0.132004826
160 -0.091060365 0.121117565
161 0.059826139 -0.091060365
162 0.016778940 0.059826139
163 0.148285214 0.016778940
164 0.108041296 0.148285214
165 -0.565821607 0.108041296
166 -0.182506232 -0.565821607
167 -0.157517561 -0.182506232
168 -0.688424310 -0.157517561
169 -0.240394620 -0.688424310
170 -0.179894009 -0.240394620
171 -0.777759279 -0.179894009
172 -0.824412604 -0.777759279
173 -0.820157220 -0.824412604
174 -0.826033966 -0.820157220
175 -0.785813113 -0.826033966
176 -0.803676079 -0.785813113
177 0.381060756 -0.803676079
178 0.349113804 0.381060756
179 0.341886673 0.349113804
180 0.295810668 0.341886673
181 0.337794434 0.295810668
182 0.331762108 0.337794434
183 -0.841727415 0.331762108
184 -0.832294470 -0.841727415
185 -0.842056067 -0.832294470
186 -0.762813588 -0.842056067
187 -0.725386889 -0.762813588
188 -0.822936681 -0.725386889
189 -0.705571997 -0.822936681
190 -0.812510092 -0.705571997
191 -0.662595878 -0.812510092
192 -0.444220679 -0.662595878
193 -0.578853524 -0.444220679
194 -0.595020096 -0.578853524
195 NA -0.595020096
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.039765405 0.040434571
[2,] 0.006933691 0.039765405
[3,] 0.030111174 0.006933691
[4,] -0.035589164 0.030111174
[5,] 0.093428814 -0.035589164
[6,] 0.225311742 0.093428814
[7,] 0.152524067 0.225311742
[8,] 0.074777456 0.152524067
[9,] 0.012431443 0.074777456
[10,] 0.021939299 0.012431443
[11,] 0.005346817 0.021939299
[12,] 0.354669045 0.005346817
[13,] 0.188088733 0.354669045
[14,] 0.245812998 0.188088733
[15,] 0.242933403 0.245812998
[16,] 0.280996607 0.242933403
[17,] 0.237556600 0.280996607
[18,] -0.132918021 0.237556600
[19,] 0.191259051 -0.132918021
[20,] -0.003387485 0.191259051
[21,] -0.047727439 -0.003387485
[22,] -0.001387602 -0.047727439
[23,] 0.101912431 -0.001387602
[24,] 0.293342198 0.101912431
[25,] -0.061601405 0.293342198
[26,] 0.213625757 -0.061601405
[27,] 0.206213659 0.213625757
[28,] 0.200733089 0.206213659
[29,] 0.203725133 0.200733089
[30,] -0.380663260 0.203725133
[31,] -0.367488364 -0.380663260
[32,] -0.385056999 -0.367488364
[33,] -0.340268116 -0.385056999
[34,] -0.343838775 -0.340268116
[35,] -0.367574046 -0.343838775
[36,] 0.446419901 -0.367574046
[37,] 0.445174044 0.446419901
[38,] 0.515407772 0.445174044
[39,] 0.517904118 0.515407772
[40,] 0.525457320 0.517904118
[41,] 0.503028165 0.525457320
[42,] -0.222596416 0.503028165
[43,] -0.209889957 -0.222596416
[44,] -0.180149967 -0.209889957
[45,] -0.185112295 -0.180149967
[46,] -0.187889758 -0.185112295
[47,] -0.255852972 -0.187889758
[48,] -0.610833159 -0.255852972
[49,] -0.625050113 -0.610833159
[50,] -0.662467802 -0.625050113
[51,] -0.634640104 -0.662467802
[52,] -0.629863355 -0.634640104
[53,] -0.647616769 -0.629863355
[54,] 0.177600499 -0.647616769
[55,] 0.180175980 0.177600499
[56,] 0.116308817 0.180175980
[57,] 0.283667894 0.116308817
[58,] 0.251388772 0.283667894
[59,] 0.265611993 0.251388772
[60,] -0.576479212 0.265611993
[61,] -0.593978381 -0.576479212
[62,] -0.315838063 -0.593978381
[63,] -0.256291068 -0.315838063
[64,] -0.240151458 -0.256291068
[65,] -0.532705798 -0.240151458
[66,] 0.120994171 -0.532705798
[67,] 0.098186323 0.120994171
[68,] 0.029958840 0.098186323
[69,] -0.037374688 0.029958840
[70,] 0.064763690 -0.037374688
[71,] 0.007190620 0.064763690
[72,] 0.250653582 0.007190620
[73,] 0.227757718 0.250653582
[74,] 0.137567753 0.227757718
[75,] 0.145931369 0.137567753
[76,] 0.055432958 0.145931369
[77,] 0.165336803 0.055432958
[78,] -0.007220431 0.165336803
[79,] 0.052681114 -0.007220431
[80,] -0.061085475 0.052681114
[81,] -0.007169479 -0.061085475
[82,] 0.065387791 -0.007169479
[83,] 0.052177399 0.065387791
[84,] 0.081302625 0.052177399
[85,] 0.311663924 0.081302625
[86,] 0.287694738 0.311663924
[87,] 0.076610874 0.287694738
[88,] -0.026391891 0.076610874
[89,] 0.287939187 -0.026391891
[90,] -0.048003937 0.287939187
[91,] 0.013349223 -0.048003937
[92,] 0.201988380 0.013349223
[93,] -0.012807495 0.201988380
[94,] 0.045303006 -0.012807495
[95,] 0.270393749 0.045303006
[96,] 0.255547274 0.270393749
[97,] 0.124765639 0.255547274
[98,] -0.010746702 0.124765639
[99,] -0.138507582 -0.010746702
[100,] -0.178763982 -0.138507582
[101,] -0.184853397 -0.178763982
[102,] -0.156532135 -0.184853397
[103,] 0.243989612 -0.156532135
[104,] 0.380874003 0.243989612
[105,] 0.378015968 0.380874003
[106,] 0.415029897 0.378015968
[107,] 0.371538552 0.415029897
[108,] 0.368308884 0.371538552
[109,] 0.254799675 0.368308884
[110,] 0.294183129 0.254799675
[111,] 0.617930197 0.294183129
[112,] 0.548143085 0.617930197
[113,] 0.599517746 0.548143085
[114,] 0.343968292 0.599517746
[115,] 0.434026911 0.343968292
[116,] 0.346555833 0.434026911
[117,] 0.382889533 0.346555833
[118,] 0.491030672 0.382889533
[119,] 0.583732668 0.491030672
[120,] 0.323348380 0.583732668
[121,] 0.379392472 0.323348380
[122,] 0.036280263 0.379392472
[123,] 0.222018112 0.036280263
[124,] 0.186740853 0.222018112
[125,] 0.159496461 0.186740853
[126,] 0.100820002 0.159496461
[127,] 0.157225397 0.100820002
[128,] 0.298920551 0.157225397
[129,] 0.255275611 0.298920551
[130,] 0.207188702 0.255275611
[131,] 0.193657831 0.207188702
[132,] 0.202413883 0.193657831
[133,] 0.248672582 0.202413883
[134,] -0.034546643 0.248672582
[135,] 0.007832907 -0.034546643
[136,] -0.083128876 0.007832907
[137,] -0.081363569 -0.083128876
[138,] -0.097823249 -0.081363569
[139,] 0.074128640 -0.097823249
[140,] 0.251183541 0.074128640
[141,] 0.094324068 0.251183541
[142,] 0.397591768 0.094324068
[143,] 0.349294833 0.397591768
[144,] 0.514047472 0.349294833
[145,] 0.384816941 0.514047472
[146,] -0.154187302 0.384816941
[147,] 0.096767659 -0.154187302
[148,] 0.066936636 0.096767659
[149,] 0.311394139 0.066936636
[150,] 0.166529464 0.311394139
[151,] -0.482011888 0.166529464
[152,] 0.061419617 -0.482011888
[153,] 0.142389955 0.061419617
[154,] 0.121142607 0.142389955
[155,] 0.101041089 0.121142607
[156,] 0.171423010 0.101041089
[157,] 0.122554652 0.171423010
[158,] 0.132004826 0.122554652
[159,] 0.121117565 0.132004826
[160,] -0.091060365 0.121117565
[161,] 0.059826139 -0.091060365
[162,] 0.016778940 0.059826139
[163,] 0.148285214 0.016778940
[164,] 0.108041296 0.148285214
[165,] -0.565821607 0.108041296
[166,] -0.182506232 -0.565821607
[167,] -0.157517561 -0.182506232
[168,] -0.688424310 -0.157517561
[169,] -0.240394620 -0.688424310
[170,] -0.179894009 -0.240394620
[171,] -0.777759279 -0.179894009
[172,] -0.824412604 -0.777759279
[173,] -0.820157220 -0.824412604
[174,] -0.826033966 -0.820157220
[175,] -0.785813113 -0.826033966
[176,] -0.803676079 -0.785813113
[177,] 0.381060756 -0.803676079
[178,] 0.349113804 0.381060756
[179,] 0.341886673 0.349113804
[180,] 0.295810668 0.341886673
[181,] 0.337794434 0.295810668
[182,] 0.331762108 0.337794434
[183,] -0.841727415 0.331762108
[184,] -0.832294470 -0.841727415
[185,] -0.842056067 -0.832294470
[186,] -0.762813588 -0.842056067
[187,] -0.725386889 -0.762813588
[188,] -0.822936681 -0.725386889
[189,] -0.705571997 -0.822936681
[190,] -0.812510092 -0.705571997
[191,] -0.662595878 -0.812510092
[192,] -0.444220679 -0.662595878
[193,] -0.578853524 -0.444220679
[194,] -0.595020096 -0.578853524
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.039765405 0.040434571
2 0.006933691 0.039765405
3 0.030111174 0.006933691
4 -0.035589164 0.030111174
5 0.093428814 -0.035589164
6 0.225311742 0.093428814
7 0.152524067 0.225311742
8 0.074777456 0.152524067
9 0.012431443 0.074777456
10 0.021939299 0.012431443
11 0.005346817 0.021939299
12 0.354669045 0.005346817
13 0.188088733 0.354669045
14 0.245812998 0.188088733
15 0.242933403 0.245812998
16 0.280996607 0.242933403
17 0.237556600 0.280996607
18 -0.132918021 0.237556600
19 0.191259051 -0.132918021
20 -0.003387485 0.191259051
21 -0.047727439 -0.003387485
22 -0.001387602 -0.047727439
23 0.101912431 -0.001387602
24 0.293342198 0.101912431
25 -0.061601405 0.293342198
26 0.213625757 -0.061601405
27 0.206213659 0.213625757
28 0.200733089 0.206213659
29 0.203725133 0.200733089
30 -0.380663260 0.203725133
31 -0.367488364 -0.380663260
32 -0.385056999 -0.367488364
33 -0.340268116 -0.385056999
34 -0.343838775 -0.340268116
35 -0.367574046 -0.343838775
36 0.446419901 -0.367574046
37 0.445174044 0.446419901
38 0.515407772 0.445174044
39 0.517904118 0.515407772
40 0.525457320 0.517904118
41 0.503028165 0.525457320
42 -0.222596416 0.503028165
43 -0.209889957 -0.222596416
44 -0.180149967 -0.209889957
45 -0.185112295 -0.180149967
46 -0.187889758 -0.185112295
47 -0.255852972 -0.187889758
48 -0.610833159 -0.255852972
49 -0.625050113 -0.610833159
50 -0.662467802 -0.625050113
51 -0.634640104 -0.662467802
52 -0.629863355 -0.634640104
53 -0.647616769 -0.629863355
54 0.177600499 -0.647616769
55 0.180175980 0.177600499
56 0.116308817 0.180175980
57 0.283667894 0.116308817
58 0.251388772 0.283667894
59 0.265611993 0.251388772
60 -0.576479212 0.265611993
61 -0.593978381 -0.576479212
62 -0.315838063 -0.593978381
63 -0.256291068 -0.315838063
64 -0.240151458 -0.256291068
65 -0.532705798 -0.240151458
66 0.120994171 -0.532705798
67 0.098186323 0.120994171
68 0.029958840 0.098186323
69 -0.037374688 0.029958840
70 0.064763690 -0.037374688
71 0.007190620 0.064763690
72 0.250653582 0.007190620
73 0.227757718 0.250653582
74 0.137567753 0.227757718
75 0.145931369 0.137567753
76 0.055432958 0.145931369
77 0.165336803 0.055432958
78 -0.007220431 0.165336803
79 0.052681114 -0.007220431
80 -0.061085475 0.052681114
81 -0.007169479 -0.061085475
82 0.065387791 -0.007169479
83 0.052177399 0.065387791
84 0.081302625 0.052177399
85 0.311663924 0.081302625
86 0.287694738 0.311663924
87 0.076610874 0.287694738
88 -0.026391891 0.076610874
89 0.287939187 -0.026391891
90 -0.048003937 0.287939187
91 0.013349223 -0.048003937
92 0.201988380 0.013349223
93 -0.012807495 0.201988380
94 0.045303006 -0.012807495
95 0.270393749 0.045303006
96 0.255547274 0.270393749
97 0.124765639 0.255547274
98 -0.010746702 0.124765639
99 -0.138507582 -0.010746702
100 -0.178763982 -0.138507582
101 -0.184853397 -0.178763982
102 -0.156532135 -0.184853397
103 0.243989612 -0.156532135
104 0.380874003 0.243989612
105 0.378015968 0.380874003
106 0.415029897 0.378015968
107 0.371538552 0.415029897
108 0.368308884 0.371538552
109 0.254799675 0.368308884
110 0.294183129 0.254799675
111 0.617930197 0.294183129
112 0.548143085 0.617930197
113 0.599517746 0.548143085
114 0.343968292 0.599517746
115 0.434026911 0.343968292
116 0.346555833 0.434026911
117 0.382889533 0.346555833
118 0.491030672 0.382889533
119 0.583732668 0.491030672
120 0.323348380 0.583732668
121 0.379392472 0.323348380
122 0.036280263 0.379392472
123 0.222018112 0.036280263
124 0.186740853 0.222018112
125 0.159496461 0.186740853
126 0.100820002 0.159496461
127 0.157225397 0.100820002
128 0.298920551 0.157225397
129 0.255275611 0.298920551
130 0.207188702 0.255275611
131 0.193657831 0.207188702
132 0.202413883 0.193657831
133 0.248672582 0.202413883
134 -0.034546643 0.248672582
135 0.007832907 -0.034546643
136 -0.083128876 0.007832907
137 -0.081363569 -0.083128876
138 -0.097823249 -0.081363569
139 0.074128640 -0.097823249
140 0.251183541 0.074128640
141 0.094324068 0.251183541
142 0.397591768 0.094324068
143 0.349294833 0.397591768
144 0.514047472 0.349294833
145 0.384816941 0.514047472
146 -0.154187302 0.384816941
147 0.096767659 -0.154187302
148 0.066936636 0.096767659
149 0.311394139 0.066936636
150 0.166529464 0.311394139
151 -0.482011888 0.166529464
152 0.061419617 -0.482011888
153 0.142389955 0.061419617
154 0.121142607 0.142389955
155 0.101041089 0.121142607
156 0.171423010 0.101041089
157 0.122554652 0.171423010
158 0.132004826 0.122554652
159 0.121117565 0.132004826
160 -0.091060365 0.121117565
161 0.059826139 -0.091060365
162 0.016778940 0.059826139
163 0.148285214 0.016778940
164 0.108041296 0.148285214
165 -0.565821607 0.108041296
166 -0.182506232 -0.565821607
167 -0.157517561 -0.182506232
168 -0.688424310 -0.157517561
169 -0.240394620 -0.688424310
170 -0.179894009 -0.240394620
171 -0.777759279 -0.179894009
172 -0.824412604 -0.777759279
173 -0.820157220 -0.824412604
174 -0.826033966 -0.820157220
175 -0.785813113 -0.826033966
176 -0.803676079 -0.785813113
177 0.381060756 -0.803676079
178 0.349113804 0.381060756
179 0.341886673 0.349113804
180 0.295810668 0.341886673
181 0.337794434 0.295810668
182 0.331762108 0.337794434
183 -0.841727415 0.331762108
184 -0.832294470 -0.841727415
185 -0.842056067 -0.832294470
186 -0.762813588 -0.842056067
187 -0.725386889 -0.762813588
188 -0.822936681 -0.725386889
189 -0.705571997 -0.822936681
190 -0.812510092 -0.705571997
191 -0.662595878 -0.812510092
192 -0.444220679 -0.662595878
193 -0.578853524 -0.444220679
194 -0.595020096 -0.578853524
> 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/wessaorg/rcomp/tmp/7gplr1386320470.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/8tfhu1386320470.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/9pic01386320470.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/wessaorg/rcomp/tmp/10w1mg1386320470.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/wessaorg/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/wessaorg/rcomp/tmp/1101jj1386320470.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/wessaorg/rcomp/tmp/12c2801386320470.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/wessaorg/rcomp/tmp/13er6b1386320470.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/wessaorg/rcomp/tmp/14rzwx1386320470.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/wessaorg/rcomp/tmp/15tlov1386320470.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/wessaorg/rcomp/tmp/167zzi1386320470.tab")
+ }
>
> try(system("convert tmp/1p5uq1386320470.ps tmp/1p5uq1386320470.png",intern=TRUE))
character(0)
> try(system("convert tmp/2w7ae1386320470.ps tmp/2w7ae1386320470.png",intern=TRUE))
character(0)
> try(system("convert tmp/3y31l1386320470.ps tmp/3y31l1386320470.png",intern=TRUE))
character(0)
> try(system("convert tmp/4g1iq1386320470.ps tmp/4g1iq1386320470.png",intern=TRUE))
character(0)
> try(system("convert tmp/57wdh1386320470.ps tmp/57wdh1386320470.png",intern=TRUE))
character(0)
> try(system("convert tmp/68w4i1386320470.ps tmp/68w4i1386320470.png",intern=TRUE))
character(0)
> try(system("convert tmp/7gplr1386320470.ps tmp/7gplr1386320470.png",intern=TRUE))
character(0)
> try(system("convert tmp/8tfhu1386320470.ps tmp/8tfhu1386320470.png",intern=TRUE))
character(0)
> try(system("convert tmp/9pic01386320470.ps tmp/9pic01386320470.png",intern=TRUE))
character(0)
> try(system("convert tmp/10w1mg1386320470.ps tmp/10w1mg1386320470.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
20.700 4.109 24.846