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(1
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+ ,0.02478
+ ,0.04689
+ ,0.422
+ ,1
+ ,126.512
+ ,141.756
+ ,99.77
+ ,0.01936
+ ,0.00015
+ ,0.01159
+ ,0.0099
+ ,0.03476
+ ,0.06734
+ ,0.659
+ ,1
+ ,125.641
+ ,141.068
+ ,116.346
+ ,0.03316
+ ,0.00026
+ ,0.02144
+ ,0.01522
+ ,0.06433
+ ,0.09178
+ ,0.891
+ ,1
+ ,128.451
+ ,150.449
+ ,75.632
+ ,0.01551
+ ,0.00012
+ ,0.00905
+ ,0.00909
+ ,0.02716
+ ,0.0617
+ ,0.584
+ ,1
+ ,139.224
+ ,586.567
+ ,66.157
+ ,0.03011
+ ,0.00022
+ ,0.01854
+ ,0.01628
+ ,0.05563
+ ,0.09419
+ ,0.93
+ ,1
+ ,150.258
+ ,154.609
+ ,75.349
+ ,0.00248
+ ,0.00002
+ ,0.00105
+ ,0.00136
+ ,0.00315
+ ,0.01131
+ ,0.107
+ ,1
+ ,154.003
+ ,160.267
+ ,128.621
+ ,0.00183
+ ,0.00001
+ ,0.00076
+ ,0.001
+ ,0.00229
+ ,0.0103
+ ,0.094
+ ,1
+ ,149.689
+ ,160.368
+ ,133.608
+ ,0.00257
+ ,0.00002
+ ,0.00116
+ ,0.00134
+ ,0.00349
+ ,0.01346
+ ,0.126
+ ,1
+ ,155.078
+ ,163.736
+ ,144.148
+ ,0.00168
+ ,0.00001
+ ,0.00068
+ ,0.00092
+ ,0.00204
+ ,0.01064
+ ,0.097
+ ,1
+ ,151.884
+ ,157.765
+ ,133.751
+ ,0.00258
+ ,0.00002
+ ,0.00115
+ ,0.00122
+ ,0.00346
+ ,0.0145
+ ,0.137
+ ,1
+ ,151.989
+ ,157.339
+ ,132.857
+ ,0.00174
+ ,0.00001
+ ,0.00075
+ ,0.00096
+ ,0.00225
+ ,0.01024
+ ,0.093
+ ,1
+ ,193.03
+ ,208.9
+ ,80.297
+ ,0.00766
+ ,0.00004
+ ,0.0045
+ ,0.00389
+ ,0.01351
+ ,0.03044
+ ,0.275
+ ,1
+ ,200.714
+ ,223.982
+ ,89.686
+ ,0.00621
+ ,0.00003
+ ,0.00371
+ ,0.00337
+ ,0.01112
+ ,0.02286
+ ,0.207
+ ,1
+ ,208.519
+ ,220.315
+ ,199.02
+ ,0.00609
+ ,0.00003
+ ,0.00368
+ ,0.00339
+ ,0.01105
+ ,0.01761
+ ,0.155
+ ,1
+ ,204.664
+ ,221.3
+ ,189.621
+ ,0.00841
+ ,0.00004
+ ,0.00502
+ ,0.00485
+ ,0.01506
+ ,0.02378
+ ,0.21
+ ,1
+ ,210.141
+ ,232.706
+ ,185.258
+ ,0.00534
+ ,0.00003
+ ,0.00321
+ ,0.0028
+ ,0.00964
+ ,0.0168
+ ,0.149
+ ,1
+ ,206.327
+ ,226.355
+ ,92.02
+ ,0.00495
+ ,0.00002
+ ,0.00302
+ ,0.00246
+ ,0.00905
+ ,0.02105
+ ,0.209
+ ,1
+ ,151.872
+ ,492.892
+ ,69.085
+ ,0.00856
+ ,0.00006
+ ,0.00404
+ ,0.00385
+ ,0.01211
+ ,0.01843
+ ,0.235
+ ,1
+ ,158.219
+ ,442.557
+ ,71.948
+ ,0.00476
+ ,0.00003
+ ,0.00214
+ ,0.00207
+ ,0.00642
+ ,0.01458
+ ,0.148
+ ,1
+ ,170.756
+ ,450.247
+ ,79.032
+ ,0.00555
+ ,0.00003
+ ,0.00244
+ ,0.00261
+ ,0.00731
+ ,0.01725
+ ,0.175
+ ,1
+ ,178.285
+ ,442.824
+ ,82.063
+ ,0.00462
+ ,0.00003
+ ,0.00157
+ ,0.00194
+ ,0.00472
+ ,0.01279
+ ,0.129
+ ,1
+ ,217.116
+ ,233.481
+ ,93.978
+ ,0.00404
+ ,0.00002
+ ,0.00127
+ ,0.00128
+ ,0.00381
+ ,0.01299
+ ,0.124
+ ,1
+ ,128.94
+ ,479.697
+ ,88.251
+ ,0.00581
+ ,0.00005
+ ,0.00241
+ ,0.00314
+ ,0.00723
+ ,0.02008
+ ,0.221
+ ,1
+ ,176.824
+ ,215.293
+ ,83.961
+ ,0.0046
+ ,0.00003
+ ,0.00209
+ ,0.00221
+ ,0.00628
+ ,0.01169
+ ,0.117
+ ,1
+ ,138.19
+ ,203.522
+ ,83.34
+ ,0.00704
+ ,0.00005
+ ,0.00406
+ ,0.00398
+ ,0.01218
+ ,0.04479
+ ,0.441
+ ,1
+ ,182.018
+ ,197.173
+ ,79.187
+ ,0.00842
+ ,0.00005
+ ,0.00506
+ ,0.00449
+ ,0.01517
+ ,0.02503
+ ,0.231
+ ,1
+ ,156.239
+ ,195.107
+ ,79.82
+ ,0.00694
+ ,0.00004
+ ,0.00403
+ ,0.00395
+ ,0.01209
+ ,0.02343
+ ,0.224
+ ,1
+ ,145.174
+ ,198.109
+ ,80.637
+ ,0.00733
+ ,0.00005
+ ,0.00414
+ ,0.00422
+ ,0.01242
+ ,0.02362
+ ,0.233
+ ,1
+ ,138.145
+ ,197.238
+ ,81.114
+ ,0.00544
+ ,0.00004
+ ,0.00294
+ ,0.00327
+ ,0.00883
+ ,0.02791
+ ,0.246
+ ,1
+ ,166.888
+ ,198.966
+ ,79.512
+ ,0.00638
+ ,0.00004
+ ,0.00368
+ ,0.00351
+ ,0.01104
+ ,0.02857
+ ,0.257
+ ,1
+ ,119.031
+ ,127.533
+ ,109.216
+ ,0.0044
+ ,0.00004
+ ,0.00214
+ ,0.00192
+ ,0.00641
+ ,0.01033
+ ,0.098
+ ,1
+ ,120.078
+ ,126.632
+ ,105.667
+ ,0.0027
+ ,0.00002
+ ,0.00116
+ ,0.00135
+ ,0.00349
+ ,0.01022
+ ,0.09
+ ,1
+ ,120.289
+ ,128.143
+ ,100.209
+ ,0.00492
+ ,0.00004
+ ,0.00269
+ ,0.00238
+ ,0.00808
+ ,0.01412
+ ,0.125
+ ,1
+ ,120.256
+ ,125.306
+ ,104.773
+ ,0.00407
+ ,0.00003
+ ,0.00224
+ ,0.00205
+ ,0.00671
+ ,0.01516
+ ,0.138
+ ,1
+ ,119.056
+ ,125.213
+ ,86.795
+ ,0.00346
+ ,0.00003
+ ,0.00169
+ ,0.0017
+ ,0.00508
+ ,0.01201
+ ,0.106
+ ,1
+ ,118.747
+ ,123.723
+ ,109.836
+ ,0.00331
+ ,0.00003
+ ,0.00168
+ ,0.00171
+ ,0.00504
+ ,0.01043
+ ,0.099
+ ,1
+ ,106.516
+ ,112.777
+ ,93.105
+ ,0.00589
+ ,0.00006
+ ,0.00291
+ ,0.00319
+ ,0.00873
+ ,0.04932
+ ,0.441
+ ,1
+ ,110.453
+ ,127.611
+ ,105.554
+ ,0.00494
+ ,0.00004
+ ,0.00244
+ ,0.00315
+ ,0.00731
+ ,0.04128
+ ,0.379
+ ,1
+ ,113.4
+ ,133.344
+ ,107.816
+ ,0.00451
+ ,0.00004
+ ,0.00219
+ ,0.00283
+ ,0.00658
+ ,0.04879
+ ,0.431
+ ,1
+ ,113.166
+ ,130.27
+ ,100.673
+ ,0.00502
+ ,0.00004
+ ,0.00257
+ ,0.00312
+ ,0.00772
+ ,0.05279
+ ,0.476
+ ,1
+ ,112.239
+ ,126.609
+ ,104.095
+ ,0.00472
+ ,0.00004
+ ,0.00238
+ ,0.0029
+ ,0.00715
+ ,0.05643
+ ,0.517
+ ,1
+ ,116.15
+ ,131.731
+ ,109.815
+ ,0.00381
+ ,0.00003
+ ,0.00181
+ ,0.00232
+ ,0.00542
+ ,0.03026
+ ,0.267
+ ,1
+ ,170.368
+ ,268.796
+ ,79.543
+ ,0.00571
+ ,0.00003
+ ,0.00232
+ ,0.00269
+ ,0.00696
+ ,0.03273
+ ,0.281
+ ,1
+ ,208.083
+ ,253.792
+ ,91.802
+ ,0.00757
+ ,0.00004
+ ,0.00428
+ ,0.00428
+ ,0.01285
+ ,0.06725
+ ,0.571
+ ,1
+ ,198.458
+ ,219.29
+ ,148.691
+ ,0.00376
+ ,0.00002
+ ,0.00182
+ ,0.00215
+ ,0.00546
+ ,0.03527
+ ,0.297
+ ,1
+ ,202.805
+ ,231.508
+ ,86.232
+ ,0.0037
+ ,0.00002
+ ,0.00189
+ ,0.00211
+ ,0.00568
+ ,0.01997
+ ,0.18
+ ,1
+ ,202.544
+ ,241.35
+ ,164.168
+ ,0.00254
+ ,0.00001
+ ,0.001
+ ,0.00133
+ ,0.00301
+ ,0.02662
+ ,0.228
+ ,1
+ ,223.361
+ ,263.872
+ ,87.638
+ ,0.00352
+ ,0.00002
+ ,0.00169
+ ,0.00188
+ ,0.00506
+ ,0.02536
+ ,0.225
+ ,1
+ ,169.774
+ ,191.759
+ ,151.451
+ ,0.01568
+ ,0.00009
+ ,0.00863
+ ,0.00946
+ ,0.02589
+ ,0.08143
+ ,0.821
+ ,1
+ ,183.52
+ ,216.814
+ ,161.34
+ ,0.01466
+ ,0.00008
+ ,0.00849
+ ,0.00819
+ ,0.02546
+ ,0.0605
+ ,0.618
+ ,1
+ ,188.62
+ ,216.302
+ ,165.982
+ ,0.01719
+ ,0.00009
+ ,0.00996
+ ,0.01027
+ ,0.02987
+ ,0.07118
+ ,0.722
+ ,1
+ ,202.632
+ ,565.74
+ ,177.258
+ ,0.01627
+ ,0.00008
+ ,0.00919
+ ,0.00963
+ ,0.02756
+ ,0.0717
+ ,0.833
+ ,1
+ ,186.695
+ ,211.961
+ ,149.442
+ ,0.01872
+ ,0.0001
+ ,0.01075
+ ,0.01154
+ ,0.03225
+ ,0.0583
+ ,0.784
+ ,1
+ ,192.818
+ ,224.429
+ ,168.793
+ ,0.03107
+ ,0.00016
+ ,0.018
+ ,0.01958
+ ,0.05401
+ ,0.11908
+ ,1.302
+ ,1
+ ,198.116
+ ,233.099
+ ,174.478
+ ,0.02714
+ ,0.00014
+ ,0.01568
+ ,0.01699
+ ,0.04705
+ ,0.08684
+ ,1.018
+ ,1
+ ,121.345
+ ,139.644
+ ,98.25
+ ,0.00684
+ ,0.00006
+ ,0.00388
+ ,0.00332
+ ,0.01164
+ ,0.02534
+ ,0.241
+ ,1
+ ,119.1
+ ,128.442
+ ,88.833
+ ,0.00692
+ ,0.00006
+ ,0.00393
+ ,0.003
+ ,0.01179
+ ,0.02682
+ ,0.236
+ ,1
+ ,117.87
+ ,127.349
+ ,95.654
+ ,0.00647
+ ,0.00005
+ ,0.00356
+ ,0.003
+ ,0.01067
+ ,0.03087
+ ,0.276
+ ,1
+ ,122.336
+ ,142.369
+ ,94.794
+ ,0.00727
+ ,0.00006
+ ,0.00415
+ ,0.00339
+ ,0.01246
+ ,0.02293
+ ,0.223
+ ,1
+ ,117.963
+ ,134.209
+ ,100.757
+ ,0.01813
+ ,0.00015
+ ,0.01117
+ ,0.00718
+ ,0.03351
+ ,0.04912
+ ,0.438
+ ,1
+ ,126.144
+ ,154.284
+ ,97.543
+ ,0.00975
+ ,0.00008
+ ,0.00593
+ ,0.00454
+ ,0.01778
+ ,0.02852
+ ,0.266
+ ,1
+ ,127.93
+ ,138.752
+ ,112.173
+ ,0.00605
+ ,0.00005
+ ,0.00321
+ ,0.00318
+ ,0.00962
+ ,0.03235
+ ,0.339
+ ,1
+ ,114.238
+ ,124.393
+ ,77.022
+ ,0.00581
+ ,0.00005
+ ,0.00299
+ ,0.00316
+ ,0.00896
+ ,0.04009
+ ,0.406
+ ,1
+ ,115.322
+ ,135.738
+ ,107.802
+ ,0.00619
+ ,0.00005
+ ,0.00352
+ ,0.00329
+ ,0.01057
+ ,0.03273
+ ,0.325
+ ,1
+ ,114.554
+ ,126.778
+ ,91.121
+ ,0.00651
+ ,0.00006
+ ,0.00366
+ ,0.0034
+ ,0.01097
+ ,0.03658
+ ,0.369
+ ,1
+ ,112.15
+ ,131.669
+ ,97.527
+ ,0.00519
+ ,0.00005
+ ,0.00291
+ ,0.00284
+ ,0.00873
+ ,0.01756
+ ,0.155
+ ,1
+ ,102.273
+ ,142.83
+ ,85.902
+ ,0.00907
+ ,0.00009
+ ,0.00493
+ ,0.00461
+ ,0.0148
+ ,0.02814
+ ,0.272
+ ,0
+ ,236.2
+ ,244.663
+ ,102.137
+ ,0.00277
+ ,0.00001
+ ,0.00154
+ ,0.00153
+ ,0.00462
+ ,0.02448
+ ,0.217
+ ,0
+ ,237.323
+ ,243.709
+ ,229.256
+ ,0.00303
+ ,0.00001
+ ,0.00173
+ ,0.00159
+ ,0.00519
+ ,0.01242
+ ,0.116
+ ,0
+ ,260.105
+ ,264.919
+ ,237.303
+ ,0.00339
+ ,0.00001
+ ,0.00205
+ ,0.00186
+ ,0.00616
+ ,0.0203
+ ,0.197
+ ,0
+ ,197.569
+ ,217.627
+ ,90.794
+ ,0.00803
+ ,0.00004
+ ,0.0049
+ ,0.00448
+ ,0.0147
+ ,0.02177
+ ,0.189
+ ,0
+ ,240.301
+ ,245.135
+ ,219.783
+ ,0.00517
+ ,0.00002
+ ,0.00316
+ ,0.00283
+ ,0.00949
+ ,0.02018
+ ,0.212
+ ,0
+ ,244.99
+ ,272.21
+ ,239.17
+ ,0.00451
+ ,0.00002
+ ,0.00279
+ ,0.00237
+ ,0.00837
+ ,0.01897
+ ,0.181
+ ,0
+ ,112.547
+ ,133.374
+ ,105.715
+ ,0.00355
+ ,0.00003
+ ,0.00166
+ ,0.0019
+ ,0.00499
+ ,0.01358
+ ,0.129
+ ,0
+ ,110.739
+ ,113.597
+ ,100.139
+ ,0.00356
+ ,0.00003
+ ,0.0017
+ ,0.002
+ ,0.0051
+ ,0.01484
+ ,0.133
+ ,0
+ ,113.715
+ ,116.443
+ ,96.913
+ ,0.00349
+ ,0.00003
+ ,0.00171
+ ,0.00203
+ ,0.00514
+ ,0.01472
+ ,0.133
+ ,0
+ ,117.004
+ ,144.466
+ ,99.923
+ ,0.00353
+ ,0.00003
+ ,0.00176
+ ,0.00218
+ ,0.00528
+ ,0.01657
+ ,0.145
+ ,0
+ ,115.38
+ ,123.109
+ ,108.634
+ ,0.00332
+ ,0.00003
+ ,0.0016
+ ,0.00199
+ ,0.0048
+ ,0.01503
+ ,0.137
+ ,0
+ ,116.388
+ ,129.038
+ ,108.97
+ ,0.00346
+ ,0.00003
+ ,0.00169
+ ,0.00213
+ ,0.00507
+ ,0.01725
+ ,0.155
+ ,1
+ ,151.737
+ ,190.204
+ ,129.859
+ ,0.00314
+ ,0.00002
+ ,0.00135
+ ,0.00162
+ ,0.00406
+ ,0.01469
+ ,0.132
+ ,1
+ ,148.79
+ ,158.359
+ ,138.99
+ ,0.00309
+ ,0.00002
+ ,0.00152
+ ,0.00186
+ ,0.00456
+ ,0.01574
+ ,0.142
+ ,1
+ ,148.143
+ ,155.982
+ ,135.041
+ ,0.00392
+ ,0.00003
+ ,0.00204
+ ,0.00231
+ ,0.00612
+ ,0.0145
+ ,0.131
+ ,1
+ ,150.44
+ ,163.441
+ ,144.736
+ ,0.00396
+ ,0.00003
+ ,0.00206
+ ,0.00233
+ ,0.00619
+ ,0.02551
+ ,0.237
+ ,1
+ ,148.462
+ ,161.078
+ ,141.998
+ ,0.00397
+ ,0.00003
+ ,0.00202
+ ,0.00235
+ ,0.00605
+ ,0.01831
+ ,0.163
+ ,1
+ ,149.818
+ ,163.417
+ ,144.786
+ ,0.00336
+ ,0.00002
+ ,0.00174
+ ,0.00198
+ ,0.00521
+ ,0.02145
+ ,0.198
+ ,0
+ ,117.226
+ ,123.925
+ ,106.656
+ ,0.00417
+ ,0.00004
+ ,0.00186
+ ,0.0027
+ ,0.00558
+ ,0.01909
+ ,0.171
+ ,0
+ ,116.848
+ ,217.552
+ ,99.503
+ ,0.00531
+ ,0.00005
+ ,0.0026
+ ,0.00346
+ ,0.0078
+ ,0.01795
+ ,0.163
+ ,0
+ ,116.286
+ ,177.291
+ ,96.983
+ ,0.00314
+ ,0.00003
+ ,0.00134
+ ,0.00192
+ ,0.00403
+ ,0.01564
+ ,0.136
+ ,0
+ ,116.556
+ ,592.03
+ ,86.228
+ ,0.00496
+ ,0.00004
+ ,0.00254
+ ,0.00263
+ ,0.00762
+ ,0.0166
+ ,0.154
+ ,0
+ ,116.342
+ ,581.289
+ ,94.246
+ ,0.00267
+ ,0.00002
+ ,0.00115
+ ,0.00148
+ ,0.00345
+ ,0.013
+ ,0.117
+ ,0
+ ,114.563
+ ,119.167
+ ,86.647
+ ,0.00327
+ ,0.00003
+ ,0.00146
+ ,0.00184
+ ,0.00439
+ ,0.01185
+ ,0.106
+ ,0
+ ,201.774
+ ,262.707
+ ,78.228
+ ,0.00694
+ ,0.00003
+ ,0.00412
+ ,0.00396
+ ,0.01235
+ ,0.02574
+ ,0.255
+ ,0
+ ,174.188
+ ,230.978
+ ,94.261
+ ,0.00459
+ ,0.00003
+ ,0.00263
+ ,0.00259
+ ,0.0079
+ ,0.04087
+ ,0.405
+ ,0
+ ,209.516
+ ,253.017
+ ,89.488
+ ,0.00564
+ ,0.00003
+ ,0.00331
+ ,0.00292
+ ,0.00994
+ ,0.02751
+ ,0.263
+ ,0
+ ,174.688
+ ,240.005
+ ,74.287
+ ,0.0136
+ ,0.00008
+ ,0.00624
+ ,0.00564
+ ,0.01873
+ ,0.02308
+ ,0.256
+ ,0
+ ,198.764
+ ,396.961
+ ,74.904
+ ,0.0074
+ ,0.00004
+ ,0.0037
+ ,0.0039
+ ,0.01109
+ ,0.02296
+ ,0.241
+ ,0
+ ,214.289
+ ,260.277
+ ,77.973
+ ,0.00567
+ ,0.00003
+ ,0.00295
+ ,0.00317
+ ,0.00885
+ ,0.01884
+ ,0.19)
+ ,dim=c(11
+ ,195)
+ ,dimnames=list(c('status'
+ ,'MDVP:Fo(Hz)'
+ ,'MDVP:Fhi(Hz)'
+ ,'MDVP:Flo(Hz)'
+ ,'MDVP:Jitter(%)'
+ ,'MDVP:Jitter(Abs)'
+ ,'MDVP:RAP'
+ ,'MDVP:PPQ'
+ ,'Jitter:DDP'
+ ,'MDVP:Shimmer'
+ ,'MDVP:Shimmer(dB)')
+ ,1:195))
> y <- array(NA,dim=c(11,195),dimnames=list(c('status','MDVP:Fo(Hz)','MDVP:Fhi(Hz)','MDVP:Flo(Hz)','MDVP:Jitter(%)','MDVP:Jitter(Abs)','MDVP:RAP','MDVP:PPQ','Jitter:DDP','MDVP:Shimmer','MDVP:Shimmer(dB)'),1:195))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = 'No Linear Trend'
> par2 = 'Do not include Seasonal Dummies'
> par1 = '1'
> par3 <- 'No Linear Trend'
> par2 <- 'Do not include Seasonal Dummies'
> par1 <- '1'
> #'GNU S' R Code compiled by R2WASP v. 1.2.327 ()
> #Author: root
> #To cite this work: Wessa P., (2013), Multiple Regression (v1.0.29) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_multipleregression.wasp/
> #Source of accompanying publication: Office for Research, Development, and Education
> #
> library(lattice)
> library(lmtest)
Loading required package: zoo
Attaching package: 'zoo'
The following objects are masked from 'package:base':
as.Date, as.Date.numeric
> n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
> par1 <- as.numeric(par1)
> x <- t(y)
> k <- length(x[1,])
> n <- length(x[,1])
> x1 <- cbind(x[,par1], x[,1:k!=par1])
> mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
> colnames(x1) <- mycolnames #colnames(x)[par1]
> x <- x1
> if (par3 == 'First Differences'){
+ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
+ for (i in 1:n-1) {
+ for (j in 1:k) {
+ x2[i,j] <- x[i+1,j] - x[i,j]
+ }
+ }
+ x <- x2
+ }
> if (par2 == 'Include Monthly Dummies'){
+ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
+ for (i in 1:11){
+ x2[seq(i,n,12),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> if (par2 == 'Include Quarterly Dummies'){
+ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
+ for (i in 1:3){
+ x2[seq(i,n,4),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> k <- length(x[1,])
> if (par3 == 'Linear Trend'){
+ x <- cbind(x, c(1:n))
+ colnames(x)[k+1] <- 't'
+ }
> x
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 Jitter:DDP MDVP:Shimmer MDVP:Shimmer(dB)
1 7.0e-05 0.00370 0.00554 0.01109 0.04374 0.426
2 8.0e-05 0.00465 0.00696 0.01394 0.06134 0.626
3 9.0e-05 0.00544 0.00781 0.01633 0.05233 0.482
4 9.0e-05 0.00502 0.00698 0.01505 0.05492 0.517
5 1.1e-04 0.00655 0.00908 0.01966 0.06425 0.584
6 8.0e-05 0.00463 0.00750 0.01388 0.04701 0.456
7 3.0e-05 0.00155 0.00202 0.00466 0.01608 0.140
8 3.0e-05 0.00144 0.00182 0.00431 0.01567 0.134
9 6.0e-05 0.00293 0.00332 0.00880 0.02093 0.191
10 6.0e-05 0.00268 0.00332 0.00803 0.02838 0.255
11 6.0e-05 0.00254 0.00330 0.00763 0.02143 0.197
12 6.0e-05 0.00281 0.00336 0.00844 0.02752 0.249
13 2.0e-05 0.00118 0.00153 0.00355 0.01259 0.112
14 3.0e-05 0.00165 0.00208 0.00496 0.01642 0.154
15 2.0e-05 0.00121 0.00149 0.00364 0.01828 0.158
16 3.0e-05 0.00157 0.00203 0.00471 0.01503 0.126
17 4.0e-05 0.00211 0.00292 0.00632 0.02047 0.192
18 4.0e-05 0.00284 0.00387 0.00853 0.03327 0.348
19 5.0e-05 0.00364 0.00432 0.01092 0.05517 0.542
20 5.0e-05 0.00372 0.00399 0.01116 0.03995 0.348
21 5.0e-05 0.00428 0.00450 0.01285 0.03810 0.328
22 3.0e-05 0.00232 0.00267 0.00696 0.04137 0.370
23 3.0e-05 0.00220 0.00247 0.00661 0.04351 0.377
24 3.0e-05 0.00221 0.00258 0.00663 0.04192 0.364
25 5.0e-05 0.00380 0.00390 0.01140 0.01659 0.164
26 6.0e-05 0.00316 0.00375 0.00948 0.03767 0.381
27 3.0e-05 0.00250 0.00234 0.00750 0.01966 0.186
28 3.0e-05 0.00250 0.00275 0.00749 0.01919 0.198
29 2.0e-05 0.00159 0.00176 0.00476 0.01718 0.161
30 3.0e-05 0.00280 0.00253 0.00841 0.01791 0.168
31 1.0e-05 0.00166 0.00168 0.00498 0.01098 0.097
32 1.0e-05 0.00134 0.00138 0.00402 0.01015 0.089
33 1.0e-05 0.00113 0.00135 0.00339 0.01263 0.111
34 9.0e-06 0.00093 0.00107 0.00278 0.00954 0.085
35 9.0e-06 0.00094 0.00106 0.00283 0.00958 0.085
36 1.0e-05 0.00105 0.00115 0.00314 0.01194 0.107
37 2.0e-05 0.00233 0.00241 0.00700 0.02126 0.189
38 2.0e-05 0.00205 0.00218 0.00616 0.01851 0.168
39 2.0e-05 0.00153 0.00166 0.00459 0.01444 0.131
40 2.0e-05 0.00168 0.00182 0.00504 0.01663 0.151
41 2.0e-05 0.00165 0.00175 0.00496 0.01495 0.135
42 1.0e-05 0.00134 0.00147 0.00403 0.01463 0.132
43 1.0e-05 0.00169 0.00182 0.00507 0.01752 0.164
44 1.0e-05 0.00157 0.00173 0.00470 0.01760 0.154
45 9.0e-06 0.00109 0.00137 0.00327 0.01419 0.126
46 9.0e-06 0.00117 0.00139 0.00350 0.01494 0.134
47 1.0e-05 0.00127 0.00148 0.00380 0.01608 0.141
48 7.0e-06 0.00092 0.00113 0.00276 0.01152 0.103
49 4.0e-05 0.00169 0.00203 0.00507 0.01613 0.143
50 3.0e-05 0.00124 0.00155 0.00373 0.01681 0.154
51 3.0e-05 0.00141 0.00167 0.00422 0.02184 0.197
52 4.0e-05 0.00131 0.00169 0.00393 0.02033 0.185
53 3.0e-05 0.00137 0.00166 0.00411 0.02297 0.210
54 4.0e-05 0.00165 0.00183 0.00495 0.02498 0.228
55 7.0e-05 0.00349 0.00486 0.01046 0.02719 0.255
56 8.0e-05 0.00398 0.00539 0.01193 0.03209 0.307
57 7.0e-05 0.00352 0.00514 0.01056 0.03715 0.334
58 6.0e-05 0.00299 0.00469 0.00898 0.02293 0.221
59 7.0e-05 0.00334 0.00493 0.01003 0.02645 0.265
60 8.0e-05 0.00373 0.00520 0.01120 0.03225 0.350
61 1.0e-05 0.00147 0.00152 0.00442 0.01861 0.170
62 1.0e-05 0.00154 0.00151 0.00461 0.01906 0.165
63 1.0e-05 0.00152 0.00144 0.00457 0.01643 0.145
64 1.0e-05 0.00175 0.00155 0.00526 0.01644 0.145
65 9.0e-06 0.00114 0.00113 0.00342 0.01457 0.129
66 1.0e-05 0.00136 0.00140 0.00408 0.01745 0.154
67 6.0e-05 0.00430 0.00440 0.01289 0.03198 0.313
68 7.0e-05 0.00507 0.00463 0.01520 0.03111 0.308
69 8.0e-05 0.00647 0.00467 0.01941 0.05384 0.478
70 5.0e-05 0.00467 0.00354 0.01400 0.05428 0.497
71 6.0e-05 0.00469 0.00419 0.01407 0.03485 0.365
72 7.0e-05 0.00534 0.00478 0.01601 0.04978 0.483
73 3.0e-05 0.00180 0.00220 0.00540 0.01706 0.152
74 5.0e-05 0.00268 0.00329 0.00805 0.02448 0.226
75 4.0e-05 0.00260 0.00283 0.00780 0.02442 0.216
76 5.0e-05 0.00277 0.00289 0.00831 0.02215 0.206
77 4.0e-05 0.00270 0.00289 0.00810 0.03999 0.350
78 4.0e-05 0.00226 0.00280 0.00677 0.02199 0.197
79 6.0e-05 0.00331 0.00332 0.00994 0.03202 0.263
80 1.0e-04 0.00622 0.00576 0.01865 0.03121 0.361
81 7.0e-05 0.00389 0.00415 0.01168 0.04024 0.364
82 7.0e-05 0.00428 0.00371 0.01283 0.03156 0.296
83 6.0e-05 0.00351 0.00348 0.01053 0.02427 0.216
84 4.0e-05 0.00247 0.00258 0.00742 0.02223 0.202
85 4.0e-05 0.00418 0.00420 0.01254 0.04795 0.435
86 2.0e-05 0.00220 0.00244 0.00659 0.03852 0.331
87 2.0e-05 0.00163 0.00194 0.00488 0.03759 0.327
88 3.0e-05 0.00287 0.00312 0.00862 0.06511 0.580
89 3.0e-05 0.00237 0.00254 0.00710 0.06727 0.650
90 4.0e-05 0.00391 0.00419 0.01172 0.04313 0.442
91 4.0e-05 0.00387 0.00453 0.01161 0.06640 0.634
92 3.0e-05 0.00224 0.00227 0.00672 0.07959 0.772
93 3.0e-05 0.00250 0.00256 0.00750 0.04190 0.383
94 3.0e-05 0.00191 0.00226 0.00574 0.05925 0.637
95 2.0e-05 0.00196 0.00196 0.00587 0.03716 0.307
96 2.0e-05 0.00201 0.00197 0.00602 0.03272 0.283
97 2.0e-05 0.00178 0.00184 0.00535 0.03381 0.307
98 1.0e-04 0.00743 0.00623 0.02228 0.03886 0.342
99 1.1e-04 0.00826 0.00655 0.02478 0.04689 0.422
100 1.5e-04 0.01159 0.00990 0.03476 0.06734 0.659
101 2.6e-04 0.02144 0.01522 0.06433 0.09178 0.891
102 1.2e-04 0.00905 0.00909 0.02716 0.06170 0.584
103 2.2e-04 0.01854 0.01628 0.05563 0.09419 0.930
104 2.0e-05 0.00105 0.00136 0.00315 0.01131 0.107
105 1.0e-05 0.00076 0.00100 0.00229 0.01030 0.094
106 2.0e-05 0.00116 0.00134 0.00349 0.01346 0.126
107 1.0e-05 0.00068 0.00092 0.00204 0.01064 0.097
108 2.0e-05 0.00115 0.00122 0.00346 0.01450 0.137
109 1.0e-05 0.00075 0.00096 0.00225 0.01024 0.093
110 4.0e-05 0.00450 0.00389 0.01351 0.03044 0.275
111 3.0e-05 0.00371 0.00337 0.01112 0.02286 0.207
112 3.0e-05 0.00368 0.00339 0.01105 0.01761 0.155
113 4.0e-05 0.00502 0.00485 0.01506 0.02378 0.210
114 3.0e-05 0.00321 0.00280 0.00964 0.01680 0.149
115 2.0e-05 0.00302 0.00246 0.00905 0.02105 0.209
116 6.0e-05 0.00404 0.00385 0.01211 0.01843 0.235
117 3.0e-05 0.00214 0.00207 0.00642 0.01458 0.148
118 3.0e-05 0.00244 0.00261 0.00731 0.01725 0.175
119 3.0e-05 0.00157 0.00194 0.00472 0.01279 0.129
120 2.0e-05 0.00127 0.00128 0.00381 0.01299 0.124
121 5.0e-05 0.00241 0.00314 0.00723 0.02008 0.221
122 3.0e-05 0.00209 0.00221 0.00628 0.01169 0.117
123 5.0e-05 0.00406 0.00398 0.01218 0.04479 0.441
124 5.0e-05 0.00506 0.00449 0.01517 0.02503 0.231
125 4.0e-05 0.00403 0.00395 0.01209 0.02343 0.224
126 5.0e-05 0.00414 0.00422 0.01242 0.02362 0.233
127 4.0e-05 0.00294 0.00327 0.00883 0.02791 0.246
128 4.0e-05 0.00368 0.00351 0.01104 0.02857 0.257
129 4.0e-05 0.00214 0.00192 0.00641 0.01033 0.098
130 2.0e-05 0.00116 0.00135 0.00349 0.01022 0.090
131 4.0e-05 0.00269 0.00238 0.00808 0.01412 0.125
132 3.0e-05 0.00224 0.00205 0.00671 0.01516 0.138
133 3.0e-05 0.00169 0.00170 0.00508 0.01201 0.106
134 3.0e-05 0.00168 0.00171 0.00504 0.01043 0.099
135 6.0e-05 0.00291 0.00319 0.00873 0.04932 0.441
136 4.0e-05 0.00244 0.00315 0.00731 0.04128 0.379
137 4.0e-05 0.00219 0.00283 0.00658 0.04879 0.431
138 4.0e-05 0.00257 0.00312 0.00772 0.05279 0.476
139 4.0e-05 0.00238 0.00290 0.00715 0.05643 0.517
140 3.0e-05 0.00181 0.00232 0.00542 0.03026 0.267
141 3.0e-05 0.00232 0.00269 0.00696 0.03273 0.281
142 4.0e-05 0.00428 0.00428 0.01285 0.06725 0.571
143 2.0e-05 0.00182 0.00215 0.00546 0.03527 0.297
144 2.0e-05 0.00189 0.00211 0.00568 0.01997 0.180
145 1.0e-05 0.00100 0.00133 0.00301 0.02662 0.228
146 2.0e-05 0.00169 0.00188 0.00506 0.02536 0.225
147 9.0e-05 0.00863 0.00946 0.02589 0.08143 0.821
148 8.0e-05 0.00849 0.00819 0.02546 0.06050 0.618
149 9.0e-05 0.00996 0.01027 0.02987 0.07118 0.722
150 8.0e-05 0.00919 0.00963 0.02756 0.07170 0.833
151 1.0e-04 0.01075 0.01154 0.03225 0.05830 0.784
152 1.6e-04 0.01800 0.01958 0.05401 0.11908 1.302
153 1.4e-04 0.01568 0.01699 0.04705 0.08684 1.018
154 6.0e-05 0.00388 0.00332 0.01164 0.02534 0.241
155 6.0e-05 0.00393 0.00300 0.01179 0.02682 0.236
156 5.0e-05 0.00356 0.00300 0.01067 0.03087 0.276
157 6.0e-05 0.00415 0.00339 0.01246 0.02293 0.223
158 1.5e-04 0.01117 0.00718 0.03351 0.04912 0.438
159 8.0e-05 0.00593 0.00454 0.01778 0.02852 0.266
160 5.0e-05 0.00321 0.00318 0.00962 0.03235 0.339
161 5.0e-05 0.00299 0.00316 0.00896 0.04009 0.406
162 5.0e-05 0.00352 0.00329 0.01057 0.03273 0.325
163 6.0e-05 0.00366 0.00340 0.01097 0.03658 0.369
164 5.0e-05 0.00291 0.00284 0.00873 0.01756 0.155
165 9.0e-05 0.00493 0.00461 0.01480 0.02814 0.272
166 1.0e-05 0.00154 0.00153 0.00462 0.02448 0.217
167 1.0e-05 0.00173 0.00159 0.00519 0.01242 0.116
168 1.0e-05 0.00205 0.00186 0.00616 0.02030 0.197
169 4.0e-05 0.00490 0.00448 0.01470 0.02177 0.189
170 2.0e-05 0.00316 0.00283 0.00949 0.02018 0.212
171 2.0e-05 0.00279 0.00237 0.00837 0.01897 0.181
172 3.0e-05 0.00166 0.00190 0.00499 0.01358 0.129
173 3.0e-05 0.00170 0.00200 0.00510 0.01484 0.133
174 3.0e-05 0.00171 0.00203 0.00514 0.01472 0.133
175 3.0e-05 0.00176 0.00218 0.00528 0.01657 0.145
176 3.0e-05 0.00160 0.00199 0.00480 0.01503 0.137
177 3.0e-05 0.00169 0.00213 0.00507 0.01725 0.155
178 2.0e-05 0.00135 0.00162 0.00406 0.01469 0.132
179 2.0e-05 0.00152 0.00186 0.00456 0.01574 0.142
180 3.0e-05 0.00204 0.00231 0.00612 0.01450 0.131
181 3.0e-05 0.00206 0.00233 0.00619 0.02551 0.237
182 3.0e-05 0.00202 0.00235 0.00605 0.01831 0.163
183 2.0e-05 0.00174 0.00198 0.00521 0.02145 0.198
184 4.0e-05 0.00186 0.00270 0.00558 0.01909 0.171
185 5.0e-05 0.00260 0.00346 0.00780 0.01795 0.163
186 3.0e-05 0.00134 0.00192 0.00403 0.01564 0.136
187 4.0e-05 0.00254 0.00263 0.00762 0.01660 0.154
188 2.0e-05 0.00115 0.00148 0.00345 0.01300 0.117
189 3.0e-05 0.00146 0.00184 0.00439 0.01185 0.106
190 3.0e-05 0.00412 0.00396 0.01235 0.02574 0.255
191 3.0e-05 0.00263 0.00259 0.00790 0.04087 0.405
192 3.0e-05 0.00331 0.00292 0.00994 0.02751 0.263
193 8.0e-05 0.00624 0.00564 0.01873 0.02308 0.256
194 4.0e-05 0.00370 0.00390 0.01109 0.02296 0.241
195 3.0e-05 0.00295 0.00317 0.00885 0.01884 0.190
> 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)`
1.397e+00 -3.040e-03 -2.532e-04 -2.327e-03
`MDVP:Jitter(%)` `MDVP:Jitter(Abs)` `MDVP:RAP` `MDVP:PPQ`
-6.571e+01 -3.241e+03 2.586e+03 4.943e+01
`Jitter:DDP` `MDVP:Shimmer` `MDVP:Shimmer(dB)`
-8.287e+02 8.937e+00 -2.380e-01
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.84149 -0.16618 0.08191 0.25421 0.61533
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.397e+00 2.105e-01 6.639 3.44e-10 ***
`MDVP:Fo(Hz)` -3.040e-03 1.336e-03 -2.275 0.02404 *
`MDVP:Fhi(Hz)` -2.532e-04 3.408e-04 -0.743 0.45839
`MDVP:Flo(Hz)` -2.327e-03 8.361e-04 -2.783 0.00595 **
`MDVP:Jitter(%)` -6.571e+01 6.335e+01 -1.037 0.30099
`MDVP:Jitter(Abs)` -3.241e+03 3.947e+03 -0.821 0.41259
`MDVP:RAP` 2.586e+03 1.014e+04 0.255 0.79907
`MDVP:PPQ` 4.943e+01 5.261e+01 0.939 0.34873
`Jitter:DDP` -8.287e+02 3.381e+03 -0.245 0.80667
`MDVP:Shimmer` 8.937e+00 1.102e+01 0.811 0.41823
`MDVP:Shimmer(dB)` -2.380e-01 1.187e+00 -0.201 0.84131
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.3722 on 184 degrees of freedom
Multiple R-squared: 0.2957, Adjusted R-squared: 0.2574
F-statistic: 7.726 on 10 and 184 DF, p-value: 2.776e-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,] 4.248031e-47 8.496062e-47 1.0000000000
[2,] 7.052731e-62 1.410546e-61 1.0000000000
[3,] 0.000000e+00 0.000000e+00 1.0000000000
[4,] 5.528388e-99 1.105678e-98 1.0000000000
[5,] 9.293466e-106 1.858693e-105 1.0000000000
[6,] 2.785503e-121 5.571006e-121 1.0000000000
[7,] 1.131756e-144 2.263512e-144 1.0000000000
[8,] 1.604368e-173 3.208736e-173 1.0000000000
[9,] 2.034498e-165 4.068995e-165 1.0000000000
[10,] 1.389273e-183 2.778545e-183 1.0000000000
[11,] 8.794292e-196 1.758858e-195 1.0000000000
[12,] 1.687256e-219 3.374513e-219 1.0000000000
[13,] 1.805943e-256 3.611886e-256 1.0000000000
[14,] 1.122320e-249 2.244640e-249 1.0000000000
[15,] 4.075615e-257 8.151231e-257 1.0000000000
[16,] 9.982547e-281 1.996509e-280 1.0000000000
[17,] 2.603675e-286 5.207349e-286 1.0000000000
[18,] 4.349227e-08 8.698454e-08 0.9999999565
[19,] 3.495397e-08 6.990794e-08 0.9999999650
[20,] 1.410259e-08 2.820518e-08 0.9999999859
[21,] 4.352439e-09 8.704878e-09 0.9999999956
[22,] 1.316068e-09 2.632136e-09 0.9999999987
[23,] 4.128696e-10 8.257391e-10 0.9999999996
[24,] 1.386846e-07 2.773693e-07 0.9999998613
[25,] 1.697710e-06 3.395420e-06 0.9999983023
[26,] 1.092167e-04 2.184335e-04 0.9998907833
[27,] 9.165458e-04 1.833092e-03 0.9990834542
[28,] 2.831056e-03 5.662113e-03 0.9971689436
[29,] 3.410486e-03 6.820972e-03 0.9965895140
[30,] 2.287438e-03 4.574876e-03 0.9977125621
[31,] 1.474354e-03 2.948709e-03 0.9985256456
[32,] 9.404525e-04 1.880905e-03 0.9990595475
[33,] 6.116214e-04 1.223243e-03 0.9993883786
[34,] 3.999296e-04 7.998592e-04 0.9996000704
[35,] 2.472727e-04 4.945454e-04 0.9997527273
[36,] 7.863412e-04 1.572682e-03 0.9992136588
[37,] 1.386062e-03 2.772124e-03 0.9986139379
[38,] 1.735580e-03 3.471160e-03 0.9982644200
[39,] 1.576734e-03 3.153468e-03 0.9984232660
[40,] 1.991058e-03 3.982115e-03 0.9980089423
[41,] 2.388930e-03 4.777861e-03 0.9976110696
[42,] 3.034430e-03 6.068859e-03 0.9969655703
[43,] 4.791734e-03 9.583467e-03 0.9952082664
[44,] 3.850474e-03 7.700948e-03 0.9961495260
[45,] 4.149428e-03 8.298856e-03 0.9958505718
[46,] 3.888511e-03 7.777022e-03 0.9961114888
[47,] 2.729598e-03 5.459196e-03 0.9972704022
[48,] 1.322228e-02 2.644456e-02 0.9867777209
[49,] 2.148614e-02 4.297228e-02 0.9785138580
[50,] 2.099510e-02 4.199021e-02 0.9790048951
[51,] 1.913797e-02 3.827595e-02 0.9808620253
[52,] 1.640446e-02 3.280893e-02 0.9835955356
[53,] 1.903366e-02 3.806733e-02 0.9809663362
[54,] 1.428434e-02 2.856867e-02 0.9857156646
[55,] 1.053344e-02 2.106688e-02 0.9894665581
[56,] 7.706129e-03 1.541226e-02 0.9922938710
[57,] 5.756600e-03 1.151320e-02 0.9942433999
[58,] 4.195825e-03 8.391649e-03 0.9958041753
[59,] 2.965653e-03 5.931306e-03 0.9970343472
[60,] 2.194236e-03 4.388471e-03 0.9978057643
[61,] 6.041598e-03 1.208320e-02 0.9939584023
[62,] 4.448032e-03 8.896064e-03 0.9955519681
[63,] 3.269203e-03 6.538406e-03 0.9967307970
[64,] 2.310822e-03 4.621645e-03 0.9976891777
[65,] 1.645283e-03 3.290565e-03 0.9983547173
[66,] 1.169098e-03 2.338195e-03 0.9988309025
[67,] 9.080188e-04 1.816038e-03 0.9990919812
[68,] 6.727597e-04 1.345519e-03 0.9993272403
[69,] 4.576394e-04 9.152788e-04 0.9995423606
[70,] 3.269983e-04 6.539965e-04 0.9996730017
[71,] 2.817403e-04 5.634805e-04 0.9997182597
[72,] 1.930124e-04 3.860249e-04 0.9998069876
[73,] 2.134151e-04 4.268302e-04 0.9997865849
[74,] 2.763717e-04 5.527434e-04 0.9997236283
[75,] 1.859544e-04 3.719089e-04 0.9998140456
[76,] 1.312324e-04 2.624648e-04 0.9998687676
[77,] 8.933645e-05 1.786729e-04 0.9999106635
[78,] 6.940953e-05 1.388191e-04 0.9999305905
[79,] 5.562651e-05 1.112530e-04 0.9999443735
[80,] 3.717866e-05 7.435731e-05 0.9999628213
[81,] 2.356743e-05 4.713486e-05 0.9999764326
[82,] 1.462214e-05 2.924429e-05 0.9999853779
[83,] 1.098497e-05 2.196995e-05 0.9999890150
[84,] 8.143765e-06 1.628753e-05 0.9999918562
[85,] 5.268122e-06 1.053624e-05 0.9999947319
[86,] 3.159845e-06 6.319690e-06 0.9999968402
[87,] 2.155507e-06 4.311013e-06 0.9999978445
[88,] 1.730467e-06 3.460933e-06 0.9999982695
[89,] 1.847890e-06 3.695779e-06 0.9999981521
[90,] 6.649593e-06 1.329919e-05 0.9999933504
[91,] 5.252336e-06 1.050467e-05 0.9999947477
[92,] 4.566981e-06 9.133963e-06 0.9999954330
[93,] 4.425059e-06 8.850117e-06 0.9999955749
[94,] 4.132966e-06 8.265933e-06 0.9999958670
[95,] 4.176266e-06 8.352531e-06 0.9999958237
[96,] 3.512531e-06 7.025061e-06 0.9999964875
[97,] 2.568195e-06 5.136390e-06 0.9999974318
[98,] 1.969480e-06 3.938961e-06 0.9999980305
[99,] 3.997367e-06 7.994734e-06 0.9999960026
[100,] 4.739413e-06 9.478827e-06 0.9999952606
[101,] 1.539046e-05 3.078092e-05 0.9999846095
[102,] 1.229366e-05 2.458732e-05 0.9999877063
[103,] 1.308295e-05 2.616590e-05 0.9999869170
[104,] 1.303684e-05 2.607369e-05 0.9999869632
[105,] 1.278115e-05 2.556231e-05 0.9999872188
[106,] 3.246151e-05 6.492302e-05 0.9999675385
[107,] 1.322082e-04 2.644164e-04 0.9998677918
[108,] 2.056494e-04 4.112988e-04 0.9997943506
[109,] 3.483460e-04 6.966921e-04 0.9996516540
[110,] 2.494252e-04 4.988504e-04 0.9997505748
[111,] 2.071792e-04 4.143583e-04 0.9997928208
[112,] 2.172189e-04 4.344378e-04 0.9997827811
[113,] 2.375771e-04 4.751542e-04 0.9997624229
[114,] 2.772606e-04 5.545213e-04 0.9997227394
[115,] 2.851901e-04 5.703801e-04 0.9997148099
[116,] 2.446352e-04 4.892703e-04 0.9997553648
[117,] 2.764328e-04 5.528656e-04 0.9997235672
[118,] 3.380668e-04 6.761336e-04 0.9996619332
[119,] 2.782565e-04 5.565130e-04 0.9997217435
[120,] 4.386985e-04 8.773971e-04 0.9995613015
[121,] 5.630216e-04 1.126043e-03 0.9994369784
[122,] 4.571954e-04 9.143907e-04 0.9995428046
[123,] 3.092370e-04 6.184740e-04 0.9996907630
[124,] 2.017940e-04 4.035881e-04 0.9997982060
[125,] 1.355849e-04 2.711697e-04 0.9998644151
[126,] 9.985452e-05 1.997090e-04 0.9999001455
[127,] 6.437001e-05 1.287400e-04 0.9999356300
[128,] 7.535707e-05 1.507141e-04 0.9999246429
[129,] 4.930415e-05 9.860831e-05 0.9999506958
[130,] 6.300851e-05 1.260170e-04 0.9999369915
[131,] 4.250404e-04 8.500807e-04 0.9995749596
[132,] 1.671723e-03 3.343446e-03 0.9983282769
[133,] 6.416411e-03 1.283282e-02 0.9935835887
[134,] 4.532367e-03 9.064734e-03 0.9954676329
[135,] 3.151452e-03 6.302904e-03 0.9968485478
[136,] 2.166434e-03 4.332868e-03 0.9978335660
[137,] 1.454942e-03 2.909884e-03 0.9985450582
[138,] 1.022384e-03 2.044768e-03 0.9989776161
[139,] 9.989391e-04 1.997878e-03 0.9990010609
[140,] 7.152863e-04 1.430573e-03 0.9992847137
[141,] 5.758458e-04 1.151692e-03 0.9994241542
[142,] 4.469492e-04 8.938984e-04 0.9995530508
[143,] 2.928369e-04 5.856739e-04 0.9997071631
[144,] 4.572980e-04 9.145961e-04 0.9995427020
[145,] 1.156701e-03 2.313402e-03 0.9988432989
[146,] 9.926592e-04 1.985318e-03 0.9990073408
[147,] 6.639376e-04 1.327875e-03 0.9993360624
[148,] 4.011035e-04 8.022069e-04 0.9995988965
[149,] 4.055399e-04 8.110797e-04 0.9995944601
[150,] 2.417722e-04 4.835445e-04 0.9997582278
[151,] 2.453560e-04 4.907120e-04 0.9997546440
[152,] 3.156600e-03 6.313201e-03 0.9968433997
[153,] 4.687785e-03 9.375571e-03 0.9953122146
[154,] 6.246761e-03 1.249352e-02 0.9937532386
[155,] 1.389775e-02 2.779549e-02 0.9861022528
[156,] 1.881365e-02 3.762731e-02 0.9811863472
[157,] 1.265010e-02 2.530020e-02 0.9873498987
[158,] 9.969298e-01 6.140452e-03 0.0030702259
[159,] 9.987249e-01 2.550248e-03 0.0012751239
[160,] 9.978442e-01 4.311698e-03 0.0021558489
[161,] 9.961680e-01 7.664001e-03 0.0038320004
[162,] 9.933592e-01 1.328154e-02 0.0066407705
[163,] 9.969571e-01 6.085716e-03 0.0030428579
[164,] 9.992624e-01 1.475215e-03 0.0007376073
[165,] 9.997369e-01 5.262780e-04 0.0002631390
[166,] 9.986374e-01 2.725144e-03 0.0013625718
[167,] 9.950510e-01 9.897911e-03 0.0049489557
[168,] 9.838216e-01 3.235686e-02 0.0161784321
> postscript(file="/var/fisher/rcomp/tmp/10x311386781243.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/2lsec1386781243.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3avmq1386781243.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/4afxt1386781243.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/54h661386781243.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
-0.016064481 -0.041719897 -0.040349639 -0.022479198 -0.078702774 0.011063273
7 8 9 10 11 12
0.230052453 0.138600776 0.108131382 0.046303974 0.069292932 0.037340066
13 14 15 16 17 18
0.351595313 0.233927433 0.231601824 0.262201672 0.309266691 0.271739344
19 20 21 22 23 24
-0.018124162 0.283453188 0.076842040 0.064133375 0.101877582 0.155241957
25 26 27 28 29 30
0.299091550 0.018195351 0.251359592 0.197857531 0.218594617 0.239302977
31 32 33 34 35 36
-0.399917746 -0.371973967 -0.384340498 -0.341578007 -0.326543911 -0.364512257
37 38 39 40 41 42
0.430346551 0.444176400 0.528946545 0.529821022 0.548626473 0.505382716
43 44 45 46 47 48
-0.236927132 -0.213119324 -0.158740240 -0.182189470 -0.176274172 -0.219919857
49 50 51 52 53 54
-0.628624298 -0.650581827 -0.710424531 -0.644374681 -0.679145542 -0.664773048
55 56 57 58 59 60
0.155672896 0.160688528 0.097178218 0.264131631 0.246579866 0.268175908
61 62 63 64 65 66
-0.568166712 -0.589577191 -0.304274906 -0.249894822 -0.226412836 -0.512245891
67 68 69 70 71 72
0.091955862 0.101701394 -0.026262233 -0.101345544 0.072058814 -0.026169152
73 74 75 76 77 78
0.221042520 0.267472559 0.110390524 0.148254810 0.004958523 0.131078071
79 80 81 82 83 84
0.013691421 0.038434372 -0.055113063 -0.016809899 0.065771441 0.031093421
85 86 87 88 89 90
0.039536364 0.283616557 0.275168133 0.051922660 -0.065983731 0.254025129
91 92 93 94 95 96
-0.091523075 -0.041582073 0.166488977 -0.038920826 0.026200300 0.247284397
97 98 99 100 101 102
0.266757893 0.133102763 0.041980433 -0.082618059 -0.173623204 -0.138904653
103 104 105 106 107 108
-0.242348878 0.254393338 0.376925774 0.379232414 0.408651651 0.386486534
109 110 111 112 113 114
0.368988081 0.223714373 0.284372818 0.606811413 0.502439998 0.615333519
115 116 117 118 119 120
0.322577419 0.397384785 0.362759642 0.388938288 0.521095411 0.594558280
121 122 123 124 125 126
0.343371284 0.404010345 -0.003278944 0.202938851 0.145997734 0.149210600
127 128 129 130 131 132
0.111577165 0.160340925 0.284547068 0.244090367 0.213379417 0.173245321
133 134 135 136 137 138
0.196816444 0.243926045 -0.029937223 -0.014923937 -0.024968111 -0.086764986
139 140 141 142 143 144
-0.095244536 0.082252614 0.256681193 0.045805478 0.387465072 0.366679286
145 146 147 148 149 150
0.520232671 0.406052321 -0.017147776 0.161711402 0.066714892 0.261220089
151 152 153 154 155 156
0.150971525 -0.306936458 -0.018981206 0.160063620 0.130168506 0.081911917
157 158 159 160 161 162
0.179183671 0.105719956 0.137631882 0.153936243 -0.019224002 0.087421794
163 164 165 166 167 168
0.037562066 0.156917535 0.140291437 -0.561260013 -0.183347417 -0.154394836
169 170 171 172 173 174
-0.731792278 -0.264795889 -0.187226883 -0.786421194 -0.836763359 -0.832220487
175 176 177 178 179 180
-0.839849813 -0.806546418 -0.823434305 0.379426473 0.336289567 0.345942742
181 182 183 184 185 186
0.312143643 0.332969875 0.298104212 -0.806229440 -0.795937621 -0.797072517
187 188 189 190 191 192
-0.731390233 -0.712830929 -0.813796742 -0.765581553 -0.841490924 -0.669025262
193 194 195
-0.516632112 -0.619544921 -0.602198241
> postscript(file="/var/fisher/rcomp/tmp/6i1ou1386781243.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 -0.016064481 NA
1 -0.041719897 -0.016064481
2 -0.040349639 -0.041719897
3 -0.022479198 -0.040349639
4 -0.078702774 -0.022479198
5 0.011063273 -0.078702774
6 0.230052453 0.011063273
7 0.138600776 0.230052453
8 0.108131382 0.138600776
9 0.046303974 0.108131382
10 0.069292932 0.046303974
11 0.037340066 0.069292932
12 0.351595313 0.037340066
13 0.233927433 0.351595313
14 0.231601824 0.233927433
15 0.262201672 0.231601824
16 0.309266691 0.262201672
17 0.271739344 0.309266691
18 -0.018124162 0.271739344
19 0.283453188 -0.018124162
20 0.076842040 0.283453188
21 0.064133375 0.076842040
22 0.101877582 0.064133375
23 0.155241957 0.101877582
24 0.299091550 0.155241957
25 0.018195351 0.299091550
26 0.251359592 0.018195351
27 0.197857531 0.251359592
28 0.218594617 0.197857531
29 0.239302977 0.218594617
30 -0.399917746 0.239302977
31 -0.371973967 -0.399917746
32 -0.384340498 -0.371973967
33 -0.341578007 -0.384340498
34 -0.326543911 -0.341578007
35 -0.364512257 -0.326543911
36 0.430346551 -0.364512257
37 0.444176400 0.430346551
38 0.528946545 0.444176400
39 0.529821022 0.528946545
40 0.548626473 0.529821022
41 0.505382716 0.548626473
42 -0.236927132 0.505382716
43 -0.213119324 -0.236927132
44 -0.158740240 -0.213119324
45 -0.182189470 -0.158740240
46 -0.176274172 -0.182189470
47 -0.219919857 -0.176274172
48 -0.628624298 -0.219919857
49 -0.650581827 -0.628624298
50 -0.710424531 -0.650581827
51 -0.644374681 -0.710424531
52 -0.679145542 -0.644374681
53 -0.664773048 -0.679145542
54 0.155672896 -0.664773048
55 0.160688528 0.155672896
56 0.097178218 0.160688528
57 0.264131631 0.097178218
58 0.246579866 0.264131631
59 0.268175908 0.246579866
60 -0.568166712 0.268175908
61 -0.589577191 -0.568166712
62 -0.304274906 -0.589577191
63 -0.249894822 -0.304274906
64 -0.226412836 -0.249894822
65 -0.512245891 -0.226412836
66 0.091955862 -0.512245891
67 0.101701394 0.091955862
68 -0.026262233 0.101701394
69 -0.101345544 -0.026262233
70 0.072058814 -0.101345544
71 -0.026169152 0.072058814
72 0.221042520 -0.026169152
73 0.267472559 0.221042520
74 0.110390524 0.267472559
75 0.148254810 0.110390524
76 0.004958523 0.148254810
77 0.131078071 0.004958523
78 0.013691421 0.131078071
79 0.038434372 0.013691421
80 -0.055113063 0.038434372
81 -0.016809899 -0.055113063
82 0.065771441 -0.016809899
83 0.031093421 0.065771441
84 0.039536364 0.031093421
85 0.283616557 0.039536364
86 0.275168133 0.283616557
87 0.051922660 0.275168133
88 -0.065983731 0.051922660
89 0.254025129 -0.065983731
90 -0.091523075 0.254025129
91 -0.041582073 -0.091523075
92 0.166488977 -0.041582073
93 -0.038920826 0.166488977
94 0.026200300 -0.038920826
95 0.247284397 0.026200300
96 0.266757893 0.247284397
97 0.133102763 0.266757893
98 0.041980433 0.133102763
99 -0.082618059 0.041980433
100 -0.173623204 -0.082618059
101 -0.138904653 -0.173623204
102 -0.242348878 -0.138904653
103 0.254393338 -0.242348878
104 0.376925774 0.254393338
105 0.379232414 0.376925774
106 0.408651651 0.379232414
107 0.386486534 0.408651651
108 0.368988081 0.386486534
109 0.223714373 0.368988081
110 0.284372818 0.223714373
111 0.606811413 0.284372818
112 0.502439998 0.606811413
113 0.615333519 0.502439998
114 0.322577419 0.615333519
115 0.397384785 0.322577419
116 0.362759642 0.397384785
117 0.388938288 0.362759642
118 0.521095411 0.388938288
119 0.594558280 0.521095411
120 0.343371284 0.594558280
121 0.404010345 0.343371284
122 -0.003278944 0.404010345
123 0.202938851 -0.003278944
124 0.145997734 0.202938851
125 0.149210600 0.145997734
126 0.111577165 0.149210600
127 0.160340925 0.111577165
128 0.284547068 0.160340925
129 0.244090367 0.284547068
130 0.213379417 0.244090367
131 0.173245321 0.213379417
132 0.196816444 0.173245321
133 0.243926045 0.196816444
134 -0.029937223 0.243926045
135 -0.014923937 -0.029937223
136 -0.024968111 -0.014923937
137 -0.086764986 -0.024968111
138 -0.095244536 -0.086764986
139 0.082252614 -0.095244536
140 0.256681193 0.082252614
141 0.045805478 0.256681193
142 0.387465072 0.045805478
143 0.366679286 0.387465072
144 0.520232671 0.366679286
145 0.406052321 0.520232671
146 -0.017147776 0.406052321
147 0.161711402 -0.017147776
148 0.066714892 0.161711402
149 0.261220089 0.066714892
150 0.150971525 0.261220089
151 -0.306936458 0.150971525
152 -0.018981206 -0.306936458
153 0.160063620 -0.018981206
154 0.130168506 0.160063620
155 0.081911917 0.130168506
156 0.179183671 0.081911917
157 0.105719956 0.179183671
158 0.137631882 0.105719956
159 0.153936243 0.137631882
160 -0.019224002 0.153936243
161 0.087421794 -0.019224002
162 0.037562066 0.087421794
163 0.156917535 0.037562066
164 0.140291437 0.156917535
165 -0.561260013 0.140291437
166 -0.183347417 -0.561260013
167 -0.154394836 -0.183347417
168 -0.731792278 -0.154394836
169 -0.264795889 -0.731792278
170 -0.187226883 -0.264795889
171 -0.786421194 -0.187226883
172 -0.836763359 -0.786421194
173 -0.832220487 -0.836763359
174 -0.839849813 -0.832220487
175 -0.806546418 -0.839849813
176 -0.823434305 -0.806546418
177 0.379426473 -0.823434305
178 0.336289567 0.379426473
179 0.345942742 0.336289567
180 0.312143643 0.345942742
181 0.332969875 0.312143643
182 0.298104212 0.332969875
183 -0.806229440 0.298104212
184 -0.795937621 -0.806229440
185 -0.797072517 -0.795937621
186 -0.731390233 -0.797072517
187 -0.712830929 -0.731390233
188 -0.813796742 -0.712830929
189 -0.765581553 -0.813796742
190 -0.841490924 -0.765581553
191 -0.669025262 -0.841490924
192 -0.516632112 -0.669025262
193 -0.619544921 -0.516632112
194 -0.602198241 -0.619544921
195 NA -0.602198241
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.041719897 -0.016064481
[2,] -0.040349639 -0.041719897
[3,] -0.022479198 -0.040349639
[4,] -0.078702774 -0.022479198
[5,] 0.011063273 -0.078702774
[6,] 0.230052453 0.011063273
[7,] 0.138600776 0.230052453
[8,] 0.108131382 0.138600776
[9,] 0.046303974 0.108131382
[10,] 0.069292932 0.046303974
[11,] 0.037340066 0.069292932
[12,] 0.351595313 0.037340066
[13,] 0.233927433 0.351595313
[14,] 0.231601824 0.233927433
[15,] 0.262201672 0.231601824
[16,] 0.309266691 0.262201672
[17,] 0.271739344 0.309266691
[18,] -0.018124162 0.271739344
[19,] 0.283453188 -0.018124162
[20,] 0.076842040 0.283453188
[21,] 0.064133375 0.076842040
[22,] 0.101877582 0.064133375
[23,] 0.155241957 0.101877582
[24,] 0.299091550 0.155241957
[25,] 0.018195351 0.299091550
[26,] 0.251359592 0.018195351
[27,] 0.197857531 0.251359592
[28,] 0.218594617 0.197857531
[29,] 0.239302977 0.218594617
[30,] -0.399917746 0.239302977
[31,] -0.371973967 -0.399917746
[32,] -0.384340498 -0.371973967
[33,] -0.341578007 -0.384340498
[34,] -0.326543911 -0.341578007
[35,] -0.364512257 -0.326543911
[36,] 0.430346551 -0.364512257
[37,] 0.444176400 0.430346551
[38,] 0.528946545 0.444176400
[39,] 0.529821022 0.528946545
[40,] 0.548626473 0.529821022
[41,] 0.505382716 0.548626473
[42,] -0.236927132 0.505382716
[43,] -0.213119324 -0.236927132
[44,] -0.158740240 -0.213119324
[45,] -0.182189470 -0.158740240
[46,] -0.176274172 -0.182189470
[47,] -0.219919857 -0.176274172
[48,] -0.628624298 -0.219919857
[49,] -0.650581827 -0.628624298
[50,] -0.710424531 -0.650581827
[51,] -0.644374681 -0.710424531
[52,] -0.679145542 -0.644374681
[53,] -0.664773048 -0.679145542
[54,] 0.155672896 -0.664773048
[55,] 0.160688528 0.155672896
[56,] 0.097178218 0.160688528
[57,] 0.264131631 0.097178218
[58,] 0.246579866 0.264131631
[59,] 0.268175908 0.246579866
[60,] -0.568166712 0.268175908
[61,] -0.589577191 -0.568166712
[62,] -0.304274906 -0.589577191
[63,] -0.249894822 -0.304274906
[64,] -0.226412836 -0.249894822
[65,] -0.512245891 -0.226412836
[66,] 0.091955862 -0.512245891
[67,] 0.101701394 0.091955862
[68,] -0.026262233 0.101701394
[69,] -0.101345544 -0.026262233
[70,] 0.072058814 -0.101345544
[71,] -0.026169152 0.072058814
[72,] 0.221042520 -0.026169152
[73,] 0.267472559 0.221042520
[74,] 0.110390524 0.267472559
[75,] 0.148254810 0.110390524
[76,] 0.004958523 0.148254810
[77,] 0.131078071 0.004958523
[78,] 0.013691421 0.131078071
[79,] 0.038434372 0.013691421
[80,] -0.055113063 0.038434372
[81,] -0.016809899 -0.055113063
[82,] 0.065771441 -0.016809899
[83,] 0.031093421 0.065771441
[84,] 0.039536364 0.031093421
[85,] 0.283616557 0.039536364
[86,] 0.275168133 0.283616557
[87,] 0.051922660 0.275168133
[88,] -0.065983731 0.051922660
[89,] 0.254025129 -0.065983731
[90,] -0.091523075 0.254025129
[91,] -0.041582073 -0.091523075
[92,] 0.166488977 -0.041582073
[93,] -0.038920826 0.166488977
[94,] 0.026200300 -0.038920826
[95,] 0.247284397 0.026200300
[96,] 0.266757893 0.247284397
[97,] 0.133102763 0.266757893
[98,] 0.041980433 0.133102763
[99,] -0.082618059 0.041980433
[100,] -0.173623204 -0.082618059
[101,] -0.138904653 -0.173623204
[102,] -0.242348878 -0.138904653
[103,] 0.254393338 -0.242348878
[104,] 0.376925774 0.254393338
[105,] 0.379232414 0.376925774
[106,] 0.408651651 0.379232414
[107,] 0.386486534 0.408651651
[108,] 0.368988081 0.386486534
[109,] 0.223714373 0.368988081
[110,] 0.284372818 0.223714373
[111,] 0.606811413 0.284372818
[112,] 0.502439998 0.606811413
[113,] 0.615333519 0.502439998
[114,] 0.322577419 0.615333519
[115,] 0.397384785 0.322577419
[116,] 0.362759642 0.397384785
[117,] 0.388938288 0.362759642
[118,] 0.521095411 0.388938288
[119,] 0.594558280 0.521095411
[120,] 0.343371284 0.594558280
[121,] 0.404010345 0.343371284
[122,] -0.003278944 0.404010345
[123,] 0.202938851 -0.003278944
[124,] 0.145997734 0.202938851
[125,] 0.149210600 0.145997734
[126,] 0.111577165 0.149210600
[127,] 0.160340925 0.111577165
[128,] 0.284547068 0.160340925
[129,] 0.244090367 0.284547068
[130,] 0.213379417 0.244090367
[131,] 0.173245321 0.213379417
[132,] 0.196816444 0.173245321
[133,] 0.243926045 0.196816444
[134,] -0.029937223 0.243926045
[135,] -0.014923937 -0.029937223
[136,] -0.024968111 -0.014923937
[137,] -0.086764986 -0.024968111
[138,] -0.095244536 -0.086764986
[139,] 0.082252614 -0.095244536
[140,] 0.256681193 0.082252614
[141,] 0.045805478 0.256681193
[142,] 0.387465072 0.045805478
[143,] 0.366679286 0.387465072
[144,] 0.520232671 0.366679286
[145,] 0.406052321 0.520232671
[146,] -0.017147776 0.406052321
[147,] 0.161711402 -0.017147776
[148,] 0.066714892 0.161711402
[149,] 0.261220089 0.066714892
[150,] 0.150971525 0.261220089
[151,] -0.306936458 0.150971525
[152,] -0.018981206 -0.306936458
[153,] 0.160063620 -0.018981206
[154,] 0.130168506 0.160063620
[155,] 0.081911917 0.130168506
[156,] 0.179183671 0.081911917
[157,] 0.105719956 0.179183671
[158,] 0.137631882 0.105719956
[159,] 0.153936243 0.137631882
[160,] -0.019224002 0.153936243
[161,] 0.087421794 -0.019224002
[162,] 0.037562066 0.087421794
[163,] 0.156917535 0.037562066
[164,] 0.140291437 0.156917535
[165,] -0.561260013 0.140291437
[166,] -0.183347417 -0.561260013
[167,] -0.154394836 -0.183347417
[168,] -0.731792278 -0.154394836
[169,] -0.264795889 -0.731792278
[170,] -0.187226883 -0.264795889
[171,] -0.786421194 -0.187226883
[172,] -0.836763359 -0.786421194
[173,] -0.832220487 -0.836763359
[174,] -0.839849813 -0.832220487
[175,] -0.806546418 -0.839849813
[176,] -0.823434305 -0.806546418
[177,] 0.379426473 -0.823434305
[178,] 0.336289567 0.379426473
[179,] 0.345942742 0.336289567
[180,] 0.312143643 0.345942742
[181,] 0.332969875 0.312143643
[182,] 0.298104212 0.332969875
[183,] -0.806229440 0.298104212
[184,] -0.795937621 -0.806229440
[185,] -0.797072517 -0.795937621
[186,] -0.731390233 -0.797072517
[187,] -0.712830929 -0.731390233
[188,] -0.813796742 -0.712830929
[189,] -0.765581553 -0.813796742
[190,] -0.841490924 -0.765581553
[191,] -0.669025262 -0.841490924
[192,] -0.516632112 -0.669025262
[193,] -0.619544921 -0.516632112
[194,] -0.602198241 -0.619544921
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.041719897 -0.016064481
2 -0.040349639 -0.041719897
3 -0.022479198 -0.040349639
4 -0.078702774 -0.022479198
5 0.011063273 -0.078702774
6 0.230052453 0.011063273
7 0.138600776 0.230052453
8 0.108131382 0.138600776
9 0.046303974 0.108131382
10 0.069292932 0.046303974
11 0.037340066 0.069292932
12 0.351595313 0.037340066
13 0.233927433 0.351595313
14 0.231601824 0.233927433
15 0.262201672 0.231601824
16 0.309266691 0.262201672
17 0.271739344 0.309266691
18 -0.018124162 0.271739344
19 0.283453188 -0.018124162
20 0.076842040 0.283453188
21 0.064133375 0.076842040
22 0.101877582 0.064133375
23 0.155241957 0.101877582
24 0.299091550 0.155241957
25 0.018195351 0.299091550
26 0.251359592 0.018195351
27 0.197857531 0.251359592
28 0.218594617 0.197857531
29 0.239302977 0.218594617
30 -0.399917746 0.239302977
31 -0.371973967 -0.399917746
32 -0.384340498 -0.371973967
33 -0.341578007 -0.384340498
34 -0.326543911 -0.341578007
35 -0.364512257 -0.326543911
36 0.430346551 -0.364512257
37 0.444176400 0.430346551
38 0.528946545 0.444176400
39 0.529821022 0.528946545
40 0.548626473 0.529821022
41 0.505382716 0.548626473
42 -0.236927132 0.505382716
43 -0.213119324 -0.236927132
44 -0.158740240 -0.213119324
45 -0.182189470 -0.158740240
46 -0.176274172 -0.182189470
47 -0.219919857 -0.176274172
48 -0.628624298 -0.219919857
49 -0.650581827 -0.628624298
50 -0.710424531 -0.650581827
51 -0.644374681 -0.710424531
52 -0.679145542 -0.644374681
53 -0.664773048 -0.679145542
54 0.155672896 -0.664773048
55 0.160688528 0.155672896
56 0.097178218 0.160688528
57 0.264131631 0.097178218
58 0.246579866 0.264131631
59 0.268175908 0.246579866
60 -0.568166712 0.268175908
61 -0.589577191 -0.568166712
62 -0.304274906 -0.589577191
63 -0.249894822 -0.304274906
64 -0.226412836 -0.249894822
65 -0.512245891 -0.226412836
66 0.091955862 -0.512245891
67 0.101701394 0.091955862
68 -0.026262233 0.101701394
69 -0.101345544 -0.026262233
70 0.072058814 -0.101345544
71 -0.026169152 0.072058814
72 0.221042520 -0.026169152
73 0.267472559 0.221042520
74 0.110390524 0.267472559
75 0.148254810 0.110390524
76 0.004958523 0.148254810
77 0.131078071 0.004958523
78 0.013691421 0.131078071
79 0.038434372 0.013691421
80 -0.055113063 0.038434372
81 -0.016809899 -0.055113063
82 0.065771441 -0.016809899
83 0.031093421 0.065771441
84 0.039536364 0.031093421
85 0.283616557 0.039536364
86 0.275168133 0.283616557
87 0.051922660 0.275168133
88 -0.065983731 0.051922660
89 0.254025129 -0.065983731
90 -0.091523075 0.254025129
91 -0.041582073 -0.091523075
92 0.166488977 -0.041582073
93 -0.038920826 0.166488977
94 0.026200300 -0.038920826
95 0.247284397 0.026200300
96 0.266757893 0.247284397
97 0.133102763 0.266757893
98 0.041980433 0.133102763
99 -0.082618059 0.041980433
100 -0.173623204 -0.082618059
101 -0.138904653 -0.173623204
102 -0.242348878 -0.138904653
103 0.254393338 -0.242348878
104 0.376925774 0.254393338
105 0.379232414 0.376925774
106 0.408651651 0.379232414
107 0.386486534 0.408651651
108 0.368988081 0.386486534
109 0.223714373 0.368988081
110 0.284372818 0.223714373
111 0.606811413 0.284372818
112 0.502439998 0.606811413
113 0.615333519 0.502439998
114 0.322577419 0.615333519
115 0.397384785 0.322577419
116 0.362759642 0.397384785
117 0.388938288 0.362759642
118 0.521095411 0.388938288
119 0.594558280 0.521095411
120 0.343371284 0.594558280
121 0.404010345 0.343371284
122 -0.003278944 0.404010345
123 0.202938851 -0.003278944
124 0.145997734 0.202938851
125 0.149210600 0.145997734
126 0.111577165 0.149210600
127 0.160340925 0.111577165
128 0.284547068 0.160340925
129 0.244090367 0.284547068
130 0.213379417 0.244090367
131 0.173245321 0.213379417
132 0.196816444 0.173245321
133 0.243926045 0.196816444
134 -0.029937223 0.243926045
135 -0.014923937 -0.029937223
136 -0.024968111 -0.014923937
137 -0.086764986 -0.024968111
138 -0.095244536 -0.086764986
139 0.082252614 -0.095244536
140 0.256681193 0.082252614
141 0.045805478 0.256681193
142 0.387465072 0.045805478
143 0.366679286 0.387465072
144 0.520232671 0.366679286
145 0.406052321 0.520232671
146 -0.017147776 0.406052321
147 0.161711402 -0.017147776
148 0.066714892 0.161711402
149 0.261220089 0.066714892
150 0.150971525 0.261220089
151 -0.306936458 0.150971525
152 -0.018981206 -0.306936458
153 0.160063620 -0.018981206
154 0.130168506 0.160063620
155 0.081911917 0.130168506
156 0.179183671 0.081911917
157 0.105719956 0.179183671
158 0.137631882 0.105719956
159 0.153936243 0.137631882
160 -0.019224002 0.153936243
161 0.087421794 -0.019224002
162 0.037562066 0.087421794
163 0.156917535 0.037562066
164 0.140291437 0.156917535
165 -0.561260013 0.140291437
166 -0.183347417 -0.561260013
167 -0.154394836 -0.183347417
168 -0.731792278 -0.154394836
169 -0.264795889 -0.731792278
170 -0.187226883 -0.264795889
171 -0.786421194 -0.187226883
172 -0.836763359 -0.786421194
173 -0.832220487 -0.836763359
174 -0.839849813 -0.832220487
175 -0.806546418 -0.839849813
176 -0.823434305 -0.806546418
177 0.379426473 -0.823434305
178 0.336289567 0.379426473
179 0.345942742 0.336289567
180 0.312143643 0.345942742
181 0.332969875 0.312143643
182 0.298104212 0.332969875
183 -0.806229440 0.298104212
184 -0.795937621 -0.806229440
185 -0.797072517 -0.795937621
186 -0.731390233 -0.797072517
187 -0.712830929 -0.731390233
188 -0.813796742 -0.712830929
189 -0.765581553 -0.813796742
190 -0.841490924 -0.765581553
191 -0.669025262 -0.841490924
192 -0.516632112 -0.669025262
193 -0.619544921 -0.516632112
194 -0.602198241 -0.619544921
> 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/7ulpp1386781244.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/83n1r1386781244.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/9m2bu1386781244.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/fisher/rcomp/tmp/10l1n31386781244.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/fisher/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/fisher/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11jh411386781244.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/1243411386781244.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/13fdd51386781244.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/14oazt1386781244.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/15old11386781244.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/16lqj11386781244.tab")
+ }
>
> try(system("convert tmp/10x311386781243.ps tmp/10x311386781243.png",intern=TRUE))
character(0)
> try(system("convert tmp/2lsec1386781243.ps tmp/2lsec1386781243.png",intern=TRUE))
character(0)
> try(system("convert tmp/3avmq1386781243.ps tmp/3avmq1386781243.png",intern=TRUE))
character(0)
> try(system("convert tmp/4afxt1386781243.ps tmp/4afxt1386781243.png",intern=TRUE))
character(0)
> try(system("convert tmp/54h661386781243.ps tmp/54h661386781243.png",intern=TRUE))
character(0)
> try(system("convert tmp/6i1ou1386781243.ps tmp/6i1ou1386781243.png",intern=TRUE))
character(0)
> try(system("convert tmp/7ulpp1386781244.ps tmp/7ulpp1386781244.png",intern=TRUE))
character(0)
> try(system("convert tmp/83n1r1386781244.ps tmp/83n1r1386781244.png",intern=TRUE))
character(0)
> try(system("convert tmp/9m2bu1386781244.ps tmp/9m2bu1386781244.png",intern=TRUE))
character(0)
> try(system("convert tmp/10l1n31386781244.ps tmp/10l1n31386781244.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
22.588 4.358 26.829