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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+ ,-4.865194
+ ,0.246404
+ ,2.01353
+ ,0.168581
+ ,1
+ ,119.1
+ ,128.442
+ ,88.833
+ ,0.00692
+ ,0.00006
+ ,0.003
+ ,0.01179
+ ,0.02682
+ ,0.01484
+ ,0.01405
+ ,0.02018
+ ,0.04451
+ ,0.04611
+ ,0.65168
+ ,0.659333
+ ,-4.239028
+ ,0.175691
+ ,2.45113
+ ,0.247455
+ ,1
+ ,117.87
+ ,127.349
+ ,95.654
+ ,0.00647
+ ,0.00005
+ ,0.003
+ ,0.01067
+ ,0.03087
+ ,0.01659
+ ,0.01804
+ ,0.02402
+ ,0.04977
+ ,0.02631
+ ,0.6283
+ ,0.652025
+ ,-3.583722
+ ,0.207914
+ ,2.439597
+ ,0.206256
+ ,1
+ ,122.336
+ ,142.369
+ ,94.794
+ ,0.00727
+ ,0.00006
+ ,0.00339
+ ,0.01246
+ ,0.02293
+ ,0.01205
+ ,0.01289
+ ,0.01771
+ ,0.03615
+ ,0.03191
+ ,0.611679
+ ,0.623731
+ ,-5.4351
+ ,0.230532
+ ,2.699645
+ ,0.220546
+ ,1
+ ,117.963
+ ,134.209
+ ,100.757
+ ,0.01813
+ ,0.00015
+ ,0.00718
+ ,0.03351
+ ,0.04912
+ ,0.0261
+ ,0.02161
+ ,0.02916
+ ,0.0783
+ ,0.10748
+ ,0.630547
+ ,0.646786
+ ,-3.444478
+ ,0.303214
+ ,2.964568
+ ,0.261305
+ ,1
+ ,126.144
+ ,154.284
+ ,97.543
+ ,0.00975
+ ,0.00008
+ ,0.00454
+ ,0.01778
+ ,0.02852
+ ,0.015
+ ,0.01581
+ ,0.02157
+ ,0.04499
+ ,0.03828
+ ,0.635015
+ ,0.627337
+ ,-5.070096
+ ,0.280091
+ ,2.8923
+ ,0.249703
+ ,1
+ ,127.93
+ ,138.752
+ ,112.173
+ ,0.00605
+ ,0.00005
+ ,0.00318
+ ,0.00962
+ ,0.03235
+ ,0.0136
+ ,0.0165
+ ,0.03105
+ ,0.04079
+ ,0.02663
+ ,0.654945
+ ,0.675865
+ ,-5.498456
+ ,0.234196
+ ,2.103014
+ ,0.216638
+ ,1
+ ,114.238
+ ,124.393
+ ,77.022
+ ,0.00581
+ ,0.00005
+ ,0.00316
+ ,0.00896
+ ,0.04009
+ ,0.01579
+ ,0.01994
+ ,0.04114
+ ,0.04736
+ ,0.02073
+ ,0.653139
+ ,0.694571
+ ,-5.185987
+ ,0.259229
+ ,2.151121
+ ,0.244948
+ ,1
+ ,115.322
+ ,135.738
+ ,107.802
+ ,0.00619
+ ,0.00005
+ ,0.00329
+ ,0.01057
+ ,0.03273
+ ,0.01644
+ ,0.01722
+ ,0.02931
+ ,0.04933
+ ,0.0281
+ ,0.577802
+ ,0.684373
+ ,-5.283009
+ ,0.226528
+ ,2.442906
+ ,0.238281
+ ,1
+ ,114.554
+ ,126.778
+ ,91.121
+ ,0.00651
+ ,0.00006
+ ,0.0034
+ ,0.01097
+ ,0.03658
+ ,0.01864
+ ,0.0194
+ ,0.03091
+ ,0.05592
+ ,0.02707
+ ,0.685151
+ ,0.719576
+ ,-5.529833
+ ,0.24275
+ ,2.408689
+ ,0.22052
+ ,1
+ ,112.15
+ ,131.669
+ ,97.527
+ ,0.00519
+ ,0.00005
+ ,0.00284
+ ,0.00873
+ ,0.01756
+ ,0.00967
+ ,0.01033
+ ,0.01363
+ ,0.02902
+ ,0.01435
+ ,0.557045
+ ,0.673086
+ ,-5.617124
+ ,0.184896
+ ,1.871871
+ ,0.212386
+ ,1
+ ,102.273
+ ,142.83
+ ,85.902
+ ,0.00907
+ ,0.00009
+ ,0.00461
+ ,0.0148
+ ,0.02814
+ ,0.01579
+ ,0.01553
+ ,0.02073
+ ,0.04736
+ ,0.03882
+ ,0.671378
+ ,0.674562
+ ,-2.929379
+ ,0.396746
+ ,2.560422
+ ,0.367233
+ ,0
+ ,236.2
+ ,244.663
+ ,102.137
+ ,0.00277
+ ,0.00001
+ ,0.00153
+ ,0.00462
+ ,0.02448
+ ,0.0141
+ ,0.01426
+ ,0.01621
+ ,0.04231
+ ,0.0062
+ ,0.469928
+ ,0.628232
+ ,-6.816086
+ ,0.17227
+ ,2.235197
+ ,0.119652
+ ,0
+ ,237.323
+ ,243.709
+ ,229.256
+ ,0.00303
+ ,0.00001
+ ,0.00159
+ ,0.00519
+ ,0.01242
+ ,0.00696
+ ,0.00747
+ ,0.00882
+ ,0.02089
+ ,0.00533
+ ,0.384868
+ ,0.62671
+ ,-7.018057
+ ,0.176316
+ ,1.852402
+ ,0.091604
+ ,0
+ ,260.105
+ ,264.919
+ ,237.303
+ ,0.00339
+ ,0.00001
+ ,0.00186
+ ,0.00616
+ ,0.0203
+ ,0.01186
+ ,0.0123
+ ,0.01367
+ ,0.03557
+ ,0.0091
+ ,0.440988
+ ,0.628058
+ ,-7.517934
+ ,0.160414
+ ,1.881767
+ ,0.075587
+ ,0
+ ,197.569
+ ,217.627
+ ,90.794
+ ,0.00803
+ ,0.00004
+ ,0.00448
+ ,0.0147
+ ,0.02177
+ ,0.01279
+ ,0.01272
+ ,0.01439
+ ,0.03836
+ ,0.01337
+ ,0.372222
+ ,0.725216
+ ,-5.736781
+ ,0.164529
+ ,2.88245
+ ,0.202879
+ ,0
+ ,240.301
+ ,245.135
+ ,219.783
+ ,0.00517
+ ,0.00002
+ ,0.00283
+ ,0.00949
+ ,0.02018
+ ,0.01176
+ ,0.01191
+ ,0.01344
+ ,0.03529
+ ,0.00965
+ ,0.371837
+ ,0.646167
+ ,-7.169701
+ ,0.073298
+ ,2.266432
+ ,0.100881
+ ,0
+ ,244.99
+ ,272.21
+ ,239.17
+ ,0.00451
+ ,0.00002
+ ,0.00237
+ ,0.00837
+ ,0.01897
+ ,0.01084
+ ,0.01121
+ ,0.01255
+ ,0.03253
+ ,0.01049
+ ,0.522812
+ ,0.646818
+ ,-7.3045
+ ,0.171088
+ ,2.095237
+ ,0.09622
+ ,0
+ ,112.547
+ ,133.374
+ ,105.715
+ ,0.00355
+ ,0.00003
+ ,0.0019
+ ,0.00499
+ ,0.01358
+ ,0.00664
+ ,0.00786
+ ,0.0114
+ ,0.01992
+ ,0.00435
+ ,0.413295
+ ,0.7567
+ ,-6.323531
+ ,0.218885
+ ,2.193412
+ ,0.160376
+ ,0
+ ,110.739
+ ,113.597
+ ,100.139
+ ,0.00356
+ ,0.00003
+ ,0.002
+ ,0.0051
+ ,0.01484
+ ,0.00754
+ ,0.0095
+ ,0.01285
+ ,0.02261
+ ,0.0043
+ ,0.36909
+ ,0.776158
+ ,-6.085567
+ ,0.192375
+ ,1.889002
+ ,0.174152
+ ,0
+ ,113.715
+ ,116.443
+ ,96.913
+ ,0.00349
+ ,0.00003
+ ,0.00203
+ ,0.00514
+ ,0.01472
+ ,0.00748
+ ,0.00905
+ ,0.01148
+ ,0.02245
+ ,0.00478
+ ,0.380253
+ ,0.7667
+ ,-5.943501
+ ,0.19215
+ ,1.852542
+ ,0.179677
+ ,0
+ ,117.004
+ ,144.466
+ ,99.923
+ ,0.00353
+ ,0.00003
+ ,0.00218
+ ,0.00528
+ ,0.01657
+ ,0.00881
+ ,0.01062
+ ,0.01318
+ ,0.02643
+ ,0.0059
+ ,0.387482
+ ,0.756482
+ ,-6.012559
+ ,0.229298
+ ,1.872946
+ ,0.163118
+ ,0
+ ,115.38
+ ,123.109
+ ,108.634
+ ,0.00332
+ ,0.00003
+ ,0.00199
+ ,0.0048
+ ,0.01503
+ ,0.00812
+ ,0.00933
+ ,0.01133
+ ,0.02436
+ ,0.00401
+ ,0.405991
+ ,0.761255
+ ,-5.966779
+ ,0.197938
+ ,1.974857
+ ,0.184067
+ ,0
+ ,116.388
+ ,129.038
+ ,108.97
+ ,0.00346
+ ,0.00003
+ ,0.00213
+ ,0.00507
+ ,0.01725
+ ,0.00874
+ ,0.01021
+ ,0.01331
+ ,0.02623
+ ,0.00415
+ ,0.361232
+ ,0.763242
+ ,-6.016891
+ ,0.109256
+ ,2.004719
+ ,0.174429
+ ,1
+ ,151.737
+ ,190.204
+ ,129.859
+ ,0.00314
+ ,0.00002
+ ,0.00162
+ ,0.00406
+ ,0.01469
+ ,0.00728
+ ,0.00886
+ ,0.0123
+ ,0.02184
+ ,0.0057
+ ,0.39661
+ ,0.745957
+ ,-6.486822
+ ,0.197919
+ ,2.449763
+ ,0.132703
+ ,1
+ ,148.79
+ ,158.359
+ ,138.99
+ ,0.00309
+ ,0.00002
+ ,0.00186
+ ,0.00456
+ ,0.01574
+ ,0.00839
+ ,0.00956
+ ,0.01309
+ ,0.02518
+ ,0.00488
+ ,0.402591
+ ,0.762508
+ ,-6.311987
+ ,0.182459
+ ,2.251553
+ ,0.160306
+ ,1
+ ,148.143
+ ,155.982
+ ,135.041
+ ,0.00392
+ ,0.00003
+ ,0.00231
+ ,0.00612
+ ,0.0145
+ ,0.00725
+ ,0.00876
+ ,0.01263
+ ,0.02175
+ ,0.0054
+ ,0.398499
+ ,0.778349
+ ,-5.711205
+ ,0.240875
+ ,2.845109
+ ,0.19273
+ ,1
+ ,150.44
+ ,163.441
+ ,144.736
+ ,0.00396
+ ,0.00003
+ ,0.00233
+ ,0.00619
+ ,0.02551
+ ,0.01321
+ ,0.01574
+ ,0.02148
+ ,0.03964
+ ,0.00611
+ ,0.352396
+ ,0.75932
+ ,-6.261446
+ ,0.183218
+ ,2.264226
+ ,0.144105
+ ,1
+ ,148.462
+ ,161.078
+ ,141.998
+ ,0.00397
+ ,0.00003
+ ,0.00235
+ ,0.00605
+ ,0.01831
+ ,0.0095
+ ,0.01103
+ ,0.01559
+ ,0.02849
+ ,0.00639
+ ,0.408598
+ ,0.768845
+ ,-5.704053
+ ,0.216204
+ ,2.679185
+ ,0.19771
+ ,1
+ ,149.818
+ ,163.417
+ ,144.786
+ ,0.00336
+ ,0.00002
+ ,0.00198
+ ,0.00521
+ ,0.02145
+ ,0.01155
+ ,0.01341
+ ,0.01666
+ ,0.03464
+ ,0.00595
+ ,0.329577
+ ,0.75718
+ ,-6.27717
+ ,0.109397
+ ,2.209021
+ ,0.156368
+ ,0
+ ,117.226
+ ,123.925
+ ,106.656
+ ,0.00417
+ ,0.00004
+ ,0.0027
+ ,0.00558
+ ,0.01909
+ ,0.00864
+ ,0.01223
+ ,0.01949
+ ,0.02592
+ ,0.00955
+ ,0.603515
+ ,0.669565
+ ,-5.61907
+ ,0.191576
+ ,2.027228
+ ,0.215724
+ ,0
+ ,116.848
+ ,217.552
+ ,99.503
+ ,0.00531
+ ,0.00005
+ ,0.00346
+ ,0.0078
+ ,0.01795
+ ,0.0081
+ ,0.01144
+ ,0.01756
+ ,0.02429
+ ,0.01179
+ ,0.663842
+ ,0.656516
+ ,-5.198864
+ ,0.206768
+ ,2.120412
+ ,0.252404
+ ,0
+ ,116.286
+ ,177.291
+ ,96.983
+ ,0.00314
+ ,0.00003
+ ,0.00192
+ ,0.00403
+ ,0.01564
+ ,0.00667
+ ,0.0099
+ ,0.01691
+ ,0.02001
+ ,0.00737
+ ,0.598515
+ ,0.654331
+ ,-5.592584
+ ,0.133917
+ ,2.058658
+ ,0.214346
+ ,0
+ ,116.556
+ ,592.03
+ ,86.228
+ ,0.00496
+ ,0.00004
+ ,0.00263
+ ,0.00762
+ ,0.0166
+ ,0.0082
+ ,0.00972
+ ,0.01491
+ ,0.0246
+ ,0.01397
+ ,0.566424
+ ,0.667654
+ ,-6.431119
+ ,0.15331
+ ,2.161936
+ ,0.120605
+ ,0
+ ,116.342
+ ,581.289
+ ,94.246
+ ,0.00267
+ ,0.00002
+ ,0.00148
+ ,0.00345
+ ,0.013
+ ,0.00631
+ ,0.00789
+ ,0.01144
+ ,0.01892
+ ,0.0068
+ ,0.528485
+ ,0.663884
+ ,-6.359018
+ ,0.116636
+ ,2.152083
+ ,0.138868
+ ,0
+ ,114.563
+ ,119.167
+ ,86.647
+ ,0.00327
+ ,0.00003
+ ,0.00184
+ ,0.00439
+ ,0.01185
+ ,0.00557
+ ,0.00721
+ ,0.01095
+ ,0.01672
+ ,0.00703
+ ,0.555303
+ ,0.659132
+ ,-6.710219
+ ,0.149694
+ ,1.91399
+ ,0.121777
+ ,0
+ ,201.774
+ ,262.707
+ ,78.228
+ ,0.00694
+ ,0.00003
+ ,0.00396
+ ,0.01235
+ ,0.02574
+ ,0.01454
+ ,0.01582
+ ,0.01758
+ ,0.04363
+ ,0.04441
+ ,0.508479
+ ,0.683761
+ ,-6.934474
+ ,0.15989
+ ,2.316346
+ ,0.112838
+ ,0
+ ,174.188
+ ,230.978
+ ,94.261
+ ,0.00459
+ ,0.00003
+ ,0.00259
+ ,0.0079
+ ,0.04087
+ ,0.02336
+ ,0.02498
+ ,0.02745
+ ,0.07008
+ ,0.02764
+ ,0.448439
+ ,0.657899
+ ,-6.538586
+ ,0.121952
+ ,2.657476
+ ,0.13305
+ ,0
+ ,209.516
+ ,253.017
+ ,89.488
+ ,0.00564
+ ,0.00003
+ ,0.00292
+ ,0.00994
+ ,0.02751
+ ,0.01604
+ ,0.01657
+ ,0.01879
+ ,0.04812
+ ,0.0181
+ ,0.431674
+ ,0.683244
+ ,-6.195325
+ ,0.129303
+ ,2.784312
+ ,0.168895
+ ,0
+ ,174.688
+ ,240.005
+ ,74.287
+ ,0.0136
+ ,0.00008
+ ,0.00564
+ ,0.01873
+ ,0.02308
+ ,0.01268
+ ,0.01365
+ ,0.01667
+ ,0.03804
+ ,0.10715
+ ,0.407567
+ ,0.655683
+ ,-6.787197
+ ,0.158453
+ ,2.679772
+ ,0.131728
+ ,0
+ ,198.764
+ ,396.961
+ ,74.904
+ ,0.0074
+ ,0.00004
+ ,0.0039
+ ,0.01109
+ ,0.02296
+ ,0.01265
+ ,0.01321
+ ,0.01588
+ ,0.03794
+ ,0.07223
+ ,0.451221
+ ,0.643956
+ ,-6.744577
+ ,0.207454
+ ,2.138608
+ ,0.123306
+ ,0
+ ,214.289
+ ,260.277
+ ,77.973
+ ,0.00567
+ ,0.00003
+ ,0.00317
+ ,0.00885
+ ,0.01884
+ ,0.01026
+ ,0.01161
+ ,0.01373
+ ,0.03078
+ ,0.04398
+ ,0.462803
+ ,0.664357
+ ,-5.724056
+ ,0.190667
+ ,2.555477
+ ,0.148569)
+ ,dim=c(20
+ ,195)
+ ,dimnames=list(c('status'
+ ,'MDVP:Fo(Hz)'
+ ,'MDVP:Fhi(Hz)'
+ ,'MDVP:Flo(Hz)'
+ ,'MDVP:Jitter(%)'
+ ,'MDVP:Jitter(Abs)'
+ ,'MDVP:PPQ'
+ ,'Jitter:DDP'
+ ,'MDVP:Shimmer'
+ ,'Shimmer:APQ3'
+ ,'Shimmer:APQ5'
+ ,'MDVP:APQ'
+ ,'Shimmer:DDA'
+ ,'NHR'
+ ,'RPDE'
+ ,'DFA'
+ ,'spread1'
+ ,'spread2'
+ ,'D2'
+ ,'PPE')
+ ,1:195))
> y <- array(NA,dim=c(20,195),dimnames=list(c('status','MDVP:Fo(Hz)','MDVP:Fhi(Hz)','MDVP:Flo(Hz)','MDVP:Jitter(%)','MDVP:Jitter(Abs)','MDVP:PPQ','Jitter:DDP','MDVP:Shimmer','Shimmer:APQ3','Shimmer:APQ5','MDVP:APQ','Shimmer:DDA','NHR','RPDE','DFA','spread1','spread2','D2','PPE'),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:PPQ Jitter:DDP MDVP:Shimmer Shimmer:APQ3 Shimmer:APQ5
1 7.0e-05 0.00554 0.01109 0.04374 0.02182 0.03130
2 8.0e-05 0.00696 0.01394 0.06134 0.03134 0.04518
3 9.0e-05 0.00781 0.01633 0.05233 0.02757 0.03858
4 9.0e-05 0.00698 0.01505 0.05492 0.02924 0.04005
5 1.1e-04 0.00908 0.01966 0.06425 0.03490 0.04825
6 8.0e-05 0.00750 0.01388 0.04701 0.02328 0.03526
7 3.0e-05 0.00202 0.00466 0.01608 0.00779 0.00937
8 3.0e-05 0.00182 0.00431 0.01567 0.00829 0.00946
9 6.0e-05 0.00332 0.00880 0.02093 0.01073 0.01277
10 6.0e-05 0.00332 0.00803 0.02838 0.01441 0.01725
11 6.0e-05 0.00330 0.00763 0.02143 0.01079 0.01342
12 6.0e-05 0.00336 0.00844 0.02752 0.01424 0.01641
13 2.0e-05 0.00153 0.00355 0.01259 0.00656 0.00717
14 3.0e-05 0.00208 0.00496 0.01642 0.00728 0.00932
15 2.0e-05 0.00149 0.00364 0.01828 0.01064 0.00972
16 3.0e-05 0.00203 0.00471 0.01503 0.00772 0.00888
17 4.0e-05 0.00292 0.00632 0.02047 0.00969 0.01200
18 4.0e-05 0.00387 0.00853 0.03327 0.01441 0.01893
19 5.0e-05 0.00432 0.01092 0.05517 0.02471 0.03572
20 5.0e-05 0.00399 0.01116 0.03995 0.01721 0.02374
21 5.0e-05 0.00450 0.01285 0.03810 0.01667 0.02383
22 3.0e-05 0.00267 0.00696 0.04137 0.02021 0.02591
23 3.0e-05 0.00247 0.00661 0.04351 0.02228 0.02540
24 3.0e-05 0.00258 0.00663 0.04192 0.02187 0.02470
25 5.0e-05 0.00390 0.01140 0.01659 0.00738 0.00948
26 6.0e-05 0.00375 0.00948 0.03767 0.01732 0.02245
27 3.0e-05 0.00234 0.00750 0.01966 0.00889 0.01169
28 3.0e-05 0.00275 0.00749 0.01919 0.00883 0.01144
29 2.0e-05 0.00176 0.00476 0.01718 0.00769 0.01012
30 3.0e-05 0.00253 0.00841 0.01791 0.00793 0.01057
31 1.0e-05 0.00168 0.00498 0.01098 0.00563 0.00680
32 1.0e-05 0.00138 0.00402 0.01015 0.00504 0.00641
33 1.0e-05 0.00135 0.00339 0.01263 0.00640 0.00825
34 9.0e-06 0.00107 0.00278 0.00954 0.00469 0.00606
35 9.0e-06 0.00106 0.00283 0.00958 0.00468 0.00610
36 1.0e-05 0.00115 0.00314 0.01194 0.00586 0.00760
37 2.0e-05 0.00241 0.00700 0.02126 0.01154 0.01347
38 2.0e-05 0.00218 0.00616 0.01851 0.00938 0.01160
39 2.0e-05 0.00166 0.00459 0.01444 0.00726 0.00885
40 2.0e-05 0.00182 0.00504 0.01663 0.00829 0.01003
41 2.0e-05 0.00175 0.00496 0.01495 0.00774 0.00941
42 1.0e-05 0.00147 0.00403 0.01463 0.00742 0.00901
43 1.0e-05 0.00182 0.00507 0.01752 0.01035 0.01024
44 1.0e-05 0.00173 0.00470 0.01760 0.01006 0.01038
45 9.0e-06 0.00137 0.00327 0.01419 0.00777 0.00898
46 9.0e-06 0.00139 0.00350 0.01494 0.00847 0.00879
47 1.0e-05 0.00148 0.00380 0.01608 0.00906 0.00977
48 7.0e-06 0.00113 0.00276 0.01152 0.00614 0.00730
49 4.0e-05 0.00203 0.00507 0.01613 0.00855 0.00776
50 3.0e-05 0.00155 0.00373 0.01681 0.00930 0.00802
51 3.0e-05 0.00167 0.00422 0.02184 0.01241 0.01024
52 4.0e-05 0.00169 0.00393 0.02033 0.01143 0.00959
53 3.0e-05 0.00166 0.00411 0.02297 0.01323 0.01072
54 4.0e-05 0.00183 0.00495 0.02498 0.01396 0.01219
55 7.0e-05 0.00486 0.01046 0.02719 0.01483 0.01609
56 8.0e-05 0.00539 0.01193 0.03209 0.01789 0.01992
57 7.0e-05 0.00514 0.01056 0.03715 0.02032 0.02302
58 6.0e-05 0.00469 0.00898 0.02293 0.01189 0.01459
59 7.0e-05 0.00493 0.01003 0.02645 0.01394 0.01625
60 8.0e-05 0.00520 0.01120 0.03225 0.01805 0.01974
61 1.0e-05 0.00152 0.00442 0.01861 0.00975 0.01258
62 1.0e-05 0.00151 0.00461 0.01906 0.01013 0.01296
63 1.0e-05 0.00144 0.00457 0.01643 0.00867 0.01108
64 1.0e-05 0.00155 0.00526 0.01644 0.00882 0.01075
65 9.0e-06 0.00113 0.00342 0.01457 0.00769 0.00957
66 1.0e-05 0.00140 0.00408 0.01745 0.00942 0.01160
67 6.0e-05 0.00440 0.01289 0.03198 0.01830 0.01810
68 7.0e-05 0.00463 0.01520 0.03111 0.01638 0.01759
69 8.0e-05 0.00467 0.01941 0.05384 0.03152 0.02422
70 5.0e-05 0.00354 0.01400 0.05428 0.03357 0.02494
71 6.0e-05 0.00419 0.01407 0.03485 0.01868 0.01906
72 7.0e-05 0.00478 0.01601 0.04978 0.02749 0.02466
73 3.0e-05 0.00220 0.00540 0.01706 0.00974 0.00925
74 5.0e-05 0.00329 0.00805 0.02448 0.01373 0.01375
75 4.0e-05 0.00283 0.00780 0.02442 0.01432 0.01325
76 5.0e-05 0.00289 0.00831 0.02215 0.01284 0.01219
77 4.0e-05 0.00289 0.00810 0.03999 0.02413 0.02231
78 4.0e-05 0.00280 0.00677 0.02199 0.01284 0.01199
79 6.0e-05 0.00332 0.00994 0.03202 0.01803 0.01886
80 1.0e-04 0.00576 0.01865 0.03121 0.01773 0.01783
81 7.0e-05 0.00415 0.01168 0.04024 0.02266 0.02451
82 7.0e-05 0.00371 0.01283 0.03156 0.01792 0.01841
83 6.0e-05 0.00348 0.01053 0.02427 0.01371 0.01421
84 4.0e-05 0.00258 0.00742 0.02223 0.01277 0.01343
85 4.0e-05 0.00420 0.01254 0.04795 0.02679 0.03022
86 2.0e-05 0.00244 0.00659 0.03852 0.02107 0.02493
87 2.0e-05 0.00194 0.00488 0.03759 0.02073 0.02415
88 3.0e-05 0.00312 0.00862 0.06511 0.03671 0.04159
89 3.0e-05 0.00254 0.00710 0.06727 0.03788 0.04254
90 4.0e-05 0.00419 0.01172 0.04313 0.02297 0.02768
91 4.0e-05 0.00453 0.01161 0.06640 0.03650 0.04282
92 3.0e-05 0.00227 0.00672 0.07959 0.04421 0.04962
93 3.0e-05 0.00256 0.00750 0.04190 0.02383 0.02521
94 3.0e-05 0.00226 0.00574 0.05925 0.03341 0.03794
95 2.0e-05 0.00196 0.00587 0.03716 0.02062 0.02321
96 2.0e-05 0.00197 0.00602 0.03272 0.01813 0.01909
97 2.0e-05 0.00184 0.00535 0.03381 0.01806 0.02024
98 1.0e-04 0.00623 0.02228 0.03886 0.02135 0.02174
99 1.1e-04 0.00655 0.02478 0.04689 0.02542 0.02630
100 1.5e-04 0.00990 0.03476 0.06734 0.03611 0.03963
101 2.6e-04 0.01522 0.06433 0.09178 0.05358 0.04791
102 1.2e-04 0.00909 0.02716 0.06170 0.03223 0.03672
103 2.2e-04 0.01628 0.05563 0.09419 0.05551 0.05005
104 2.0e-05 0.00136 0.00315 0.01131 0.00522 0.00659
105 1.0e-05 0.00100 0.00229 0.01030 0.00469 0.00582
106 2.0e-05 0.00134 0.00349 0.01346 0.00660 0.00818
107 1.0e-05 0.00092 0.00204 0.01064 0.00522 0.00632
108 2.0e-05 0.00122 0.00346 0.01450 0.00633 0.00788
109 1.0e-05 0.00096 0.00225 0.01024 0.00455 0.00576
110 4.0e-05 0.00389 0.01351 0.03044 0.01771 0.01815
111 3.0e-05 0.00337 0.01112 0.02286 0.01192 0.01439
112 3.0e-05 0.00339 0.01105 0.01761 0.00952 0.01058
113 4.0e-05 0.00485 0.01506 0.02378 0.01277 0.01483
114 3.0e-05 0.00280 0.00964 0.01680 0.00861 0.01017
115 2.0e-05 0.00246 0.00905 0.02105 0.01107 0.01284
116 6.0e-05 0.00385 0.01211 0.01843 0.00796 0.00832
117 3.0e-05 0.00207 0.00642 0.01458 0.00606 0.00747
118 3.0e-05 0.00261 0.00731 0.01725 0.00757 0.00971
119 3.0e-05 0.00194 0.00472 0.01279 0.00617 0.00744
120 2.0e-05 0.00128 0.00381 0.01299 0.00679 0.00631
121 5.0e-05 0.00314 0.00723 0.02008 0.00849 0.01117
122 3.0e-05 0.00221 0.00628 0.01169 0.00534 0.00630
123 5.0e-05 0.00398 0.01218 0.04479 0.02587 0.02567
124 5.0e-05 0.00449 0.01517 0.02503 0.01372 0.01580
125 4.0e-05 0.00395 0.01209 0.02343 0.01289 0.01420
126 5.0e-05 0.00422 0.01242 0.02362 0.01235 0.01495
127 4.0e-05 0.00327 0.00883 0.02791 0.01484 0.01805
128 4.0e-05 0.00351 0.01104 0.02857 0.01547 0.01859
129 4.0e-05 0.00192 0.00641 0.01033 0.00538 0.00570
130 2.0e-05 0.00135 0.00349 0.01022 0.00476 0.00588
131 4.0e-05 0.00238 0.00808 0.01412 0.00703 0.00820
132 3.0e-05 0.00205 0.00671 0.01516 0.00721 0.00815
133 3.0e-05 0.00170 0.00508 0.01201 0.00633 0.00701
134 3.0e-05 0.00171 0.00504 0.01043 0.00490 0.00621
135 6.0e-05 0.00319 0.00873 0.04932 0.02683 0.03112
136 4.0e-05 0.00315 0.00731 0.04128 0.02229 0.02592
137 4.0e-05 0.00283 0.00658 0.04879 0.02385 0.02973
138 4.0e-05 0.00312 0.00772 0.05279 0.02896 0.03347
139 4.0e-05 0.00290 0.00715 0.05643 0.03070 0.03530
140 3.0e-05 0.00232 0.00542 0.03026 0.01514 0.01812
141 3.0e-05 0.00269 0.00696 0.03273 0.01713 0.01964
142 4.0e-05 0.00428 0.01285 0.06725 0.04016 0.04003
143 2.0e-05 0.00215 0.00546 0.03527 0.02055 0.02076
144 2.0e-05 0.00211 0.00568 0.01997 0.01117 0.01177
145 1.0e-05 0.00133 0.00301 0.02662 0.01475 0.01558
146 2.0e-05 0.00188 0.00506 0.02536 0.01379 0.01478
147 9.0e-05 0.00946 0.02589 0.08143 0.03804 0.05426
148 8.0e-05 0.00819 0.02546 0.06050 0.02865 0.04101
149 9.0e-05 0.01027 0.02987 0.07118 0.03474 0.04580
150 8.0e-05 0.00963 0.02756 0.07170 0.03515 0.04265
151 1.0e-04 0.01154 0.03225 0.05830 0.02699 0.03714
152 1.6e-04 0.01958 0.05401 0.11908 0.05647 0.07940
153 1.4e-04 0.01699 0.04705 0.08684 0.04284 0.05556
154 6.0e-05 0.00332 0.01164 0.02534 0.01340 0.01399
155 6.0e-05 0.00300 0.01179 0.02682 0.01484 0.01405
156 5.0e-05 0.00300 0.01067 0.03087 0.01659 0.01804
157 6.0e-05 0.00339 0.01246 0.02293 0.01205 0.01289
158 1.5e-04 0.00718 0.03351 0.04912 0.02610 0.02161
159 8.0e-05 0.00454 0.01778 0.02852 0.01500 0.01581
160 5.0e-05 0.00318 0.00962 0.03235 0.01360 0.01650
161 5.0e-05 0.00316 0.00896 0.04009 0.01579 0.01994
162 5.0e-05 0.00329 0.01057 0.03273 0.01644 0.01722
163 6.0e-05 0.00340 0.01097 0.03658 0.01864 0.01940
164 5.0e-05 0.00284 0.00873 0.01756 0.00967 0.01033
165 9.0e-05 0.00461 0.01480 0.02814 0.01579 0.01553
166 1.0e-05 0.00153 0.00462 0.02448 0.01410 0.01426
167 1.0e-05 0.00159 0.00519 0.01242 0.00696 0.00747
168 1.0e-05 0.00186 0.00616 0.02030 0.01186 0.01230
169 4.0e-05 0.00448 0.01470 0.02177 0.01279 0.01272
170 2.0e-05 0.00283 0.00949 0.02018 0.01176 0.01191
171 2.0e-05 0.00237 0.00837 0.01897 0.01084 0.01121
172 3.0e-05 0.00190 0.00499 0.01358 0.00664 0.00786
173 3.0e-05 0.00200 0.00510 0.01484 0.00754 0.00950
174 3.0e-05 0.00203 0.00514 0.01472 0.00748 0.00905
175 3.0e-05 0.00218 0.00528 0.01657 0.00881 0.01062
176 3.0e-05 0.00199 0.00480 0.01503 0.00812 0.00933
177 3.0e-05 0.00213 0.00507 0.01725 0.00874 0.01021
178 2.0e-05 0.00162 0.00406 0.01469 0.00728 0.00886
179 2.0e-05 0.00186 0.00456 0.01574 0.00839 0.00956
180 3.0e-05 0.00231 0.00612 0.01450 0.00725 0.00876
181 3.0e-05 0.00233 0.00619 0.02551 0.01321 0.01574
182 3.0e-05 0.00235 0.00605 0.01831 0.00950 0.01103
183 2.0e-05 0.00198 0.00521 0.02145 0.01155 0.01341
184 4.0e-05 0.00270 0.00558 0.01909 0.00864 0.01223
185 5.0e-05 0.00346 0.00780 0.01795 0.00810 0.01144
186 3.0e-05 0.00192 0.00403 0.01564 0.00667 0.00990
187 4.0e-05 0.00263 0.00762 0.01660 0.00820 0.00972
188 2.0e-05 0.00148 0.00345 0.01300 0.00631 0.00789
189 3.0e-05 0.00184 0.00439 0.01185 0.00557 0.00721
190 3.0e-05 0.00396 0.01235 0.02574 0.01454 0.01582
191 3.0e-05 0.00259 0.00790 0.04087 0.02336 0.02498
192 3.0e-05 0.00292 0.00994 0.02751 0.01604 0.01657
193 8.0e-05 0.00564 0.01873 0.02308 0.01268 0.01365
194 4.0e-05 0.00390 0.01109 0.02296 0.01265 0.01321
195 3.0e-05 0.00317 0.00885 0.01884 0.01026 0.01161
MDVP:APQ Shimmer:DDA NHR RPDE DFA spread1 spread2 D2
1 0.02971 0.06545 0.02211 0.414783 0.815285 -4.813031 0.266482 2.301442
2 0.04368 0.09403 0.01929 0.458359 0.819521 -4.075192 0.335590 2.486855
3 0.03590 0.08270 0.01309 0.429895 0.825288 -4.443179 0.311173 2.342259
4 0.03772 0.08771 0.01353 0.434969 0.819235 -4.117501 0.334147 2.405554
5 0.04465 0.10470 0.01767 0.417356 0.823484 -3.747787 0.234513 2.332180
6 0.03243 0.06985 0.01222 0.415564 0.825069 -4.242867 0.299111 2.187560
7 0.01351 0.02337 0.00607 0.596040 0.764112 -5.634322 0.257682 1.854785
8 0.01256 0.02487 0.00344 0.637420 0.763262 -6.167603 0.183721 2.064693
9 0.01717 0.03218 0.01070 0.615551 0.773587 -5.498678 0.327769 2.322511
10 0.02444 0.04324 0.01022 0.547037 0.798463 -5.011879 0.325996 2.432792
11 0.01892 0.03237 0.01166 0.611137 0.776156 -5.249770 0.391002 2.407313
12 0.02214 0.04272 0.01141 0.583390 0.792520 -4.960234 0.363566 2.642476
13 0.01140 0.01968 0.00581 0.460600 0.646846 -6.547148 0.152813 2.041277
14 0.01797 0.02184 0.01041 0.430166 0.665833 -5.660217 0.254989 2.519422
15 0.01246 0.03191 0.00609 0.474791 0.654027 -6.105098 0.203653 2.125618
16 0.01359 0.02316 0.00839 0.565924 0.658245 -5.340115 0.210185 2.205546
17 0.02074 0.02908 0.01859 0.567380 0.644692 -5.440040 0.239764 2.264501
18 0.03430 0.04322 0.02919 0.631099 0.605417 -2.931070 0.434326 3.007463
19 0.05767 0.07413 0.03160 0.665318 0.719467 -3.949079 0.357870 3.109010
20 0.04310 0.05164 0.03365 0.649554 0.686080 -4.554466 0.340176 2.856676
21 0.04055 0.05000 0.03871 0.660125 0.704087 -4.095442 0.262564 2.739710
22 0.04525 0.06062 0.01849 0.629017 0.698951 -5.186960 0.237622 2.557536
23 0.04246 0.06685 0.01280 0.619060 0.679834 -4.330956 0.262384 2.916777
24 0.03772 0.06562 0.01840 0.537264 0.686894 -5.248776 0.210279 2.547508
25 0.01497 0.02214 0.01778 0.397937 0.732479 -5.557447 0.220890 2.692176
26 0.03780 0.05197 0.02887 0.522746 0.737948 -5.571843 0.236853 2.846369
27 0.01872 0.02666 0.01095 0.418622 0.720916 -6.183590 0.226278 2.589702
28 0.01826 0.02650 0.01328 0.358773 0.726652 -6.271690 0.196102 2.314209
29 0.01661 0.02307 0.00677 0.470478 0.676258 -7.120925 0.279789 2.241742
30 0.01799 0.02380 0.01170 0.427785 0.723797 -6.635729 0.209866 1.957961
31 0.00802 0.01689 0.00339 0.422229 0.741367 -7.348300 0.177551 1.743867
32 0.00762 0.01513 0.00167 0.432439 0.742055 -7.682587 0.173319 2.103106
33 0.00951 0.01919 0.00119 0.465946 0.738703 -7.067931 0.175181 1.512275
34 0.00719 0.01407 0.00072 0.368535 0.742133 -7.695734 0.178540 1.544609
35 0.00726 0.01403 0.00065 0.340068 0.741899 -7.964984 0.163519 1.423287
36 0.00957 0.01758 0.00135 0.344252 0.742737 -7.777685 0.170183 2.447064
37 0.01612 0.03463 0.00586 0.360148 0.778834 -6.149653 0.218037 2.477082
38 0.01491 0.02814 0.00340 0.341435 0.783626 -6.006414 0.196371 2.536527
39 0.01190 0.02177 0.00231 0.403884 0.766209 -6.452058 0.212294 2.269398
40 0.01366 0.02488 0.00265 0.396793 0.758324 -6.006647 0.266892 2.382544
41 0.01233 0.02321 0.00231 0.326480 0.765623 -6.647379 0.201095 2.374073
42 0.01234 0.02226 0.00257 0.306443 0.759203 -7.044105 0.063412 2.361532
43 0.01133 0.03104 0.00740 0.305062 0.654172 -7.310550 0.098648 2.416838
44 0.01251 0.03017 0.00675 0.457702 0.634267 -6.793547 0.158266 2.256699
45 0.01033 0.02330 0.00454 0.438296 0.635285 -7.057869 0.091608 2.330716
46 0.01014 0.02542 0.00476 0.431285 0.638928 -6.995820 0.102083 2.365800
47 0.01149 0.02719 0.00476 0.467489 0.631653 -7.156076 0.127642 2.392122
48 0.00860 0.01841 0.00432 0.610367 0.635204 -7.319510 0.200873 2.028612
49 0.01433 0.02566 0.00839 0.579597 0.733659 -6.439398 0.266392 2.079922
50 0.01400 0.02789 0.00462 0.538688 0.754073 -6.482096 0.264967 2.054419
51 0.01685 0.03724 0.00479 0.553134 0.775933 -6.650471 0.254498 1.840198
52 0.01614 0.03429 0.00474 0.507504 0.760361 -6.689151 0.291954 2.431854
53 0.01677 0.03969 0.00481 0.459766 0.766204 -7.072419 0.220434 1.972297
54 0.01947 0.04188 0.00484 0.420383 0.785714 -6.836811 0.269866 2.223719
55 0.02067 0.04450 0.01036 0.536009 0.819032 -4.649573 0.205558 1.986899
56 0.02454 0.05368 0.01180 0.558586 0.811843 -4.333543 0.221727 2.014606
57 0.02802 0.06097 0.00969 0.541781 0.821364 -4.438453 0.238298 1.922940
58 0.01948 0.03568 0.00681 0.530529 0.817756 -4.608260 0.290024 2.021591
59 0.02137 0.04183 0.00786 0.540049 0.813432 -4.476755 0.262633 1.827012
60 0.02519 0.05414 0.01143 0.547975 0.817396 -4.609161 0.221711 1.831691
61 0.01382 0.02925 0.00871 0.341788 0.678874 -7.040508 0.066994 2.460791
62 0.01340 0.03039 0.00301 0.447979 0.686264 -7.293801 0.086372 2.321560
63 0.01200 0.02602 0.00340 0.364867 0.694399 -6.966321 0.095882 2.278687
64 0.01179 0.02647 0.00351 0.256570 0.683296 -7.245620 0.018689 2.498224
65 0.01016 0.02308 0.00300 0.276850 0.673636 -7.496264 0.056844 2.003032
66 0.01234 0.02827 0.00420 0.305429 0.681811 -7.314237 0.006274 2.118596
67 0.02428 0.05490 0.02183 0.460139 0.720908 -5.409423 0.226850 2.359973
68 0.02603 0.04914 0.02659 0.498133 0.729067 -5.324574 0.205660 2.291558
69 0.03392 0.09455 0.04882 0.513237 0.731444 -5.869750 0.151814 2.118496
70 0.03635 0.10070 0.02431 0.487407 0.727313 -6.261141 0.120956 2.137075
71 0.02949 0.05605 0.02599 0.489345 0.730387 -5.720868 0.158830 2.277927
72 0.03736 0.08247 0.03361 0.543299 0.733232 -5.207985 0.224852 2.642276
73 0.01345 0.02921 0.00442 0.495954 0.762959 -5.791820 0.329066 2.205024
74 0.01956 0.04120 0.00623 0.509127 0.789532 -5.389129 0.306636 1.928708
75 0.01831 0.04295 0.00479 0.437031 0.815908 -5.313360 0.201861 2.225815
76 0.01715 0.03851 0.00472 0.463514 0.807217 -5.477592 0.315074 1.862092
77 0.02704 0.07238 0.00905 0.489538 0.789977 -5.775966 0.341169 2.007923
78 0.01636 0.03852 0.00420 0.429484 0.816340 -5.391029 0.250572 1.777901
79 0.02455 0.05408 0.01062 0.644954 0.779612 -5.115212 0.249494 2.017753
80 0.02139 0.05320 0.02220 0.594387 0.790117 -4.913885 0.265699 2.398422
81 0.02876 0.06799 0.01823 0.544805 0.770466 -4.441519 0.155097 2.645959
82 0.02190 0.05377 0.01825 0.576084 0.778747 -5.132032 0.210458 2.232576
83 0.01751 0.04114 0.01237 0.554610 0.787896 -5.022288 0.146948 2.428306
84 0.01552 0.03831 0.00882 0.576644 0.772416 -6.025367 0.078202 2.053601
85 0.03510 0.08037 0.05470 0.556494 0.729586 -5.288912 0.343073 3.099301
86 0.02877 0.06321 0.02782 0.583574 0.727747 -5.657899 0.315903 3.098256
87 0.02784 0.06219 0.03151 0.598714 0.712199 -6.366916 0.335753 2.654271
88 0.04683 0.11012 0.04824 0.602874 0.740837 -5.515071 0.299549 3.136550
89 0.04802 0.11363 0.04214 0.599371 0.743937 -5.783272 0.299793 3.007096
90 0.03455 0.06892 0.07223 0.590951 0.745526 -4.379411 0.375531 3.671155
91 0.05114 0.10949 0.08725 0.653410 0.733165 -4.508984 0.389232 3.317586
92 0.05690 0.13262 0.01658 0.501037 0.714360 -6.411497 0.207156 2.344876
93 0.03051 0.07150 0.01914 0.454444 0.734504 -5.952058 0.087840 2.344336
94 0.04398 0.10024 0.01211 0.447456 0.697790 -6.152551 0.173520 2.080121
95 0.02764 0.06185 0.00850 0.502380 0.712170 -6.251425 0.188056 2.143851
96 0.02571 0.05439 0.01018 0.447285 0.705658 -6.247076 0.180528 2.344348
97 0.02809 0.05417 0.00852 0.366329 0.693429 -6.417440 0.194627 2.473239
98 0.03088 0.06406 0.08151 0.629574 0.714485 -4.020042 0.265315 2.671825
99 0.03908 0.07625 0.10323 0.571010 0.690892 -5.159169 0.202146 2.441612
100 0.05783 0.10833 0.16744 0.638545 0.674953 -3.760348 0.242861 2.634633
101 0.06196 0.16074 0.31482 0.671299 0.656846 -3.700544 0.260481 2.991063
102 0.05174 0.09669 0.11843 0.639808 0.643327 -4.202730 0.310163 2.638279
103 0.06023 0.16654 0.25930 0.596362 0.641418 -3.269487 0.270641 2.690917
104 0.01009 0.01567 0.00495 0.296888 0.722356 -6.878393 0.089267 2.004055
105 0.00871 0.01406 0.00243 0.263654 0.691483 -7.111576 0.144780 2.065477
106 0.01059 0.01979 0.00578 0.365488 0.719974 -6.997403 0.210279 1.994387
107 0.00928 0.01567 0.00233 0.334171 0.677930 -6.981201 0.184550 2.129924
108 0.01267 0.01898 0.00659 0.393563 0.700246 -6.600023 0.249172 2.499148
109 0.00993 0.01364 0.00238 0.311369 0.676066 -6.739151 0.160686 2.296873
110 0.02084 0.05312 0.00947 0.497554 0.740539 -5.845099 0.278679 2.608749
111 0.01852 0.03576 0.00704 0.436084 0.727863 -5.258320 0.256454 2.550961
112 0.01307 0.02855 0.00830 0.338097 0.712466 -6.471427 0.184378 2.502336
113 0.01767 0.03831 0.01316 0.498877 0.722085 -4.876336 0.212054 2.376749
114 0.01301 0.02583 0.00620 0.441097 0.722254 -5.963040 0.250283 2.489191
115 0.01604 0.03320 0.01048 0.331508 0.715121 -6.729713 0.181701 2.938114
116 0.01271 0.02389 0.06051 0.407701 0.662668 -4.673241 0.261549 2.702355
117 0.01312 0.01818 0.01554 0.450798 0.653823 -6.051233 0.273280 2.640798
118 0.01652 0.02270 0.01802 0.486738 0.676023 -4.597834 0.372114 2.975889
119 0.01151 0.01851 0.00856 0.470422 0.655239 -4.913137 0.393056 2.816781
120 0.01075 0.02038 0.00681 0.462516 0.582710 -5.517173 0.389295 2.925862
121 0.01734 0.02548 0.02350 0.487756 0.684130 -6.186128 0.279933 2.686240
122 0.01104 0.01603 0.01161 0.400088 0.656182 -4.711007 0.281618 2.655744
123 0.03220 0.07761 0.01968 0.538016 0.741480 -5.418787 0.160267 2.090438
124 0.01931 0.04115 0.01813 0.589956 0.732903 -5.445140 0.142466 2.174306
125 0.01720 0.03867 0.02020 0.618663 0.728421 -5.944191 0.143359 1.929715
126 0.01944 0.03706 0.01874 0.637518 0.735546 -5.594275 0.127950 1.765957
127 0.02259 0.04451 0.01794 0.623209 0.738245 -5.540351 0.087165 1.821297
128 0.02301 0.04641 0.01796 0.585169 0.736964 -5.825257 0.115697 1.996146
129 0.00811 0.01614 0.01724 0.457541 0.699787 -6.890021 0.152941 2.328513
130 0.00903 0.01428 0.00487 0.491345 0.718839 -5.892061 0.195976 2.108873
131 0.01194 0.02110 0.01610 0.467160 0.724045 -6.135296 0.203630 2.539724
132 0.01310 0.02164 0.01015 0.468621 0.735136 -6.112667 0.217013 2.527742
133 0.00915 0.01898 0.00903 0.470972 0.721308 -5.436135 0.254909 2.516320
134 0.00903 0.01471 0.00504 0.482296 0.723096 -6.448134 0.178713 2.034827
135 0.03651 0.08050 0.03031 0.637814 0.744064 -5.301321 0.320385 2.375138
136 0.03316 0.06688 0.02529 0.653427 0.706687 -5.333619 0.322044 2.631793
137 0.04370 0.07154 0.02278 0.647900 0.708144 -4.378916 0.300067 2.445502
138 0.04134 0.08689 0.03690 0.625362 0.708617 -4.654894 0.304107 2.672362
139 0.04451 0.09211 0.02629 0.640945 0.701404 -5.634576 0.306014 2.419253
140 0.02770 0.04543 0.01827 0.624811 0.696049 -5.866357 0.233070 2.445646
141 0.02824 0.05139 0.02485 0.677131 0.685057 -4.796845 0.397749 2.963799
142 0.04464 0.12047 0.04238 0.606344 0.665945 -5.410336 0.288917 2.665133
143 0.02530 0.06165 0.01728 0.606273 0.661735 -5.585259 0.310746 2.465528
144 0.01506 0.03350 0.02010 0.536102 0.632631 -5.898673 0.213353 2.470746
145 0.02006 0.04426 0.01049 0.497480 0.630409 -6.132663 0.220617 2.576563
146 0.01909 0.04137 0.01493 0.566849 0.574282 -5.456811 0.345238 2.840556
147 0.08808 0.11411 0.07530 0.561610 0.793509 -3.297668 0.414758 3.413649
148 0.06359 0.08595 0.06057 0.478024 0.768974 -4.276605 0.355736 3.142364
149 0.06824 0.10422 0.08069 0.552870 0.764036 -3.377325 0.335357 3.274865
150 0.06460 0.10546 0.07889 0.427627 0.775708 -4.892495 0.262281 2.910213
151 0.06259 0.08096 0.10952 0.507826 0.762726 -4.484303 0.340256 2.958815
152 0.13778 0.16942 0.21713 0.625866 0.768320 -2.434031 0.450493 3.079221
153 0.08318 0.12851 0.16265 0.584164 0.754449 -2.839756 0.356224 3.184027
154 0.02056 0.04019 0.04179 0.566867 0.670475 -4.865194 0.246404 2.013530
155 0.02018 0.04451 0.04611 0.651680 0.659333 -4.239028 0.175691 2.451130
156 0.02402 0.04977 0.02631 0.628300 0.652025 -3.583722 0.207914 2.439597
157 0.01771 0.03615 0.03191 0.611679 0.623731 -5.435100 0.230532 2.699645
158 0.02916 0.07830 0.10748 0.630547 0.646786 -3.444478 0.303214 2.964568
159 0.02157 0.04499 0.03828 0.635015 0.627337 -5.070096 0.280091 2.892300
160 0.03105 0.04079 0.02663 0.654945 0.675865 -5.498456 0.234196 2.103014
161 0.04114 0.04736 0.02073 0.653139 0.694571 -5.185987 0.259229 2.151121
162 0.02931 0.04933 0.02810 0.577802 0.684373 -5.283009 0.226528 2.442906
163 0.03091 0.05592 0.02707 0.685151 0.719576 -5.529833 0.242750 2.408689
164 0.01363 0.02902 0.01435 0.557045 0.673086 -5.617124 0.184896 1.871871
165 0.02073 0.04736 0.03882 0.671378 0.674562 -2.929379 0.396746 2.560422
166 0.01621 0.04231 0.00620 0.469928 0.628232 -6.816086 0.172270 2.235197
167 0.00882 0.02089 0.00533 0.384868 0.626710 -7.018057 0.176316 1.852402
168 0.01367 0.03557 0.00910 0.440988 0.628058 -7.517934 0.160414 1.881767
169 0.01439 0.03836 0.01337 0.372222 0.725216 -5.736781 0.164529 2.882450
170 0.01344 0.03529 0.00965 0.371837 0.646167 -7.169701 0.073298 2.266432
171 0.01255 0.03253 0.01049 0.522812 0.646818 -7.304500 0.171088 2.095237
172 0.01140 0.01992 0.00435 0.413295 0.756700 -6.323531 0.218885 2.193412
173 0.01285 0.02261 0.00430 0.369090 0.776158 -6.085567 0.192375 1.889002
174 0.01148 0.02245 0.00478 0.380253 0.766700 -5.943501 0.192150 1.852542
175 0.01318 0.02643 0.00590 0.387482 0.756482 -6.012559 0.229298 1.872946
176 0.01133 0.02436 0.00401 0.405991 0.761255 -5.966779 0.197938 1.974857
177 0.01331 0.02623 0.00415 0.361232 0.763242 -6.016891 0.109256 2.004719
178 0.01230 0.02184 0.00570 0.396610 0.745957 -6.486822 0.197919 2.449763
179 0.01309 0.02518 0.00488 0.402591 0.762508 -6.311987 0.182459 2.251553
180 0.01263 0.02175 0.00540 0.398499 0.778349 -5.711205 0.240875 2.845109
181 0.02148 0.03964 0.00611 0.352396 0.759320 -6.261446 0.183218 2.264226
182 0.01559 0.02849 0.00639 0.408598 0.768845 -5.704053 0.216204 2.679185
183 0.01666 0.03464 0.00595 0.329577 0.757180 -6.277170 0.109397 2.209021
184 0.01949 0.02592 0.00955 0.603515 0.669565 -5.619070 0.191576 2.027228
185 0.01756 0.02429 0.01179 0.663842 0.656516 -5.198864 0.206768 2.120412
186 0.01691 0.02001 0.00737 0.598515 0.654331 -5.592584 0.133917 2.058658
187 0.01491 0.02460 0.01397 0.566424 0.667654 -6.431119 0.153310 2.161936
188 0.01144 0.01892 0.00680 0.528485 0.663884 -6.359018 0.116636 2.152083
189 0.01095 0.01672 0.00703 0.555303 0.659132 -6.710219 0.149694 1.913990
190 0.01758 0.04363 0.04441 0.508479 0.683761 -6.934474 0.159890 2.316346
191 0.02745 0.07008 0.02764 0.448439 0.657899 -6.538586 0.121952 2.657476
192 0.01879 0.04812 0.01810 0.431674 0.683244 -6.195325 0.129303 2.784312
193 0.01667 0.03804 0.10715 0.407567 0.655683 -6.787197 0.158453 2.679772
194 0.01588 0.03794 0.07223 0.451221 0.643956 -6.744577 0.207454 2.138608
195 0.01373 0.03078 0.04398 0.462803 0.664357 -5.724056 0.190667 2.555477
PPE
1 0.284654
2 0.368674
3 0.332634
4 0.368975
5 0.410335
6 0.357775
7 0.211756
8 0.163755
9 0.231571
10 0.271362
11 0.249740
12 0.275931
13 0.138512
14 0.199889
15 0.170100
16 0.234589
17 0.218164
18 0.430788
19 0.377429
20 0.322111
21 0.365391
22 0.259765
23 0.285695
24 0.253556
25 0.215961
26 0.219514
27 0.147403
28 0.162999
29 0.108514
30 0.135242
31 0.085569
32 0.068501
33 0.096320
34 0.056141
35 0.044539
36 0.057610
37 0.165827
38 0.173218
39 0.141929
40 0.160691
41 0.130554
42 0.115730
43 0.095032
44 0.117399
45 0.091470
46 0.102706
47 0.097336
48 0.086398
49 0.133867
50 0.128872
51 0.103561
52 0.105993
53 0.119308
54 0.147491
55 0.316700
56 0.344834
57 0.335041
58 0.314464
59 0.326197
60 0.316395
61 0.101516
62 0.098555
63 0.103224
64 0.093534
65 0.073581
66 0.091546
67 0.226156
68 0.226247
69 0.185580
70 0.141958
71 0.180828
72 0.242981
73 0.188180
74 0.225461
75 0.244512
76 0.228624
77 0.193918
78 0.232744
79 0.260015
80 0.277948
81 0.327978
82 0.260633
83 0.264666
84 0.177275
85 0.242119
86 0.200423
87 0.144614
88 0.220968
89 0.194052
90 0.332086
91 0.301952
92 0.134120
93 0.186489
94 0.160809
95 0.160812
96 0.164916
97 0.151709
98 0.340623
99 0.260375
100 0.378483
101 0.370961
102 0.356881
103 0.444774
104 0.113942
105 0.093193
106 0.112878
107 0.106802
108 0.105306
109 0.115130
110 0.185668
111 0.232520
112 0.136390
113 0.268144
114 0.177807
115 0.115515
116 0.274407
117 0.170106
118 0.282780
119 0.251972
120 0.220657
121 0.152428
122 0.234809
123 0.229892
124 0.215558
125 0.181988
126 0.222716
127 0.214075
128 0.196535
129 0.112856
130 0.183572
131 0.169923
132 0.170633
133 0.232209
134 0.141422
135 0.243080
136 0.228319
137 0.259451
138 0.274387
139 0.209191
140 0.184985
141 0.277227
142 0.231723
143 0.209863
144 0.189032
145 0.159777
146 0.232861
147 0.457533
148 0.336085
149 0.418646
150 0.270173
151 0.301487
152 0.527367
153 0.454721
154 0.168581
155 0.247455
156 0.206256
157 0.220546
158 0.261305
159 0.249703
160 0.216638
161 0.244948
162 0.238281
163 0.220520
164 0.212386
165 0.367233
166 0.119652
167 0.091604
168 0.075587
169 0.202879
170 0.100881
171 0.096220
172 0.160376
173 0.174152
174 0.179677
175 0.163118
176 0.184067
177 0.174429
178 0.132703
179 0.160306
180 0.192730
181 0.144105
182 0.197710
183 0.156368
184 0.215724
185 0.252404
186 0.214346
187 0.120605
188 0.138868
189 0.121777
190 0.112838
191 0.133050
192 0.168895
193 0.131728
194 0.123306
195 0.148569
> 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)`
1.583e+00 -2.395e-03 -1.387e-04 -1.596e-03
`MDVP:Jitter(%)` `MDVP:Jitter(Abs)` `MDVP:PPQ` `Jitter:DDP`
-1.680e+02 -4.462e+03 -1.673e+01 1.037e+02
`MDVP:Shimmer` `Shimmer:APQ3` `Shimmer:APQ5` `MDVP:APQ`
3.287e+01 -1.636e+03 -2.607e+01 -3.558e+00
`Shimmer:DDA` NHR RPDE DFA
5.393e+02 -2.255e+00 -7.713e-01 4.038e-01
spread1 spread2 D2 PPE
1.285e-01 1.132e+00 1.030e-01 1.231e+00
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.92948 -0.15996 0.04966 0.21289 0.55443
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.583e+00 9.558e-01 1.657 0.099399 .
`MDVP:Fo(Hz)` -2.395e-03 1.494e-03 -1.603 0.110781
`MDVP:Fhi(Hz)` -1.387e-04 3.165e-04 -0.438 0.661816
`MDVP:Flo(Hz)` -1.596e-03 7.948e-04 -2.008 0.046227 *
`MDVP:Jitter(%)` -1.680e+02 6.630e+01 -2.535 0.012135 *
`MDVP:Jitter(Abs)` -4.462e+03 4.514e+03 -0.988 0.324280
`MDVP:PPQ` -1.673e+01 8.203e+01 -0.204 0.838625
`Jitter:DDP` 1.037e+02 2.648e+01 3.915 0.000129 ***
`MDVP:Shimmer` 3.287e+01 2.927e+01 1.123 0.262884
`Shimmer:APQ3` -1.636e+03 8.870e+03 -0.184 0.853920
`Shimmer:APQ5` -2.607e+01 1.939e+01 -1.345 0.180445
`MDVP:APQ` -3.558e+00 1.069e+01 -0.333 0.739639
`Shimmer:DDA` 5.393e+02 2.956e+03 0.182 0.855454
NHR -2.255e+00 1.956e+00 -1.153 0.250583
RPDE -7.713e-01 3.572e-01 -2.159 0.032186 *
DFA 4.038e-01 7.252e-01 0.557 0.578361
spread1 1.285e-01 9.676e-02 1.328 0.185969
spread2 1.132e+00 4.585e-01 2.468 0.014542 *
D2 1.030e-01 1.045e-01 0.986 0.325662
PPE 1.231e+00 1.316e+00 0.935 0.350916
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.3254 on 175 degrees of freedom
Multiple R-squared: 0.4881, Adjusted R-squared: 0.4325
F-statistic: 8.781 on 19 and 175 DF, p-value: < 2.2e-16
> if (n > n25) {
+ kp3 <- k + 3
+ nmkm3 <- n - k - 3
+ gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
+ numgqtests <- 0
+ numsignificant1 <- 0
+ numsignificant5 <- 0
+ numsignificant10 <- 0
+ for (mypoint in kp3:nmkm3) {
+ j <- 0
+ numgqtests <- numgqtests + 1
+ for (myalt in c('greater', 'two.sided', 'less')) {
+ j <- j + 1
+ gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
+ }
+ if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
+ }
+ gqarr
+ }
[,1] [,2] [,3]
[1,] 2.739566e-48 5.479132e-48 1.0000000
[2,] 6.905067e-65 1.381013e-64 1.0000000
[3,] 1.870821e-81 3.741642e-81 1.0000000
[4,] 2.495428e-104 4.990856e-104 1.0000000
[5,] 5.179815e-111 1.035963e-110 1.0000000
[6,] 5.685381e-124 1.137076e-123 1.0000000
[7,] 5.316945e-141 1.063389e-140 1.0000000
[8,] 4.718273e-157 9.436547e-157 1.0000000
[9,] 3.799930e-05 7.599860e-05 0.9999620
[10,] 1.149044e-05 2.298089e-05 0.9999885
[11,] 3.421473e-06 6.842947e-06 0.9999966
[12,] 9.592301e-07 1.918460e-06 0.9999990
[13,] 2.555717e-07 5.111435e-07 0.9999997
[14,] 9.607751e-08 1.921550e-07 0.9999999
[15,] 4.064657e-05 8.129314e-05 0.9999594
[16,] 5.070797e-05 1.014159e-04 0.9999493
[17,] 9.719185e-04 1.943837e-03 0.9990281
[18,] 1.758964e-03 3.517927e-03 0.9982410
[19,] 3.117086e-03 6.234171e-03 0.9968829
[20,] 2.624882e-03 5.249764e-03 0.9973751
[21,] 1.476380e-03 2.952760e-03 0.9985236
[22,] 1.023428e-03 2.046855e-03 0.9989766
[23,] 6.535651e-04 1.307130e-03 0.9993464
[24,] 3.794414e-04 7.588828e-04 0.9996206
[25,] 2.724169e-04 5.448338e-04 0.9997276
[26,] 5.670819e-04 1.134164e-03 0.9994329
[27,] 5.400176e-04 1.080035e-03 0.9994600
[28,] 4.440110e-04 8.880219e-04 0.9995560
[29,] 3.005324e-04 6.010649e-04 0.9996995
[30,] 2.309707e-04 4.619414e-04 0.9997690
[31,] 1.800133e-04 3.600265e-04 0.9998200
[32,] 2.075818e-04 4.151636e-04 0.9997924
[33,] 1.737025e-04 3.474050e-04 0.9998263
[34,] 1.394758e-04 2.789516e-04 0.9998605
[35,] 8.644331e-05 1.728866e-04 0.9999136
[36,] 6.763198e-05 1.352640e-04 0.9999324
[37,] 4.380160e-05 8.760320e-05 0.9999562
[38,] 2.743653e-05 5.487306e-05 0.9999726
[39,] 3.362787e-04 6.725573e-04 0.9996637
[40,] 3.487407e-04 6.974815e-04 0.9996513
[41,] 4.245121e-04 8.490241e-04 0.9995755
[42,] 4.273107e-04 8.546214e-04 0.9995727
[43,] 2.743955e-04 5.487909e-04 0.9997256
[44,] 2.309744e-04 4.619488e-04 0.9997690
[45,] 1.527800e-04 3.055600e-04 0.9998472
[46,] 9.883653e-05 1.976731e-04 0.9999012
[47,] 9.792016e-05 1.958403e-04 0.9999021
[48,] 7.693360e-05 1.538672e-04 0.9999231
[49,] 4.570463e-05 9.140926e-05 0.9999543
[50,] 3.135272e-05 6.270544e-05 0.9999686
[51,] 1.817417e-05 3.634834e-05 0.9999818
[52,] 5.389994e-05 1.077999e-04 0.9999461
[53,] 6.851882e-05 1.370376e-04 0.9999315
[54,] 4.963307e-05 9.926615e-05 0.9999504
[55,] 3.186175e-05 6.372350e-05 0.9999681
[56,] 2.499115e-05 4.998230e-05 0.9999750
[57,] 1.462698e-05 2.925395e-05 0.9999854
[58,] 1.084395e-05 2.168790e-05 0.9999892
[59,] 8.202084e-06 1.640417e-05 0.9999918
[60,] 4.726747e-06 9.453494e-06 0.9999953
[61,] 3.112382e-06 6.224763e-06 0.9999969
[62,] 2.037352e-06 4.074705e-06 0.9999980
[63,] 1.228532e-06 2.457063e-06 0.9999988
[64,] 1.596438e-06 3.192875e-06 0.9999984
[65,] 3.291105e-06 6.582209e-06 0.9999967
[66,] 1.979904e-06 3.959807e-06 0.9999980
[67,] 1.487407e-06 2.974813e-06 0.9999985
[68,] 2.660625e-06 5.321250e-06 0.9999973
[69,] 3.688406e-06 7.376812e-06 0.9999963
[70,] 4.855495e-06 9.710989e-06 0.9999951
[71,] 3.409726e-06 6.819452e-06 0.9999966
[72,] 2.891751e-06 5.783503e-06 0.9999971
[73,] 1.999731e-06 3.999462e-06 0.9999980
[74,] 1.352697e-06 2.705394e-06 0.9999986
[75,] 9.097206e-07 1.819441e-06 0.9999991
[76,] 5.239762e-07 1.047952e-06 0.9999995
[77,] 3.148759e-07 6.297517e-07 0.9999997
[78,] 2.349981e-07 4.699962e-07 0.9999998
[79,] 1.434241e-07 2.868482e-07 0.9999999
[80,] 1.288289e-07 2.576578e-07 0.9999999
[81,] 1.665957e-07 3.331913e-07 0.9999998
[82,] 3.385825e-07 6.771649e-07 0.9999997
[83,] 5.107381e-07 1.021476e-06 0.9999995
[84,] 1.010200e-06 2.020401e-06 0.9999990
[85,] 1.782724e-06 3.565448e-06 0.9999982
[86,] 1.255056e-06 2.510112e-06 0.9999987
[87,] 2.352229e-06 4.704457e-06 0.9999976
[88,] 1.744800e-06 3.489601e-06 0.9999983
[89,] 1.059707e-06 2.119414e-06 0.9999989
[90,] 2.110068e-06 4.220137e-06 0.9999979
[91,] 1.268071e-06 2.536141e-06 0.9999987
[92,] 1.386674e-06 2.773348e-06 0.9999986
[93,] 1.606024e-06 3.212048e-06 0.9999984
[94,] 1.391620e-06 2.783240e-06 0.9999986
[95,] 1.590879e-06 3.181758e-06 0.9999984
[96,] 1.102063e-06 2.204126e-06 0.9999989
[97,] 7.598981e-07 1.519796e-06 0.9999992
[98,] 1.443880e-06 2.887761e-06 0.9999986
[99,] 4.671236e-06 9.342473e-06 0.9999953
[100,] 1.032289e-05 2.064578e-05 0.9999897
[101,] 6.832689e-06 1.366538e-05 0.9999932
[102,] 4.970225e-06 9.940450e-06 0.9999950
[103,] 3.837343e-06 7.674687e-06 0.9999962
[104,] 5.434756e-06 1.086951e-05 0.9999946
[105,] 1.054043e-05 2.108085e-05 0.9999895
[106,] 1.813176e-04 3.626351e-04 0.9998187
[107,] 3.287305e-04 6.574610e-04 0.9996713
[108,] 3.294201e-04 6.588402e-04 0.9996706
[109,] 2.744859e-04 5.489719e-04 0.9997255
[110,] 1.837958e-04 3.675917e-04 0.9998162
[111,] 1.525650e-04 3.051300e-04 0.9998474
[112,] 4.923610e-04 9.847220e-04 0.9995076
[113,] 6.529283e-04 1.305857e-03 0.9993471
[114,] 4.655303e-04 9.310606e-04 0.9995345
[115,] 5.550434e-04 1.110087e-03 0.9994450
[116,] 5.101859e-04 1.020372e-03 0.9994898
[117,] 3.246894e-04 6.493788e-04 0.9996753
[118,] 2.079140e-04 4.158279e-04 0.9997921
[119,] 2.111769e-04 4.223537e-04 0.9997888
[120,] 1.496019e-04 2.992039e-04 0.9998504
[121,] 1.493529e-04 2.987057e-04 0.9998506
[122,] 4.703177e-04 9.406354e-04 0.9995297
[123,] 4.597305e-04 9.194610e-04 0.9995403
[124,] 4.060782e-04 8.121564e-04 0.9995939
[125,] 3.456218e-04 6.912436e-04 0.9996544
[126,] 2.745100e-04 5.490201e-04 0.9997255
[127,] 1.763917e-04 3.527833e-04 0.9998236
[128,] 1.648692e-04 3.297383e-04 0.9998351
[129,] 9.678030e-05 1.935606e-04 0.9999032
[130,] 6.796498e-04 1.359300e-03 0.9993204
[131,] 5.110571e-04 1.022114e-03 0.9994889
[132,] 3.622368e-04 7.244735e-04 0.9996378
[133,] 2.632146e-04 5.264291e-04 0.9997368
[134,] 2.715977e-04 5.431954e-04 0.9997284
[135,] 2.486369e-04 4.972738e-04 0.9997514
[136,] 2.408824e-04 4.817647e-04 0.9997591
[137,] 1.477074e-04 2.954148e-04 0.9998523
[138,] 1.418538e-04 2.837075e-04 0.9998581
[139,] 6.228144e-04 1.245629e-03 0.9993772
[140,] 5.423799e-04 1.084760e-03 0.9994576
[141,] 5.298718e-04 1.059744e-03 0.9994701
[142,] 1.256905e-01 2.513811e-01 0.8743095
[143,] 3.291138e-01 6.582275e-01 0.6708862
[144,] 2.856703e-01 5.713406e-01 0.7143297
[145,] 2.654855e-01 5.309710e-01 0.7345145
[146,] 1.994336e-01 3.988673e-01 0.8005664
[147,] 2.552179e-01 5.104358e-01 0.7447821
[148,] 2.231724e-01 4.463448e-01 0.7768276
[149,] 7.864592e-01 4.270816e-01 0.2135408
[150,] 8.664251e-01 2.671499e-01 0.1335749
> postscript(file="/var/wessaorg/rcomp/tmp/1h5vh1386796538.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/2ja6p1386796538.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/307wy1386796538.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/4bn2c1386796538.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/5alu61386796538.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
0.062644053 -0.068371726 -0.002732081 -0.094932142 0.073830662 0.025930123
7 8 9 10 11 12
0.197695701 0.344668576 0.046009753 -0.143068671 -0.077550844 -0.230155447
13 14 15 16 17 18
0.539362362 0.121821631 0.288617642 0.263472139 0.453068366 -0.326136298
19 20 21 22 23 24
-0.302278934 0.035449947 -0.087310463 0.095033450 -0.185426058 0.108181148
25 26 27 28 29 30
0.169634327 0.067530854 0.169664638 0.205407432 0.326807276 0.301503133
31 32 33 34 35 36
-0.297846679 -0.243088416 -0.246969087 -0.185467765 -0.131912720 -0.292967142
37 38 39 40 41 42
0.200473028 0.174507414 0.393888207 0.235137128 0.387111136 0.548003211
43 44 45 46 47 48
-0.202459137 -0.192905093 -0.028634342 -0.107352429 -0.072338513 0.001733650
49 50 51 52 53 54
-0.335743199 -0.435599676 -0.434400780 -0.446054580 -0.411825386 -0.553045784
55 56 57 58 59 60
0.179993468 0.208592283 0.115323228 0.233814659 0.231486816 0.385760032
61 62 63 64 65 66
-0.392462560 -0.330412161 -0.276767656 -0.224138409 -0.109977028 -0.271188280
67 68 69 70 71 72
0.119580258 0.142298159 0.051195520 0.015750169 0.180718596 -0.096324211
73 74 75 76 77 78
0.095309458 0.093956917 -0.073318895 -0.057748888 -0.110237545 -0.005785127
79 80 81 82 83 84
0.021420158 -0.082696765 -0.183945074 -0.103033383 -0.009025775 0.294527016
85 86 87 88 89 90
-0.065736391 0.135591724 0.342614965 0.050238245 0.029541849 -0.170293504
91 92 93 94 95 96
-0.138322171 0.201251191 0.287248529 0.199097255 0.184789360 0.243697718
97 98 99 100 101 102
0.217187042 -0.013768589 0.231558806 0.114257609 0.016970153 0.004657129
103 104 105 106 107 108
-0.044495022 0.427418223 0.428605068 0.443455999 0.432760090 0.305672590
109 110 111 112 113 114
0.346989373 0.075230512 -0.052955481 0.438206718 0.179213163 0.307631244
115 116 117 118 119 120
0.218226495 0.136928347 0.249050667 -0.091694058 0.116273140 0.235682486
121 122 123 124 125 126
0.439003501 -0.001513816 0.063392856 0.301001421 0.398539473 0.408172503
127 128 129 130 131 132
0.422083554 0.400154548 0.554432024 0.219248590 0.191779505 0.085011065
133 134 135 136 137 138
-0.030447610 0.355705638 0.064504317 0.039960947 -0.149623247 -0.149177431
139 140 141 142 143 144
0.049662908 0.229777874 0.065940934 0.095560936 0.278210097 0.368532469
145 146 147 148 149 150
0.465206186 0.148733853 -0.347719240 -0.121803924 -0.259677284 0.151892354
151 152 153 154 155 156
0.175190171 -0.022101531 0.031600118 0.188096668 0.072739366 -0.042013578
157 158 159 160 161 162
0.172606279 -0.306587007 -0.002806673 0.189917717 -0.093877523 -0.021613883
163 164 165 166 167 168
0.092663675 0.251019748 -0.330798684 -0.469861835 -0.183470417 -0.017075354
169 170 171 172 173 174
-0.929480054 -0.176408308 -0.082760476 -0.801142667 -0.825604457 -0.870676377
175 176 177 178 179 180
-0.847009838 -0.829026648 -0.784236030 0.368282057 0.293471676 0.065961582
181 182 183 184 185 186
0.268539977 0.136288581 0.313529948 -0.608798561 -0.652610221 -0.622750929
187 188 189 190 191 192
-0.432700242 -0.485684278 -0.441280372 -0.424168943 -0.629370739 -0.673334288
193 194 195
0.207554827 -0.209970568 -0.512845953
> postscript(file="/var/wessaorg/rcomp/tmp/6u5a31386796538.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 0.062644053 NA
1 -0.068371726 0.062644053
2 -0.002732081 -0.068371726
3 -0.094932142 -0.002732081
4 0.073830662 -0.094932142
5 0.025930123 0.073830662
6 0.197695701 0.025930123
7 0.344668576 0.197695701
8 0.046009753 0.344668576
9 -0.143068671 0.046009753
10 -0.077550844 -0.143068671
11 -0.230155447 -0.077550844
12 0.539362362 -0.230155447
13 0.121821631 0.539362362
14 0.288617642 0.121821631
15 0.263472139 0.288617642
16 0.453068366 0.263472139
17 -0.326136298 0.453068366
18 -0.302278934 -0.326136298
19 0.035449947 -0.302278934
20 -0.087310463 0.035449947
21 0.095033450 -0.087310463
22 -0.185426058 0.095033450
23 0.108181148 -0.185426058
24 0.169634327 0.108181148
25 0.067530854 0.169634327
26 0.169664638 0.067530854
27 0.205407432 0.169664638
28 0.326807276 0.205407432
29 0.301503133 0.326807276
30 -0.297846679 0.301503133
31 -0.243088416 -0.297846679
32 -0.246969087 -0.243088416
33 -0.185467765 -0.246969087
34 -0.131912720 -0.185467765
35 -0.292967142 -0.131912720
36 0.200473028 -0.292967142
37 0.174507414 0.200473028
38 0.393888207 0.174507414
39 0.235137128 0.393888207
40 0.387111136 0.235137128
41 0.548003211 0.387111136
42 -0.202459137 0.548003211
43 -0.192905093 -0.202459137
44 -0.028634342 -0.192905093
45 -0.107352429 -0.028634342
46 -0.072338513 -0.107352429
47 0.001733650 -0.072338513
48 -0.335743199 0.001733650
49 -0.435599676 -0.335743199
50 -0.434400780 -0.435599676
51 -0.446054580 -0.434400780
52 -0.411825386 -0.446054580
53 -0.553045784 -0.411825386
54 0.179993468 -0.553045784
55 0.208592283 0.179993468
56 0.115323228 0.208592283
57 0.233814659 0.115323228
58 0.231486816 0.233814659
59 0.385760032 0.231486816
60 -0.392462560 0.385760032
61 -0.330412161 -0.392462560
62 -0.276767656 -0.330412161
63 -0.224138409 -0.276767656
64 -0.109977028 -0.224138409
65 -0.271188280 -0.109977028
66 0.119580258 -0.271188280
67 0.142298159 0.119580258
68 0.051195520 0.142298159
69 0.015750169 0.051195520
70 0.180718596 0.015750169
71 -0.096324211 0.180718596
72 0.095309458 -0.096324211
73 0.093956917 0.095309458
74 -0.073318895 0.093956917
75 -0.057748888 -0.073318895
76 -0.110237545 -0.057748888
77 -0.005785127 -0.110237545
78 0.021420158 -0.005785127
79 -0.082696765 0.021420158
80 -0.183945074 -0.082696765
81 -0.103033383 -0.183945074
82 -0.009025775 -0.103033383
83 0.294527016 -0.009025775
84 -0.065736391 0.294527016
85 0.135591724 -0.065736391
86 0.342614965 0.135591724
87 0.050238245 0.342614965
88 0.029541849 0.050238245
89 -0.170293504 0.029541849
90 -0.138322171 -0.170293504
91 0.201251191 -0.138322171
92 0.287248529 0.201251191
93 0.199097255 0.287248529
94 0.184789360 0.199097255
95 0.243697718 0.184789360
96 0.217187042 0.243697718
97 -0.013768589 0.217187042
98 0.231558806 -0.013768589
99 0.114257609 0.231558806
100 0.016970153 0.114257609
101 0.004657129 0.016970153
102 -0.044495022 0.004657129
103 0.427418223 -0.044495022
104 0.428605068 0.427418223
105 0.443455999 0.428605068
106 0.432760090 0.443455999
107 0.305672590 0.432760090
108 0.346989373 0.305672590
109 0.075230512 0.346989373
110 -0.052955481 0.075230512
111 0.438206718 -0.052955481
112 0.179213163 0.438206718
113 0.307631244 0.179213163
114 0.218226495 0.307631244
115 0.136928347 0.218226495
116 0.249050667 0.136928347
117 -0.091694058 0.249050667
118 0.116273140 -0.091694058
119 0.235682486 0.116273140
120 0.439003501 0.235682486
121 -0.001513816 0.439003501
122 0.063392856 -0.001513816
123 0.301001421 0.063392856
124 0.398539473 0.301001421
125 0.408172503 0.398539473
126 0.422083554 0.408172503
127 0.400154548 0.422083554
128 0.554432024 0.400154548
129 0.219248590 0.554432024
130 0.191779505 0.219248590
131 0.085011065 0.191779505
132 -0.030447610 0.085011065
133 0.355705638 -0.030447610
134 0.064504317 0.355705638
135 0.039960947 0.064504317
136 -0.149623247 0.039960947
137 -0.149177431 -0.149623247
138 0.049662908 -0.149177431
139 0.229777874 0.049662908
140 0.065940934 0.229777874
141 0.095560936 0.065940934
142 0.278210097 0.095560936
143 0.368532469 0.278210097
144 0.465206186 0.368532469
145 0.148733853 0.465206186
146 -0.347719240 0.148733853
147 -0.121803924 -0.347719240
148 -0.259677284 -0.121803924
149 0.151892354 -0.259677284
150 0.175190171 0.151892354
151 -0.022101531 0.175190171
152 0.031600118 -0.022101531
153 0.188096668 0.031600118
154 0.072739366 0.188096668
155 -0.042013578 0.072739366
156 0.172606279 -0.042013578
157 -0.306587007 0.172606279
158 -0.002806673 -0.306587007
159 0.189917717 -0.002806673
160 -0.093877523 0.189917717
161 -0.021613883 -0.093877523
162 0.092663675 -0.021613883
163 0.251019748 0.092663675
164 -0.330798684 0.251019748
165 -0.469861835 -0.330798684
166 -0.183470417 -0.469861835
167 -0.017075354 -0.183470417
168 -0.929480054 -0.017075354
169 -0.176408308 -0.929480054
170 -0.082760476 -0.176408308
171 -0.801142667 -0.082760476
172 -0.825604457 -0.801142667
173 -0.870676377 -0.825604457
174 -0.847009838 -0.870676377
175 -0.829026648 -0.847009838
176 -0.784236030 -0.829026648
177 0.368282057 -0.784236030
178 0.293471676 0.368282057
179 0.065961582 0.293471676
180 0.268539977 0.065961582
181 0.136288581 0.268539977
182 0.313529948 0.136288581
183 -0.608798561 0.313529948
184 -0.652610221 -0.608798561
185 -0.622750929 -0.652610221
186 -0.432700242 -0.622750929
187 -0.485684278 -0.432700242
188 -0.441280372 -0.485684278
189 -0.424168943 -0.441280372
190 -0.629370739 -0.424168943
191 -0.673334288 -0.629370739
192 0.207554827 -0.673334288
193 -0.209970568 0.207554827
194 -0.512845953 -0.209970568
195 NA -0.512845953
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.068371726 0.062644053
[2,] -0.002732081 -0.068371726
[3,] -0.094932142 -0.002732081
[4,] 0.073830662 -0.094932142
[5,] 0.025930123 0.073830662
[6,] 0.197695701 0.025930123
[7,] 0.344668576 0.197695701
[8,] 0.046009753 0.344668576
[9,] -0.143068671 0.046009753
[10,] -0.077550844 -0.143068671
[11,] -0.230155447 -0.077550844
[12,] 0.539362362 -0.230155447
[13,] 0.121821631 0.539362362
[14,] 0.288617642 0.121821631
[15,] 0.263472139 0.288617642
[16,] 0.453068366 0.263472139
[17,] -0.326136298 0.453068366
[18,] -0.302278934 -0.326136298
[19,] 0.035449947 -0.302278934
[20,] -0.087310463 0.035449947
[21,] 0.095033450 -0.087310463
[22,] -0.185426058 0.095033450
[23,] 0.108181148 -0.185426058
[24,] 0.169634327 0.108181148
[25,] 0.067530854 0.169634327
[26,] 0.169664638 0.067530854
[27,] 0.205407432 0.169664638
[28,] 0.326807276 0.205407432
[29,] 0.301503133 0.326807276
[30,] -0.297846679 0.301503133
[31,] -0.243088416 -0.297846679
[32,] -0.246969087 -0.243088416
[33,] -0.185467765 -0.246969087
[34,] -0.131912720 -0.185467765
[35,] -0.292967142 -0.131912720
[36,] 0.200473028 -0.292967142
[37,] 0.174507414 0.200473028
[38,] 0.393888207 0.174507414
[39,] 0.235137128 0.393888207
[40,] 0.387111136 0.235137128
[41,] 0.548003211 0.387111136
[42,] -0.202459137 0.548003211
[43,] -0.192905093 -0.202459137
[44,] -0.028634342 -0.192905093
[45,] -0.107352429 -0.028634342
[46,] -0.072338513 -0.107352429
[47,] 0.001733650 -0.072338513
[48,] -0.335743199 0.001733650
[49,] -0.435599676 -0.335743199
[50,] -0.434400780 -0.435599676
[51,] -0.446054580 -0.434400780
[52,] -0.411825386 -0.446054580
[53,] -0.553045784 -0.411825386
[54,] 0.179993468 -0.553045784
[55,] 0.208592283 0.179993468
[56,] 0.115323228 0.208592283
[57,] 0.233814659 0.115323228
[58,] 0.231486816 0.233814659
[59,] 0.385760032 0.231486816
[60,] -0.392462560 0.385760032
[61,] -0.330412161 -0.392462560
[62,] -0.276767656 -0.330412161
[63,] -0.224138409 -0.276767656
[64,] -0.109977028 -0.224138409
[65,] -0.271188280 -0.109977028
[66,] 0.119580258 -0.271188280
[67,] 0.142298159 0.119580258
[68,] 0.051195520 0.142298159
[69,] 0.015750169 0.051195520
[70,] 0.180718596 0.015750169
[71,] -0.096324211 0.180718596
[72,] 0.095309458 -0.096324211
[73,] 0.093956917 0.095309458
[74,] -0.073318895 0.093956917
[75,] -0.057748888 -0.073318895
[76,] -0.110237545 -0.057748888
[77,] -0.005785127 -0.110237545
[78,] 0.021420158 -0.005785127
[79,] -0.082696765 0.021420158
[80,] -0.183945074 -0.082696765
[81,] -0.103033383 -0.183945074
[82,] -0.009025775 -0.103033383
[83,] 0.294527016 -0.009025775
[84,] -0.065736391 0.294527016
[85,] 0.135591724 -0.065736391
[86,] 0.342614965 0.135591724
[87,] 0.050238245 0.342614965
[88,] 0.029541849 0.050238245
[89,] -0.170293504 0.029541849
[90,] -0.138322171 -0.170293504
[91,] 0.201251191 -0.138322171
[92,] 0.287248529 0.201251191
[93,] 0.199097255 0.287248529
[94,] 0.184789360 0.199097255
[95,] 0.243697718 0.184789360
[96,] 0.217187042 0.243697718
[97,] -0.013768589 0.217187042
[98,] 0.231558806 -0.013768589
[99,] 0.114257609 0.231558806
[100,] 0.016970153 0.114257609
[101,] 0.004657129 0.016970153
[102,] -0.044495022 0.004657129
[103,] 0.427418223 -0.044495022
[104,] 0.428605068 0.427418223
[105,] 0.443455999 0.428605068
[106,] 0.432760090 0.443455999
[107,] 0.305672590 0.432760090
[108,] 0.346989373 0.305672590
[109,] 0.075230512 0.346989373
[110,] -0.052955481 0.075230512
[111,] 0.438206718 -0.052955481
[112,] 0.179213163 0.438206718
[113,] 0.307631244 0.179213163
[114,] 0.218226495 0.307631244
[115,] 0.136928347 0.218226495
[116,] 0.249050667 0.136928347
[117,] -0.091694058 0.249050667
[118,] 0.116273140 -0.091694058
[119,] 0.235682486 0.116273140
[120,] 0.439003501 0.235682486
[121,] -0.001513816 0.439003501
[122,] 0.063392856 -0.001513816
[123,] 0.301001421 0.063392856
[124,] 0.398539473 0.301001421
[125,] 0.408172503 0.398539473
[126,] 0.422083554 0.408172503
[127,] 0.400154548 0.422083554
[128,] 0.554432024 0.400154548
[129,] 0.219248590 0.554432024
[130,] 0.191779505 0.219248590
[131,] 0.085011065 0.191779505
[132,] -0.030447610 0.085011065
[133,] 0.355705638 -0.030447610
[134,] 0.064504317 0.355705638
[135,] 0.039960947 0.064504317
[136,] -0.149623247 0.039960947
[137,] -0.149177431 -0.149623247
[138,] 0.049662908 -0.149177431
[139,] 0.229777874 0.049662908
[140,] 0.065940934 0.229777874
[141,] 0.095560936 0.065940934
[142,] 0.278210097 0.095560936
[143,] 0.368532469 0.278210097
[144,] 0.465206186 0.368532469
[145,] 0.148733853 0.465206186
[146,] -0.347719240 0.148733853
[147,] -0.121803924 -0.347719240
[148,] -0.259677284 -0.121803924
[149,] 0.151892354 -0.259677284
[150,] 0.175190171 0.151892354
[151,] -0.022101531 0.175190171
[152,] 0.031600118 -0.022101531
[153,] 0.188096668 0.031600118
[154,] 0.072739366 0.188096668
[155,] -0.042013578 0.072739366
[156,] 0.172606279 -0.042013578
[157,] -0.306587007 0.172606279
[158,] -0.002806673 -0.306587007
[159,] 0.189917717 -0.002806673
[160,] -0.093877523 0.189917717
[161,] -0.021613883 -0.093877523
[162,] 0.092663675 -0.021613883
[163,] 0.251019748 0.092663675
[164,] -0.330798684 0.251019748
[165,] -0.469861835 -0.330798684
[166,] -0.183470417 -0.469861835
[167,] -0.017075354 -0.183470417
[168,] -0.929480054 -0.017075354
[169,] -0.176408308 -0.929480054
[170,] -0.082760476 -0.176408308
[171,] -0.801142667 -0.082760476
[172,] -0.825604457 -0.801142667
[173,] -0.870676377 -0.825604457
[174,] -0.847009838 -0.870676377
[175,] -0.829026648 -0.847009838
[176,] -0.784236030 -0.829026648
[177,] 0.368282057 -0.784236030
[178,] 0.293471676 0.368282057
[179,] 0.065961582 0.293471676
[180,] 0.268539977 0.065961582
[181,] 0.136288581 0.268539977
[182,] 0.313529948 0.136288581
[183,] -0.608798561 0.313529948
[184,] -0.652610221 -0.608798561
[185,] -0.622750929 -0.652610221
[186,] -0.432700242 -0.622750929
[187,] -0.485684278 -0.432700242
[188,] -0.441280372 -0.485684278
[189,] -0.424168943 -0.441280372
[190,] -0.629370739 -0.424168943
[191,] -0.673334288 -0.629370739
[192,] 0.207554827 -0.673334288
[193,] -0.209970568 0.207554827
[194,] -0.512845953 -0.209970568
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.068371726 0.062644053
2 -0.002732081 -0.068371726
3 -0.094932142 -0.002732081
4 0.073830662 -0.094932142
5 0.025930123 0.073830662
6 0.197695701 0.025930123
7 0.344668576 0.197695701
8 0.046009753 0.344668576
9 -0.143068671 0.046009753
10 -0.077550844 -0.143068671
11 -0.230155447 -0.077550844
12 0.539362362 -0.230155447
13 0.121821631 0.539362362
14 0.288617642 0.121821631
15 0.263472139 0.288617642
16 0.453068366 0.263472139
17 -0.326136298 0.453068366
18 -0.302278934 -0.326136298
19 0.035449947 -0.302278934
20 -0.087310463 0.035449947
21 0.095033450 -0.087310463
22 -0.185426058 0.095033450
23 0.108181148 -0.185426058
24 0.169634327 0.108181148
25 0.067530854 0.169634327
26 0.169664638 0.067530854
27 0.205407432 0.169664638
28 0.326807276 0.205407432
29 0.301503133 0.326807276
30 -0.297846679 0.301503133
31 -0.243088416 -0.297846679
32 -0.246969087 -0.243088416
33 -0.185467765 -0.246969087
34 -0.131912720 -0.185467765
35 -0.292967142 -0.131912720
36 0.200473028 -0.292967142
37 0.174507414 0.200473028
38 0.393888207 0.174507414
39 0.235137128 0.393888207
40 0.387111136 0.235137128
41 0.548003211 0.387111136
42 -0.202459137 0.548003211
43 -0.192905093 -0.202459137
44 -0.028634342 -0.192905093
45 -0.107352429 -0.028634342
46 -0.072338513 -0.107352429
47 0.001733650 -0.072338513
48 -0.335743199 0.001733650
49 -0.435599676 -0.335743199
50 -0.434400780 -0.435599676
51 -0.446054580 -0.434400780
52 -0.411825386 -0.446054580
53 -0.553045784 -0.411825386
54 0.179993468 -0.553045784
55 0.208592283 0.179993468
56 0.115323228 0.208592283
57 0.233814659 0.115323228
58 0.231486816 0.233814659
59 0.385760032 0.231486816
60 -0.392462560 0.385760032
61 -0.330412161 -0.392462560
62 -0.276767656 -0.330412161
63 -0.224138409 -0.276767656
64 -0.109977028 -0.224138409
65 -0.271188280 -0.109977028
66 0.119580258 -0.271188280
67 0.142298159 0.119580258
68 0.051195520 0.142298159
69 0.015750169 0.051195520
70 0.180718596 0.015750169
71 -0.096324211 0.180718596
72 0.095309458 -0.096324211
73 0.093956917 0.095309458
74 -0.073318895 0.093956917
75 -0.057748888 -0.073318895
76 -0.110237545 -0.057748888
77 -0.005785127 -0.110237545
78 0.021420158 -0.005785127
79 -0.082696765 0.021420158
80 -0.183945074 -0.082696765
81 -0.103033383 -0.183945074
82 -0.009025775 -0.103033383
83 0.294527016 -0.009025775
84 -0.065736391 0.294527016
85 0.135591724 -0.065736391
86 0.342614965 0.135591724
87 0.050238245 0.342614965
88 0.029541849 0.050238245
89 -0.170293504 0.029541849
90 -0.138322171 -0.170293504
91 0.201251191 -0.138322171
92 0.287248529 0.201251191
93 0.199097255 0.287248529
94 0.184789360 0.199097255
95 0.243697718 0.184789360
96 0.217187042 0.243697718
97 -0.013768589 0.217187042
98 0.231558806 -0.013768589
99 0.114257609 0.231558806
100 0.016970153 0.114257609
101 0.004657129 0.016970153
102 -0.044495022 0.004657129
103 0.427418223 -0.044495022
104 0.428605068 0.427418223
105 0.443455999 0.428605068
106 0.432760090 0.443455999
107 0.305672590 0.432760090
108 0.346989373 0.305672590
109 0.075230512 0.346989373
110 -0.052955481 0.075230512
111 0.438206718 -0.052955481
112 0.179213163 0.438206718
113 0.307631244 0.179213163
114 0.218226495 0.307631244
115 0.136928347 0.218226495
116 0.249050667 0.136928347
117 -0.091694058 0.249050667
118 0.116273140 -0.091694058
119 0.235682486 0.116273140
120 0.439003501 0.235682486
121 -0.001513816 0.439003501
122 0.063392856 -0.001513816
123 0.301001421 0.063392856
124 0.398539473 0.301001421
125 0.408172503 0.398539473
126 0.422083554 0.408172503
127 0.400154548 0.422083554
128 0.554432024 0.400154548
129 0.219248590 0.554432024
130 0.191779505 0.219248590
131 0.085011065 0.191779505
132 -0.030447610 0.085011065
133 0.355705638 -0.030447610
134 0.064504317 0.355705638
135 0.039960947 0.064504317
136 -0.149623247 0.039960947
137 -0.149177431 -0.149623247
138 0.049662908 -0.149177431
139 0.229777874 0.049662908
140 0.065940934 0.229777874
141 0.095560936 0.065940934
142 0.278210097 0.095560936
143 0.368532469 0.278210097
144 0.465206186 0.368532469
145 0.148733853 0.465206186
146 -0.347719240 0.148733853
147 -0.121803924 -0.347719240
148 -0.259677284 -0.121803924
149 0.151892354 -0.259677284
150 0.175190171 0.151892354
151 -0.022101531 0.175190171
152 0.031600118 -0.022101531
153 0.188096668 0.031600118
154 0.072739366 0.188096668
155 -0.042013578 0.072739366
156 0.172606279 -0.042013578
157 -0.306587007 0.172606279
158 -0.002806673 -0.306587007
159 0.189917717 -0.002806673
160 -0.093877523 0.189917717
161 -0.021613883 -0.093877523
162 0.092663675 -0.021613883
163 0.251019748 0.092663675
164 -0.330798684 0.251019748
165 -0.469861835 -0.330798684
166 -0.183470417 -0.469861835
167 -0.017075354 -0.183470417
168 -0.929480054 -0.017075354
169 -0.176408308 -0.929480054
170 -0.082760476 -0.176408308
171 -0.801142667 -0.082760476
172 -0.825604457 -0.801142667
173 -0.870676377 -0.825604457
174 -0.847009838 -0.870676377
175 -0.829026648 -0.847009838
176 -0.784236030 -0.829026648
177 0.368282057 -0.784236030
178 0.293471676 0.368282057
179 0.065961582 0.293471676
180 0.268539977 0.065961582
181 0.136288581 0.268539977
182 0.313529948 0.136288581
183 -0.608798561 0.313529948
184 -0.652610221 -0.608798561
185 -0.622750929 -0.652610221
186 -0.432700242 -0.622750929
187 -0.485684278 -0.432700242
188 -0.441280372 -0.485684278
189 -0.424168943 -0.441280372
190 -0.629370739 -0.424168943
191 -0.673334288 -0.629370739
192 0.207554827 -0.673334288
193 -0.209970568 0.207554827
194 -0.512845953 -0.209970568
> 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/77hyn1386796538.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/8h8av1386796538.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/91v511386796538.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/wessaorg/rcomp/tmp/10x6t21386796538.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/wessaorg/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11mg7u1386796538.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/12t1421386796538.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/13n7xc1386796538.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/14jr3j1386796538.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/15r7u91386796538.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/167fx71386796539.tab")
+ }
>
> try(system("convert tmp/1h5vh1386796538.ps tmp/1h5vh1386796538.png",intern=TRUE))
character(0)
> try(system("convert tmp/2ja6p1386796538.ps tmp/2ja6p1386796538.png",intern=TRUE))
character(0)
> try(system("convert tmp/307wy1386796538.ps tmp/307wy1386796538.png",intern=TRUE))
character(0)
> try(system("convert tmp/4bn2c1386796538.ps tmp/4bn2c1386796538.png",intern=TRUE))
character(0)
> try(system("convert tmp/5alu61386796538.ps tmp/5alu61386796538.png",intern=TRUE))
character(0)
> try(system("convert tmp/6u5a31386796538.ps tmp/6u5a31386796538.png",intern=TRUE))
character(0)
> try(system("convert tmp/77hyn1386796538.ps tmp/77hyn1386796538.png",intern=TRUE))
character(0)
> try(system("convert tmp/8h8av1386796538.ps tmp/8h8av1386796538.png",intern=TRUE))
character(0)
> try(system("convert tmp/91v511386796538.ps tmp/91v511386796538.png",intern=TRUE))
character(0)
> try(system("convert tmp/10x6t21386796538.ps tmp/10x6t21386796538.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
24.681 4.029 28.700