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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You are welcome to redistribute it under certain conditions.
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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(0.284654
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+ ,0.00611
+ ,0.03964
+ ,0.02148
+ ,0.01574
+ ,0.01321
+ ,0.237
+ ,0.02551
+ ,0.00619
+ ,0.00233
+ ,0.00206
+ ,0.00003
+ ,0.00396
+ ,144.736
+ ,163.441
+ ,150.44
+ ,0.19771
+ ,2.679185
+ ,0.216204
+ ,-5.704053
+ ,0.768845
+ ,0.408598
+ ,1
+ ,22.866
+ ,0.00639
+ ,0.02849
+ ,0.01559
+ ,0.01103
+ ,0.0095
+ ,0.163
+ ,0.01831
+ ,0.00605
+ ,0.00235
+ ,0.00202
+ ,0.00003
+ ,0.00397
+ ,141.998
+ ,161.078
+ ,148.462
+ ,0.156368
+ ,2.209021
+ ,0.109397
+ ,-6.27717
+ ,0.75718
+ ,0.329577
+ ,1
+ ,23.008
+ ,0.00595
+ ,0.03464
+ ,0.01666
+ ,0.01341
+ ,0.01155
+ ,0.198
+ ,0.02145
+ ,0.00521
+ ,0.00198
+ ,0.00174
+ ,0.00002
+ ,0.00336
+ ,144.786
+ ,163.417
+ ,149.818
+ ,0.215724
+ ,2.027228
+ ,0.191576
+ ,-5.61907
+ ,0.669565
+ ,0.603515
+ ,0
+ ,23.079
+ ,0.00955
+ ,0.02592
+ ,0.01949
+ ,0.01223
+ ,0.00864
+ ,0.171
+ ,0.01909
+ ,0.00558
+ ,0.0027
+ ,0.00186
+ ,0.00004
+ ,0.00417
+ ,106.656
+ ,123.925
+ ,117.226
+ ,0.252404
+ ,2.120412
+ ,0.206768
+ ,-5.198864
+ ,0.656516
+ ,0.663842
+ ,0
+ ,22.085
+ ,0.01179
+ ,0.02429
+ ,0.01756
+ ,0.01144
+ ,0.0081
+ ,0.163
+ ,0.01795
+ ,0.0078
+ ,0.00346
+ ,0.0026
+ ,0.00005
+ ,0.00531
+ ,99.503
+ ,217.552
+ ,116.848
+ ,0.214346
+ ,2.058658
+ ,0.133917
+ ,-5.592584
+ ,0.654331
+ ,0.598515
+ ,0
+ ,24.199
+ ,0.00737
+ ,0.02001
+ ,0.01691
+ ,0.0099
+ ,0.00667
+ ,0.136
+ ,0.01564
+ ,0.00403
+ ,0.00192
+ ,0.00134
+ ,0.00003
+ ,0.00314
+ ,96.983
+ ,177.291
+ ,116.286
+ ,0.120605
+ ,2.161936
+ ,0.15331
+ ,-6.431119
+ ,0.667654
+ ,0.566424
+ ,0
+ ,23.958
+ ,0.01397
+ ,0.0246
+ ,0.01491
+ ,0.00972
+ ,0.0082
+ ,0.154
+ ,0.0166
+ ,0.00762
+ ,0.00263
+ ,0.00254
+ ,0.00004
+ ,0.00496
+ ,86.228
+ ,592.03
+ ,116.556
+ ,0.138868
+ ,2.152083
+ ,0.116636
+ ,-6.359018
+ ,0.663884
+ ,0.528485
+ ,0
+ ,25.023
+ ,0.0068
+ ,0.01892
+ ,0.01144
+ ,0.00789
+ ,0.00631
+ ,0.117
+ ,0.013
+ ,0.00345
+ ,0.00148
+ ,0.00115
+ ,0.00002
+ ,0.00267
+ ,94.246
+ ,581.289
+ ,116.342
+ ,0.121777
+ ,1.91399
+ ,0.149694
+ ,-6.710219
+ ,0.659132
+ ,0.555303
+ ,0
+ ,24.775
+ ,0.00703
+ ,0.01672
+ ,0.01095
+ ,0.00721
+ ,0.00557
+ ,0.106
+ ,0.01185
+ ,0.00439
+ ,0.00184
+ ,0.00146
+ ,0.00003
+ ,0.00327
+ ,86.647
+ ,119.167
+ ,114.563
+ ,0.112838
+ ,2.316346
+ ,0.15989
+ ,-6.934474
+ ,0.683761
+ ,0.508479
+ ,0
+ ,19.368
+ ,0.04441
+ ,0.04363
+ ,0.01758
+ ,0.01582
+ ,0.01454
+ ,0.255
+ ,0.02574
+ ,0.01235
+ ,0.00396
+ ,0.00412
+ ,0.00003
+ ,0.00694
+ ,78.228
+ ,262.707
+ ,201.774
+ ,0.13305
+ ,2.657476
+ ,0.121952
+ ,-6.538586
+ ,0.657899
+ ,0.448439
+ ,0
+ ,19.517
+ ,0.02764
+ ,0.07008
+ ,0.02745
+ ,0.02498
+ ,0.02336
+ ,0.405
+ ,0.04087
+ ,0.0079
+ ,0.00259
+ ,0.00263
+ ,0.00003
+ ,0.00459
+ ,94.261
+ ,230.978
+ ,174.188
+ ,0.168895
+ ,2.784312
+ ,0.129303
+ ,-6.195325
+ ,0.683244
+ ,0.431674
+ ,0
+ ,19.147
+ ,0.0181
+ ,0.04812
+ ,0.01879
+ ,0.01657
+ ,0.01604
+ ,0.263
+ ,0.02751
+ ,0.00994
+ ,0.00292
+ ,0.00331
+ ,0.00003
+ ,0.00564
+ ,89.488
+ ,253.017
+ ,209.516
+ ,0.131728
+ ,2.679772
+ ,0.158453
+ ,-6.787197
+ ,0.655683
+ ,0.407567
+ ,0
+ ,17.883
+ ,0.10715
+ ,0.03804
+ ,0.01667
+ ,0.01365
+ ,0.01268
+ ,0.256
+ ,0.02308
+ ,0.01873
+ ,0.00564
+ ,0.00624
+ ,0.00008
+ ,0.0136
+ ,74.287
+ ,240.005
+ ,174.688
+ ,0.123306
+ ,2.138608
+ ,0.207454
+ ,-6.744577
+ ,0.643956
+ ,0.451221
+ ,0
+ ,19.02
+ ,0.07223
+ ,0.03794
+ ,0.01588
+ ,0.01321
+ ,0.01265
+ ,0.241
+ ,0.02296
+ ,0.01109
+ ,0.0039
+ ,0.0037
+ ,0.00004
+ ,0.0074
+ ,74.904
+ ,396.961
+ ,198.764
+ ,0.148569
+ ,2.555477
+ ,0.190667
+ ,-5.724056
+ ,0.664357
+ ,0.462803
+ ,0
+ ,21.209
+ ,0.04398
+ ,0.03078
+ ,0.01373
+ ,0.01161
+ ,0.01026
+ ,0.19
+ ,0.01884
+ ,0.00885
+ ,0.00317
+ ,0.00295
+ ,0.00003
+ ,0.00567
+ ,77.973
+ ,260.277
+ ,214.289)
+ ,dim=c(23
+ ,195)
+ ,dimnames=list(c('PPE'
+ ,'D2'
+ ,'spread2'
+ ,'spread1'
+ ,'DFA'
+ ,'RPDE'
+ ,'status'
+ ,'HNR'
+ ,'NHR'
+ ,'Shimmer:DDA'
+ ,'MDVP:APQ'
+ ,'Shimmer:APQ5'
+ ,'Shimmer:APQ3'
+ ,'MDVP:Shimmer(dB)'
+ ,'MDVP:Shimmer'
+ ,'Jitter:DDP'
+ ,'MDVP:PPQ'
+ ,'MDVP:RAP'
+ ,'MDVP:Jitter(Abs)'
+ ,'MDVP:Jitter(%)'
+ ,'MDVP:Flo(Hz)'
+ ,'MDVP:Fhi(Hz)'
+ ,'MDVP:Fo(Hz)')
+ ,1:195))
> y <- array(NA,dim=c(23,195),dimnames=list(c('PPE','D2','spread2','spread1','DFA','RPDE','status','HNR','NHR','Shimmer:DDA','MDVP:APQ','Shimmer:APQ5','Shimmer:APQ3','MDVP:Shimmer(dB)','MDVP:Shimmer','Jitter:DDP','MDVP:PPQ','MDVP:RAP','MDVP:Jitter(Abs)','MDVP:Jitter(%)','MDVP:Flo(Hz)','MDVP:Fhi(Hz)','MDVP:Fo(Hz)'),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 = '7'
> par3 <- 'No Linear Trend'
> par2 <- 'Do not include Seasonal Dummies'
> par1 <- '7'
> #'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 PPE D2 spread2 spread1 DFA RPDE HNR
1 1 0.284654 2.301442 0.266482 -4.813031 0.815285 0.414783 21.033
2 1 0.368674 2.486855 0.335590 -4.075192 0.819521 0.458359 19.085
3 1 0.332634 2.342259 0.311173 -4.443179 0.825288 0.429895 20.651
4 1 0.368975 2.405554 0.334147 -4.117501 0.819235 0.434969 20.644
5 1 0.410335 2.332180 0.234513 -3.747787 0.823484 0.417356 19.649
6 1 0.357775 2.187560 0.299111 -4.242867 0.825069 0.415564 21.378
7 1 0.211756 1.854785 0.257682 -5.634322 0.764112 0.596040 24.886
8 1 0.163755 2.064693 0.183721 -6.167603 0.763262 0.637420 26.892
9 1 0.231571 2.322511 0.327769 -5.498678 0.773587 0.615551 21.812
10 1 0.271362 2.432792 0.325996 -5.011879 0.798463 0.547037 21.862
11 1 0.249740 2.407313 0.391002 -5.249770 0.776156 0.611137 21.118
12 1 0.275931 2.642476 0.363566 -4.960234 0.792520 0.583390 21.414
13 1 0.138512 2.041277 0.152813 -6.547148 0.646846 0.460600 25.703
14 1 0.199889 2.519422 0.254989 -5.660217 0.665833 0.430166 24.889
15 1 0.170100 2.125618 0.203653 -6.105098 0.654027 0.474791 24.922
16 1 0.234589 2.205546 0.210185 -5.340115 0.658245 0.565924 25.175
17 1 0.218164 2.264501 0.239764 -5.440040 0.644692 0.567380 22.333
18 1 0.430788 3.007463 0.434326 -2.931070 0.605417 0.631099 20.376
19 1 0.377429 3.109010 0.357870 -3.949079 0.719467 0.665318 17.280
20 1 0.322111 2.856676 0.340176 -4.554466 0.686080 0.649554 17.153
21 1 0.365391 2.739710 0.262564 -4.095442 0.704087 0.660125 17.536
22 1 0.259765 2.557536 0.237622 -5.186960 0.698951 0.629017 19.493
23 1 0.285695 2.916777 0.262384 -4.330956 0.679834 0.619060 22.468
24 1 0.253556 2.547508 0.210279 -5.248776 0.686894 0.537264 20.422
25 1 0.215961 2.692176 0.220890 -5.557447 0.732479 0.397937 23.831
26 1 0.219514 2.846369 0.236853 -5.571843 0.737948 0.522746 22.066
27 1 0.147403 2.589702 0.226278 -6.183590 0.720916 0.418622 25.908
28 1 0.162999 2.314209 0.196102 -6.271690 0.726652 0.358773 25.119
29 1 0.108514 2.241742 0.279789 -7.120925 0.676258 0.470478 25.970
30 1 0.135242 1.957961 0.209866 -6.635729 0.723797 0.427785 25.678
31 0 0.085569 1.743867 0.177551 -7.348300 0.741367 0.422229 26.775
32 0 0.068501 2.103106 0.173319 -7.682587 0.742055 0.432439 30.940
33 0 0.096320 1.512275 0.175181 -7.067931 0.738703 0.465946 30.775
34 0 0.056141 1.544609 0.178540 -7.695734 0.742133 0.368535 32.684
35 0 0.044539 1.423287 0.163519 -7.964984 0.741899 0.340068 33.047
36 0 0.057610 2.447064 0.170183 -7.777685 0.742737 0.344252 31.732
37 1 0.165827 2.477082 0.218037 -6.149653 0.778834 0.360148 23.216
38 1 0.173218 2.536527 0.196371 -6.006414 0.783626 0.341435 24.951
39 1 0.141929 2.269398 0.212294 -6.452058 0.766209 0.403884 26.738
40 1 0.160691 2.382544 0.266892 -6.006647 0.758324 0.396793 26.310
41 1 0.130554 2.374073 0.201095 -6.647379 0.765623 0.326480 26.822
42 1 0.115730 2.361532 0.063412 -7.044105 0.759203 0.306443 26.453
43 0 0.095032 2.416838 0.098648 -7.310550 0.654172 0.305062 22.736
44 0 0.117399 2.256699 0.158266 -6.793547 0.634267 0.457702 23.145
45 0 0.091470 2.330716 0.091608 -7.057869 0.635285 0.438296 25.368
46 0 0.102706 2.365800 0.102083 -6.995820 0.638928 0.431285 25.032
47 0 0.097336 2.392122 0.127642 -7.156076 0.631653 0.467489 24.602
48 0 0.086398 2.028612 0.200873 -7.319510 0.635204 0.610367 26.805
49 0 0.133867 2.079922 0.266392 -6.439398 0.733659 0.579597 23.162
50 0 0.128872 2.054419 0.264967 -6.482096 0.754073 0.538688 24.971
51 0 0.103561 1.840198 0.254498 -6.650471 0.775933 0.553134 25.135
52 0 0.105993 2.431854 0.291954 -6.689151 0.760361 0.507504 25.030
53 0 0.119308 1.972297 0.220434 -7.072419 0.766204 0.459766 24.692
54 0 0.147491 2.223719 0.269866 -6.836811 0.785714 0.420383 25.429
55 1 0.316700 1.986899 0.205558 -4.649573 0.819032 0.536009 21.028
56 1 0.344834 2.014606 0.221727 -4.333543 0.811843 0.558586 20.767
57 1 0.335041 1.922940 0.238298 -4.438453 0.821364 0.541781 21.422
58 1 0.314464 2.021591 0.290024 -4.608260 0.817756 0.530529 22.817
59 1 0.326197 1.827012 0.262633 -4.476755 0.813432 0.540049 22.603
60 1 0.316395 1.831691 0.221711 -4.609161 0.817396 0.547975 21.660
61 0 0.101516 2.460791 0.066994 -7.040508 0.678874 0.341788 25.554
62 0 0.098555 2.321560 0.086372 -7.293801 0.686264 0.447979 26.138
63 0 0.103224 2.278687 0.095882 -6.966321 0.694399 0.364867 25.856
64 0 0.093534 2.498224 0.018689 -7.245620 0.683296 0.256570 25.964
65 0 0.073581 2.003032 0.056844 -7.496264 0.673636 0.276850 26.415
66 0 0.091546 2.118596 0.006274 -7.314237 0.681811 0.305429 24.547
67 1 0.226156 2.359973 0.226850 -5.409423 0.720908 0.460139 19.560
68 1 0.226247 2.291558 0.205660 -5.324574 0.729067 0.498133 19.979
69 1 0.185580 2.118496 0.151814 -5.869750 0.731444 0.513237 20.338
70 1 0.141958 2.137075 0.120956 -6.261141 0.727313 0.487407 21.718
71 1 0.180828 2.277927 0.158830 -5.720868 0.730387 0.489345 20.264
72 1 0.242981 2.642276 0.224852 -5.207985 0.733232 0.543299 18.570
73 1 0.188180 2.205024 0.329066 -5.791820 0.762959 0.495954 25.742
74 1 0.225461 1.928708 0.306636 -5.389129 0.789532 0.509127 24.178
75 1 0.244512 2.225815 0.201861 -5.313360 0.815908 0.437031 25.438
76 1 0.228624 1.862092 0.315074 -5.477592 0.807217 0.463514 25.197
77 1 0.193918 2.007923 0.341169 -5.775966 0.789977 0.489538 23.370
78 1 0.232744 1.777901 0.250572 -5.391029 0.816340 0.429484 25.820
79 1 0.260015 2.017753 0.249494 -5.115212 0.779612 0.644954 21.875
80 1 0.277948 2.398422 0.265699 -4.913885 0.790117 0.594387 19.200
81 1 0.327978 2.645959 0.155097 -4.441519 0.770466 0.544805 19.055
82 1 0.260633 2.232576 0.210458 -5.132032 0.778747 0.576084 19.659
83 1 0.264666 2.428306 0.146948 -5.022288 0.787896 0.554610 20.536
84 1 0.177275 2.053601 0.078202 -6.025367 0.772416 0.576644 22.244
85 1 0.242119 3.099301 0.343073 -5.288912 0.729586 0.556494 13.893
86 1 0.200423 3.098256 0.315903 -5.657899 0.727747 0.583574 16.176
87 1 0.144614 2.654271 0.335753 -6.366916 0.712199 0.598714 15.924
88 1 0.220968 3.136550 0.299549 -5.515071 0.740837 0.602874 13.922
89 1 0.194052 3.007096 0.299793 -5.783272 0.743937 0.599371 14.739
90 1 0.332086 3.671155 0.375531 -4.379411 0.745526 0.590951 11.866
91 1 0.301952 3.317586 0.389232 -4.508984 0.733165 0.653410 11.744
92 1 0.134120 2.344876 0.207156 -6.411497 0.714360 0.501037 19.664
93 1 0.186489 2.344336 0.087840 -5.952058 0.734504 0.454444 18.780
94 1 0.160809 2.080121 0.173520 -6.152551 0.697790 0.447456 20.969
95 1 0.160812 2.143851 0.188056 -6.251425 0.712170 0.502380 22.219
96 1 0.164916 2.344348 0.180528 -6.247076 0.705658 0.447285 21.693
97 1 0.151709 2.473239 0.194627 -6.417440 0.693429 0.366329 22.663
98 1 0.340623 2.671825 0.265315 -4.020042 0.714485 0.629574 15.338
99 1 0.260375 2.441612 0.202146 -5.159169 0.690892 0.571010 15.433
100 1 0.378483 2.634633 0.242861 -3.760348 0.674953 0.638545 12.435
101 1 0.370961 2.991063 0.260481 -3.700544 0.656846 0.671299 8.867
102 1 0.356881 2.638279 0.310163 -4.202730 0.643327 0.639808 15.060
103 1 0.444774 2.690917 0.270641 -3.269487 0.641418 0.596362 10.489
104 1 0.113942 2.004055 0.089267 -6.878393 0.722356 0.296888 26.759
105 1 0.093193 2.065477 0.144780 -7.111576 0.691483 0.263654 28.409
106 1 0.112878 1.994387 0.210279 -6.997403 0.719974 0.365488 27.421
107 1 0.106802 2.129924 0.184550 -6.981201 0.677930 0.334171 29.746
108 1 0.105306 2.499148 0.249172 -6.600023 0.700246 0.393563 26.833
109 1 0.115130 2.296873 0.160686 -6.739151 0.676066 0.311369 29.928
110 1 0.185668 2.608749 0.278679 -5.845099 0.740539 0.497554 21.934
111 1 0.232520 2.550961 0.256454 -5.258320 0.727863 0.436084 23.239
112 1 0.136390 2.502336 0.184378 -6.471427 0.712466 0.338097 22.407
113 1 0.268144 2.376749 0.212054 -4.876336 0.722085 0.498877 21.305
114 1 0.177807 2.489191 0.250283 -5.963040 0.722254 0.441097 23.671
115 1 0.115515 2.938114 0.181701 -6.729713 0.715121 0.331508 21.864
116 1 0.274407 2.702355 0.261549 -4.673241 0.662668 0.407701 23.693
117 1 0.170106 2.640798 0.273280 -6.051233 0.653823 0.450798 26.356
118 1 0.282780 2.975889 0.372114 -4.597834 0.676023 0.486738 25.690
119 1 0.251972 2.816781 0.393056 -4.913137 0.655239 0.470422 25.020
120 1 0.220657 2.925862 0.389295 -5.517173 0.582710 0.462516 24.581
121 1 0.152428 2.686240 0.279933 -6.186128 0.684130 0.487756 24.743
122 1 0.234809 2.655744 0.281618 -4.711007 0.656182 0.400088 27.166
123 1 0.229892 2.090438 0.160267 -5.418787 0.741480 0.538016 18.305
124 1 0.215558 2.174306 0.142466 -5.445140 0.732903 0.589956 18.784
125 1 0.181988 1.929715 0.143359 -5.944191 0.728421 0.618663 19.196
126 1 0.222716 1.765957 0.127950 -5.594275 0.735546 0.637518 18.857
127 1 0.214075 1.821297 0.087165 -5.540351 0.738245 0.623209 18.178
128 1 0.196535 1.996146 0.115697 -5.825257 0.736964 0.585169 18.330
129 1 0.112856 2.328513 0.152941 -6.890021 0.699787 0.457541 26.842
130 1 0.183572 2.108873 0.195976 -5.892061 0.718839 0.491345 26.369
131 1 0.169923 2.539724 0.203630 -6.135296 0.724045 0.467160 23.949
132 1 0.170633 2.527742 0.217013 -6.112667 0.735136 0.468621 26.017
133 1 0.232209 2.516320 0.254909 -5.436135 0.721308 0.470972 23.389
134 1 0.141422 2.034827 0.178713 -6.448134 0.723096 0.482296 25.619
135 1 0.243080 2.375138 0.320385 -5.301321 0.744064 0.637814 17.060
136 1 0.228319 2.631793 0.322044 -5.333619 0.706687 0.653427 17.707
137 1 0.259451 2.445502 0.300067 -4.378916 0.708144 0.647900 19.013
138 1 0.274387 2.672362 0.304107 -4.654894 0.708617 0.625362 16.747
139 1 0.209191 2.419253 0.306014 -5.634576 0.701404 0.640945 17.366
140 1 0.184985 2.445646 0.233070 -5.866357 0.696049 0.624811 18.801
141 1 0.277227 2.963799 0.397749 -4.796845 0.685057 0.677131 18.540
142 1 0.231723 2.665133 0.288917 -5.410336 0.665945 0.606344 15.648
143 1 0.209863 2.465528 0.310746 -5.585259 0.661735 0.606273 18.702
144 1 0.189032 2.470746 0.213353 -5.898673 0.632631 0.536102 18.687
145 1 0.159777 2.576563 0.220617 -6.132663 0.630409 0.497480 20.680
146 1 0.232861 2.840556 0.345238 -5.456811 0.574282 0.566849 20.366
147 1 0.457533 3.413649 0.414758 -3.297668 0.793509 0.561610 12.359
148 1 0.336085 3.142364 0.355736 -4.276605 0.768974 0.478024 14.367
149 1 0.418646 3.274865 0.335357 -3.377325 0.764036 0.552870 12.298
150 1 0.270173 2.910213 0.262281 -4.892495 0.775708 0.427627 14.989
151 1 0.301487 2.958815 0.340256 -4.484303 0.762726 0.507826 12.529
152 1 0.527367 3.079221 0.450493 -2.434031 0.768320 0.625866 8.441
153 1 0.454721 3.184027 0.356224 -2.839756 0.754449 0.584164 9.449
154 1 0.168581 2.013530 0.246404 -4.865194 0.670475 0.566867 21.520
155 1 0.247455 2.451130 0.175691 -4.239028 0.659333 0.651680 21.824
156 1 0.206256 2.439597 0.207914 -3.583722 0.652025 0.628300 22.431
157 1 0.220546 2.699645 0.230532 -5.435100 0.623731 0.611679 22.953
158 1 0.261305 2.964568 0.303214 -3.444478 0.646786 0.630547 19.075
159 1 0.249703 2.892300 0.280091 -5.070096 0.627337 0.635015 21.534
160 1 0.216638 2.103014 0.234196 -5.498456 0.675865 0.654945 19.651
161 1 0.244948 2.151121 0.259229 -5.185987 0.694571 0.653139 20.437
162 1 0.238281 2.442906 0.226528 -5.283009 0.684373 0.577802 19.388
163 1 0.220520 2.408689 0.242750 -5.529833 0.719576 0.685151 18.954
164 1 0.212386 1.871871 0.184896 -5.617124 0.673086 0.557045 21.219
165 1 0.367233 2.560422 0.396746 -2.929379 0.674562 0.671378 18.447
166 0 0.119652 2.235197 0.172270 -6.816086 0.628232 0.469928 24.078
167 0 0.091604 1.852402 0.176316 -7.018057 0.626710 0.384868 24.679
168 0 0.075587 1.881767 0.160414 -7.517934 0.628058 0.440988 21.083
169 0 0.202879 2.882450 0.164529 -5.736781 0.725216 0.372222 19.269
170 0 0.100881 2.266432 0.073298 -7.169701 0.646167 0.371837 21.020
171 0 0.096220 2.095237 0.171088 -7.304500 0.646818 0.522812 21.528
172 0 0.160376 2.193412 0.218885 -6.323531 0.756700 0.413295 26.436
173 0 0.174152 1.889002 0.192375 -6.085567 0.776158 0.369090 26.550
174 0 0.179677 1.852542 0.192150 -5.943501 0.766700 0.380253 26.547
175 0 0.163118 1.872946 0.229298 -6.012559 0.756482 0.387482 25.445
176 0 0.184067 1.974857 0.197938 -5.966779 0.761255 0.405991 26.005
177 0 0.174429 2.004719 0.109256 -6.016891 0.763242 0.361232 26.143
178 1 0.132703 2.449763 0.197919 -6.486822 0.745957 0.396610 24.151
179 1 0.160306 2.251553 0.182459 -6.311987 0.762508 0.402591 24.412
180 1 0.192730 2.845109 0.240875 -5.711205 0.778349 0.398499 23.683
181 1 0.144105 2.264226 0.183218 -6.261446 0.759320 0.352396 23.133
182 1 0.197710 2.679185 0.216204 -5.704053 0.768845 0.408598 22.866
183 1 0.156368 2.209021 0.109397 -6.277170 0.757180 0.329577 23.008
184 0 0.215724 2.027228 0.191576 -5.619070 0.669565 0.603515 23.079
185 0 0.252404 2.120412 0.206768 -5.198864 0.656516 0.663842 22.085
186 0 0.214346 2.058658 0.133917 -5.592584 0.654331 0.598515 24.199
187 0 0.120605 2.161936 0.153310 -6.431119 0.667654 0.566424 23.958
188 0 0.138868 2.152083 0.116636 -6.359018 0.663884 0.528485 25.023
189 0 0.121777 1.913990 0.149694 -6.710219 0.659132 0.555303 24.775
190 0 0.112838 2.316346 0.159890 -6.934474 0.683761 0.508479 19.368
191 0 0.133050 2.657476 0.121952 -6.538586 0.657899 0.448439 19.517
192 0 0.168895 2.784312 0.129303 -6.195325 0.683244 0.431674 19.147
193 0 0.131728 2.679772 0.158453 -6.787197 0.655683 0.407567 17.883
194 0 0.123306 2.138608 0.207454 -6.744577 0.643956 0.451221 19.020
195 0 0.148569 2.555477 0.190667 -5.724056 0.664357 0.462803 21.209
NHR Shimmer:DDA MDVP:APQ Shimmer:APQ5 Shimmer:APQ3 MDVP:Shimmer(dB)
1 0.02211 0.06545 0.02971 0.03130 0.02182 0.426
2 0.01929 0.09403 0.04368 0.04518 0.03134 0.626
3 0.01309 0.08270 0.03590 0.03858 0.02757 0.482
4 0.01353 0.08771 0.03772 0.04005 0.02924 0.517
5 0.01767 0.10470 0.04465 0.04825 0.03490 0.584
6 0.01222 0.06985 0.03243 0.03526 0.02328 0.456
7 0.00607 0.02337 0.01351 0.00937 0.00779 0.140
8 0.00344 0.02487 0.01256 0.00946 0.00829 0.134
9 0.01070 0.03218 0.01717 0.01277 0.01073 0.191
10 0.01022 0.04324 0.02444 0.01725 0.01441 0.255
11 0.01166 0.03237 0.01892 0.01342 0.01079 0.197
12 0.01141 0.04272 0.02214 0.01641 0.01424 0.249
13 0.00581 0.01968 0.01140 0.00717 0.00656 0.112
14 0.01041 0.02184 0.01797 0.00932 0.00728 0.154
15 0.00609 0.03191 0.01246 0.00972 0.01064 0.158
16 0.00839 0.02316 0.01359 0.00888 0.00772 0.126
17 0.01859 0.02908 0.02074 0.01200 0.00969 0.192
18 0.02919 0.04322 0.03430 0.01893 0.01441 0.348
19 0.03160 0.07413 0.05767 0.03572 0.02471 0.542
20 0.03365 0.05164 0.04310 0.02374 0.01721 0.348
21 0.03871 0.05000 0.04055 0.02383 0.01667 0.328
22 0.01849 0.06062 0.04525 0.02591 0.02021 0.370
23 0.01280 0.06685 0.04246 0.02540 0.02228 0.377
24 0.01840 0.06562 0.03772 0.02470 0.02187 0.364
25 0.01778 0.02214 0.01497 0.00948 0.00738 0.164
26 0.02887 0.05197 0.03780 0.02245 0.01732 0.381
27 0.01095 0.02666 0.01872 0.01169 0.00889 0.186
28 0.01328 0.02650 0.01826 0.01144 0.00883 0.198
29 0.00677 0.02307 0.01661 0.01012 0.00769 0.161
30 0.01170 0.02380 0.01799 0.01057 0.00793 0.168
31 0.00339 0.01689 0.00802 0.00680 0.00563 0.097
32 0.00167 0.01513 0.00762 0.00641 0.00504 0.089
33 0.00119 0.01919 0.00951 0.00825 0.00640 0.111
34 0.00072 0.01407 0.00719 0.00606 0.00469 0.085
35 0.00065 0.01403 0.00726 0.00610 0.00468 0.085
36 0.00135 0.01758 0.00957 0.00760 0.00586 0.107
37 0.00586 0.03463 0.01612 0.01347 0.01154 0.189
38 0.00340 0.02814 0.01491 0.01160 0.00938 0.168
39 0.00231 0.02177 0.01190 0.00885 0.00726 0.131
40 0.00265 0.02488 0.01366 0.01003 0.00829 0.151
41 0.00231 0.02321 0.01233 0.00941 0.00774 0.135
42 0.00257 0.02226 0.01234 0.00901 0.00742 0.132
43 0.00740 0.03104 0.01133 0.01024 0.01035 0.164
44 0.00675 0.03017 0.01251 0.01038 0.01006 0.154
45 0.00454 0.02330 0.01033 0.00898 0.00777 0.126
46 0.00476 0.02542 0.01014 0.00879 0.00847 0.134
47 0.00476 0.02719 0.01149 0.00977 0.00906 0.141
48 0.00432 0.01841 0.00860 0.00730 0.00614 0.103
49 0.00839 0.02566 0.01433 0.00776 0.00855 0.143
50 0.00462 0.02789 0.01400 0.00802 0.00930 0.154
51 0.00479 0.03724 0.01685 0.01024 0.01241 0.197
52 0.00474 0.03429 0.01614 0.00959 0.01143 0.185
53 0.00481 0.03969 0.01677 0.01072 0.01323 0.210
54 0.00484 0.04188 0.01947 0.01219 0.01396 0.228
55 0.01036 0.04450 0.02067 0.01609 0.01483 0.255
56 0.01180 0.05368 0.02454 0.01992 0.01789 0.307
57 0.00969 0.06097 0.02802 0.02302 0.02032 0.334
58 0.00681 0.03568 0.01948 0.01459 0.01189 0.221
59 0.00786 0.04183 0.02137 0.01625 0.01394 0.265
60 0.01143 0.05414 0.02519 0.01974 0.01805 0.350
61 0.00871 0.02925 0.01382 0.01258 0.00975 0.170
62 0.00301 0.03039 0.01340 0.01296 0.01013 0.165
63 0.00340 0.02602 0.01200 0.01108 0.00867 0.145
64 0.00351 0.02647 0.01179 0.01075 0.00882 0.145
65 0.00300 0.02308 0.01016 0.00957 0.00769 0.129
66 0.00420 0.02827 0.01234 0.01160 0.00942 0.154
67 0.02183 0.05490 0.02428 0.01810 0.01830 0.313
68 0.02659 0.04914 0.02603 0.01759 0.01638 0.308
69 0.04882 0.09455 0.03392 0.02422 0.03152 0.478
70 0.02431 0.10070 0.03635 0.02494 0.03357 0.497
71 0.02599 0.05605 0.02949 0.01906 0.01868 0.365
72 0.03361 0.08247 0.03736 0.02466 0.02749 0.483
73 0.00442 0.02921 0.01345 0.00925 0.00974 0.152
74 0.00623 0.04120 0.01956 0.01375 0.01373 0.226
75 0.00479 0.04295 0.01831 0.01325 0.01432 0.216
76 0.00472 0.03851 0.01715 0.01219 0.01284 0.206
77 0.00905 0.07238 0.02704 0.02231 0.02413 0.350
78 0.00420 0.03852 0.01636 0.01199 0.01284 0.197
79 0.01062 0.05408 0.02455 0.01886 0.01803 0.263
80 0.02220 0.05320 0.02139 0.01783 0.01773 0.361
81 0.01823 0.06799 0.02876 0.02451 0.02266 0.364
82 0.01825 0.05377 0.02190 0.01841 0.01792 0.296
83 0.01237 0.04114 0.01751 0.01421 0.01371 0.216
84 0.00882 0.03831 0.01552 0.01343 0.01277 0.202
85 0.05470 0.08037 0.03510 0.03022 0.02679 0.435
86 0.02782 0.06321 0.02877 0.02493 0.02107 0.331
87 0.03151 0.06219 0.02784 0.02415 0.02073 0.327
88 0.04824 0.11012 0.04683 0.04159 0.03671 0.580
89 0.04214 0.11363 0.04802 0.04254 0.03788 0.650
90 0.07223 0.06892 0.03455 0.02768 0.02297 0.442
91 0.08725 0.10949 0.05114 0.04282 0.03650 0.634
92 0.01658 0.13262 0.05690 0.04962 0.04421 0.772
93 0.01914 0.07150 0.03051 0.02521 0.02383 0.383
94 0.01211 0.10024 0.04398 0.03794 0.03341 0.637
95 0.00850 0.06185 0.02764 0.02321 0.02062 0.307
96 0.01018 0.05439 0.02571 0.01909 0.01813 0.283
97 0.00852 0.05417 0.02809 0.02024 0.01806 0.307
98 0.08151 0.06406 0.03088 0.02174 0.02135 0.342
99 0.10323 0.07625 0.03908 0.02630 0.02542 0.422
100 0.16744 0.10833 0.05783 0.03963 0.03611 0.659
101 0.31482 0.16074 0.06196 0.04791 0.05358 0.891
102 0.11843 0.09669 0.05174 0.03672 0.03223 0.584
103 0.25930 0.16654 0.06023 0.05005 0.05551 0.930
104 0.00495 0.01567 0.01009 0.00659 0.00522 0.107
105 0.00243 0.01406 0.00871 0.00582 0.00469 0.094
106 0.00578 0.01979 0.01059 0.00818 0.00660 0.126
107 0.00233 0.01567 0.00928 0.00632 0.00522 0.097
108 0.00659 0.01898 0.01267 0.00788 0.00633 0.137
109 0.00238 0.01364 0.00993 0.00576 0.00455 0.093
110 0.00947 0.05312 0.02084 0.01815 0.01771 0.275
111 0.00704 0.03576 0.01852 0.01439 0.01192 0.207
112 0.00830 0.02855 0.01307 0.01058 0.00952 0.155
113 0.01316 0.03831 0.01767 0.01483 0.01277 0.210
114 0.00620 0.02583 0.01301 0.01017 0.00861 0.149
115 0.01048 0.03320 0.01604 0.01284 0.01107 0.209
116 0.06051 0.02389 0.01271 0.00832 0.00796 0.235
117 0.01554 0.01818 0.01312 0.00747 0.00606 0.148
118 0.01802 0.02270 0.01652 0.00971 0.00757 0.175
119 0.00856 0.01851 0.01151 0.00744 0.00617 0.129
120 0.00681 0.02038 0.01075 0.00631 0.00679 0.124
121 0.02350 0.02548 0.01734 0.01117 0.00849 0.221
122 0.01161 0.01603 0.01104 0.00630 0.00534 0.117
123 0.01968 0.07761 0.03220 0.02567 0.02587 0.441
124 0.01813 0.04115 0.01931 0.01580 0.01372 0.231
125 0.02020 0.03867 0.01720 0.01420 0.01289 0.224
126 0.01874 0.03706 0.01944 0.01495 0.01235 0.233
127 0.01794 0.04451 0.02259 0.01805 0.01484 0.246
128 0.01796 0.04641 0.02301 0.01859 0.01547 0.257
129 0.01724 0.01614 0.00811 0.00570 0.00538 0.098
130 0.00487 0.01428 0.00903 0.00588 0.00476 0.090
131 0.01610 0.02110 0.01194 0.00820 0.00703 0.125
132 0.01015 0.02164 0.01310 0.00815 0.00721 0.138
133 0.00903 0.01898 0.00915 0.00701 0.00633 0.106
134 0.00504 0.01471 0.00903 0.00621 0.00490 0.099
135 0.03031 0.08050 0.03651 0.03112 0.02683 0.441
136 0.02529 0.06688 0.03316 0.02592 0.02229 0.379
137 0.02278 0.07154 0.04370 0.02973 0.02385 0.431
138 0.03690 0.08689 0.04134 0.03347 0.02896 0.476
139 0.02629 0.09211 0.04451 0.03530 0.03070 0.517
140 0.01827 0.04543 0.02770 0.01812 0.01514 0.267
141 0.02485 0.05139 0.02824 0.01964 0.01713 0.281
142 0.04238 0.12047 0.04464 0.04003 0.04016 0.571
143 0.01728 0.06165 0.02530 0.02076 0.02055 0.297
144 0.02010 0.03350 0.01506 0.01177 0.01117 0.180
145 0.01049 0.04426 0.02006 0.01558 0.01475 0.228
146 0.01493 0.04137 0.01909 0.01478 0.01379 0.225
147 0.07530 0.11411 0.08808 0.05426 0.03804 0.821
148 0.06057 0.08595 0.06359 0.04101 0.02865 0.618
149 0.08069 0.10422 0.06824 0.04580 0.03474 0.722
150 0.07889 0.10546 0.06460 0.04265 0.03515 0.833
151 0.10952 0.08096 0.06259 0.03714 0.02699 0.784
152 0.21713 0.16942 0.13778 0.07940 0.05647 1.302
153 0.16265 0.12851 0.08318 0.05556 0.04284 1.018
154 0.04179 0.04019 0.02056 0.01399 0.01340 0.241
155 0.04611 0.04451 0.02018 0.01405 0.01484 0.236
156 0.02631 0.04977 0.02402 0.01804 0.01659 0.276
157 0.03191 0.03615 0.01771 0.01289 0.01205 0.223
158 0.10748 0.07830 0.02916 0.02161 0.02610 0.438
159 0.03828 0.04499 0.02157 0.01581 0.01500 0.266
160 0.02663 0.04079 0.03105 0.01650 0.01360 0.339
161 0.02073 0.04736 0.04114 0.01994 0.01579 0.406
162 0.02810 0.04933 0.02931 0.01722 0.01644 0.325
163 0.02707 0.05592 0.03091 0.01940 0.01864 0.369
164 0.01435 0.02902 0.01363 0.01033 0.00967 0.155
165 0.03882 0.04736 0.02073 0.01553 0.01579 0.272
166 0.00620 0.04231 0.01621 0.01426 0.01410 0.217
167 0.00533 0.02089 0.00882 0.00747 0.00696 0.116
168 0.00910 0.03557 0.01367 0.01230 0.01186 0.197
169 0.01337 0.03836 0.01439 0.01272 0.01279 0.189
170 0.00965 0.03529 0.01344 0.01191 0.01176 0.212
171 0.01049 0.03253 0.01255 0.01121 0.01084 0.181
172 0.00435 0.01992 0.01140 0.00786 0.00664 0.129
173 0.00430 0.02261 0.01285 0.00950 0.00754 0.133
174 0.00478 0.02245 0.01148 0.00905 0.00748 0.133
175 0.00590 0.02643 0.01318 0.01062 0.00881 0.145
176 0.00401 0.02436 0.01133 0.00933 0.00812 0.137
177 0.00415 0.02623 0.01331 0.01021 0.00874 0.155
178 0.00570 0.02184 0.01230 0.00886 0.00728 0.132
179 0.00488 0.02518 0.01309 0.00956 0.00839 0.142
180 0.00540 0.02175 0.01263 0.00876 0.00725 0.131
181 0.00611 0.03964 0.02148 0.01574 0.01321 0.237
182 0.00639 0.02849 0.01559 0.01103 0.00950 0.163
183 0.00595 0.03464 0.01666 0.01341 0.01155 0.198
184 0.00955 0.02592 0.01949 0.01223 0.00864 0.171
185 0.01179 0.02429 0.01756 0.01144 0.00810 0.163
186 0.00737 0.02001 0.01691 0.00990 0.00667 0.136
187 0.01397 0.02460 0.01491 0.00972 0.00820 0.154
188 0.00680 0.01892 0.01144 0.00789 0.00631 0.117
189 0.00703 0.01672 0.01095 0.00721 0.00557 0.106
190 0.04441 0.04363 0.01758 0.01582 0.01454 0.255
191 0.02764 0.07008 0.02745 0.02498 0.02336 0.405
192 0.01810 0.04812 0.01879 0.01657 0.01604 0.263
193 0.10715 0.03804 0.01667 0.01365 0.01268 0.256
194 0.07223 0.03794 0.01588 0.01321 0.01265 0.241
195 0.04398 0.03078 0.01373 0.01161 0.01026 0.190
MDVP:Shimmer Jitter:DDP MDVP:PPQ MDVP:RAP MDVP:Jitter(Abs) MDVP:Jitter(%)
1 0.04374 0.01109 0.00554 0.00370 7.0e-05 0.00784
2 0.06134 0.01394 0.00696 0.00465 8.0e-05 0.00968
3 0.05233 0.01633 0.00781 0.00544 9.0e-05 0.01050
4 0.05492 0.01505 0.00698 0.00502 9.0e-05 0.00997
5 0.06425 0.01966 0.00908 0.00655 1.1e-04 0.01284
6 0.04701 0.01388 0.00750 0.00463 8.0e-05 0.00968
7 0.01608 0.00466 0.00202 0.00155 3.0e-05 0.00333
8 0.01567 0.00431 0.00182 0.00144 3.0e-05 0.00290
9 0.02093 0.00880 0.00332 0.00293 6.0e-05 0.00551
10 0.02838 0.00803 0.00332 0.00268 6.0e-05 0.00532
11 0.02143 0.00763 0.00330 0.00254 6.0e-05 0.00505
12 0.02752 0.00844 0.00336 0.00281 6.0e-05 0.00540
13 0.01259 0.00355 0.00153 0.00118 2.0e-05 0.00293
14 0.01642 0.00496 0.00208 0.00165 3.0e-05 0.00390
15 0.01828 0.00364 0.00149 0.00121 2.0e-05 0.00294
16 0.01503 0.00471 0.00203 0.00157 3.0e-05 0.00369
17 0.02047 0.00632 0.00292 0.00211 4.0e-05 0.00544
18 0.03327 0.00853 0.00387 0.00284 4.0e-05 0.00718
19 0.05517 0.01092 0.00432 0.00364 5.0e-05 0.00742
20 0.03995 0.01116 0.00399 0.00372 5.0e-05 0.00768
21 0.03810 0.01285 0.00450 0.00428 5.0e-05 0.00840
22 0.04137 0.00696 0.00267 0.00232 3.0e-05 0.00480
23 0.04351 0.00661 0.00247 0.00220 3.0e-05 0.00442
24 0.04192 0.00663 0.00258 0.00221 3.0e-05 0.00476
25 0.01659 0.01140 0.00390 0.00380 5.0e-05 0.00742
26 0.03767 0.00948 0.00375 0.00316 6.0e-05 0.00633
27 0.01966 0.00750 0.00234 0.00250 3.0e-05 0.00455
28 0.01919 0.00749 0.00275 0.00250 3.0e-05 0.00496
29 0.01718 0.00476 0.00176 0.00159 2.0e-05 0.00310
30 0.01791 0.00841 0.00253 0.00280 3.0e-05 0.00502
31 0.01098 0.00498 0.00168 0.00166 1.0e-05 0.00289
32 0.01015 0.00402 0.00138 0.00134 1.0e-05 0.00241
33 0.01263 0.00339 0.00135 0.00113 1.0e-05 0.00212
34 0.00954 0.00278 0.00107 0.00093 9.0e-06 0.00180
35 0.00958 0.00283 0.00106 0.00094 9.0e-06 0.00178
36 0.01194 0.00314 0.00115 0.00105 1.0e-05 0.00198
37 0.02126 0.00700 0.00241 0.00233 2.0e-05 0.00411
38 0.01851 0.00616 0.00218 0.00205 2.0e-05 0.00369
39 0.01444 0.00459 0.00166 0.00153 2.0e-05 0.00284
40 0.01663 0.00504 0.00182 0.00168 2.0e-05 0.00316
41 0.01495 0.00496 0.00175 0.00165 2.0e-05 0.00298
42 0.01463 0.00403 0.00147 0.00134 1.0e-05 0.00258
43 0.01752 0.00507 0.00182 0.00169 1.0e-05 0.00298
44 0.01760 0.00470 0.00173 0.00157 1.0e-05 0.00281
45 0.01419 0.00327 0.00137 0.00109 9.0e-06 0.00210
46 0.01494 0.00350 0.00139 0.00117 9.0e-06 0.00225
47 0.01608 0.00380 0.00148 0.00127 1.0e-05 0.00235
48 0.01152 0.00276 0.00113 0.00092 7.0e-06 0.00185
49 0.01613 0.00507 0.00203 0.00169 4.0e-05 0.00524
50 0.01681 0.00373 0.00155 0.00124 3.0e-05 0.00428
51 0.02184 0.00422 0.00167 0.00141 3.0e-05 0.00431
52 0.02033 0.00393 0.00169 0.00131 4.0e-05 0.00448
53 0.02297 0.00411 0.00166 0.00137 3.0e-05 0.00436
54 0.02498 0.00495 0.00183 0.00165 4.0e-05 0.00490
55 0.02719 0.01046 0.00486 0.00349 7.0e-05 0.00761
56 0.03209 0.01193 0.00539 0.00398 8.0e-05 0.00874
57 0.03715 0.01056 0.00514 0.00352 7.0e-05 0.00784
58 0.02293 0.00898 0.00469 0.00299 6.0e-05 0.00752
59 0.02645 0.01003 0.00493 0.00334 7.0e-05 0.00788
60 0.03225 0.01120 0.00520 0.00373 8.0e-05 0.00867
61 0.01861 0.00442 0.00152 0.00147 1.0e-05 0.00282
62 0.01906 0.00461 0.00151 0.00154 1.0e-05 0.00264
63 0.01643 0.00457 0.00144 0.00152 1.0e-05 0.00266
64 0.01644 0.00526 0.00155 0.00175 1.0e-05 0.00296
65 0.01457 0.00342 0.00113 0.00114 9.0e-06 0.00205
66 0.01745 0.00408 0.00140 0.00136 1.0e-05 0.00238
67 0.03198 0.01289 0.00440 0.00430 6.0e-05 0.00817
68 0.03111 0.01520 0.00463 0.00507 7.0e-05 0.00923
69 0.05384 0.01941 0.00467 0.00647 8.0e-05 0.01101
70 0.05428 0.01400 0.00354 0.00467 5.0e-05 0.00762
71 0.03485 0.01407 0.00419 0.00469 6.0e-05 0.00831
72 0.04978 0.01601 0.00478 0.00534 7.0e-05 0.00971
73 0.01706 0.00540 0.00220 0.00180 3.0e-05 0.00405
74 0.02448 0.00805 0.00329 0.00268 5.0e-05 0.00533
75 0.02442 0.00780 0.00283 0.00260 4.0e-05 0.00494
76 0.02215 0.00831 0.00289 0.00277 5.0e-05 0.00516
77 0.03999 0.00810 0.00289 0.00270 4.0e-05 0.00500
78 0.02199 0.00677 0.00280 0.00226 4.0e-05 0.00462
79 0.03202 0.00994 0.00332 0.00331 6.0e-05 0.00608
80 0.03121 0.01865 0.00576 0.00622 1.0e-04 0.01038
81 0.04024 0.01168 0.00415 0.00389 7.0e-05 0.00694
82 0.03156 0.01283 0.00371 0.00428 7.0e-05 0.00702
83 0.02427 0.01053 0.00348 0.00351 6.0e-05 0.00606
84 0.02223 0.00742 0.00258 0.00247 4.0e-05 0.00432
85 0.04795 0.01254 0.00420 0.00418 4.0e-05 0.00747
86 0.03852 0.00659 0.00244 0.00220 2.0e-05 0.00406
87 0.03759 0.00488 0.00194 0.00163 2.0e-05 0.00321
88 0.06511 0.00862 0.00312 0.00287 3.0e-05 0.00520
89 0.06727 0.00710 0.00254 0.00237 3.0e-05 0.00448
90 0.04313 0.01172 0.00419 0.00391 4.0e-05 0.00709
91 0.06640 0.01161 0.00453 0.00387 4.0e-05 0.00742
92 0.07959 0.00672 0.00227 0.00224 3.0e-05 0.00419
93 0.04190 0.00750 0.00256 0.00250 3.0e-05 0.00459
94 0.05925 0.00574 0.00226 0.00191 3.0e-05 0.00382
95 0.03716 0.00587 0.00196 0.00196 2.0e-05 0.00358
96 0.03272 0.00602 0.00197 0.00201 2.0e-05 0.00369
97 0.03381 0.00535 0.00184 0.00178 2.0e-05 0.00342
98 0.03886 0.02228 0.00623 0.00743 1.0e-04 0.01280
99 0.04689 0.02478 0.00655 0.00826 1.1e-04 0.01378
100 0.06734 0.03476 0.00990 0.01159 1.5e-04 0.01936
101 0.09178 0.06433 0.01522 0.02144 2.6e-04 0.03316
102 0.06170 0.02716 0.00909 0.00905 1.2e-04 0.01551
103 0.09419 0.05563 0.01628 0.01854 2.2e-04 0.03011
104 0.01131 0.00315 0.00136 0.00105 2.0e-05 0.00248
105 0.01030 0.00229 0.00100 0.00076 1.0e-05 0.00183
106 0.01346 0.00349 0.00134 0.00116 2.0e-05 0.00257
107 0.01064 0.00204 0.00092 0.00068 1.0e-05 0.00168
108 0.01450 0.00346 0.00122 0.00115 2.0e-05 0.00258
109 0.01024 0.00225 0.00096 0.00075 1.0e-05 0.00174
110 0.03044 0.01351 0.00389 0.00450 4.0e-05 0.00766
111 0.02286 0.01112 0.00337 0.00371 3.0e-05 0.00621
112 0.01761 0.01105 0.00339 0.00368 3.0e-05 0.00609
113 0.02378 0.01506 0.00485 0.00502 4.0e-05 0.00841
114 0.01680 0.00964 0.00280 0.00321 3.0e-05 0.00534
115 0.02105 0.00905 0.00246 0.00302 2.0e-05 0.00495
116 0.01843 0.01211 0.00385 0.00404 6.0e-05 0.00856
117 0.01458 0.00642 0.00207 0.00214 3.0e-05 0.00476
118 0.01725 0.00731 0.00261 0.00244 3.0e-05 0.00555
119 0.01279 0.00472 0.00194 0.00157 3.0e-05 0.00462
120 0.01299 0.00381 0.00128 0.00127 2.0e-05 0.00404
121 0.02008 0.00723 0.00314 0.00241 5.0e-05 0.00581
122 0.01169 0.00628 0.00221 0.00209 3.0e-05 0.00460
123 0.04479 0.01218 0.00398 0.00406 5.0e-05 0.00704
124 0.02503 0.01517 0.00449 0.00506 5.0e-05 0.00842
125 0.02343 0.01209 0.00395 0.00403 4.0e-05 0.00694
126 0.02362 0.01242 0.00422 0.00414 5.0e-05 0.00733
127 0.02791 0.00883 0.00327 0.00294 4.0e-05 0.00544
128 0.02857 0.01104 0.00351 0.00368 4.0e-05 0.00638
129 0.01033 0.00641 0.00192 0.00214 4.0e-05 0.00440
130 0.01022 0.00349 0.00135 0.00116 2.0e-05 0.00270
131 0.01412 0.00808 0.00238 0.00269 4.0e-05 0.00492
132 0.01516 0.00671 0.00205 0.00224 3.0e-05 0.00407
133 0.01201 0.00508 0.00170 0.00169 3.0e-05 0.00346
134 0.01043 0.00504 0.00171 0.00168 3.0e-05 0.00331
135 0.04932 0.00873 0.00319 0.00291 6.0e-05 0.00589
136 0.04128 0.00731 0.00315 0.00244 4.0e-05 0.00494
137 0.04879 0.00658 0.00283 0.00219 4.0e-05 0.00451
138 0.05279 0.00772 0.00312 0.00257 4.0e-05 0.00502
139 0.05643 0.00715 0.00290 0.00238 4.0e-05 0.00472
140 0.03026 0.00542 0.00232 0.00181 3.0e-05 0.00381
141 0.03273 0.00696 0.00269 0.00232 3.0e-05 0.00571
142 0.06725 0.01285 0.00428 0.00428 4.0e-05 0.00757
143 0.03527 0.00546 0.00215 0.00182 2.0e-05 0.00376
144 0.01997 0.00568 0.00211 0.00189 2.0e-05 0.00370
145 0.02662 0.00301 0.00133 0.00100 1.0e-05 0.00254
146 0.02536 0.00506 0.00188 0.00169 2.0e-05 0.00352
147 0.08143 0.02589 0.00946 0.00863 9.0e-05 0.01568
148 0.06050 0.02546 0.00819 0.00849 8.0e-05 0.01466
149 0.07118 0.02987 0.01027 0.00996 9.0e-05 0.01719
150 0.07170 0.02756 0.00963 0.00919 8.0e-05 0.01627
151 0.05830 0.03225 0.01154 0.01075 1.0e-04 0.01872
152 0.11908 0.05401 0.01958 0.01800 1.6e-04 0.03107
153 0.08684 0.04705 0.01699 0.01568 1.4e-04 0.02714
154 0.02534 0.01164 0.00332 0.00388 6.0e-05 0.00684
155 0.02682 0.01179 0.00300 0.00393 6.0e-05 0.00692
156 0.03087 0.01067 0.00300 0.00356 5.0e-05 0.00647
157 0.02293 0.01246 0.00339 0.00415 6.0e-05 0.00727
158 0.04912 0.03351 0.00718 0.01117 1.5e-04 0.01813
159 0.02852 0.01778 0.00454 0.00593 8.0e-05 0.00975
160 0.03235 0.00962 0.00318 0.00321 5.0e-05 0.00605
161 0.04009 0.00896 0.00316 0.00299 5.0e-05 0.00581
162 0.03273 0.01057 0.00329 0.00352 5.0e-05 0.00619
163 0.03658 0.01097 0.00340 0.00366 6.0e-05 0.00651
164 0.01756 0.00873 0.00284 0.00291 5.0e-05 0.00519
165 0.02814 0.01480 0.00461 0.00493 9.0e-05 0.00907
166 0.02448 0.00462 0.00153 0.00154 1.0e-05 0.00277
167 0.01242 0.00519 0.00159 0.00173 1.0e-05 0.00303
168 0.02030 0.00616 0.00186 0.00205 1.0e-05 0.00339
169 0.02177 0.01470 0.00448 0.00490 4.0e-05 0.00803
170 0.02018 0.00949 0.00283 0.00316 2.0e-05 0.00517
171 0.01897 0.00837 0.00237 0.00279 2.0e-05 0.00451
172 0.01358 0.00499 0.00190 0.00166 3.0e-05 0.00355
173 0.01484 0.00510 0.00200 0.00170 3.0e-05 0.00356
174 0.01472 0.00514 0.00203 0.00171 3.0e-05 0.00349
175 0.01657 0.00528 0.00218 0.00176 3.0e-05 0.00353
176 0.01503 0.00480 0.00199 0.00160 3.0e-05 0.00332
177 0.01725 0.00507 0.00213 0.00169 3.0e-05 0.00346
178 0.01469 0.00406 0.00162 0.00135 2.0e-05 0.00314
179 0.01574 0.00456 0.00186 0.00152 2.0e-05 0.00309
180 0.01450 0.00612 0.00231 0.00204 3.0e-05 0.00392
181 0.02551 0.00619 0.00233 0.00206 3.0e-05 0.00396
182 0.01831 0.00605 0.00235 0.00202 3.0e-05 0.00397
183 0.02145 0.00521 0.00198 0.00174 2.0e-05 0.00336
184 0.01909 0.00558 0.00270 0.00186 4.0e-05 0.00417
185 0.01795 0.00780 0.00346 0.00260 5.0e-05 0.00531
186 0.01564 0.00403 0.00192 0.00134 3.0e-05 0.00314
187 0.01660 0.00762 0.00263 0.00254 4.0e-05 0.00496
188 0.01300 0.00345 0.00148 0.00115 2.0e-05 0.00267
189 0.01185 0.00439 0.00184 0.00146 3.0e-05 0.00327
190 0.02574 0.01235 0.00396 0.00412 3.0e-05 0.00694
191 0.04087 0.00790 0.00259 0.00263 3.0e-05 0.00459
192 0.02751 0.00994 0.00292 0.00331 3.0e-05 0.00564
193 0.02308 0.01873 0.00564 0.00624 8.0e-05 0.01360
194 0.02296 0.01109 0.00390 0.00370 4.0e-05 0.00740
195 0.01884 0.00885 0.00317 0.00295 3.0e-05 0.00567
MDVP:Flo(Hz) MDVP:Fhi(Hz) MDVP:Fo(Hz)
1 74.997 157.302 119.992
2 113.819 148.650 122.400
3 111.555 131.111 116.682
4 111.366 137.871 116.676
5 110.655 141.781 116.014
6 113.787 131.162 120.552
7 114.820 137.244 120.267
8 104.315 113.840 107.332
9 91.754 132.068 95.730
10 91.226 120.103 95.056
11 84.072 112.240 88.333
12 86.292 115.871 91.904
13 131.276 159.866 136.926
14 76.556 179.139 139.173
15 75.836 163.305 152.845
16 83.159 217.455 142.167
17 82.764 349.259 144.188
18 75.603 232.181 168.778
19 68.623 175.829 153.046
20 142.822 189.398 156.405
21 65.782 165.738 153.848
22 78.128 172.860 153.880
23 79.068 193.221 167.930
24 86.180 192.735 173.917
25 76.779 200.841 163.656
26 77.968 206.002 104.400
27 75.501 208.313 171.041
28 81.737 208.701 146.845
29 80.055 227.383 155.358
30 77.630 198.346 162.568
31 192.055 206.896 197.076
32 192.091 209.512 199.228
33 193.104 215.203 198.383
34 197.079 211.604 202.266
35 196.160 211.526 203.184
36 195.708 210.565 201.464
37 168.013 192.921 177.876
38 163.564 185.604 176.170
39 175.456 201.249 180.198
40 173.015 202.324 187.733
41 177.584 197.724 186.163
42 166.977 196.537 184.055
43 225.227 247.326 237.226
44 232.483 248.834 241.404
45 232.435 250.912 243.439
46 227.911 255.034 242.852
47 231.848 262.090 245.510
48 182.786 261.487 252.455
49 115.765 128.611 122.188
50 114.676 130.049 122.964
51 117.495 135.069 124.445
52 112.773 134.231 126.344
53 122.080 138.052 128.001
54 118.604 139.867 129.336
55 102.874 134.656 108.807
56 104.437 126.358 109.860
57 103.370 131.067 110.417
58 110.402 129.916 117.274
59 108.153 131.897 116.879
60 104.680 271.314 114.847
61 109.379 237.494 209.144
62 98.664 238.987 223.365
63 205.495 231.345 222.236
64 223.634 234.619 228.832
65 221.156 252.221 229.401
66 113.201 239.541 228.969
67 67.021 159.774 140.341
68 66.004 166.607 136.969
69 65.809 162.215 143.533
70 67.343 162.824 148.090
71 65.476 162.408 142.729
72 65.750 176.595 136.358
73 111.208 139.710 120.080
74 107.024 588.518 112.014
75 107.316 128.101 110.793
76 105.007 122.611 110.707
77 106.981 148.826 112.876
78 106.821 125.394 110.568
79 90.264 102.145 95.385
80 85.545 115.697 100.770
81 84.510 108.664 96.106
82 87.549 107.715 95.605
83 95.628 110.019 100.960
84 87.804 102.305 98.804
85 75.344 205.560 176.858
86 155.495 200.125 180.978
87 141.047 202.450 178.222
88 125.610 227.381 176.281
89 74.677 211.350 173.898
90 144.878 225.930 179.711
91 78.032 206.008 166.605
92 147.226 163.335 151.955
93 142.299 164.989 148.272
94 76.596 161.469 152.125
95 68.401 172.975 157.821
96 149.605 163.267 157.447
97 144.811 168.913 159.116
98 116.187 143.946 125.036
99 96.206 140.557 125.791
100 99.770 141.756 126.512
101 116.346 141.068 125.641
102 75.632 150.449 128.451
103 66.157 586.567 139.224
104 75.349 154.609 150.258
105 128.621 160.267 154.003
106 133.608 160.368 149.689
107 144.148 163.736 155.078
108 133.751 157.765 151.884
109 132.857 157.339 151.989
110 80.297 208.900 193.030
111 89.686 223.982 200.714
112 199.020 220.315 208.519
113 189.621 221.300 204.664
114 185.258 232.706 210.141
115 92.020 226.355 206.327
116 69.085 492.892 151.872
117 71.948 442.557 158.219
118 79.032 450.247 170.756
119 82.063 442.824 178.285
120 93.978 233.481 217.116
121 88.251 479.697 128.940
122 83.961 215.293 176.824
123 83.340 203.522 138.190
124 79.187 197.173 182.018
125 79.820 195.107 156.239
126 80.637 198.109 145.174
127 81.114 197.238 138.145
128 79.512 198.966 166.888
129 109.216 127.533 119.031
130 105.667 126.632 120.078
131 100.209 128.143 120.289
132 104.773 125.306 120.256
133 86.795 125.213 119.056
134 109.836 123.723 118.747
135 93.105 112.777 106.516
136 105.554 127.611 110.453
137 107.816 133.344 113.400
138 100.673 130.270 113.166
139 104.095 126.609 112.239
140 109.815 131.731 116.150
141 79.543 268.796 170.368
142 91.802 253.792 208.083
143 148.691 219.290 198.458
144 86.232 231.508 202.805
145 164.168 241.350 202.544
146 87.638 263.872 223.361
147 151.451 191.759 169.774
148 161.340 216.814 183.520
149 165.982 216.302 188.620
150 177.258 565.740 202.632
151 149.442 211.961 186.695
152 168.793 224.429 192.818
153 174.478 233.099 198.116
154 98.250 139.644 121.345
155 88.833 128.442 119.100
156 95.654 127.349 117.870
157 94.794 142.369 122.336
158 100.757 134.209 117.963
159 97.543 154.284 126.144
160 112.173 138.752 127.930
161 77.022 124.393 114.238
162 107.802 135.738 115.322
163 91.121 126.778 114.554
164 97.527 131.669 112.150
165 85.902 142.830 102.273
166 102.137 244.663 236.200
167 229.256 243.709 237.323
168 237.303 264.919 260.105
169 90.794 217.627 197.569
170 219.783 245.135 240.301
171 239.170 272.210 244.990
172 105.715 133.374 112.547
173 100.139 113.597 110.739
174 96.913 116.443 113.715
175 99.923 144.466 117.004
176 108.634 123.109 115.380
177 108.970 129.038 116.388
178 129.859 190.204 151.737
179 138.990 158.359 148.790
180 135.041 155.982 148.143
181 144.736 163.441 150.440
182 141.998 161.078 148.462
183 144.786 163.417 149.818
184 106.656 123.925 117.226
185 99.503 217.552 116.848
186 96.983 177.291 116.286
187 86.228 592.030 116.556
188 94.246 581.289 116.342
189 86.647 119.167 114.563
190 78.228 262.707 201.774
191 94.261 230.978 174.188
192 89.488 253.017 209.516
193 74.287 240.005 174.688
194 74.904 396.961 198.764
195 77.973 260.277 214.289
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) PPE D2 spread2
2.225e+00 1.263e+00 4.946e-02 1.266e+00
spread1 DFA RPDE HNR
1.273e-01 3.551e-01 -1.014e+00 -1.569e-02
NHR `Shimmer:DDA` `MDVP:APQ` `Shimmer:APQ5`
-2.526e+00 2.837e+02 -3.075e+00 -2.640e+01
`Shimmer:APQ3` `MDVP:Shimmer(dB)` `MDVP:Shimmer` `Jitter:DDP`
-8.712e+02 5.710e-01 2.745e+01 3.606e+02
`MDVP:PPQ` `MDVP:RAP` `MDVP:Jitter(Abs)` `MDVP:Jitter(%)`
-3.614e+01 -7.592e+02 -3.322e+03 -1.769e+02
`MDVP:Flo(Hz)` `MDVP:Fhi(Hz)` `MDVP:Fo(Hz)`
-1.535e-03 -1.152e-04 -2.384e-03
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.94184 -0.15077 0.04547 0.20496 0.58507
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 2.225e+00 1.158e+00 1.921 0.05644 .
PPE 1.263e+00 1.383e+00 0.913 0.36235
D2 4.946e-02 1.143e-01 0.433 0.66582
spread2 1.266e+00 4.780e-01 2.648 0.00886 **
spread1 1.273e-01 9.790e-02 1.300 0.19523
DFA 3.551e-01 7.394e-01 0.480 0.63161
RPDE -1.014e+00 4.395e-01 -2.308 0.02219 *
HNR -1.569e-02 1.434e-02 -1.094 0.27544
NHR -2.526e+00 1.981e+00 -1.275 0.20397
`Shimmer:DDA` 2.837e+02 2.990e+03 0.095 0.92450
`MDVP:APQ` -3.075e+00 1.089e+01 -0.282 0.77804
`Shimmer:APQ5` -2.640e+01 2.012e+01 -1.312 0.19136
`Shimmer:APQ3` -8.712e+02 8.972e+03 -0.097 0.92276
`MDVP:Shimmer(dB)` 5.710e-01 1.199e+00 0.476 0.63459
`MDVP:Shimmer` 2.745e+01 3.428e+01 0.801 0.42442
`Jitter:DDP` 3.606e+02 3.111e+03 0.116 0.90788
`MDVP:PPQ` -3.614e+01 8.838e+01 -0.409 0.68316
`MDVP:RAP` -7.592e+02 9.332e+03 -0.081 0.93525
`MDVP:Jitter(Abs)` -3.322e+03 4.626e+03 -0.718 0.47368
`MDVP:Jitter(%)` -1.769e+02 6.703e+01 -2.639 0.00907 **
`MDVP:Flo(Hz)` -1.535e-03 8.023e-04 -1.913 0.05737 .
`MDVP:Fhi(Hz)` -1.152e-04 3.211e-04 -0.359 0.72011
`MDVP:Fo(Hz)` -2.384e-03 1.510e-03 -1.579 0.11617
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.3267 on 172 degrees of freedom
Multiple R-squared: 0.4927, Adjusted R-squared: 0.4279
F-statistic: 7.594 on 22 and 172 DF, p-value: 4.808e-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,] 1.814384e-53 3.628768e-53 1.00000000
[2,] 3.497289e-65 6.994578e-65 1.00000000
[3,] 1.346058e-76 2.692115e-76 1.00000000
[4,] 3.416667e-94 6.833335e-94 1.00000000
[5,] 3.724507e-106 7.449015e-106 1.00000000
[6,] 2.546253e-04 5.092506e-04 0.99974537
[7,] 6.628473e-05 1.325695e-04 0.99993372
[8,] 1.634550e-05 3.269099e-05 0.99998365
[9,] 4.921244e-06 9.842488e-06 0.99999508
[10,] 1.493053e-06 2.986107e-06 0.99999851
[11,] 3.663781e-07 7.327561e-07 0.99999963
[12,] 5.203943e-05 1.040789e-04 0.99994796
[13,] 2.996809e-05 5.993618e-05 0.99997003
[14,] 5.278445e-04 1.055689e-03 0.99947216
[15,] 8.676116e-04 1.735223e-03 0.99913239
[16,] 1.089027e-03 2.178054e-03 0.99891097
[17,] 6.345594e-04 1.269119e-03 0.99936544
[18,] 3.910895e-04 7.821790e-04 0.99960891
[19,] 2.188681e-04 4.377363e-04 0.99978113
[20,] 1.019935e-04 2.039871e-04 0.99989801
[21,] 5.006918e-05 1.001384e-04 0.99994993
[22,] 2.996916e-05 5.993832e-05 0.99997003
[23,] 3.988068e-05 7.976137e-05 0.99996012
[24,] 1.743572e-04 3.487145e-04 0.99982564
[25,] 1.438208e-04 2.876416e-04 0.99985618
[26,] 9.518950e-05 1.903790e-04 0.99990481
[27,] 6.312518e-05 1.262504e-04 0.99993687
[28,] 4.435794e-05 8.871587e-05 0.99995564
[29,] 4.615947e-05 9.231894e-05 0.99995384
[30,] 5.316994e-05 1.063399e-04 0.99994683
[31,] 6.654600e-05 1.330920e-04 0.99993345
[32,] 3.769786e-05 7.539572e-05 0.99996230
[33,] 2.599145e-05 5.198290e-05 0.99997401
[34,] 1.475256e-05 2.950512e-05 0.99998525
[35,] 8.440881e-06 1.688176e-05 0.99999156
[36,] 3.517339e-04 7.034678e-04 0.99964827
[37,] 4.571405e-04 9.142810e-04 0.99954286
[38,] 6.235912e-04 1.247182e-03 0.99937641
[39,] 6.232405e-04 1.246481e-03 0.99937676
[40,] 4.260821e-04 8.521643e-04 0.99957392
[41,] 4.220822e-04 8.441644e-04 0.99957792
[42,] 2.832624e-04 5.665249e-04 0.99971674
[43,] 1.823968e-04 3.647937e-04 0.99981760
[44,] 2.316280e-04 4.632559e-04 0.99976837
[45,] 1.786081e-04 3.572162e-04 0.99982139
[46,] 1.078655e-04 2.157309e-04 0.99989213
[47,] 7.362955e-05 1.472591e-04 0.99992637
[48,] 4.320487e-05 8.640973e-05 0.99995680
[49,] 1.196078e-04 2.392156e-04 0.99988039
[50,] 1.563976e-04 3.127953e-04 0.99984360
[51,] 1.157693e-04 2.315385e-04 0.99988423
[52,] 7.497239e-05 1.499448e-04 0.99992503
[53,] 5.881014e-05 1.176203e-04 0.99994119
[54,] 3.488782e-05 6.977565e-05 0.99996511
[55,] 2.490667e-05 4.981333e-05 0.99997509
[56,] 1.848991e-05 3.697981e-05 0.99998151
[57,] 1.077432e-05 2.154865e-05 0.99998923
[58,] 7.130719e-06 1.426144e-05 0.99999287
[59,] 4.579435e-06 9.158871e-06 0.99999542
[60,] 2.768345e-06 5.536690e-06 0.99999723
[61,] 3.233183e-06 6.466366e-06 0.99999677
[62,] 5.280663e-06 1.056133e-05 0.99999472
[63,] 3.341635e-06 6.683270e-06 0.99999666
[64,] 2.597860e-06 5.195721e-06 0.99999740
[65,] 3.214897e-06 6.429795e-06 0.99999679
[66,] 3.172618e-06 6.345235e-06 0.99999683
[67,] 3.961379e-06 7.922758e-06 0.99999604
[68,] 2.519518e-06 5.039035e-06 0.99999748
[69,] 1.688523e-06 3.377046e-06 0.99999831
[70,] 1.089697e-06 2.179394e-06 0.99999891
[71,] 6.756170e-07 1.351234e-06 0.99999932
[72,] 4.200856e-07 8.401713e-07 0.99999958
[73,] 2.393840e-07 4.787681e-07 0.99999976
[74,] 1.489749e-07 2.979497e-07 0.99999985
[75,] 8.739952e-08 1.747990e-07 0.99999991
[76,] 6.861254e-08 1.372251e-07 0.99999993
[77,] 6.592061e-08 1.318412e-07 0.99999993
[78,] 7.737128e-08 1.547426e-07 0.99999992
[79,] 1.488163e-07 2.976327e-07 0.99999985
[80,] 2.309658e-07 4.619316e-07 0.99999977
[81,] 4.220153e-07 8.440305e-07 0.99999958
[82,] 9.940501e-07 1.988100e-06 0.99999901
[83,] 7.281823e-07 1.456365e-06 0.99999927
[84,] 1.655143e-06 3.310286e-06 0.99999834
[85,] 1.356737e-06 2.713473e-06 0.99999864
[86,] 8.088825e-07 1.617765e-06 0.99999919
[87,] 1.638931e-06 3.277863e-06 0.99999836
[88,] 1.084246e-06 2.168492e-06 0.99999892
[89,] 1.262980e-06 2.525959e-06 0.99999874
[90,] 1.248066e-06 2.496133e-06 0.99999875
[91,] 8.450619e-07 1.690124e-06 0.99999915
[92,] 1.162028e-06 2.324055e-06 0.99999884
[93,] 6.706285e-07 1.341257e-06 0.99999933
[94,] 4.737373e-07 9.474746e-07 0.99999953
[95,] 1.087439e-06 2.174877e-06 0.99999891
[96,] 6.793447e-06 1.358689e-05 0.99999321
[97,] 1.714571e-05 3.429142e-05 0.99998285
[98,] 1.094495e-05 2.188990e-05 0.99998906
[99,] 7.511780e-06 1.502356e-05 0.99999249
[100,] 5.283168e-06 1.056634e-05 0.99999472
[101,] 5.531585e-06 1.106317e-05 0.99999447
[102,] 1.110809e-05 2.221618e-05 0.99998889
[103,] 1.462569e-04 2.925139e-04 0.99985374
[104,] 3.825555e-04 7.651110e-04 0.99961744
[105,] 4.874166e-04 9.748332e-04 0.99951258
[106,] 5.018673e-04 1.003735e-03 0.99949813
[107,] 3.434651e-04 6.869302e-04 0.99965653
[108,] 3.079671e-04 6.159342e-04 0.99969203
[109,] 1.383024e-03 2.766049e-03 0.99861698
[110,] 1.606070e-03 3.212141e-03 0.99839393
[111,] 1.563284e-03 3.126568e-03 0.99843672
[112,] 1.689852e-03 3.379705e-03 0.99831015
[113,] 1.735729e-03 3.471459e-03 0.99826427
[114,] 1.172495e-03 2.344991e-03 0.99882750
[115,] 1.453524e-03 2.907049e-03 0.99854648
[116,] 1.228730e-03 2.457461e-03 0.99877127
[117,] 1.564035e-03 3.128070e-03 0.99843596
[118,] 1.314530e-03 2.629060e-03 0.99868547
[119,] 4.516192e-03 9.032385e-03 0.99548381
[120,] 3.920266e-03 7.840531e-03 0.99607973
[121,] 3.016489e-03 6.032978e-03 0.99698351
[122,] 2.145464e-03 4.290927e-03 0.99785454
[123,] 1.383528e-03 2.767056e-03 0.99861647
[124,] 1.399000e-03 2.798000e-03 0.99860100
[125,] 9.274371e-04 1.854874e-03 0.99907256
[126,] 8.691156e-04 1.738231e-03 0.99913088
[127,] 6.385178e-03 1.277036e-02 0.99361482
[128,] 3.427698e-02 6.855396e-02 0.96572302
[129,] 3.053679e-02 6.107359e-02 0.96946321
[130,] 2.584514e-02 5.169028e-02 0.97415486
[131,] 1.922083e-02 3.844166e-02 0.98077917
[132,] 8.907862e-02 1.781572e-01 0.91092138
[133,] 8.425213e-02 1.685043e-01 0.91574787
[134,] 2.873580e-01 5.747160e-01 0.71264199
[135,] 2.263667e-01 4.527334e-01 0.77363329
[136,] 1.699163e-01 3.398326e-01 0.83008369
[137,] 1.292572e-01 2.585143e-01 0.87074283
[138,] 1.478543e-01 2.957086e-01 0.85214568
[139,] 3.161248e-01 6.322496e-01 0.68387519
[140,] 4.253480e-01 8.506959e-01 0.57465204
[141,] 5.208676e-01 9.582648e-01 0.47913240
[142,] 9.017518e-01 1.964964e-01 0.09824818
[143,] 9.450033e-01 1.099934e-01 0.05499670
[144,] 9.629543e-01 7.409143e-02 0.03704571
> postscript(file="/var/fisher/rcomp/tmp/1m3sr1386781580.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/2dwjb1386781580.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/34km11386781580.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/4gt8j1386781580.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/5yboj1386781580.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
0.0564281266 -0.0705871710 0.0273891732 -0.0777826472 0.1289663200
6 7 8 9 10
0.0505447902 0.2065940405 0.4193252967 0.0253973938 -0.1509099713
11 12 13 14 15
-0.1115990369 -0.2381056691 0.5431177417 0.1198483763 0.2951541293
16 17 18 19 20
0.2972892567 0.4555582492 -0.3229152531 -0.2934121147 0.0339362094
21 22 23 24 25
-0.0659459012 0.1095260220 -0.1034321130 0.1342289531 0.1796414412
26 27 28 29 30
0.0825911180 0.1959813824 0.2041000665 0.3386043677 0.3006759523
31 32 33 34 35
-0.2963270110 -0.1593586542 -0.1923513261 -0.1280861823 -0.0881745263
36 37 38 39 40
-0.2036277196 0.1893057590 0.1765375230 0.4039567429 0.2469086122
41 42 43 44 45
0.3874036392 0.5634980566 -0.2446206322 -0.2042351963 -0.0134396038
46 47 48 49 50
-0.0888886766 -0.0510556031 0.0454685804 -0.3374627122 -0.4294787668
51 52 53 54 55
-0.4106123628 -0.4259158307 -0.4115231996 -0.5484716508 0.1728758516
56 57 58 59 60
0.2084448843 0.1325855833 0.2401098424 0.2207200741 0.3491883720
61 62 63 64 65
-0.3697711123 -0.2746523739 -0.2644755661 -0.2136066078 -0.1283357512
66 67 68 69 70
-0.2822755512 0.0852200123 0.1108957245 0.0777503964 0.0576307634
71 72 73 74 75
0.1491522115 -0.0929952919 0.1127812153 0.0770648551 -0.0422682654
76 77 78 79 80
-0.0786140032 -0.0985768523 -0.0004832498 0.0388675548 -0.1412748538
81 82 83 84 85
-0.1801439924 -0.1352195816 -0.0136507016 0.3040125754 -0.0875400146
86 87 88 89 90
0.1285309038 0.2968687880 0.0553590707 0.0096593355 -0.2252495501
91 92 93 94 95
-0.1444663951 0.2025294735 0.2663284516 0.1467443301 0.2114405807
96 97 98 99 100
0.2423275791 0.1969934283 -0.0269041154 0.2002605665 0.1029306555
101 102 103 104 105
0.0361471681 0.0261951899 0.0110830427 0.4142247986 0.4193350458
106 107 108 109 110
0.4327364370 0.4647109258 0.3098959351 0.3826380366 0.1015785013
111 112 113 114 115
-0.0349901184 0.4100113720 0.1983687987 0.3011294351 0.1977753708
116 117 118 119 120
0.1149057253 0.2711061162 -0.0518569667 0.1139358570 0.2383985845
121 122 123 124 125
0.4484696084 0.0263668326 0.0271514850 0.2893986811 0.3817899266
126 127 128 129 130
0.3753790565 0.3873613391 0.3724147706 0.5850678575 0.2347514154
131 132 133 134 135
0.1968833498 0.1250420776 -0.0533167362 0.3529596528 0.0367175561
136 137 138 139 140
0.0384846911 -0.1463688729 -0.1506276986 0.0402465326 0.2185196681
141 142 143 144 145
0.0899151144 0.1417015350 0.2710047917 0.3171741249 0.4506066443
146 147 148 149 150
0.1397667810 -0.3515142193 -0.1522854818 -0.2447742538 0.1062984528
151 152 153 154 155
0.0657469871 0.0440683480 0.0409203842 0.1545941737 0.1055613373
156 157 158 159 160
0.0051826515 0.2013160095 -0.2686981811 0.0292525535 0.1380999139
161 162 163 164 165
-0.1281321791 -0.0587124904 0.0691747997 0.2058241998 -0.3824425267
166 167 168 169 170
-0.4502115660 -0.2251905676 -0.0938475184 -0.9418426114 -0.2302780047
171 172 173 174 175
-0.1145762029 -0.7993405843 -0.8358743948 -0.8783338466 -0.8660101226
176 177 178 179 180
-0.8346308649 -0.7768488137 0.3556600296 0.2877673101 0.0656761316
181 182 183 184 185
0.2360214910 0.1275739354 0.2808066129 -0.6043862370 -0.6484630863
186 187 188 189 190
-0.6067838036 -0.4232493950 -0.4753615206 -0.4359967657 -0.4282774574
191 192 193 194 195
-0.6511288011 -0.7009783841 0.1745042657 -0.2670808288 -0.5194153557
> postscript(file="/var/fisher/rcomp/tmp/65t4q1386781580.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 0.0564281266 NA
1 -0.0705871710 0.0564281266
2 0.0273891732 -0.0705871710
3 -0.0777826472 0.0273891732
4 0.1289663200 -0.0777826472
5 0.0505447902 0.1289663200
6 0.2065940405 0.0505447902
7 0.4193252967 0.2065940405
8 0.0253973938 0.4193252967
9 -0.1509099713 0.0253973938
10 -0.1115990369 -0.1509099713
11 -0.2381056691 -0.1115990369
12 0.5431177417 -0.2381056691
13 0.1198483763 0.5431177417
14 0.2951541293 0.1198483763
15 0.2972892567 0.2951541293
16 0.4555582492 0.2972892567
17 -0.3229152531 0.4555582492
18 -0.2934121147 -0.3229152531
19 0.0339362094 -0.2934121147
20 -0.0659459012 0.0339362094
21 0.1095260220 -0.0659459012
22 -0.1034321130 0.1095260220
23 0.1342289531 -0.1034321130
24 0.1796414412 0.1342289531
25 0.0825911180 0.1796414412
26 0.1959813824 0.0825911180
27 0.2041000665 0.1959813824
28 0.3386043677 0.2041000665
29 0.3006759523 0.3386043677
30 -0.2963270110 0.3006759523
31 -0.1593586542 -0.2963270110
32 -0.1923513261 -0.1593586542
33 -0.1280861823 -0.1923513261
34 -0.0881745263 -0.1280861823
35 -0.2036277196 -0.0881745263
36 0.1893057590 -0.2036277196
37 0.1765375230 0.1893057590
38 0.4039567429 0.1765375230
39 0.2469086122 0.4039567429
40 0.3874036392 0.2469086122
41 0.5634980566 0.3874036392
42 -0.2446206322 0.5634980566
43 -0.2042351963 -0.2446206322
44 -0.0134396038 -0.2042351963
45 -0.0888886766 -0.0134396038
46 -0.0510556031 -0.0888886766
47 0.0454685804 -0.0510556031
48 -0.3374627122 0.0454685804
49 -0.4294787668 -0.3374627122
50 -0.4106123628 -0.4294787668
51 -0.4259158307 -0.4106123628
52 -0.4115231996 -0.4259158307
53 -0.5484716508 -0.4115231996
54 0.1728758516 -0.5484716508
55 0.2084448843 0.1728758516
56 0.1325855833 0.2084448843
57 0.2401098424 0.1325855833
58 0.2207200741 0.2401098424
59 0.3491883720 0.2207200741
60 -0.3697711123 0.3491883720
61 -0.2746523739 -0.3697711123
62 -0.2644755661 -0.2746523739
63 -0.2136066078 -0.2644755661
64 -0.1283357512 -0.2136066078
65 -0.2822755512 -0.1283357512
66 0.0852200123 -0.2822755512
67 0.1108957245 0.0852200123
68 0.0777503964 0.1108957245
69 0.0576307634 0.0777503964
70 0.1491522115 0.0576307634
71 -0.0929952919 0.1491522115
72 0.1127812153 -0.0929952919
73 0.0770648551 0.1127812153
74 -0.0422682654 0.0770648551
75 -0.0786140032 -0.0422682654
76 -0.0985768523 -0.0786140032
77 -0.0004832498 -0.0985768523
78 0.0388675548 -0.0004832498
79 -0.1412748538 0.0388675548
80 -0.1801439924 -0.1412748538
81 -0.1352195816 -0.1801439924
82 -0.0136507016 -0.1352195816
83 0.3040125754 -0.0136507016
84 -0.0875400146 0.3040125754
85 0.1285309038 -0.0875400146
86 0.2968687880 0.1285309038
87 0.0553590707 0.2968687880
88 0.0096593355 0.0553590707
89 -0.2252495501 0.0096593355
90 -0.1444663951 -0.2252495501
91 0.2025294735 -0.1444663951
92 0.2663284516 0.2025294735
93 0.1467443301 0.2663284516
94 0.2114405807 0.1467443301
95 0.2423275791 0.2114405807
96 0.1969934283 0.2423275791
97 -0.0269041154 0.1969934283
98 0.2002605665 -0.0269041154
99 0.1029306555 0.2002605665
100 0.0361471681 0.1029306555
101 0.0261951899 0.0361471681
102 0.0110830427 0.0261951899
103 0.4142247986 0.0110830427
104 0.4193350458 0.4142247986
105 0.4327364370 0.4193350458
106 0.4647109258 0.4327364370
107 0.3098959351 0.4647109258
108 0.3826380366 0.3098959351
109 0.1015785013 0.3826380366
110 -0.0349901184 0.1015785013
111 0.4100113720 -0.0349901184
112 0.1983687987 0.4100113720
113 0.3011294351 0.1983687987
114 0.1977753708 0.3011294351
115 0.1149057253 0.1977753708
116 0.2711061162 0.1149057253
117 -0.0518569667 0.2711061162
118 0.1139358570 -0.0518569667
119 0.2383985845 0.1139358570
120 0.4484696084 0.2383985845
121 0.0263668326 0.4484696084
122 0.0271514850 0.0263668326
123 0.2893986811 0.0271514850
124 0.3817899266 0.2893986811
125 0.3753790565 0.3817899266
126 0.3873613391 0.3753790565
127 0.3724147706 0.3873613391
128 0.5850678575 0.3724147706
129 0.2347514154 0.5850678575
130 0.1968833498 0.2347514154
131 0.1250420776 0.1968833498
132 -0.0533167362 0.1250420776
133 0.3529596528 -0.0533167362
134 0.0367175561 0.3529596528
135 0.0384846911 0.0367175561
136 -0.1463688729 0.0384846911
137 -0.1506276986 -0.1463688729
138 0.0402465326 -0.1506276986
139 0.2185196681 0.0402465326
140 0.0899151144 0.2185196681
141 0.1417015350 0.0899151144
142 0.2710047917 0.1417015350
143 0.3171741249 0.2710047917
144 0.4506066443 0.3171741249
145 0.1397667810 0.4506066443
146 -0.3515142193 0.1397667810
147 -0.1522854818 -0.3515142193
148 -0.2447742538 -0.1522854818
149 0.1062984528 -0.2447742538
150 0.0657469871 0.1062984528
151 0.0440683480 0.0657469871
152 0.0409203842 0.0440683480
153 0.1545941737 0.0409203842
154 0.1055613373 0.1545941737
155 0.0051826515 0.1055613373
156 0.2013160095 0.0051826515
157 -0.2686981811 0.2013160095
158 0.0292525535 -0.2686981811
159 0.1380999139 0.0292525535
160 -0.1281321791 0.1380999139
161 -0.0587124904 -0.1281321791
162 0.0691747997 -0.0587124904
163 0.2058241998 0.0691747997
164 -0.3824425267 0.2058241998
165 -0.4502115660 -0.3824425267
166 -0.2251905676 -0.4502115660
167 -0.0938475184 -0.2251905676
168 -0.9418426114 -0.0938475184
169 -0.2302780047 -0.9418426114
170 -0.1145762029 -0.2302780047
171 -0.7993405843 -0.1145762029
172 -0.8358743948 -0.7993405843
173 -0.8783338466 -0.8358743948
174 -0.8660101226 -0.8783338466
175 -0.8346308649 -0.8660101226
176 -0.7768488137 -0.8346308649
177 0.3556600296 -0.7768488137
178 0.2877673101 0.3556600296
179 0.0656761316 0.2877673101
180 0.2360214910 0.0656761316
181 0.1275739354 0.2360214910
182 0.2808066129 0.1275739354
183 -0.6043862370 0.2808066129
184 -0.6484630863 -0.6043862370
185 -0.6067838036 -0.6484630863
186 -0.4232493950 -0.6067838036
187 -0.4753615206 -0.4232493950
188 -0.4359967657 -0.4753615206
189 -0.4282774574 -0.4359967657
190 -0.6511288011 -0.4282774574
191 -0.7009783841 -0.6511288011
192 0.1745042657 -0.7009783841
193 -0.2670808288 0.1745042657
194 -0.5194153557 -0.2670808288
195 NA -0.5194153557
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.0705871710 0.0564281266
[2,] 0.0273891732 -0.0705871710
[3,] -0.0777826472 0.0273891732
[4,] 0.1289663200 -0.0777826472
[5,] 0.0505447902 0.1289663200
[6,] 0.2065940405 0.0505447902
[7,] 0.4193252967 0.2065940405
[8,] 0.0253973938 0.4193252967
[9,] -0.1509099713 0.0253973938
[10,] -0.1115990369 -0.1509099713
[11,] -0.2381056691 -0.1115990369
[12,] 0.5431177417 -0.2381056691
[13,] 0.1198483763 0.5431177417
[14,] 0.2951541293 0.1198483763
[15,] 0.2972892567 0.2951541293
[16,] 0.4555582492 0.2972892567
[17,] -0.3229152531 0.4555582492
[18,] -0.2934121147 -0.3229152531
[19,] 0.0339362094 -0.2934121147
[20,] -0.0659459012 0.0339362094
[21,] 0.1095260220 -0.0659459012
[22,] -0.1034321130 0.1095260220
[23,] 0.1342289531 -0.1034321130
[24,] 0.1796414412 0.1342289531
[25,] 0.0825911180 0.1796414412
[26,] 0.1959813824 0.0825911180
[27,] 0.2041000665 0.1959813824
[28,] 0.3386043677 0.2041000665
[29,] 0.3006759523 0.3386043677
[30,] -0.2963270110 0.3006759523
[31,] -0.1593586542 -0.2963270110
[32,] -0.1923513261 -0.1593586542
[33,] -0.1280861823 -0.1923513261
[34,] -0.0881745263 -0.1280861823
[35,] -0.2036277196 -0.0881745263
[36,] 0.1893057590 -0.2036277196
[37,] 0.1765375230 0.1893057590
[38,] 0.4039567429 0.1765375230
[39,] 0.2469086122 0.4039567429
[40,] 0.3874036392 0.2469086122
[41,] 0.5634980566 0.3874036392
[42,] -0.2446206322 0.5634980566
[43,] -0.2042351963 -0.2446206322
[44,] -0.0134396038 -0.2042351963
[45,] -0.0888886766 -0.0134396038
[46,] -0.0510556031 -0.0888886766
[47,] 0.0454685804 -0.0510556031
[48,] -0.3374627122 0.0454685804
[49,] -0.4294787668 -0.3374627122
[50,] -0.4106123628 -0.4294787668
[51,] -0.4259158307 -0.4106123628
[52,] -0.4115231996 -0.4259158307
[53,] -0.5484716508 -0.4115231996
[54,] 0.1728758516 -0.5484716508
[55,] 0.2084448843 0.1728758516
[56,] 0.1325855833 0.2084448843
[57,] 0.2401098424 0.1325855833
[58,] 0.2207200741 0.2401098424
[59,] 0.3491883720 0.2207200741
[60,] -0.3697711123 0.3491883720
[61,] -0.2746523739 -0.3697711123
[62,] -0.2644755661 -0.2746523739
[63,] -0.2136066078 -0.2644755661
[64,] -0.1283357512 -0.2136066078
[65,] -0.2822755512 -0.1283357512
[66,] 0.0852200123 -0.2822755512
[67,] 0.1108957245 0.0852200123
[68,] 0.0777503964 0.1108957245
[69,] 0.0576307634 0.0777503964
[70,] 0.1491522115 0.0576307634
[71,] -0.0929952919 0.1491522115
[72,] 0.1127812153 -0.0929952919
[73,] 0.0770648551 0.1127812153
[74,] -0.0422682654 0.0770648551
[75,] -0.0786140032 -0.0422682654
[76,] -0.0985768523 -0.0786140032
[77,] -0.0004832498 -0.0985768523
[78,] 0.0388675548 -0.0004832498
[79,] -0.1412748538 0.0388675548
[80,] -0.1801439924 -0.1412748538
[81,] -0.1352195816 -0.1801439924
[82,] -0.0136507016 -0.1352195816
[83,] 0.3040125754 -0.0136507016
[84,] -0.0875400146 0.3040125754
[85,] 0.1285309038 -0.0875400146
[86,] 0.2968687880 0.1285309038
[87,] 0.0553590707 0.2968687880
[88,] 0.0096593355 0.0553590707
[89,] -0.2252495501 0.0096593355
[90,] -0.1444663951 -0.2252495501
[91,] 0.2025294735 -0.1444663951
[92,] 0.2663284516 0.2025294735
[93,] 0.1467443301 0.2663284516
[94,] 0.2114405807 0.1467443301
[95,] 0.2423275791 0.2114405807
[96,] 0.1969934283 0.2423275791
[97,] -0.0269041154 0.1969934283
[98,] 0.2002605665 -0.0269041154
[99,] 0.1029306555 0.2002605665
[100,] 0.0361471681 0.1029306555
[101,] 0.0261951899 0.0361471681
[102,] 0.0110830427 0.0261951899
[103,] 0.4142247986 0.0110830427
[104,] 0.4193350458 0.4142247986
[105,] 0.4327364370 0.4193350458
[106,] 0.4647109258 0.4327364370
[107,] 0.3098959351 0.4647109258
[108,] 0.3826380366 0.3098959351
[109,] 0.1015785013 0.3826380366
[110,] -0.0349901184 0.1015785013
[111,] 0.4100113720 -0.0349901184
[112,] 0.1983687987 0.4100113720
[113,] 0.3011294351 0.1983687987
[114,] 0.1977753708 0.3011294351
[115,] 0.1149057253 0.1977753708
[116,] 0.2711061162 0.1149057253
[117,] -0.0518569667 0.2711061162
[118,] 0.1139358570 -0.0518569667
[119,] 0.2383985845 0.1139358570
[120,] 0.4484696084 0.2383985845
[121,] 0.0263668326 0.4484696084
[122,] 0.0271514850 0.0263668326
[123,] 0.2893986811 0.0271514850
[124,] 0.3817899266 0.2893986811
[125,] 0.3753790565 0.3817899266
[126,] 0.3873613391 0.3753790565
[127,] 0.3724147706 0.3873613391
[128,] 0.5850678575 0.3724147706
[129,] 0.2347514154 0.5850678575
[130,] 0.1968833498 0.2347514154
[131,] 0.1250420776 0.1968833498
[132,] -0.0533167362 0.1250420776
[133,] 0.3529596528 -0.0533167362
[134,] 0.0367175561 0.3529596528
[135,] 0.0384846911 0.0367175561
[136,] -0.1463688729 0.0384846911
[137,] -0.1506276986 -0.1463688729
[138,] 0.0402465326 -0.1506276986
[139,] 0.2185196681 0.0402465326
[140,] 0.0899151144 0.2185196681
[141,] 0.1417015350 0.0899151144
[142,] 0.2710047917 0.1417015350
[143,] 0.3171741249 0.2710047917
[144,] 0.4506066443 0.3171741249
[145,] 0.1397667810 0.4506066443
[146,] -0.3515142193 0.1397667810
[147,] -0.1522854818 -0.3515142193
[148,] -0.2447742538 -0.1522854818
[149,] 0.1062984528 -0.2447742538
[150,] 0.0657469871 0.1062984528
[151,] 0.0440683480 0.0657469871
[152,] 0.0409203842 0.0440683480
[153,] 0.1545941737 0.0409203842
[154,] 0.1055613373 0.1545941737
[155,] 0.0051826515 0.1055613373
[156,] 0.2013160095 0.0051826515
[157,] -0.2686981811 0.2013160095
[158,] 0.0292525535 -0.2686981811
[159,] 0.1380999139 0.0292525535
[160,] -0.1281321791 0.1380999139
[161,] -0.0587124904 -0.1281321791
[162,] 0.0691747997 -0.0587124904
[163,] 0.2058241998 0.0691747997
[164,] -0.3824425267 0.2058241998
[165,] -0.4502115660 -0.3824425267
[166,] -0.2251905676 -0.4502115660
[167,] -0.0938475184 -0.2251905676
[168,] -0.9418426114 -0.0938475184
[169,] -0.2302780047 -0.9418426114
[170,] -0.1145762029 -0.2302780047
[171,] -0.7993405843 -0.1145762029
[172,] -0.8358743948 -0.7993405843
[173,] -0.8783338466 -0.8358743948
[174,] -0.8660101226 -0.8783338466
[175,] -0.8346308649 -0.8660101226
[176,] -0.7768488137 -0.8346308649
[177,] 0.3556600296 -0.7768488137
[178,] 0.2877673101 0.3556600296
[179,] 0.0656761316 0.2877673101
[180,] 0.2360214910 0.0656761316
[181,] 0.1275739354 0.2360214910
[182,] 0.2808066129 0.1275739354
[183,] -0.6043862370 0.2808066129
[184,] -0.6484630863 -0.6043862370
[185,] -0.6067838036 -0.6484630863
[186,] -0.4232493950 -0.6067838036
[187,] -0.4753615206 -0.4232493950
[188,] -0.4359967657 -0.4753615206
[189,] -0.4282774574 -0.4359967657
[190,] -0.6511288011 -0.4282774574
[191,] -0.7009783841 -0.6511288011
[192,] 0.1745042657 -0.7009783841
[193,] -0.2670808288 0.1745042657
[194,] -0.5194153557 -0.2670808288
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.0705871710 0.0564281266
2 0.0273891732 -0.0705871710
3 -0.0777826472 0.0273891732
4 0.1289663200 -0.0777826472
5 0.0505447902 0.1289663200
6 0.2065940405 0.0505447902
7 0.4193252967 0.2065940405
8 0.0253973938 0.4193252967
9 -0.1509099713 0.0253973938
10 -0.1115990369 -0.1509099713
11 -0.2381056691 -0.1115990369
12 0.5431177417 -0.2381056691
13 0.1198483763 0.5431177417
14 0.2951541293 0.1198483763
15 0.2972892567 0.2951541293
16 0.4555582492 0.2972892567
17 -0.3229152531 0.4555582492
18 -0.2934121147 -0.3229152531
19 0.0339362094 -0.2934121147
20 -0.0659459012 0.0339362094
21 0.1095260220 -0.0659459012
22 -0.1034321130 0.1095260220
23 0.1342289531 -0.1034321130
24 0.1796414412 0.1342289531
25 0.0825911180 0.1796414412
26 0.1959813824 0.0825911180
27 0.2041000665 0.1959813824
28 0.3386043677 0.2041000665
29 0.3006759523 0.3386043677
30 -0.2963270110 0.3006759523
31 -0.1593586542 -0.2963270110
32 -0.1923513261 -0.1593586542
33 -0.1280861823 -0.1923513261
34 -0.0881745263 -0.1280861823
35 -0.2036277196 -0.0881745263
36 0.1893057590 -0.2036277196
37 0.1765375230 0.1893057590
38 0.4039567429 0.1765375230
39 0.2469086122 0.4039567429
40 0.3874036392 0.2469086122
41 0.5634980566 0.3874036392
42 -0.2446206322 0.5634980566
43 -0.2042351963 -0.2446206322
44 -0.0134396038 -0.2042351963
45 -0.0888886766 -0.0134396038
46 -0.0510556031 -0.0888886766
47 0.0454685804 -0.0510556031
48 -0.3374627122 0.0454685804
49 -0.4294787668 -0.3374627122
50 -0.4106123628 -0.4294787668
51 -0.4259158307 -0.4106123628
52 -0.4115231996 -0.4259158307
53 -0.5484716508 -0.4115231996
54 0.1728758516 -0.5484716508
55 0.2084448843 0.1728758516
56 0.1325855833 0.2084448843
57 0.2401098424 0.1325855833
58 0.2207200741 0.2401098424
59 0.3491883720 0.2207200741
60 -0.3697711123 0.3491883720
61 -0.2746523739 -0.3697711123
62 -0.2644755661 -0.2746523739
63 -0.2136066078 -0.2644755661
64 -0.1283357512 -0.2136066078
65 -0.2822755512 -0.1283357512
66 0.0852200123 -0.2822755512
67 0.1108957245 0.0852200123
68 0.0777503964 0.1108957245
69 0.0576307634 0.0777503964
70 0.1491522115 0.0576307634
71 -0.0929952919 0.1491522115
72 0.1127812153 -0.0929952919
73 0.0770648551 0.1127812153
74 -0.0422682654 0.0770648551
75 -0.0786140032 -0.0422682654
76 -0.0985768523 -0.0786140032
77 -0.0004832498 -0.0985768523
78 0.0388675548 -0.0004832498
79 -0.1412748538 0.0388675548
80 -0.1801439924 -0.1412748538
81 -0.1352195816 -0.1801439924
82 -0.0136507016 -0.1352195816
83 0.3040125754 -0.0136507016
84 -0.0875400146 0.3040125754
85 0.1285309038 -0.0875400146
86 0.2968687880 0.1285309038
87 0.0553590707 0.2968687880
88 0.0096593355 0.0553590707
89 -0.2252495501 0.0096593355
90 -0.1444663951 -0.2252495501
91 0.2025294735 -0.1444663951
92 0.2663284516 0.2025294735
93 0.1467443301 0.2663284516
94 0.2114405807 0.1467443301
95 0.2423275791 0.2114405807
96 0.1969934283 0.2423275791
97 -0.0269041154 0.1969934283
98 0.2002605665 -0.0269041154
99 0.1029306555 0.2002605665
100 0.0361471681 0.1029306555
101 0.0261951899 0.0361471681
102 0.0110830427 0.0261951899
103 0.4142247986 0.0110830427
104 0.4193350458 0.4142247986
105 0.4327364370 0.4193350458
106 0.4647109258 0.4327364370
107 0.3098959351 0.4647109258
108 0.3826380366 0.3098959351
109 0.1015785013 0.3826380366
110 -0.0349901184 0.1015785013
111 0.4100113720 -0.0349901184
112 0.1983687987 0.4100113720
113 0.3011294351 0.1983687987
114 0.1977753708 0.3011294351
115 0.1149057253 0.1977753708
116 0.2711061162 0.1149057253
117 -0.0518569667 0.2711061162
118 0.1139358570 -0.0518569667
119 0.2383985845 0.1139358570
120 0.4484696084 0.2383985845
121 0.0263668326 0.4484696084
122 0.0271514850 0.0263668326
123 0.2893986811 0.0271514850
124 0.3817899266 0.2893986811
125 0.3753790565 0.3817899266
126 0.3873613391 0.3753790565
127 0.3724147706 0.3873613391
128 0.5850678575 0.3724147706
129 0.2347514154 0.5850678575
130 0.1968833498 0.2347514154
131 0.1250420776 0.1968833498
132 -0.0533167362 0.1250420776
133 0.3529596528 -0.0533167362
134 0.0367175561 0.3529596528
135 0.0384846911 0.0367175561
136 -0.1463688729 0.0384846911
137 -0.1506276986 -0.1463688729
138 0.0402465326 -0.1506276986
139 0.2185196681 0.0402465326
140 0.0899151144 0.2185196681
141 0.1417015350 0.0899151144
142 0.2710047917 0.1417015350
143 0.3171741249 0.2710047917
144 0.4506066443 0.3171741249
145 0.1397667810 0.4506066443
146 -0.3515142193 0.1397667810
147 -0.1522854818 -0.3515142193
148 -0.2447742538 -0.1522854818
149 0.1062984528 -0.2447742538
150 0.0657469871 0.1062984528
151 0.0440683480 0.0657469871
152 0.0409203842 0.0440683480
153 0.1545941737 0.0409203842
154 0.1055613373 0.1545941737
155 0.0051826515 0.1055613373
156 0.2013160095 0.0051826515
157 -0.2686981811 0.2013160095
158 0.0292525535 -0.2686981811
159 0.1380999139 0.0292525535
160 -0.1281321791 0.1380999139
161 -0.0587124904 -0.1281321791
162 0.0691747997 -0.0587124904
163 0.2058241998 0.0691747997
164 -0.3824425267 0.2058241998
165 -0.4502115660 -0.3824425267
166 -0.2251905676 -0.4502115660
167 -0.0938475184 -0.2251905676
168 -0.9418426114 -0.0938475184
169 -0.2302780047 -0.9418426114
170 -0.1145762029 -0.2302780047
171 -0.7993405843 -0.1145762029
172 -0.8358743948 -0.7993405843
173 -0.8783338466 -0.8358743948
174 -0.8660101226 -0.8783338466
175 -0.8346308649 -0.8660101226
176 -0.7768488137 -0.8346308649
177 0.3556600296 -0.7768488137
178 0.2877673101 0.3556600296
179 0.0656761316 0.2877673101
180 0.2360214910 0.0656761316
181 0.1275739354 0.2360214910
182 0.2808066129 0.1275739354
183 -0.6043862370 0.2808066129
184 -0.6484630863 -0.6043862370
185 -0.6067838036 -0.6484630863
186 -0.4232493950 -0.6067838036
187 -0.4753615206 -0.4232493950
188 -0.4359967657 -0.4753615206
189 -0.4282774574 -0.4359967657
190 -0.6511288011 -0.4282774574
191 -0.7009783841 -0.6511288011
192 0.1745042657 -0.7009783841
193 -0.2670808288 0.1745042657
194 -0.5194153557 -0.2670808288
> 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/7e2m31386781580.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/83b2v1386781580.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/9hnsd1386781580.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/fisher/rcomp/tmp/10ixy71386781580.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/fisher/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/fisher/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11mduf1386781581.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/12iqio1386781581.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/13a7731386781581.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/14mvbg1386781581.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/15tvs31386781581.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/16pr2z1386781581.tab")
+ }
>
> try(system("convert tmp/1m3sr1386781580.ps tmp/1m3sr1386781580.png",intern=TRUE))
character(0)
> try(system("convert tmp/2dwjb1386781580.ps tmp/2dwjb1386781580.png",intern=TRUE))
character(0)
> try(system("convert tmp/34km11386781580.ps tmp/34km11386781580.png",intern=TRUE))
character(0)
> try(system("convert tmp/4gt8j1386781580.ps tmp/4gt8j1386781580.png",intern=TRUE))
character(0)
> try(system("convert tmp/5yboj1386781580.ps tmp/5yboj1386781580.png",intern=TRUE))
character(0)
> try(system("convert tmp/65t4q1386781580.ps tmp/65t4q1386781580.png",intern=TRUE))
character(0)
> try(system("convert tmp/7e2m31386781580.ps tmp/7e2m31386781580.png",intern=TRUE))
character(0)
> try(system("convert tmp/83b2v1386781580.ps tmp/83b2v1386781580.png",intern=TRUE))
character(0)
> try(system("convert tmp/9hnsd1386781580.ps tmp/9hnsd1386781580.png",intern=TRUE))
character(0)
> try(system("convert tmp/10ixy71386781580.ps tmp/10ixy71386781580.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
29.630 4.145 33.805