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
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> x <- array(list(119.992
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+ ,0.02551
+ ,0.237
+ ,0.01321
+ ,0.01574
+ ,0.02148
+ ,0.03964
+ ,0.00611
+ ,23.133
+ ,1
+ ,0.352396
+ ,0.75932
+ ,-6.261446
+ ,0.183218
+ ,2.264226
+ ,0.144105
+ ,148.462
+ ,161.078
+ ,141.998
+ ,0.00397
+ ,0.00003
+ ,0.00202
+ ,0.00235
+ ,0.00605
+ ,0.01831
+ ,0.163
+ ,0.0095
+ ,0.01103
+ ,0.01559
+ ,0.02849
+ ,0.00639
+ ,22.866
+ ,1
+ ,0.408598
+ ,0.768845
+ ,-5.704053
+ ,0.216204
+ ,2.679185
+ ,0.19771
+ ,149.818
+ ,163.417
+ ,144.786
+ ,0.00336
+ ,0.00002
+ ,0.00174
+ ,0.00198
+ ,0.00521
+ ,0.02145
+ ,0.198
+ ,0.01155
+ ,0.01341
+ ,0.01666
+ ,0.03464
+ ,0.00595
+ ,23.008
+ ,1
+ ,0.329577
+ ,0.75718
+ ,-6.27717
+ ,0.109397
+ ,2.209021
+ ,0.156368
+ ,117.226
+ ,123.925
+ ,106.656
+ ,0.00417
+ ,0.00004
+ ,0.00186
+ ,0.0027
+ ,0.00558
+ ,0.01909
+ ,0.171
+ ,0.00864
+ ,0.01223
+ ,0.01949
+ ,0.02592
+ ,0.00955
+ ,23.079
+ ,0
+ ,0.603515
+ ,0.669565
+ ,-5.61907
+ ,0.191576
+ ,2.027228
+ ,0.215724
+ ,116.848
+ ,217.552
+ ,99.503
+ ,0.00531
+ ,0.00005
+ ,0.0026
+ ,0.00346
+ ,0.0078
+ ,0.01795
+ ,0.163
+ ,0.0081
+ ,0.01144
+ ,0.01756
+ ,0.02429
+ ,0.01179
+ ,22.085
+ ,0
+ ,0.663842
+ ,0.656516
+ ,-5.198864
+ ,0.206768
+ ,2.120412
+ ,0.252404
+ ,116.286
+ ,177.291
+ ,96.983
+ ,0.00314
+ ,0.00003
+ ,0.00134
+ ,0.00192
+ ,0.00403
+ ,0.01564
+ ,0.136
+ ,0.00667
+ ,0.0099
+ ,0.01691
+ ,0.02001
+ ,0.00737
+ ,24.199
+ ,0
+ ,0.598515
+ ,0.654331
+ ,-5.592584
+ ,0.133917
+ ,2.058658
+ ,0.214346
+ ,116.556
+ ,592.03
+ ,86.228
+ ,0.00496
+ ,0.00004
+ ,0.00254
+ ,0.00263
+ ,0.00762
+ ,0.0166
+ ,0.154
+ ,0.0082
+ ,0.00972
+ ,0.01491
+ ,0.0246
+ ,0.01397
+ ,23.958
+ ,0
+ ,0.566424
+ ,0.667654
+ ,-6.431119
+ ,0.15331
+ ,2.161936
+ ,0.120605
+ ,116.342
+ ,581.289
+ ,94.246
+ ,0.00267
+ ,0.00002
+ ,0.00115
+ ,0.00148
+ ,0.00345
+ ,0.013
+ ,0.117
+ ,0.00631
+ ,0.00789
+ ,0.01144
+ ,0.01892
+ ,0.0068
+ ,25.023
+ ,0
+ ,0.528485
+ ,0.663884
+ ,-6.359018
+ ,0.116636
+ ,2.152083
+ ,0.138868
+ ,114.563
+ ,119.167
+ ,86.647
+ ,0.00327
+ ,0.00003
+ ,0.00146
+ ,0.00184
+ ,0.00439
+ ,0.01185
+ ,0.106
+ ,0.00557
+ ,0.00721
+ ,0.01095
+ ,0.01672
+ ,0.00703
+ ,24.775
+ ,0
+ ,0.555303
+ ,0.659132
+ ,-6.710219
+ ,0.149694
+ ,1.91399
+ ,0.121777
+ ,201.774
+ ,262.707
+ ,78.228
+ ,0.00694
+ ,0.00003
+ ,0.00412
+ ,0.00396
+ ,0.01235
+ ,0.02574
+ ,0.255
+ ,0.01454
+ ,0.01582
+ ,0.01758
+ ,0.04363
+ ,0.04441
+ ,19.368
+ ,0
+ ,0.508479
+ ,0.683761
+ ,-6.934474
+ ,0.15989
+ ,2.316346
+ ,0.112838
+ ,174.188
+ ,230.978
+ ,94.261
+ ,0.00459
+ ,0.00003
+ ,0.00263
+ ,0.00259
+ ,0.0079
+ ,0.04087
+ ,0.405
+ ,0.02336
+ ,0.02498
+ ,0.02745
+ ,0.07008
+ ,0.02764
+ ,19.517
+ ,0
+ ,0.448439
+ ,0.657899
+ ,-6.538586
+ ,0.121952
+ ,2.657476
+ ,0.13305
+ ,209.516
+ ,253.017
+ ,89.488
+ ,0.00564
+ ,0.00003
+ ,0.00331
+ ,0.00292
+ ,0.00994
+ ,0.02751
+ ,0.263
+ ,0.01604
+ ,0.01657
+ ,0.01879
+ ,0.04812
+ ,0.0181
+ ,19.147
+ ,0
+ ,0.431674
+ ,0.683244
+ ,-6.195325
+ ,0.129303
+ ,2.784312
+ ,0.168895
+ ,174.688
+ ,240.005
+ ,74.287
+ ,0.0136
+ ,0.00008
+ ,0.00624
+ ,0.00564
+ ,0.01873
+ ,0.02308
+ ,0.256
+ ,0.01268
+ ,0.01365
+ ,0.01667
+ ,0.03804
+ ,0.10715
+ ,17.883
+ ,0
+ ,0.407567
+ ,0.655683
+ ,-6.787197
+ ,0.158453
+ ,2.679772
+ ,0.131728
+ ,198.764
+ ,396.961
+ ,74.904
+ ,0.0074
+ ,0.00004
+ ,0.0037
+ ,0.0039
+ ,0.01109
+ ,0.02296
+ ,0.241
+ ,0.01265
+ ,0.01321
+ ,0.01588
+ ,0.03794
+ ,0.07223
+ ,19.02
+ ,0
+ ,0.451221
+ ,0.643956
+ ,-6.744577
+ ,0.207454
+ ,2.138608
+ ,0.123306
+ ,214.289
+ ,260.277
+ ,77.973
+ ,0.00567
+ ,0.00003
+ ,0.00295
+ ,0.00317
+ ,0.00885
+ ,0.01884
+ ,0.19
+ ,0.01026
+ ,0.01161
+ ,0.01373
+ ,0.03078
+ ,0.04398
+ ,21.209
+ ,0
+ ,0.462803
+ ,0.664357
+ ,-5.724056
+ ,0.190667
+ ,2.555477
+ ,0.148569)
+ ,dim=c(23
+ ,195)
+ ,dimnames=list(c('MDVP:Fo(Hz)'
+ ,'MDVP:Fhi(Hz)'
+ ,'MDVP:Flo(Hz)'
+ ,'MDVP:Jitter(%)'
+ ,'MDVP:Jitter(Abs)'
+ ,'MDVP:RAP'
+ ,'MDVP:PPQ'
+ ,'Jitter:DDP'
+ ,'MDVP:Shimmer'
+ ,'MDVP:Shimmer(dB)'
+ ,'Shimmer:APQ3'
+ ,'Shimmer:APQ5'
+ ,'MDVP:APQ'
+ ,'Shimmer:DDA'
+ ,'NHR'
+ ,'HNR'
+ ,'status'
+ ,'RPDE'
+ ,'DFA'
+ ,'spread1'
+ ,'spread2'
+ ,'D2'
+ ,'PPE')
+ ,1:195))
> y <- array(NA,dim=c(23,195),dimnames=list(c('MDVP:Fo(Hz)','MDVP:Fhi(Hz)','MDVP:Flo(Hz)','MDVP:Jitter(%)','MDVP:Jitter(Abs)','MDVP:RAP','MDVP:PPQ','Jitter:DDP','MDVP:Shimmer','MDVP:Shimmer(dB)','Shimmer:APQ3','Shimmer:APQ5','MDVP:APQ','Shimmer:DDA','NHR','HNR','status','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 = '18'
> par3 <- 'No Linear Trend'
> par2 <- 'Do not include Seasonal Dummies'
> par1 <- '18'
> #'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
RPDE MDVP:Fo(Hz) MDVP:Fhi(Hz) MDVP:Flo(Hz) MDVP:Jitter(%)
1 0.414783 119.992 157.302 74.997 0.00784
2 0.458359 122.400 148.650 113.819 0.00968
3 0.429895 116.682 131.111 111.555 0.01050
4 0.434969 116.676 137.871 111.366 0.00997
5 0.417356 116.014 141.781 110.655 0.01284
6 0.415564 120.552 131.162 113.787 0.00968
7 0.596040 120.267 137.244 114.820 0.00333
8 0.637420 107.332 113.840 104.315 0.00290
9 0.615551 95.730 132.068 91.754 0.00551
10 0.547037 95.056 120.103 91.226 0.00532
11 0.611137 88.333 112.240 84.072 0.00505
12 0.583390 91.904 115.871 86.292 0.00540
13 0.460600 136.926 159.866 131.276 0.00293
14 0.430166 139.173 179.139 76.556 0.00390
15 0.474791 152.845 163.305 75.836 0.00294
16 0.565924 142.167 217.455 83.159 0.00369
17 0.567380 144.188 349.259 82.764 0.00544
18 0.631099 168.778 232.181 75.603 0.00718
19 0.665318 153.046 175.829 68.623 0.00742
20 0.649554 156.405 189.398 142.822 0.00768
21 0.660125 153.848 165.738 65.782 0.00840
22 0.629017 153.880 172.860 78.128 0.00480
23 0.619060 167.930 193.221 79.068 0.00442
24 0.537264 173.917 192.735 86.180 0.00476
25 0.397937 163.656 200.841 76.779 0.00742
26 0.522746 104.400 206.002 77.968 0.00633
27 0.418622 171.041 208.313 75.501 0.00455
28 0.358773 146.845 208.701 81.737 0.00496
29 0.470478 155.358 227.383 80.055 0.00310
30 0.427785 162.568 198.346 77.630 0.00502
31 0.422229 197.076 206.896 192.055 0.00289
32 0.432439 199.228 209.512 192.091 0.00241
33 0.465946 198.383 215.203 193.104 0.00212
34 0.368535 202.266 211.604 197.079 0.00180
35 0.340068 203.184 211.526 196.160 0.00178
36 0.344252 201.464 210.565 195.708 0.00198
37 0.360148 177.876 192.921 168.013 0.00411
38 0.341435 176.170 185.604 163.564 0.00369
39 0.403884 180.198 201.249 175.456 0.00284
40 0.396793 187.733 202.324 173.015 0.00316
41 0.326480 186.163 197.724 177.584 0.00298
42 0.306443 184.055 196.537 166.977 0.00258
43 0.305062 237.226 247.326 225.227 0.00298
44 0.457702 241.404 248.834 232.483 0.00281
45 0.438296 243.439 250.912 232.435 0.00210
46 0.431285 242.852 255.034 227.911 0.00225
47 0.467489 245.510 262.090 231.848 0.00235
48 0.610367 252.455 261.487 182.786 0.00185
49 0.579597 122.188 128.611 115.765 0.00524
50 0.538688 122.964 130.049 114.676 0.00428
51 0.553134 124.445 135.069 117.495 0.00431
52 0.507504 126.344 134.231 112.773 0.00448
53 0.459766 128.001 138.052 122.080 0.00436
54 0.420383 129.336 139.867 118.604 0.00490
55 0.536009 108.807 134.656 102.874 0.00761
56 0.558586 109.860 126.358 104.437 0.00874
57 0.541781 110.417 131.067 103.370 0.00784
58 0.530529 117.274 129.916 110.402 0.00752
59 0.540049 116.879 131.897 108.153 0.00788
60 0.547975 114.847 271.314 104.680 0.00867
61 0.341788 209.144 237.494 109.379 0.00282
62 0.447979 223.365 238.987 98.664 0.00264
63 0.364867 222.236 231.345 205.495 0.00266
64 0.256570 228.832 234.619 223.634 0.00296
65 0.276850 229.401 252.221 221.156 0.00205
66 0.305429 228.969 239.541 113.201 0.00238
67 0.460139 140.341 159.774 67.021 0.00817
68 0.498133 136.969 166.607 66.004 0.00923
69 0.513237 143.533 162.215 65.809 0.01101
70 0.487407 148.090 162.824 67.343 0.00762
71 0.489345 142.729 162.408 65.476 0.00831
72 0.543299 136.358 176.595 65.750 0.00971
73 0.495954 120.080 139.710 111.208 0.00405
74 0.509127 112.014 588.518 107.024 0.00533
75 0.437031 110.793 128.101 107.316 0.00494
76 0.463514 110.707 122.611 105.007 0.00516
77 0.489538 112.876 148.826 106.981 0.00500
78 0.429484 110.568 125.394 106.821 0.00462
79 0.644954 95.385 102.145 90.264 0.00608
80 0.594387 100.770 115.697 85.545 0.01038
81 0.544805 96.106 108.664 84.510 0.00694
82 0.576084 95.605 107.715 87.549 0.00702
83 0.554610 100.960 110.019 95.628 0.00606
84 0.576644 98.804 102.305 87.804 0.00432
85 0.556494 176.858 205.560 75.344 0.00747
86 0.583574 180.978 200.125 155.495 0.00406
87 0.598714 178.222 202.450 141.047 0.00321
88 0.602874 176.281 227.381 125.610 0.00520
89 0.599371 173.898 211.350 74.677 0.00448
90 0.590951 179.711 225.930 144.878 0.00709
91 0.653410 166.605 206.008 78.032 0.00742
92 0.501037 151.955 163.335 147.226 0.00419
93 0.454444 148.272 164.989 142.299 0.00459
94 0.447456 152.125 161.469 76.596 0.00382
95 0.502380 157.821 172.975 68.401 0.00358
96 0.447285 157.447 163.267 149.605 0.00369
97 0.366329 159.116 168.913 144.811 0.00342
98 0.629574 125.036 143.946 116.187 0.01280
99 0.571010 125.791 140.557 96.206 0.01378
100 0.638545 126.512 141.756 99.770 0.01936
101 0.671299 125.641 141.068 116.346 0.03316
102 0.639808 128.451 150.449 75.632 0.01551
103 0.596362 139.224 586.567 66.157 0.03011
104 0.296888 150.258 154.609 75.349 0.00248
105 0.263654 154.003 160.267 128.621 0.00183
106 0.365488 149.689 160.368 133.608 0.00257
107 0.334171 155.078 163.736 144.148 0.00168
108 0.393563 151.884 157.765 133.751 0.00258
109 0.311369 151.989 157.339 132.857 0.00174
110 0.497554 193.030 208.900 80.297 0.00766
111 0.436084 200.714 223.982 89.686 0.00621
112 0.338097 208.519 220.315 199.020 0.00609
113 0.498877 204.664 221.300 189.621 0.00841
114 0.441097 210.141 232.706 185.258 0.00534
115 0.331508 206.327 226.355 92.020 0.00495
116 0.407701 151.872 492.892 69.085 0.00856
117 0.450798 158.219 442.557 71.948 0.00476
118 0.486738 170.756 450.247 79.032 0.00555
119 0.470422 178.285 442.824 82.063 0.00462
120 0.462516 217.116 233.481 93.978 0.00404
121 0.487756 128.940 479.697 88.251 0.00581
122 0.400088 176.824 215.293 83.961 0.00460
123 0.538016 138.190 203.522 83.340 0.00704
124 0.589956 182.018 197.173 79.187 0.00842
125 0.618663 156.239 195.107 79.820 0.00694
126 0.637518 145.174 198.109 80.637 0.00733
127 0.623209 138.145 197.238 81.114 0.00544
128 0.585169 166.888 198.966 79.512 0.00638
129 0.457541 119.031 127.533 109.216 0.00440
130 0.491345 120.078 126.632 105.667 0.00270
131 0.467160 120.289 128.143 100.209 0.00492
132 0.468621 120.256 125.306 104.773 0.00407
133 0.470972 119.056 125.213 86.795 0.00346
134 0.482296 118.747 123.723 109.836 0.00331
135 0.637814 106.516 112.777 93.105 0.00589
136 0.653427 110.453 127.611 105.554 0.00494
137 0.647900 113.400 133.344 107.816 0.00451
138 0.625362 113.166 130.270 100.673 0.00502
139 0.640945 112.239 126.609 104.095 0.00472
140 0.624811 116.150 131.731 109.815 0.00381
141 0.677131 170.368 268.796 79.543 0.00571
142 0.606344 208.083 253.792 91.802 0.00757
143 0.606273 198.458 219.290 148.691 0.00376
144 0.536102 202.805 231.508 86.232 0.00370
145 0.497480 202.544 241.350 164.168 0.00254
146 0.566849 223.361 263.872 87.638 0.00352
147 0.561610 169.774 191.759 151.451 0.01568
148 0.478024 183.520 216.814 161.340 0.01466
149 0.552870 188.620 216.302 165.982 0.01719
150 0.427627 202.632 565.740 177.258 0.01627
151 0.507826 186.695 211.961 149.442 0.01872
152 0.625866 192.818 224.429 168.793 0.03107
153 0.584164 198.116 233.099 174.478 0.02714
154 0.566867 121.345 139.644 98.250 0.00684
155 0.651680 119.100 128.442 88.833 0.00692
156 0.628300 117.870 127.349 95.654 0.00647
157 0.611679 122.336 142.369 94.794 0.00727
158 0.630547 117.963 134.209 100.757 0.01813
159 0.635015 126.144 154.284 97.543 0.00975
160 0.654945 127.930 138.752 112.173 0.00605
161 0.653139 114.238 124.393 77.022 0.00581
162 0.577802 115.322 135.738 107.802 0.00619
163 0.685151 114.554 126.778 91.121 0.00651
164 0.557045 112.150 131.669 97.527 0.00519
165 0.671378 102.273 142.830 85.902 0.00907
166 0.469928 236.200 244.663 102.137 0.00277
167 0.384868 237.323 243.709 229.256 0.00303
168 0.440988 260.105 264.919 237.303 0.00339
169 0.372222 197.569 217.627 90.794 0.00803
170 0.371837 240.301 245.135 219.783 0.00517
171 0.522812 244.990 272.210 239.170 0.00451
172 0.413295 112.547 133.374 105.715 0.00355
173 0.369090 110.739 113.597 100.139 0.00356
174 0.380253 113.715 116.443 96.913 0.00349
175 0.387482 117.004 144.466 99.923 0.00353
176 0.405991 115.380 123.109 108.634 0.00332
177 0.361232 116.388 129.038 108.970 0.00346
178 0.396610 151.737 190.204 129.859 0.00314
179 0.402591 148.790 158.359 138.990 0.00309
180 0.398499 148.143 155.982 135.041 0.00392
181 0.352396 150.440 163.441 144.736 0.00396
182 0.408598 148.462 161.078 141.998 0.00397
183 0.329577 149.818 163.417 144.786 0.00336
184 0.603515 117.226 123.925 106.656 0.00417
185 0.663842 116.848 217.552 99.503 0.00531
186 0.598515 116.286 177.291 96.983 0.00314
187 0.566424 116.556 592.030 86.228 0.00496
188 0.528485 116.342 581.289 94.246 0.00267
189 0.555303 114.563 119.167 86.647 0.00327
190 0.508479 201.774 262.707 78.228 0.00694
191 0.448439 174.188 230.978 94.261 0.00459
192 0.431674 209.516 253.017 89.488 0.00564
193 0.407567 174.688 240.005 74.287 0.01360
194 0.451221 198.764 396.961 74.904 0.00740
195 0.462803 214.289 260.277 77.973 0.00567
MDVP:Jitter(Abs) MDVP:RAP MDVP:PPQ Jitter:DDP MDVP:Shimmer MDVP:Shimmer(dB)
1 7.0e-05 0.00370 0.00554 0.01109 0.04374 0.426
2 8.0e-05 0.00465 0.00696 0.01394 0.06134 0.626
3 9.0e-05 0.00544 0.00781 0.01633 0.05233 0.482
4 9.0e-05 0.00502 0.00698 0.01505 0.05492 0.517
5 1.1e-04 0.00655 0.00908 0.01966 0.06425 0.584
6 8.0e-05 0.00463 0.00750 0.01388 0.04701 0.456
7 3.0e-05 0.00155 0.00202 0.00466 0.01608 0.140
8 3.0e-05 0.00144 0.00182 0.00431 0.01567 0.134
9 6.0e-05 0.00293 0.00332 0.00880 0.02093 0.191
10 6.0e-05 0.00268 0.00332 0.00803 0.02838 0.255
11 6.0e-05 0.00254 0.00330 0.00763 0.02143 0.197
12 6.0e-05 0.00281 0.00336 0.00844 0.02752 0.249
13 2.0e-05 0.00118 0.00153 0.00355 0.01259 0.112
14 3.0e-05 0.00165 0.00208 0.00496 0.01642 0.154
15 2.0e-05 0.00121 0.00149 0.00364 0.01828 0.158
16 3.0e-05 0.00157 0.00203 0.00471 0.01503 0.126
17 4.0e-05 0.00211 0.00292 0.00632 0.02047 0.192
18 4.0e-05 0.00284 0.00387 0.00853 0.03327 0.348
19 5.0e-05 0.00364 0.00432 0.01092 0.05517 0.542
20 5.0e-05 0.00372 0.00399 0.01116 0.03995 0.348
21 5.0e-05 0.00428 0.00450 0.01285 0.03810 0.328
22 3.0e-05 0.00232 0.00267 0.00696 0.04137 0.370
23 3.0e-05 0.00220 0.00247 0.00661 0.04351 0.377
24 3.0e-05 0.00221 0.00258 0.00663 0.04192 0.364
25 5.0e-05 0.00380 0.00390 0.01140 0.01659 0.164
26 6.0e-05 0.00316 0.00375 0.00948 0.03767 0.381
27 3.0e-05 0.00250 0.00234 0.00750 0.01966 0.186
28 3.0e-05 0.00250 0.00275 0.00749 0.01919 0.198
29 2.0e-05 0.00159 0.00176 0.00476 0.01718 0.161
30 3.0e-05 0.00280 0.00253 0.00841 0.01791 0.168
31 1.0e-05 0.00166 0.00168 0.00498 0.01098 0.097
32 1.0e-05 0.00134 0.00138 0.00402 0.01015 0.089
33 1.0e-05 0.00113 0.00135 0.00339 0.01263 0.111
34 9.0e-06 0.00093 0.00107 0.00278 0.00954 0.085
35 9.0e-06 0.00094 0.00106 0.00283 0.00958 0.085
36 1.0e-05 0.00105 0.00115 0.00314 0.01194 0.107
37 2.0e-05 0.00233 0.00241 0.00700 0.02126 0.189
38 2.0e-05 0.00205 0.00218 0.00616 0.01851 0.168
39 2.0e-05 0.00153 0.00166 0.00459 0.01444 0.131
40 2.0e-05 0.00168 0.00182 0.00504 0.01663 0.151
41 2.0e-05 0.00165 0.00175 0.00496 0.01495 0.135
42 1.0e-05 0.00134 0.00147 0.00403 0.01463 0.132
43 1.0e-05 0.00169 0.00182 0.00507 0.01752 0.164
44 1.0e-05 0.00157 0.00173 0.00470 0.01760 0.154
45 9.0e-06 0.00109 0.00137 0.00327 0.01419 0.126
46 9.0e-06 0.00117 0.00139 0.00350 0.01494 0.134
47 1.0e-05 0.00127 0.00148 0.00380 0.01608 0.141
48 7.0e-06 0.00092 0.00113 0.00276 0.01152 0.103
49 4.0e-05 0.00169 0.00203 0.00507 0.01613 0.143
50 3.0e-05 0.00124 0.00155 0.00373 0.01681 0.154
51 3.0e-05 0.00141 0.00167 0.00422 0.02184 0.197
52 4.0e-05 0.00131 0.00169 0.00393 0.02033 0.185
53 3.0e-05 0.00137 0.00166 0.00411 0.02297 0.210
54 4.0e-05 0.00165 0.00183 0.00495 0.02498 0.228
55 7.0e-05 0.00349 0.00486 0.01046 0.02719 0.255
56 8.0e-05 0.00398 0.00539 0.01193 0.03209 0.307
57 7.0e-05 0.00352 0.00514 0.01056 0.03715 0.334
58 6.0e-05 0.00299 0.00469 0.00898 0.02293 0.221
59 7.0e-05 0.00334 0.00493 0.01003 0.02645 0.265
60 8.0e-05 0.00373 0.00520 0.01120 0.03225 0.350
61 1.0e-05 0.00147 0.00152 0.00442 0.01861 0.170
62 1.0e-05 0.00154 0.00151 0.00461 0.01906 0.165
63 1.0e-05 0.00152 0.00144 0.00457 0.01643 0.145
64 1.0e-05 0.00175 0.00155 0.00526 0.01644 0.145
65 9.0e-06 0.00114 0.00113 0.00342 0.01457 0.129
66 1.0e-05 0.00136 0.00140 0.00408 0.01745 0.154
67 6.0e-05 0.00430 0.00440 0.01289 0.03198 0.313
68 7.0e-05 0.00507 0.00463 0.01520 0.03111 0.308
69 8.0e-05 0.00647 0.00467 0.01941 0.05384 0.478
70 5.0e-05 0.00467 0.00354 0.01400 0.05428 0.497
71 6.0e-05 0.00469 0.00419 0.01407 0.03485 0.365
72 7.0e-05 0.00534 0.00478 0.01601 0.04978 0.483
73 3.0e-05 0.00180 0.00220 0.00540 0.01706 0.152
74 5.0e-05 0.00268 0.00329 0.00805 0.02448 0.226
75 4.0e-05 0.00260 0.00283 0.00780 0.02442 0.216
76 5.0e-05 0.00277 0.00289 0.00831 0.02215 0.206
77 4.0e-05 0.00270 0.00289 0.00810 0.03999 0.350
78 4.0e-05 0.00226 0.00280 0.00677 0.02199 0.197
79 6.0e-05 0.00331 0.00332 0.00994 0.03202 0.263
80 1.0e-04 0.00622 0.00576 0.01865 0.03121 0.361
81 7.0e-05 0.00389 0.00415 0.01168 0.04024 0.364
82 7.0e-05 0.00428 0.00371 0.01283 0.03156 0.296
83 6.0e-05 0.00351 0.00348 0.01053 0.02427 0.216
84 4.0e-05 0.00247 0.00258 0.00742 0.02223 0.202
85 4.0e-05 0.00418 0.00420 0.01254 0.04795 0.435
86 2.0e-05 0.00220 0.00244 0.00659 0.03852 0.331
87 2.0e-05 0.00163 0.00194 0.00488 0.03759 0.327
88 3.0e-05 0.00287 0.00312 0.00862 0.06511 0.580
89 3.0e-05 0.00237 0.00254 0.00710 0.06727 0.650
90 4.0e-05 0.00391 0.00419 0.01172 0.04313 0.442
91 4.0e-05 0.00387 0.00453 0.01161 0.06640 0.634
92 3.0e-05 0.00224 0.00227 0.00672 0.07959 0.772
93 3.0e-05 0.00250 0.00256 0.00750 0.04190 0.383
94 3.0e-05 0.00191 0.00226 0.00574 0.05925 0.637
95 2.0e-05 0.00196 0.00196 0.00587 0.03716 0.307
96 2.0e-05 0.00201 0.00197 0.00602 0.03272 0.283
97 2.0e-05 0.00178 0.00184 0.00535 0.03381 0.307
98 1.0e-04 0.00743 0.00623 0.02228 0.03886 0.342
99 1.1e-04 0.00826 0.00655 0.02478 0.04689 0.422
100 1.5e-04 0.01159 0.00990 0.03476 0.06734 0.659
101 2.6e-04 0.02144 0.01522 0.06433 0.09178 0.891
102 1.2e-04 0.00905 0.00909 0.02716 0.06170 0.584
103 2.2e-04 0.01854 0.01628 0.05563 0.09419 0.930
104 2.0e-05 0.00105 0.00136 0.00315 0.01131 0.107
105 1.0e-05 0.00076 0.00100 0.00229 0.01030 0.094
106 2.0e-05 0.00116 0.00134 0.00349 0.01346 0.126
107 1.0e-05 0.00068 0.00092 0.00204 0.01064 0.097
108 2.0e-05 0.00115 0.00122 0.00346 0.01450 0.137
109 1.0e-05 0.00075 0.00096 0.00225 0.01024 0.093
110 4.0e-05 0.00450 0.00389 0.01351 0.03044 0.275
111 3.0e-05 0.00371 0.00337 0.01112 0.02286 0.207
112 3.0e-05 0.00368 0.00339 0.01105 0.01761 0.155
113 4.0e-05 0.00502 0.00485 0.01506 0.02378 0.210
114 3.0e-05 0.00321 0.00280 0.00964 0.01680 0.149
115 2.0e-05 0.00302 0.00246 0.00905 0.02105 0.209
116 6.0e-05 0.00404 0.00385 0.01211 0.01843 0.235
117 3.0e-05 0.00214 0.00207 0.00642 0.01458 0.148
118 3.0e-05 0.00244 0.00261 0.00731 0.01725 0.175
119 3.0e-05 0.00157 0.00194 0.00472 0.01279 0.129
120 2.0e-05 0.00127 0.00128 0.00381 0.01299 0.124
121 5.0e-05 0.00241 0.00314 0.00723 0.02008 0.221
122 3.0e-05 0.00209 0.00221 0.00628 0.01169 0.117
123 5.0e-05 0.00406 0.00398 0.01218 0.04479 0.441
124 5.0e-05 0.00506 0.00449 0.01517 0.02503 0.231
125 4.0e-05 0.00403 0.00395 0.01209 0.02343 0.224
126 5.0e-05 0.00414 0.00422 0.01242 0.02362 0.233
127 4.0e-05 0.00294 0.00327 0.00883 0.02791 0.246
128 4.0e-05 0.00368 0.00351 0.01104 0.02857 0.257
129 4.0e-05 0.00214 0.00192 0.00641 0.01033 0.098
130 2.0e-05 0.00116 0.00135 0.00349 0.01022 0.090
131 4.0e-05 0.00269 0.00238 0.00808 0.01412 0.125
132 3.0e-05 0.00224 0.00205 0.00671 0.01516 0.138
133 3.0e-05 0.00169 0.00170 0.00508 0.01201 0.106
134 3.0e-05 0.00168 0.00171 0.00504 0.01043 0.099
135 6.0e-05 0.00291 0.00319 0.00873 0.04932 0.441
136 4.0e-05 0.00244 0.00315 0.00731 0.04128 0.379
137 4.0e-05 0.00219 0.00283 0.00658 0.04879 0.431
138 4.0e-05 0.00257 0.00312 0.00772 0.05279 0.476
139 4.0e-05 0.00238 0.00290 0.00715 0.05643 0.517
140 3.0e-05 0.00181 0.00232 0.00542 0.03026 0.267
141 3.0e-05 0.00232 0.00269 0.00696 0.03273 0.281
142 4.0e-05 0.00428 0.00428 0.01285 0.06725 0.571
143 2.0e-05 0.00182 0.00215 0.00546 0.03527 0.297
144 2.0e-05 0.00189 0.00211 0.00568 0.01997 0.180
145 1.0e-05 0.00100 0.00133 0.00301 0.02662 0.228
146 2.0e-05 0.00169 0.00188 0.00506 0.02536 0.225
147 9.0e-05 0.00863 0.00946 0.02589 0.08143 0.821
148 8.0e-05 0.00849 0.00819 0.02546 0.06050 0.618
149 9.0e-05 0.00996 0.01027 0.02987 0.07118 0.722
150 8.0e-05 0.00919 0.00963 0.02756 0.07170 0.833
151 1.0e-04 0.01075 0.01154 0.03225 0.05830 0.784
152 1.6e-04 0.01800 0.01958 0.05401 0.11908 1.302
153 1.4e-04 0.01568 0.01699 0.04705 0.08684 1.018
154 6.0e-05 0.00388 0.00332 0.01164 0.02534 0.241
155 6.0e-05 0.00393 0.00300 0.01179 0.02682 0.236
156 5.0e-05 0.00356 0.00300 0.01067 0.03087 0.276
157 6.0e-05 0.00415 0.00339 0.01246 0.02293 0.223
158 1.5e-04 0.01117 0.00718 0.03351 0.04912 0.438
159 8.0e-05 0.00593 0.00454 0.01778 0.02852 0.266
160 5.0e-05 0.00321 0.00318 0.00962 0.03235 0.339
161 5.0e-05 0.00299 0.00316 0.00896 0.04009 0.406
162 5.0e-05 0.00352 0.00329 0.01057 0.03273 0.325
163 6.0e-05 0.00366 0.00340 0.01097 0.03658 0.369
164 5.0e-05 0.00291 0.00284 0.00873 0.01756 0.155
165 9.0e-05 0.00493 0.00461 0.01480 0.02814 0.272
166 1.0e-05 0.00154 0.00153 0.00462 0.02448 0.217
167 1.0e-05 0.00173 0.00159 0.00519 0.01242 0.116
168 1.0e-05 0.00205 0.00186 0.00616 0.02030 0.197
169 4.0e-05 0.00490 0.00448 0.01470 0.02177 0.189
170 2.0e-05 0.00316 0.00283 0.00949 0.02018 0.212
171 2.0e-05 0.00279 0.00237 0.00837 0.01897 0.181
172 3.0e-05 0.00166 0.00190 0.00499 0.01358 0.129
173 3.0e-05 0.00170 0.00200 0.00510 0.01484 0.133
174 3.0e-05 0.00171 0.00203 0.00514 0.01472 0.133
175 3.0e-05 0.00176 0.00218 0.00528 0.01657 0.145
176 3.0e-05 0.00160 0.00199 0.00480 0.01503 0.137
177 3.0e-05 0.00169 0.00213 0.00507 0.01725 0.155
178 2.0e-05 0.00135 0.00162 0.00406 0.01469 0.132
179 2.0e-05 0.00152 0.00186 0.00456 0.01574 0.142
180 3.0e-05 0.00204 0.00231 0.00612 0.01450 0.131
181 3.0e-05 0.00206 0.00233 0.00619 0.02551 0.237
182 3.0e-05 0.00202 0.00235 0.00605 0.01831 0.163
183 2.0e-05 0.00174 0.00198 0.00521 0.02145 0.198
184 4.0e-05 0.00186 0.00270 0.00558 0.01909 0.171
185 5.0e-05 0.00260 0.00346 0.00780 0.01795 0.163
186 3.0e-05 0.00134 0.00192 0.00403 0.01564 0.136
187 4.0e-05 0.00254 0.00263 0.00762 0.01660 0.154
188 2.0e-05 0.00115 0.00148 0.00345 0.01300 0.117
189 3.0e-05 0.00146 0.00184 0.00439 0.01185 0.106
190 3.0e-05 0.00412 0.00396 0.01235 0.02574 0.255
191 3.0e-05 0.00263 0.00259 0.00790 0.04087 0.405
192 3.0e-05 0.00331 0.00292 0.00994 0.02751 0.263
193 8.0e-05 0.00624 0.00564 0.01873 0.02308 0.256
194 4.0e-05 0.00370 0.00390 0.01109 0.02296 0.241
195 3.0e-05 0.00295 0.00317 0.00885 0.01884 0.190
Shimmer:APQ3 Shimmer:APQ5 MDVP:APQ Shimmer:DDA NHR HNR status
1 0.02182 0.03130 0.02971 0.06545 0.02211 21.033 1
2 0.03134 0.04518 0.04368 0.09403 0.01929 19.085 1
3 0.02757 0.03858 0.03590 0.08270 0.01309 20.651 1
4 0.02924 0.04005 0.03772 0.08771 0.01353 20.644 1
5 0.03490 0.04825 0.04465 0.10470 0.01767 19.649 1
6 0.02328 0.03526 0.03243 0.06985 0.01222 21.378 1
7 0.00779 0.00937 0.01351 0.02337 0.00607 24.886 1
8 0.00829 0.00946 0.01256 0.02487 0.00344 26.892 1
9 0.01073 0.01277 0.01717 0.03218 0.01070 21.812 1
10 0.01441 0.01725 0.02444 0.04324 0.01022 21.862 1
11 0.01079 0.01342 0.01892 0.03237 0.01166 21.118 1
12 0.01424 0.01641 0.02214 0.04272 0.01141 21.414 1
13 0.00656 0.00717 0.01140 0.01968 0.00581 25.703 1
14 0.00728 0.00932 0.01797 0.02184 0.01041 24.889 1
15 0.01064 0.00972 0.01246 0.03191 0.00609 24.922 1
16 0.00772 0.00888 0.01359 0.02316 0.00839 25.175 1
17 0.00969 0.01200 0.02074 0.02908 0.01859 22.333 1
18 0.01441 0.01893 0.03430 0.04322 0.02919 20.376 1
19 0.02471 0.03572 0.05767 0.07413 0.03160 17.280 1
20 0.01721 0.02374 0.04310 0.05164 0.03365 17.153 1
21 0.01667 0.02383 0.04055 0.05000 0.03871 17.536 1
22 0.02021 0.02591 0.04525 0.06062 0.01849 19.493 1
23 0.02228 0.02540 0.04246 0.06685 0.01280 22.468 1
24 0.02187 0.02470 0.03772 0.06562 0.01840 20.422 1
25 0.00738 0.00948 0.01497 0.02214 0.01778 23.831 1
26 0.01732 0.02245 0.03780 0.05197 0.02887 22.066 1
27 0.00889 0.01169 0.01872 0.02666 0.01095 25.908 1
28 0.00883 0.01144 0.01826 0.02650 0.01328 25.119 1
29 0.00769 0.01012 0.01661 0.02307 0.00677 25.970 1
30 0.00793 0.01057 0.01799 0.02380 0.01170 25.678 1
31 0.00563 0.00680 0.00802 0.01689 0.00339 26.775 0
32 0.00504 0.00641 0.00762 0.01513 0.00167 30.940 0
33 0.00640 0.00825 0.00951 0.01919 0.00119 30.775 0
34 0.00469 0.00606 0.00719 0.01407 0.00072 32.684 0
35 0.00468 0.00610 0.00726 0.01403 0.00065 33.047 0
36 0.00586 0.00760 0.00957 0.01758 0.00135 31.732 0
37 0.01154 0.01347 0.01612 0.03463 0.00586 23.216 1
38 0.00938 0.01160 0.01491 0.02814 0.00340 24.951 1
39 0.00726 0.00885 0.01190 0.02177 0.00231 26.738 1
40 0.00829 0.01003 0.01366 0.02488 0.00265 26.310 1
41 0.00774 0.00941 0.01233 0.02321 0.00231 26.822 1
42 0.00742 0.00901 0.01234 0.02226 0.00257 26.453 1
43 0.01035 0.01024 0.01133 0.03104 0.00740 22.736 0
44 0.01006 0.01038 0.01251 0.03017 0.00675 23.145 0
45 0.00777 0.00898 0.01033 0.02330 0.00454 25.368 0
46 0.00847 0.00879 0.01014 0.02542 0.00476 25.032 0
47 0.00906 0.00977 0.01149 0.02719 0.00476 24.602 0
48 0.00614 0.00730 0.00860 0.01841 0.00432 26.805 0
49 0.00855 0.00776 0.01433 0.02566 0.00839 23.162 0
50 0.00930 0.00802 0.01400 0.02789 0.00462 24.971 0
51 0.01241 0.01024 0.01685 0.03724 0.00479 25.135 0
52 0.01143 0.00959 0.01614 0.03429 0.00474 25.030 0
53 0.01323 0.01072 0.01677 0.03969 0.00481 24.692 0
54 0.01396 0.01219 0.01947 0.04188 0.00484 25.429 0
55 0.01483 0.01609 0.02067 0.04450 0.01036 21.028 1
56 0.01789 0.01992 0.02454 0.05368 0.01180 20.767 1
57 0.02032 0.02302 0.02802 0.06097 0.00969 21.422 1
58 0.01189 0.01459 0.01948 0.03568 0.00681 22.817 1
59 0.01394 0.01625 0.02137 0.04183 0.00786 22.603 1
60 0.01805 0.01974 0.02519 0.05414 0.01143 21.660 1
61 0.00975 0.01258 0.01382 0.02925 0.00871 25.554 0
62 0.01013 0.01296 0.01340 0.03039 0.00301 26.138 0
63 0.00867 0.01108 0.01200 0.02602 0.00340 25.856 0
64 0.00882 0.01075 0.01179 0.02647 0.00351 25.964 0
65 0.00769 0.00957 0.01016 0.02308 0.00300 26.415 0
66 0.00942 0.01160 0.01234 0.02827 0.00420 24.547 0
67 0.01830 0.01810 0.02428 0.05490 0.02183 19.560 1
68 0.01638 0.01759 0.02603 0.04914 0.02659 19.979 1
69 0.03152 0.02422 0.03392 0.09455 0.04882 20.338 1
70 0.03357 0.02494 0.03635 0.10070 0.02431 21.718 1
71 0.01868 0.01906 0.02949 0.05605 0.02599 20.264 1
72 0.02749 0.02466 0.03736 0.08247 0.03361 18.570 1
73 0.00974 0.00925 0.01345 0.02921 0.00442 25.742 1
74 0.01373 0.01375 0.01956 0.04120 0.00623 24.178 1
75 0.01432 0.01325 0.01831 0.04295 0.00479 25.438 1
76 0.01284 0.01219 0.01715 0.03851 0.00472 25.197 1
77 0.02413 0.02231 0.02704 0.07238 0.00905 23.370 1
78 0.01284 0.01199 0.01636 0.03852 0.00420 25.820 1
79 0.01803 0.01886 0.02455 0.05408 0.01062 21.875 1
80 0.01773 0.01783 0.02139 0.05320 0.02220 19.200 1
81 0.02266 0.02451 0.02876 0.06799 0.01823 19.055 1
82 0.01792 0.01841 0.02190 0.05377 0.01825 19.659 1
83 0.01371 0.01421 0.01751 0.04114 0.01237 20.536 1
84 0.01277 0.01343 0.01552 0.03831 0.00882 22.244 1
85 0.02679 0.03022 0.03510 0.08037 0.05470 13.893 1
86 0.02107 0.02493 0.02877 0.06321 0.02782 16.176 1
87 0.02073 0.02415 0.02784 0.06219 0.03151 15.924 1
88 0.03671 0.04159 0.04683 0.11012 0.04824 13.922 1
89 0.03788 0.04254 0.04802 0.11363 0.04214 14.739 1
90 0.02297 0.02768 0.03455 0.06892 0.07223 11.866 1
91 0.03650 0.04282 0.05114 0.10949 0.08725 11.744 1
92 0.04421 0.04962 0.05690 0.13262 0.01658 19.664 1
93 0.02383 0.02521 0.03051 0.07150 0.01914 18.780 1
94 0.03341 0.03794 0.04398 0.10024 0.01211 20.969 1
95 0.02062 0.02321 0.02764 0.06185 0.00850 22.219 1
96 0.01813 0.01909 0.02571 0.05439 0.01018 21.693 1
97 0.01806 0.02024 0.02809 0.05417 0.00852 22.663 1
98 0.02135 0.02174 0.03088 0.06406 0.08151 15.338 1
99 0.02542 0.02630 0.03908 0.07625 0.10323 15.433 1
100 0.03611 0.03963 0.05783 0.10833 0.16744 12.435 1
101 0.05358 0.04791 0.06196 0.16074 0.31482 8.867 1
102 0.03223 0.03672 0.05174 0.09669 0.11843 15.060 1
103 0.05551 0.05005 0.06023 0.16654 0.25930 10.489 1
104 0.00522 0.00659 0.01009 0.01567 0.00495 26.759 1
105 0.00469 0.00582 0.00871 0.01406 0.00243 28.409 1
106 0.00660 0.00818 0.01059 0.01979 0.00578 27.421 1
107 0.00522 0.00632 0.00928 0.01567 0.00233 29.746 1
108 0.00633 0.00788 0.01267 0.01898 0.00659 26.833 1
109 0.00455 0.00576 0.00993 0.01364 0.00238 29.928 1
110 0.01771 0.01815 0.02084 0.05312 0.00947 21.934 1
111 0.01192 0.01439 0.01852 0.03576 0.00704 23.239 1
112 0.00952 0.01058 0.01307 0.02855 0.00830 22.407 1
113 0.01277 0.01483 0.01767 0.03831 0.01316 21.305 1
114 0.00861 0.01017 0.01301 0.02583 0.00620 23.671 1
115 0.01107 0.01284 0.01604 0.03320 0.01048 21.864 1
116 0.00796 0.00832 0.01271 0.02389 0.06051 23.693 1
117 0.00606 0.00747 0.01312 0.01818 0.01554 26.356 1
118 0.00757 0.00971 0.01652 0.02270 0.01802 25.690 1
119 0.00617 0.00744 0.01151 0.01851 0.00856 25.020 1
120 0.00679 0.00631 0.01075 0.02038 0.00681 24.581 1
121 0.00849 0.01117 0.01734 0.02548 0.02350 24.743 1
122 0.00534 0.00630 0.01104 0.01603 0.01161 27.166 1
123 0.02587 0.02567 0.03220 0.07761 0.01968 18.305 1
124 0.01372 0.01580 0.01931 0.04115 0.01813 18.784 1
125 0.01289 0.01420 0.01720 0.03867 0.02020 19.196 1
126 0.01235 0.01495 0.01944 0.03706 0.01874 18.857 1
127 0.01484 0.01805 0.02259 0.04451 0.01794 18.178 1
128 0.01547 0.01859 0.02301 0.04641 0.01796 18.330 1
129 0.00538 0.00570 0.00811 0.01614 0.01724 26.842 1
130 0.00476 0.00588 0.00903 0.01428 0.00487 26.369 1
131 0.00703 0.00820 0.01194 0.02110 0.01610 23.949 1
132 0.00721 0.00815 0.01310 0.02164 0.01015 26.017 1
133 0.00633 0.00701 0.00915 0.01898 0.00903 23.389 1
134 0.00490 0.00621 0.00903 0.01471 0.00504 25.619 1
135 0.02683 0.03112 0.03651 0.08050 0.03031 17.060 1
136 0.02229 0.02592 0.03316 0.06688 0.02529 17.707 1
137 0.02385 0.02973 0.04370 0.07154 0.02278 19.013 1
138 0.02896 0.03347 0.04134 0.08689 0.03690 16.747 1
139 0.03070 0.03530 0.04451 0.09211 0.02629 17.366 1
140 0.01514 0.01812 0.02770 0.04543 0.01827 18.801 1
141 0.01713 0.01964 0.02824 0.05139 0.02485 18.540 1
142 0.04016 0.04003 0.04464 0.12047 0.04238 15.648 1
143 0.02055 0.02076 0.02530 0.06165 0.01728 18.702 1
144 0.01117 0.01177 0.01506 0.03350 0.02010 18.687 1
145 0.01475 0.01558 0.02006 0.04426 0.01049 20.680 1
146 0.01379 0.01478 0.01909 0.04137 0.01493 20.366 1
147 0.03804 0.05426 0.08808 0.11411 0.07530 12.359 1
148 0.02865 0.04101 0.06359 0.08595 0.06057 14.367 1
149 0.03474 0.04580 0.06824 0.10422 0.08069 12.298 1
150 0.03515 0.04265 0.06460 0.10546 0.07889 14.989 1
151 0.02699 0.03714 0.06259 0.08096 0.10952 12.529 1
152 0.05647 0.07940 0.13778 0.16942 0.21713 8.441 1
153 0.04284 0.05556 0.08318 0.12851 0.16265 9.449 1
154 0.01340 0.01399 0.02056 0.04019 0.04179 21.520 1
155 0.01484 0.01405 0.02018 0.04451 0.04611 21.824 1
156 0.01659 0.01804 0.02402 0.04977 0.02631 22.431 1
157 0.01205 0.01289 0.01771 0.03615 0.03191 22.953 1
158 0.02610 0.02161 0.02916 0.07830 0.10748 19.075 1
159 0.01500 0.01581 0.02157 0.04499 0.03828 21.534 1
160 0.01360 0.01650 0.03105 0.04079 0.02663 19.651 1
161 0.01579 0.01994 0.04114 0.04736 0.02073 20.437 1
162 0.01644 0.01722 0.02931 0.04933 0.02810 19.388 1
163 0.01864 0.01940 0.03091 0.05592 0.02707 18.954 1
164 0.00967 0.01033 0.01363 0.02902 0.01435 21.219 1
165 0.01579 0.01553 0.02073 0.04736 0.03882 18.447 1
166 0.01410 0.01426 0.01621 0.04231 0.00620 24.078 0
167 0.00696 0.00747 0.00882 0.02089 0.00533 24.679 0
168 0.01186 0.01230 0.01367 0.03557 0.00910 21.083 0
169 0.01279 0.01272 0.01439 0.03836 0.01337 19.269 0
170 0.01176 0.01191 0.01344 0.03529 0.00965 21.020 0
171 0.01084 0.01121 0.01255 0.03253 0.01049 21.528 0
172 0.00664 0.00786 0.01140 0.01992 0.00435 26.436 0
173 0.00754 0.00950 0.01285 0.02261 0.00430 26.550 0
174 0.00748 0.00905 0.01148 0.02245 0.00478 26.547 0
175 0.00881 0.01062 0.01318 0.02643 0.00590 25.445 0
176 0.00812 0.00933 0.01133 0.02436 0.00401 26.005 0
177 0.00874 0.01021 0.01331 0.02623 0.00415 26.143 0
178 0.00728 0.00886 0.01230 0.02184 0.00570 24.151 1
179 0.00839 0.00956 0.01309 0.02518 0.00488 24.412 1
180 0.00725 0.00876 0.01263 0.02175 0.00540 23.683 1
181 0.01321 0.01574 0.02148 0.03964 0.00611 23.133 1
182 0.00950 0.01103 0.01559 0.02849 0.00639 22.866 1
183 0.01155 0.01341 0.01666 0.03464 0.00595 23.008 1
184 0.00864 0.01223 0.01949 0.02592 0.00955 23.079 0
185 0.00810 0.01144 0.01756 0.02429 0.01179 22.085 0
186 0.00667 0.00990 0.01691 0.02001 0.00737 24.199 0
187 0.00820 0.00972 0.01491 0.02460 0.01397 23.958 0
188 0.00631 0.00789 0.01144 0.01892 0.00680 25.023 0
189 0.00557 0.00721 0.01095 0.01672 0.00703 24.775 0
190 0.01454 0.01582 0.01758 0.04363 0.04441 19.368 0
191 0.02336 0.02498 0.02745 0.07008 0.02764 19.517 0
192 0.01604 0.01657 0.01879 0.04812 0.01810 19.147 0
193 0.01268 0.01365 0.01667 0.03804 0.10715 17.883 0
194 0.01265 0.01321 0.01588 0.03794 0.07223 19.020 0
195 0.01026 0.01161 0.01373 0.03078 0.04398 21.209 0
DFA spread1 spread2 D2 PPE
1 0.815285 -4.813031 0.266482 2.301442 0.284654
2 0.819521 -4.075192 0.335590 2.486855 0.368674
3 0.825288 -4.443179 0.311173 2.342259 0.332634
4 0.819235 -4.117501 0.334147 2.405554 0.368975
5 0.823484 -3.747787 0.234513 2.332180 0.410335
6 0.825069 -4.242867 0.299111 2.187560 0.357775
7 0.764112 -5.634322 0.257682 1.854785 0.211756
8 0.763262 -6.167603 0.183721 2.064693 0.163755
9 0.773587 -5.498678 0.327769 2.322511 0.231571
10 0.798463 -5.011879 0.325996 2.432792 0.271362
11 0.776156 -5.249770 0.391002 2.407313 0.249740
12 0.792520 -4.960234 0.363566 2.642476 0.275931
13 0.646846 -6.547148 0.152813 2.041277 0.138512
14 0.665833 -5.660217 0.254989 2.519422 0.199889
15 0.654027 -6.105098 0.203653 2.125618 0.170100
16 0.658245 -5.340115 0.210185 2.205546 0.234589
17 0.644692 -5.440040 0.239764 2.264501 0.218164
18 0.605417 -2.931070 0.434326 3.007463 0.430788
19 0.719467 -3.949079 0.357870 3.109010 0.377429
20 0.686080 -4.554466 0.340176 2.856676 0.322111
21 0.704087 -4.095442 0.262564 2.739710 0.365391
22 0.698951 -5.186960 0.237622 2.557536 0.259765
23 0.679834 -4.330956 0.262384 2.916777 0.285695
24 0.686894 -5.248776 0.210279 2.547508 0.253556
25 0.732479 -5.557447 0.220890 2.692176 0.215961
26 0.737948 -5.571843 0.236853 2.846369 0.219514
27 0.720916 -6.183590 0.226278 2.589702 0.147403
28 0.726652 -6.271690 0.196102 2.314209 0.162999
29 0.676258 -7.120925 0.279789 2.241742 0.108514
30 0.723797 -6.635729 0.209866 1.957961 0.135242
31 0.741367 -7.348300 0.177551 1.743867 0.085569
32 0.742055 -7.682587 0.173319 2.103106 0.068501
33 0.738703 -7.067931 0.175181 1.512275 0.096320
34 0.742133 -7.695734 0.178540 1.544609 0.056141
35 0.741899 -7.964984 0.163519 1.423287 0.044539
36 0.742737 -7.777685 0.170183 2.447064 0.057610
37 0.778834 -6.149653 0.218037 2.477082 0.165827
38 0.783626 -6.006414 0.196371 2.536527 0.173218
39 0.766209 -6.452058 0.212294 2.269398 0.141929
40 0.758324 -6.006647 0.266892 2.382544 0.160691
41 0.765623 -6.647379 0.201095 2.374073 0.130554
42 0.759203 -7.044105 0.063412 2.361532 0.115730
43 0.654172 -7.310550 0.098648 2.416838 0.095032
44 0.634267 -6.793547 0.158266 2.256699 0.117399
45 0.635285 -7.057869 0.091608 2.330716 0.091470
46 0.638928 -6.995820 0.102083 2.365800 0.102706
47 0.631653 -7.156076 0.127642 2.392122 0.097336
48 0.635204 -7.319510 0.200873 2.028612 0.086398
49 0.733659 -6.439398 0.266392 2.079922 0.133867
50 0.754073 -6.482096 0.264967 2.054419 0.128872
51 0.775933 -6.650471 0.254498 1.840198 0.103561
52 0.760361 -6.689151 0.291954 2.431854 0.105993
53 0.766204 -7.072419 0.220434 1.972297 0.119308
54 0.785714 -6.836811 0.269866 2.223719 0.147491
55 0.819032 -4.649573 0.205558 1.986899 0.316700
56 0.811843 -4.333543 0.221727 2.014606 0.344834
57 0.821364 -4.438453 0.238298 1.922940 0.335041
58 0.817756 -4.608260 0.290024 2.021591 0.314464
59 0.813432 -4.476755 0.262633 1.827012 0.326197
60 0.817396 -4.609161 0.221711 1.831691 0.316395
61 0.678874 -7.040508 0.066994 2.460791 0.101516
62 0.686264 -7.293801 0.086372 2.321560 0.098555
63 0.694399 -6.966321 0.095882 2.278687 0.103224
64 0.683296 -7.245620 0.018689 2.498224 0.093534
65 0.673636 -7.496264 0.056844 2.003032 0.073581
66 0.681811 -7.314237 0.006274 2.118596 0.091546
67 0.720908 -5.409423 0.226850 2.359973 0.226156
68 0.729067 -5.324574 0.205660 2.291558 0.226247
69 0.731444 -5.869750 0.151814 2.118496 0.185580
70 0.727313 -6.261141 0.120956 2.137075 0.141958
71 0.730387 -5.720868 0.158830 2.277927 0.180828
72 0.733232 -5.207985 0.224852 2.642276 0.242981
73 0.762959 -5.791820 0.329066 2.205024 0.188180
74 0.789532 -5.389129 0.306636 1.928708 0.225461
75 0.815908 -5.313360 0.201861 2.225815 0.244512
76 0.807217 -5.477592 0.315074 1.862092 0.228624
77 0.789977 -5.775966 0.341169 2.007923 0.193918
78 0.816340 -5.391029 0.250572 1.777901 0.232744
79 0.779612 -5.115212 0.249494 2.017753 0.260015
80 0.790117 -4.913885 0.265699 2.398422 0.277948
81 0.770466 -4.441519 0.155097 2.645959 0.327978
82 0.778747 -5.132032 0.210458 2.232576 0.260633
83 0.787896 -5.022288 0.146948 2.428306 0.264666
84 0.772416 -6.025367 0.078202 2.053601 0.177275
85 0.729586 -5.288912 0.343073 3.099301 0.242119
86 0.727747 -5.657899 0.315903 3.098256 0.200423
87 0.712199 -6.366916 0.335753 2.654271 0.144614
88 0.740837 -5.515071 0.299549 3.136550 0.220968
89 0.743937 -5.783272 0.299793 3.007096 0.194052
90 0.745526 -4.379411 0.375531 3.671155 0.332086
91 0.733165 -4.508984 0.389232 3.317586 0.301952
92 0.714360 -6.411497 0.207156 2.344876 0.134120
93 0.734504 -5.952058 0.087840 2.344336 0.186489
94 0.697790 -6.152551 0.173520 2.080121 0.160809
95 0.712170 -6.251425 0.188056 2.143851 0.160812
96 0.705658 -6.247076 0.180528 2.344348 0.164916
97 0.693429 -6.417440 0.194627 2.473239 0.151709
98 0.714485 -4.020042 0.265315 2.671825 0.340623
99 0.690892 -5.159169 0.202146 2.441612 0.260375
100 0.674953 -3.760348 0.242861 2.634633 0.378483
101 0.656846 -3.700544 0.260481 2.991063 0.370961
102 0.643327 -4.202730 0.310163 2.638279 0.356881
103 0.641418 -3.269487 0.270641 2.690917 0.444774
104 0.722356 -6.878393 0.089267 2.004055 0.113942
105 0.691483 -7.111576 0.144780 2.065477 0.093193
106 0.719974 -6.997403 0.210279 1.994387 0.112878
107 0.677930 -6.981201 0.184550 2.129924 0.106802
108 0.700246 -6.600023 0.249172 2.499148 0.105306
109 0.676066 -6.739151 0.160686 2.296873 0.115130
110 0.740539 -5.845099 0.278679 2.608749 0.185668
111 0.727863 -5.258320 0.256454 2.550961 0.232520
112 0.712466 -6.471427 0.184378 2.502336 0.136390
113 0.722085 -4.876336 0.212054 2.376749 0.268144
114 0.722254 -5.963040 0.250283 2.489191 0.177807
115 0.715121 -6.729713 0.181701 2.938114 0.115515
116 0.662668 -4.673241 0.261549 2.702355 0.274407
117 0.653823 -6.051233 0.273280 2.640798 0.170106
118 0.676023 -4.597834 0.372114 2.975889 0.282780
119 0.655239 -4.913137 0.393056 2.816781 0.251972
120 0.582710 -5.517173 0.389295 2.925862 0.220657
121 0.684130 -6.186128 0.279933 2.686240 0.152428
122 0.656182 -4.711007 0.281618 2.655744 0.234809
123 0.741480 -5.418787 0.160267 2.090438 0.229892
124 0.732903 -5.445140 0.142466 2.174306 0.215558
125 0.728421 -5.944191 0.143359 1.929715 0.181988
126 0.735546 -5.594275 0.127950 1.765957 0.222716
127 0.738245 -5.540351 0.087165 1.821297 0.214075
128 0.736964 -5.825257 0.115697 1.996146 0.196535
129 0.699787 -6.890021 0.152941 2.328513 0.112856
130 0.718839 -5.892061 0.195976 2.108873 0.183572
131 0.724045 -6.135296 0.203630 2.539724 0.169923
132 0.735136 -6.112667 0.217013 2.527742 0.170633
133 0.721308 -5.436135 0.254909 2.516320 0.232209
134 0.723096 -6.448134 0.178713 2.034827 0.141422
135 0.744064 -5.301321 0.320385 2.375138 0.243080
136 0.706687 -5.333619 0.322044 2.631793 0.228319
137 0.708144 -4.378916 0.300067 2.445502 0.259451
138 0.708617 -4.654894 0.304107 2.672362 0.274387
139 0.701404 -5.634576 0.306014 2.419253 0.209191
140 0.696049 -5.866357 0.233070 2.445646 0.184985
141 0.685057 -4.796845 0.397749 2.963799 0.277227
142 0.665945 -5.410336 0.288917 2.665133 0.231723
143 0.661735 -5.585259 0.310746 2.465528 0.209863
144 0.632631 -5.898673 0.213353 2.470746 0.189032
145 0.630409 -6.132663 0.220617 2.576563 0.159777
146 0.574282 -5.456811 0.345238 2.840556 0.232861
147 0.793509 -3.297668 0.414758 3.413649 0.457533
148 0.768974 -4.276605 0.355736 3.142364 0.336085
149 0.764036 -3.377325 0.335357 3.274865 0.418646
150 0.775708 -4.892495 0.262281 2.910213 0.270173
151 0.762726 -4.484303 0.340256 2.958815 0.301487
152 0.768320 -2.434031 0.450493 3.079221 0.527367
153 0.754449 -2.839756 0.356224 3.184027 0.454721
154 0.670475 -4.865194 0.246404 2.013530 0.168581
155 0.659333 -4.239028 0.175691 2.451130 0.247455
156 0.652025 -3.583722 0.207914 2.439597 0.206256
157 0.623731 -5.435100 0.230532 2.699645 0.220546
158 0.646786 -3.444478 0.303214 2.964568 0.261305
159 0.627337 -5.070096 0.280091 2.892300 0.249703
160 0.675865 -5.498456 0.234196 2.103014 0.216638
161 0.694571 -5.185987 0.259229 2.151121 0.244948
162 0.684373 -5.283009 0.226528 2.442906 0.238281
163 0.719576 -5.529833 0.242750 2.408689 0.220520
164 0.673086 -5.617124 0.184896 1.871871 0.212386
165 0.674562 -2.929379 0.396746 2.560422 0.367233
166 0.628232 -6.816086 0.172270 2.235197 0.119652
167 0.626710 -7.018057 0.176316 1.852402 0.091604
168 0.628058 -7.517934 0.160414 1.881767 0.075587
169 0.725216 -5.736781 0.164529 2.882450 0.202879
170 0.646167 -7.169701 0.073298 2.266432 0.100881
171 0.646818 -7.304500 0.171088 2.095237 0.096220
172 0.756700 -6.323531 0.218885 2.193412 0.160376
173 0.776158 -6.085567 0.192375 1.889002 0.174152
174 0.766700 -5.943501 0.192150 1.852542 0.179677
175 0.756482 -6.012559 0.229298 1.872946 0.163118
176 0.761255 -5.966779 0.197938 1.974857 0.184067
177 0.763242 -6.016891 0.109256 2.004719 0.174429
178 0.745957 -6.486822 0.197919 2.449763 0.132703
179 0.762508 -6.311987 0.182459 2.251553 0.160306
180 0.778349 -5.711205 0.240875 2.845109 0.192730
181 0.759320 -6.261446 0.183218 2.264226 0.144105
182 0.768845 -5.704053 0.216204 2.679185 0.197710
183 0.757180 -6.277170 0.109397 2.209021 0.156368
184 0.669565 -5.619070 0.191576 2.027228 0.215724
185 0.656516 -5.198864 0.206768 2.120412 0.252404
186 0.654331 -5.592584 0.133917 2.058658 0.214346
187 0.667654 -6.431119 0.153310 2.161936 0.120605
188 0.663884 -6.359018 0.116636 2.152083 0.138868
189 0.659132 -6.710219 0.149694 1.913990 0.121777
190 0.683761 -6.934474 0.159890 2.316346 0.112838
191 0.657899 -6.538586 0.121952 2.657476 0.133050
192 0.683244 -6.195325 0.129303 2.784312 0.168895
193 0.655683 -6.787197 0.158453 2.679772 0.131728
194 0.643956 -6.744577 0.207454 2.138608 0.123306
195 0.664357 -5.724056 0.190667 2.555477 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)`
1.667e+00 -3.832e-04 2.678e-05 -8.035e-05
`MDVP:Jitter(%)` `MDVP:Jitter(Abs)` `MDVP:RAP` `MDVP:PPQ`
-2.707e+01 1.456e+03 2.473e+03 -4.281e+00
`Jitter:DDP` `MDVP:Shimmer` `MDVP:Shimmer(dB)` `Shimmer:APQ3`
-8.164e+02 2.186e+00 -3.966e-01 6.651e+02
`Shimmer:APQ5` `MDVP:APQ` `Shimmer:DDA` NHR
-6.000e+00 5.436e+00 -2.211e+02 -2.153e-01
HNR status DFA spread1
-1.802e-02 -2.961e-02 -4.946e-01 2.707e-02
spread2 D2 PPE
4.020e-01 -9.320e-02 -3.883e-02
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.128974 -0.032597 -0.003913 0.033093 0.193170
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.667e+00 1.544e-01 10.796 < 2e-16 ***
`MDVP:Fo(Hz)` -3.832e-04 2.582e-04 -1.484 0.13965
`MDVP:Fhi(Hz)` 2.678e-05 5.484e-05 0.488 0.62586
`MDVP:Flo(Hz)` -8.035e-05 1.384e-04 -0.581 0.56227
`MDVP:Jitter(%)` -2.707e+01 1.150e+01 -2.354 0.01970 *
`MDVP:Jitter(Abs)` 1.456e+03 7.836e+02 1.858 0.06485 .
`MDVP:RAP` 2.473e+03 1.583e+03 1.562 0.12013
`MDVP:PPQ` -4.281e+00 1.510e+01 -0.283 0.77721
`Jitter:DDP` -8.164e+02 5.280e+02 -1.546 0.12387
`MDVP:Shimmer` 2.186e+00 5.866e+00 0.373 0.70984
`MDVP:Shimmer(dB)` -3.966e-01 2.028e-01 -1.956 0.05211 .
`Shimmer:APQ3` 6.651e+02 1.532e+03 0.434 0.66476
`Shimmer:APQ5` -6.000e+00 3.425e+00 -1.752 0.08154 .
`MDVP:APQ` 5.436e+00 1.814e+00 2.996 0.00314 **
`Shimmer:DDA` -2.211e+02 5.106e+02 -0.433 0.66556
NHR -2.153e-01 3.396e-01 -0.634 0.52691
HNR -1.802e-02 2.039e-03 -8.834 1.14e-15 ***
status -2.961e-02 1.283e-02 -2.308 0.02219 *
DFA -4.946e-01 1.207e-01 -4.100 6.37e-05 ***
spread1 2.707e-02 1.668e-02 1.623 0.10647
spread2 4.020e-01 7.747e-02 5.189 5.91e-07 ***
D2 -9.320e-02 1.821e-02 -5.119 8.15e-07 ***
PPE -3.883e-02 2.369e-01 -0.164 0.86998
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.05581 on 172 degrees of freedom
Multiple R-squared: 0.7444, Adjusted R-squared: 0.7117
F-statistic: 22.77 on 22 and 172 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,] 0.7689165 0.46216698 0.231083490
[2,] 0.6594566 0.68108687 0.340543433
[3,] 0.8388896 0.32222075 0.161110375
[4,] 0.8192113 0.36157736 0.180788680
[5,] 0.7448546 0.51029088 0.255145442
[6,] 0.6526512 0.69469769 0.347348843
[7,] 0.6471766 0.70564684 0.352823421
[8,] 0.5898795 0.82024104 0.410120518
[9,] 0.5389804 0.92203914 0.461019568
[10,] 0.4930552 0.98611033 0.506944833
[11,] 0.4580257 0.91605149 0.541974257
[12,] 0.5412539 0.91749213 0.458746064
[13,] 0.4849876 0.96997526 0.515012369
[14,] 0.4696052 0.93921041 0.530394795
[15,] 0.4007871 0.80157412 0.599212941
[16,] 0.3307982 0.66159636 0.669201818
[17,] 0.2750622 0.55012438 0.724937812
[18,] 0.2403077 0.48061546 0.759692270
[19,] 0.2186876 0.43737516 0.781312422
[20,] 0.2330246 0.46604912 0.766975438
[21,] 0.2012499 0.40249972 0.798750142
[22,] 0.2018085 0.40361704 0.798191478
[23,] 0.7642050 0.47159007 0.235795036
[24,] 0.7897307 0.42053862 0.210269309
[25,] 0.7692943 0.46141144 0.230705720
[26,] 0.7789058 0.44218849 0.221094243
[27,] 0.7596087 0.48078258 0.240391289
[28,] 0.7234691 0.55306183 0.276530916
[29,] 0.7008747 0.59825062 0.299125312
[30,] 0.6791648 0.64167031 0.320835155
[31,] 0.6444209 0.71115811 0.355579056
[32,] 0.5957129 0.80857417 0.404287084
[33,] 0.5594591 0.88108177 0.440540883
[34,] 0.5190052 0.96198954 0.480994771
[35,] 0.5950563 0.80988742 0.404943709
[36,] 0.5950577 0.80988454 0.404942268
[37,] 0.6774816 0.64503673 0.322518366
[38,] 0.6839905 0.63201903 0.316009515
[39,] 0.6687954 0.66240913 0.331204565
[40,] 0.7580451 0.48390977 0.241954885
[41,] 0.7810335 0.43793302 0.218966511
[42,] 0.7996962 0.40060756 0.200303780
[43,] 0.7841039 0.43179213 0.215896065
[44,] 0.8176386 0.36472281 0.182361406
[45,] 0.7892321 0.42153576 0.210767878
[46,] 0.7645092 0.47098153 0.235490767
[47,] 0.7490279 0.50194417 0.250972085
[48,] 0.7286740 0.54265209 0.271326047
[49,] 0.6873581 0.62528375 0.312641873
[50,] 0.6876758 0.62464835 0.312324173
[51,] 0.7539365 0.49212698 0.246063488
[52,] 0.7381770 0.52364607 0.261823035
[53,] 0.7819657 0.43606857 0.218034285
[54,] 0.8185176 0.36296474 0.181482371
[55,] 0.8275022 0.34499552 0.172497762
[56,] 0.7968054 0.40638917 0.203194587
[57,] 0.7618149 0.47637019 0.238185096
[58,] 0.7511708 0.49765837 0.248829187
[59,] 0.8686705 0.26265895 0.131329475
[60,] 0.8653902 0.26921962 0.134609810
[61,] 0.8682465 0.26350709 0.131753546
[62,] 0.8430955 0.31380894 0.156904472
[63,] 0.8373133 0.32537348 0.162686738
[64,] 0.8434816 0.31303686 0.156518428
[65,] 0.8296245 0.34075096 0.170375481
[66,] 0.8370993 0.32580139 0.162900697
[67,] 0.8268065 0.34638696 0.173193480
[68,] 0.8004597 0.39908066 0.199540330
[69,] 0.7716908 0.45661850 0.228309248
[70,] 0.7350350 0.52992994 0.264964970
[71,] 0.7160410 0.56791795 0.283958977
[72,] 0.7817987 0.43640252 0.218201260
[73,] 0.7891122 0.42177552 0.210887761
[74,] 0.8324983 0.33500339 0.167501697
[75,] 0.8159202 0.36815964 0.184079820
[76,] 0.8443089 0.31138211 0.155691054
[77,] 0.8298924 0.34021524 0.170107622
[78,] 0.8243980 0.35120401 0.175602006
[79,] 0.8766487 0.24670267 0.123351335
[80,] 0.9284288 0.14314243 0.071571217
[81,] 0.9124326 0.17513481 0.087567405
[82,] 0.8969905 0.20601905 0.103009525
[83,] 0.8758854 0.24822913 0.124114567
[84,] 0.8687246 0.26255082 0.131275409
[85,] 0.8945375 0.21092509 0.105462543
[86,] 0.8735577 0.25288459 0.126442297
[87,] 0.8843390 0.23132200 0.115660999
[88,] 0.8786221 0.24275575 0.121377875
[89,] 0.8787332 0.24253361 0.121266804
[90,] 0.8748296 0.25034078 0.125170391
[91,] 0.8784070 0.24318609 0.121593046
[92,] 0.8637396 0.27252071 0.136260353
[93,] 0.8425005 0.31499893 0.157499464
[94,] 0.8160778 0.36784448 0.183922238
[95,] 0.7977535 0.40449291 0.202246457
[96,] 0.7906102 0.41877965 0.209389823
[97,] 0.7986864 0.40262724 0.201313621
[98,] 0.7768710 0.44625800 0.223128998
[99,] 0.7915267 0.41694664 0.208473319
[100,] 0.8293610 0.34127795 0.170638974
[101,] 0.8436930 0.31261410 0.156307048
[102,] 0.8612642 0.27747157 0.138735785
[103,] 0.9379244 0.12415115 0.062075576
[104,] 0.9373524 0.12529513 0.062647565
[105,] 0.9383477 0.12330453 0.061652265
[106,] 0.9230326 0.15393489 0.076967445
[107,] 0.9080053 0.18398938 0.091994688
[108,] 0.8892135 0.22157298 0.110786488
[109,] 0.8861731 0.22765371 0.113826855
[110,] 0.8894988 0.22100233 0.110501164
[111,] 0.8634581 0.27308372 0.136541859
[112,] 0.8373802 0.32523965 0.162619827
[113,] 0.8027637 0.39447253 0.197236263
[114,] 0.7613487 0.47730264 0.238651322
[115,] 0.7279830 0.54403404 0.272017019
[116,] 0.8218904 0.35621924 0.178109621
[117,] 0.8216042 0.35679162 0.178395808
[118,] 0.8483904 0.30321919 0.151609594
[119,] 0.8157205 0.36855905 0.184279527
[120,] 0.7762133 0.44757347 0.223786736
[121,] 0.8113650 0.37727008 0.188635039
[122,] 0.7734988 0.45300249 0.226501244
[123,] 0.7304037 0.53919251 0.269596253
[124,] 0.6795319 0.64093614 0.320468071
[125,] 0.6330021 0.73399577 0.366997884
[126,] 0.5969719 0.80605627 0.403028137
[127,] 0.6761787 0.64764263 0.323821316
[128,] 0.7809146 0.43817086 0.219085429
[129,] 0.7488082 0.50238351 0.251191757
[130,] 0.8149789 0.37004223 0.185021117
[131,] 0.8671802 0.26563958 0.132819790
[132,] 0.8770651 0.24586977 0.122934885
[133,] 0.8729293 0.25414147 0.127070737
[134,] 0.8296533 0.34069334 0.170346671
[135,] 0.7673588 0.46528243 0.232641214
[136,] 0.8423882 0.31522369 0.157611843
[137,] 0.8004330 0.39913403 0.199567014
[138,] 0.7355135 0.52897304 0.264486521
[139,] 0.7941596 0.41168085 0.205840423
[140,] 0.8542450 0.29150992 0.145754958
[141,] 0.7791258 0.44174847 0.220874234
[142,] 0.9860128 0.02797447 0.013987236
[143,] 0.9925793 0.01484139 0.007420696
[144,] 0.9881181 0.02376373 0.011881867
> postscript(file="/var/wessaorg/rcomp/tmp/1zouh1386790450.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/2jevi1386790450.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/39udr1386790450.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/468981386790450.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/5rtzp1386790450.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
-4.444846e-02 -3.711888e-03 -2.274669e-02 -5.184791e-02 -1.303356e-02
6 7 8 9 10
-3.004261e-02 8.286117e-02 1.931696e-01 4.465732e-02 -3.286575e-02
11 12 13 14 15
-2.756609e-03 1.510675e-02 8.034284e-05 -6.980058e-02 -1.300558e-02
16 17 18 19 20
4.913747e-02 -3.912712e-03 -1.050460e-02 3.803441e-02 -7.861252e-03
21 22 23 24 25
4.626037e-02 1.490350e-02 6.479613e-02 -1.424073e-02 -1.465345e-02
26 27 28 29 30
1.712489e-02 6.860326e-03 -6.719225e-02 -1.112212e-02 -1.513912e-02
31 32 33 34 35
1.629830e-02 1.409465e-01 9.208586e-02 3.914520e-02 3.202293e-02
36 37 38 39 40
8.601405e-02 -3.232814e-02 -1.896315e-02 2.322169e-02 -6.523539e-03
41 42 43 44 45
-2.121215e-02 2.144957e-02 -9.888493e-02 -1.821240e-02 4.733019e-02
46 47 48 49 50
3.186202e-02 5.116698e-02 1.774137e-01 4.192417e-02 4.800806e-02
51 52 53 54 55
4.823206e-02 3.336574e-02 -5.545850e-03 -3.386358e-02 6.049335e-03
56 57 58 59 60
2.288520e-02 1.242683e-02 4.180620e-02 2.848336e-02 4.532089e-02
61 62 63 64 65
2.669414e-03 9.032165e-02 1.420339e-02 -3.305232e-02 -8.250061e-02
66 67 68 69 70
-6.076084e-02 -8.570378e-02 -4.906117e-02 -7.191868e-03 -1.505567e-02
71 72 73 74 75
-1.605124e-02 -7.745728e-03 6.689852e-03 -3.502609e-02 -1.087028e-03
76 77 78 79 80
-6.760500e-02 -5.597239e-02 -6.450568e-02 6.608449e-02 4.033968e-02
81 82 83 84 85
2.020683e-02 -1.964535e-03 4.393917e-02 1.191537e-01 -1.515581e-02
86 87 88 89 90
3.450210e-02 -6.568944e-03 5.700447e-02 5.158803e-02 1.797865e-02
91 92 93 94 95
4.527665e-02 1.692032e-02 -2.617146e-02 -2.350701e-02 -9.298022e-03
96 97 98 99 100
-5.370974e-02 -1.001076e-01 -2.413658e-02 -7.077257e-02 -5.280696e-02
101 102 103 104 105
1.569186e-02 -2.938812e-02 -2.108659e-02 -8.542933e-02 -1.032971e-01
106 107 108 109 110
-3.737974e-02 -3.371223e-02 -1.872499e-02 -4.779196e-02 6.494192e-02
111 112 113 114 115
-1.890290e-02 -6.006423e-02 4.302417e-02 1.890069e-02 -4.888438e-02
116 117 118 119 120
-4.990640e-02 8.297748e-03 8.677885e-03 -2.573946e-02 -3.856536e-02
121 122 123 124 125
3.787728e-02 -4.156505e-02 -1.225025e-02 7.424041e-02 9.902617e-02
126 127 128 129 130
8.700091e-02 6.294399e-02 5.536771e-02 5.729102e-02 4.199472e-02
131 132 133 134 135
2.244353e-02 3.915677e-02 -2.963115e-02 2.495647e-02 -2.711325e-03
136 137 138 139 140
2.531172e-02 -1.680039e-02 -4.571559e-03 1.023166e-02 1.440494e-02
141 142 143 144 145
7.889684e-02 2.892316e-02 1.118432e-02 -2.073620e-02 -9.673034e-03
146 147 148 149 150
-2.927007e-02 -6.356316e-02 -8.547313e-02 -6.286902e-03 3.166478e-04
151 152 153 154 155
1.580738e-02 -5.634029e-03 1.095322e-01 -5.973461e-02 7.412073e-02
156 157 158 159 160
3.257950e-02 9.887833e-02 1.406885e-02 6.847811e-02 1.152124e-02
161 162 163 164 165
-1.874648e-02 -2.139630e-02 7.225242e-02 -4.855794e-02 -5.625824e-02
166 167 168 169 170
5.400982e-03 -9.024693e-02 -5.112799e-02 -8.193670e-02 -3.723084e-02
171 172 173 174 175
4.771475e-02 -3.689886e-02 -1.051736e-01 -8.907027e-02 -1.289742e-01
176 177 178 179 180
-7.500234e-02 -7.647941e-02 -1.075904e-02 -1.832546e-02 -1.783339e-02
181 182 183 184 185
-8.785106e-02 -4.260441e-02 -9.643693e-02 7.425478e-03 3.911600e-02
186 187 188 189 190
3.858395e-02 3.226214e-02 2.112312e-02 3.217684e-02 3.282109e-02
191 192 193 194 195
-2.089350e-02 -2.353185e-02 5.909980e-03 -8.904637e-02 -1.711114e-02
> postscript(file="/var/wessaorg/rcomp/tmp/6v1g81386790450.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 -4.444846e-02 NA
1 -3.711888e-03 -4.444846e-02
2 -2.274669e-02 -3.711888e-03
3 -5.184791e-02 -2.274669e-02
4 -1.303356e-02 -5.184791e-02
5 -3.004261e-02 -1.303356e-02
6 8.286117e-02 -3.004261e-02
7 1.931696e-01 8.286117e-02
8 4.465732e-02 1.931696e-01
9 -3.286575e-02 4.465732e-02
10 -2.756609e-03 -3.286575e-02
11 1.510675e-02 -2.756609e-03
12 8.034284e-05 1.510675e-02
13 -6.980058e-02 8.034284e-05
14 -1.300558e-02 -6.980058e-02
15 4.913747e-02 -1.300558e-02
16 -3.912712e-03 4.913747e-02
17 -1.050460e-02 -3.912712e-03
18 3.803441e-02 -1.050460e-02
19 -7.861252e-03 3.803441e-02
20 4.626037e-02 -7.861252e-03
21 1.490350e-02 4.626037e-02
22 6.479613e-02 1.490350e-02
23 -1.424073e-02 6.479613e-02
24 -1.465345e-02 -1.424073e-02
25 1.712489e-02 -1.465345e-02
26 6.860326e-03 1.712489e-02
27 -6.719225e-02 6.860326e-03
28 -1.112212e-02 -6.719225e-02
29 -1.513912e-02 -1.112212e-02
30 1.629830e-02 -1.513912e-02
31 1.409465e-01 1.629830e-02
32 9.208586e-02 1.409465e-01
33 3.914520e-02 9.208586e-02
34 3.202293e-02 3.914520e-02
35 8.601405e-02 3.202293e-02
36 -3.232814e-02 8.601405e-02
37 -1.896315e-02 -3.232814e-02
38 2.322169e-02 -1.896315e-02
39 -6.523539e-03 2.322169e-02
40 -2.121215e-02 -6.523539e-03
41 2.144957e-02 -2.121215e-02
42 -9.888493e-02 2.144957e-02
43 -1.821240e-02 -9.888493e-02
44 4.733019e-02 -1.821240e-02
45 3.186202e-02 4.733019e-02
46 5.116698e-02 3.186202e-02
47 1.774137e-01 5.116698e-02
48 4.192417e-02 1.774137e-01
49 4.800806e-02 4.192417e-02
50 4.823206e-02 4.800806e-02
51 3.336574e-02 4.823206e-02
52 -5.545850e-03 3.336574e-02
53 -3.386358e-02 -5.545850e-03
54 6.049335e-03 -3.386358e-02
55 2.288520e-02 6.049335e-03
56 1.242683e-02 2.288520e-02
57 4.180620e-02 1.242683e-02
58 2.848336e-02 4.180620e-02
59 4.532089e-02 2.848336e-02
60 2.669414e-03 4.532089e-02
61 9.032165e-02 2.669414e-03
62 1.420339e-02 9.032165e-02
63 -3.305232e-02 1.420339e-02
64 -8.250061e-02 -3.305232e-02
65 -6.076084e-02 -8.250061e-02
66 -8.570378e-02 -6.076084e-02
67 -4.906117e-02 -8.570378e-02
68 -7.191868e-03 -4.906117e-02
69 -1.505567e-02 -7.191868e-03
70 -1.605124e-02 -1.505567e-02
71 -7.745728e-03 -1.605124e-02
72 6.689852e-03 -7.745728e-03
73 -3.502609e-02 6.689852e-03
74 -1.087028e-03 -3.502609e-02
75 -6.760500e-02 -1.087028e-03
76 -5.597239e-02 -6.760500e-02
77 -6.450568e-02 -5.597239e-02
78 6.608449e-02 -6.450568e-02
79 4.033968e-02 6.608449e-02
80 2.020683e-02 4.033968e-02
81 -1.964535e-03 2.020683e-02
82 4.393917e-02 -1.964535e-03
83 1.191537e-01 4.393917e-02
84 -1.515581e-02 1.191537e-01
85 3.450210e-02 -1.515581e-02
86 -6.568944e-03 3.450210e-02
87 5.700447e-02 -6.568944e-03
88 5.158803e-02 5.700447e-02
89 1.797865e-02 5.158803e-02
90 4.527665e-02 1.797865e-02
91 1.692032e-02 4.527665e-02
92 -2.617146e-02 1.692032e-02
93 -2.350701e-02 -2.617146e-02
94 -9.298022e-03 -2.350701e-02
95 -5.370974e-02 -9.298022e-03
96 -1.001076e-01 -5.370974e-02
97 -2.413658e-02 -1.001076e-01
98 -7.077257e-02 -2.413658e-02
99 -5.280696e-02 -7.077257e-02
100 1.569186e-02 -5.280696e-02
101 -2.938812e-02 1.569186e-02
102 -2.108659e-02 -2.938812e-02
103 -8.542933e-02 -2.108659e-02
104 -1.032971e-01 -8.542933e-02
105 -3.737974e-02 -1.032971e-01
106 -3.371223e-02 -3.737974e-02
107 -1.872499e-02 -3.371223e-02
108 -4.779196e-02 -1.872499e-02
109 6.494192e-02 -4.779196e-02
110 -1.890290e-02 6.494192e-02
111 -6.006423e-02 -1.890290e-02
112 4.302417e-02 -6.006423e-02
113 1.890069e-02 4.302417e-02
114 -4.888438e-02 1.890069e-02
115 -4.990640e-02 -4.888438e-02
116 8.297748e-03 -4.990640e-02
117 8.677885e-03 8.297748e-03
118 -2.573946e-02 8.677885e-03
119 -3.856536e-02 -2.573946e-02
120 3.787728e-02 -3.856536e-02
121 -4.156505e-02 3.787728e-02
122 -1.225025e-02 -4.156505e-02
123 7.424041e-02 -1.225025e-02
124 9.902617e-02 7.424041e-02
125 8.700091e-02 9.902617e-02
126 6.294399e-02 8.700091e-02
127 5.536771e-02 6.294399e-02
128 5.729102e-02 5.536771e-02
129 4.199472e-02 5.729102e-02
130 2.244353e-02 4.199472e-02
131 3.915677e-02 2.244353e-02
132 -2.963115e-02 3.915677e-02
133 2.495647e-02 -2.963115e-02
134 -2.711325e-03 2.495647e-02
135 2.531172e-02 -2.711325e-03
136 -1.680039e-02 2.531172e-02
137 -4.571559e-03 -1.680039e-02
138 1.023166e-02 -4.571559e-03
139 1.440494e-02 1.023166e-02
140 7.889684e-02 1.440494e-02
141 2.892316e-02 7.889684e-02
142 1.118432e-02 2.892316e-02
143 -2.073620e-02 1.118432e-02
144 -9.673034e-03 -2.073620e-02
145 -2.927007e-02 -9.673034e-03
146 -6.356316e-02 -2.927007e-02
147 -8.547313e-02 -6.356316e-02
148 -6.286902e-03 -8.547313e-02
149 3.166478e-04 -6.286902e-03
150 1.580738e-02 3.166478e-04
151 -5.634029e-03 1.580738e-02
152 1.095322e-01 -5.634029e-03
153 -5.973461e-02 1.095322e-01
154 7.412073e-02 -5.973461e-02
155 3.257950e-02 7.412073e-02
156 9.887833e-02 3.257950e-02
157 1.406885e-02 9.887833e-02
158 6.847811e-02 1.406885e-02
159 1.152124e-02 6.847811e-02
160 -1.874648e-02 1.152124e-02
161 -2.139630e-02 -1.874648e-02
162 7.225242e-02 -2.139630e-02
163 -4.855794e-02 7.225242e-02
164 -5.625824e-02 -4.855794e-02
165 5.400982e-03 -5.625824e-02
166 -9.024693e-02 5.400982e-03
167 -5.112799e-02 -9.024693e-02
168 -8.193670e-02 -5.112799e-02
169 -3.723084e-02 -8.193670e-02
170 4.771475e-02 -3.723084e-02
171 -3.689886e-02 4.771475e-02
172 -1.051736e-01 -3.689886e-02
173 -8.907027e-02 -1.051736e-01
174 -1.289742e-01 -8.907027e-02
175 -7.500234e-02 -1.289742e-01
176 -7.647941e-02 -7.500234e-02
177 -1.075904e-02 -7.647941e-02
178 -1.832546e-02 -1.075904e-02
179 -1.783339e-02 -1.832546e-02
180 -8.785106e-02 -1.783339e-02
181 -4.260441e-02 -8.785106e-02
182 -9.643693e-02 -4.260441e-02
183 7.425478e-03 -9.643693e-02
184 3.911600e-02 7.425478e-03
185 3.858395e-02 3.911600e-02
186 3.226214e-02 3.858395e-02
187 2.112312e-02 3.226214e-02
188 3.217684e-02 2.112312e-02
189 3.282109e-02 3.217684e-02
190 -2.089350e-02 3.282109e-02
191 -2.353185e-02 -2.089350e-02
192 5.909980e-03 -2.353185e-02
193 -8.904637e-02 5.909980e-03
194 -1.711114e-02 -8.904637e-02
195 NA -1.711114e-02
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -3.711888e-03 -4.444846e-02
[2,] -2.274669e-02 -3.711888e-03
[3,] -5.184791e-02 -2.274669e-02
[4,] -1.303356e-02 -5.184791e-02
[5,] -3.004261e-02 -1.303356e-02
[6,] 8.286117e-02 -3.004261e-02
[7,] 1.931696e-01 8.286117e-02
[8,] 4.465732e-02 1.931696e-01
[9,] -3.286575e-02 4.465732e-02
[10,] -2.756609e-03 -3.286575e-02
[11,] 1.510675e-02 -2.756609e-03
[12,] 8.034284e-05 1.510675e-02
[13,] -6.980058e-02 8.034284e-05
[14,] -1.300558e-02 -6.980058e-02
[15,] 4.913747e-02 -1.300558e-02
[16,] -3.912712e-03 4.913747e-02
[17,] -1.050460e-02 -3.912712e-03
[18,] 3.803441e-02 -1.050460e-02
[19,] -7.861252e-03 3.803441e-02
[20,] 4.626037e-02 -7.861252e-03
[21,] 1.490350e-02 4.626037e-02
[22,] 6.479613e-02 1.490350e-02
[23,] -1.424073e-02 6.479613e-02
[24,] -1.465345e-02 -1.424073e-02
[25,] 1.712489e-02 -1.465345e-02
[26,] 6.860326e-03 1.712489e-02
[27,] -6.719225e-02 6.860326e-03
[28,] -1.112212e-02 -6.719225e-02
[29,] -1.513912e-02 -1.112212e-02
[30,] 1.629830e-02 -1.513912e-02
[31,] 1.409465e-01 1.629830e-02
[32,] 9.208586e-02 1.409465e-01
[33,] 3.914520e-02 9.208586e-02
[34,] 3.202293e-02 3.914520e-02
[35,] 8.601405e-02 3.202293e-02
[36,] -3.232814e-02 8.601405e-02
[37,] -1.896315e-02 -3.232814e-02
[38,] 2.322169e-02 -1.896315e-02
[39,] -6.523539e-03 2.322169e-02
[40,] -2.121215e-02 -6.523539e-03
[41,] 2.144957e-02 -2.121215e-02
[42,] -9.888493e-02 2.144957e-02
[43,] -1.821240e-02 -9.888493e-02
[44,] 4.733019e-02 -1.821240e-02
[45,] 3.186202e-02 4.733019e-02
[46,] 5.116698e-02 3.186202e-02
[47,] 1.774137e-01 5.116698e-02
[48,] 4.192417e-02 1.774137e-01
[49,] 4.800806e-02 4.192417e-02
[50,] 4.823206e-02 4.800806e-02
[51,] 3.336574e-02 4.823206e-02
[52,] -5.545850e-03 3.336574e-02
[53,] -3.386358e-02 -5.545850e-03
[54,] 6.049335e-03 -3.386358e-02
[55,] 2.288520e-02 6.049335e-03
[56,] 1.242683e-02 2.288520e-02
[57,] 4.180620e-02 1.242683e-02
[58,] 2.848336e-02 4.180620e-02
[59,] 4.532089e-02 2.848336e-02
[60,] 2.669414e-03 4.532089e-02
[61,] 9.032165e-02 2.669414e-03
[62,] 1.420339e-02 9.032165e-02
[63,] -3.305232e-02 1.420339e-02
[64,] -8.250061e-02 -3.305232e-02
[65,] -6.076084e-02 -8.250061e-02
[66,] -8.570378e-02 -6.076084e-02
[67,] -4.906117e-02 -8.570378e-02
[68,] -7.191868e-03 -4.906117e-02
[69,] -1.505567e-02 -7.191868e-03
[70,] -1.605124e-02 -1.505567e-02
[71,] -7.745728e-03 -1.605124e-02
[72,] 6.689852e-03 -7.745728e-03
[73,] -3.502609e-02 6.689852e-03
[74,] -1.087028e-03 -3.502609e-02
[75,] -6.760500e-02 -1.087028e-03
[76,] -5.597239e-02 -6.760500e-02
[77,] -6.450568e-02 -5.597239e-02
[78,] 6.608449e-02 -6.450568e-02
[79,] 4.033968e-02 6.608449e-02
[80,] 2.020683e-02 4.033968e-02
[81,] -1.964535e-03 2.020683e-02
[82,] 4.393917e-02 -1.964535e-03
[83,] 1.191537e-01 4.393917e-02
[84,] -1.515581e-02 1.191537e-01
[85,] 3.450210e-02 -1.515581e-02
[86,] -6.568944e-03 3.450210e-02
[87,] 5.700447e-02 -6.568944e-03
[88,] 5.158803e-02 5.700447e-02
[89,] 1.797865e-02 5.158803e-02
[90,] 4.527665e-02 1.797865e-02
[91,] 1.692032e-02 4.527665e-02
[92,] -2.617146e-02 1.692032e-02
[93,] -2.350701e-02 -2.617146e-02
[94,] -9.298022e-03 -2.350701e-02
[95,] -5.370974e-02 -9.298022e-03
[96,] -1.001076e-01 -5.370974e-02
[97,] -2.413658e-02 -1.001076e-01
[98,] -7.077257e-02 -2.413658e-02
[99,] -5.280696e-02 -7.077257e-02
[100,] 1.569186e-02 -5.280696e-02
[101,] -2.938812e-02 1.569186e-02
[102,] -2.108659e-02 -2.938812e-02
[103,] -8.542933e-02 -2.108659e-02
[104,] -1.032971e-01 -8.542933e-02
[105,] -3.737974e-02 -1.032971e-01
[106,] -3.371223e-02 -3.737974e-02
[107,] -1.872499e-02 -3.371223e-02
[108,] -4.779196e-02 -1.872499e-02
[109,] 6.494192e-02 -4.779196e-02
[110,] -1.890290e-02 6.494192e-02
[111,] -6.006423e-02 -1.890290e-02
[112,] 4.302417e-02 -6.006423e-02
[113,] 1.890069e-02 4.302417e-02
[114,] -4.888438e-02 1.890069e-02
[115,] -4.990640e-02 -4.888438e-02
[116,] 8.297748e-03 -4.990640e-02
[117,] 8.677885e-03 8.297748e-03
[118,] -2.573946e-02 8.677885e-03
[119,] -3.856536e-02 -2.573946e-02
[120,] 3.787728e-02 -3.856536e-02
[121,] -4.156505e-02 3.787728e-02
[122,] -1.225025e-02 -4.156505e-02
[123,] 7.424041e-02 -1.225025e-02
[124,] 9.902617e-02 7.424041e-02
[125,] 8.700091e-02 9.902617e-02
[126,] 6.294399e-02 8.700091e-02
[127,] 5.536771e-02 6.294399e-02
[128,] 5.729102e-02 5.536771e-02
[129,] 4.199472e-02 5.729102e-02
[130,] 2.244353e-02 4.199472e-02
[131,] 3.915677e-02 2.244353e-02
[132,] -2.963115e-02 3.915677e-02
[133,] 2.495647e-02 -2.963115e-02
[134,] -2.711325e-03 2.495647e-02
[135,] 2.531172e-02 -2.711325e-03
[136,] -1.680039e-02 2.531172e-02
[137,] -4.571559e-03 -1.680039e-02
[138,] 1.023166e-02 -4.571559e-03
[139,] 1.440494e-02 1.023166e-02
[140,] 7.889684e-02 1.440494e-02
[141,] 2.892316e-02 7.889684e-02
[142,] 1.118432e-02 2.892316e-02
[143,] -2.073620e-02 1.118432e-02
[144,] -9.673034e-03 -2.073620e-02
[145,] -2.927007e-02 -9.673034e-03
[146,] -6.356316e-02 -2.927007e-02
[147,] -8.547313e-02 -6.356316e-02
[148,] -6.286902e-03 -8.547313e-02
[149,] 3.166478e-04 -6.286902e-03
[150,] 1.580738e-02 3.166478e-04
[151,] -5.634029e-03 1.580738e-02
[152,] 1.095322e-01 -5.634029e-03
[153,] -5.973461e-02 1.095322e-01
[154,] 7.412073e-02 -5.973461e-02
[155,] 3.257950e-02 7.412073e-02
[156,] 9.887833e-02 3.257950e-02
[157,] 1.406885e-02 9.887833e-02
[158,] 6.847811e-02 1.406885e-02
[159,] 1.152124e-02 6.847811e-02
[160,] -1.874648e-02 1.152124e-02
[161,] -2.139630e-02 -1.874648e-02
[162,] 7.225242e-02 -2.139630e-02
[163,] -4.855794e-02 7.225242e-02
[164,] -5.625824e-02 -4.855794e-02
[165,] 5.400982e-03 -5.625824e-02
[166,] -9.024693e-02 5.400982e-03
[167,] -5.112799e-02 -9.024693e-02
[168,] -8.193670e-02 -5.112799e-02
[169,] -3.723084e-02 -8.193670e-02
[170,] 4.771475e-02 -3.723084e-02
[171,] -3.689886e-02 4.771475e-02
[172,] -1.051736e-01 -3.689886e-02
[173,] -8.907027e-02 -1.051736e-01
[174,] -1.289742e-01 -8.907027e-02
[175,] -7.500234e-02 -1.289742e-01
[176,] -7.647941e-02 -7.500234e-02
[177,] -1.075904e-02 -7.647941e-02
[178,] -1.832546e-02 -1.075904e-02
[179,] -1.783339e-02 -1.832546e-02
[180,] -8.785106e-02 -1.783339e-02
[181,] -4.260441e-02 -8.785106e-02
[182,] -9.643693e-02 -4.260441e-02
[183,] 7.425478e-03 -9.643693e-02
[184,] 3.911600e-02 7.425478e-03
[185,] 3.858395e-02 3.911600e-02
[186,] 3.226214e-02 3.858395e-02
[187,] 2.112312e-02 3.226214e-02
[188,] 3.217684e-02 2.112312e-02
[189,] 3.282109e-02 3.217684e-02
[190,] -2.089350e-02 3.282109e-02
[191,] -2.353185e-02 -2.089350e-02
[192,] 5.909980e-03 -2.353185e-02
[193,] -8.904637e-02 5.909980e-03
[194,] -1.711114e-02 -8.904637e-02
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -3.711888e-03 -4.444846e-02
2 -2.274669e-02 -3.711888e-03
3 -5.184791e-02 -2.274669e-02
4 -1.303356e-02 -5.184791e-02
5 -3.004261e-02 -1.303356e-02
6 8.286117e-02 -3.004261e-02
7 1.931696e-01 8.286117e-02
8 4.465732e-02 1.931696e-01
9 -3.286575e-02 4.465732e-02
10 -2.756609e-03 -3.286575e-02
11 1.510675e-02 -2.756609e-03
12 8.034284e-05 1.510675e-02
13 -6.980058e-02 8.034284e-05
14 -1.300558e-02 -6.980058e-02
15 4.913747e-02 -1.300558e-02
16 -3.912712e-03 4.913747e-02
17 -1.050460e-02 -3.912712e-03
18 3.803441e-02 -1.050460e-02
19 -7.861252e-03 3.803441e-02
20 4.626037e-02 -7.861252e-03
21 1.490350e-02 4.626037e-02
22 6.479613e-02 1.490350e-02
23 -1.424073e-02 6.479613e-02
24 -1.465345e-02 -1.424073e-02
25 1.712489e-02 -1.465345e-02
26 6.860326e-03 1.712489e-02
27 -6.719225e-02 6.860326e-03
28 -1.112212e-02 -6.719225e-02
29 -1.513912e-02 -1.112212e-02
30 1.629830e-02 -1.513912e-02
31 1.409465e-01 1.629830e-02
32 9.208586e-02 1.409465e-01
33 3.914520e-02 9.208586e-02
34 3.202293e-02 3.914520e-02
35 8.601405e-02 3.202293e-02
36 -3.232814e-02 8.601405e-02
37 -1.896315e-02 -3.232814e-02
38 2.322169e-02 -1.896315e-02
39 -6.523539e-03 2.322169e-02
40 -2.121215e-02 -6.523539e-03
41 2.144957e-02 -2.121215e-02
42 -9.888493e-02 2.144957e-02
43 -1.821240e-02 -9.888493e-02
44 4.733019e-02 -1.821240e-02
45 3.186202e-02 4.733019e-02
46 5.116698e-02 3.186202e-02
47 1.774137e-01 5.116698e-02
48 4.192417e-02 1.774137e-01
49 4.800806e-02 4.192417e-02
50 4.823206e-02 4.800806e-02
51 3.336574e-02 4.823206e-02
52 -5.545850e-03 3.336574e-02
53 -3.386358e-02 -5.545850e-03
54 6.049335e-03 -3.386358e-02
55 2.288520e-02 6.049335e-03
56 1.242683e-02 2.288520e-02
57 4.180620e-02 1.242683e-02
58 2.848336e-02 4.180620e-02
59 4.532089e-02 2.848336e-02
60 2.669414e-03 4.532089e-02
61 9.032165e-02 2.669414e-03
62 1.420339e-02 9.032165e-02
63 -3.305232e-02 1.420339e-02
64 -8.250061e-02 -3.305232e-02
65 -6.076084e-02 -8.250061e-02
66 -8.570378e-02 -6.076084e-02
67 -4.906117e-02 -8.570378e-02
68 -7.191868e-03 -4.906117e-02
69 -1.505567e-02 -7.191868e-03
70 -1.605124e-02 -1.505567e-02
71 -7.745728e-03 -1.605124e-02
72 6.689852e-03 -7.745728e-03
73 -3.502609e-02 6.689852e-03
74 -1.087028e-03 -3.502609e-02
75 -6.760500e-02 -1.087028e-03
76 -5.597239e-02 -6.760500e-02
77 -6.450568e-02 -5.597239e-02
78 6.608449e-02 -6.450568e-02
79 4.033968e-02 6.608449e-02
80 2.020683e-02 4.033968e-02
81 -1.964535e-03 2.020683e-02
82 4.393917e-02 -1.964535e-03
83 1.191537e-01 4.393917e-02
84 -1.515581e-02 1.191537e-01
85 3.450210e-02 -1.515581e-02
86 -6.568944e-03 3.450210e-02
87 5.700447e-02 -6.568944e-03
88 5.158803e-02 5.700447e-02
89 1.797865e-02 5.158803e-02
90 4.527665e-02 1.797865e-02
91 1.692032e-02 4.527665e-02
92 -2.617146e-02 1.692032e-02
93 -2.350701e-02 -2.617146e-02
94 -9.298022e-03 -2.350701e-02
95 -5.370974e-02 -9.298022e-03
96 -1.001076e-01 -5.370974e-02
97 -2.413658e-02 -1.001076e-01
98 -7.077257e-02 -2.413658e-02
99 -5.280696e-02 -7.077257e-02
100 1.569186e-02 -5.280696e-02
101 -2.938812e-02 1.569186e-02
102 -2.108659e-02 -2.938812e-02
103 -8.542933e-02 -2.108659e-02
104 -1.032971e-01 -8.542933e-02
105 -3.737974e-02 -1.032971e-01
106 -3.371223e-02 -3.737974e-02
107 -1.872499e-02 -3.371223e-02
108 -4.779196e-02 -1.872499e-02
109 6.494192e-02 -4.779196e-02
110 -1.890290e-02 6.494192e-02
111 -6.006423e-02 -1.890290e-02
112 4.302417e-02 -6.006423e-02
113 1.890069e-02 4.302417e-02
114 -4.888438e-02 1.890069e-02
115 -4.990640e-02 -4.888438e-02
116 8.297748e-03 -4.990640e-02
117 8.677885e-03 8.297748e-03
118 -2.573946e-02 8.677885e-03
119 -3.856536e-02 -2.573946e-02
120 3.787728e-02 -3.856536e-02
121 -4.156505e-02 3.787728e-02
122 -1.225025e-02 -4.156505e-02
123 7.424041e-02 -1.225025e-02
124 9.902617e-02 7.424041e-02
125 8.700091e-02 9.902617e-02
126 6.294399e-02 8.700091e-02
127 5.536771e-02 6.294399e-02
128 5.729102e-02 5.536771e-02
129 4.199472e-02 5.729102e-02
130 2.244353e-02 4.199472e-02
131 3.915677e-02 2.244353e-02
132 -2.963115e-02 3.915677e-02
133 2.495647e-02 -2.963115e-02
134 -2.711325e-03 2.495647e-02
135 2.531172e-02 -2.711325e-03
136 -1.680039e-02 2.531172e-02
137 -4.571559e-03 -1.680039e-02
138 1.023166e-02 -4.571559e-03
139 1.440494e-02 1.023166e-02
140 7.889684e-02 1.440494e-02
141 2.892316e-02 7.889684e-02
142 1.118432e-02 2.892316e-02
143 -2.073620e-02 1.118432e-02
144 -9.673034e-03 -2.073620e-02
145 -2.927007e-02 -9.673034e-03
146 -6.356316e-02 -2.927007e-02
147 -8.547313e-02 -6.356316e-02
148 -6.286902e-03 -8.547313e-02
149 3.166478e-04 -6.286902e-03
150 1.580738e-02 3.166478e-04
151 -5.634029e-03 1.580738e-02
152 1.095322e-01 -5.634029e-03
153 -5.973461e-02 1.095322e-01
154 7.412073e-02 -5.973461e-02
155 3.257950e-02 7.412073e-02
156 9.887833e-02 3.257950e-02
157 1.406885e-02 9.887833e-02
158 6.847811e-02 1.406885e-02
159 1.152124e-02 6.847811e-02
160 -1.874648e-02 1.152124e-02
161 -2.139630e-02 -1.874648e-02
162 7.225242e-02 -2.139630e-02
163 -4.855794e-02 7.225242e-02
164 -5.625824e-02 -4.855794e-02
165 5.400982e-03 -5.625824e-02
166 -9.024693e-02 5.400982e-03
167 -5.112799e-02 -9.024693e-02
168 -8.193670e-02 -5.112799e-02
169 -3.723084e-02 -8.193670e-02
170 4.771475e-02 -3.723084e-02
171 -3.689886e-02 4.771475e-02
172 -1.051736e-01 -3.689886e-02
173 -8.907027e-02 -1.051736e-01
174 -1.289742e-01 -8.907027e-02
175 -7.500234e-02 -1.289742e-01
176 -7.647941e-02 -7.500234e-02
177 -1.075904e-02 -7.647941e-02
178 -1.832546e-02 -1.075904e-02
179 -1.783339e-02 -1.832546e-02
180 -8.785106e-02 -1.783339e-02
181 -4.260441e-02 -8.785106e-02
182 -9.643693e-02 -4.260441e-02
183 7.425478e-03 -9.643693e-02
184 3.911600e-02 7.425478e-03
185 3.858395e-02 3.911600e-02
186 3.226214e-02 3.858395e-02
187 2.112312e-02 3.226214e-02
188 3.217684e-02 2.112312e-02
189 3.282109e-02 3.217684e-02
190 -2.089350e-02 3.282109e-02
191 -2.353185e-02 -2.089350e-02
192 5.909980e-03 -2.353185e-02
193 -8.904637e-02 5.909980e-03
194 -1.711114e-02 -8.904637e-02
> 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/7axfi1386790450.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/87gml1386790450.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/9euam1386790450.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/wessaorg/rcomp/tmp/10c03d1386790450.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/wessaorg/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11xs921386790450.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/12tmdo1386790450.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/13qhkl1386790450.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/149a1l1386790450.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/15y4cw1386790450.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/16slah1386790450.tab")
+ }
>
> try(system("convert tmp/1zouh1386790450.ps tmp/1zouh1386790450.png",intern=TRUE))
character(0)
> try(system("convert tmp/2jevi1386790450.ps tmp/2jevi1386790450.png",intern=TRUE))
character(0)
> try(system("convert tmp/39udr1386790450.ps tmp/39udr1386790450.png",intern=TRUE))
character(0)
> try(system("convert tmp/468981386790450.ps tmp/468981386790450.png",intern=TRUE))
character(0)
> try(system("convert tmp/5rtzp1386790450.ps tmp/5rtzp1386790450.png",intern=TRUE))
character(0)
> try(system("convert tmp/6v1g81386790450.ps tmp/6v1g81386790450.png",intern=TRUE))
character(0)
> try(system("convert tmp/7axfi1386790450.ps tmp/7axfi1386790450.png",intern=TRUE))
character(0)
> try(system("convert tmp/87gml1386790450.ps tmp/87gml1386790450.png",intern=TRUE))
character(0)
> try(system("convert tmp/9euam1386790450.ps tmp/9euam1386790450.png",intern=TRUE))
character(0)
> try(system("convert tmp/10c03d1386790450.ps tmp/10c03d1386790450.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
33.634 5.934 40.427