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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+ ,0.00619
+ ,0.02551
+ ,0.237
+ ,0.01321
+ ,0.01574
+ ,0.02148
+ ,0.03964
+ ,0.00611
+ ,23.133
+ ,0.352396
+ ,0.75932
+ ,-6.261446
+ ,0.183218
+ ,2.264226
+ ,0.144105
+ ,1
+ ,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
+ ,0.408598
+ ,0.768845
+ ,-5.704053
+ ,0.216204
+ ,2.679185
+ ,0.19771
+ ,1
+ ,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
+ ,0.329577
+ ,0.75718
+ ,-6.27717
+ ,0.109397
+ ,2.209021
+ ,0.156368
+ ,1
+ ,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
+ ,0.603515
+ ,0.669565
+ ,-5.61907
+ ,0.191576
+ ,2.027228
+ ,0.215724
+ ,0
+ ,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
+ ,0.663842
+ ,0.656516
+ ,-5.198864
+ ,0.206768
+ ,2.120412
+ ,0.252404
+ ,0
+ ,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
+ ,0.598515
+ ,0.654331
+ ,-5.592584
+ ,0.133917
+ ,2.058658
+ ,0.214346
+ ,0
+ ,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
+ ,0.566424
+ ,0.667654
+ ,-6.431119
+ ,0.15331
+ ,2.161936
+ ,0.120605
+ ,0
+ ,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
+ ,0.528485
+ ,0.663884
+ ,-6.359018
+ ,0.116636
+ ,2.152083
+ ,0.138868
+ ,0
+ ,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
+ ,0.555303
+ ,0.659132
+ ,-6.710219
+ ,0.149694
+ ,1.91399
+ ,0.121777
+ ,0
+ ,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
+ ,0.508479
+ ,0.683761
+ ,-6.934474
+ ,0.15989
+ ,2.316346
+ ,0.112838
+ ,0
+ ,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
+ ,0.448439
+ ,0.657899
+ ,-6.538586
+ ,0.121952
+ ,2.657476
+ ,0.13305
+ ,0
+ ,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
+ ,0.431674
+ ,0.683244
+ ,-6.195325
+ ,0.129303
+ ,2.784312
+ ,0.168895
+ ,0
+ ,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
+ ,0.407567
+ ,0.655683
+ ,-6.787197
+ ,0.158453
+ ,2.679772
+ ,0.131728
+ ,0
+ ,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
+ ,0.451221
+ ,0.643956
+ ,-6.744577
+ ,0.207454
+ ,2.138608
+ ,0.123306
+ ,0
+ ,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
+ ,0.462803
+ ,0.664357
+ ,-5.724056
+ ,0.190667
+ ,2.555477
+ ,0.148569
+ ,0)
+ ,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'
+ ,'RPDE'
+ ,'DFA'
+ ,'spread1'
+ ,'spread2'
+ ,'D2'
+ ,'PPE'
+ ,'status')
+ ,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','RPDE','DFA','spread1','spread2','D2','PPE','status'),1:195))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = 'No Linear Trend'
> par2 = 'Do not include Seasonal Dummies'
> par1 = '1'
> par3 <- 'No Linear Trend'
> par2 <- 'Do not include Seasonal Dummies'
> par1 <- '1'
> #'GNU S' R Code compiled by R2WASP v. 1.2.327 ()
> #Author: root
> #To cite this work: Wessa P., (2013), Multiple Regression (v1.0.29) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_multipleregression.wasp/
> #Source of accompanying publication: Office for Research, Development, and Education
> #
> library(lattice)
> library(lmtest)
Loading required package: zoo
Attaching package: 'zoo'
The following objects are masked from 'package:base':
as.Date, as.Date.numeric
> n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
> par1 <- as.numeric(par1)
> x <- t(y)
> k <- length(x[1,])
> n <- length(x[,1])
> x1 <- cbind(x[,par1], x[,1:k!=par1])
> mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
> colnames(x1) <- mycolnames #colnames(x)[par1]
> x <- x1
> if (par3 == 'First Differences'){
+ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
+ for (i in 1:n-1) {
+ for (j in 1:k) {
+ x2[i,j] <- x[i+1,j] - x[i,j]
+ }
+ }
+ x <- x2
+ }
> if (par2 == 'Include Monthly Dummies'){
+ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
+ for (i in 1:11){
+ x2[seq(i,n,12),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> if (par2 == 'Include Quarterly Dummies'){
+ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
+ for (i in 1:3){
+ x2[seq(i,n,4),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> k <- length(x[1,])
> if (par3 == 'Linear Trend'){
+ x <- cbind(x, c(1:n))
+ colnames(x)[k+1] <- 't'
+ }
> x
MDVP:Fo(Hz) MDVP:Fhi(Hz) MDVP:Flo(Hz) MDVP:Jitter(%) MDVP:Jitter(Abs)
1 119.992 157.302 74.997 0.00784 7.0e-05
2 122.400 148.650 113.819 0.00968 8.0e-05
3 116.682 131.111 111.555 0.01050 9.0e-05
4 116.676 137.871 111.366 0.00997 9.0e-05
5 116.014 141.781 110.655 0.01284 1.1e-04
6 120.552 131.162 113.787 0.00968 8.0e-05
7 120.267 137.244 114.820 0.00333 3.0e-05
8 107.332 113.840 104.315 0.00290 3.0e-05
9 95.730 132.068 91.754 0.00551 6.0e-05
10 95.056 120.103 91.226 0.00532 6.0e-05
11 88.333 112.240 84.072 0.00505 6.0e-05
12 91.904 115.871 86.292 0.00540 6.0e-05
13 136.926 159.866 131.276 0.00293 2.0e-05
14 139.173 179.139 76.556 0.00390 3.0e-05
15 152.845 163.305 75.836 0.00294 2.0e-05
16 142.167 217.455 83.159 0.00369 3.0e-05
17 144.188 349.259 82.764 0.00544 4.0e-05
18 168.778 232.181 75.603 0.00718 4.0e-05
19 153.046 175.829 68.623 0.00742 5.0e-05
20 156.405 189.398 142.822 0.00768 5.0e-05
21 153.848 165.738 65.782 0.00840 5.0e-05
22 153.880 172.860 78.128 0.00480 3.0e-05
23 167.930 193.221 79.068 0.00442 3.0e-05
24 173.917 192.735 86.180 0.00476 3.0e-05
25 163.656 200.841 76.779 0.00742 5.0e-05
26 104.400 206.002 77.968 0.00633 6.0e-05
27 171.041 208.313 75.501 0.00455 3.0e-05
28 146.845 208.701 81.737 0.00496 3.0e-05
29 155.358 227.383 80.055 0.00310 2.0e-05
30 162.568 198.346 77.630 0.00502 3.0e-05
31 197.076 206.896 192.055 0.00289 1.0e-05
32 199.228 209.512 192.091 0.00241 1.0e-05
33 198.383 215.203 193.104 0.00212 1.0e-05
34 202.266 211.604 197.079 0.00180 9.0e-06
35 203.184 211.526 196.160 0.00178 9.0e-06
36 201.464 210.565 195.708 0.00198 1.0e-05
37 177.876 192.921 168.013 0.00411 2.0e-05
38 176.170 185.604 163.564 0.00369 2.0e-05
39 180.198 201.249 175.456 0.00284 2.0e-05
40 187.733 202.324 173.015 0.00316 2.0e-05
41 186.163 197.724 177.584 0.00298 2.0e-05
42 184.055 196.537 166.977 0.00258 1.0e-05
43 237.226 247.326 225.227 0.00298 1.0e-05
44 241.404 248.834 232.483 0.00281 1.0e-05
45 243.439 250.912 232.435 0.00210 9.0e-06
46 242.852 255.034 227.911 0.00225 9.0e-06
47 245.510 262.090 231.848 0.00235 1.0e-05
48 252.455 261.487 182.786 0.00185 7.0e-06
49 122.188 128.611 115.765 0.00524 4.0e-05
50 122.964 130.049 114.676 0.00428 3.0e-05
51 124.445 135.069 117.495 0.00431 3.0e-05
52 126.344 134.231 112.773 0.00448 4.0e-05
53 128.001 138.052 122.080 0.00436 3.0e-05
54 129.336 139.867 118.604 0.00490 4.0e-05
55 108.807 134.656 102.874 0.00761 7.0e-05
56 109.860 126.358 104.437 0.00874 8.0e-05
57 110.417 131.067 103.370 0.00784 7.0e-05
58 117.274 129.916 110.402 0.00752 6.0e-05
59 116.879 131.897 108.153 0.00788 7.0e-05
60 114.847 271.314 104.680 0.00867 8.0e-05
61 209.144 237.494 109.379 0.00282 1.0e-05
62 223.365 238.987 98.664 0.00264 1.0e-05
63 222.236 231.345 205.495 0.00266 1.0e-05
64 228.832 234.619 223.634 0.00296 1.0e-05
65 229.401 252.221 221.156 0.00205 9.0e-06
66 228.969 239.541 113.201 0.00238 1.0e-05
67 140.341 159.774 67.021 0.00817 6.0e-05
68 136.969 166.607 66.004 0.00923 7.0e-05
69 143.533 162.215 65.809 0.01101 8.0e-05
70 148.090 162.824 67.343 0.00762 5.0e-05
71 142.729 162.408 65.476 0.00831 6.0e-05
72 136.358 176.595 65.750 0.00971 7.0e-05
73 120.080 139.710 111.208 0.00405 3.0e-05
74 112.014 588.518 107.024 0.00533 5.0e-05
75 110.793 128.101 107.316 0.00494 4.0e-05
76 110.707 122.611 105.007 0.00516 5.0e-05
77 112.876 148.826 106.981 0.00500 4.0e-05
78 110.568 125.394 106.821 0.00462 4.0e-05
79 95.385 102.145 90.264 0.00608 6.0e-05
80 100.770 115.697 85.545 0.01038 1.0e-04
81 96.106 108.664 84.510 0.00694 7.0e-05
82 95.605 107.715 87.549 0.00702 7.0e-05
83 100.960 110.019 95.628 0.00606 6.0e-05
84 98.804 102.305 87.804 0.00432 4.0e-05
85 176.858 205.560 75.344 0.00747 4.0e-05
86 180.978 200.125 155.495 0.00406 2.0e-05
87 178.222 202.450 141.047 0.00321 2.0e-05
88 176.281 227.381 125.610 0.00520 3.0e-05
89 173.898 211.350 74.677 0.00448 3.0e-05
90 179.711 225.930 144.878 0.00709 4.0e-05
91 166.605 206.008 78.032 0.00742 4.0e-05
92 151.955 163.335 147.226 0.00419 3.0e-05
93 148.272 164.989 142.299 0.00459 3.0e-05
94 152.125 161.469 76.596 0.00382 3.0e-05
95 157.821 172.975 68.401 0.00358 2.0e-05
96 157.447 163.267 149.605 0.00369 2.0e-05
97 159.116 168.913 144.811 0.00342 2.0e-05
98 125.036 143.946 116.187 0.01280 1.0e-04
99 125.791 140.557 96.206 0.01378 1.1e-04
100 126.512 141.756 99.770 0.01936 1.5e-04
101 125.641 141.068 116.346 0.03316 2.6e-04
102 128.451 150.449 75.632 0.01551 1.2e-04
103 139.224 586.567 66.157 0.03011 2.2e-04
104 150.258 154.609 75.349 0.00248 2.0e-05
105 154.003 160.267 128.621 0.00183 1.0e-05
106 149.689 160.368 133.608 0.00257 2.0e-05
107 155.078 163.736 144.148 0.00168 1.0e-05
108 151.884 157.765 133.751 0.00258 2.0e-05
109 151.989 157.339 132.857 0.00174 1.0e-05
110 193.030 208.900 80.297 0.00766 4.0e-05
111 200.714 223.982 89.686 0.00621 3.0e-05
112 208.519 220.315 199.020 0.00609 3.0e-05
113 204.664 221.300 189.621 0.00841 4.0e-05
114 210.141 232.706 185.258 0.00534 3.0e-05
115 206.327 226.355 92.020 0.00495 2.0e-05
116 151.872 492.892 69.085 0.00856 6.0e-05
117 158.219 442.557 71.948 0.00476 3.0e-05
118 170.756 450.247 79.032 0.00555 3.0e-05
119 178.285 442.824 82.063 0.00462 3.0e-05
120 217.116 233.481 93.978 0.00404 2.0e-05
121 128.940 479.697 88.251 0.00581 5.0e-05
122 176.824 215.293 83.961 0.00460 3.0e-05
123 138.190 203.522 83.340 0.00704 5.0e-05
124 182.018 197.173 79.187 0.00842 5.0e-05
125 156.239 195.107 79.820 0.00694 4.0e-05
126 145.174 198.109 80.637 0.00733 5.0e-05
127 138.145 197.238 81.114 0.00544 4.0e-05
128 166.888 198.966 79.512 0.00638 4.0e-05
129 119.031 127.533 109.216 0.00440 4.0e-05
130 120.078 126.632 105.667 0.00270 2.0e-05
131 120.289 128.143 100.209 0.00492 4.0e-05
132 120.256 125.306 104.773 0.00407 3.0e-05
133 119.056 125.213 86.795 0.00346 3.0e-05
134 118.747 123.723 109.836 0.00331 3.0e-05
135 106.516 112.777 93.105 0.00589 6.0e-05
136 110.453 127.611 105.554 0.00494 4.0e-05
137 113.400 133.344 107.816 0.00451 4.0e-05
138 113.166 130.270 100.673 0.00502 4.0e-05
139 112.239 126.609 104.095 0.00472 4.0e-05
140 116.150 131.731 109.815 0.00381 3.0e-05
141 170.368 268.796 79.543 0.00571 3.0e-05
142 208.083 253.792 91.802 0.00757 4.0e-05
143 198.458 219.290 148.691 0.00376 2.0e-05
144 202.805 231.508 86.232 0.00370 2.0e-05
145 202.544 241.350 164.168 0.00254 1.0e-05
146 223.361 263.872 87.638 0.00352 2.0e-05
147 169.774 191.759 151.451 0.01568 9.0e-05
148 183.520 216.814 161.340 0.01466 8.0e-05
149 188.620 216.302 165.982 0.01719 9.0e-05
150 202.632 565.740 177.258 0.01627 8.0e-05
151 186.695 211.961 149.442 0.01872 1.0e-04
152 192.818 224.429 168.793 0.03107 1.6e-04
153 198.116 233.099 174.478 0.02714 1.4e-04
154 121.345 139.644 98.250 0.00684 6.0e-05
155 119.100 128.442 88.833 0.00692 6.0e-05
156 117.870 127.349 95.654 0.00647 5.0e-05
157 122.336 142.369 94.794 0.00727 6.0e-05
158 117.963 134.209 100.757 0.01813 1.5e-04
159 126.144 154.284 97.543 0.00975 8.0e-05
160 127.930 138.752 112.173 0.00605 5.0e-05
161 114.238 124.393 77.022 0.00581 5.0e-05
162 115.322 135.738 107.802 0.00619 5.0e-05
163 114.554 126.778 91.121 0.00651 6.0e-05
164 112.150 131.669 97.527 0.00519 5.0e-05
165 102.273 142.830 85.902 0.00907 9.0e-05
166 236.200 244.663 102.137 0.00277 1.0e-05
167 237.323 243.709 229.256 0.00303 1.0e-05
168 260.105 264.919 237.303 0.00339 1.0e-05
169 197.569 217.627 90.794 0.00803 4.0e-05
170 240.301 245.135 219.783 0.00517 2.0e-05
171 244.990 272.210 239.170 0.00451 2.0e-05
172 112.547 133.374 105.715 0.00355 3.0e-05
173 110.739 113.597 100.139 0.00356 3.0e-05
174 113.715 116.443 96.913 0.00349 3.0e-05
175 117.004 144.466 99.923 0.00353 3.0e-05
176 115.380 123.109 108.634 0.00332 3.0e-05
177 116.388 129.038 108.970 0.00346 3.0e-05
178 151.737 190.204 129.859 0.00314 2.0e-05
179 148.790 158.359 138.990 0.00309 2.0e-05
180 148.143 155.982 135.041 0.00392 3.0e-05
181 150.440 163.441 144.736 0.00396 3.0e-05
182 148.462 161.078 141.998 0.00397 3.0e-05
183 149.818 163.417 144.786 0.00336 2.0e-05
184 117.226 123.925 106.656 0.00417 4.0e-05
185 116.848 217.552 99.503 0.00531 5.0e-05
186 116.286 177.291 96.983 0.00314 3.0e-05
187 116.556 592.030 86.228 0.00496 4.0e-05
188 116.342 581.289 94.246 0.00267 2.0e-05
189 114.563 119.167 86.647 0.00327 3.0e-05
190 201.774 262.707 78.228 0.00694 3.0e-05
191 174.188 230.978 94.261 0.00459 3.0e-05
192 209.516 253.017 89.488 0.00564 3.0e-05
193 174.688 240.005 74.287 0.01360 8.0e-05
194 198.764 396.961 74.904 0.00740 4.0e-05
195 214.289 260.277 77.973 0.00567 3.0e-05
MDVP:RAP MDVP:PPQ Jitter:DDP MDVP:Shimmer MDVP:Shimmer(dB) Shimmer:APQ3
1 0.00370 0.00554 0.01109 0.04374 0.426 0.02182
2 0.00465 0.00696 0.01394 0.06134 0.626 0.03134
3 0.00544 0.00781 0.01633 0.05233 0.482 0.02757
4 0.00502 0.00698 0.01505 0.05492 0.517 0.02924
5 0.00655 0.00908 0.01966 0.06425 0.584 0.03490
6 0.00463 0.00750 0.01388 0.04701 0.456 0.02328
7 0.00155 0.00202 0.00466 0.01608 0.140 0.00779
8 0.00144 0.00182 0.00431 0.01567 0.134 0.00829
9 0.00293 0.00332 0.00880 0.02093 0.191 0.01073
10 0.00268 0.00332 0.00803 0.02838 0.255 0.01441
11 0.00254 0.00330 0.00763 0.02143 0.197 0.01079
12 0.00281 0.00336 0.00844 0.02752 0.249 0.01424
13 0.00118 0.00153 0.00355 0.01259 0.112 0.00656
14 0.00165 0.00208 0.00496 0.01642 0.154 0.00728
15 0.00121 0.00149 0.00364 0.01828 0.158 0.01064
16 0.00157 0.00203 0.00471 0.01503 0.126 0.00772
17 0.00211 0.00292 0.00632 0.02047 0.192 0.00969
18 0.00284 0.00387 0.00853 0.03327 0.348 0.01441
19 0.00364 0.00432 0.01092 0.05517 0.542 0.02471
20 0.00372 0.00399 0.01116 0.03995 0.348 0.01721
21 0.00428 0.00450 0.01285 0.03810 0.328 0.01667
22 0.00232 0.00267 0.00696 0.04137 0.370 0.02021
23 0.00220 0.00247 0.00661 0.04351 0.377 0.02228
24 0.00221 0.00258 0.00663 0.04192 0.364 0.02187
25 0.00380 0.00390 0.01140 0.01659 0.164 0.00738
26 0.00316 0.00375 0.00948 0.03767 0.381 0.01732
27 0.00250 0.00234 0.00750 0.01966 0.186 0.00889
28 0.00250 0.00275 0.00749 0.01919 0.198 0.00883
29 0.00159 0.00176 0.00476 0.01718 0.161 0.00769
30 0.00280 0.00253 0.00841 0.01791 0.168 0.00793
31 0.00166 0.00168 0.00498 0.01098 0.097 0.00563
32 0.00134 0.00138 0.00402 0.01015 0.089 0.00504
33 0.00113 0.00135 0.00339 0.01263 0.111 0.00640
34 0.00093 0.00107 0.00278 0.00954 0.085 0.00469
35 0.00094 0.00106 0.00283 0.00958 0.085 0.00468
36 0.00105 0.00115 0.00314 0.01194 0.107 0.00586
37 0.00233 0.00241 0.00700 0.02126 0.189 0.01154
38 0.00205 0.00218 0.00616 0.01851 0.168 0.00938
39 0.00153 0.00166 0.00459 0.01444 0.131 0.00726
40 0.00168 0.00182 0.00504 0.01663 0.151 0.00829
41 0.00165 0.00175 0.00496 0.01495 0.135 0.00774
42 0.00134 0.00147 0.00403 0.01463 0.132 0.00742
43 0.00169 0.00182 0.00507 0.01752 0.164 0.01035
44 0.00157 0.00173 0.00470 0.01760 0.154 0.01006
45 0.00109 0.00137 0.00327 0.01419 0.126 0.00777
46 0.00117 0.00139 0.00350 0.01494 0.134 0.00847
47 0.00127 0.00148 0.00380 0.01608 0.141 0.00906
48 0.00092 0.00113 0.00276 0.01152 0.103 0.00614
49 0.00169 0.00203 0.00507 0.01613 0.143 0.00855
50 0.00124 0.00155 0.00373 0.01681 0.154 0.00930
51 0.00141 0.00167 0.00422 0.02184 0.197 0.01241
52 0.00131 0.00169 0.00393 0.02033 0.185 0.01143
53 0.00137 0.00166 0.00411 0.02297 0.210 0.01323
54 0.00165 0.00183 0.00495 0.02498 0.228 0.01396
55 0.00349 0.00486 0.01046 0.02719 0.255 0.01483
56 0.00398 0.00539 0.01193 0.03209 0.307 0.01789
57 0.00352 0.00514 0.01056 0.03715 0.334 0.02032
58 0.00299 0.00469 0.00898 0.02293 0.221 0.01189
59 0.00334 0.00493 0.01003 0.02645 0.265 0.01394
60 0.00373 0.00520 0.01120 0.03225 0.350 0.01805
61 0.00147 0.00152 0.00442 0.01861 0.170 0.00975
62 0.00154 0.00151 0.00461 0.01906 0.165 0.01013
63 0.00152 0.00144 0.00457 0.01643 0.145 0.00867
64 0.00175 0.00155 0.00526 0.01644 0.145 0.00882
65 0.00114 0.00113 0.00342 0.01457 0.129 0.00769
66 0.00136 0.00140 0.00408 0.01745 0.154 0.00942
67 0.00430 0.00440 0.01289 0.03198 0.313 0.01830
68 0.00507 0.00463 0.01520 0.03111 0.308 0.01638
69 0.00647 0.00467 0.01941 0.05384 0.478 0.03152
70 0.00467 0.00354 0.01400 0.05428 0.497 0.03357
71 0.00469 0.00419 0.01407 0.03485 0.365 0.01868
72 0.00534 0.00478 0.01601 0.04978 0.483 0.02749
73 0.00180 0.00220 0.00540 0.01706 0.152 0.00974
74 0.00268 0.00329 0.00805 0.02448 0.226 0.01373
75 0.00260 0.00283 0.00780 0.02442 0.216 0.01432
76 0.00277 0.00289 0.00831 0.02215 0.206 0.01284
77 0.00270 0.00289 0.00810 0.03999 0.350 0.02413
78 0.00226 0.00280 0.00677 0.02199 0.197 0.01284
79 0.00331 0.00332 0.00994 0.03202 0.263 0.01803
80 0.00622 0.00576 0.01865 0.03121 0.361 0.01773
81 0.00389 0.00415 0.01168 0.04024 0.364 0.02266
82 0.00428 0.00371 0.01283 0.03156 0.296 0.01792
83 0.00351 0.00348 0.01053 0.02427 0.216 0.01371
84 0.00247 0.00258 0.00742 0.02223 0.202 0.01277
85 0.00418 0.00420 0.01254 0.04795 0.435 0.02679
86 0.00220 0.00244 0.00659 0.03852 0.331 0.02107
87 0.00163 0.00194 0.00488 0.03759 0.327 0.02073
88 0.00287 0.00312 0.00862 0.06511 0.580 0.03671
89 0.00237 0.00254 0.00710 0.06727 0.650 0.03788
90 0.00391 0.00419 0.01172 0.04313 0.442 0.02297
91 0.00387 0.00453 0.01161 0.06640 0.634 0.03650
92 0.00224 0.00227 0.00672 0.07959 0.772 0.04421
93 0.00250 0.00256 0.00750 0.04190 0.383 0.02383
94 0.00191 0.00226 0.00574 0.05925 0.637 0.03341
95 0.00196 0.00196 0.00587 0.03716 0.307 0.02062
96 0.00201 0.00197 0.00602 0.03272 0.283 0.01813
97 0.00178 0.00184 0.00535 0.03381 0.307 0.01806
98 0.00743 0.00623 0.02228 0.03886 0.342 0.02135
99 0.00826 0.00655 0.02478 0.04689 0.422 0.02542
100 0.01159 0.00990 0.03476 0.06734 0.659 0.03611
101 0.02144 0.01522 0.06433 0.09178 0.891 0.05358
102 0.00905 0.00909 0.02716 0.06170 0.584 0.03223
103 0.01854 0.01628 0.05563 0.09419 0.930 0.05551
104 0.00105 0.00136 0.00315 0.01131 0.107 0.00522
105 0.00076 0.00100 0.00229 0.01030 0.094 0.00469
106 0.00116 0.00134 0.00349 0.01346 0.126 0.00660
107 0.00068 0.00092 0.00204 0.01064 0.097 0.00522
108 0.00115 0.00122 0.00346 0.01450 0.137 0.00633
109 0.00075 0.00096 0.00225 0.01024 0.093 0.00455
110 0.00450 0.00389 0.01351 0.03044 0.275 0.01771
111 0.00371 0.00337 0.01112 0.02286 0.207 0.01192
112 0.00368 0.00339 0.01105 0.01761 0.155 0.00952
113 0.00502 0.00485 0.01506 0.02378 0.210 0.01277
114 0.00321 0.00280 0.00964 0.01680 0.149 0.00861
115 0.00302 0.00246 0.00905 0.02105 0.209 0.01107
116 0.00404 0.00385 0.01211 0.01843 0.235 0.00796
117 0.00214 0.00207 0.00642 0.01458 0.148 0.00606
118 0.00244 0.00261 0.00731 0.01725 0.175 0.00757
119 0.00157 0.00194 0.00472 0.01279 0.129 0.00617
120 0.00127 0.00128 0.00381 0.01299 0.124 0.00679
121 0.00241 0.00314 0.00723 0.02008 0.221 0.00849
122 0.00209 0.00221 0.00628 0.01169 0.117 0.00534
123 0.00406 0.00398 0.01218 0.04479 0.441 0.02587
124 0.00506 0.00449 0.01517 0.02503 0.231 0.01372
125 0.00403 0.00395 0.01209 0.02343 0.224 0.01289
126 0.00414 0.00422 0.01242 0.02362 0.233 0.01235
127 0.00294 0.00327 0.00883 0.02791 0.246 0.01484
128 0.00368 0.00351 0.01104 0.02857 0.257 0.01547
129 0.00214 0.00192 0.00641 0.01033 0.098 0.00538
130 0.00116 0.00135 0.00349 0.01022 0.090 0.00476
131 0.00269 0.00238 0.00808 0.01412 0.125 0.00703
132 0.00224 0.00205 0.00671 0.01516 0.138 0.00721
133 0.00169 0.00170 0.00508 0.01201 0.106 0.00633
134 0.00168 0.00171 0.00504 0.01043 0.099 0.00490
135 0.00291 0.00319 0.00873 0.04932 0.441 0.02683
136 0.00244 0.00315 0.00731 0.04128 0.379 0.02229
137 0.00219 0.00283 0.00658 0.04879 0.431 0.02385
138 0.00257 0.00312 0.00772 0.05279 0.476 0.02896
139 0.00238 0.00290 0.00715 0.05643 0.517 0.03070
140 0.00181 0.00232 0.00542 0.03026 0.267 0.01514
141 0.00232 0.00269 0.00696 0.03273 0.281 0.01713
142 0.00428 0.00428 0.01285 0.06725 0.571 0.04016
143 0.00182 0.00215 0.00546 0.03527 0.297 0.02055
144 0.00189 0.00211 0.00568 0.01997 0.180 0.01117
145 0.00100 0.00133 0.00301 0.02662 0.228 0.01475
146 0.00169 0.00188 0.00506 0.02536 0.225 0.01379
147 0.00863 0.00946 0.02589 0.08143 0.821 0.03804
148 0.00849 0.00819 0.02546 0.06050 0.618 0.02865
149 0.00996 0.01027 0.02987 0.07118 0.722 0.03474
150 0.00919 0.00963 0.02756 0.07170 0.833 0.03515
151 0.01075 0.01154 0.03225 0.05830 0.784 0.02699
152 0.01800 0.01958 0.05401 0.11908 1.302 0.05647
153 0.01568 0.01699 0.04705 0.08684 1.018 0.04284
154 0.00388 0.00332 0.01164 0.02534 0.241 0.01340
155 0.00393 0.00300 0.01179 0.02682 0.236 0.01484
156 0.00356 0.00300 0.01067 0.03087 0.276 0.01659
157 0.00415 0.00339 0.01246 0.02293 0.223 0.01205
158 0.01117 0.00718 0.03351 0.04912 0.438 0.02610
159 0.00593 0.00454 0.01778 0.02852 0.266 0.01500
160 0.00321 0.00318 0.00962 0.03235 0.339 0.01360
161 0.00299 0.00316 0.00896 0.04009 0.406 0.01579
162 0.00352 0.00329 0.01057 0.03273 0.325 0.01644
163 0.00366 0.00340 0.01097 0.03658 0.369 0.01864
164 0.00291 0.00284 0.00873 0.01756 0.155 0.00967
165 0.00493 0.00461 0.01480 0.02814 0.272 0.01579
166 0.00154 0.00153 0.00462 0.02448 0.217 0.01410
167 0.00173 0.00159 0.00519 0.01242 0.116 0.00696
168 0.00205 0.00186 0.00616 0.02030 0.197 0.01186
169 0.00490 0.00448 0.01470 0.02177 0.189 0.01279
170 0.00316 0.00283 0.00949 0.02018 0.212 0.01176
171 0.00279 0.00237 0.00837 0.01897 0.181 0.01084
172 0.00166 0.00190 0.00499 0.01358 0.129 0.00664
173 0.00170 0.00200 0.00510 0.01484 0.133 0.00754
174 0.00171 0.00203 0.00514 0.01472 0.133 0.00748
175 0.00176 0.00218 0.00528 0.01657 0.145 0.00881
176 0.00160 0.00199 0.00480 0.01503 0.137 0.00812
177 0.00169 0.00213 0.00507 0.01725 0.155 0.00874
178 0.00135 0.00162 0.00406 0.01469 0.132 0.00728
179 0.00152 0.00186 0.00456 0.01574 0.142 0.00839
180 0.00204 0.00231 0.00612 0.01450 0.131 0.00725
181 0.00206 0.00233 0.00619 0.02551 0.237 0.01321
182 0.00202 0.00235 0.00605 0.01831 0.163 0.00950
183 0.00174 0.00198 0.00521 0.02145 0.198 0.01155
184 0.00186 0.00270 0.00558 0.01909 0.171 0.00864
185 0.00260 0.00346 0.00780 0.01795 0.163 0.00810
186 0.00134 0.00192 0.00403 0.01564 0.136 0.00667
187 0.00254 0.00263 0.00762 0.01660 0.154 0.00820
188 0.00115 0.00148 0.00345 0.01300 0.117 0.00631
189 0.00146 0.00184 0.00439 0.01185 0.106 0.00557
190 0.00412 0.00396 0.01235 0.02574 0.255 0.01454
191 0.00263 0.00259 0.00790 0.04087 0.405 0.02336
192 0.00331 0.00292 0.00994 0.02751 0.263 0.01604
193 0.00624 0.00564 0.01873 0.02308 0.256 0.01268
194 0.00370 0.00390 0.01109 0.02296 0.241 0.01265
195 0.00295 0.00317 0.00885 0.01884 0.190 0.01026
Shimmer:APQ5 MDVP:APQ Shimmer:DDA NHR HNR RPDE DFA
1 0.03130 0.02971 0.06545 0.02211 21.033 0.414783 0.815285
2 0.04518 0.04368 0.09403 0.01929 19.085 0.458359 0.819521
3 0.03858 0.03590 0.08270 0.01309 20.651 0.429895 0.825288
4 0.04005 0.03772 0.08771 0.01353 20.644 0.434969 0.819235
5 0.04825 0.04465 0.10470 0.01767 19.649 0.417356 0.823484
6 0.03526 0.03243 0.06985 0.01222 21.378 0.415564 0.825069
7 0.00937 0.01351 0.02337 0.00607 24.886 0.596040 0.764112
8 0.00946 0.01256 0.02487 0.00344 26.892 0.637420 0.763262
9 0.01277 0.01717 0.03218 0.01070 21.812 0.615551 0.773587
10 0.01725 0.02444 0.04324 0.01022 21.862 0.547037 0.798463
11 0.01342 0.01892 0.03237 0.01166 21.118 0.611137 0.776156
12 0.01641 0.02214 0.04272 0.01141 21.414 0.583390 0.792520
13 0.00717 0.01140 0.01968 0.00581 25.703 0.460600 0.646846
14 0.00932 0.01797 0.02184 0.01041 24.889 0.430166 0.665833
15 0.00972 0.01246 0.03191 0.00609 24.922 0.474791 0.654027
16 0.00888 0.01359 0.02316 0.00839 25.175 0.565924 0.658245
17 0.01200 0.02074 0.02908 0.01859 22.333 0.567380 0.644692
18 0.01893 0.03430 0.04322 0.02919 20.376 0.631099 0.605417
19 0.03572 0.05767 0.07413 0.03160 17.280 0.665318 0.719467
20 0.02374 0.04310 0.05164 0.03365 17.153 0.649554 0.686080
21 0.02383 0.04055 0.05000 0.03871 17.536 0.660125 0.704087
22 0.02591 0.04525 0.06062 0.01849 19.493 0.629017 0.698951
23 0.02540 0.04246 0.06685 0.01280 22.468 0.619060 0.679834
24 0.02470 0.03772 0.06562 0.01840 20.422 0.537264 0.686894
25 0.00948 0.01497 0.02214 0.01778 23.831 0.397937 0.732479
26 0.02245 0.03780 0.05197 0.02887 22.066 0.522746 0.737948
27 0.01169 0.01872 0.02666 0.01095 25.908 0.418622 0.720916
28 0.01144 0.01826 0.02650 0.01328 25.119 0.358773 0.726652
29 0.01012 0.01661 0.02307 0.00677 25.970 0.470478 0.676258
30 0.01057 0.01799 0.02380 0.01170 25.678 0.427785 0.723797
31 0.00680 0.00802 0.01689 0.00339 26.775 0.422229 0.741367
32 0.00641 0.00762 0.01513 0.00167 30.940 0.432439 0.742055
33 0.00825 0.00951 0.01919 0.00119 30.775 0.465946 0.738703
34 0.00606 0.00719 0.01407 0.00072 32.684 0.368535 0.742133
35 0.00610 0.00726 0.01403 0.00065 33.047 0.340068 0.741899
36 0.00760 0.00957 0.01758 0.00135 31.732 0.344252 0.742737
37 0.01347 0.01612 0.03463 0.00586 23.216 0.360148 0.778834
38 0.01160 0.01491 0.02814 0.00340 24.951 0.341435 0.783626
39 0.00885 0.01190 0.02177 0.00231 26.738 0.403884 0.766209
40 0.01003 0.01366 0.02488 0.00265 26.310 0.396793 0.758324
41 0.00941 0.01233 0.02321 0.00231 26.822 0.326480 0.765623
42 0.00901 0.01234 0.02226 0.00257 26.453 0.306443 0.759203
43 0.01024 0.01133 0.03104 0.00740 22.736 0.305062 0.654172
44 0.01038 0.01251 0.03017 0.00675 23.145 0.457702 0.634267
45 0.00898 0.01033 0.02330 0.00454 25.368 0.438296 0.635285
46 0.00879 0.01014 0.02542 0.00476 25.032 0.431285 0.638928
47 0.00977 0.01149 0.02719 0.00476 24.602 0.467489 0.631653
48 0.00730 0.00860 0.01841 0.00432 26.805 0.610367 0.635204
49 0.00776 0.01433 0.02566 0.00839 23.162 0.579597 0.733659
50 0.00802 0.01400 0.02789 0.00462 24.971 0.538688 0.754073
51 0.01024 0.01685 0.03724 0.00479 25.135 0.553134 0.775933
52 0.00959 0.01614 0.03429 0.00474 25.030 0.507504 0.760361
53 0.01072 0.01677 0.03969 0.00481 24.692 0.459766 0.766204
54 0.01219 0.01947 0.04188 0.00484 25.429 0.420383 0.785714
55 0.01609 0.02067 0.04450 0.01036 21.028 0.536009 0.819032
56 0.01992 0.02454 0.05368 0.01180 20.767 0.558586 0.811843
57 0.02302 0.02802 0.06097 0.00969 21.422 0.541781 0.821364
58 0.01459 0.01948 0.03568 0.00681 22.817 0.530529 0.817756
59 0.01625 0.02137 0.04183 0.00786 22.603 0.540049 0.813432
60 0.01974 0.02519 0.05414 0.01143 21.660 0.547975 0.817396
61 0.01258 0.01382 0.02925 0.00871 25.554 0.341788 0.678874
62 0.01296 0.01340 0.03039 0.00301 26.138 0.447979 0.686264
63 0.01108 0.01200 0.02602 0.00340 25.856 0.364867 0.694399
64 0.01075 0.01179 0.02647 0.00351 25.964 0.256570 0.683296
65 0.00957 0.01016 0.02308 0.00300 26.415 0.276850 0.673636
66 0.01160 0.01234 0.02827 0.00420 24.547 0.305429 0.681811
67 0.01810 0.02428 0.05490 0.02183 19.560 0.460139 0.720908
68 0.01759 0.02603 0.04914 0.02659 19.979 0.498133 0.729067
69 0.02422 0.03392 0.09455 0.04882 20.338 0.513237 0.731444
70 0.02494 0.03635 0.10070 0.02431 21.718 0.487407 0.727313
71 0.01906 0.02949 0.05605 0.02599 20.264 0.489345 0.730387
72 0.02466 0.03736 0.08247 0.03361 18.570 0.543299 0.733232
73 0.00925 0.01345 0.02921 0.00442 25.742 0.495954 0.762959
74 0.01375 0.01956 0.04120 0.00623 24.178 0.509127 0.789532
75 0.01325 0.01831 0.04295 0.00479 25.438 0.437031 0.815908
76 0.01219 0.01715 0.03851 0.00472 25.197 0.463514 0.807217
77 0.02231 0.02704 0.07238 0.00905 23.370 0.489538 0.789977
78 0.01199 0.01636 0.03852 0.00420 25.820 0.429484 0.816340
79 0.01886 0.02455 0.05408 0.01062 21.875 0.644954 0.779612
80 0.01783 0.02139 0.05320 0.02220 19.200 0.594387 0.790117
81 0.02451 0.02876 0.06799 0.01823 19.055 0.544805 0.770466
82 0.01841 0.02190 0.05377 0.01825 19.659 0.576084 0.778747
83 0.01421 0.01751 0.04114 0.01237 20.536 0.554610 0.787896
84 0.01343 0.01552 0.03831 0.00882 22.244 0.576644 0.772416
85 0.03022 0.03510 0.08037 0.05470 13.893 0.556494 0.729586
86 0.02493 0.02877 0.06321 0.02782 16.176 0.583574 0.727747
87 0.02415 0.02784 0.06219 0.03151 15.924 0.598714 0.712199
88 0.04159 0.04683 0.11012 0.04824 13.922 0.602874 0.740837
89 0.04254 0.04802 0.11363 0.04214 14.739 0.599371 0.743937
90 0.02768 0.03455 0.06892 0.07223 11.866 0.590951 0.745526
91 0.04282 0.05114 0.10949 0.08725 11.744 0.653410 0.733165
92 0.04962 0.05690 0.13262 0.01658 19.664 0.501037 0.714360
93 0.02521 0.03051 0.07150 0.01914 18.780 0.454444 0.734504
94 0.03794 0.04398 0.10024 0.01211 20.969 0.447456 0.697790
95 0.02321 0.02764 0.06185 0.00850 22.219 0.502380 0.712170
96 0.01909 0.02571 0.05439 0.01018 21.693 0.447285 0.705658
97 0.02024 0.02809 0.05417 0.00852 22.663 0.366329 0.693429
98 0.02174 0.03088 0.06406 0.08151 15.338 0.629574 0.714485
99 0.02630 0.03908 0.07625 0.10323 15.433 0.571010 0.690892
100 0.03963 0.05783 0.10833 0.16744 12.435 0.638545 0.674953
101 0.04791 0.06196 0.16074 0.31482 8.867 0.671299 0.656846
102 0.03672 0.05174 0.09669 0.11843 15.060 0.639808 0.643327
103 0.05005 0.06023 0.16654 0.25930 10.489 0.596362 0.641418
104 0.00659 0.01009 0.01567 0.00495 26.759 0.296888 0.722356
105 0.00582 0.00871 0.01406 0.00243 28.409 0.263654 0.691483
106 0.00818 0.01059 0.01979 0.00578 27.421 0.365488 0.719974
107 0.00632 0.00928 0.01567 0.00233 29.746 0.334171 0.677930
108 0.00788 0.01267 0.01898 0.00659 26.833 0.393563 0.700246
109 0.00576 0.00993 0.01364 0.00238 29.928 0.311369 0.676066
110 0.01815 0.02084 0.05312 0.00947 21.934 0.497554 0.740539
111 0.01439 0.01852 0.03576 0.00704 23.239 0.436084 0.727863
112 0.01058 0.01307 0.02855 0.00830 22.407 0.338097 0.712466
113 0.01483 0.01767 0.03831 0.01316 21.305 0.498877 0.722085
114 0.01017 0.01301 0.02583 0.00620 23.671 0.441097 0.722254
115 0.01284 0.01604 0.03320 0.01048 21.864 0.331508 0.715121
116 0.00832 0.01271 0.02389 0.06051 23.693 0.407701 0.662668
117 0.00747 0.01312 0.01818 0.01554 26.356 0.450798 0.653823
118 0.00971 0.01652 0.02270 0.01802 25.690 0.486738 0.676023
119 0.00744 0.01151 0.01851 0.00856 25.020 0.470422 0.655239
120 0.00631 0.01075 0.02038 0.00681 24.581 0.462516 0.582710
121 0.01117 0.01734 0.02548 0.02350 24.743 0.487756 0.684130
122 0.00630 0.01104 0.01603 0.01161 27.166 0.400088 0.656182
123 0.02567 0.03220 0.07761 0.01968 18.305 0.538016 0.741480
124 0.01580 0.01931 0.04115 0.01813 18.784 0.589956 0.732903
125 0.01420 0.01720 0.03867 0.02020 19.196 0.618663 0.728421
126 0.01495 0.01944 0.03706 0.01874 18.857 0.637518 0.735546
127 0.01805 0.02259 0.04451 0.01794 18.178 0.623209 0.738245
128 0.01859 0.02301 0.04641 0.01796 18.330 0.585169 0.736964
129 0.00570 0.00811 0.01614 0.01724 26.842 0.457541 0.699787
130 0.00588 0.00903 0.01428 0.00487 26.369 0.491345 0.718839
131 0.00820 0.01194 0.02110 0.01610 23.949 0.467160 0.724045
132 0.00815 0.01310 0.02164 0.01015 26.017 0.468621 0.735136
133 0.00701 0.00915 0.01898 0.00903 23.389 0.470972 0.721308
134 0.00621 0.00903 0.01471 0.00504 25.619 0.482296 0.723096
135 0.03112 0.03651 0.08050 0.03031 17.060 0.637814 0.744064
136 0.02592 0.03316 0.06688 0.02529 17.707 0.653427 0.706687
137 0.02973 0.04370 0.07154 0.02278 19.013 0.647900 0.708144
138 0.03347 0.04134 0.08689 0.03690 16.747 0.625362 0.708617
139 0.03530 0.04451 0.09211 0.02629 17.366 0.640945 0.701404
140 0.01812 0.02770 0.04543 0.01827 18.801 0.624811 0.696049
141 0.01964 0.02824 0.05139 0.02485 18.540 0.677131 0.685057
142 0.04003 0.04464 0.12047 0.04238 15.648 0.606344 0.665945
143 0.02076 0.02530 0.06165 0.01728 18.702 0.606273 0.661735
144 0.01177 0.01506 0.03350 0.02010 18.687 0.536102 0.632631
145 0.01558 0.02006 0.04426 0.01049 20.680 0.497480 0.630409
146 0.01478 0.01909 0.04137 0.01493 20.366 0.566849 0.574282
147 0.05426 0.08808 0.11411 0.07530 12.359 0.561610 0.793509
148 0.04101 0.06359 0.08595 0.06057 14.367 0.478024 0.768974
149 0.04580 0.06824 0.10422 0.08069 12.298 0.552870 0.764036
150 0.04265 0.06460 0.10546 0.07889 14.989 0.427627 0.775708
151 0.03714 0.06259 0.08096 0.10952 12.529 0.507826 0.762726
152 0.07940 0.13778 0.16942 0.21713 8.441 0.625866 0.768320
153 0.05556 0.08318 0.12851 0.16265 9.449 0.584164 0.754449
154 0.01399 0.02056 0.04019 0.04179 21.520 0.566867 0.670475
155 0.01405 0.02018 0.04451 0.04611 21.824 0.651680 0.659333
156 0.01804 0.02402 0.04977 0.02631 22.431 0.628300 0.652025
157 0.01289 0.01771 0.03615 0.03191 22.953 0.611679 0.623731
158 0.02161 0.02916 0.07830 0.10748 19.075 0.630547 0.646786
159 0.01581 0.02157 0.04499 0.03828 21.534 0.635015 0.627337
160 0.01650 0.03105 0.04079 0.02663 19.651 0.654945 0.675865
161 0.01994 0.04114 0.04736 0.02073 20.437 0.653139 0.694571
162 0.01722 0.02931 0.04933 0.02810 19.388 0.577802 0.684373
163 0.01940 0.03091 0.05592 0.02707 18.954 0.685151 0.719576
164 0.01033 0.01363 0.02902 0.01435 21.219 0.557045 0.673086
165 0.01553 0.02073 0.04736 0.03882 18.447 0.671378 0.674562
166 0.01426 0.01621 0.04231 0.00620 24.078 0.469928 0.628232
167 0.00747 0.00882 0.02089 0.00533 24.679 0.384868 0.626710
168 0.01230 0.01367 0.03557 0.00910 21.083 0.440988 0.628058
169 0.01272 0.01439 0.03836 0.01337 19.269 0.372222 0.725216
170 0.01191 0.01344 0.03529 0.00965 21.020 0.371837 0.646167
171 0.01121 0.01255 0.03253 0.01049 21.528 0.522812 0.646818
172 0.00786 0.01140 0.01992 0.00435 26.436 0.413295 0.756700
173 0.00950 0.01285 0.02261 0.00430 26.550 0.369090 0.776158
174 0.00905 0.01148 0.02245 0.00478 26.547 0.380253 0.766700
175 0.01062 0.01318 0.02643 0.00590 25.445 0.387482 0.756482
176 0.00933 0.01133 0.02436 0.00401 26.005 0.405991 0.761255
177 0.01021 0.01331 0.02623 0.00415 26.143 0.361232 0.763242
178 0.00886 0.01230 0.02184 0.00570 24.151 0.396610 0.745957
179 0.00956 0.01309 0.02518 0.00488 24.412 0.402591 0.762508
180 0.00876 0.01263 0.02175 0.00540 23.683 0.398499 0.778349
181 0.01574 0.02148 0.03964 0.00611 23.133 0.352396 0.759320
182 0.01103 0.01559 0.02849 0.00639 22.866 0.408598 0.768845
183 0.01341 0.01666 0.03464 0.00595 23.008 0.329577 0.757180
184 0.01223 0.01949 0.02592 0.00955 23.079 0.603515 0.669565
185 0.01144 0.01756 0.02429 0.01179 22.085 0.663842 0.656516
186 0.00990 0.01691 0.02001 0.00737 24.199 0.598515 0.654331
187 0.00972 0.01491 0.02460 0.01397 23.958 0.566424 0.667654
188 0.00789 0.01144 0.01892 0.00680 25.023 0.528485 0.663884
189 0.00721 0.01095 0.01672 0.00703 24.775 0.555303 0.659132
190 0.01582 0.01758 0.04363 0.04441 19.368 0.508479 0.683761
191 0.02498 0.02745 0.07008 0.02764 19.517 0.448439 0.657899
192 0.01657 0.01879 0.04812 0.01810 19.147 0.431674 0.683244
193 0.01365 0.01667 0.03804 0.10715 17.883 0.407567 0.655683
194 0.01321 0.01588 0.03794 0.07223 19.020 0.451221 0.643956
195 0.01161 0.01373 0.03078 0.04398 21.209 0.462803 0.664357
spread1 spread2 D2 PPE status
1 -4.813031 0.266482 2.301442 0.284654 1
2 -4.075192 0.335590 2.486855 0.368674 1
3 -4.443179 0.311173 2.342259 0.332634 1
4 -4.117501 0.334147 2.405554 0.368975 1
5 -3.747787 0.234513 2.332180 0.410335 1
6 -4.242867 0.299111 2.187560 0.357775 1
7 -5.634322 0.257682 1.854785 0.211756 1
8 -6.167603 0.183721 2.064693 0.163755 1
9 -5.498678 0.327769 2.322511 0.231571 1
10 -5.011879 0.325996 2.432792 0.271362 1
11 -5.249770 0.391002 2.407313 0.249740 1
12 -4.960234 0.363566 2.642476 0.275931 1
13 -6.547148 0.152813 2.041277 0.138512 1
14 -5.660217 0.254989 2.519422 0.199889 1
15 -6.105098 0.203653 2.125618 0.170100 1
16 -5.340115 0.210185 2.205546 0.234589 1
17 -5.440040 0.239764 2.264501 0.218164 1
18 -2.931070 0.434326 3.007463 0.430788 1
19 -3.949079 0.357870 3.109010 0.377429 1
20 -4.554466 0.340176 2.856676 0.322111 1
21 -4.095442 0.262564 2.739710 0.365391 1
22 -5.186960 0.237622 2.557536 0.259765 1
23 -4.330956 0.262384 2.916777 0.285695 1
24 -5.248776 0.210279 2.547508 0.253556 1
25 -5.557447 0.220890 2.692176 0.215961 1
26 -5.571843 0.236853 2.846369 0.219514 1
27 -6.183590 0.226278 2.589702 0.147403 1
28 -6.271690 0.196102 2.314209 0.162999 1
29 -7.120925 0.279789 2.241742 0.108514 1
30 -6.635729 0.209866 1.957961 0.135242 1
31 -7.348300 0.177551 1.743867 0.085569 0
32 -7.682587 0.173319 2.103106 0.068501 0
33 -7.067931 0.175181 1.512275 0.096320 0
34 -7.695734 0.178540 1.544609 0.056141 0
35 -7.964984 0.163519 1.423287 0.044539 0
36 -7.777685 0.170183 2.447064 0.057610 0
37 -6.149653 0.218037 2.477082 0.165827 1
38 -6.006414 0.196371 2.536527 0.173218 1
39 -6.452058 0.212294 2.269398 0.141929 1
40 -6.006647 0.266892 2.382544 0.160691 1
41 -6.647379 0.201095 2.374073 0.130554 1
42 -7.044105 0.063412 2.361532 0.115730 1
43 -7.310550 0.098648 2.416838 0.095032 0
44 -6.793547 0.158266 2.256699 0.117399 0
45 -7.057869 0.091608 2.330716 0.091470 0
46 -6.995820 0.102083 2.365800 0.102706 0
47 -7.156076 0.127642 2.392122 0.097336 0
48 -7.319510 0.200873 2.028612 0.086398 0
49 -6.439398 0.266392 2.079922 0.133867 0
50 -6.482096 0.264967 2.054419 0.128872 0
51 -6.650471 0.254498 1.840198 0.103561 0
52 -6.689151 0.291954 2.431854 0.105993 0
53 -7.072419 0.220434 1.972297 0.119308 0
54 -6.836811 0.269866 2.223719 0.147491 0
55 -4.649573 0.205558 1.986899 0.316700 1
56 -4.333543 0.221727 2.014606 0.344834 1
57 -4.438453 0.238298 1.922940 0.335041 1
58 -4.608260 0.290024 2.021591 0.314464 1
59 -4.476755 0.262633 1.827012 0.326197 1
60 -4.609161 0.221711 1.831691 0.316395 1
61 -7.040508 0.066994 2.460791 0.101516 0
62 -7.293801 0.086372 2.321560 0.098555 0
63 -6.966321 0.095882 2.278687 0.103224 0
64 -7.245620 0.018689 2.498224 0.093534 0
65 -7.496264 0.056844 2.003032 0.073581 0
66 -7.314237 0.006274 2.118596 0.091546 0
67 -5.409423 0.226850 2.359973 0.226156 1
68 -5.324574 0.205660 2.291558 0.226247 1
69 -5.869750 0.151814 2.118496 0.185580 1
70 -6.261141 0.120956 2.137075 0.141958 1
71 -5.720868 0.158830 2.277927 0.180828 1
72 -5.207985 0.224852 2.642276 0.242981 1
73 -5.791820 0.329066 2.205024 0.188180 1
74 -5.389129 0.306636 1.928708 0.225461 1
75 -5.313360 0.201861 2.225815 0.244512 1
76 -5.477592 0.315074 1.862092 0.228624 1
77 -5.775966 0.341169 2.007923 0.193918 1
78 -5.391029 0.250572 1.777901 0.232744 1
79 -5.115212 0.249494 2.017753 0.260015 1
80 -4.913885 0.265699 2.398422 0.277948 1
81 -4.441519 0.155097 2.645959 0.327978 1
82 -5.132032 0.210458 2.232576 0.260633 1
83 -5.022288 0.146948 2.428306 0.264666 1
84 -6.025367 0.078202 2.053601 0.177275 1
85 -5.288912 0.343073 3.099301 0.242119 1
86 -5.657899 0.315903 3.098256 0.200423 1
87 -6.366916 0.335753 2.654271 0.144614 1
88 -5.515071 0.299549 3.136550 0.220968 1
89 -5.783272 0.299793 3.007096 0.194052 1
90 -4.379411 0.375531 3.671155 0.332086 1
91 -4.508984 0.389232 3.317586 0.301952 1
92 -6.411497 0.207156 2.344876 0.134120 1
93 -5.952058 0.087840 2.344336 0.186489 1
94 -6.152551 0.173520 2.080121 0.160809 1
95 -6.251425 0.188056 2.143851 0.160812 1
96 -6.247076 0.180528 2.344348 0.164916 1
97 -6.417440 0.194627 2.473239 0.151709 1
98 -4.020042 0.265315 2.671825 0.340623 1
99 -5.159169 0.202146 2.441612 0.260375 1
100 -3.760348 0.242861 2.634633 0.378483 1
101 -3.700544 0.260481 2.991063 0.370961 1
102 -4.202730 0.310163 2.638279 0.356881 1
103 -3.269487 0.270641 2.690917 0.444774 1
104 -6.878393 0.089267 2.004055 0.113942 1
105 -7.111576 0.144780 2.065477 0.093193 1
106 -6.997403 0.210279 1.994387 0.112878 1
107 -6.981201 0.184550 2.129924 0.106802 1
108 -6.600023 0.249172 2.499148 0.105306 1
109 -6.739151 0.160686 2.296873 0.115130 1
110 -5.845099 0.278679 2.608749 0.185668 1
111 -5.258320 0.256454 2.550961 0.232520 1
112 -6.471427 0.184378 2.502336 0.136390 1
113 -4.876336 0.212054 2.376749 0.268144 1
114 -5.963040 0.250283 2.489191 0.177807 1
115 -6.729713 0.181701 2.938114 0.115515 1
116 -4.673241 0.261549 2.702355 0.274407 1
117 -6.051233 0.273280 2.640798 0.170106 1
118 -4.597834 0.372114 2.975889 0.282780 1
119 -4.913137 0.393056 2.816781 0.251972 1
120 -5.517173 0.389295 2.925862 0.220657 1
121 -6.186128 0.279933 2.686240 0.152428 1
122 -4.711007 0.281618 2.655744 0.234809 1
123 -5.418787 0.160267 2.090438 0.229892 1
124 -5.445140 0.142466 2.174306 0.215558 1
125 -5.944191 0.143359 1.929715 0.181988 1
126 -5.594275 0.127950 1.765957 0.222716 1
127 -5.540351 0.087165 1.821297 0.214075 1
128 -5.825257 0.115697 1.996146 0.196535 1
129 -6.890021 0.152941 2.328513 0.112856 1
130 -5.892061 0.195976 2.108873 0.183572 1
131 -6.135296 0.203630 2.539724 0.169923 1
132 -6.112667 0.217013 2.527742 0.170633 1
133 -5.436135 0.254909 2.516320 0.232209 1
134 -6.448134 0.178713 2.034827 0.141422 1
135 -5.301321 0.320385 2.375138 0.243080 1
136 -5.333619 0.322044 2.631793 0.228319 1
137 -4.378916 0.300067 2.445502 0.259451 1
138 -4.654894 0.304107 2.672362 0.274387 1
139 -5.634576 0.306014 2.419253 0.209191 1
140 -5.866357 0.233070 2.445646 0.184985 1
141 -4.796845 0.397749 2.963799 0.277227 1
142 -5.410336 0.288917 2.665133 0.231723 1
143 -5.585259 0.310746 2.465528 0.209863 1
144 -5.898673 0.213353 2.470746 0.189032 1
145 -6.132663 0.220617 2.576563 0.159777 1
146 -5.456811 0.345238 2.840556 0.232861 1
147 -3.297668 0.414758 3.413649 0.457533 1
148 -4.276605 0.355736 3.142364 0.336085 1
149 -3.377325 0.335357 3.274865 0.418646 1
150 -4.892495 0.262281 2.910213 0.270173 1
151 -4.484303 0.340256 2.958815 0.301487 1
152 -2.434031 0.450493 3.079221 0.527367 1
153 -2.839756 0.356224 3.184027 0.454721 1
154 -4.865194 0.246404 2.013530 0.168581 1
155 -4.239028 0.175691 2.451130 0.247455 1
156 -3.583722 0.207914 2.439597 0.206256 1
157 -5.435100 0.230532 2.699645 0.220546 1
158 -3.444478 0.303214 2.964568 0.261305 1
159 -5.070096 0.280091 2.892300 0.249703 1
160 -5.498456 0.234196 2.103014 0.216638 1
161 -5.185987 0.259229 2.151121 0.244948 1
162 -5.283009 0.226528 2.442906 0.238281 1
163 -5.529833 0.242750 2.408689 0.220520 1
164 -5.617124 0.184896 1.871871 0.212386 1
165 -2.929379 0.396746 2.560422 0.367233 1
166 -6.816086 0.172270 2.235197 0.119652 0
167 -7.018057 0.176316 1.852402 0.091604 0
168 -7.517934 0.160414 1.881767 0.075587 0
169 -5.736781 0.164529 2.882450 0.202879 0
170 -7.169701 0.073298 2.266432 0.100881 0
171 -7.304500 0.171088 2.095237 0.096220 0
172 -6.323531 0.218885 2.193412 0.160376 0
173 -6.085567 0.192375 1.889002 0.174152 0
174 -5.943501 0.192150 1.852542 0.179677 0
175 -6.012559 0.229298 1.872946 0.163118 0
176 -5.966779 0.197938 1.974857 0.184067 0
177 -6.016891 0.109256 2.004719 0.174429 0
178 -6.486822 0.197919 2.449763 0.132703 1
179 -6.311987 0.182459 2.251553 0.160306 1
180 -5.711205 0.240875 2.845109 0.192730 1
181 -6.261446 0.183218 2.264226 0.144105 1
182 -5.704053 0.216204 2.679185 0.197710 1
183 -6.277170 0.109397 2.209021 0.156368 1
184 -5.619070 0.191576 2.027228 0.215724 0
185 -5.198864 0.206768 2.120412 0.252404 0
186 -5.592584 0.133917 2.058658 0.214346 0
187 -6.431119 0.153310 2.161936 0.120605 0
188 -6.359018 0.116636 2.152083 0.138868 0
189 -6.710219 0.149694 1.913990 0.121777 0
190 -6.934474 0.159890 2.316346 0.112838 0
191 -6.538586 0.121952 2.657476 0.133050 0
192 -6.195325 0.129303 2.784312 0.168895 0
193 -6.787197 0.158453 2.679772 0.131728 0
194 -6.744577 0.207454 2.138608 0.123306 0
195 -5.724056 0.190667 2.555477 0.148569 0
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) `MDVP:Fhi(Hz)` `MDVP:Flo(Hz)` `MDVP:Jitter(%)`
1.595e+02 2.986e-02 2.523e-01 1.158e+04
`MDVP:Jitter(Abs)` `MDVP:RAP` `MDVP:PPQ` `Jitter:DDP`
-2.022e+06 6.830e+05 -7.137e+03 -2.237e+05
`MDVP:Shimmer` `MDVP:Shimmer(dB)` `Shimmer:APQ3` `Shimmer:APQ5`
-2.195e+02 -1.468e+01 1.945e+05 2.270e+03
`MDVP:APQ` `Shimmer:DDA` NHR HNR
-1.662e+03 -6.465e+04 -2.345e+02 -1.636e-01
RPDE DFA spread1 spread2
-3.299e+01 -2.340e+02 -1.659e+01 2.140e+01
D2 PPE status
5.817e+00 2.350e+02 -5.993e+00
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-51.065 -10.258 -0.036 10.691 43.166
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.595e+02 5.742e+01 2.779 0.006064 **
`MDVP:Fhi(Hz)` 2.986e-02 1.594e-02 1.873 0.062741 .
`MDVP:Flo(Hz)` 2.523e-01 3.581e-02 7.047 4.23e-11 ***
`MDVP:Jitter(%)` 1.158e+04 3.312e+03 3.496 0.000601 ***
`MDVP:Jitter(Abs)` -2.022e+06 1.737e+05 -11.641 < 2e-16 ***
`MDVP:RAP` 6.830e+05 4.649e+05 1.469 0.143636
`MDVP:PPQ` -7.137e+03 4.400e+03 -1.622 0.106564
`Jitter:DDP` -2.237e+05 1.551e+05 -1.443 0.150947
`MDVP:Shimmer` -2.195e+02 1.722e+03 -0.127 0.898694
`MDVP:Shimmer(dB)` -1.468e+01 6.015e+01 -0.244 0.807540
`Shimmer:APQ3` 1.945e+05 4.496e+05 0.433 0.665815
`Shimmer:APQ5` 2.270e+03 9.990e+02 2.272 0.024304 *
`MDVP:APQ` -1.662e+03 5.312e+02 -3.130 0.002056 **
`Shimmer:DDA` -6.465e+04 1.498e+05 -0.432 0.666613
NHR -2.345e+02 9.815e+01 -2.389 0.017973 *
HNR -1.636e-01 7.214e-01 -0.227 0.820860
RPDE -3.299e+01 2.223e+01 -1.484 0.139653
DFA -2.340e+02 3.252e+01 -7.196 1.84e-11 ***
spread1 -1.659e+01 4.767e+00 -3.480 0.000635 ***
spread2 2.140e+01 2.439e+01 0.878 0.381436
D2 5.817e+00 5.717e+00 1.017 0.310407
PPE 2.350e+02 6.717e+01 3.498 0.000597 ***
status -5.993e+00 3.795e+00 -1.579 0.116169
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 16.38 on 172 degrees of freedom
Multiple R-squared: 0.8612, Adjusted R-squared: 0.8434
F-statistic: 48.51 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,] 0.1973287935 0.3946575870 0.80267121
[2,] 0.0974327096 0.1948654192 0.90256729
[3,] 0.0886111069 0.1772222137 0.91138889
[4,] 0.0411813775 0.0823627549 0.95881862
[5,] 0.0186798930 0.0373597860 0.98132011
[6,] 0.0076980782 0.0153961563 0.99230192
[7,] 0.0042243245 0.0084486490 0.99577568
[8,] 0.0026309738 0.0052619477 0.99736903
[9,] 0.0017365832 0.0034731663 0.99826342
[10,] 0.0007963196 0.0015926392 0.99920368
[11,] 0.0003192302 0.0006384604 0.99968077
[12,] 0.0002090002 0.0004180005 0.99979100
[13,] 0.0000834131 0.0001668262 0.99991659
[14,] 0.0003187246 0.0006374492 0.99968128
[15,] 0.0004394246 0.0008788491 0.99956058
[16,] 0.0003313199 0.0006626398 0.99966868
[17,] 0.0001604510 0.0003209020 0.99983955
[18,] 0.0001893187 0.0003786375 0.99981068
[19,] 0.0001529991 0.0003059982 0.99984700
[20,] 0.0002587712 0.0005175425 0.99974123
[21,] 0.0002912820 0.0005825640 0.99970872
[22,] 0.0003056158 0.0006112317 0.99969438
[23,] 0.0139532048 0.0279064096 0.98604680
[24,] 0.0095039046 0.0190078092 0.99049610
[25,] 0.0074037804 0.0148075608 0.99259622
[26,] 0.0046426109 0.0092852217 0.99535739
[27,] 0.0037453807 0.0074907614 0.99625462
[28,] 0.0024397014 0.0048794028 0.99756030
[29,] 0.0015499647 0.0030999294 0.99845004
[30,] 0.0076698143 0.0153396286 0.99233019
[31,] 0.0134244162 0.0268488325 0.98657558
[32,] 0.0115658034 0.0231316069 0.98843420
[33,] 0.0154739401 0.0309478801 0.98452606
[34,] 0.0244886558 0.0489773116 0.97551134
[35,] 0.0467768872 0.0935537743 0.95322311
[36,] 0.0379842268 0.0759684537 0.96201577
[37,] 0.0538505348 0.1077010695 0.94614947
[38,] 0.0543579252 0.1087158504 0.94564207
[39,] 0.0581946266 0.1163892532 0.94180537
[40,] 0.0596511527 0.1193023054 0.94034885
[41,] 0.1239353696 0.2478707392 0.87606463
[42,] 0.1065884365 0.2131768730 0.89341156
[43,] 0.0858163683 0.1716327365 0.91418363
[44,] 0.0687441607 0.1374883215 0.93125584
[45,] 0.0814137705 0.1628275409 0.91858623
[46,] 0.0685332403 0.1370664805 0.93146676
[47,] 0.0575436843 0.1150873685 0.94245632
[48,] 0.0520384206 0.1040768412 0.94796158
[49,] 0.2164331728 0.4328663455 0.78356683
[50,] 0.2883976623 0.5767953245 0.71160234
[51,] 0.2535441633 0.5070883267 0.74645584
[52,] 0.2361911432 0.4723822865 0.76380886
[53,] 0.2072224770 0.4144449540 0.79277752
[54,] 0.2060826670 0.4121653340 0.79391733
[55,] 0.2213897728 0.4427795456 0.77861023
[56,] 0.2513592436 0.5027184871 0.74864076
[57,] 0.2333253890 0.4666507781 0.76667461
[58,] 0.2601385105 0.5202770209 0.73986149
[59,] 0.3069486967 0.6138973934 0.69305130
[60,] 0.3653749480 0.7307498960 0.63462505
[61,] 0.3248753756 0.6497507512 0.67512462
[62,] 0.3284974554 0.6569949108 0.67150254
[63,] 0.2906165171 0.5812330341 0.70938348
[64,] 0.3387010695 0.6774021391 0.66129893
[65,] 0.3216408184 0.6432816367 0.67835918
[66,] 0.2843921561 0.5687843122 0.71560784
[67,] 0.2931307908 0.5862615816 0.70686921
[68,] 0.2902025889 0.5804051778 0.70979741
[69,] 0.2807667066 0.5615334132 0.71923329
[70,] 0.2430209196 0.4860418392 0.75697908
[71,] 0.2846803691 0.5693607382 0.71531963
[72,] 0.3078744353 0.6157488705 0.69212556
[73,] 0.2757012532 0.5514025063 0.72429875
[74,] 0.2418704237 0.4837408474 0.75812958
[75,] 0.2279303144 0.4558606288 0.77206969
[76,] 0.2115277498 0.4230554996 0.78847225
[77,] 0.2061048884 0.4122097768 0.79389511
[78,] 0.2580450423 0.5160900847 0.74195496
[79,] 0.2744397875 0.5488795750 0.72556021
[80,] 0.2715653446 0.5431306892 0.72843466
[81,] 0.2350320826 0.4700641651 0.76496792
[82,] 0.2124760281 0.4249520562 0.78752397
[83,] 0.1806849917 0.3613699834 0.81931501
[84,] 0.1798783561 0.3597567122 0.82012164
[85,] 0.1593433343 0.3186866685 0.84065667
[86,] 0.1411041830 0.2822083661 0.85889582
[87,] 0.1246305796 0.2492611591 0.87536942
[88,] 0.1300637406 0.2601274812 0.86993626
[89,] 0.1512300701 0.3024601403 0.84876993
[90,] 0.1256003894 0.2512007789 0.87439961
[91,] 0.1070215248 0.2140430497 0.89297848
[92,] 0.0956565654 0.1913131308 0.90434343
[93,] 0.0815522505 0.1631045009 0.91844775
[94,] 0.0796458060 0.1592916121 0.92035419
[95,] 0.1139180626 0.2278361253 0.88608194
[96,] 0.1478723837 0.2957447674 0.85212762
[97,] 0.1906597211 0.3813194422 0.80934028
[98,] 0.1692706972 0.3385413944 0.83072930
[99,] 0.1566382831 0.3132765663 0.84336172
[100,] 0.1332788451 0.2665576902 0.86672115
[101,] 0.1299442536 0.2598885072 0.87005575
[102,] 0.1395715978 0.2791431955 0.86042840
[103,] 0.2883861464 0.5767722927 0.71161385
[104,] 0.2771876069 0.5543752137 0.72281239
[105,] 0.2689268513 0.5378537026 0.73107315
[106,] 0.2515143014 0.5030286028 0.74848570
[107,] 0.2617979310 0.5235958621 0.73820207
[108,] 0.2782973062 0.5565946125 0.72170269
[109,] 0.2599679828 0.5199359657 0.74003202
[110,] 0.4102034199 0.8204068399 0.58979658
[111,] 0.4711016618 0.9422033235 0.52889834
[112,] 0.4215356071 0.8430712143 0.57846439
[113,] 0.4737621599 0.9475243199 0.52623784
[114,] 0.5036524154 0.9926951692 0.49634758
[115,] 0.7079583244 0.5840833511 0.29204168
[116,] 0.6669912677 0.6660174646 0.33300873
[117,] 0.6382192166 0.7235615669 0.36178078
[118,] 0.6439569737 0.7120860525 0.35604303
[119,] 0.6626426099 0.6747147801 0.33735739
[120,] 0.7193336247 0.5613327506 0.28066638
[121,] 0.7087994607 0.5824010787 0.29120054
[122,] 0.6995957856 0.6008084288 0.30040421
[123,] 0.6770063079 0.6459873843 0.32299369
[124,] 0.6973115482 0.6053769035 0.30268845
[125,] 0.6411060509 0.7177878983 0.35889395
[126,] 0.6790627480 0.6418745040 0.32093725
[127,] 0.6434180313 0.7131639374 0.35658197
[128,] 0.6320574211 0.7358851578 0.36794258
[129,] 0.6252157921 0.7495684158 0.37478421
[130,] 0.6337484636 0.7325030727 0.36625154
[131,] 0.7906586715 0.4186826570 0.20934133
[132,] 0.7561714869 0.4876570262 0.24382851
[133,] 0.6916410725 0.6167178549 0.30835893
[134,] 0.6951793936 0.6096412127 0.30482061
[135,] 0.6242518811 0.7514962378 0.37574812
[136,] 0.5391115375 0.9217769249 0.46088846
[137,] 0.6880524056 0.6238951888 0.31194759
[138,] 0.5957275281 0.8085449437 0.40427247
[139,] 0.5860671292 0.8278657415 0.41393287
[140,] 0.6747146071 0.6505707859 0.32528539
[141,] 0.9036699047 0.1926601905 0.09633010
[142,] 0.9687833085 0.0624333831 0.03121669
[143,] 0.9517506598 0.0964986804 0.04824934
[144,] 0.8974242100 0.2051515800 0.10257579
> postscript(file="/var/wessaorg/rcomp/tmp/1rz721386190257.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/2o5s81386190257.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/32nda1386190257.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/4yph71386190257.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/5tqem1386190257.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
> qqline(mysum$resid)
> grid()
> dev.off()
null device
1
> (myerror <- as.ts(mysum$resid))
Time Series:
Start = 1
End = 195
Frequency = 1
1 2 3 4 5 6
15.58378197 -7.83745428 1.01188287 -3.66699803 -2.19155427 -1.59712982
7 8 9 10 11 12
0.83602402 -6.91847654 5.21717314 11.11098990 11.02424285 6.63448853
13 14 15 16 17 18
-29.62443663 -0.03638896 -5.12858914 -4.07846117 -1.70750557 -3.13300460
19 20 21 22 23 24
21.43387460 -2.21813265 5.67017834 15.91522694 32.86186726 20.47358411
25 26 27 28 29 30
16.98532760 13.65895272 25.33696072 -6.39301574 -5.78741711 14.18071741
31 32 33 34 35 36
-2.90828318 4.53008498 13.95273179 14.96817202 18.36367974 6.84813825
37 38 39 40 41 42
-2.12888471 6.89492175 17.06952331 21.67014094 18.16543841 4.28008922
43 44 45 46 47 48
-3.65143593 4.11640752 19.49568415 15.02794967 14.16887228 42.90655360
49 50 51 52 53 54
-7.60199554 -10.55160642 -4.61205932 9.37025417 -18.27713268 -4.66738697
55 56 57 58 59 60
17.24859345 18.60988517 16.40884076 13.79436539 27.81942393 31.08372351
61 62 63 64 65 66
13.63799097 26.35825281 8.58084541 -2.72520313 9.41360813 35.21204183
67 68 69 70 71 72
1.24093592 9.73309001 7.52994405 0.13322839 18.39735026 4.01260996
73 74 75 76 77 78
-16.39219171 -3.08270821 -12.65773566 4.19329680 -19.75299755 -4.28322580
79 80 81 82 83 84
-1.54092946 13.29463224 -4.62613731 -8.29142989 -1.30104187 -11.52419434
85 86 87 88 89 90
-1.19631980 -9.41647654 5.83419745 -0.72146790 18.55177218 -5.98206239
91 92 93 94 95 96
1.54407290 -21.15724418 -17.87526932 6.84872145 -5.92727039 -26.41463096
97 98 99 100 101 102
-19.48989495 -8.56117661 3.81940476 11.95832892 12.66261113 6.82392124
103 104 105 106 107 108
-12.24244890 15.54560438 -15.02227172 -6.48003188 -20.05778832 0.58596394
109 110 111 112 113 114
-19.62490921 9.01441388 11.71100052 -5.65457358 -18.45545664 13.64139077
115 116 117 118 119 120
8.21471077 -5.37914984 -10.78336990 -10.87405468 8.30998347 14.17097655
121 122 123 124 125 126
-0.95226933 21.26437888 -6.17238362 14.12364566 -1.90409862 5.59542852
127 128 129 130 131 132
9.96487928 15.12729703 -19.11172204 -21.56418938 -16.72026803 -25.70379572
133 134 135 136 137 138
-20.54325654 -14.49660460 -0.08579711 -27.94252987 3.42535807 -22.84268036
139 140 141 142 143 144
-21.84042553 -21.08222990 0.11139455 5.05682857 6.18236149 23.78123816
145 146 147 148 149 150
1.52204660 20.78972893 1.80713067 -14.66927708 -24.24126121 -13.11572255
151 152 153 154 155 156
10.31546762 5.33894619 -23.99452778 13.44025601 0.87453919 -3.46404726
157 158 159 160 161 162
-28.75978743 6.65150321 -32.53678486 3.10358591 15.21280344 -16.85912071
163 164 165 166 167 168
6.75690185 -17.95726596 9.91434197 30.36897792 -0.41595178 10.03242760
169 170 171 172 173 174
-9.96539346 -7.92185596 0.67766670 -23.76077377 -21.65106490 -15.21830705
175 176 177 178 179 180
-17.51737450 -20.64858207 -17.04416735 -1.33567019 -5.65064563 4.18316643
181 182 183 184 185 186
6.74592185 -1.84375642 -17.43550441 -13.85870970 -16.98383710 -20.79900671
187 188 189 190 191 192
-35.17991523 -51.06506620 -22.09339481 -0.93833461 -7.49909837 13.55731616
193 194 195
-7.55574018 10.35759738 43.16642382
> postscript(file="/var/wessaorg/rcomp/tmp/6kzo91386190257.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 15.58378197 NA
1 -7.83745428 15.58378197
2 1.01188287 -7.83745428
3 -3.66699803 1.01188287
4 -2.19155427 -3.66699803
5 -1.59712982 -2.19155427
6 0.83602402 -1.59712982
7 -6.91847654 0.83602402
8 5.21717314 -6.91847654
9 11.11098990 5.21717314
10 11.02424285 11.11098990
11 6.63448853 11.02424285
12 -29.62443663 6.63448853
13 -0.03638896 -29.62443663
14 -5.12858914 -0.03638896
15 -4.07846117 -5.12858914
16 -1.70750557 -4.07846117
17 -3.13300460 -1.70750557
18 21.43387460 -3.13300460
19 -2.21813265 21.43387460
20 5.67017834 -2.21813265
21 15.91522694 5.67017834
22 32.86186726 15.91522694
23 20.47358411 32.86186726
24 16.98532760 20.47358411
25 13.65895272 16.98532760
26 25.33696072 13.65895272
27 -6.39301574 25.33696072
28 -5.78741711 -6.39301574
29 14.18071741 -5.78741711
30 -2.90828318 14.18071741
31 4.53008498 -2.90828318
32 13.95273179 4.53008498
33 14.96817202 13.95273179
34 18.36367974 14.96817202
35 6.84813825 18.36367974
36 -2.12888471 6.84813825
37 6.89492175 -2.12888471
38 17.06952331 6.89492175
39 21.67014094 17.06952331
40 18.16543841 21.67014094
41 4.28008922 18.16543841
42 -3.65143593 4.28008922
43 4.11640752 -3.65143593
44 19.49568415 4.11640752
45 15.02794967 19.49568415
46 14.16887228 15.02794967
47 42.90655360 14.16887228
48 -7.60199554 42.90655360
49 -10.55160642 -7.60199554
50 -4.61205932 -10.55160642
51 9.37025417 -4.61205932
52 -18.27713268 9.37025417
53 -4.66738697 -18.27713268
54 17.24859345 -4.66738697
55 18.60988517 17.24859345
56 16.40884076 18.60988517
57 13.79436539 16.40884076
58 27.81942393 13.79436539
59 31.08372351 27.81942393
60 13.63799097 31.08372351
61 26.35825281 13.63799097
62 8.58084541 26.35825281
63 -2.72520313 8.58084541
64 9.41360813 -2.72520313
65 35.21204183 9.41360813
66 1.24093592 35.21204183
67 9.73309001 1.24093592
68 7.52994405 9.73309001
69 0.13322839 7.52994405
70 18.39735026 0.13322839
71 4.01260996 18.39735026
72 -16.39219171 4.01260996
73 -3.08270821 -16.39219171
74 -12.65773566 -3.08270821
75 4.19329680 -12.65773566
76 -19.75299755 4.19329680
77 -4.28322580 -19.75299755
78 -1.54092946 -4.28322580
79 13.29463224 -1.54092946
80 -4.62613731 13.29463224
81 -8.29142989 -4.62613731
82 -1.30104187 -8.29142989
83 -11.52419434 -1.30104187
84 -1.19631980 -11.52419434
85 -9.41647654 -1.19631980
86 5.83419745 -9.41647654
87 -0.72146790 5.83419745
88 18.55177218 -0.72146790
89 -5.98206239 18.55177218
90 1.54407290 -5.98206239
91 -21.15724418 1.54407290
92 -17.87526932 -21.15724418
93 6.84872145 -17.87526932
94 -5.92727039 6.84872145
95 -26.41463096 -5.92727039
96 -19.48989495 -26.41463096
97 -8.56117661 -19.48989495
98 3.81940476 -8.56117661
99 11.95832892 3.81940476
100 12.66261113 11.95832892
101 6.82392124 12.66261113
102 -12.24244890 6.82392124
103 15.54560438 -12.24244890
104 -15.02227172 15.54560438
105 -6.48003188 -15.02227172
106 -20.05778832 -6.48003188
107 0.58596394 -20.05778832
108 -19.62490921 0.58596394
109 9.01441388 -19.62490921
110 11.71100052 9.01441388
111 -5.65457358 11.71100052
112 -18.45545664 -5.65457358
113 13.64139077 -18.45545664
114 8.21471077 13.64139077
115 -5.37914984 8.21471077
116 -10.78336990 -5.37914984
117 -10.87405468 -10.78336990
118 8.30998347 -10.87405468
119 14.17097655 8.30998347
120 -0.95226933 14.17097655
121 21.26437888 -0.95226933
122 -6.17238362 21.26437888
123 14.12364566 -6.17238362
124 -1.90409862 14.12364566
125 5.59542852 -1.90409862
126 9.96487928 5.59542852
127 15.12729703 9.96487928
128 -19.11172204 15.12729703
129 -21.56418938 -19.11172204
130 -16.72026803 -21.56418938
131 -25.70379572 -16.72026803
132 -20.54325654 -25.70379572
133 -14.49660460 -20.54325654
134 -0.08579711 -14.49660460
135 -27.94252987 -0.08579711
136 3.42535807 -27.94252987
137 -22.84268036 3.42535807
138 -21.84042553 -22.84268036
139 -21.08222990 -21.84042553
140 0.11139455 -21.08222990
141 5.05682857 0.11139455
142 6.18236149 5.05682857
143 23.78123816 6.18236149
144 1.52204660 23.78123816
145 20.78972893 1.52204660
146 1.80713067 20.78972893
147 -14.66927708 1.80713067
148 -24.24126121 -14.66927708
149 -13.11572255 -24.24126121
150 10.31546762 -13.11572255
151 5.33894619 10.31546762
152 -23.99452778 5.33894619
153 13.44025601 -23.99452778
154 0.87453919 13.44025601
155 -3.46404726 0.87453919
156 -28.75978743 -3.46404726
157 6.65150321 -28.75978743
158 -32.53678486 6.65150321
159 3.10358591 -32.53678486
160 15.21280344 3.10358591
161 -16.85912071 15.21280344
162 6.75690185 -16.85912071
163 -17.95726596 6.75690185
164 9.91434197 -17.95726596
165 30.36897792 9.91434197
166 -0.41595178 30.36897792
167 10.03242760 -0.41595178
168 -9.96539346 10.03242760
169 -7.92185596 -9.96539346
170 0.67766670 -7.92185596
171 -23.76077377 0.67766670
172 -21.65106490 -23.76077377
173 -15.21830705 -21.65106490
174 -17.51737450 -15.21830705
175 -20.64858207 -17.51737450
176 -17.04416735 -20.64858207
177 -1.33567019 -17.04416735
178 -5.65064563 -1.33567019
179 4.18316643 -5.65064563
180 6.74592185 4.18316643
181 -1.84375642 6.74592185
182 -17.43550441 -1.84375642
183 -13.85870970 -17.43550441
184 -16.98383710 -13.85870970
185 -20.79900671 -16.98383710
186 -35.17991523 -20.79900671
187 -51.06506620 -35.17991523
188 -22.09339481 -51.06506620
189 -0.93833461 -22.09339481
190 -7.49909837 -0.93833461
191 13.55731616 -7.49909837
192 -7.55574018 13.55731616
193 10.35759738 -7.55574018
194 43.16642382 10.35759738
195 NA 43.16642382
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -7.83745428 15.58378197
[2,] 1.01188287 -7.83745428
[3,] -3.66699803 1.01188287
[4,] -2.19155427 -3.66699803
[5,] -1.59712982 -2.19155427
[6,] 0.83602402 -1.59712982
[7,] -6.91847654 0.83602402
[8,] 5.21717314 -6.91847654
[9,] 11.11098990 5.21717314
[10,] 11.02424285 11.11098990
[11,] 6.63448853 11.02424285
[12,] -29.62443663 6.63448853
[13,] -0.03638896 -29.62443663
[14,] -5.12858914 -0.03638896
[15,] -4.07846117 -5.12858914
[16,] -1.70750557 -4.07846117
[17,] -3.13300460 -1.70750557
[18,] 21.43387460 -3.13300460
[19,] -2.21813265 21.43387460
[20,] 5.67017834 -2.21813265
[21,] 15.91522694 5.67017834
[22,] 32.86186726 15.91522694
[23,] 20.47358411 32.86186726
[24,] 16.98532760 20.47358411
[25,] 13.65895272 16.98532760
[26,] 25.33696072 13.65895272
[27,] -6.39301574 25.33696072
[28,] -5.78741711 -6.39301574
[29,] 14.18071741 -5.78741711
[30,] -2.90828318 14.18071741
[31,] 4.53008498 -2.90828318
[32,] 13.95273179 4.53008498
[33,] 14.96817202 13.95273179
[34,] 18.36367974 14.96817202
[35,] 6.84813825 18.36367974
[36,] -2.12888471 6.84813825
[37,] 6.89492175 -2.12888471
[38,] 17.06952331 6.89492175
[39,] 21.67014094 17.06952331
[40,] 18.16543841 21.67014094
[41,] 4.28008922 18.16543841
[42,] -3.65143593 4.28008922
[43,] 4.11640752 -3.65143593
[44,] 19.49568415 4.11640752
[45,] 15.02794967 19.49568415
[46,] 14.16887228 15.02794967
[47,] 42.90655360 14.16887228
[48,] -7.60199554 42.90655360
[49,] -10.55160642 -7.60199554
[50,] -4.61205932 -10.55160642
[51,] 9.37025417 -4.61205932
[52,] -18.27713268 9.37025417
[53,] -4.66738697 -18.27713268
[54,] 17.24859345 -4.66738697
[55,] 18.60988517 17.24859345
[56,] 16.40884076 18.60988517
[57,] 13.79436539 16.40884076
[58,] 27.81942393 13.79436539
[59,] 31.08372351 27.81942393
[60,] 13.63799097 31.08372351
[61,] 26.35825281 13.63799097
[62,] 8.58084541 26.35825281
[63,] -2.72520313 8.58084541
[64,] 9.41360813 -2.72520313
[65,] 35.21204183 9.41360813
[66,] 1.24093592 35.21204183
[67,] 9.73309001 1.24093592
[68,] 7.52994405 9.73309001
[69,] 0.13322839 7.52994405
[70,] 18.39735026 0.13322839
[71,] 4.01260996 18.39735026
[72,] -16.39219171 4.01260996
[73,] -3.08270821 -16.39219171
[74,] -12.65773566 -3.08270821
[75,] 4.19329680 -12.65773566
[76,] -19.75299755 4.19329680
[77,] -4.28322580 -19.75299755
[78,] -1.54092946 -4.28322580
[79,] 13.29463224 -1.54092946
[80,] -4.62613731 13.29463224
[81,] -8.29142989 -4.62613731
[82,] -1.30104187 -8.29142989
[83,] -11.52419434 -1.30104187
[84,] -1.19631980 -11.52419434
[85,] -9.41647654 -1.19631980
[86,] 5.83419745 -9.41647654
[87,] -0.72146790 5.83419745
[88,] 18.55177218 -0.72146790
[89,] -5.98206239 18.55177218
[90,] 1.54407290 -5.98206239
[91,] -21.15724418 1.54407290
[92,] -17.87526932 -21.15724418
[93,] 6.84872145 -17.87526932
[94,] -5.92727039 6.84872145
[95,] -26.41463096 -5.92727039
[96,] -19.48989495 -26.41463096
[97,] -8.56117661 -19.48989495
[98,] 3.81940476 -8.56117661
[99,] 11.95832892 3.81940476
[100,] 12.66261113 11.95832892
[101,] 6.82392124 12.66261113
[102,] -12.24244890 6.82392124
[103,] 15.54560438 -12.24244890
[104,] -15.02227172 15.54560438
[105,] -6.48003188 -15.02227172
[106,] -20.05778832 -6.48003188
[107,] 0.58596394 -20.05778832
[108,] -19.62490921 0.58596394
[109,] 9.01441388 -19.62490921
[110,] 11.71100052 9.01441388
[111,] -5.65457358 11.71100052
[112,] -18.45545664 -5.65457358
[113,] 13.64139077 -18.45545664
[114,] 8.21471077 13.64139077
[115,] -5.37914984 8.21471077
[116,] -10.78336990 -5.37914984
[117,] -10.87405468 -10.78336990
[118,] 8.30998347 -10.87405468
[119,] 14.17097655 8.30998347
[120,] -0.95226933 14.17097655
[121,] 21.26437888 -0.95226933
[122,] -6.17238362 21.26437888
[123,] 14.12364566 -6.17238362
[124,] -1.90409862 14.12364566
[125,] 5.59542852 -1.90409862
[126,] 9.96487928 5.59542852
[127,] 15.12729703 9.96487928
[128,] -19.11172204 15.12729703
[129,] -21.56418938 -19.11172204
[130,] -16.72026803 -21.56418938
[131,] -25.70379572 -16.72026803
[132,] -20.54325654 -25.70379572
[133,] -14.49660460 -20.54325654
[134,] -0.08579711 -14.49660460
[135,] -27.94252987 -0.08579711
[136,] 3.42535807 -27.94252987
[137,] -22.84268036 3.42535807
[138,] -21.84042553 -22.84268036
[139,] -21.08222990 -21.84042553
[140,] 0.11139455 -21.08222990
[141,] 5.05682857 0.11139455
[142,] 6.18236149 5.05682857
[143,] 23.78123816 6.18236149
[144,] 1.52204660 23.78123816
[145,] 20.78972893 1.52204660
[146,] 1.80713067 20.78972893
[147,] -14.66927708 1.80713067
[148,] -24.24126121 -14.66927708
[149,] -13.11572255 -24.24126121
[150,] 10.31546762 -13.11572255
[151,] 5.33894619 10.31546762
[152,] -23.99452778 5.33894619
[153,] 13.44025601 -23.99452778
[154,] 0.87453919 13.44025601
[155,] -3.46404726 0.87453919
[156,] -28.75978743 -3.46404726
[157,] 6.65150321 -28.75978743
[158,] -32.53678486 6.65150321
[159,] 3.10358591 -32.53678486
[160,] 15.21280344 3.10358591
[161,] -16.85912071 15.21280344
[162,] 6.75690185 -16.85912071
[163,] -17.95726596 6.75690185
[164,] 9.91434197 -17.95726596
[165,] 30.36897792 9.91434197
[166,] -0.41595178 30.36897792
[167,] 10.03242760 -0.41595178
[168,] -9.96539346 10.03242760
[169,] -7.92185596 -9.96539346
[170,] 0.67766670 -7.92185596
[171,] -23.76077377 0.67766670
[172,] -21.65106490 -23.76077377
[173,] -15.21830705 -21.65106490
[174,] -17.51737450 -15.21830705
[175,] -20.64858207 -17.51737450
[176,] -17.04416735 -20.64858207
[177,] -1.33567019 -17.04416735
[178,] -5.65064563 -1.33567019
[179,] 4.18316643 -5.65064563
[180,] 6.74592185 4.18316643
[181,] -1.84375642 6.74592185
[182,] -17.43550441 -1.84375642
[183,] -13.85870970 -17.43550441
[184,] -16.98383710 -13.85870970
[185,] -20.79900671 -16.98383710
[186,] -35.17991523 -20.79900671
[187,] -51.06506620 -35.17991523
[188,] -22.09339481 -51.06506620
[189,] -0.93833461 -22.09339481
[190,] -7.49909837 -0.93833461
[191,] 13.55731616 -7.49909837
[192,] -7.55574018 13.55731616
[193,] 10.35759738 -7.55574018
[194,] 43.16642382 10.35759738
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -7.83745428 15.58378197
2 1.01188287 -7.83745428
3 -3.66699803 1.01188287
4 -2.19155427 -3.66699803
5 -1.59712982 -2.19155427
6 0.83602402 -1.59712982
7 -6.91847654 0.83602402
8 5.21717314 -6.91847654
9 11.11098990 5.21717314
10 11.02424285 11.11098990
11 6.63448853 11.02424285
12 -29.62443663 6.63448853
13 -0.03638896 -29.62443663
14 -5.12858914 -0.03638896
15 -4.07846117 -5.12858914
16 -1.70750557 -4.07846117
17 -3.13300460 -1.70750557
18 21.43387460 -3.13300460
19 -2.21813265 21.43387460
20 5.67017834 -2.21813265
21 15.91522694 5.67017834
22 32.86186726 15.91522694
23 20.47358411 32.86186726
24 16.98532760 20.47358411
25 13.65895272 16.98532760
26 25.33696072 13.65895272
27 -6.39301574 25.33696072
28 -5.78741711 -6.39301574
29 14.18071741 -5.78741711
30 -2.90828318 14.18071741
31 4.53008498 -2.90828318
32 13.95273179 4.53008498
33 14.96817202 13.95273179
34 18.36367974 14.96817202
35 6.84813825 18.36367974
36 -2.12888471 6.84813825
37 6.89492175 -2.12888471
38 17.06952331 6.89492175
39 21.67014094 17.06952331
40 18.16543841 21.67014094
41 4.28008922 18.16543841
42 -3.65143593 4.28008922
43 4.11640752 -3.65143593
44 19.49568415 4.11640752
45 15.02794967 19.49568415
46 14.16887228 15.02794967
47 42.90655360 14.16887228
48 -7.60199554 42.90655360
49 -10.55160642 -7.60199554
50 -4.61205932 -10.55160642
51 9.37025417 -4.61205932
52 -18.27713268 9.37025417
53 -4.66738697 -18.27713268
54 17.24859345 -4.66738697
55 18.60988517 17.24859345
56 16.40884076 18.60988517
57 13.79436539 16.40884076
58 27.81942393 13.79436539
59 31.08372351 27.81942393
60 13.63799097 31.08372351
61 26.35825281 13.63799097
62 8.58084541 26.35825281
63 -2.72520313 8.58084541
64 9.41360813 -2.72520313
65 35.21204183 9.41360813
66 1.24093592 35.21204183
67 9.73309001 1.24093592
68 7.52994405 9.73309001
69 0.13322839 7.52994405
70 18.39735026 0.13322839
71 4.01260996 18.39735026
72 -16.39219171 4.01260996
73 -3.08270821 -16.39219171
74 -12.65773566 -3.08270821
75 4.19329680 -12.65773566
76 -19.75299755 4.19329680
77 -4.28322580 -19.75299755
78 -1.54092946 -4.28322580
79 13.29463224 -1.54092946
80 -4.62613731 13.29463224
81 -8.29142989 -4.62613731
82 -1.30104187 -8.29142989
83 -11.52419434 -1.30104187
84 -1.19631980 -11.52419434
85 -9.41647654 -1.19631980
86 5.83419745 -9.41647654
87 -0.72146790 5.83419745
88 18.55177218 -0.72146790
89 -5.98206239 18.55177218
90 1.54407290 -5.98206239
91 -21.15724418 1.54407290
92 -17.87526932 -21.15724418
93 6.84872145 -17.87526932
94 -5.92727039 6.84872145
95 -26.41463096 -5.92727039
96 -19.48989495 -26.41463096
97 -8.56117661 -19.48989495
98 3.81940476 -8.56117661
99 11.95832892 3.81940476
100 12.66261113 11.95832892
101 6.82392124 12.66261113
102 -12.24244890 6.82392124
103 15.54560438 -12.24244890
104 -15.02227172 15.54560438
105 -6.48003188 -15.02227172
106 -20.05778832 -6.48003188
107 0.58596394 -20.05778832
108 -19.62490921 0.58596394
109 9.01441388 -19.62490921
110 11.71100052 9.01441388
111 -5.65457358 11.71100052
112 -18.45545664 -5.65457358
113 13.64139077 -18.45545664
114 8.21471077 13.64139077
115 -5.37914984 8.21471077
116 -10.78336990 -5.37914984
117 -10.87405468 -10.78336990
118 8.30998347 -10.87405468
119 14.17097655 8.30998347
120 -0.95226933 14.17097655
121 21.26437888 -0.95226933
122 -6.17238362 21.26437888
123 14.12364566 -6.17238362
124 -1.90409862 14.12364566
125 5.59542852 -1.90409862
126 9.96487928 5.59542852
127 15.12729703 9.96487928
128 -19.11172204 15.12729703
129 -21.56418938 -19.11172204
130 -16.72026803 -21.56418938
131 -25.70379572 -16.72026803
132 -20.54325654 -25.70379572
133 -14.49660460 -20.54325654
134 -0.08579711 -14.49660460
135 -27.94252987 -0.08579711
136 3.42535807 -27.94252987
137 -22.84268036 3.42535807
138 -21.84042553 -22.84268036
139 -21.08222990 -21.84042553
140 0.11139455 -21.08222990
141 5.05682857 0.11139455
142 6.18236149 5.05682857
143 23.78123816 6.18236149
144 1.52204660 23.78123816
145 20.78972893 1.52204660
146 1.80713067 20.78972893
147 -14.66927708 1.80713067
148 -24.24126121 -14.66927708
149 -13.11572255 -24.24126121
150 10.31546762 -13.11572255
151 5.33894619 10.31546762
152 -23.99452778 5.33894619
153 13.44025601 -23.99452778
154 0.87453919 13.44025601
155 -3.46404726 0.87453919
156 -28.75978743 -3.46404726
157 6.65150321 -28.75978743
158 -32.53678486 6.65150321
159 3.10358591 -32.53678486
160 15.21280344 3.10358591
161 -16.85912071 15.21280344
162 6.75690185 -16.85912071
163 -17.95726596 6.75690185
164 9.91434197 -17.95726596
165 30.36897792 9.91434197
166 -0.41595178 30.36897792
167 10.03242760 -0.41595178
168 -9.96539346 10.03242760
169 -7.92185596 -9.96539346
170 0.67766670 -7.92185596
171 -23.76077377 0.67766670
172 -21.65106490 -23.76077377
173 -15.21830705 -21.65106490
174 -17.51737450 -15.21830705
175 -20.64858207 -17.51737450
176 -17.04416735 -20.64858207
177 -1.33567019 -17.04416735
178 -5.65064563 -1.33567019
179 4.18316643 -5.65064563
180 6.74592185 4.18316643
181 -1.84375642 6.74592185
182 -17.43550441 -1.84375642
183 -13.85870970 -17.43550441
184 -16.98383710 -13.85870970
185 -20.79900671 -16.98383710
186 -35.17991523 -20.79900671
187 -51.06506620 -35.17991523
188 -22.09339481 -51.06506620
189 -0.93833461 -22.09339481
190 -7.49909837 -0.93833461
191 13.55731616 -7.49909837
192 -7.55574018 13.55731616
193 10.35759738 -7.55574018
194 43.16642382 10.35759738
> 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/7vddp1386190257.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/8uw3o1386190257.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/9nfx21386190257.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/wessaorg/rcomp/tmp/105les1386190257.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/wessaorg/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11m3yq1386190257.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/12cwsx1386190257.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/13lznm1386190258.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/14oou71386190258.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/152h4c1386190258.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/166rgw1386190258.tab")
+ }
>
> try(system("convert tmp/1rz721386190257.ps tmp/1rz721386190257.png",intern=TRUE))
character(0)
> try(system("convert tmp/2o5s81386190257.ps tmp/2o5s81386190257.png",intern=TRUE))
character(0)
> try(system("convert tmp/32nda1386190257.ps tmp/32nda1386190257.png",intern=TRUE))
character(0)
> try(system("convert tmp/4yph71386190257.ps tmp/4yph71386190257.png",intern=TRUE))
character(0)
> try(system("convert tmp/5tqem1386190257.ps tmp/5tqem1386190257.png",intern=TRUE))
character(0)
> try(system("convert tmp/6kzo91386190257.ps tmp/6kzo91386190257.png",intern=TRUE))
character(0)
> try(system("convert tmp/7vddp1386190257.ps tmp/7vddp1386190257.png",intern=TRUE))
character(0)
> try(system("convert tmp/8uw3o1386190257.ps tmp/8uw3o1386190257.png",intern=TRUE))
character(0)
> try(system("convert tmp/9nfx21386190257.ps tmp/9nfx21386190257.png",intern=TRUE))
character(0)
> try(system("convert tmp/105les1386190257.ps tmp/105les1386190257.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
36.299 6.340 42.637