R version 3.0.2 (2013-09-25) -- "Frisbee Sailing"
Copyright (C) 2013 The R Foundation for Statistical Computing
Platform: i686-pc-linux-gnu (32-bit)
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Type 'contributors()' for more information and
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Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> x <- array(list(1
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+ ,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
+ ,0.04363
+ ,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
+ ,0.07008
+ ,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
+ ,0.04812
+ ,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
+ ,0.03804
+ ,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
+ ,0.03794
+ ,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
+ ,0.03078
+ ,0.04398
+ ,21.209
+ ,0.462803
+ ,0.664357
+ ,-5.724056
+ ,0.190667
+ ,2.555477
+ ,0.148569)
+ ,dim=c(22
+ ,195)
+ ,dimnames=list(c('status'
+ ,'MDVP:Fo(Hz)'
+ ,'MDVP:Fhi(Hz)'
+ ,'MDVP:Flo(Hz)'
+ ,'MDVP:Jitter(%)'
+ ,'MDVP:Jitter(Abs)'
+ ,'MDVP:RAP'
+ ,'MDVP:PPQ'
+ ,'Jitter:DDP'
+ ,'MDVP:Shimmer'
+ ,'MDVP:Shimmer(dB)'
+ ,'Shimmer:APQ3'
+ ,'Shimmer:APQ5'
+ ,'Shimmer:DDA'
+ ,'NHR'
+ ,'HNR'
+ ,'RPDE'
+ ,'DFA'
+ ,'spread1'
+ ,'spread2'
+ ,'D2'
+ ,'PPE')
+ ,1:195))
> y <- array(NA,dim=c(22,195),dimnames=list(c('status','MDVP:Fo(Hz)','MDVP:Fhi(Hz)','MDVP:Flo(Hz)','MDVP:Jitter(%)','MDVP:Jitter(Abs)','MDVP:RAP','MDVP:PPQ','Jitter:DDP','MDVP:Shimmer','MDVP:Shimmer(dB)','Shimmer:APQ3','Shimmer:APQ5','Shimmer:DDA','NHR','HNR','RPDE','DFA','spread1','spread2','D2','PPE'),1:195))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = 'No Linear Trend'
> par2 = 'Do not include Seasonal Dummies'
> par1 = '1'
> par3 <- 'No Linear Trend'
> par2 <- 'Do not include Seasonal Dummies'
> par1 <- '1'
> #'GNU S' R Code compiled by R2WASP v. 1.2.327 ()
> #Author: root
> #To cite this work: Wessa P., (2013), Multiple Regression (v1.0.29) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_multipleregression.wasp/
> #Source of accompanying publication: Office for Research, Development, and Education
> #
> library(lattice)
> library(lmtest)
Loading required package: zoo
Attaching package: 'zoo'
The following objects are masked from 'package:base':
as.Date, as.Date.numeric
> n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
> par1 <- as.numeric(par1)
> x <- t(y)
> k <- length(x[1,])
> n <- length(x[,1])
> x1 <- cbind(x[,par1], x[,1:k!=par1])
> mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
> colnames(x1) <- mycolnames #colnames(x)[par1]
> x <- x1
> if (par3 == 'First Differences'){
+ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
+ for (i in 1:n-1) {
+ for (j in 1:k) {
+ x2[i,j] <- x[i+1,j] - x[i,j]
+ }
+ }
+ x <- x2
+ }
> if (par2 == 'Include Monthly Dummies'){
+ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
+ for (i in 1:11){
+ x2[seq(i,n,12),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> if (par2 == 'Include Quarterly Dummies'){
+ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
+ for (i in 1:3){
+ x2[seq(i,n,4),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> k <- length(x[1,])
> if (par3 == 'Linear Trend'){
+ x <- cbind(x, c(1:n))
+ colnames(x)[k+1] <- 't'
+ }
> x
status MDVP:Fo(Hz) MDVP:Fhi(Hz) MDVP:Flo(Hz) MDVP:Jitter(%)
1 1 119.992 157.302 74.997 0.00784
2 1 122.400 148.650 113.819 0.00968
3 1 116.682 131.111 111.555 0.01050
4 1 116.676 137.871 111.366 0.00997
5 1 116.014 141.781 110.655 0.01284
6 1 120.552 131.162 113.787 0.00968
7 1 120.267 137.244 114.820 0.00333
8 1 107.332 113.840 104.315 0.00290
9 1 95.730 132.068 91.754 0.00551
10 1 95.056 120.103 91.226 0.00532
11 1 88.333 112.240 84.072 0.00505
12 1 91.904 115.871 86.292 0.00540
13 1 136.926 159.866 131.276 0.00293
14 1 139.173 179.139 76.556 0.00390
15 1 152.845 163.305 75.836 0.00294
16 1 142.167 217.455 83.159 0.00369
17 1 144.188 349.259 82.764 0.00544
18 1 168.778 232.181 75.603 0.00718
19 1 153.046 175.829 68.623 0.00742
20 1 156.405 189.398 142.822 0.00768
21 1 153.848 165.738 65.782 0.00840
22 1 153.880 172.860 78.128 0.00480
23 1 167.930 193.221 79.068 0.00442
24 1 173.917 192.735 86.180 0.00476
25 1 163.656 200.841 76.779 0.00742
26 1 104.400 206.002 77.968 0.00633
27 1 171.041 208.313 75.501 0.00455
28 1 146.845 208.701 81.737 0.00496
29 1 155.358 227.383 80.055 0.00310
30 1 162.568 198.346 77.630 0.00502
31 0 197.076 206.896 192.055 0.00289
32 0 199.228 209.512 192.091 0.00241
33 0 198.383 215.203 193.104 0.00212
34 0 202.266 211.604 197.079 0.00180
35 0 203.184 211.526 196.160 0.00178
36 0 201.464 210.565 195.708 0.00198
37 1 177.876 192.921 168.013 0.00411
38 1 176.170 185.604 163.564 0.00369
39 1 180.198 201.249 175.456 0.00284
40 1 187.733 202.324 173.015 0.00316
41 1 186.163 197.724 177.584 0.00298
42 1 184.055 196.537 166.977 0.00258
43 0 237.226 247.326 225.227 0.00298
44 0 241.404 248.834 232.483 0.00281
45 0 243.439 250.912 232.435 0.00210
46 0 242.852 255.034 227.911 0.00225
47 0 245.510 262.090 231.848 0.00235
48 0 252.455 261.487 182.786 0.00185
49 0 122.188 128.611 115.765 0.00524
50 0 122.964 130.049 114.676 0.00428
51 0 124.445 135.069 117.495 0.00431
52 0 126.344 134.231 112.773 0.00448
53 0 128.001 138.052 122.080 0.00436
54 0 129.336 139.867 118.604 0.00490
55 1 108.807 134.656 102.874 0.00761
56 1 109.860 126.358 104.437 0.00874
57 1 110.417 131.067 103.370 0.00784
58 1 117.274 129.916 110.402 0.00752
59 1 116.879 131.897 108.153 0.00788
60 1 114.847 271.314 104.680 0.00867
61 0 209.144 237.494 109.379 0.00282
62 0 223.365 238.987 98.664 0.00264
63 0 222.236 231.345 205.495 0.00266
64 0 228.832 234.619 223.634 0.00296
65 0 229.401 252.221 221.156 0.00205
66 0 228.969 239.541 113.201 0.00238
67 1 140.341 159.774 67.021 0.00817
68 1 136.969 166.607 66.004 0.00923
69 1 143.533 162.215 65.809 0.01101
70 1 148.090 162.824 67.343 0.00762
71 1 142.729 162.408 65.476 0.00831
72 1 136.358 176.595 65.750 0.00971
73 1 120.080 139.710 111.208 0.00405
74 1 112.014 588.518 107.024 0.00533
75 1 110.793 128.101 107.316 0.00494
76 1 110.707 122.611 105.007 0.00516
77 1 112.876 148.826 106.981 0.00500
78 1 110.568 125.394 106.821 0.00462
79 1 95.385 102.145 90.264 0.00608
80 1 100.770 115.697 85.545 0.01038
81 1 96.106 108.664 84.510 0.00694
82 1 95.605 107.715 87.549 0.00702
83 1 100.960 110.019 95.628 0.00606
84 1 98.804 102.305 87.804 0.00432
85 1 176.858 205.560 75.344 0.00747
86 1 180.978 200.125 155.495 0.00406
87 1 178.222 202.450 141.047 0.00321
88 1 176.281 227.381 125.610 0.00520
89 1 173.898 211.350 74.677 0.00448
90 1 179.711 225.930 144.878 0.00709
91 1 166.605 206.008 78.032 0.00742
92 1 151.955 163.335 147.226 0.00419
93 1 148.272 164.989 142.299 0.00459
94 1 152.125 161.469 76.596 0.00382
95 1 157.821 172.975 68.401 0.00358
96 1 157.447 163.267 149.605 0.00369
97 1 159.116 168.913 144.811 0.00342
98 1 125.036 143.946 116.187 0.01280
99 1 125.791 140.557 96.206 0.01378
100 1 126.512 141.756 99.770 0.01936
101 1 125.641 141.068 116.346 0.03316
102 1 128.451 150.449 75.632 0.01551
103 1 139.224 586.567 66.157 0.03011
104 1 150.258 154.609 75.349 0.00248
105 1 154.003 160.267 128.621 0.00183
106 1 149.689 160.368 133.608 0.00257
107 1 155.078 163.736 144.148 0.00168
108 1 151.884 157.765 133.751 0.00258
109 1 151.989 157.339 132.857 0.00174
110 1 193.030 208.900 80.297 0.00766
111 1 200.714 223.982 89.686 0.00621
112 1 208.519 220.315 199.020 0.00609
113 1 204.664 221.300 189.621 0.00841
114 1 210.141 232.706 185.258 0.00534
115 1 206.327 226.355 92.020 0.00495
116 1 151.872 492.892 69.085 0.00856
117 1 158.219 442.557 71.948 0.00476
118 1 170.756 450.247 79.032 0.00555
119 1 178.285 442.824 82.063 0.00462
120 1 217.116 233.481 93.978 0.00404
121 1 128.940 479.697 88.251 0.00581
122 1 176.824 215.293 83.961 0.00460
123 1 138.190 203.522 83.340 0.00704
124 1 182.018 197.173 79.187 0.00842
125 1 156.239 195.107 79.820 0.00694
126 1 145.174 198.109 80.637 0.00733
127 1 138.145 197.238 81.114 0.00544
128 1 166.888 198.966 79.512 0.00638
129 1 119.031 127.533 109.216 0.00440
130 1 120.078 126.632 105.667 0.00270
131 1 120.289 128.143 100.209 0.00492
132 1 120.256 125.306 104.773 0.00407
133 1 119.056 125.213 86.795 0.00346
134 1 118.747 123.723 109.836 0.00331
135 1 106.516 112.777 93.105 0.00589
136 1 110.453 127.611 105.554 0.00494
137 1 113.400 133.344 107.816 0.00451
138 1 113.166 130.270 100.673 0.00502
139 1 112.239 126.609 104.095 0.00472
140 1 116.150 131.731 109.815 0.00381
141 1 170.368 268.796 79.543 0.00571
142 1 208.083 253.792 91.802 0.00757
143 1 198.458 219.290 148.691 0.00376
144 1 202.805 231.508 86.232 0.00370
145 1 202.544 241.350 164.168 0.00254
146 1 223.361 263.872 87.638 0.00352
147 1 169.774 191.759 151.451 0.01568
148 1 183.520 216.814 161.340 0.01466
149 1 188.620 216.302 165.982 0.01719
150 1 202.632 565.740 177.258 0.01627
151 1 186.695 211.961 149.442 0.01872
152 1 192.818 224.429 168.793 0.03107
153 1 198.116 233.099 174.478 0.02714
154 1 121.345 139.644 98.250 0.00684
155 1 119.100 128.442 88.833 0.00692
156 1 117.870 127.349 95.654 0.00647
157 1 122.336 142.369 94.794 0.00727
158 1 117.963 134.209 100.757 0.01813
159 1 126.144 154.284 97.543 0.00975
160 1 127.930 138.752 112.173 0.00605
161 1 114.238 124.393 77.022 0.00581
162 1 115.322 135.738 107.802 0.00619
163 1 114.554 126.778 91.121 0.00651
164 1 112.150 131.669 97.527 0.00519
165 1 102.273 142.830 85.902 0.00907
166 0 236.200 244.663 102.137 0.00277
167 0 237.323 243.709 229.256 0.00303
168 0 260.105 264.919 237.303 0.00339
169 0 197.569 217.627 90.794 0.00803
170 0 240.301 245.135 219.783 0.00517
171 0 244.990 272.210 239.170 0.00451
172 0 112.547 133.374 105.715 0.00355
173 0 110.739 113.597 100.139 0.00356
174 0 113.715 116.443 96.913 0.00349
175 0 117.004 144.466 99.923 0.00353
176 0 115.380 123.109 108.634 0.00332
177 0 116.388 129.038 108.970 0.00346
178 1 151.737 190.204 129.859 0.00314
179 1 148.790 158.359 138.990 0.00309
180 1 148.143 155.982 135.041 0.00392
181 1 150.440 163.441 144.736 0.00396
182 1 148.462 161.078 141.998 0.00397
183 1 149.818 163.417 144.786 0.00336
184 0 117.226 123.925 106.656 0.00417
185 0 116.848 217.552 99.503 0.00531
186 0 116.286 177.291 96.983 0.00314
187 0 116.556 592.030 86.228 0.00496
188 0 116.342 581.289 94.246 0.00267
189 0 114.563 119.167 86.647 0.00327
190 0 201.774 262.707 78.228 0.00694
191 0 174.188 230.978 94.261 0.00459
192 0 209.516 253.017 89.488 0.00564
193 0 174.688 240.005 74.287 0.01360
194 0 198.764 396.961 74.904 0.00740
195 0 214.289 260.277 77.973 0.00567
MDVP:Jitter(Abs) MDVP: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 Shimmer:DDA NHR HNR RPDE DFA
1 0.02182 0.03130 0.06545 0.02211 21.033 0.414783 0.815285
2 0.03134 0.04518 0.09403 0.01929 19.085 0.458359 0.819521
3 0.02757 0.03858 0.08270 0.01309 20.651 0.429895 0.825288
4 0.02924 0.04005 0.08771 0.01353 20.644 0.434969 0.819235
5 0.03490 0.04825 0.10470 0.01767 19.649 0.417356 0.823484
6 0.02328 0.03526 0.06985 0.01222 21.378 0.415564 0.825069
7 0.00779 0.00937 0.02337 0.00607 24.886 0.596040 0.764112
8 0.00829 0.00946 0.02487 0.00344 26.892 0.637420 0.763262
9 0.01073 0.01277 0.03218 0.01070 21.812 0.615551 0.773587
10 0.01441 0.01725 0.04324 0.01022 21.862 0.547037 0.798463
11 0.01079 0.01342 0.03237 0.01166 21.118 0.611137 0.776156
12 0.01424 0.01641 0.04272 0.01141 21.414 0.583390 0.792520
13 0.00656 0.00717 0.01968 0.00581 25.703 0.460600 0.646846
14 0.00728 0.00932 0.02184 0.01041 24.889 0.430166 0.665833
15 0.01064 0.00972 0.03191 0.00609 24.922 0.474791 0.654027
16 0.00772 0.00888 0.02316 0.00839 25.175 0.565924 0.658245
17 0.00969 0.01200 0.02908 0.01859 22.333 0.567380 0.644692
18 0.01441 0.01893 0.04322 0.02919 20.376 0.631099 0.605417
19 0.02471 0.03572 0.07413 0.03160 17.280 0.665318 0.719467
20 0.01721 0.02374 0.05164 0.03365 17.153 0.649554 0.686080
21 0.01667 0.02383 0.05000 0.03871 17.536 0.660125 0.704087
22 0.02021 0.02591 0.06062 0.01849 19.493 0.629017 0.698951
23 0.02228 0.02540 0.06685 0.01280 22.468 0.619060 0.679834
24 0.02187 0.02470 0.06562 0.01840 20.422 0.537264 0.686894
25 0.00738 0.00948 0.02214 0.01778 23.831 0.397937 0.732479
26 0.01732 0.02245 0.05197 0.02887 22.066 0.522746 0.737948
27 0.00889 0.01169 0.02666 0.01095 25.908 0.418622 0.720916
28 0.00883 0.01144 0.02650 0.01328 25.119 0.358773 0.726652
29 0.00769 0.01012 0.02307 0.00677 25.970 0.470478 0.676258
30 0.00793 0.01057 0.02380 0.01170 25.678 0.427785 0.723797
31 0.00563 0.00680 0.01689 0.00339 26.775 0.422229 0.741367
32 0.00504 0.00641 0.01513 0.00167 30.940 0.432439 0.742055
33 0.00640 0.00825 0.01919 0.00119 30.775 0.465946 0.738703
34 0.00469 0.00606 0.01407 0.00072 32.684 0.368535 0.742133
35 0.00468 0.00610 0.01403 0.00065 33.047 0.340068 0.741899
36 0.00586 0.00760 0.01758 0.00135 31.732 0.344252 0.742737
37 0.01154 0.01347 0.03463 0.00586 23.216 0.360148 0.778834
38 0.00938 0.01160 0.02814 0.00340 24.951 0.341435 0.783626
39 0.00726 0.00885 0.02177 0.00231 26.738 0.403884 0.766209
40 0.00829 0.01003 0.02488 0.00265 26.310 0.396793 0.758324
41 0.00774 0.00941 0.02321 0.00231 26.822 0.326480 0.765623
42 0.00742 0.00901 0.02226 0.00257 26.453 0.306443 0.759203
43 0.01035 0.01024 0.03104 0.00740 22.736 0.305062 0.654172
44 0.01006 0.01038 0.03017 0.00675 23.145 0.457702 0.634267
45 0.00777 0.00898 0.02330 0.00454 25.368 0.438296 0.635285
46 0.00847 0.00879 0.02542 0.00476 25.032 0.431285 0.638928
47 0.00906 0.00977 0.02719 0.00476 24.602 0.467489 0.631653
48 0.00614 0.00730 0.01841 0.00432 26.805 0.610367 0.635204
49 0.00855 0.00776 0.02566 0.00839 23.162 0.579597 0.733659
50 0.00930 0.00802 0.02789 0.00462 24.971 0.538688 0.754073
51 0.01241 0.01024 0.03724 0.00479 25.135 0.553134 0.775933
52 0.01143 0.00959 0.03429 0.00474 25.030 0.507504 0.760361
53 0.01323 0.01072 0.03969 0.00481 24.692 0.459766 0.766204
54 0.01396 0.01219 0.04188 0.00484 25.429 0.420383 0.785714
55 0.01483 0.01609 0.04450 0.01036 21.028 0.536009 0.819032
56 0.01789 0.01992 0.05368 0.01180 20.767 0.558586 0.811843
57 0.02032 0.02302 0.06097 0.00969 21.422 0.541781 0.821364
58 0.01189 0.01459 0.03568 0.00681 22.817 0.530529 0.817756
59 0.01394 0.01625 0.04183 0.00786 22.603 0.540049 0.813432
60 0.01805 0.01974 0.05414 0.01143 21.660 0.547975 0.817396
61 0.00975 0.01258 0.02925 0.00871 25.554 0.341788 0.678874
62 0.01013 0.01296 0.03039 0.00301 26.138 0.447979 0.686264
63 0.00867 0.01108 0.02602 0.00340 25.856 0.364867 0.694399
64 0.00882 0.01075 0.02647 0.00351 25.964 0.256570 0.683296
65 0.00769 0.00957 0.02308 0.00300 26.415 0.276850 0.673636
66 0.00942 0.01160 0.02827 0.00420 24.547 0.305429 0.681811
67 0.01830 0.01810 0.05490 0.02183 19.560 0.460139 0.720908
68 0.01638 0.01759 0.04914 0.02659 19.979 0.498133 0.729067
69 0.03152 0.02422 0.09455 0.04882 20.338 0.513237 0.731444
70 0.03357 0.02494 0.10070 0.02431 21.718 0.487407 0.727313
71 0.01868 0.01906 0.05605 0.02599 20.264 0.489345 0.730387
72 0.02749 0.02466 0.08247 0.03361 18.570 0.543299 0.733232
73 0.00974 0.00925 0.02921 0.00442 25.742 0.495954 0.762959
74 0.01373 0.01375 0.04120 0.00623 24.178 0.509127 0.789532
75 0.01432 0.01325 0.04295 0.00479 25.438 0.437031 0.815908
76 0.01284 0.01219 0.03851 0.00472 25.197 0.463514 0.807217
77 0.02413 0.02231 0.07238 0.00905 23.370 0.489538 0.789977
78 0.01284 0.01199 0.03852 0.00420 25.820 0.429484 0.816340
79 0.01803 0.01886 0.05408 0.01062 21.875 0.644954 0.779612
80 0.01773 0.01783 0.05320 0.02220 19.200 0.594387 0.790117
81 0.02266 0.02451 0.06799 0.01823 19.055 0.544805 0.770466
82 0.01792 0.01841 0.05377 0.01825 19.659 0.576084 0.778747
83 0.01371 0.01421 0.04114 0.01237 20.536 0.554610 0.787896
84 0.01277 0.01343 0.03831 0.00882 22.244 0.576644 0.772416
85 0.02679 0.03022 0.08037 0.05470 13.893 0.556494 0.729586
86 0.02107 0.02493 0.06321 0.02782 16.176 0.583574 0.727747
87 0.02073 0.02415 0.06219 0.03151 15.924 0.598714 0.712199
88 0.03671 0.04159 0.11012 0.04824 13.922 0.602874 0.740837
89 0.03788 0.04254 0.11363 0.04214 14.739 0.599371 0.743937
90 0.02297 0.02768 0.06892 0.07223 11.866 0.590951 0.745526
91 0.03650 0.04282 0.10949 0.08725 11.744 0.653410 0.733165
92 0.04421 0.04962 0.13262 0.01658 19.664 0.501037 0.714360
93 0.02383 0.02521 0.07150 0.01914 18.780 0.454444 0.734504
94 0.03341 0.03794 0.10024 0.01211 20.969 0.447456 0.697790
95 0.02062 0.02321 0.06185 0.00850 22.219 0.502380 0.712170
96 0.01813 0.01909 0.05439 0.01018 21.693 0.447285 0.705658
97 0.01806 0.02024 0.05417 0.00852 22.663 0.366329 0.693429
98 0.02135 0.02174 0.06406 0.08151 15.338 0.629574 0.714485
99 0.02542 0.02630 0.07625 0.10323 15.433 0.571010 0.690892
100 0.03611 0.03963 0.10833 0.16744 12.435 0.638545 0.674953
101 0.05358 0.04791 0.16074 0.31482 8.867 0.671299 0.656846
102 0.03223 0.03672 0.09669 0.11843 15.060 0.639808 0.643327
103 0.05551 0.05005 0.16654 0.25930 10.489 0.596362 0.641418
104 0.00522 0.00659 0.01567 0.00495 26.759 0.296888 0.722356
105 0.00469 0.00582 0.01406 0.00243 28.409 0.263654 0.691483
106 0.00660 0.00818 0.01979 0.00578 27.421 0.365488 0.719974
107 0.00522 0.00632 0.01567 0.00233 29.746 0.334171 0.677930
108 0.00633 0.00788 0.01898 0.00659 26.833 0.393563 0.700246
109 0.00455 0.00576 0.01364 0.00238 29.928 0.311369 0.676066
110 0.01771 0.01815 0.05312 0.00947 21.934 0.497554 0.740539
111 0.01192 0.01439 0.03576 0.00704 23.239 0.436084 0.727863
112 0.00952 0.01058 0.02855 0.00830 22.407 0.338097 0.712466
113 0.01277 0.01483 0.03831 0.01316 21.305 0.498877 0.722085
114 0.00861 0.01017 0.02583 0.00620 23.671 0.441097 0.722254
115 0.01107 0.01284 0.03320 0.01048 21.864 0.331508 0.715121
116 0.00796 0.00832 0.02389 0.06051 23.693 0.407701 0.662668
117 0.00606 0.00747 0.01818 0.01554 26.356 0.450798 0.653823
118 0.00757 0.00971 0.02270 0.01802 25.690 0.486738 0.676023
119 0.00617 0.00744 0.01851 0.00856 25.020 0.470422 0.655239
120 0.00679 0.00631 0.02038 0.00681 24.581 0.462516 0.582710
121 0.00849 0.01117 0.02548 0.02350 24.743 0.487756 0.684130
122 0.00534 0.00630 0.01603 0.01161 27.166 0.400088 0.656182
123 0.02587 0.02567 0.07761 0.01968 18.305 0.538016 0.741480
124 0.01372 0.01580 0.04115 0.01813 18.784 0.589956 0.732903
125 0.01289 0.01420 0.03867 0.02020 19.196 0.618663 0.728421
126 0.01235 0.01495 0.03706 0.01874 18.857 0.637518 0.735546
127 0.01484 0.01805 0.04451 0.01794 18.178 0.623209 0.738245
128 0.01547 0.01859 0.04641 0.01796 18.330 0.585169 0.736964
129 0.00538 0.00570 0.01614 0.01724 26.842 0.457541 0.699787
130 0.00476 0.00588 0.01428 0.00487 26.369 0.491345 0.718839
131 0.00703 0.00820 0.02110 0.01610 23.949 0.467160 0.724045
132 0.00721 0.00815 0.02164 0.01015 26.017 0.468621 0.735136
133 0.00633 0.00701 0.01898 0.00903 23.389 0.470972 0.721308
134 0.00490 0.00621 0.01471 0.00504 25.619 0.482296 0.723096
135 0.02683 0.03112 0.08050 0.03031 17.060 0.637814 0.744064
136 0.02229 0.02592 0.06688 0.02529 17.707 0.653427 0.706687
137 0.02385 0.02973 0.07154 0.02278 19.013 0.647900 0.708144
138 0.02896 0.03347 0.08689 0.03690 16.747 0.625362 0.708617
139 0.03070 0.03530 0.09211 0.02629 17.366 0.640945 0.701404
140 0.01514 0.01812 0.04543 0.01827 18.801 0.624811 0.696049
141 0.01713 0.01964 0.05139 0.02485 18.540 0.677131 0.685057
142 0.04016 0.04003 0.12047 0.04238 15.648 0.606344 0.665945
143 0.02055 0.02076 0.06165 0.01728 18.702 0.606273 0.661735
144 0.01117 0.01177 0.03350 0.02010 18.687 0.536102 0.632631
145 0.01475 0.01558 0.04426 0.01049 20.680 0.497480 0.630409
146 0.01379 0.01478 0.04137 0.01493 20.366 0.566849 0.574282
147 0.03804 0.05426 0.11411 0.07530 12.359 0.561610 0.793509
148 0.02865 0.04101 0.08595 0.06057 14.367 0.478024 0.768974
149 0.03474 0.04580 0.10422 0.08069 12.298 0.552870 0.764036
150 0.03515 0.04265 0.10546 0.07889 14.989 0.427627 0.775708
151 0.02699 0.03714 0.08096 0.10952 12.529 0.507826 0.762726
152 0.05647 0.07940 0.16942 0.21713 8.441 0.625866 0.768320
153 0.04284 0.05556 0.12851 0.16265 9.449 0.584164 0.754449
154 0.01340 0.01399 0.04019 0.04179 21.520 0.566867 0.670475
155 0.01484 0.01405 0.04451 0.04611 21.824 0.651680 0.659333
156 0.01659 0.01804 0.04977 0.02631 22.431 0.628300 0.652025
157 0.01205 0.01289 0.03615 0.03191 22.953 0.611679 0.623731
158 0.02610 0.02161 0.07830 0.10748 19.075 0.630547 0.646786
159 0.01500 0.01581 0.04499 0.03828 21.534 0.635015 0.627337
160 0.01360 0.01650 0.04079 0.02663 19.651 0.654945 0.675865
161 0.01579 0.01994 0.04736 0.02073 20.437 0.653139 0.694571
162 0.01644 0.01722 0.04933 0.02810 19.388 0.577802 0.684373
163 0.01864 0.01940 0.05592 0.02707 18.954 0.685151 0.719576
164 0.00967 0.01033 0.02902 0.01435 21.219 0.557045 0.673086
165 0.01579 0.01553 0.04736 0.03882 18.447 0.671378 0.674562
166 0.01410 0.01426 0.04231 0.00620 24.078 0.469928 0.628232
167 0.00696 0.00747 0.02089 0.00533 24.679 0.384868 0.626710
168 0.01186 0.01230 0.03557 0.00910 21.083 0.440988 0.628058
169 0.01279 0.01272 0.03836 0.01337 19.269 0.372222 0.725216
170 0.01176 0.01191 0.03529 0.00965 21.020 0.371837 0.646167
171 0.01084 0.01121 0.03253 0.01049 21.528 0.522812 0.646818
172 0.00664 0.00786 0.01992 0.00435 26.436 0.413295 0.756700
173 0.00754 0.00950 0.02261 0.00430 26.550 0.369090 0.776158
174 0.00748 0.00905 0.02245 0.00478 26.547 0.380253 0.766700
175 0.00881 0.01062 0.02643 0.00590 25.445 0.387482 0.756482
176 0.00812 0.00933 0.02436 0.00401 26.005 0.405991 0.761255
177 0.00874 0.01021 0.02623 0.00415 26.143 0.361232 0.763242
178 0.00728 0.00886 0.02184 0.00570 24.151 0.396610 0.745957
179 0.00839 0.00956 0.02518 0.00488 24.412 0.402591 0.762508
180 0.00725 0.00876 0.02175 0.00540 23.683 0.398499 0.778349
181 0.01321 0.01574 0.03964 0.00611 23.133 0.352396 0.759320
182 0.00950 0.01103 0.02849 0.00639 22.866 0.408598 0.768845
183 0.01155 0.01341 0.03464 0.00595 23.008 0.329577 0.757180
184 0.00864 0.01223 0.02592 0.00955 23.079 0.603515 0.669565
185 0.00810 0.01144 0.02429 0.01179 22.085 0.663842 0.656516
186 0.00667 0.00990 0.02001 0.00737 24.199 0.598515 0.654331
187 0.00820 0.00972 0.02460 0.01397 23.958 0.566424 0.667654
188 0.00631 0.00789 0.01892 0.00680 25.023 0.528485 0.663884
189 0.00557 0.00721 0.01672 0.00703 24.775 0.555303 0.659132
190 0.01454 0.01582 0.04363 0.04441 19.368 0.508479 0.683761
191 0.02336 0.02498 0.07008 0.02764 19.517 0.448439 0.657899
192 0.01604 0.01657 0.04812 0.01810 19.147 0.431674 0.683244
193 0.01268 0.01365 0.03804 0.10715 17.883 0.407567 0.655683
194 0.01265 0.01321 0.03794 0.07223 19.020 0.451221 0.643956
195 0.01026 0.01161 0.03078 0.04398 21.209 0.462803 0.664357
spread1 spread2 D2 PPE
1 -4.813031 0.266482 2.301442 0.284654
2 -4.075192 0.335590 2.486855 0.368674
3 -4.443179 0.311173 2.342259 0.332634
4 -4.117501 0.334147 2.405554 0.368975
5 -3.747787 0.234513 2.332180 0.410335
6 -4.242867 0.299111 2.187560 0.357775
7 -5.634322 0.257682 1.854785 0.211756
8 -6.167603 0.183721 2.064693 0.163755
9 -5.498678 0.327769 2.322511 0.231571
10 -5.011879 0.325996 2.432792 0.271362
11 -5.249770 0.391002 2.407313 0.249740
12 -4.960234 0.363566 2.642476 0.275931
13 -6.547148 0.152813 2.041277 0.138512
14 -5.660217 0.254989 2.519422 0.199889
15 -6.105098 0.203653 2.125618 0.170100
16 -5.340115 0.210185 2.205546 0.234589
17 -5.440040 0.239764 2.264501 0.218164
18 -2.931070 0.434326 3.007463 0.430788
19 -3.949079 0.357870 3.109010 0.377429
20 -4.554466 0.340176 2.856676 0.322111
21 -4.095442 0.262564 2.739710 0.365391
22 -5.186960 0.237622 2.557536 0.259765
23 -4.330956 0.262384 2.916777 0.285695
24 -5.248776 0.210279 2.547508 0.253556
25 -5.557447 0.220890 2.692176 0.215961
26 -5.571843 0.236853 2.846369 0.219514
27 -6.183590 0.226278 2.589702 0.147403
28 -6.271690 0.196102 2.314209 0.162999
29 -7.120925 0.279789 2.241742 0.108514
30 -6.635729 0.209866 1.957961 0.135242
31 -7.348300 0.177551 1.743867 0.085569
32 -7.682587 0.173319 2.103106 0.068501
33 -7.067931 0.175181 1.512275 0.096320
34 -7.695734 0.178540 1.544609 0.056141
35 -7.964984 0.163519 1.423287 0.044539
36 -7.777685 0.170183 2.447064 0.057610
37 -6.149653 0.218037 2.477082 0.165827
38 -6.006414 0.196371 2.536527 0.173218
39 -6.452058 0.212294 2.269398 0.141929
40 -6.006647 0.266892 2.382544 0.160691
41 -6.647379 0.201095 2.374073 0.130554
42 -7.044105 0.063412 2.361532 0.115730
43 -7.310550 0.098648 2.416838 0.095032
44 -6.793547 0.158266 2.256699 0.117399
45 -7.057869 0.091608 2.330716 0.091470
46 -6.995820 0.102083 2.365800 0.102706
47 -7.156076 0.127642 2.392122 0.097336
48 -7.319510 0.200873 2.028612 0.086398
49 -6.439398 0.266392 2.079922 0.133867
50 -6.482096 0.264967 2.054419 0.128872
51 -6.650471 0.254498 1.840198 0.103561
52 -6.689151 0.291954 2.431854 0.105993
53 -7.072419 0.220434 1.972297 0.119308
54 -6.836811 0.269866 2.223719 0.147491
55 -4.649573 0.205558 1.986899 0.316700
56 -4.333543 0.221727 2.014606 0.344834
57 -4.438453 0.238298 1.922940 0.335041
58 -4.608260 0.290024 2.021591 0.314464
59 -4.476755 0.262633 1.827012 0.326197
60 -4.609161 0.221711 1.831691 0.316395
61 -7.040508 0.066994 2.460791 0.101516
62 -7.293801 0.086372 2.321560 0.098555
63 -6.966321 0.095882 2.278687 0.103224
64 -7.245620 0.018689 2.498224 0.093534
65 -7.496264 0.056844 2.003032 0.073581
66 -7.314237 0.006274 2.118596 0.091546
67 -5.409423 0.226850 2.359973 0.226156
68 -5.324574 0.205660 2.291558 0.226247
69 -5.869750 0.151814 2.118496 0.185580
70 -6.261141 0.120956 2.137075 0.141958
71 -5.720868 0.158830 2.277927 0.180828
72 -5.207985 0.224852 2.642276 0.242981
73 -5.791820 0.329066 2.205024 0.188180
74 -5.389129 0.306636 1.928708 0.225461
75 -5.313360 0.201861 2.225815 0.244512
76 -5.477592 0.315074 1.862092 0.228624
77 -5.775966 0.341169 2.007923 0.193918
78 -5.391029 0.250572 1.777901 0.232744
79 -5.115212 0.249494 2.017753 0.260015
80 -4.913885 0.265699 2.398422 0.277948
81 -4.441519 0.155097 2.645959 0.327978
82 -5.132032 0.210458 2.232576 0.260633
83 -5.022288 0.146948 2.428306 0.264666
84 -6.025367 0.078202 2.053601 0.177275
85 -5.288912 0.343073 3.099301 0.242119
86 -5.657899 0.315903 3.098256 0.200423
87 -6.366916 0.335753 2.654271 0.144614
88 -5.515071 0.299549 3.136550 0.220968
89 -5.783272 0.299793 3.007096 0.194052
90 -4.379411 0.375531 3.671155 0.332086
91 -4.508984 0.389232 3.317586 0.301952
92 -6.411497 0.207156 2.344876 0.134120
93 -5.952058 0.087840 2.344336 0.186489
94 -6.152551 0.173520 2.080121 0.160809
95 -6.251425 0.188056 2.143851 0.160812
96 -6.247076 0.180528 2.344348 0.164916
97 -6.417440 0.194627 2.473239 0.151709
98 -4.020042 0.265315 2.671825 0.340623
99 -5.159169 0.202146 2.441612 0.260375
100 -3.760348 0.242861 2.634633 0.378483
101 -3.700544 0.260481 2.991063 0.370961
102 -4.202730 0.310163 2.638279 0.356881
103 -3.269487 0.270641 2.690917 0.444774
104 -6.878393 0.089267 2.004055 0.113942
105 -7.111576 0.144780 2.065477 0.093193
106 -6.997403 0.210279 1.994387 0.112878
107 -6.981201 0.184550 2.129924 0.106802
108 -6.600023 0.249172 2.499148 0.105306
109 -6.739151 0.160686 2.296873 0.115130
110 -5.845099 0.278679 2.608749 0.185668
111 -5.258320 0.256454 2.550961 0.232520
112 -6.471427 0.184378 2.502336 0.136390
113 -4.876336 0.212054 2.376749 0.268144
114 -5.963040 0.250283 2.489191 0.177807
115 -6.729713 0.181701 2.938114 0.115515
116 -4.673241 0.261549 2.702355 0.274407
117 -6.051233 0.273280 2.640798 0.170106
118 -4.597834 0.372114 2.975889 0.282780
119 -4.913137 0.393056 2.816781 0.251972
120 -5.517173 0.389295 2.925862 0.220657
121 -6.186128 0.279933 2.686240 0.152428
122 -4.711007 0.281618 2.655744 0.234809
123 -5.418787 0.160267 2.090438 0.229892
124 -5.445140 0.142466 2.174306 0.215558
125 -5.944191 0.143359 1.929715 0.181988
126 -5.594275 0.127950 1.765957 0.222716
127 -5.540351 0.087165 1.821297 0.214075
128 -5.825257 0.115697 1.996146 0.196535
129 -6.890021 0.152941 2.328513 0.112856
130 -5.892061 0.195976 2.108873 0.183572
131 -6.135296 0.203630 2.539724 0.169923
132 -6.112667 0.217013 2.527742 0.170633
133 -5.436135 0.254909 2.516320 0.232209
134 -6.448134 0.178713 2.034827 0.141422
135 -5.301321 0.320385 2.375138 0.243080
136 -5.333619 0.322044 2.631793 0.228319
137 -4.378916 0.300067 2.445502 0.259451
138 -4.654894 0.304107 2.672362 0.274387
139 -5.634576 0.306014 2.419253 0.209191
140 -5.866357 0.233070 2.445646 0.184985
141 -4.796845 0.397749 2.963799 0.277227
142 -5.410336 0.288917 2.665133 0.231723
143 -5.585259 0.310746 2.465528 0.209863
144 -5.898673 0.213353 2.470746 0.189032
145 -6.132663 0.220617 2.576563 0.159777
146 -5.456811 0.345238 2.840556 0.232861
147 -3.297668 0.414758 3.413649 0.457533
148 -4.276605 0.355736 3.142364 0.336085
149 -3.377325 0.335357 3.274865 0.418646
150 -4.892495 0.262281 2.910213 0.270173
151 -4.484303 0.340256 2.958815 0.301487
152 -2.434031 0.450493 3.079221 0.527367
153 -2.839756 0.356224 3.184027 0.454721
154 -4.865194 0.246404 2.013530 0.168581
155 -4.239028 0.175691 2.451130 0.247455
156 -3.583722 0.207914 2.439597 0.206256
157 -5.435100 0.230532 2.699645 0.220546
158 -3.444478 0.303214 2.964568 0.261305
159 -5.070096 0.280091 2.892300 0.249703
160 -5.498456 0.234196 2.103014 0.216638
161 -5.185987 0.259229 2.151121 0.244948
162 -5.283009 0.226528 2.442906 0.238281
163 -5.529833 0.242750 2.408689 0.220520
164 -5.617124 0.184896 1.871871 0.212386
165 -2.929379 0.396746 2.560422 0.367233
166 -6.816086 0.172270 2.235197 0.119652
167 -7.018057 0.176316 1.852402 0.091604
168 -7.517934 0.160414 1.881767 0.075587
169 -5.736781 0.164529 2.882450 0.202879
170 -7.169701 0.073298 2.266432 0.100881
171 -7.304500 0.171088 2.095237 0.096220
172 -6.323531 0.218885 2.193412 0.160376
173 -6.085567 0.192375 1.889002 0.174152
174 -5.943501 0.192150 1.852542 0.179677
175 -6.012559 0.229298 1.872946 0.163118
176 -5.966779 0.197938 1.974857 0.184067
177 -6.016891 0.109256 2.004719 0.174429
178 -6.486822 0.197919 2.449763 0.132703
179 -6.311987 0.182459 2.251553 0.160306
180 -5.711205 0.240875 2.845109 0.192730
181 -6.261446 0.183218 2.264226 0.144105
182 -5.704053 0.216204 2.679185 0.197710
183 -6.277170 0.109397 2.209021 0.156368
184 -5.619070 0.191576 2.027228 0.215724
185 -5.198864 0.206768 2.120412 0.252404
186 -5.592584 0.133917 2.058658 0.214346
187 -6.431119 0.153310 2.161936 0.120605
188 -6.359018 0.116636 2.152083 0.138868
189 -6.710219 0.149694 1.913990 0.121777
190 -6.934474 0.159890 2.316346 0.112838
191 -6.538586 0.121952 2.657476 0.133050
192 -6.195325 0.129303 2.784312 0.168895
193 -6.787197 0.158453 2.679772 0.131728
194 -6.744577 0.207454 2.138608 0.123306
195 -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)`
2.270e+00 -2.286e-03 -9.960e-05 -1.558e-03
`MDVP:Jitter(%)` `MDVP:Jitter(Abs)` `MDVP:RAP` `MDVP:PPQ`
-1.807e+02 -2.632e+03 -4.068e+02 -3.516e+01
`Jitter:DDP` `MDVP:Shimmer` `MDVP:Shimmer(dB)` `Shimmer:APQ3`
2.426e+02 2.118e+01 5.449e-01 -6.739e+02
`Shimmer:APQ5` `Shimmer:DDA` NHR HNR
-2.603e+01 2.204e+02 -2.586e+00 -1.602e-02
RPDE DFA spread1 spread2
-1.043e+00 3.505e-01 1.318e-01 1.267e+00
D2 PPE
5.130e-02 1.180e+00
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.94281 -0.15029 0.04766 0.20812 0.58217
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 2.270e+00 1.144e+00 1.984 0.04887 *
`MDVP:Fo(Hz)` -2.286e-03 1.465e-03 -1.560 0.12057
`MDVP:Fhi(Hz)` -9.960e-05 3.154e-04 -0.316 0.75255
`MDVP:Flo(Hz)` -1.558e-03 7.960e-04 -1.958 0.05188 .
`MDVP:Jitter(%)` -1.807e+02 6.548e+01 -2.760 0.00640 **
`MDVP:Jitter(Abs)` -2.632e+03 3.917e+03 -0.672 0.50256
`MDVP:RAP` -4.068e+02 9.223e+03 -0.044 0.96487
`MDVP:PPQ` -3.516e+01 8.808e+01 -0.399 0.69029
`Jitter:DDP` 2.426e+02 3.075e+03 0.079 0.93720
`MDVP:Shimmer` 2.118e+01 2.605e+01 0.813 0.41733
`MDVP:Shimmer(dB)` 5.449e-01 1.193e+00 0.457 0.64832
`Shimmer:APQ3` -6.739e+02 8.921e+03 -0.076 0.93987
`Shimmer:APQ5` -2.603e+01 2.003e+01 -1.300 0.19547
`Shimmer:DDA` 2.204e+02 2.974e+03 0.074 0.94101
NHR -2.586e+00 1.964e+00 -1.317 0.18957
HNR -1.602e-02 1.426e-02 -1.123 0.26288
RPDE -1.043e+00 4.266e-01 -2.445 0.01550 *
DFA 3.505e-01 7.372e-01 0.475 0.63509
spread1 1.318e-01 9.632e-02 1.369 0.17286
spread2 1.267e+00 4.767e-01 2.657 0.00862 **
D2 5.130e-02 1.138e-01 0.451 0.65277
PPE 1.180e+00 1.347e+00 0.875 0.38258
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.3258 on 173 degrees of freedom
Multiple R-squared: 0.4925, Adjusted R-squared: 0.4309
F-statistic: 7.995 on 21 and 173 DF, p-value: < 2.2e-16
> if (n > n25) {
+ kp3 <- k + 3
+ nmkm3 <- n - k - 3
+ gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
+ numgqtests <- 0
+ numsignificant1 <- 0
+ numsignificant5 <- 0
+ numsignificant10 <- 0
+ for (mypoint in kp3:nmkm3) {
+ j <- 0
+ numgqtests <- numgqtests + 1
+ for (myalt in c('greater', 'two.sided', 'less')) {
+ j <- j + 1
+ gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
+ }
+ if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
+ }
+ gqarr
+ }
[,1] [,2] [,3]
[1,] 2.542585e-49 5.085170e-49 1.00000000
[2,] 3.200967e-70 6.401933e-70 1.00000000
[3,] 3.243071e-80 6.486142e-80 1.00000000
[4,] 7.678565e-92 1.535713e-91 1.00000000
[5,] 8.363704e-110 1.672741e-109 1.00000000
[6,] 3.065328e-121 6.130656e-121 1.00000000
[7,] 3.312769e-04 6.625538e-04 0.99966872
[8,] 9.476026e-05 1.895205e-04 0.99990524
[9,] 2.761903e-05 5.523807e-05 0.99997238
[10,] 7.249990e-06 1.449998e-05 0.99999275
[11,] 2.318831e-06 4.637662e-06 0.99999768
[12,] 6.510161e-07 1.302032e-06 0.99999935
[13,] 4.263066e-05 8.526131e-05 0.99995737
[14,] 3.296660e-05 6.593319e-05 0.99996703
[15,] 5.381629e-04 1.076326e-03 0.99946184
[16,] 7.382982e-04 1.476596e-03 0.99926170
[17,] 8.945070e-04 1.789014e-03 0.99910549
[18,] 5.581095e-04 1.116219e-03 0.99944189
[19,] 3.304338e-04 6.608677e-04 0.99966957
[20,] 1.889292e-04 3.778584e-04 0.99981107
[21,] 9.039295e-05 1.807859e-04 0.99990961
[22,] 4.601249e-05 9.202498e-05 0.99995399
[23,] 2.880337e-05 5.760673e-05 0.99997120
[24,] 3.684783e-05 7.369567e-05 0.99996315
[25,] 1.959926e-04 3.919851e-04 0.99980401
[26,] 1.553175e-04 3.106350e-04 0.99984468
[27,] 1.040494e-04 2.080988e-04 0.99989595
[28,] 7.090766e-05 1.418153e-04 0.99992909
[29,] 5.333506e-05 1.066701e-04 0.99994666
[30,] 5.529667e-05 1.105933e-04 0.99994470
[31,] 4.333263e-05 8.666527e-05 0.99995667
[32,] 4.398262e-05 8.796524e-05 0.99995602
[33,] 2.468572e-05 4.937145e-05 0.99997531
[34,] 1.585459e-05 3.170918e-05 0.99998415
[35,] 8.854622e-06 1.770924e-05 0.99999115
[36,] 5.045770e-06 1.009154e-05 0.99999495
[37,] 2.508399e-04 5.016798e-04 0.99974916
[38,] 3.823141e-04 7.646283e-04 0.99961769
[39,] 6.260249e-04 1.252050e-03 0.99937398
[40,] 7.443919e-04 1.488784e-03 0.99925561
[41,] 5.327956e-04 1.065591e-03 0.99946720
[42,] 5.347926e-04 1.069585e-03 0.99946521
[43,] 4.117990e-04 8.235979e-04 0.99958820
[44,] 2.641959e-04 5.283917e-04 0.99973580
[45,] 2.066948e-04 4.133895e-04 0.99979331
[46,] 1.499057e-04 2.998114e-04 0.99985009
[47,] 9.286552e-05 1.857310e-04 0.99990713
[48,] 6.392835e-05 1.278567e-04 0.99993607
[49,] 3.712688e-05 7.425377e-05 0.99996287
[50,] 1.296265e-04 2.592530e-04 0.99987037
[51,] 1.583747e-04 3.167494e-04 0.99984163
[52,] 1.092359e-04 2.184718e-04 0.99989076
[53,] 6.974057e-05 1.394811e-04 0.99993026
[54,] 5.093130e-05 1.018626e-04 0.99994907
[55,] 3.063298e-05 6.126596e-05 0.99996937
[56,] 2.228956e-05 4.457912e-05 0.99997771
[57,] 1.816548e-05 3.633096e-05 0.99998183
[58,] 1.091103e-05 2.182207e-05 0.99998909
[59,] 7.687149e-06 1.537430e-05 0.99999231
[60,] 5.026557e-06 1.005311e-05 0.99999497
[61,] 3.624360e-06 7.248719e-06 0.99999638
[62,] 3.741303e-06 7.482606e-06 0.99999626
[63,] 6.099887e-06 1.219977e-05 0.99999390
[64,] 3.891708e-06 7.783415e-06 0.99999611
[65,] 2.913975e-06 5.827950e-06 0.99999709
[66,] 3.105474e-06 6.210948e-06 0.99999689
[67,] 2.861832e-06 5.723664e-06 0.99999714
[68,] 3.334987e-06 6.669974e-06 0.99999667
[69,] 2.137840e-06 4.275681e-06 0.99999786
[70,] 1.437111e-06 2.874223e-06 0.99999856
[71,] 9.098060e-07 1.819612e-06 0.99999909
[72,] 5.759366e-07 1.151873e-06 0.99999942
[73,] 3.681128e-07 7.362255e-07 0.99999963
[74,] 2.137790e-07 4.275581e-07 0.99999979
[75,] 1.473809e-07 2.947619e-07 0.99999985
[76,] 8.484970e-08 1.696994e-07 0.99999992
[77,] 6.184009e-08 1.236802e-07 0.99999994
[78,] 5.590367e-08 1.118073e-07 0.99999994
[79,] 6.760528e-08 1.352106e-07 0.99999993
[80,] 1.336403e-07 2.672805e-07 0.99999987
[81,] 2.015059e-07 4.030118e-07 0.99999980
[82,] 3.427429e-07 6.854858e-07 0.99999966
[83,] 7.951648e-07 1.590330e-06 0.99999920
[84,] 5.585143e-07 1.117029e-06 0.99999944
[85,] 1.236067e-06 2.472134e-06 0.99999876
[86,] 1.030757e-06 2.061514e-06 0.99999897
[87,] 6.038895e-07 1.207779e-06 0.99999940
[88,] 1.265273e-06 2.530545e-06 0.99999873
[89,] 7.780694e-07 1.556139e-06 0.99999922
[90,] 8.479460e-07 1.695892e-06 0.99999915
[91,] 8.390880e-07 1.678176e-06 0.99999916
[92,] 4.968462e-07 9.936924e-07 0.99999950
[93,] 6.171099e-07 1.234220e-06 0.99999938
[94,] 3.508326e-07 7.016652e-07 0.99999965
[95,] 2.479412e-07 4.958824e-07 0.99999975
[96,] 6.227586e-07 1.245517e-06 0.99999938
[97,] 3.223712e-06 6.447424e-06 0.99999678
[98,] 8.056822e-06 1.611364e-05 0.99999194
[99,] 5.471975e-06 1.094395e-05 0.99999453
[100,] 3.857092e-06 7.714184e-06 0.99999614
[101,] 2.666566e-06 5.333131e-06 0.99999733
[102,] 2.710302e-06 5.420604e-06 0.99999729
[103,] 4.720534e-06 9.441068e-06 0.99999528
[104,] 5.733389e-05 1.146678e-04 0.99994267
[105,] 1.971389e-04 3.942778e-04 0.99980286
[106,] 2.078994e-04 4.157989e-04 0.99979210
[107,] 2.017614e-04 4.035228e-04 0.99979824
[108,] 1.335066e-04 2.670133e-04 0.99986649
[109,] 1.112461e-04 2.224922e-04 0.99988875
[110,] 4.202961e-04 8.405922e-04 0.99957970
[111,] 3.318479e-04 6.636957e-04 0.99966815
[112,] 3.431238e-04 6.862476e-04 0.99965688
[113,] 4.378250e-04 8.756500e-04 0.99956218
[114,] 8.020040e-04 1.604008e-03 0.99919800
[115,] 6.701368e-04 1.340274e-03 0.99932986
[116,] 8.895838e-04 1.779168e-03 0.99911042
[117,] 7.378485e-04 1.475697e-03 0.99926215
[118,] 9.630031e-04 1.926006e-03 0.99903700
[119,] 7.922516e-04 1.584503e-03 0.99920775
[120,] 2.702960e-03 5.405921e-03 0.99729704
[121,] 2.354841e-03 4.709682e-03 0.99764516
[122,] 1.863254e-03 3.726508e-03 0.99813675
[123,] 1.376988e-03 2.753975e-03 0.99862301
[124,] 9.343288e-04 1.868658e-03 0.99906567
[125,] 1.778761e-03 3.557521e-03 0.99822124
[126,] 1.189494e-03 2.378988e-03 0.99881051
[127,] 1.012757e-03 2.025514e-03 0.99898724
[128,] 1.011031e-03 2.022062e-03 0.99898897
[129,] 2.255904e-02 4.511807e-02 0.97744096
[130,] 2.897953e-02 5.795906e-02 0.97102047
[131,] 3.085113e-02 6.170225e-02 0.96914887
[132,] 2.355814e-02 4.711628e-02 0.97644186
[133,] 1.063476e-01 2.126953e-01 0.89365237
[134,] 1.086464e-01 2.172928e-01 0.89135361
[135,] 3.460340e-01 6.920681e-01 0.65396597
[136,] 2.806283e-01 5.612567e-01 0.71937166
[137,] 2.168288e-01 4.336575e-01 0.78317125
[138,] 1.677847e-01 3.355693e-01 0.83221533
[139,] 1.911700e-01 3.823401e-01 0.80882997
[140,] 3.783363e-01 7.566727e-01 0.62166367
[141,] 5.020622e-01 9.958756e-01 0.49793781
[142,] 6.209635e-01 7.580730e-01 0.37903650
[143,] 9.263567e-01 1.472866e-01 0.07364328
[144,] 9.065595e-01 1.868810e-01 0.09344048
[145,] 9.551134e-01 8.977311e-02 0.04488656
[146,] 9.151082e-01 1.697837e-01 0.08489183
> postscript(file="/var/fisher/rcomp/tmp/1dbzz1386423751.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
> points(x[,1]-mysum$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/2n8bl1386423751.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/317sp1386423751.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/4mbtb1386423751.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/5473e1386423751.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
> qqline(mysum$resid)
> grid()
> dev.off()
null device
1
> (myerror <- as.ts(mysum$resid))
Time Series:
Start = 1
End = 195
Frequency = 1
1 2 3 4 5
0.0701258623 -0.0514529296 0.0362719818 -0.0725332996 0.1315158840
6 7 8 9 10
0.0706676596 0.2160464438 0.4225534430 0.0227766828 -0.1583029451
11 12 13 14 15
-0.1179025351 -0.2393499326 0.5433445603 0.1135752305 0.2961330853
16 17 18 19 20
0.2951712703 0.4475557243 -0.3109197352 -0.2935133450 0.0374387511
21 22 23 24 25
-0.0584070862 0.0910770005 -0.1171105862 0.1271901579 0.1803200855
26 27 28 29 30
0.0734406974 0.1923698917 0.2040232674 0.3384398352 0.3031297454
31 32 33 34 35
-0.2892991187 -0.1528163535 -0.1870295956 -0.1267568588 -0.0854029670
36 37 38 39 40
-0.2048818481 0.1923074378 0.1791320195 0.4018848174 0.2465487868
41 42 43 44 45
0.3825606085 0.5648101997 -0.2495419163 -0.2085917433 -0.0187574361
46 47 48 49 50
-0.0926866683 -0.0557239090 0.0451515782 -0.3393060640 -0.4292672534
51 52 53 54 55
-0.4109190090 -0.4349002979 -0.4096176794 -0.5524029928 0.1698969863
56 57 58 59 60
0.2015264323 0.1324084202 0.2421741366 0.2206706319 0.3384044733
61 62 63 64 65
-0.3690941287 -0.2734134993 -0.2624328769 -0.2136757077 -0.1308833071
66 67 68 69 70
-0.2867052798 0.0755973788 0.1037763756 0.0859517697 0.0476560691
71 72 73 74 75
0.1417965024 -0.0918238408 0.1113384846 0.0645111014 -0.0458090661
76 77 78 79 80
-0.0873647303 -0.1024155043 -0.0048980470 0.0359645542 -0.1473980021
81 82 83 84 85
-0.1813392122 -0.1370282150 -0.0160901256 0.3076263989 -0.0817867276
86 87 88 89 90
0.1327567692 0.2968729647 0.0603967527 0.0107407408 -0.2171890472
91 92 93 94 95
-0.1366057668 0.2098975616 0.2671478485 0.1433173936 0.2118478480
96 97 98 99 100
0.2409902950 0.1932511329 -0.0299143810 0.1893785791 0.0902200984
101 102 103 104 105
0.0380810421 0.0260995536 0.0214677351 0.4108419344 0.4210545774
106 107 108 109 110
0.4356404043 0.4653584524 0.3123733418 0.3815574820 0.1040295318
111 112 113 114 115
-0.0338821476 0.4097161158 0.2099593094 0.3038102705 0.1955950307
116 117 118 119 120
0.1292696340 0.2745232910 -0.0499362452 0.1089664039 0.2360458305
121 122 123 124 125
0.4496455531 0.0237243230 0.0264153346 0.2897285114 0.3905362482
126 127 128 129 130
0.3809525355 0.3893559944 0.3722594451 0.5821732248 0.2430294327
131 132 133 134 135
0.1992634392 0.1309263925 -0.0499954721 0.3559000408 0.0384285430
136 137 138 139 140
0.0394350255 -0.1440930690 -0.1477572853 0.0425783478 0.2184198005
141 142 143 144 145
0.0927353453 0.1426011132 0.2696295275 0.3148098810 0.4510685199
146 147 148 149 150
0.1368546881 -0.3680690359 -0.1640555410 -0.2386483982 0.1173537192
151 152 153 154 155
0.0642281920 0.0082886324 0.0571992474 0.1462046260 0.1038403026
156 157 158 159 160
0.0003592216 0.2063450760 -0.2493911304 0.0310806775 0.1509187768
161 162 163 164 165
-0.1126607571 -0.0553105168 0.0703788753 0.2030341446 -0.3912657849
166 167 168 169 170
-0.4505415003 -0.2265153348 -0.0946885232 -0.9428134891 -0.2281599826
171 172 173 174 175
-0.1105664221 -0.7954817912 -0.8374265143 -0.8738884760 -0.8686731218
176 177 178 179 180
-0.8342017850 -0.7736445551 0.3565946892 0.2873994732 0.0616521112
181 182 183 184 185
0.2313055231 0.1213822982 0.2786509081 -0.6061113367 -0.6494359886
186 187 188 189 190
-0.6052159039 -0.4315454219 -0.4783835049 -0.4330765767 -0.4207740756
191 192 193 194 195
-0.6495521784 -0.7039654166 0.1798504075 -0.2664820661 -0.5231375976
> postscript(file="/var/fisher/rcomp/tmp/6qfjn1386423751.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> dum <- cbind(lag(myerror,k=1),myerror)
> dum
Time Series:
Start = 0
End = 195
Frequency = 1
lag(myerror, k = 1) myerror
0 0.0701258623 NA
1 -0.0514529296 0.0701258623
2 0.0362719818 -0.0514529296
3 -0.0725332996 0.0362719818
4 0.1315158840 -0.0725332996
5 0.0706676596 0.1315158840
6 0.2160464438 0.0706676596
7 0.4225534430 0.2160464438
8 0.0227766828 0.4225534430
9 -0.1583029451 0.0227766828
10 -0.1179025351 -0.1583029451
11 -0.2393499326 -0.1179025351
12 0.5433445603 -0.2393499326
13 0.1135752305 0.5433445603
14 0.2961330853 0.1135752305
15 0.2951712703 0.2961330853
16 0.4475557243 0.2951712703
17 -0.3109197352 0.4475557243
18 -0.2935133450 -0.3109197352
19 0.0374387511 -0.2935133450
20 -0.0584070862 0.0374387511
21 0.0910770005 -0.0584070862
22 -0.1171105862 0.0910770005
23 0.1271901579 -0.1171105862
24 0.1803200855 0.1271901579
25 0.0734406974 0.1803200855
26 0.1923698917 0.0734406974
27 0.2040232674 0.1923698917
28 0.3384398352 0.2040232674
29 0.3031297454 0.3384398352
30 -0.2892991187 0.3031297454
31 -0.1528163535 -0.2892991187
32 -0.1870295956 -0.1528163535
33 -0.1267568588 -0.1870295956
34 -0.0854029670 -0.1267568588
35 -0.2048818481 -0.0854029670
36 0.1923074378 -0.2048818481
37 0.1791320195 0.1923074378
38 0.4018848174 0.1791320195
39 0.2465487868 0.4018848174
40 0.3825606085 0.2465487868
41 0.5648101997 0.3825606085
42 -0.2495419163 0.5648101997
43 -0.2085917433 -0.2495419163
44 -0.0187574361 -0.2085917433
45 -0.0926866683 -0.0187574361
46 -0.0557239090 -0.0926866683
47 0.0451515782 -0.0557239090
48 -0.3393060640 0.0451515782
49 -0.4292672534 -0.3393060640
50 -0.4109190090 -0.4292672534
51 -0.4349002979 -0.4109190090
52 -0.4096176794 -0.4349002979
53 -0.5524029928 -0.4096176794
54 0.1698969863 -0.5524029928
55 0.2015264323 0.1698969863
56 0.1324084202 0.2015264323
57 0.2421741366 0.1324084202
58 0.2206706319 0.2421741366
59 0.3384044733 0.2206706319
60 -0.3690941287 0.3384044733
61 -0.2734134993 -0.3690941287
62 -0.2624328769 -0.2734134993
63 -0.2136757077 -0.2624328769
64 -0.1308833071 -0.2136757077
65 -0.2867052798 -0.1308833071
66 0.0755973788 -0.2867052798
67 0.1037763756 0.0755973788
68 0.0859517697 0.1037763756
69 0.0476560691 0.0859517697
70 0.1417965024 0.0476560691
71 -0.0918238408 0.1417965024
72 0.1113384846 -0.0918238408
73 0.0645111014 0.1113384846
74 -0.0458090661 0.0645111014
75 -0.0873647303 -0.0458090661
76 -0.1024155043 -0.0873647303
77 -0.0048980470 -0.1024155043
78 0.0359645542 -0.0048980470
79 -0.1473980021 0.0359645542
80 -0.1813392122 -0.1473980021
81 -0.1370282150 -0.1813392122
82 -0.0160901256 -0.1370282150
83 0.3076263989 -0.0160901256
84 -0.0817867276 0.3076263989
85 0.1327567692 -0.0817867276
86 0.2968729647 0.1327567692
87 0.0603967527 0.2968729647
88 0.0107407408 0.0603967527
89 -0.2171890472 0.0107407408
90 -0.1366057668 -0.2171890472
91 0.2098975616 -0.1366057668
92 0.2671478485 0.2098975616
93 0.1433173936 0.2671478485
94 0.2118478480 0.1433173936
95 0.2409902950 0.2118478480
96 0.1932511329 0.2409902950
97 -0.0299143810 0.1932511329
98 0.1893785791 -0.0299143810
99 0.0902200984 0.1893785791
100 0.0380810421 0.0902200984
101 0.0260995536 0.0380810421
102 0.0214677351 0.0260995536
103 0.4108419344 0.0214677351
104 0.4210545774 0.4108419344
105 0.4356404043 0.4210545774
106 0.4653584524 0.4356404043
107 0.3123733418 0.4653584524
108 0.3815574820 0.3123733418
109 0.1040295318 0.3815574820
110 -0.0338821476 0.1040295318
111 0.4097161158 -0.0338821476
112 0.2099593094 0.4097161158
113 0.3038102705 0.2099593094
114 0.1955950307 0.3038102705
115 0.1292696340 0.1955950307
116 0.2745232910 0.1292696340
117 -0.0499362452 0.2745232910
118 0.1089664039 -0.0499362452
119 0.2360458305 0.1089664039
120 0.4496455531 0.2360458305
121 0.0237243230 0.4496455531
122 0.0264153346 0.0237243230
123 0.2897285114 0.0264153346
124 0.3905362482 0.2897285114
125 0.3809525355 0.3905362482
126 0.3893559944 0.3809525355
127 0.3722594451 0.3893559944
128 0.5821732248 0.3722594451
129 0.2430294327 0.5821732248
130 0.1992634392 0.2430294327
131 0.1309263925 0.1992634392
132 -0.0499954721 0.1309263925
133 0.3559000408 -0.0499954721
134 0.0384285430 0.3559000408
135 0.0394350255 0.0384285430
136 -0.1440930690 0.0394350255
137 -0.1477572853 -0.1440930690
138 0.0425783478 -0.1477572853
139 0.2184198005 0.0425783478
140 0.0927353453 0.2184198005
141 0.1426011132 0.0927353453
142 0.2696295275 0.1426011132
143 0.3148098810 0.2696295275
144 0.4510685199 0.3148098810
145 0.1368546881 0.4510685199
146 -0.3680690359 0.1368546881
147 -0.1640555410 -0.3680690359
148 -0.2386483982 -0.1640555410
149 0.1173537192 -0.2386483982
150 0.0642281920 0.1173537192
151 0.0082886324 0.0642281920
152 0.0571992474 0.0082886324
153 0.1462046260 0.0571992474
154 0.1038403026 0.1462046260
155 0.0003592216 0.1038403026
156 0.2063450760 0.0003592216
157 -0.2493911304 0.2063450760
158 0.0310806775 -0.2493911304
159 0.1509187768 0.0310806775
160 -0.1126607571 0.1509187768
161 -0.0553105168 -0.1126607571
162 0.0703788753 -0.0553105168
163 0.2030341446 0.0703788753
164 -0.3912657849 0.2030341446
165 -0.4505415003 -0.3912657849
166 -0.2265153348 -0.4505415003
167 -0.0946885232 -0.2265153348
168 -0.9428134891 -0.0946885232
169 -0.2281599826 -0.9428134891
170 -0.1105664221 -0.2281599826
171 -0.7954817912 -0.1105664221
172 -0.8374265143 -0.7954817912
173 -0.8738884760 -0.8374265143
174 -0.8686731218 -0.8738884760
175 -0.8342017850 -0.8686731218
176 -0.7736445551 -0.8342017850
177 0.3565946892 -0.7736445551
178 0.2873994732 0.3565946892
179 0.0616521112 0.2873994732
180 0.2313055231 0.0616521112
181 0.1213822982 0.2313055231
182 0.2786509081 0.1213822982
183 -0.6061113367 0.2786509081
184 -0.6494359886 -0.6061113367
185 -0.6052159039 -0.6494359886
186 -0.4315454219 -0.6052159039
187 -0.4783835049 -0.4315454219
188 -0.4330765767 -0.4783835049
189 -0.4207740756 -0.4330765767
190 -0.6495521784 -0.4207740756
191 -0.7039654166 -0.6495521784
192 0.1798504075 -0.7039654166
193 -0.2664820661 0.1798504075
194 -0.5231375976 -0.2664820661
195 NA -0.5231375976
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.0514529296 0.0701258623
[2,] 0.0362719818 -0.0514529296
[3,] -0.0725332996 0.0362719818
[4,] 0.1315158840 -0.0725332996
[5,] 0.0706676596 0.1315158840
[6,] 0.2160464438 0.0706676596
[7,] 0.4225534430 0.2160464438
[8,] 0.0227766828 0.4225534430
[9,] -0.1583029451 0.0227766828
[10,] -0.1179025351 -0.1583029451
[11,] -0.2393499326 -0.1179025351
[12,] 0.5433445603 -0.2393499326
[13,] 0.1135752305 0.5433445603
[14,] 0.2961330853 0.1135752305
[15,] 0.2951712703 0.2961330853
[16,] 0.4475557243 0.2951712703
[17,] -0.3109197352 0.4475557243
[18,] -0.2935133450 -0.3109197352
[19,] 0.0374387511 -0.2935133450
[20,] -0.0584070862 0.0374387511
[21,] 0.0910770005 -0.0584070862
[22,] -0.1171105862 0.0910770005
[23,] 0.1271901579 -0.1171105862
[24,] 0.1803200855 0.1271901579
[25,] 0.0734406974 0.1803200855
[26,] 0.1923698917 0.0734406974
[27,] 0.2040232674 0.1923698917
[28,] 0.3384398352 0.2040232674
[29,] 0.3031297454 0.3384398352
[30,] -0.2892991187 0.3031297454
[31,] -0.1528163535 -0.2892991187
[32,] -0.1870295956 -0.1528163535
[33,] -0.1267568588 -0.1870295956
[34,] -0.0854029670 -0.1267568588
[35,] -0.2048818481 -0.0854029670
[36,] 0.1923074378 -0.2048818481
[37,] 0.1791320195 0.1923074378
[38,] 0.4018848174 0.1791320195
[39,] 0.2465487868 0.4018848174
[40,] 0.3825606085 0.2465487868
[41,] 0.5648101997 0.3825606085
[42,] -0.2495419163 0.5648101997
[43,] -0.2085917433 -0.2495419163
[44,] -0.0187574361 -0.2085917433
[45,] -0.0926866683 -0.0187574361
[46,] -0.0557239090 -0.0926866683
[47,] 0.0451515782 -0.0557239090
[48,] -0.3393060640 0.0451515782
[49,] -0.4292672534 -0.3393060640
[50,] -0.4109190090 -0.4292672534
[51,] -0.4349002979 -0.4109190090
[52,] -0.4096176794 -0.4349002979
[53,] -0.5524029928 -0.4096176794
[54,] 0.1698969863 -0.5524029928
[55,] 0.2015264323 0.1698969863
[56,] 0.1324084202 0.2015264323
[57,] 0.2421741366 0.1324084202
[58,] 0.2206706319 0.2421741366
[59,] 0.3384044733 0.2206706319
[60,] -0.3690941287 0.3384044733
[61,] -0.2734134993 -0.3690941287
[62,] -0.2624328769 -0.2734134993
[63,] -0.2136757077 -0.2624328769
[64,] -0.1308833071 -0.2136757077
[65,] -0.2867052798 -0.1308833071
[66,] 0.0755973788 -0.2867052798
[67,] 0.1037763756 0.0755973788
[68,] 0.0859517697 0.1037763756
[69,] 0.0476560691 0.0859517697
[70,] 0.1417965024 0.0476560691
[71,] -0.0918238408 0.1417965024
[72,] 0.1113384846 -0.0918238408
[73,] 0.0645111014 0.1113384846
[74,] -0.0458090661 0.0645111014
[75,] -0.0873647303 -0.0458090661
[76,] -0.1024155043 -0.0873647303
[77,] -0.0048980470 -0.1024155043
[78,] 0.0359645542 -0.0048980470
[79,] -0.1473980021 0.0359645542
[80,] -0.1813392122 -0.1473980021
[81,] -0.1370282150 -0.1813392122
[82,] -0.0160901256 -0.1370282150
[83,] 0.3076263989 -0.0160901256
[84,] -0.0817867276 0.3076263989
[85,] 0.1327567692 -0.0817867276
[86,] 0.2968729647 0.1327567692
[87,] 0.0603967527 0.2968729647
[88,] 0.0107407408 0.0603967527
[89,] -0.2171890472 0.0107407408
[90,] -0.1366057668 -0.2171890472
[91,] 0.2098975616 -0.1366057668
[92,] 0.2671478485 0.2098975616
[93,] 0.1433173936 0.2671478485
[94,] 0.2118478480 0.1433173936
[95,] 0.2409902950 0.2118478480
[96,] 0.1932511329 0.2409902950
[97,] -0.0299143810 0.1932511329
[98,] 0.1893785791 -0.0299143810
[99,] 0.0902200984 0.1893785791
[100,] 0.0380810421 0.0902200984
[101,] 0.0260995536 0.0380810421
[102,] 0.0214677351 0.0260995536
[103,] 0.4108419344 0.0214677351
[104,] 0.4210545774 0.4108419344
[105,] 0.4356404043 0.4210545774
[106,] 0.4653584524 0.4356404043
[107,] 0.3123733418 0.4653584524
[108,] 0.3815574820 0.3123733418
[109,] 0.1040295318 0.3815574820
[110,] -0.0338821476 0.1040295318
[111,] 0.4097161158 -0.0338821476
[112,] 0.2099593094 0.4097161158
[113,] 0.3038102705 0.2099593094
[114,] 0.1955950307 0.3038102705
[115,] 0.1292696340 0.1955950307
[116,] 0.2745232910 0.1292696340
[117,] -0.0499362452 0.2745232910
[118,] 0.1089664039 -0.0499362452
[119,] 0.2360458305 0.1089664039
[120,] 0.4496455531 0.2360458305
[121,] 0.0237243230 0.4496455531
[122,] 0.0264153346 0.0237243230
[123,] 0.2897285114 0.0264153346
[124,] 0.3905362482 0.2897285114
[125,] 0.3809525355 0.3905362482
[126,] 0.3893559944 0.3809525355
[127,] 0.3722594451 0.3893559944
[128,] 0.5821732248 0.3722594451
[129,] 0.2430294327 0.5821732248
[130,] 0.1992634392 0.2430294327
[131,] 0.1309263925 0.1992634392
[132,] -0.0499954721 0.1309263925
[133,] 0.3559000408 -0.0499954721
[134,] 0.0384285430 0.3559000408
[135,] 0.0394350255 0.0384285430
[136,] -0.1440930690 0.0394350255
[137,] -0.1477572853 -0.1440930690
[138,] 0.0425783478 -0.1477572853
[139,] 0.2184198005 0.0425783478
[140,] 0.0927353453 0.2184198005
[141,] 0.1426011132 0.0927353453
[142,] 0.2696295275 0.1426011132
[143,] 0.3148098810 0.2696295275
[144,] 0.4510685199 0.3148098810
[145,] 0.1368546881 0.4510685199
[146,] -0.3680690359 0.1368546881
[147,] -0.1640555410 -0.3680690359
[148,] -0.2386483982 -0.1640555410
[149,] 0.1173537192 -0.2386483982
[150,] 0.0642281920 0.1173537192
[151,] 0.0082886324 0.0642281920
[152,] 0.0571992474 0.0082886324
[153,] 0.1462046260 0.0571992474
[154,] 0.1038403026 0.1462046260
[155,] 0.0003592216 0.1038403026
[156,] 0.2063450760 0.0003592216
[157,] -0.2493911304 0.2063450760
[158,] 0.0310806775 -0.2493911304
[159,] 0.1509187768 0.0310806775
[160,] -0.1126607571 0.1509187768
[161,] -0.0553105168 -0.1126607571
[162,] 0.0703788753 -0.0553105168
[163,] 0.2030341446 0.0703788753
[164,] -0.3912657849 0.2030341446
[165,] -0.4505415003 -0.3912657849
[166,] -0.2265153348 -0.4505415003
[167,] -0.0946885232 -0.2265153348
[168,] -0.9428134891 -0.0946885232
[169,] -0.2281599826 -0.9428134891
[170,] -0.1105664221 -0.2281599826
[171,] -0.7954817912 -0.1105664221
[172,] -0.8374265143 -0.7954817912
[173,] -0.8738884760 -0.8374265143
[174,] -0.8686731218 -0.8738884760
[175,] -0.8342017850 -0.8686731218
[176,] -0.7736445551 -0.8342017850
[177,] 0.3565946892 -0.7736445551
[178,] 0.2873994732 0.3565946892
[179,] 0.0616521112 0.2873994732
[180,] 0.2313055231 0.0616521112
[181,] 0.1213822982 0.2313055231
[182,] 0.2786509081 0.1213822982
[183,] -0.6061113367 0.2786509081
[184,] -0.6494359886 -0.6061113367
[185,] -0.6052159039 -0.6494359886
[186,] -0.4315454219 -0.6052159039
[187,] -0.4783835049 -0.4315454219
[188,] -0.4330765767 -0.4783835049
[189,] -0.4207740756 -0.4330765767
[190,] -0.6495521784 -0.4207740756
[191,] -0.7039654166 -0.6495521784
[192,] 0.1798504075 -0.7039654166
[193,] -0.2664820661 0.1798504075
[194,] -0.5231375976 -0.2664820661
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.0514529296 0.0701258623
2 0.0362719818 -0.0514529296
3 -0.0725332996 0.0362719818
4 0.1315158840 -0.0725332996
5 0.0706676596 0.1315158840
6 0.2160464438 0.0706676596
7 0.4225534430 0.2160464438
8 0.0227766828 0.4225534430
9 -0.1583029451 0.0227766828
10 -0.1179025351 -0.1583029451
11 -0.2393499326 -0.1179025351
12 0.5433445603 -0.2393499326
13 0.1135752305 0.5433445603
14 0.2961330853 0.1135752305
15 0.2951712703 0.2961330853
16 0.4475557243 0.2951712703
17 -0.3109197352 0.4475557243
18 -0.2935133450 -0.3109197352
19 0.0374387511 -0.2935133450
20 -0.0584070862 0.0374387511
21 0.0910770005 -0.0584070862
22 -0.1171105862 0.0910770005
23 0.1271901579 -0.1171105862
24 0.1803200855 0.1271901579
25 0.0734406974 0.1803200855
26 0.1923698917 0.0734406974
27 0.2040232674 0.1923698917
28 0.3384398352 0.2040232674
29 0.3031297454 0.3384398352
30 -0.2892991187 0.3031297454
31 -0.1528163535 -0.2892991187
32 -0.1870295956 -0.1528163535
33 -0.1267568588 -0.1870295956
34 -0.0854029670 -0.1267568588
35 -0.2048818481 -0.0854029670
36 0.1923074378 -0.2048818481
37 0.1791320195 0.1923074378
38 0.4018848174 0.1791320195
39 0.2465487868 0.4018848174
40 0.3825606085 0.2465487868
41 0.5648101997 0.3825606085
42 -0.2495419163 0.5648101997
43 -0.2085917433 -0.2495419163
44 -0.0187574361 -0.2085917433
45 -0.0926866683 -0.0187574361
46 -0.0557239090 -0.0926866683
47 0.0451515782 -0.0557239090
48 -0.3393060640 0.0451515782
49 -0.4292672534 -0.3393060640
50 -0.4109190090 -0.4292672534
51 -0.4349002979 -0.4109190090
52 -0.4096176794 -0.4349002979
53 -0.5524029928 -0.4096176794
54 0.1698969863 -0.5524029928
55 0.2015264323 0.1698969863
56 0.1324084202 0.2015264323
57 0.2421741366 0.1324084202
58 0.2206706319 0.2421741366
59 0.3384044733 0.2206706319
60 -0.3690941287 0.3384044733
61 -0.2734134993 -0.3690941287
62 -0.2624328769 -0.2734134993
63 -0.2136757077 -0.2624328769
64 -0.1308833071 -0.2136757077
65 -0.2867052798 -0.1308833071
66 0.0755973788 -0.2867052798
67 0.1037763756 0.0755973788
68 0.0859517697 0.1037763756
69 0.0476560691 0.0859517697
70 0.1417965024 0.0476560691
71 -0.0918238408 0.1417965024
72 0.1113384846 -0.0918238408
73 0.0645111014 0.1113384846
74 -0.0458090661 0.0645111014
75 -0.0873647303 -0.0458090661
76 -0.1024155043 -0.0873647303
77 -0.0048980470 -0.1024155043
78 0.0359645542 -0.0048980470
79 -0.1473980021 0.0359645542
80 -0.1813392122 -0.1473980021
81 -0.1370282150 -0.1813392122
82 -0.0160901256 -0.1370282150
83 0.3076263989 -0.0160901256
84 -0.0817867276 0.3076263989
85 0.1327567692 -0.0817867276
86 0.2968729647 0.1327567692
87 0.0603967527 0.2968729647
88 0.0107407408 0.0603967527
89 -0.2171890472 0.0107407408
90 -0.1366057668 -0.2171890472
91 0.2098975616 -0.1366057668
92 0.2671478485 0.2098975616
93 0.1433173936 0.2671478485
94 0.2118478480 0.1433173936
95 0.2409902950 0.2118478480
96 0.1932511329 0.2409902950
97 -0.0299143810 0.1932511329
98 0.1893785791 -0.0299143810
99 0.0902200984 0.1893785791
100 0.0380810421 0.0902200984
101 0.0260995536 0.0380810421
102 0.0214677351 0.0260995536
103 0.4108419344 0.0214677351
104 0.4210545774 0.4108419344
105 0.4356404043 0.4210545774
106 0.4653584524 0.4356404043
107 0.3123733418 0.4653584524
108 0.3815574820 0.3123733418
109 0.1040295318 0.3815574820
110 -0.0338821476 0.1040295318
111 0.4097161158 -0.0338821476
112 0.2099593094 0.4097161158
113 0.3038102705 0.2099593094
114 0.1955950307 0.3038102705
115 0.1292696340 0.1955950307
116 0.2745232910 0.1292696340
117 -0.0499362452 0.2745232910
118 0.1089664039 -0.0499362452
119 0.2360458305 0.1089664039
120 0.4496455531 0.2360458305
121 0.0237243230 0.4496455531
122 0.0264153346 0.0237243230
123 0.2897285114 0.0264153346
124 0.3905362482 0.2897285114
125 0.3809525355 0.3905362482
126 0.3893559944 0.3809525355
127 0.3722594451 0.3893559944
128 0.5821732248 0.3722594451
129 0.2430294327 0.5821732248
130 0.1992634392 0.2430294327
131 0.1309263925 0.1992634392
132 -0.0499954721 0.1309263925
133 0.3559000408 -0.0499954721
134 0.0384285430 0.3559000408
135 0.0394350255 0.0384285430
136 -0.1440930690 0.0394350255
137 -0.1477572853 -0.1440930690
138 0.0425783478 -0.1477572853
139 0.2184198005 0.0425783478
140 0.0927353453 0.2184198005
141 0.1426011132 0.0927353453
142 0.2696295275 0.1426011132
143 0.3148098810 0.2696295275
144 0.4510685199 0.3148098810
145 0.1368546881 0.4510685199
146 -0.3680690359 0.1368546881
147 -0.1640555410 -0.3680690359
148 -0.2386483982 -0.1640555410
149 0.1173537192 -0.2386483982
150 0.0642281920 0.1173537192
151 0.0082886324 0.0642281920
152 0.0571992474 0.0082886324
153 0.1462046260 0.0571992474
154 0.1038403026 0.1462046260
155 0.0003592216 0.1038403026
156 0.2063450760 0.0003592216
157 -0.2493911304 0.2063450760
158 0.0310806775 -0.2493911304
159 0.1509187768 0.0310806775
160 -0.1126607571 0.1509187768
161 -0.0553105168 -0.1126607571
162 0.0703788753 -0.0553105168
163 0.2030341446 0.0703788753
164 -0.3912657849 0.2030341446
165 -0.4505415003 -0.3912657849
166 -0.2265153348 -0.4505415003
167 -0.0946885232 -0.2265153348
168 -0.9428134891 -0.0946885232
169 -0.2281599826 -0.9428134891
170 -0.1105664221 -0.2281599826
171 -0.7954817912 -0.1105664221
172 -0.8374265143 -0.7954817912
173 -0.8738884760 -0.8374265143
174 -0.8686731218 -0.8738884760
175 -0.8342017850 -0.8686731218
176 -0.7736445551 -0.8342017850
177 0.3565946892 -0.7736445551
178 0.2873994732 0.3565946892
179 0.0616521112 0.2873994732
180 0.2313055231 0.0616521112
181 0.1213822982 0.2313055231
182 0.2786509081 0.1213822982
183 -0.6061113367 0.2786509081
184 -0.6494359886 -0.6061113367
185 -0.6052159039 -0.6494359886
186 -0.4315454219 -0.6052159039
187 -0.4783835049 -0.4315454219
188 -0.4330765767 -0.4783835049
189 -0.4207740756 -0.4330765767
190 -0.6495521784 -0.4207740756
191 -0.7039654166 -0.6495521784
192 0.1798504075 -0.7039654166
193 -0.2664820661 0.1798504075
194 -0.5231375976 -0.2664820661
> plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
> lines(lowess(z))
> abline(lm(z))
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/726tc1386423751.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/81t131386423751.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/9ey271386423751.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/fisher/rcomp/tmp/101q7g1386423751.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/fisher/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/fisher/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
+ myeq <- paste(myeq, signif(mysum$coefficients[i,1],6), sep=' ')
+ if (rownames(mysum$coefficients)[i] != '(Intercept)') {
+ myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
+ if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/fisher/rcomp/tmp/11f7191386423751.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
+ a<-table.element(a,signif(mysum$coefficients[i,1],6))
+ a<-table.element(a, signif(mysum$coefficients[i,2],6))
+ a<-table.element(a, signif(mysum$coefficients[i,3],4))
+ a<-table.element(a, signif(mysum$coefficients[i,4],6))
+ a<-table.element(a, signif(mysum$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/fisher/rcomp/tmp/12fo5r1386423751.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, signif(sqrt(mysum$r.squared),6))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'R-squared',1,TRUE)
> a<-table.element(a, signif(mysum$r.squared,6))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Adjusted R-squared',1,TRUE)
> a<-table.element(a, signif(mysum$adj.r.squared,6))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (value)',1,TRUE)
> a<-table.element(a, signif(mysum$fstatistic[1],6))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
> a<-table.element(a, signif(mysum$fstatistic[2],6))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
> a<-table.element(a, signif(mysum$fstatistic[3],6))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'p-value',1,TRUE)
> a<-table.element(a, signif(1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]),6))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
> a<-table.element(a, signif(mysum$sigma,6))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, signif(sum(myerror*myerror),6))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/fisher/rcomp/tmp/13p5tv1386423751.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,signif(x[i],6))
+ a<-table.element(a,signif(x[i]-mysum$resid[i],6))
+ a<-table.element(a,signif(mysum$resid[i],6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/fisher/rcomp/tmp/140mpt1386423751.tab")
> if (n > n25) {
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'p-values',header=TRUE)
+ a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'breakpoint index',header=TRUE)
+ a<-table.element(a,'greater',header=TRUE)
+ a<-table.element(a,'2-sided',header=TRUE)
+ a<-table.element(a,'less',header=TRUE)
+ a<-table.row.end(a)
+ for (mypoint in kp3:nmkm3) {
+ a<-table.row.start(a)
+ a<-table.element(a,mypoint,header=TRUE)
+ a<-table.element(a,signif(gqarr[mypoint-kp3+1,1],6))
+ a<-table.element(a,signif(gqarr[mypoint-kp3+1,2],6))
+ a<-table.element(a,signif(gqarr[mypoint-kp3+1,3],6))
+ a<-table.row.end(a)
+ }
+ a<-table.end(a)
+ table.save(a,file="/var/fisher/rcomp/tmp/155x9s1386423751.tab")
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'Description',header=TRUE)
+ a<-table.element(a,'# significant tests',header=TRUE)
+ a<-table.element(a,'% significant tests',header=TRUE)
+ a<-table.element(a,'OK/NOK',header=TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'1% type I error level',header=TRUE)
+ a<-table.element(a,signif(numsignificant1,6))
+ a<-table.element(a,signif(numsignificant1/numgqtests,6))
+ if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'5% type I error level',header=TRUE)
+ a<-table.element(a,signif(numsignificant5,6))
+ a<-table.element(a,signif(numsignificant5/numgqtests,6))
+ if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'10% type I error level',header=TRUE)
+ a<-table.element(a,signif(numsignificant10,6))
+ a<-table.element(a,signif(numsignificant10/numgqtests,6))
+ if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.end(a)
+ table.save(a,file="/var/fisher/rcomp/tmp/16xxsq1386423751.tab")
+ }
>
> try(system("convert tmp/1dbzz1386423751.ps tmp/1dbzz1386423751.png",intern=TRUE))
character(0)
> try(system("convert tmp/2n8bl1386423751.ps tmp/2n8bl1386423751.png",intern=TRUE))
character(0)
> try(system("convert tmp/317sp1386423751.ps tmp/317sp1386423751.png",intern=TRUE))
character(0)
> try(system("convert tmp/4mbtb1386423751.ps tmp/4mbtb1386423751.png",intern=TRUE))
character(0)
> try(system("convert tmp/5473e1386423751.ps tmp/5473e1386423751.png",intern=TRUE))
character(0)
> try(system("convert tmp/6qfjn1386423751.ps tmp/6qfjn1386423751.png",intern=TRUE))
character(0)
> try(system("convert tmp/726tc1386423751.ps tmp/726tc1386423751.png",intern=TRUE))
character(0)
> try(system("convert tmp/81t131386423751.ps tmp/81t131386423751.png",intern=TRUE))
character(0)
> try(system("convert tmp/9ey271386423751.ps tmp/9ey271386423751.png",intern=TRUE))
character(0)
> try(system("convert tmp/101q7g1386423751.ps tmp/101q7g1386423751.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
36.044 5.652 41.768