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) R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. 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 + ,157.302 + ,74.997 + ,0.00784 + ,0.00007 + ,0.0037 + ,0.00554 + ,0.01109 + ,0.04374 + ,0.426 + ,0.02182 + ,0.0313 + ,0.02971 + ,0.06545 + ,0.02211 + ,21.033 + ,1 + ,0.414783 + ,0.815285 + ,-4.813031 + ,0.266482 + ,2.301442 + ,0.284654 + ,122.4 + ,148.65 + ,113.819 + ,0.00968 + ,0.00008 + ,0.00465 + ,0.00696 + ,0.01394 + ,0.06134 + ,0.626 + ,0.03134 + ,0.04518 + ,0.04368 + ,0.09403 + ,0.01929 + ,19.085 + ,1 + ,0.458359 + ,0.819521 + ,-4.075192 + ,0.33559 + ,2.486855 + ,0.368674 + ,116.682 + ,131.111 + ,111.555 + ,0.0105 + ,0.00009 + ,0.00544 + ,0.00781 + ,0.01633 + ,0.05233 + ,0.482 + ,0.02757 + ,0.03858 + ,0.0359 + ,0.0827 + ,0.01309 + ,20.651 + ,1 + ,0.429895 + ,0.825288 + ,-4.443179 + ,0.311173 + ,2.342259 + ,0.332634 + ,116.676 + ,137.871 + ,111.366 + ,0.00997 + ,0.00009 + ,0.00502 + ,0.00698 + ,0.01505 + ,0.05492 + ,0.517 + ,0.02924 + ,0.04005 + ,0.03772 + ,0.08771 + ,0.01353 + ,20.644 + ,1 + ,0.434969 + ,0.819235 + ,-4.117501 + 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,0.655683 + ,-6.787197 + ,0.158453 + ,2.679772 + ,0.131728 + ,198.764 + ,396.961 + ,74.904 + ,0.0074 + ,0.00004 + ,0.0037 + ,0.0039 + ,0.01109 + ,0.02296 + ,0.241 + ,0.01265 + ,0.01321 + ,0.01588 + ,0.03794 + ,0.07223 + ,19.02 + ,0 + ,0.451221 + ,0.643956 + ,-6.744577 + ,0.207454 + ,2.138608 + ,0.123306 + ,214.289 + ,260.277 + ,77.973 + ,0.00567 + ,0.00003 + ,0.00295 + ,0.00317 + ,0.00885 + ,0.01884 + ,0.19 + ,0.01026 + ,0.01161 + ,0.01373 + ,0.03078 + ,0.04398 + ,21.209 + ,0 + ,0.462803 + ,0.664357 + ,-5.724056 + ,0.190667 + ,2.555477 + ,0.148569) + ,dim=c(23 + ,195) + ,dimnames=list(c('MDVP:Fo(Hz)' + ,'MDVP:Fhi(Hz)' + ,'MDVP:Flo(Hz)' + ,'MDVP:Jitter(%)' + ,'MDVP:Jitter(Abs)' + ,'MDVP:RAP' + ,'MDVP:PPQ' + ,'Jitter:DDP' + ,'MDVP:Shimmer' + ,'MDVP:Shimmer(dB)' + ,'Shimmer:APQ3' + ,'Shimmer:APQ5' + ,'MDVP:APQ' + ,'Shimmer:DDA' + ,'NHR' + ,'HNR' + ,'status' + ,'RPDE' + ,'DFA' + ,'spread1' + ,'spread2' + ,'D2' + ,'PPE') + ,1:195)) > y <- array(NA,dim=c(23,195),dimnames=list(c('MDVP:Fo(Hz)','MDVP:Fhi(Hz)','MDVP:Flo(Hz)','MDVP:Jitter(%)','MDVP:Jitter(Abs)','MDVP:RAP','MDVP:PPQ','Jitter:DDP','MDVP:Shimmer','MDVP:Shimmer(dB)','Shimmer:APQ3','Shimmer:APQ5','MDVP:APQ','Shimmer:DDA','NHR','HNR','status','RPDE','DFA','spread1','spread2','D2','PPE'),1:195)) > for (i in 1:dim(x)[1]) + { + for (j in 1:dim(x)[2]) + { + y[i,j] <- as.numeric(x[i,j]) + } + } > par3 = 'No Linear Trend' > par2 = 'Do not include Seasonal Dummies' > par1 = '16' > par3 <- 'No Linear Trend' > par2 <- 'Do not include Seasonal Dummies' > par1 <- '16' > #'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 HNR MDVP:Fo(Hz) MDVP:Fhi(Hz) MDVP:Flo(Hz) MDVP:Jitter(%) 1 21.033 119.992 157.302 74.997 0.00784 2 19.085 122.400 148.650 113.819 0.00968 3 20.651 116.682 131.111 111.555 0.01050 4 20.644 116.676 137.871 111.366 0.00997 5 19.649 116.014 141.781 110.655 0.01284 6 21.378 120.552 131.162 113.787 0.00968 7 24.886 120.267 137.244 114.820 0.00333 8 26.892 107.332 113.840 104.315 0.00290 9 21.812 95.730 132.068 91.754 0.00551 10 21.862 95.056 120.103 91.226 0.00532 11 21.118 88.333 112.240 84.072 0.00505 12 21.414 91.904 115.871 86.292 0.00540 13 25.703 136.926 159.866 131.276 0.00293 14 24.889 139.173 179.139 76.556 0.00390 15 24.922 152.845 163.305 75.836 0.00294 16 25.175 142.167 217.455 83.159 0.00369 17 22.333 144.188 349.259 82.764 0.00544 18 20.376 168.778 232.181 75.603 0.00718 19 17.280 153.046 175.829 68.623 0.00742 20 17.153 156.405 189.398 142.822 0.00768 21 17.536 153.848 165.738 65.782 0.00840 22 19.493 153.880 172.860 78.128 0.00480 23 22.468 167.930 193.221 79.068 0.00442 24 20.422 173.917 192.735 86.180 0.00476 25 23.831 163.656 200.841 76.779 0.00742 26 22.066 104.400 206.002 77.968 0.00633 27 25.908 171.041 208.313 75.501 0.00455 28 25.119 146.845 208.701 81.737 0.00496 29 25.970 155.358 227.383 80.055 0.00310 30 25.678 162.568 198.346 77.630 0.00502 31 26.775 197.076 206.896 192.055 0.00289 32 30.940 199.228 209.512 192.091 0.00241 33 30.775 198.383 215.203 193.104 0.00212 34 32.684 202.266 211.604 197.079 0.00180 35 33.047 203.184 211.526 196.160 0.00178 36 31.732 201.464 210.565 195.708 0.00198 37 23.216 177.876 192.921 168.013 0.00411 38 24.951 176.170 185.604 163.564 0.00369 39 26.738 180.198 201.249 175.456 0.00284 40 26.310 187.733 202.324 173.015 0.00316 41 26.822 186.163 197.724 177.584 0.00298 42 26.453 184.055 196.537 166.977 0.00258 43 22.736 237.226 247.326 225.227 0.00298 44 23.145 241.404 248.834 232.483 0.00281 45 25.368 243.439 250.912 232.435 0.00210 46 25.032 242.852 255.034 227.911 0.00225 47 24.602 245.510 262.090 231.848 0.00235 48 26.805 252.455 261.487 182.786 0.00185 49 23.162 122.188 128.611 115.765 0.00524 50 24.971 122.964 130.049 114.676 0.00428 51 25.135 124.445 135.069 117.495 0.00431 52 25.030 126.344 134.231 112.773 0.00448 53 24.692 128.001 138.052 122.080 0.00436 54 25.429 129.336 139.867 118.604 0.00490 55 21.028 108.807 134.656 102.874 0.00761 56 20.767 109.860 126.358 104.437 0.00874 57 21.422 110.417 131.067 103.370 0.00784 58 22.817 117.274 129.916 110.402 0.00752 59 22.603 116.879 131.897 108.153 0.00788 60 21.660 114.847 271.314 104.680 0.00867 61 25.554 209.144 237.494 109.379 0.00282 62 26.138 223.365 238.987 98.664 0.00264 63 25.856 222.236 231.345 205.495 0.00266 64 25.964 228.832 234.619 223.634 0.00296 65 26.415 229.401 252.221 221.156 0.00205 66 24.547 228.969 239.541 113.201 0.00238 67 19.560 140.341 159.774 67.021 0.00817 68 19.979 136.969 166.607 66.004 0.00923 69 20.338 143.533 162.215 65.809 0.01101 70 21.718 148.090 162.824 67.343 0.00762 71 20.264 142.729 162.408 65.476 0.00831 72 18.570 136.358 176.595 65.750 0.00971 73 25.742 120.080 139.710 111.208 0.00405 74 24.178 112.014 588.518 107.024 0.00533 75 25.438 110.793 128.101 107.316 0.00494 76 25.197 110.707 122.611 105.007 0.00516 77 23.370 112.876 148.826 106.981 0.00500 78 25.820 110.568 125.394 106.821 0.00462 79 21.875 95.385 102.145 90.264 0.00608 80 19.200 100.770 115.697 85.545 0.01038 81 19.055 96.106 108.664 84.510 0.00694 82 19.659 95.605 107.715 87.549 0.00702 83 20.536 100.960 110.019 95.628 0.00606 84 22.244 98.804 102.305 87.804 0.00432 85 13.893 176.858 205.560 75.344 0.00747 86 16.176 180.978 200.125 155.495 0.00406 87 15.924 178.222 202.450 141.047 0.00321 88 13.922 176.281 227.381 125.610 0.00520 89 14.739 173.898 211.350 74.677 0.00448 90 11.866 179.711 225.930 144.878 0.00709 91 11.744 166.605 206.008 78.032 0.00742 92 19.664 151.955 163.335 147.226 0.00419 93 18.780 148.272 164.989 142.299 0.00459 94 20.969 152.125 161.469 76.596 0.00382 95 22.219 157.821 172.975 68.401 0.00358 96 21.693 157.447 163.267 149.605 0.00369 97 22.663 159.116 168.913 144.811 0.00342 98 15.338 125.036 143.946 116.187 0.01280 99 15.433 125.791 140.557 96.206 0.01378 100 12.435 126.512 141.756 99.770 0.01936 101 8.867 125.641 141.068 116.346 0.03316 102 15.060 128.451 150.449 75.632 0.01551 103 10.489 139.224 586.567 66.157 0.03011 104 26.759 150.258 154.609 75.349 0.00248 105 28.409 154.003 160.267 128.621 0.00183 106 27.421 149.689 160.368 133.608 0.00257 107 29.746 155.078 163.736 144.148 0.00168 108 26.833 151.884 157.765 133.751 0.00258 109 29.928 151.989 157.339 132.857 0.00174 110 21.934 193.030 208.900 80.297 0.00766 111 23.239 200.714 223.982 89.686 0.00621 112 22.407 208.519 220.315 199.020 0.00609 113 21.305 204.664 221.300 189.621 0.00841 114 23.671 210.141 232.706 185.258 0.00534 115 21.864 206.327 226.355 92.020 0.00495 116 23.693 151.872 492.892 69.085 0.00856 117 26.356 158.219 442.557 71.948 0.00476 118 25.690 170.756 450.247 79.032 0.00555 119 25.020 178.285 442.824 82.063 0.00462 120 24.581 217.116 233.481 93.978 0.00404 121 24.743 128.940 479.697 88.251 0.00581 122 27.166 176.824 215.293 83.961 0.00460 123 18.305 138.190 203.522 83.340 0.00704 124 18.784 182.018 197.173 79.187 0.00842 125 19.196 156.239 195.107 79.820 0.00694 126 18.857 145.174 198.109 80.637 0.00733 127 18.178 138.145 197.238 81.114 0.00544 128 18.330 166.888 198.966 79.512 0.00638 129 26.842 119.031 127.533 109.216 0.00440 130 26.369 120.078 126.632 105.667 0.00270 131 23.949 120.289 128.143 100.209 0.00492 132 26.017 120.256 125.306 104.773 0.00407 133 23.389 119.056 125.213 86.795 0.00346 134 25.619 118.747 123.723 109.836 0.00331 135 17.060 106.516 112.777 93.105 0.00589 136 17.707 110.453 127.611 105.554 0.00494 137 19.013 113.400 133.344 107.816 0.00451 138 16.747 113.166 130.270 100.673 0.00502 139 17.366 112.239 126.609 104.095 0.00472 140 18.801 116.150 131.731 109.815 0.00381 141 18.540 170.368 268.796 79.543 0.00571 142 15.648 208.083 253.792 91.802 0.00757 143 18.702 198.458 219.290 148.691 0.00376 144 18.687 202.805 231.508 86.232 0.00370 145 20.680 202.544 241.350 164.168 0.00254 146 20.366 223.361 263.872 87.638 0.00352 147 12.359 169.774 191.759 151.451 0.01568 148 14.367 183.520 216.814 161.340 0.01466 149 12.298 188.620 216.302 165.982 0.01719 150 14.989 202.632 565.740 177.258 0.01627 151 12.529 186.695 211.961 149.442 0.01872 152 8.441 192.818 224.429 168.793 0.03107 153 9.449 198.116 233.099 174.478 0.02714 154 21.520 121.345 139.644 98.250 0.00684 155 21.824 119.100 128.442 88.833 0.00692 156 22.431 117.870 127.349 95.654 0.00647 157 22.953 122.336 142.369 94.794 0.00727 158 19.075 117.963 134.209 100.757 0.01813 159 21.534 126.144 154.284 97.543 0.00975 160 19.651 127.930 138.752 112.173 0.00605 161 20.437 114.238 124.393 77.022 0.00581 162 19.388 115.322 135.738 107.802 0.00619 163 18.954 114.554 126.778 91.121 0.00651 164 21.219 112.150 131.669 97.527 0.00519 165 18.447 102.273 142.830 85.902 0.00907 166 24.078 236.200 244.663 102.137 0.00277 167 24.679 237.323 243.709 229.256 0.00303 168 21.083 260.105 264.919 237.303 0.00339 169 19.269 197.569 217.627 90.794 0.00803 170 21.020 240.301 245.135 219.783 0.00517 171 21.528 244.990 272.210 239.170 0.00451 172 26.436 112.547 133.374 105.715 0.00355 173 26.550 110.739 113.597 100.139 0.00356 174 26.547 113.715 116.443 96.913 0.00349 175 25.445 117.004 144.466 99.923 0.00353 176 26.005 115.380 123.109 108.634 0.00332 177 26.143 116.388 129.038 108.970 0.00346 178 24.151 151.737 190.204 129.859 0.00314 179 24.412 148.790 158.359 138.990 0.00309 180 23.683 148.143 155.982 135.041 0.00392 181 23.133 150.440 163.441 144.736 0.00396 182 22.866 148.462 161.078 141.998 0.00397 183 23.008 149.818 163.417 144.786 0.00336 184 23.079 117.226 123.925 106.656 0.00417 185 22.085 116.848 217.552 99.503 0.00531 186 24.199 116.286 177.291 96.983 0.00314 187 23.958 116.556 592.030 86.228 0.00496 188 25.023 116.342 581.289 94.246 0.00267 189 24.775 114.563 119.167 86.647 0.00327 190 19.368 201.774 262.707 78.228 0.00694 191 19.517 174.188 230.978 94.261 0.00459 192 19.147 209.516 253.017 89.488 0.00564 193 17.883 174.688 240.005 74.287 0.01360 194 19.020 198.764 396.961 74.904 0.00740 195 21.209 214.289 260.277 77.973 0.00567 MDVP:Jitter(Abs) MDVP:RAP MDVP:PPQ Jitter:DDP MDVP:Shimmer MDVP:Shimmer(dB) 1 7.0e-05 0.00370 0.00554 0.01109 0.04374 0.426 2 8.0e-05 0.00465 0.00696 0.01394 0.06134 0.626 3 9.0e-05 0.00544 0.00781 0.01633 0.05233 0.482 4 9.0e-05 0.00502 0.00698 0.01505 0.05492 0.517 5 1.1e-04 0.00655 0.00908 0.01966 0.06425 0.584 6 8.0e-05 0.00463 0.00750 0.01388 0.04701 0.456 7 3.0e-05 0.00155 0.00202 0.00466 0.01608 0.140 8 3.0e-05 0.00144 0.00182 0.00431 0.01567 0.134 9 6.0e-05 0.00293 0.00332 0.00880 0.02093 0.191 10 6.0e-05 0.00268 0.00332 0.00803 0.02838 0.255 11 6.0e-05 0.00254 0.00330 0.00763 0.02143 0.197 12 6.0e-05 0.00281 0.00336 0.00844 0.02752 0.249 13 2.0e-05 0.00118 0.00153 0.00355 0.01259 0.112 14 3.0e-05 0.00165 0.00208 0.00496 0.01642 0.154 15 2.0e-05 0.00121 0.00149 0.00364 0.01828 0.158 16 3.0e-05 0.00157 0.00203 0.00471 0.01503 0.126 17 4.0e-05 0.00211 0.00292 0.00632 0.02047 0.192 18 4.0e-05 0.00284 0.00387 0.00853 0.03327 0.348 19 5.0e-05 0.00364 0.00432 0.01092 0.05517 0.542 20 5.0e-05 0.00372 0.00399 0.01116 0.03995 0.348 21 5.0e-05 0.00428 0.00450 0.01285 0.03810 0.328 22 3.0e-05 0.00232 0.00267 0.00696 0.04137 0.370 23 3.0e-05 0.00220 0.00247 0.00661 0.04351 0.377 24 3.0e-05 0.00221 0.00258 0.00663 0.04192 0.364 25 5.0e-05 0.00380 0.00390 0.01140 0.01659 0.164 26 6.0e-05 0.00316 0.00375 0.00948 0.03767 0.381 27 3.0e-05 0.00250 0.00234 0.00750 0.01966 0.186 28 3.0e-05 0.00250 0.00275 0.00749 0.01919 0.198 29 2.0e-05 0.00159 0.00176 0.00476 0.01718 0.161 30 3.0e-05 0.00280 0.00253 0.00841 0.01791 0.168 31 1.0e-05 0.00166 0.00168 0.00498 0.01098 0.097 32 1.0e-05 0.00134 0.00138 0.00402 0.01015 0.089 33 1.0e-05 0.00113 0.00135 0.00339 0.01263 0.111 34 9.0e-06 0.00093 0.00107 0.00278 0.00954 0.085 35 9.0e-06 0.00094 0.00106 0.00283 0.00958 0.085 36 1.0e-05 0.00105 0.00115 0.00314 0.01194 0.107 37 2.0e-05 0.00233 0.00241 0.00700 0.02126 0.189 38 2.0e-05 0.00205 0.00218 0.00616 0.01851 0.168 39 2.0e-05 0.00153 0.00166 0.00459 0.01444 0.131 40 2.0e-05 0.00168 0.00182 0.00504 0.01663 0.151 41 2.0e-05 0.00165 0.00175 0.00496 0.01495 0.135 42 1.0e-05 0.00134 0.00147 0.00403 0.01463 0.132 43 1.0e-05 0.00169 0.00182 0.00507 0.01752 0.164 44 1.0e-05 0.00157 0.00173 0.00470 0.01760 0.154 45 9.0e-06 0.00109 0.00137 0.00327 0.01419 0.126 46 9.0e-06 0.00117 0.00139 0.00350 0.01494 0.134 47 1.0e-05 0.00127 0.00148 0.00380 0.01608 0.141 48 7.0e-06 0.00092 0.00113 0.00276 0.01152 0.103 49 4.0e-05 0.00169 0.00203 0.00507 0.01613 0.143 50 3.0e-05 0.00124 0.00155 0.00373 0.01681 0.154 51 3.0e-05 0.00141 0.00167 0.00422 0.02184 0.197 52 4.0e-05 0.00131 0.00169 0.00393 0.02033 0.185 53 3.0e-05 0.00137 0.00166 0.00411 0.02297 0.210 54 4.0e-05 0.00165 0.00183 0.00495 0.02498 0.228 55 7.0e-05 0.00349 0.00486 0.01046 0.02719 0.255 56 8.0e-05 0.00398 0.00539 0.01193 0.03209 0.307 57 7.0e-05 0.00352 0.00514 0.01056 0.03715 0.334 58 6.0e-05 0.00299 0.00469 0.00898 0.02293 0.221 59 7.0e-05 0.00334 0.00493 0.01003 0.02645 0.265 60 8.0e-05 0.00373 0.00520 0.01120 0.03225 0.350 61 1.0e-05 0.00147 0.00152 0.00442 0.01861 0.170 62 1.0e-05 0.00154 0.00151 0.00461 0.01906 0.165 63 1.0e-05 0.00152 0.00144 0.00457 0.01643 0.145 64 1.0e-05 0.00175 0.00155 0.00526 0.01644 0.145 65 9.0e-06 0.00114 0.00113 0.00342 0.01457 0.129 66 1.0e-05 0.00136 0.00140 0.00408 0.01745 0.154 67 6.0e-05 0.00430 0.00440 0.01289 0.03198 0.313 68 7.0e-05 0.00507 0.00463 0.01520 0.03111 0.308 69 8.0e-05 0.00647 0.00467 0.01941 0.05384 0.478 70 5.0e-05 0.00467 0.00354 0.01400 0.05428 0.497 71 6.0e-05 0.00469 0.00419 0.01407 0.03485 0.365 72 7.0e-05 0.00534 0.00478 0.01601 0.04978 0.483 73 3.0e-05 0.00180 0.00220 0.00540 0.01706 0.152 74 5.0e-05 0.00268 0.00329 0.00805 0.02448 0.226 75 4.0e-05 0.00260 0.00283 0.00780 0.02442 0.216 76 5.0e-05 0.00277 0.00289 0.00831 0.02215 0.206 77 4.0e-05 0.00270 0.00289 0.00810 0.03999 0.350 78 4.0e-05 0.00226 0.00280 0.00677 0.02199 0.197 79 6.0e-05 0.00331 0.00332 0.00994 0.03202 0.263 80 1.0e-04 0.00622 0.00576 0.01865 0.03121 0.361 81 7.0e-05 0.00389 0.00415 0.01168 0.04024 0.364 82 7.0e-05 0.00428 0.00371 0.01283 0.03156 0.296 83 6.0e-05 0.00351 0.00348 0.01053 0.02427 0.216 84 4.0e-05 0.00247 0.00258 0.00742 0.02223 0.202 85 4.0e-05 0.00418 0.00420 0.01254 0.04795 0.435 86 2.0e-05 0.00220 0.00244 0.00659 0.03852 0.331 87 2.0e-05 0.00163 0.00194 0.00488 0.03759 0.327 88 3.0e-05 0.00287 0.00312 0.00862 0.06511 0.580 89 3.0e-05 0.00237 0.00254 0.00710 0.06727 0.650 90 4.0e-05 0.00391 0.00419 0.01172 0.04313 0.442 91 4.0e-05 0.00387 0.00453 0.01161 0.06640 0.634 92 3.0e-05 0.00224 0.00227 0.00672 0.07959 0.772 93 3.0e-05 0.00250 0.00256 0.00750 0.04190 0.383 94 3.0e-05 0.00191 0.00226 0.00574 0.05925 0.637 95 2.0e-05 0.00196 0.00196 0.00587 0.03716 0.307 96 2.0e-05 0.00201 0.00197 0.00602 0.03272 0.283 97 2.0e-05 0.00178 0.00184 0.00535 0.03381 0.307 98 1.0e-04 0.00743 0.00623 0.02228 0.03886 0.342 99 1.1e-04 0.00826 0.00655 0.02478 0.04689 0.422 100 1.5e-04 0.01159 0.00990 0.03476 0.06734 0.659 101 2.6e-04 0.02144 0.01522 0.06433 0.09178 0.891 102 1.2e-04 0.00905 0.00909 0.02716 0.06170 0.584 103 2.2e-04 0.01854 0.01628 0.05563 0.09419 0.930 104 2.0e-05 0.00105 0.00136 0.00315 0.01131 0.107 105 1.0e-05 0.00076 0.00100 0.00229 0.01030 0.094 106 2.0e-05 0.00116 0.00134 0.00349 0.01346 0.126 107 1.0e-05 0.00068 0.00092 0.00204 0.01064 0.097 108 2.0e-05 0.00115 0.00122 0.00346 0.01450 0.137 109 1.0e-05 0.00075 0.00096 0.00225 0.01024 0.093 110 4.0e-05 0.00450 0.00389 0.01351 0.03044 0.275 111 3.0e-05 0.00371 0.00337 0.01112 0.02286 0.207 112 3.0e-05 0.00368 0.00339 0.01105 0.01761 0.155 113 4.0e-05 0.00502 0.00485 0.01506 0.02378 0.210 114 3.0e-05 0.00321 0.00280 0.00964 0.01680 0.149 115 2.0e-05 0.00302 0.00246 0.00905 0.02105 0.209 116 6.0e-05 0.00404 0.00385 0.01211 0.01843 0.235 117 3.0e-05 0.00214 0.00207 0.00642 0.01458 0.148 118 3.0e-05 0.00244 0.00261 0.00731 0.01725 0.175 119 3.0e-05 0.00157 0.00194 0.00472 0.01279 0.129 120 2.0e-05 0.00127 0.00128 0.00381 0.01299 0.124 121 5.0e-05 0.00241 0.00314 0.00723 0.02008 0.221 122 3.0e-05 0.00209 0.00221 0.00628 0.01169 0.117 123 5.0e-05 0.00406 0.00398 0.01218 0.04479 0.441 124 5.0e-05 0.00506 0.00449 0.01517 0.02503 0.231 125 4.0e-05 0.00403 0.00395 0.01209 0.02343 0.224 126 5.0e-05 0.00414 0.00422 0.01242 0.02362 0.233 127 4.0e-05 0.00294 0.00327 0.00883 0.02791 0.246 128 4.0e-05 0.00368 0.00351 0.01104 0.02857 0.257 129 4.0e-05 0.00214 0.00192 0.00641 0.01033 0.098 130 2.0e-05 0.00116 0.00135 0.00349 0.01022 0.090 131 4.0e-05 0.00269 0.00238 0.00808 0.01412 0.125 132 3.0e-05 0.00224 0.00205 0.00671 0.01516 0.138 133 3.0e-05 0.00169 0.00170 0.00508 0.01201 0.106 134 3.0e-05 0.00168 0.00171 0.00504 0.01043 0.099 135 6.0e-05 0.00291 0.00319 0.00873 0.04932 0.441 136 4.0e-05 0.00244 0.00315 0.00731 0.04128 0.379 137 4.0e-05 0.00219 0.00283 0.00658 0.04879 0.431 138 4.0e-05 0.00257 0.00312 0.00772 0.05279 0.476 139 4.0e-05 0.00238 0.00290 0.00715 0.05643 0.517 140 3.0e-05 0.00181 0.00232 0.00542 0.03026 0.267 141 3.0e-05 0.00232 0.00269 0.00696 0.03273 0.281 142 4.0e-05 0.00428 0.00428 0.01285 0.06725 0.571 143 2.0e-05 0.00182 0.00215 0.00546 0.03527 0.297 144 2.0e-05 0.00189 0.00211 0.00568 0.01997 0.180 145 1.0e-05 0.00100 0.00133 0.00301 0.02662 0.228 146 2.0e-05 0.00169 0.00188 0.00506 0.02536 0.225 147 9.0e-05 0.00863 0.00946 0.02589 0.08143 0.821 148 8.0e-05 0.00849 0.00819 0.02546 0.06050 0.618 149 9.0e-05 0.00996 0.01027 0.02987 0.07118 0.722 150 8.0e-05 0.00919 0.00963 0.02756 0.07170 0.833 151 1.0e-04 0.01075 0.01154 0.03225 0.05830 0.784 152 1.6e-04 0.01800 0.01958 0.05401 0.11908 1.302 153 1.4e-04 0.01568 0.01699 0.04705 0.08684 1.018 154 6.0e-05 0.00388 0.00332 0.01164 0.02534 0.241 155 6.0e-05 0.00393 0.00300 0.01179 0.02682 0.236 156 5.0e-05 0.00356 0.00300 0.01067 0.03087 0.276 157 6.0e-05 0.00415 0.00339 0.01246 0.02293 0.223 158 1.5e-04 0.01117 0.00718 0.03351 0.04912 0.438 159 8.0e-05 0.00593 0.00454 0.01778 0.02852 0.266 160 5.0e-05 0.00321 0.00318 0.00962 0.03235 0.339 161 5.0e-05 0.00299 0.00316 0.00896 0.04009 0.406 162 5.0e-05 0.00352 0.00329 0.01057 0.03273 0.325 163 6.0e-05 0.00366 0.00340 0.01097 0.03658 0.369 164 5.0e-05 0.00291 0.00284 0.00873 0.01756 0.155 165 9.0e-05 0.00493 0.00461 0.01480 0.02814 0.272 166 1.0e-05 0.00154 0.00153 0.00462 0.02448 0.217 167 1.0e-05 0.00173 0.00159 0.00519 0.01242 0.116 168 1.0e-05 0.00205 0.00186 0.00616 0.02030 0.197 169 4.0e-05 0.00490 0.00448 0.01470 0.02177 0.189 170 2.0e-05 0.00316 0.00283 0.00949 0.02018 0.212 171 2.0e-05 0.00279 0.00237 0.00837 0.01897 0.181 172 3.0e-05 0.00166 0.00190 0.00499 0.01358 0.129 173 3.0e-05 0.00170 0.00200 0.00510 0.01484 0.133 174 3.0e-05 0.00171 0.00203 0.00514 0.01472 0.133 175 3.0e-05 0.00176 0.00218 0.00528 0.01657 0.145 176 3.0e-05 0.00160 0.00199 0.00480 0.01503 0.137 177 3.0e-05 0.00169 0.00213 0.00507 0.01725 0.155 178 2.0e-05 0.00135 0.00162 0.00406 0.01469 0.132 179 2.0e-05 0.00152 0.00186 0.00456 0.01574 0.142 180 3.0e-05 0.00204 0.00231 0.00612 0.01450 0.131 181 3.0e-05 0.00206 0.00233 0.00619 0.02551 0.237 182 3.0e-05 0.00202 0.00235 0.00605 0.01831 0.163 183 2.0e-05 0.00174 0.00198 0.00521 0.02145 0.198 184 4.0e-05 0.00186 0.00270 0.00558 0.01909 0.171 185 5.0e-05 0.00260 0.00346 0.00780 0.01795 0.163 186 3.0e-05 0.00134 0.00192 0.00403 0.01564 0.136 187 4.0e-05 0.00254 0.00263 0.00762 0.01660 0.154 188 2.0e-05 0.00115 0.00148 0.00345 0.01300 0.117 189 3.0e-05 0.00146 0.00184 0.00439 0.01185 0.106 190 3.0e-05 0.00412 0.00396 0.01235 0.02574 0.255 191 3.0e-05 0.00263 0.00259 0.00790 0.04087 0.405 192 3.0e-05 0.00331 0.00292 0.00994 0.02751 0.263 193 8.0e-05 0.00624 0.00564 0.01873 0.02308 0.256 194 4.0e-05 0.00370 0.00390 0.01109 0.02296 0.241 195 3.0e-05 0.00295 0.00317 0.00885 0.01884 0.190 Shimmer:APQ3 Shimmer:APQ5 MDVP:APQ Shimmer:DDA NHR status RPDE 1 0.02182 0.03130 0.02971 0.06545 0.02211 1 0.414783 2 0.03134 0.04518 0.04368 0.09403 0.01929 1 0.458359 3 0.02757 0.03858 0.03590 0.08270 0.01309 1 0.429895 4 0.02924 0.04005 0.03772 0.08771 0.01353 1 0.434969 5 0.03490 0.04825 0.04465 0.10470 0.01767 1 0.417356 6 0.02328 0.03526 0.03243 0.06985 0.01222 1 0.415564 7 0.00779 0.00937 0.01351 0.02337 0.00607 1 0.596040 8 0.00829 0.00946 0.01256 0.02487 0.00344 1 0.637420 9 0.01073 0.01277 0.01717 0.03218 0.01070 1 0.615551 10 0.01441 0.01725 0.02444 0.04324 0.01022 1 0.547037 11 0.01079 0.01342 0.01892 0.03237 0.01166 1 0.611137 12 0.01424 0.01641 0.02214 0.04272 0.01141 1 0.583390 13 0.00656 0.00717 0.01140 0.01968 0.00581 1 0.460600 14 0.00728 0.00932 0.01797 0.02184 0.01041 1 0.430166 15 0.01064 0.00972 0.01246 0.03191 0.00609 1 0.474791 16 0.00772 0.00888 0.01359 0.02316 0.00839 1 0.565924 17 0.00969 0.01200 0.02074 0.02908 0.01859 1 0.567380 18 0.01441 0.01893 0.03430 0.04322 0.02919 1 0.631099 19 0.02471 0.03572 0.05767 0.07413 0.03160 1 0.665318 20 0.01721 0.02374 0.04310 0.05164 0.03365 1 0.649554 21 0.01667 0.02383 0.04055 0.05000 0.03871 1 0.660125 22 0.02021 0.02591 0.04525 0.06062 0.01849 1 0.629017 23 0.02228 0.02540 0.04246 0.06685 0.01280 1 0.619060 24 0.02187 0.02470 0.03772 0.06562 0.01840 1 0.537264 25 0.00738 0.00948 0.01497 0.02214 0.01778 1 0.397937 26 0.01732 0.02245 0.03780 0.05197 0.02887 1 0.522746 27 0.00889 0.01169 0.01872 0.02666 0.01095 1 0.418622 28 0.00883 0.01144 0.01826 0.02650 0.01328 1 0.358773 29 0.00769 0.01012 0.01661 0.02307 0.00677 1 0.470478 30 0.00793 0.01057 0.01799 0.02380 0.01170 1 0.427785 31 0.00563 0.00680 0.00802 0.01689 0.00339 0 0.422229 32 0.00504 0.00641 0.00762 0.01513 0.00167 0 0.432439 33 0.00640 0.00825 0.00951 0.01919 0.00119 0 0.465946 34 0.00469 0.00606 0.00719 0.01407 0.00072 0 0.368535 35 0.00468 0.00610 0.00726 0.01403 0.00065 0 0.340068 36 0.00586 0.00760 0.00957 0.01758 0.00135 0 0.344252 37 0.01154 0.01347 0.01612 0.03463 0.00586 1 0.360148 38 0.00938 0.01160 0.01491 0.02814 0.00340 1 0.341435 39 0.00726 0.00885 0.01190 0.02177 0.00231 1 0.403884 40 0.00829 0.01003 0.01366 0.02488 0.00265 1 0.396793 41 0.00774 0.00941 0.01233 0.02321 0.00231 1 0.326480 42 0.00742 0.00901 0.01234 0.02226 0.00257 1 0.306443 43 0.01035 0.01024 0.01133 0.03104 0.00740 0 0.305062 44 0.01006 0.01038 0.01251 0.03017 0.00675 0 0.457702 45 0.00777 0.00898 0.01033 0.02330 0.00454 0 0.438296 46 0.00847 0.00879 0.01014 0.02542 0.00476 0 0.431285 47 0.00906 0.00977 0.01149 0.02719 0.00476 0 0.467489 48 0.00614 0.00730 0.00860 0.01841 0.00432 0 0.610367 49 0.00855 0.00776 0.01433 0.02566 0.00839 0 0.579597 50 0.00930 0.00802 0.01400 0.02789 0.00462 0 0.538688 51 0.01241 0.01024 0.01685 0.03724 0.00479 0 0.553134 52 0.01143 0.00959 0.01614 0.03429 0.00474 0 0.507504 53 0.01323 0.01072 0.01677 0.03969 0.00481 0 0.459766 54 0.01396 0.01219 0.01947 0.04188 0.00484 0 0.420383 55 0.01483 0.01609 0.02067 0.04450 0.01036 1 0.536009 56 0.01789 0.01992 0.02454 0.05368 0.01180 1 0.558586 57 0.02032 0.02302 0.02802 0.06097 0.00969 1 0.541781 58 0.01189 0.01459 0.01948 0.03568 0.00681 1 0.530529 59 0.01394 0.01625 0.02137 0.04183 0.00786 1 0.540049 60 0.01805 0.01974 0.02519 0.05414 0.01143 1 0.547975 61 0.00975 0.01258 0.01382 0.02925 0.00871 0 0.341788 62 0.01013 0.01296 0.01340 0.03039 0.00301 0 0.447979 63 0.00867 0.01108 0.01200 0.02602 0.00340 0 0.364867 64 0.00882 0.01075 0.01179 0.02647 0.00351 0 0.256570 65 0.00769 0.00957 0.01016 0.02308 0.00300 0 0.276850 66 0.00942 0.01160 0.01234 0.02827 0.00420 0 0.305429 67 0.01830 0.01810 0.02428 0.05490 0.02183 1 0.460139 68 0.01638 0.01759 0.02603 0.04914 0.02659 1 0.498133 69 0.03152 0.02422 0.03392 0.09455 0.04882 1 0.513237 70 0.03357 0.02494 0.03635 0.10070 0.02431 1 0.487407 71 0.01868 0.01906 0.02949 0.05605 0.02599 1 0.489345 72 0.02749 0.02466 0.03736 0.08247 0.03361 1 0.543299 73 0.00974 0.00925 0.01345 0.02921 0.00442 1 0.495954 74 0.01373 0.01375 0.01956 0.04120 0.00623 1 0.509127 75 0.01432 0.01325 0.01831 0.04295 0.00479 1 0.437031 76 0.01284 0.01219 0.01715 0.03851 0.00472 1 0.463514 77 0.02413 0.02231 0.02704 0.07238 0.00905 1 0.489538 78 0.01284 0.01199 0.01636 0.03852 0.00420 1 0.429484 79 0.01803 0.01886 0.02455 0.05408 0.01062 1 0.644954 80 0.01773 0.01783 0.02139 0.05320 0.02220 1 0.594387 81 0.02266 0.02451 0.02876 0.06799 0.01823 1 0.544805 82 0.01792 0.01841 0.02190 0.05377 0.01825 1 0.576084 83 0.01371 0.01421 0.01751 0.04114 0.01237 1 0.554610 84 0.01277 0.01343 0.01552 0.03831 0.00882 1 0.576644 85 0.02679 0.03022 0.03510 0.08037 0.05470 1 0.556494 86 0.02107 0.02493 0.02877 0.06321 0.02782 1 0.583574 87 0.02073 0.02415 0.02784 0.06219 0.03151 1 0.598714 88 0.03671 0.04159 0.04683 0.11012 0.04824 1 0.602874 89 0.03788 0.04254 0.04802 0.11363 0.04214 1 0.599371 90 0.02297 0.02768 0.03455 0.06892 0.07223 1 0.590951 91 0.03650 0.04282 0.05114 0.10949 0.08725 1 0.653410 92 0.04421 0.04962 0.05690 0.13262 0.01658 1 0.501037 93 0.02383 0.02521 0.03051 0.07150 0.01914 1 0.454444 94 0.03341 0.03794 0.04398 0.10024 0.01211 1 0.447456 95 0.02062 0.02321 0.02764 0.06185 0.00850 1 0.502380 96 0.01813 0.01909 0.02571 0.05439 0.01018 1 0.447285 97 0.01806 0.02024 0.02809 0.05417 0.00852 1 0.366329 98 0.02135 0.02174 0.03088 0.06406 0.08151 1 0.629574 99 0.02542 0.02630 0.03908 0.07625 0.10323 1 0.571010 100 0.03611 0.03963 0.05783 0.10833 0.16744 1 0.638545 101 0.05358 0.04791 0.06196 0.16074 0.31482 1 0.671299 102 0.03223 0.03672 0.05174 0.09669 0.11843 1 0.639808 103 0.05551 0.05005 0.06023 0.16654 0.25930 1 0.596362 104 0.00522 0.00659 0.01009 0.01567 0.00495 1 0.296888 105 0.00469 0.00582 0.00871 0.01406 0.00243 1 0.263654 106 0.00660 0.00818 0.01059 0.01979 0.00578 1 0.365488 107 0.00522 0.00632 0.00928 0.01567 0.00233 1 0.334171 108 0.00633 0.00788 0.01267 0.01898 0.00659 1 0.393563 109 0.00455 0.00576 0.00993 0.01364 0.00238 1 0.311369 110 0.01771 0.01815 0.02084 0.05312 0.00947 1 0.497554 111 0.01192 0.01439 0.01852 0.03576 0.00704 1 0.436084 112 0.00952 0.01058 0.01307 0.02855 0.00830 1 0.338097 113 0.01277 0.01483 0.01767 0.03831 0.01316 1 0.498877 114 0.00861 0.01017 0.01301 0.02583 0.00620 1 0.441097 115 0.01107 0.01284 0.01604 0.03320 0.01048 1 0.331508 116 0.00796 0.00832 0.01271 0.02389 0.06051 1 0.407701 117 0.00606 0.00747 0.01312 0.01818 0.01554 1 0.450798 118 0.00757 0.00971 0.01652 0.02270 0.01802 1 0.486738 119 0.00617 0.00744 0.01151 0.01851 0.00856 1 0.470422 120 0.00679 0.00631 0.01075 0.02038 0.00681 1 0.462516 121 0.00849 0.01117 0.01734 0.02548 0.02350 1 0.487756 122 0.00534 0.00630 0.01104 0.01603 0.01161 1 0.400088 123 0.02587 0.02567 0.03220 0.07761 0.01968 1 0.538016 124 0.01372 0.01580 0.01931 0.04115 0.01813 1 0.589956 125 0.01289 0.01420 0.01720 0.03867 0.02020 1 0.618663 126 0.01235 0.01495 0.01944 0.03706 0.01874 1 0.637518 127 0.01484 0.01805 0.02259 0.04451 0.01794 1 0.623209 128 0.01547 0.01859 0.02301 0.04641 0.01796 1 0.585169 129 0.00538 0.00570 0.00811 0.01614 0.01724 1 0.457541 130 0.00476 0.00588 0.00903 0.01428 0.00487 1 0.491345 131 0.00703 0.00820 0.01194 0.02110 0.01610 1 0.467160 132 0.00721 0.00815 0.01310 0.02164 0.01015 1 0.468621 133 0.00633 0.00701 0.00915 0.01898 0.00903 1 0.470972 134 0.00490 0.00621 0.00903 0.01471 0.00504 1 0.482296 135 0.02683 0.03112 0.03651 0.08050 0.03031 1 0.637814 136 0.02229 0.02592 0.03316 0.06688 0.02529 1 0.653427 137 0.02385 0.02973 0.04370 0.07154 0.02278 1 0.647900 138 0.02896 0.03347 0.04134 0.08689 0.03690 1 0.625362 139 0.03070 0.03530 0.04451 0.09211 0.02629 1 0.640945 140 0.01514 0.01812 0.02770 0.04543 0.01827 1 0.624811 141 0.01713 0.01964 0.02824 0.05139 0.02485 1 0.677131 142 0.04016 0.04003 0.04464 0.12047 0.04238 1 0.606344 143 0.02055 0.02076 0.02530 0.06165 0.01728 1 0.606273 144 0.01117 0.01177 0.01506 0.03350 0.02010 1 0.536102 145 0.01475 0.01558 0.02006 0.04426 0.01049 1 0.497480 146 0.01379 0.01478 0.01909 0.04137 0.01493 1 0.566849 147 0.03804 0.05426 0.08808 0.11411 0.07530 1 0.561610 148 0.02865 0.04101 0.06359 0.08595 0.06057 1 0.478024 149 0.03474 0.04580 0.06824 0.10422 0.08069 1 0.552870 150 0.03515 0.04265 0.06460 0.10546 0.07889 1 0.427627 151 0.02699 0.03714 0.06259 0.08096 0.10952 1 0.507826 152 0.05647 0.07940 0.13778 0.16942 0.21713 1 0.625866 153 0.04284 0.05556 0.08318 0.12851 0.16265 1 0.584164 154 0.01340 0.01399 0.02056 0.04019 0.04179 1 0.566867 155 0.01484 0.01405 0.02018 0.04451 0.04611 1 0.651680 156 0.01659 0.01804 0.02402 0.04977 0.02631 1 0.628300 157 0.01205 0.01289 0.01771 0.03615 0.03191 1 0.611679 158 0.02610 0.02161 0.02916 0.07830 0.10748 1 0.630547 159 0.01500 0.01581 0.02157 0.04499 0.03828 1 0.635015 160 0.01360 0.01650 0.03105 0.04079 0.02663 1 0.654945 161 0.01579 0.01994 0.04114 0.04736 0.02073 1 0.653139 162 0.01644 0.01722 0.02931 0.04933 0.02810 1 0.577802 163 0.01864 0.01940 0.03091 0.05592 0.02707 1 0.685151 164 0.00967 0.01033 0.01363 0.02902 0.01435 1 0.557045 165 0.01579 0.01553 0.02073 0.04736 0.03882 1 0.671378 166 0.01410 0.01426 0.01621 0.04231 0.00620 0 0.469928 167 0.00696 0.00747 0.00882 0.02089 0.00533 0 0.384868 168 0.01186 0.01230 0.01367 0.03557 0.00910 0 0.440988 169 0.01279 0.01272 0.01439 0.03836 0.01337 0 0.372222 170 0.01176 0.01191 0.01344 0.03529 0.00965 0 0.371837 171 0.01084 0.01121 0.01255 0.03253 0.01049 0 0.522812 172 0.00664 0.00786 0.01140 0.01992 0.00435 0 0.413295 173 0.00754 0.00950 0.01285 0.02261 0.00430 0 0.369090 174 0.00748 0.00905 0.01148 0.02245 0.00478 0 0.380253 175 0.00881 0.01062 0.01318 0.02643 0.00590 0 0.387482 176 0.00812 0.00933 0.01133 0.02436 0.00401 0 0.405991 177 0.00874 0.01021 0.01331 0.02623 0.00415 0 0.361232 178 0.00728 0.00886 0.01230 0.02184 0.00570 1 0.396610 179 0.00839 0.00956 0.01309 0.02518 0.00488 1 0.402591 180 0.00725 0.00876 0.01263 0.02175 0.00540 1 0.398499 181 0.01321 0.01574 0.02148 0.03964 0.00611 1 0.352396 182 0.00950 0.01103 0.01559 0.02849 0.00639 1 0.408598 183 0.01155 0.01341 0.01666 0.03464 0.00595 1 0.329577 184 0.00864 0.01223 0.01949 0.02592 0.00955 0 0.603515 185 0.00810 0.01144 0.01756 0.02429 0.01179 0 0.663842 186 0.00667 0.00990 0.01691 0.02001 0.00737 0 0.598515 187 0.00820 0.00972 0.01491 0.02460 0.01397 0 0.566424 188 0.00631 0.00789 0.01144 0.01892 0.00680 0 0.528485 189 0.00557 0.00721 0.01095 0.01672 0.00703 0 0.555303 190 0.01454 0.01582 0.01758 0.04363 0.04441 0 0.508479 191 0.02336 0.02498 0.02745 0.07008 0.02764 0 0.448439 192 0.01604 0.01657 0.01879 0.04812 0.01810 0 0.431674 193 0.01268 0.01365 0.01667 0.03804 0.10715 0 0.407567 194 0.01265 0.01321 0.01588 0.03794 0.07223 0 0.451221 195 0.01026 0.01161 0.01373 0.03078 0.04398 0 0.462803 DFA spread1 spread2 D2 PPE 1 0.815285 -4.813031 0.266482 2.301442 0.284654 2 0.819521 -4.075192 0.335590 2.486855 0.368674 3 0.825288 -4.443179 0.311173 2.342259 0.332634 4 0.819235 -4.117501 0.334147 2.405554 0.368975 5 0.823484 -3.747787 0.234513 2.332180 0.410335 6 0.825069 -4.242867 0.299111 2.187560 0.357775 7 0.764112 -5.634322 0.257682 1.854785 0.211756 8 0.763262 -6.167603 0.183721 2.064693 0.163755 9 0.773587 -5.498678 0.327769 2.322511 0.231571 10 0.798463 -5.011879 0.325996 2.432792 0.271362 11 0.776156 -5.249770 0.391002 2.407313 0.249740 12 0.792520 -4.960234 0.363566 2.642476 0.275931 13 0.646846 -6.547148 0.152813 2.041277 0.138512 14 0.665833 -5.660217 0.254989 2.519422 0.199889 15 0.654027 -6.105098 0.203653 2.125618 0.170100 16 0.658245 -5.340115 0.210185 2.205546 0.234589 17 0.644692 -5.440040 0.239764 2.264501 0.218164 18 0.605417 -2.931070 0.434326 3.007463 0.430788 19 0.719467 -3.949079 0.357870 3.109010 0.377429 20 0.686080 -4.554466 0.340176 2.856676 0.322111 21 0.704087 -4.095442 0.262564 2.739710 0.365391 22 0.698951 -5.186960 0.237622 2.557536 0.259765 23 0.679834 -4.330956 0.262384 2.916777 0.285695 24 0.686894 -5.248776 0.210279 2.547508 0.253556 25 0.732479 -5.557447 0.220890 2.692176 0.215961 26 0.737948 -5.571843 0.236853 2.846369 0.219514 27 0.720916 -6.183590 0.226278 2.589702 0.147403 28 0.726652 -6.271690 0.196102 2.314209 0.162999 29 0.676258 -7.120925 0.279789 2.241742 0.108514 30 0.723797 -6.635729 0.209866 1.957961 0.135242 31 0.741367 -7.348300 0.177551 1.743867 0.085569 32 0.742055 -7.682587 0.173319 2.103106 0.068501 33 0.738703 -7.067931 0.175181 1.512275 0.096320 34 0.742133 -7.695734 0.178540 1.544609 0.056141 35 0.741899 -7.964984 0.163519 1.423287 0.044539 36 0.742737 -7.777685 0.170183 2.447064 0.057610 37 0.778834 -6.149653 0.218037 2.477082 0.165827 38 0.783626 -6.006414 0.196371 2.536527 0.173218 39 0.766209 -6.452058 0.212294 2.269398 0.141929 40 0.758324 -6.006647 0.266892 2.382544 0.160691 41 0.765623 -6.647379 0.201095 2.374073 0.130554 42 0.759203 -7.044105 0.063412 2.361532 0.115730 43 0.654172 -7.310550 0.098648 2.416838 0.095032 44 0.634267 -6.793547 0.158266 2.256699 0.117399 45 0.635285 -7.057869 0.091608 2.330716 0.091470 46 0.638928 -6.995820 0.102083 2.365800 0.102706 47 0.631653 -7.156076 0.127642 2.392122 0.097336 48 0.635204 -7.319510 0.200873 2.028612 0.086398 49 0.733659 -6.439398 0.266392 2.079922 0.133867 50 0.754073 -6.482096 0.264967 2.054419 0.128872 51 0.775933 -6.650471 0.254498 1.840198 0.103561 52 0.760361 -6.689151 0.291954 2.431854 0.105993 53 0.766204 -7.072419 0.220434 1.972297 0.119308 54 0.785714 -6.836811 0.269866 2.223719 0.147491 55 0.819032 -4.649573 0.205558 1.986899 0.316700 56 0.811843 -4.333543 0.221727 2.014606 0.344834 57 0.821364 -4.438453 0.238298 1.922940 0.335041 58 0.817756 -4.608260 0.290024 2.021591 0.314464 59 0.813432 -4.476755 0.262633 1.827012 0.326197 60 0.817396 -4.609161 0.221711 1.831691 0.316395 61 0.678874 -7.040508 0.066994 2.460791 0.101516 62 0.686264 -7.293801 0.086372 2.321560 0.098555 63 0.694399 -6.966321 0.095882 2.278687 0.103224 64 0.683296 -7.245620 0.018689 2.498224 0.093534 65 0.673636 -7.496264 0.056844 2.003032 0.073581 66 0.681811 -7.314237 0.006274 2.118596 0.091546 67 0.720908 -5.409423 0.226850 2.359973 0.226156 68 0.729067 -5.324574 0.205660 2.291558 0.226247 69 0.731444 -5.869750 0.151814 2.118496 0.185580 70 0.727313 -6.261141 0.120956 2.137075 0.141958 71 0.730387 -5.720868 0.158830 2.277927 0.180828 72 0.733232 -5.207985 0.224852 2.642276 0.242981 73 0.762959 -5.791820 0.329066 2.205024 0.188180 74 0.789532 -5.389129 0.306636 1.928708 0.225461 75 0.815908 -5.313360 0.201861 2.225815 0.244512 76 0.807217 -5.477592 0.315074 1.862092 0.228624 77 0.789977 -5.775966 0.341169 2.007923 0.193918 78 0.816340 -5.391029 0.250572 1.777901 0.232744 79 0.779612 -5.115212 0.249494 2.017753 0.260015 80 0.790117 -4.913885 0.265699 2.398422 0.277948 81 0.770466 -4.441519 0.155097 2.645959 0.327978 82 0.778747 -5.132032 0.210458 2.232576 0.260633 83 0.787896 -5.022288 0.146948 2.428306 0.264666 84 0.772416 -6.025367 0.078202 2.053601 0.177275 85 0.729586 -5.288912 0.343073 3.099301 0.242119 86 0.727747 -5.657899 0.315903 3.098256 0.200423 87 0.712199 -6.366916 0.335753 2.654271 0.144614 88 0.740837 -5.515071 0.299549 3.136550 0.220968 89 0.743937 -5.783272 0.299793 3.007096 0.194052 90 0.745526 -4.379411 0.375531 3.671155 0.332086 91 0.733165 -4.508984 0.389232 3.317586 0.301952 92 0.714360 -6.411497 0.207156 2.344876 0.134120 93 0.734504 -5.952058 0.087840 2.344336 0.186489 94 0.697790 -6.152551 0.173520 2.080121 0.160809 95 0.712170 -6.251425 0.188056 2.143851 0.160812 96 0.705658 -6.247076 0.180528 2.344348 0.164916 97 0.693429 -6.417440 0.194627 2.473239 0.151709 98 0.714485 -4.020042 0.265315 2.671825 0.340623 99 0.690892 -5.159169 0.202146 2.441612 0.260375 100 0.674953 -3.760348 0.242861 2.634633 0.378483 101 0.656846 -3.700544 0.260481 2.991063 0.370961 102 0.643327 -4.202730 0.310163 2.638279 0.356881 103 0.641418 -3.269487 0.270641 2.690917 0.444774 104 0.722356 -6.878393 0.089267 2.004055 0.113942 105 0.691483 -7.111576 0.144780 2.065477 0.093193 106 0.719974 -6.997403 0.210279 1.994387 0.112878 107 0.677930 -6.981201 0.184550 2.129924 0.106802 108 0.700246 -6.600023 0.249172 2.499148 0.105306 109 0.676066 -6.739151 0.160686 2.296873 0.115130 110 0.740539 -5.845099 0.278679 2.608749 0.185668 111 0.727863 -5.258320 0.256454 2.550961 0.232520 112 0.712466 -6.471427 0.184378 2.502336 0.136390 113 0.722085 -4.876336 0.212054 2.376749 0.268144 114 0.722254 -5.963040 0.250283 2.489191 0.177807 115 0.715121 -6.729713 0.181701 2.938114 0.115515 116 0.662668 -4.673241 0.261549 2.702355 0.274407 117 0.653823 -6.051233 0.273280 2.640798 0.170106 118 0.676023 -4.597834 0.372114 2.975889 0.282780 119 0.655239 -4.913137 0.393056 2.816781 0.251972 120 0.582710 -5.517173 0.389295 2.925862 0.220657 121 0.684130 -6.186128 0.279933 2.686240 0.152428 122 0.656182 -4.711007 0.281618 2.655744 0.234809 123 0.741480 -5.418787 0.160267 2.090438 0.229892 124 0.732903 -5.445140 0.142466 2.174306 0.215558 125 0.728421 -5.944191 0.143359 1.929715 0.181988 126 0.735546 -5.594275 0.127950 1.765957 0.222716 127 0.738245 -5.540351 0.087165 1.821297 0.214075 128 0.736964 -5.825257 0.115697 1.996146 0.196535 129 0.699787 -6.890021 0.152941 2.328513 0.112856 130 0.718839 -5.892061 0.195976 2.108873 0.183572 131 0.724045 -6.135296 0.203630 2.539724 0.169923 132 0.735136 -6.112667 0.217013 2.527742 0.170633 133 0.721308 -5.436135 0.254909 2.516320 0.232209 134 0.723096 -6.448134 0.178713 2.034827 0.141422 135 0.744064 -5.301321 0.320385 2.375138 0.243080 136 0.706687 -5.333619 0.322044 2.631793 0.228319 137 0.708144 -4.378916 0.300067 2.445502 0.259451 138 0.708617 -4.654894 0.304107 2.672362 0.274387 139 0.701404 -5.634576 0.306014 2.419253 0.209191 140 0.696049 -5.866357 0.233070 2.445646 0.184985 141 0.685057 -4.796845 0.397749 2.963799 0.277227 142 0.665945 -5.410336 0.288917 2.665133 0.231723 143 0.661735 -5.585259 0.310746 2.465528 0.209863 144 0.632631 -5.898673 0.213353 2.470746 0.189032 145 0.630409 -6.132663 0.220617 2.576563 0.159777 146 0.574282 -5.456811 0.345238 2.840556 0.232861 147 0.793509 -3.297668 0.414758 3.413649 0.457533 148 0.768974 -4.276605 0.355736 3.142364 0.336085 149 0.764036 -3.377325 0.335357 3.274865 0.418646 150 0.775708 -4.892495 0.262281 2.910213 0.270173 151 0.762726 -4.484303 0.340256 2.958815 0.301487 152 0.768320 -2.434031 0.450493 3.079221 0.527367 153 0.754449 -2.839756 0.356224 3.184027 0.454721 154 0.670475 -4.865194 0.246404 2.013530 0.168581 155 0.659333 -4.239028 0.175691 2.451130 0.247455 156 0.652025 -3.583722 0.207914 2.439597 0.206256 157 0.623731 -5.435100 0.230532 2.699645 0.220546 158 0.646786 -3.444478 0.303214 2.964568 0.261305 159 0.627337 -5.070096 0.280091 2.892300 0.249703 160 0.675865 -5.498456 0.234196 2.103014 0.216638 161 0.694571 -5.185987 0.259229 2.151121 0.244948 162 0.684373 -5.283009 0.226528 2.442906 0.238281 163 0.719576 -5.529833 0.242750 2.408689 0.220520 164 0.673086 -5.617124 0.184896 1.871871 0.212386 165 0.674562 -2.929379 0.396746 2.560422 0.367233 166 0.628232 -6.816086 0.172270 2.235197 0.119652 167 0.626710 -7.018057 0.176316 1.852402 0.091604 168 0.628058 -7.517934 0.160414 1.881767 0.075587 169 0.725216 -5.736781 0.164529 2.882450 0.202879 170 0.646167 -7.169701 0.073298 2.266432 0.100881 171 0.646818 -7.304500 0.171088 2.095237 0.096220 172 0.756700 -6.323531 0.218885 2.193412 0.160376 173 0.776158 -6.085567 0.192375 1.889002 0.174152 174 0.766700 -5.943501 0.192150 1.852542 0.179677 175 0.756482 -6.012559 0.229298 1.872946 0.163118 176 0.761255 -5.966779 0.197938 1.974857 0.184067 177 0.763242 -6.016891 0.109256 2.004719 0.174429 178 0.745957 -6.486822 0.197919 2.449763 0.132703 179 0.762508 -6.311987 0.182459 2.251553 0.160306 180 0.778349 -5.711205 0.240875 2.845109 0.192730 181 0.759320 -6.261446 0.183218 2.264226 0.144105 182 0.768845 -5.704053 0.216204 2.679185 0.197710 183 0.757180 -6.277170 0.109397 2.209021 0.156368 184 0.669565 -5.619070 0.191576 2.027228 0.215724 185 0.656516 -5.198864 0.206768 2.120412 0.252404 186 0.654331 -5.592584 0.133917 2.058658 0.214346 187 0.667654 -6.431119 0.153310 2.161936 0.120605 188 0.663884 -6.359018 0.116636 2.152083 0.138868 189 0.659132 -6.710219 0.149694 1.913990 0.121777 190 0.683761 -6.934474 0.159890 2.316346 0.112838 191 0.657899 -6.538586 0.121952 2.657476 0.133050 192 0.683244 -6.195325 0.129303 2.784312 0.168895 193 0.655683 -6.787197 0.158453 2.679772 0.131728 194 0.643956 -6.744577 0.207454 2.138608 0.123306 195 0.664357 -5.724056 0.190667 2.555477 0.148569 > k <- length(x[1,]) > df <- as.data.frame(x) > (mylm <- lm(df)) Call: lm(formula = df) Coefficients: (Intercept) `MDVP:Fo(Hz)` `MDVP:Fhi(Hz)` `MDVP:Flo(Hz)` 4.454e+01 -1.827e-03 2.478e-03 2.102e-03 `MDVP:Jitter(%)` `MDVP:Jitter(Abs)` `MDVP:RAP` `MDVP:PPQ` -6.075e+02 5.789e+04 -2.710e+04 2.541e+01 `Jitter:DDP` `MDVP:Shimmer` `MDVP:Shimmer(dB)` `Shimmer:APQ3` 9.120e+03 3.089e+02 -1.075e+01 2.766e+04 `Shimmer:APQ5` `MDVP:APQ` `Shimmer:DDA` NHR -1.930e+02 5.899e+01 -9.349e+03 -1.643e+01 status RPDE DFA spread1 -4.404e-01 -1.732e+01 -2.398e+00 4.123e-01 spread2 D2 PPE 9.433e+00 -3.015e+00 -1.159e+01 > (mysum <- summary(mylm)) Call: lm(formula = df) Residuals: Min 1Q Median 3Q Max -3.9904 -0.9766 -0.1385 0.8344 5.0830 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 4.454e+01 5.190e+00 8.583 5.32e-15 *** `MDVP:Fo(Hz)` -1.827e-03 8.056e-03 -0.227 0.820860 `MDVP:Fhi(Hz)` 2.478e-03 1.691e-03 1.465 0.144700 `MDVP:Flo(Hz)` 2.102e-03 4.292e-03 0.490 0.624890 `MDVP:Jitter(%)` -6.075e+02 3.592e+02 -1.691 0.092640 . `MDVP:Jitter(Abs)` 5.789e+04 2.414e+04 2.398 0.017565 * `MDVP:RAP` -2.710e+04 4.940e+04 -0.549 0.584023 `MDVP:PPQ` 2.541e+01 4.685e+02 0.054 0.956811 `Jitter:DDP` 9.120e+03 1.647e+04 0.554 0.580490 `MDVP:Shimmer` 3.089e+02 1.804e+02 1.712 0.088658 . `MDVP:Shimmer(dB)` -1.075e+01 6.305e+00 -1.706 0.089856 . `Shimmer:APQ3` 2.766e+04 4.749e+04 0.582 0.561007 `Shimmer:APQ5` -1.930e+02 1.061e+02 -1.819 0.070652 . `MDVP:APQ` 5.899e+01 5.754e+01 1.025 0.306707 `Shimmer:DDA` -9.349e+03 1.582e+04 -0.591 0.555429 NHR -1.643e+01 1.047e+01 -1.570 0.118322 status -4.404e-01 4.025e-01 -1.094 0.275444 RPDE -1.732e+01 1.961e+00 -8.834 1.14e-15 *** DFA -2.398e+00 3.915e+00 -0.612 0.541082 spread1 4.123e-01 5.202e-01 0.792 0.429161 spread2 9.433e+00 2.481e+00 3.801 0.000199 *** D2 -3.015e+00 5.607e-01 -5.378 2.43e-07 *** PPE -1.159e+01 7.293e+00 -1.590 0.113756 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 1.731 on 172 degrees of freedom Multiple R-squared: 0.8644, Adjusted R-squared: 0.8471 F-statistic: 49.85 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,] 0.24568624 4.913725e-01 7.543138e-01 [2,] 0.16488642 3.297728e-01 8.351136e-01 [3,] 0.08467087 1.693417e-01 9.153291e-01 [4,] 0.09452461 1.890492e-01 9.054754e-01 [5,] 0.05068873 1.013775e-01 9.493113e-01 [6,] 0.02471842 4.943684e-02 9.752816e-01 [7,] 0.13964927 2.792985e-01 8.603507e-01 [8,] 0.16419877 3.283975e-01 8.358012e-01 [9,] 0.28109113 5.621823e-01 7.189089e-01 [10,] 0.27228137 5.445627e-01 7.277186e-01 [11,] 0.28131805 5.626361e-01 7.186820e-01 [12,] 0.21633476 4.326695e-01 7.836652e-01 [13,] 0.17897606 3.579521e-01 8.210239e-01 [14,] 0.13535545 2.707109e-01 8.646446e-01 [15,] 0.10550606 2.110121e-01 8.944939e-01 [16,] 0.07513833 1.502767e-01 9.248617e-01 [17,] 0.07150447 1.430089e-01 9.284955e-01 [18,] 0.37416101 7.483220e-01 6.258390e-01 [19,] 0.33002153 6.600431e-01 6.699785e-01 [20,] 0.33066161 6.613232e-01 6.693384e-01 [21,] 0.28345355 5.669071e-01 7.165465e-01 [22,] 0.24607515 4.921503e-01 7.539248e-01 [23,] 0.31218644 6.243729e-01 6.878136e-01 [24,] 0.66995048 6.600990e-01 3.300495e-01 [25,] 0.62947669 7.410466e-01 3.705233e-01 [26,] 0.59670926 8.065815e-01 4.032907e-01 [27,] 0.55089824 8.982035e-01 4.491018e-01 [28,] 0.49880918 9.976184e-01 5.011908e-01 [29,] 0.44771973 8.954395e-01 5.522803e-01 [30,] 0.39221572 7.844314e-01 6.077843e-01 [31,] 0.38216521 7.643304e-01 6.178348e-01 [32,] 0.33729527 6.745905e-01 6.627047e-01 [33,] 0.29599796 5.919959e-01 7.040020e-01 [34,] 0.26386631 5.277326e-01 7.361337e-01 [35,] 0.24313773 4.862755e-01 7.568623e-01 [36,] 0.35611534 7.122307e-01 6.438847e-01 [37,] 0.55315968 8.936806e-01 4.468403e-01 [38,] 0.60280071 7.943986e-01 3.971993e-01 [39,] 0.59563448 8.087310e-01 4.043655e-01 [40,] 0.65317769 6.936446e-01 3.468223e-01 [41,] 0.76196314 4.760737e-01 2.380369e-01 [42,] 0.77784645 4.443071e-01 2.221536e-01 [43,] 0.75884124 4.823175e-01 2.411588e-01 [44,] 0.85418810 2.916238e-01 1.458119e-01 [45,] 0.82872253 3.425549e-01 1.712775e-01 [46,] 0.80505459 3.898908e-01 1.949454e-01 [47,] 0.77314120 4.537176e-01 2.268588e-01 [48,] 0.75104568 4.979086e-01 2.489543e-01 [49,] 0.75704593 4.859081e-01 2.429541e-01 [50,] 0.77240158 4.551968e-01 2.275984e-01 [51,] 0.73647026 5.270595e-01 2.635297e-01 [52,] 0.69648357 6.070329e-01 3.035164e-01 [53,] 0.67186375 6.562725e-01 3.281363e-01 [54,] 0.66881858 6.623628e-01 3.311814e-01 [55,] 0.63050620 7.389876e-01 3.694938e-01 [56,] 0.60092381 7.981524e-01 3.990762e-01 [57,] 0.56690197 8.661961e-01 4.330980e-01 [58,] 0.53530137 9.293973e-01 4.646986e-01 [59,] 0.54201348 9.159730e-01 4.579865e-01 [60,] 0.56789501 8.642100e-01 4.321050e-01 [61,] 0.56072232 8.785554e-01 4.392777e-01 [62,] 0.62839383 7.432123e-01 3.716062e-01 [63,] 0.60939952 7.812010e-01 3.906005e-01 [64,] 0.59556490 8.088702e-01 4.044351e-01 [65,] 0.66727418 6.654516e-01 3.327258e-01 [66,] 0.69769426 6.046115e-01 3.023057e-01 [67,] 0.76111761 4.777648e-01 2.388824e-01 [68,] 0.74448920 5.110216e-01 2.555108e-01 [69,] 0.76796474 4.640705e-01 2.320353e-01 [70,] 0.76051181 4.789764e-01 2.394882e-01 [71,] 0.73055282 5.388944e-01 2.694472e-01 [72,] 0.71694617 5.661077e-01 2.830538e-01 [73,] 0.80205008 3.958998e-01 1.979499e-01 [74,] 0.82238374 3.552325e-01 1.776163e-01 [75,] 0.85226577 2.954685e-01 1.477342e-01 [76,] 0.93454260 1.309148e-01 6.545740e-02 [77,] 0.92157889 1.568422e-01 7.842111e-02 [78,] 0.92175077 1.564985e-01 7.824923e-02 [79,] 0.90603411 1.879318e-01 9.396589e-02 [80,] 0.88712530 2.257494e-01 1.128747e-01 [81,] 0.87510077 2.497985e-01 1.248992e-01 [82,] 0.91111659 1.777668e-01 8.888341e-02 [83,] 0.89682420 2.063516e-01 1.031758e-01 [84,] 0.93214803 1.357039e-01 6.785197e-02 [85,] 0.93754255 1.249149e-01 6.245745e-02 [86,] 0.92840069 1.431986e-01 7.159931e-02 [87,] 0.93317458 1.336508e-01 6.682542e-02 [88,] 0.91688529 1.662294e-01 8.311471e-02 [89,] 0.90729865 1.854027e-01 9.270135e-02 [90,] 0.88868475 2.226305e-01 1.113152e-01 [91,] 0.86541583 2.691683e-01 1.345842e-01 [92,] 0.85663854 2.867229e-01 1.433615e-01 [93,] 0.90301053 1.939789e-01 9.698947e-02 [94,] 0.89127714 2.174457e-01 1.087229e-01 [95,] 0.88522157 2.295569e-01 1.147784e-01 [96,] 0.87854076 2.429185e-01 1.214592e-01 [97,] 0.90547934 1.890413e-01 9.452066e-02 [98,] 0.89379351 2.124130e-01 1.062065e-01 [99,] 0.87822763 2.435447e-01 1.217724e-01 [100,] 0.85986351 2.802730e-01 1.401365e-01 [101,] 0.84214721 3.157056e-01 1.578528e-01 [102,] 0.84146135 3.170773e-01 1.585386e-01 [103,] 0.81821267 3.635747e-01 1.817873e-01 [104,] 0.90777046 1.844591e-01 9.222954e-02 [105,] 0.90988107 1.802379e-01 9.011893e-02 [106,] 0.88700945 2.259811e-01 1.129906e-01 [107,] 0.90880868 1.823826e-01 9.119132e-02 [108,] 0.88829722 2.234056e-01 1.117028e-01 [109,] 0.88679754 2.264049e-01 1.132025e-01 [110,] 0.86944736 2.611053e-01 1.305526e-01 [111,] 0.85222811 2.955438e-01 1.477719e-01 [112,] 0.83216362 3.356728e-01 1.678364e-01 [113,] 0.83046726 3.390655e-01 1.695327e-01 [114,] 0.82118978 3.576204e-01 1.788102e-01 [115,] 0.92089690 1.582062e-01 7.910310e-02 [116,] 0.90007338 1.998532e-01 9.992662e-02 [117,] 0.90061137 1.987773e-01 9.938863e-02 [118,] 0.87348322 2.530336e-01 1.265168e-01 [119,] 0.86741763 2.651647e-01 1.325824e-01 [120,] 0.85672469 2.865506e-01 1.432753e-01 [121,] 0.82950633 3.409873e-01 1.704937e-01 [122,] 0.78591766 4.281647e-01 2.140823e-01 [123,] 0.81874419 3.625116e-01 1.812558e-01 [124,] 0.96223694 7.552612e-02 3.776306e-02 [125,] 0.94685765 1.062847e-01 5.314235e-02 [126,] 0.92680116 1.463977e-01 7.319884e-02 [127,] 0.93283289 1.343342e-01 6.716711e-02 [128,] 0.99970666 5.866790e-04 2.933395e-04 [129,] 0.99943442 1.131153e-03 5.655767e-04 [130,] 0.99948130 1.037399e-03 5.186993e-04 [131,] 0.99902116 1.957688e-03 9.788441e-04 [132,] 0.99966490 6.702049e-04 3.351024e-04 [133,] 0.99920599 1.588018e-03 7.940091e-04 [134,] 0.99999110 1.779145e-05 8.895724e-06 [135,] 0.99997333 5.333376e-05 2.666688e-05 [136,] 0.99995109 9.781153e-05 4.890577e-05 [137,] 0.99984231 3.153770e-04 1.576885e-04 [138,] 0.99975672 4.865590e-04 2.432795e-04 [139,] 0.99925140 1.497191e-03 7.485956e-04 [140,] 0.99779269 4.414619e-03 2.207309e-03 [141,] 0.99904942 1.901169e-03 9.505845e-04 [142,] 0.99987161 2.567850e-04 1.283925e-04 [143,] 0.99967090 6.581927e-04 3.290963e-04 [144,] 0.99934407 1.311855e-03 6.559274e-04 > postscript(file="/var/fisher/rcomp/tmp/1fwgw1386010214.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$resid) > grid() > dev.off() null device 1 > postscript(file="/var/fisher/rcomp/tmp/2jmrv1386010214.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(mysum$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/3qkey1386010214.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') > grid() > dev.off() null device 1 > postscript(file="/var/fisher/rcomp/tmp/40j6d1386010214.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') > dev.off() null device 1 > postscript(file="/var/fisher/rcomp/tmp/59ksr1386010214.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > qqnorm(mysum$resid, main='Residual Normal Q-Q Plot') > qqline(mysum$resid) > grid() > dev.off() null device 1 > (myerror <- as.ts(mysum$resid)) Time Series: Start = 1 End = 195 Frequency = 1 1 2 3 4 5 6 -0.78167302 1.20500037 0.03680685 0.74630019 1.80562469 0.29687459 7 8 9 10 11 12 0.92253225 4.93477988 -1.04783311 -0.92660013 -2.10477933 -0.56849523 13 14 15 16 17 18 0.57249264 -0.30478438 0.80681628 2.38278566 -0.39458531 1.57806450 19 20 21 22 23 24 1.04271289 -2.07514587 -0.16058805 0.34056754 3.95752227 0.61398952 25 26 27 28 29 30 0.09901452 0.78039640 1.27574125 -0.13847132 0.34717701 -0.37624433 31 32 33 34 35 36 -0.20194973 4.95761446 3.54286679 3.46628999 2.78757054 5.08304862 37 38 39 40 41 42 -1.06901341 -0.02383687 0.91086854 0.62211917 0.15168048 1.08127477 43 44 45 46 47 48 -2.69692617 -0.97785272 0.92885126 1.04126517 1.07754545 3.28396658 49 50 51 52 53 54 -0.83212491 0.26774677 0.66317855 0.45976010 -0.29268438 -0.01298221 55 56 57 58 59 60 -0.67861649 0.31053859 0.28039393 0.30072732 -0.05879324 -0.03053857 61 62 63 64 65 66 1.22046964 3.18780350 0.66646461 0.35494562 -1.28887434 -1.01611450 67 68 69 70 71 72 -1.96253344 -1.72193603 0.46316491 2.11717535 -0.97538758 0.10253027 73 74 75 76 77 78 1.11672031 -1.58573067 2.07202550 -0.60179774 0.17920249 0.33312729 79 80 81 82 83 84 0.76643325 -0.44799932 0.74520004 -0.87636359 0.05455290 1.29420919 85 86 87 88 89 90 -2.15088828 -1.39554995 -3.55991005 -0.19925813 0.55538312 -1.70740831 91 92 93 94 95 96 0.12655964 2.22672592 -1.06370948 1.48158889 0.62047758 -0.65895394 97 98 99 100 101 102 -1.37637686 -1.64226920 -2.90478230 -0.59899660 1.71331478 -0.35779842 103 104 105 106 107 108 2.02005298 -0.80878622 -0.56100056 -0.12222503 2.25280272 0.17941287 109 110 111 112 113 114 2.30012061 1.40540430 0.84336666 -2.61176688 0.53342767 -0.42985436 115 116 117 118 119 120 -1.17212192 0.46174373 1.42205810 2.91376493 0.66740264 1.16169027 121 122 123 124 125 126 0.36538798 2.12264776 -0.91973173 -0.65920462 -0.81956432 -1.33126260 127 128 129 130 131 132 -2.16430228 -1.65727930 2.43245331 1.64227509 0.36013787 2.39783196 133 134 135 136 137 138 -0.44740552 0.40512571 -1.74778750 -0.40815013 -1.23626845 -0.22726431 139 140 141 142 143 144 -0.63326915 -1.63691659 0.79635809 1.00181100 -0.91812843 -2.77390666 145 146 147 148 149 150 -1.19094088 -0.23436528 -0.91960526 -2.23641263 -0.16505404 -0.88542851 151 152 153 154 155 156 -1.86965600 2.84993573 2.22009759 -2.16676498 2.46098446 2.20730793 157 158 159 160 161 162 3.00121280 0.71805629 2.52675656 -2.35672515 -2.30271095 -1.58014903 163 164 165 166 167 168 -0.26467050 -2.33973247 -1.85015728 0.98808303 -2.49245021 -3.99043155 169 170 171 172 173 174 -2.44540174 -2.54690496 -1.54367371 0.05999685 -0.87077744 -0.73469146 175 176 177 178 179 180 -1.95941718 -0.33767514 -0.19389743 -0.98450661 -0.33830326 -0.33182903 181 182 183 184 185 186 -2.40146875 -0.88579336 -1.92270207 -0.28130365 -0.22780146 0.82545061 187 188 189 190 191 192 -0.59092370 0.09909850 0.02161336 -0.88205594 -0.39627918 -1.44100537 193 194 195 -1.84580325 -3.93442473 -0.94920296 > postscript(file="/var/fisher/rcomp/tmp/68kms1386010214.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 -0.78167302 NA 1 1.20500037 -0.78167302 2 0.03680685 1.20500037 3 0.74630019 0.03680685 4 1.80562469 0.74630019 5 0.29687459 1.80562469 6 0.92253225 0.29687459 7 4.93477988 0.92253225 8 -1.04783311 4.93477988 9 -0.92660013 -1.04783311 10 -2.10477933 -0.92660013 11 -0.56849523 -2.10477933 12 0.57249264 -0.56849523 13 -0.30478438 0.57249264 14 0.80681628 -0.30478438 15 2.38278566 0.80681628 16 -0.39458531 2.38278566 17 1.57806450 -0.39458531 18 1.04271289 1.57806450 19 -2.07514587 1.04271289 20 -0.16058805 -2.07514587 21 0.34056754 -0.16058805 22 3.95752227 0.34056754 23 0.61398952 3.95752227 24 0.09901452 0.61398952 25 0.78039640 0.09901452 26 1.27574125 0.78039640 27 -0.13847132 1.27574125 28 0.34717701 -0.13847132 29 -0.37624433 0.34717701 30 -0.20194973 -0.37624433 31 4.95761446 -0.20194973 32 3.54286679 4.95761446 33 3.46628999 3.54286679 34 2.78757054 3.46628999 35 5.08304862 2.78757054 36 -1.06901341 5.08304862 37 -0.02383687 -1.06901341 38 0.91086854 -0.02383687 39 0.62211917 0.91086854 40 0.15168048 0.62211917 41 1.08127477 0.15168048 42 -2.69692617 1.08127477 43 -0.97785272 -2.69692617 44 0.92885126 -0.97785272 45 1.04126517 0.92885126 46 1.07754545 1.04126517 47 3.28396658 1.07754545 48 -0.83212491 3.28396658 49 0.26774677 -0.83212491 50 0.66317855 0.26774677 51 0.45976010 0.66317855 52 -0.29268438 0.45976010 53 -0.01298221 -0.29268438 54 -0.67861649 -0.01298221 55 0.31053859 -0.67861649 56 0.28039393 0.31053859 57 0.30072732 0.28039393 58 -0.05879324 0.30072732 59 -0.03053857 -0.05879324 60 1.22046964 -0.03053857 61 3.18780350 1.22046964 62 0.66646461 3.18780350 63 0.35494562 0.66646461 64 -1.28887434 0.35494562 65 -1.01611450 -1.28887434 66 -1.96253344 -1.01611450 67 -1.72193603 -1.96253344 68 0.46316491 -1.72193603 69 2.11717535 0.46316491 70 -0.97538758 2.11717535 71 0.10253027 -0.97538758 72 1.11672031 0.10253027 73 -1.58573067 1.11672031 74 2.07202550 -1.58573067 75 -0.60179774 2.07202550 76 0.17920249 -0.60179774 77 0.33312729 0.17920249 78 0.76643325 0.33312729 79 -0.44799932 0.76643325 80 0.74520004 -0.44799932 81 -0.87636359 0.74520004 82 0.05455290 -0.87636359 83 1.29420919 0.05455290 84 -2.15088828 1.29420919 85 -1.39554995 -2.15088828 86 -3.55991005 -1.39554995 87 -0.19925813 -3.55991005 88 0.55538312 -0.19925813 89 -1.70740831 0.55538312 90 0.12655964 -1.70740831 91 2.22672592 0.12655964 92 -1.06370948 2.22672592 93 1.48158889 -1.06370948 94 0.62047758 1.48158889 95 -0.65895394 0.62047758 96 -1.37637686 -0.65895394 97 -1.64226920 -1.37637686 98 -2.90478230 -1.64226920 99 -0.59899660 -2.90478230 100 1.71331478 -0.59899660 101 -0.35779842 1.71331478 102 2.02005298 -0.35779842 103 -0.80878622 2.02005298 104 -0.56100056 -0.80878622 105 -0.12222503 -0.56100056 106 2.25280272 -0.12222503 107 0.17941287 2.25280272 108 2.30012061 0.17941287 109 1.40540430 2.30012061 110 0.84336666 1.40540430 111 -2.61176688 0.84336666 112 0.53342767 -2.61176688 113 -0.42985436 0.53342767 114 -1.17212192 -0.42985436 115 0.46174373 -1.17212192 116 1.42205810 0.46174373 117 2.91376493 1.42205810 118 0.66740264 2.91376493 119 1.16169027 0.66740264 120 0.36538798 1.16169027 121 2.12264776 0.36538798 122 -0.91973173 2.12264776 123 -0.65920462 -0.91973173 124 -0.81956432 -0.65920462 125 -1.33126260 -0.81956432 126 -2.16430228 -1.33126260 127 -1.65727930 -2.16430228 128 2.43245331 -1.65727930 129 1.64227509 2.43245331 130 0.36013787 1.64227509 131 2.39783196 0.36013787 132 -0.44740552 2.39783196 133 0.40512571 -0.44740552 134 -1.74778750 0.40512571 135 -0.40815013 -1.74778750 136 -1.23626845 -0.40815013 137 -0.22726431 -1.23626845 138 -0.63326915 -0.22726431 139 -1.63691659 -0.63326915 140 0.79635809 -1.63691659 141 1.00181100 0.79635809 142 -0.91812843 1.00181100 143 -2.77390666 -0.91812843 144 -1.19094088 -2.77390666 145 -0.23436528 -1.19094088 146 -0.91960526 -0.23436528 147 -2.23641263 -0.91960526 148 -0.16505404 -2.23641263 149 -0.88542851 -0.16505404 150 -1.86965600 -0.88542851 151 2.84993573 -1.86965600 152 2.22009759 2.84993573 153 -2.16676498 2.22009759 154 2.46098446 -2.16676498 155 2.20730793 2.46098446 156 3.00121280 2.20730793 157 0.71805629 3.00121280 158 2.52675656 0.71805629 159 -2.35672515 2.52675656 160 -2.30271095 -2.35672515 161 -1.58014903 -2.30271095 162 -0.26467050 -1.58014903 163 -2.33973247 -0.26467050 164 -1.85015728 -2.33973247 165 0.98808303 -1.85015728 166 -2.49245021 0.98808303 167 -3.99043155 -2.49245021 168 -2.44540174 -3.99043155 169 -2.54690496 -2.44540174 170 -1.54367371 -2.54690496 171 0.05999685 -1.54367371 172 -0.87077744 0.05999685 173 -0.73469146 -0.87077744 174 -1.95941718 -0.73469146 175 -0.33767514 -1.95941718 176 -0.19389743 -0.33767514 177 -0.98450661 -0.19389743 178 -0.33830326 -0.98450661 179 -0.33182903 -0.33830326 180 -2.40146875 -0.33182903 181 -0.88579336 -2.40146875 182 -1.92270207 -0.88579336 183 -0.28130365 -1.92270207 184 -0.22780146 -0.28130365 185 0.82545061 -0.22780146 186 -0.59092370 0.82545061 187 0.09909850 -0.59092370 188 0.02161336 0.09909850 189 -0.88205594 0.02161336 190 -0.39627918 -0.88205594 191 -1.44100537 -0.39627918 192 -1.84580325 -1.44100537 193 -3.93442473 -1.84580325 194 -0.94920296 -3.93442473 195 NA -0.94920296 > dum1 <- dum[2:length(myerror),] > dum1 lag(myerror, k = 1) myerror [1,] 1.20500037 -0.78167302 [2,] 0.03680685 1.20500037 [3,] 0.74630019 0.03680685 [4,] 1.80562469 0.74630019 [5,] 0.29687459 1.80562469 [6,] 0.92253225 0.29687459 [7,] 4.93477988 0.92253225 [8,] -1.04783311 4.93477988 [9,] -0.92660013 -1.04783311 [10,] -2.10477933 -0.92660013 [11,] -0.56849523 -2.10477933 [12,] 0.57249264 -0.56849523 [13,] -0.30478438 0.57249264 [14,] 0.80681628 -0.30478438 [15,] 2.38278566 0.80681628 [16,] -0.39458531 2.38278566 [17,] 1.57806450 -0.39458531 [18,] 1.04271289 1.57806450 [19,] -2.07514587 1.04271289 [20,] -0.16058805 -2.07514587 [21,] 0.34056754 -0.16058805 [22,] 3.95752227 0.34056754 [23,] 0.61398952 3.95752227 [24,] 0.09901452 0.61398952 [25,] 0.78039640 0.09901452 [26,] 1.27574125 0.78039640 [27,] -0.13847132 1.27574125 [28,] 0.34717701 -0.13847132 [29,] -0.37624433 0.34717701 [30,] -0.20194973 -0.37624433 [31,] 4.95761446 -0.20194973 [32,] 3.54286679 4.95761446 [33,] 3.46628999 3.54286679 [34,] 2.78757054 3.46628999 [35,] 5.08304862 2.78757054 [36,] -1.06901341 5.08304862 [37,] -0.02383687 -1.06901341 [38,] 0.91086854 -0.02383687 [39,] 0.62211917 0.91086854 [40,] 0.15168048 0.62211917 [41,] 1.08127477 0.15168048 [42,] -2.69692617 1.08127477 [43,] -0.97785272 -2.69692617 [44,] 0.92885126 -0.97785272 [45,] 1.04126517 0.92885126 [46,] 1.07754545 1.04126517 [47,] 3.28396658 1.07754545 [48,] -0.83212491 3.28396658 [49,] 0.26774677 -0.83212491 [50,] 0.66317855 0.26774677 [51,] 0.45976010 0.66317855 [52,] -0.29268438 0.45976010 [53,] -0.01298221 -0.29268438 [54,] -0.67861649 -0.01298221 [55,] 0.31053859 -0.67861649 [56,] 0.28039393 0.31053859 [57,] 0.30072732 0.28039393 [58,] -0.05879324 0.30072732 [59,] -0.03053857 -0.05879324 [60,] 1.22046964 -0.03053857 [61,] 3.18780350 1.22046964 [62,] 0.66646461 3.18780350 [63,] 0.35494562 0.66646461 [64,] -1.28887434 0.35494562 [65,] -1.01611450 -1.28887434 [66,] -1.96253344 -1.01611450 [67,] -1.72193603 -1.96253344 [68,] 0.46316491 -1.72193603 [69,] 2.11717535 0.46316491 [70,] -0.97538758 2.11717535 [71,] 0.10253027 -0.97538758 [72,] 1.11672031 0.10253027 [73,] -1.58573067 1.11672031 [74,] 2.07202550 -1.58573067 [75,] -0.60179774 2.07202550 [76,] 0.17920249 -0.60179774 [77,] 0.33312729 0.17920249 [78,] 0.76643325 0.33312729 [79,] -0.44799932 0.76643325 [80,] 0.74520004 -0.44799932 [81,] -0.87636359 0.74520004 [82,] 0.05455290 -0.87636359 [83,] 1.29420919 0.05455290 [84,] -2.15088828 1.29420919 [85,] -1.39554995 -2.15088828 [86,] -3.55991005 -1.39554995 [87,] -0.19925813 -3.55991005 [88,] 0.55538312 -0.19925813 [89,] -1.70740831 0.55538312 [90,] 0.12655964 -1.70740831 [91,] 2.22672592 0.12655964 [92,] -1.06370948 2.22672592 [93,] 1.48158889 -1.06370948 [94,] 0.62047758 1.48158889 [95,] -0.65895394 0.62047758 [96,] -1.37637686 -0.65895394 [97,] -1.64226920 -1.37637686 [98,] -2.90478230 -1.64226920 [99,] -0.59899660 -2.90478230 [100,] 1.71331478 -0.59899660 [101,] -0.35779842 1.71331478 [102,] 2.02005298 -0.35779842 [103,] -0.80878622 2.02005298 [104,] -0.56100056 -0.80878622 [105,] -0.12222503 -0.56100056 [106,] 2.25280272 -0.12222503 [107,] 0.17941287 2.25280272 [108,] 2.30012061 0.17941287 [109,] 1.40540430 2.30012061 [110,] 0.84336666 1.40540430 [111,] -2.61176688 0.84336666 [112,] 0.53342767 -2.61176688 [113,] -0.42985436 0.53342767 [114,] -1.17212192 -0.42985436 [115,] 0.46174373 -1.17212192 [116,] 1.42205810 0.46174373 [117,] 2.91376493 1.42205810 [118,] 0.66740264 2.91376493 [119,] 1.16169027 0.66740264 [120,] 0.36538798 1.16169027 [121,] 2.12264776 0.36538798 [122,] -0.91973173 2.12264776 [123,] -0.65920462 -0.91973173 [124,] -0.81956432 -0.65920462 [125,] -1.33126260 -0.81956432 [126,] -2.16430228 -1.33126260 [127,] -1.65727930 -2.16430228 [128,] 2.43245331 -1.65727930 [129,] 1.64227509 2.43245331 [130,] 0.36013787 1.64227509 [131,] 2.39783196 0.36013787 [132,] -0.44740552 2.39783196 [133,] 0.40512571 -0.44740552 [134,] -1.74778750 0.40512571 [135,] -0.40815013 -1.74778750 [136,] -1.23626845 -0.40815013 [137,] -0.22726431 -1.23626845 [138,] -0.63326915 -0.22726431 [139,] -1.63691659 -0.63326915 [140,] 0.79635809 -1.63691659 [141,] 1.00181100 0.79635809 [142,] -0.91812843 1.00181100 [143,] -2.77390666 -0.91812843 [144,] -1.19094088 -2.77390666 [145,] -0.23436528 -1.19094088 [146,] -0.91960526 -0.23436528 [147,] -2.23641263 -0.91960526 [148,] -0.16505404 -2.23641263 [149,] -0.88542851 -0.16505404 [150,] -1.86965600 -0.88542851 [151,] 2.84993573 -1.86965600 [152,] 2.22009759 2.84993573 [153,] -2.16676498 2.22009759 [154,] 2.46098446 -2.16676498 [155,] 2.20730793 2.46098446 [156,] 3.00121280 2.20730793 [157,] 0.71805629 3.00121280 [158,] 2.52675656 0.71805629 [159,] -2.35672515 2.52675656 [160,] -2.30271095 -2.35672515 [161,] -1.58014903 -2.30271095 [162,] -0.26467050 -1.58014903 [163,] -2.33973247 -0.26467050 [164,] -1.85015728 -2.33973247 [165,] 0.98808303 -1.85015728 [166,] -2.49245021 0.98808303 [167,] -3.99043155 -2.49245021 [168,] -2.44540174 -3.99043155 [169,] -2.54690496 -2.44540174 [170,] -1.54367371 -2.54690496 [171,] 0.05999685 -1.54367371 [172,] -0.87077744 0.05999685 [173,] -0.73469146 -0.87077744 [174,] -1.95941718 -0.73469146 [175,] -0.33767514 -1.95941718 [176,] -0.19389743 -0.33767514 [177,] -0.98450661 -0.19389743 [178,] -0.33830326 -0.98450661 [179,] -0.33182903 -0.33830326 [180,] -2.40146875 -0.33182903 [181,] -0.88579336 -2.40146875 [182,] -1.92270207 -0.88579336 [183,] -0.28130365 -1.92270207 [184,] -0.22780146 -0.28130365 [185,] 0.82545061 -0.22780146 [186,] -0.59092370 0.82545061 [187,] 0.09909850 -0.59092370 [188,] 0.02161336 0.09909850 [189,] -0.88205594 0.02161336 [190,] -0.39627918 -0.88205594 [191,] -1.44100537 -0.39627918 [192,] -1.84580325 -1.44100537 [193,] -3.93442473 -1.84580325 [194,] -0.94920296 -3.93442473 > z <- as.data.frame(dum1) > z lag(myerror, k = 1) myerror 1 1.20500037 -0.78167302 2 0.03680685 1.20500037 3 0.74630019 0.03680685 4 1.80562469 0.74630019 5 0.29687459 1.80562469 6 0.92253225 0.29687459 7 4.93477988 0.92253225 8 -1.04783311 4.93477988 9 -0.92660013 -1.04783311 10 -2.10477933 -0.92660013 11 -0.56849523 -2.10477933 12 0.57249264 -0.56849523 13 -0.30478438 0.57249264 14 0.80681628 -0.30478438 15 2.38278566 0.80681628 16 -0.39458531 2.38278566 17 1.57806450 -0.39458531 18 1.04271289 1.57806450 19 -2.07514587 1.04271289 20 -0.16058805 -2.07514587 21 0.34056754 -0.16058805 22 3.95752227 0.34056754 23 0.61398952 3.95752227 24 0.09901452 0.61398952 25 0.78039640 0.09901452 26 1.27574125 0.78039640 27 -0.13847132 1.27574125 28 0.34717701 -0.13847132 29 -0.37624433 0.34717701 30 -0.20194973 -0.37624433 31 4.95761446 -0.20194973 32 3.54286679 4.95761446 33 3.46628999 3.54286679 34 2.78757054 3.46628999 35 5.08304862 2.78757054 36 -1.06901341 5.08304862 37 -0.02383687 -1.06901341 38 0.91086854 -0.02383687 39 0.62211917 0.91086854 40 0.15168048 0.62211917 41 1.08127477 0.15168048 42 -2.69692617 1.08127477 43 -0.97785272 -2.69692617 44 0.92885126 -0.97785272 45 1.04126517 0.92885126 46 1.07754545 1.04126517 47 3.28396658 1.07754545 48 -0.83212491 3.28396658 49 0.26774677 -0.83212491 50 0.66317855 0.26774677 51 0.45976010 0.66317855 52 -0.29268438 0.45976010 53 -0.01298221 -0.29268438 54 -0.67861649 -0.01298221 55 0.31053859 -0.67861649 56 0.28039393 0.31053859 57 0.30072732 0.28039393 58 -0.05879324 0.30072732 59 -0.03053857 -0.05879324 60 1.22046964 -0.03053857 61 3.18780350 1.22046964 62 0.66646461 3.18780350 63 0.35494562 0.66646461 64 -1.28887434 0.35494562 65 -1.01611450 -1.28887434 66 -1.96253344 -1.01611450 67 -1.72193603 -1.96253344 68 0.46316491 -1.72193603 69 2.11717535 0.46316491 70 -0.97538758 2.11717535 71 0.10253027 -0.97538758 72 1.11672031 0.10253027 73 -1.58573067 1.11672031 74 2.07202550 -1.58573067 75 -0.60179774 2.07202550 76 0.17920249 -0.60179774 77 0.33312729 0.17920249 78 0.76643325 0.33312729 79 -0.44799932 0.76643325 80 0.74520004 -0.44799932 81 -0.87636359 0.74520004 82 0.05455290 -0.87636359 83 1.29420919 0.05455290 84 -2.15088828 1.29420919 85 -1.39554995 -2.15088828 86 -3.55991005 -1.39554995 87 -0.19925813 -3.55991005 88 0.55538312 -0.19925813 89 -1.70740831 0.55538312 90 0.12655964 -1.70740831 91 2.22672592 0.12655964 92 -1.06370948 2.22672592 93 1.48158889 -1.06370948 94 0.62047758 1.48158889 95 -0.65895394 0.62047758 96 -1.37637686 -0.65895394 97 -1.64226920 -1.37637686 98 -2.90478230 -1.64226920 99 -0.59899660 -2.90478230 100 1.71331478 -0.59899660 101 -0.35779842 1.71331478 102 2.02005298 -0.35779842 103 -0.80878622 2.02005298 104 -0.56100056 -0.80878622 105 -0.12222503 -0.56100056 106 2.25280272 -0.12222503 107 0.17941287 2.25280272 108 2.30012061 0.17941287 109 1.40540430 2.30012061 110 0.84336666 1.40540430 111 -2.61176688 0.84336666 112 0.53342767 -2.61176688 113 -0.42985436 0.53342767 114 -1.17212192 -0.42985436 115 0.46174373 -1.17212192 116 1.42205810 0.46174373 117 2.91376493 1.42205810 118 0.66740264 2.91376493 119 1.16169027 0.66740264 120 0.36538798 1.16169027 121 2.12264776 0.36538798 122 -0.91973173 2.12264776 123 -0.65920462 -0.91973173 124 -0.81956432 -0.65920462 125 -1.33126260 -0.81956432 126 -2.16430228 -1.33126260 127 -1.65727930 -2.16430228 128 2.43245331 -1.65727930 129 1.64227509 2.43245331 130 0.36013787 1.64227509 131 2.39783196 0.36013787 132 -0.44740552 2.39783196 133 0.40512571 -0.44740552 134 -1.74778750 0.40512571 135 -0.40815013 -1.74778750 136 -1.23626845 -0.40815013 137 -0.22726431 -1.23626845 138 -0.63326915 -0.22726431 139 -1.63691659 -0.63326915 140 0.79635809 -1.63691659 141 1.00181100 0.79635809 142 -0.91812843 1.00181100 143 -2.77390666 -0.91812843 144 -1.19094088 -2.77390666 145 -0.23436528 -1.19094088 146 -0.91960526 -0.23436528 147 -2.23641263 -0.91960526 148 -0.16505404 -2.23641263 149 -0.88542851 -0.16505404 150 -1.86965600 -0.88542851 151 2.84993573 -1.86965600 152 2.22009759 2.84993573 153 -2.16676498 2.22009759 154 2.46098446 -2.16676498 155 2.20730793 2.46098446 156 3.00121280 2.20730793 157 0.71805629 3.00121280 158 2.52675656 0.71805629 159 -2.35672515 2.52675656 160 -2.30271095 -2.35672515 161 -1.58014903 -2.30271095 162 -0.26467050 -1.58014903 163 -2.33973247 -0.26467050 164 -1.85015728 -2.33973247 165 0.98808303 -1.85015728 166 -2.49245021 0.98808303 167 -3.99043155 -2.49245021 168 -2.44540174 -3.99043155 169 -2.54690496 -2.44540174 170 -1.54367371 -2.54690496 171 0.05999685 -1.54367371 172 -0.87077744 0.05999685 173 -0.73469146 -0.87077744 174 -1.95941718 -0.73469146 175 -0.33767514 -1.95941718 176 -0.19389743 -0.33767514 177 -0.98450661 -0.19389743 178 -0.33830326 -0.98450661 179 -0.33182903 -0.33830326 180 -2.40146875 -0.33182903 181 -0.88579336 -2.40146875 182 -1.92270207 -0.88579336 183 -0.28130365 -1.92270207 184 -0.22780146 -0.28130365 185 0.82545061 -0.22780146 186 -0.59092370 0.82545061 187 0.09909850 -0.59092370 188 0.02161336 0.09909850 189 -0.88205594 0.02161336 190 -0.39627918 -0.88205594 191 -1.44100537 -0.39627918 192 -1.84580325 -1.44100537 193 -3.93442473 -1.84580325 194 -0.94920296 -3.93442473 > 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/7krju1386010214.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/fisher/rcomp/tmp/8pskz1386010214.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/fisher/rcomp/tmp/95c7u1386010214.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0)) > plot(mylm, las = 1, sub='Residual Diagnostics') > par(opar) > dev.off() null device 1 > if (n > n25) { + postscript(file="/var/fisher/rcomp/tmp/10tvvq1386010214.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) + plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint') + grid() + dev.off() + } null device 1 > > #Note: the /var/fisher/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab > load(file="/var/fisher/rcomp/createtable") > > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE) > a<-table.row.end(a) > myeq <- colnames(x)[1] > myeq <- paste(myeq, '[t] = ', sep='') > for (i in 1:k){ + if (mysum$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/11dog71386010214.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/12rr6x1386010214.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/137mxe1386010214.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/1413nd1386010214.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/15i54c1386010214.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/168grh1386010214.tab") + } > > try(system("convert tmp/1fwgw1386010214.ps tmp/1fwgw1386010214.png",intern=TRUE)) character(0) > try(system("convert tmp/2jmrv1386010214.ps tmp/2jmrv1386010214.png",intern=TRUE)) character(0) > try(system("convert tmp/3qkey1386010214.ps tmp/3qkey1386010214.png",intern=TRUE)) character(0) > try(system("convert tmp/40j6d1386010214.ps tmp/40j6d1386010214.png",intern=TRUE)) character(0) > try(system("convert tmp/59ksr1386010214.ps tmp/59ksr1386010214.png",intern=TRUE)) character(0) > try(system("convert tmp/68kms1386010214.ps tmp/68kms1386010214.png",intern=TRUE)) character(0) > try(system("convert tmp/7krju1386010214.ps tmp/7krju1386010214.png",intern=TRUE)) character(0) > try(system("convert tmp/8pskz1386010214.ps tmp/8pskz1386010214.png",intern=TRUE)) character(0) > try(system("convert tmp/95c7u1386010214.ps tmp/95c7u1386010214.png",intern=TRUE)) character(0) > try(system("convert tmp/10tvvq1386010214.ps tmp/10tvvq1386010214.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 29.750 3.909 33.664