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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+ ,0.659132 + ,-6.710219 + ,0.149694 + ,1.91399 + ,0.121777 + ,201.774 + ,262.707 + ,78.228 + ,0.00694 + ,0.00003 + ,0.00412 + ,0.00396 + ,0.01235 + ,0.02574 + ,0.255 + ,0.01454 + ,0.01582 + ,0.01758 + ,0.04363 + ,0.04441 + ,19.368 + ,0 + ,0.508479 + ,0.683761 + ,-6.934474 + ,0.15989 + ,2.316346 + ,0.112838 + ,174.188 + ,230.978 + ,94.261 + ,0.00459 + ,0.00003 + ,0.00263 + ,0.00259 + ,0.0079 + ,0.04087 + ,0.405 + ,0.02336 + ,0.02498 + ,0.02745 + ,0.07008 + ,0.02764 + ,19.517 + ,0 + ,0.448439 + ,0.657899 + ,-6.538586 + ,0.121952 + ,2.657476 + ,0.13305 + ,209.516 + ,253.017 + ,89.488 + ,0.00564 + ,0.00003 + ,0.00331 + ,0.00292 + ,0.00994 + ,0.02751 + ,0.263 + ,0.01604 + ,0.01657 + ,0.01879 + ,0.04812 + ,0.0181 + ,19.147 + ,0 + ,0.431674 + ,0.683244 + ,-6.195325 + ,0.129303 + ,2.784312 + ,0.168895 + ,174.688 + ,240.005 + ,74.287 + ,0.0136 + ,0.00008 + ,0.00624 + ,0.00564 + ,0.01873 + ,0.02308 + ,0.256 + ,0.01268 + ,0.01365 + ,0.01667 + ,0.03804 + ,0.10715 + ,17.883 + ,0 + ,0.407567 + ,0.655683 + ,-6.787197 + ,0.158453 + ,2.679772 + ,0.131728 + ,198.764 + ,396.961 + ,74.904 + ,0.0074 + ,0.00004 + ,0.0037 + ,0.0039 + ,0.01109 + ,0.02296 + ,0.241 + ,0.01265 + ,0.01321 + ,0.01588 + ,0.03794 + ,0.07223 + ,19.02 + ,0 + ,0.451221 + ,0.643956 + ,-6.744577 + ,0.207454 + ,2.138608 + ,0.123306 + ,214.289 + ,260.277 + ,77.973 + ,0.00567 + ,0.00003 + ,0.00295 + ,0.00317 + ,0.00885 + ,0.01884 + ,0.19 + ,0.01026 + ,0.01161 + ,0.01373 + ,0.03078 + ,0.04398 + ,21.209 + ,0 + ,0.462803 + ,0.664357 + ,-5.724056 + ,0.190667 + ,2.555477 + ,0.148569) + ,dim=c(23 + ,195) + ,dimnames=list(c('MDVP:Fo(Hz)' + ,'MDVP:Fhi(Hz)' + ,'MDVP:Flo(Hz)' + ,'MDVP:Jitter(%)' + ,'MDVP:Jitter(Abs)' + ,'MDVP:RAP' + ,'MDVP:PPQ' + ,'Jitter:DDP' + ,'MDVP:Shimmer' + ,'MDVP:Shimmer(dB)' + ,'Shimmer:APQ3' + ,'Shimmer:APQ5' + ,'MDVP:APQ' + ,'Shimmer:DDA' + ,'NHR' + ,'HNR' + ,'status' + ,'RPDE' + ,'DFA' + ,'spread1' + ,'spread2' + ,'D2' + ,'PPE') + ,1:195)) > y <- array(NA,dim=c(23,195),dimnames=list(c('MDVP:Fo(Hz)','MDVP:Fhi(Hz)','MDVP:Flo(Hz)','MDVP:Jitter(%)','MDVP:Jitter(Abs)','MDVP:RAP','MDVP:PPQ','Jitter:DDP','MDVP:Shimmer','MDVP:Shimmer(dB)','Shimmer:APQ3','Shimmer:APQ5','MDVP:APQ','Shimmer:DDA','NHR','HNR','status','RPDE','DFA','spread1','spread2','D2','PPE'),1:195)) > for (i in 1:dim(x)[1]) + { + for (j in 1:dim(x)[2]) + { + y[i,j] <- as.numeric(x[i,j]) + } + } > par3 = 'No Linear Trend' > par2 = 'Do not include Seasonal Dummies' > par1 = '17' > par3 <- 'No Linear Trend' > par2 <- 'Do not include Seasonal Dummies' > par1 <- '17' > #'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 MDVP:APQ Shimmer:DDA NHR HNR RPDE 1 0.02182 0.03130 0.02971 0.06545 0.02211 21.033 0.414783 2 0.03134 0.04518 0.04368 0.09403 0.01929 19.085 0.458359 3 0.02757 0.03858 0.03590 0.08270 0.01309 20.651 0.429895 4 0.02924 0.04005 0.03772 0.08771 0.01353 20.644 0.434969 5 0.03490 0.04825 0.04465 0.10470 0.01767 19.649 0.417356 6 0.02328 0.03526 0.03243 0.06985 0.01222 21.378 0.415564 7 0.00779 0.00937 0.01351 0.02337 0.00607 24.886 0.596040 8 0.00829 0.00946 0.01256 0.02487 0.00344 26.892 0.637420 9 0.01073 0.01277 0.01717 0.03218 0.01070 21.812 0.615551 10 0.01441 0.01725 0.02444 0.04324 0.01022 21.862 0.547037 11 0.01079 0.01342 0.01892 0.03237 0.01166 21.118 0.611137 12 0.01424 0.01641 0.02214 0.04272 0.01141 21.414 0.583390 13 0.00656 0.00717 0.01140 0.01968 0.00581 25.703 0.460600 14 0.00728 0.00932 0.01797 0.02184 0.01041 24.889 0.430166 15 0.01064 0.00972 0.01246 0.03191 0.00609 24.922 0.474791 16 0.00772 0.00888 0.01359 0.02316 0.00839 25.175 0.565924 17 0.00969 0.01200 0.02074 0.02908 0.01859 22.333 0.567380 18 0.01441 0.01893 0.03430 0.04322 0.02919 20.376 0.631099 19 0.02471 0.03572 0.05767 0.07413 0.03160 17.280 0.665318 20 0.01721 0.02374 0.04310 0.05164 0.03365 17.153 0.649554 21 0.01667 0.02383 0.04055 0.05000 0.03871 17.536 0.660125 22 0.02021 0.02591 0.04525 0.06062 0.01849 19.493 0.629017 23 0.02228 0.02540 0.04246 0.06685 0.01280 22.468 0.619060 24 0.02187 0.02470 0.03772 0.06562 0.01840 20.422 0.537264 25 0.00738 0.00948 0.01497 0.02214 0.01778 23.831 0.397937 26 0.01732 0.02245 0.03780 0.05197 0.02887 22.066 0.522746 27 0.00889 0.01169 0.01872 0.02666 0.01095 25.908 0.418622 28 0.00883 0.01144 0.01826 0.02650 0.01328 25.119 0.358773 29 0.00769 0.01012 0.01661 0.02307 0.00677 25.970 0.470478 30 0.00793 0.01057 0.01799 0.02380 0.01170 25.678 0.427785 31 0.00563 0.00680 0.00802 0.01689 0.00339 26.775 0.422229 32 0.00504 0.00641 0.00762 0.01513 0.00167 30.940 0.432439 33 0.00640 0.00825 0.00951 0.01919 0.00119 30.775 0.465946 34 0.00469 0.00606 0.00719 0.01407 0.00072 32.684 0.368535 35 0.00468 0.00610 0.00726 0.01403 0.00065 33.047 0.340068 36 0.00586 0.00760 0.00957 0.01758 0.00135 31.732 0.344252 37 0.01154 0.01347 0.01612 0.03463 0.00586 23.216 0.360148 38 0.00938 0.01160 0.01491 0.02814 0.00340 24.951 0.341435 39 0.00726 0.00885 0.01190 0.02177 0.00231 26.738 0.403884 40 0.00829 0.01003 0.01366 0.02488 0.00265 26.310 0.396793 41 0.00774 0.00941 0.01233 0.02321 0.00231 26.822 0.326480 42 0.00742 0.00901 0.01234 0.02226 0.00257 26.453 0.306443 43 0.01035 0.01024 0.01133 0.03104 0.00740 22.736 0.305062 44 0.01006 0.01038 0.01251 0.03017 0.00675 23.145 0.457702 45 0.00777 0.00898 0.01033 0.02330 0.00454 25.368 0.438296 46 0.00847 0.00879 0.01014 0.02542 0.00476 25.032 0.431285 47 0.00906 0.00977 0.01149 0.02719 0.00476 24.602 0.467489 48 0.00614 0.00730 0.00860 0.01841 0.00432 26.805 0.610367 49 0.00855 0.00776 0.01433 0.02566 0.00839 23.162 0.579597 50 0.00930 0.00802 0.01400 0.02789 0.00462 24.971 0.538688 51 0.01241 0.01024 0.01685 0.03724 0.00479 25.135 0.553134 52 0.01143 0.00959 0.01614 0.03429 0.00474 25.030 0.507504 53 0.01323 0.01072 0.01677 0.03969 0.00481 24.692 0.459766 54 0.01396 0.01219 0.01947 0.04188 0.00484 25.429 0.420383 55 0.01483 0.01609 0.02067 0.04450 0.01036 21.028 0.536009 56 0.01789 0.01992 0.02454 0.05368 0.01180 20.767 0.558586 57 0.02032 0.02302 0.02802 0.06097 0.00969 21.422 0.541781 58 0.01189 0.01459 0.01948 0.03568 0.00681 22.817 0.530529 59 0.01394 0.01625 0.02137 0.04183 0.00786 22.603 0.540049 60 0.01805 0.01974 0.02519 0.05414 0.01143 21.660 0.547975 61 0.00975 0.01258 0.01382 0.02925 0.00871 25.554 0.341788 62 0.01013 0.01296 0.01340 0.03039 0.00301 26.138 0.447979 63 0.00867 0.01108 0.01200 0.02602 0.00340 25.856 0.364867 64 0.00882 0.01075 0.01179 0.02647 0.00351 25.964 0.256570 65 0.00769 0.00957 0.01016 0.02308 0.00300 26.415 0.276850 66 0.00942 0.01160 0.01234 0.02827 0.00420 24.547 0.305429 67 0.01830 0.01810 0.02428 0.05490 0.02183 19.560 0.460139 68 0.01638 0.01759 0.02603 0.04914 0.02659 19.979 0.498133 69 0.03152 0.02422 0.03392 0.09455 0.04882 20.338 0.513237 70 0.03357 0.02494 0.03635 0.10070 0.02431 21.718 0.487407 71 0.01868 0.01906 0.02949 0.05605 0.02599 20.264 0.489345 72 0.02749 0.02466 0.03736 0.08247 0.03361 18.570 0.543299 73 0.00974 0.00925 0.01345 0.02921 0.00442 25.742 0.495954 74 0.01373 0.01375 0.01956 0.04120 0.00623 24.178 0.509127 75 0.01432 0.01325 0.01831 0.04295 0.00479 25.438 0.437031 76 0.01284 0.01219 0.01715 0.03851 0.00472 25.197 0.463514 77 0.02413 0.02231 0.02704 0.07238 0.00905 23.370 0.489538 78 0.01284 0.01199 0.01636 0.03852 0.00420 25.820 0.429484 79 0.01803 0.01886 0.02455 0.05408 0.01062 21.875 0.644954 80 0.01773 0.01783 0.02139 0.05320 0.02220 19.200 0.594387 81 0.02266 0.02451 0.02876 0.06799 0.01823 19.055 0.544805 82 0.01792 0.01841 0.02190 0.05377 0.01825 19.659 0.576084 83 0.01371 0.01421 0.01751 0.04114 0.01237 20.536 0.554610 84 0.01277 0.01343 0.01552 0.03831 0.00882 22.244 0.576644 85 0.02679 0.03022 0.03510 0.08037 0.05470 13.893 0.556494 86 0.02107 0.02493 0.02877 0.06321 0.02782 16.176 0.583574 87 0.02073 0.02415 0.02784 0.06219 0.03151 15.924 0.598714 88 0.03671 0.04159 0.04683 0.11012 0.04824 13.922 0.602874 89 0.03788 0.04254 0.04802 0.11363 0.04214 14.739 0.599371 90 0.02297 0.02768 0.03455 0.06892 0.07223 11.866 0.590951 91 0.03650 0.04282 0.05114 0.10949 0.08725 11.744 0.653410 92 0.04421 0.04962 0.05690 0.13262 0.01658 19.664 0.501037 93 0.02383 0.02521 0.03051 0.07150 0.01914 18.780 0.454444 94 0.03341 0.03794 0.04398 0.10024 0.01211 20.969 0.447456 95 0.02062 0.02321 0.02764 0.06185 0.00850 22.219 0.502380 96 0.01813 0.01909 0.02571 0.05439 0.01018 21.693 0.447285 97 0.01806 0.02024 0.02809 0.05417 0.00852 22.663 0.366329 98 0.02135 0.02174 0.03088 0.06406 0.08151 15.338 0.629574 99 0.02542 0.02630 0.03908 0.07625 0.10323 15.433 0.571010 100 0.03611 0.03963 0.05783 0.10833 0.16744 12.435 0.638545 101 0.05358 0.04791 0.06196 0.16074 0.31482 8.867 0.671299 102 0.03223 0.03672 0.05174 0.09669 0.11843 15.060 0.639808 103 0.05551 0.05005 0.06023 0.16654 0.25930 10.489 0.596362 104 0.00522 0.00659 0.01009 0.01567 0.00495 26.759 0.296888 105 0.00469 0.00582 0.00871 0.01406 0.00243 28.409 0.263654 106 0.00660 0.00818 0.01059 0.01979 0.00578 27.421 0.365488 107 0.00522 0.00632 0.00928 0.01567 0.00233 29.746 0.334171 108 0.00633 0.00788 0.01267 0.01898 0.00659 26.833 0.393563 109 0.00455 0.00576 0.00993 0.01364 0.00238 29.928 0.311369 110 0.01771 0.01815 0.02084 0.05312 0.00947 21.934 0.497554 111 0.01192 0.01439 0.01852 0.03576 0.00704 23.239 0.436084 112 0.00952 0.01058 0.01307 0.02855 0.00830 22.407 0.338097 113 0.01277 0.01483 0.01767 0.03831 0.01316 21.305 0.498877 114 0.00861 0.01017 0.01301 0.02583 0.00620 23.671 0.441097 115 0.01107 0.01284 0.01604 0.03320 0.01048 21.864 0.331508 116 0.00796 0.00832 0.01271 0.02389 0.06051 23.693 0.407701 117 0.00606 0.00747 0.01312 0.01818 0.01554 26.356 0.450798 118 0.00757 0.00971 0.01652 0.02270 0.01802 25.690 0.486738 119 0.00617 0.00744 0.01151 0.01851 0.00856 25.020 0.470422 120 0.00679 0.00631 0.01075 0.02038 0.00681 24.581 0.462516 121 0.00849 0.01117 0.01734 0.02548 0.02350 24.743 0.487756 122 0.00534 0.00630 0.01104 0.01603 0.01161 27.166 0.400088 123 0.02587 0.02567 0.03220 0.07761 0.01968 18.305 0.538016 124 0.01372 0.01580 0.01931 0.04115 0.01813 18.784 0.589956 125 0.01289 0.01420 0.01720 0.03867 0.02020 19.196 0.618663 126 0.01235 0.01495 0.01944 0.03706 0.01874 18.857 0.637518 127 0.01484 0.01805 0.02259 0.04451 0.01794 18.178 0.623209 128 0.01547 0.01859 0.02301 0.04641 0.01796 18.330 0.585169 129 0.00538 0.00570 0.00811 0.01614 0.01724 26.842 0.457541 130 0.00476 0.00588 0.00903 0.01428 0.00487 26.369 0.491345 131 0.00703 0.00820 0.01194 0.02110 0.01610 23.949 0.467160 132 0.00721 0.00815 0.01310 0.02164 0.01015 26.017 0.468621 133 0.00633 0.00701 0.00915 0.01898 0.00903 23.389 0.470972 134 0.00490 0.00621 0.00903 0.01471 0.00504 25.619 0.482296 135 0.02683 0.03112 0.03651 0.08050 0.03031 17.060 0.637814 136 0.02229 0.02592 0.03316 0.06688 0.02529 17.707 0.653427 137 0.02385 0.02973 0.04370 0.07154 0.02278 19.013 0.647900 138 0.02896 0.03347 0.04134 0.08689 0.03690 16.747 0.625362 139 0.03070 0.03530 0.04451 0.09211 0.02629 17.366 0.640945 140 0.01514 0.01812 0.02770 0.04543 0.01827 18.801 0.624811 141 0.01713 0.01964 0.02824 0.05139 0.02485 18.540 0.677131 142 0.04016 0.04003 0.04464 0.12047 0.04238 15.648 0.606344 143 0.02055 0.02076 0.02530 0.06165 0.01728 18.702 0.606273 144 0.01117 0.01177 0.01506 0.03350 0.02010 18.687 0.536102 145 0.01475 0.01558 0.02006 0.04426 0.01049 20.680 0.497480 146 0.01379 0.01478 0.01909 0.04137 0.01493 20.366 0.566849 147 0.03804 0.05426 0.08808 0.11411 0.07530 12.359 0.561610 148 0.02865 0.04101 0.06359 0.08595 0.06057 14.367 0.478024 149 0.03474 0.04580 0.06824 0.10422 0.08069 12.298 0.552870 150 0.03515 0.04265 0.06460 0.10546 0.07889 14.989 0.427627 151 0.02699 0.03714 0.06259 0.08096 0.10952 12.529 0.507826 152 0.05647 0.07940 0.13778 0.16942 0.21713 8.441 0.625866 153 0.04284 0.05556 0.08318 0.12851 0.16265 9.449 0.584164 154 0.01340 0.01399 0.02056 0.04019 0.04179 21.520 0.566867 155 0.01484 0.01405 0.02018 0.04451 0.04611 21.824 0.651680 156 0.01659 0.01804 0.02402 0.04977 0.02631 22.431 0.628300 157 0.01205 0.01289 0.01771 0.03615 0.03191 22.953 0.611679 158 0.02610 0.02161 0.02916 0.07830 0.10748 19.075 0.630547 159 0.01500 0.01581 0.02157 0.04499 0.03828 21.534 0.635015 160 0.01360 0.01650 0.03105 0.04079 0.02663 19.651 0.654945 161 0.01579 0.01994 0.04114 0.04736 0.02073 20.437 0.653139 162 0.01644 0.01722 0.02931 0.04933 0.02810 19.388 0.577802 163 0.01864 0.01940 0.03091 0.05592 0.02707 18.954 0.685151 164 0.00967 0.01033 0.01363 0.02902 0.01435 21.219 0.557045 165 0.01579 0.01553 0.02073 0.04736 0.03882 18.447 0.671378 166 0.01410 0.01426 0.01621 0.04231 0.00620 24.078 0.469928 167 0.00696 0.00747 0.00882 0.02089 0.00533 24.679 0.384868 168 0.01186 0.01230 0.01367 0.03557 0.00910 21.083 0.440988 169 0.01279 0.01272 0.01439 0.03836 0.01337 19.269 0.372222 170 0.01176 0.01191 0.01344 0.03529 0.00965 21.020 0.371837 171 0.01084 0.01121 0.01255 0.03253 0.01049 21.528 0.522812 172 0.00664 0.00786 0.01140 0.01992 0.00435 26.436 0.413295 173 0.00754 0.00950 0.01285 0.02261 0.00430 26.550 0.369090 174 0.00748 0.00905 0.01148 0.02245 0.00478 26.547 0.380253 175 0.00881 0.01062 0.01318 0.02643 0.00590 25.445 0.387482 176 0.00812 0.00933 0.01133 0.02436 0.00401 26.005 0.405991 177 0.00874 0.01021 0.01331 0.02623 0.00415 26.143 0.361232 178 0.00728 0.00886 0.01230 0.02184 0.00570 24.151 0.396610 179 0.00839 0.00956 0.01309 0.02518 0.00488 24.412 0.402591 180 0.00725 0.00876 0.01263 0.02175 0.00540 23.683 0.398499 181 0.01321 0.01574 0.02148 0.03964 0.00611 23.133 0.352396 182 0.00950 0.01103 0.01559 0.02849 0.00639 22.866 0.408598 183 0.01155 0.01341 0.01666 0.03464 0.00595 23.008 0.329577 184 0.00864 0.01223 0.01949 0.02592 0.00955 23.079 0.603515 185 0.00810 0.01144 0.01756 0.02429 0.01179 22.085 0.663842 186 0.00667 0.00990 0.01691 0.02001 0.00737 24.199 0.598515 187 0.00820 0.00972 0.01491 0.02460 0.01397 23.958 0.566424 188 0.00631 0.00789 0.01144 0.01892 0.00680 25.023 0.528485 189 0.00557 0.00721 0.01095 0.01672 0.00703 24.775 0.555303 190 0.01454 0.01582 0.01758 0.04363 0.04441 19.368 0.508479 191 0.02336 0.02498 0.02745 0.07008 0.02764 19.517 0.448439 192 0.01604 0.01657 0.01879 0.04812 0.01810 19.147 0.431674 193 0.01268 0.01365 0.01667 0.03804 0.10715 17.883 0.407567 194 0.01265 0.01321 0.01588 0.03794 0.07223 19.020 0.451221 195 0.01026 0.01161 0.01373 0.03078 0.04398 21.209 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)` 2.225e+00 -2.384e-03 -1.152e-04 -1.535e-03 `MDVP:Jitter(%)` `MDVP:Jitter(Abs)` `MDVP:RAP` `MDVP:PPQ` -1.769e+02 -3.322e+03 -7.592e+02 -3.614e+01 `Jitter:DDP` `MDVP:Shimmer` `MDVP:Shimmer(dB)` `Shimmer:APQ3` 3.606e+02 2.745e+01 5.710e-01 -8.712e+02 `Shimmer:APQ5` `MDVP:APQ` `Shimmer:DDA` NHR -2.640e+01 -3.075e+00 2.837e+02 -2.526e+00 HNR RPDE DFA spread1 -1.569e-02 -1.014e+00 3.551e-01 1.273e-01 spread2 D2 PPE 1.266e+00 4.946e-02 1.263e+00 > (mysum <- summary(mylm)) Call: lm(formula = df) Residuals: Min 1Q Median 3Q Max -0.94184 -0.15077 0.04547 0.20496 0.58507 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 2.225e+00 1.158e+00 1.921 0.05644 . `MDVP:Fo(Hz)` -2.384e-03 1.510e-03 -1.579 0.11617 `MDVP:Fhi(Hz)` -1.152e-04 3.211e-04 -0.359 0.72011 `MDVP:Flo(Hz)` -1.535e-03 8.023e-04 -1.913 0.05737 . `MDVP:Jitter(%)` -1.769e+02 6.703e+01 -2.639 0.00907 ** `MDVP:Jitter(Abs)` -3.322e+03 4.626e+03 -0.718 0.47368 `MDVP:RAP` -7.592e+02 9.332e+03 -0.081 0.93525 `MDVP:PPQ` -3.614e+01 8.838e+01 -0.409 0.68316 `Jitter:DDP` 3.606e+02 3.111e+03 0.116 0.90788 `MDVP:Shimmer` 2.745e+01 3.428e+01 0.801 0.42442 `MDVP:Shimmer(dB)` 5.710e-01 1.199e+00 0.476 0.63459 `Shimmer:APQ3` -8.712e+02 8.972e+03 -0.097 0.92276 `Shimmer:APQ5` -2.640e+01 2.012e+01 -1.312 0.19136 `MDVP:APQ` -3.075e+00 1.089e+01 -0.282 0.77804 `Shimmer:DDA` 2.837e+02 2.990e+03 0.095 0.92450 NHR -2.526e+00 1.981e+00 -1.275 0.20397 HNR -1.569e-02 1.434e-02 -1.094 0.27544 RPDE -1.014e+00 4.395e-01 -2.308 0.02219 * DFA 3.551e-01 7.394e-01 0.480 0.63161 spread1 1.273e-01 9.790e-02 1.300 0.19523 spread2 1.266e+00 4.780e-01 2.648 0.00886 ** D2 4.946e-02 1.143e-01 0.433 0.66582 PPE 1.263e+00 1.383e+00 0.913 0.36235 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 0.3267 on 172 degrees of freedom Multiple R-squared: 0.4927, Adjusted R-squared: 0.4279 F-statistic: 7.594 on 22 and 172 DF, p-value: 4.808e-16 > if (n > n25) { + kp3 <- k + 3 + nmkm3 <- n - k - 3 + gqarr <- array(NA, dim=c(nmkm3-kp3+1,3)) + numgqtests <- 0 + numsignificant1 <- 0 + numsignificant5 <- 0 + numsignificant10 <- 0 + for (mypoint in kp3:nmkm3) { + j <- 0 + numgqtests <- numgqtests + 1 + for (myalt in c('greater', 'two.sided', 'less')) { + j <- j + 1 + gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value + } + if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1 + if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1 + if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1 + } + gqarr + } [,1] [,2] [,3] [1,] 1.814384e-53 3.628768e-53 1.00000000 [2,] 3.497289e-65 6.994578e-65 1.00000000 [3,] 9.123684e-78 1.824737e-77 1.00000000 [4,] 3.416667e-94 6.833335e-94 1.00000000 [5,] 4.419698e-110 8.839395e-110 1.00000000 [6,] 2.546253e-04 5.092506e-04 0.99974537 [7,] 6.628473e-05 1.325695e-04 0.99993372 [8,] 1.634550e-05 3.269099e-05 0.99998365 [9,] 4.921244e-06 9.842488e-06 0.99999508 [10,] 1.493053e-06 2.986107e-06 0.99999851 [11,] 3.663781e-07 7.327561e-07 0.99999963 [12,] 5.203943e-05 1.040789e-04 0.99994796 [13,] 2.996809e-05 5.993618e-05 0.99997003 [14,] 5.278445e-04 1.055689e-03 0.99947216 [15,] 8.676116e-04 1.735223e-03 0.99913239 [16,] 1.089027e-03 2.178054e-03 0.99891097 [17,] 6.345594e-04 1.269119e-03 0.99936544 [18,] 3.910895e-04 7.821790e-04 0.99960891 [19,] 2.188681e-04 4.377363e-04 0.99978113 [20,] 1.019935e-04 2.039871e-04 0.99989801 [21,] 5.006918e-05 1.001384e-04 0.99994993 [22,] 2.996916e-05 5.993832e-05 0.99997003 [23,] 3.988068e-05 7.976137e-05 0.99996012 [24,] 1.743572e-04 3.487145e-04 0.99982564 [25,] 1.438208e-04 2.876416e-04 0.99985618 [26,] 9.518950e-05 1.903790e-04 0.99990481 [27,] 6.312518e-05 1.262504e-04 0.99993687 [28,] 4.435794e-05 8.871587e-05 0.99995564 [29,] 4.615947e-05 9.231894e-05 0.99995384 [30,] 5.316994e-05 1.063399e-04 0.99994683 [31,] 6.654600e-05 1.330920e-04 0.99993345 [32,] 3.769786e-05 7.539572e-05 0.99996230 [33,] 2.599145e-05 5.198290e-05 0.99997401 [34,] 1.475256e-05 2.950512e-05 0.99998525 [35,] 8.440881e-06 1.688176e-05 0.99999156 [36,] 3.517339e-04 7.034678e-04 0.99964827 [37,] 4.571405e-04 9.142810e-04 0.99954286 [38,] 6.235912e-04 1.247182e-03 0.99937641 [39,] 6.232405e-04 1.246481e-03 0.99937676 [40,] 4.260821e-04 8.521643e-04 0.99957392 [41,] 4.220822e-04 8.441644e-04 0.99957792 [42,] 2.832624e-04 5.665249e-04 0.99971674 [43,] 1.823968e-04 3.647937e-04 0.99981760 [44,] 2.316280e-04 4.632559e-04 0.99976837 [45,] 1.786081e-04 3.572162e-04 0.99982139 [46,] 1.078655e-04 2.157309e-04 0.99989213 [47,] 7.362955e-05 1.472591e-04 0.99992637 [48,] 4.320487e-05 8.640973e-05 0.99995680 [49,] 1.196078e-04 2.392156e-04 0.99988039 [50,] 1.563976e-04 3.127953e-04 0.99984360 [51,] 1.157693e-04 2.315385e-04 0.99988423 [52,] 7.497239e-05 1.499448e-04 0.99992503 [53,] 5.881014e-05 1.176203e-04 0.99994119 [54,] 3.488782e-05 6.977565e-05 0.99996511 [55,] 2.490667e-05 4.981333e-05 0.99997509 [56,] 1.848991e-05 3.697981e-05 0.99998151 [57,] 1.077432e-05 2.154865e-05 0.99998923 [58,] 7.130719e-06 1.426144e-05 0.99999287 [59,] 4.579435e-06 9.158871e-06 0.99999542 [60,] 2.768345e-06 5.536690e-06 0.99999723 [61,] 3.233183e-06 6.466366e-06 0.99999677 [62,] 5.280663e-06 1.056133e-05 0.99999472 [63,] 3.341635e-06 6.683270e-06 0.99999666 [64,] 2.597860e-06 5.195721e-06 0.99999740 [65,] 3.214897e-06 6.429795e-06 0.99999679 [66,] 3.172618e-06 6.345235e-06 0.99999683 [67,] 3.961379e-06 7.922758e-06 0.99999604 [68,] 2.519518e-06 5.039035e-06 0.99999748 [69,] 1.688523e-06 3.377046e-06 0.99999831 [70,] 1.089697e-06 2.179394e-06 0.99999891 [71,] 6.756170e-07 1.351234e-06 0.99999932 [72,] 4.200856e-07 8.401713e-07 0.99999958 [73,] 2.393840e-07 4.787681e-07 0.99999976 [74,] 1.489749e-07 2.979497e-07 0.99999985 [75,] 8.739952e-08 1.747990e-07 0.99999991 [76,] 6.861254e-08 1.372251e-07 0.99999993 [77,] 6.592061e-08 1.318412e-07 0.99999993 [78,] 7.737128e-08 1.547426e-07 0.99999992 [79,] 1.488163e-07 2.976327e-07 0.99999985 [80,] 2.309658e-07 4.619316e-07 0.99999977 [81,] 4.220153e-07 8.440305e-07 0.99999958 [82,] 9.940501e-07 1.988100e-06 0.99999901 [83,] 7.281823e-07 1.456365e-06 0.99999927 [84,] 1.655143e-06 3.310286e-06 0.99999834 [85,] 1.356737e-06 2.713473e-06 0.99999864 [86,] 8.088825e-07 1.617765e-06 0.99999919 [87,] 1.638931e-06 3.277863e-06 0.99999836 [88,] 1.084246e-06 2.168492e-06 0.99999892 [89,] 1.262980e-06 2.525959e-06 0.99999874 [90,] 1.248066e-06 2.496133e-06 0.99999875 [91,] 8.450619e-07 1.690124e-06 0.99999915 [92,] 1.162028e-06 2.324055e-06 0.99999884 [93,] 6.706285e-07 1.341257e-06 0.99999933 [94,] 4.737373e-07 9.474746e-07 0.99999953 [95,] 1.087439e-06 2.174877e-06 0.99999891 [96,] 6.793447e-06 1.358689e-05 0.99999321 [97,] 1.714571e-05 3.429142e-05 0.99998285 [98,] 1.094495e-05 2.188990e-05 0.99998906 [99,] 7.511780e-06 1.502356e-05 0.99999249 [100,] 5.283168e-06 1.056634e-05 0.99999472 [101,] 5.531585e-06 1.106317e-05 0.99999447 [102,] 1.110809e-05 2.221618e-05 0.99998889 [103,] 1.462569e-04 2.925139e-04 0.99985374 [104,] 3.825555e-04 7.651110e-04 0.99961744 [105,] 4.874166e-04 9.748332e-04 0.99951258 [106,] 5.018673e-04 1.003735e-03 0.99949813 [107,] 3.434651e-04 6.869302e-04 0.99965653 [108,] 3.079671e-04 6.159342e-04 0.99969203 [109,] 1.383024e-03 2.766049e-03 0.99861698 [110,] 1.606070e-03 3.212141e-03 0.99839393 [111,] 1.563284e-03 3.126568e-03 0.99843672 [112,] 1.689852e-03 3.379705e-03 0.99831015 [113,] 1.735729e-03 3.471459e-03 0.99826427 [114,] 1.172495e-03 2.344991e-03 0.99882750 [115,] 1.453524e-03 2.907049e-03 0.99854648 [116,] 1.228730e-03 2.457461e-03 0.99877127 [117,] 1.564035e-03 3.128070e-03 0.99843596 [118,] 1.314530e-03 2.629060e-03 0.99868547 [119,] 4.516192e-03 9.032385e-03 0.99548381 [120,] 3.920266e-03 7.840531e-03 0.99607973 [121,] 3.016489e-03 6.032978e-03 0.99698351 [122,] 2.145464e-03 4.290927e-03 0.99785454 [123,] 1.383528e-03 2.767056e-03 0.99861647 [124,] 1.399000e-03 2.798000e-03 0.99860100 [125,] 9.274371e-04 1.854874e-03 0.99907256 [126,] 8.691156e-04 1.738231e-03 0.99913088 [127,] 6.385178e-03 1.277036e-02 0.99361482 [128,] 3.427698e-02 6.855396e-02 0.96572302 [129,] 3.053679e-02 6.107359e-02 0.96946321 [130,] 2.584514e-02 5.169028e-02 0.97415486 [131,] 1.922083e-02 3.844166e-02 0.98077917 [132,] 8.907862e-02 1.781572e-01 0.91092138 [133,] 8.425213e-02 1.685043e-01 0.91574787 [134,] 2.873580e-01 5.747160e-01 0.71264199 [135,] 2.263667e-01 4.527334e-01 0.77363329 [136,] 1.699163e-01 3.398326e-01 0.83008369 [137,] 1.292572e-01 2.585143e-01 0.87074283 [138,] 1.478543e-01 2.957086e-01 0.85214568 [139,] 3.161248e-01 6.322496e-01 0.68387519 [140,] 4.253480e-01 8.506959e-01 0.57465204 [141,] 5.208676e-01 9.582648e-01 0.47913240 [142,] 9.017518e-01 1.964964e-01 0.09824818 [143,] 9.450033e-01 1.099934e-01 0.05499670 [144,] 9.629543e-01 7.409143e-02 0.03704571 > postscript(file="/var/wessaorg/rcomp/tmp/10udb1386779266.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index') > points(x[,1]-mysum$resid) > grid() > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/2xpuj1386779266.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index') > grid() > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/37bj61386779266.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') > grid() > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/4f9cd1386779266.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/5sqxy1386779266.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > qqnorm(mysum$resid, main='Residual Normal Q-Q Plot') > qqline(mysum$resid) > grid() > dev.off() null device 1 > (myerror <- as.ts(mysum$resid)) Time Series: Start = 1 End = 195 Frequency = 1 1 2 3 4 5 0.0564281266 -0.0705871710 0.0273891732 -0.0777826472 0.1289663200 6 7 8 9 10 0.0505447902 0.2065940405 0.4193252967 0.0253973938 -0.1509099713 11 12 13 14 15 -0.1115990369 -0.2381056691 0.5431177417 0.1198483763 0.2951541293 16 17 18 19 20 0.2972892567 0.4555582492 -0.3229152531 -0.2934121147 0.0339362094 21 22 23 24 25 -0.0659459012 0.1095260220 -0.1034321130 0.1342289531 0.1796414412 26 27 28 29 30 0.0825911180 0.1959813824 0.2041000665 0.3386043677 0.3006759523 31 32 33 34 35 -0.2963270110 -0.1593586542 -0.1923513261 -0.1280861823 -0.0881745263 36 37 38 39 40 -0.2036277196 0.1893057590 0.1765375230 0.4039567429 0.2469086122 41 42 43 44 45 0.3874036392 0.5634980566 -0.2446206322 -0.2042351963 -0.0134396038 46 47 48 49 50 -0.0888886766 -0.0510556031 0.0454685804 -0.3374627122 -0.4294787668 51 52 53 54 55 -0.4106123628 -0.4259158307 -0.4115231996 -0.5484716508 0.1728758516 56 57 58 59 60 0.2084448843 0.1325855833 0.2401098424 0.2207200741 0.3491883720 61 62 63 64 65 -0.3697711123 -0.2746523739 -0.2644755661 -0.2136066078 -0.1283357512 66 67 68 69 70 -0.2822755512 0.0852200123 0.1108957245 0.0777503964 0.0576307634 71 72 73 74 75 0.1491522115 -0.0929952919 0.1127812153 0.0770648551 -0.0422682654 76 77 78 79 80 -0.0786140032 -0.0985768523 -0.0004832498 0.0388675548 -0.1412748538 81 82 83 84 85 -0.1801439924 -0.1352195816 -0.0136507016 0.3040125754 -0.0875400146 86 87 88 89 90 0.1285309038 0.2968687880 0.0553590707 0.0096593355 -0.2252495501 91 92 93 94 95 -0.1444663951 0.2025294735 0.2663284516 0.1467443301 0.2114405807 96 97 98 99 100 0.2423275791 0.1969934283 -0.0269041154 0.2002605665 0.1029306555 101 102 103 104 105 0.0361471681 0.0261951899 0.0110830427 0.4142247986 0.4193350458 106 107 108 109 110 0.4327364370 0.4647109258 0.3098959351 0.3826380366 0.1015785013 111 112 113 114 115 -0.0349901184 0.4100113720 0.1983687987 0.3011294351 0.1977753708 116 117 118 119 120 0.1149057253 0.2711061162 -0.0518569667 0.1139358570 0.2383985845 121 122 123 124 125 0.4484696084 0.0263668326 0.0271514850 0.2893986811 0.3817899266 126 127 128 129 130 0.3753790565 0.3873613391 0.3724147706 0.5850678575 0.2347514154 131 132 133 134 135 0.1968833498 0.1250420776 -0.0533167362 0.3529596528 0.0367175561 136 137 138 139 140 0.0384846911 -0.1463688729 -0.1506276986 0.0402465326 0.2185196681 141 142 143 144 145 0.0899151144 0.1417015350 0.2710047917 0.3171741249 0.4506066443 146 147 148 149 150 0.1397667810 -0.3515142193 -0.1522854818 -0.2447742538 0.1062984528 151 152 153 154 155 0.0657469871 0.0440683480 0.0409203842 0.1545941737 0.1055613373 156 157 158 159 160 0.0051826515 0.2013160095 -0.2686981811 0.0292525535 0.1380999139 161 162 163 164 165 -0.1281321791 -0.0587124904 0.0691747997 0.2058241998 -0.3824425267 166 167 168 169 170 -0.4502115660 -0.2251905676 -0.0938475184 -0.9418426114 -0.2302780047 171 172 173 174 175 -0.1145762029 -0.7993405843 -0.8358743948 -0.8783338466 -0.8660101226 176 177 178 179 180 -0.8346308649 -0.7768488137 0.3556600296 0.2877673101 0.0656761316 181 182 183 184 185 0.2360214910 0.1275739354 0.2808066129 -0.6043862370 -0.6484630863 186 187 188 189 190 -0.6067838036 -0.4232493950 -0.4753615206 -0.4359967657 -0.4282774574 191 192 193 194 195 -0.6511288011 -0.7009783841 0.1745042657 -0.2670808288 -0.5194153557 > postscript(file="/var/wessaorg/rcomp/tmp/6tzud1386779266.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > dum <- cbind(lag(myerror,k=1),myerror) > dum Time Series: Start = 0 End = 195 Frequency = 1 lag(myerror, k = 1) myerror 0 0.0564281266 NA 1 -0.0705871710 0.0564281266 2 0.0273891732 -0.0705871710 3 -0.0777826472 0.0273891732 4 0.1289663200 -0.0777826472 5 0.0505447902 0.1289663200 6 0.2065940405 0.0505447902 7 0.4193252967 0.2065940405 8 0.0253973938 0.4193252967 9 -0.1509099713 0.0253973938 10 -0.1115990369 -0.1509099713 11 -0.2381056691 -0.1115990369 12 0.5431177417 -0.2381056691 13 0.1198483763 0.5431177417 14 0.2951541293 0.1198483763 15 0.2972892567 0.2951541293 16 0.4555582492 0.2972892567 17 -0.3229152531 0.4555582492 18 -0.2934121147 -0.3229152531 19 0.0339362094 -0.2934121147 20 -0.0659459012 0.0339362094 21 0.1095260220 -0.0659459012 22 -0.1034321130 0.1095260220 23 0.1342289531 -0.1034321130 24 0.1796414412 0.1342289531 25 0.0825911180 0.1796414412 26 0.1959813824 0.0825911180 27 0.2041000665 0.1959813824 28 0.3386043677 0.2041000665 29 0.3006759523 0.3386043677 30 -0.2963270110 0.3006759523 31 -0.1593586542 -0.2963270110 32 -0.1923513261 -0.1593586542 33 -0.1280861823 -0.1923513261 34 -0.0881745263 -0.1280861823 35 -0.2036277196 -0.0881745263 36 0.1893057590 -0.2036277196 37 0.1765375230 0.1893057590 38 0.4039567429 0.1765375230 39 0.2469086122 0.4039567429 40 0.3874036392 0.2469086122 41 0.5634980566 0.3874036392 42 -0.2446206322 0.5634980566 43 -0.2042351963 -0.2446206322 44 -0.0134396038 -0.2042351963 45 -0.0888886766 -0.0134396038 46 -0.0510556031 -0.0888886766 47 0.0454685804 -0.0510556031 48 -0.3374627122 0.0454685804 49 -0.4294787668 -0.3374627122 50 -0.4106123628 -0.4294787668 51 -0.4259158307 -0.4106123628 52 -0.4115231996 -0.4259158307 53 -0.5484716508 -0.4115231996 54 0.1728758516 -0.5484716508 55 0.2084448843 0.1728758516 56 0.1325855833 0.2084448843 57 0.2401098424 0.1325855833 58 0.2207200741 0.2401098424 59 0.3491883720 0.2207200741 60 -0.3697711123 0.3491883720 61 -0.2746523739 -0.3697711123 62 -0.2644755661 -0.2746523739 63 -0.2136066078 -0.2644755661 64 -0.1283357512 -0.2136066078 65 -0.2822755512 -0.1283357512 66 0.0852200123 -0.2822755512 67 0.1108957245 0.0852200123 68 0.0777503964 0.1108957245 69 0.0576307634 0.0777503964 70 0.1491522115 0.0576307634 71 -0.0929952919 0.1491522115 72 0.1127812153 -0.0929952919 73 0.0770648551 0.1127812153 74 -0.0422682654 0.0770648551 75 -0.0786140032 -0.0422682654 76 -0.0985768523 -0.0786140032 77 -0.0004832498 -0.0985768523 78 0.0388675548 -0.0004832498 79 -0.1412748538 0.0388675548 80 -0.1801439924 -0.1412748538 81 -0.1352195816 -0.1801439924 82 -0.0136507016 -0.1352195816 83 0.3040125754 -0.0136507016 84 -0.0875400146 0.3040125754 85 0.1285309038 -0.0875400146 86 0.2968687880 0.1285309038 87 0.0553590707 0.2968687880 88 0.0096593355 0.0553590707 89 -0.2252495501 0.0096593355 90 -0.1444663951 -0.2252495501 91 0.2025294735 -0.1444663951 92 0.2663284516 0.2025294735 93 0.1467443301 0.2663284516 94 0.2114405807 0.1467443301 95 0.2423275791 0.2114405807 96 0.1969934283 0.2423275791 97 -0.0269041154 0.1969934283 98 0.2002605665 -0.0269041154 99 0.1029306555 0.2002605665 100 0.0361471681 0.1029306555 101 0.0261951899 0.0361471681 102 0.0110830427 0.0261951899 103 0.4142247986 0.0110830427 104 0.4193350458 0.4142247986 105 0.4327364370 0.4193350458 106 0.4647109258 0.4327364370 107 0.3098959351 0.4647109258 108 0.3826380366 0.3098959351 109 0.1015785013 0.3826380366 110 -0.0349901184 0.1015785013 111 0.4100113720 -0.0349901184 112 0.1983687987 0.4100113720 113 0.3011294351 0.1983687987 114 0.1977753708 0.3011294351 115 0.1149057253 0.1977753708 116 0.2711061162 0.1149057253 117 -0.0518569667 0.2711061162 118 0.1139358570 -0.0518569667 119 0.2383985845 0.1139358570 120 0.4484696084 0.2383985845 121 0.0263668326 0.4484696084 122 0.0271514850 0.0263668326 123 0.2893986811 0.0271514850 124 0.3817899266 0.2893986811 125 0.3753790565 0.3817899266 126 0.3873613391 0.3753790565 127 0.3724147706 0.3873613391 128 0.5850678575 0.3724147706 129 0.2347514154 0.5850678575 130 0.1968833498 0.2347514154 131 0.1250420776 0.1968833498 132 -0.0533167362 0.1250420776 133 0.3529596528 -0.0533167362 134 0.0367175561 0.3529596528 135 0.0384846911 0.0367175561 136 -0.1463688729 0.0384846911 137 -0.1506276986 -0.1463688729 138 0.0402465326 -0.1506276986 139 0.2185196681 0.0402465326 140 0.0899151144 0.2185196681 141 0.1417015350 0.0899151144 142 0.2710047917 0.1417015350 143 0.3171741249 0.2710047917 144 0.4506066443 0.3171741249 145 0.1397667810 0.4506066443 146 -0.3515142193 0.1397667810 147 -0.1522854818 -0.3515142193 148 -0.2447742538 -0.1522854818 149 0.1062984528 -0.2447742538 150 0.0657469871 0.1062984528 151 0.0440683480 0.0657469871 152 0.0409203842 0.0440683480 153 0.1545941737 0.0409203842 154 0.1055613373 0.1545941737 155 0.0051826515 0.1055613373 156 0.2013160095 0.0051826515 157 -0.2686981811 0.2013160095 158 0.0292525535 -0.2686981811 159 0.1380999139 0.0292525535 160 -0.1281321791 0.1380999139 161 -0.0587124904 -0.1281321791 162 0.0691747997 -0.0587124904 163 0.2058241998 0.0691747997 164 -0.3824425267 0.2058241998 165 -0.4502115660 -0.3824425267 166 -0.2251905676 -0.4502115660 167 -0.0938475184 -0.2251905676 168 -0.9418426114 -0.0938475184 169 -0.2302780047 -0.9418426114 170 -0.1145762029 -0.2302780047 171 -0.7993405843 -0.1145762029 172 -0.8358743948 -0.7993405843 173 -0.8783338466 -0.8358743948 174 -0.8660101226 -0.8783338466 175 -0.8346308649 -0.8660101226 176 -0.7768488137 -0.8346308649 177 0.3556600296 -0.7768488137 178 0.2877673101 0.3556600296 179 0.0656761316 0.2877673101 180 0.2360214910 0.0656761316 181 0.1275739354 0.2360214910 182 0.2808066129 0.1275739354 183 -0.6043862370 0.2808066129 184 -0.6484630863 -0.6043862370 185 -0.6067838036 -0.6484630863 186 -0.4232493950 -0.6067838036 187 -0.4753615206 -0.4232493950 188 -0.4359967657 -0.4753615206 189 -0.4282774574 -0.4359967657 190 -0.6511288011 -0.4282774574 191 -0.7009783841 -0.6511288011 192 0.1745042657 -0.7009783841 193 -0.2670808288 0.1745042657 194 -0.5194153557 -0.2670808288 195 NA -0.5194153557 > dum1 <- dum[2:length(myerror),] > dum1 lag(myerror, k = 1) myerror [1,] -0.0705871710 0.0564281266 [2,] 0.0273891732 -0.0705871710 [3,] -0.0777826472 0.0273891732 [4,] 0.1289663200 -0.0777826472 [5,] 0.0505447902 0.1289663200 [6,] 0.2065940405 0.0505447902 [7,] 0.4193252967 0.2065940405 [8,] 0.0253973938 0.4193252967 [9,] -0.1509099713 0.0253973938 [10,] -0.1115990369 -0.1509099713 [11,] -0.2381056691 -0.1115990369 [12,] 0.5431177417 -0.2381056691 [13,] 0.1198483763 0.5431177417 [14,] 0.2951541293 0.1198483763 [15,] 0.2972892567 0.2951541293 [16,] 0.4555582492 0.2972892567 [17,] -0.3229152531 0.4555582492 [18,] -0.2934121147 -0.3229152531 [19,] 0.0339362094 -0.2934121147 [20,] -0.0659459012 0.0339362094 [21,] 0.1095260220 -0.0659459012 [22,] -0.1034321130 0.1095260220 [23,] 0.1342289531 -0.1034321130 [24,] 0.1796414412 0.1342289531 [25,] 0.0825911180 0.1796414412 [26,] 0.1959813824 0.0825911180 [27,] 0.2041000665 0.1959813824 [28,] 0.3386043677 0.2041000665 [29,] 0.3006759523 0.3386043677 [30,] -0.2963270110 0.3006759523 [31,] -0.1593586542 -0.2963270110 [32,] -0.1923513261 -0.1593586542 [33,] -0.1280861823 -0.1923513261 [34,] -0.0881745263 -0.1280861823 [35,] -0.2036277196 -0.0881745263 [36,] 0.1893057590 -0.2036277196 [37,] 0.1765375230 0.1893057590 [38,] 0.4039567429 0.1765375230 [39,] 0.2469086122 0.4039567429 [40,] 0.3874036392 0.2469086122 [41,] 0.5634980566 0.3874036392 [42,] -0.2446206322 0.5634980566 [43,] -0.2042351963 -0.2446206322 [44,] -0.0134396038 -0.2042351963 [45,] -0.0888886766 -0.0134396038 [46,] -0.0510556031 -0.0888886766 [47,] 0.0454685804 -0.0510556031 [48,] -0.3374627122 0.0454685804 [49,] -0.4294787668 -0.3374627122 [50,] -0.4106123628 -0.4294787668 [51,] -0.4259158307 -0.4106123628 [52,] -0.4115231996 -0.4259158307 [53,] -0.5484716508 -0.4115231996 [54,] 0.1728758516 -0.5484716508 [55,] 0.2084448843 0.1728758516 [56,] 0.1325855833 0.2084448843 [57,] 0.2401098424 0.1325855833 [58,] 0.2207200741 0.2401098424 [59,] 0.3491883720 0.2207200741 [60,] -0.3697711123 0.3491883720 [61,] -0.2746523739 -0.3697711123 [62,] -0.2644755661 -0.2746523739 [63,] -0.2136066078 -0.2644755661 [64,] -0.1283357512 -0.2136066078 [65,] -0.2822755512 -0.1283357512 [66,] 0.0852200123 -0.2822755512 [67,] 0.1108957245 0.0852200123 [68,] 0.0777503964 0.1108957245 [69,] 0.0576307634 0.0777503964 [70,] 0.1491522115 0.0576307634 [71,] -0.0929952919 0.1491522115 [72,] 0.1127812153 -0.0929952919 [73,] 0.0770648551 0.1127812153 [74,] -0.0422682654 0.0770648551 [75,] -0.0786140032 -0.0422682654 [76,] -0.0985768523 -0.0786140032 [77,] -0.0004832498 -0.0985768523 [78,] 0.0388675548 -0.0004832498 [79,] -0.1412748538 0.0388675548 [80,] -0.1801439924 -0.1412748538 [81,] -0.1352195816 -0.1801439924 [82,] -0.0136507016 -0.1352195816 [83,] 0.3040125754 -0.0136507016 [84,] -0.0875400146 0.3040125754 [85,] 0.1285309038 -0.0875400146 [86,] 0.2968687880 0.1285309038 [87,] 0.0553590707 0.2968687880 [88,] 0.0096593355 0.0553590707 [89,] -0.2252495501 0.0096593355 [90,] -0.1444663951 -0.2252495501 [91,] 0.2025294735 -0.1444663951 [92,] 0.2663284516 0.2025294735 [93,] 0.1467443301 0.2663284516 [94,] 0.2114405807 0.1467443301 [95,] 0.2423275791 0.2114405807 [96,] 0.1969934283 0.2423275791 [97,] -0.0269041154 0.1969934283 [98,] 0.2002605665 -0.0269041154 [99,] 0.1029306555 0.2002605665 [100,] 0.0361471681 0.1029306555 [101,] 0.0261951899 0.0361471681 [102,] 0.0110830427 0.0261951899 [103,] 0.4142247986 0.0110830427 [104,] 0.4193350458 0.4142247986 [105,] 0.4327364370 0.4193350458 [106,] 0.4647109258 0.4327364370 [107,] 0.3098959351 0.4647109258 [108,] 0.3826380366 0.3098959351 [109,] 0.1015785013 0.3826380366 [110,] -0.0349901184 0.1015785013 [111,] 0.4100113720 -0.0349901184 [112,] 0.1983687987 0.4100113720 [113,] 0.3011294351 0.1983687987 [114,] 0.1977753708 0.3011294351 [115,] 0.1149057253 0.1977753708 [116,] 0.2711061162 0.1149057253 [117,] -0.0518569667 0.2711061162 [118,] 0.1139358570 -0.0518569667 [119,] 0.2383985845 0.1139358570 [120,] 0.4484696084 0.2383985845 [121,] 0.0263668326 0.4484696084 [122,] 0.0271514850 0.0263668326 [123,] 0.2893986811 0.0271514850 [124,] 0.3817899266 0.2893986811 [125,] 0.3753790565 0.3817899266 [126,] 0.3873613391 0.3753790565 [127,] 0.3724147706 0.3873613391 [128,] 0.5850678575 0.3724147706 [129,] 0.2347514154 0.5850678575 [130,] 0.1968833498 0.2347514154 [131,] 0.1250420776 0.1968833498 [132,] -0.0533167362 0.1250420776 [133,] 0.3529596528 -0.0533167362 [134,] 0.0367175561 0.3529596528 [135,] 0.0384846911 0.0367175561 [136,] -0.1463688729 0.0384846911 [137,] -0.1506276986 -0.1463688729 [138,] 0.0402465326 -0.1506276986 [139,] 0.2185196681 0.0402465326 [140,] 0.0899151144 0.2185196681 [141,] 0.1417015350 0.0899151144 [142,] 0.2710047917 0.1417015350 [143,] 0.3171741249 0.2710047917 [144,] 0.4506066443 0.3171741249 [145,] 0.1397667810 0.4506066443 [146,] -0.3515142193 0.1397667810 [147,] -0.1522854818 -0.3515142193 [148,] -0.2447742538 -0.1522854818 [149,] 0.1062984528 -0.2447742538 [150,] 0.0657469871 0.1062984528 [151,] 0.0440683480 0.0657469871 [152,] 0.0409203842 0.0440683480 [153,] 0.1545941737 0.0409203842 [154,] 0.1055613373 0.1545941737 [155,] 0.0051826515 0.1055613373 [156,] 0.2013160095 0.0051826515 [157,] -0.2686981811 0.2013160095 [158,] 0.0292525535 -0.2686981811 [159,] 0.1380999139 0.0292525535 [160,] -0.1281321791 0.1380999139 [161,] -0.0587124904 -0.1281321791 [162,] 0.0691747997 -0.0587124904 [163,] 0.2058241998 0.0691747997 [164,] -0.3824425267 0.2058241998 [165,] -0.4502115660 -0.3824425267 [166,] -0.2251905676 -0.4502115660 [167,] -0.0938475184 -0.2251905676 [168,] -0.9418426114 -0.0938475184 [169,] -0.2302780047 -0.9418426114 [170,] -0.1145762029 -0.2302780047 [171,] -0.7993405843 -0.1145762029 [172,] -0.8358743948 -0.7993405843 [173,] -0.8783338466 -0.8358743948 [174,] -0.8660101226 -0.8783338466 [175,] -0.8346308649 -0.8660101226 [176,] -0.7768488137 -0.8346308649 [177,] 0.3556600296 -0.7768488137 [178,] 0.2877673101 0.3556600296 [179,] 0.0656761316 0.2877673101 [180,] 0.2360214910 0.0656761316 [181,] 0.1275739354 0.2360214910 [182,] 0.2808066129 0.1275739354 [183,] -0.6043862370 0.2808066129 [184,] -0.6484630863 -0.6043862370 [185,] -0.6067838036 -0.6484630863 [186,] -0.4232493950 -0.6067838036 [187,] -0.4753615206 -0.4232493950 [188,] -0.4359967657 -0.4753615206 [189,] -0.4282774574 -0.4359967657 [190,] -0.6511288011 -0.4282774574 [191,] -0.7009783841 -0.6511288011 [192,] 0.1745042657 -0.7009783841 [193,] -0.2670808288 0.1745042657 [194,] -0.5194153557 -0.2670808288 > z <- as.data.frame(dum1) > z lag(myerror, k = 1) myerror 1 -0.0705871710 0.0564281266 2 0.0273891732 -0.0705871710 3 -0.0777826472 0.0273891732 4 0.1289663200 -0.0777826472 5 0.0505447902 0.1289663200 6 0.2065940405 0.0505447902 7 0.4193252967 0.2065940405 8 0.0253973938 0.4193252967 9 -0.1509099713 0.0253973938 10 -0.1115990369 -0.1509099713 11 -0.2381056691 -0.1115990369 12 0.5431177417 -0.2381056691 13 0.1198483763 0.5431177417 14 0.2951541293 0.1198483763 15 0.2972892567 0.2951541293 16 0.4555582492 0.2972892567 17 -0.3229152531 0.4555582492 18 -0.2934121147 -0.3229152531 19 0.0339362094 -0.2934121147 20 -0.0659459012 0.0339362094 21 0.1095260220 -0.0659459012 22 -0.1034321130 0.1095260220 23 0.1342289531 -0.1034321130 24 0.1796414412 0.1342289531 25 0.0825911180 0.1796414412 26 0.1959813824 0.0825911180 27 0.2041000665 0.1959813824 28 0.3386043677 0.2041000665 29 0.3006759523 0.3386043677 30 -0.2963270110 0.3006759523 31 -0.1593586542 -0.2963270110 32 -0.1923513261 -0.1593586542 33 -0.1280861823 -0.1923513261 34 -0.0881745263 -0.1280861823 35 -0.2036277196 -0.0881745263 36 0.1893057590 -0.2036277196 37 0.1765375230 0.1893057590 38 0.4039567429 0.1765375230 39 0.2469086122 0.4039567429 40 0.3874036392 0.2469086122 41 0.5634980566 0.3874036392 42 -0.2446206322 0.5634980566 43 -0.2042351963 -0.2446206322 44 -0.0134396038 -0.2042351963 45 -0.0888886766 -0.0134396038 46 -0.0510556031 -0.0888886766 47 0.0454685804 -0.0510556031 48 -0.3374627122 0.0454685804 49 -0.4294787668 -0.3374627122 50 -0.4106123628 -0.4294787668 51 -0.4259158307 -0.4106123628 52 -0.4115231996 -0.4259158307 53 -0.5484716508 -0.4115231996 54 0.1728758516 -0.5484716508 55 0.2084448843 0.1728758516 56 0.1325855833 0.2084448843 57 0.2401098424 0.1325855833 58 0.2207200741 0.2401098424 59 0.3491883720 0.2207200741 60 -0.3697711123 0.3491883720 61 -0.2746523739 -0.3697711123 62 -0.2644755661 -0.2746523739 63 -0.2136066078 -0.2644755661 64 -0.1283357512 -0.2136066078 65 -0.2822755512 -0.1283357512 66 0.0852200123 -0.2822755512 67 0.1108957245 0.0852200123 68 0.0777503964 0.1108957245 69 0.0576307634 0.0777503964 70 0.1491522115 0.0576307634 71 -0.0929952919 0.1491522115 72 0.1127812153 -0.0929952919 73 0.0770648551 0.1127812153 74 -0.0422682654 0.0770648551 75 -0.0786140032 -0.0422682654 76 -0.0985768523 -0.0786140032 77 -0.0004832498 -0.0985768523 78 0.0388675548 -0.0004832498 79 -0.1412748538 0.0388675548 80 -0.1801439924 -0.1412748538 81 -0.1352195816 -0.1801439924 82 -0.0136507016 -0.1352195816 83 0.3040125754 -0.0136507016 84 -0.0875400146 0.3040125754 85 0.1285309038 -0.0875400146 86 0.2968687880 0.1285309038 87 0.0553590707 0.2968687880 88 0.0096593355 0.0553590707 89 -0.2252495501 0.0096593355 90 -0.1444663951 -0.2252495501 91 0.2025294735 -0.1444663951 92 0.2663284516 0.2025294735 93 0.1467443301 0.2663284516 94 0.2114405807 0.1467443301 95 0.2423275791 0.2114405807 96 0.1969934283 0.2423275791 97 -0.0269041154 0.1969934283 98 0.2002605665 -0.0269041154 99 0.1029306555 0.2002605665 100 0.0361471681 0.1029306555 101 0.0261951899 0.0361471681 102 0.0110830427 0.0261951899 103 0.4142247986 0.0110830427 104 0.4193350458 0.4142247986 105 0.4327364370 0.4193350458 106 0.4647109258 0.4327364370 107 0.3098959351 0.4647109258 108 0.3826380366 0.3098959351 109 0.1015785013 0.3826380366 110 -0.0349901184 0.1015785013 111 0.4100113720 -0.0349901184 112 0.1983687987 0.4100113720 113 0.3011294351 0.1983687987 114 0.1977753708 0.3011294351 115 0.1149057253 0.1977753708 116 0.2711061162 0.1149057253 117 -0.0518569667 0.2711061162 118 0.1139358570 -0.0518569667 119 0.2383985845 0.1139358570 120 0.4484696084 0.2383985845 121 0.0263668326 0.4484696084 122 0.0271514850 0.0263668326 123 0.2893986811 0.0271514850 124 0.3817899266 0.2893986811 125 0.3753790565 0.3817899266 126 0.3873613391 0.3753790565 127 0.3724147706 0.3873613391 128 0.5850678575 0.3724147706 129 0.2347514154 0.5850678575 130 0.1968833498 0.2347514154 131 0.1250420776 0.1968833498 132 -0.0533167362 0.1250420776 133 0.3529596528 -0.0533167362 134 0.0367175561 0.3529596528 135 0.0384846911 0.0367175561 136 -0.1463688729 0.0384846911 137 -0.1506276986 -0.1463688729 138 0.0402465326 -0.1506276986 139 0.2185196681 0.0402465326 140 0.0899151144 0.2185196681 141 0.1417015350 0.0899151144 142 0.2710047917 0.1417015350 143 0.3171741249 0.2710047917 144 0.4506066443 0.3171741249 145 0.1397667810 0.4506066443 146 -0.3515142193 0.1397667810 147 -0.1522854818 -0.3515142193 148 -0.2447742538 -0.1522854818 149 0.1062984528 -0.2447742538 150 0.0657469871 0.1062984528 151 0.0440683480 0.0657469871 152 0.0409203842 0.0440683480 153 0.1545941737 0.0409203842 154 0.1055613373 0.1545941737 155 0.0051826515 0.1055613373 156 0.2013160095 0.0051826515 157 -0.2686981811 0.2013160095 158 0.0292525535 -0.2686981811 159 0.1380999139 0.0292525535 160 -0.1281321791 0.1380999139 161 -0.0587124904 -0.1281321791 162 0.0691747997 -0.0587124904 163 0.2058241998 0.0691747997 164 -0.3824425267 0.2058241998 165 -0.4502115660 -0.3824425267 166 -0.2251905676 -0.4502115660 167 -0.0938475184 -0.2251905676 168 -0.9418426114 -0.0938475184 169 -0.2302780047 -0.9418426114 170 -0.1145762029 -0.2302780047 171 -0.7993405843 -0.1145762029 172 -0.8358743948 -0.7993405843 173 -0.8783338466 -0.8358743948 174 -0.8660101226 -0.8783338466 175 -0.8346308649 -0.8660101226 176 -0.7768488137 -0.8346308649 177 0.3556600296 -0.7768488137 178 0.2877673101 0.3556600296 179 0.0656761316 0.2877673101 180 0.2360214910 0.0656761316 181 0.1275739354 0.2360214910 182 0.2808066129 0.1275739354 183 -0.6043862370 0.2808066129 184 -0.6484630863 -0.6043862370 185 -0.6067838036 -0.6484630863 186 -0.4232493950 -0.6067838036 187 -0.4753615206 -0.4232493950 188 -0.4359967657 -0.4753615206 189 -0.4282774574 -0.4359967657 190 -0.6511288011 -0.4282774574 191 -0.7009783841 -0.6511288011 192 0.1745042657 -0.7009783841 193 -0.2670808288 0.1745042657 194 -0.5194153557 -0.2670808288 > plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals') > lines(lowess(z)) > abline(lm(z)) > grid() > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/7eie51386779266.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/8ff2n1386779266.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/9lcu41386779266.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0)) > plot(mylm, las = 1, sub='Residual Diagnostics') > par(opar) > dev.off() null device 1 > if (n > n25) { + postscript(file="/var/wessaorg/rcomp/tmp/10uxfk1386779266.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) + plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint') + grid() + dev.off() + } null device 1 > > #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab > load(file="/var/wessaorg/rcomp/createtable") > > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE) > a<-table.row.end(a) > myeq <- colnames(x)[1] > myeq <- paste(myeq, '[t] = ', sep='') > for (i in 1:k){ + if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '') + myeq <- paste(myeq, signif(mysum$coefficients[i,1],6), sep=' ') + if (rownames(mysum$coefficients)[i] != '(Intercept)') { + myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='') + if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='') + } + } > myeq <- paste(myeq, ' + e[t]') > a<-table.row.start(a) > a<-table.element(a, myeq) > a<-table.row.end(a) > a<-table.end(a) > table.save(a,file="/var/wessaorg/rcomp/tmp/11bxxf1386779266.tab") > a<-table.start() > a<-table.row.start(a) > a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a,'Variable',header=TRUE) > a<-table.element(a,'Parameter',header=TRUE) > a<-table.element(a,'S.D.',header=TRUE) > a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE) > a<-table.element(a,'2-tail p-value',header=TRUE) > a<-table.element(a,'1-tail p-value',header=TRUE) > a<-table.row.end(a) > for (i in 1:k){ + a<-table.row.start(a) + a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE) + a<-table.element(a,signif(mysum$coefficients[i,1],6)) + a<-table.element(a, signif(mysum$coefficients[i,2],6)) + a<-table.element(a, signif(mysum$coefficients[i,3],4)) + a<-table.element(a, signif(mysum$coefficients[i,4],6)) + a<-table.element(a, signif(mysum$coefficients[i,4]/2,6)) + a<-table.row.end(a) + } > a<-table.end(a) > table.save(a,file="/var/wessaorg/rcomp/tmp/12z4p81386779266.tab") > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Multiple R',1,TRUE) > a<-table.element(a, signif(sqrt(mysum$r.squared),6)) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'R-squared',1,TRUE) > a<-table.element(a, signif(mysum$r.squared,6)) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Adjusted R-squared',1,TRUE) > a<-table.element(a, signif(mysum$adj.r.squared,6)) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'F-TEST (value)',1,TRUE) > a<-table.element(a, signif(mysum$fstatistic[1],6)) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE) > a<-table.element(a, signif(mysum$fstatistic[2],6)) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE) > a<-table.element(a, signif(mysum$fstatistic[3],6)) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'p-value',1,TRUE) > a<-table.element(a, signif(1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]),6)) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Residual Standard Deviation',1,TRUE) > a<-table.element(a, signif(mysum$sigma,6)) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Sum Squared Residuals',1,TRUE) > a<-table.element(a, signif(sum(myerror*myerror),6)) > a<-table.row.end(a) > a<-table.end(a) > table.save(a,file="/var/wessaorg/rcomp/tmp/13s0s71386779266.tab") > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Time or Index', 1, TRUE) > a<-table.element(a, 'Actuals', 1, TRUE) > a<-table.element(a, 'Interpolation
Forecast', 1, TRUE) > a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE) > a<-table.row.end(a) > for (i in 1:n) { + a<-table.row.start(a) + a<-table.element(a,i, 1, TRUE) + a<-table.element(a,signif(x[i],6)) + a<-table.element(a,signif(x[i]-mysum$resid[i],6)) + a<-table.element(a,signif(mysum$resid[i],6)) + a<-table.row.end(a) + } > a<-table.end(a) > table.save(a,file="/var/wessaorg/rcomp/tmp/14eafj1386779266.tab") > if (n > n25) { + a<-table.start() + a<-table.row.start(a) + a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'p-values',header=TRUE) + a<-table.element(a,'Alternative Hypothesis',3,header=TRUE) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'breakpoint index',header=TRUE) + a<-table.element(a,'greater',header=TRUE) + a<-table.element(a,'2-sided',header=TRUE) + a<-table.element(a,'less',header=TRUE) + a<-table.row.end(a) + for (mypoint in kp3:nmkm3) { + a<-table.row.start(a) + a<-table.element(a,mypoint,header=TRUE) + a<-table.element(a,signif(gqarr[mypoint-kp3+1,1],6)) + a<-table.element(a,signif(gqarr[mypoint-kp3+1,2],6)) + a<-table.element(a,signif(gqarr[mypoint-kp3+1,3],6)) + a<-table.row.end(a) + } + a<-table.end(a) + table.save(a,file="/var/wessaorg/rcomp/tmp/15kcbi1386779266.tab") + a<-table.start() + a<-table.row.start(a) + a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'Description',header=TRUE) + a<-table.element(a,'# significant tests',header=TRUE) + a<-table.element(a,'% significant tests',header=TRUE) + a<-table.element(a,'OK/NOK',header=TRUE) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'1% type I error level',header=TRUE) + a<-table.element(a,signif(numsignificant1,6)) + a<-table.element(a,signif(numsignificant1/numgqtests,6)) + if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK' + a<-table.element(a,dum) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'5% type I error level',header=TRUE) + a<-table.element(a,signif(numsignificant5,6)) + a<-table.element(a,signif(numsignificant5/numgqtests,6)) + if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK' + a<-table.element(a,dum) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'10% type I error level',header=TRUE) + a<-table.element(a,signif(numsignificant10,6)) + a<-table.element(a,signif(numsignificant10/numgqtests,6)) + if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK' + a<-table.element(a,dum) + a<-table.row.end(a) + a<-table.end(a) + table.save(a,file="/var/wessaorg/rcomp/tmp/16ot8e1386779266.tab") + } > > try(system("convert tmp/10udb1386779266.ps tmp/10udb1386779266.png",intern=TRUE)) character(0) > try(system("convert tmp/2xpuj1386779266.ps tmp/2xpuj1386779266.png",intern=TRUE)) character(0) > try(system("convert tmp/37bj61386779266.ps tmp/37bj61386779266.png",intern=TRUE)) character(0) > try(system("convert tmp/4f9cd1386779266.ps tmp/4f9cd1386779266.png",intern=TRUE)) character(0) > try(system("convert tmp/5sqxy1386779266.ps tmp/5sqxy1386779266.png",intern=TRUE)) character(0) > try(system("convert tmp/6tzud1386779266.ps tmp/6tzud1386779266.png",intern=TRUE)) character(0) > try(system("convert tmp/7eie51386779266.ps tmp/7eie51386779266.png",intern=TRUE)) character(0) > try(system("convert tmp/8ff2n1386779266.ps tmp/8ff2n1386779266.png",intern=TRUE)) character(0) > try(system("convert tmp/9lcu41386779266.ps tmp/9lcu41386779266.png",intern=TRUE)) character(0) > try(system("convert tmp/10uxfk1386779266.ps tmp/10uxfk1386779266.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 28.178 4.245 32.407