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 + ,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 + ,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 + ,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 + ,20.644 + ,1 + ,0.434969 + ,0.819235 + ,-4.117501 + ,0.334147 + ,2.405554 + ,0.368975 + ,116.014 + 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,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 + ,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 + ,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 + ,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 + ,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 + ,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 + ,21.209 + ,0 + ,0.462803 + ,0.664357 + ,-5.724056 + ,0.190667 + ,2.555477 + ,0.148569) + ,dim=c(22 + ,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' + ,'HNR' + ,'status' + ,'RPDE' + ,'DFA' + ,'spread1' + ,'spread2' + ,'D2' + ,'PPE') + ,1:195)) > y <- array(NA,dim=c(22,195),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','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 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 HNR RPDE DFA 1 0.02182 0.03130 0.02971 0.06545 21.033 0.414783 0.815285 2 0.03134 0.04518 0.04368 0.09403 19.085 0.458359 0.819521 3 0.02757 0.03858 0.03590 0.08270 20.651 0.429895 0.825288 4 0.02924 0.04005 0.03772 0.08771 20.644 0.434969 0.819235 5 0.03490 0.04825 0.04465 0.10470 19.649 0.417356 0.823484 6 0.02328 0.03526 0.03243 0.06985 21.378 0.415564 0.825069 7 0.00779 0.00937 0.01351 0.02337 24.886 0.596040 0.764112 8 0.00829 0.00946 0.01256 0.02487 26.892 0.637420 0.763262 9 0.01073 0.01277 0.01717 0.03218 21.812 0.615551 0.773587 10 0.01441 0.01725 0.02444 0.04324 21.862 0.547037 0.798463 11 0.01079 0.01342 0.01892 0.03237 21.118 0.611137 0.776156 12 0.01424 0.01641 0.02214 0.04272 21.414 0.583390 0.792520 13 0.00656 0.00717 0.01140 0.01968 25.703 0.460600 0.646846 14 0.00728 0.00932 0.01797 0.02184 24.889 0.430166 0.665833 15 0.01064 0.00972 0.01246 0.03191 24.922 0.474791 0.654027 16 0.00772 0.00888 0.01359 0.02316 25.175 0.565924 0.658245 17 0.00969 0.01200 0.02074 0.02908 22.333 0.567380 0.644692 18 0.01441 0.01893 0.03430 0.04322 20.376 0.631099 0.605417 19 0.02471 0.03572 0.05767 0.07413 17.280 0.665318 0.719467 20 0.01721 0.02374 0.04310 0.05164 17.153 0.649554 0.686080 21 0.01667 0.02383 0.04055 0.05000 17.536 0.660125 0.704087 22 0.02021 0.02591 0.04525 0.06062 19.493 0.629017 0.698951 23 0.02228 0.02540 0.04246 0.06685 22.468 0.619060 0.679834 24 0.02187 0.02470 0.03772 0.06562 20.422 0.537264 0.686894 25 0.00738 0.00948 0.01497 0.02214 23.831 0.397937 0.732479 26 0.01732 0.02245 0.03780 0.05197 22.066 0.522746 0.737948 27 0.00889 0.01169 0.01872 0.02666 25.908 0.418622 0.720916 28 0.00883 0.01144 0.01826 0.02650 25.119 0.358773 0.726652 29 0.00769 0.01012 0.01661 0.02307 25.970 0.470478 0.676258 30 0.00793 0.01057 0.01799 0.02380 25.678 0.427785 0.723797 31 0.00563 0.00680 0.00802 0.01689 26.775 0.422229 0.741367 32 0.00504 0.00641 0.00762 0.01513 30.940 0.432439 0.742055 33 0.00640 0.00825 0.00951 0.01919 30.775 0.465946 0.738703 34 0.00469 0.00606 0.00719 0.01407 32.684 0.368535 0.742133 35 0.00468 0.00610 0.00726 0.01403 33.047 0.340068 0.741899 36 0.00586 0.00760 0.00957 0.01758 31.732 0.344252 0.742737 37 0.01154 0.01347 0.01612 0.03463 23.216 0.360148 0.778834 38 0.00938 0.01160 0.01491 0.02814 24.951 0.341435 0.783626 39 0.00726 0.00885 0.01190 0.02177 26.738 0.403884 0.766209 40 0.00829 0.01003 0.01366 0.02488 26.310 0.396793 0.758324 41 0.00774 0.00941 0.01233 0.02321 26.822 0.326480 0.765623 42 0.00742 0.00901 0.01234 0.02226 26.453 0.306443 0.759203 43 0.01035 0.01024 0.01133 0.03104 22.736 0.305062 0.654172 44 0.01006 0.01038 0.01251 0.03017 23.145 0.457702 0.634267 45 0.00777 0.00898 0.01033 0.02330 25.368 0.438296 0.635285 46 0.00847 0.00879 0.01014 0.02542 25.032 0.431285 0.638928 47 0.00906 0.00977 0.01149 0.02719 24.602 0.467489 0.631653 48 0.00614 0.00730 0.00860 0.01841 26.805 0.610367 0.635204 49 0.00855 0.00776 0.01433 0.02566 23.162 0.579597 0.733659 50 0.00930 0.00802 0.01400 0.02789 24.971 0.538688 0.754073 51 0.01241 0.01024 0.01685 0.03724 25.135 0.553134 0.775933 52 0.01143 0.00959 0.01614 0.03429 25.030 0.507504 0.760361 53 0.01323 0.01072 0.01677 0.03969 24.692 0.459766 0.766204 54 0.01396 0.01219 0.01947 0.04188 25.429 0.420383 0.785714 55 0.01483 0.01609 0.02067 0.04450 21.028 0.536009 0.819032 56 0.01789 0.01992 0.02454 0.05368 20.767 0.558586 0.811843 57 0.02032 0.02302 0.02802 0.06097 21.422 0.541781 0.821364 58 0.01189 0.01459 0.01948 0.03568 22.817 0.530529 0.817756 59 0.01394 0.01625 0.02137 0.04183 22.603 0.540049 0.813432 60 0.01805 0.01974 0.02519 0.05414 21.660 0.547975 0.817396 61 0.00975 0.01258 0.01382 0.02925 25.554 0.341788 0.678874 62 0.01013 0.01296 0.01340 0.03039 26.138 0.447979 0.686264 63 0.00867 0.01108 0.01200 0.02602 25.856 0.364867 0.694399 64 0.00882 0.01075 0.01179 0.02647 25.964 0.256570 0.683296 65 0.00769 0.00957 0.01016 0.02308 26.415 0.276850 0.673636 66 0.00942 0.01160 0.01234 0.02827 24.547 0.305429 0.681811 67 0.01830 0.01810 0.02428 0.05490 19.560 0.460139 0.720908 68 0.01638 0.01759 0.02603 0.04914 19.979 0.498133 0.729067 69 0.03152 0.02422 0.03392 0.09455 20.338 0.513237 0.731444 70 0.03357 0.02494 0.03635 0.10070 21.718 0.487407 0.727313 71 0.01868 0.01906 0.02949 0.05605 20.264 0.489345 0.730387 72 0.02749 0.02466 0.03736 0.08247 18.570 0.543299 0.733232 73 0.00974 0.00925 0.01345 0.02921 25.742 0.495954 0.762959 74 0.01373 0.01375 0.01956 0.04120 24.178 0.509127 0.789532 75 0.01432 0.01325 0.01831 0.04295 25.438 0.437031 0.815908 76 0.01284 0.01219 0.01715 0.03851 25.197 0.463514 0.807217 77 0.02413 0.02231 0.02704 0.07238 23.370 0.489538 0.789977 78 0.01284 0.01199 0.01636 0.03852 25.820 0.429484 0.816340 79 0.01803 0.01886 0.02455 0.05408 21.875 0.644954 0.779612 80 0.01773 0.01783 0.02139 0.05320 19.200 0.594387 0.790117 81 0.02266 0.02451 0.02876 0.06799 19.055 0.544805 0.770466 82 0.01792 0.01841 0.02190 0.05377 19.659 0.576084 0.778747 83 0.01371 0.01421 0.01751 0.04114 20.536 0.554610 0.787896 84 0.01277 0.01343 0.01552 0.03831 22.244 0.576644 0.772416 85 0.02679 0.03022 0.03510 0.08037 13.893 0.556494 0.729586 86 0.02107 0.02493 0.02877 0.06321 16.176 0.583574 0.727747 87 0.02073 0.02415 0.02784 0.06219 15.924 0.598714 0.712199 88 0.03671 0.04159 0.04683 0.11012 13.922 0.602874 0.740837 89 0.03788 0.04254 0.04802 0.11363 14.739 0.599371 0.743937 90 0.02297 0.02768 0.03455 0.06892 11.866 0.590951 0.745526 91 0.03650 0.04282 0.05114 0.10949 11.744 0.653410 0.733165 92 0.04421 0.04962 0.05690 0.13262 19.664 0.501037 0.714360 93 0.02383 0.02521 0.03051 0.07150 18.780 0.454444 0.734504 94 0.03341 0.03794 0.04398 0.10024 20.969 0.447456 0.697790 95 0.02062 0.02321 0.02764 0.06185 22.219 0.502380 0.712170 96 0.01813 0.01909 0.02571 0.05439 21.693 0.447285 0.705658 97 0.01806 0.02024 0.02809 0.05417 22.663 0.366329 0.693429 98 0.02135 0.02174 0.03088 0.06406 15.338 0.629574 0.714485 99 0.02542 0.02630 0.03908 0.07625 15.433 0.571010 0.690892 100 0.03611 0.03963 0.05783 0.10833 12.435 0.638545 0.674953 101 0.05358 0.04791 0.06196 0.16074 8.867 0.671299 0.656846 102 0.03223 0.03672 0.05174 0.09669 15.060 0.639808 0.643327 103 0.05551 0.05005 0.06023 0.16654 10.489 0.596362 0.641418 104 0.00522 0.00659 0.01009 0.01567 26.759 0.296888 0.722356 105 0.00469 0.00582 0.00871 0.01406 28.409 0.263654 0.691483 106 0.00660 0.00818 0.01059 0.01979 27.421 0.365488 0.719974 107 0.00522 0.00632 0.00928 0.01567 29.746 0.334171 0.677930 108 0.00633 0.00788 0.01267 0.01898 26.833 0.393563 0.700246 109 0.00455 0.00576 0.00993 0.01364 29.928 0.311369 0.676066 110 0.01771 0.01815 0.02084 0.05312 21.934 0.497554 0.740539 111 0.01192 0.01439 0.01852 0.03576 23.239 0.436084 0.727863 112 0.00952 0.01058 0.01307 0.02855 22.407 0.338097 0.712466 113 0.01277 0.01483 0.01767 0.03831 21.305 0.498877 0.722085 114 0.00861 0.01017 0.01301 0.02583 23.671 0.441097 0.722254 115 0.01107 0.01284 0.01604 0.03320 21.864 0.331508 0.715121 116 0.00796 0.00832 0.01271 0.02389 23.693 0.407701 0.662668 117 0.00606 0.00747 0.01312 0.01818 26.356 0.450798 0.653823 118 0.00757 0.00971 0.01652 0.02270 25.690 0.486738 0.676023 119 0.00617 0.00744 0.01151 0.01851 25.020 0.470422 0.655239 120 0.00679 0.00631 0.01075 0.02038 24.581 0.462516 0.582710 121 0.00849 0.01117 0.01734 0.02548 24.743 0.487756 0.684130 122 0.00534 0.00630 0.01104 0.01603 27.166 0.400088 0.656182 123 0.02587 0.02567 0.03220 0.07761 18.305 0.538016 0.741480 124 0.01372 0.01580 0.01931 0.04115 18.784 0.589956 0.732903 125 0.01289 0.01420 0.01720 0.03867 19.196 0.618663 0.728421 126 0.01235 0.01495 0.01944 0.03706 18.857 0.637518 0.735546 127 0.01484 0.01805 0.02259 0.04451 18.178 0.623209 0.738245 128 0.01547 0.01859 0.02301 0.04641 18.330 0.585169 0.736964 129 0.00538 0.00570 0.00811 0.01614 26.842 0.457541 0.699787 130 0.00476 0.00588 0.00903 0.01428 26.369 0.491345 0.718839 131 0.00703 0.00820 0.01194 0.02110 23.949 0.467160 0.724045 132 0.00721 0.00815 0.01310 0.02164 26.017 0.468621 0.735136 133 0.00633 0.00701 0.00915 0.01898 23.389 0.470972 0.721308 134 0.00490 0.00621 0.00903 0.01471 25.619 0.482296 0.723096 135 0.02683 0.03112 0.03651 0.08050 17.060 0.637814 0.744064 136 0.02229 0.02592 0.03316 0.06688 17.707 0.653427 0.706687 137 0.02385 0.02973 0.04370 0.07154 19.013 0.647900 0.708144 138 0.02896 0.03347 0.04134 0.08689 16.747 0.625362 0.708617 139 0.03070 0.03530 0.04451 0.09211 17.366 0.640945 0.701404 140 0.01514 0.01812 0.02770 0.04543 18.801 0.624811 0.696049 141 0.01713 0.01964 0.02824 0.05139 18.540 0.677131 0.685057 142 0.04016 0.04003 0.04464 0.12047 15.648 0.606344 0.665945 143 0.02055 0.02076 0.02530 0.06165 18.702 0.606273 0.661735 144 0.01117 0.01177 0.01506 0.03350 18.687 0.536102 0.632631 145 0.01475 0.01558 0.02006 0.04426 20.680 0.497480 0.630409 146 0.01379 0.01478 0.01909 0.04137 20.366 0.566849 0.574282 147 0.03804 0.05426 0.08808 0.11411 12.359 0.561610 0.793509 148 0.02865 0.04101 0.06359 0.08595 14.367 0.478024 0.768974 149 0.03474 0.04580 0.06824 0.10422 12.298 0.552870 0.764036 150 0.03515 0.04265 0.06460 0.10546 14.989 0.427627 0.775708 151 0.02699 0.03714 0.06259 0.08096 12.529 0.507826 0.762726 152 0.05647 0.07940 0.13778 0.16942 8.441 0.625866 0.768320 153 0.04284 0.05556 0.08318 0.12851 9.449 0.584164 0.754449 154 0.01340 0.01399 0.02056 0.04019 21.520 0.566867 0.670475 155 0.01484 0.01405 0.02018 0.04451 21.824 0.651680 0.659333 156 0.01659 0.01804 0.02402 0.04977 22.431 0.628300 0.652025 157 0.01205 0.01289 0.01771 0.03615 22.953 0.611679 0.623731 158 0.02610 0.02161 0.02916 0.07830 19.075 0.630547 0.646786 159 0.01500 0.01581 0.02157 0.04499 21.534 0.635015 0.627337 160 0.01360 0.01650 0.03105 0.04079 19.651 0.654945 0.675865 161 0.01579 0.01994 0.04114 0.04736 20.437 0.653139 0.694571 162 0.01644 0.01722 0.02931 0.04933 19.388 0.577802 0.684373 163 0.01864 0.01940 0.03091 0.05592 18.954 0.685151 0.719576 164 0.00967 0.01033 0.01363 0.02902 21.219 0.557045 0.673086 165 0.01579 0.01553 0.02073 0.04736 18.447 0.671378 0.674562 166 0.01410 0.01426 0.01621 0.04231 24.078 0.469928 0.628232 167 0.00696 0.00747 0.00882 0.02089 24.679 0.384868 0.626710 168 0.01186 0.01230 0.01367 0.03557 21.083 0.440988 0.628058 169 0.01279 0.01272 0.01439 0.03836 19.269 0.372222 0.725216 170 0.01176 0.01191 0.01344 0.03529 21.020 0.371837 0.646167 171 0.01084 0.01121 0.01255 0.03253 21.528 0.522812 0.646818 172 0.00664 0.00786 0.01140 0.01992 26.436 0.413295 0.756700 173 0.00754 0.00950 0.01285 0.02261 26.550 0.369090 0.776158 174 0.00748 0.00905 0.01148 0.02245 26.547 0.380253 0.766700 175 0.00881 0.01062 0.01318 0.02643 25.445 0.387482 0.756482 176 0.00812 0.00933 0.01133 0.02436 26.005 0.405991 0.761255 177 0.00874 0.01021 0.01331 0.02623 26.143 0.361232 0.763242 178 0.00728 0.00886 0.01230 0.02184 24.151 0.396610 0.745957 179 0.00839 0.00956 0.01309 0.02518 24.412 0.402591 0.762508 180 0.00725 0.00876 0.01263 0.02175 23.683 0.398499 0.778349 181 0.01321 0.01574 0.02148 0.03964 23.133 0.352396 0.759320 182 0.00950 0.01103 0.01559 0.02849 22.866 0.408598 0.768845 183 0.01155 0.01341 0.01666 0.03464 23.008 0.329577 0.757180 184 0.00864 0.01223 0.01949 0.02592 23.079 0.603515 0.669565 185 0.00810 0.01144 0.01756 0.02429 22.085 0.663842 0.656516 186 0.00667 0.00990 0.01691 0.02001 24.199 0.598515 0.654331 187 0.00820 0.00972 0.01491 0.02460 23.958 0.566424 0.667654 188 0.00631 0.00789 0.01144 0.01892 25.023 0.528485 0.663884 189 0.00557 0.00721 0.01095 0.01672 24.775 0.555303 0.659132 190 0.01454 0.01582 0.01758 0.04363 19.368 0.508479 0.683761 191 0.02336 0.02498 0.02745 0.07008 19.517 0.448439 0.657899 192 0.01604 0.01657 0.01879 0.04812 19.147 0.431674 0.683244 193 0.01268 0.01365 0.01667 0.03804 17.883 0.407567 0.655683 194 0.01265 0.01321 0.01588 0.03794 19.020 0.451221 0.643956 195 0.01026 0.01161 0.01373 0.03078 21.209 0.462803 0.664357 spread1 spread2 D2 PPE 1 -4.813031 0.266482 2.301442 0.284654 2 -4.075192 0.335590 2.486855 0.368674 3 -4.443179 0.311173 2.342259 0.332634 4 -4.117501 0.334147 2.405554 0.368975 5 -3.747787 0.234513 2.332180 0.410335 6 -4.242867 0.299111 2.187560 0.357775 7 -5.634322 0.257682 1.854785 0.211756 8 -6.167603 0.183721 2.064693 0.163755 9 -5.498678 0.327769 2.322511 0.231571 10 -5.011879 0.325996 2.432792 0.271362 11 -5.249770 0.391002 2.407313 0.249740 12 -4.960234 0.363566 2.642476 0.275931 13 -6.547148 0.152813 2.041277 0.138512 14 -5.660217 0.254989 2.519422 0.199889 15 -6.105098 0.203653 2.125618 0.170100 16 -5.340115 0.210185 2.205546 0.234589 17 -5.440040 0.239764 2.264501 0.218164 18 -2.931070 0.434326 3.007463 0.430788 19 -3.949079 0.357870 3.109010 0.377429 20 -4.554466 0.340176 2.856676 0.322111 21 -4.095442 0.262564 2.739710 0.365391 22 -5.186960 0.237622 2.557536 0.259765 23 -4.330956 0.262384 2.916777 0.285695 24 -5.248776 0.210279 2.547508 0.253556 25 -5.557447 0.220890 2.692176 0.215961 26 -5.571843 0.236853 2.846369 0.219514 27 -6.183590 0.226278 2.589702 0.147403 28 -6.271690 0.196102 2.314209 0.162999 29 -7.120925 0.279789 2.241742 0.108514 30 -6.635729 0.209866 1.957961 0.135242 31 -7.348300 0.177551 1.743867 0.085569 32 -7.682587 0.173319 2.103106 0.068501 33 -7.067931 0.175181 1.512275 0.096320 34 -7.695734 0.178540 1.544609 0.056141 35 -7.964984 0.163519 1.423287 0.044539 36 -7.777685 0.170183 2.447064 0.057610 37 -6.149653 0.218037 2.477082 0.165827 38 -6.006414 0.196371 2.536527 0.173218 39 -6.452058 0.212294 2.269398 0.141929 40 -6.006647 0.266892 2.382544 0.160691 41 -6.647379 0.201095 2.374073 0.130554 42 -7.044105 0.063412 2.361532 0.115730 43 -7.310550 0.098648 2.416838 0.095032 44 -6.793547 0.158266 2.256699 0.117399 45 -7.057869 0.091608 2.330716 0.091470 46 -6.995820 0.102083 2.365800 0.102706 47 -7.156076 0.127642 2.392122 0.097336 48 -7.319510 0.200873 2.028612 0.086398 49 -6.439398 0.266392 2.079922 0.133867 50 -6.482096 0.264967 2.054419 0.128872 51 -6.650471 0.254498 1.840198 0.103561 52 -6.689151 0.291954 2.431854 0.105993 53 -7.072419 0.220434 1.972297 0.119308 54 -6.836811 0.269866 2.223719 0.147491 55 -4.649573 0.205558 1.986899 0.316700 56 -4.333543 0.221727 2.014606 0.344834 57 -4.438453 0.238298 1.922940 0.335041 58 -4.608260 0.290024 2.021591 0.314464 59 -4.476755 0.262633 1.827012 0.326197 60 -4.609161 0.221711 1.831691 0.316395 61 -7.040508 0.066994 2.460791 0.101516 62 -7.293801 0.086372 2.321560 0.098555 63 -6.966321 0.095882 2.278687 0.103224 64 -7.245620 0.018689 2.498224 0.093534 65 -7.496264 0.056844 2.003032 0.073581 66 -7.314237 0.006274 2.118596 0.091546 67 -5.409423 0.226850 2.359973 0.226156 68 -5.324574 0.205660 2.291558 0.226247 69 -5.869750 0.151814 2.118496 0.185580 70 -6.261141 0.120956 2.137075 0.141958 71 -5.720868 0.158830 2.277927 0.180828 72 -5.207985 0.224852 2.642276 0.242981 73 -5.791820 0.329066 2.205024 0.188180 74 -5.389129 0.306636 1.928708 0.225461 75 -5.313360 0.201861 2.225815 0.244512 76 -5.477592 0.315074 1.862092 0.228624 77 -5.775966 0.341169 2.007923 0.193918 78 -5.391029 0.250572 1.777901 0.232744 79 -5.115212 0.249494 2.017753 0.260015 80 -4.913885 0.265699 2.398422 0.277948 81 -4.441519 0.155097 2.645959 0.327978 82 -5.132032 0.210458 2.232576 0.260633 83 -5.022288 0.146948 2.428306 0.264666 84 -6.025367 0.078202 2.053601 0.177275 85 -5.288912 0.343073 3.099301 0.242119 86 -5.657899 0.315903 3.098256 0.200423 87 -6.366916 0.335753 2.654271 0.144614 88 -5.515071 0.299549 3.136550 0.220968 89 -5.783272 0.299793 3.007096 0.194052 90 -4.379411 0.375531 3.671155 0.332086 91 -4.508984 0.389232 3.317586 0.301952 92 -6.411497 0.207156 2.344876 0.134120 93 -5.952058 0.087840 2.344336 0.186489 94 -6.152551 0.173520 2.080121 0.160809 95 -6.251425 0.188056 2.143851 0.160812 96 -6.247076 0.180528 2.344348 0.164916 97 -6.417440 0.194627 2.473239 0.151709 98 -4.020042 0.265315 2.671825 0.340623 99 -5.159169 0.202146 2.441612 0.260375 100 -3.760348 0.242861 2.634633 0.378483 101 -3.700544 0.260481 2.991063 0.370961 102 -4.202730 0.310163 2.638279 0.356881 103 -3.269487 0.270641 2.690917 0.444774 104 -6.878393 0.089267 2.004055 0.113942 105 -7.111576 0.144780 2.065477 0.093193 106 -6.997403 0.210279 1.994387 0.112878 107 -6.981201 0.184550 2.129924 0.106802 108 -6.600023 0.249172 2.499148 0.105306 109 -6.739151 0.160686 2.296873 0.115130 110 -5.845099 0.278679 2.608749 0.185668 111 -5.258320 0.256454 2.550961 0.232520 112 -6.471427 0.184378 2.502336 0.136390 113 -4.876336 0.212054 2.376749 0.268144 114 -5.963040 0.250283 2.489191 0.177807 115 -6.729713 0.181701 2.938114 0.115515 116 -4.673241 0.261549 2.702355 0.274407 117 -6.051233 0.273280 2.640798 0.170106 118 -4.597834 0.372114 2.975889 0.282780 119 -4.913137 0.393056 2.816781 0.251972 120 -5.517173 0.389295 2.925862 0.220657 121 -6.186128 0.279933 2.686240 0.152428 122 -4.711007 0.281618 2.655744 0.234809 123 -5.418787 0.160267 2.090438 0.229892 124 -5.445140 0.142466 2.174306 0.215558 125 -5.944191 0.143359 1.929715 0.181988 126 -5.594275 0.127950 1.765957 0.222716 127 -5.540351 0.087165 1.821297 0.214075 128 -5.825257 0.115697 1.996146 0.196535 129 -6.890021 0.152941 2.328513 0.112856 130 -5.892061 0.195976 2.108873 0.183572 131 -6.135296 0.203630 2.539724 0.169923 132 -6.112667 0.217013 2.527742 0.170633 133 -5.436135 0.254909 2.516320 0.232209 134 -6.448134 0.178713 2.034827 0.141422 135 -5.301321 0.320385 2.375138 0.243080 136 -5.333619 0.322044 2.631793 0.228319 137 -4.378916 0.300067 2.445502 0.259451 138 -4.654894 0.304107 2.672362 0.274387 139 -5.634576 0.306014 2.419253 0.209191 140 -5.866357 0.233070 2.445646 0.184985 141 -4.796845 0.397749 2.963799 0.277227 142 -5.410336 0.288917 2.665133 0.231723 143 -5.585259 0.310746 2.465528 0.209863 144 -5.898673 0.213353 2.470746 0.189032 145 -6.132663 0.220617 2.576563 0.159777 146 -5.456811 0.345238 2.840556 0.232861 147 -3.297668 0.414758 3.413649 0.457533 148 -4.276605 0.355736 3.142364 0.336085 149 -3.377325 0.335357 3.274865 0.418646 150 -4.892495 0.262281 2.910213 0.270173 151 -4.484303 0.340256 2.958815 0.301487 152 -2.434031 0.450493 3.079221 0.527367 153 -2.839756 0.356224 3.184027 0.454721 154 -4.865194 0.246404 2.013530 0.168581 155 -4.239028 0.175691 2.451130 0.247455 156 -3.583722 0.207914 2.439597 0.206256 157 -5.435100 0.230532 2.699645 0.220546 158 -3.444478 0.303214 2.964568 0.261305 159 -5.070096 0.280091 2.892300 0.249703 160 -5.498456 0.234196 2.103014 0.216638 161 -5.185987 0.259229 2.151121 0.244948 162 -5.283009 0.226528 2.442906 0.238281 163 -5.529833 0.242750 2.408689 0.220520 164 -5.617124 0.184896 1.871871 0.212386 165 -2.929379 0.396746 2.560422 0.367233 166 -6.816086 0.172270 2.235197 0.119652 167 -7.018057 0.176316 1.852402 0.091604 168 -7.517934 0.160414 1.881767 0.075587 169 -5.736781 0.164529 2.882450 0.202879 170 -7.169701 0.073298 2.266432 0.100881 171 -7.304500 0.171088 2.095237 0.096220 172 -6.323531 0.218885 2.193412 0.160376 173 -6.085567 0.192375 1.889002 0.174152 174 -5.943501 0.192150 1.852542 0.179677 175 -6.012559 0.229298 1.872946 0.163118 176 -5.966779 0.197938 1.974857 0.184067 177 -6.016891 0.109256 2.004719 0.174429 178 -6.486822 0.197919 2.449763 0.132703 179 -6.311987 0.182459 2.251553 0.160306 180 -5.711205 0.240875 2.845109 0.192730 181 -6.261446 0.183218 2.264226 0.144105 182 -5.704053 0.216204 2.679185 0.197710 183 -6.277170 0.109397 2.209021 0.156368 184 -5.619070 0.191576 2.027228 0.215724 185 -5.198864 0.206768 2.120412 0.252404 186 -5.592584 0.133917 2.058658 0.214346 187 -6.431119 0.153310 2.161936 0.120605 188 -6.359018 0.116636 2.152083 0.138868 189 -6.710219 0.149694 1.913990 0.121777 190 -6.934474 0.159890 2.316346 0.112838 191 -6.538586 0.121952 2.657476 0.133050 192 -6.195325 0.129303 2.784312 0.168895 193 -6.787197 0.158453 2.679772 0.131728 194 -6.744577 0.207454 2.138608 0.123306 195 -5.724056 0.190667 2.555477 0.148569 > k <- length(x[1,]) > df <- as.data.frame(x) > (mylm <- lm(df)) Call: lm(formula = df) Coefficients: (Intercept) `MDVP:Fo(Hz)` `MDVP:Fhi(Hz)` `MDVP:Flo(Hz)` 2.122e+00 -2.058e-03 -1.605e-04 -1.600e-03 `MDVP:Jitter(%)` `MDVP:Jitter(Abs)` `MDVP:RAP` `MDVP:PPQ` -1.891e+02 -3.487e+03 5.059e+01 -1.905e+01 `Jitter:DDP` `MDVP:Shimmer` `MDVP:Shimmer(dB)` `Shimmer:APQ3` 8.268e+01 3.230e+01 5.596e-01 -5.274e+02 `Shimmer:APQ5` `MDVP:APQ` `Shimmer:DDA` HNR -2.862e+01 -4.581e+00 1.674e+02 -1.365e-02 RPDE DFA spread1 spread2 -9.964e-01 7.148e-01 1.503e-01 1.164e+00 D2 PPE 3.764e-02 1.237e+00 > (mysum <- summary(mylm)) Call: lm(formula = df) Residuals: Min 1Q Median 3Q Max -0.9037 -0.1714 0.0501 0.2126 0.5872 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 2.122e+00 1.158e+00 1.833 0.06858 . `MDVP:Fo(Hz)` -2.058e-03 1.491e-03 -1.380 0.16930 `MDVP:Fhi(Hz)` -1.605e-04 3.197e-04 -0.502 0.61617 `MDVP:Flo(Hz)` -1.600e-03 8.021e-04 -1.995 0.04759 * `MDVP:Jitter(%)` -1.891e+02 6.647e+01 -2.844 0.00499 ** `MDVP:Jitter(Abs)` -3.487e+03 4.632e+03 -0.753 0.45265 `MDVP:RAP` 5.059e+01 9.327e+03 0.005 0.99568 `MDVP:PPQ` -1.905e+01 8.752e+01 -0.218 0.82793 `Jitter:DDP` 8.268e+01 3.109e+03 0.027 0.97882 `MDVP:Shimmer` 3.230e+01 3.413e+01 0.946 0.34531 `MDVP:Shimmer(dB)` 5.596e-01 1.201e+00 0.466 0.64199 `Shimmer:APQ3` -5.274e+02 8.984e+03 -0.059 0.95325 `Shimmer:APQ5` -2.862e+01 2.008e+01 -1.425 0.15594 `MDVP:APQ` -4.581e+00 1.085e+01 -0.422 0.67332 `Shimmer:DDA` 1.674e+02 2.994e+03 0.056 0.95546 HNR -1.365e-02 1.428e-02 -0.956 0.34045 RPDE -9.964e-01 4.401e-01 -2.264 0.02481 * DFA 7.148e-01 6.847e-01 1.044 0.29793 spread1 1.503e-01 9.640e-02 1.559 0.12088 spread2 1.164e+00 4.722e-01 2.466 0.01466 * D2 3.764e-02 1.142e-01 0.330 0.74203 PPE 1.237e+00 1.386e+00 0.892 0.37341 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 0.3273 on 173 degrees of freedom Multiple R-squared: 0.4879, Adjusted R-squared: 0.4258 F-statistic: 7.85 on 21 and 173 DF, p-value: 3.432e-16 > if (n > n25) { + kp3 <- k + 3 + nmkm3 <- n - k - 3 + gqarr <- array(NA, dim=c(nmkm3-kp3+1,3)) + numgqtests <- 0 + numsignificant1 <- 0 + numsignificant5 <- 0 + numsignificant10 <- 0 + for (mypoint in kp3:nmkm3) { + j <- 0 + numgqtests <- numgqtests + 1 + for (myalt in c('greater', 'two.sided', 'less')) { + j <- j + 1 + gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value + } + if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1 + if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1 + if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1 + } + gqarr + } [,1] [,2] [,3] [1,] 2.279922e-49 4.559843e-49 1.00000000 [2,] 3.554255e-71 7.108510e-71 1.00000000 [3,] 1.984899e-81 3.969798e-81 1.00000000 [4,] 7.487846e-92 1.497569e-91 1.00000000 [5,] 7.673098e-110 1.534620e-109 1.00000000 [6,] 2.612553e-121 5.225106e-121 1.00000000 [7,] 8.543645e-04 1.708729e-03 0.99914564 [8,] 2.996577e-04 5.993155e-04 0.99970034 [9,] 9.836953e-05 1.967391e-04 0.99990163 [10,] 2.963263e-05 5.926525e-05 0.99997037 [11,] 1.496413e-05 2.992827e-05 0.99998504 [12,] 4.741391e-06 9.482781e-06 0.99999526 [13,] 6.655941e-05 1.331188e-04 0.99993344 [14,] 8.012915e-05 1.602583e-04 0.99991987 [15,] 1.683900e-03 3.367800e-03 0.99831610 [16,] 1.626722e-03 3.253443e-03 0.99837328 [17,] 2.299419e-03 4.598838e-03 0.99770058 [18,] 1.891960e-03 3.783920e-03 0.99810804 [19,] 1.496786e-03 2.993572e-03 0.99850321 [20,] 8.716899e-04 1.743380e-03 0.99912831 [21,] 4.655412e-04 9.310824e-04 0.99953446 [22,] 2.461639e-04 4.923279e-04 0.99975384 [23,] 1.524320e-04 3.048639e-04 0.99984757 [24,] 1.996534e-04 3.993069e-04 0.99980035 [25,] 2.411187e-04 4.822375e-04 0.99975888 [26,] 1.741719e-04 3.483439e-04 0.99982583 [27,] 1.201616e-04 2.403231e-04 0.99987984 [28,] 8.235532e-05 1.647106e-04 0.99991764 [29,] 6.646361e-05 1.329272e-04 0.99993354 [30,] 7.048632e-05 1.409726e-04 0.99992951 [31,] 9.582466e-05 1.916493e-04 0.99990418 [32,] 1.106809e-04 2.213618e-04 0.99988932 [33,] 6.685127e-05 1.337025e-04 0.99993315 [34,] 4.308011e-05 8.616021e-05 0.99995692 [35,] 2.781390e-05 5.562780e-05 0.99997219 [36,] 1.852904e-05 3.705809e-05 0.99998147 [37,] 6.832380e-04 1.366476e-03 0.99931676 [38,] 6.194649e-04 1.238930e-03 0.99938054 [39,] 6.938531e-04 1.387706e-03 0.99930615 [40,] 5.897099e-04 1.179420e-03 0.99941029 [41,] 3.946783e-04 7.893566e-04 0.99960532 [42,] 3.907152e-04 7.814304e-04 0.99960928 [43,] 2.501428e-04 5.002855e-04 0.99974986 [44,] 1.649035e-04 3.298070e-04 0.99983510 [45,] 1.875926e-04 3.751852e-04 0.99981241 [46,] 1.388421e-04 2.776842e-04 0.99986116 [47,] 8.392411e-05 1.678482e-04 0.99991608 [48,] 5.520846e-05 1.104169e-04 0.99994479 [49,] 3.255314e-05 6.510628e-05 0.99996745 [50,] 8.888459e-05 1.777692e-04 0.99991112 [51,] 1.181805e-04 2.363610e-04 0.99988182 [52,] 8.744604e-05 1.748921e-04 0.99991255 [53,] 5.567977e-05 1.113595e-04 0.99994432 [54,] 4.396856e-05 8.793712e-05 0.99995603 [55,] 2.591124e-05 5.182249e-05 0.99997409 [56,] 1.836978e-05 3.673955e-05 0.99998163 [57,] 1.369521e-05 2.739043e-05 0.99998630 [58,] 7.932255e-06 1.586451e-05 0.99999207 [59,] 5.259831e-06 1.051966e-05 0.99999474 [60,] 3.355904e-06 6.711809e-06 0.99999664 [61,] 2.341446e-06 4.682893e-06 0.99999766 [62,] 2.935642e-06 5.871283e-06 0.99999706 [63,] 6.210133e-06 1.242027e-05 0.99999379 [64,] 4.952374e-06 9.904748e-06 0.99999505 [65,] 4.292510e-06 8.585020e-06 0.99999571 [66,] 3.226186e-06 6.452373e-06 0.99999677 [67,] 2.636028e-06 5.272056e-06 0.99999736 [68,] 3.334371e-06 6.668742e-06 0.99999667 [69,] 2.123288e-06 4.246575e-06 0.99999788 [70,] 1.425792e-06 2.851584e-06 0.99999857 [71,] 9.282131e-07 1.856426e-06 0.99999907 [72,] 5.807345e-07 1.161469e-06 0.99999942 [73,] 3.611052e-07 7.222104e-07 0.99999964 [74,] 2.011918e-07 4.023836e-07 0.99999980 [75,] 1.239011e-07 2.478023e-07 0.99999988 [76,] 7.365632e-08 1.473126e-07 0.99999993 [77,] 5.350922e-08 1.070184e-07 0.99999995 [78,] 5.157132e-08 1.031426e-07 0.99999995 [79,] 6.702548e-08 1.340510e-07 0.99999993 [80,] 1.311891e-07 2.623783e-07 0.99999987 [81,] 2.086355e-07 4.172710e-07 0.99999979 [82,] 3.909340e-07 7.818679e-07 0.99999961 [83,] 9.480702e-07 1.896140e-06 0.99999905 [84,] 7.013525e-07 1.402705e-06 0.99999930 [85,] 1.605313e-06 3.210627e-06 0.99999839 [86,] 1.252240e-06 2.504480e-06 0.99999875 [87,] 7.507246e-07 1.501449e-06 0.99999925 [88,] 1.590232e-06 3.180464e-06 0.99999841 [89,] 1.063281e-06 2.126561e-06 0.99999894 [90,] 1.297834e-06 2.595667e-06 0.99999870 [91,] 1.310244e-06 2.620489e-06 0.99999869 [92,] 8.647464e-07 1.729493e-06 0.99999914 [93,] 1.245588e-06 2.491176e-06 0.99999875 [94,] 7.160026e-07 1.432005e-06 0.99999928 [95,] 5.216211e-07 1.043242e-06 0.99999948 [96,] 1.337866e-06 2.675732e-06 0.99999866 [97,] 8.514498e-06 1.702900e-05 0.99999149 [98,] 2.159221e-05 4.318442e-05 0.99997841 [99,] 1.360114e-05 2.720227e-05 0.99998640 [100,] 9.316273e-06 1.863255e-05 0.99999068 [101,] 6.463960e-06 1.292792e-05 0.99999354 [102,] 6.425684e-06 1.285137e-05 0.99999357 [103,] 1.359043e-05 2.718087e-05 0.99998641 [104,] 1.566585e-04 3.133170e-04 0.99984334 [105,] 3.933754e-04 7.867508e-04 0.99960662 [106,] 5.552122e-04 1.110424e-03 0.99944479 [107,] 5.894905e-04 1.178981e-03 0.99941051 [108,] 4.176382e-04 8.352764e-04 0.99958236 [109,] 4.050382e-04 8.100765e-04 0.99959496 [110,] 1.811773e-03 3.623546e-03 0.99818823 [111,] 2.146756e-03 4.293513e-03 0.99785324 [112,] 2.077444e-03 4.154888e-03 0.99792256 [113,] 2.384514e-03 4.769028e-03 0.99761549 [114,] 2.198829e-03 4.397658e-03 0.99780117 [115,] 1.471260e-03 2.942520e-03 0.99852874 [116,] 1.637134e-03 3.274269e-03 0.99836287 [117,] 1.443352e-03 2.886704e-03 0.99855665 [118,] 1.478965e-03 2.957930e-03 0.99852103 [119,] 1.286974e-03 2.573949e-03 0.99871303 [120,] 4.795564e-03 9.591128e-03 0.99520444 [121,] 4.204150e-03 8.408300e-03 0.99579585 [122,] 3.262579e-03 6.525158e-03 0.99673742 [123,] 2.290641e-03 4.581281e-03 0.99770936 [124,] 1.467080e-03 2.934160e-03 0.99853292 [125,] 1.365001e-03 2.730001e-03 0.99863500 [126,] 9.006569e-04 1.801314e-03 0.99909934 [127,] 1.052251e-03 2.104503e-03 0.99894775 [128,] 3.025265e-03 6.050529e-03 0.99697474 [129,] 3.319626e-02 6.639252e-02 0.96680374 [130,] 3.186693e-02 6.373386e-02 0.96813307 [131,] 3.680197e-02 7.360395e-02 0.96319803 [132,] 2.750580e-02 5.501159e-02 0.97249420 [133,] 1.252429e-01 2.504857e-01 0.87475713 [134,] 1.010982e-01 2.021964e-01 0.89890179 [135,] 3.068025e-01 6.136050e-01 0.69319748 [136,] 2.552068e-01 5.104137e-01 0.74479317 [137,] 2.118363e-01 4.236726e-01 0.78816371 [138,] 1.753820e-01 3.507640e-01 0.82461798 [139,] 1.629334e-01 3.258668e-01 0.83706660 [140,] 2.647592e-01 5.295185e-01 0.73524076 [141,] 5.057228e-01 9.885543e-01 0.49427715 [142,] 6.393910e-01 7.212179e-01 0.36060897 [143,] 9.246541e-01 1.506919e-01 0.07534595 [144,] 8.808272e-01 2.383455e-01 0.11917276 [145,] 9.367200e-01 1.265601e-01 0.06328004 [146,] 8.566303e-01 2.867394e-01 0.14336971 > postscript(file="/var/fisher/rcomp/tmp/1utbo1386503693.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$resid) > grid() > dev.off() null device 1 > postscript(file="/var/fisher/rcomp/tmp/25jnd1386503693.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(mysum$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/3s3sl1386503693.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') > grid() > dev.off() null device 1 > postscript(file="/var/fisher/rcomp/tmp/4rf5m1386503693.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') > dev.off() null device 1 > postscript(file="/var/fisher/rcomp/tmp/5uerh1386503693.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 0.020237114 -0.060009283 0.068735226 -0.044073427 0.193638597 0.046978094 7 8 9 10 11 12 0.170836851 0.387344023 0.042941253 -0.156564024 -0.104080142 -0.232933609 13 14 15 16 17 18 0.553489969 0.123351736 0.293102957 0.286812855 0.461909002 -0.338409038 19 20 21 22 23 24 -0.297367471 0.046021514 -0.081763813 0.110843948 -0.106625326 0.125550714 25 26 27 28 29 30 0.192155389 0.080987400 0.194890801 0.200591565 0.357392112 0.314875317 31 32 33 34 35 36 -0.311941665 -0.176845070 -0.235515457 -0.168604237 -0.121596763 -0.226265303 37 38 39 40 41 42 0.186125700 0.157074289 0.376809757 0.224507577 0.371725486 0.536185418 43 44 45 46 47 48 -0.234415928 -0.202829771 -0.025266635 -0.099081779 -0.048257950 0.030564065 49 50 51 52 53 54 -0.317604229 -0.424936962 -0.415118344 -0.410603276 -0.401264477 -0.529848051 55 56 57 58 59 60 0.155881373 0.210168311 0.121643655 0.228385612 0.211158215 0.357333789 61 62 63 64 65 66 -0.383185948 -0.284648917 -0.269771389 -0.206423157 -0.137344445 -0.301142647 67 68 69 70 71 72 0.125228074 0.155723821 0.119279068 0.104926151 0.187734915 -0.050261573 73 74 75 76 77 78 0.108620757 0.101400252 -0.059186428 -0.075460171 -0.081208823 -0.030513290 79 80 81 82 83 84 0.057765762 -0.068553164 -0.167819620 -0.107109578 -0.011002902 0.294363968 85 86 87 88 89 90 -0.112979339 0.104078058 0.264733517 0.017884697 -0.030622465 -0.316063550 91 92 93 94 95 96 -0.256625343 0.238263411 0.259619602 0.171041823 0.213451452 0.251132524 97 98 99 100 101 102 0.218388280 -0.035429561 0.192978219 0.032610095 -0.053832363 -0.001658070 103 104 105 106 107 108 -0.062204332 0.381001805 0.402857540 0.420416397 0.452578457 0.301178786 109 110 111 112 113 114 0.362897092 0.170564069 -0.012258844 0.458224060 0.238050775 0.330901120 115 116 117 118 119 120 0.225405593 0.056123652 0.290249746 -0.057158717 0.133179652 0.274008849 121 122 123 124 125 126 0.461897589 0.012907812 0.056003387 0.326028651 0.395215079 0.390681546 127 128 129 130 131 132 0.369767237 0.382574360 0.587232112 0.208789068 0.209785950 0.120635867 133 134 135 136 137 138 -0.064956441 0.350573544 0.039049364 0.039459768 -0.174255372 -0.175442009 139 140 141 142 143 144 0.057687473 0.211701193 0.090339293 0.166849289 0.269871855 0.305468264 145 146 147 148 149 150 0.453975842 0.154785119 -0.315349207 -0.077675431 -0.206594174 0.141891335 151 152 153 154 155 156 0.065264440 0.016893285 0.036977422 0.133122823 0.067691290 -0.006284525 157 158 159 160 161 162 0.250725290 -0.212893350 0.113240682 0.131458854 -0.140556657 -0.041061948 163 164 165 166 167 168 0.081484366 0.231465692 -0.364842566 -0.443169966 -0.206044707 -0.060958598 169 170 171 172 173 174 -0.884849693 -0.176505258 -0.068951405 -0.804733376 -0.859847482 -0.903740455 175 176 177 178 179 180 -0.883740268 -0.858033450 -0.808415767 0.343189535 0.264870844 0.050101651 181 182 183 184 185 186 0.237893139 0.108855685 0.257138364 -0.606097223 -0.635264892 -0.616985602 187 188 189 190 191 192 -0.378459351 -0.465225775 -0.422687830 -0.442634571 -0.640622746 -0.672476830 193 194 195 0.138197603 -0.338932926 -0.586277167 > postscript(file="/var/fisher/rcomp/tmp/6fohb1386503693.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 0.020237114 NA 1 -0.060009283 0.020237114 2 0.068735226 -0.060009283 3 -0.044073427 0.068735226 4 0.193638597 -0.044073427 5 0.046978094 0.193638597 6 0.170836851 0.046978094 7 0.387344023 0.170836851 8 0.042941253 0.387344023 9 -0.156564024 0.042941253 10 -0.104080142 -0.156564024 11 -0.232933609 -0.104080142 12 0.553489969 -0.232933609 13 0.123351736 0.553489969 14 0.293102957 0.123351736 15 0.286812855 0.293102957 16 0.461909002 0.286812855 17 -0.338409038 0.461909002 18 -0.297367471 -0.338409038 19 0.046021514 -0.297367471 20 -0.081763813 0.046021514 21 0.110843948 -0.081763813 22 -0.106625326 0.110843948 23 0.125550714 -0.106625326 24 0.192155389 0.125550714 25 0.080987400 0.192155389 26 0.194890801 0.080987400 27 0.200591565 0.194890801 28 0.357392112 0.200591565 29 0.314875317 0.357392112 30 -0.311941665 0.314875317 31 -0.176845070 -0.311941665 32 -0.235515457 -0.176845070 33 -0.168604237 -0.235515457 34 -0.121596763 -0.168604237 35 -0.226265303 -0.121596763 36 0.186125700 -0.226265303 37 0.157074289 0.186125700 38 0.376809757 0.157074289 39 0.224507577 0.376809757 40 0.371725486 0.224507577 41 0.536185418 0.371725486 42 -0.234415928 0.536185418 43 -0.202829771 -0.234415928 44 -0.025266635 -0.202829771 45 -0.099081779 -0.025266635 46 -0.048257950 -0.099081779 47 0.030564065 -0.048257950 48 -0.317604229 0.030564065 49 -0.424936962 -0.317604229 50 -0.415118344 -0.424936962 51 -0.410603276 -0.415118344 52 -0.401264477 -0.410603276 53 -0.529848051 -0.401264477 54 0.155881373 -0.529848051 55 0.210168311 0.155881373 56 0.121643655 0.210168311 57 0.228385612 0.121643655 58 0.211158215 0.228385612 59 0.357333789 0.211158215 60 -0.383185948 0.357333789 61 -0.284648917 -0.383185948 62 -0.269771389 -0.284648917 63 -0.206423157 -0.269771389 64 -0.137344445 -0.206423157 65 -0.301142647 -0.137344445 66 0.125228074 -0.301142647 67 0.155723821 0.125228074 68 0.119279068 0.155723821 69 0.104926151 0.119279068 70 0.187734915 0.104926151 71 -0.050261573 0.187734915 72 0.108620757 -0.050261573 73 0.101400252 0.108620757 74 -0.059186428 0.101400252 75 -0.075460171 -0.059186428 76 -0.081208823 -0.075460171 77 -0.030513290 -0.081208823 78 0.057765762 -0.030513290 79 -0.068553164 0.057765762 80 -0.167819620 -0.068553164 81 -0.107109578 -0.167819620 82 -0.011002902 -0.107109578 83 0.294363968 -0.011002902 84 -0.112979339 0.294363968 85 0.104078058 -0.112979339 86 0.264733517 0.104078058 87 0.017884697 0.264733517 88 -0.030622465 0.017884697 89 -0.316063550 -0.030622465 90 -0.256625343 -0.316063550 91 0.238263411 -0.256625343 92 0.259619602 0.238263411 93 0.171041823 0.259619602 94 0.213451452 0.171041823 95 0.251132524 0.213451452 96 0.218388280 0.251132524 97 -0.035429561 0.218388280 98 0.192978219 -0.035429561 99 0.032610095 0.192978219 100 -0.053832363 0.032610095 101 -0.001658070 -0.053832363 102 -0.062204332 -0.001658070 103 0.381001805 -0.062204332 104 0.402857540 0.381001805 105 0.420416397 0.402857540 106 0.452578457 0.420416397 107 0.301178786 0.452578457 108 0.362897092 0.301178786 109 0.170564069 0.362897092 110 -0.012258844 0.170564069 111 0.458224060 -0.012258844 112 0.238050775 0.458224060 113 0.330901120 0.238050775 114 0.225405593 0.330901120 115 0.056123652 0.225405593 116 0.290249746 0.056123652 117 -0.057158717 0.290249746 118 0.133179652 -0.057158717 119 0.274008849 0.133179652 120 0.461897589 0.274008849 121 0.012907812 0.461897589 122 0.056003387 0.012907812 123 0.326028651 0.056003387 124 0.395215079 0.326028651 125 0.390681546 0.395215079 126 0.369767237 0.390681546 127 0.382574360 0.369767237 128 0.587232112 0.382574360 129 0.208789068 0.587232112 130 0.209785950 0.208789068 131 0.120635867 0.209785950 132 -0.064956441 0.120635867 133 0.350573544 -0.064956441 134 0.039049364 0.350573544 135 0.039459768 0.039049364 136 -0.174255372 0.039459768 137 -0.175442009 -0.174255372 138 0.057687473 -0.175442009 139 0.211701193 0.057687473 140 0.090339293 0.211701193 141 0.166849289 0.090339293 142 0.269871855 0.166849289 143 0.305468264 0.269871855 144 0.453975842 0.305468264 145 0.154785119 0.453975842 146 -0.315349207 0.154785119 147 -0.077675431 -0.315349207 148 -0.206594174 -0.077675431 149 0.141891335 -0.206594174 150 0.065264440 0.141891335 151 0.016893285 0.065264440 152 0.036977422 0.016893285 153 0.133122823 0.036977422 154 0.067691290 0.133122823 155 -0.006284525 0.067691290 156 0.250725290 -0.006284525 157 -0.212893350 0.250725290 158 0.113240682 -0.212893350 159 0.131458854 0.113240682 160 -0.140556657 0.131458854 161 -0.041061948 -0.140556657 162 0.081484366 -0.041061948 163 0.231465692 0.081484366 164 -0.364842566 0.231465692 165 -0.443169966 -0.364842566 166 -0.206044707 -0.443169966 167 -0.060958598 -0.206044707 168 -0.884849693 -0.060958598 169 -0.176505258 -0.884849693 170 -0.068951405 -0.176505258 171 -0.804733376 -0.068951405 172 -0.859847482 -0.804733376 173 -0.903740455 -0.859847482 174 -0.883740268 -0.903740455 175 -0.858033450 -0.883740268 176 -0.808415767 -0.858033450 177 0.343189535 -0.808415767 178 0.264870844 0.343189535 179 0.050101651 0.264870844 180 0.237893139 0.050101651 181 0.108855685 0.237893139 182 0.257138364 0.108855685 183 -0.606097223 0.257138364 184 -0.635264892 -0.606097223 185 -0.616985602 -0.635264892 186 -0.378459351 -0.616985602 187 -0.465225775 -0.378459351 188 -0.422687830 -0.465225775 189 -0.442634571 -0.422687830 190 -0.640622746 -0.442634571 191 -0.672476830 -0.640622746 192 0.138197603 -0.672476830 193 -0.338932926 0.138197603 194 -0.586277167 -0.338932926 195 NA -0.586277167 > dum1 <- dum[2:length(myerror),] > dum1 lag(myerror, k = 1) myerror [1,] -0.060009283 0.020237114 [2,] 0.068735226 -0.060009283 [3,] -0.044073427 0.068735226 [4,] 0.193638597 -0.044073427 [5,] 0.046978094 0.193638597 [6,] 0.170836851 0.046978094 [7,] 0.387344023 0.170836851 [8,] 0.042941253 0.387344023 [9,] -0.156564024 0.042941253 [10,] -0.104080142 -0.156564024 [11,] -0.232933609 -0.104080142 [12,] 0.553489969 -0.232933609 [13,] 0.123351736 0.553489969 [14,] 0.293102957 0.123351736 [15,] 0.286812855 0.293102957 [16,] 0.461909002 0.286812855 [17,] -0.338409038 0.461909002 [18,] -0.297367471 -0.338409038 [19,] 0.046021514 -0.297367471 [20,] -0.081763813 0.046021514 [21,] 0.110843948 -0.081763813 [22,] -0.106625326 0.110843948 [23,] 0.125550714 -0.106625326 [24,] 0.192155389 0.125550714 [25,] 0.080987400 0.192155389 [26,] 0.194890801 0.080987400 [27,] 0.200591565 0.194890801 [28,] 0.357392112 0.200591565 [29,] 0.314875317 0.357392112 [30,] -0.311941665 0.314875317 [31,] -0.176845070 -0.311941665 [32,] -0.235515457 -0.176845070 [33,] -0.168604237 -0.235515457 [34,] -0.121596763 -0.168604237 [35,] -0.226265303 -0.121596763 [36,] 0.186125700 -0.226265303 [37,] 0.157074289 0.186125700 [38,] 0.376809757 0.157074289 [39,] 0.224507577 0.376809757 [40,] 0.371725486 0.224507577 [41,] 0.536185418 0.371725486 [42,] -0.234415928 0.536185418 [43,] -0.202829771 -0.234415928 [44,] -0.025266635 -0.202829771 [45,] -0.099081779 -0.025266635 [46,] -0.048257950 -0.099081779 [47,] 0.030564065 -0.048257950 [48,] -0.317604229 0.030564065 [49,] -0.424936962 -0.317604229 [50,] -0.415118344 -0.424936962 [51,] -0.410603276 -0.415118344 [52,] -0.401264477 -0.410603276 [53,] -0.529848051 -0.401264477 [54,] 0.155881373 -0.529848051 [55,] 0.210168311 0.155881373 [56,] 0.121643655 0.210168311 [57,] 0.228385612 0.121643655 [58,] 0.211158215 0.228385612 [59,] 0.357333789 0.211158215 [60,] -0.383185948 0.357333789 [61,] -0.284648917 -0.383185948 [62,] -0.269771389 -0.284648917 [63,] -0.206423157 -0.269771389 [64,] -0.137344445 -0.206423157 [65,] -0.301142647 -0.137344445 [66,] 0.125228074 -0.301142647 [67,] 0.155723821 0.125228074 [68,] 0.119279068 0.155723821 [69,] 0.104926151 0.119279068 [70,] 0.187734915 0.104926151 [71,] -0.050261573 0.187734915 [72,] 0.108620757 -0.050261573 [73,] 0.101400252 0.108620757 [74,] -0.059186428 0.101400252 [75,] -0.075460171 -0.059186428 [76,] -0.081208823 -0.075460171 [77,] -0.030513290 -0.081208823 [78,] 0.057765762 -0.030513290 [79,] -0.068553164 0.057765762 [80,] -0.167819620 -0.068553164 [81,] -0.107109578 -0.167819620 [82,] -0.011002902 -0.107109578 [83,] 0.294363968 -0.011002902 [84,] -0.112979339 0.294363968 [85,] 0.104078058 -0.112979339 [86,] 0.264733517 0.104078058 [87,] 0.017884697 0.264733517 [88,] -0.030622465 0.017884697 [89,] -0.316063550 -0.030622465 [90,] -0.256625343 -0.316063550 [91,] 0.238263411 -0.256625343 [92,] 0.259619602 0.238263411 [93,] 0.171041823 0.259619602 [94,] 0.213451452 0.171041823 [95,] 0.251132524 0.213451452 [96,] 0.218388280 0.251132524 [97,] -0.035429561 0.218388280 [98,] 0.192978219 -0.035429561 [99,] 0.032610095 0.192978219 [100,] -0.053832363 0.032610095 [101,] -0.001658070 -0.053832363 [102,] -0.062204332 -0.001658070 [103,] 0.381001805 -0.062204332 [104,] 0.402857540 0.381001805 [105,] 0.420416397 0.402857540 [106,] 0.452578457 0.420416397 [107,] 0.301178786 0.452578457 [108,] 0.362897092 0.301178786 [109,] 0.170564069 0.362897092 [110,] -0.012258844 0.170564069 [111,] 0.458224060 -0.012258844 [112,] 0.238050775 0.458224060 [113,] 0.330901120 0.238050775 [114,] 0.225405593 0.330901120 [115,] 0.056123652 0.225405593 [116,] 0.290249746 0.056123652 [117,] -0.057158717 0.290249746 [118,] 0.133179652 -0.057158717 [119,] 0.274008849 0.133179652 [120,] 0.461897589 0.274008849 [121,] 0.012907812 0.461897589 [122,] 0.056003387 0.012907812 [123,] 0.326028651 0.056003387 [124,] 0.395215079 0.326028651 [125,] 0.390681546 0.395215079 [126,] 0.369767237 0.390681546 [127,] 0.382574360 0.369767237 [128,] 0.587232112 0.382574360 [129,] 0.208789068 0.587232112 [130,] 0.209785950 0.208789068 [131,] 0.120635867 0.209785950 [132,] -0.064956441 0.120635867 [133,] 0.350573544 -0.064956441 [134,] 0.039049364 0.350573544 [135,] 0.039459768 0.039049364 [136,] -0.174255372 0.039459768 [137,] -0.175442009 -0.174255372 [138,] 0.057687473 -0.175442009 [139,] 0.211701193 0.057687473 [140,] 0.090339293 0.211701193 [141,] 0.166849289 0.090339293 [142,] 0.269871855 0.166849289 [143,] 0.305468264 0.269871855 [144,] 0.453975842 0.305468264 [145,] 0.154785119 0.453975842 [146,] -0.315349207 0.154785119 [147,] -0.077675431 -0.315349207 [148,] -0.206594174 -0.077675431 [149,] 0.141891335 -0.206594174 [150,] 0.065264440 0.141891335 [151,] 0.016893285 0.065264440 [152,] 0.036977422 0.016893285 [153,] 0.133122823 0.036977422 [154,] 0.067691290 0.133122823 [155,] -0.006284525 0.067691290 [156,] 0.250725290 -0.006284525 [157,] -0.212893350 0.250725290 [158,] 0.113240682 -0.212893350 [159,] 0.131458854 0.113240682 [160,] -0.140556657 0.131458854 [161,] -0.041061948 -0.140556657 [162,] 0.081484366 -0.041061948 [163,] 0.231465692 0.081484366 [164,] -0.364842566 0.231465692 [165,] -0.443169966 -0.364842566 [166,] -0.206044707 -0.443169966 [167,] -0.060958598 -0.206044707 [168,] -0.884849693 -0.060958598 [169,] -0.176505258 -0.884849693 [170,] -0.068951405 -0.176505258 [171,] -0.804733376 -0.068951405 [172,] -0.859847482 -0.804733376 [173,] -0.903740455 -0.859847482 [174,] -0.883740268 -0.903740455 [175,] -0.858033450 -0.883740268 [176,] -0.808415767 -0.858033450 [177,] 0.343189535 -0.808415767 [178,] 0.264870844 0.343189535 [179,] 0.050101651 0.264870844 [180,] 0.237893139 0.050101651 [181,] 0.108855685 0.237893139 [182,] 0.257138364 0.108855685 [183,] -0.606097223 0.257138364 [184,] -0.635264892 -0.606097223 [185,] -0.616985602 -0.635264892 [186,] -0.378459351 -0.616985602 [187,] -0.465225775 -0.378459351 [188,] -0.422687830 -0.465225775 [189,] -0.442634571 -0.422687830 [190,] -0.640622746 -0.442634571 [191,] -0.672476830 -0.640622746 [192,] 0.138197603 -0.672476830 [193,] -0.338932926 0.138197603 [194,] -0.586277167 -0.338932926 > z <- as.data.frame(dum1) > z lag(myerror, k = 1) myerror 1 -0.060009283 0.020237114 2 0.068735226 -0.060009283 3 -0.044073427 0.068735226 4 0.193638597 -0.044073427 5 0.046978094 0.193638597 6 0.170836851 0.046978094 7 0.387344023 0.170836851 8 0.042941253 0.387344023 9 -0.156564024 0.042941253 10 -0.104080142 -0.156564024 11 -0.232933609 -0.104080142 12 0.553489969 -0.232933609 13 0.123351736 0.553489969 14 0.293102957 0.123351736 15 0.286812855 0.293102957 16 0.461909002 0.286812855 17 -0.338409038 0.461909002 18 -0.297367471 -0.338409038 19 0.046021514 -0.297367471 20 -0.081763813 0.046021514 21 0.110843948 -0.081763813 22 -0.106625326 0.110843948 23 0.125550714 -0.106625326 24 0.192155389 0.125550714 25 0.080987400 0.192155389 26 0.194890801 0.080987400 27 0.200591565 0.194890801 28 0.357392112 0.200591565 29 0.314875317 0.357392112 30 -0.311941665 0.314875317 31 -0.176845070 -0.311941665 32 -0.235515457 -0.176845070 33 -0.168604237 -0.235515457 34 -0.121596763 -0.168604237 35 -0.226265303 -0.121596763 36 0.186125700 -0.226265303 37 0.157074289 0.186125700 38 0.376809757 0.157074289 39 0.224507577 0.376809757 40 0.371725486 0.224507577 41 0.536185418 0.371725486 42 -0.234415928 0.536185418 43 -0.202829771 -0.234415928 44 -0.025266635 -0.202829771 45 -0.099081779 -0.025266635 46 -0.048257950 -0.099081779 47 0.030564065 -0.048257950 48 -0.317604229 0.030564065 49 -0.424936962 -0.317604229 50 -0.415118344 -0.424936962 51 -0.410603276 -0.415118344 52 -0.401264477 -0.410603276 53 -0.529848051 -0.401264477 54 0.155881373 -0.529848051 55 0.210168311 0.155881373 56 0.121643655 0.210168311 57 0.228385612 0.121643655 58 0.211158215 0.228385612 59 0.357333789 0.211158215 60 -0.383185948 0.357333789 61 -0.284648917 -0.383185948 62 -0.269771389 -0.284648917 63 -0.206423157 -0.269771389 64 -0.137344445 -0.206423157 65 -0.301142647 -0.137344445 66 0.125228074 -0.301142647 67 0.155723821 0.125228074 68 0.119279068 0.155723821 69 0.104926151 0.119279068 70 0.187734915 0.104926151 71 -0.050261573 0.187734915 72 0.108620757 -0.050261573 73 0.101400252 0.108620757 74 -0.059186428 0.101400252 75 -0.075460171 -0.059186428 76 -0.081208823 -0.075460171 77 -0.030513290 -0.081208823 78 0.057765762 -0.030513290 79 -0.068553164 0.057765762 80 -0.167819620 -0.068553164 81 -0.107109578 -0.167819620 82 -0.011002902 -0.107109578 83 0.294363968 -0.011002902 84 -0.112979339 0.294363968 85 0.104078058 -0.112979339 86 0.264733517 0.104078058 87 0.017884697 0.264733517 88 -0.030622465 0.017884697 89 -0.316063550 -0.030622465 90 -0.256625343 -0.316063550 91 0.238263411 -0.256625343 92 0.259619602 0.238263411 93 0.171041823 0.259619602 94 0.213451452 0.171041823 95 0.251132524 0.213451452 96 0.218388280 0.251132524 97 -0.035429561 0.218388280 98 0.192978219 -0.035429561 99 0.032610095 0.192978219 100 -0.053832363 0.032610095 101 -0.001658070 -0.053832363 102 -0.062204332 -0.001658070 103 0.381001805 -0.062204332 104 0.402857540 0.381001805 105 0.420416397 0.402857540 106 0.452578457 0.420416397 107 0.301178786 0.452578457 108 0.362897092 0.301178786 109 0.170564069 0.362897092 110 -0.012258844 0.170564069 111 0.458224060 -0.012258844 112 0.238050775 0.458224060 113 0.330901120 0.238050775 114 0.225405593 0.330901120 115 0.056123652 0.225405593 116 0.290249746 0.056123652 117 -0.057158717 0.290249746 118 0.133179652 -0.057158717 119 0.274008849 0.133179652 120 0.461897589 0.274008849 121 0.012907812 0.461897589 122 0.056003387 0.012907812 123 0.326028651 0.056003387 124 0.395215079 0.326028651 125 0.390681546 0.395215079 126 0.369767237 0.390681546 127 0.382574360 0.369767237 128 0.587232112 0.382574360 129 0.208789068 0.587232112 130 0.209785950 0.208789068 131 0.120635867 0.209785950 132 -0.064956441 0.120635867 133 0.350573544 -0.064956441 134 0.039049364 0.350573544 135 0.039459768 0.039049364 136 -0.174255372 0.039459768 137 -0.175442009 -0.174255372 138 0.057687473 -0.175442009 139 0.211701193 0.057687473 140 0.090339293 0.211701193 141 0.166849289 0.090339293 142 0.269871855 0.166849289 143 0.305468264 0.269871855 144 0.453975842 0.305468264 145 0.154785119 0.453975842 146 -0.315349207 0.154785119 147 -0.077675431 -0.315349207 148 -0.206594174 -0.077675431 149 0.141891335 -0.206594174 150 0.065264440 0.141891335 151 0.016893285 0.065264440 152 0.036977422 0.016893285 153 0.133122823 0.036977422 154 0.067691290 0.133122823 155 -0.006284525 0.067691290 156 0.250725290 -0.006284525 157 -0.212893350 0.250725290 158 0.113240682 -0.212893350 159 0.131458854 0.113240682 160 -0.140556657 0.131458854 161 -0.041061948 -0.140556657 162 0.081484366 -0.041061948 163 0.231465692 0.081484366 164 -0.364842566 0.231465692 165 -0.443169966 -0.364842566 166 -0.206044707 -0.443169966 167 -0.060958598 -0.206044707 168 -0.884849693 -0.060958598 169 -0.176505258 -0.884849693 170 -0.068951405 -0.176505258 171 -0.804733376 -0.068951405 172 -0.859847482 -0.804733376 173 -0.903740455 -0.859847482 174 -0.883740268 -0.903740455 175 -0.858033450 -0.883740268 176 -0.808415767 -0.858033450 177 0.343189535 -0.808415767 178 0.264870844 0.343189535 179 0.050101651 0.264870844 180 0.237893139 0.050101651 181 0.108855685 0.237893139 182 0.257138364 0.108855685 183 -0.606097223 0.257138364 184 -0.635264892 -0.606097223 185 -0.616985602 -0.635264892 186 -0.378459351 -0.616985602 187 -0.465225775 -0.378459351 188 -0.422687830 -0.465225775 189 -0.442634571 -0.422687830 190 -0.640622746 -0.442634571 191 -0.672476830 -0.640622746 192 0.138197603 -0.672476830 193 -0.338932926 0.138197603 194 -0.586277167 -0.338932926 > 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/7obhx1386503693.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/fisher/rcomp/tmp/82j5j1386503693.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/fisher/rcomp/tmp/9wque1386503693.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0)) > plot(mylm, las = 1, sub='Residual Diagnostics') > par(opar) > dev.off() null device 1 > if (n > n25) { + postscript(file="/var/fisher/rcomp/tmp/106utf1386503693.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) + plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint') + grid() + dev.off() + } null device 1 > > #Note: the /var/fisher/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab > load(file="/var/fisher/rcomp/createtable") > > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE) > a<-table.row.end(a) > myeq <- colnames(x)[1] > myeq <- paste(myeq, '[t] = ', sep='') > for (i in 1:k){ + if (mysum$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/11skml1386503693.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/12vlmh1386503693.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/13megw1386503694.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/14k7om1386503694.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/15j9td1386503694.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/16nag81386503694.tab") + } > > try(system("convert tmp/1utbo1386503693.ps tmp/1utbo1386503693.png",intern=TRUE)) character(0) > try(system("convert tmp/25jnd1386503693.ps tmp/25jnd1386503693.png",intern=TRUE)) character(0) > try(system("convert tmp/3s3sl1386503693.ps tmp/3s3sl1386503693.png",intern=TRUE)) character(0) > try(system("convert tmp/4rf5m1386503693.ps tmp/4rf5m1386503693.png",intern=TRUE)) character(0) > try(system("convert tmp/5uerh1386503693.ps tmp/5uerh1386503693.png",intern=TRUE)) character(0) > try(system("convert tmp/6fohb1386503693.ps tmp/6fohb1386503693.png",intern=TRUE)) character(0) > try(system("convert tmp/7obhx1386503693.ps tmp/7obhx1386503693.png",intern=TRUE)) character(0) > try(system("convert tmp/82j5j1386503693.ps tmp/82j5j1386503693.png",intern=TRUE)) character(0) > try(system("convert tmp/9wque1386503693.ps tmp/9wque1386503693.png",intern=TRUE)) character(0) > try(system("convert tmp/106utf1386503693.ps tmp/106utf1386503693.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 32.477 5.781 38.253