R version 3.0.2 (2013-09-25) -- "Frisbee Sailing" Copyright (C) 2013 The R Foundation for Statistical Computing Platform: i686-pc-linux-gnu (32-bit) R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > x <- array(list(119.992 + ,157.302 + ,74.997 + ,0.00784 + ,0.00007 + ,0.0037 + ,0.00554 + ,0.01109 + ,0.04374 + ,0.426 + ,0.02182 + ,0.0313 + ,0.02971 + ,0.06545 + ,0.02211 + ,21.033 + ,1 + ,0.414783 + ,0.815285 + ,-4.813031 + ,0.266482 + ,2.301442 + ,0.284654 + ,122.4 + ,148.65 + ,113.819 + ,0.00968 + ,0.00008 + ,0.00465 + ,0.00696 + ,0.01394 + ,0.06134 + ,0.626 + ,0.03134 + ,0.04518 + ,0.04368 + ,0.09403 + ,0.01929 + ,19.085 + ,1 + ,0.458359 + ,0.819521 + ,-4.075192 + ,0.33559 + ,2.486855 + ,0.368674 + ,116.682 + ,131.111 + ,111.555 + ,0.0105 + ,0.00009 + ,0.00544 + ,0.00781 + ,0.01633 + ,0.05233 + ,0.482 + ,0.02757 + ,0.03858 + ,0.0359 + ,0.0827 + ,0.01309 + ,20.651 + ,1 + ,0.429895 + ,0.825288 + ,-4.443179 + ,0.311173 + ,2.342259 + ,0.332634 + ,116.676 + ,137.871 + ,111.366 + ,0.00997 + ,0.00009 + ,0.00502 + ,0.00698 + ,0.01505 + ,0.05492 + ,0.517 + ,0.02924 + ,0.04005 + ,0.03772 + ,0.08771 + ,0.01353 + ,20.644 + ,1 + ,0.434969 + ,0.819235 + ,-4.117501 + 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,0.655683 + ,-6.787197 + ,0.158453 + ,2.679772 + ,0.131728 + ,198.764 + ,396.961 + ,74.904 + ,0.0074 + ,0.00004 + ,0.0037 + ,0.0039 + ,0.01109 + ,0.02296 + ,0.241 + ,0.01265 + ,0.01321 + ,0.01588 + ,0.03794 + ,0.07223 + ,19.02 + ,0 + ,0.451221 + ,0.643956 + ,-6.744577 + ,0.207454 + ,2.138608 + ,0.123306 + ,214.289 + ,260.277 + ,77.973 + ,0.00567 + ,0.00003 + ,0.00295 + ,0.00317 + ,0.00885 + ,0.01884 + ,0.19 + ,0.01026 + ,0.01161 + ,0.01373 + ,0.03078 + ,0.04398 + ,21.209 + ,0 + ,0.462803 + ,0.664357 + ,-5.724056 + ,0.190667 + ,2.555477 + ,0.148569) + ,dim=c(23 + ,195) + ,dimnames=list(c('MDVP:Fo(Hz)' + ,'MDVP:Fhi(Hz)' + ,'MDVP:Flo(Hz)' + ,'MDVP:Jitter(%)' + ,'MDVP:Jitter(Abs)' + ,'MDVP:RAP' + ,'MDVP:PPQ' + ,'Jitter:DDP' + ,'MDVP:Shimmer' + ,'MDVP:Shimmer(dB)' + ,'Shimmer:APQ3' + ,'Shimmer:APQ5' + ,'MDVP:APQ' + ,'Shimmer:DDA' + ,'NHR' + ,'HNR' + ,'status' + ,'RPDE' + ,'DFA' + ,'spread1' + ,'spread2' + ,'D2' + ,'PPE') + ,1:195)) > y <- array(NA,dim=c(23,195),dimnames=list(c('MDVP:Fo(Hz)','MDVP:Fhi(Hz)','MDVP:Flo(Hz)','MDVP:Jitter(%)','MDVP:Jitter(Abs)','MDVP:RAP','MDVP:PPQ','Jitter:DDP','MDVP:Shimmer','MDVP:Shimmer(dB)','Shimmer:APQ3','Shimmer:APQ5','MDVP:APQ','Shimmer:DDA','NHR','HNR','status','RPDE','DFA','spread1','spread2','D2','PPE'),1:195)) > for (i in 1:dim(x)[1]) + { + for (j in 1:dim(x)[2]) + { + y[i,j] <- as.numeric(x[i,j]) + } + } > par3 = 'No Linear Trend' > par2 = 'Do not include Seasonal Dummies' > par1 = '1' > par3 <- 'No Linear Trend' > par2 <- 'Do not include Seasonal Dummies' > par1 <- '1' > #'GNU S' R Code compiled by R2WASP v. 1.2.327 () > #Author: root > #To cite this work: Wessa P., (2013), Multiple Regression (v1.0.29) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_multipleregression.wasp/ > #Source of accompanying publication: Office for Research, Development, and Education > # > library(lattice) > library(lmtest) Loading required package: zoo Attaching package: 'zoo' The following objects are masked from 'package:base': as.Date, as.Date.numeric > n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test > par1 <- as.numeric(par1) > x <- t(y) > k <- length(x[1,]) > n <- length(x[,1]) > x1 <- cbind(x[,par1], x[,1:k!=par1]) > mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1]) > colnames(x1) <- mycolnames #colnames(x)[par1] > x <- x1 > if (par3 == 'First Differences'){ + x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep=''))) + for (i in 1:n-1) { + for (j in 1:k) { + x2[i,j] <- x[i+1,j] - x[i,j] + } + } + x <- x2 + } > if (par2 == 'Include Monthly Dummies'){ + x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep =''))) + for (i in 1:11){ + x2[seq(i,n,12),i] <- 1 + } + x <- cbind(x, x2) + } > if (par2 == 'Include Quarterly Dummies'){ + x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep =''))) + for (i in 1:3){ + x2[seq(i,n,4),i] <- 1 + } + x <- cbind(x, x2) + } > k <- length(x[1,]) > if (par3 == 'Linear Trend'){ + x <- cbind(x, c(1:n)) + colnames(x)[k+1] <- 't' + } > x MDVP:Fo(Hz) MDVP:Fhi(Hz) MDVP:Flo(Hz) MDVP:Jitter(%) MDVP:Jitter(Abs) 1 119.992 157.302 74.997 0.00784 7.0e-05 2 122.400 148.650 113.819 0.00968 8.0e-05 3 116.682 131.111 111.555 0.01050 9.0e-05 4 116.676 137.871 111.366 0.00997 9.0e-05 5 116.014 141.781 110.655 0.01284 1.1e-04 6 120.552 131.162 113.787 0.00968 8.0e-05 7 120.267 137.244 114.820 0.00333 3.0e-05 8 107.332 113.840 104.315 0.00290 3.0e-05 9 95.730 132.068 91.754 0.00551 6.0e-05 10 95.056 120.103 91.226 0.00532 6.0e-05 11 88.333 112.240 84.072 0.00505 6.0e-05 12 91.904 115.871 86.292 0.00540 6.0e-05 13 136.926 159.866 131.276 0.00293 2.0e-05 14 139.173 179.139 76.556 0.00390 3.0e-05 15 152.845 163.305 75.836 0.00294 2.0e-05 16 142.167 217.455 83.159 0.00369 3.0e-05 17 144.188 349.259 82.764 0.00544 4.0e-05 18 168.778 232.181 75.603 0.00718 4.0e-05 19 153.046 175.829 68.623 0.00742 5.0e-05 20 156.405 189.398 142.822 0.00768 5.0e-05 21 153.848 165.738 65.782 0.00840 5.0e-05 22 153.880 172.860 78.128 0.00480 3.0e-05 23 167.930 193.221 79.068 0.00442 3.0e-05 24 173.917 192.735 86.180 0.00476 3.0e-05 25 163.656 200.841 76.779 0.00742 5.0e-05 26 104.400 206.002 77.968 0.00633 6.0e-05 27 171.041 208.313 75.501 0.00455 3.0e-05 28 146.845 208.701 81.737 0.00496 3.0e-05 29 155.358 227.383 80.055 0.00310 2.0e-05 30 162.568 198.346 77.630 0.00502 3.0e-05 31 197.076 206.896 192.055 0.00289 1.0e-05 32 199.228 209.512 192.091 0.00241 1.0e-05 33 198.383 215.203 193.104 0.00212 1.0e-05 34 202.266 211.604 197.079 0.00180 9.0e-06 35 203.184 211.526 196.160 0.00178 9.0e-06 36 201.464 210.565 195.708 0.00198 1.0e-05 37 177.876 192.921 168.013 0.00411 2.0e-05 38 176.170 185.604 163.564 0.00369 2.0e-05 39 180.198 201.249 175.456 0.00284 2.0e-05 40 187.733 202.324 173.015 0.00316 2.0e-05 41 186.163 197.724 177.584 0.00298 2.0e-05 42 184.055 196.537 166.977 0.00258 1.0e-05 43 237.226 247.326 225.227 0.00298 1.0e-05 44 241.404 248.834 232.483 0.00281 1.0e-05 45 243.439 250.912 232.435 0.00210 9.0e-06 46 242.852 255.034 227.911 0.00225 9.0e-06 47 245.510 262.090 231.848 0.00235 1.0e-05 48 252.455 261.487 182.786 0.00185 7.0e-06 49 122.188 128.611 115.765 0.00524 4.0e-05 50 122.964 130.049 114.676 0.00428 3.0e-05 51 124.445 135.069 117.495 0.00431 3.0e-05 52 126.344 134.231 112.773 0.00448 4.0e-05 53 128.001 138.052 122.080 0.00436 3.0e-05 54 129.336 139.867 118.604 0.00490 4.0e-05 55 108.807 134.656 102.874 0.00761 7.0e-05 56 109.860 126.358 104.437 0.00874 8.0e-05 57 110.417 131.067 103.370 0.00784 7.0e-05 58 117.274 129.916 110.402 0.00752 6.0e-05 59 116.879 131.897 108.153 0.00788 7.0e-05 60 114.847 271.314 104.680 0.00867 8.0e-05 61 209.144 237.494 109.379 0.00282 1.0e-05 62 223.365 238.987 98.664 0.00264 1.0e-05 63 222.236 231.345 205.495 0.00266 1.0e-05 64 228.832 234.619 223.634 0.00296 1.0e-05 65 229.401 252.221 221.156 0.00205 9.0e-06 66 228.969 239.541 113.201 0.00238 1.0e-05 67 140.341 159.774 67.021 0.00817 6.0e-05 68 136.969 166.607 66.004 0.00923 7.0e-05 69 143.533 162.215 65.809 0.01101 8.0e-05 70 148.090 162.824 67.343 0.00762 5.0e-05 71 142.729 162.408 65.476 0.00831 6.0e-05 72 136.358 176.595 65.750 0.00971 7.0e-05 73 120.080 139.710 111.208 0.00405 3.0e-05 74 112.014 588.518 107.024 0.00533 5.0e-05 75 110.793 128.101 107.316 0.00494 4.0e-05 76 110.707 122.611 105.007 0.00516 5.0e-05 77 112.876 148.826 106.981 0.00500 4.0e-05 78 110.568 125.394 106.821 0.00462 4.0e-05 79 95.385 102.145 90.264 0.00608 6.0e-05 80 100.770 115.697 85.545 0.01038 1.0e-04 81 96.106 108.664 84.510 0.00694 7.0e-05 82 95.605 107.715 87.549 0.00702 7.0e-05 83 100.960 110.019 95.628 0.00606 6.0e-05 84 98.804 102.305 87.804 0.00432 4.0e-05 85 176.858 205.560 75.344 0.00747 4.0e-05 86 180.978 200.125 155.495 0.00406 2.0e-05 87 178.222 202.450 141.047 0.00321 2.0e-05 88 176.281 227.381 125.610 0.00520 3.0e-05 89 173.898 211.350 74.677 0.00448 3.0e-05 90 179.711 225.930 144.878 0.00709 4.0e-05 91 166.605 206.008 78.032 0.00742 4.0e-05 92 151.955 163.335 147.226 0.00419 3.0e-05 93 148.272 164.989 142.299 0.00459 3.0e-05 94 152.125 161.469 76.596 0.00382 3.0e-05 95 157.821 172.975 68.401 0.00358 2.0e-05 96 157.447 163.267 149.605 0.00369 2.0e-05 97 159.116 168.913 144.811 0.00342 2.0e-05 98 125.036 143.946 116.187 0.01280 1.0e-04 99 125.791 140.557 96.206 0.01378 1.1e-04 100 126.512 141.756 99.770 0.01936 1.5e-04 101 125.641 141.068 116.346 0.03316 2.6e-04 102 128.451 150.449 75.632 0.01551 1.2e-04 103 139.224 586.567 66.157 0.03011 2.2e-04 104 150.258 154.609 75.349 0.00248 2.0e-05 105 154.003 160.267 128.621 0.00183 1.0e-05 106 149.689 160.368 133.608 0.00257 2.0e-05 107 155.078 163.736 144.148 0.00168 1.0e-05 108 151.884 157.765 133.751 0.00258 2.0e-05 109 151.989 157.339 132.857 0.00174 1.0e-05 110 193.030 208.900 80.297 0.00766 4.0e-05 111 200.714 223.982 89.686 0.00621 3.0e-05 112 208.519 220.315 199.020 0.00609 3.0e-05 113 204.664 221.300 189.621 0.00841 4.0e-05 114 210.141 232.706 185.258 0.00534 3.0e-05 115 206.327 226.355 92.020 0.00495 2.0e-05 116 151.872 492.892 69.085 0.00856 6.0e-05 117 158.219 442.557 71.948 0.00476 3.0e-05 118 170.756 450.247 79.032 0.00555 3.0e-05 119 178.285 442.824 82.063 0.00462 3.0e-05 120 217.116 233.481 93.978 0.00404 2.0e-05 121 128.940 479.697 88.251 0.00581 5.0e-05 122 176.824 215.293 83.961 0.00460 3.0e-05 123 138.190 203.522 83.340 0.00704 5.0e-05 124 182.018 197.173 79.187 0.00842 5.0e-05 125 156.239 195.107 79.820 0.00694 4.0e-05 126 145.174 198.109 80.637 0.00733 5.0e-05 127 138.145 197.238 81.114 0.00544 4.0e-05 128 166.888 198.966 79.512 0.00638 4.0e-05 129 119.031 127.533 109.216 0.00440 4.0e-05 130 120.078 126.632 105.667 0.00270 2.0e-05 131 120.289 128.143 100.209 0.00492 4.0e-05 132 120.256 125.306 104.773 0.00407 3.0e-05 133 119.056 125.213 86.795 0.00346 3.0e-05 134 118.747 123.723 109.836 0.00331 3.0e-05 135 106.516 112.777 93.105 0.00589 6.0e-05 136 110.453 127.611 105.554 0.00494 4.0e-05 137 113.400 133.344 107.816 0.00451 4.0e-05 138 113.166 130.270 100.673 0.00502 4.0e-05 139 112.239 126.609 104.095 0.00472 4.0e-05 140 116.150 131.731 109.815 0.00381 3.0e-05 141 170.368 268.796 79.543 0.00571 3.0e-05 142 208.083 253.792 91.802 0.00757 4.0e-05 143 198.458 219.290 148.691 0.00376 2.0e-05 144 202.805 231.508 86.232 0.00370 2.0e-05 145 202.544 241.350 164.168 0.00254 1.0e-05 146 223.361 263.872 87.638 0.00352 2.0e-05 147 169.774 191.759 151.451 0.01568 9.0e-05 148 183.520 216.814 161.340 0.01466 8.0e-05 149 188.620 216.302 165.982 0.01719 9.0e-05 150 202.632 565.740 177.258 0.01627 8.0e-05 151 186.695 211.961 149.442 0.01872 1.0e-04 152 192.818 224.429 168.793 0.03107 1.6e-04 153 198.116 233.099 174.478 0.02714 1.4e-04 154 121.345 139.644 98.250 0.00684 6.0e-05 155 119.100 128.442 88.833 0.00692 6.0e-05 156 117.870 127.349 95.654 0.00647 5.0e-05 157 122.336 142.369 94.794 0.00727 6.0e-05 158 117.963 134.209 100.757 0.01813 1.5e-04 159 126.144 154.284 97.543 0.00975 8.0e-05 160 127.930 138.752 112.173 0.00605 5.0e-05 161 114.238 124.393 77.022 0.00581 5.0e-05 162 115.322 135.738 107.802 0.00619 5.0e-05 163 114.554 126.778 91.121 0.00651 6.0e-05 164 112.150 131.669 97.527 0.00519 5.0e-05 165 102.273 142.830 85.902 0.00907 9.0e-05 166 236.200 244.663 102.137 0.00277 1.0e-05 167 237.323 243.709 229.256 0.00303 1.0e-05 168 260.105 264.919 237.303 0.00339 1.0e-05 169 197.569 217.627 90.794 0.00803 4.0e-05 170 240.301 245.135 219.783 0.00517 2.0e-05 171 244.990 272.210 239.170 0.00451 2.0e-05 172 112.547 133.374 105.715 0.00355 3.0e-05 173 110.739 113.597 100.139 0.00356 3.0e-05 174 113.715 116.443 96.913 0.00349 3.0e-05 175 117.004 144.466 99.923 0.00353 3.0e-05 176 115.380 123.109 108.634 0.00332 3.0e-05 177 116.388 129.038 108.970 0.00346 3.0e-05 178 151.737 190.204 129.859 0.00314 2.0e-05 179 148.790 158.359 138.990 0.00309 2.0e-05 180 148.143 155.982 135.041 0.00392 3.0e-05 181 150.440 163.441 144.736 0.00396 3.0e-05 182 148.462 161.078 141.998 0.00397 3.0e-05 183 149.818 163.417 144.786 0.00336 2.0e-05 184 117.226 123.925 106.656 0.00417 4.0e-05 185 116.848 217.552 99.503 0.00531 5.0e-05 186 116.286 177.291 96.983 0.00314 3.0e-05 187 116.556 592.030 86.228 0.00496 4.0e-05 188 116.342 581.289 94.246 0.00267 2.0e-05 189 114.563 119.167 86.647 0.00327 3.0e-05 190 201.774 262.707 78.228 0.00694 3.0e-05 191 174.188 230.978 94.261 0.00459 3.0e-05 192 209.516 253.017 89.488 0.00564 3.0e-05 193 174.688 240.005 74.287 0.01360 8.0e-05 194 198.764 396.961 74.904 0.00740 4.0e-05 195 214.289 260.277 77.973 0.00567 3.0e-05 MDVP:RAP MDVP:PPQ Jitter:DDP MDVP:Shimmer MDVP:Shimmer(dB) Shimmer:APQ3 1 0.00370 0.00554 0.01109 0.04374 0.426 0.02182 2 0.00465 0.00696 0.01394 0.06134 0.626 0.03134 3 0.00544 0.00781 0.01633 0.05233 0.482 0.02757 4 0.00502 0.00698 0.01505 0.05492 0.517 0.02924 5 0.00655 0.00908 0.01966 0.06425 0.584 0.03490 6 0.00463 0.00750 0.01388 0.04701 0.456 0.02328 7 0.00155 0.00202 0.00466 0.01608 0.140 0.00779 8 0.00144 0.00182 0.00431 0.01567 0.134 0.00829 9 0.00293 0.00332 0.00880 0.02093 0.191 0.01073 10 0.00268 0.00332 0.00803 0.02838 0.255 0.01441 11 0.00254 0.00330 0.00763 0.02143 0.197 0.01079 12 0.00281 0.00336 0.00844 0.02752 0.249 0.01424 13 0.00118 0.00153 0.00355 0.01259 0.112 0.00656 14 0.00165 0.00208 0.00496 0.01642 0.154 0.00728 15 0.00121 0.00149 0.00364 0.01828 0.158 0.01064 16 0.00157 0.00203 0.00471 0.01503 0.126 0.00772 17 0.00211 0.00292 0.00632 0.02047 0.192 0.00969 18 0.00284 0.00387 0.00853 0.03327 0.348 0.01441 19 0.00364 0.00432 0.01092 0.05517 0.542 0.02471 20 0.00372 0.00399 0.01116 0.03995 0.348 0.01721 21 0.00428 0.00450 0.01285 0.03810 0.328 0.01667 22 0.00232 0.00267 0.00696 0.04137 0.370 0.02021 23 0.00220 0.00247 0.00661 0.04351 0.377 0.02228 24 0.00221 0.00258 0.00663 0.04192 0.364 0.02187 25 0.00380 0.00390 0.01140 0.01659 0.164 0.00738 26 0.00316 0.00375 0.00948 0.03767 0.381 0.01732 27 0.00250 0.00234 0.00750 0.01966 0.186 0.00889 28 0.00250 0.00275 0.00749 0.01919 0.198 0.00883 29 0.00159 0.00176 0.00476 0.01718 0.161 0.00769 30 0.00280 0.00253 0.00841 0.01791 0.168 0.00793 31 0.00166 0.00168 0.00498 0.01098 0.097 0.00563 32 0.00134 0.00138 0.00402 0.01015 0.089 0.00504 33 0.00113 0.00135 0.00339 0.01263 0.111 0.00640 34 0.00093 0.00107 0.00278 0.00954 0.085 0.00469 35 0.00094 0.00106 0.00283 0.00958 0.085 0.00468 36 0.00105 0.00115 0.00314 0.01194 0.107 0.00586 37 0.00233 0.00241 0.00700 0.02126 0.189 0.01154 38 0.00205 0.00218 0.00616 0.01851 0.168 0.00938 39 0.00153 0.00166 0.00459 0.01444 0.131 0.00726 40 0.00168 0.00182 0.00504 0.01663 0.151 0.00829 41 0.00165 0.00175 0.00496 0.01495 0.135 0.00774 42 0.00134 0.00147 0.00403 0.01463 0.132 0.00742 43 0.00169 0.00182 0.00507 0.01752 0.164 0.01035 44 0.00157 0.00173 0.00470 0.01760 0.154 0.01006 45 0.00109 0.00137 0.00327 0.01419 0.126 0.00777 46 0.00117 0.00139 0.00350 0.01494 0.134 0.00847 47 0.00127 0.00148 0.00380 0.01608 0.141 0.00906 48 0.00092 0.00113 0.00276 0.01152 0.103 0.00614 49 0.00169 0.00203 0.00507 0.01613 0.143 0.00855 50 0.00124 0.00155 0.00373 0.01681 0.154 0.00930 51 0.00141 0.00167 0.00422 0.02184 0.197 0.01241 52 0.00131 0.00169 0.00393 0.02033 0.185 0.01143 53 0.00137 0.00166 0.00411 0.02297 0.210 0.01323 54 0.00165 0.00183 0.00495 0.02498 0.228 0.01396 55 0.00349 0.00486 0.01046 0.02719 0.255 0.01483 56 0.00398 0.00539 0.01193 0.03209 0.307 0.01789 57 0.00352 0.00514 0.01056 0.03715 0.334 0.02032 58 0.00299 0.00469 0.00898 0.02293 0.221 0.01189 59 0.00334 0.00493 0.01003 0.02645 0.265 0.01394 60 0.00373 0.00520 0.01120 0.03225 0.350 0.01805 61 0.00147 0.00152 0.00442 0.01861 0.170 0.00975 62 0.00154 0.00151 0.00461 0.01906 0.165 0.01013 63 0.00152 0.00144 0.00457 0.01643 0.145 0.00867 64 0.00175 0.00155 0.00526 0.01644 0.145 0.00882 65 0.00114 0.00113 0.00342 0.01457 0.129 0.00769 66 0.00136 0.00140 0.00408 0.01745 0.154 0.00942 67 0.00430 0.00440 0.01289 0.03198 0.313 0.01830 68 0.00507 0.00463 0.01520 0.03111 0.308 0.01638 69 0.00647 0.00467 0.01941 0.05384 0.478 0.03152 70 0.00467 0.00354 0.01400 0.05428 0.497 0.03357 71 0.00469 0.00419 0.01407 0.03485 0.365 0.01868 72 0.00534 0.00478 0.01601 0.04978 0.483 0.02749 73 0.00180 0.00220 0.00540 0.01706 0.152 0.00974 74 0.00268 0.00329 0.00805 0.02448 0.226 0.01373 75 0.00260 0.00283 0.00780 0.02442 0.216 0.01432 76 0.00277 0.00289 0.00831 0.02215 0.206 0.01284 77 0.00270 0.00289 0.00810 0.03999 0.350 0.02413 78 0.00226 0.00280 0.00677 0.02199 0.197 0.01284 79 0.00331 0.00332 0.00994 0.03202 0.263 0.01803 80 0.00622 0.00576 0.01865 0.03121 0.361 0.01773 81 0.00389 0.00415 0.01168 0.04024 0.364 0.02266 82 0.00428 0.00371 0.01283 0.03156 0.296 0.01792 83 0.00351 0.00348 0.01053 0.02427 0.216 0.01371 84 0.00247 0.00258 0.00742 0.02223 0.202 0.01277 85 0.00418 0.00420 0.01254 0.04795 0.435 0.02679 86 0.00220 0.00244 0.00659 0.03852 0.331 0.02107 87 0.00163 0.00194 0.00488 0.03759 0.327 0.02073 88 0.00287 0.00312 0.00862 0.06511 0.580 0.03671 89 0.00237 0.00254 0.00710 0.06727 0.650 0.03788 90 0.00391 0.00419 0.01172 0.04313 0.442 0.02297 91 0.00387 0.00453 0.01161 0.06640 0.634 0.03650 92 0.00224 0.00227 0.00672 0.07959 0.772 0.04421 93 0.00250 0.00256 0.00750 0.04190 0.383 0.02383 94 0.00191 0.00226 0.00574 0.05925 0.637 0.03341 95 0.00196 0.00196 0.00587 0.03716 0.307 0.02062 96 0.00201 0.00197 0.00602 0.03272 0.283 0.01813 97 0.00178 0.00184 0.00535 0.03381 0.307 0.01806 98 0.00743 0.00623 0.02228 0.03886 0.342 0.02135 99 0.00826 0.00655 0.02478 0.04689 0.422 0.02542 100 0.01159 0.00990 0.03476 0.06734 0.659 0.03611 101 0.02144 0.01522 0.06433 0.09178 0.891 0.05358 102 0.00905 0.00909 0.02716 0.06170 0.584 0.03223 103 0.01854 0.01628 0.05563 0.09419 0.930 0.05551 104 0.00105 0.00136 0.00315 0.01131 0.107 0.00522 105 0.00076 0.00100 0.00229 0.01030 0.094 0.00469 106 0.00116 0.00134 0.00349 0.01346 0.126 0.00660 107 0.00068 0.00092 0.00204 0.01064 0.097 0.00522 108 0.00115 0.00122 0.00346 0.01450 0.137 0.00633 109 0.00075 0.00096 0.00225 0.01024 0.093 0.00455 110 0.00450 0.00389 0.01351 0.03044 0.275 0.01771 111 0.00371 0.00337 0.01112 0.02286 0.207 0.01192 112 0.00368 0.00339 0.01105 0.01761 0.155 0.00952 113 0.00502 0.00485 0.01506 0.02378 0.210 0.01277 114 0.00321 0.00280 0.00964 0.01680 0.149 0.00861 115 0.00302 0.00246 0.00905 0.02105 0.209 0.01107 116 0.00404 0.00385 0.01211 0.01843 0.235 0.00796 117 0.00214 0.00207 0.00642 0.01458 0.148 0.00606 118 0.00244 0.00261 0.00731 0.01725 0.175 0.00757 119 0.00157 0.00194 0.00472 0.01279 0.129 0.00617 120 0.00127 0.00128 0.00381 0.01299 0.124 0.00679 121 0.00241 0.00314 0.00723 0.02008 0.221 0.00849 122 0.00209 0.00221 0.00628 0.01169 0.117 0.00534 123 0.00406 0.00398 0.01218 0.04479 0.441 0.02587 124 0.00506 0.00449 0.01517 0.02503 0.231 0.01372 125 0.00403 0.00395 0.01209 0.02343 0.224 0.01289 126 0.00414 0.00422 0.01242 0.02362 0.233 0.01235 127 0.00294 0.00327 0.00883 0.02791 0.246 0.01484 128 0.00368 0.00351 0.01104 0.02857 0.257 0.01547 129 0.00214 0.00192 0.00641 0.01033 0.098 0.00538 130 0.00116 0.00135 0.00349 0.01022 0.090 0.00476 131 0.00269 0.00238 0.00808 0.01412 0.125 0.00703 132 0.00224 0.00205 0.00671 0.01516 0.138 0.00721 133 0.00169 0.00170 0.00508 0.01201 0.106 0.00633 134 0.00168 0.00171 0.00504 0.01043 0.099 0.00490 135 0.00291 0.00319 0.00873 0.04932 0.441 0.02683 136 0.00244 0.00315 0.00731 0.04128 0.379 0.02229 137 0.00219 0.00283 0.00658 0.04879 0.431 0.02385 138 0.00257 0.00312 0.00772 0.05279 0.476 0.02896 139 0.00238 0.00290 0.00715 0.05643 0.517 0.03070 140 0.00181 0.00232 0.00542 0.03026 0.267 0.01514 141 0.00232 0.00269 0.00696 0.03273 0.281 0.01713 142 0.00428 0.00428 0.01285 0.06725 0.571 0.04016 143 0.00182 0.00215 0.00546 0.03527 0.297 0.02055 144 0.00189 0.00211 0.00568 0.01997 0.180 0.01117 145 0.00100 0.00133 0.00301 0.02662 0.228 0.01475 146 0.00169 0.00188 0.00506 0.02536 0.225 0.01379 147 0.00863 0.00946 0.02589 0.08143 0.821 0.03804 148 0.00849 0.00819 0.02546 0.06050 0.618 0.02865 149 0.00996 0.01027 0.02987 0.07118 0.722 0.03474 150 0.00919 0.00963 0.02756 0.07170 0.833 0.03515 151 0.01075 0.01154 0.03225 0.05830 0.784 0.02699 152 0.01800 0.01958 0.05401 0.11908 1.302 0.05647 153 0.01568 0.01699 0.04705 0.08684 1.018 0.04284 154 0.00388 0.00332 0.01164 0.02534 0.241 0.01340 155 0.00393 0.00300 0.01179 0.02682 0.236 0.01484 156 0.00356 0.00300 0.01067 0.03087 0.276 0.01659 157 0.00415 0.00339 0.01246 0.02293 0.223 0.01205 158 0.01117 0.00718 0.03351 0.04912 0.438 0.02610 159 0.00593 0.00454 0.01778 0.02852 0.266 0.01500 160 0.00321 0.00318 0.00962 0.03235 0.339 0.01360 161 0.00299 0.00316 0.00896 0.04009 0.406 0.01579 162 0.00352 0.00329 0.01057 0.03273 0.325 0.01644 163 0.00366 0.00340 0.01097 0.03658 0.369 0.01864 164 0.00291 0.00284 0.00873 0.01756 0.155 0.00967 165 0.00493 0.00461 0.01480 0.02814 0.272 0.01579 166 0.00154 0.00153 0.00462 0.02448 0.217 0.01410 167 0.00173 0.00159 0.00519 0.01242 0.116 0.00696 168 0.00205 0.00186 0.00616 0.02030 0.197 0.01186 169 0.00490 0.00448 0.01470 0.02177 0.189 0.01279 170 0.00316 0.00283 0.00949 0.02018 0.212 0.01176 171 0.00279 0.00237 0.00837 0.01897 0.181 0.01084 172 0.00166 0.00190 0.00499 0.01358 0.129 0.00664 173 0.00170 0.00200 0.00510 0.01484 0.133 0.00754 174 0.00171 0.00203 0.00514 0.01472 0.133 0.00748 175 0.00176 0.00218 0.00528 0.01657 0.145 0.00881 176 0.00160 0.00199 0.00480 0.01503 0.137 0.00812 177 0.00169 0.00213 0.00507 0.01725 0.155 0.00874 178 0.00135 0.00162 0.00406 0.01469 0.132 0.00728 179 0.00152 0.00186 0.00456 0.01574 0.142 0.00839 180 0.00204 0.00231 0.00612 0.01450 0.131 0.00725 181 0.00206 0.00233 0.00619 0.02551 0.237 0.01321 182 0.00202 0.00235 0.00605 0.01831 0.163 0.00950 183 0.00174 0.00198 0.00521 0.02145 0.198 0.01155 184 0.00186 0.00270 0.00558 0.01909 0.171 0.00864 185 0.00260 0.00346 0.00780 0.01795 0.163 0.00810 186 0.00134 0.00192 0.00403 0.01564 0.136 0.00667 187 0.00254 0.00263 0.00762 0.01660 0.154 0.00820 188 0.00115 0.00148 0.00345 0.01300 0.117 0.00631 189 0.00146 0.00184 0.00439 0.01185 0.106 0.00557 190 0.00412 0.00396 0.01235 0.02574 0.255 0.01454 191 0.00263 0.00259 0.00790 0.04087 0.405 0.02336 192 0.00331 0.00292 0.00994 0.02751 0.263 0.01604 193 0.00624 0.00564 0.01873 0.02308 0.256 0.01268 194 0.00370 0.00390 0.01109 0.02296 0.241 0.01265 195 0.00295 0.00317 0.00885 0.01884 0.190 0.01026 Shimmer:APQ5 MDVP:APQ Shimmer:DDA NHR HNR status RPDE DFA 1 0.03130 0.02971 0.06545 0.02211 21.033 1 0.414783 0.815285 2 0.04518 0.04368 0.09403 0.01929 19.085 1 0.458359 0.819521 3 0.03858 0.03590 0.08270 0.01309 20.651 1 0.429895 0.825288 4 0.04005 0.03772 0.08771 0.01353 20.644 1 0.434969 0.819235 5 0.04825 0.04465 0.10470 0.01767 19.649 1 0.417356 0.823484 6 0.03526 0.03243 0.06985 0.01222 21.378 1 0.415564 0.825069 7 0.00937 0.01351 0.02337 0.00607 24.886 1 0.596040 0.764112 8 0.00946 0.01256 0.02487 0.00344 26.892 1 0.637420 0.763262 9 0.01277 0.01717 0.03218 0.01070 21.812 1 0.615551 0.773587 10 0.01725 0.02444 0.04324 0.01022 21.862 1 0.547037 0.798463 11 0.01342 0.01892 0.03237 0.01166 21.118 1 0.611137 0.776156 12 0.01641 0.02214 0.04272 0.01141 21.414 1 0.583390 0.792520 13 0.00717 0.01140 0.01968 0.00581 25.703 1 0.460600 0.646846 14 0.00932 0.01797 0.02184 0.01041 24.889 1 0.430166 0.665833 15 0.00972 0.01246 0.03191 0.00609 24.922 1 0.474791 0.654027 16 0.00888 0.01359 0.02316 0.00839 25.175 1 0.565924 0.658245 17 0.01200 0.02074 0.02908 0.01859 22.333 1 0.567380 0.644692 18 0.01893 0.03430 0.04322 0.02919 20.376 1 0.631099 0.605417 19 0.03572 0.05767 0.07413 0.03160 17.280 1 0.665318 0.719467 20 0.02374 0.04310 0.05164 0.03365 17.153 1 0.649554 0.686080 21 0.02383 0.04055 0.05000 0.03871 17.536 1 0.660125 0.704087 22 0.02591 0.04525 0.06062 0.01849 19.493 1 0.629017 0.698951 23 0.02540 0.04246 0.06685 0.01280 22.468 1 0.619060 0.679834 24 0.02470 0.03772 0.06562 0.01840 20.422 1 0.537264 0.686894 25 0.00948 0.01497 0.02214 0.01778 23.831 1 0.397937 0.732479 26 0.02245 0.03780 0.05197 0.02887 22.066 1 0.522746 0.737948 27 0.01169 0.01872 0.02666 0.01095 25.908 1 0.418622 0.720916 28 0.01144 0.01826 0.02650 0.01328 25.119 1 0.358773 0.726652 29 0.01012 0.01661 0.02307 0.00677 25.970 1 0.470478 0.676258 30 0.01057 0.01799 0.02380 0.01170 25.678 1 0.427785 0.723797 31 0.00680 0.00802 0.01689 0.00339 26.775 0 0.422229 0.741367 32 0.00641 0.00762 0.01513 0.00167 30.940 0 0.432439 0.742055 33 0.00825 0.00951 0.01919 0.00119 30.775 0 0.465946 0.738703 34 0.00606 0.00719 0.01407 0.00072 32.684 0 0.368535 0.742133 35 0.00610 0.00726 0.01403 0.00065 33.047 0 0.340068 0.741899 36 0.00760 0.00957 0.01758 0.00135 31.732 0 0.344252 0.742737 37 0.01347 0.01612 0.03463 0.00586 23.216 1 0.360148 0.778834 38 0.01160 0.01491 0.02814 0.00340 24.951 1 0.341435 0.783626 39 0.00885 0.01190 0.02177 0.00231 26.738 1 0.403884 0.766209 40 0.01003 0.01366 0.02488 0.00265 26.310 1 0.396793 0.758324 41 0.00941 0.01233 0.02321 0.00231 26.822 1 0.326480 0.765623 42 0.00901 0.01234 0.02226 0.00257 26.453 1 0.306443 0.759203 43 0.01024 0.01133 0.03104 0.00740 22.736 0 0.305062 0.654172 44 0.01038 0.01251 0.03017 0.00675 23.145 0 0.457702 0.634267 45 0.00898 0.01033 0.02330 0.00454 25.368 0 0.438296 0.635285 46 0.00879 0.01014 0.02542 0.00476 25.032 0 0.431285 0.638928 47 0.00977 0.01149 0.02719 0.00476 24.602 0 0.467489 0.631653 48 0.00730 0.00860 0.01841 0.00432 26.805 0 0.610367 0.635204 49 0.00776 0.01433 0.02566 0.00839 23.162 0 0.579597 0.733659 50 0.00802 0.01400 0.02789 0.00462 24.971 0 0.538688 0.754073 51 0.01024 0.01685 0.03724 0.00479 25.135 0 0.553134 0.775933 52 0.00959 0.01614 0.03429 0.00474 25.030 0 0.507504 0.760361 53 0.01072 0.01677 0.03969 0.00481 24.692 0 0.459766 0.766204 54 0.01219 0.01947 0.04188 0.00484 25.429 0 0.420383 0.785714 55 0.01609 0.02067 0.04450 0.01036 21.028 1 0.536009 0.819032 56 0.01992 0.02454 0.05368 0.01180 20.767 1 0.558586 0.811843 57 0.02302 0.02802 0.06097 0.00969 21.422 1 0.541781 0.821364 58 0.01459 0.01948 0.03568 0.00681 22.817 1 0.530529 0.817756 59 0.01625 0.02137 0.04183 0.00786 22.603 1 0.540049 0.813432 60 0.01974 0.02519 0.05414 0.01143 21.660 1 0.547975 0.817396 61 0.01258 0.01382 0.02925 0.00871 25.554 0 0.341788 0.678874 62 0.01296 0.01340 0.03039 0.00301 26.138 0 0.447979 0.686264 63 0.01108 0.01200 0.02602 0.00340 25.856 0 0.364867 0.694399 64 0.01075 0.01179 0.02647 0.00351 25.964 0 0.256570 0.683296 65 0.00957 0.01016 0.02308 0.00300 26.415 0 0.276850 0.673636 66 0.01160 0.01234 0.02827 0.00420 24.547 0 0.305429 0.681811 67 0.01810 0.02428 0.05490 0.02183 19.560 1 0.460139 0.720908 68 0.01759 0.02603 0.04914 0.02659 19.979 1 0.498133 0.729067 69 0.02422 0.03392 0.09455 0.04882 20.338 1 0.513237 0.731444 70 0.02494 0.03635 0.10070 0.02431 21.718 1 0.487407 0.727313 71 0.01906 0.02949 0.05605 0.02599 20.264 1 0.489345 0.730387 72 0.02466 0.03736 0.08247 0.03361 18.570 1 0.543299 0.733232 73 0.00925 0.01345 0.02921 0.00442 25.742 1 0.495954 0.762959 74 0.01375 0.01956 0.04120 0.00623 24.178 1 0.509127 0.789532 75 0.01325 0.01831 0.04295 0.00479 25.438 1 0.437031 0.815908 76 0.01219 0.01715 0.03851 0.00472 25.197 1 0.463514 0.807217 77 0.02231 0.02704 0.07238 0.00905 23.370 1 0.489538 0.789977 78 0.01199 0.01636 0.03852 0.00420 25.820 1 0.429484 0.816340 79 0.01886 0.02455 0.05408 0.01062 21.875 1 0.644954 0.779612 80 0.01783 0.02139 0.05320 0.02220 19.200 1 0.594387 0.790117 81 0.02451 0.02876 0.06799 0.01823 19.055 1 0.544805 0.770466 82 0.01841 0.02190 0.05377 0.01825 19.659 1 0.576084 0.778747 83 0.01421 0.01751 0.04114 0.01237 20.536 1 0.554610 0.787896 84 0.01343 0.01552 0.03831 0.00882 22.244 1 0.576644 0.772416 85 0.03022 0.03510 0.08037 0.05470 13.893 1 0.556494 0.729586 86 0.02493 0.02877 0.06321 0.02782 16.176 1 0.583574 0.727747 87 0.02415 0.02784 0.06219 0.03151 15.924 1 0.598714 0.712199 88 0.04159 0.04683 0.11012 0.04824 13.922 1 0.602874 0.740837 89 0.04254 0.04802 0.11363 0.04214 14.739 1 0.599371 0.743937 90 0.02768 0.03455 0.06892 0.07223 11.866 1 0.590951 0.745526 91 0.04282 0.05114 0.10949 0.08725 11.744 1 0.653410 0.733165 92 0.04962 0.05690 0.13262 0.01658 19.664 1 0.501037 0.714360 93 0.02521 0.03051 0.07150 0.01914 18.780 1 0.454444 0.734504 94 0.03794 0.04398 0.10024 0.01211 20.969 1 0.447456 0.697790 95 0.02321 0.02764 0.06185 0.00850 22.219 1 0.502380 0.712170 96 0.01909 0.02571 0.05439 0.01018 21.693 1 0.447285 0.705658 97 0.02024 0.02809 0.05417 0.00852 22.663 1 0.366329 0.693429 98 0.02174 0.03088 0.06406 0.08151 15.338 1 0.629574 0.714485 99 0.02630 0.03908 0.07625 0.10323 15.433 1 0.571010 0.690892 100 0.03963 0.05783 0.10833 0.16744 12.435 1 0.638545 0.674953 101 0.04791 0.06196 0.16074 0.31482 8.867 1 0.671299 0.656846 102 0.03672 0.05174 0.09669 0.11843 15.060 1 0.639808 0.643327 103 0.05005 0.06023 0.16654 0.25930 10.489 1 0.596362 0.641418 104 0.00659 0.01009 0.01567 0.00495 26.759 1 0.296888 0.722356 105 0.00582 0.00871 0.01406 0.00243 28.409 1 0.263654 0.691483 106 0.00818 0.01059 0.01979 0.00578 27.421 1 0.365488 0.719974 107 0.00632 0.00928 0.01567 0.00233 29.746 1 0.334171 0.677930 108 0.00788 0.01267 0.01898 0.00659 26.833 1 0.393563 0.700246 109 0.00576 0.00993 0.01364 0.00238 29.928 1 0.311369 0.676066 110 0.01815 0.02084 0.05312 0.00947 21.934 1 0.497554 0.740539 111 0.01439 0.01852 0.03576 0.00704 23.239 1 0.436084 0.727863 112 0.01058 0.01307 0.02855 0.00830 22.407 1 0.338097 0.712466 113 0.01483 0.01767 0.03831 0.01316 21.305 1 0.498877 0.722085 114 0.01017 0.01301 0.02583 0.00620 23.671 1 0.441097 0.722254 115 0.01284 0.01604 0.03320 0.01048 21.864 1 0.331508 0.715121 116 0.00832 0.01271 0.02389 0.06051 23.693 1 0.407701 0.662668 117 0.00747 0.01312 0.01818 0.01554 26.356 1 0.450798 0.653823 118 0.00971 0.01652 0.02270 0.01802 25.690 1 0.486738 0.676023 119 0.00744 0.01151 0.01851 0.00856 25.020 1 0.470422 0.655239 120 0.00631 0.01075 0.02038 0.00681 24.581 1 0.462516 0.582710 121 0.01117 0.01734 0.02548 0.02350 24.743 1 0.487756 0.684130 122 0.00630 0.01104 0.01603 0.01161 27.166 1 0.400088 0.656182 123 0.02567 0.03220 0.07761 0.01968 18.305 1 0.538016 0.741480 124 0.01580 0.01931 0.04115 0.01813 18.784 1 0.589956 0.732903 125 0.01420 0.01720 0.03867 0.02020 19.196 1 0.618663 0.728421 126 0.01495 0.01944 0.03706 0.01874 18.857 1 0.637518 0.735546 127 0.01805 0.02259 0.04451 0.01794 18.178 1 0.623209 0.738245 128 0.01859 0.02301 0.04641 0.01796 18.330 1 0.585169 0.736964 129 0.00570 0.00811 0.01614 0.01724 26.842 1 0.457541 0.699787 130 0.00588 0.00903 0.01428 0.00487 26.369 1 0.491345 0.718839 131 0.00820 0.01194 0.02110 0.01610 23.949 1 0.467160 0.724045 132 0.00815 0.01310 0.02164 0.01015 26.017 1 0.468621 0.735136 133 0.00701 0.00915 0.01898 0.00903 23.389 1 0.470972 0.721308 134 0.00621 0.00903 0.01471 0.00504 25.619 1 0.482296 0.723096 135 0.03112 0.03651 0.08050 0.03031 17.060 1 0.637814 0.744064 136 0.02592 0.03316 0.06688 0.02529 17.707 1 0.653427 0.706687 137 0.02973 0.04370 0.07154 0.02278 19.013 1 0.647900 0.708144 138 0.03347 0.04134 0.08689 0.03690 16.747 1 0.625362 0.708617 139 0.03530 0.04451 0.09211 0.02629 17.366 1 0.640945 0.701404 140 0.01812 0.02770 0.04543 0.01827 18.801 1 0.624811 0.696049 141 0.01964 0.02824 0.05139 0.02485 18.540 1 0.677131 0.685057 142 0.04003 0.04464 0.12047 0.04238 15.648 1 0.606344 0.665945 143 0.02076 0.02530 0.06165 0.01728 18.702 1 0.606273 0.661735 144 0.01177 0.01506 0.03350 0.02010 18.687 1 0.536102 0.632631 145 0.01558 0.02006 0.04426 0.01049 20.680 1 0.497480 0.630409 146 0.01478 0.01909 0.04137 0.01493 20.366 1 0.566849 0.574282 147 0.05426 0.08808 0.11411 0.07530 12.359 1 0.561610 0.793509 148 0.04101 0.06359 0.08595 0.06057 14.367 1 0.478024 0.768974 149 0.04580 0.06824 0.10422 0.08069 12.298 1 0.552870 0.764036 150 0.04265 0.06460 0.10546 0.07889 14.989 1 0.427627 0.775708 151 0.03714 0.06259 0.08096 0.10952 12.529 1 0.507826 0.762726 152 0.07940 0.13778 0.16942 0.21713 8.441 1 0.625866 0.768320 153 0.05556 0.08318 0.12851 0.16265 9.449 1 0.584164 0.754449 154 0.01399 0.02056 0.04019 0.04179 21.520 1 0.566867 0.670475 155 0.01405 0.02018 0.04451 0.04611 21.824 1 0.651680 0.659333 156 0.01804 0.02402 0.04977 0.02631 22.431 1 0.628300 0.652025 157 0.01289 0.01771 0.03615 0.03191 22.953 1 0.611679 0.623731 158 0.02161 0.02916 0.07830 0.10748 19.075 1 0.630547 0.646786 159 0.01581 0.02157 0.04499 0.03828 21.534 1 0.635015 0.627337 160 0.01650 0.03105 0.04079 0.02663 19.651 1 0.654945 0.675865 161 0.01994 0.04114 0.04736 0.02073 20.437 1 0.653139 0.694571 162 0.01722 0.02931 0.04933 0.02810 19.388 1 0.577802 0.684373 163 0.01940 0.03091 0.05592 0.02707 18.954 1 0.685151 0.719576 164 0.01033 0.01363 0.02902 0.01435 21.219 1 0.557045 0.673086 165 0.01553 0.02073 0.04736 0.03882 18.447 1 0.671378 0.674562 166 0.01426 0.01621 0.04231 0.00620 24.078 0 0.469928 0.628232 167 0.00747 0.00882 0.02089 0.00533 24.679 0 0.384868 0.626710 168 0.01230 0.01367 0.03557 0.00910 21.083 0 0.440988 0.628058 169 0.01272 0.01439 0.03836 0.01337 19.269 0 0.372222 0.725216 170 0.01191 0.01344 0.03529 0.00965 21.020 0 0.371837 0.646167 171 0.01121 0.01255 0.03253 0.01049 21.528 0 0.522812 0.646818 172 0.00786 0.01140 0.01992 0.00435 26.436 0 0.413295 0.756700 173 0.00950 0.01285 0.02261 0.00430 26.550 0 0.369090 0.776158 174 0.00905 0.01148 0.02245 0.00478 26.547 0 0.380253 0.766700 175 0.01062 0.01318 0.02643 0.00590 25.445 0 0.387482 0.756482 176 0.00933 0.01133 0.02436 0.00401 26.005 0 0.405991 0.761255 177 0.01021 0.01331 0.02623 0.00415 26.143 0 0.361232 0.763242 178 0.00886 0.01230 0.02184 0.00570 24.151 1 0.396610 0.745957 179 0.00956 0.01309 0.02518 0.00488 24.412 1 0.402591 0.762508 180 0.00876 0.01263 0.02175 0.00540 23.683 1 0.398499 0.778349 181 0.01574 0.02148 0.03964 0.00611 23.133 1 0.352396 0.759320 182 0.01103 0.01559 0.02849 0.00639 22.866 1 0.408598 0.768845 183 0.01341 0.01666 0.03464 0.00595 23.008 1 0.329577 0.757180 184 0.01223 0.01949 0.02592 0.00955 23.079 0 0.603515 0.669565 185 0.01144 0.01756 0.02429 0.01179 22.085 0 0.663842 0.656516 186 0.00990 0.01691 0.02001 0.00737 24.199 0 0.598515 0.654331 187 0.00972 0.01491 0.02460 0.01397 23.958 0 0.566424 0.667654 188 0.00789 0.01144 0.01892 0.00680 25.023 0 0.528485 0.663884 189 0.00721 0.01095 0.01672 0.00703 24.775 0 0.555303 0.659132 190 0.01582 0.01758 0.04363 0.04441 19.368 0 0.508479 0.683761 191 0.02498 0.02745 0.07008 0.02764 19.517 0 0.448439 0.657899 192 0.01657 0.01879 0.04812 0.01810 19.147 0 0.431674 0.683244 193 0.01365 0.01667 0.03804 0.10715 17.883 0 0.407567 0.655683 194 0.01321 0.01588 0.03794 0.07223 19.020 0 0.451221 0.643956 195 0.01161 0.01373 0.03078 0.04398 21.209 0 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:Fhi(Hz)` `MDVP:Flo(Hz)` `MDVP:Jitter(%)` 1.595e+02 2.986e-02 2.523e-01 1.158e+04 `MDVP:Jitter(Abs)` `MDVP:RAP` `MDVP:PPQ` `Jitter:DDP` -2.022e+06 6.830e+05 -7.137e+03 -2.237e+05 `MDVP:Shimmer` `MDVP:Shimmer(dB)` `Shimmer:APQ3` `Shimmer:APQ5` -2.195e+02 -1.468e+01 1.945e+05 2.270e+03 `MDVP:APQ` `Shimmer:DDA` NHR HNR -1.662e+03 -6.465e+04 -2.345e+02 -1.636e-01 status RPDE DFA spread1 -5.993e+00 -3.299e+01 -2.340e+02 -1.659e+01 spread2 D2 PPE 2.140e+01 5.817e+00 2.350e+02 > (mysum <- summary(mylm)) Call: lm(formula = df) Residuals: Min 1Q Median 3Q Max -51.065 -10.258 -0.036 10.691 43.166 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 1.595e+02 5.742e+01 2.779 0.006064 ** `MDVP:Fhi(Hz)` 2.986e-02 1.594e-02 1.873 0.062741 . `MDVP:Flo(Hz)` 2.523e-01 3.581e-02 7.047 4.23e-11 *** `MDVP:Jitter(%)` 1.158e+04 3.312e+03 3.496 0.000601 *** `MDVP:Jitter(Abs)` -2.022e+06 1.737e+05 -11.641 < 2e-16 *** `MDVP:RAP` 6.830e+05 4.649e+05 1.469 0.143636 `MDVP:PPQ` -7.137e+03 4.400e+03 -1.622 0.106564 `Jitter:DDP` -2.237e+05 1.551e+05 -1.443 0.150947 `MDVP:Shimmer` -2.195e+02 1.722e+03 -0.127 0.898694 `MDVP:Shimmer(dB)` -1.468e+01 6.015e+01 -0.244 0.807540 `Shimmer:APQ3` 1.945e+05 4.496e+05 0.433 0.665815 `Shimmer:APQ5` 2.270e+03 9.990e+02 2.272 0.024304 * `MDVP:APQ` -1.662e+03 5.312e+02 -3.130 0.002056 ** `Shimmer:DDA` -6.465e+04 1.498e+05 -0.432 0.666613 NHR -2.345e+02 9.815e+01 -2.389 0.017973 * HNR -1.636e-01 7.214e-01 -0.227 0.820860 status -5.993e+00 3.795e+00 -1.579 0.116169 RPDE -3.299e+01 2.223e+01 -1.484 0.139653 DFA -2.340e+02 3.252e+01 -7.196 1.84e-11 *** spread1 -1.659e+01 4.767e+00 -3.480 0.000635 *** spread2 2.140e+01 2.439e+01 0.878 0.381436 D2 5.817e+00 5.717e+00 1.017 0.310407 PPE 2.350e+02 6.717e+01 3.498 0.000597 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 16.38 on 172 degrees of freedom Multiple R-squared: 0.8612, Adjusted R-squared: 0.8434 F-statistic: 48.51 on 22 and 172 DF, p-value: < 2.2e-16 > if (n > n25) { + kp3 <- k + 3 + nmkm3 <- n - k - 3 + gqarr <- array(NA, dim=c(nmkm3-kp3+1,3)) + numgqtests <- 0 + numsignificant1 <- 0 + numsignificant5 <- 0 + numsignificant10 <- 0 + for (mypoint in kp3:nmkm3) { + j <- 0 + numgqtests <- numgqtests + 1 + for (myalt in c('greater', 'two.sided', 'less')) { + j <- j + 1 + gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value + } + if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1 + if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1 + if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1 + } + gqarr + } [,1] [,2] [,3] [1,] 0.1973287935 0.3946575870 0.80267121 [2,] 0.0974327096 0.1948654192 0.90256729 [3,] 0.0886111069 0.1772222137 0.91138889 [4,] 0.0411813775 0.0823627549 0.95881862 [5,] 0.0186798930 0.0373597860 0.98132011 [6,] 0.0076980782 0.0153961563 0.99230192 [7,] 0.0042243245 0.0084486490 0.99577568 [8,] 0.0026309738 0.0052619477 0.99736903 [9,] 0.0017365832 0.0034731663 0.99826342 [10,] 0.0007963196 0.0015926392 0.99920368 [11,] 0.0003192302 0.0006384604 0.99968077 [12,] 0.0002090002 0.0004180005 0.99979100 [13,] 0.0000834131 0.0001668262 0.99991659 [14,] 0.0003187246 0.0006374492 0.99968128 [15,] 0.0004394246 0.0008788491 0.99956058 [16,] 0.0003313199 0.0006626398 0.99966868 [17,] 0.0001604510 0.0003209020 0.99983955 [18,] 0.0001893187 0.0003786375 0.99981068 [19,] 0.0001529991 0.0003059982 0.99984700 [20,] 0.0002587712 0.0005175425 0.99974123 [21,] 0.0002912820 0.0005825640 0.99970872 [22,] 0.0003056158 0.0006112317 0.99969438 [23,] 0.0139532048 0.0279064096 0.98604680 [24,] 0.0095039046 0.0190078092 0.99049610 [25,] 0.0074037804 0.0148075608 0.99259622 [26,] 0.0046426109 0.0092852217 0.99535739 [27,] 0.0037453807 0.0074907614 0.99625462 [28,] 0.0024397014 0.0048794028 0.99756030 [29,] 0.0015499647 0.0030999294 0.99845004 [30,] 0.0076698143 0.0153396286 0.99233019 [31,] 0.0134244162 0.0268488325 0.98657558 [32,] 0.0115658034 0.0231316069 0.98843420 [33,] 0.0154739401 0.0309478801 0.98452606 [34,] 0.0244886558 0.0489773116 0.97551134 [35,] 0.0467768872 0.0935537743 0.95322311 [36,] 0.0379842268 0.0759684537 0.96201577 [37,] 0.0538505348 0.1077010695 0.94614947 [38,] 0.0543579252 0.1087158504 0.94564207 [39,] 0.0581946266 0.1163892532 0.94180537 [40,] 0.0596511527 0.1193023054 0.94034885 [41,] 0.1239353696 0.2478707392 0.87606463 [42,] 0.1065884365 0.2131768730 0.89341156 [43,] 0.0858163683 0.1716327365 0.91418363 [44,] 0.0687441607 0.1374883215 0.93125584 [45,] 0.0814137705 0.1628275409 0.91858623 [46,] 0.0685332403 0.1370664805 0.93146676 [47,] 0.0575436843 0.1150873685 0.94245632 [48,] 0.0520384206 0.1040768412 0.94796158 [49,] 0.2164331728 0.4328663455 0.78356683 [50,] 0.2883976623 0.5767953245 0.71160234 [51,] 0.2535441633 0.5070883267 0.74645584 [52,] 0.2361911432 0.4723822865 0.76380886 [53,] 0.2072224770 0.4144449540 0.79277752 [54,] 0.2060826670 0.4121653340 0.79391733 [55,] 0.2213897728 0.4427795456 0.77861023 [56,] 0.2513592436 0.5027184871 0.74864076 [57,] 0.2333253890 0.4666507781 0.76667461 [58,] 0.2601385105 0.5202770209 0.73986149 [59,] 0.3069486967 0.6138973934 0.69305130 [60,] 0.3653749480 0.7307498960 0.63462505 [61,] 0.3248753756 0.6497507512 0.67512462 [62,] 0.3284974554 0.6569949108 0.67150254 [63,] 0.2906165171 0.5812330341 0.70938348 [64,] 0.3387010695 0.6774021391 0.66129893 [65,] 0.3216408184 0.6432816367 0.67835918 [66,] 0.2843921561 0.5687843122 0.71560784 [67,] 0.2931307908 0.5862615816 0.70686921 [68,] 0.2902025889 0.5804051778 0.70979741 [69,] 0.2807667066 0.5615334132 0.71923329 [70,] 0.2430209196 0.4860418392 0.75697908 [71,] 0.2846803691 0.5693607382 0.71531963 [72,] 0.3078744353 0.6157488705 0.69212556 [73,] 0.2757012532 0.5514025063 0.72429875 [74,] 0.2418704237 0.4837408474 0.75812958 [75,] 0.2279303144 0.4558606288 0.77206969 [76,] 0.2115277498 0.4230554996 0.78847225 [77,] 0.2061048884 0.4122097768 0.79389511 [78,] 0.2580450423 0.5160900847 0.74195496 [79,] 0.2744397875 0.5488795750 0.72556021 [80,] 0.2715653446 0.5431306892 0.72843466 [81,] 0.2350320826 0.4700641651 0.76496792 [82,] 0.2124760281 0.4249520562 0.78752397 [83,] 0.1806849917 0.3613699834 0.81931501 [84,] 0.1798783561 0.3597567122 0.82012164 [85,] 0.1593433343 0.3186866685 0.84065667 [86,] 0.1411041830 0.2822083661 0.85889582 [87,] 0.1246305796 0.2492611591 0.87536942 [88,] 0.1300637406 0.2601274812 0.86993626 [89,] 0.1512300701 0.3024601403 0.84876993 [90,] 0.1256003894 0.2512007789 0.87439961 [91,] 0.1070215248 0.2140430497 0.89297848 [92,] 0.0956565654 0.1913131308 0.90434343 [93,] 0.0815522505 0.1631045009 0.91844775 [94,] 0.0796458060 0.1592916121 0.92035419 [95,] 0.1139180626 0.2278361253 0.88608194 [96,] 0.1478723837 0.2957447674 0.85212762 [97,] 0.1906597211 0.3813194422 0.80934028 [98,] 0.1692706972 0.3385413944 0.83072930 [99,] 0.1566382831 0.3132765663 0.84336172 [100,] 0.1332788451 0.2665576902 0.86672115 [101,] 0.1299442536 0.2598885072 0.87005575 [102,] 0.1395715978 0.2791431955 0.86042840 [103,] 0.2883861464 0.5767722927 0.71161385 [104,] 0.2771876069 0.5543752137 0.72281239 [105,] 0.2689268513 0.5378537026 0.73107315 [106,] 0.2515143014 0.5030286028 0.74848570 [107,] 0.2617979310 0.5235958621 0.73820207 [108,] 0.2782973062 0.5565946125 0.72170269 [109,] 0.2599679828 0.5199359657 0.74003202 [110,] 0.4102034199 0.8204068399 0.58979658 [111,] 0.4711016618 0.9422033235 0.52889834 [112,] 0.4215356071 0.8430712143 0.57846439 [113,] 0.4737621599 0.9475243199 0.52623784 [114,] 0.5036524154 0.9926951692 0.49634758 [115,] 0.7079583244 0.5840833511 0.29204168 [116,] 0.6669912677 0.6660174646 0.33300873 [117,] 0.6382192166 0.7235615669 0.36178078 [118,] 0.6439569737 0.7120860525 0.35604303 [119,] 0.6626426099 0.6747147801 0.33735739 [120,] 0.7193336247 0.5613327506 0.28066638 [121,] 0.7087994607 0.5824010787 0.29120054 [122,] 0.6995957856 0.6008084288 0.30040421 [123,] 0.6770063079 0.6459873843 0.32299369 [124,] 0.6973115482 0.6053769035 0.30268845 [125,] 0.6411060509 0.7177878983 0.35889395 [126,] 0.6790627480 0.6418745040 0.32093725 [127,] 0.6434180313 0.7131639374 0.35658197 [128,] 0.6320574211 0.7358851578 0.36794258 [129,] 0.6252157921 0.7495684158 0.37478421 [130,] 0.6337484636 0.7325030727 0.36625154 [131,] 0.7906586715 0.4186826570 0.20934133 [132,] 0.7561714869 0.4876570262 0.24382851 [133,] 0.6916410725 0.6167178549 0.30835893 [134,] 0.6951793936 0.6096412127 0.30482061 [135,] 0.6242518811 0.7514962378 0.37574812 [136,] 0.5391115375 0.9217769249 0.46088846 [137,] 0.6880524056 0.6238951888 0.31194759 [138,] 0.5957275281 0.8085449437 0.40427247 [139,] 0.5860671292 0.8278657415 0.41393287 [140,] 0.6747146071 0.6505707859 0.32528539 [141,] 0.9036699047 0.1926601905 0.09633010 [142,] 0.9687833085 0.0624333831 0.03121669 [143,] 0.9517506598 0.0964986804 0.04824934 [144,] 0.8974242100 0.2051515800 0.10257579 > postscript(file="/var/fisher/rcomp/tmp/1r34f1386705982.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$resid) > grid() > dev.off() null device 1 > postscript(file="/var/fisher/rcomp/tmp/2cr6u1386705982.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(mysum$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/3rn941386705982.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') > grid() > dev.off() null device 1 > postscript(file="/var/fisher/rcomp/tmp/4ykb01386705982.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') > dev.off() null device 1 > postscript(file="/var/fisher/rcomp/tmp/5vb5f1386705982.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > qqnorm(mysum$resid, main='Residual Normal Q-Q Plot') > qqline(mysum$resid) > grid() > dev.off() null device 1 > (myerror <- as.ts(mysum$resid)) Time Series: Start = 1 End = 195 Frequency = 1 1 2 3 4 5 6 15.58378197 -7.83745428 1.01188287 -3.66699803 -2.19155427 -1.59712982 7 8 9 10 11 12 0.83602402 -6.91847654 5.21717314 11.11098990 11.02424285 6.63448853 13 14 15 16 17 18 -29.62443663 -0.03638896 -5.12858914 -4.07846117 -1.70750557 -3.13300460 19 20 21 22 23 24 21.43387460 -2.21813265 5.67017834 15.91522694 32.86186726 20.47358411 25 26 27 28 29 30 16.98532760 13.65895272 25.33696072 -6.39301574 -5.78741711 14.18071741 31 32 33 34 35 36 -2.90828318 4.53008498 13.95273179 14.96817202 18.36367974 6.84813825 37 38 39 40 41 42 -2.12888471 6.89492175 17.06952331 21.67014094 18.16543841 4.28008922 43 44 45 46 47 48 -3.65143593 4.11640752 19.49568415 15.02794967 14.16887228 42.90655360 49 50 51 52 53 54 -7.60199554 -10.55160642 -4.61205932 9.37025417 -18.27713268 -4.66738697 55 56 57 58 59 60 17.24859345 18.60988517 16.40884076 13.79436539 27.81942393 31.08372351 61 62 63 64 65 66 13.63799097 26.35825281 8.58084541 -2.72520313 9.41360813 35.21204183 67 68 69 70 71 72 1.24093592 9.73309001 7.52994405 0.13322839 18.39735026 4.01260996 73 74 75 76 77 78 -16.39219171 -3.08270821 -12.65773566 4.19329680 -19.75299755 -4.28322580 79 80 81 82 83 84 -1.54092946 13.29463224 -4.62613731 -8.29142989 -1.30104187 -11.52419434 85 86 87 88 89 90 -1.19631980 -9.41647654 5.83419745 -0.72146790 18.55177218 -5.98206239 91 92 93 94 95 96 1.54407290 -21.15724418 -17.87526932 6.84872145 -5.92727039 -26.41463096 97 98 99 100 101 102 -19.48989495 -8.56117661 3.81940476 11.95832892 12.66261113 6.82392124 103 104 105 106 107 108 -12.24244890 15.54560438 -15.02227172 -6.48003188 -20.05778832 0.58596394 109 110 111 112 113 114 -19.62490921 9.01441388 11.71100052 -5.65457358 -18.45545664 13.64139077 115 116 117 118 119 120 8.21471077 -5.37914984 -10.78336990 -10.87405468 8.30998347 14.17097655 121 122 123 124 125 126 -0.95226933 21.26437888 -6.17238362 14.12364566 -1.90409862 5.59542852 127 128 129 130 131 132 9.96487928 15.12729703 -19.11172204 -21.56418938 -16.72026803 -25.70379572 133 134 135 136 137 138 -20.54325654 -14.49660460 -0.08579711 -27.94252987 3.42535807 -22.84268036 139 140 141 142 143 144 -21.84042553 -21.08222990 0.11139455 5.05682857 6.18236149 23.78123816 145 146 147 148 149 150 1.52204660 20.78972893 1.80713067 -14.66927708 -24.24126121 -13.11572255 151 152 153 154 155 156 10.31546762 5.33894619 -23.99452778 13.44025601 0.87453919 -3.46404726 157 158 159 160 161 162 -28.75978743 6.65150321 -32.53678486 3.10358591 15.21280344 -16.85912071 163 164 165 166 167 168 6.75690185 -17.95726596 9.91434197 30.36897792 -0.41595178 10.03242760 169 170 171 172 173 174 -9.96539346 -7.92185596 0.67766670 -23.76077377 -21.65106490 -15.21830705 175 176 177 178 179 180 -17.51737450 -20.64858207 -17.04416735 -1.33567019 -5.65064563 4.18316643 181 182 183 184 185 186 6.74592185 -1.84375642 -17.43550441 -13.85870970 -16.98383710 -20.79900671 187 188 189 190 191 192 -35.17991523 -51.06506620 -22.09339481 -0.93833461 -7.49909837 13.55731616 193 194 195 -7.55574018 10.35759738 43.16642382 > postscript(file="/var/fisher/rcomp/tmp/6jmrs1386705982.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > dum <- cbind(lag(myerror,k=1),myerror) > dum Time Series: Start = 0 End = 195 Frequency = 1 lag(myerror, k = 1) myerror 0 15.58378197 NA 1 -7.83745428 15.58378197 2 1.01188287 -7.83745428 3 -3.66699803 1.01188287 4 -2.19155427 -3.66699803 5 -1.59712982 -2.19155427 6 0.83602402 -1.59712982 7 -6.91847654 0.83602402 8 5.21717314 -6.91847654 9 11.11098990 5.21717314 10 11.02424285 11.11098990 11 6.63448853 11.02424285 12 -29.62443663 6.63448853 13 -0.03638896 -29.62443663 14 -5.12858914 -0.03638896 15 -4.07846117 -5.12858914 16 -1.70750557 -4.07846117 17 -3.13300460 -1.70750557 18 21.43387460 -3.13300460 19 -2.21813265 21.43387460 20 5.67017834 -2.21813265 21 15.91522694 5.67017834 22 32.86186726 15.91522694 23 20.47358411 32.86186726 24 16.98532760 20.47358411 25 13.65895272 16.98532760 26 25.33696072 13.65895272 27 -6.39301574 25.33696072 28 -5.78741711 -6.39301574 29 14.18071741 -5.78741711 30 -2.90828318 14.18071741 31 4.53008498 -2.90828318 32 13.95273179 4.53008498 33 14.96817202 13.95273179 34 18.36367974 14.96817202 35 6.84813825 18.36367974 36 -2.12888471 6.84813825 37 6.89492175 -2.12888471 38 17.06952331 6.89492175 39 21.67014094 17.06952331 40 18.16543841 21.67014094 41 4.28008922 18.16543841 42 -3.65143593 4.28008922 43 4.11640752 -3.65143593 44 19.49568415 4.11640752 45 15.02794967 19.49568415 46 14.16887228 15.02794967 47 42.90655360 14.16887228 48 -7.60199554 42.90655360 49 -10.55160642 -7.60199554 50 -4.61205932 -10.55160642 51 9.37025417 -4.61205932 52 -18.27713268 9.37025417 53 -4.66738697 -18.27713268 54 17.24859345 -4.66738697 55 18.60988517 17.24859345 56 16.40884076 18.60988517 57 13.79436539 16.40884076 58 27.81942393 13.79436539 59 31.08372351 27.81942393 60 13.63799097 31.08372351 61 26.35825281 13.63799097 62 8.58084541 26.35825281 63 -2.72520313 8.58084541 64 9.41360813 -2.72520313 65 35.21204183 9.41360813 66 1.24093592 35.21204183 67 9.73309001 1.24093592 68 7.52994405 9.73309001 69 0.13322839 7.52994405 70 18.39735026 0.13322839 71 4.01260996 18.39735026 72 -16.39219171 4.01260996 73 -3.08270821 -16.39219171 74 -12.65773566 -3.08270821 75 4.19329680 -12.65773566 76 -19.75299755 4.19329680 77 -4.28322580 -19.75299755 78 -1.54092946 -4.28322580 79 13.29463224 -1.54092946 80 -4.62613731 13.29463224 81 -8.29142989 -4.62613731 82 -1.30104187 -8.29142989 83 -11.52419434 -1.30104187 84 -1.19631980 -11.52419434 85 -9.41647654 -1.19631980 86 5.83419745 -9.41647654 87 -0.72146790 5.83419745 88 18.55177218 -0.72146790 89 -5.98206239 18.55177218 90 1.54407290 -5.98206239 91 -21.15724418 1.54407290 92 -17.87526932 -21.15724418 93 6.84872145 -17.87526932 94 -5.92727039 6.84872145 95 -26.41463096 -5.92727039 96 -19.48989495 -26.41463096 97 -8.56117661 -19.48989495 98 3.81940476 -8.56117661 99 11.95832892 3.81940476 100 12.66261113 11.95832892 101 6.82392124 12.66261113 102 -12.24244890 6.82392124 103 15.54560438 -12.24244890 104 -15.02227172 15.54560438 105 -6.48003188 -15.02227172 106 -20.05778832 -6.48003188 107 0.58596394 -20.05778832 108 -19.62490921 0.58596394 109 9.01441388 -19.62490921 110 11.71100052 9.01441388 111 -5.65457358 11.71100052 112 -18.45545664 -5.65457358 113 13.64139077 -18.45545664 114 8.21471077 13.64139077 115 -5.37914984 8.21471077 116 -10.78336990 -5.37914984 117 -10.87405468 -10.78336990 118 8.30998347 -10.87405468 119 14.17097655 8.30998347 120 -0.95226933 14.17097655 121 21.26437888 -0.95226933 122 -6.17238362 21.26437888 123 14.12364566 -6.17238362 124 -1.90409862 14.12364566 125 5.59542852 -1.90409862 126 9.96487928 5.59542852 127 15.12729703 9.96487928 128 -19.11172204 15.12729703 129 -21.56418938 -19.11172204 130 -16.72026803 -21.56418938 131 -25.70379572 -16.72026803 132 -20.54325654 -25.70379572 133 -14.49660460 -20.54325654 134 -0.08579711 -14.49660460 135 -27.94252987 -0.08579711 136 3.42535807 -27.94252987 137 -22.84268036 3.42535807 138 -21.84042553 -22.84268036 139 -21.08222990 -21.84042553 140 0.11139455 -21.08222990 141 5.05682857 0.11139455 142 6.18236149 5.05682857 143 23.78123816 6.18236149 144 1.52204660 23.78123816 145 20.78972893 1.52204660 146 1.80713067 20.78972893 147 -14.66927708 1.80713067 148 -24.24126121 -14.66927708 149 -13.11572255 -24.24126121 150 10.31546762 -13.11572255 151 5.33894619 10.31546762 152 -23.99452778 5.33894619 153 13.44025601 -23.99452778 154 0.87453919 13.44025601 155 -3.46404726 0.87453919 156 -28.75978743 -3.46404726 157 6.65150321 -28.75978743 158 -32.53678486 6.65150321 159 3.10358591 -32.53678486 160 15.21280344 3.10358591 161 -16.85912071 15.21280344 162 6.75690185 -16.85912071 163 -17.95726596 6.75690185 164 9.91434197 -17.95726596 165 30.36897792 9.91434197 166 -0.41595178 30.36897792 167 10.03242760 -0.41595178 168 -9.96539346 10.03242760 169 -7.92185596 -9.96539346 170 0.67766670 -7.92185596 171 -23.76077377 0.67766670 172 -21.65106490 -23.76077377 173 -15.21830705 -21.65106490 174 -17.51737450 -15.21830705 175 -20.64858207 -17.51737450 176 -17.04416735 -20.64858207 177 -1.33567019 -17.04416735 178 -5.65064563 -1.33567019 179 4.18316643 -5.65064563 180 6.74592185 4.18316643 181 -1.84375642 6.74592185 182 -17.43550441 -1.84375642 183 -13.85870970 -17.43550441 184 -16.98383710 -13.85870970 185 -20.79900671 -16.98383710 186 -35.17991523 -20.79900671 187 -51.06506620 -35.17991523 188 -22.09339481 -51.06506620 189 -0.93833461 -22.09339481 190 -7.49909837 -0.93833461 191 13.55731616 -7.49909837 192 -7.55574018 13.55731616 193 10.35759738 -7.55574018 194 43.16642382 10.35759738 195 NA 43.16642382 > dum1 <- dum[2:length(myerror),] > dum1 lag(myerror, k = 1) myerror [1,] -7.83745428 15.58378197 [2,] 1.01188287 -7.83745428 [3,] -3.66699803 1.01188287 [4,] -2.19155427 -3.66699803 [5,] -1.59712982 -2.19155427 [6,] 0.83602402 -1.59712982 [7,] -6.91847654 0.83602402 [8,] 5.21717314 -6.91847654 [9,] 11.11098990 5.21717314 [10,] 11.02424285 11.11098990 [11,] 6.63448853 11.02424285 [12,] -29.62443663 6.63448853 [13,] -0.03638896 -29.62443663 [14,] -5.12858914 -0.03638896 [15,] -4.07846117 -5.12858914 [16,] -1.70750557 -4.07846117 [17,] -3.13300460 -1.70750557 [18,] 21.43387460 -3.13300460 [19,] -2.21813265 21.43387460 [20,] 5.67017834 -2.21813265 [21,] 15.91522694 5.67017834 [22,] 32.86186726 15.91522694 [23,] 20.47358411 32.86186726 [24,] 16.98532760 20.47358411 [25,] 13.65895272 16.98532760 [26,] 25.33696072 13.65895272 [27,] -6.39301574 25.33696072 [28,] -5.78741711 -6.39301574 [29,] 14.18071741 -5.78741711 [30,] -2.90828318 14.18071741 [31,] 4.53008498 -2.90828318 [32,] 13.95273179 4.53008498 [33,] 14.96817202 13.95273179 [34,] 18.36367974 14.96817202 [35,] 6.84813825 18.36367974 [36,] -2.12888471 6.84813825 [37,] 6.89492175 -2.12888471 [38,] 17.06952331 6.89492175 [39,] 21.67014094 17.06952331 [40,] 18.16543841 21.67014094 [41,] 4.28008922 18.16543841 [42,] -3.65143593 4.28008922 [43,] 4.11640752 -3.65143593 [44,] 19.49568415 4.11640752 [45,] 15.02794967 19.49568415 [46,] 14.16887228 15.02794967 [47,] 42.90655360 14.16887228 [48,] -7.60199554 42.90655360 [49,] -10.55160642 -7.60199554 [50,] -4.61205932 -10.55160642 [51,] 9.37025417 -4.61205932 [52,] -18.27713268 9.37025417 [53,] -4.66738697 -18.27713268 [54,] 17.24859345 -4.66738697 [55,] 18.60988517 17.24859345 [56,] 16.40884076 18.60988517 [57,] 13.79436539 16.40884076 [58,] 27.81942393 13.79436539 [59,] 31.08372351 27.81942393 [60,] 13.63799097 31.08372351 [61,] 26.35825281 13.63799097 [62,] 8.58084541 26.35825281 [63,] -2.72520313 8.58084541 [64,] 9.41360813 -2.72520313 [65,] 35.21204183 9.41360813 [66,] 1.24093592 35.21204183 [67,] 9.73309001 1.24093592 [68,] 7.52994405 9.73309001 [69,] 0.13322839 7.52994405 [70,] 18.39735026 0.13322839 [71,] 4.01260996 18.39735026 [72,] -16.39219171 4.01260996 [73,] -3.08270821 -16.39219171 [74,] -12.65773566 -3.08270821 [75,] 4.19329680 -12.65773566 [76,] -19.75299755 4.19329680 [77,] -4.28322580 -19.75299755 [78,] -1.54092946 -4.28322580 [79,] 13.29463224 -1.54092946 [80,] -4.62613731 13.29463224 [81,] -8.29142989 -4.62613731 [82,] -1.30104187 -8.29142989 [83,] -11.52419434 -1.30104187 [84,] -1.19631980 -11.52419434 [85,] -9.41647654 -1.19631980 [86,] 5.83419745 -9.41647654 [87,] -0.72146790 5.83419745 [88,] 18.55177218 -0.72146790 [89,] -5.98206239 18.55177218 [90,] 1.54407290 -5.98206239 [91,] -21.15724418 1.54407290 [92,] -17.87526932 -21.15724418 [93,] 6.84872145 -17.87526932 [94,] -5.92727039 6.84872145 [95,] -26.41463096 -5.92727039 [96,] -19.48989495 -26.41463096 [97,] -8.56117661 -19.48989495 [98,] 3.81940476 -8.56117661 [99,] 11.95832892 3.81940476 [100,] 12.66261113 11.95832892 [101,] 6.82392124 12.66261113 [102,] -12.24244890 6.82392124 [103,] 15.54560438 -12.24244890 [104,] -15.02227172 15.54560438 [105,] -6.48003188 -15.02227172 [106,] -20.05778832 -6.48003188 [107,] 0.58596394 -20.05778832 [108,] -19.62490921 0.58596394 [109,] 9.01441388 -19.62490921 [110,] 11.71100052 9.01441388 [111,] -5.65457358 11.71100052 [112,] -18.45545664 -5.65457358 [113,] 13.64139077 -18.45545664 [114,] 8.21471077 13.64139077 [115,] -5.37914984 8.21471077 [116,] -10.78336990 -5.37914984 [117,] -10.87405468 -10.78336990 [118,] 8.30998347 -10.87405468 [119,] 14.17097655 8.30998347 [120,] -0.95226933 14.17097655 [121,] 21.26437888 -0.95226933 [122,] -6.17238362 21.26437888 [123,] 14.12364566 -6.17238362 [124,] -1.90409862 14.12364566 [125,] 5.59542852 -1.90409862 [126,] 9.96487928 5.59542852 [127,] 15.12729703 9.96487928 [128,] -19.11172204 15.12729703 [129,] -21.56418938 -19.11172204 [130,] -16.72026803 -21.56418938 [131,] -25.70379572 -16.72026803 [132,] -20.54325654 -25.70379572 [133,] -14.49660460 -20.54325654 [134,] -0.08579711 -14.49660460 [135,] -27.94252987 -0.08579711 [136,] 3.42535807 -27.94252987 [137,] -22.84268036 3.42535807 [138,] -21.84042553 -22.84268036 [139,] -21.08222990 -21.84042553 [140,] 0.11139455 -21.08222990 [141,] 5.05682857 0.11139455 [142,] 6.18236149 5.05682857 [143,] 23.78123816 6.18236149 [144,] 1.52204660 23.78123816 [145,] 20.78972893 1.52204660 [146,] 1.80713067 20.78972893 [147,] -14.66927708 1.80713067 [148,] -24.24126121 -14.66927708 [149,] -13.11572255 -24.24126121 [150,] 10.31546762 -13.11572255 [151,] 5.33894619 10.31546762 [152,] -23.99452778 5.33894619 [153,] 13.44025601 -23.99452778 [154,] 0.87453919 13.44025601 [155,] -3.46404726 0.87453919 [156,] -28.75978743 -3.46404726 [157,] 6.65150321 -28.75978743 [158,] -32.53678486 6.65150321 [159,] 3.10358591 -32.53678486 [160,] 15.21280344 3.10358591 [161,] -16.85912071 15.21280344 [162,] 6.75690185 -16.85912071 [163,] -17.95726596 6.75690185 [164,] 9.91434197 -17.95726596 [165,] 30.36897792 9.91434197 [166,] -0.41595178 30.36897792 [167,] 10.03242760 -0.41595178 [168,] -9.96539346 10.03242760 [169,] -7.92185596 -9.96539346 [170,] 0.67766670 -7.92185596 [171,] -23.76077377 0.67766670 [172,] -21.65106490 -23.76077377 [173,] -15.21830705 -21.65106490 [174,] -17.51737450 -15.21830705 [175,] -20.64858207 -17.51737450 [176,] -17.04416735 -20.64858207 [177,] -1.33567019 -17.04416735 [178,] -5.65064563 -1.33567019 [179,] 4.18316643 -5.65064563 [180,] 6.74592185 4.18316643 [181,] -1.84375642 6.74592185 [182,] -17.43550441 -1.84375642 [183,] -13.85870970 -17.43550441 [184,] -16.98383710 -13.85870970 [185,] -20.79900671 -16.98383710 [186,] -35.17991523 -20.79900671 [187,] -51.06506620 -35.17991523 [188,] -22.09339481 -51.06506620 [189,] -0.93833461 -22.09339481 [190,] -7.49909837 -0.93833461 [191,] 13.55731616 -7.49909837 [192,] -7.55574018 13.55731616 [193,] 10.35759738 -7.55574018 [194,] 43.16642382 10.35759738 > z <- as.data.frame(dum1) > z lag(myerror, k = 1) myerror 1 -7.83745428 15.58378197 2 1.01188287 -7.83745428 3 -3.66699803 1.01188287 4 -2.19155427 -3.66699803 5 -1.59712982 -2.19155427 6 0.83602402 -1.59712982 7 -6.91847654 0.83602402 8 5.21717314 -6.91847654 9 11.11098990 5.21717314 10 11.02424285 11.11098990 11 6.63448853 11.02424285 12 -29.62443663 6.63448853 13 -0.03638896 -29.62443663 14 -5.12858914 -0.03638896 15 -4.07846117 -5.12858914 16 -1.70750557 -4.07846117 17 -3.13300460 -1.70750557 18 21.43387460 -3.13300460 19 -2.21813265 21.43387460 20 5.67017834 -2.21813265 21 15.91522694 5.67017834 22 32.86186726 15.91522694 23 20.47358411 32.86186726 24 16.98532760 20.47358411 25 13.65895272 16.98532760 26 25.33696072 13.65895272 27 -6.39301574 25.33696072 28 -5.78741711 -6.39301574 29 14.18071741 -5.78741711 30 -2.90828318 14.18071741 31 4.53008498 -2.90828318 32 13.95273179 4.53008498 33 14.96817202 13.95273179 34 18.36367974 14.96817202 35 6.84813825 18.36367974 36 -2.12888471 6.84813825 37 6.89492175 -2.12888471 38 17.06952331 6.89492175 39 21.67014094 17.06952331 40 18.16543841 21.67014094 41 4.28008922 18.16543841 42 -3.65143593 4.28008922 43 4.11640752 -3.65143593 44 19.49568415 4.11640752 45 15.02794967 19.49568415 46 14.16887228 15.02794967 47 42.90655360 14.16887228 48 -7.60199554 42.90655360 49 -10.55160642 -7.60199554 50 -4.61205932 -10.55160642 51 9.37025417 -4.61205932 52 -18.27713268 9.37025417 53 -4.66738697 -18.27713268 54 17.24859345 -4.66738697 55 18.60988517 17.24859345 56 16.40884076 18.60988517 57 13.79436539 16.40884076 58 27.81942393 13.79436539 59 31.08372351 27.81942393 60 13.63799097 31.08372351 61 26.35825281 13.63799097 62 8.58084541 26.35825281 63 -2.72520313 8.58084541 64 9.41360813 -2.72520313 65 35.21204183 9.41360813 66 1.24093592 35.21204183 67 9.73309001 1.24093592 68 7.52994405 9.73309001 69 0.13322839 7.52994405 70 18.39735026 0.13322839 71 4.01260996 18.39735026 72 -16.39219171 4.01260996 73 -3.08270821 -16.39219171 74 -12.65773566 -3.08270821 75 4.19329680 -12.65773566 76 -19.75299755 4.19329680 77 -4.28322580 -19.75299755 78 -1.54092946 -4.28322580 79 13.29463224 -1.54092946 80 -4.62613731 13.29463224 81 -8.29142989 -4.62613731 82 -1.30104187 -8.29142989 83 -11.52419434 -1.30104187 84 -1.19631980 -11.52419434 85 -9.41647654 -1.19631980 86 5.83419745 -9.41647654 87 -0.72146790 5.83419745 88 18.55177218 -0.72146790 89 -5.98206239 18.55177218 90 1.54407290 -5.98206239 91 -21.15724418 1.54407290 92 -17.87526932 -21.15724418 93 6.84872145 -17.87526932 94 -5.92727039 6.84872145 95 -26.41463096 -5.92727039 96 -19.48989495 -26.41463096 97 -8.56117661 -19.48989495 98 3.81940476 -8.56117661 99 11.95832892 3.81940476 100 12.66261113 11.95832892 101 6.82392124 12.66261113 102 -12.24244890 6.82392124 103 15.54560438 -12.24244890 104 -15.02227172 15.54560438 105 -6.48003188 -15.02227172 106 -20.05778832 -6.48003188 107 0.58596394 -20.05778832 108 -19.62490921 0.58596394 109 9.01441388 -19.62490921 110 11.71100052 9.01441388 111 -5.65457358 11.71100052 112 -18.45545664 -5.65457358 113 13.64139077 -18.45545664 114 8.21471077 13.64139077 115 -5.37914984 8.21471077 116 -10.78336990 -5.37914984 117 -10.87405468 -10.78336990 118 8.30998347 -10.87405468 119 14.17097655 8.30998347 120 -0.95226933 14.17097655 121 21.26437888 -0.95226933 122 -6.17238362 21.26437888 123 14.12364566 -6.17238362 124 -1.90409862 14.12364566 125 5.59542852 -1.90409862 126 9.96487928 5.59542852 127 15.12729703 9.96487928 128 -19.11172204 15.12729703 129 -21.56418938 -19.11172204 130 -16.72026803 -21.56418938 131 -25.70379572 -16.72026803 132 -20.54325654 -25.70379572 133 -14.49660460 -20.54325654 134 -0.08579711 -14.49660460 135 -27.94252987 -0.08579711 136 3.42535807 -27.94252987 137 -22.84268036 3.42535807 138 -21.84042553 -22.84268036 139 -21.08222990 -21.84042553 140 0.11139455 -21.08222990 141 5.05682857 0.11139455 142 6.18236149 5.05682857 143 23.78123816 6.18236149 144 1.52204660 23.78123816 145 20.78972893 1.52204660 146 1.80713067 20.78972893 147 -14.66927708 1.80713067 148 -24.24126121 -14.66927708 149 -13.11572255 -24.24126121 150 10.31546762 -13.11572255 151 5.33894619 10.31546762 152 -23.99452778 5.33894619 153 13.44025601 -23.99452778 154 0.87453919 13.44025601 155 -3.46404726 0.87453919 156 -28.75978743 -3.46404726 157 6.65150321 -28.75978743 158 -32.53678486 6.65150321 159 3.10358591 -32.53678486 160 15.21280344 3.10358591 161 -16.85912071 15.21280344 162 6.75690185 -16.85912071 163 -17.95726596 6.75690185 164 9.91434197 -17.95726596 165 30.36897792 9.91434197 166 -0.41595178 30.36897792 167 10.03242760 -0.41595178 168 -9.96539346 10.03242760 169 -7.92185596 -9.96539346 170 0.67766670 -7.92185596 171 -23.76077377 0.67766670 172 -21.65106490 -23.76077377 173 -15.21830705 -21.65106490 174 -17.51737450 -15.21830705 175 -20.64858207 -17.51737450 176 -17.04416735 -20.64858207 177 -1.33567019 -17.04416735 178 -5.65064563 -1.33567019 179 4.18316643 -5.65064563 180 6.74592185 4.18316643 181 -1.84375642 6.74592185 182 -17.43550441 -1.84375642 183 -13.85870970 -17.43550441 184 -16.98383710 -13.85870970 185 -20.79900671 -16.98383710 186 -35.17991523 -20.79900671 187 -51.06506620 -35.17991523 188 -22.09339481 -51.06506620 189 -0.93833461 -22.09339481 190 -7.49909837 -0.93833461 191 13.55731616 -7.49909837 192 -7.55574018 13.55731616 193 10.35759738 -7.55574018 194 43.16642382 10.35759738 > plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals') > lines(lowess(z)) > abline(lm(z)) > grid() > dev.off() null device 1 > postscript(file="/var/fisher/rcomp/tmp/7stpo1386705982.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/fisher/rcomp/tmp/8kk181386705982.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/fisher/rcomp/tmp/98hgp1386705982.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0)) > plot(mylm, las = 1, sub='Residual Diagnostics') > par(opar) > dev.off() null device 1 > if (n > n25) { + postscript(file="/var/fisher/rcomp/tmp/10ibzd1386705982.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) + plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint') + grid() + dev.off() + } null device 1 > > #Note: the /var/fisher/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab > load(file="/var/fisher/rcomp/createtable") > > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE) > a<-table.row.end(a) > myeq <- colnames(x)[1] > myeq <- paste(myeq, '[t] = ', sep='') > for (i in 1:k){ + if (mysum$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/11662x1386705982.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/122frx1386705982.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/13ij311386705983.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/14tado1386705983.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/15mqwf1386705983.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/16e26t1386705983.tab") + } > > try(system("convert tmp/1r34f1386705982.ps tmp/1r34f1386705982.png",intern=TRUE)) character(0) > try(system("convert tmp/2cr6u1386705982.ps tmp/2cr6u1386705982.png",intern=TRUE)) character(0) > try(system("convert tmp/3rn941386705982.ps tmp/3rn941386705982.png",intern=TRUE)) character(0) > try(system("convert tmp/4ykb01386705982.ps tmp/4ykb01386705982.png",intern=TRUE)) character(0) > try(system("convert tmp/5vb5f1386705982.ps tmp/5vb5f1386705982.png",intern=TRUE)) character(0) > try(system("convert tmp/6jmrs1386705982.ps tmp/6jmrs1386705982.png",intern=TRUE)) character(0) > try(system("convert tmp/7stpo1386705982.ps tmp/7stpo1386705982.png",intern=TRUE)) character(0) > try(system("convert tmp/8kk181386705982.ps tmp/8kk181386705982.png",intern=TRUE)) character(0) > try(system("convert tmp/98hgp1386705982.ps tmp/98hgp1386705982.png",intern=TRUE)) character(0) > try(system("convert tmp/10ibzd1386705982.ps tmp/10ibzd1386705982.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 29.785 4.181 33.961