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(1 + ,119.992 + ,157.302 + ,74.997 + ,0.00784 + ,0.00007 + ,0.00554 + ,0.01109 + ,0.04374 + ,0.02182 + ,0.0313 + ,0.02971 + ,0.06545 + ,0.02211 + ,0.414783 + ,0.815285 + ,-4.813031 + ,0.266482 + ,2.301442 + ,0.284654 + ,1 + ,122.4 + ,148.65 + ,113.819 + ,0.00968 + ,0.00008 + ,0.00696 + ,0.01394 + ,0.06134 + ,0.03134 + ,0.04518 + ,0.04368 + ,0.09403 + ,0.01929 + ,0.458359 + ,0.819521 + ,-4.075192 + ,0.33559 + ,2.486855 + ,0.368674 + ,1 + ,116.682 + ,131.111 + ,111.555 + ,0.0105 + ,0.00009 + ,0.00781 + ,0.01633 + ,0.05233 + ,0.02757 + ,0.03858 + ,0.0359 + ,0.0827 + ,0.01309 + ,0.429895 + ,0.825288 + ,-4.443179 + ,0.311173 + ,2.342259 + ,0.332634 + ,1 + ,116.676 + ,137.871 + ,111.366 + ,0.00997 + ,0.00009 + ,0.00698 + ,0.01505 + ,0.05492 + ,0.02924 + ,0.04005 + ,0.03772 + ,0.08771 + ,0.01353 + ,0.434969 + ,0.819235 + ,-4.117501 + ,0.334147 + ,2.405554 + ,0.368975 + ,1 + ,116.014 + ,141.781 + ,110.655 + ,0.01284 + ,0.00011 + ,0.00908 + ,0.01966 + 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,0.02145 + ,0.01155 + ,0.01341 + ,0.01666 + ,0.03464 + ,0.00595 + ,0.329577 + ,0.75718 + ,-6.27717 + ,0.109397 + ,2.209021 + ,0.156368 + ,0 + ,117.226 + ,123.925 + ,106.656 + ,0.00417 + ,0.00004 + ,0.0027 + ,0.00558 + ,0.01909 + ,0.00864 + ,0.01223 + ,0.01949 + ,0.02592 + ,0.00955 + ,0.603515 + ,0.669565 + ,-5.61907 + ,0.191576 + ,2.027228 + ,0.215724 + ,0 + ,116.848 + ,217.552 + ,99.503 + ,0.00531 + ,0.00005 + ,0.00346 + ,0.0078 + ,0.01795 + ,0.0081 + ,0.01144 + ,0.01756 + ,0.02429 + ,0.01179 + ,0.663842 + ,0.656516 + ,-5.198864 + ,0.206768 + ,2.120412 + ,0.252404 + ,0 + ,116.286 + ,177.291 + ,96.983 + ,0.00314 + ,0.00003 + ,0.00192 + ,0.00403 + ,0.01564 + ,0.00667 + ,0.0099 + ,0.01691 + ,0.02001 + ,0.00737 + ,0.598515 + ,0.654331 + ,-5.592584 + ,0.133917 + ,2.058658 + ,0.214346 + ,0 + ,116.556 + ,592.03 + ,86.228 + ,0.00496 + ,0.00004 + ,0.00263 + ,0.00762 + ,0.0166 + ,0.0082 + ,0.00972 + ,0.01491 + ,0.0246 + ,0.01397 + ,0.566424 + ,0.667654 + ,-6.431119 + ,0.15331 + ,2.161936 + ,0.120605 + ,0 + ,116.342 + ,581.289 + ,94.246 + ,0.00267 + ,0.00002 + ,0.00148 + ,0.00345 + ,0.013 + ,0.00631 + ,0.00789 + ,0.01144 + ,0.01892 + ,0.0068 + ,0.528485 + ,0.663884 + ,-6.359018 + ,0.116636 + ,2.152083 + ,0.138868 + ,0 + ,114.563 + ,119.167 + ,86.647 + ,0.00327 + ,0.00003 + ,0.00184 + ,0.00439 + ,0.01185 + ,0.00557 + ,0.00721 + ,0.01095 + ,0.01672 + ,0.00703 + ,0.555303 + ,0.659132 + ,-6.710219 + ,0.149694 + ,1.91399 + ,0.121777 + ,0 + ,201.774 + ,262.707 + ,78.228 + ,0.00694 + ,0.00003 + ,0.00396 + ,0.01235 + ,0.02574 + ,0.01454 + ,0.01582 + ,0.01758 + ,0.04363 + ,0.04441 + ,0.508479 + ,0.683761 + ,-6.934474 + ,0.15989 + ,2.316346 + ,0.112838 + ,0 + ,174.188 + ,230.978 + ,94.261 + ,0.00459 + ,0.00003 + ,0.00259 + ,0.0079 + ,0.04087 + ,0.02336 + ,0.02498 + ,0.02745 + ,0.07008 + ,0.02764 + ,0.448439 + ,0.657899 + ,-6.538586 + ,0.121952 + ,2.657476 + ,0.13305 + ,0 + ,209.516 + ,253.017 + ,89.488 + ,0.00564 + ,0.00003 + ,0.00292 + ,0.00994 + ,0.02751 + ,0.01604 + ,0.01657 + ,0.01879 + ,0.04812 + ,0.0181 + ,0.431674 + ,0.683244 + ,-6.195325 + ,0.129303 + ,2.784312 + ,0.168895 + ,0 + ,174.688 + ,240.005 + ,74.287 + ,0.0136 + ,0.00008 + ,0.00564 + ,0.01873 + ,0.02308 + ,0.01268 + ,0.01365 + ,0.01667 + ,0.03804 + ,0.10715 + ,0.407567 + ,0.655683 + ,-6.787197 + ,0.158453 + ,2.679772 + ,0.131728 + ,0 + ,198.764 + ,396.961 + ,74.904 + ,0.0074 + ,0.00004 + ,0.0039 + ,0.01109 + ,0.02296 + ,0.01265 + ,0.01321 + ,0.01588 + ,0.03794 + ,0.07223 + ,0.451221 + ,0.643956 + ,-6.744577 + ,0.207454 + ,2.138608 + ,0.123306 + ,0 + ,214.289 + ,260.277 + ,77.973 + ,0.00567 + ,0.00003 + ,0.00317 + ,0.00885 + ,0.01884 + ,0.01026 + ,0.01161 + ,0.01373 + ,0.03078 + ,0.04398 + ,0.462803 + ,0.664357 + ,-5.724056 + ,0.190667 + ,2.555477 + ,0.148569) + ,dim=c(20 + ,195) + ,dimnames=list(c('status' + ,'MDVP:Fo(Hz)' + ,'MDVP:Fhi(Hz)' + ,'MDVP:Flo(Hz)' + ,'MDVP:Jitter(%)' + ,'MDVP:Jitter(Abs)' + ,'MDVP:PPQ' + ,'Jitter:DDP' + ,'MDVP:Shimmer' + ,'Shimmer:APQ3' + ,'Shimmer:APQ5' + ,'MDVP:APQ' + ,'Shimmer:DDA' + ,'NHR' + ,'RPDE' + ,'DFA' + ,'spread1' + ,'spread2' + ,'D2' + ,'PPE') + ,1:195)) > y <- array(NA,dim=c(20,195),dimnames=list(c('status','MDVP:Fo(Hz)','MDVP:Fhi(Hz)','MDVP:Flo(Hz)','MDVP:Jitter(%)','MDVP:Jitter(Abs)','MDVP:PPQ','Jitter:DDP','MDVP:Shimmer','Shimmer:APQ3','Shimmer:APQ5','MDVP:APQ','Shimmer:DDA','NHR','RPDE','DFA','spread1','spread2','D2','PPE'),1:195)) > for (i in 1:dim(x)[1]) + { + for (j in 1:dim(x)[2]) + { + y[i,j] <- as.numeric(x[i,j]) + } + } > par3 = 'No Linear Trend' > par2 = 'Do not include Seasonal Dummies' > par1 = '1' > par3 <- 'No Linear Trend' > par2 <- 'Do not include Seasonal Dummies' > par1 <- '1' > #'GNU S' R Code compiled by R2WASP v. 1.2.327 () > #Author: root > #To cite this work: Wessa P., (2013), Multiple Regression (v1.0.29) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_multipleregression.wasp/ > #Source of accompanying publication: Office for Research, Development, and Education > # > library(lattice) > library(lmtest) Loading required package: zoo Attaching package: 'zoo' The following objects are masked from 'package:base': as.Date, as.Date.numeric > n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test > par1 <- as.numeric(par1) > x <- t(y) > k <- length(x[1,]) > n <- length(x[,1]) > x1 <- cbind(x[,par1], x[,1:k!=par1]) > mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1]) > colnames(x1) <- mycolnames #colnames(x)[par1] > x <- x1 > if (par3 == 'First Differences'){ + x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep=''))) + for (i in 1:n-1) { + for (j in 1:k) { + x2[i,j] <- x[i+1,j] - x[i,j] + } + } + x <- x2 + } > if (par2 == 'Include Monthly Dummies'){ + x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep =''))) + for (i in 1:11){ + x2[seq(i,n,12),i] <- 1 + } + x <- cbind(x, x2) + } > if (par2 == 'Include Quarterly Dummies'){ + x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep =''))) + for (i in 1:3){ + x2[seq(i,n,4),i] <- 1 + } + x <- cbind(x, x2) + } > k <- length(x[1,]) > if (par3 == 'Linear Trend'){ + x <- cbind(x, c(1:n)) + colnames(x)[k+1] <- 't' + } > x status MDVP:Fo(Hz) MDVP:Fhi(Hz) MDVP:Flo(Hz) MDVP:Jitter(%) 1 1 119.992 157.302 74.997 0.00784 2 1 122.400 148.650 113.819 0.00968 3 1 116.682 131.111 111.555 0.01050 4 1 116.676 137.871 111.366 0.00997 5 1 116.014 141.781 110.655 0.01284 6 1 120.552 131.162 113.787 0.00968 7 1 120.267 137.244 114.820 0.00333 8 1 107.332 113.840 104.315 0.00290 9 1 95.730 132.068 91.754 0.00551 10 1 95.056 120.103 91.226 0.00532 11 1 88.333 112.240 84.072 0.00505 12 1 91.904 115.871 86.292 0.00540 13 1 136.926 159.866 131.276 0.00293 14 1 139.173 179.139 76.556 0.00390 15 1 152.845 163.305 75.836 0.00294 16 1 142.167 217.455 83.159 0.00369 17 1 144.188 349.259 82.764 0.00544 18 1 168.778 232.181 75.603 0.00718 19 1 153.046 175.829 68.623 0.00742 20 1 156.405 189.398 142.822 0.00768 21 1 153.848 165.738 65.782 0.00840 22 1 153.880 172.860 78.128 0.00480 23 1 167.930 193.221 79.068 0.00442 24 1 173.917 192.735 86.180 0.00476 25 1 163.656 200.841 76.779 0.00742 26 1 104.400 206.002 77.968 0.00633 27 1 171.041 208.313 75.501 0.00455 28 1 146.845 208.701 81.737 0.00496 29 1 155.358 227.383 80.055 0.00310 30 1 162.568 198.346 77.630 0.00502 31 0 197.076 206.896 192.055 0.00289 32 0 199.228 209.512 192.091 0.00241 33 0 198.383 215.203 193.104 0.00212 34 0 202.266 211.604 197.079 0.00180 35 0 203.184 211.526 196.160 0.00178 36 0 201.464 210.565 195.708 0.00198 37 1 177.876 192.921 168.013 0.00411 38 1 176.170 185.604 163.564 0.00369 39 1 180.198 201.249 175.456 0.00284 40 1 187.733 202.324 173.015 0.00316 41 1 186.163 197.724 177.584 0.00298 42 1 184.055 196.537 166.977 0.00258 43 0 237.226 247.326 225.227 0.00298 44 0 241.404 248.834 232.483 0.00281 45 0 243.439 250.912 232.435 0.00210 46 0 242.852 255.034 227.911 0.00225 47 0 245.510 262.090 231.848 0.00235 48 0 252.455 261.487 182.786 0.00185 49 0 122.188 128.611 115.765 0.00524 50 0 122.964 130.049 114.676 0.00428 51 0 124.445 135.069 117.495 0.00431 52 0 126.344 134.231 112.773 0.00448 53 0 128.001 138.052 122.080 0.00436 54 0 129.336 139.867 118.604 0.00490 55 1 108.807 134.656 102.874 0.00761 56 1 109.860 126.358 104.437 0.00874 57 1 110.417 131.067 103.370 0.00784 58 1 117.274 129.916 110.402 0.00752 59 1 116.879 131.897 108.153 0.00788 60 1 114.847 271.314 104.680 0.00867 61 0 209.144 237.494 109.379 0.00282 62 0 223.365 238.987 98.664 0.00264 63 0 222.236 231.345 205.495 0.00266 64 0 228.832 234.619 223.634 0.00296 65 0 229.401 252.221 221.156 0.00205 66 0 228.969 239.541 113.201 0.00238 67 1 140.341 159.774 67.021 0.00817 68 1 136.969 166.607 66.004 0.00923 69 1 143.533 162.215 65.809 0.01101 70 1 148.090 162.824 67.343 0.00762 71 1 142.729 162.408 65.476 0.00831 72 1 136.358 176.595 65.750 0.00971 73 1 120.080 139.710 111.208 0.00405 74 1 112.014 588.518 107.024 0.00533 75 1 110.793 128.101 107.316 0.00494 76 1 110.707 122.611 105.007 0.00516 77 1 112.876 148.826 106.981 0.00500 78 1 110.568 125.394 106.821 0.00462 79 1 95.385 102.145 90.264 0.00608 80 1 100.770 115.697 85.545 0.01038 81 1 96.106 108.664 84.510 0.00694 82 1 95.605 107.715 87.549 0.00702 83 1 100.960 110.019 95.628 0.00606 84 1 98.804 102.305 87.804 0.00432 85 1 176.858 205.560 75.344 0.00747 86 1 180.978 200.125 155.495 0.00406 87 1 178.222 202.450 141.047 0.00321 88 1 176.281 227.381 125.610 0.00520 89 1 173.898 211.350 74.677 0.00448 90 1 179.711 225.930 144.878 0.00709 91 1 166.605 206.008 78.032 0.00742 92 1 151.955 163.335 147.226 0.00419 93 1 148.272 164.989 142.299 0.00459 94 1 152.125 161.469 76.596 0.00382 95 1 157.821 172.975 68.401 0.00358 96 1 157.447 163.267 149.605 0.00369 97 1 159.116 168.913 144.811 0.00342 98 1 125.036 143.946 116.187 0.01280 99 1 125.791 140.557 96.206 0.01378 100 1 126.512 141.756 99.770 0.01936 101 1 125.641 141.068 116.346 0.03316 102 1 128.451 150.449 75.632 0.01551 103 1 139.224 586.567 66.157 0.03011 104 1 150.258 154.609 75.349 0.00248 105 1 154.003 160.267 128.621 0.00183 106 1 149.689 160.368 133.608 0.00257 107 1 155.078 163.736 144.148 0.00168 108 1 151.884 157.765 133.751 0.00258 109 1 151.989 157.339 132.857 0.00174 110 1 193.030 208.900 80.297 0.00766 111 1 200.714 223.982 89.686 0.00621 112 1 208.519 220.315 199.020 0.00609 113 1 204.664 221.300 189.621 0.00841 114 1 210.141 232.706 185.258 0.00534 115 1 206.327 226.355 92.020 0.00495 116 1 151.872 492.892 69.085 0.00856 117 1 158.219 442.557 71.948 0.00476 118 1 170.756 450.247 79.032 0.00555 119 1 178.285 442.824 82.063 0.00462 120 1 217.116 233.481 93.978 0.00404 121 1 128.940 479.697 88.251 0.00581 122 1 176.824 215.293 83.961 0.00460 123 1 138.190 203.522 83.340 0.00704 124 1 182.018 197.173 79.187 0.00842 125 1 156.239 195.107 79.820 0.00694 126 1 145.174 198.109 80.637 0.00733 127 1 138.145 197.238 81.114 0.00544 128 1 166.888 198.966 79.512 0.00638 129 1 119.031 127.533 109.216 0.00440 130 1 120.078 126.632 105.667 0.00270 131 1 120.289 128.143 100.209 0.00492 132 1 120.256 125.306 104.773 0.00407 133 1 119.056 125.213 86.795 0.00346 134 1 118.747 123.723 109.836 0.00331 135 1 106.516 112.777 93.105 0.00589 136 1 110.453 127.611 105.554 0.00494 137 1 113.400 133.344 107.816 0.00451 138 1 113.166 130.270 100.673 0.00502 139 1 112.239 126.609 104.095 0.00472 140 1 116.150 131.731 109.815 0.00381 141 1 170.368 268.796 79.543 0.00571 142 1 208.083 253.792 91.802 0.00757 143 1 198.458 219.290 148.691 0.00376 144 1 202.805 231.508 86.232 0.00370 145 1 202.544 241.350 164.168 0.00254 146 1 223.361 263.872 87.638 0.00352 147 1 169.774 191.759 151.451 0.01568 148 1 183.520 216.814 161.340 0.01466 149 1 188.620 216.302 165.982 0.01719 150 1 202.632 565.740 177.258 0.01627 151 1 186.695 211.961 149.442 0.01872 152 1 192.818 224.429 168.793 0.03107 153 1 198.116 233.099 174.478 0.02714 154 1 121.345 139.644 98.250 0.00684 155 1 119.100 128.442 88.833 0.00692 156 1 117.870 127.349 95.654 0.00647 157 1 122.336 142.369 94.794 0.00727 158 1 117.963 134.209 100.757 0.01813 159 1 126.144 154.284 97.543 0.00975 160 1 127.930 138.752 112.173 0.00605 161 1 114.238 124.393 77.022 0.00581 162 1 115.322 135.738 107.802 0.00619 163 1 114.554 126.778 91.121 0.00651 164 1 112.150 131.669 97.527 0.00519 165 1 102.273 142.830 85.902 0.00907 166 0 236.200 244.663 102.137 0.00277 167 0 237.323 243.709 229.256 0.00303 168 0 260.105 264.919 237.303 0.00339 169 0 197.569 217.627 90.794 0.00803 170 0 240.301 245.135 219.783 0.00517 171 0 244.990 272.210 239.170 0.00451 172 0 112.547 133.374 105.715 0.00355 173 0 110.739 113.597 100.139 0.00356 174 0 113.715 116.443 96.913 0.00349 175 0 117.004 144.466 99.923 0.00353 176 0 115.380 123.109 108.634 0.00332 177 0 116.388 129.038 108.970 0.00346 178 1 151.737 190.204 129.859 0.00314 179 1 148.790 158.359 138.990 0.00309 180 1 148.143 155.982 135.041 0.00392 181 1 150.440 163.441 144.736 0.00396 182 1 148.462 161.078 141.998 0.00397 183 1 149.818 163.417 144.786 0.00336 184 0 117.226 123.925 106.656 0.00417 185 0 116.848 217.552 99.503 0.00531 186 0 116.286 177.291 96.983 0.00314 187 0 116.556 592.030 86.228 0.00496 188 0 116.342 581.289 94.246 0.00267 189 0 114.563 119.167 86.647 0.00327 190 0 201.774 262.707 78.228 0.00694 191 0 174.188 230.978 94.261 0.00459 192 0 209.516 253.017 89.488 0.00564 193 0 174.688 240.005 74.287 0.01360 194 0 198.764 396.961 74.904 0.00740 195 0 214.289 260.277 77.973 0.00567 MDVP:Jitter(Abs) MDVP:PPQ Jitter:DDP MDVP:Shimmer Shimmer:APQ3 Shimmer:APQ5 1 7.0e-05 0.00554 0.01109 0.04374 0.02182 0.03130 2 8.0e-05 0.00696 0.01394 0.06134 0.03134 0.04518 3 9.0e-05 0.00781 0.01633 0.05233 0.02757 0.03858 4 9.0e-05 0.00698 0.01505 0.05492 0.02924 0.04005 5 1.1e-04 0.00908 0.01966 0.06425 0.03490 0.04825 6 8.0e-05 0.00750 0.01388 0.04701 0.02328 0.03526 7 3.0e-05 0.00202 0.00466 0.01608 0.00779 0.00937 8 3.0e-05 0.00182 0.00431 0.01567 0.00829 0.00946 9 6.0e-05 0.00332 0.00880 0.02093 0.01073 0.01277 10 6.0e-05 0.00332 0.00803 0.02838 0.01441 0.01725 11 6.0e-05 0.00330 0.00763 0.02143 0.01079 0.01342 12 6.0e-05 0.00336 0.00844 0.02752 0.01424 0.01641 13 2.0e-05 0.00153 0.00355 0.01259 0.00656 0.00717 14 3.0e-05 0.00208 0.00496 0.01642 0.00728 0.00932 15 2.0e-05 0.00149 0.00364 0.01828 0.01064 0.00972 16 3.0e-05 0.00203 0.00471 0.01503 0.00772 0.00888 17 4.0e-05 0.00292 0.00632 0.02047 0.00969 0.01200 18 4.0e-05 0.00387 0.00853 0.03327 0.01441 0.01893 19 5.0e-05 0.00432 0.01092 0.05517 0.02471 0.03572 20 5.0e-05 0.00399 0.01116 0.03995 0.01721 0.02374 21 5.0e-05 0.00450 0.01285 0.03810 0.01667 0.02383 22 3.0e-05 0.00267 0.00696 0.04137 0.02021 0.02591 23 3.0e-05 0.00247 0.00661 0.04351 0.02228 0.02540 24 3.0e-05 0.00258 0.00663 0.04192 0.02187 0.02470 25 5.0e-05 0.00390 0.01140 0.01659 0.00738 0.00948 26 6.0e-05 0.00375 0.00948 0.03767 0.01732 0.02245 27 3.0e-05 0.00234 0.00750 0.01966 0.00889 0.01169 28 3.0e-05 0.00275 0.00749 0.01919 0.00883 0.01144 29 2.0e-05 0.00176 0.00476 0.01718 0.00769 0.01012 30 3.0e-05 0.00253 0.00841 0.01791 0.00793 0.01057 31 1.0e-05 0.00168 0.00498 0.01098 0.00563 0.00680 32 1.0e-05 0.00138 0.00402 0.01015 0.00504 0.00641 33 1.0e-05 0.00135 0.00339 0.01263 0.00640 0.00825 34 9.0e-06 0.00107 0.00278 0.00954 0.00469 0.00606 35 9.0e-06 0.00106 0.00283 0.00958 0.00468 0.00610 36 1.0e-05 0.00115 0.00314 0.01194 0.00586 0.00760 37 2.0e-05 0.00241 0.00700 0.02126 0.01154 0.01347 38 2.0e-05 0.00218 0.00616 0.01851 0.00938 0.01160 39 2.0e-05 0.00166 0.00459 0.01444 0.00726 0.00885 40 2.0e-05 0.00182 0.00504 0.01663 0.00829 0.01003 41 2.0e-05 0.00175 0.00496 0.01495 0.00774 0.00941 42 1.0e-05 0.00147 0.00403 0.01463 0.00742 0.00901 43 1.0e-05 0.00182 0.00507 0.01752 0.01035 0.01024 44 1.0e-05 0.00173 0.00470 0.01760 0.01006 0.01038 45 9.0e-06 0.00137 0.00327 0.01419 0.00777 0.00898 46 9.0e-06 0.00139 0.00350 0.01494 0.00847 0.00879 47 1.0e-05 0.00148 0.00380 0.01608 0.00906 0.00977 48 7.0e-06 0.00113 0.00276 0.01152 0.00614 0.00730 49 4.0e-05 0.00203 0.00507 0.01613 0.00855 0.00776 50 3.0e-05 0.00155 0.00373 0.01681 0.00930 0.00802 51 3.0e-05 0.00167 0.00422 0.02184 0.01241 0.01024 52 4.0e-05 0.00169 0.00393 0.02033 0.01143 0.00959 53 3.0e-05 0.00166 0.00411 0.02297 0.01323 0.01072 54 4.0e-05 0.00183 0.00495 0.02498 0.01396 0.01219 55 7.0e-05 0.00486 0.01046 0.02719 0.01483 0.01609 56 8.0e-05 0.00539 0.01193 0.03209 0.01789 0.01992 57 7.0e-05 0.00514 0.01056 0.03715 0.02032 0.02302 58 6.0e-05 0.00469 0.00898 0.02293 0.01189 0.01459 59 7.0e-05 0.00493 0.01003 0.02645 0.01394 0.01625 60 8.0e-05 0.00520 0.01120 0.03225 0.01805 0.01974 61 1.0e-05 0.00152 0.00442 0.01861 0.00975 0.01258 62 1.0e-05 0.00151 0.00461 0.01906 0.01013 0.01296 63 1.0e-05 0.00144 0.00457 0.01643 0.00867 0.01108 64 1.0e-05 0.00155 0.00526 0.01644 0.00882 0.01075 65 9.0e-06 0.00113 0.00342 0.01457 0.00769 0.00957 66 1.0e-05 0.00140 0.00408 0.01745 0.00942 0.01160 67 6.0e-05 0.00440 0.01289 0.03198 0.01830 0.01810 68 7.0e-05 0.00463 0.01520 0.03111 0.01638 0.01759 69 8.0e-05 0.00467 0.01941 0.05384 0.03152 0.02422 70 5.0e-05 0.00354 0.01400 0.05428 0.03357 0.02494 71 6.0e-05 0.00419 0.01407 0.03485 0.01868 0.01906 72 7.0e-05 0.00478 0.01601 0.04978 0.02749 0.02466 73 3.0e-05 0.00220 0.00540 0.01706 0.00974 0.00925 74 5.0e-05 0.00329 0.00805 0.02448 0.01373 0.01375 75 4.0e-05 0.00283 0.00780 0.02442 0.01432 0.01325 76 5.0e-05 0.00289 0.00831 0.02215 0.01284 0.01219 77 4.0e-05 0.00289 0.00810 0.03999 0.02413 0.02231 78 4.0e-05 0.00280 0.00677 0.02199 0.01284 0.01199 79 6.0e-05 0.00332 0.00994 0.03202 0.01803 0.01886 80 1.0e-04 0.00576 0.01865 0.03121 0.01773 0.01783 81 7.0e-05 0.00415 0.01168 0.04024 0.02266 0.02451 82 7.0e-05 0.00371 0.01283 0.03156 0.01792 0.01841 83 6.0e-05 0.00348 0.01053 0.02427 0.01371 0.01421 84 4.0e-05 0.00258 0.00742 0.02223 0.01277 0.01343 85 4.0e-05 0.00420 0.01254 0.04795 0.02679 0.03022 86 2.0e-05 0.00244 0.00659 0.03852 0.02107 0.02493 87 2.0e-05 0.00194 0.00488 0.03759 0.02073 0.02415 88 3.0e-05 0.00312 0.00862 0.06511 0.03671 0.04159 89 3.0e-05 0.00254 0.00710 0.06727 0.03788 0.04254 90 4.0e-05 0.00419 0.01172 0.04313 0.02297 0.02768 91 4.0e-05 0.00453 0.01161 0.06640 0.03650 0.04282 92 3.0e-05 0.00227 0.00672 0.07959 0.04421 0.04962 93 3.0e-05 0.00256 0.00750 0.04190 0.02383 0.02521 94 3.0e-05 0.00226 0.00574 0.05925 0.03341 0.03794 95 2.0e-05 0.00196 0.00587 0.03716 0.02062 0.02321 96 2.0e-05 0.00197 0.00602 0.03272 0.01813 0.01909 97 2.0e-05 0.00184 0.00535 0.03381 0.01806 0.02024 98 1.0e-04 0.00623 0.02228 0.03886 0.02135 0.02174 99 1.1e-04 0.00655 0.02478 0.04689 0.02542 0.02630 100 1.5e-04 0.00990 0.03476 0.06734 0.03611 0.03963 101 2.6e-04 0.01522 0.06433 0.09178 0.05358 0.04791 102 1.2e-04 0.00909 0.02716 0.06170 0.03223 0.03672 103 2.2e-04 0.01628 0.05563 0.09419 0.05551 0.05005 104 2.0e-05 0.00136 0.00315 0.01131 0.00522 0.00659 105 1.0e-05 0.00100 0.00229 0.01030 0.00469 0.00582 106 2.0e-05 0.00134 0.00349 0.01346 0.00660 0.00818 107 1.0e-05 0.00092 0.00204 0.01064 0.00522 0.00632 108 2.0e-05 0.00122 0.00346 0.01450 0.00633 0.00788 109 1.0e-05 0.00096 0.00225 0.01024 0.00455 0.00576 110 4.0e-05 0.00389 0.01351 0.03044 0.01771 0.01815 111 3.0e-05 0.00337 0.01112 0.02286 0.01192 0.01439 112 3.0e-05 0.00339 0.01105 0.01761 0.00952 0.01058 113 4.0e-05 0.00485 0.01506 0.02378 0.01277 0.01483 114 3.0e-05 0.00280 0.00964 0.01680 0.00861 0.01017 115 2.0e-05 0.00246 0.00905 0.02105 0.01107 0.01284 116 6.0e-05 0.00385 0.01211 0.01843 0.00796 0.00832 117 3.0e-05 0.00207 0.00642 0.01458 0.00606 0.00747 118 3.0e-05 0.00261 0.00731 0.01725 0.00757 0.00971 119 3.0e-05 0.00194 0.00472 0.01279 0.00617 0.00744 120 2.0e-05 0.00128 0.00381 0.01299 0.00679 0.00631 121 5.0e-05 0.00314 0.00723 0.02008 0.00849 0.01117 122 3.0e-05 0.00221 0.00628 0.01169 0.00534 0.00630 123 5.0e-05 0.00398 0.01218 0.04479 0.02587 0.02567 124 5.0e-05 0.00449 0.01517 0.02503 0.01372 0.01580 125 4.0e-05 0.00395 0.01209 0.02343 0.01289 0.01420 126 5.0e-05 0.00422 0.01242 0.02362 0.01235 0.01495 127 4.0e-05 0.00327 0.00883 0.02791 0.01484 0.01805 128 4.0e-05 0.00351 0.01104 0.02857 0.01547 0.01859 129 4.0e-05 0.00192 0.00641 0.01033 0.00538 0.00570 130 2.0e-05 0.00135 0.00349 0.01022 0.00476 0.00588 131 4.0e-05 0.00238 0.00808 0.01412 0.00703 0.00820 132 3.0e-05 0.00205 0.00671 0.01516 0.00721 0.00815 133 3.0e-05 0.00170 0.00508 0.01201 0.00633 0.00701 134 3.0e-05 0.00171 0.00504 0.01043 0.00490 0.00621 135 6.0e-05 0.00319 0.00873 0.04932 0.02683 0.03112 136 4.0e-05 0.00315 0.00731 0.04128 0.02229 0.02592 137 4.0e-05 0.00283 0.00658 0.04879 0.02385 0.02973 138 4.0e-05 0.00312 0.00772 0.05279 0.02896 0.03347 139 4.0e-05 0.00290 0.00715 0.05643 0.03070 0.03530 140 3.0e-05 0.00232 0.00542 0.03026 0.01514 0.01812 141 3.0e-05 0.00269 0.00696 0.03273 0.01713 0.01964 142 4.0e-05 0.00428 0.01285 0.06725 0.04016 0.04003 143 2.0e-05 0.00215 0.00546 0.03527 0.02055 0.02076 144 2.0e-05 0.00211 0.00568 0.01997 0.01117 0.01177 145 1.0e-05 0.00133 0.00301 0.02662 0.01475 0.01558 146 2.0e-05 0.00188 0.00506 0.02536 0.01379 0.01478 147 9.0e-05 0.00946 0.02589 0.08143 0.03804 0.05426 148 8.0e-05 0.00819 0.02546 0.06050 0.02865 0.04101 149 9.0e-05 0.01027 0.02987 0.07118 0.03474 0.04580 150 8.0e-05 0.00963 0.02756 0.07170 0.03515 0.04265 151 1.0e-04 0.01154 0.03225 0.05830 0.02699 0.03714 152 1.6e-04 0.01958 0.05401 0.11908 0.05647 0.07940 153 1.4e-04 0.01699 0.04705 0.08684 0.04284 0.05556 154 6.0e-05 0.00332 0.01164 0.02534 0.01340 0.01399 155 6.0e-05 0.00300 0.01179 0.02682 0.01484 0.01405 156 5.0e-05 0.00300 0.01067 0.03087 0.01659 0.01804 157 6.0e-05 0.00339 0.01246 0.02293 0.01205 0.01289 158 1.5e-04 0.00718 0.03351 0.04912 0.02610 0.02161 159 8.0e-05 0.00454 0.01778 0.02852 0.01500 0.01581 160 5.0e-05 0.00318 0.00962 0.03235 0.01360 0.01650 161 5.0e-05 0.00316 0.00896 0.04009 0.01579 0.01994 162 5.0e-05 0.00329 0.01057 0.03273 0.01644 0.01722 163 6.0e-05 0.00340 0.01097 0.03658 0.01864 0.01940 164 5.0e-05 0.00284 0.00873 0.01756 0.00967 0.01033 165 9.0e-05 0.00461 0.01480 0.02814 0.01579 0.01553 166 1.0e-05 0.00153 0.00462 0.02448 0.01410 0.01426 167 1.0e-05 0.00159 0.00519 0.01242 0.00696 0.00747 168 1.0e-05 0.00186 0.00616 0.02030 0.01186 0.01230 169 4.0e-05 0.00448 0.01470 0.02177 0.01279 0.01272 170 2.0e-05 0.00283 0.00949 0.02018 0.01176 0.01191 171 2.0e-05 0.00237 0.00837 0.01897 0.01084 0.01121 172 3.0e-05 0.00190 0.00499 0.01358 0.00664 0.00786 173 3.0e-05 0.00200 0.00510 0.01484 0.00754 0.00950 174 3.0e-05 0.00203 0.00514 0.01472 0.00748 0.00905 175 3.0e-05 0.00218 0.00528 0.01657 0.00881 0.01062 176 3.0e-05 0.00199 0.00480 0.01503 0.00812 0.00933 177 3.0e-05 0.00213 0.00507 0.01725 0.00874 0.01021 178 2.0e-05 0.00162 0.00406 0.01469 0.00728 0.00886 179 2.0e-05 0.00186 0.00456 0.01574 0.00839 0.00956 180 3.0e-05 0.00231 0.00612 0.01450 0.00725 0.00876 181 3.0e-05 0.00233 0.00619 0.02551 0.01321 0.01574 182 3.0e-05 0.00235 0.00605 0.01831 0.00950 0.01103 183 2.0e-05 0.00198 0.00521 0.02145 0.01155 0.01341 184 4.0e-05 0.00270 0.00558 0.01909 0.00864 0.01223 185 5.0e-05 0.00346 0.00780 0.01795 0.00810 0.01144 186 3.0e-05 0.00192 0.00403 0.01564 0.00667 0.00990 187 4.0e-05 0.00263 0.00762 0.01660 0.00820 0.00972 188 2.0e-05 0.00148 0.00345 0.01300 0.00631 0.00789 189 3.0e-05 0.00184 0.00439 0.01185 0.00557 0.00721 190 3.0e-05 0.00396 0.01235 0.02574 0.01454 0.01582 191 3.0e-05 0.00259 0.00790 0.04087 0.02336 0.02498 192 3.0e-05 0.00292 0.00994 0.02751 0.01604 0.01657 193 8.0e-05 0.00564 0.01873 0.02308 0.01268 0.01365 194 4.0e-05 0.00390 0.01109 0.02296 0.01265 0.01321 195 3.0e-05 0.00317 0.00885 0.01884 0.01026 0.01161 MDVP:APQ Shimmer:DDA NHR RPDE DFA spread1 spread2 D2 1 0.02971 0.06545 0.02211 0.414783 0.815285 -4.813031 0.266482 2.301442 2 0.04368 0.09403 0.01929 0.458359 0.819521 -4.075192 0.335590 2.486855 3 0.03590 0.08270 0.01309 0.429895 0.825288 -4.443179 0.311173 2.342259 4 0.03772 0.08771 0.01353 0.434969 0.819235 -4.117501 0.334147 2.405554 5 0.04465 0.10470 0.01767 0.417356 0.823484 -3.747787 0.234513 2.332180 6 0.03243 0.06985 0.01222 0.415564 0.825069 -4.242867 0.299111 2.187560 7 0.01351 0.02337 0.00607 0.596040 0.764112 -5.634322 0.257682 1.854785 8 0.01256 0.02487 0.00344 0.637420 0.763262 -6.167603 0.183721 2.064693 9 0.01717 0.03218 0.01070 0.615551 0.773587 -5.498678 0.327769 2.322511 10 0.02444 0.04324 0.01022 0.547037 0.798463 -5.011879 0.325996 2.432792 11 0.01892 0.03237 0.01166 0.611137 0.776156 -5.249770 0.391002 2.407313 12 0.02214 0.04272 0.01141 0.583390 0.792520 -4.960234 0.363566 2.642476 13 0.01140 0.01968 0.00581 0.460600 0.646846 -6.547148 0.152813 2.041277 14 0.01797 0.02184 0.01041 0.430166 0.665833 -5.660217 0.254989 2.519422 15 0.01246 0.03191 0.00609 0.474791 0.654027 -6.105098 0.203653 2.125618 16 0.01359 0.02316 0.00839 0.565924 0.658245 -5.340115 0.210185 2.205546 17 0.02074 0.02908 0.01859 0.567380 0.644692 -5.440040 0.239764 2.264501 18 0.03430 0.04322 0.02919 0.631099 0.605417 -2.931070 0.434326 3.007463 19 0.05767 0.07413 0.03160 0.665318 0.719467 -3.949079 0.357870 3.109010 20 0.04310 0.05164 0.03365 0.649554 0.686080 -4.554466 0.340176 2.856676 21 0.04055 0.05000 0.03871 0.660125 0.704087 -4.095442 0.262564 2.739710 22 0.04525 0.06062 0.01849 0.629017 0.698951 -5.186960 0.237622 2.557536 23 0.04246 0.06685 0.01280 0.619060 0.679834 -4.330956 0.262384 2.916777 24 0.03772 0.06562 0.01840 0.537264 0.686894 -5.248776 0.210279 2.547508 25 0.01497 0.02214 0.01778 0.397937 0.732479 -5.557447 0.220890 2.692176 26 0.03780 0.05197 0.02887 0.522746 0.737948 -5.571843 0.236853 2.846369 27 0.01872 0.02666 0.01095 0.418622 0.720916 -6.183590 0.226278 2.589702 28 0.01826 0.02650 0.01328 0.358773 0.726652 -6.271690 0.196102 2.314209 29 0.01661 0.02307 0.00677 0.470478 0.676258 -7.120925 0.279789 2.241742 30 0.01799 0.02380 0.01170 0.427785 0.723797 -6.635729 0.209866 1.957961 31 0.00802 0.01689 0.00339 0.422229 0.741367 -7.348300 0.177551 1.743867 32 0.00762 0.01513 0.00167 0.432439 0.742055 -7.682587 0.173319 2.103106 33 0.00951 0.01919 0.00119 0.465946 0.738703 -7.067931 0.175181 1.512275 34 0.00719 0.01407 0.00072 0.368535 0.742133 -7.695734 0.178540 1.544609 35 0.00726 0.01403 0.00065 0.340068 0.741899 -7.964984 0.163519 1.423287 36 0.00957 0.01758 0.00135 0.344252 0.742737 -7.777685 0.170183 2.447064 37 0.01612 0.03463 0.00586 0.360148 0.778834 -6.149653 0.218037 2.477082 38 0.01491 0.02814 0.00340 0.341435 0.783626 -6.006414 0.196371 2.536527 39 0.01190 0.02177 0.00231 0.403884 0.766209 -6.452058 0.212294 2.269398 40 0.01366 0.02488 0.00265 0.396793 0.758324 -6.006647 0.266892 2.382544 41 0.01233 0.02321 0.00231 0.326480 0.765623 -6.647379 0.201095 2.374073 42 0.01234 0.02226 0.00257 0.306443 0.759203 -7.044105 0.063412 2.361532 43 0.01133 0.03104 0.00740 0.305062 0.654172 -7.310550 0.098648 2.416838 44 0.01251 0.03017 0.00675 0.457702 0.634267 -6.793547 0.158266 2.256699 45 0.01033 0.02330 0.00454 0.438296 0.635285 -7.057869 0.091608 2.330716 46 0.01014 0.02542 0.00476 0.431285 0.638928 -6.995820 0.102083 2.365800 47 0.01149 0.02719 0.00476 0.467489 0.631653 -7.156076 0.127642 2.392122 48 0.00860 0.01841 0.00432 0.610367 0.635204 -7.319510 0.200873 2.028612 49 0.01433 0.02566 0.00839 0.579597 0.733659 -6.439398 0.266392 2.079922 50 0.01400 0.02789 0.00462 0.538688 0.754073 -6.482096 0.264967 2.054419 51 0.01685 0.03724 0.00479 0.553134 0.775933 -6.650471 0.254498 1.840198 52 0.01614 0.03429 0.00474 0.507504 0.760361 -6.689151 0.291954 2.431854 53 0.01677 0.03969 0.00481 0.459766 0.766204 -7.072419 0.220434 1.972297 54 0.01947 0.04188 0.00484 0.420383 0.785714 -6.836811 0.269866 2.223719 55 0.02067 0.04450 0.01036 0.536009 0.819032 -4.649573 0.205558 1.986899 56 0.02454 0.05368 0.01180 0.558586 0.811843 -4.333543 0.221727 2.014606 57 0.02802 0.06097 0.00969 0.541781 0.821364 -4.438453 0.238298 1.922940 58 0.01948 0.03568 0.00681 0.530529 0.817756 -4.608260 0.290024 2.021591 59 0.02137 0.04183 0.00786 0.540049 0.813432 -4.476755 0.262633 1.827012 60 0.02519 0.05414 0.01143 0.547975 0.817396 -4.609161 0.221711 1.831691 61 0.01382 0.02925 0.00871 0.341788 0.678874 -7.040508 0.066994 2.460791 62 0.01340 0.03039 0.00301 0.447979 0.686264 -7.293801 0.086372 2.321560 63 0.01200 0.02602 0.00340 0.364867 0.694399 -6.966321 0.095882 2.278687 64 0.01179 0.02647 0.00351 0.256570 0.683296 -7.245620 0.018689 2.498224 65 0.01016 0.02308 0.00300 0.276850 0.673636 -7.496264 0.056844 2.003032 66 0.01234 0.02827 0.00420 0.305429 0.681811 -7.314237 0.006274 2.118596 67 0.02428 0.05490 0.02183 0.460139 0.720908 -5.409423 0.226850 2.359973 68 0.02603 0.04914 0.02659 0.498133 0.729067 -5.324574 0.205660 2.291558 69 0.03392 0.09455 0.04882 0.513237 0.731444 -5.869750 0.151814 2.118496 70 0.03635 0.10070 0.02431 0.487407 0.727313 -6.261141 0.120956 2.137075 71 0.02949 0.05605 0.02599 0.489345 0.730387 -5.720868 0.158830 2.277927 72 0.03736 0.08247 0.03361 0.543299 0.733232 -5.207985 0.224852 2.642276 73 0.01345 0.02921 0.00442 0.495954 0.762959 -5.791820 0.329066 2.205024 74 0.01956 0.04120 0.00623 0.509127 0.789532 -5.389129 0.306636 1.928708 75 0.01831 0.04295 0.00479 0.437031 0.815908 -5.313360 0.201861 2.225815 76 0.01715 0.03851 0.00472 0.463514 0.807217 -5.477592 0.315074 1.862092 77 0.02704 0.07238 0.00905 0.489538 0.789977 -5.775966 0.341169 2.007923 78 0.01636 0.03852 0.00420 0.429484 0.816340 -5.391029 0.250572 1.777901 79 0.02455 0.05408 0.01062 0.644954 0.779612 -5.115212 0.249494 2.017753 80 0.02139 0.05320 0.02220 0.594387 0.790117 -4.913885 0.265699 2.398422 81 0.02876 0.06799 0.01823 0.544805 0.770466 -4.441519 0.155097 2.645959 82 0.02190 0.05377 0.01825 0.576084 0.778747 -5.132032 0.210458 2.232576 83 0.01751 0.04114 0.01237 0.554610 0.787896 -5.022288 0.146948 2.428306 84 0.01552 0.03831 0.00882 0.576644 0.772416 -6.025367 0.078202 2.053601 85 0.03510 0.08037 0.05470 0.556494 0.729586 -5.288912 0.343073 3.099301 86 0.02877 0.06321 0.02782 0.583574 0.727747 -5.657899 0.315903 3.098256 87 0.02784 0.06219 0.03151 0.598714 0.712199 -6.366916 0.335753 2.654271 88 0.04683 0.11012 0.04824 0.602874 0.740837 -5.515071 0.299549 3.136550 89 0.04802 0.11363 0.04214 0.599371 0.743937 -5.783272 0.299793 3.007096 90 0.03455 0.06892 0.07223 0.590951 0.745526 -4.379411 0.375531 3.671155 91 0.05114 0.10949 0.08725 0.653410 0.733165 -4.508984 0.389232 3.317586 92 0.05690 0.13262 0.01658 0.501037 0.714360 -6.411497 0.207156 2.344876 93 0.03051 0.07150 0.01914 0.454444 0.734504 -5.952058 0.087840 2.344336 94 0.04398 0.10024 0.01211 0.447456 0.697790 -6.152551 0.173520 2.080121 95 0.02764 0.06185 0.00850 0.502380 0.712170 -6.251425 0.188056 2.143851 96 0.02571 0.05439 0.01018 0.447285 0.705658 -6.247076 0.180528 2.344348 97 0.02809 0.05417 0.00852 0.366329 0.693429 -6.417440 0.194627 2.473239 98 0.03088 0.06406 0.08151 0.629574 0.714485 -4.020042 0.265315 2.671825 99 0.03908 0.07625 0.10323 0.571010 0.690892 -5.159169 0.202146 2.441612 100 0.05783 0.10833 0.16744 0.638545 0.674953 -3.760348 0.242861 2.634633 101 0.06196 0.16074 0.31482 0.671299 0.656846 -3.700544 0.260481 2.991063 102 0.05174 0.09669 0.11843 0.639808 0.643327 -4.202730 0.310163 2.638279 103 0.06023 0.16654 0.25930 0.596362 0.641418 -3.269487 0.270641 2.690917 104 0.01009 0.01567 0.00495 0.296888 0.722356 -6.878393 0.089267 2.004055 105 0.00871 0.01406 0.00243 0.263654 0.691483 -7.111576 0.144780 2.065477 106 0.01059 0.01979 0.00578 0.365488 0.719974 -6.997403 0.210279 1.994387 107 0.00928 0.01567 0.00233 0.334171 0.677930 -6.981201 0.184550 2.129924 108 0.01267 0.01898 0.00659 0.393563 0.700246 -6.600023 0.249172 2.499148 109 0.00993 0.01364 0.00238 0.311369 0.676066 -6.739151 0.160686 2.296873 110 0.02084 0.05312 0.00947 0.497554 0.740539 -5.845099 0.278679 2.608749 111 0.01852 0.03576 0.00704 0.436084 0.727863 -5.258320 0.256454 2.550961 112 0.01307 0.02855 0.00830 0.338097 0.712466 -6.471427 0.184378 2.502336 113 0.01767 0.03831 0.01316 0.498877 0.722085 -4.876336 0.212054 2.376749 114 0.01301 0.02583 0.00620 0.441097 0.722254 -5.963040 0.250283 2.489191 115 0.01604 0.03320 0.01048 0.331508 0.715121 -6.729713 0.181701 2.938114 116 0.01271 0.02389 0.06051 0.407701 0.662668 -4.673241 0.261549 2.702355 117 0.01312 0.01818 0.01554 0.450798 0.653823 -6.051233 0.273280 2.640798 118 0.01652 0.02270 0.01802 0.486738 0.676023 -4.597834 0.372114 2.975889 119 0.01151 0.01851 0.00856 0.470422 0.655239 -4.913137 0.393056 2.816781 120 0.01075 0.02038 0.00681 0.462516 0.582710 -5.517173 0.389295 2.925862 121 0.01734 0.02548 0.02350 0.487756 0.684130 -6.186128 0.279933 2.686240 122 0.01104 0.01603 0.01161 0.400088 0.656182 -4.711007 0.281618 2.655744 123 0.03220 0.07761 0.01968 0.538016 0.741480 -5.418787 0.160267 2.090438 124 0.01931 0.04115 0.01813 0.589956 0.732903 -5.445140 0.142466 2.174306 125 0.01720 0.03867 0.02020 0.618663 0.728421 -5.944191 0.143359 1.929715 126 0.01944 0.03706 0.01874 0.637518 0.735546 -5.594275 0.127950 1.765957 127 0.02259 0.04451 0.01794 0.623209 0.738245 -5.540351 0.087165 1.821297 128 0.02301 0.04641 0.01796 0.585169 0.736964 -5.825257 0.115697 1.996146 129 0.00811 0.01614 0.01724 0.457541 0.699787 -6.890021 0.152941 2.328513 130 0.00903 0.01428 0.00487 0.491345 0.718839 -5.892061 0.195976 2.108873 131 0.01194 0.02110 0.01610 0.467160 0.724045 -6.135296 0.203630 2.539724 132 0.01310 0.02164 0.01015 0.468621 0.735136 -6.112667 0.217013 2.527742 133 0.00915 0.01898 0.00903 0.470972 0.721308 -5.436135 0.254909 2.516320 134 0.00903 0.01471 0.00504 0.482296 0.723096 -6.448134 0.178713 2.034827 135 0.03651 0.08050 0.03031 0.637814 0.744064 -5.301321 0.320385 2.375138 136 0.03316 0.06688 0.02529 0.653427 0.706687 -5.333619 0.322044 2.631793 137 0.04370 0.07154 0.02278 0.647900 0.708144 -4.378916 0.300067 2.445502 138 0.04134 0.08689 0.03690 0.625362 0.708617 -4.654894 0.304107 2.672362 139 0.04451 0.09211 0.02629 0.640945 0.701404 -5.634576 0.306014 2.419253 140 0.02770 0.04543 0.01827 0.624811 0.696049 -5.866357 0.233070 2.445646 141 0.02824 0.05139 0.02485 0.677131 0.685057 -4.796845 0.397749 2.963799 142 0.04464 0.12047 0.04238 0.606344 0.665945 -5.410336 0.288917 2.665133 143 0.02530 0.06165 0.01728 0.606273 0.661735 -5.585259 0.310746 2.465528 144 0.01506 0.03350 0.02010 0.536102 0.632631 -5.898673 0.213353 2.470746 145 0.02006 0.04426 0.01049 0.497480 0.630409 -6.132663 0.220617 2.576563 146 0.01909 0.04137 0.01493 0.566849 0.574282 -5.456811 0.345238 2.840556 147 0.08808 0.11411 0.07530 0.561610 0.793509 -3.297668 0.414758 3.413649 148 0.06359 0.08595 0.06057 0.478024 0.768974 -4.276605 0.355736 3.142364 149 0.06824 0.10422 0.08069 0.552870 0.764036 -3.377325 0.335357 3.274865 150 0.06460 0.10546 0.07889 0.427627 0.775708 -4.892495 0.262281 2.910213 151 0.06259 0.08096 0.10952 0.507826 0.762726 -4.484303 0.340256 2.958815 152 0.13778 0.16942 0.21713 0.625866 0.768320 -2.434031 0.450493 3.079221 153 0.08318 0.12851 0.16265 0.584164 0.754449 -2.839756 0.356224 3.184027 154 0.02056 0.04019 0.04179 0.566867 0.670475 -4.865194 0.246404 2.013530 155 0.02018 0.04451 0.04611 0.651680 0.659333 -4.239028 0.175691 2.451130 156 0.02402 0.04977 0.02631 0.628300 0.652025 -3.583722 0.207914 2.439597 157 0.01771 0.03615 0.03191 0.611679 0.623731 -5.435100 0.230532 2.699645 158 0.02916 0.07830 0.10748 0.630547 0.646786 -3.444478 0.303214 2.964568 159 0.02157 0.04499 0.03828 0.635015 0.627337 -5.070096 0.280091 2.892300 160 0.03105 0.04079 0.02663 0.654945 0.675865 -5.498456 0.234196 2.103014 161 0.04114 0.04736 0.02073 0.653139 0.694571 -5.185987 0.259229 2.151121 162 0.02931 0.04933 0.02810 0.577802 0.684373 -5.283009 0.226528 2.442906 163 0.03091 0.05592 0.02707 0.685151 0.719576 -5.529833 0.242750 2.408689 164 0.01363 0.02902 0.01435 0.557045 0.673086 -5.617124 0.184896 1.871871 165 0.02073 0.04736 0.03882 0.671378 0.674562 -2.929379 0.396746 2.560422 166 0.01621 0.04231 0.00620 0.469928 0.628232 -6.816086 0.172270 2.235197 167 0.00882 0.02089 0.00533 0.384868 0.626710 -7.018057 0.176316 1.852402 168 0.01367 0.03557 0.00910 0.440988 0.628058 -7.517934 0.160414 1.881767 169 0.01439 0.03836 0.01337 0.372222 0.725216 -5.736781 0.164529 2.882450 170 0.01344 0.03529 0.00965 0.371837 0.646167 -7.169701 0.073298 2.266432 171 0.01255 0.03253 0.01049 0.522812 0.646818 -7.304500 0.171088 2.095237 172 0.01140 0.01992 0.00435 0.413295 0.756700 -6.323531 0.218885 2.193412 173 0.01285 0.02261 0.00430 0.369090 0.776158 -6.085567 0.192375 1.889002 174 0.01148 0.02245 0.00478 0.380253 0.766700 -5.943501 0.192150 1.852542 175 0.01318 0.02643 0.00590 0.387482 0.756482 -6.012559 0.229298 1.872946 176 0.01133 0.02436 0.00401 0.405991 0.761255 -5.966779 0.197938 1.974857 177 0.01331 0.02623 0.00415 0.361232 0.763242 -6.016891 0.109256 2.004719 178 0.01230 0.02184 0.00570 0.396610 0.745957 -6.486822 0.197919 2.449763 179 0.01309 0.02518 0.00488 0.402591 0.762508 -6.311987 0.182459 2.251553 180 0.01263 0.02175 0.00540 0.398499 0.778349 -5.711205 0.240875 2.845109 181 0.02148 0.03964 0.00611 0.352396 0.759320 -6.261446 0.183218 2.264226 182 0.01559 0.02849 0.00639 0.408598 0.768845 -5.704053 0.216204 2.679185 183 0.01666 0.03464 0.00595 0.329577 0.757180 -6.277170 0.109397 2.209021 184 0.01949 0.02592 0.00955 0.603515 0.669565 -5.619070 0.191576 2.027228 185 0.01756 0.02429 0.01179 0.663842 0.656516 -5.198864 0.206768 2.120412 186 0.01691 0.02001 0.00737 0.598515 0.654331 -5.592584 0.133917 2.058658 187 0.01491 0.02460 0.01397 0.566424 0.667654 -6.431119 0.153310 2.161936 188 0.01144 0.01892 0.00680 0.528485 0.663884 -6.359018 0.116636 2.152083 189 0.01095 0.01672 0.00703 0.555303 0.659132 -6.710219 0.149694 1.913990 190 0.01758 0.04363 0.04441 0.508479 0.683761 -6.934474 0.159890 2.316346 191 0.02745 0.07008 0.02764 0.448439 0.657899 -6.538586 0.121952 2.657476 192 0.01879 0.04812 0.01810 0.431674 0.683244 -6.195325 0.129303 2.784312 193 0.01667 0.03804 0.10715 0.407567 0.655683 -6.787197 0.158453 2.679772 194 0.01588 0.03794 0.07223 0.451221 0.643956 -6.744577 0.207454 2.138608 195 0.01373 0.03078 0.04398 0.462803 0.664357 -5.724056 0.190667 2.555477 PPE 1 0.284654 2 0.368674 3 0.332634 4 0.368975 5 0.410335 6 0.357775 7 0.211756 8 0.163755 9 0.231571 10 0.271362 11 0.249740 12 0.275931 13 0.138512 14 0.199889 15 0.170100 16 0.234589 17 0.218164 18 0.430788 19 0.377429 20 0.322111 21 0.365391 22 0.259765 23 0.285695 24 0.253556 25 0.215961 26 0.219514 27 0.147403 28 0.162999 29 0.108514 30 0.135242 31 0.085569 32 0.068501 33 0.096320 34 0.056141 35 0.044539 36 0.057610 37 0.165827 38 0.173218 39 0.141929 40 0.160691 41 0.130554 42 0.115730 43 0.095032 44 0.117399 45 0.091470 46 0.102706 47 0.097336 48 0.086398 49 0.133867 50 0.128872 51 0.103561 52 0.105993 53 0.119308 54 0.147491 55 0.316700 56 0.344834 57 0.335041 58 0.314464 59 0.326197 60 0.316395 61 0.101516 62 0.098555 63 0.103224 64 0.093534 65 0.073581 66 0.091546 67 0.226156 68 0.226247 69 0.185580 70 0.141958 71 0.180828 72 0.242981 73 0.188180 74 0.225461 75 0.244512 76 0.228624 77 0.193918 78 0.232744 79 0.260015 80 0.277948 81 0.327978 82 0.260633 83 0.264666 84 0.177275 85 0.242119 86 0.200423 87 0.144614 88 0.220968 89 0.194052 90 0.332086 91 0.301952 92 0.134120 93 0.186489 94 0.160809 95 0.160812 96 0.164916 97 0.151709 98 0.340623 99 0.260375 100 0.378483 101 0.370961 102 0.356881 103 0.444774 104 0.113942 105 0.093193 106 0.112878 107 0.106802 108 0.105306 109 0.115130 110 0.185668 111 0.232520 112 0.136390 113 0.268144 114 0.177807 115 0.115515 116 0.274407 117 0.170106 118 0.282780 119 0.251972 120 0.220657 121 0.152428 122 0.234809 123 0.229892 124 0.215558 125 0.181988 126 0.222716 127 0.214075 128 0.196535 129 0.112856 130 0.183572 131 0.169923 132 0.170633 133 0.232209 134 0.141422 135 0.243080 136 0.228319 137 0.259451 138 0.274387 139 0.209191 140 0.184985 141 0.277227 142 0.231723 143 0.209863 144 0.189032 145 0.159777 146 0.232861 147 0.457533 148 0.336085 149 0.418646 150 0.270173 151 0.301487 152 0.527367 153 0.454721 154 0.168581 155 0.247455 156 0.206256 157 0.220546 158 0.261305 159 0.249703 160 0.216638 161 0.244948 162 0.238281 163 0.220520 164 0.212386 165 0.367233 166 0.119652 167 0.091604 168 0.075587 169 0.202879 170 0.100881 171 0.096220 172 0.160376 173 0.174152 174 0.179677 175 0.163118 176 0.184067 177 0.174429 178 0.132703 179 0.160306 180 0.192730 181 0.144105 182 0.197710 183 0.156368 184 0.215724 185 0.252404 186 0.214346 187 0.120605 188 0.138868 189 0.121777 190 0.112838 191 0.133050 192 0.168895 193 0.131728 194 0.123306 195 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)` 1.583e+00 -2.395e-03 -1.387e-04 -1.596e-03 `MDVP:Jitter(%)` `MDVP:Jitter(Abs)` `MDVP:PPQ` `Jitter:DDP` -1.680e+02 -4.462e+03 -1.673e+01 1.037e+02 `MDVP:Shimmer` `Shimmer:APQ3` `Shimmer:APQ5` `MDVP:APQ` 3.287e+01 -1.636e+03 -2.607e+01 -3.558e+00 `Shimmer:DDA` NHR RPDE DFA 5.393e+02 -2.255e+00 -7.713e-01 4.038e-01 spread1 spread2 D2 PPE 1.285e-01 1.132e+00 1.030e-01 1.231e+00 > (mysum <- summary(mylm)) Call: lm(formula = df) Residuals: Min 1Q Median 3Q Max -0.92948 -0.15996 0.04966 0.21289 0.55443 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 1.583e+00 9.558e-01 1.657 0.099399 . `MDVP:Fo(Hz)` -2.395e-03 1.494e-03 -1.603 0.110781 `MDVP:Fhi(Hz)` -1.387e-04 3.165e-04 -0.438 0.661816 `MDVP:Flo(Hz)` -1.596e-03 7.948e-04 -2.008 0.046227 * `MDVP:Jitter(%)` -1.680e+02 6.630e+01 -2.535 0.012135 * `MDVP:Jitter(Abs)` -4.462e+03 4.514e+03 -0.988 0.324280 `MDVP:PPQ` -1.673e+01 8.203e+01 -0.204 0.838625 `Jitter:DDP` 1.037e+02 2.648e+01 3.915 0.000129 *** `MDVP:Shimmer` 3.287e+01 2.927e+01 1.123 0.262884 `Shimmer:APQ3` -1.636e+03 8.870e+03 -0.184 0.853920 `Shimmer:APQ5` -2.607e+01 1.939e+01 -1.345 0.180445 `MDVP:APQ` -3.558e+00 1.069e+01 -0.333 0.739639 `Shimmer:DDA` 5.393e+02 2.956e+03 0.182 0.855454 NHR -2.255e+00 1.956e+00 -1.153 0.250583 RPDE -7.713e-01 3.572e-01 -2.159 0.032186 * DFA 4.038e-01 7.252e-01 0.557 0.578361 spread1 1.285e-01 9.676e-02 1.328 0.185969 spread2 1.132e+00 4.585e-01 2.468 0.014542 * D2 1.030e-01 1.045e-01 0.986 0.325662 PPE 1.231e+00 1.316e+00 0.935 0.350916 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 0.3254 on 175 degrees of freedom Multiple R-squared: 0.4881, Adjusted R-squared: 0.4325 F-statistic: 8.781 on 19 and 175 DF, p-value: < 2.2e-16 > if (n > n25) { + kp3 <- k + 3 + nmkm3 <- n - k - 3 + gqarr <- array(NA, dim=c(nmkm3-kp3+1,3)) + numgqtests <- 0 + numsignificant1 <- 0 + numsignificant5 <- 0 + numsignificant10 <- 0 + for (mypoint in kp3:nmkm3) { + j <- 0 + numgqtests <- numgqtests + 1 + for (myalt in c('greater', 'two.sided', 'less')) { + j <- j + 1 + gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value + } + if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1 + if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1 + if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1 + } + gqarr + } [,1] [,2] [,3] [1,] 2.739566e-48 5.479132e-48 1.0000000 [2,] 6.905067e-65 1.381013e-64 1.0000000 [3,] 1.870821e-81 3.741642e-81 1.0000000 [4,] 2.495428e-104 4.990856e-104 1.0000000 [5,] 5.179815e-111 1.035963e-110 1.0000000 [6,] 5.685381e-124 1.137076e-123 1.0000000 [7,] 5.316945e-141 1.063389e-140 1.0000000 [8,] 4.718273e-157 9.436547e-157 1.0000000 [9,] 3.799930e-05 7.599860e-05 0.9999620 [10,] 1.149044e-05 2.298089e-05 0.9999885 [11,] 3.421473e-06 6.842947e-06 0.9999966 [12,] 9.592301e-07 1.918460e-06 0.9999990 [13,] 2.555717e-07 5.111435e-07 0.9999997 [14,] 9.607751e-08 1.921550e-07 0.9999999 [15,] 4.064657e-05 8.129314e-05 0.9999594 [16,] 5.070797e-05 1.014159e-04 0.9999493 [17,] 9.719185e-04 1.943837e-03 0.9990281 [18,] 1.758964e-03 3.517927e-03 0.9982410 [19,] 3.117086e-03 6.234171e-03 0.9968829 [20,] 2.624882e-03 5.249764e-03 0.9973751 [21,] 1.476380e-03 2.952760e-03 0.9985236 [22,] 1.023428e-03 2.046855e-03 0.9989766 [23,] 6.535651e-04 1.307130e-03 0.9993464 [24,] 3.794414e-04 7.588828e-04 0.9996206 [25,] 2.724169e-04 5.448338e-04 0.9997276 [26,] 5.670819e-04 1.134164e-03 0.9994329 [27,] 5.400176e-04 1.080035e-03 0.9994600 [28,] 4.440110e-04 8.880219e-04 0.9995560 [29,] 3.005324e-04 6.010649e-04 0.9996995 [30,] 2.309707e-04 4.619414e-04 0.9997690 [31,] 1.800133e-04 3.600265e-04 0.9998200 [32,] 2.075818e-04 4.151636e-04 0.9997924 [33,] 1.737025e-04 3.474050e-04 0.9998263 [34,] 1.394758e-04 2.789516e-04 0.9998605 [35,] 8.644331e-05 1.728866e-04 0.9999136 [36,] 6.763198e-05 1.352640e-04 0.9999324 [37,] 4.380160e-05 8.760320e-05 0.9999562 [38,] 2.743653e-05 5.487306e-05 0.9999726 [39,] 3.362787e-04 6.725573e-04 0.9996637 [40,] 3.487407e-04 6.974815e-04 0.9996513 [41,] 4.245121e-04 8.490241e-04 0.9995755 [42,] 4.273107e-04 8.546214e-04 0.9995727 [43,] 2.743955e-04 5.487909e-04 0.9997256 [44,] 2.309744e-04 4.619488e-04 0.9997690 [45,] 1.527800e-04 3.055600e-04 0.9998472 [46,] 9.883653e-05 1.976731e-04 0.9999012 [47,] 9.792016e-05 1.958403e-04 0.9999021 [48,] 7.693360e-05 1.538672e-04 0.9999231 [49,] 4.570463e-05 9.140926e-05 0.9999543 [50,] 3.135272e-05 6.270544e-05 0.9999686 [51,] 1.817417e-05 3.634834e-05 0.9999818 [52,] 5.389994e-05 1.077999e-04 0.9999461 [53,] 6.851882e-05 1.370376e-04 0.9999315 [54,] 4.963307e-05 9.926615e-05 0.9999504 [55,] 3.186175e-05 6.372350e-05 0.9999681 [56,] 2.499115e-05 4.998230e-05 0.9999750 [57,] 1.462698e-05 2.925395e-05 0.9999854 [58,] 1.084395e-05 2.168790e-05 0.9999892 [59,] 8.202084e-06 1.640417e-05 0.9999918 [60,] 4.726747e-06 9.453494e-06 0.9999953 [61,] 3.112382e-06 6.224763e-06 0.9999969 [62,] 2.037352e-06 4.074705e-06 0.9999980 [63,] 1.228532e-06 2.457063e-06 0.9999988 [64,] 1.596438e-06 3.192875e-06 0.9999984 [65,] 3.291105e-06 6.582209e-06 0.9999967 [66,] 1.979904e-06 3.959807e-06 0.9999980 [67,] 1.487407e-06 2.974813e-06 0.9999985 [68,] 2.660625e-06 5.321250e-06 0.9999973 [69,] 3.688406e-06 7.376812e-06 0.9999963 [70,] 4.855495e-06 9.710989e-06 0.9999951 [71,] 3.409726e-06 6.819452e-06 0.9999966 [72,] 2.891751e-06 5.783503e-06 0.9999971 [73,] 1.999731e-06 3.999462e-06 0.9999980 [74,] 1.352697e-06 2.705394e-06 0.9999986 [75,] 9.097206e-07 1.819441e-06 0.9999991 [76,] 5.239762e-07 1.047952e-06 0.9999995 [77,] 3.148759e-07 6.297517e-07 0.9999997 [78,] 2.349981e-07 4.699962e-07 0.9999998 [79,] 1.434241e-07 2.868482e-07 0.9999999 [80,] 1.288289e-07 2.576578e-07 0.9999999 [81,] 1.665957e-07 3.331913e-07 0.9999998 [82,] 3.385825e-07 6.771649e-07 0.9999997 [83,] 5.107381e-07 1.021476e-06 0.9999995 [84,] 1.010200e-06 2.020401e-06 0.9999990 [85,] 1.782724e-06 3.565448e-06 0.9999982 [86,] 1.255056e-06 2.510112e-06 0.9999987 [87,] 2.352229e-06 4.704457e-06 0.9999976 [88,] 1.744800e-06 3.489601e-06 0.9999983 [89,] 1.059707e-06 2.119414e-06 0.9999989 [90,] 2.110068e-06 4.220137e-06 0.9999979 [91,] 1.268071e-06 2.536141e-06 0.9999987 [92,] 1.386674e-06 2.773348e-06 0.9999986 [93,] 1.606024e-06 3.212048e-06 0.9999984 [94,] 1.391620e-06 2.783240e-06 0.9999986 [95,] 1.590879e-06 3.181758e-06 0.9999984 [96,] 1.102063e-06 2.204126e-06 0.9999989 [97,] 7.598981e-07 1.519796e-06 0.9999992 [98,] 1.443880e-06 2.887761e-06 0.9999986 [99,] 4.671236e-06 9.342473e-06 0.9999953 [100,] 1.032289e-05 2.064578e-05 0.9999897 [101,] 6.832689e-06 1.366538e-05 0.9999932 [102,] 4.970225e-06 9.940450e-06 0.9999950 [103,] 3.837343e-06 7.674687e-06 0.9999962 [104,] 5.434756e-06 1.086951e-05 0.9999946 [105,] 1.054043e-05 2.108085e-05 0.9999895 [106,] 1.813176e-04 3.626351e-04 0.9998187 [107,] 3.287305e-04 6.574610e-04 0.9996713 [108,] 3.294201e-04 6.588402e-04 0.9996706 [109,] 2.744859e-04 5.489719e-04 0.9997255 [110,] 1.837958e-04 3.675917e-04 0.9998162 [111,] 1.525650e-04 3.051300e-04 0.9998474 [112,] 4.923610e-04 9.847220e-04 0.9995076 [113,] 6.529283e-04 1.305857e-03 0.9993471 [114,] 4.655303e-04 9.310606e-04 0.9995345 [115,] 5.550434e-04 1.110087e-03 0.9994450 [116,] 5.101859e-04 1.020372e-03 0.9994898 [117,] 3.246894e-04 6.493788e-04 0.9996753 [118,] 2.079140e-04 4.158279e-04 0.9997921 [119,] 2.111769e-04 4.223537e-04 0.9997888 [120,] 1.496019e-04 2.992039e-04 0.9998504 [121,] 1.493529e-04 2.987057e-04 0.9998506 [122,] 4.703177e-04 9.406354e-04 0.9995297 [123,] 4.597305e-04 9.194610e-04 0.9995403 [124,] 4.060782e-04 8.121564e-04 0.9995939 [125,] 3.456218e-04 6.912436e-04 0.9996544 [126,] 2.745100e-04 5.490201e-04 0.9997255 [127,] 1.763917e-04 3.527833e-04 0.9998236 [128,] 1.648692e-04 3.297383e-04 0.9998351 [129,] 9.678030e-05 1.935606e-04 0.9999032 [130,] 6.796498e-04 1.359300e-03 0.9993204 [131,] 5.110571e-04 1.022114e-03 0.9994889 [132,] 3.622368e-04 7.244735e-04 0.9996378 [133,] 2.632146e-04 5.264291e-04 0.9997368 [134,] 2.715977e-04 5.431954e-04 0.9997284 [135,] 2.486369e-04 4.972738e-04 0.9997514 [136,] 2.408824e-04 4.817647e-04 0.9997591 [137,] 1.477074e-04 2.954148e-04 0.9998523 [138,] 1.418538e-04 2.837075e-04 0.9998581 [139,] 6.228144e-04 1.245629e-03 0.9993772 [140,] 5.423799e-04 1.084760e-03 0.9994576 [141,] 5.298718e-04 1.059744e-03 0.9994701 [142,] 1.256905e-01 2.513811e-01 0.8743095 [143,] 3.291138e-01 6.582275e-01 0.6708862 [144,] 2.856703e-01 5.713406e-01 0.7143297 [145,] 2.654855e-01 5.309710e-01 0.7345145 [146,] 1.994336e-01 3.988673e-01 0.8005664 [147,] 2.552179e-01 5.104358e-01 0.7447821 [148,] 2.231724e-01 4.463448e-01 0.7768276 [149,] 7.864592e-01 4.270816e-01 0.2135408 [150,] 8.664251e-01 2.671499e-01 0.1335749 > postscript(file="/var/wessaorg/rcomp/tmp/1h5vh1386796538.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index') > points(x[,1]-mysum$resid) > grid() > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/2ja6p1386796538.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index') > grid() > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/307wy1386796538.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') > grid() > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/4bn2c1386796538.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/5alu61386796538.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 0.062644053 -0.068371726 -0.002732081 -0.094932142 0.073830662 0.025930123 7 8 9 10 11 12 0.197695701 0.344668576 0.046009753 -0.143068671 -0.077550844 -0.230155447 13 14 15 16 17 18 0.539362362 0.121821631 0.288617642 0.263472139 0.453068366 -0.326136298 19 20 21 22 23 24 -0.302278934 0.035449947 -0.087310463 0.095033450 -0.185426058 0.108181148 25 26 27 28 29 30 0.169634327 0.067530854 0.169664638 0.205407432 0.326807276 0.301503133 31 32 33 34 35 36 -0.297846679 -0.243088416 -0.246969087 -0.185467765 -0.131912720 -0.292967142 37 38 39 40 41 42 0.200473028 0.174507414 0.393888207 0.235137128 0.387111136 0.548003211 43 44 45 46 47 48 -0.202459137 -0.192905093 -0.028634342 -0.107352429 -0.072338513 0.001733650 49 50 51 52 53 54 -0.335743199 -0.435599676 -0.434400780 -0.446054580 -0.411825386 -0.553045784 55 56 57 58 59 60 0.179993468 0.208592283 0.115323228 0.233814659 0.231486816 0.385760032 61 62 63 64 65 66 -0.392462560 -0.330412161 -0.276767656 -0.224138409 -0.109977028 -0.271188280 67 68 69 70 71 72 0.119580258 0.142298159 0.051195520 0.015750169 0.180718596 -0.096324211 73 74 75 76 77 78 0.095309458 0.093956917 -0.073318895 -0.057748888 -0.110237545 -0.005785127 79 80 81 82 83 84 0.021420158 -0.082696765 -0.183945074 -0.103033383 -0.009025775 0.294527016 85 86 87 88 89 90 -0.065736391 0.135591724 0.342614965 0.050238245 0.029541849 -0.170293504 91 92 93 94 95 96 -0.138322171 0.201251191 0.287248529 0.199097255 0.184789360 0.243697718 97 98 99 100 101 102 0.217187042 -0.013768589 0.231558806 0.114257609 0.016970153 0.004657129 103 104 105 106 107 108 -0.044495022 0.427418223 0.428605068 0.443455999 0.432760090 0.305672590 109 110 111 112 113 114 0.346989373 0.075230512 -0.052955481 0.438206718 0.179213163 0.307631244 115 116 117 118 119 120 0.218226495 0.136928347 0.249050667 -0.091694058 0.116273140 0.235682486 121 122 123 124 125 126 0.439003501 -0.001513816 0.063392856 0.301001421 0.398539473 0.408172503 127 128 129 130 131 132 0.422083554 0.400154548 0.554432024 0.219248590 0.191779505 0.085011065 133 134 135 136 137 138 -0.030447610 0.355705638 0.064504317 0.039960947 -0.149623247 -0.149177431 139 140 141 142 143 144 0.049662908 0.229777874 0.065940934 0.095560936 0.278210097 0.368532469 145 146 147 148 149 150 0.465206186 0.148733853 -0.347719240 -0.121803924 -0.259677284 0.151892354 151 152 153 154 155 156 0.175190171 -0.022101531 0.031600118 0.188096668 0.072739366 -0.042013578 157 158 159 160 161 162 0.172606279 -0.306587007 -0.002806673 0.189917717 -0.093877523 -0.021613883 163 164 165 166 167 168 0.092663675 0.251019748 -0.330798684 -0.469861835 -0.183470417 -0.017075354 169 170 171 172 173 174 -0.929480054 -0.176408308 -0.082760476 -0.801142667 -0.825604457 -0.870676377 175 176 177 178 179 180 -0.847009838 -0.829026648 -0.784236030 0.368282057 0.293471676 0.065961582 181 182 183 184 185 186 0.268539977 0.136288581 0.313529948 -0.608798561 -0.652610221 -0.622750929 187 188 189 190 191 192 -0.432700242 -0.485684278 -0.441280372 -0.424168943 -0.629370739 -0.673334288 193 194 195 0.207554827 -0.209970568 -0.512845953 > postscript(file="/var/wessaorg/rcomp/tmp/6u5a31386796538.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 0.062644053 NA 1 -0.068371726 0.062644053 2 -0.002732081 -0.068371726 3 -0.094932142 -0.002732081 4 0.073830662 -0.094932142 5 0.025930123 0.073830662 6 0.197695701 0.025930123 7 0.344668576 0.197695701 8 0.046009753 0.344668576 9 -0.143068671 0.046009753 10 -0.077550844 -0.143068671 11 -0.230155447 -0.077550844 12 0.539362362 -0.230155447 13 0.121821631 0.539362362 14 0.288617642 0.121821631 15 0.263472139 0.288617642 16 0.453068366 0.263472139 17 -0.326136298 0.453068366 18 -0.302278934 -0.326136298 19 0.035449947 -0.302278934 20 -0.087310463 0.035449947 21 0.095033450 -0.087310463 22 -0.185426058 0.095033450 23 0.108181148 -0.185426058 24 0.169634327 0.108181148 25 0.067530854 0.169634327 26 0.169664638 0.067530854 27 0.205407432 0.169664638 28 0.326807276 0.205407432 29 0.301503133 0.326807276 30 -0.297846679 0.301503133 31 -0.243088416 -0.297846679 32 -0.246969087 -0.243088416 33 -0.185467765 -0.246969087 34 -0.131912720 -0.185467765 35 -0.292967142 -0.131912720 36 0.200473028 -0.292967142 37 0.174507414 0.200473028 38 0.393888207 0.174507414 39 0.235137128 0.393888207 40 0.387111136 0.235137128 41 0.548003211 0.387111136 42 -0.202459137 0.548003211 43 -0.192905093 -0.202459137 44 -0.028634342 -0.192905093 45 -0.107352429 -0.028634342 46 -0.072338513 -0.107352429 47 0.001733650 -0.072338513 48 -0.335743199 0.001733650 49 -0.435599676 -0.335743199 50 -0.434400780 -0.435599676 51 -0.446054580 -0.434400780 52 -0.411825386 -0.446054580 53 -0.553045784 -0.411825386 54 0.179993468 -0.553045784 55 0.208592283 0.179993468 56 0.115323228 0.208592283 57 0.233814659 0.115323228 58 0.231486816 0.233814659 59 0.385760032 0.231486816 60 -0.392462560 0.385760032 61 -0.330412161 -0.392462560 62 -0.276767656 -0.330412161 63 -0.224138409 -0.276767656 64 -0.109977028 -0.224138409 65 -0.271188280 -0.109977028 66 0.119580258 -0.271188280 67 0.142298159 0.119580258 68 0.051195520 0.142298159 69 0.015750169 0.051195520 70 0.180718596 0.015750169 71 -0.096324211 0.180718596 72 0.095309458 -0.096324211 73 0.093956917 0.095309458 74 -0.073318895 0.093956917 75 -0.057748888 -0.073318895 76 -0.110237545 -0.057748888 77 -0.005785127 -0.110237545 78 0.021420158 -0.005785127 79 -0.082696765 0.021420158 80 -0.183945074 -0.082696765 81 -0.103033383 -0.183945074 82 -0.009025775 -0.103033383 83 0.294527016 -0.009025775 84 -0.065736391 0.294527016 85 0.135591724 -0.065736391 86 0.342614965 0.135591724 87 0.050238245 0.342614965 88 0.029541849 0.050238245 89 -0.170293504 0.029541849 90 -0.138322171 -0.170293504 91 0.201251191 -0.138322171 92 0.287248529 0.201251191 93 0.199097255 0.287248529 94 0.184789360 0.199097255 95 0.243697718 0.184789360 96 0.217187042 0.243697718 97 -0.013768589 0.217187042 98 0.231558806 -0.013768589 99 0.114257609 0.231558806 100 0.016970153 0.114257609 101 0.004657129 0.016970153 102 -0.044495022 0.004657129 103 0.427418223 -0.044495022 104 0.428605068 0.427418223 105 0.443455999 0.428605068 106 0.432760090 0.443455999 107 0.305672590 0.432760090 108 0.346989373 0.305672590 109 0.075230512 0.346989373 110 -0.052955481 0.075230512 111 0.438206718 -0.052955481 112 0.179213163 0.438206718 113 0.307631244 0.179213163 114 0.218226495 0.307631244 115 0.136928347 0.218226495 116 0.249050667 0.136928347 117 -0.091694058 0.249050667 118 0.116273140 -0.091694058 119 0.235682486 0.116273140 120 0.439003501 0.235682486 121 -0.001513816 0.439003501 122 0.063392856 -0.001513816 123 0.301001421 0.063392856 124 0.398539473 0.301001421 125 0.408172503 0.398539473 126 0.422083554 0.408172503 127 0.400154548 0.422083554 128 0.554432024 0.400154548 129 0.219248590 0.554432024 130 0.191779505 0.219248590 131 0.085011065 0.191779505 132 -0.030447610 0.085011065 133 0.355705638 -0.030447610 134 0.064504317 0.355705638 135 0.039960947 0.064504317 136 -0.149623247 0.039960947 137 -0.149177431 -0.149623247 138 0.049662908 -0.149177431 139 0.229777874 0.049662908 140 0.065940934 0.229777874 141 0.095560936 0.065940934 142 0.278210097 0.095560936 143 0.368532469 0.278210097 144 0.465206186 0.368532469 145 0.148733853 0.465206186 146 -0.347719240 0.148733853 147 -0.121803924 -0.347719240 148 -0.259677284 -0.121803924 149 0.151892354 -0.259677284 150 0.175190171 0.151892354 151 -0.022101531 0.175190171 152 0.031600118 -0.022101531 153 0.188096668 0.031600118 154 0.072739366 0.188096668 155 -0.042013578 0.072739366 156 0.172606279 -0.042013578 157 -0.306587007 0.172606279 158 -0.002806673 -0.306587007 159 0.189917717 -0.002806673 160 -0.093877523 0.189917717 161 -0.021613883 -0.093877523 162 0.092663675 -0.021613883 163 0.251019748 0.092663675 164 -0.330798684 0.251019748 165 -0.469861835 -0.330798684 166 -0.183470417 -0.469861835 167 -0.017075354 -0.183470417 168 -0.929480054 -0.017075354 169 -0.176408308 -0.929480054 170 -0.082760476 -0.176408308 171 -0.801142667 -0.082760476 172 -0.825604457 -0.801142667 173 -0.870676377 -0.825604457 174 -0.847009838 -0.870676377 175 -0.829026648 -0.847009838 176 -0.784236030 -0.829026648 177 0.368282057 -0.784236030 178 0.293471676 0.368282057 179 0.065961582 0.293471676 180 0.268539977 0.065961582 181 0.136288581 0.268539977 182 0.313529948 0.136288581 183 -0.608798561 0.313529948 184 -0.652610221 -0.608798561 185 -0.622750929 -0.652610221 186 -0.432700242 -0.622750929 187 -0.485684278 -0.432700242 188 -0.441280372 -0.485684278 189 -0.424168943 -0.441280372 190 -0.629370739 -0.424168943 191 -0.673334288 -0.629370739 192 0.207554827 -0.673334288 193 -0.209970568 0.207554827 194 -0.512845953 -0.209970568 195 NA -0.512845953 > dum1 <- dum[2:length(myerror),] > dum1 lag(myerror, k = 1) myerror [1,] -0.068371726 0.062644053 [2,] -0.002732081 -0.068371726 [3,] -0.094932142 -0.002732081 [4,] 0.073830662 -0.094932142 [5,] 0.025930123 0.073830662 [6,] 0.197695701 0.025930123 [7,] 0.344668576 0.197695701 [8,] 0.046009753 0.344668576 [9,] -0.143068671 0.046009753 [10,] -0.077550844 -0.143068671 [11,] -0.230155447 -0.077550844 [12,] 0.539362362 -0.230155447 [13,] 0.121821631 0.539362362 [14,] 0.288617642 0.121821631 [15,] 0.263472139 0.288617642 [16,] 0.453068366 0.263472139 [17,] -0.326136298 0.453068366 [18,] -0.302278934 -0.326136298 [19,] 0.035449947 -0.302278934 [20,] -0.087310463 0.035449947 [21,] 0.095033450 -0.087310463 [22,] -0.185426058 0.095033450 [23,] 0.108181148 -0.185426058 [24,] 0.169634327 0.108181148 [25,] 0.067530854 0.169634327 [26,] 0.169664638 0.067530854 [27,] 0.205407432 0.169664638 [28,] 0.326807276 0.205407432 [29,] 0.301503133 0.326807276 [30,] -0.297846679 0.301503133 [31,] -0.243088416 -0.297846679 [32,] -0.246969087 -0.243088416 [33,] -0.185467765 -0.246969087 [34,] -0.131912720 -0.185467765 [35,] -0.292967142 -0.131912720 [36,] 0.200473028 -0.292967142 [37,] 0.174507414 0.200473028 [38,] 0.393888207 0.174507414 [39,] 0.235137128 0.393888207 [40,] 0.387111136 0.235137128 [41,] 0.548003211 0.387111136 [42,] -0.202459137 0.548003211 [43,] -0.192905093 -0.202459137 [44,] -0.028634342 -0.192905093 [45,] -0.107352429 -0.028634342 [46,] -0.072338513 -0.107352429 [47,] 0.001733650 -0.072338513 [48,] -0.335743199 0.001733650 [49,] -0.435599676 -0.335743199 [50,] -0.434400780 -0.435599676 [51,] -0.446054580 -0.434400780 [52,] -0.411825386 -0.446054580 [53,] -0.553045784 -0.411825386 [54,] 0.179993468 -0.553045784 [55,] 0.208592283 0.179993468 [56,] 0.115323228 0.208592283 [57,] 0.233814659 0.115323228 [58,] 0.231486816 0.233814659 [59,] 0.385760032 0.231486816 [60,] -0.392462560 0.385760032 [61,] -0.330412161 -0.392462560 [62,] -0.276767656 -0.330412161 [63,] -0.224138409 -0.276767656 [64,] -0.109977028 -0.224138409 [65,] -0.271188280 -0.109977028 [66,] 0.119580258 -0.271188280 [67,] 0.142298159 0.119580258 [68,] 0.051195520 0.142298159 [69,] 0.015750169 0.051195520 [70,] 0.180718596 0.015750169 [71,] -0.096324211 0.180718596 [72,] 0.095309458 -0.096324211 [73,] 0.093956917 0.095309458 [74,] -0.073318895 0.093956917 [75,] -0.057748888 -0.073318895 [76,] -0.110237545 -0.057748888 [77,] -0.005785127 -0.110237545 [78,] 0.021420158 -0.005785127 [79,] -0.082696765 0.021420158 [80,] -0.183945074 -0.082696765 [81,] -0.103033383 -0.183945074 [82,] -0.009025775 -0.103033383 [83,] 0.294527016 -0.009025775 [84,] -0.065736391 0.294527016 [85,] 0.135591724 -0.065736391 [86,] 0.342614965 0.135591724 [87,] 0.050238245 0.342614965 [88,] 0.029541849 0.050238245 [89,] -0.170293504 0.029541849 [90,] -0.138322171 -0.170293504 [91,] 0.201251191 -0.138322171 [92,] 0.287248529 0.201251191 [93,] 0.199097255 0.287248529 [94,] 0.184789360 0.199097255 [95,] 0.243697718 0.184789360 [96,] 0.217187042 0.243697718 [97,] -0.013768589 0.217187042 [98,] 0.231558806 -0.013768589 [99,] 0.114257609 0.231558806 [100,] 0.016970153 0.114257609 [101,] 0.004657129 0.016970153 [102,] -0.044495022 0.004657129 [103,] 0.427418223 -0.044495022 [104,] 0.428605068 0.427418223 [105,] 0.443455999 0.428605068 [106,] 0.432760090 0.443455999 [107,] 0.305672590 0.432760090 [108,] 0.346989373 0.305672590 [109,] 0.075230512 0.346989373 [110,] -0.052955481 0.075230512 [111,] 0.438206718 -0.052955481 [112,] 0.179213163 0.438206718 [113,] 0.307631244 0.179213163 [114,] 0.218226495 0.307631244 [115,] 0.136928347 0.218226495 [116,] 0.249050667 0.136928347 [117,] -0.091694058 0.249050667 [118,] 0.116273140 -0.091694058 [119,] 0.235682486 0.116273140 [120,] 0.439003501 0.235682486 [121,] -0.001513816 0.439003501 [122,] 0.063392856 -0.001513816 [123,] 0.301001421 0.063392856 [124,] 0.398539473 0.301001421 [125,] 0.408172503 0.398539473 [126,] 0.422083554 0.408172503 [127,] 0.400154548 0.422083554 [128,] 0.554432024 0.400154548 [129,] 0.219248590 0.554432024 [130,] 0.191779505 0.219248590 [131,] 0.085011065 0.191779505 [132,] -0.030447610 0.085011065 [133,] 0.355705638 -0.030447610 [134,] 0.064504317 0.355705638 [135,] 0.039960947 0.064504317 [136,] -0.149623247 0.039960947 [137,] -0.149177431 -0.149623247 [138,] 0.049662908 -0.149177431 [139,] 0.229777874 0.049662908 [140,] 0.065940934 0.229777874 [141,] 0.095560936 0.065940934 [142,] 0.278210097 0.095560936 [143,] 0.368532469 0.278210097 [144,] 0.465206186 0.368532469 [145,] 0.148733853 0.465206186 [146,] -0.347719240 0.148733853 [147,] -0.121803924 -0.347719240 [148,] -0.259677284 -0.121803924 [149,] 0.151892354 -0.259677284 [150,] 0.175190171 0.151892354 [151,] -0.022101531 0.175190171 [152,] 0.031600118 -0.022101531 [153,] 0.188096668 0.031600118 [154,] 0.072739366 0.188096668 [155,] -0.042013578 0.072739366 [156,] 0.172606279 -0.042013578 [157,] -0.306587007 0.172606279 [158,] -0.002806673 -0.306587007 [159,] 0.189917717 -0.002806673 [160,] -0.093877523 0.189917717 [161,] -0.021613883 -0.093877523 [162,] 0.092663675 -0.021613883 [163,] 0.251019748 0.092663675 [164,] -0.330798684 0.251019748 [165,] -0.469861835 -0.330798684 [166,] -0.183470417 -0.469861835 [167,] -0.017075354 -0.183470417 [168,] -0.929480054 -0.017075354 [169,] -0.176408308 -0.929480054 [170,] -0.082760476 -0.176408308 [171,] -0.801142667 -0.082760476 [172,] -0.825604457 -0.801142667 [173,] -0.870676377 -0.825604457 [174,] -0.847009838 -0.870676377 [175,] -0.829026648 -0.847009838 [176,] -0.784236030 -0.829026648 [177,] 0.368282057 -0.784236030 [178,] 0.293471676 0.368282057 [179,] 0.065961582 0.293471676 [180,] 0.268539977 0.065961582 [181,] 0.136288581 0.268539977 [182,] 0.313529948 0.136288581 [183,] -0.608798561 0.313529948 [184,] -0.652610221 -0.608798561 [185,] -0.622750929 -0.652610221 [186,] -0.432700242 -0.622750929 [187,] -0.485684278 -0.432700242 [188,] -0.441280372 -0.485684278 [189,] -0.424168943 -0.441280372 [190,] -0.629370739 -0.424168943 [191,] -0.673334288 -0.629370739 [192,] 0.207554827 -0.673334288 [193,] -0.209970568 0.207554827 [194,] -0.512845953 -0.209970568 > z <- as.data.frame(dum1) > z lag(myerror, k = 1) myerror 1 -0.068371726 0.062644053 2 -0.002732081 -0.068371726 3 -0.094932142 -0.002732081 4 0.073830662 -0.094932142 5 0.025930123 0.073830662 6 0.197695701 0.025930123 7 0.344668576 0.197695701 8 0.046009753 0.344668576 9 -0.143068671 0.046009753 10 -0.077550844 -0.143068671 11 -0.230155447 -0.077550844 12 0.539362362 -0.230155447 13 0.121821631 0.539362362 14 0.288617642 0.121821631 15 0.263472139 0.288617642 16 0.453068366 0.263472139 17 -0.326136298 0.453068366 18 -0.302278934 -0.326136298 19 0.035449947 -0.302278934 20 -0.087310463 0.035449947 21 0.095033450 -0.087310463 22 -0.185426058 0.095033450 23 0.108181148 -0.185426058 24 0.169634327 0.108181148 25 0.067530854 0.169634327 26 0.169664638 0.067530854 27 0.205407432 0.169664638 28 0.326807276 0.205407432 29 0.301503133 0.326807276 30 -0.297846679 0.301503133 31 -0.243088416 -0.297846679 32 -0.246969087 -0.243088416 33 -0.185467765 -0.246969087 34 -0.131912720 -0.185467765 35 -0.292967142 -0.131912720 36 0.200473028 -0.292967142 37 0.174507414 0.200473028 38 0.393888207 0.174507414 39 0.235137128 0.393888207 40 0.387111136 0.235137128 41 0.548003211 0.387111136 42 -0.202459137 0.548003211 43 -0.192905093 -0.202459137 44 -0.028634342 -0.192905093 45 -0.107352429 -0.028634342 46 -0.072338513 -0.107352429 47 0.001733650 -0.072338513 48 -0.335743199 0.001733650 49 -0.435599676 -0.335743199 50 -0.434400780 -0.435599676 51 -0.446054580 -0.434400780 52 -0.411825386 -0.446054580 53 -0.553045784 -0.411825386 54 0.179993468 -0.553045784 55 0.208592283 0.179993468 56 0.115323228 0.208592283 57 0.233814659 0.115323228 58 0.231486816 0.233814659 59 0.385760032 0.231486816 60 -0.392462560 0.385760032 61 -0.330412161 -0.392462560 62 -0.276767656 -0.330412161 63 -0.224138409 -0.276767656 64 -0.109977028 -0.224138409 65 -0.271188280 -0.109977028 66 0.119580258 -0.271188280 67 0.142298159 0.119580258 68 0.051195520 0.142298159 69 0.015750169 0.051195520 70 0.180718596 0.015750169 71 -0.096324211 0.180718596 72 0.095309458 -0.096324211 73 0.093956917 0.095309458 74 -0.073318895 0.093956917 75 -0.057748888 -0.073318895 76 -0.110237545 -0.057748888 77 -0.005785127 -0.110237545 78 0.021420158 -0.005785127 79 -0.082696765 0.021420158 80 -0.183945074 -0.082696765 81 -0.103033383 -0.183945074 82 -0.009025775 -0.103033383 83 0.294527016 -0.009025775 84 -0.065736391 0.294527016 85 0.135591724 -0.065736391 86 0.342614965 0.135591724 87 0.050238245 0.342614965 88 0.029541849 0.050238245 89 -0.170293504 0.029541849 90 -0.138322171 -0.170293504 91 0.201251191 -0.138322171 92 0.287248529 0.201251191 93 0.199097255 0.287248529 94 0.184789360 0.199097255 95 0.243697718 0.184789360 96 0.217187042 0.243697718 97 -0.013768589 0.217187042 98 0.231558806 -0.013768589 99 0.114257609 0.231558806 100 0.016970153 0.114257609 101 0.004657129 0.016970153 102 -0.044495022 0.004657129 103 0.427418223 -0.044495022 104 0.428605068 0.427418223 105 0.443455999 0.428605068 106 0.432760090 0.443455999 107 0.305672590 0.432760090 108 0.346989373 0.305672590 109 0.075230512 0.346989373 110 -0.052955481 0.075230512 111 0.438206718 -0.052955481 112 0.179213163 0.438206718 113 0.307631244 0.179213163 114 0.218226495 0.307631244 115 0.136928347 0.218226495 116 0.249050667 0.136928347 117 -0.091694058 0.249050667 118 0.116273140 -0.091694058 119 0.235682486 0.116273140 120 0.439003501 0.235682486 121 -0.001513816 0.439003501 122 0.063392856 -0.001513816 123 0.301001421 0.063392856 124 0.398539473 0.301001421 125 0.408172503 0.398539473 126 0.422083554 0.408172503 127 0.400154548 0.422083554 128 0.554432024 0.400154548 129 0.219248590 0.554432024 130 0.191779505 0.219248590 131 0.085011065 0.191779505 132 -0.030447610 0.085011065 133 0.355705638 -0.030447610 134 0.064504317 0.355705638 135 0.039960947 0.064504317 136 -0.149623247 0.039960947 137 -0.149177431 -0.149623247 138 0.049662908 -0.149177431 139 0.229777874 0.049662908 140 0.065940934 0.229777874 141 0.095560936 0.065940934 142 0.278210097 0.095560936 143 0.368532469 0.278210097 144 0.465206186 0.368532469 145 0.148733853 0.465206186 146 -0.347719240 0.148733853 147 -0.121803924 -0.347719240 148 -0.259677284 -0.121803924 149 0.151892354 -0.259677284 150 0.175190171 0.151892354 151 -0.022101531 0.175190171 152 0.031600118 -0.022101531 153 0.188096668 0.031600118 154 0.072739366 0.188096668 155 -0.042013578 0.072739366 156 0.172606279 -0.042013578 157 -0.306587007 0.172606279 158 -0.002806673 -0.306587007 159 0.189917717 -0.002806673 160 -0.093877523 0.189917717 161 -0.021613883 -0.093877523 162 0.092663675 -0.021613883 163 0.251019748 0.092663675 164 -0.330798684 0.251019748 165 -0.469861835 -0.330798684 166 -0.183470417 -0.469861835 167 -0.017075354 -0.183470417 168 -0.929480054 -0.017075354 169 -0.176408308 -0.929480054 170 -0.082760476 -0.176408308 171 -0.801142667 -0.082760476 172 -0.825604457 -0.801142667 173 -0.870676377 -0.825604457 174 -0.847009838 -0.870676377 175 -0.829026648 -0.847009838 176 -0.784236030 -0.829026648 177 0.368282057 -0.784236030 178 0.293471676 0.368282057 179 0.065961582 0.293471676 180 0.268539977 0.065961582 181 0.136288581 0.268539977 182 0.313529948 0.136288581 183 -0.608798561 0.313529948 184 -0.652610221 -0.608798561 185 -0.622750929 -0.652610221 186 -0.432700242 -0.622750929 187 -0.485684278 -0.432700242 188 -0.441280372 -0.485684278 189 -0.424168943 -0.441280372 190 -0.629370739 -0.424168943 191 -0.673334288 -0.629370739 192 0.207554827 -0.673334288 193 -0.209970568 0.207554827 194 -0.512845953 -0.209970568 > plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals') > lines(lowess(z)) > abline(lm(z)) > grid() > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/77hyn1386796538.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/8h8av1386796538.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/91v511386796538.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0)) > plot(mylm, las = 1, sub='Residual Diagnostics') > par(opar) > dev.off() null device 1 > if (n > n25) { + postscript(file="/var/wessaorg/rcomp/tmp/10x6t21386796538.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) + plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint') + grid() + dev.off() + } null device 1 > > #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab > load(file="/var/wessaorg/rcomp/createtable") > > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE) > a<-table.row.end(a) > myeq <- colnames(x)[1] > myeq <- paste(myeq, '[t] = ', sep='') > for (i in 1:k){ + if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '') + myeq <- paste(myeq, signif(mysum$coefficients[i,1],6), sep=' ') + if (rownames(mysum$coefficients)[i] != '(Intercept)') { + myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='') + if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='') + } + } > myeq <- paste(myeq, ' + e[t]') > a<-table.row.start(a) > a<-table.element(a, myeq) > a<-table.row.end(a) > a<-table.end(a) > table.save(a,file="/var/wessaorg/rcomp/tmp/11mg7u1386796538.tab") > a<-table.start() > a<-table.row.start(a) > a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a,'Variable',header=TRUE) > a<-table.element(a,'Parameter',header=TRUE) > a<-table.element(a,'S.D.',header=TRUE) > a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE) > a<-table.element(a,'2-tail p-value',header=TRUE) > a<-table.element(a,'1-tail p-value',header=TRUE) > a<-table.row.end(a) > for (i in 1:k){ + a<-table.row.start(a) + a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE) + a<-table.element(a,signif(mysum$coefficients[i,1],6)) + a<-table.element(a, signif(mysum$coefficients[i,2],6)) + a<-table.element(a, signif(mysum$coefficients[i,3],4)) + a<-table.element(a, signif(mysum$coefficients[i,4],6)) + a<-table.element(a, signif(mysum$coefficients[i,4]/2,6)) + a<-table.row.end(a) + } > a<-table.end(a) > table.save(a,file="/var/wessaorg/rcomp/tmp/12t1421386796538.tab") > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Multiple R',1,TRUE) > a<-table.element(a, signif(sqrt(mysum$r.squared),6)) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'R-squared',1,TRUE) > a<-table.element(a, signif(mysum$r.squared,6)) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Adjusted R-squared',1,TRUE) > a<-table.element(a, signif(mysum$adj.r.squared,6)) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'F-TEST (value)',1,TRUE) > a<-table.element(a, signif(mysum$fstatistic[1],6)) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE) > a<-table.element(a, signif(mysum$fstatistic[2],6)) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE) > a<-table.element(a, signif(mysum$fstatistic[3],6)) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'p-value',1,TRUE) > a<-table.element(a, signif(1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]),6)) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Residual Standard Deviation',1,TRUE) > a<-table.element(a, signif(mysum$sigma,6)) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Sum Squared Residuals',1,TRUE) > a<-table.element(a, signif(sum(myerror*myerror),6)) > a<-table.row.end(a) > a<-table.end(a) > table.save(a,file="/var/wessaorg/rcomp/tmp/13n7xc1386796538.tab") > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Time or Index', 1, TRUE) > a<-table.element(a, 'Actuals', 1, TRUE) > a<-table.element(a, 'Interpolation
Forecast', 1, TRUE) > a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE) > a<-table.row.end(a) > for (i in 1:n) { + a<-table.row.start(a) + a<-table.element(a,i, 1, TRUE) + a<-table.element(a,signif(x[i],6)) + a<-table.element(a,signif(x[i]-mysum$resid[i],6)) + a<-table.element(a,signif(mysum$resid[i],6)) + a<-table.row.end(a) + } > a<-table.end(a) > table.save(a,file="/var/wessaorg/rcomp/tmp/14jr3j1386796538.tab") > if (n > n25) { + a<-table.start() + a<-table.row.start(a) + a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'p-values',header=TRUE) + a<-table.element(a,'Alternative Hypothesis',3,header=TRUE) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'breakpoint index',header=TRUE) + a<-table.element(a,'greater',header=TRUE) + a<-table.element(a,'2-sided',header=TRUE) + a<-table.element(a,'less',header=TRUE) + a<-table.row.end(a) + for (mypoint in kp3:nmkm3) { + a<-table.row.start(a) + a<-table.element(a,mypoint,header=TRUE) + a<-table.element(a,signif(gqarr[mypoint-kp3+1,1],6)) + a<-table.element(a,signif(gqarr[mypoint-kp3+1,2],6)) + a<-table.element(a,signif(gqarr[mypoint-kp3+1,3],6)) + a<-table.row.end(a) + } + a<-table.end(a) + table.save(a,file="/var/wessaorg/rcomp/tmp/15r7u91386796538.tab") + a<-table.start() + a<-table.row.start(a) + a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'Description',header=TRUE) + a<-table.element(a,'# significant tests',header=TRUE) + a<-table.element(a,'% significant tests',header=TRUE) + a<-table.element(a,'OK/NOK',header=TRUE) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'1% type I error level',header=TRUE) + a<-table.element(a,signif(numsignificant1,6)) + a<-table.element(a,signif(numsignificant1/numgqtests,6)) + if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK' + a<-table.element(a,dum) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'5% type I error level',header=TRUE) + a<-table.element(a,signif(numsignificant5,6)) + a<-table.element(a,signif(numsignificant5/numgqtests,6)) + if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK' + a<-table.element(a,dum) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'10% type I error level',header=TRUE) + a<-table.element(a,signif(numsignificant10,6)) + a<-table.element(a,signif(numsignificant10/numgqtests,6)) + if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK' + a<-table.element(a,dum) + a<-table.row.end(a) + a<-table.end(a) + table.save(a,file="/var/wessaorg/rcomp/tmp/167fx71386796539.tab") + } > > try(system("convert tmp/1h5vh1386796538.ps tmp/1h5vh1386796538.png",intern=TRUE)) character(0) > try(system("convert tmp/2ja6p1386796538.ps tmp/2ja6p1386796538.png",intern=TRUE)) character(0) > try(system("convert tmp/307wy1386796538.ps tmp/307wy1386796538.png",intern=TRUE)) character(0) > try(system("convert tmp/4bn2c1386796538.ps tmp/4bn2c1386796538.png",intern=TRUE)) character(0) > try(system("convert tmp/5alu61386796538.ps tmp/5alu61386796538.png",intern=TRUE)) character(0) > try(system("convert tmp/6u5a31386796538.ps tmp/6u5a31386796538.png",intern=TRUE)) character(0) > try(system("convert tmp/77hyn1386796538.ps tmp/77hyn1386796538.png",intern=TRUE)) character(0) > try(system("convert tmp/8h8av1386796538.ps tmp/8h8av1386796538.png",intern=TRUE)) character(0) > try(system("convert tmp/91v511386796538.ps tmp/91v511386796538.png",intern=TRUE)) character(0) > try(system("convert tmp/10x6t21386796538.ps tmp/10x6t21386796538.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 24.681 4.029 28.700