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(0.284654 + ,2.301442 + ,0.266482 + ,-4.813031 + ,0.815285 + ,0.414783 + ,1 + ,21.033 + ,0.02211 + ,0.06545 + ,0.02971 + ,0.0313 + ,0.02182 + ,0.426 + ,0.04374 + ,0.01109 + ,0.00554 + ,0.0037 + ,0.00007 + ,0.00784 + ,74.997 + ,157.302 + ,119.992 + ,0.368674 + ,2.486855 + ,0.33559 + ,-4.075192 + ,0.819521 + ,0.458359 + ,1 + ,19.085 + ,0.01929 + ,0.09403 + ,0.04368 + ,0.04518 + ,0.03134 + ,0.626 + ,0.06134 + ,0.01394 + ,0.00696 + ,0.00465 + ,0.00008 + ,0.00968 + ,113.819 + ,148.65 + ,122.4 + ,0.332634 + ,2.342259 + ,0.311173 + ,-4.443179 + ,0.825288 + ,0.429895 + ,1 + ,20.651 + ,0.01309 + ,0.0827 + ,0.0359 + ,0.03858 + ,0.02757 + ,0.482 + ,0.05233 + ,0.01633 + ,0.00781 + ,0.00544 + ,0.00009 + ,0.0105 + ,111.555 + ,131.111 + ,116.682 + ,0.368975 + ,2.405554 + ,0.334147 + ,-4.117501 + ,0.819235 + ,0.434969 + ,1 + ,20.644 + ,0.01353 + ,0.08771 + ,0.03772 + ,0.04005 + ,0.02924 + ,0.517 + ,0.05492 + ,0.01505 + ,0.00698 + ,0.00502 + ,0.00009 + ,0.00997 + 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,0.00008 + ,0.0136 + ,74.287 + ,240.005 + ,174.688 + ,0.123306 + ,2.138608 + ,0.207454 + ,-6.744577 + ,0.643956 + ,0.451221 + ,0 + ,19.02 + ,0.07223 + ,0.03794 + ,0.01588 + ,0.01321 + ,0.01265 + ,0.241 + ,0.02296 + ,0.01109 + ,0.0039 + ,0.0037 + ,0.00004 + ,0.0074 + ,74.904 + ,396.961 + ,198.764 + ,0.148569 + ,2.555477 + ,0.190667 + ,-5.724056 + ,0.664357 + ,0.462803 + ,0 + ,21.209 + ,0.04398 + ,0.03078 + ,0.01373 + ,0.01161 + ,0.01026 + ,0.19 + ,0.01884 + ,0.00885 + ,0.00317 + ,0.00295 + ,0.00003 + ,0.00567 + ,77.973 + ,260.277 + ,214.289) + ,dim=c(23 + ,195) + ,dimnames=list(c('PPE' + ,'D2' + ,'spread2' + ,'spread1' + ,'DFA' + ,'RPDE' + ,'status' + ,'HNR' + ,'NHR' + ,'Shimmer:DDA' + ,'MDVP:APQ' + ,'Shimmer:APQ5' + ,'Shimmer:APQ3' + ,'MDVP:Shimmer(dB)' + ,'MDVP:Shimmer' + ,'Jitter:DDP' + ,'MDVP:PPQ' + ,'MDVP:RAP' + ,'MDVP:Jitter(Abs)' + ,'MDVP:Jitter(%)' + ,'MDVP:Flo(Hz)' + ,'MDVP:Fhi(Hz)' + ,'MDVP:Fo(Hz)') + ,1:195)) > y <- array(NA,dim=c(23,195),dimnames=list(c('PPE','D2','spread2','spread1','DFA','RPDE','status','HNR','NHR','Shimmer:DDA','MDVP:APQ','Shimmer:APQ5','Shimmer:APQ3','MDVP:Shimmer(dB)','MDVP:Shimmer','Jitter:DDP','MDVP:PPQ','MDVP:RAP','MDVP:Jitter(Abs)','MDVP:Jitter(%)','MDVP:Flo(Hz)','MDVP:Fhi(Hz)','MDVP:Fo(Hz)'),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 = '7' > par3 <- 'No Linear Trend' > par2 <- 'Do not include Seasonal Dummies' > par1 <- '7' > #'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 PPE D2 spread2 spread1 DFA RPDE HNR 1 1 0.284654 2.301442 0.266482 -4.813031 0.815285 0.414783 21.033 2 1 0.368674 2.486855 0.335590 -4.075192 0.819521 0.458359 19.085 3 1 0.332634 2.342259 0.311173 -4.443179 0.825288 0.429895 20.651 4 1 0.368975 2.405554 0.334147 -4.117501 0.819235 0.434969 20.644 5 1 0.410335 2.332180 0.234513 -3.747787 0.823484 0.417356 19.649 6 1 0.357775 2.187560 0.299111 -4.242867 0.825069 0.415564 21.378 7 1 0.211756 1.854785 0.257682 -5.634322 0.764112 0.596040 24.886 8 1 0.163755 2.064693 0.183721 -6.167603 0.763262 0.637420 26.892 9 1 0.231571 2.322511 0.327769 -5.498678 0.773587 0.615551 21.812 10 1 0.271362 2.432792 0.325996 -5.011879 0.798463 0.547037 21.862 11 1 0.249740 2.407313 0.391002 -5.249770 0.776156 0.611137 21.118 12 1 0.275931 2.642476 0.363566 -4.960234 0.792520 0.583390 21.414 13 1 0.138512 2.041277 0.152813 -6.547148 0.646846 0.460600 25.703 14 1 0.199889 2.519422 0.254989 -5.660217 0.665833 0.430166 24.889 15 1 0.170100 2.125618 0.203653 -6.105098 0.654027 0.474791 24.922 16 1 0.234589 2.205546 0.210185 -5.340115 0.658245 0.565924 25.175 17 1 0.218164 2.264501 0.239764 -5.440040 0.644692 0.567380 22.333 18 1 0.430788 3.007463 0.434326 -2.931070 0.605417 0.631099 20.376 19 1 0.377429 3.109010 0.357870 -3.949079 0.719467 0.665318 17.280 20 1 0.322111 2.856676 0.340176 -4.554466 0.686080 0.649554 17.153 21 1 0.365391 2.739710 0.262564 -4.095442 0.704087 0.660125 17.536 22 1 0.259765 2.557536 0.237622 -5.186960 0.698951 0.629017 19.493 23 1 0.285695 2.916777 0.262384 -4.330956 0.679834 0.619060 22.468 24 1 0.253556 2.547508 0.210279 -5.248776 0.686894 0.537264 20.422 25 1 0.215961 2.692176 0.220890 -5.557447 0.732479 0.397937 23.831 26 1 0.219514 2.846369 0.236853 -5.571843 0.737948 0.522746 22.066 27 1 0.147403 2.589702 0.226278 -6.183590 0.720916 0.418622 25.908 28 1 0.162999 2.314209 0.196102 -6.271690 0.726652 0.358773 25.119 29 1 0.108514 2.241742 0.279789 -7.120925 0.676258 0.470478 25.970 30 1 0.135242 1.957961 0.209866 -6.635729 0.723797 0.427785 25.678 31 0 0.085569 1.743867 0.177551 -7.348300 0.741367 0.422229 26.775 32 0 0.068501 2.103106 0.173319 -7.682587 0.742055 0.432439 30.940 33 0 0.096320 1.512275 0.175181 -7.067931 0.738703 0.465946 30.775 34 0 0.056141 1.544609 0.178540 -7.695734 0.742133 0.368535 32.684 35 0 0.044539 1.423287 0.163519 -7.964984 0.741899 0.340068 33.047 36 0 0.057610 2.447064 0.170183 -7.777685 0.742737 0.344252 31.732 37 1 0.165827 2.477082 0.218037 -6.149653 0.778834 0.360148 23.216 38 1 0.173218 2.536527 0.196371 -6.006414 0.783626 0.341435 24.951 39 1 0.141929 2.269398 0.212294 -6.452058 0.766209 0.403884 26.738 40 1 0.160691 2.382544 0.266892 -6.006647 0.758324 0.396793 26.310 41 1 0.130554 2.374073 0.201095 -6.647379 0.765623 0.326480 26.822 42 1 0.115730 2.361532 0.063412 -7.044105 0.759203 0.306443 26.453 43 0 0.095032 2.416838 0.098648 -7.310550 0.654172 0.305062 22.736 44 0 0.117399 2.256699 0.158266 -6.793547 0.634267 0.457702 23.145 45 0 0.091470 2.330716 0.091608 -7.057869 0.635285 0.438296 25.368 46 0 0.102706 2.365800 0.102083 -6.995820 0.638928 0.431285 25.032 47 0 0.097336 2.392122 0.127642 -7.156076 0.631653 0.467489 24.602 48 0 0.086398 2.028612 0.200873 -7.319510 0.635204 0.610367 26.805 49 0 0.133867 2.079922 0.266392 -6.439398 0.733659 0.579597 23.162 50 0 0.128872 2.054419 0.264967 -6.482096 0.754073 0.538688 24.971 51 0 0.103561 1.840198 0.254498 -6.650471 0.775933 0.553134 25.135 52 0 0.105993 2.431854 0.291954 -6.689151 0.760361 0.507504 25.030 53 0 0.119308 1.972297 0.220434 -7.072419 0.766204 0.459766 24.692 54 0 0.147491 2.223719 0.269866 -6.836811 0.785714 0.420383 25.429 55 1 0.316700 1.986899 0.205558 -4.649573 0.819032 0.536009 21.028 56 1 0.344834 2.014606 0.221727 -4.333543 0.811843 0.558586 20.767 57 1 0.335041 1.922940 0.238298 -4.438453 0.821364 0.541781 21.422 58 1 0.314464 2.021591 0.290024 -4.608260 0.817756 0.530529 22.817 59 1 0.326197 1.827012 0.262633 -4.476755 0.813432 0.540049 22.603 60 1 0.316395 1.831691 0.221711 -4.609161 0.817396 0.547975 21.660 61 0 0.101516 2.460791 0.066994 -7.040508 0.678874 0.341788 25.554 62 0 0.098555 2.321560 0.086372 -7.293801 0.686264 0.447979 26.138 63 0 0.103224 2.278687 0.095882 -6.966321 0.694399 0.364867 25.856 64 0 0.093534 2.498224 0.018689 -7.245620 0.683296 0.256570 25.964 65 0 0.073581 2.003032 0.056844 -7.496264 0.673636 0.276850 26.415 66 0 0.091546 2.118596 0.006274 -7.314237 0.681811 0.305429 24.547 67 1 0.226156 2.359973 0.226850 -5.409423 0.720908 0.460139 19.560 68 1 0.226247 2.291558 0.205660 -5.324574 0.729067 0.498133 19.979 69 1 0.185580 2.118496 0.151814 -5.869750 0.731444 0.513237 20.338 70 1 0.141958 2.137075 0.120956 -6.261141 0.727313 0.487407 21.718 71 1 0.180828 2.277927 0.158830 -5.720868 0.730387 0.489345 20.264 72 1 0.242981 2.642276 0.224852 -5.207985 0.733232 0.543299 18.570 73 1 0.188180 2.205024 0.329066 -5.791820 0.762959 0.495954 25.742 74 1 0.225461 1.928708 0.306636 -5.389129 0.789532 0.509127 24.178 75 1 0.244512 2.225815 0.201861 -5.313360 0.815908 0.437031 25.438 76 1 0.228624 1.862092 0.315074 -5.477592 0.807217 0.463514 25.197 77 1 0.193918 2.007923 0.341169 -5.775966 0.789977 0.489538 23.370 78 1 0.232744 1.777901 0.250572 -5.391029 0.816340 0.429484 25.820 79 1 0.260015 2.017753 0.249494 -5.115212 0.779612 0.644954 21.875 80 1 0.277948 2.398422 0.265699 -4.913885 0.790117 0.594387 19.200 81 1 0.327978 2.645959 0.155097 -4.441519 0.770466 0.544805 19.055 82 1 0.260633 2.232576 0.210458 -5.132032 0.778747 0.576084 19.659 83 1 0.264666 2.428306 0.146948 -5.022288 0.787896 0.554610 20.536 84 1 0.177275 2.053601 0.078202 -6.025367 0.772416 0.576644 22.244 85 1 0.242119 3.099301 0.343073 -5.288912 0.729586 0.556494 13.893 86 1 0.200423 3.098256 0.315903 -5.657899 0.727747 0.583574 16.176 87 1 0.144614 2.654271 0.335753 -6.366916 0.712199 0.598714 15.924 88 1 0.220968 3.136550 0.299549 -5.515071 0.740837 0.602874 13.922 89 1 0.194052 3.007096 0.299793 -5.783272 0.743937 0.599371 14.739 90 1 0.332086 3.671155 0.375531 -4.379411 0.745526 0.590951 11.866 91 1 0.301952 3.317586 0.389232 -4.508984 0.733165 0.653410 11.744 92 1 0.134120 2.344876 0.207156 -6.411497 0.714360 0.501037 19.664 93 1 0.186489 2.344336 0.087840 -5.952058 0.734504 0.454444 18.780 94 1 0.160809 2.080121 0.173520 -6.152551 0.697790 0.447456 20.969 95 1 0.160812 2.143851 0.188056 -6.251425 0.712170 0.502380 22.219 96 1 0.164916 2.344348 0.180528 -6.247076 0.705658 0.447285 21.693 97 1 0.151709 2.473239 0.194627 -6.417440 0.693429 0.366329 22.663 98 1 0.340623 2.671825 0.265315 -4.020042 0.714485 0.629574 15.338 99 1 0.260375 2.441612 0.202146 -5.159169 0.690892 0.571010 15.433 100 1 0.378483 2.634633 0.242861 -3.760348 0.674953 0.638545 12.435 101 1 0.370961 2.991063 0.260481 -3.700544 0.656846 0.671299 8.867 102 1 0.356881 2.638279 0.310163 -4.202730 0.643327 0.639808 15.060 103 1 0.444774 2.690917 0.270641 -3.269487 0.641418 0.596362 10.489 104 1 0.113942 2.004055 0.089267 -6.878393 0.722356 0.296888 26.759 105 1 0.093193 2.065477 0.144780 -7.111576 0.691483 0.263654 28.409 106 1 0.112878 1.994387 0.210279 -6.997403 0.719974 0.365488 27.421 107 1 0.106802 2.129924 0.184550 -6.981201 0.677930 0.334171 29.746 108 1 0.105306 2.499148 0.249172 -6.600023 0.700246 0.393563 26.833 109 1 0.115130 2.296873 0.160686 -6.739151 0.676066 0.311369 29.928 110 1 0.185668 2.608749 0.278679 -5.845099 0.740539 0.497554 21.934 111 1 0.232520 2.550961 0.256454 -5.258320 0.727863 0.436084 23.239 112 1 0.136390 2.502336 0.184378 -6.471427 0.712466 0.338097 22.407 113 1 0.268144 2.376749 0.212054 -4.876336 0.722085 0.498877 21.305 114 1 0.177807 2.489191 0.250283 -5.963040 0.722254 0.441097 23.671 115 1 0.115515 2.938114 0.181701 -6.729713 0.715121 0.331508 21.864 116 1 0.274407 2.702355 0.261549 -4.673241 0.662668 0.407701 23.693 117 1 0.170106 2.640798 0.273280 -6.051233 0.653823 0.450798 26.356 118 1 0.282780 2.975889 0.372114 -4.597834 0.676023 0.486738 25.690 119 1 0.251972 2.816781 0.393056 -4.913137 0.655239 0.470422 25.020 120 1 0.220657 2.925862 0.389295 -5.517173 0.582710 0.462516 24.581 121 1 0.152428 2.686240 0.279933 -6.186128 0.684130 0.487756 24.743 122 1 0.234809 2.655744 0.281618 -4.711007 0.656182 0.400088 27.166 123 1 0.229892 2.090438 0.160267 -5.418787 0.741480 0.538016 18.305 124 1 0.215558 2.174306 0.142466 -5.445140 0.732903 0.589956 18.784 125 1 0.181988 1.929715 0.143359 -5.944191 0.728421 0.618663 19.196 126 1 0.222716 1.765957 0.127950 -5.594275 0.735546 0.637518 18.857 127 1 0.214075 1.821297 0.087165 -5.540351 0.738245 0.623209 18.178 128 1 0.196535 1.996146 0.115697 -5.825257 0.736964 0.585169 18.330 129 1 0.112856 2.328513 0.152941 -6.890021 0.699787 0.457541 26.842 130 1 0.183572 2.108873 0.195976 -5.892061 0.718839 0.491345 26.369 131 1 0.169923 2.539724 0.203630 -6.135296 0.724045 0.467160 23.949 132 1 0.170633 2.527742 0.217013 -6.112667 0.735136 0.468621 26.017 133 1 0.232209 2.516320 0.254909 -5.436135 0.721308 0.470972 23.389 134 1 0.141422 2.034827 0.178713 -6.448134 0.723096 0.482296 25.619 135 1 0.243080 2.375138 0.320385 -5.301321 0.744064 0.637814 17.060 136 1 0.228319 2.631793 0.322044 -5.333619 0.706687 0.653427 17.707 137 1 0.259451 2.445502 0.300067 -4.378916 0.708144 0.647900 19.013 138 1 0.274387 2.672362 0.304107 -4.654894 0.708617 0.625362 16.747 139 1 0.209191 2.419253 0.306014 -5.634576 0.701404 0.640945 17.366 140 1 0.184985 2.445646 0.233070 -5.866357 0.696049 0.624811 18.801 141 1 0.277227 2.963799 0.397749 -4.796845 0.685057 0.677131 18.540 142 1 0.231723 2.665133 0.288917 -5.410336 0.665945 0.606344 15.648 143 1 0.209863 2.465528 0.310746 -5.585259 0.661735 0.606273 18.702 144 1 0.189032 2.470746 0.213353 -5.898673 0.632631 0.536102 18.687 145 1 0.159777 2.576563 0.220617 -6.132663 0.630409 0.497480 20.680 146 1 0.232861 2.840556 0.345238 -5.456811 0.574282 0.566849 20.366 147 1 0.457533 3.413649 0.414758 -3.297668 0.793509 0.561610 12.359 148 1 0.336085 3.142364 0.355736 -4.276605 0.768974 0.478024 14.367 149 1 0.418646 3.274865 0.335357 -3.377325 0.764036 0.552870 12.298 150 1 0.270173 2.910213 0.262281 -4.892495 0.775708 0.427627 14.989 151 1 0.301487 2.958815 0.340256 -4.484303 0.762726 0.507826 12.529 152 1 0.527367 3.079221 0.450493 -2.434031 0.768320 0.625866 8.441 153 1 0.454721 3.184027 0.356224 -2.839756 0.754449 0.584164 9.449 154 1 0.168581 2.013530 0.246404 -4.865194 0.670475 0.566867 21.520 155 1 0.247455 2.451130 0.175691 -4.239028 0.659333 0.651680 21.824 156 1 0.206256 2.439597 0.207914 -3.583722 0.652025 0.628300 22.431 157 1 0.220546 2.699645 0.230532 -5.435100 0.623731 0.611679 22.953 158 1 0.261305 2.964568 0.303214 -3.444478 0.646786 0.630547 19.075 159 1 0.249703 2.892300 0.280091 -5.070096 0.627337 0.635015 21.534 160 1 0.216638 2.103014 0.234196 -5.498456 0.675865 0.654945 19.651 161 1 0.244948 2.151121 0.259229 -5.185987 0.694571 0.653139 20.437 162 1 0.238281 2.442906 0.226528 -5.283009 0.684373 0.577802 19.388 163 1 0.220520 2.408689 0.242750 -5.529833 0.719576 0.685151 18.954 164 1 0.212386 1.871871 0.184896 -5.617124 0.673086 0.557045 21.219 165 1 0.367233 2.560422 0.396746 -2.929379 0.674562 0.671378 18.447 166 0 0.119652 2.235197 0.172270 -6.816086 0.628232 0.469928 24.078 167 0 0.091604 1.852402 0.176316 -7.018057 0.626710 0.384868 24.679 168 0 0.075587 1.881767 0.160414 -7.517934 0.628058 0.440988 21.083 169 0 0.202879 2.882450 0.164529 -5.736781 0.725216 0.372222 19.269 170 0 0.100881 2.266432 0.073298 -7.169701 0.646167 0.371837 21.020 171 0 0.096220 2.095237 0.171088 -7.304500 0.646818 0.522812 21.528 172 0 0.160376 2.193412 0.218885 -6.323531 0.756700 0.413295 26.436 173 0 0.174152 1.889002 0.192375 -6.085567 0.776158 0.369090 26.550 174 0 0.179677 1.852542 0.192150 -5.943501 0.766700 0.380253 26.547 175 0 0.163118 1.872946 0.229298 -6.012559 0.756482 0.387482 25.445 176 0 0.184067 1.974857 0.197938 -5.966779 0.761255 0.405991 26.005 177 0 0.174429 2.004719 0.109256 -6.016891 0.763242 0.361232 26.143 178 1 0.132703 2.449763 0.197919 -6.486822 0.745957 0.396610 24.151 179 1 0.160306 2.251553 0.182459 -6.311987 0.762508 0.402591 24.412 180 1 0.192730 2.845109 0.240875 -5.711205 0.778349 0.398499 23.683 181 1 0.144105 2.264226 0.183218 -6.261446 0.759320 0.352396 23.133 182 1 0.197710 2.679185 0.216204 -5.704053 0.768845 0.408598 22.866 183 1 0.156368 2.209021 0.109397 -6.277170 0.757180 0.329577 23.008 184 0 0.215724 2.027228 0.191576 -5.619070 0.669565 0.603515 23.079 185 0 0.252404 2.120412 0.206768 -5.198864 0.656516 0.663842 22.085 186 0 0.214346 2.058658 0.133917 -5.592584 0.654331 0.598515 24.199 187 0 0.120605 2.161936 0.153310 -6.431119 0.667654 0.566424 23.958 188 0 0.138868 2.152083 0.116636 -6.359018 0.663884 0.528485 25.023 189 0 0.121777 1.913990 0.149694 -6.710219 0.659132 0.555303 24.775 190 0 0.112838 2.316346 0.159890 -6.934474 0.683761 0.508479 19.368 191 0 0.133050 2.657476 0.121952 -6.538586 0.657899 0.448439 19.517 192 0 0.168895 2.784312 0.129303 -6.195325 0.683244 0.431674 19.147 193 0 0.131728 2.679772 0.158453 -6.787197 0.655683 0.407567 17.883 194 0 0.123306 2.138608 0.207454 -6.744577 0.643956 0.451221 19.020 195 0 0.148569 2.555477 0.190667 -5.724056 0.664357 0.462803 21.209 NHR Shimmer:DDA MDVP:APQ Shimmer:APQ5 Shimmer:APQ3 MDVP:Shimmer(dB) 1 0.02211 0.06545 0.02971 0.03130 0.02182 0.426 2 0.01929 0.09403 0.04368 0.04518 0.03134 0.626 3 0.01309 0.08270 0.03590 0.03858 0.02757 0.482 4 0.01353 0.08771 0.03772 0.04005 0.02924 0.517 5 0.01767 0.10470 0.04465 0.04825 0.03490 0.584 6 0.01222 0.06985 0.03243 0.03526 0.02328 0.456 7 0.00607 0.02337 0.01351 0.00937 0.00779 0.140 8 0.00344 0.02487 0.01256 0.00946 0.00829 0.134 9 0.01070 0.03218 0.01717 0.01277 0.01073 0.191 10 0.01022 0.04324 0.02444 0.01725 0.01441 0.255 11 0.01166 0.03237 0.01892 0.01342 0.01079 0.197 12 0.01141 0.04272 0.02214 0.01641 0.01424 0.249 13 0.00581 0.01968 0.01140 0.00717 0.00656 0.112 14 0.01041 0.02184 0.01797 0.00932 0.00728 0.154 15 0.00609 0.03191 0.01246 0.00972 0.01064 0.158 16 0.00839 0.02316 0.01359 0.00888 0.00772 0.126 17 0.01859 0.02908 0.02074 0.01200 0.00969 0.192 18 0.02919 0.04322 0.03430 0.01893 0.01441 0.348 19 0.03160 0.07413 0.05767 0.03572 0.02471 0.542 20 0.03365 0.05164 0.04310 0.02374 0.01721 0.348 21 0.03871 0.05000 0.04055 0.02383 0.01667 0.328 22 0.01849 0.06062 0.04525 0.02591 0.02021 0.370 23 0.01280 0.06685 0.04246 0.02540 0.02228 0.377 24 0.01840 0.06562 0.03772 0.02470 0.02187 0.364 25 0.01778 0.02214 0.01497 0.00948 0.00738 0.164 26 0.02887 0.05197 0.03780 0.02245 0.01732 0.381 27 0.01095 0.02666 0.01872 0.01169 0.00889 0.186 28 0.01328 0.02650 0.01826 0.01144 0.00883 0.198 29 0.00677 0.02307 0.01661 0.01012 0.00769 0.161 30 0.01170 0.02380 0.01799 0.01057 0.00793 0.168 31 0.00339 0.01689 0.00802 0.00680 0.00563 0.097 32 0.00167 0.01513 0.00762 0.00641 0.00504 0.089 33 0.00119 0.01919 0.00951 0.00825 0.00640 0.111 34 0.00072 0.01407 0.00719 0.00606 0.00469 0.085 35 0.00065 0.01403 0.00726 0.00610 0.00468 0.085 36 0.00135 0.01758 0.00957 0.00760 0.00586 0.107 37 0.00586 0.03463 0.01612 0.01347 0.01154 0.189 38 0.00340 0.02814 0.01491 0.01160 0.00938 0.168 39 0.00231 0.02177 0.01190 0.00885 0.00726 0.131 40 0.00265 0.02488 0.01366 0.01003 0.00829 0.151 41 0.00231 0.02321 0.01233 0.00941 0.00774 0.135 42 0.00257 0.02226 0.01234 0.00901 0.00742 0.132 43 0.00740 0.03104 0.01133 0.01024 0.01035 0.164 44 0.00675 0.03017 0.01251 0.01038 0.01006 0.154 45 0.00454 0.02330 0.01033 0.00898 0.00777 0.126 46 0.00476 0.02542 0.01014 0.00879 0.00847 0.134 47 0.00476 0.02719 0.01149 0.00977 0.00906 0.141 48 0.00432 0.01841 0.00860 0.00730 0.00614 0.103 49 0.00839 0.02566 0.01433 0.00776 0.00855 0.143 50 0.00462 0.02789 0.01400 0.00802 0.00930 0.154 51 0.00479 0.03724 0.01685 0.01024 0.01241 0.197 52 0.00474 0.03429 0.01614 0.00959 0.01143 0.185 53 0.00481 0.03969 0.01677 0.01072 0.01323 0.210 54 0.00484 0.04188 0.01947 0.01219 0.01396 0.228 55 0.01036 0.04450 0.02067 0.01609 0.01483 0.255 56 0.01180 0.05368 0.02454 0.01992 0.01789 0.307 57 0.00969 0.06097 0.02802 0.02302 0.02032 0.334 58 0.00681 0.03568 0.01948 0.01459 0.01189 0.221 59 0.00786 0.04183 0.02137 0.01625 0.01394 0.265 60 0.01143 0.05414 0.02519 0.01974 0.01805 0.350 61 0.00871 0.02925 0.01382 0.01258 0.00975 0.170 62 0.00301 0.03039 0.01340 0.01296 0.01013 0.165 63 0.00340 0.02602 0.01200 0.01108 0.00867 0.145 64 0.00351 0.02647 0.01179 0.01075 0.00882 0.145 65 0.00300 0.02308 0.01016 0.00957 0.00769 0.129 66 0.00420 0.02827 0.01234 0.01160 0.00942 0.154 67 0.02183 0.05490 0.02428 0.01810 0.01830 0.313 68 0.02659 0.04914 0.02603 0.01759 0.01638 0.308 69 0.04882 0.09455 0.03392 0.02422 0.03152 0.478 70 0.02431 0.10070 0.03635 0.02494 0.03357 0.497 71 0.02599 0.05605 0.02949 0.01906 0.01868 0.365 72 0.03361 0.08247 0.03736 0.02466 0.02749 0.483 73 0.00442 0.02921 0.01345 0.00925 0.00974 0.152 74 0.00623 0.04120 0.01956 0.01375 0.01373 0.226 75 0.00479 0.04295 0.01831 0.01325 0.01432 0.216 76 0.00472 0.03851 0.01715 0.01219 0.01284 0.206 77 0.00905 0.07238 0.02704 0.02231 0.02413 0.350 78 0.00420 0.03852 0.01636 0.01199 0.01284 0.197 79 0.01062 0.05408 0.02455 0.01886 0.01803 0.263 80 0.02220 0.05320 0.02139 0.01783 0.01773 0.361 81 0.01823 0.06799 0.02876 0.02451 0.02266 0.364 82 0.01825 0.05377 0.02190 0.01841 0.01792 0.296 83 0.01237 0.04114 0.01751 0.01421 0.01371 0.216 84 0.00882 0.03831 0.01552 0.01343 0.01277 0.202 85 0.05470 0.08037 0.03510 0.03022 0.02679 0.435 86 0.02782 0.06321 0.02877 0.02493 0.02107 0.331 87 0.03151 0.06219 0.02784 0.02415 0.02073 0.327 88 0.04824 0.11012 0.04683 0.04159 0.03671 0.580 89 0.04214 0.11363 0.04802 0.04254 0.03788 0.650 90 0.07223 0.06892 0.03455 0.02768 0.02297 0.442 91 0.08725 0.10949 0.05114 0.04282 0.03650 0.634 92 0.01658 0.13262 0.05690 0.04962 0.04421 0.772 93 0.01914 0.07150 0.03051 0.02521 0.02383 0.383 94 0.01211 0.10024 0.04398 0.03794 0.03341 0.637 95 0.00850 0.06185 0.02764 0.02321 0.02062 0.307 96 0.01018 0.05439 0.02571 0.01909 0.01813 0.283 97 0.00852 0.05417 0.02809 0.02024 0.01806 0.307 98 0.08151 0.06406 0.03088 0.02174 0.02135 0.342 99 0.10323 0.07625 0.03908 0.02630 0.02542 0.422 100 0.16744 0.10833 0.05783 0.03963 0.03611 0.659 101 0.31482 0.16074 0.06196 0.04791 0.05358 0.891 102 0.11843 0.09669 0.05174 0.03672 0.03223 0.584 103 0.25930 0.16654 0.06023 0.05005 0.05551 0.930 104 0.00495 0.01567 0.01009 0.00659 0.00522 0.107 105 0.00243 0.01406 0.00871 0.00582 0.00469 0.094 106 0.00578 0.01979 0.01059 0.00818 0.00660 0.126 107 0.00233 0.01567 0.00928 0.00632 0.00522 0.097 108 0.00659 0.01898 0.01267 0.00788 0.00633 0.137 109 0.00238 0.01364 0.00993 0.00576 0.00455 0.093 110 0.00947 0.05312 0.02084 0.01815 0.01771 0.275 111 0.00704 0.03576 0.01852 0.01439 0.01192 0.207 112 0.00830 0.02855 0.01307 0.01058 0.00952 0.155 113 0.01316 0.03831 0.01767 0.01483 0.01277 0.210 114 0.00620 0.02583 0.01301 0.01017 0.00861 0.149 115 0.01048 0.03320 0.01604 0.01284 0.01107 0.209 116 0.06051 0.02389 0.01271 0.00832 0.00796 0.235 117 0.01554 0.01818 0.01312 0.00747 0.00606 0.148 118 0.01802 0.02270 0.01652 0.00971 0.00757 0.175 119 0.00856 0.01851 0.01151 0.00744 0.00617 0.129 120 0.00681 0.02038 0.01075 0.00631 0.00679 0.124 121 0.02350 0.02548 0.01734 0.01117 0.00849 0.221 122 0.01161 0.01603 0.01104 0.00630 0.00534 0.117 123 0.01968 0.07761 0.03220 0.02567 0.02587 0.441 124 0.01813 0.04115 0.01931 0.01580 0.01372 0.231 125 0.02020 0.03867 0.01720 0.01420 0.01289 0.224 126 0.01874 0.03706 0.01944 0.01495 0.01235 0.233 127 0.01794 0.04451 0.02259 0.01805 0.01484 0.246 128 0.01796 0.04641 0.02301 0.01859 0.01547 0.257 129 0.01724 0.01614 0.00811 0.00570 0.00538 0.098 130 0.00487 0.01428 0.00903 0.00588 0.00476 0.090 131 0.01610 0.02110 0.01194 0.00820 0.00703 0.125 132 0.01015 0.02164 0.01310 0.00815 0.00721 0.138 133 0.00903 0.01898 0.00915 0.00701 0.00633 0.106 134 0.00504 0.01471 0.00903 0.00621 0.00490 0.099 135 0.03031 0.08050 0.03651 0.03112 0.02683 0.441 136 0.02529 0.06688 0.03316 0.02592 0.02229 0.379 137 0.02278 0.07154 0.04370 0.02973 0.02385 0.431 138 0.03690 0.08689 0.04134 0.03347 0.02896 0.476 139 0.02629 0.09211 0.04451 0.03530 0.03070 0.517 140 0.01827 0.04543 0.02770 0.01812 0.01514 0.267 141 0.02485 0.05139 0.02824 0.01964 0.01713 0.281 142 0.04238 0.12047 0.04464 0.04003 0.04016 0.571 143 0.01728 0.06165 0.02530 0.02076 0.02055 0.297 144 0.02010 0.03350 0.01506 0.01177 0.01117 0.180 145 0.01049 0.04426 0.02006 0.01558 0.01475 0.228 146 0.01493 0.04137 0.01909 0.01478 0.01379 0.225 147 0.07530 0.11411 0.08808 0.05426 0.03804 0.821 148 0.06057 0.08595 0.06359 0.04101 0.02865 0.618 149 0.08069 0.10422 0.06824 0.04580 0.03474 0.722 150 0.07889 0.10546 0.06460 0.04265 0.03515 0.833 151 0.10952 0.08096 0.06259 0.03714 0.02699 0.784 152 0.21713 0.16942 0.13778 0.07940 0.05647 1.302 153 0.16265 0.12851 0.08318 0.05556 0.04284 1.018 154 0.04179 0.04019 0.02056 0.01399 0.01340 0.241 155 0.04611 0.04451 0.02018 0.01405 0.01484 0.236 156 0.02631 0.04977 0.02402 0.01804 0.01659 0.276 157 0.03191 0.03615 0.01771 0.01289 0.01205 0.223 158 0.10748 0.07830 0.02916 0.02161 0.02610 0.438 159 0.03828 0.04499 0.02157 0.01581 0.01500 0.266 160 0.02663 0.04079 0.03105 0.01650 0.01360 0.339 161 0.02073 0.04736 0.04114 0.01994 0.01579 0.406 162 0.02810 0.04933 0.02931 0.01722 0.01644 0.325 163 0.02707 0.05592 0.03091 0.01940 0.01864 0.369 164 0.01435 0.02902 0.01363 0.01033 0.00967 0.155 165 0.03882 0.04736 0.02073 0.01553 0.01579 0.272 166 0.00620 0.04231 0.01621 0.01426 0.01410 0.217 167 0.00533 0.02089 0.00882 0.00747 0.00696 0.116 168 0.00910 0.03557 0.01367 0.01230 0.01186 0.197 169 0.01337 0.03836 0.01439 0.01272 0.01279 0.189 170 0.00965 0.03529 0.01344 0.01191 0.01176 0.212 171 0.01049 0.03253 0.01255 0.01121 0.01084 0.181 172 0.00435 0.01992 0.01140 0.00786 0.00664 0.129 173 0.00430 0.02261 0.01285 0.00950 0.00754 0.133 174 0.00478 0.02245 0.01148 0.00905 0.00748 0.133 175 0.00590 0.02643 0.01318 0.01062 0.00881 0.145 176 0.00401 0.02436 0.01133 0.00933 0.00812 0.137 177 0.00415 0.02623 0.01331 0.01021 0.00874 0.155 178 0.00570 0.02184 0.01230 0.00886 0.00728 0.132 179 0.00488 0.02518 0.01309 0.00956 0.00839 0.142 180 0.00540 0.02175 0.01263 0.00876 0.00725 0.131 181 0.00611 0.03964 0.02148 0.01574 0.01321 0.237 182 0.00639 0.02849 0.01559 0.01103 0.00950 0.163 183 0.00595 0.03464 0.01666 0.01341 0.01155 0.198 184 0.00955 0.02592 0.01949 0.01223 0.00864 0.171 185 0.01179 0.02429 0.01756 0.01144 0.00810 0.163 186 0.00737 0.02001 0.01691 0.00990 0.00667 0.136 187 0.01397 0.02460 0.01491 0.00972 0.00820 0.154 188 0.00680 0.01892 0.01144 0.00789 0.00631 0.117 189 0.00703 0.01672 0.01095 0.00721 0.00557 0.106 190 0.04441 0.04363 0.01758 0.01582 0.01454 0.255 191 0.02764 0.07008 0.02745 0.02498 0.02336 0.405 192 0.01810 0.04812 0.01879 0.01657 0.01604 0.263 193 0.10715 0.03804 0.01667 0.01365 0.01268 0.256 194 0.07223 0.03794 0.01588 0.01321 0.01265 0.241 195 0.04398 0.03078 0.01373 0.01161 0.01026 0.190 MDVP:Shimmer Jitter:DDP MDVP:PPQ MDVP:RAP MDVP:Jitter(Abs) MDVP:Jitter(%) 1 0.04374 0.01109 0.00554 0.00370 7.0e-05 0.00784 2 0.06134 0.01394 0.00696 0.00465 8.0e-05 0.00968 3 0.05233 0.01633 0.00781 0.00544 9.0e-05 0.01050 4 0.05492 0.01505 0.00698 0.00502 9.0e-05 0.00997 5 0.06425 0.01966 0.00908 0.00655 1.1e-04 0.01284 6 0.04701 0.01388 0.00750 0.00463 8.0e-05 0.00968 7 0.01608 0.00466 0.00202 0.00155 3.0e-05 0.00333 8 0.01567 0.00431 0.00182 0.00144 3.0e-05 0.00290 9 0.02093 0.00880 0.00332 0.00293 6.0e-05 0.00551 10 0.02838 0.00803 0.00332 0.00268 6.0e-05 0.00532 11 0.02143 0.00763 0.00330 0.00254 6.0e-05 0.00505 12 0.02752 0.00844 0.00336 0.00281 6.0e-05 0.00540 13 0.01259 0.00355 0.00153 0.00118 2.0e-05 0.00293 14 0.01642 0.00496 0.00208 0.00165 3.0e-05 0.00390 15 0.01828 0.00364 0.00149 0.00121 2.0e-05 0.00294 16 0.01503 0.00471 0.00203 0.00157 3.0e-05 0.00369 17 0.02047 0.00632 0.00292 0.00211 4.0e-05 0.00544 18 0.03327 0.00853 0.00387 0.00284 4.0e-05 0.00718 19 0.05517 0.01092 0.00432 0.00364 5.0e-05 0.00742 20 0.03995 0.01116 0.00399 0.00372 5.0e-05 0.00768 21 0.03810 0.01285 0.00450 0.00428 5.0e-05 0.00840 22 0.04137 0.00696 0.00267 0.00232 3.0e-05 0.00480 23 0.04351 0.00661 0.00247 0.00220 3.0e-05 0.00442 24 0.04192 0.00663 0.00258 0.00221 3.0e-05 0.00476 25 0.01659 0.01140 0.00390 0.00380 5.0e-05 0.00742 26 0.03767 0.00948 0.00375 0.00316 6.0e-05 0.00633 27 0.01966 0.00750 0.00234 0.00250 3.0e-05 0.00455 28 0.01919 0.00749 0.00275 0.00250 3.0e-05 0.00496 29 0.01718 0.00476 0.00176 0.00159 2.0e-05 0.00310 30 0.01791 0.00841 0.00253 0.00280 3.0e-05 0.00502 31 0.01098 0.00498 0.00168 0.00166 1.0e-05 0.00289 32 0.01015 0.00402 0.00138 0.00134 1.0e-05 0.00241 33 0.01263 0.00339 0.00135 0.00113 1.0e-05 0.00212 34 0.00954 0.00278 0.00107 0.00093 9.0e-06 0.00180 35 0.00958 0.00283 0.00106 0.00094 9.0e-06 0.00178 36 0.01194 0.00314 0.00115 0.00105 1.0e-05 0.00198 37 0.02126 0.00700 0.00241 0.00233 2.0e-05 0.00411 38 0.01851 0.00616 0.00218 0.00205 2.0e-05 0.00369 39 0.01444 0.00459 0.00166 0.00153 2.0e-05 0.00284 40 0.01663 0.00504 0.00182 0.00168 2.0e-05 0.00316 41 0.01495 0.00496 0.00175 0.00165 2.0e-05 0.00298 42 0.01463 0.00403 0.00147 0.00134 1.0e-05 0.00258 43 0.01752 0.00507 0.00182 0.00169 1.0e-05 0.00298 44 0.01760 0.00470 0.00173 0.00157 1.0e-05 0.00281 45 0.01419 0.00327 0.00137 0.00109 9.0e-06 0.00210 46 0.01494 0.00350 0.00139 0.00117 9.0e-06 0.00225 47 0.01608 0.00380 0.00148 0.00127 1.0e-05 0.00235 48 0.01152 0.00276 0.00113 0.00092 7.0e-06 0.00185 49 0.01613 0.00507 0.00203 0.00169 4.0e-05 0.00524 50 0.01681 0.00373 0.00155 0.00124 3.0e-05 0.00428 51 0.02184 0.00422 0.00167 0.00141 3.0e-05 0.00431 52 0.02033 0.00393 0.00169 0.00131 4.0e-05 0.00448 53 0.02297 0.00411 0.00166 0.00137 3.0e-05 0.00436 54 0.02498 0.00495 0.00183 0.00165 4.0e-05 0.00490 55 0.02719 0.01046 0.00486 0.00349 7.0e-05 0.00761 56 0.03209 0.01193 0.00539 0.00398 8.0e-05 0.00874 57 0.03715 0.01056 0.00514 0.00352 7.0e-05 0.00784 58 0.02293 0.00898 0.00469 0.00299 6.0e-05 0.00752 59 0.02645 0.01003 0.00493 0.00334 7.0e-05 0.00788 60 0.03225 0.01120 0.00520 0.00373 8.0e-05 0.00867 61 0.01861 0.00442 0.00152 0.00147 1.0e-05 0.00282 62 0.01906 0.00461 0.00151 0.00154 1.0e-05 0.00264 63 0.01643 0.00457 0.00144 0.00152 1.0e-05 0.00266 64 0.01644 0.00526 0.00155 0.00175 1.0e-05 0.00296 65 0.01457 0.00342 0.00113 0.00114 9.0e-06 0.00205 66 0.01745 0.00408 0.00140 0.00136 1.0e-05 0.00238 67 0.03198 0.01289 0.00440 0.00430 6.0e-05 0.00817 68 0.03111 0.01520 0.00463 0.00507 7.0e-05 0.00923 69 0.05384 0.01941 0.00467 0.00647 8.0e-05 0.01101 70 0.05428 0.01400 0.00354 0.00467 5.0e-05 0.00762 71 0.03485 0.01407 0.00419 0.00469 6.0e-05 0.00831 72 0.04978 0.01601 0.00478 0.00534 7.0e-05 0.00971 73 0.01706 0.00540 0.00220 0.00180 3.0e-05 0.00405 74 0.02448 0.00805 0.00329 0.00268 5.0e-05 0.00533 75 0.02442 0.00780 0.00283 0.00260 4.0e-05 0.00494 76 0.02215 0.00831 0.00289 0.00277 5.0e-05 0.00516 77 0.03999 0.00810 0.00289 0.00270 4.0e-05 0.00500 78 0.02199 0.00677 0.00280 0.00226 4.0e-05 0.00462 79 0.03202 0.00994 0.00332 0.00331 6.0e-05 0.00608 80 0.03121 0.01865 0.00576 0.00622 1.0e-04 0.01038 81 0.04024 0.01168 0.00415 0.00389 7.0e-05 0.00694 82 0.03156 0.01283 0.00371 0.00428 7.0e-05 0.00702 83 0.02427 0.01053 0.00348 0.00351 6.0e-05 0.00606 84 0.02223 0.00742 0.00258 0.00247 4.0e-05 0.00432 85 0.04795 0.01254 0.00420 0.00418 4.0e-05 0.00747 86 0.03852 0.00659 0.00244 0.00220 2.0e-05 0.00406 87 0.03759 0.00488 0.00194 0.00163 2.0e-05 0.00321 88 0.06511 0.00862 0.00312 0.00287 3.0e-05 0.00520 89 0.06727 0.00710 0.00254 0.00237 3.0e-05 0.00448 90 0.04313 0.01172 0.00419 0.00391 4.0e-05 0.00709 91 0.06640 0.01161 0.00453 0.00387 4.0e-05 0.00742 92 0.07959 0.00672 0.00227 0.00224 3.0e-05 0.00419 93 0.04190 0.00750 0.00256 0.00250 3.0e-05 0.00459 94 0.05925 0.00574 0.00226 0.00191 3.0e-05 0.00382 95 0.03716 0.00587 0.00196 0.00196 2.0e-05 0.00358 96 0.03272 0.00602 0.00197 0.00201 2.0e-05 0.00369 97 0.03381 0.00535 0.00184 0.00178 2.0e-05 0.00342 98 0.03886 0.02228 0.00623 0.00743 1.0e-04 0.01280 99 0.04689 0.02478 0.00655 0.00826 1.1e-04 0.01378 100 0.06734 0.03476 0.00990 0.01159 1.5e-04 0.01936 101 0.09178 0.06433 0.01522 0.02144 2.6e-04 0.03316 102 0.06170 0.02716 0.00909 0.00905 1.2e-04 0.01551 103 0.09419 0.05563 0.01628 0.01854 2.2e-04 0.03011 104 0.01131 0.00315 0.00136 0.00105 2.0e-05 0.00248 105 0.01030 0.00229 0.00100 0.00076 1.0e-05 0.00183 106 0.01346 0.00349 0.00134 0.00116 2.0e-05 0.00257 107 0.01064 0.00204 0.00092 0.00068 1.0e-05 0.00168 108 0.01450 0.00346 0.00122 0.00115 2.0e-05 0.00258 109 0.01024 0.00225 0.00096 0.00075 1.0e-05 0.00174 110 0.03044 0.01351 0.00389 0.00450 4.0e-05 0.00766 111 0.02286 0.01112 0.00337 0.00371 3.0e-05 0.00621 112 0.01761 0.01105 0.00339 0.00368 3.0e-05 0.00609 113 0.02378 0.01506 0.00485 0.00502 4.0e-05 0.00841 114 0.01680 0.00964 0.00280 0.00321 3.0e-05 0.00534 115 0.02105 0.00905 0.00246 0.00302 2.0e-05 0.00495 116 0.01843 0.01211 0.00385 0.00404 6.0e-05 0.00856 117 0.01458 0.00642 0.00207 0.00214 3.0e-05 0.00476 118 0.01725 0.00731 0.00261 0.00244 3.0e-05 0.00555 119 0.01279 0.00472 0.00194 0.00157 3.0e-05 0.00462 120 0.01299 0.00381 0.00128 0.00127 2.0e-05 0.00404 121 0.02008 0.00723 0.00314 0.00241 5.0e-05 0.00581 122 0.01169 0.00628 0.00221 0.00209 3.0e-05 0.00460 123 0.04479 0.01218 0.00398 0.00406 5.0e-05 0.00704 124 0.02503 0.01517 0.00449 0.00506 5.0e-05 0.00842 125 0.02343 0.01209 0.00395 0.00403 4.0e-05 0.00694 126 0.02362 0.01242 0.00422 0.00414 5.0e-05 0.00733 127 0.02791 0.00883 0.00327 0.00294 4.0e-05 0.00544 128 0.02857 0.01104 0.00351 0.00368 4.0e-05 0.00638 129 0.01033 0.00641 0.00192 0.00214 4.0e-05 0.00440 130 0.01022 0.00349 0.00135 0.00116 2.0e-05 0.00270 131 0.01412 0.00808 0.00238 0.00269 4.0e-05 0.00492 132 0.01516 0.00671 0.00205 0.00224 3.0e-05 0.00407 133 0.01201 0.00508 0.00170 0.00169 3.0e-05 0.00346 134 0.01043 0.00504 0.00171 0.00168 3.0e-05 0.00331 135 0.04932 0.00873 0.00319 0.00291 6.0e-05 0.00589 136 0.04128 0.00731 0.00315 0.00244 4.0e-05 0.00494 137 0.04879 0.00658 0.00283 0.00219 4.0e-05 0.00451 138 0.05279 0.00772 0.00312 0.00257 4.0e-05 0.00502 139 0.05643 0.00715 0.00290 0.00238 4.0e-05 0.00472 140 0.03026 0.00542 0.00232 0.00181 3.0e-05 0.00381 141 0.03273 0.00696 0.00269 0.00232 3.0e-05 0.00571 142 0.06725 0.01285 0.00428 0.00428 4.0e-05 0.00757 143 0.03527 0.00546 0.00215 0.00182 2.0e-05 0.00376 144 0.01997 0.00568 0.00211 0.00189 2.0e-05 0.00370 145 0.02662 0.00301 0.00133 0.00100 1.0e-05 0.00254 146 0.02536 0.00506 0.00188 0.00169 2.0e-05 0.00352 147 0.08143 0.02589 0.00946 0.00863 9.0e-05 0.01568 148 0.06050 0.02546 0.00819 0.00849 8.0e-05 0.01466 149 0.07118 0.02987 0.01027 0.00996 9.0e-05 0.01719 150 0.07170 0.02756 0.00963 0.00919 8.0e-05 0.01627 151 0.05830 0.03225 0.01154 0.01075 1.0e-04 0.01872 152 0.11908 0.05401 0.01958 0.01800 1.6e-04 0.03107 153 0.08684 0.04705 0.01699 0.01568 1.4e-04 0.02714 154 0.02534 0.01164 0.00332 0.00388 6.0e-05 0.00684 155 0.02682 0.01179 0.00300 0.00393 6.0e-05 0.00692 156 0.03087 0.01067 0.00300 0.00356 5.0e-05 0.00647 157 0.02293 0.01246 0.00339 0.00415 6.0e-05 0.00727 158 0.04912 0.03351 0.00718 0.01117 1.5e-04 0.01813 159 0.02852 0.01778 0.00454 0.00593 8.0e-05 0.00975 160 0.03235 0.00962 0.00318 0.00321 5.0e-05 0.00605 161 0.04009 0.00896 0.00316 0.00299 5.0e-05 0.00581 162 0.03273 0.01057 0.00329 0.00352 5.0e-05 0.00619 163 0.03658 0.01097 0.00340 0.00366 6.0e-05 0.00651 164 0.01756 0.00873 0.00284 0.00291 5.0e-05 0.00519 165 0.02814 0.01480 0.00461 0.00493 9.0e-05 0.00907 166 0.02448 0.00462 0.00153 0.00154 1.0e-05 0.00277 167 0.01242 0.00519 0.00159 0.00173 1.0e-05 0.00303 168 0.02030 0.00616 0.00186 0.00205 1.0e-05 0.00339 169 0.02177 0.01470 0.00448 0.00490 4.0e-05 0.00803 170 0.02018 0.00949 0.00283 0.00316 2.0e-05 0.00517 171 0.01897 0.00837 0.00237 0.00279 2.0e-05 0.00451 172 0.01358 0.00499 0.00190 0.00166 3.0e-05 0.00355 173 0.01484 0.00510 0.00200 0.00170 3.0e-05 0.00356 174 0.01472 0.00514 0.00203 0.00171 3.0e-05 0.00349 175 0.01657 0.00528 0.00218 0.00176 3.0e-05 0.00353 176 0.01503 0.00480 0.00199 0.00160 3.0e-05 0.00332 177 0.01725 0.00507 0.00213 0.00169 3.0e-05 0.00346 178 0.01469 0.00406 0.00162 0.00135 2.0e-05 0.00314 179 0.01574 0.00456 0.00186 0.00152 2.0e-05 0.00309 180 0.01450 0.00612 0.00231 0.00204 3.0e-05 0.00392 181 0.02551 0.00619 0.00233 0.00206 3.0e-05 0.00396 182 0.01831 0.00605 0.00235 0.00202 3.0e-05 0.00397 183 0.02145 0.00521 0.00198 0.00174 2.0e-05 0.00336 184 0.01909 0.00558 0.00270 0.00186 4.0e-05 0.00417 185 0.01795 0.00780 0.00346 0.00260 5.0e-05 0.00531 186 0.01564 0.00403 0.00192 0.00134 3.0e-05 0.00314 187 0.01660 0.00762 0.00263 0.00254 4.0e-05 0.00496 188 0.01300 0.00345 0.00148 0.00115 2.0e-05 0.00267 189 0.01185 0.00439 0.00184 0.00146 3.0e-05 0.00327 190 0.02574 0.01235 0.00396 0.00412 3.0e-05 0.00694 191 0.04087 0.00790 0.00259 0.00263 3.0e-05 0.00459 192 0.02751 0.00994 0.00292 0.00331 3.0e-05 0.00564 193 0.02308 0.01873 0.00564 0.00624 8.0e-05 0.01360 194 0.02296 0.01109 0.00390 0.00370 4.0e-05 0.00740 195 0.01884 0.00885 0.00317 0.00295 3.0e-05 0.00567 MDVP:Flo(Hz) MDVP:Fhi(Hz) MDVP:Fo(Hz) 1 74.997 157.302 119.992 2 113.819 148.650 122.400 3 111.555 131.111 116.682 4 111.366 137.871 116.676 5 110.655 141.781 116.014 6 113.787 131.162 120.552 7 114.820 137.244 120.267 8 104.315 113.840 107.332 9 91.754 132.068 95.730 10 91.226 120.103 95.056 11 84.072 112.240 88.333 12 86.292 115.871 91.904 13 131.276 159.866 136.926 14 76.556 179.139 139.173 15 75.836 163.305 152.845 16 83.159 217.455 142.167 17 82.764 349.259 144.188 18 75.603 232.181 168.778 19 68.623 175.829 153.046 20 142.822 189.398 156.405 21 65.782 165.738 153.848 22 78.128 172.860 153.880 23 79.068 193.221 167.930 24 86.180 192.735 173.917 25 76.779 200.841 163.656 26 77.968 206.002 104.400 27 75.501 208.313 171.041 28 81.737 208.701 146.845 29 80.055 227.383 155.358 30 77.630 198.346 162.568 31 192.055 206.896 197.076 32 192.091 209.512 199.228 33 193.104 215.203 198.383 34 197.079 211.604 202.266 35 196.160 211.526 203.184 36 195.708 210.565 201.464 37 168.013 192.921 177.876 38 163.564 185.604 176.170 39 175.456 201.249 180.198 40 173.015 202.324 187.733 41 177.584 197.724 186.163 42 166.977 196.537 184.055 43 225.227 247.326 237.226 44 232.483 248.834 241.404 45 232.435 250.912 243.439 46 227.911 255.034 242.852 47 231.848 262.090 245.510 48 182.786 261.487 252.455 49 115.765 128.611 122.188 50 114.676 130.049 122.964 51 117.495 135.069 124.445 52 112.773 134.231 126.344 53 122.080 138.052 128.001 54 118.604 139.867 129.336 55 102.874 134.656 108.807 56 104.437 126.358 109.860 57 103.370 131.067 110.417 58 110.402 129.916 117.274 59 108.153 131.897 116.879 60 104.680 271.314 114.847 61 109.379 237.494 209.144 62 98.664 238.987 223.365 63 205.495 231.345 222.236 64 223.634 234.619 228.832 65 221.156 252.221 229.401 66 113.201 239.541 228.969 67 67.021 159.774 140.341 68 66.004 166.607 136.969 69 65.809 162.215 143.533 70 67.343 162.824 148.090 71 65.476 162.408 142.729 72 65.750 176.595 136.358 73 111.208 139.710 120.080 74 107.024 588.518 112.014 75 107.316 128.101 110.793 76 105.007 122.611 110.707 77 106.981 148.826 112.876 78 106.821 125.394 110.568 79 90.264 102.145 95.385 80 85.545 115.697 100.770 81 84.510 108.664 96.106 82 87.549 107.715 95.605 83 95.628 110.019 100.960 84 87.804 102.305 98.804 85 75.344 205.560 176.858 86 155.495 200.125 180.978 87 141.047 202.450 178.222 88 125.610 227.381 176.281 89 74.677 211.350 173.898 90 144.878 225.930 179.711 91 78.032 206.008 166.605 92 147.226 163.335 151.955 93 142.299 164.989 148.272 94 76.596 161.469 152.125 95 68.401 172.975 157.821 96 149.605 163.267 157.447 97 144.811 168.913 159.116 98 116.187 143.946 125.036 99 96.206 140.557 125.791 100 99.770 141.756 126.512 101 116.346 141.068 125.641 102 75.632 150.449 128.451 103 66.157 586.567 139.224 104 75.349 154.609 150.258 105 128.621 160.267 154.003 106 133.608 160.368 149.689 107 144.148 163.736 155.078 108 133.751 157.765 151.884 109 132.857 157.339 151.989 110 80.297 208.900 193.030 111 89.686 223.982 200.714 112 199.020 220.315 208.519 113 189.621 221.300 204.664 114 185.258 232.706 210.141 115 92.020 226.355 206.327 116 69.085 492.892 151.872 117 71.948 442.557 158.219 118 79.032 450.247 170.756 119 82.063 442.824 178.285 120 93.978 233.481 217.116 121 88.251 479.697 128.940 122 83.961 215.293 176.824 123 83.340 203.522 138.190 124 79.187 197.173 182.018 125 79.820 195.107 156.239 126 80.637 198.109 145.174 127 81.114 197.238 138.145 128 79.512 198.966 166.888 129 109.216 127.533 119.031 130 105.667 126.632 120.078 131 100.209 128.143 120.289 132 104.773 125.306 120.256 133 86.795 125.213 119.056 134 109.836 123.723 118.747 135 93.105 112.777 106.516 136 105.554 127.611 110.453 137 107.816 133.344 113.400 138 100.673 130.270 113.166 139 104.095 126.609 112.239 140 109.815 131.731 116.150 141 79.543 268.796 170.368 142 91.802 253.792 208.083 143 148.691 219.290 198.458 144 86.232 231.508 202.805 145 164.168 241.350 202.544 146 87.638 263.872 223.361 147 151.451 191.759 169.774 148 161.340 216.814 183.520 149 165.982 216.302 188.620 150 177.258 565.740 202.632 151 149.442 211.961 186.695 152 168.793 224.429 192.818 153 174.478 233.099 198.116 154 98.250 139.644 121.345 155 88.833 128.442 119.100 156 95.654 127.349 117.870 157 94.794 142.369 122.336 158 100.757 134.209 117.963 159 97.543 154.284 126.144 160 112.173 138.752 127.930 161 77.022 124.393 114.238 162 107.802 135.738 115.322 163 91.121 126.778 114.554 164 97.527 131.669 112.150 165 85.902 142.830 102.273 166 102.137 244.663 236.200 167 229.256 243.709 237.323 168 237.303 264.919 260.105 169 90.794 217.627 197.569 170 219.783 245.135 240.301 171 239.170 272.210 244.990 172 105.715 133.374 112.547 173 100.139 113.597 110.739 174 96.913 116.443 113.715 175 99.923 144.466 117.004 176 108.634 123.109 115.380 177 108.970 129.038 116.388 178 129.859 190.204 151.737 179 138.990 158.359 148.790 180 135.041 155.982 148.143 181 144.736 163.441 150.440 182 141.998 161.078 148.462 183 144.786 163.417 149.818 184 106.656 123.925 117.226 185 99.503 217.552 116.848 186 96.983 177.291 116.286 187 86.228 592.030 116.556 188 94.246 581.289 116.342 189 86.647 119.167 114.563 190 78.228 262.707 201.774 191 94.261 230.978 174.188 192 89.488 253.017 209.516 193 74.287 240.005 174.688 194 74.904 396.961 198.764 195 77.973 260.277 214.289 > k <- length(x[1,]) > df <- as.data.frame(x) > (mylm <- lm(df)) Call: lm(formula = df) Coefficients: (Intercept) PPE D2 spread2 2.225e+00 1.263e+00 4.946e-02 1.266e+00 spread1 DFA RPDE HNR 1.273e-01 3.551e-01 -1.014e+00 -1.569e-02 NHR `Shimmer:DDA` `MDVP:APQ` `Shimmer:APQ5` -2.526e+00 2.837e+02 -3.075e+00 -2.640e+01 `Shimmer:APQ3` `MDVP:Shimmer(dB)` `MDVP:Shimmer` `Jitter:DDP` -8.712e+02 5.710e-01 2.745e+01 3.606e+02 `MDVP:PPQ` `MDVP:RAP` `MDVP:Jitter(Abs)` `MDVP:Jitter(%)` -3.614e+01 -7.592e+02 -3.322e+03 -1.769e+02 `MDVP:Flo(Hz)` `MDVP:Fhi(Hz)` `MDVP:Fo(Hz)` -1.535e-03 -1.152e-04 -2.384e-03 > (mysum <- summary(mylm)) Call: lm(formula = df) Residuals: Min 1Q Median 3Q Max -0.94184 -0.15077 0.04547 0.20496 0.58507 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 2.225e+00 1.158e+00 1.921 0.05644 . PPE 1.263e+00 1.383e+00 0.913 0.36235 D2 4.946e-02 1.143e-01 0.433 0.66582 spread2 1.266e+00 4.780e-01 2.648 0.00886 ** spread1 1.273e-01 9.790e-02 1.300 0.19523 DFA 3.551e-01 7.394e-01 0.480 0.63161 RPDE -1.014e+00 4.395e-01 -2.308 0.02219 * HNR -1.569e-02 1.434e-02 -1.094 0.27544 NHR -2.526e+00 1.981e+00 -1.275 0.20397 `Shimmer:DDA` 2.837e+02 2.990e+03 0.095 0.92450 `MDVP:APQ` -3.075e+00 1.089e+01 -0.282 0.77804 `Shimmer:APQ5` -2.640e+01 2.012e+01 -1.312 0.19136 `Shimmer:APQ3` -8.712e+02 8.972e+03 -0.097 0.92276 `MDVP:Shimmer(dB)` 5.710e-01 1.199e+00 0.476 0.63459 `MDVP:Shimmer` 2.745e+01 3.428e+01 0.801 0.42442 `Jitter:DDP` 3.606e+02 3.111e+03 0.116 0.90788 `MDVP:PPQ` -3.614e+01 8.838e+01 -0.409 0.68316 `MDVP:RAP` -7.592e+02 9.332e+03 -0.081 0.93525 `MDVP:Jitter(Abs)` -3.322e+03 4.626e+03 -0.718 0.47368 `MDVP:Jitter(%)` -1.769e+02 6.703e+01 -2.639 0.00907 ** `MDVP:Flo(Hz)` -1.535e-03 8.023e-04 -1.913 0.05737 . `MDVP:Fhi(Hz)` -1.152e-04 3.211e-04 -0.359 0.72011 `MDVP:Fo(Hz)` -2.384e-03 1.510e-03 -1.579 0.11617 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 0.3267 on 172 degrees of freedom Multiple R-squared: 0.4927, Adjusted R-squared: 0.4279 F-statistic: 7.594 on 22 and 172 DF, p-value: 4.808e-16 > if (n > n25) { + kp3 <- k + 3 + nmkm3 <- n - k - 3 + gqarr <- array(NA, dim=c(nmkm3-kp3+1,3)) + numgqtests <- 0 + numsignificant1 <- 0 + numsignificant5 <- 0 + numsignificant10 <- 0 + for (mypoint in kp3:nmkm3) { + j <- 0 + numgqtests <- numgqtests + 1 + for (myalt in c('greater', 'two.sided', 'less')) { + j <- j + 1 + gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value + } + if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1 + if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1 + if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1 + } + gqarr + } [,1] [,2] [,3] [1,] 1.814384e-53 3.628768e-53 1.00000000 [2,] 3.497289e-65 6.994578e-65 1.00000000 [3,] 1.346058e-76 2.692115e-76 1.00000000 [4,] 3.416667e-94 6.833335e-94 1.00000000 [5,] 3.724507e-106 7.449015e-106 1.00000000 [6,] 2.546253e-04 5.092506e-04 0.99974537 [7,] 6.628473e-05 1.325695e-04 0.99993372 [8,] 1.634550e-05 3.269099e-05 0.99998365 [9,] 4.921244e-06 9.842488e-06 0.99999508 [10,] 1.493053e-06 2.986107e-06 0.99999851 [11,] 3.663781e-07 7.327561e-07 0.99999963 [12,] 5.203943e-05 1.040789e-04 0.99994796 [13,] 2.996809e-05 5.993618e-05 0.99997003 [14,] 5.278445e-04 1.055689e-03 0.99947216 [15,] 8.676116e-04 1.735223e-03 0.99913239 [16,] 1.089027e-03 2.178054e-03 0.99891097 [17,] 6.345594e-04 1.269119e-03 0.99936544 [18,] 3.910895e-04 7.821790e-04 0.99960891 [19,] 2.188681e-04 4.377363e-04 0.99978113 [20,] 1.019935e-04 2.039871e-04 0.99989801 [21,] 5.006918e-05 1.001384e-04 0.99994993 [22,] 2.996916e-05 5.993832e-05 0.99997003 [23,] 3.988068e-05 7.976137e-05 0.99996012 [24,] 1.743572e-04 3.487145e-04 0.99982564 [25,] 1.438208e-04 2.876416e-04 0.99985618 [26,] 9.518950e-05 1.903790e-04 0.99990481 [27,] 6.312518e-05 1.262504e-04 0.99993687 [28,] 4.435794e-05 8.871587e-05 0.99995564 [29,] 4.615947e-05 9.231894e-05 0.99995384 [30,] 5.316994e-05 1.063399e-04 0.99994683 [31,] 6.654600e-05 1.330920e-04 0.99993345 [32,] 3.769786e-05 7.539572e-05 0.99996230 [33,] 2.599145e-05 5.198290e-05 0.99997401 [34,] 1.475256e-05 2.950512e-05 0.99998525 [35,] 8.440881e-06 1.688176e-05 0.99999156 [36,] 3.517339e-04 7.034678e-04 0.99964827 [37,] 4.571405e-04 9.142810e-04 0.99954286 [38,] 6.235912e-04 1.247182e-03 0.99937641 [39,] 6.232405e-04 1.246481e-03 0.99937676 [40,] 4.260821e-04 8.521643e-04 0.99957392 [41,] 4.220822e-04 8.441644e-04 0.99957792 [42,] 2.832624e-04 5.665249e-04 0.99971674 [43,] 1.823968e-04 3.647937e-04 0.99981760 [44,] 2.316280e-04 4.632559e-04 0.99976837 [45,] 1.786081e-04 3.572162e-04 0.99982139 [46,] 1.078655e-04 2.157309e-04 0.99989213 [47,] 7.362955e-05 1.472591e-04 0.99992637 [48,] 4.320487e-05 8.640973e-05 0.99995680 [49,] 1.196078e-04 2.392156e-04 0.99988039 [50,] 1.563976e-04 3.127953e-04 0.99984360 [51,] 1.157693e-04 2.315385e-04 0.99988423 [52,] 7.497239e-05 1.499448e-04 0.99992503 [53,] 5.881014e-05 1.176203e-04 0.99994119 [54,] 3.488782e-05 6.977565e-05 0.99996511 [55,] 2.490667e-05 4.981333e-05 0.99997509 [56,] 1.848991e-05 3.697981e-05 0.99998151 [57,] 1.077432e-05 2.154865e-05 0.99998923 [58,] 7.130719e-06 1.426144e-05 0.99999287 [59,] 4.579435e-06 9.158871e-06 0.99999542 [60,] 2.768345e-06 5.536690e-06 0.99999723 [61,] 3.233183e-06 6.466366e-06 0.99999677 [62,] 5.280663e-06 1.056133e-05 0.99999472 [63,] 3.341635e-06 6.683270e-06 0.99999666 [64,] 2.597860e-06 5.195721e-06 0.99999740 [65,] 3.214897e-06 6.429795e-06 0.99999679 [66,] 3.172618e-06 6.345235e-06 0.99999683 [67,] 3.961379e-06 7.922758e-06 0.99999604 [68,] 2.519518e-06 5.039035e-06 0.99999748 [69,] 1.688523e-06 3.377046e-06 0.99999831 [70,] 1.089697e-06 2.179394e-06 0.99999891 [71,] 6.756170e-07 1.351234e-06 0.99999932 [72,] 4.200856e-07 8.401713e-07 0.99999958 [73,] 2.393840e-07 4.787681e-07 0.99999976 [74,] 1.489749e-07 2.979497e-07 0.99999985 [75,] 8.739952e-08 1.747990e-07 0.99999991 [76,] 6.861254e-08 1.372251e-07 0.99999993 [77,] 6.592061e-08 1.318412e-07 0.99999993 [78,] 7.737128e-08 1.547426e-07 0.99999992 [79,] 1.488163e-07 2.976327e-07 0.99999985 [80,] 2.309658e-07 4.619316e-07 0.99999977 [81,] 4.220153e-07 8.440305e-07 0.99999958 [82,] 9.940501e-07 1.988100e-06 0.99999901 [83,] 7.281823e-07 1.456365e-06 0.99999927 [84,] 1.655143e-06 3.310286e-06 0.99999834 [85,] 1.356737e-06 2.713473e-06 0.99999864 [86,] 8.088825e-07 1.617765e-06 0.99999919 [87,] 1.638931e-06 3.277863e-06 0.99999836 [88,] 1.084246e-06 2.168492e-06 0.99999892 [89,] 1.262980e-06 2.525959e-06 0.99999874 [90,] 1.248066e-06 2.496133e-06 0.99999875 [91,] 8.450619e-07 1.690124e-06 0.99999915 [92,] 1.162028e-06 2.324055e-06 0.99999884 [93,] 6.706285e-07 1.341257e-06 0.99999933 [94,] 4.737373e-07 9.474746e-07 0.99999953 [95,] 1.087439e-06 2.174877e-06 0.99999891 [96,] 6.793447e-06 1.358689e-05 0.99999321 [97,] 1.714571e-05 3.429142e-05 0.99998285 [98,] 1.094495e-05 2.188990e-05 0.99998906 [99,] 7.511780e-06 1.502356e-05 0.99999249 [100,] 5.283168e-06 1.056634e-05 0.99999472 [101,] 5.531585e-06 1.106317e-05 0.99999447 [102,] 1.110809e-05 2.221618e-05 0.99998889 [103,] 1.462569e-04 2.925139e-04 0.99985374 [104,] 3.825555e-04 7.651110e-04 0.99961744 [105,] 4.874166e-04 9.748332e-04 0.99951258 [106,] 5.018673e-04 1.003735e-03 0.99949813 [107,] 3.434651e-04 6.869302e-04 0.99965653 [108,] 3.079671e-04 6.159342e-04 0.99969203 [109,] 1.383024e-03 2.766049e-03 0.99861698 [110,] 1.606070e-03 3.212141e-03 0.99839393 [111,] 1.563284e-03 3.126568e-03 0.99843672 [112,] 1.689852e-03 3.379705e-03 0.99831015 [113,] 1.735729e-03 3.471459e-03 0.99826427 [114,] 1.172495e-03 2.344991e-03 0.99882750 [115,] 1.453524e-03 2.907049e-03 0.99854648 [116,] 1.228730e-03 2.457461e-03 0.99877127 [117,] 1.564035e-03 3.128070e-03 0.99843596 [118,] 1.314530e-03 2.629060e-03 0.99868547 [119,] 4.516192e-03 9.032385e-03 0.99548381 [120,] 3.920266e-03 7.840531e-03 0.99607973 [121,] 3.016489e-03 6.032978e-03 0.99698351 [122,] 2.145464e-03 4.290927e-03 0.99785454 [123,] 1.383528e-03 2.767056e-03 0.99861647 [124,] 1.399000e-03 2.798000e-03 0.99860100 [125,] 9.274371e-04 1.854874e-03 0.99907256 [126,] 8.691156e-04 1.738231e-03 0.99913088 [127,] 6.385178e-03 1.277036e-02 0.99361482 [128,] 3.427698e-02 6.855396e-02 0.96572302 [129,] 3.053679e-02 6.107359e-02 0.96946321 [130,] 2.584514e-02 5.169028e-02 0.97415486 [131,] 1.922083e-02 3.844166e-02 0.98077917 [132,] 8.907862e-02 1.781572e-01 0.91092138 [133,] 8.425213e-02 1.685043e-01 0.91574787 [134,] 2.873580e-01 5.747160e-01 0.71264199 [135,] 2.263667e-01 4.527334e-01 0.77363329 [136,] 1.699163e-01 3.398326e-01 0.83008369 [137,] 1.292572e-01 2.585143e-01 0.87074283 [138,] 1.478543e-01 2.957086e-01 0.85214568 [139,] 3.161248e-01 6.322496e-01 0.68387519 [140,] 4.253480e-01 8.506959e-01 0.57465204 [141,] 5.208676e-01 9.582648e-01 0.47913240 [142,] 9.017518e-01 1.964964e-01 0.09824818 [143,] 9.450033e-01 1.099934e-01 0.05499670 [144,] 9.629543e-01 7.409143e-02 0.03704571 > postscript(file="/var/fisher/rcomp/tmp/1m3sr1386781580.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$resid) > grid() > dev.off() null device 1 > postscript(file="/var/fisher/rcomp/tmp/2dwjb1386781580.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(mysum$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/34km11386781580.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') > grid() > dev.off() null device 1 > postscript(file="/var/fisher/rcomp/tmp/4gt8j1386781580.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') > dev.off() null device 1 > postscript(file="/var/fisher/rcomp/tmp/5yboj1386781580.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > qqnorm(mysum$resid, main='Residual Normal Q-Q Plot') > qqline(mysum$resid) > grid() > dev.off() null device 1 > (myerror <- as.ts(mysum$resid)) Time Series: Start = 1 End = 195 Frequency = 1 1 2 3 4 5 0.0564281266 -0.0705871710 0.0273891732 -0.0777826472 0.1289663200 6 7 8 9 10 0.0505447902 0.2065940405 0.4193252967 0.0253973938 -0.1509099713 11 12 13 14 15 -0.1115990369 -0.2381056691 0.5431177417 0.1198483763 0.2951541293 16 17 18 19 20 0.2972892567 0.4555582492 -0.3229152531 -0.2934121147 0.0339362094 21 22 23 24 25 -0.0659459012 0.1095260220 -0.1034321130 0.1342289531 0.1796414412 26 27 28 29 30 0.0825911180 0.1959813824 0.2041000665 0.3386043677 0.3006759523 31 32 33 34 35 -0.2963270110 -0.1593586542 -0.1923513261 -0.1280861823 -0.0881745263 36 37 38 39 40 -0.2036277196 0.1893057590 0.1765375230 0.4039567429 0.2469086122 41 42 43 44 45 0.3874036392 0.5634980566 -0.2446206322 -0.2042351963 -0.0134396038 46 47 48 49 50 -0.0888886766 -0.0510556031 0.0454685804 -0.3374627122 -0.4294787668 51 52 53 54 55 -0.4106123628 -0.4259158307 -0.4115231996 -0.5484716508 0.1728758516 56 57 58 59 60 0.2084448843 0.1325855833 0.2401098424 0.2207200741 0.3491883720 61 62 63 64 65 -0.3697711123 -0.2746523739 -0.2644755661 -0.2136066078 -0.1283357512 66 67 68 69 70 -0.2822755512 0.0852200123 0.1108957245 0.0777503964 0.0576307634 71 72 73 74 75 0.1491522115 -0.0929952919 0.1127812153 0.0770648551 -0.0422682654 76 77 78 79 80 -0.0786140032 -0.0985768523 -0.0004832498 0.0388675548 -0.1412748538 81 82 83 84 85 -0.1801439924 -0.1352195816 -0.0136507016 0.3040125754 -0.0875400146 86 87 88 89 90 0.1285309038 0.2968687880 0.0553590707 0.0096593355 -0.2252495501 91 92 93 94 95 -0.1444663951 0.2025294735 0.2663284516 0.1467443301 0.2114405807 96 97 98 99 100 0.2423275791 0.1969934283 -0.0269041154 0.2002605665 0.1029306555 101 102 103 104 105 0.0361471681 0.0261951899 0.0110830427 0.4142247986 0.4193350458 106 107 108 109 110 0.4327364370 0.4647109258 0.3098959351 0.3826380366 0.1015785013 111 112 113 114 115 -0.0349901184 0.4100113720 0.1983687987 0.3011294351 0.1977753708 116 117 118 119 120 0.1149057253 0.2711061162 -0.0518569667 0.1139358570 0.2383985845 121 122 123 124 125 0.4484696084 0.0263668326 0.0271514850 0.2893986811 0.3817899266 126 127 128 129 130 0.3753790565 0.3873613391 0.3724147706 0.5850678575 0.2347514154 131 132 133 134 135 0.1968833498 0.1250420776 -0.0533167362 0.3529596528 0.0367175561 136 137 138 139 140 0.0384846911 -0.1463688729 -0.1506276986 0.0402465326 0.2185196681 141 142 143 144 145 0.0899151144 0.1417015350 0.2710047917 0.3171741249 0.4506066443 146 147 148 149 150 0.1397667810 -0.3515142193 -0.1522854818 -0.2447742538 0.1062984528 151 152 153 154 155 0.0657469871 0.0440683480 0.0409203842 0.1545941737 0.1055613373 156 157 158 159 160 0.0051826515 0.2013160095 -0.2686981811 0.0292525535 0.1380999139 161 162 163 164 165 -0.1281321791 -0.0587124904 0.0691747997 0.2058241998 -0.3824425267 166 167 168 169 170 -0.4502115660 -0.2251905676 -0.0938475184 -0.9418426114 -0.2302780047 171 172 173 174 175 -0.1145762029 -0.7993405843 -0.8358743948 -0.8783338466 -0.8660101226 176 177 178 179 180 -0.8346308649 -0.7768488137 0.3556600296 0.2877673101 0.0656761316 181 182 183 184 185 0.2360214910 0.1275739354 0.2808066129 -0.6043862370 -0.6484630863 186 187 188 189 190 -0.6067838036 -0.4232493950 -0.4753615206 -0.4359967657 -0.4282774574 191 192 193 194 195 -0.6511288011 -0.7009783841 0.1745042657 -0.2670808288 -0.5194153557 > postscript(file="/var/fisher/rcomp/tmp/65t4q1386781580.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > dum <- cbind(lag(myerror,k=1),myerror) > dum Time Series: Start = 0 End = 195 Frequency = 1 lag(myerror, k = 1) myerror 0 0.0564281266 NA 1 -0.0705871710 0.0564281266 2 0.0273891732 -0.0705871710 3 -0.0777826472 0.0273891732 4 0.1289663200 -0.0777826472 5 0.0505447902 0.1289663200 6 0.2065940405 0.0505447902 7 0.4193252967 0.2065940405 8 0.0253973938 0.4193252967 9 -0.1509099713 0.0253973938 10 -0.1115990369 -0.1509099713 11 -0.2381056691 -0.1115990369 12 0.5431177417 -0.2381056691 13 0.1198483763 0.5431177417 14 0.2951541293 0.1198483763 15 0.2972892567 0.2951541293 16 0.4555582492 0.2972892567 17 -0.3229152531 0.4555582492 18 -0.2934121147 -0.3229152531 19 0.0339362094 -0.2934121147 20 -0.0659459012 0.0339362094 21 0.1095260220 -0.0659459012 22 -0.1034321130 0.1095260220 23 0.1342289531 -0.1034321130 24 0.1796414412 0.1342289531 25 0.0825911180 0.1796414412 26 0.1959813824 0.0825911180 27 0.2041000665 0.1959813824 28 0.3386043677 0.2041000665 29 0.3006759523 0.3386043677 30 -0.2963270110 0.3006759523 31 -0.1593586542 -0.2963270110 32 -0.1923513261 -0.1593586542 33 -0.1280861823 -0.1923513261 34 -0.0881745263 -0.1280861823 35 -0.2036277196 -0.0881745263 36 0.1893057590 -0.2036277196 37 0.1765375230 0.1893057590 38 0.4039567429 0.1765375230 39 0.2469086122 0.4039567429 40 0.3874036392 0.2469086122 41 0.5634980566 0.3874036392 42 -0.2446206322 0.5634980566 43 -0.2042351963 -0.2446206322 44 -0.0134396038 -0.2042351963 45 -0.0888886766 -0.0134396038 46 -0.0510556031 -0.0888886766 47 0.0454685804 -0.0510556031 48 -0.3374627122 0.0454685804 49 -0.4294787668 -0.3374627122 50 -0.4106123628 -0.4294787668 51 -0.4259158307 -0.4106123628 52 -0.4115231996 -0.4259158307 53 -0.5484716508 -0.4115231996 54 0.1728758516 -0.5484716508 55 0.2084448843 0.1728758516 56 0.1325855833 0.2084448843 57 0.2401098424 0.1325855833 58 0.2207200741 0.2401098424 59 0.3491883720 0.2207200741 60 -0.3697711123 0.3491883720 61 -0.2746523739 -0.3697711123 62 -0.2644755661 -0.2746523739 63 -0.2136066078 -0.2644755661 64 -0.1283357512 -0.2136066078 65 -0.2822755512 -0.1283357512 66 0.0852200123 -0.2822755512 67 0.1108957245 0.0852200123 68 0.0777503964 0.1108957245 69 0.0576307634 0.0777503964 70 0.1491522115 0.0576307634 71 -0.0929952919 0.1491522115 72 0.1127812153 -0.0929952919 73 0.0770648551 0.1127812153 74 -0.0422682654 0.0770648551 75 -0.0786140032 -0.0422682654 76 -0.0985768523 -0.0786140032 77 -0.0004832498 -0.0985768523 78 0.0388675548 -0.0004832498 79 -0.1412748538 0.0388675548 80 -0.1801439924 -0.1412748538 81 -0.1352195816 -0.1801439924 82 -0.0136507016 -0.1352195816 83 0.3040125754 -0.0136507016 84 -0.0875400146 0.3040125754 85 0.1285309038 -0.0875400146 86 0.2968687880 0.1285309038 87 0.0553590707 0.2968687880 88 0.0096593355 0.0553590707 89 -0.2252495501 0.0096593355 90 -0.1444663951 -0.2252495501 91 0.2025294735 -0.1444663951 92 0.2663284516 0.2025294735 93 0.1467443301 0.2663284516 94 0.2114405807 0.1467443301 95 0.2423275791 0.2114405807 96 0.1969934283 0.2423275791 97 -0.0269041154 0.1969934283 98 0.2002605665 -0.0269041154 99 0.1029306555 0.2002605665 100 0.0361471681 0.1029306555 101 0.0261951899 0.0361471681 102 0.0110830427 0.0261951899 103 0.4142247986 0.0110830427 104 0.4193350458 0.4142247986 105 0.4327364370 0.4193350458 106 0.4647109258 0.4327364370 107 0.3098959351 0.4647109258 108 0.3826380366 0.3098959351 109 0.1015785013 0.3826380366 110 -0.0349901184 0.1015785013 111 0.4100113720 -0.0349901184 112 0.1983687987 0.4100113720 113 0.3011294351 0.1983687987 114 0.1977753708 0.3011294351 115 0.1149057253 0.1977753708 116 0.2711061162 0.1149057253 117 -0.0518569667 0.2711061162 118 0.1139358570 -0.0518569667 119 0.2383985845 0.1139358570 120 0.4484696084 0.2383985845 121 0.0263668326 0.4484696084 122 0.0271514850 0.0263668326 123 0.2893986811 0.0271514850 124 0.3817899266 0.2893986811 125 0.3753790565 0.3817899266 126 0.3873613391 0.3753790565 127 0.3724147706 0.3873613391 128 0.5850678575 0.3724147706 129 0.2347514154 0.5850678575 130 0.1968833498 0.2347514154 131 0.1250420776 0.1968833498 132 -0.0533167362 0.1250420776 133 0.3529596528 -0.0533167362 134 0.0367175561 0.3529596528 135 0.0384846911 0.0367175561 136 -0.1463688729 0.0384846911 137 -0.1506276986 -0.1463688729 138 0.0402465326 -0.1506276986 139 0.2185196681 0.0402465326 140 0.0899151144 0.2185196681 141 0.1417015350 0.0899151144 142 0.2710047917 0.1417015350 143 0.3171741249 0.2710047917 144 0.4506066443 0.3171741249 145 0.1397667810 0.4506066443 146 -0.3515142193 0.1397667810 147 -0.1522854818 -0.3515142193 148 -0.2447742538 -0.1522854818 149 0.1062984528 -0.2447742538 150 0.0657469871 0.1062984528 151 0.0440683480 0.0657469871 152 0.0409203842 0.0440683480 153 0.1545941737 0.0409203842 154 0.1055613373 0.1545941737 155 0.0051826515 0.1055613373 156 0.2013160095 0.0051826515 157 -0.2686981811 0.2013160095 158 0.0292525535 -0.2686981811 159 0.1380999139 0.0292525535 160 -0.1281321791 0.1380999139 161 -0.0587124904 -0.1281321791 162 0.0691747997 -0.0587124904 163 0.2058241998 0.0691747997 164 -0.3824425267 0.2058241998 165 -0.4502115660 -0.3824425267 166 -0.2251905676 -0.4502115660 167 -0.0938475184 -0.2251905676 168 -0.9418426114 -0.0938475184 169 -0.2302780047 -0.9418426114 170 -0.1145762029 -0.2302780047 171 -0.7993405843 -0.1145762029 172 -0.8358743948 -0.7993405843 173 -0.8783338466 -0.8358743948 174 -0.8660101226 -0.8783338466 175 -0.8346308649 -0.8660101226 176 -0.7768488137 -0.8346308649 177 0.3556600296 -0.7768488137 178 0.2877673101 0.3556600296 179 0.0656761316 0.2877673101 180 0.2360214910 0.0656761316 181 0.1275739354 0.2360214910 182 0.2808066129 0.1275739354 183 -0.6043862370 0.2808066129 184 -0.6484630863 -0.6043862370 185 -0.6067838036 -0.6484630863 186 -0.4232493950 -0.6067838036 187 -0.4753615206 -0.4232493950 188 -0.4359967657 -0.4753615206 189 -0.4282774574 -0.4359967657 190 -0.6511288011 -0.4282774574 191 -0.7009783841 -0.6511288011 192 0.1745042657 -0.7009783841 193 -0.2670808288 0.1745042657 194 -0.5194153557 -0.2670808288 195 NA -0.5194153557 > dum1 <- dum[2:length(myerror),] > dum1 lag(myerror, k = 1) myerror [1,] -0.0705871710 0.0564281266 [2,] 0.0273891732 -0.0705871710 [3,] -0.0777826472 0.0273891732 [4,] 0.1289663200 -0.0777826472 [5,] 0.0505447902 0.1289663200 [6,] 0.2065940405 0.0505447902 [7,] 0.4193252967 0.2065940405 [8,] 0.0253973938 0.4193252967 [9,] -0.1509099713 0.0253973938 [10,] -0.1115990369 -0.1509099713 [11,] -0.2381056691 -0.1115990369 [12,] 0.5431177417 -0.2381056691 [13,] 0.1198483763 0.5431177417 [14,] 0.2951541293 0.1198483763 [15,] 0.2972892567 0.2951541293 [16,] 0.4555582492 0.2972892567 [17,] -0.3229152531 0.4555582492 [18,] -0.2934121147 -0.3229152531 [19,] 0.0339362094 -0.2934121147 [20,] -0.0659459012 0.0339362094 [21,] 0.1095260220 -0.0659459012 [22,] -0.1034321130 0.1095260220 [23,] 0.1342289531 -0.1034321130 [24,] 0.1796414412 0.1342289531 [25,] 0.0825911180 0.1796414412 [26,] 0.1959813824 0.0825911180 [27,] 0.2041000665 0.1959813824 [28,] 0.3386043677 0.2041000665 [29,] 0.3006759523 0.3386043677 [30,] -0.2963270110 0.3006759523 [31,] -0.1593586542 -0.2963270110 [32,] -0.1923513261 -0.1593586542 [33,] -0.1280861823 -0.1923513261 [34,] -0.0881745263 -0.1280861823 [35,] -0.2036277196 -0.0881745263 [36,] 0.1893057590 -0.2036277196 [37,] 0.1765375230 0.1893057590 [38,] 0.4039567429 0.1765375230 [39,] 0.2469086122 0.4039567429 [40,] 0.3874036392 0.2469086122 [41,] 0.5634980566 0.3874036392 [42,] -0.2446206322 0.5634980566 [43,] -0.2042351963 -0.2446206322 [44,] -0.0134396038 -0.2042351963 [45,] -0.0888886766 -0.0134396038 [46,] -0.0510556031 -0.0888886766 [47,] 0.0454685804 -0.0510556031 [48,] -0.3374627122 0.0454685804 [49,] -0.4294787668 -0.3374627122 [50,] -0.4106123628 -0.4294787668 [51,] -0.4259158307 -0.4106123628 [52,] -0.4115231996 -0.4259158307 [53,] -0.5484716508 -0.4115231996 [54,] 0.1728758516 -0.5484716508 [55,] 0.2084448843 0.1728758516 [56,] 0.1325855833 0.2084448843 [57,] 0.2401098424 0.1325855833 [58,] 0.2207200741 0.2401098424 [59,] 0.3491883720 0.2207200741 [60,] -0.3697711123 0.3491883720 [61,] -0.2746523739 -0.3697711123 [62,] -0.2644755661 -0.2746523739 [63,] -0.2136066078 -0.2644755661 [64,] -0.1283357512 -0.2136066078 [65,] -0.2822755512 -0.1283357512 [66,] 0.0852200123 -0.2822755512 [67,] 0.1108957245 0.0852200123 [68,] 0.0777503964 0.1108957245 [69,] 0.0576307634 0.0777503964 [70,] 0.1491522115 0.0576307634 [71,] -0.0929952919 0.1491522115 [72,] 0.1127812153 -0.0929952919 [73,] 0.0770648551 0.1127812153 [74,] -0.0422682654 0.0770648551 [75,] -0.0786140032 -0.0422682654 [76,] -0.0985768523 -0.0786140032 [77,] -0.0004832498 -0.0985768523 [78,] 0.0388675548 -0.0004832498 [79,] -0.1412748538 0.0388675548 [80,] -0.1801439924 -0.1412748538 [81,] -0.1352195816 -0.1801439924 [82,] -0.0136507016 -0.1352195816 [83,] 0.3040125754 -0.0136507016 [84,] -0.0875400146 0.3040125754 [85,] 0.1285309038 -0.0875400146 [86,] 0.2968687880 0.1285309038 [87,] 0.0553590707 0.2968687880 [88,] 0.0096593355 0.0553590707 [89,] -0.2252495501 0.0096593355 [90,] -0.1444663951 -0.2252495501 [91,] 0.2025294735 -0.1444663951 [92,] 0.2663284516 0.2025294735 [93,] 0.1467443301 0.2663284516 [94,] 0.2114405807 0.1467443301 [95,] 0.2423275791 0.2114405807 [96,] 0.1969934283 0.2423275791 [97,] -0.0269041154 0.1969934283 [98,] 0.2002605665 -0.0269041154 [99,] 0.1029306555 0.2002605665 [100,] 0.0361471681 0.1029306555 [101,] 0.0261951899 0.0361471681 [102,] 0.0110830427 0.0261951899 [103,] 0.4142247986 0.0110830427 [104,] 0.4193350458 0.4142247986 [105,] 0.4327364370 0.4193350458 [106,] 0.4647109258 0.4327364370 [107,] 0.3098959351 0.4647109258 [108,] 0.3826380366 0.3098959351 [109,] 0.1015785013 0.3826380366 [110,] -0.0349901184 0.1015785013 [111,] 0.4100113720 -0.0349901184 [112,] 0.1983687987 0.4100113720 [113,] 0.3011294351 0.1983687987 [114,] 0.1977753708 0.3011294351 [115,] 0.1149057253 0.1977753708 [116,] 0.2711061162 0.1149057253 [117,] -0.0518569667 0.2711061162 [118,] 0.1139358570 -0.0518569667 [119,] 0.2383985845 0.1139358570 [120,] 0.4484696084 0.2383985845 [121,] 0.0263668326 0.4484696084 [122,] 0.0271514850 0.0263668326 [123,] 0.2893986811 0.0271514850 [124,] 0.3817899266 0.2893986811 [125,] 0.3753790565 0.3817899266 [126,] 0.3873613391 0.3753790565 [127,] 0.3724147706 0.3873613391 [128,] 0.5850678575 0.3724147706 [129,] 0.2347514154 0.5850678575 [130,] 0.1968833498 0.2347514154 [131,] 0.1250420776 0.1968833498 [132,] -0.0533167362 0.1250420776 [133,] 0.3529596528 -0.0533167362 [134,] 0.0367175561 0.3529596528 [135,] 0.0384846911 0.0367175561 [136,] -0.1463688729 0.0384846911 [137,] -0.1506276986 -0.1463688729 [138,] 0.0402465326 -0.1506276986 [139,] 0.2185196681 0.0402465326 [140,] 0.0899151144 0.2185196681 [141,] 0.1417015350 0.0899151144 [142,] 0.2710047917 0.1417015350 [143,] 0.3171741249 0.2710047917 [144,] 0.4506066443 0.3171741249 [145,] 0.1397667810 0.4506066443 [146,] -0.3515142193 0.1397667810 [147,] -0.1522854818 -0.3515142193 [148,] -0.2447742538 -0.1522854818 [149,] 0.1062984528 -0.2447742538 [150,] 0.0657469871 0.1062984528 [151,] 0.0440683480 0.0657469871 [152,] 0.0409203842 0.0440683480 [153,] 0.1545941737 0.0409203842 [154,] 0.1055613373 0.1545941737 [155,] 0.0051826515 0.1055613373 [156,] 0.2013160095 0.0051826515 [157,] -0.2686981811 0.2013160095 [158,] 0.0292525535 -0.2686981811 [159,] 0.1380999139 0.0292525535 [160,] -0.1281321791 0.1380999139 [161,] -0.0587124904 -0.1281321791 [162,] 0.0691747997 -0.0587124904 [163,] 0.2058241998 0.0691747997 [164,] -0.3824425267 0.2058241998 [165,] -0.4502115660 -0.3824425267 [166,] -0.2251905676 -0.4502115660 [167,] -0.0938475184 -0.2251905676 [168,] -0.9418426114 -0.0938475184 [169,] -0.2302780047 -0.9418426114 [170,] -0.1145762029 -0.2302780047 [171,] -0.7993405843 -0.1145762029 [172,] -0.8358743948 -0.7993405843 [173,] -0.8783338466 -0.8358743948 [174,] -0.8660101226 -0.8783338466 [175,] -0.8346308649 -0.8660101226 [176,] -0.7768488137 -0.8346308649 [177,] 0.3556600296 -0.7768488137 [178,] 0.2877673101 0.3556600296 [179,] 0.0656761316 0.2877673101 [180,] 0.2360214910 0.0656761316 [181,] 0.1275739354 0.2360214910 [182,] 0.2808066129 0.1275739354 [183,] -0.6043862370 0.2808066129 [184,] -0.6484630863 -0.6043862370 [185,] -0.6067838036 -0.6484630863 [186,] -0.4232493950 -0.6067838036 [187,] -0.4753615206 -0.4232493950 [188,] -0.4359967657 -0.4753615206 [189,] -0.4282774574 -0.4359967657 [190,] -0.6511288011 -0.4282774574 [191,] -0.7009783841 -0.6511288011 [192,] 0.1745042657 -0.7009783841 [193,] -0.2670808288 0.1745042657 [194,] -0.5194153557 -0.2670808288 > z <- as.data.frame(dum1) > z lag(myerror, k = 1) myerror 1 -0.0705871710 0.0564281266 2 0.0273891732 -0.0705871710 3 -0.0777826472 0.0273891732 4 0.1289663200 -0.0777826472 5 0.0505447902 0.1289663200 6 0.2065940405 0.0505447902 7 0.4193252967 0.2065940405 8 0.0253973938 0.4193252967 9 -0.1509099713 0.0253973938 10 -0.1115990369 -0.1509099713 11 -0.2381056691 -0.1115990369 12 0.5431177417 -0.2381056691 13 0.1198483763 0.5431177417 14 0.2951541293 0.1198483763 15 0.2972892567 0.2951541293 16 0.4555582492 0.2972892567 17 -0.3229152531 0.4555582492 18 -0.2934121147 -0.3229152531 19 0.0339362094 -0.2934121147 20 -0.0659459012 0.0339362094 21 0.1095260220 -0.0659459012 22 -0.1034321130 0.1095260220 23 0.1342289531 -0.1034321130 24 0.1796414412 0.1342289531 25 0.0825911180 0.1796414412 26 0.1959813824 0.0825911180 27 0.2041000665 0.1959813824 28 0.3386043677 0.2041000665 29 0.3006759523 0.3386043677 30 -0.2963270110 0.3006759523 31 -0.1593586542 -0.2963270110 32 -0.1923513261 -0.1593586542 33 -0.1280861823 -0.1923513261 34 -0.0881745263 -0.1280861823 35 -0.2036277196 -0.0881745263 36 0.1893057590 -0.2036277196 37 0.1765375230 0.1893057590 38 0.4039567429 0.1765375230 39 0.2469086122 0.4039567429 40 0.3874036392 0.2469086122 41 0.5634980566 0.3874036392 42 -0.2446206322 0.5634980566 43 -0.2042351963 -0.2446206322 44 -0.0134396038 -0.2042351963 45 -0.0888886766 -0.0134396038 46 -0.0510556031 -0.0888886766 47 0.0454685804 -0.0510556031 48 -0.3374627122 0.0454685804 49 -0.4294787668 -0.3374627122 50 -0.4106123628 -0.4294787668 51 -0.4259158307 -0.4106123628 52 -0.4115231996 -0.4259158307 53 -0.5484716508 -0.4115231996 54 0.1728758516 -0.5484716508 55 0.2084448843 0.1728758516 56 0.1325855833 0.2084448843 57 0.2401098424 0.1325855833 58 0.2207200741 0.2401098424 59 0.3491883720 0.2207200741 60 -0.3697711123 0.3491883720 61 -0.2746523739 -0.3697711123 62 -0.2644755661 -0.2746523739 63 -0.2136066078 -0.2644755661 64 -0.1283357512 -0.2136066078 65 -0.2822755512 -0.1283357512 66 0.0852200123 -0.2822755512 67 0.1108957245 0.0852200123 68 0.0777503964 0.1108957245 69 0.0576307634 0.0777503964 70 0.1491522115 0.0576307634 71 -0.0929952919 0.1491522115 72 0.1127812153 -0.0929952919 73 0.0770648551 0.1127812153 74 -0.0422682654 0.0770648551 75 -0.0786140032 -0.0422682654 76 -0.0985768523 -0.0786140032 77 -0.0004832498 -0.0985768523 78 0.0388675548 -0.0004832498 79 -0.1412748538 0.0388675548 80 -0.1801439924 -0.1412748538 81 -0.1352195816 -0.1801439924 82 -0.0136507016 -0.1352195816 83 0.3040125754 -0.0136507016 84 -0.0875400146 0.3040125754 85 0.1285309038 -0.0875400146 86 0.2968687880 0.1285309038 87 0.0553590707 0.2968687880 88 0.0096593355 0.0553590707 89 -0.2252495501 0.0096593355 90 -0.1444663951 -0.2252495501 91 0.2025294735 -0.1444663951 92 0.2663284516 0.2025294735 93 0.1467443301 0.2663284516 94 0.2114405807 0.1467443301 95 0.2423275791 0.2114405807 96 0.1969934283 0.2423275791 97 -0.0269041154 0.1969934283 98 0.2002605665 -0.0269041154 99 0.1029306555 0.2002605665 100 0.0361471681 0.1029306555 101 0.0261951899 0.0361471681 102 0.0110830427 0.0261951899 103 0.4142247986 0.0110830427 104 0.4193350458 0.4142247986 105 0.4327364370 0.4193350458 106 0.4647109258 0.4327364370 107 0.3098959351 0.4647109258 108 0.3826380366 0.3098959351 109 0.1015785013 0.3826380366 110 -0.0349901184 0.1015785013 111 0.4100113720 -0.0349901184 112 0.1983687987 0.4100113720 113 0.3011294351 0.1983687987 114 0.1977753708 0.3011294351 115 0.1149057253 0.1977753708 116 0.2711061162 0.1149057253 117 -0.0518569667 0.2711061162 118 0.1139358570 -0.0518569667 119 0.2383985845 0.1139358570 120 0.4484696084 0.2383985845 121 0.0263668326 0.4484696084 122 0.0271514850 0.0263668326 123 0.2893986811 0.0271514850 124 0.3817899266 0.2893986811 125 0.3753790565 0.3817899266 126 0.3873613391 0.3753790565 127 0.3724147706 0.3873613391 128 0.5850678575 0.3724147706 129 0.2347514154 0.5850678575 130 0.1968833498 0.2347514154 131 0.1250420776 0.1968833498 132 -0.0533167362 0.1250420776 133 0.3529596528 -0.0533167362 134 0.0367175561 0.3529596528 135 0.0384846911 0.0367175561 136 -0.1463688729 0.0384846911 137 -0.1506276986 -0.1463688729 138 0.0402465326 -0.1506276986 139 0.2185196681 0.0402465326 140 0.0899151144 0.2185196681 141 0.1417015350 0.0899151144 142 0.2710047917 0.1417015350 143 0.3171741249 0.2710047917 144 0.4506066443 0.3171741249 145 0.1397667810 0.4506066443 146 -0.3515142193 0.1397667810 147 -0.1522854818 -0.3515142193 148 -0.2447742538 -0.1522854818 149 0.1062984528 -0.2447742538 150 0.0657469871 0.1062984528 151 0.0440683480 0.0657469871 152 0.0409203842 0.0440683480 153 0.1545941737 0.0409203842 154 0.1055613373 0.1545941737 155 0.0051826515 0.1055613373 156 0.2013160095 0.0051826515 157 -0.2686981811 0.2013160095 158 0.0292525535 -0.2686981811 159 0.1380999139 0.0292525535 160 -0.1281321791 0.1380999139 161 -0.0587124904 -0.1281321791 162 0.0691747997 -0.0587124904 163 0.2058241998 0.0691747997 164 -0.3824425267 0.2058241998 165 -0.4502115660 -0.3824425267 166 -0.2251905676 -0.4502115660 167 -0.0938475184 -0.2251905676 168 -0.9418426114 -0.0938475184 169 -0.2302780047 -0.9418426114 170 -0.1145762029 -0.2302780047 171 -0.7993405843 -0.1145762029 172 -0.8358743948 -0.7993405843 173 -0.8783338466 -0.8358743948 174 -0.8660101226 -0.8783338466 175 -0.8346308649 -0.8660101226 176 -0.7768488137 -0.8346308649 177 0.3556600296 -0.7768488137 178 0.2877673101 0.3556600296 179 0.0656761316 0.2877673101 180 0.2360214910 0.0656761316 181 0.1275739354 0.2360214910 182 0.2808066129 0.1275739354 183 -0.6043862370 0.2808066129 184 -0.6484630863 -0.6043862370 185 -0.6067838036 -0.6484630863 186 -0.4232493950 -0.6067838036 187 -0.4753615206 -0.4232493950 188 -0.4359967657 -0.4753615206 189 -0.4282774574 -0.4359967657 190 -0.6511288011 -0.4282774574 191 -0.7009783841 -0.6511288011 192 0.1745042657 -0.7009783841 193 -0.2670808288 0.1745042657 194 -0.5194153557 -0.2670808288 > plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals') > lines(lowess(z)) > abline(lm(z)) > grid() > dev.off() null device 1 > postscript(file="/var/fisher/rcomp/tmp/7e2m31386781580.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/fisher/rcomp/tmp/83b2v1386781580.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/fisher/rcomp/tmp/9hnsd1386781580.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0)) > plot(mylm, las = 1, sub='Residual Diagnostics') > par(opar) > dev.off() null device 1 > if (n > n25) { + postscript(file="/var/fisher/rcomp/tmp/10ixy71386781580.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) + plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint') + grid() + dev.off() + } null device 1 > > #Note: the /var/fisher/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab > load(file="/var/fisher/rcomp/createtable") > > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE) > a<-table.row.end(a) > myeq <- colnames(x)[1] > myeq <- paste(myeq, '[t] = ', sep='') > for (i in 1:k){ + if (mysum$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/11mduf1386781581.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/12iqio1386781581.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/13a7731386781581.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/14mvbg1386781581.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/15tvs31386781581.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/16pr2z1386781581.tab") + } > > try(system("convert tmp/1m3sr1386781580.ps tmp/1m3sr1386781580.png",intern=TRUE)) character(0) > try(system("convert tmp/2dwjb1386781580.ps tmp/2dwjb1386781580.png",intern=TRUE)) character(0) > try(system("convert tmp/34km11386781580.ps tmp/34km11386781580.png",intern=TRUE)) character(0) > try(system("convert tmp/4gt8j1386781580.ps tmp/4gt8j1386781580.png",intern=TRUE)) character(0) > try(system("convert tmp/5yboj1386781580.ps tmp/5yboj1386781580.png",intern=TRUE)) character(0) > try(system("convert tmp/65t4q1386781580.ps tmp/65t4q1386781580.png",intern=TRUE)) character(0) > try(system("convert tmp/7e2m31386781580.ps tmp/7e2m31386781580.png",intern=TRUE)) character(0) > try(system("convert tmp/83b2v1386781580.ps tmp/83b2v1386781580.png",intern=TRUE)) character(0) > try(system("convert tmp/9hnsd1386781580.ps tmp/9hnsd1386781580.png",intern=TRUE)) character(0) > try(system("convert tmp/10ixy71386781580.ps tmp/10ixy71386781580.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 29.630 4.145 33.805