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 + ,0.00968 + ,0.01394 + ,0.03134 + ,0.01929 + ,19.085 + ,0.458359 + ,0.819521 + ,-4.075192 + ,2.486855 + ,0.368674 + ,1 + ,0.0105 + ,0.01633 + ,0.02757 + ,0.01309 + ,20.651 + ,0.429895 + ,0.825288 + ,-4.443179 + ,2.342259 + ,0.332634 + ,1 + ,0.00997 + ,0.01505 + ,0.02924 + ,0.01353 + ,20.644 + ,0.434969 + ,0.819235 + ,-4.117501 + ,2.405554 + ,0.368975 + ,1 + ,0.01284 + ,0.01966 + ,0.0349 + ,0.01767 + ,19.649 + ,0.417356 + ,0.823484 + ,-3.747787 + ,2.33218 + ,0.410335 + ,1 + ,0.00968 + ,0.01388 + ,0.02328 + ,0.01222 + ,21.378 + ,0.415564 + ,0.825069 + ,-4.242867 + ,2.18756 + ,0.357775 + ,1 + ,0.00333 + ,0.00466 + ,0.00779 + ,0.00607 + ,24.886 + ,0.59604 + ,0.764112 + ,-5.634322 + ,1.854785 + ,0.211756 + ,1 + ,0.0029 + ,0.00431 + ,0.00829 + ,0.00344 + ,26.892 + ,0.63742 + ,0.763262 + ,-6.167603 + ,2.064693 + ,0.163755 + ,1 + ,0.00551 + ,0.0088 + ,0.01073 + ,0.0107 + ,21.812 + ,0.615551 + ,0.773587 + ,-5.498678 + ,2.322511 + ,0.231571 + ,1 + 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+ ,0.598515 + ,0.654331 + ,-5.592584 + ,2.058658 + ,0.214346 + ,0 + ,0.00496 + ,0.00762 + ,0.0082 + ,0.01397 + ,23.958 + ,0.566424 + ,0.667654 + ,-6.431119 + ,2.161936 + ,0.120605 + ,0 + ,0.00267 + ,0.00345 + ,0.00631 + ,0.0068 + ,25.023 + ,0.528485 + ,0.663884 + ,-6.359018 + ,2.152083 + ,0.138868 + ,0 + ,0.00327 + ,0.00439 + ,0.00557 + ,0.00703 + ,24.775 + ,0.555303 + ,0.659132 + ,-6.710219 + ,1.91399 + ,0.121777 + ,0 + ,0.00694 + ,0.01235 + ,0.01454 + ,0.04441 + ,19.368 + ,0.508479 + ,0.683761 + ,-6.934474 + ,2.316346 + ,0.112838 + ,0 + ,0.00459 + ,0.0079 + ,0.02336 + ,0.02764 + ,19.517 + ,0.448439 + ,0.657899 + ,-6.538586 + ,2.657476 + ,0.13305 + ,0 + ,0.00564 + ,0.00994 + ,0.01604 + ,0.0181 + ,19.147 + ,0.431674 + ,0.683244 + ,-6.195325 + ,2.784312 + ,0.168895 + ,0 + ,0.0136 + ,0.01873 + ,0.01268 + ,0.10715 + ,17.883 + ,0.407567 + ,0.655683 + ,-6.787197 + ,2.679772 + ,0.131728 + ,0 + ,0.0074 + ,0.01109 + ,0.01265 + ,0.07223 + ,19.02 + ,0.451221 + ,0.643956 + ,-6.744577 + ,2.138608 + ,0.123306 + ,0 + ,0.00567 + ,0.00885 + ,0.01026 + ,0.04398 + ,21.209 + ,0.462803 + ,0.664357 + ,-5.724056 + ,2.555477 + ,0.148569) + ,dim=c(11 + ,194) + ,dimnames=list(c('status' + ,'MDVP:Jitter(%)' + ,'Jitter:DDP' + ,'Shimmer:APQ3' + ,'NHR' + ,'HNR' + ,'RPDE' + ,'DFA' + ,'spread1' + ,'D2' + ,'PPE') + ,1:194)) > y <- array(NA,dim=c(11,194),dimnames=list(c('status','MDVP:Jitter(%)','Jitter:DDP','Shimmer:APQ3','NHR','HNR','RPDE','DFA','spread1','D2','PPE'),1:194)) > 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:Jitter(%) Jitter:DDP Shimmer:APQ3 NHR HNR RPDE 1 1 0.00968 0.01394 0.03134 0.01929 19.085 0.458359 2 1 0.01050 0.01633 0.02757 0.01309 20.651 0.429895 3 1 0.00997 0.01505 0.02924 0.01353 20.644 0.434969 4 1 0.01284 0.01966 0.03490 0.01767 19.649 0.417356 5 1 0.00968 0.01388 0.02328 0.01222 21.378 0.415564 6 1 0.00333 0.00466 0.00779 0.00607 24.886 0.596040 7 1 0.00290 0.00431 0.00829 0.00344 26.892 0.637420 8 1 0.00551 0.00880 0.01073 0.01070 21.812 0.615551 9 1 0.00532 0.00803 0.01441 0.01022 21.862 0.547037 10 1 0.00505 0.00763 0.01079 0.01166 21.118 0.611137 11 1 0.00540 0.00844 0.01424 0.01141 21.414 0.583390 12 1 0.00293 0.00355 0.00656 0.00581 25.703 0.460600 13 1 0.00390 0.00496 0.00728 0.01041 24.889 0.430166 14 1 0.00294 0.00364 0.01064 0.00609 24.922 0.474791 15 1 0.00369 0.00471 0.00772 0.00839 25.175 0.565924 16 1 0.00544 0.00632 0.00969 0.01859 22.333 0.567380 17 1 0.00718 0.00853 0.01441 0.02919 20.376 0.631099 18 1 0.00742 0.01092 0.02471 0.03160 17.280 0.665318 19 1 0.00768 0.01116 0.01721 0.03365 17.153 0.649554 20 1 0.00840 0.01285 0.01667 0.03871 17.536 0.660125 21 1 0.00480 0.00696 0.02021 0.01849 19.493 0.629017 22 1 0.00442 0.00661 0.02228 0.01280 22.468 0.619060 23 1 0.00476 0.00663 0.02187 0.01840 20.422 0.537264 24 1 0.00742 0.01140 0.00738 0.01778 23.831 0.397937 25 1 0.00633 0.00948 0.01732 0.02887 22.066 0.522746 26 1 0.00455 0.00750 0.00889 0.01095 25.908 0.418622 27 1 0.00496 0.00749 0.00883 0.01328 25.119 0.358773 28 1 0.00310 0.00476 0.00769 0.00677 25.970 0.470478 29 1 0.00502 0.00841 0.00793 0.01170 25.678 0.427785 30 0 0.00289 0.00498 0.00563 0.00339 26.775 0.422229 31 0 0.00241 0.00402 0.00504 0.00167 30.940 0.432439 32 0 0.00212 0.00339 0.00640 0.00119 30.775 0.465946 33 0 0.00180 0.00278 0.00469 0.00072 32.684 0.368535 34 0 0.00178 0.00283 0.00468 0.00065 33.047 0.340068 35 0 0.00198 0.00314 0.00586 0.00135 31.732 0.344252 36 1 0.00411 0.00700 0.01154 0.00586 23.216 0.360148 37 1 0.00369 0.00616 0.00938 0.00340 24.951 0.341435 38 1 0.00284 0.00459 0.00726 0.00231 26.738 0.403884 39 1 0.00316 0.00504 0.00829 0.00265 26.310 0.396793 40 1 0.00298 0.00496 0.00774 0.00231 26.822 0.326480 41 1 0.00258 0.00403 0.00742 0.00257 26.453 0.306443 42 0 0.00298 0.00507 0.01035 0.00740 22.736 0.305062 43 0 0.00281 0.00470 0.01006 0.00675 23.145 0.457702 44 0 0.00210 0.00327 0.00777 0.00454 25.368 0.438296 45 0 0.00225 0.00350 0.00847 0.00476 25.032 0.431285 46 0 0.00235 0.00380 0.00906 0.00476 24.602 0.467489 47 0 0.00185 0.00276 0.00614 0.00432 26.805 0.610367 48 0 0.00524 0.00507 0.00855 0.00839 23.162 0.579597 49 0 0.00428 0.00373 0.00930 0.00462 24.971 0.538688 50 0 0.00431 0.00422 0.01241 0.00479 25.135 0.553134 51 0 0.00448 0.00393 0.01143 0.00474 25.030 0.507504 52 0 0.00436 0.00411 0.01323 0.00481 24.692 0.459766 53 0 0.00490 0.00495 0.01396 0.00484 25.429 0.420383 54 1 0.00761 0.01046 0.01483 0.01036 21.028 0.536009 55 1 0.00874 0.01193 0.01789 0.01180 20.767 0.558586 56 1 0.00784 0.01056 0.02032 0.00969 21.422 0.541781 57 1 0.00752 0.00898 0.01189 0.00681 22.817 0.530529 58 1 0.00788 0.01003 0.01394 0.00786 22.603 0.540049 59 1 0.00867 0.01120 0.01805 0.01143 21.660 0.547975 60 0 0.00282 0.00442 0.00975 0.00871 25.554 0.341788 61 0 0.00264 0.00461 0.01013 0.00301 26.138 0.447979 62 0 0.00266 0.00457 0.00867 0.00340 25.856 0.364867 63 0 0.00296 0.00526 0.00882 0.00351 25.964 0.256570 64 0 0.00205 0.00342 0.00769 0.00300 26.415 0.276850 65 0 0.00238 0.00408 0.00942 0.00420 24.547 0.305429 66 1 0.00817 0.01289 0.01830 0.02183 19.560 0.460139 67 1 0.00923 0.01520 0.01638 0.02659 19.979 0.498133 68 1 0.01101 0.01941 0.03152 0.04882 20.338 0.513237 69 1 0.00762 0.01400 0.03357 0.02431 21.718 0.487407 70 1 0.00831 0.01407 0.01868 0.02599 20.264 0.489345 71 1 0.00971 0.01601 0.02749 0.03361 18.570 0.543299 72 1 0.00405 0.00540 0.00974 0.00442 25.742 0.495954 73 1 0.00533 0.00805 0.01373 0.00623 24.178 0.509127 74 1 0.00494 0.00780 0.01432 0.00479 25.438 0.437031 75 1 0.00516 0.00831 0.01284 0.00472 25.197 0.463514 76 1 0.00500 0.00810 0.02413 0.00905 23.370 0.489538 77 1 0.00462 0.00677 0.01284 0.00420 25.820 0.429484 78 1 0.00608 0.00994 0.01803 0.01062 21.875 0.644954 79 1 0.01038 0.01865 0.01773 0.02220 19.200 0.594387 80 1 0.00694 0.01168 0.02266 0.01823 19.055 0.544805 81 1 0.00702 0.01283 0.01792 0.01825 19.659 0.576084 82 1 0.00606 0.01053 0.01371 0.01237 20.536 0.554610 83 1 0.00432 0.00742 0.01277 0.00882 22.244 0.576644 84 1 0.00747 0.01254 0.02679 0.05470 13.893 0.556494 85 1 0.00406 0.00659 0.02107 0.02782 16.176 0.583574 86 1 0.00321 0.00488 0.02073 0.03151 15.924 0.598714 87 1 0.00520 0.00862 0.03671 0.04824 13.922 0.602874 88 1 0.00448 0.00710 0.03788 0.04214 14.739 0.599371 89 1 0.00709 0.01172 0.02297 0.07223 11.866 0.590951 90 1 0.00742 0.01161 0.03650 0.08725 11.744 0.653410 91 1 0.00419 0.00672 0.04421 0.01658 19.664 0.501037 92 1 0.00459 0.00750 0.02383 0.01914 18.780 0.454444 93 1 0.00382 0.00574 0.03341 0.01211 20.969 0.447456 94 1 0.00358 0.00587 0.02062 0.00850 22.219 0.502380 95 1 0.00369 0.00602 0.01813 0.01018 21.693 0.447285 96 1 0.00342 0.00535 0.01806 0.00852 22.663 0.366329 97 1 0.01280 0.02228 0.02135 0.08151 15.338 0.629574 98 1 0.01378 0.02478 0.02542 0.10323 15.433 0.571010 99 1 0.01936 0.03476 0.03611 0.16744 12.435 0.638545 100 1 0.03316 0.06433 0.05358 0.31482 8.867 0.671299 101 1 0.01551 0.02716 0.03223 0.11843 15.060 0.639808 102 1 0.03011 0.05563 0.05551 0.25930 10.489 0.596362 103 1 0.00248 0.00315 0.00522 0.00495 26.759 0.296888 104 1 0.00183 0.00229 0.00469 0.00243 28.409 0.263654 105 1 0.00257 0.00349 0.00660 0.00578 27.421 0.365488 106 1 0.00168 0.00204 0.00522 0.00233 29.746 0.334171 107 1 0.00258 0.00346 0.00633 0.00659 26.833 0.393563 108 1 0.00174 0.00225 0.00455 0.00238 29.928 0.311369 109 1 0.00766 0.01351 0.01771 0.00947 21.934 0.497554 110 1 0.00621 0.01112 0.01192 0.00704 23.239 0.436084 111 1 0.00609 0.01105 0.00952 0.00830 22.407 0.338097 112 1 0.00841 0.01506 0.01277 0.01316 21.305 0.498877 113 1 0.00534 0.00964 0.00861 0.00620 23.671 0.441097 114 1 0.00495 0.00905 0.01107 0.01048 21.864 0.331508 115 1 0.00856 0.01211 0.00796 0.06051 23.693 0.407701 116 1 0.00476 0.00642 0.00606 0.01554 26.356 0.450798 117 1 0.00555 0.00731 0.00757 0.01802 25.690 0.486738 118 1 0.00462 0.00472 0.00617 0.00856 25.020 0.470422 119 1 0.00404 0.00381 0.00679 0.00681 24.581 0.462516 120 1 0.00581 0.00723 0.00849 0.02350 24.743 0.487756 121 1 0.00460 0.00628 0.00534 0.01161 27.166 0.400088 122 1 0.00704 0.01218 0.02587 0.01968 18.305 0.538016 123 1 0.00842 0.01517 0.01372 0.01813 18.784 0.589956 124 1 0.00694 0.01209 0.01289 0.02020 19.196 0.618663 125 1 0.00733 0.01242 0.01235 0.01874 18.857 0.637518 126 1 0.00544 0.00883 0.01484 0.01794 18.178 0.623209 127 1 0.00638 0.01104 0.01547 0.01796 18.330 0.585169 128 1 0.00440 0.00641 0.00538 0.01724 26.842 0.457541 129 1 0.00270 0.00349 0.00476 0.00487 26.369 0.491345 130 1 0.00492 0.00808 0.00703 0.01610 23.949 0.467160 131 1 0.00407 0.00671 0.00721 0.01015 26.017 0.468621 132 1 0.00346 0.00508 0.00633 0.00903 23.389 0.470972 133 1 0.00331 0.00504 0.00490 0.00504 25.619 0.482296 134 1 0.00589 0.00873 0.02683 0.03031 17.060 0.637814 135 1 0.00494 0.00731 0.02229 0.02529 17.707 0.653427 136 1 0.00451 0.00658 0.02385 0.02278 19.013 0.647900 137 1 0.00502 0.00772 0.02896 0.03690 16.747 0.625362 138 1 0.00472 0.00715 0.03070 0.02629 17.366 0.640945 139 1 0.00381 0.00542 0.01514 0.01827 18.801 0.624811 140 1 0.00571 0.00696 0.01713 0.02485 18.540 0.677131 141 1 0.00757 0.01285 0.04016 0.04238 15.648 0.606344 142 1 0.00376 0.00546 0.02055 0.01728 18.702 0.606273 143 1 0.00370 0.00568 0.01117 0.02010 18.687 0.536102 144 1 0.00254 0.00301 0.01475 0.01049 20.680 0.497480 145 1 0.00352 0.00506 0.01379 0.01493 20.366 0.566849 146 1 0.01568 0.02589 0.03804 0.07530 12.359 0.561610 147 1 0.01466 0.02546 0.02865 0.06057 14.367 0.478024 148 1 0.01719 0.02987 0.03474 0.08069 12.298 0.552870 149 1 0.01627 0.02756 0.03515 0.07889 14.989 0.427627 150 1 0.01872 0.03225 0.02699 0.10952 12.529 0.507826 151 1 0.03107 0.05401 0.05647 0.21713 8.441 0.625866 152 1 0.02714 0.04705 0.04284 0.16265 9.449 0.584164 153 1 0.00684 0.01164 0.01340 0.04179 21.520 0.566867 154 1 0.00692 0.01179 0.01484 0.04611 21.824 0.651680 155 1 0.00647 0.01067 0.01659 0.02631 22.431 0.628300 156 1 0.00727 0.01246 0.01205 0.03191 22.953 0.611679 157 1 0.01813 0.03351 0.02610 0.10748 19.075 0.630547 158 1 0.00975 0.01778 0.01500 0.03828 21.534 0.635015 159 1 0.00605 0.00962 0.01360 0.02663 19.651 0.654945 160 1 0.00581 0.00896 0.01579 0.02073 20.437 0.653139 161 1 0.00619 0.01057 0.01644 0.02810 19.388 0.577802 162 1 0.00651 0.01097 0.01864 0.02707 18.954 0.685151 163 1 0.00519 0.00873 0.00967 0.01435 21.219 0.557045 164 1 0.00907 0.01480 0.01579 0.03882 18.447 0.671378 165 0 0.00277 0.00462 0.01410 0.00620 24.078 0.469928 166 0 0.00303 0.00519 0.00696 0.00533 24.679 0.384868 167 0 0.00339 0.00616 0.01186 0.00910 21.083 0.440988 168 0 0.00803 0.01470 0.01279 0.01337 19.269 0.372222 169 0 0.00517 0.00949 0.01176 0.00965 21.020 0.371837 170 0 0.00451 0.00837 0.01084 0.01049 21.528 0.522812 171 0 0.00355 0.00499 0.00664 0.00435 26.436 0.413295 172 0 0.00356 0.00510 0.00754 0.00430 26.550 0.369090 173 0 0.00349 0.00514 0.00748 0.00478 26.547 0.380253 174 0 0.00353 0.00528 0.00881 0.00590 25.445 0.387482 175 0 0.00332 0.00480 0.00812 0.00401 26.005 0.405991 176 0 0.00346 0.00507 0.00874 0.00415 26.143 0.361232 177 1 0.00314 0.00406 0.00728 0.00570 24.151 0.396610 178 1 0.00309 0.00456 0.00839 0.00488 24.412 0.402591 179 1 0.00392 0.00612 0.00725 0.00540 23.683 0.398499 180 1 0.00396 0.00619 0.01321 0.00611 23.133 0.352396 181 1 0.00397 0.00605 0.00950 0.00639 22.866 0.408598 182 1 0.00336 0.00521 0.01155 0.00595 23.008 0.329577 183 0 0.00417 0.00558 0.00864 0.00955 23.079 0.603515 184 0 0.00531 0.00780 0.00810 0.01179 22.085 0.663842 185 0 0.00314 0.00403 0.00667 0.00737 24.199 0.598515 186 0 0.00496 0.00762 0.00820 0.01397 23.958 0.566424 187 0 0.00267 0.00345 0.00631 0.00680 25.023 0.528485 188 0 0.00327 0.00439 0.00557 0.00703 24.775 0.555303 189 0 0.00694 0.01235 0.01454 0.04441 19.368 0.508479 190 0 0.00459 0.00790 0.02336 0.02764 19.517 0.448439 191 0 0.00564 0.00994 0.01604 0.01810 19.147 0.431674 192 0 0.01360 0.01873 0.01268 0.10715 17.883 0.407567 193 0 0.00740 0.01109 0.01265 0.07223 19.020 0.451221 194 0 0.00567 0.00885 0.01026 0.04398 21.209 0.462803 DFA spread1 D2 PPE 1 0.819521 -4.075192 2.486855 0.368674 2 0.825288 -4.443179 2.342259 0.332634 3 0.819235 -4.117501 2.405554 0.368975 4 0.823484 -3.747787 2.332180 0.410335 5 0.825069 -4.242867 2.187560 0.357775 6 0.764112 -5.634322 1.854785 0.211756 7 0.763262 -6.167603 2.064693 0.163755 8 0.773587 -5.498678 2.322511 0.231571 9 0.798463 -5.011879 2.432792 0.271362 10 0.776156 -5.249770 2.407313 0.249740 11 0.792520 -4.960234 2.642476 0.275931 12 0.646846 -6.547148 2.041277 0.138512 13 0.665833 -5.660217 2.519422 0.199889 14 0.654027 -6.105098 2.125618 0.170100 15 0.658245 -5.340115 2.205546 0.234589 16 0.644692 -5.440040 2.264501 0.218164 17 0.605417 -2.931070 3.007463 0.430788 18 0.719467 -3.949079 3.109010 0.377429 19 0.686080 -4.554466 2.856676 0.322111 20 0.704087 -4.095442 2.739710 0.365391 21 0.698951 -5.186960 2.557536 0.259765 22 0.679834 -4.330956 2.916777 0.285695 23 0.686894 -5.248776 2.547508 0.253556 24 0.732479 -5.557447 2.692176 0.215961 25 0.737948 -5.571843 2.846369 0.219514 26 0.720916 -6.183590 2.589702 0.147403 27 0.726652 -6.271690 2.314209 0.162999 28 0.676258 -7.120925 2.241742 0.108514 29 0.723797 -6.635729 1.957961 0.135242 30 0.741367 -7.348300 1.743867 0.085569 31 0.742055 -7.682587 2.103106 0.068501 32 0.738703 -7.067931 1.512275 0.096320 33 0.742133 -7.695734 1.544609 0.056141 34 0.741899 -7.964984 1.423287 0.044539 35 0.742737 -7.777685 2.447064 0.057610 36 0.778834 -6.149653 2.477082 0.165827 37 0.783626 -6.006414 2.536527 0.173218 38 0.766209 -6.452058 2.269398 0.141929 39 0.758324 -6.006647 2.382544 0.160691 40 0.765623 -6.647379 2.374073 0.130554 41 0.759203 -7.044105 2.361532 0.115730 42 0.654172 -7.310550 2.416838 0.095032 43 0.634267 -6.793547 2.256699 0.117399 44 0.635285 -7.057869 2.330716 0.091470 45 0.638928 -6.995820 2.365800 0.102706 46 0.631653 -7.156076 2.392122 0.097336 47 0.635204 -7.319510 2.028612 0.086398 48 0.733659 -6.439398 2.079922 0.133867 49 0.754073 -6.482096 2.054419 0.128872 50 0.775933 -6.650471 1.840198 0.103561 51 0.760361 -6.689151 2.431854 0.105993 52 0.766204 -7.072419 1.972297 0.119308 53 0.785714 -6.836811 2.223719 0.147491 54 0.819032 -4.649573 1.986899 0.316700 55 0.811843 -4.333543 2.014606 0.344834 56 0.821364 -4.438453 1.922940 0.335041 57 0.817756 -4.608260 2.021591 0.314464 58 0.813432 -4.476755 1.827012 0.326197 59 0.817396 -4.609161 1.831691 0.316395 60 0.678874 -7.040508 2.460791 0.101516 61 0.686264 -7.293801 2.321560 0.098555 62 0.694399 -6.966321 2.278687 0.103224 63 0.683296 -7.245620 2.498224 0.093534 64 0.673636 -7.496264 2.003032 0.073581 65 0.681811 -7.314237 2.118596 0.091546 66 0.720908 -5.409423 2.359973 0.226156 67 0.729067 -5.324574 2.291558 0.226247 68 0.731444 -5.869750 2.118496 0.185580 69 0.727313 -6.261141 2.137075 0.141958 70 0.730387 -5.720868 2.277927 0.180828 71 0.733232 -5.207985 2.642276 0.242981 72 0.762959 -5.791820 2.205024 0.188180 73 0.789532 -5.389129 1.928708 0.225461 74 0.815908 -5.313360 2.225815 0.244512 75 0.807217 -5.477592 1.862092 0.228624 76 0.789977 -5.775966 2.007923 0.193918 77 0.816340 -5.391029 1.777901 0.232744 78 0.779612 -5.115212 2.017753 0.260015 79 0.790117 -4.913885 2.398422 0.277948 80 0.770466 -4.441519 2.645959 0.327978 81 0.778747 -5.132032 2.232576 0.260633 82 0.787896 -5.022288 2.428306 0.264666 83 0.772416 -6.025367 2.053601 0.177275 84 0.729586 -5.288912 3.099301 0.242119 85 0.727747 -5.657899 3.098256 0.200423 86 0.712199 -6.366916 2.654271 0.144614 87 0.740837 -5.515071 3.136550 0.220968 88 0.743937 -5.783272 3.007096 0.194052 89 0.745526 -4.379411 3.671155 0.332086 90 0.733165 -4.508984 3.317586 0.301952 91 0.714360 -6.411497 2.344876 0.134120 92 0.734504 -5.952058 2.344336 0.186489 93 0.697790 -6.152551 2.080121 0.160809 94 0.712170 -6.251425 2.143851 0.160812 95 0.705658 -6.247076 2.344348 0.164916 96 0.693429 -6.417440 2.473239 0.151709 97 0.714485 -4.020042 2.671825 0.340623 98 0.690892 -5.159169 2.441612 0.260375 99 0.674953 -3.760348 2.634633 0.378483 100 0.656846 -3.700544 2.991063 0.370961 101 0.643327 -4.202730 2.638279 0.356881 102 0.641418 -3.269487 2.690917 0.444774 103 0.722356 -6.878393 2.004055 0.113942 104 0.691483 -7.111576 2.065477 0.093193 105 0.719974 -6.997403 1.994387 0.112878 106 0.677930 -6.981201 2.129924 0.106802 107 0.700246 -6.600023 2.499148 0.105306 108 0.676066 -6.739151 2.296873 0.115130 109 0.740539 -5.845099 2.608749 0.185668 110 0.727863 -5.258320 2.550961 0.232520 111 0.712466 -6.471427 2.502336 0.136390 112 0.722085 -4.876336 2.376749 0.268144 113 0.722254 -5.963040 2.489191 0.177807 114 0.715121 -6.729713 2.938114 0.115515 115 0.662668 -4.673241 2.702355 0.274407 116 0.653823 -6.051233 2.640798 0.170106 117 0.676023 -4.597834 2.975889 0.282780 118 0.655239 -4.913137 2.816781 0.251972 119 0.582710 -5.517173 2.925862 0.220657 120 0.684130 -6.186128 2.686240 0.152428 121 0.656182 -4.711007 2.655744 0.234809 122 0.741480 -5.418787 2.090438 0.229892 123 0.732903 -5.445140 2.174306 0.215558 124 0.728421 -5.944191 1.929715 0.181988 125 0.735546 -5.594275 1.765957 0.222716 126 0.738245 -5.540351 1.821297 0.214075 127 0.736964 -5.825257 1.996146 0.196535 128 0.699787 -6.890021 2.328513 0.112856 129 0.718839 -5.892061 2.108873 0.183572 130 0.724045 -6.135296 2.539724 0.169923 131 0.735136 -6.112667 2.527742 0.170633 132 0.721308 -5.436135 2.516320 0.232209 133 0.723096 -6.448134 2.034827 0.141422 134 0.744064 -5.301321 2.375138 0.243080 135 0.706687 -5.333619 2.631793 0.228319 136 0.708144 -4.378916 2.445502 0.259451 137 0.708617 -4.654894 2.672362 0.274387 138 0.701404 -5.634576 2.419253 0.209191 139 0.696049 -5.866357 2.445646 0.184985 140 0.685057 -4.796845 2.963799 0.277227 141 0.665945 -5.410336 2.665133 0.231723 142 0.661735 -5.585259 2.465528 0.209863 143 0.632631 -5.898673 2.470746 0.189032 144 0.630409 -6.132663 2.576563 0.159777 145 0.574282 -5.456811 2.840556 0.232861 146 0.793509 -3.297668 3.413649 0.457533 147 0.768974 -4.276605 3.142364 0.336085 148 0.764036 -3.377325 3.274865 0.418646 149 0.775708 -4.892495 2.910213 0.270173 150 0.762726 -4.484303 2.958815 0.301487 151 0.768320 -2.434031 3.079221 0.527367 152 0.754449 -2.839756 3.184027 0.454721 153 0.670475 -4.865194 2.013530 0.168581 154 0.659333 -4.239028 2.451130 0.247455 155 0.652025 -3.583722 2.439597 0.206256 156 0.623731 -5.435100 2.699645 0.220546 157 0.646786 -3.444478 2.964568 0.261305 158 0.627337 -5.070096 2.892300 0.249703 159 0.675865 -5.498456 2.103014 0.216638 160 0.694571 -5.185987 2.151121 0.244948 161 0.684373 -5.283009 2.442906 0.238281 162 0.719576 -5.529833 2.408689 0.220520 163 0.673086 -5.617124 1.871871 0.212386 164 0.674562 -2.929379 2.560422 0.367233 165 0.628232 -6.816086 2.235197 0.119652 166 0.626710 -7.018057 1.852402 0.091604 167 0.628058 -7.517934 1.881767 0.075587 168 0.725216 -5.736781 2.882450 0.202879 169 0.646167 -7.169701 2.266432 0.100881 170 0.646818 -7.304500 2.095237 0.096220 171 0.756700 -6.323531 2.193412 0.160376 172 0.776158 -6.085567 1.889002 0.174152 173 0.766700 -5.943501 1.852542 0.179677 174 0.756482 -6.012559 1.872946 0.163118 175 0.761255 -5.966779 1.974857 0.184067 176 0.763242 -6.016891 2.004719 0.174429 177 0.745957 -6.486822 2.449763 0.132703 178 0.762508 -6.311987 2.251553 0.160306 179 0.778349 -5.711205 2.845109 0.192730 180 0.759320 -6.261446 2.264226 0.144105 181 0.768845 -5.704053 2.679185 0.197710 182 0.757180 -6.277170 2.209021 0.156368 183 0.669565 -5.619070 2.027228 0.215724 184 0.656516 -5.198864 2.120412 0.252404 185 0.654331 -5.592584 2.058658 0.214346 186 0.667654 -6.431119 2.161936 0.120605 187 0.663884 -6.359018 2.152083 0.138868 188 0.659132 -6.710219 1.913990 0.121777 189 0.683761 -6.934474 2.316346 0.112838 190 0.657899 -6.538586 2.657476 0.133050 191 0.683244 -6.195325 2.784312 0.168895 192 0.655683 -6.787197 2.679772 0.131728 193 0.643956 -6.744577 2.138608 0.123306 194 0.664357 -5.724056 2.555477 0.148569 > k <- length(x[1,]) > df <- as.data.frame(x) > (mylm <- lm(df)) Call: lm(formula = df) Coefficients: (Intercept) `MDVP:Jitter(%)` `Jitter:DDP` `Shimmer:APQ3` 1.34068 -134.46053 58.44675 4.34436 NHR HNR RPDE DFA -0.15760 0.00299 -0.02044 1.43089 spread1 D2 PPE 0.31225 0.16732 -0.48774 > (mysum <- summary(mylm)) Call: lm(formula = df) Residuals: Min 1Q Median 3Q Max -0.85332 -0.25971 0.08751 0.26075 0.63127 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 1.34068 1.09812 1.221 0.223701 `MDVP:Jitter(%)` -134.46053 45.27396 -2.970 0.003378 ** `Jitter:DDP` 58.44675 23.45324 2.492 0.013590 * `Shimmer:APQ3` 4.34436 4.88767 0.889 0.375256 NHR -0.15760 1.92417 -0.082 0.934813 HNR 0.00299 0.01337 0.224 0.823301 RPDE -0.02043 0.37459 -0.055 0.956553 DFA 1.43089 0.59184 2.418 0.016599 * spread1 0.31225 0.09112 3.427 0.000754 *** D2 0.16732 0.09332 1.793 0.074629 . PPE -0.48774 1.17483 -0.415 0.678512 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 0.3428 on 183 degrees of freedom Multiple R-squared: 0.4046, Adjusted R-squared: 0.3721 F-statistic: 12.44 on 10 and 183 DF, p-value: 2.298e-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,] 3.133036e-48 6.266072e-48 1.0000000000 [2,] 7.868538e-63 1.573708e-62 1.0000000000 [3,] 0.000000e+00 0.000000e+00 1.0000000000 [4,] 6.848492e-100 1.369698e-99 1.0000000000 [5,] 4.367692e-106 8.735383e-106 1.0000000000 [6,] 1.387778e-121 2.775557e-121 1.0000000000 [7,] 3.471389e-145 6.942778e-145 1.0000000000 [8,] 6.568853e-174 1.313771e-173 1.0000000000 [9,] 7.724629e-166 1.544926e-165 1.0000000000 [10,] 8.781128e-184 1.756226e-183 1.0000000000 [11,] 5.060632e-196 1.012126e-195 1.0000000000 [12,] 2.226779e-219 4.453558e-219 1.0000000000 [13,] 2.665894e-256 5.331788e-256 1.0000000000 [14,] 1.784933e-249 3.569866e-249 1.0000000000 [15,] 1.086937e-256 2.173874e-256 1.0000000000 [16,] 3.831234e-280 7.662468e-280 1.0000000000 [17,] 2.108061e-03 4.216123e-03 0.9978919387 [18,] 6.745854e-03 1.349171e-02 0.9932541460 [19,] 8.728813e-03 1.745763e-02 0.9912711872 [20,] 5.325955e-03 1.065191e-02 0.9946740446 [21,] 3.340886e-03 6.681772e-03 0.9966591141 [22,] 2.425094e-03 4.850188e-03 0.9975749060 [23,] 1.415495e-03 2.830990e-03 0.9985845050 [24,] 8.116640e-04 1.623328e-03 0.9991883360 [25,] 7.507372e-04 1.501474e-03 0.9992492628 [26,] 4.215206e-04 8.430412e-04 0.9995784794 [27,] 3.695545e-04 7.391090e-04 0.9996304455 [28,] 4.031126e-04 8.062251e-04 0.9995968874 [29,] 1.040309e-02 2.080618e-02 0.9895969101 [30,] 3.028213e-02 6.056426e-02 0.9697178720 [31,] 4.569234e-02 9.138467e-02 0.9543076643 [32,] 5.262895e-02 1.052579e-01 0.9473710486 [33,] 4.539844e-02 9.079688e-02 0.9546015580 [34,] 3.514932e-02 7.029863e-02 0.9648506848 [35,] 1.228572e-01 2.457143e-01 0.8771428361 [36,] 1.331927e-01 2.663854e-01 0.8668072807 [37,] 1.273402e-01 2.546804e-01 0.8726598013 [38,] 1.183346e-01 2.366691e-01 0.8816654495 [39,] 1.061351e-01 2.122701e-01 0.8938649439 [40,] 1.016075e-01 2.032150e-01 0.8983925214 [41,] 8.134044e-02 1.626809e-01 0.9186595632 [42,] 6.363261e-02 1.272652e-01 0.9363673857 [43,] 4.933658e-02 9.867317e-02 0.9506634174 [44,] 3.776252e-02 7.552504e-02 0.9622374815 [45,] 2.844622e-02 5.689244e-02 0.9715537811 [46,] 2.259835e-02 4.519671e-02 0.9774016473 [47,] 3.068776e-02 6.137551e-02 0.9693122450 [48,] 2.977177e-02 5.954354e-02 0.9702282285 [49,] 4.634518e-02 9.269036e-02 0.9536548199 [50,] 5.353799e-02 1.070760e-01 0.9464620094 [51,] 4.970741e-02 9.941481e-02 0.9502925934 [52,] 4.839072e-02 9.678144e-02 0.9516092810 [53,] 4.154854e-02 8.309708e-02 0.9584514585 [54,] 3.358278e-02 6.716557e-02 0.9664172168 [55,] 2.831598e-02 5.663196e-02 0.9716840213 [56,] 2.999486e-02 5.998971e-02 0.9700051441 [57,] 2.418154e-02 4.836308e-02 0.9758184599 [58,] 1.832099e-02 3.664198e-02 0.9816790106 [59,] 1.467366e-02 2.934733e-02 0.9853263360 [60,] 1.107894e-02 2.215787e-02 0.9889210635 [61,] 8.445627e-03 1.689125e-02 0.9915543730 [62,] 6.334781e-03 1.266956e-02 0.9936652190 [63,] 4.872402e-03 9.744804e-03 0.9951275982 [64,] 3.689083e-03 7.378167e-03 0.9963109166 [65,] 2.616712e-03 5.233424e-03 0.9973832882 [66,] 2.291561e-03 4.583121e-03 0.9977084393 [67,] 1.948193e-03 3.896387e-03 0.9980518065 [68,] 1.521299e-03 3.042597e-03 0.9984787014 [69,] 1.188727e-03 2.377454e-03 0.9988112731 [70,] 9.208810e-04 1.841762e-03 0.9990791190 [71,] 7.146287e-04 1.429257e-03 0.9992853713 [72,] 4.994694e-04 9.989388e-04 0.9995005306 [73,] 4.681394e-04 9.362788e-04 0.9995318606 [74,] 3.233390e-04 6.466780e-04 0.9996766610 [75,] 2.273566e-04 4.547132e-04 0.9997726434 [76,] 4.872503e-04 9.745006e-04 0.9995127497 [77,] 5.527401e-04 1.105480e-03 0.9994472599 [78,] 5.963927e-04 1.192785e-03 0.9994036073 [79,] 4.470976e-04 8.941953e-04 0.9995529024 [80,] 3.808865e-04 7.617729e-04 0.9996191135 [81,] 3.408744e-04 6.817488e-04 0.9996591256 [82,] 3.169443e-04 6.338886e-04 0.9996830557 [83,] 3.510722e-04 7.021443e-04 0.9996489278 [84,] 3.211887e-04 6.423775e-04 0.9996788113 [85,] 2.545985e-04 5.091969e-04 0.9997454015 [86,] 1.810485e-04 3.620970e-04 0.9998189515 [87,] 1.217764e-04 2.435529e-04 0.9998782236 [88,] 9.054827e-05 1.810965e-04 0.9999094517 [89,] 6.956108e-05 1.391222e-04 0.9999304389 [90,] 1.432323e-04 2.864647e-04 0.9998567677 [91,] 3.718266e-04 7.436533e-04 0.9996281734 [92,] 9.106967e-04 1.821393e-03 0.9990893033 [93,] 2.127402e-03 4.254805e-03 0.9978725977 [94,] 1.998352e-03 3.996703e-03 0.9980016484 [95,] 3.288615e-03 6.577229e-03 0.9967113855 [96,] 2.612357e-03 5.224713e-03 0.9973876433 [97,] 2.069975e-03 4.139951e-03 0.9979300246 [98,] 2.322781e-03 4.645563e-03 0.9976772186 [99,] 1.915805e-03 3.831609e-03 0.9980841953 [100,] 1.577359e-03 3.154718e-03 0.9984226411 [101,] 1.597443e-03 3.194885e-03 0.9984025574 [102,] 1.493200e-03 2.986401e-03 0.9985067997 [103,] 1.990067e-03 3.980134e-03 0.9980099329 [104,] 1.444422e-03 2.888844e-03 0.9985555782 [105,] 1.096791e-03 2.193581e-03 0.9989032094 [106,] 1.782061e-03 3.564121e-03 0.9982179395 [107,] 2.780063e-03 5.560126e-03 0.9972199372 [108,] 5.935613e-03 1.187123e-02 0.9940643871 [109,] 5.045750e-03 1.009150e-02 0.9949542503 [110,] 3.751333e-03 7.502666e-03 0.9962486670 [111,] 3.128460e-03 6.256920e-03 0.9968715400 [112,] 2.794100e-03 5.588200e-03 0.9972059001 [113,] 2.185431e-03 4.370861e-03 0.9978145693 [114,] 1.932496e-03 3.864992e-03 0.9980675041 [115,] 5.547048e-03 1.109410e-02 0.9944529517 [116,] 6.466075e-03 1.293215e-02 0.9935339250 [117,] 6.422195e-03 1.284439e-02 0.9935778055 [118,] 6.591083e-03 1.318217e-02 0.9934089167 [119,] 5.654041e-03 1.130808e-02 0.9943459591 [120,] 1.168944e-02 2.337888e-02 0.9883105587 [121,] 8.696457e-03 1.739291e-02 0.9913035431 [122,] 6.427868e-03 1.285574e-02 0.9935721320 [123,] 7.415066e-03 1.483013e-02 0.9925849344 [124,] 7.204136e-03 1.440827e-02 0.9927958636 [125,] 5.435365e-03 1.087073e-02 0.9945646349 [126,] 4.040802e-03 8.081603e-03 0.9959591985 [127,] 2.965902e-03 5.931804e-03 0.9970340982 [128,] 2.116838e-03 4.233676e-03 0.9978831619 [129,] 1.506429e-03 3.012858e-03 0.9984935708 [130,] 1.371396e-03 2.742792e-03 0.9986286039 [131,] 1.746490e-03 3.492980e-03 0.9982535099 [132,] 3.249269e-03 6.498539e-03 0.9967507307 [133,] 2.549925e-03 5.099850e-03 0.9974500752 [134,] 1.785320e-03 3.570639e-03 0.9982146805 [135,] 1.538077e-03 3.076154e-03 0.9984619230 [136,] 1.157979e-03 2.315958e-03 0.9988420210 [137,] 7.544719e-04 1.508944e-03 0.9992455281 [138,] 4.860716e-04 9.721432e-04 0.9995139284 [139,] 3.336855e-04 6.673709e-04 0.9996663145 [140,] 4.684459e-04 9.368917e-04 0.9995315541 [141,] 4.004555e-04 8.009110e-04 0.9995995445 [142,] 5.277914e-04 1.055583e-03 0.9994722086 [143,] 1.136707e-03 2.273414e-03 0.9988632932 [144,] 8.972705e-04 1.794541e-03 0.9991027295 [145,] 6.521873e-02 1.304375e-01 0.9347812701 [146,] 5.226029e-02 1.045206e-01 0.9477397128 [147,] 4.270429e-02 8.540858e-02 0.9572957079 [148,] 6.938941e-02 1.387788e-01 0.9306105867 [149,] 6.605484e-02 1.321097e-01 0.9339451644 [150,] 1.745461e-01 3.490921e-01 0.8254539467 [151,] 8.335701e-01 3.328597e-01 0.1664298556 [152,] 9.183970e-01 1.632059e-01 0.0816029691 [153,] 9.864461e-01 2.710780e-02 0.0135538989 [154,] 9.881176e-01 2.376474e-02 0.0118823710 [155,] 9.856489e-01 2.870224e-02 0.0143511178 [156,] 9.880147e-01 2.397067e-02 0.0119853341 [157,] 9.892337e-01 2.153263e-02 0.0107663153 [158,] 9.849440e-01 3.011197e-02 0.0150559832 [159,] 9.795004e-01 4.099922e-02 0.0204996112 [160,] 9.700467e-01 5.990654e-02 0.0299532724 [161,] 9.798636e-01 4.027285e-02 0.0201364234 [162,] 9.821041e-01 3.579186e-02 0.0178959324 [163,] 9.994264e-01 1.147287e-03 0.0005736434 [164,] 9.992063e-01 1.587423e-03 0.0007937117 [165,] 9.965802e-01 6.839689e-03 0.0034198444 [166,] 9.862602e-01 2.747970e-02 0.0137398481 [167,] 9.502195e-01 9.956100e-02 0.0497804987 > postscript(file="/var/fisher/rcomp/tmp/129sj1386679785.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$resid) > grid() > dev.off() null device 1 > postscript(file="/var/fisher/rcomp/tmp/2malr1386679785.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(mysum$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/3zzmg1386679785.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') > grid() > dev.off() null device 1 > postscript(file="/var/fisher/rcomp/tmp/4rfxz1386679785.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') > dev.off() null device 1 > postscript(file="/var/fisher/rcomp/tmp/5r3jj1386679785.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 = 194 Frequency = 1 1 2 3 4 5 -1.711085e-01 -7.713330e-02 -1.665448e-01 -1.604777e-01 -5.225077e-02 6 7 8 9 10 1.984958e-01 2.625949e-01 1.226895e-01 -6.211017e-02 4.437058e-02 11 12 13 14 15 -1.127865e-01 5.955781e-01 2.888217e-01 4.295955e-01 2.552337e-01 16 17 18 19 20 4.307367e-01 -2.239912e-01 -2.541524e-01 5.179023e-02 -7.637611e-02 21 22 23 24 25 8.590556e-02 -2.511186e-01 1.231397e-01 2.404379e-01 1.450897e-01 26 27 28 29 30 2.648215e-01 3.953035e-01 6.312659e-01 5.168267e-01 -3.548573e-01 31 32 33 34 35 -3.382791e-01 -4.199612e-01 -2.615626e-01 -1.697592e-01 -3.865921e-01 36 37 38 39 40 1.637810e-01 1.019033e-01 2.778548e-01 1.537162e-01 3.099012e-01 41 42 43 44 45 4.405306e-01 -3.532138e-01 -4.466426e-01 -3.999272e-01 -4.203235e-01 46 47 48 49 50 -3.715223e-01 -2.675800e-01 -3.474627e-01 -4.223708e-01 -4.158605e-01 51 52 53 54 55 -4.358723e-01 -2.755980e-01 -3.880681e-01 5.840255e-02 3.329301e-02 56 57 58 59 60 8.855297e-03 1.215841e-01 1.041191e-01 1.577576e-01 -4.654591e-01 61 62 63 64 65 -4.125232e-01 -5.063929e-01 -4.479138e-01 -2.936254e-01 -3.680950e-01 66 67 68 69 70 2.523314e-01 2.417851e-01 3.479822e-01 2.946737e-01 2.749909e-01 71 72 73 74 75 1.240755e-01 2.194477e-01 1.252234e-01 -2.245646e-02 1.018246e-01 76 77 78 79 80 1.267228e-01 9.259652e-02 3.789536e-02 -1.574539e-02 -2.299341e-01 81 82 83 84 85 -2.687667e-02 -8.537131e-02 2.167398e-01 -8.964336e-05 1.184662e-03 86 87 88 89 90 2.806663e-01 -8.148587e-02 -1.017428e-02 -3.357398e-01 -2.371007e-01 91 92 93 94 95 2.448358e-01 1.970123e-01 2.937505e-01 3.058972e-01 2.998643e-01 96 97 98 99 100 3.408940e-01 -8.268481e-02 2.760636e-01 2.840936e-02 5.829153e-02 101 102 103 104 105 1.284822e-01 1.068575e-01 5.472617e-01 6.030093e-01 5.747174e-01 106 107 108 109 110 5.671140e-01 3.974434e-01 4.680305e-01 1.883782e-01 2.014630e-02 111 112 113 114 115 3.812892e-01 2.553450e-02 2.144509e-01 3.337514e-01 2.076523e-01 116 117 118 119 120 4.257144e-01 -1.022770e-02 1.621520e-01 3.944010e-01 4.983618e-01 121 122 123 124 125 1.862685e-02 1.344157e-01 1.968615e-01 3.679575e-01 3.324323e-01 126 127 128 129 130 2.447462e-01 2.909907e-01 6.002723e-01 2.775681e-01 2.962040e-01 131 132 133 134 135 2.335260e-01 9.880834e-02 4.298479e-01 1.031138e-01 8.911148e-02 136 137 138 139 140 -1.910696e-01 -1.479587e-01 1.610347e-01 2.653162e-01 6.508751e-02 141 142 143 144 145 1.275365e-01 2.026471e-01 3.500022e-01 3.705337e-01 2.504658e-01 146 147 148 149 150 -3.781371e-01 -1.324563e-01 -3.212296e-01 1.224192e-01 1.252623e-01 151 152 153 154 155 -1.406098e-01 -1.219212e-01 9.942352e-02 -1.176661e-01 -3.380574e-01 156 157 158 159 160 2.656375e-01 -2.205850e-01 1.439237e-01 3.138847e-01 1.887809e-01 161 162 163 164 165 1.385255e-01 1.757079e-01 3.363470e-01 -3.898741e-01 -4.471533e-01 166 167 168 169 170 -3.025487e-01 -1.781961e-01 -8.533247e-01 -3.208621e-01 -2.709193e-01 171 172 173 174 175 -6.507616e-01 -7.055019e-01 -7.387103e-01 -7.189790e-01 -7.457167e-01 176 177 178 179 180 -7.435646e-01 3.623591e-01 2.891555e-01 2.294920e-02 2.716786e-01 181 182 183 184 185 6.835174e-02 2.785641e-01 -6.363103e-01 -7.161128e-01 -6.718517e-01 186 187 188 189 190 -4.627306e-01 -5.303643e-01 -3.521222e-01 -3.786546e-01 -6.110127e-01 191 192 193 194 -7.051888e-01 1.069023e-01 -1.981882e-01 -7.055433e-01 > postscript(file="/var/fisher/rcomp/tmp/685ty1386679785.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 = 194 Frequency = 1 lag(myerror, k = 1) myerror 0 -1.711085e-01 NA 1 -7.713330e-02 -1.711085e-01 2 -1.665448e-01 -7.713330e-02 3 -1.604777e-01 -1.665448e-01 4 -5.225077e-02 -1.604777e-01 5 1.984958e-01 -5.225077e-02 6 2.625949e-01 1.984958e-01 7 1.226895e-01 2.625949e-01 8 -6.211017e-02 1.226895e-01 9 4.437058e-02 -6.211017e-02 10 -1.127865e-01 4.437058e-02 11 5.955781e-01 -1.127865e-01 12 2.888217e-01 5.955781e-01 13 4.295955e-01 2.888217e-01 14 2.552337e-01 4.295955e-01 15 4.307367e-01 2.552337e-01 16 -2.239912e-01 4.307367e-01 17 -2.541524e-01 -2.239912e-01 18 5.179023e-02 -2.541524e-01 19 -7.637611e-02 5.179023e-02 20 8.590556e-02 -7.637611e-02 21 -2.511186e-01 8.590556e-02 22 1.231397e-01 -2.511186e-01 23 2.404379e-01 1.231397e-01 24 1.450897e-01 2.404379e-01 25 2.648215e-01 1.450897e-01 26 3.953035e-01 2.648215e-01 27 6.312659e-01 3.953035e-01 28 5.168267e-01 6.312659e-01 29 -3.548573e-01 5.168267e-01 30 -3.382791e-01 -3.548573e-01 31 -4.199612e-01 -3.382791e-01 32 -2.615626e-01 -4.199612e-01 33 -1.697592e-01 -2.615626e-01 34 -3.865921e-01 -1.697592e-01 35 1.637810e-01 -3.865921e-01 36 1.019033e-01 1.637810e-01 37 2.778548e-01 1.019033e-01 38 1.537162e-01 2.778548e-01 39 3.099012e-01 1.537162e-01 40 4.405306e-01 3.099012e-01 41 -3.532138e-01 4.405306e-01 42 -4.466426e-01 -3.532138e-01 43 -3.999272e-01 -4.466426e-01 44 -4.203235e-01 -3.999272e-01 45 -3.715223e-01 -4.203235e-01 46 -2.675800e-01 -3.715223e-01 47 -3.474627e-01 -2.675800e-01 48 -4.223708e-01 -3.474627e-01 49 -4.158605e-01 -4.223708e-01 50 -4.358723e-01 -4.158605e-01 51 -2.755980e-01 -4.358723e-01 52 -3.880681e-01 -2.755980e-01 53 5.840255e-02 -3.880681e-01 54 3.329301e-02 5.840255e-02 55 8.855297e-03 3.329301e-02 56 1.215841e-01 8.855297e-03 57 1.041191e-01 1.215841e-01 58 1.577576e-01 1.041191e-01 59 -4.654591e-01 1.577576e-01 60 -4.125232e-01 -4.654591e-01 61 -5.063929e-01 -4.125232e-01 62 -4.479138e-01 -5.063929e-01 63 -2.936254e-01 -4.479138e-01 64 -3.680950e-01 -2.936254e-01 65 2.523314e-01 -3.680950e-01 66 2.417851e-01 2.523314e-01 67 3.479822e-01 2.417851e-01 68 2.946737e-01 3.479822e-01 69 2.749909e-01 2.946737e-01 70 1.240755e-01 2.749909e-01 71 2.194477e-01 1.240755e-01 72 1.252234e-01 2.194477e-01 73 -2.245646e-02 1.252234e-01 74 1.018246e-01 -2.245646e-02 75 1.267228e-01 1.018246e-01 76 9.259652e-02 1.267228e-01 77 3.789536e-02 9.259652e-02 78 -1.574539e-02 3.789536e-02 79 -2.299341e-01 -1.574539e-02 80 -2.687667e-02 -2.299341e-01 81 -8.537131e-02 -2.687667e-02 82 2.167398e-01 -8.537131e-02 83 -8.964336e-05 2.167398e-01 84 1.184662e-03 -8.964336e-05 85 2.806663e-01 1.184662e-03 86 -8.148587e-02 2.806663e-01 87 -1.017428e-02 -8.148587e-02 88 -3.357398e-01 -1.017428e-02 89 -2.371007e-01 -3.357398e-01 90 2.448358e-01 -2.371007e-01 91 1.970123e-01 2.448358e-01 92 2.937505e-01 1.970123e-01 93 3.058972e-01 2.937505e-01 94 2.998643e-01 3.058972e-01 95 3.408940e-01 2.998643e-01 96 -8.268481e-02 3.408940e-01 97 2.760636e-01 -8.268481e-02 98 2.840936e-02 2.760636e-01 99 5.829153e-02 2.840936e-02 100 1.284822e-01 5.829153e-02 101 1.068575e-01 1.284822e-01 102 5.472617e-01 1.068575e-01 103 6.030093e-01 5.472617e-01 104 5.747174e-01 6.030093e-01 105 5.671140e-01 5.747174e-01 106 3.974434e-01 5.671140e-01 107 4.680305e-01 3.974434e-01 108 1.883782e-01 4.680305e-01 109 2.014630e-02 1.883782e-01 110 3.812892e-01 2.014630e-02 111 2.553450e-02 3.812892e-01 112 2.144509e-01 2.553450e-02 113 3.337514e-01 2.144509e-01 114 2.076523e-01 3.337514e-01 115 4.257144e-01 2.076523e-01 116 -1.022770e-02 4.257144e-01 117 1.621520e-01 -1.022770e-02 118 3.944010e-01 1.621520e-01 119 4.983618e-01 3.944010e-01 120 1.862685e-02 4.983618e-01 121 1.344157e-01 1.862685e-02 122 1.968615e-01 1.344157e-01 123 3.679575e-01 1.968615e-01 124 3.324323e-01 3.679575e-01 125 2.447462e-01 3.324323e-01 126 2.909907e-01 2.447462e-01 127 6.002723e-01 2.909907e-01 128 2.775681e-01 6.002723e-01 129 2.962040e-01 2.775681e-01 130 2.335260e-01 2.962040e-01 131 9.880834e-02 2.335260e-01 132 4.298479e-01 9.880834e-02 133 1.031138e-01 4.298479e-01 134 8.911148e-02 1.031138e-01 135 -1.910696e-01 8.911148e-02 136 -1.479587e-01 -1.910696e-01 137 1.610347e-01 -1.479587e-01 138 2.653162e-01 1.610347e-01 139 6.508751e-02 2.653162e-01 140 1.275365e-01 6.508751e-02 141 2.026471e-01 1.275365e-01 142 3.500022e-01 2.026471e-01 143 3.705337e-01 3.500022e-01 144 2.504658e-01 3.705337e-01 145 -3.781371e-01 2.504658e-01 146 -1.324563e-01 -3.781371e-01 147 -3.212296e-01 -1.324563e-01 148 1.224192e-01 -3.212296e-01 149 1.252623e-01 1.224192e-01 150 -1.406098e-01 1.252623e-01 151 -1.219212e-01 -1.406098e-01 152 9.942352e-02 -1.219212e-01 153 -1.176661e-01 9.942352e-02 154 -3.380574e-01 -1.176661e-01 155 2.656375e-01 -3.380574e-01 156 -2.205850e-01 2.656375e-01 157 1.439237e-01 -2.205850e-01 158 3.138847e-01 1.439237e-01 159 1.887809e-01 3.138847e-01 160 1.385255e-01 1.887809e-01 161 1.757079e-01 1.385255e-01 162 3.363470e-01 1.757079e-01 163 -3.898741e-01 3.363470e-01 164 -4.471533e-01 -3.898741e-01 165 -3.025487e-01 -4.471533e-01 166 -1.781961e-01 -3.025487e-01 167 -8.533247e-01 -1.781961e-01 168 -3.208621e-01 -8.533247e-01 169 -2.709193e-01 -3.208621e-01 170 -6.507616e-01 -2.709193e-01 171 -7.055019e-01 -6.507616e-01 172 -7.387103e-01 -7.055019e-01 173 -7.189790e-01 -7.387103e-01 174 -7.457167e-01 -7.189790e-01 175 -7.435646e-01 -7.457167e-01 176 3.623591e-01 -7.435646e-01 177 2.891555e-01 3.623591e-01 178 2.294920e-02 2.891555e-01 179 2.716786e-01 2.294920e-02 180 6.835174e-02 2.716786e-01 181 2.785641e-01 6.835174e-02 182 -6.363103e-01 2.785641e-01 183 -7.161128e-01 -6.363103e-01 184 -6.718517e-01 -7.161128e-01 185 -4.627306e-01 -6.718517e-01 186 -5.303643e-01 -4.627306e-01 187 -3.521222e-01 -5.303643e-01 188 -3.786546e-01 -3.521222e-01 189 -6.110127e-01 -3.786546e-01 190 -7.051888e-01 -6.110127e-01 191 1.069023e-01 -7.051888e-01 192 -1.981882e-01 1.069023e-01 193 -7.055433e-01 -1.981882e-01 194 NA -7.055433e-01 > dum1 <- dum[2:length(myerror),] > dum1 lag(myerror, k = 1) myerror [1,] -7.713330e-02 -1.711085e-01 [2,] -1.665448e-01 -7.713330e-02 [3,] -1.604777e-01 -1.665448e-01 [4,] -5.225077e-02 -1.604777e-01 [5,] 1.984958e-01 -5.225077e-02 [6,] 2.625949e-01 1.984958e-01 [7,] 1.226895e-01 2.625949e-01 [8,] -6.211017e-02 1.226895e-01 [9,] 4.437058e-02 -6.211017e-02 [10,] -1.127865e-01 4.437058e-02 [11,] 5.955781e-01 -1.127865e-01 [12,] 2.888217e-01 5.955781e-01 [13,] 4.295955e-01 2.888217e-01 [14,] 2.552337e-01 4.295955e-01 [15,] 4.307367e-01 2.552337e-01 [16,] -2.239912e-01 4.307367e-01 [17,] -2.541524e-01 -2.239912e-01 [18,] 5.179023e-02 -2.541524e-01 [19,] -7.637611e-02 5.179023e-02 [20,] 8.590556e-02 -7.637611e-02 [21,] -2.511186e-01 8.590556e-02 [22,] 1.231397e-01 -2.511186e-01 [23,] 2.404379e-01 1.231397e-01 [24,] 1.450897e-01 2.404379e-01 [25,] 2.648215e-01 1.450897e-01 [26,] 3.953035e-01 2.648215e-01 [27,] 6.312659e-01 3.953035e-01 [28,] 5.168267e-01 6.312659e-01 [29,] -3.548573e-01 5.168267e-01 [30,] -3.382791e-01 -3.548573e-01 [31,] -4.199612e-01 -3.382791e-01 [32,] -2.615626e-01 -4.199612e-01 [33,] -1.697592e-01 -2.615626e-01 [34,] -3.865921e-01 -1.697592e-01 [35,] 1.637810e-01 -3.865921e-01 [36,] 1.019033e-01 1.637810e-01 [37,] 2.778548e-01 1.019033e-01 [38,] 1.537162e-01 2.778548e-01 [39,] 3.099012e-01 1.537162e-01 [40,] 4.405306e-01 3.099012e-01 [41,] -3.532138e-01 4.405306e-01 [42,] -4.466426e-01 -3.532138e-01 [43,] -3.999272e-01 -4.466426e-01 [44,] -4.203235e-01 -3.999272e-01 [45,] -3.715223e-01 -4.203235e-01 [46,] -2.675800e-01 -3.715223e-01 [47,] -3.474627e-01 -2.675800e-01 [48,] -4.223708e-01 -3.474627e-01 [49,] -4.158605e-01 -4.223708e-01 [50,] -4.358723e-01 -4.158605e-01 [51,] -2.755980e-01 -4.358723e-01 [52,] -3.880681e-01 -2.755980e-01 [53,] 5.840255e-02 -3.880681e-01 [54,] 3.329301e-02 5.840255e-02 [55,] 8.855297e-03 3.329301e-02 [56,] 1.215841e-01 8.855297e-03 [57,] 1.041191e-01 1.215841e-01 [58,] 1.577576e-01 1.041191e-01 [59,] -4.654591e-01 1.577576e-01 [60,] -4.125232e-01 -4.654591e-01 [61,] -5.063929e-01 -4.125232e-01 [62,] -4.479138e-01 -5.063929e-01 [63,] -2.936254e-01 -4.479138e-01 [64,] -3.680950e-01 -2.936254e-01 [65,] 2.523314e-01 -3.680950e-01 [66,] 2.417851e-01 2.523314e-01 [67,] 3.479822e-01 2.417851e-01 [68,] 2.946737e-01 3.479822e-01 [69,] 2.749909e-01 2.946737e-01 [70,] 1.240755e-01 2.749909e-01 [71,] 2.194477e-01 1.240755e-01 [72,] 1.252234e-01 2.194477e-01 [73,] -2.245646e-02 1.252234e-01 [74,] 1.018246e-01 -2.245646e-02 [75,] 1.267228e-01 1.018246e-01 [76,] 9.259652e-02 1.267228e-01 [77,] 3.789536e-02 9.259652e-02 [78,] -1.574539e-02 3.789536e-02 [79,] -2.299341e-01 -1.574539e-02 [80,] -2.687667e-02 -2.299341e-01 [81,] -8.537131e-02 -2.687667e-02 [82,] 2.167398e-01 -8.537131e-02 [83,] -8.964336e-05 2.167398e-01 [84,] 1.184662e-03 -8.964336e-05 [85,] 2.806663e-01 1.184662e-03 [86,] -8.148587e-02 2.806663e-01 [87,] -1.017428e-02 -8.148587e-02 [88,] -3.357398e-01 -1.017428e-02 [89,] -2.371007e-01 -3.357398e-01 [90,] 2.448358e-01 -2.371007e-01 [91,] 1.970123e-01 2.448358e-01 [92,] 2.937505e-01 1.970123e-01 [93,] 3.058972e-01 2.937505e-01 [94,] 2.998643e-01 3.058972e-01 [95,] 3.408940e-01 2.998643e-01 [96,] -8.268481e-02 3.408940e-01 [97,] 2.760636e-01 -8.268481e-02 [98,] 2.840936e-02 2.760636e-01 [99,] 5.829153e-02 2.840936e-02 [100,] 1.284822e-01 5.829153e-02 [101,] 1.068575e-01 1.284822e-01 [102,] 5.472617e-01 1.068575e-01 [103,] 6.030093e-01 5.472617e-01 [104,] 5.747174e-01 6.030093e-01 [105,] 5.671140e-01 5.747174e-01 [106,] 3.974434e-01 5.671140e-01 [107,] 4.680305e-01 3.974434e-01 [108,] 1.883782e-01 4.680305e-01 [109,] 2.014630e-02 1.883782e-01 [110,] 3.812892e-01 2.014630e-02 [111,] 2.553450e-02 3.812892e-01 [112,] 2.144509e-01 2.553450e-02 [113,] 3.337514e-01 2.144509e-01 [114,] 2.076523e-01 3.337514e-01 [115,] 4.257144e-01 2.076523e-01 [116,] -1.022770e-02 4.257144e-01 [117,] 1.621520e-01 -1.022770e-02 [118,] 3.944010e-01 1.621520e-01 [119,] 4.983618e-01 3.944010e-01 [120,] 1.862685e-02 4.983618e-01 [121,] 1.344157e-01 1.862685e-02 [122,] 1.968615e-01 1.344157e-01 [123,] 3.679575e-01 1.968615e-01 [124,] 3.324323e-01 3.679575e-01 [125,] 2.447462e-01 3.324323e-01 [126,] 2.909907e-01 2.447462e-01 [127,] 6.002723e-01 2.909907e-01 [128,] 2.775681e-01 6.002723e-01 [129,] 2.962040e-01 2.775681e-01 [130,] 2.335260e-01 2.962040e-01 [131,] 9.880834e-02 2.335260e-01 [132,] 4.298479e-01 9.880834e-02 [133,] 1.031138e-01 4.298479e-01 [134,] 8.911148e-02 1.031138e-01 [135,] -1.910696e-01 8.911148e-02 [136,] -1.479587e-01 -1.910696e-01 [137,] 1.610347e-01 -1.479587e-01 [138,] 2.653162e-01 1.610347e-01 [139,] 6.508751e-02 2.653162e-01 [140,] 1.275365e-01 6.508751e-02 [141,] 2.026471e-01 1.275365e-01 [142,] 3.500022e-01 2.026471e-01 [143,] 3.705337e-01 3.500022e-01 [144,] 2.504658e-01 3.705337e-01 [145,] -3.781371e-01 2.504658e-01 [146,] -1.324563e-01 -3.781371e-01 [147,] -3.212296e-01 -1.324563e-01 [148,] 1.224192e-01 -3.212296e-01 [149,] 1.252623e-01 1.224192e-01 [150,] -1.406098e-01 1.252623e-01 [151,] -1.219212e-01 -1.406098e-01 [152,] 9.942352e-02 -1.219212e-01 [153,] -1.176661e-01 9.942352e-02 [154,] -3.380574e-01 -1.176661e-01 [155,] 2.656375e-01 -3.380574e-01 [156,] -2.205850e-01 2.656375e-01 [157,] 1.439237e-01 -2.205850e-01 [158,] 3.138847e-01 1.439237e-01 [159,] 1.887809e-01 3.138847e-01 [160,] 1.385255e-01 1.887809e-01 [161,] 1.757079e-01 1.385255e-01 [162,] 3.363470e-01 1.757079e-01 [163,] -3.898741e-01 3.363470e-01 [164,] -4.471533e-01 -3.898741e-01 [165,] -3.025487e-01 -4.471533e-01 [166,] -1.781961e-01 -3.025487e-01 [167,] -8.533247e-01 -1.781961e-01 [168,] -3.208621e-01 -8.533247e-01 [169,] -2.709193e-01 -3.208621e-01 [170,] -6.507616e-01 -2.709193e-01 [171,] -7.055019e-01 -6.507616e-01 [172,] -7.387103e-01 -7.055019e-01 [173,] -7.189790e-01 -7.387103e-01 [174,] -7.457167e-01 -7.189790e-01 [175,] -7.435646e-01 -7.457167e-01 [176,] 3.623591e-01 -7.435646e-01 [177,] 2.891555e-01 3.623591e-01 [178,] 2.294920e-02 2.891555e-01 [179,] 2.716786e-01 2.294920e-02 [180,] 6.835174e-02 2.716786e-01 [181,] 2.785641e-01 6.835174e-02 [182,] -6.363103e-01 2.785641e-01 [183,] -7.161128e-01 -6.363103e-01 [184,] -6.718517e-01 -7.161128e-01 [185,] -4.627306e-01 -6.718517e-01 [186,] -5.303643e-01 -4.627306e-01 [187,] -3.521222e-01 -5.303643e-01 [188,] -3.786546e-01 -3.521222e-01 [189,] -6.110127e-01 -3.786546e-01 [190,] -7.051888e-01 -6.110127e-01 [191,] 1.069023e-01 -7.051888e-01 [192,] -1.981882e-01 1.069023e-01 [193,] -7.055433e-01 -1.981882e-01 > z <- as.data.frame(dum1) > z lag(myerror, k = 1) myerror 1 -7.713330e-02 -1.711085e-01 2 -1.665448e-01 -7.713330e-02 3 -1.604777e-01 -1.665448e-01 4 -5.225077e-02 -1.604777e-01 5 1.984958e-01 -5.225077e-02 6 2.625949e-01 1.984958e-01 7 1.226895e-01 2.625949e-01 8 -6.211017e-02 1.226895e-01 9 4.437058e-02 -6.211017e-02 10 -1.127865e-01 4.437058e-02 11 5.955781e-01 -1.127865e-01 12 2.888217e-01 5.955781e-01 13 4.295955e-01 2.888217e-01 14 2.552337e-01 4.295955e-01 15 4.307367e-01 2.552337e-01 16 -2.239912e-01 4.307367e-01 17 -2.541524e-01 -2.239912e-01 18 5.179023e-02 -2.541524e-01 19 -7.637611e-02 5.179023e-02 20 8.590556e-02 -7.637611e-02 21 -2.511186e-01 8.590556e-02 22 1.231397e-01 -2.511186e-01 23 2.404379e-01 1.231397e-01 24 1.450897e-01 2.404379e-01 25 2.648215e-01 1.450897e-01 26 3.953035e-01 2.648215e-01 27 6.312659e-01 3.953035e-01 28 5.168267e-01 6.312659e-01 29 -3.548573e-01 5.168267e-01 30 -3.382791e-01 -3.548573e-01 31 -4.199612e-01 -3.382791e-01 32 -2.615626e-01 -4.199612e-01 33 -1.697592e-01 -2.615626e-01 34 -3.865921e-01 -1.697592e-01 35 1.637810e-01 -3.865921e-01 36 1.019033e-01 1.637810e-01 37 2.778548e-01 1.019033e-01 38 1.537162e-01 2.778548e-01 39 3.099012e-01 1.537162e-01 40 4.405306e-01 3.099012e-01 41 -3.532138e-01 4.405306e-01 42 -4.466426e-01 -3.532138e-01 43 -3.999272e-01 -4.466426e-01 44 -4.203235e-01 -3.999272e-01 45 -3.715223e-01 -4.203235e-01 46 -2.675800e-01 -3.715223e-01 47 -3.474627e-01 -2.675800e-01 48 -4.223708e-01 -3.474627e-01 49 -4.158605e-01 -4.223708e-01 50 -4.358723e-01 -4.158605e-01 51 -2.755980e-01 -4.358723e-01 52 -3.880681e-01 -2.755980e-01 53 5.840255e-02 -3.880681e-01 54 3.329301e-02 5.840255e-02 55 8.855297e-03 3.329301e-02 56 1.215841e-01 8.855297e-03 57 1.041191e-01 1.215841e-01 58 1.577576e-01 1.041191e-01 59 -4.654591e-01 1.577576e-01 60 -4.125232e-01 -4.654591e-01 61 -5.063929e-01 -4.125232e-01 62 -4.479138e-01 -5.063929e-01 63 -2.936254e-01 -4.479138e-01 64 -3.680950e-01 -2.936254e-01 65 2.523314e-01 -3.680950e-01 66 2.417851e-01 2.523314e-01 67 3.479822e-01 2.417851e-01 68 2.946737e-01 3.479822e-01 69 2.749909e-01 2.946737e-01 70 1.240755e-01 2.749909e-01 71 2.194477e-01 1.240755e-01 72 1.252234e-01 2.194477e-01 73 -2.245646e-02 1.252234e-01 74 1.018246e-01 -2.245646e-02 75 1.267228e-01 1.018246e-01 76 9.259652e-02 1.267228e-01 77 3.789536e-02 9.259652e-02 78 -1.574539e-02 3.789536e-02 79 -2.299341e-01 -1.574539e-02 80 -2.687667e-02 -2.299341e-01 81 -8.537131e-02 -2.687667e-02 82 2.167398e-01 -8.537131e-02 83 -8.964336e-05 2.167398e-01 84 1.184662e-03 -8.964336e-05 85 2.806663e-01 1.184662e-03 86 -8.148587e-02 2.806663e-01 87 -1.017428e-02 -8.148587e-02 88 -3.357398e-01 -1.017428e-02 89 -2.371007e-01 -3.357398e-01 90 2.448358e-01 -2.371007e-01 91 1.970123e-01 2.448358e-01 92 2.937505e-01 1.970123e-01 93 3.058972e-01 2.937505e-01 94 2.998643e-01 3.058972e-01 95 3.408940e-01 2.998643e-01 96 -8.268481e-02 3.408940e-01 97 2.760636e-01 -8.268481e-02 98 2.840936e-02 2.760636e-01 99 5.829153e-02 2.840936e-02 100 1.284822e-01 5.829153e-02 101 1.068575e-01 1.284822e-01 102 5.472617e-01 1.068575e-01 103 6.030093e-01 5.472617e-01 104 5.747174e-01 6.030093e-01 105 5.671140e-01 5.747174e-01 106 3.974434e-01 5.671140e-01 107 4.680305e-01 3.974434e-01 108 1.883782e-01 4.680305e-01 109 2.014630e-02 1.883782e-01 110 3.812892e-01 2.014630e-02 111 2.553450e-02 3.812892e-01 112 2.144509e-01 2.553450e-02 113 3.337514e-01 2.144509e-01 114 2.076523e-01 3.337514e-01 115 4.257144e-01 2.076523e-01 116 -1.022770e-02 4.257144e-01 117 1.621520e-01 -1.022770e-02 118 3.944010e-01 1.621520e-01 119 4.983618e-01 3.944010e-01 120 1.862685e-02 4.983618e-01 121 1.344157e-01 1.862685e-02 122 1.968615e-01 1.344157e-01 123 3.679575e-01 1.968615e-01 124 3.324323e-01 3.679575e-01 125 2.447462e-01 3.324323e-01 126 2.909907e-01 2.447462e-01 127 6.002723e-01 2.909907e-01 128 2.775681e-01 6.002723e-01 129 2.962040e-01 2.775681e-01 130 2.335260e-01 2.962040e-01 131 9.880834e-02 2.335260e-01 132 4.298479e-01 9.880834e-02 133 1.031138e-01 4.298479e-01 134 8.911148e-02 1.031138e-01 135 -1.910696e-01 8.911148e-02 136 -1.479587e-01 -1.910696e-01 137 1.610347e-01 -1.479587e-01 138 2.653162e-01 1.610347e-01 139 6.508751e-02 2.653162e-01 140 1.275365e-01 6.508751e-02 141 2.026471e-01 1.275365e-01 142 3.500022e-01 2.026471e-01 143 3.705337e-01 3.500022e-01 144 2.504658e-01 3.705337e-01 145 -3.781371e-01 2.504658e-01 146 -1.324563e-01 -3.781371e-01 147 -3.212296e-01 -1.324563e-01 148 1.224192e-01 -3.212296e-01 149 1.252623e-01 1.224192e-01 150 -1.406098e-01 1.252623e-01 151 -1.219212e-01 -1.406098e-01 152 9.942352e-02 -1.219212e-01 153 -1.176661e-01 9.942352e-02 154 -3.380574e-01 -1.176661e-01 155 2.656375e-01 -3.380574e-01 156 -2.205850e-01 2.656375e-01 157 1.439237e-01 -2.205850e-01 158 3.138847e-01 1.439237e-01 159 1.887809e-01 3.138847e-01 160 1.385255e-01 1.887809e-01 161 1.757079e-01 1.385255e-01 162 3.363470e-01 1.757079e-01 163 -3.898741e-01 3.363470e-01 164 -4.471533e-01 -3.898741e-01 165 -3.025487e-01 -4.471533e-01 166 -1.781961e-01 -3.025487e-01 167 -8.533247e-01 -1.781961e-01 168 -3.208621e-01 -8.533247e-01 169 -2.709193e-01 -3.208621e-01 170 -6.507616e-01 -2.709193e-01 171 -7.055019e-01 -6.507616e-01 172 -7.387103e-01 -7.055019e-01 173 -7.189790e-01 -7.387103e-01 174 -7.457167e-01 -7.189790e-01 175 -7.435646e-01 -7.457167e-01 176 3.623591e-01 -7.435646e-01 177 2.891555e-01 3.623591e-01 178 2.294920e-02 2.891555e-01 179 2.716786e-01 2.294920e-02 180 6.835174e-02 2.716786e-01 181 2.785641e-01 6.835174e-02 182 -6.363103e-01 2.785641e-01 183 -7.161128e-01 -6.363103e-01 184 -6.718517e-01 -7.161128e-01 185 -4.627306e-01 -6.718517e-01 186 -5.303643e-01 -4.627306e-01 187 -3.521222e-01 -5.303643e-01 188 -3.786546e-01 -3.521222e-01 189 -6.110127e-01 -3.786546e-01 190 -7.051888e-01 -6.110127e-01 191 1.069023e-01 -7.051888e-01 192 -1.981882e-01 1.069023e-01 193 -7.055433e-01 -1.981882e-01 > 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/7tsyo1386679785.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/fisher/rcomp/tmp/8od4c1386679785.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/fisher/rcomp/tmp/9vvem1386679785.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0)) > plot(mylm, las = 1, sub='Residual Diagnostics') > par(opar) > dev.off() null device 1 > if (n > n25) { + postscript(file="/var/fisher/rcomp/tmp/10a38n1386679785.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) + plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint') + grid() + dev.off() + } null device 1 > > #Note: the /var/fisher/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab > load(file="/var/fisher/rcomp/createtable") > > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE) > a<-table.row.end(a) > myeq <- colnames(x)[1] > myeq <- paste(myeq, '[t] = ', sep='') > for (i in 1:k){ + if (mysum$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/113b1v1386679785.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/12d2z41386679785.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/13syyb1386679785.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/1497au1386679785.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/15u02g1386679785.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/16l5121386679785.tab") + } > > try(system("convert tmp/129sj1386679785.ps tmp/129sj1386679785.png",intern=TRUE)) character(0) > try(system("convert tmp/2malr1386679785.ps tmp/2malr1386679785.png",intern=TRUE)) character(0) > try(system("convert tmp/3zzmg1386679785.ps tmp/3zzmg1386679785.png",intern=TRUE)) character(0) > try(system("convert tmp/4rfxz1386679785.ps tmp/4rfxz1386679785.png",intern=TRUE)) character(0) > try(system("convert tmp/5r3jj1386679785.ps tmp/5r3jj1386679785.png",intern=TRUE)) character(0) > try(system("convert tmp/685ty1386679785.ps tmp/685ty1386679785.png",intern=TRUE)) character(0) > try(system("convert tmp/7tsyo1386679785.ps tmp/7tsyo1386679785.png",intern=TRUE)) character(0) > try(system("convert tmp/8od4c1386679785.ps tmp/8od4c1386679785.png",intern=TRUE)) character(0) > try(system("convert tmp/9vvem1386679785.ps tmp/9vvem1386679785.png",intern=TRUE)) character(0) > try(system("convert tmp/10a38n1386679785.ps tmp/10a38n1386679785.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 20.010 4.093 24.140