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Type 'q()' to quit R. > x <- array(list(1 + ,119.992 + ,0.06545 + ,0.02971 + ,0.00784 + ,2.301442 + ,0.414783 + ,1 + ,122.4 + ,0.09403 + ,0.04368 + ,0.00968 + ,2.486855 + ,0.458359 + ,1 + ,116.682 + ,0.0827 + ,0.0359 + ,0.0105 + ,2.342259 + ,0.429895 + ,1 + ,116.676 + ,0.08771 + ,0.03772 + ,0.00997 + ,2.405554 + ,0.434969 + ,1 + ,116.014 + ,0.1047 + ,0.04465 + ,0.01284 + ,2.33218 + ,0.417356 + ,1 + ,120.552 + ,0.06985 + ,0.03243 + ,0.00968 + ,2.18756 + ,0.415564 + ,1 + ,120.267 + ,0.02337 + ,0.01351 + ,0.00333 + ,1.854785 + ,0.59604 + ,1 + ,107.332 + ,0.02487 + ,0.01256 + ,0.0029 + ,2.064693 + ,0.63742 + ,1 + ,95.73 + ,0.03218 + ,0.01717 + ,0.00551 + ,2.322511 + ,0.615551 + ,1 + ,95.056 + ,0.04324 + ,0.02444 + ,0.00532 + ,2.432792 + ,0.547037 + ,1 + ,88.333 + ,0.03237 + ,0.01892 + ,0.00505 + ,2.407313 + ,0.611137 + ,1 + ,91.904 + ,0.04272 + ,0.02214 + ,0.0054 + ,2.642476 + ,0.58339 + ,1 + ,136.926 + ,0.01968 + ,0.0114 + ,0.00293 + ,2.041277 + ,0.4606 + ,1 + ,139.173 + ,0.02184 + 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+ ,0.02175 + ,0.01263 + ,0.00392 + ,2.845109 + ,0.398499 + ,1 + ,150.44 + ,0.03964 + ,0.02148 + ,0.00396 + ,2.264226 + ,0.352396 + ,1 + ,148.462 + ,0.02849 + ,0.01559 + ,0.00397 + ,2.679185 + ,0.408598 + ,1 + ,149.818 + ,0.03464 + ,0.01666 + ,0.00336 + ,2.209021 + ,0.329577 + ,0 + ,117.226 + ,0.02592 + ,0.01949 + ,0.00417 + ,2.027228 + ,0.603515 + ,0 + ,116.848 + ,0.02429 + ,0.01756 + ,0.00531 + ,2.120412 + ,0.663842 + ,0 + ,116.286 + ,0.02001 + ,0.01691 + ,0.00314 + ,2.058658 + ,0.598515 + ,0 + ,116.556 + ,0.0246 + ,0.01491 + ,0.00496 + ,2.161936 + ,0.566424 + ,0 + ,116.342 + ,0.01892 + ,0.01144 + ,0.00267 + ,2.152083 + ,0.528485 + ,0 + ,114.563 + ,0.01672 + ,0.01095 + ,0.00327 + ,1.91399 + ,0.555303 + ,0 + ,201.774 + ,0.04363 + ,0.01758 + ,0.00694 + ,2.316346 + ,0.508479 + ,0 + ,174.188 + ,0.07008 + ,0.02745 + ,0.00459 + ,2.657476 + ,0.448439 + ,0 + ,209.516 + ,0.04812 + ,0.01879 + ,0.00564 + ,2.784312 + ,0.431674 + ,0 + ,174.688 + ,0.03804 + ,0.01667 + ,0.0136 + ,2.679772 + ,0.407567 + ,0 + ,198.764 + ,0.03794 + ,0.01588 + ,0.0074 + ,2.138608 + ,0.451221 + ,0 + ,214.289 + ,0.03078 + ,0.01373 + ,0.00567 + ,2.555477 + ,0.462803) + ,dim=c(7 + ,195) + ,dimnames=list(c('status' + ,'MDVP:Fo(Hz)' + ,'Shimmer:DDA' + ,'MDVP:APQ' + ,'MDVP:Jitter(%)' + ,'D2' + ,'RPDE') + ,1:195)) > y <- array(NA,dim=c(7,195),dimnames=list(c('status','MDVP:Fo(Hz)','Shimmer:DDA','MDVP:APQ','MDVP:Jitter(%)','D2','RPDE'),1:195)) > for (i in 1:dim(x)[1]) + { + for (j in 1:dim(x)[2]) + { + y[i,j] <- as.numeric(x[i,j]) + } + } > par3 = 'No Linear Trend' > par2 = 'Do not include Seasonal Dummies' > par1 = '1' > par3 <- 'No Linear Trend' > par2 <- 'Do not include Seasonal Dummies' > par1 <- '1' > #'GNU S' R Code compiled by R2WASP v. 1.2.327 () > #Author: root > #To cite this work: Wessa P., (2013), Multiple Regression (v1.0.29) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_multipleregression.wasp/ > #Source of accompanying publication: Office for Research, Development, and Education > # > library(lattice) > library(lmtest) Loading required package: zoo Attaching package: 'zoo' The following objects are masked from 'package:base': as.Date, as.Date.numeric > n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test > par1 <- as.numeric(par1) > x <- t(y) > k <- length(x[1,]) > n <- length(x[,1]) > x1 <- cbind(x[,par1], x[,1:k!=par1]) > mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1]) > colnames(x1) <- mycolnames #colnames(x)[par1] > x <- x1 > if (par3 == 'First Differences'){ + x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep=''))) + for (i in 1:n-1) { + for (j in 1:k) { + x2[i,j] <- x[i+1,j] - x[i,j] + } + } + x <- x2 + } > if (par2 == 'Include Monthly Dummies'){ + x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep =''))) + for (i in 1:11){ + x2[seq(i,n,12),i] <- 1 + } + x <- cbind(x, x2) + } > if (par2 == 'Include Quarterly Dummies'){ + x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep =''))) + for (i in 1:3){ + x2[seq(i,n,4),i] <- 1 + } + x <- cbind(x, x2) + } > k <- length(x[1,]) > if (par3 == 'Linear Trend'){ + x <- cbind(x, c(1:n)) + colnames(x)[k+1] <- 't' + } > x status MDVP:Fo(Hz) Shimmer:DDA MDVP:APQ MDVP:Jitter(%) D2 RPDE 1 1 119.992 0.06545 0.02971 0.00784 2.301442 0.414783 2 1 122.400 0.09403 0.04368 0.00968 2.486855 0.458359 3 1 116.682 0.08270 0.03590 0.01050 2.342259 0.429895 4 1 116.676 0.08771 0.03772 0.00997 2.405554 0.434969 5 1 116.014 0.10470 0.04465 0.01284 2.332180 0.417356 6 1 120.552 0.06985 0.03243 0.00968 2.187560 0.415564 7 1 120.267 0.02337 0.01351 0.00333 1.854785 0.596040 8 1 107.332 0.02487 0.01256 0.00290 2.064693 0.637420 9 1 95.730 0.03218 0.01717 0.00551 2.322511 0.615551 10 1 95.056 0.04324 0.02444 0.00532 2.432792 0.547037 11 1 88.333 0.03237 0.01892 0.00505 2.407313 0.611137 12 1 91.904 0.04272 0.02214 0.00540 2.642476 0.583390 13 1 136.926 0.01968 0.01140 0.00293 2.041277 0.460600 14 1 139.173 0.02184 0.01797 0.00390 2.519422 0.430166 15 1 152.845 0.03191 0.01246 0.00294 2.125618 0.474791 16 1 142.167 0.02316 0.01359 0.00369 2.205546 0.565924 17 1 144.188 0.02908 0.02074 0.00544 2.264501 0.567380 18 1 168.778 0.04322 0.03430 0.00718 3.007463 0.631099 19 1 153.046 0.07413 0.05767 0.00742 3.109010 0.665318 20 1 156.405 0.05164 0.04310 0.00768 2.856676 0.649554 21 1 153.848 0.05000 0.04055 0.00840 2.739710 0.660125 22 1 153.880 0.06062 0.04525 0.00480 2.557536 0.629017 23 1 167.930 0.06685 0.04246 0.00442 2.916777 0.619060 24 1 173.917 0.06562 0.03772 0.00476 2.547508 0.537264 25 1 163.656 0.02214 0.01497 0.00742 2.692176 0.397937 26 1 104.400 0.05197 0.03780 0.00633 2.846369 0.522746 27 1 171.041 0.02666 0.01872 0.00455 2.589702 0.418622 28 1 146.845 0.02650 0.01826 0.00496 2.314209 0.358773 29 1 155.358 0.02307 0.01661 0.00310 2.241742 0.470478 30 1 162.568 0.02380 0.01799 0.00502 1.957961 0.427785 31 0 197.076 0.01689 0.00802 0.00289 1.743867 0.422229 32 0 199.228 0.01513 0.00762 0.00241 2.103106 0.432439 33 0 198.383 0.01919 0.00951 0.00212 1.512275 0.465946 34 0 202.266 0.01407 0.00719 0.00180 1.544609 0.368535 35 0 203.184 0.01403 0.00726 0.00178 1.423287 0.340068 36 0 201.464 0.01758 0.00957 0.00198 2.447064 0.344252 37 1 177.876 0.03463 0.01612 0.00411 2.477082 0.360148 38 1 176.170 0.02814 0.01491 0.00369 2.536527 0.341435 39 1 180.198 0.02177 0.01190 0.00284 2.269398 0.403884 40 1 187.733 0.02488 0.01366 0.00316 2.382544 0.396793 41 1 186.163 0.02321 0.01233 0.00298 2.374073 0.326480 42 1 184.055 0.02226 0.01234 0.00258 2.361532 0.306443 43 0 237.226 0.03104 0.01133 0.00298 2.416838 0.305062 44 0 241.404 0.03017 0.01251 0.00281 2.256699 0.457702 45 0 243.439 0.02330 0.01033 0.00210 2.330716 0.438296 46 0 242.852 0.02542 0.01014 0.00225 2.365800 0.431285 47 0 245.510 0.02719 0.01149 0.00235 2.392122 0.467489 48 0 252.455 0.01841 0.00860 0.00185 2.028612 0.610367 49 0 122.188 0.02566 0.01433 0.00524 2.079922 0.579597 50 0 122.964 0.02789 0.01400 0.00428 2.054419 0.538688 51 0 124.445 0.03724 0.01685 0.00431 1.840198 0.553134 52 0 126.344 0.03429 0.01614 0.00448 2.431854 0.507504 53 0 128.001 0.03969 0.01677 0.00436 1.972297 0.459766 54 0 129.336 0.04188 0.01947 0.00490 2.223719 0.420383 55 1 108.807 0.04450 0.02067 0.00761 1.986899 0.536009 56 1 109.860 0.05368 0.02454 0.00874 2.014606 0.558586 57 1 110.417 0.06097 0.02802 0.00784 1.922940 0.541781 58 1 117.274 0.03568 0.01948 0.00752 2.021591 0.530529 59 1 116.879 0.04183 0.02137 0.00788 1.827012 0.540049 60 1 114.847 0.05414 0.02519 0.00867 1.831691 0.547975 61 0 209.144 0.02925 0.01382 0.00282 2.460791 0.341788 62 0 223.365 0.03039 0.01340 0.00264 2.321560 0.447979 63 0 222.236 0.02602 0.01200 0.00266 2.278687 0.364867 64 0 228.832 0.02647 0.01179 0.00296 2.498224 0.256570 65 0 229.401 0.02308 0.01016 0.00205 2.003032 0.276850 66 0 228.969 0.02827 0.01234 0.00238 2.118596 0.305429 67 1 140.341 0.05490 0.02428 0.00817 2.359973 0.460139 68 1 136.969 0.04914 0.02603 0.00923 2.291558 0.498133 69 1 143.533 0.09455 0.03392 0.01101 2.118496 0.513237 70 1 148.090 0.10070 0.03635 0.00762 2.137075 0.487407 71 1 142.729 0.05605 0.02949 0.00831 2.277927 0.489345 72 1 136.358 0.08247 0.03736 0.00971 2.642276 0.543299 73 1 120.080 0.02921 0.01345 0.00405 2.205024 0.495954 74 1 112.014 0.04120 0.01956 0.00533 1.928708 0.509127 75 1 110.793 0.04295 0.01831 0.00494 2.225815 0.437031 76 1 110.707 0.03851 0.01715 0.00516 1.862092 0.463514 77 1 112.876 0.07238 0.02704 0.00500 2.007923 0.489538 78 1 110.568 0.03852 0.01636 0.00462 1.777901 0.429484 79 1 95.385 0.05408 0.02455 0.00608 2.017753 0.644954 80 1 100.770 0.05320 0.02139 0.01038 2.398422 0.594387 81 1 96.106 0.06799 0.02876 0.00694 2.645959 0.544805 82 1 95.605 0.05377 0.02190 0.00702 2.232576 0.576084 83 1 100.960 0.04114 0.01751 0.00606 2.428306 0.554610 84 1 98.804 0.03831 0.01552 0.00432 2.053601 0.576644 85 1 176.858 0.08037 0.03510 0.00747 3.099301 0.556494 86 1 180.978 0.06321 0.02877 0.00406 3.098256 0.583574 87 1 178.222 0.06219 0.02784 0.00321 2.654271 0.598714 88 1 176.281 0.11012 0.04683 0.00520 3.136550 0.602874 89 1 173.898 0.11363 0.04802 0.00448 3.007096 0.599371 90 1 179.711 0.06892 0.03455 0.00709 3.671155 0.590951 91 1 166.605 0.10949 0.05114 0.00742 3.317586 0.653410 92 1 151.955 0.13262 0.05690 0.00419 2.344876 0.501037 93 1 148.272 0.07150 0.03051 0.00459 2.344336 0.454444 94 1 152.125 0.10024 0.04398 0.00382 2.080121 0.447456 95 1 157.821 0.06185 0.02764 0.00358 2.143851 0.502380 96 1 157.447 0.05439 0.02571 0.00369 2.344348 0.447285 97 1 159.116 0.05417 0.02809 0.00342 2.473239 0.366329 98 1 125.036 0.06406 0.03088 0.01280 2.671825 0.629574 99 1 125.791 0.07625 0.03908 0.01378 2.441612 0.571010 100 1 126.512 0.10833 0.05783 0.01936 2.634633 0.638545 101 1 125.641 0.16074 0.06196 0.03316 2.991063 0.671299 102 1 128.451 0.09669 0.05174 0.01551 2.638279 0.639808 103 1 139.224 0.16654 0.06023 0.03011 2.690917 0.596362 104 1 150.258 0.01567 0.01009 0.00248 2.004055 0.296888 105 1 154.003 0.01406 0.00871 0.00183 2.065477 0.263654 106 1 149.689 0.01979 0.01059 0.00257 1.994387 0.365488 107 1 155.078 0.01567 0.00928 0.00168 2.129924 0.334171 108 1 151.884 0.01898 0.01267 0.00258 2.499148 0.393563 109 1 151.989 0.01364 0.00993 0.00174 2.296873 0.311369 110 1 193.030 0.05312 0.02084 0.00766 2.608749 0.497554 111 1 200.714 0.03576 0.01852 0.00621 2.550961 0.436084 112 1 208.519 0.02855 0.01307 0.00609 2.502336 0.338097 113 1 204.664 0.03831 0.01767 0.00841 2.376749 0.498877 114 1 210.141 0.02583 0.01301 0.00534 2.489191 0.441097 115 1 206.327 0.03320 0.01604 0.00495 2.938114 0.331508 116 1 151.872 0.02389 0.01271 0.00856 2.702355 0.407701 117 1 158.219 0.01818 0.01312 0.00476 2.640798 0.450798 118 1 170.756 0.02270 0.01652 0.00555 2.975889 0.486738 119 1 178.285 0.01851 0.01151 0.00462 2.816781 0.470422 120 1 217.116 0.02038 0.01075 0.00404 2.925862 0.462516 121 1 128.940 0.02548 0.01734 0.00581 2.686240 0.487756 122 1 176.824 0.01603 0.01104 0.00460 2.655744 0.400088 123 1 138.190 0.07761 0.03220 0.00704 2.090438 0.538016 124 1 182.018 0.04115 0.01931 0.00842 2.174306 0.589956 125 1 156.239 0.03867 0.01720 0.00694 1.929715 0.618663 126 1 145.174 0.03706 0.01944 0.00733 1.765957 0.637518 127 1 138.145 0.04451 0.02259 0.00544 1.821297 0.623209 128 1 166.888 0.04641 0.02301 0.00638 1.996146 0.585169 129 1 119.031 0.01614 0.00811 0.00440 2.328513 0.457541 130 1 120.078 0.01428 0.00903 0.00270 2.108873 0.491345 131 1 120.289 0.02110 0.01194 0.00492 2.539724 0.467160 132 1 120.256 0.02164 0.01310 0.00407 2.527742 0.468621 133 1 119.056 0.01898 0.00915 0.00346 2.516320 0.470972 134 1 118.747 0.01471 0.00903 0.00331 2.034827 0.482296 135 1 106.516 0.08050 0.03651 0.00589 2.375138 0.637814 136 1 110.453 0.06688 0.03316 0.00494 2.631793 0.653427 137 1 113.400 0.07154 0.04370 0.00451 2.445502 0.647900 138 1 113.166 0.08689 0.04134 0.00502 2.672362 0.625362 139 1 112.239 0.09211 0.04451 0.00472 2.419253 0.640945 140 1 116.150 0.04543 0.02770 0.00381 2.445646 0.624811 141 1 170.368 0.05139 0.02824 0.00571 2.963799 0.677131 142 1 208.083 0.12047 0.04464 0.00757 2.665133 0.606344 143 1 198.458 0.06165 0.02530 0.00376 2.465528 0.606273 144 1 202.805 0.03350 0.01506 0.00370 2.470746 0.536102 145 1 202.544 0.04426 0.02006 0.00254 2.576563 0.497480 146 1 223.361 0.04137 0.01909 0.00352 2.840556 0.566849 147 1 169.774 0.11411 0.08808 0.01568 3.413649 0.561610 148 1 183.520 0.08595 0.06359 0.01466 3.142364 0.478024 149 1 188.620 0.10422 0.06824 0.01719 3.274865 0.552870 150 1 202.632 0.10546 0.06460 0.01627 2.910213 0.427627 151 1 186.695 0.08096 0.06259 0.01872 2.958815 0.507826 152 1 192.818 0.16942 0.13778 0.03107 3.079221 0.625866 153 1 198.116 0.12851 0.08318 0.02714 3.184027 0.584164 154 1 121.345 0.04019 0.02056 0.00684 2.013530 0.566867 155 1 119.100 0.04451 0.02018 0.00692 2.451130 0.651680 156 1 117.870 0.04977 0.02402 0.00647 2.439597 0.628300 157 1 122.336 0.03615 0.01771 0.00727 2.699645 0.611679 158 1 117.963 0.07830 0.02916 0.01813 2.964568 0.630547 159 1 126.144 0.04499 0.02157 0.00975 2.892300 0.635015 160 1 127.930 0.04079 0.03105 0.00605 2.103014 0.654945 161 1 114.238 0.04736 0.04114 0.00581 2.151121 0.653139 162 1 115.322 0.04933 0.02931 0.00619 2.442906 0.577802 163 1 114.554 0.05592 0.03091 0.00651 2.408689 0.685151 164 1 112.150 0.02902 0.01363 0.00519 1.871871 0.557045 165 1 102.273 0.04736 0.02073 0.00907 2.560422 0.671378 166 0 236.200 0.04231 0.01621 0.00277 2.235197 0.469928 167 0 237.323 0.02089 0.00882 0.00303 1.852402 0.384868 168 0 260.105 0.03557 0.01367 0.00339 1.881767 0.440988 169 0 197.569 0.03836 0.01439 0.00803 2.882450 0.372222 170 0 240.301 0.03529 0.01344 0.00517 2.266432 0.371837 171 0 244.990 0.03253 0.01255 0.00451 2.095237 0.522812 172 0 112.547 0.01992 0.01140 0.00355 2.193412 0.413295 173 0 110.739 0.02261 0.01285 0.00356 1.889002 0.369090 174 0 113.715 0.02245 0.01148 0.00349 1.852542 0.380253 175 0 117.004 0.02643 0.01318 0.00353 1.872946 0.387482 176 0 115.380 0.02436 0.01133 0.00332 1.974857 0.405991 177 0 116.388 0.02623 0.01331 0.00346 2.004719 0.361232 178 1 151.737 0.02184 0.01230 0.00314 2.449763 0.396610 179 1 148.790 0.02518 0.01309 0.00309 2.251553 0.402591 180 1 148.143 0.02175 0.01263 0.00392 2.845109 0.398499 181 1 150.440 0.03964 0.02148 0.00396 2.264226 0.352396 182 1 148.462 0.02849 0.01559 0.00397 2.679185 0.408598 183 1 149.818 0.03464 0.01666 0.00336 2.209021 0.329577 184 0 117.226 0.02592 0.01949 0.00417 2.027228 0.603515 185 0 116.848 0.02429 0.01756 0.00531 2.120412 0.663842 186 0 116.286 0.02001 0.01691 0.00314 2.058658 0.598515 187 0 116.556 0.02460 0.01491 0.00496 2.161936 0.566424 188 0 116.342 0.01892 0.01144 0.00267 2.152083 0.528485 189 0 114.563 0.01672 0.01095 0.00327 1.913990 0.555303 190 0 201.774 0.04363 0.01758 0.00694 2.316346 0.508479 191 0 174.188 0.07008 0.02745 0.00459 2.657476 0.448439 192 0 209.516 0.04812 0.01879 0.00564 2.784312 0.431674 193 0 174.688 0.03804 0.01667 0.01360 2.679772 0.407567 194 0 198.764 0.03794 0.01588 0.00740 2.138608 0.451221 195 0 214.289 0.03078 0.01373 0.00567 2.555477 0.462803 > k <- length(x[1,]) > df <- as.data.frame(x) > (mylm <- lm(df)) Call: lm(formula = df) Coefficients: (Intercept) `MDVP:Fo(Hz)` `Shimmer:DDA` `MDVP:APQ` 0.463870 -0.004597 1.553194 3.152706 `MDVP:Jitter(%)` D2 RPDE -8.427733 0.389008 -0.048370 > (mysum <- summary(mylm)) Call: lm(formula = df) Residuals: Min 1Q Median 3Q Max -0.87095 -0.17401 0.06666 0.26230 0.53213 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 0.4638699 0.2398993 1.934 0.0547 . `MDVP:Fo(Hz)` -0.0045967 0.0007064 -6.507 6.76e-10 *** `Shimmer:DDA` 1.5531936 1.9586049 0.793 0.4288 `MDVP:APQ` 3.1527057 3.7567873 0.839 0.4024 `MDVP:Jitter(%)` -8.4277327 8.3827349 -1.005 0.3160 D2 0.3890080 0.0829281 4.691 5.22e-06 *** RPDE -0.0483704 0.3024676 -0.160 0.8731 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 0.3565 on 188 degrees of freedom Multiple R-squared: 0.3395, Adjusted R-squared: 0.3184 F-statistic: 16.11 on 6 and 188 DF, p-value: 6.398e-15 > 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,] 5.554537e-48 1.110907e-47 1.00000000 [2,] 9.694563e-67 1.938913e-66 1.00000000 [3,] 7.877504e-78 1.575501e-77 1.00000000 [4,] 3.394295e-105 6.788589e-105 1.00000000 [5,] 2.617049e-107 5.234098e-107 1.00000000 [6,] 1.046881e-122 2.093762e-122 1.00000000 [7,] 0.000000e+00 0.000000e+00 1.00000000 [8,] 1.899656e-164 3.799312e-164 1.00000000 [9,] 1.433005e-169 2.866010e-169 1.00000000 [10,] 9.316034e-184 1.863207e-183 1.00000000 [11,] 1.177416e-208 2.354832e-208 1.00000000 [12,] 1.404943e-242 2.809886e-242 1.00000000 [13,] 6.889065e-233 1.377813e-232 1.00000000 [14,] 2.161122e-244 4.322245e-244 1.00000000 [15,] 6.908181e-263 1.381636e-262 1.00000000 [16,] 9.220697e-282 1.844139e-281 1.00000000 [17,] 4.940656e-324 9.881313e-324 1.00000000 [18,] 1.741507e-311 3.483014e-311 1.00000000 [19,] 8.596742e-322 1.719348e-321 1.00000000 [20,] 0.000000e+00 0.000000e+00 1.00000000 [21,] 0.000000e+00 0.000000e+00 1.00000000 [22,] 7.528210e-06 1.505642e-05 0.99999247 [23,] 2.832349e-04 5.664698e-04 0.99971677 [24,] 4.267357e-04 8.534714e-04 0.99957326 [25,] 4.250234e-04 8.500469e-04 0.99957498 [26,] 2.921199e-04 5.842398e-04 0.99970788 [27,] 8.649546e-04 1.729909e-03 0.99913505 [28,] 1.280587e-03 2.561175e-03 0.99871941 [29,] 1.224197e-03 2.448393e-03 0.99877580 [30,] 1.784397e-03 3.568794e-03 0.99821560 [31,] 2.205398e-03 4.410797e-03 0.99779460 [32,] 2.231452e-03 4.462904e-03 0.99776855 [33,] 1.997844e-03 3.995688e-03 0.99800216 [34,] 2.638849e-03 5.277698e-03 0.99736115 [35,] 1.833691e-03 3.667381e-03 0.99816631 [36,] 1.295033e-03 2.590067e-03 0.99870497 [37,] 8.984947e-04 1.796989e-03 0.99910151 [38,] 5.966946e-04 1.193389e-03 0.99940331 [39,] 4.870787e-04 9.741573e-04 0.99951292 [40,] 6.169760e-03 1.233952e-02 0.99383024 [41,] 2.897474e-02 5.794947e-02 0.97102526 [42,] 5.469901e-02 1.093980e-01 0.94530099 [43,] 1.780463e-01 3.560926e-01 0.82195368 [44,] 2.746902e-01 5.493803e-01 0.72530984 [45,] 4.628258e-01 9.256516e-01 0.53717418 [46,] 4.453615e-01 8.907231e-01 0.55463846 [47,] 4.236425e-01 8.472850e-01 0.57635748 [48,] 4.047240e-01 8.094480e-01 0.59527600 [49,] 3.737123e-01 7.474246e-01 0.62628772 [50,] 3.539821e-01 7.079643e-01 0.64601785 [51,] 3.336263e-01 6.672526e-01 0.66637368 [52,] 3.670171e-01 7.340342e-01 0.63298289 [53,] 3.522640e-01 7.045281e-01 0.64773597 [54,] 3.458276e-01 6.916553e-01 0.65417237 [55,] 3.600192e-01 7.200384e-01 0.63998078 [56,] 3.360671e-01 6.721343e-01 0.66393286 [57,] 3.189534e-01 6.379067e-01 0.68104663 [58,] 2.912610e-01 5.825219e-01 0.70873903 [59,] 2.588342e-01 5.176683e-01 0.74116584 [60,] 2.610576e-01 5.221152e-01 0.73894242 [61,] 2.772426e-01 5.544853e-01 0.72275736 [62,] 2.477457e-01 4.954913e-01 0.75225433 [63,] 2.139967e-01 4.279934e-01 0.78600328 [64,] 1.974887e-01 3.949774e-01 0.80251128 [65,] 1.810854e-01 3.621707e-01 0.81891465 [66,] 1.580688e-01 3.161376e-01 0.84193120 [67,] 1.456422e-01 2.912843e-01 0.85435784 [68,] 1.329028e-01 2.658056e-01 0.86709719 [69,] 1.244798e-01 2.489597e-01 0.87552016 [70,] 1.066191e-01 2.132383e-01 0.89338085 [71,] 8.914279e-02 1.782856e-01 0.91085721 [72,] 7.384061e-02 1.476812e-01 0.92615939 [73,] 6.069602e-02 1.213920e-01 0.93930398 [74,] 4.914904e-02 9.829807e-02 0.95085096 [75,] 4.216085e-02 8.432170e-02 0.95783915 [76,] 3.576070e-02 7.152140e-02 0.96423930 [77,] 3.195435e-02 6.390869e-02 0.96804565 [78,] 3.123391e-02 6.246783e-02 0.96876609 [79,] 2.520452e-02 5.040903e-02 0.97479548 [80,] 2.016692e-02 4.033385e-02 0.97983308 [81,] 1.678110e-02 3.356219e-02 0.98321890 [82,] 1.440993e-02 2.881986e-02 0.98559007 [83,] 1.101093e-02 2.202186e-02 0.98898907 [84,] 8.906418e-03 1.781284e-02 0.99109358 [85,] 7.206841e-03 1.441368e-02 0.99279316 [86,] 6.839775e-03 1.367955e-02 0.99316023 [87,] 5.881704e-03 1.176341e-02 0.99411830 [88,] 4.674782e-03 9.349563e-03 0.99532522 [89,] 3.424984e-03 6.849968e-03 0.99657502 [90,] 2.576178e-03 5.152355e-03 0.99742382 [91,] 2.018834e-03 4.037668e-03 0.99798117 [92,] 1.452563e-03 2.905127e-03 0.99854744 [93,] 1.051917e-03 2.103835e-03 0.99894808 [94,] 8.165301e-04 1.633060e-03 0.99918347 [95,] 9.567903e-04 1.913581e-03 0.99904321 [96,] 1.139023e-03 2.278046e-03 0.99886098 [97,] 1.435741e-03 2.871483e-03 0.99856426 [98,] 1.694359e-03 3.388718e-03 0.99830564 [99,] 1.407821e-03 2.815642e-03 0.99859218 [100,] 1.461603e-03 2.923206e-03 0.99853840 [101,] 1.766405e-03 3.532809e-03 0.99823360 [102,] 2.245905e-03 4.491810e-03 0.99775410 [103,] 3.615880e-03 7.231761e-03 0.99638412 [104,] 6.047639e-03 1.209528e-02 0.99395236 [105,] 8.870522e-03 1.774104e-02 0.99112948 [106,] 8.363645e-03 1.672729e-02 0.99163635 [107,] 7.340176e-03 1.468035e-02 0.99265982 [108,] 6.246599e-03 1.249320e-02 0.99375340 [109,] 4.736710e-03 9.473419e-03 0.99526329 [110,] 4.029955e-03 8.059911e-03 0.99597004 [111,] 4.232321e-03 8.464643e-03 0.99576768 [112,] 3.310794e-03 6.621588e-03 0.99668921 [113,] 3.551192e-03 7.102383e-03 0.99644881 [114,] 3.238910e-03 6.477819e-03 0.99676109 [115,] 4.790365e-03 9.580730e-03 0.99520963 [116,] 6.774940e-03 1.354988e-02 0.99322506 [117,] 1.034238e-02 2.068475e-02 0.98965762 [118,] 1.339966e-02 2.679932e-02 0.98660034 [119,] 2.093203e-02 4.186407e-02 0.97906797 [120,] 2.066209e-02 4.132418e-02 0.97933791 [121,] 2.329883e-02 4.659766e-02 0.97670117 [122,] 2.051446e-02 4.102893e-02 0.97948554 [123,] 1.789884e-02 3.579767e-02 0.98210116 [124,] 1.601135e-02 3.202270e-02 0.98398865 [125,] 2.334194e-02 4.668387e-02 0.97665806 [126,] 1.788991e-02 3.577983e-02 0.98211009 [127,] 1.392711e-02 2.785422e-02 0.98607289 [128,] 1.059269e-02 2.118539e-02 0.98940731 [129,] 8.730909e-03 1.746182e-02 0.99126909 [130,] 6.789162e-03 1.357832e-02 0.99321084 [131,] 4.949527e-03 9.899055e-03 0.99505047 [132,] 3.649647e-03 7.299295e-03 0.99635035 [133,] 3.215417e-03 6.430834e-03 0.99678458 [134,] 3.058331e-03 6.116661e-03 0.99694167 [135,] 4.501453e-03 9.002907e-03 0.99549855 [136,] 5.099110e-03 1.019822e-02 0.99490089 [137,] 5.724282e-03 1.144856e-02 0.99427572 [138,] 6.301765e-03 1.260353e-02 0.99369824 [139,] 4.457100e-03 8.914199e-03 0.99554290 [140,] 3.262896e-03 6.525792e-03 0.99673710 [141,] 2.474875e-03 4.949749e-03 0.99752513 [142,] 2.045820e-03 4.091640e-03 0.99795418 [143,] 2.050751e-03 4.101502e-03 0.99794925 [144,] 2.007140e-03 4.014280e-03 0.99799286 [145,] 3.146750e-03 6.293500e-03 0.99685325 [146,] 2.991998e-03 5.983995e-03 0.99700800 [147,] 2.534844e-03 5.069688e-03 0.99746516 [148,] 2.345706e-03 4.691412e-03 0.99765429 [149,] 2.189880e-03 4.379759e-03 0.99781012 [150,] 2.104804e-03 4.209607e-03 0.99789520 [151,] 1.814670e-03 3.629341e-03 0.99818533 [152,] 1.203923e-03 2.407845e-03 0.99879608 [153,] 8.702349e-04 1.740470e-03 0.99912977 [154,] 1.013433e-03 2.026865e-03 0.99898657 [155,] 1.605952e-02 3.211904e-02 0.98394048 [156,] 5.668190e-01 8.663621e-01 0.43318104 [157,] 5.177784e-01 9.644431e-01 0.48222156 [158,] 5.018697e-01 9.962607e-01 0.49813035 [159,] 4.380839e-01 8.761678e-01 0.56191612 [160,] 5.066728e-01 9.866544e-01 0.49332719 [161,] 5.697568e-01 8.604865e-01 0.43024324 [162,] 5.044962e-01 9.910077e-01 0.49550383 [163,] 5.878896e-01 8.242208e-01 0.41211038 [164,] 6.550668e-01 6.898664e-01 0.34493320 [165,] 6.468202e-01 7.063596e-01 0.35317981 [166,] 6.460040e-01 7.079919e-01 0.35399597 [167,] 6.273784e-01 7.452431e-01 0.37262156 [168,] 9.355089e-01 1.289822e-01 0.06449111 [169,] 9.079337e-01 1.841326e-01 0.09206631 [170,] 9.206079e-01 1.587842e-01 0.07939212 [171,] 8.895071e-01 2.209857e-01 0.11049285 [172,] 8.226799e-01 3.546402e-01 0.17732010 [173,] 9.280763e-01 1.438474e-01 0.07192370 [174,] 1.000000e+00 0.000000e+00 0.00000000 [175,] 1.000000e+00 0.000000e+00 0.00000000 [176,] 1.000000e+00 0.000000e+00 0.00000000 > postscript(file="/var/wessaorg/rcomp/tmp/13tbw1386616249.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index') > points(x[,1]-mysum$resid) > grid() > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/2podr1386616249.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index') > grid() > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/3lw2j1386616249.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') > grid() > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/4wtfl1386616249.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/5em1n1386616249.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > qqnorm(mysum$resid, main='Residual Normal Q-Q Plot') > qqline(mysum$resid) > grid() > dev.off() null device 1 > (myerror <- as.ts(mysum$resid)) Time Series: Start = 1 End = 195 Frequency = 1 1 2 3 4 5 6 0.083232827 -0.048644181 0.028980470 -0.013410062 -0.012811379 0.130243389 7 8 9 10 11 12 0.345440687 0.203369231 0.044795649 -0.046216537 -0.032097502 -0.131782619 13 14 15 16 17 18 0.351931569 0.158892778 0.370730603 0.311311093 0.280749292 0.057797841 19 20 21 22 23 24 -0.172030353 0.023864896 0.074777737 0.082634797 0.002906490 0.189838783 25 26 27 28 29 30 0.241330274 -0.212492090 0.273110076 0.271316182 0.338895299 0.491062284 31 32 33 34 35 36 -0.243084824 -0.372496286 -0.159630401 -0.146501603 -0.096790669 -0.513863113 37 38 39 40 41 42 0.337620161 0.316103785 0.453775262 0.436371485 0.434318803 0.426611196 43 44 45 46 47 48 -0.357639943 -0.272557892 -0.281375813 -0.299490762 -0.301923495 -0.103145464 49 50 51 52 53 54 -0.724149537 -0.723154254 -0.655568820 -0.870952660 -0.698258444 -0.799194795 55 56 57 58 59 60 0.219143566 0.197361770 0.204888925 0.260995796 0.322856596 0.287574050 61 62 63 64 65 66 -0.508464925 -0.385760084 -0.366922197 -0.424750843 -0.225785714 -0.283497258 67 68 69 70 71 72 0.192482746 0.217796953 0.235618863 0.202306070 0.219757068 -0.002701834 73 74 75 76 77 78 0.200680609 0.244631509 0.117890588 0.272674839 0.142039113 0.301064952 79 80 81 82 83 84 0.110707216 0.032499898 -0.162829922 0.041577587 0.014380337 0.147304024 85 86 87 88 89 90 -0.002174370 0.036351335 0.194481536 -0.119393113 -0.095429278 -0.193533508 91 92 93 94 95 96 -0.225750999 -0.003377759 0.159151076 0.185710864 0.298878618 0.235098141 97 98 99 100 101 102 0.179277326 0.012998057 0.066664070 -0.063754650 -0.182949023 -0.051366473 103 104 105 106 107 108 -0.036636331 0.426341663 0.419428513 0.423588258 0.397148674 0.233464715 109 110 111 112 113 114 0.318511377 0.349024756 0.425910073 0.503332708 0.532134443 0.518977289 115 116 117 118 119 120 0.297223372 0.197689820 0.228446687 0.146379313 0.256558186 0.386840868 121 122 123 124 125 126 0.062176275 0.314250121 0.221447634 0.501697514 0.477766347 0.490244304 127 128 129 130 131 132 0.398283441 0.464195971 0.186047790 0.263598684 0.094736681 0.087657317 133 134 135 136 137 138 0.098141994 0.290320284 -0.057840757 -0.115119421 -0.073462886 -0.175982036 139 140 141 142 143 144 -0.101658076 0.023102997 0.078345885 0.221146994 0.374770647 0.464828959 145 146 147 148 149 150 0.378345334 0.390501170 -0.307017323 -0.029990549 -0.076185692 0.125814320 151 152 153 154 155 156 0.102567316 -0.180781208 0.003342556 0.268461810 0.087177180 0.060810083 157 158 159 160 161 162 0.027164546 -0.105121808 -0.034145631 0.227519838 0.101742335 0.027013648 163 164 165 166 167 168 0.029403573 0.306118083 -0.019775344 -0.318381253 -0.109664103 -0.048707970 169 170 171 172 173 174 -0.696266649 -0.276562526 -0.179579171 -0.816749174 -0.717445559 -0.685064773 175 176 177 178 179 180 -0.688738033 -0.727674209 -0.744789236 0.253591440 0.309339813 0.089042495 181 182 183 184 185 186 0.267988287 0.136163955 0.303405923 -0.750992584 -0.767837378 -0.759149078 187 188 189 190 191 192 -0.785121461 -0.783644867 -0.687886650 -0.477556370 -0.831972111 -0.649470884 193 194 195 -0.680639711 -0.406946539 -0.493868504 > postscript(file="/var/wessaorg/rcomp/tmp/6fvme1386616249.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 0.083232827 NA 1 -0.048644181 0.083232827 2 0.028980470 -0.048644181 3 -0.013410062 0.028980470 4 -0.012811379 -0.013410062 5 0.130243389 -0.012811379 6 0.345440687 0.130243389 7 0.203369231 0.345440687 8 0.044795649 0.203369231 9 -0.046216537 0.044795649 10 -0.032097502 -0.046216537 11 -0.131782619 -0.032097502 12 0.351931569 -0.131782619 13 0.158892778 0.351931569 14 0.370730603 0.158892778 15 0.311311093 0.370730603 16 0.280749292 0.311311093 17 0.057797841 0.280749292 18 -0.172030353 0.057797841 19 0.023864896 -0.172030353 20 0.074777737 0.023864896 21 0.082634797 0.074777737 22 0.002906490 0.082634797 23 0.189838783 0.002906490 24 0.241330274 0.189838783 25 -0.212492090 0.241330274 26 0.273110076 -0.212492090 27 0.271316182 0.273110076 28 0.338895299 0.271316182 29 0.491062284 0.338895299 30 -0.243084824 0.491062284 31 -0.372496286 -0.243084824 32 -0.159630401 -0.372496286 33 -0.146501603 -0.159630401 34 -0.096790669 -0.146501603 35 -0.513863113 -0.096790669 36 0.337620161 -0.513863113 37 0.316103785 0.337620161 38 0.453775262 0.316103785 39 0.436371485 0.453775262 40 0.434318803 0.436371485 41 0.426611196 0.434318803 42 -0.357639943 0.426611196 43 -0.272557892 -0.357639943 44 -0.281375813 -0.272557892 45 -0.299490762 -0.281375813 46 -0.301923495 -0.299490762 47 -0.103145464 -0.301923495 48 -0.724149537 -0.103145464 49 -0.723154254 -0.724149537 50 -0.655568820 -0.723154254 51 -0.870952660 -0.655568820 52 -0.698258444 -0.870952660 53 -0.799194795 -0.698258444 54 0.219143566 -0.799194795 55 0.197361770 0.219143566 56 0.204888925 0.197361770 57 0.260995796 0.204888925 58 0.322856596 0.260995796 59 0.287574050 0.322856596 60 -0.508464925 0.287574050 61 -0.385760084 -0.508464925 62 -0.366922197 -0.385760084 63 -0.424750843 -0.366922197 64 -0.225785714 -0.424750843 65 -0.283497258 -0.225785714 66 0.192482746 -0.283497258 67 0.217796953 0.192482746 68 0.235618863 0.217796953 69 0.202306070 0.235618863 70 0.219757068 0.202306070 71 -0.002701834 0.219757068 72 0.200680609 -0.002701834 73 0.244631509 0.200680609 74 0.117890588 0.244631509 75 0.272674839 0.117890588 76 0.142039113 0.272674839 77 0.301064952 0.142039113 78 0.110707216 0.301064952 79 0.032499898 0.110707216 80 -0.162829922 0.032499898 81 0.041577587 -0.162829922 82 0.014380337 0.041577587 83 0.147304024 0.014380337 84 -0.002174370 0.147304024 85 0.036351335 -0.002174370 86 0.194481536 0.036351335 87 -0.119393113 0.194481536 88 -0.095429278 -0.119393113 89 -0.193533508 -0.095429278 90 -0.225750999 -0.193533508 91 -0.003377759 -0.225750999 92 0.159151076 -0.003377759 93 0.185710864 0.159151076 94 0.298878618 0.185710864 95 0.235098141 0.298878618 96 0.179277326 0.235098141 97 0.012998057 0.179277326 98 0.066664070 0.012998057 99 -0.063754650 0.066664070 100 -0.182949023 -0.063754650 101 -0.051366473 -0.182949023 102 -0.036636331 -0.051366473 103 0.426341663 -0.036636331 104 0.419428513 0.426341663 105 0.423588258 0.419428513 106 0.397148674 0.423588258 107 0.233464715 0.397148674 108 0.318511377 0.233464715 109 0.349024756 0.318511377 110 0.425910073 0.349024756 111 0.503332708 0.425910073 112 0.532134443 0.503332708 113 0.518977289 0.532134443 114 0.297223372 0.518977289 115 0.197689820 0.297223372 116 0.228446687 0.197689820 117 0.146379313 0.228446687 118 0.256558186 0.146379313 119 0.386840868 0.256558186 120 0.062176275 0.386840868 121 0.314250121 0.062176275 122 0.221447634 0.314250121 123 0.501697514 0.221447634 124 0.477766347 0.501697514 125 0.490244304 0.477766347 126 0.398283441 0.490244304 127 0.464195971 0.398283441 128 0.186047790 0.464195971 129 0.263598684 0.186047790 130 0.094736681 0.263598684 131 0.087657317 0.094736681 132 0.098141994 0.087657317 133 0.290320284 0.098141994 134 -0.057840757 0.290320284 135 -0.115119421 -0.057840757 136 -0.073462886 -0.115119421 137 -0.175982036 -0.073462886 138 -0.101658076 -0.175982036 139 0.023102997 -0.101658076 140 0.078345885 0.023102997 141 0.221146994 0.078345885 142 0.374770647 0.221146994 143 0.464828959 0.374770647 144 0.378345334 0.464828959 145 0.390501170 0.378345334 146 -0.307017323 0.390501170 147 -0.029990549 -0.307017323 148 -0.076185692 -0.029990549 149 0.125814320 -0.076185692 150 0.102567316 0.125814320 151 -0.180781208 0.102567316 152 0.003342556 -0.180781208 153 0.268461810 0.003342556 154 0.087177180 0.268461810 155 0.060810083 0.087177180 156 0.027164546 0.060810083 157 -0.105121808 0.027164546 158 -0.034145631 -0.105121808 159 0.227519838 -0.034145631 160 0.101742335 0.227519838 161 0.027013648 0.101742335 162 0.029403573 0.027013648 163 0.306118083 0.029403573 164 -0.019775344 0.306118083 165 -0.318381253 -0.019775344 166 -0.109664103 -0.318381253 167 -0.048707970 -0.109664103 168 -0.696266649 -0.048707970 169 -0.276562526 -0.696266649 170 -0.179579171 -0.276562526 171 -0.816749174 -0.179579171 172 -0.717445559 -0.816749174 173 -0.685064773 -0.717445559 174 -0.688738033 -0.685064773 175 -0.727674209 -0.688738033 176 -0.744789236 -0.727674209 177 0.253591440 -0.744789236 178 0.309339813 0.253591440 179 0.089042495 0.309339813 180 0.267988287 0.089042495 181 0.136163955 0.267988287 182 0.303405923 0.136163955 183 -0.750992584 0.303405923 184 -0.767837378 -0.750992584 185 -0.759149078 -0.767837378 186 -0.785121461 -0.759149078 187 -0.783644867 -0.785121461 188 -0.687886650 -0.783644867 189 -0.477556370 -0.687886650 190 -0.831972111 -0.477556370 191 -0.649470884 -0.831972111 192 -0.680639711 -0.649470884 193 -0.406946539 -0.680639711 194 -0.493868504 -0.406946539 195 NA -0.493868504 > dum1 <- dum[2:length(myerror),] > dum1 lag(myerror, k = 1) myerror [1,] -0.048644181 0.083232827 [2,] 0.028980470 -0.048644181 [3,] -0.013410062 0.028980470 [4,] -0.012811379 -0.013410062 [5,] 0.130243389 -0.012811379 [6,] 0.345440687 0.130243389 [7,] 0.203369231 0.345440687 [8,] 0.044795649 0.203369231 [9,] -0.046216537 0.044795649 [10,] -0.032097502 -0.046216537 [11,] -0.131782619 -0.032097502 [12,] 0.351931569 -0.131782619 [13,] 0.158892778 0.351931569 [14,] 0.370730603 0.158892778 [15,] 0.311311093 0.370730603 [16,] 0.280749292 0.311311093 [17,] 0.057797841 0.280749292 [18,] -0.172030353 0.057797841 [19,] 0.023864896 -0.172030353 [20,] 0.074777737 0.023864896 [21,] 0.082634797 0.074777737 [22,] 0.002906490 0.082634797 [23,] 0.189838783 0.002906490 [24,] 0.241330274 0.189838783 [25,] -0.212492090 0.241330274 [26,] 0.273110076 -0.212492090 [27,] 0.271316182 0.273110076 [28,] 0.338895299 0.271316182 [29,] 0.491062284 0.338895299 [30,] -0.243084824 0.491062284 [31,] -0.372496286 -0.243084824 [32,] -0.159630401 -0.372496286 [33,] -0.146501603 -0.159630401 [34,] -0.096790669 -0.146501603 [35,] -0.513863113 -0.096790669 [36,] 0.337620161 -0.513863113 [37,] 0.316103785 0.337620161 [38,] 0.453775262 0.316103785 [39,] 0.436371485 0.453775262 [40,] 0.434318803 0.436371485 [41,] 0.426611196 0.434318803 [42,] -0.357639943 0.426611196 [43,] -0.272557892 -0.357639943 [44,] -0.281375813 -0.272557892 [45,] -0.299490762 -0.281375813 [46,] -0.301923495 -0.299490762 [47,] -0.103145464 -0.301923495 [48,] -0.724149537 -0.103145464 [49,] -0.723154254 -0.724149537 [50,] -0.655568820 -0.723154254 [51,] -0.870952660 -0.655568820 [52,] -0.698258444 -0.870952660 [53,] -0.799194795 -0.698258444 [54,] 0.219143566 -0.799194795 [55,] 0.197361770 0.219143566 [56,] 0.204888925 0.197361770 [57,] 0.260995796 0.204888925 [58,] 0.322856596 0.260995796 [59,] 0.287574050 0.322856596 [60,] -0.508464925 0.287574050 [61,] -0.385760084 -0.508464925 [62,] -0.366922197 -0.385760084 [63,] -0.424750843 -0.366922197 [64,] -0.225785714 -0.424750843 [65,] -0.283497258 -0.225785714 [66,] 0.192482746 -0.283497258 [67,] 0.217796953 0.192482746 [68,] 0.235618863 0.217796953 [69,] 0.202306070 0.235618863 [70,] 0.219757068 0.202306070 [71,] -0.002701834 0.219757068 [72,] 0.200680609 -0.002701834 [73,] 0.244631509 0.200680609 [74,] 0.117890588 0.244631509 [75,] 0.272674839 0.117890588 [76,] 0.142039113 0.272674839 [77,] 0.301064952 0.142039113 [78,] 0.110707216 0.301064952 [79,] 0.032499898 0.110707216 [80,] -0.162829922 0.032499898 [81,] 0.041577587 -0.162829922 [82,] 0.014380337 0.041577587 [83,] 0.147304024 0.014380337 [84,] -0.002174370 0.147304024 [85,] 0.036351335 -0.002174370 [86,] 0.194481536 0.036351335 [87,] -0.119393113 0.194481536 [88,] -0.095429278 -0.119393113 [89,] -0.193533508 -0.095429278 [90,] -0.225750999 -0.193533508 [91,] -0.003377759 -0.225750999 [92,] 0.159151076 -0.003377759 [93,] 0.185710864 0.159151076 [94,] 0.298878618 0.185710864 [95,] 0.235098141 0.298878618 [96,] 0.179277326 0.235098141 [97,] 0.012998057 0.179277326 [98,] 0.066664070 0.012998057 [99,] -0.063754650 0.066664070 [100,] -0.182949023 -0.063754650 [101,] -0.051366473 -0.182949023 [102,] -0.036636331 -0.051366473 [103,] 0.426341663 -0.036636331 [104,] 0.419428513 0.426341663 [105,] 0.423588258 0.419428513 [106,] 0.397148674 0.423588258 [107,] 0.233464715 0.397148674 [108,] 0.318511377 0.233464715 [109,] 0.349024756 0.318511377 [110,] 0.425910073 0.349024756 [111,] 0.503332708 0.425910073 [112,] 0.532134443 0.503332708 [113,] 0.518977289 0.532134443 [114,] 0.297223372 0.518977289 [115,] 0.197689820 0.297223372 [116,] 0.228446687 0.197689820 [117,] 0.146379313 0.228446687 [118,] 0.256558186 0.146379313 [119,] 0.386840868 0.256558186 [120,] 0.062176275 0.386840868 [121,] 0.314250121 0.062176275 [122,] 0.221447634 0.314250121 [123,] 0.501697514 0.221447634 [124,] 0.477766347 0.501697514 [125,] 0.490244304 0.477766347 [126,] 0.398283441 0.490244304 [127,] 0.464195971 0.398283441 [128,] 0.186047790 0.464195971 [129,] 0.263598684 0.186047790 [130,] 0.094736681 0.263598684 [131,] 0.087657317 0.094736681 [132,] 0.098141994 0.087657317 [133,] 0.290320284 0.098141994 [134,] -0.057840757 0.290320284 [135,] -0.115119421 -0.057840757 [136,] -0.073462886 -0.115119421 [137,] -0.175982036 -0.073462886 [138,] -0.101658076 -0.175982036 [139,] 0.023102997 -0.101658076 [140,] 0.078345885 0.023102997 [141,] 0.221146994 0.078345885 [142,] 0.374770647 0.221146994 [143,] 0.464828959 0.374770647 [144,] 0.378345334 0.464828959 [145,] 0.390501170 0.378345334 [146,] -0.307017323 0.390501170 [147,] -0.029990549 -0.307017323 [148,] -0.076185692 -0.029990549 [149,] 0.125814320 -0.076185692 [150,] 0.102567316 0.125814320 [151,] -0.180781208 0.102567316 [152,] 0.003342556 -0.180781208 [153,] 0.268461810 0.003342556 [154,] 0.087177180 0.268461810 [155,] 0.060810083 0.087177180 [156,] 0.027164546 0.060810083 [157,] -0.105121808 0.027164546 [158,] -0.034145631 -0.105121808 [159,] 0.227519838 -0.034145631 [160,] 0.101742335 0.227519838 [161,] 0.027013648 0.101742335 [162,] 0.029403573 0.027013648 [163,] 0.306118083 0.029403573 [164,] -0.019775344 0.306118083 [165,] -0.318381253 -0.019775344 [166,] -0.109664103 -0.318381253 [167,] -0.048707970 -0.109664103 [168,] -0.696266649 -0.048707970 [169,] -0.276562526 -0.696266649 [170,] -0.179579171 -0.276562526 [171,] -0.816749174 -0.179579171 [172,] -0.717445559 -0.816749174 [173,] -0.685064773 -0.717445559 [174,] -0.688738033 -0.685064773 [175,] -0.727674209 -0.688738033 [176,] -0.744789236 -0.727674209 [177,] 0.253591440 -0.744789236 [178,] 0.309339813 0.253591440 [179,] 0.089042495 0.309339813 [180,] 0.267988287 0.089042495 [181,] 0.136163955 0.267988287 [182,] 0.303405923 0.136163955 [183,] -0.750992584 0.303405923 [184,] -0.767837378 -0.750992584 [185,] -0.759149078 -0.767837378 [186,] -0.785121461 -0.759149078 [187,] -0.783644867 -0.785121461 [188,] -0.687886650 -0.783644867 [189,] -0.477556370 -0.687886650 [190,] -0.831972111 -0.477556370 [191,] -0.649470884 -0.831972111 [192,] -0.680639711 -0.649470884 [193,] -0.406946539 -0.680639711 [194,] -0.493868504 -0.406946539 > z <- as.data.frame(dum1) > z lag(myerror, k = 1) myerror 1 -0.048644181 0.083232827 2 0.028980470 -0.048644181 3 -0.013410062 0.028980470 4 -0.012811379 -0.013410062 5 0.130243389 -0.012811379 6 0.345440687 0.130243389 7 0.203369231 0.345440687 8 0.044795649 0.203369231 9 -0.046216537 0.044795649 10 -0.032097502 -0.046216537 11 -0.131782619 -0.032097502 12 0.351931569 -0.131782619 13 0.158892778 0.351931569 14 0.370730603 0.158892778 15 0.311311093 0.370730603 16 0.280749292 0.311311093 17 0.057797841 0.280749292 18 -0.172030353 0.057797841 19 0.023864896 -0.172030353 20 0.074777737 0.023864896 21 0.082634797 0.074777737 22 0.002906490 0.082634797 23 0.189838783 0.002906490 24 0.241330274 0.189838783 25 -0.212492090 0.241330274 26 0.273110076 -0.212492090 27 0.271316182 0.273110076 28 0.338895299 0.271316182 29 0.491062284 0.338895299 30 -0.243084824 0.491062284 31 -0.372496286 -0.243084824 32 -0.159630401 -0.372496286 33 -0.146501603 -0.159630401 34 -0.096790669 -0.146501603 35 -0.513863113 -0.096790669 36 0.337620161 -0.513863113 37 0.316103785 0.337620161 38 0.453775262 0.316103785 39 0.436371485 0.453775262 40 0.434318803 0.436371485 41 0.426611196 0.434318803 42 -0.357639943 0.426611196 43 -0.272557892 -0.357639943 44 -0.281375813 -0.272557892 45 -0.299490762 -0.281375813 46 -0.301923495 -0.299490762 47 -0.103145464 -0.301923495 48 -0.724149537 -0.103145464 49 -0.723154254 -0.724149537 50 -0.655568820 -0.723154254 51 -0.870952660 -0.655568820 52 -0.698258444 -0.870952660 53 -0.799194795 -0.698258444 54 0.219143566 -0.799194795 55 0.197361770 0.219143566 56 0.204888925 0.197361770 57 0.260995796 0.204888925 58 0.322856596 0.260995796 59 0.287574050 0.322856596 60 -0.508464925 0.287574050 61 -0.385760084 -0.508464925 62 -0.366922197 -0.385760084 63 -0.424750843 -0.366922197 64 -0.225785714 -0.424750843 65 -0.283497258 -0.225785714 66 0.192482746 -0.283497258 67 0.217796953 0.192482746 68 0.235618863 0.217796953 69 0.202306070 0.235618863 70 0.219757068 0.202306070 71 -0.002701834 0.219757068 72 0.200680609 -0.002701834 73 0.244631509 0.200680609 74 0.117890588 0.244631509 75 0.272674839 0.117890588 76 0.142039113 0.272674839 77 0.301064952 0.142039113 78 0.110707216 0.301064952 79 0.032499898 0.110707216 80 -0.162829922 0.032499898 81 0.041577587 -0.162829922 82 0.014380337 0.041577587 83 0.147304024 0.014380337 84 -0.002174370 0.147304024 85 0.036351335 -0.002174370 86 0.194481536 0.036351335 87 -0.119393113 0.194481536 88 -0.095429278 -0.119393113 89 -0.193533508 -0.095429278 90 -0.225750999 -0.193533508 91 -0.003377759 -0.225750999 92 0.159151076 -0.003377759 93 0.185710864 0.159151076 94 0.298878618 0.185710864 95 0.235098141 0.298878618 96 0.179277326 0.235098141 97 0.012998057 0.179277326 98 0.066664070 0.012998057 99 -0.063754650 0.066664070 100 -0.182949023 -0.063754650 101 -0.051366473 -0.182949023 102 -0.036636331 -0.051366473 103 0.426341663 -0.036636331 104 0.419428513 0.426341663 105 0.423588258 0.419428513 106 0.397148674 0.423588258 107 0.233464715 0.397148674 108 0.318511377 0.233464715 109 0.349024756 0.318511377 110 0.425910073 0.349024756 111 0.503332708 0.425910073 112 0.532134443 0.503332708 113 0.518977289 0.532134443 114 0.297223372 0.518977289 115 0.197689820 0.297223372 116 0.228446687 0.197689820 117 0.146379313 0.228446687 118 0.256558186 0.146379313 119 0.386840868 0.256558186 120 0.062176275 0.386840868 121 0.314250121 0.062176275 122 0.221447634 0.314250121 123 0.501697514 0.221447634 124 0.477766347 0.501697514 125 0.490244304 0.477766347 126 0.398283441 0.490244304 127 0.464195971 0.398283441 128 0.186047790 0.464195971 129 0.263598684 0.186047790 130 0.094736681 0.263598684 131 0.087657317 0.094736681 132 0.098141994 0.087657317 133 0.290320284 0.098141994 134 -0.057840757 0.290320284 135 -0.115119421 -0.057840757 136 -0.073462886 -0.115119421 137 -0.175982036 -0.073462886 138 -0.101658076 -0.175982036 139 0.023102997 -0.101658076 140 0.078345885 0.023102997 141 0.221146994 0.078345885 142 0.374770647 0.221146994 143 0.464828959 0.374770647 144 0.378345334 0.464828959 145 0.390501170 0.378345334 146 -0.307017323 0.390501170 147 -0.029990549 -0.307017323 148 -0.076185692 -0.029990549 149 0.125814320 -0.076185692 150 0.102567316 0.125814320 151 -0.180781208 0.102567316 152 0.003342556 -0.180781208 153 0.268461810 0.003342556 154 0.087177180 0.268461810 155 0.060810083 0.087177180 156 0.027164546 0.060810083 157 -0.105121808 0.027164546 158 -0.034145631 -0.105121808 159 0.227519838 -0.034145631 160 0.101742335 0.227519838 161 0.027013648 0.101742335 162 0.029403573 0.027013648 163 0.306118083 0.029403573 164 -0.019775344 0.306118083 165 -0.318381253 -0.019775344 166 -0.109664103 -0.318381253 167 -0.048707970 -0.109664103 168 -0.696266649 -0.048707970 169 -0.276562526 -0.696266649 170 -0.179579171 -0.276562526 171 -0.816749174 -0.179579171 172 -0.717445559 -0.816749174 173 -0.685064773 -0.717445559 174 -0.688738033 -0.685064773 175 -0.727674209 -0.688738033 176 -0.744789236 -0.727674209 177 0.253591440 -0.744789236 178 0.309339813 0.253591440 179 0.089042495 0.309339813 180 0.267988287 0.089042495 181 0.136163955 0.267988287 182 0.303405923 0.136163955 183 -0.750992584 0.303405923 184 -0.767837378 -0.750992584 185 -0.759149078 -0.767837378 186 -0.785121461 -0.759149078 187 -0.783644867 -0.785121461 188 -0.687886650 -0.783644867 189 -0.477556370 -0.687886650 190 -0.831972111 -0.477556370 191 -0.649470884 -0.831972111 192 -0.680639711 -0.649470884 193 -0.406946539 -0.680639711 194 -0.493868504 -0.406946539 > plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals') > lines(lowess(z)) > abline(lm(z)) > grid() > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/7jkvq1386616249.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/8o0pr1386616249.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/97tvz1386616249.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0)) > plot(mylm, las = 1, sub='Residual Diagnostics') > par(opar) > dev.off() null device 1 > if (n > n25) { + postscript(file="/var/wessaorg/rcomp/tmp/1036de1386616249.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) + plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint') + grid() + dev.off() + } null device 1 > > #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab > load(file="/var/wessaorg/rcomp/createtable") > > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE) > a<-table.row.end(a) > myeq <- colnames(x)[1] > myeq <- paste(myeq, '[t] = ', sep='') > for (i in 1:k){ + if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '') + myeq <- paste(myeq, signif(mysum$coefficients[i,1],6), sep=' ') + if (rownames(mysum$coefficients)[i] != '(Intercept)') { + myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='') + if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='') + } + } > myeq <- paste(myeq, ' + e[t]') > a<-table.row.start(a) > a<-table.element(a, myeq) > a<-table.row.end(a) > a<-table.end(a) > table.save(a,file="/var/wessaorg/rcomp/tmp/11wgcb1386616249.tab") > a<-table.start() > a<-table.row.start(a) > a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a,'Variable',header=TRUE) > a<-table.element(a,'Parameter',header=TRUE) > a<-table.element(a,'S.D.',header=TRUE) > a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE) > a<-table.element(a,'2-tail p-value',header=TRUE) > a<-table.element(a,'1-tail p-value',header=TRUE) > a<-table.row.end(a) > for (i in 1:k){ + a<-table.row.start(a) + a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE) + a<-table.element(a,signif(mysum$coefficients[i,1],6)) + a<-table.element(a, signif(mysum$coefficients[i,2],6)) + a<-table.element(a, signif(mysum$coefficients[i,3],4)) + a<-table.element(a, signif(mysum$coefficients[i,4],6)) + a<-table.element(a, signif(mysum$coefficients[i,4]/2,6)) + a<-table.row.end(a) + } > a<-table.end(a) > table.save(a,file="/var/wessaorg/rcomp/tmp/12mvy11386616249.tab") > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Multiple R',1,TRUE) > a<-table.element(a, signif(sqrt(mysum$r.squared),6)) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'R-squared',1,TRUE) > a<-table.element(a, signif(mysum$r.squared,6)) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Adjusted R-squared',1,TRUE) > a<-table.element(a, signif(mysum$adj.r.squared,6)) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'F-TEST (value)',1,TRUE) > a<-table.element(a, signif(mysum$fstatistic[1],6)) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE) > a<-table.element(a, signif(mysum$fstatistic[2],6)) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE) > a<-table.element(a, signif(mysum$fstatistic[3],6)) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'p-value',1,TRUE) > a<-table.element(a, signif(1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]),6)) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Residual Standard Deviation',1,TRUE) > a<-table.element(a, signif(mysum$sigma,6)) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Sum Squared Residuals',1,TRUE) > a<-table.element(a, signif(sum(myerror*myerror),6)) > a<-table.row.end(a) > a<-table.end(a) > table.save(a,file="/var/wessaorg/rcomp/tmp/13ie1f1386616249.tab") > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Time or Index', 1, TRUE) > a<-table.element(a, 'Actuals', 1, TRUE) > a<-table.element(a, 'Interpolation
Forecast', 1, TRUE) > a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE) > a<-table.row.end(a) > for (i in 1:n) { + a<-table.row.start(a) + a<-table.element(a,i, 1, TRUE) + a<-table.element(a,signif(x[i],6)) + a<-table.element(a,signif(x[i]-mysum$resid[i],6)) + a<-table.element(a,signif(mysum$resid[i],6)) + a<-table.row.end(a) + } > a<-table.end(a) > table.save(a,file="/var/wessaorg/rcomp/tmp/14a6ug1386616249.tab") > if (n > n25) { + a<-table.start() + a<-table.row.start(a) + a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'p-values',header=TRUE) + a<-table.element(a,'Alternative Hypothesis',3,header=TRUE) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'breakpoint index',header=TRUE) + a<-table.element(a,'greater',header=TRUE) + a<-table.element(a,'2-sided',header=TRUE) + a<-table.element(a,'less',header=TRUE) + a<-table.row.end(a) + for (mypoint in kp3:nmkm3) { + a<-table.row.start(a) + a<-table.element(a,mypoint,header=TRUE) + a<-table.element(a,signif(gqarr[mypoint-kp3+1,1],6)) + a<-table.element(a,signif(gqarr[mypoint-kp3+1,2],6)) + a<-table.element(a,signif(gqarr[mypoint-kp3+1,3],6)) + a<-table.row.end(a) + } + a<-table.end(a) + table.save(a,file="/var/wessaorg/rcomp/tmp/15srrq1386616249.tab") + a<-table.start() + a<-table.row.start(a) + a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'Description',header=TRUE) + a<-table.element(a,'# significant tests',header=TRUE) + a<-table.element(a,'% significant tests',header=TRUE) + a<-table.element(a,'OK/NOK',header=TRUE) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'1% type I error level',header=TRUE) + a<-table.element(a,signif(numsignificant1,6)) + a<-table.element(a,signif(numsignificant1/numgqtests,6)) + if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK' + a<-table.element(a,dum) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'5% type I error level',header=TRUE) + a<-table.element(a,signif(numsignificant5,6)) + a<-table.element(a,signif(numsignificant5/numgqtests,6)) + if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK' + a<-table.element(a,dum) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'10% type I error level',header=TRUE) + a<-table.element(a,signif(numsignificant10,6)) + a<-table.element(a,signif(numsignificant10/numgqtests,6)) + if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK' + a<-table.element(a,dum) + a<-table.row.end(a) + a<-table.end(a) + table.save(a,file="/var/wessaorg/rcomp/tmp/162u6k1386616249.tab") + } > > try(system("convert tmp/13tbw1386616249.ps tmp/13tbw1386616249.png",intern=TRUE)) character(0) > try(system("convert tmp/2podr1386616249.ps tmp/2podr1386616249.png",intern=TRUE)) character(0) > try(system("convert tmp/3lw2j1386616249.ps tmp/3lw2j1386616249.png",intern=TRUE)) character(0) > try(system("convert tmp/4wtfl1386616249.ps tmp/4wtfl1386616249.png",intern=TRUE)) character(0) > try(system("convert tmp/5em1n1386616249.ps tmp/5em1n1386616249.png",intern=TRUE)) character(0) > try(system("convert tmp/6fvme1386616249.ps tmp/6fvme1386616249.png",intern=TRUE)) character(0) > try(system("convert tmp/7jkvq1386616249.ps tmp/7jkvq1386616249.png",intern=TRUE)) character(0) > try(system("convert tmp/8o0pr1386616249.ps tmp/8o0pr1386616249.png",intern=TRUE)) character(0) > try(system("convert tmp/97tvz1386616249.ps tmp/97tvz1386616249.png",intern=TRUE)) character(0) > try(system("convert tmp/1036de1386616249.ps tmp/1036de1386616249.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 16.063 3.187 19.351