R version 3.0.2 (2013-09-25) -- "Frisbee Sailing" Copyright (C) 2013 The R Foundation for Statistical Computing Platform: i686-pc-linux-gnu (32-bit) R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > x <- array(list(119.992 + ,157.302 + ,74.997 + ,0.00784 + ,0.00007 + ,0.0037 + ,0.00554 + ,122.4 + ,148.65 + ,113.819 + ,0.00968 + ,0.00008 + ,0.00465 + ,0.00696 + ,116.682 + ,131.111 + ,111.555 + ,0.0105 + ,0.00009 + ,0.00544 + ,0.00781 + ,116.676 + ,137.871 + ,111.366 + ,0.00997 + ,0.00009 + ,0.00502 + ,0.00698 + ,116.014 + ,141.781 + ,110.655 + ,0.01284 + ,0.00011 + ,0.00655 + ,0.00908 + ,120.552 + ,131.162 + ,113.787 + ,0.00968 + ,0.00008 + ,0.00463 + ,0.0075 + ,120.267 + ,137.244 + ,114.82 + ,0.00333 + ,0.00003 + ,0.00155 + ,0.00202 + ,107.332 + ,113.84 + ,104.315 + ,0.0029 + ,0.00003 + ,0.00144 + ,0.00182 + ,95.73 + ,132.068 + ,91.754 + ,0.00551 + ,0.00006 + ,0.00293 + ,0.00332 + ,95.056 + ,120.103 + ,91.226 + ,0.00532 + ,0.00006 + ,0.00268 + ,0.00332 + ,88.333 + ,112.24 + ,84.072 + ,0.00505 + ,0.00006 + ,0.00254 + ,0.0033 + ,91.904 + ,115.871 + ,86.292 + ,0.0054 + ,0.00006 + ,0.00281 + ,0.00336 + ,136.926 + ,159.866 + ,131.276 + ,0.00293 + ,0.00002 + 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,1:195)) > y <- array(NA,dim=c(7,195),dimnames=list(c('MDVP:Fo(Hz)','MDVP:Fhi(Hz)','MDVP:Flo(Hz)','MDVP:Jitter(%)','MDVP:Jitter(Abs)','MDVP:RAP','MDVP:PPQ'),1:195)) > for (i in 1:dim(x)[1]) + { + for (j in 1:dim(x)[2]) + { + y[i,j] <- as.numeric(x[i,j]) + } + } > par3 = 'No Linear Trend' > par2 = 'Do not include Seasonal Dummies' > par1 = '1' > par3 <- 'No Linear Trend' > par2 <- 'Do not include Seasonal Dummies' > par1 <- '1' > #'GNU S' R Code compiled by R2WASP v. 1.2.327 () > #Author: root > #To cite this work: Wessa P., (2013), Multiple Regression (v1.0.29) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_multipleregression.wasp/ > #Source of accompanying publication: Office for Research, Development, and Education > # > library(lattice) > library(lmtest) Loading required package: zoo Attaching package: 'zoo' The following objects are masked from 'package:base': as.Date, as.Date.numeric > n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test > par1 <- as.numeric(par1) > x <- t(y) > k <- length(x[1,]) > n <- length(x[,1]) > x1 <- cbind(x[,par1], x[,1:k!=par1]) > mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1]) > colnames(x1) <- mycolnames #colnames(x)[par1] > x <- x1 > if (par3 == 'First Differences'){ + x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep=''))) + for (i in 1:n-1) { + for (j in 1:k) { + x2[i,j] <- x[i+1,j] - x[i,j] + } + } + x <- x2 + } > if (par2 == 'Include Monthly Dummies'){ + x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep =''))) + for (i in 1:11){ + x2[seq(i,n,12),i] <- 1 + } + x <- cbind(x, x2) + } > if (par2 == 'Include Quarterly Dummies'){ + x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep =''))) + for (i in 1:3){ + x2[seq(i,n,4),i] <- 1 + } + x <- cbind(x, x2) + } > k <- length(x[1,]) > if (par3 == 'Linear Trend'){ + x <- cbind(x, c(1:n)) + colnames(x)[k+1] <- 't' + } > x MDVP:Fo(Hz) MDVP:Fhi(Hz) MDVP:Flo(Hz) MDVP:Jitter(%) MDVP:Jitter(Abs) 1 119.992 157.302 74.997 0.00784 7.0e-05 2 122.400 148.650 113.819 0.00968 8.0e-05 3 116.682 131.111 111.555 0.01050 9.0e-05 4 116.676 137.871 111.366 0.00997 9.0e-05 5 116.014 141.781 110.655 0.01284 1.1e-04 6 120.552 131.162 113.787 0.00968 8.0e-05 7 120.267 137.244 114.820 0.00333 3.0e-05 8 107.332 113.840 104.315 0.00290 3.0e-05 9 95.730 132.068 91.754 0.00551 6.0e-05 10 95.056 120.103 91.226 0.00532 6.0e-05 11 88.333 112.240 84.072 0.00505 6.0e-05 12 91.904 115.871 86.292 0.00540 6.0e-05 13 136.926 159.866 131.276 0.00293 2.0e-05 14 139.173 179.139 76.556 0.00390 3.0e-05 15 152.845 163.305 75.836 0.00294 2.0e-05 16 142.167 217.455 83.159 0.00369 3.0e-05 17 144.188 349.259 82.764 0.00544 4.0e-05 18 168.778 232.181 75.603 0.00718 4.0e-05 19 153.046 175.829 68.623 0.00742 5.0e-05 20 156.405 189.398 142.822 0.00768 5.0e-05 21 153.848 165.738 65.782 0.00840 5.0e-05 22 153.880 172.860 78.128 0.00480 3.0e-05 23 167.930 193.221 79.068 0.00442 3.0e-05 24 173.917 192.735 86.180 0.00476 3.0e-05 25 163.656 200.841 76.779 0.00742 5.0e-05 26 104.400 206.002 77.968 0.00633 6.0e-05 27 171.041 208.313 75.501 0.00455 3.0e-05 28 146.845 208.701 81.737 0.00496 3.0e-05 29 155.358 227.383 80.055 0.00310 2.0e-05 30 162.568 198.346 77.630 0.00502 3.0e-05 31 197.076 206.896 192.055 0.00289 1.0e-05 32 199.228 209.512 192.091 0.00241 1.0e-05 33 198.383 215.203 193.104 0.00212 1.0e-05 34 202.266 211.604 197.079 0.00180 9.0e-06 35 203.184 211.526 196.160 0.00178 9.0e-06 36 201.464 210.565 195.708 0.00198 1.0e-05 37 177.876 192.921 168.013 0.00411 2.0e-05 38 176.170 185.604 163.564 0.00369 2.0e-05 39 180.198 201.249 175.456 0.00284 2.0e-05 40 187.733 202.324 173.015 0.00316 2.0e-05 41 186.163 197.724 177.584 0.00298 2.0e-05 42 184.055 196.537 166.977 0.00258 1.0e-05 43 237.226 247.326 225.227 0.00298 1.0e-05 44 241.404 248.834 232.483 0.00281 1.0e-05 45 243.439 250.912 232.435 0.00210 9.0e-06 46 242.852 255.034 227.911 0.00225 9.0e-06 47 245.510 262.090 231.848 0.00235 1.0e-05 48 252.455 261.487 182.786 0.00185 7.0e-06 49 122.188 128.611 115.765 0.00524 4.0e-05 50 122.964 130.049 114.676 0.00428 3.0e-05 51 124.445 135.069 117.495 0.00431 3.0e-05 52 126.344 134.231 112.773 0.00448 4.0e-05 53 128.001 138.052 122.080 0.00436 3.0e-05 54 129.336 139.867 118.604 0.00490 4.0e-05 55 108.807 134.656 102.874 0.00761 7.0e-05 56 109.860 126.358 104.437 0.00874 8.0e-05 57 110.417 131.067 103.370 0.00784 7.0e-05 58 117.274 129.916 110.402 0.00752 6.0e-05 59 116.879 131.897 108.153 0.00788 7.0e-05 60 114.847 271.314 104.680 0.00867 8.0e-05 61 209.144 237.494 109.379 0.00282 1.0e-05 62 223.365 238.987 98.664 0.00264 1.0e-05 63 222.236 231.345 205.495 0.00266 1.0e-05 64 228.832 234.619 223.634 0.00296 1.0e-05 65 229.401 252.221 221.156 0.00205 9.0e-06 66 228.969 239.541 113.201 0.00238 1.0e-05 67 140.341 159.774 67.021 0.00817 6.0e-05 68 136.969 166.607 66.004 0.00923 7.0e-05 69 143.533 162.215 65.809 0.01101 8.0e-05 70 148.090 162.824 67.343 0.00762 5.0e-05 71 142.729 162.408 65.476 0.00831 6.0e-05 72 136.358 176.595 65.750 0.00971 7.0e-05 73 120.080 139.710 111.208 0.00405 3.0e-05 74 112.014 588.518 107.024 0.00533 5.0e-05 75 110.793 128.101 107.316 0.00494 4.0e-05 76 110.707 122.611 105.007 0.00516 5.0e-05 77 112.876 148.826 106.981 0.00500 4.0e-05 78 110.568 125.394 106.821 0.00462 4.0e-05 79 95.385 102.145 90.264 0.00608 6.0e-05 80 100.770 115.697 85.545 0.01038 1.0e-04 81 96.106 108.664 84.510 0.00694 7.0e-05 82 95.605 107.715 87.549 0.00702 7.0e-05 83 100.960 110.019 95.628 0.00606 6.0e-05 84 98.804 102.305 87.804 0.00432 4.0e-05 85 176.858 205.560 75.344 0.00747 4.0e-05 86 180.978 200.125 155.495 0.00406 2.0e-05 87 178.222 202.450 141.047 0.00321 2.0e-05 88 176.281 227.381 125.610 0.00520 3.0e-05 89 173.898 211.350 74.677 0.00448 3.0e-05 90 179.711 225.930 144.878 0.00709 4.0e-05 91 166.605 206.008 78.032 0.00742 4.0e-05 92 151.955 163.335 147.226 0.00419 3.0e-05 93 148.272 164.989 142.299 0.00459 3.0e-05 94 152.125 161.469 76.596 0.00382 3.0e-05 95 157.821 172.975 68.401 0.00358 2.0e-05 96 157.447 163.267 149.605 0.00369 2.0e-05 97 159.116 168.913 144.811 0.00342 2.0e-05 98 125.036 143.946 116.187 0.01280 1.0e-04 99 125.791 140.557 96.206 0.01378 1.1e-04 100 126.512 141.756 99.770 0.01936 1.5e-04 101 125.641 141.068 116.346 0.03316 2.6e-04 102 128.451 150.449 75.632 0.01551 1.2e-04 103 139.224 586.567 66.157 0.03011 2.2e-04 104 150.258 154.609 75.349 0.00248 2.0e-05 105 154.003 160.267 128.621 0.00183 1.0e-05 106 149.689 160.368 133.608 0.00257 2.0e-05 107 155.078 163.736 144.148 0.00168 1.0e-05 108 151.884 157.765 133.751 0.00258 2.0e-05 109 151.989 157.339 132.857 0.00174 1.0e-05 110 193.030 208.900 80.297 0.00766 4.0e-05 111 200.714 223.982 89.686 0.00621 3.0e-05 112 208.519 220.315 199.020 0.00609 3.0e-05 113 204.664 221.300 189.621 0.00841 4.0e-05 114 210.141 232.706 185.258 0.00534 3.0e-05 115 206.327 226.355 92.020 0.00495 2.0e-05 116 151.872 492.892 69.085 0.00856 6.0e-05 117 158.219 442.557 71.948 0.00476 3.0e-05 118 170.756 450.247 79.032 0.00555 3.0e-05 119 178.285 442.824 82.063 0.00462 3.0e-05 120 217.116 233.481 93.978 0.00404 2.0e-05 121 128.940 479.697 88.251 0.00581 5.0e-05 122 176.824 215.293 83.961 0.00460 3.0e-05 123 138.190 203.522 83.340 0.00704 5.0e-05 124 182.018 197.173 79.187 0.00842 5.0e-05 125 156.239 195.107 79.820 0.00694 4.0e-05 126 145.174 198.109 80.637 0.00733 5.0e-05 127 138.145 197.238 81.114 0.00544 4.0e-05 128 166.888 198.966 79.512 0.00638 4.0e-05 129 119.031 127.533 109.216 0.00440 4.0e-05 130 120.078 126.632 105.667 0.00270 2.0e-05 131 120.289 128.143 100.209 0.00492 4.0e-05 132 120.256 125.306 104.773 0.00407 3.0e-05 133 119.056 125.213 86.795 0.00346 3.0e-05 134 118.747 123.723 109.836 0.00331 3.0e-05 135 106.516 112.777 93.105 0.00589 6.0e-05 136 110.453 127.611 105.554 0.00494 4.0e-05 137 113.400 133.344 107.816 0.00451 4.0e-05 138 113.166 130.270 100.673 0.00502 4.0e-05 139 112.239 126.609 104.095 0.00472 4.0e-05 140 116.150 131.731 109.815 0.00381 3.0e-05 141 170.368 268.796 79.543 0.00571 3.0e-05 142 208.083 253.792 91.802 0.00757 4.0e-05 143 198.458 219.290 148.691 0.00376 2.0e-05 144 202.805 231.508 86.232 0.00370 2.0e-05 145 202.544 241.350 164.168 0.00254 1.0e-05 146 223.361 263.872 87.638 0.00352 2.0e-05 147 169.774 191.759 151.451 0.01568 9.0e-05 148 183.520 216.814 161.340 0.01466 8.0e-05 149 188.620 216.302 165.982 0.01719 9.0e-05 150 202.632 565.740 177.258 0.01627 8.0e-05 151 186.695 211.961 149.442 0.01872 1.0e-04 152 192.818 224.429 168.793 0.03107 1.6e-04 153 198.116 233.099 174.478 0.02714 1.4e-04 154 121.345 139.644 98.250 0.00684 6.0e-05 155 119.100 128.442 88.833 0.00692 6.0e-05 156 117.870 127.349 95.654 0.00647 5.0e-05 157 122.336 142.369 94.794 0.00727 6.0e-05 158 117.963 134.209 100.757 0.01813 1.5e-04 159 126.144 154.284 97.543 0.00975 8.0e-05 160 127.930 138.752 112.173 0.00605 5.0e-05 161 114.238 124.393 77.022 0.00581 5.0e-05 162 115.322 135.738 107.802 0.00619 5.0e-05 163 114.554 126.778 91.121 0.00651 6.0e-05 164 112.150 131.669 97.527 0.00519 5.0e-05 165 102.273 142.830 85.902 0.00907 9.0e-05 166 236.200 244.663 102.137 0.00277 1.0e-05 167 237.323 243.709 229.256 0.00303 1.0e-05 168 260.105 264.919 237.303 0.00339 1.0e-05 169 197.569 217.627 90.794 0.00803 4.0e-05 170 240.301 245.135 219.783 0.00517 2.0e-05 171 244.990 272.210 239.170 0.00451 2.0e-05 172 112.547 133.374 105.715 0.00355 3.0e-05 173 110.739 113.597 100.139 0.00356 3.0e-05 174 113.715 116.443 96.913 0.00349 3.0e-05 175 117.004 144.466 99.923 0.00353 3.0e-05 176 115.380 123.109 108.634 0.00332 3.0e-05 177 116.388 129.038 108.970 0.00346 3.0e-05 178 151.737 190.204 129.859 0.00314 2.0e-05 179 148.790 158.359 138.990 0.00309 2.0e-05 180 148.143 155.982 135.041 0.00392 3.0e-05 181 150.440 163.441 144.736 0.00396 3.0e-05 182 148.462 161.078 141.998 0.00397 3.0e-05 183 149.818 163.417 144.786 0.00336 2.0e-05 184 117.226 123.925 106.656 0.00417 4.0e-05 185 116.848 217.552 99.503 0.00531 5.0e-05 186 116.286 177.291 96.983 0.00314 3.0e-05 187 116.556 592.030 86.228 0.00496 4.0e-05 188 116.342 581.289 94.246 0.00267 2.0e-05 189 114.563 119.167 86.647 0.00327 3.0e-05 190 201.774 262.707 78.228 0.00694 3.0e-05 191 174.188 230.978 94.261 0.00459 3.0e-05 192 209.516 253.017 89.488 0.00564 3.0e-05 193 174.688 240.005 74.287 0.01360 8.0e-05 194 198.764 396.961 74.904 0.00740 4.0e-05 195 214.289 260.277 77.973 0.00567 3.0e-05 MDVP:RAP MDVP:PPQ 1 0.00370 0.00554 2 0.00465 0.00696 3 0.00544 0.00781 4 0.00502 0.00698 5 0.00655 0.00908 6 0.00463 0.00750 7 0.00155 0.00202 8 0.00144 0.00182 9 0.00293 0.00332 10 0.00268 0.00332 11 0.00254 0.00330 12 0.00281 0.00336 13 0.00118 0.00153 14 0.00165 0.00208 15 0.00121 0.00149 16 0.00157 0.00203 17 0.00211 0.00292 18 0.00284 0.00387 19 0.00364 0.00432 20 0.00372 0.00399 21 0.00428 0.00450 22 0.00232 0.00267 23 0.00220 0.00247 24 0.00221 0.00258 25 0.00380 0.00390 26 0.00316 0.00375 27 0.00250 0.00234 28 0.00250 0.00275 29 0.00159 0.00176 30 0.00280 0.00253 31 0.00166 0.00168 32 0.00134 0.00138 33 0.00113 0.00135 34 0.00093 0.00107 35 0.00094 0.00106 36 0.00105 0.00115 37 0.00233 0.00241 38 0.00205 0.00218 39 0.00153 0.00166 40 0.00168 0.00182 41 0.00165 0.00175 42 0.00134 0.00147 43 0.00169 0.00182 44 0.00157 0.00173 45 0.00109 0.00137 46 0.00117 0.00139 47 0.00127 0.00148 48 0.00092 0.00113 49 0.00169 0.00203 50 0.00124 0.00155 51 0.00141 0.00167 52 0.00131 0.00169 53 0.00137 0.00166 54 0.00165 0.00183 55 0.00349 0.00486 56 0.00398 0.00539 57 0.00352 0.00514 58 0.00299 0.00469 59 0.00334 0.00493 60 0.00373 0.00520 61 0.00147 0.00152 62 0.00154 0.00151 63 0.00152 0.00144 64 0.00175 0.00155 65 0.00114 0.00113 66 0.00136 0.00140 67 0.00430 0.00440 68 0.00507 0.00463 69 0.00647 0.00467 70 0.00467 0.00354 71 0.00469 0.00419 72 0.00534 0.00478 73 0.00180 0.00220 74 0.00268 0.00329 75 0.00260 0.00283 76 0.00277 0.00289 77 0.00270 0.00289 78 0.00226 0.00280 79 0.00331 0.00332 80 0.00622 0.00576 81 0.00389 0.00415 82 0.00428 0.00371 83 0.00351 0.00348 84 0.00247 0.00258 85 0.00418 0.00420 86 0.00220 0.00244 87 0.00163 0.00194 88 0.00287 0.00312 89 0.00237 0.00254 90 0.00391 0.00419 91 0.00387 0.00453 92 0.00224 0.00227 93 0.00250 0.00256 94 0.00191 0.00226 95 0.00196 0.00196 96 0.00201 0.00197 97 0.00178 0.00184 98 0.00743 0.00623 99 0.00826 0.00655 100 0.01159 0.00990 101 0.02144 0.01522 102 0.00905 0.00909 103 0.01854 0.01628 104 0.00105 0.00136 105 0.00076 0.00100 106 0.00116 0.00134 107 0.00068 0.00092 108 0.00115 0.00122 109 0.00075 0.00096 110 0.00450 0.00389 111 0.00371 0.00337 112 0.00368 0.00339 113 0.00502 0.00485 114 0.00321 0.00280 115 0.00302 0.00246 116 0.00404 0.00385 117 0.00214 0.00207 118 0.00244 0.00261 119 0.00157 0.00194 120 0.00127 0.00128 121 0.00241 0.00314 122 0.00209 0.00221 123 0.00406 0.00398 124 0.00506 0.00449 125 0.00403 0.00395 126 0.00414 0.00422 127 0.00294 0.00327 128 0.00368 0.00351 129 0.00214 0.00192 130 0.00116 0.00135 131 0.00269 0.00238 132 0.00224 0.00205 133 0.00169 0.00170 134 0.00168 0.00171 135 0.00291 0.00319 136 0.00244 0.00315 137 0.00219 0.00283 138 0.00257 0.00312 139 0.00238 0.00290 140 0.00181 0.00232 141 0.00232 0.00269 142 0.00428 0.00428 143 0.00182 0.00215 144 0.00189 0.00211 145 0.00100 0.00133 146 0.00169 0.00188 147 0.00863 0.00946 148 0.00849 0.00819 149 0.00996 0.01027 150 0.00919 0.00963 151 0.01075 0.01154 152 0.01800 0.01958 153 0.01568 0.01699 154 0.00388 0.00332 155 0.00393 0.00300 156 0.00356 0.00300 157 0.00415 0.00339 158 0.01117 0.00718 159 0.00593 0.00454 160 0.00321 0.00318 161 0.00299 0.00316 162 0.00352 0.00329 163 0.00366 0.00340 164 0.00291 0.00284 165 0.00493 0.00461 166 0.00154 0.00153 167 0.00173 0.00159 168 0.00205 0.00186 169 0.00490 0.00448 170 0.00316 0.00283 171 0.00279 0.00237 172 0.00166 0.00190 173 0.00170 0.00200 174 0.00171 0.00203 175 0.00176 0.00218 176 0.00160 0.00199 177 0.00169 0.00213 178 0.00135 0.00162 179 0.00152 0.00186 180 0.00204 0.00231 181 0.00206 0.00233 182 0.00202 0.00235 183 0.00174 0.00198 184 0.00186 0.00270 185 0.00260 0.00346 186 0.00134 0.00192 187 0.00254 0.00263 188 0.00115 0.00148 189 0.00146 0.00184 190 0.00412 0.00396 191 0.00263 0.00259 192 0.00331 0.00292 193 0.00624 0.00564 194 0.00370 0.00390 195 0.00295 0.00317 > k <- length(x[1,]) > df <- as.data.frame(x) > (mylm <- lm(df)) Call: lm(formula = df) Coefficients: (Intercept) `MDVP:Fhi(Hz)` `MDVP:Flo(Hz)` `MDVP:Jitter(%)` 1.211e+02 8.013e-02 2.872e-01 7.471e+03 `MDVP:Jitter(Abs)` `MDVP:RAP` `MDVP:PPQ` -1.983e+06 1.461e+04 -6.857e+03 > (mysum <- summary(mylm)) Call: lm(formula = df) Residuals: Min 1Q Median 3Q Max -65.292 -14.417 -1.565 14.205 57.561 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 1.211e+02 7.239e+00 16.721 < 2e-16 *** `MDVP:Fhi(Hz)` 8.013e-02 1.768e-02 4.532 1.04e-05 *** `MDVP:Flo(Hz)` 2.872e-01 4.041e-02 7.106 2.41e-11 *** `MDVP:Jitter(%)` 7.471e+03 3.394e+03 2.201 0.028941 * `MDVP:Jitter(Abs)` -1.983e+06 1.438e+05 -13.787 < 2e-16 *** `MDVP:RAP` 1.461e+04 3.953e+03 3.696 0.000287 *** `MDVP:PPQ` -6.857e+03 2.588e+03 -2.650 0.008740 ** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 20.74 on 188 degrees of freedom Multiple R-squared: 0.7567, Adjusted R-squared: 0.7489 F-statistic: 97.45 on 6 and 188 DF, p-value: < 2.2e-16 > if (n > n25) { + kp3 <- k + 3 + nmkm3 <- n - k - 3 + gqarr <- array(NA, dim=c(nmkm3-kp3+1,3)) + numgqtests <- 0 + numsignificant1 <- 0 + numsignificant5 <- 0 + numsignificant10 <- 0 + for (mypoint in kp3:nmkm3) { + j <- 0 + numgqtests <- numgqtests + 1 + for (myalt in c('greater', 'two.sided', 'less')) { + j <- j + 1 + gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value + } + if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1 + if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1 + if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1 + } + gqarr + } [,1] [,2] [,3] [1,] 1.208914e-02 2.417827e-02 0.98791086 [2,] 2.115518e-03 4.231036e-03 0.99788448 [3,] 3.299734e-04 6.599469e-04 0.99967003 [4,] 4.804118e-05 9.608236e-05 0.99995196 [5,] 1.101170e-05 2.202340e-05 0.99998899 [6,] 8.433055e-05 1.686611e-04 0.99991567 [7,] 1.832268e-05 3.664537e-05 0.99998168 [8,] 1.701085e-05 3.402169e-05 0.99998299 [9,] 2.872696e-05 5.745392e-05 0.99997127 [10,] 1.352156e-05 2.704311e-05 0.99998648 [11,] 4.534008e-06 9.068016e-06 0.99999547 [12,] 1.342578e-05 2.685157e-05 0.99998657 [13,] 5.379286e-06 1.075857e-05 0.99999462 [14,] 5.327619e-05 1.065524e-04 0.99994672 [15,] 1.898414e-04 3.796827e-04 0.99981016 [16,] 1.001254e-04 2.002508e-04 0.99989987 [17,] 1.015978e-04 2.031957e-04 0.99989840 [18,] 6.497470e-05 1.299494e-04 0.99993503 [19,] 1.416906e-04 2.833812e-04 0.99985831 [20,] 6.743820e-05 1.348764e-04 0.99993256 [21,] 3.137589e-05 6.275178e-05 0.99996862 [22,] 3.893583e-05 7.787167e-05 0.99996106 [23,] 4.953922e-05 9.907845e-05 0.99995046 [24,] 4.569896e-05 9.139792e-05 0.99995430 [25,] 6.117296e-05 1.223459e-04 0.99993883 [26,] 5.918442e-05 1.183688e-04 0.99994082 [27,] 3.984612e-05 7.969224e-05 0.99996015 [28,] 6.439691e-05 1.287938e-04 0.99993560 [29,] 4.316761e-05 8.633522e-05 0.99995683 [30,] 2.332417e-05 4.664833e-05 0.99997668 [31,] 1.394020e-05 2.788041e-05 0.99998606 [32,] 7.911368e-06 1.582274e-05 0.99999209 [33,] 4.707482e-06 9.414965e-06 0.99999529 [34,] 4.522209e-06 9.044417e-06 0.99999548 [35,] 5.690795e-06 1.138159e-05 0.99999431 [36,] 2.121514e-05 4.243029e-05 0.99997878 [37,] 3.752872e-05 7.505743e-05 0.99996247 [38,] 5.342525e-05 1.068505e-04 0.99994657 [39,] 1.030167e-03 2.060333e-03 0.99896983 [40,] 7.579145e-04 1.515829e-03 0.99924209 [41,] 5.315836e-04 1.063167e-03 0.99946842 [42,] 3.959332e-04 7.918664e-04 0.99960407 [43,] 4.202405e-04 8.404809e-04 0.99957976 [44,] 3.200784e-04 6.401568e-04 0.99967992 [45,] 2.334317e-04 4.668634e-04 0.99976657 [46,] 1.694456e-04 3.388912e-04 0.99983055 [47,] 1.819389e-04 3.638778e-04 0.99981806 [48,] 1.502417e-04 3.004834e-04 0.99984976 [49,] 1.058723e-04 2.117446e-04 0.99989413 [50,] 1.314623e-04 2.629246e-04 0.99986854 [51,] 2.709913e-04 5.419826e-04 0.99972901 [52,] 3.276857e-04 6.553714e-04 0.99967231 [53,] 1.411547e-03 2.823094e-03 0.99858845 [54,] 1.015839e-03 2.031678e-03 0.99898416 [55,] 7.116634e-04 1.423327e-03 0.99928834 [56,] 6.493553e-04 1.298711e-03 0.99935064 [57,] 3.642600e-03 7.285200e-03 0.99635740 [58,] 3.021032e-03 6.042064e-03 0.99697897 [59,] 2.379710e-03 4.759420e-03 0.99762029 [60,] 1.879126e-03 3.758252e-03 0.99812087 [61,] 1.745248e-03 3.490496e-03 0.99825475 [62,] 1.205726e-03 2.411452e-03 0.99879427 [63,] 8.319711e-04 1.663942e-03 0.99916803 [64,] 1.120867e-03 2.241733e-03 0.99887913 [65,] 6.504301e-02 1.300860e-01 0.93495699 [66,] 9.195479e-02 1.839096e-01 0.90804521 [67,] 7.686369e-02 1.537274e-01 0.92313631 [68,] 1.078442e-01 2.156884e-01 0.89215579 [69,] 1.092719e-01 2.185438e-01 0.89072811 [70,] 9.061920e-02 1.812384e-01 0.90938080 [71,] 1.078908e-01 2.157816e-01 0.89210918 [72,] 9.841052e-02 1.968210e-01 0.90158948 [73,] 8.119183e-02 1.623837e-01 0.91880817 [74,] 6.789241e-02 1.357848e-01 0.93210759 [75,] 7.790961e-02 1.558192e-01 0.92209039 [76,] 6.637699e-02 1.327540e-01 0.93362301 [77,] 6.347236e-02 1.269447e-01 0.93652764 [78,] 5.367123e-02 1.073425e-01 0.94632877 [79,] 4.645680e-02 9.291361e-02 0.95354320 [80,] 5.052928e-02 1.010586e-01 0.94947072 [81,] 5.187519e-02 1.037504e-01 0.94812481 [82,] 4.626260e-02 9.252521e-02 0.95373740 [83,] 4.128537e-02 8.257074e-02 0.95871463 [84,] 4.586496e-02 9.172993e-02 0.95413504 [85,] 4.435984e-02 8.871969e-02 0.95564016 [86,] 3.577426e-02 7.154852e-02 0.96422574 [87,] 4.413071e-02 8.826143e-02 0.95586929 [88,] 4.229308e-02 8.458616e-02 0.95770692 [89,] 3.380438e-02 6.760876e-02 0.96619562 [90,] 2.743757e-02 5.487515e-02 0.97256243 [91,] 2.585982e-02 5.171964e-02 0.97414018 [92,] 2.059354e-02 4.118708e-02 0.97940646 [93,] 3.860775e-02 7.721551e-02 0.96139225 [94,] 9.305126e-02 1.861025e-01 0.90694874 [95,] 8.403786e-02 1.680757e-01 0.91596214 [96,] 8.134040e-02 1.626808e-01 0.91865960 [97,] 6.885360e-02 1.377072e-01 0.93114640 [98,] 6.731608e-02 1.346322e-01 0.93268392 [99,] 5.619940e-02 1.123988e-01 0.94380060 [100,] 5.744454e-02 1.148891e-01 0.94255546 [101,] 4.723586e-02 9.447171e-02 0.95276414 [102,] 3.892255e-02 7.784511e-02 0.96107745 [103,] 3.478470e-02 6.956940e-02 0.96521530 [104,] 4.319815e-02 8.639629e-02 0.95680185 [105,] 3.551872e-02 7.103744e-02 0.96448128 [106,] 2.986600e-02 5.973199e-02 0.97013400 [107,] 2.334597e-02 4.669195e-02 0.97665403 [108,] 2.001625e-02 4.003249e-02 0.97998375 [109,] 1.713680e-02 3.427360e-02 0.98286320 [110,] 1.661922e-02 3.323845e-02 0.98338078 [111,] 5.082697e-02 1.016539e-01 0.94917303 [112,] 4.876980e-02 9.753961e-02 0.95123020 [113,] 5.383920e-02 1.076784e-01 0.94616080 [114,] 4.561257e-02 9.122515e-02 0.95438743 [115,] 3.684663e-02 7.369325e-02 0.96315337 [116,] 3.438905e-02 6.877809e-02 0.96561095 [117,] 2.773881e-02 5.547762e-02 0.97226119 [118,] 2.210500e-02 4.421000e-02 0.97789500 [119,] 1.715385e-02 3.430771e-02 0.98284615 [120,] 1.409895e-02 2.819790e-02 0.98590105 [121,] 2.269925e-02 4.539851e-02 0.97730075 [122,] 2.320456e-02 4.640911e-02 0.97679544 [123,] 4.163794e-02 8.327587e-02 0.95836206 [124,] 4.010837e-02 8.021674e-02 0.95989163 [125,] 4.107165e-02 8.214329e-02 0.95892835 [126,] 4.937627e-02 9.875254e-02 0.95062373 [127,] 4.716584e-02 9.433168e-02 0.95283416 [128,] 3.965875e-02 7.931750e-02 0.96034125 [129,] 3.713780e-02 7.427561e-02 0.96286220 [130,] 3.227792e-02 6.455584e-02 0.96772208 [131,] 3.650389e-02 7.300779e-02 0.96349611 [132,] 2.836916e-02 5.673832e-02 0.97163084 [133,] 2.888410e-02 5.776820e-02 0.97111590 [134,] 2.609709e-02 5.219419e-02 0.97390291 [135,] 4.151969e-02 8.303939e-02 0.95848031 [136,] 3.346319e-02 6.692638e-02 0.96653681 [137,] 1.814385e-01 3.628771e-01 0.81856147 [138,] 2.110759e-01 4.221518e-01 0.78892412 [139,] 2.465547e-01 4.931093e-01 0.75344535 [140,] 2.784836e-01 5.569672e-01 0.72151642 [141,] 3.800358e-01 7.600715e-01 0.61996424 [142,] 3.458922e-01 6.917844e-01 0.65410781 [143,] 3.537043e-01 7.074085e-01 0.64629573 [144,] 6.635102e-01 6.729795e-01 0.33648975 [145,] 6.171376e-01 7.657247e-01 0.38286235 [146,] 5.717477e-01 8.565045e-01 0.42825226 [147,] 5.624965e-01 8.750070e-01 0.43750350 [148,] 5.110885e-01 9.778230e-01 0.48891149 [149,] 4.614424e-01 9.228849e-01 0.53855756 [150,] 4.312435e-01 8.624871e-01 0.56875647 [151,] 3.789846e-01 7.579692e-01 0.62101539 [152,] 3.259505e-01 6.519009e-01 0.67404953 [153,] 3.742687e-01 7.485373e-01 0.62573134 [154,] 3.193451e-01 6.386902e-01 0.68065491 [155,] 2.714526e-01 5.429051e-01 0.72854744 [156,] 4.725701e-01 9.451402e-01 0.52742990 [157,] 8.434183e-01 3.131634e-01 0.15658172 [158,] 8.145131e-01 3.709738e-01 0.18548690 [159,] 8.396441e-01 3.207117e-01 0.16035586 [160,] 9.131366e-01 1.737267e-01 0.08686336 [161,] 8.908316e-01 2.183368e-01 0.10916838 [162,] 8.984392e-01 2.031217e-01 0.10156084 [163,] 8.725036e-01 2.549927e-01 0.12749636 [164,] 8.591101e-01 2.817798e-01 0.14088991 [165,] 8.394781e-01 3.210438e-01 0.16052191 [166,] 8.295099e-01 3.409803e-01 0.17049015 [167,] 7.968833e-01 4.062335e-01 0.20311674 [168,] 7.952747e-01 4.094506e-01 0.20472528 [169,] 7.316957e-01 5.366086e-01 0.26830431 [170,] 6.494132e-01 7.011736e-01 0.35058681 [171,] 5.516794e-01 8.966412e-01 0.44832059 [172,] 4.605224e-01 9.210449e-01 0.53947757 [173,] 3.783931e-01 7.567863e-01 0.62160687 [174,] 2.741743e-01 5.483487e-01 0.72582566 [175,] 1.739434e-01 3.478868e-01 0.82605660 [176,] 9.934414e-02 1.986883e-01 0.90065586 > postscript(file="/var/wessaorg/rcomp/tmp/18rfg1386167226.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$resid) > grid() > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/2tcf11386167226.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(mysum$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/3y6gy1386167226.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') > grid() > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/43phb1386167226.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/5ay3s1386167226.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 28.9681605 22.8613659 27.1888789 31.1005518 40.5933544 26.4190634 7 8 9 10 11 12 -18.9364688 -23.5310425 -4.4791520 1.0296847 0.9168305 -2.5892800 13 14 15 16 17 18 -23.6118946 2.2922123 7.1645009 2.7148866 -0.7429827 18.1327106 19 20 21 22 23 24 18.3547645 -6.0551033 5.3421781 4.5827902 19.9523539 22.0039727 25 26 27 28 29 30 19.4005020 -4.3130024 16.6321134 -9.6374021 -1.5649121 1.7542858 31 32 33 34 35 36 -10.2009562 -2.0641726 1.3733209 5.8132181 6.9361039 4.9216236 37 38 39 40 41 42 -15.4451444 -9.6352426 0.1070196 4.7714709 3.5611717 -9.6386319 43 44 45 46 47 48 17.0329322 21.4127985 31.1615409 29.3908990 30.7449065 52.3273866 49 50 51 52 53 54 -13.0112561 -21.4125612 -23.0285732 0.4527019 -20.8859038 -5.8272320 55 56 57 58 59 60 11.7163536 20.8485183 13.2348340 5.3830159 19.1480482 17.0236090 61 62 63 64 65 66 25.3587946 42.7905355 11.2587611 7.5358083 18.2547677 47.9939788 67 68 69 70 71 72 12.5286045 11.1386994 4.4606026 -7.0847045 6.9646044 3.2975564 73 74 75 76 77 78 -26.0817245 -44.1938244 -27.5085983 -10.3769317 -28.4880764 -20.2214746 79 80 81 82 83 84 -11.8096490 15.2537791 0.6637988 -9.9473771 -10.0816761 -27.0101285 85 86 87 88 89 90 8.9367055 -6.8468739 5.6103146 1.0408683 23.2763735 -3.0944475 91 92 93 94 95 96 5.0420546 -13.4379388 -20.6375285 14.6815664 0.9832717 -23.4154730 97 98 99 100 101 102 -16.3357439 -4.0806483 5.2594378 16.8102336 18.8270423 25.8150407 103 104 105 106 107 108 4.2315975 10.2973338 -14.9146601 -9.8798107 -16.8353732 -8.2687785 109 110 111 112 113 114 -17.3662332 15.1976751 17.9568001 -3.8690042 -12.1741902 9.1371518 115 116 117 118 119 120 16.1341356 -6.1117254 -12.0995039 -8.7957689 13.5236677 50.0675767 121 122 123 124 125 126 -13.8268245 24.1522210 -8.5758082 15.5287303 -7.6928824 -2.0716944 127 128 129 130 131 132 -3.8585862 9.0161037 -15.2550316 -29.6666463 -20.2265474 -30.5108626 133 134 135 136 137 138 -16.3469588 -21.8177165 4.0263713 -22.7711125 -16.2618474 -21.5724806 139 140 141 142 143 144 -19.6798215 -26.5024999 6.3155554 29.9110935 16.8565137 37.3115699 145 146 147 148 149 150 10.3730136 57.5605065 -10.0332099 -20.0080910 -22.4872961 -45.8084689 151 152 153 154 155 156 -13.7492519 -38.2682172 -29.4587150 -5.1545162 -7.3203428 -21.4840254 157 158 159 160 161 162 -10.0672162 5.2570278 -4.9930985 -7.5950244 -5.1718532 -23.5276342 163 164 165 166 167 168 -2.6387135 -10.1244117 15.3996989 53.3392963 13.7275517 26.9853044 169 170 171 172 173 174 11.4598315 10.7624172 14.8976658 -27.8055972 -26.4011140 -22.1441842 175 176 177 178 179 180 -21.9658268 -21.7760545 -22.7406227 -14.2607259 -17.7427798 -7.9477932 181 182 183 184 185 186 -9.4864564 -9.8419536 -23.1934013 -4.8773660 -4.9908790 -17.2019959 187 188 189 190 191 192 -53.5081870 -65.2923811 -14.5727302 11.8051607 12.0917788 31.5066015 193 194 195 17.6097187 21.1117409 45.7549912 > postscript(file="/var/wessaorg/rcomp/tmp/6ng3z1386167226.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 28.9681605 NA 1 22.8613659 28.9681605 2 27.1888789 22.8613659 3 31.1005518 27.1888789 4 40.5933544 31.1005518 5 26.4190634 40.5933544 6 -18.9364688 26.4190634 7 -23.5310425 -18.9364688 8 -4.4791520 -23.5310425 9 1.0296847 -4.4791520 10 0.9168305 1.0296847 11 -2.5892800 0.9168305 12 -23.6118946 -2.5892800 13 2.2922123 -23.6118946 14 7.1645009 2.2922123 15 2.7148866 7.1645009 16 -0.7429827 2.7148866 17 18.1327106 -0.7429827 18 18.3547645 18.1327106 19 -6.0551033 18.3547645 20 5.3421781 -6.0551033 21 4.5827902 5.3421781 22 19.9523539 4.5827902 23 22.0039727 19.9523539 24 19.4005020 22.0039727 25 -4.3130024 19.4005020 26 16.6321134 -4.3130024 27 -9.6374021 16.6321134 28 -1.5649121 -9.6374021 29 1.7542858 -1.5649121 30 -10.2009562 1.7542858 31 -2.0641726 -10.2009562 32 1.3733209 -2.0641726 33 5.8132181 1.3733209 34 6.9361039 5.8132181 35 4.9216236 6.9361039 36 -15.4451444 4.9216236 37 -9.6352426 -15.4451444 38 0.1070196 -9.6352426 39 4.7714709 0.1070196 40 3.5611717 4.7714709 41 -9.6386319 3.5611717 42 17.0329322 -9.6386319 43 21.4127985 17.0329322 44 31.1615409 21.4127985 45 29.3908990 31.1615409 46 30.7449065 29.3908990 47 52.3273866 30.7449065 48 -13.0112561 52.3273866 49 -21.4125612 -13.0112561 50 -23.0285732 -21.4125612 51 0.4527019 -23.0285732 52 -20.8859038 0.4527019 53 -5.8272320 -20.8859038 54 11.7163536 -5.8272320 55 20.8485183 11.7163536 56 13.2348340 20.8485183 57 5.3830159 13.2348340 58 19.1480482 5.3830159 59 17.0236090 19.1480482 60 25.3587946 17.0236090 61 42.7905355 25.3587946 62 11.2587611 42.7905355 63 7.5358083 11.2587611 64 18.2547677 7.5358083 65 47.9939788 18.2547677 66 12.5286045 47.9939788 67 11.1386994 12.5286045 68 4.4606026 11.1386994 69 -7.0847045 4.4606026 70 6.9646044 -7.0847045 71 3.2975564 6.9646044 72 -26.0817245 3.2975564 73 -44.1938244 -26.0817245 74 -27.5085983 -44.1938244 75 -10.3769317 -27.5085983 76 -28.4880764 -10.3769317 77 -20.2214746 -28.4880764 78 -11.8096490 -20.2214746 79 15.2537791 -11.8096490 80 0.6637988 15.2537791 81 -9.9473771 0.6637988 82 -10.0816761 -9.9473771 83 -27.0101285 -10.0816761 84 8.9367055 -27.0101285 85 -6.8468739 8.9367055 86 5.6103146 -6.8468739 87 1.0408683 5.6103146 88 23.2763735 1.0408683 89 -3.0944475 23.2763735 90 5.0420546 -3.0944475 91 -13.4379388 5.0420546 92 -20.6375285 -13.4379388 93 14.6815664 -20.6375285 94 0.9832717 14.6815664 95 -23.4154730 0.9832717 96 -16.3357439 -23.4154730 97 -4.0806483 -16.3357439 98 5.2594378 -4.0806483 99 16.8102336 5.2594378 100 18.8270423 16.8102336 101 25.8150407 18.8270423 102 4.2315975 25.8150407 103 10.2973338 4.2315975 104 -14.9146601 10.2973338 105 -9.8798107 -14.9146601 106 -16.8353732 -9.8798107 107 -8.2687785 -16.8353732 108 -17.3662332 -8.2687785 109 15.1976751 -17.3662332 110 17.9568001 15.1976751 111 -3.8690042 17.9568001 112 -12.1741902 -3.8690042 113 9.1371518 -12.1741902 114 16.1341356 9.1371518 115 -6.1117254 16.1341356 116 -12.0995039 -6.1117254 117 -8.7957689 -12.0995039 118 13.5236677 -8.7957689 119 50.0675767 13.5236677 120 -13.8268245 50.0675767 121 24.1522210 -13.8268245 122 -8.5758082 24.1522210 123 15.5287303 -8.5758082 124 -7.6928824 15.5287303 125 -2.0716944 -7.6928824 126 -3.8585862 -2.0716944 127 9.0161037 -3.8585862 128 -15.2550316 9.0161037 129 -29.6666463 -15.2550316 130 -20.2265474 -29.6666463 131 -30.5108626 -20.2265474 132 -16.3469588 -30.5108626 133 -21.8177165 -16.3469588 134 4.0263713 -21.8177165 135 -22.7711125 4.0263713 136 -16.2618474 -22.7711125 137 -21.5724806 -16.2618474 138 -19.6798215 -21.5724806 139 -26.5024999 -19.6798215 140 6.3155554 -26.5024999 141 29.9110935 6.3155554 142 16.8565137 29.9110935 143 37.3115699 16.8565137 144 10.3730136 37.3115699 145 57.5605065 10.3730136 146 -10.0332099 57.5605065 147 -20.0080910 -10.0332099 148 -22.4872961 -20.0080910 149 -45.8084689 -22.4872961 150 -13.7492519 -45.8084689 151 -38.2682172 -13.7492519 152 -29.4587150 -38.2682172 153 -5.1545162 -29.4587150 154 -7.3203428 -5.1545162 155 -21.4840254 -7.3203428 156 -10.0672162 -21.4840254 157 5.2570278 -10.0672162 158 -4.9930985 5.2570278 159 -7.5950244 -4.9930985 160 -5.1718532 -7.5950244 161 -23.5276342 -5.1718532 162 -2.6387135 -23.5276342 163 -10.1244117 -2.6387135 164 15.3996989 -10.1244117 165 53.3392963 15.3996989 166 13.7275517 53.3392963 167 26.9853044 13.7275517 168 11.4598315 26.9853044 169 10.7624172 11.4598315 170 14.8976658 10.7624172 171 -27.8055972 14.8976658 172 -26.4011140 -27.8055972 173 -22.1441842 -26.4011140 174 -21.9658268 -22.1441842 175 -21.7760545 -21.9658268 176 -22.7406227 -21.7760545 177 -14.2607259 -22.7406227 178 -17.7427798 -14.2607259 179 -7.9477932 -17.7427798 180 -9.4864564 -7.9477932 181 -9.8419536 -9.4864564 182 -23.1934013 -9.8419536 183 -4.8773660 -23.1934013 184 -4.9908790 -4.8773660 185 -17.2019959 -4.9908790 186 -53.5081870 -17.2019959 187 -65.2923811 -53.5081870 188 -14.5727302 -65.2923811 189 11.8051607 -14.5727302 190 12.0917788 11.8051607 191 31.5066015 12.0917788 192 17.6097187 31.5066015 193 21.1117409 17.6097187 194 45.7549912 21.1117409 195 NA 45.7549912 > dum1 <- dum[2:length(myerror),] > dum1 lag(myerror, k = 1) myerror [1,] 22.8613659 28.9681605 [2,] 27.1888789 22.8613659 [3,] 31.1005518 27.1888789 [4,] 40.5933544 31.1005518 [5,] 26.4190634 40.5933544 [6,] -18.9364688 26.4190634 [7,] -23.5310425 -18.9364688 [8,] -4.4791520 -23.5310425 [9,] 1.0296847 -4.4791520 [10,] 0.9168305 1.0296847 [11,] -2.5892800 0.9168305 [12,] -23.6118946 -2.5892800 [13,] 2.2922123 -23.6118946 [14,] 7.1645009 2.2922123 [15,] 2.7148866 7.1645009 [16,] -0.7429827 2.7148866 [17,] 18.1327106 -0.7429827 [18,] 18.3547645 18.1327106 [19,] -6.0551033 18.3547645 [20,] 5.3421781 -6.0551033 [21,] 4.5827902 5.3421781 [22,] 19.9523539 4.5827902 [23,] 22.0039727 19.9523539 [24,] 19.4005020 22.0039727 [25,] -4.3130024 19.4005020 [26,] 16.6321134 -4.3130024 [27,] -9.6374021 16.6321134 [28,] -1.5649121 -9.6374021 [29,] 1.7542858 -1.5649121 [30,] -10.2009562 1.7542858 [31,] -2.0641726 -10.2009562 [32,] 1.3733209 -2.0641726 [33,] 5.8132181 1.3733209 [34,] 6.9361039 5.8132181 [35,] 4.9216236 6.9361039 [36,] -15.4451444 4.9216236 [37,] -9.6352426 -15.4451444 [38,] 0.1070196 -9.6352426 [39,] 4.7714709 0.1070196 [40,] 3.5611717 4.7714709 [41,] -9.6386319 3.5611717 [42,] 17.0329322 -9.6386319 [43,] 21.4127985 17.0329322 [44,] 31.1615409 21.4127985 [45,] 29.3908990 31.1615409 [46,] 30.7449065 29.3908990 [47,] 52.3273866 30.7449065 [48,] -13.0112561 52.3273866 [49,] -21.4125612 -13.0112561 [50,] -23.0285732 -21.4125612 [51,] 0.4527019 -23.0285732 [52,] -20.8859038 0.4527019 [53,] -5.8272320 -20.8859038 [54,] 11.7163536 -5.8272320 [55,] 20.8485183 11.7163536 [56,] 13.2348340 20.8485183 [57,] 5.3830159 13.2348340 [58,] 19.1480482 5.3830159 [59,] 17.0236090 19.1480482 [60,] 25.3587946 17.0236090 [61,] 42.7905355 25.3587946 [62,] 11.2587611 42.7905355 [63,] 7.5358083 11.2587611 [64,] 18.2547677 7.5358083 [65,] 47.9939788 18.2547677 [66,] 12.5286045 47.9939788 [67,] 11.1386994 12.5286045 [68,] 4.4606026 11.1386994 [69,] -7.0847045 4.4606026 [70,] 6.9646044 -7.0847045 [71,] 3.2975564 6.9646044 [72,] -26.0817245 3.2975564 [73,] -44.1938244 -26.0817245 [74,] -27.5085983 -44.1938244 [75,] -10.3769317 -27.5085983 [76,] -28.4880764 -10.3769317 [77,] -20.2214746 -28.4880764 [78,] -11.8096490 -20.2214746 [79,] 15.2537791 -11.8096490 [80,] 0.6637988 15.2537791 [81,] -9.9473771 0.6637988 [82,] -10.0816761 -9.9473771 [83,] -27.0101285 -10.0816761 [84,] 8.9367055 -27.0101285 [85,] -6.8468739 8.9367055 [86,] 5.6103146 -6.8468739 [87,] 1.0408683 5.6103146 [88,] 23.2763735 1.0408683 [89,] -3.0944475 23.2763735 [90,] 5.0420546 -3.0944475 [91,] -13.4379388 5.0420546 [92,] -20.6375285 -13.4379388 [93,] 14.6815664 -20.6375285 [94,] 0.9832717 14.6815664 [95,] -23.4154730 0.9832717 [96,] -16.3357439 -23.4154730 [97,] -4.0806483 -16.3357439 [98,] 5.2594378 -4.0806483 [99,] 16.8102336 5.2594378 [100,] 18.8270423 16.8102336 [101,] 25.8150407 18.8270423 [102,] 4.2315975 25.8150407 [103,] 10.2973338 4.2315975 [104,] -14.9146601 10.2973338 [105,] -9.8798107 -14.9146601 [106,] -16.8353732 -9.8798107 [107,] -8.2687785 -16.8353732 [108,] -17.3662332 -8.2687785 [109,] 15.1976751 -17.3662332 [110,] 17.9568001 15.1976751 [111,] -3.8690042 17.9568001 [112,] -12.1741902 -3.8690042 [113,] 9.1371518 -12.1741902 [114,] 16.1341356 9.1371518 [115,] -6.1117254 16.1341356 [116,] -12.0995039 -6.1117254 [117,] -8.7957689 -12.0995039 [118,] 13.5236677 -8.7957689 [119,] 50.0675767 13.5236677 [120,] -13.8268245 50.0675767 [121,] 24.1522210 -13.8268245 [122,] -8.5758082 24.1522210 [123,] 15.5287303 -8.5758082 [124,] -7.6928824 15.5287303 [125,] -2.0716944 -7.6928824 [126,] -3.8585862 -2.0716944 [127,] 9.0161037 -3.8585862 [128,] -15.2550316 9.0161037 [129,] -29.6666463 -15.2550316 [130,] -20.2265474 -29.6666463 [131,] -30.5108626 -20.2265474 [132,] -16.3469588 -30.5108626 [133,] -21.8177165 -16.3469588 [134,] 4.0263713 -21.8177165 [135,] -22.7711125 4.0263713 [136,] -16.2618474 -22.7711125 [137,] -21.5724806 -16.2618474 [138,] -19.6798215 -21.5724806 [139,] -26.5024999 -19.6798215 [140,] 6.3155554 -26.5024999 [141,] 29.9110935 6.3155554 [142,] 16.8565137 29.9110935 [143,] 37.3115699 16.8565137 [144,] 10.3730136 37.3115699 [145,] 57.5605065 10.3730136 [146,] -10.0332099 57.5605065 [147,] -20.0080910 -10.0332099 [148,] -22.4872961 -20.0080910 [149,] -45.8084689 -22.4872961 [150,] -13.7492519 -45.8084689 [151,] -38.2682172 -13.7492519 [152,] -29.4587150 -38.2682172 [153,] -5.1545162 -29.4587150 [154,] -7.3203428 -5.1545162 [155,] -21.4840254 -7.3203428 [156,] -10.0672162 -21.4840254 [157,] 5.2570278 -10.0672162 [158,] -4.9930985 5.2570278 [159,] -7.5950244 -4.9930985 [160,] -5.1718532 -7.5950244 [161,] -23.5276342 -5.1718532 [162,] -2.6387135 -23.5276342 [163,] -10.1244117 -2.6387135 [164,] 15.3996989 -10.1244117 [165,] 53.3392963 15.3996989 [166,] 13.7275517 53.3392963 [167,] 26.9853044 13.7275517 [168,] 11.4598315 26.9853044 [169,] 10.7624172 11.4598315 [170,] 14.8976658 10.7624172 [171,] -27.8055972 14.8976658 [172,] -26.4011140 -27.8055972 [173,] -22.1441842 -26.4011140 [174,] -21.9658268 -22.1441842 [175,] -21.7760545 -21.9658268 [176,] -22.7406227 -21.7760545 [177,] -14.2607259 -22.7406227 [178,] -17.7427798 -14.2607259 [179,] -7.9477932 -17.7427798 [180,] -9.4864564 -7.9477932 [181,] -9.8419536 -9.4864564 [182,] -23.1934013 -9.8419536 [183,] -4.8773660 -23.1934013 [184,] -4.9908790 -4.8773660 [185,] -17.2019959 -4.9908790 [186,] -53.5081870 -17.2019959 [187,] -65.2923811 -53.5081870 [188,] -14.5727302 -65.2923811 [189,] 11.8051607 -14.5727302 [190,] 12.0917788 11.8051607 [191,] 31.5066015 12.0917788 [192,] 17.6097187 31.5066015 [193,] 21.1117409 17.6097187 [194,] 45.7549912 21.1117409 > z <- as.data.frame(dum1) > z lag(myerror, k = 1) myerror 1 22.8613659 28.9681605 2 27.1888789 22.8613659 3 31.1005518 27.1888789 4 40.5933544 31.1005518 5 26.4190634 40.5933544 6 -18.9364688 26.4190634 7 -23.5310425 -18.9364688 8 -4.4791520 -23.5310425 9 1.0296847 -4.4791520 10 0.9168305 1.0296847 11 -2.5892800 0.9168305 12 -23.6118946 -2.5892800 13 2.2922123 -23.6118946 14 7.1645009 2.2922123 15 2.7148866 7.1645009 16 -0.7429827 2.7148866 17 18.1327106 -0.7429827 18 18.3547645 18.1327106 19 -6.0551033 18.3547645 20 5.3421781 -6.0551033 21 4.5827902 5.3421781 22 19.9523539 4.5827902 23 22.0039727 19.9523539 24 19.4005020 22.0039727 25 -4.3130024 19.4005020 26 16.6321134 -4.3130024 27 -9.6374021 16.6321134 28 -1.5649121 -9.6374021 29 1.7542858 -1.5649121 30 -10.2009562 1.7542858 31 -2.0641726 -10.2009562 32 1.3733209 -2.0641726 33 5.8132181 1.3733209 34 6.9361039 5.8132181 35 4.9216236 6.9361039 36 -15.4451444 4.9216236 37 -9.6352426 -15.4451444 38 0.1070196 -9.6352426 39 4.7714709 0.1070196 40 3.5611717 4.7714709 41 -9.6386319 3.5611717 42 17.0329322 -9.6386319 43 21.4127985 17.0329322 44 31.1615409 21.4127985 45 29.3908990 31.1615409 46 30.7449065 29.3908990 47 52.3273866 30.7449065 48 -13.0112561 52.3273866 49 -21.4125612 -13.0112561 50 -23.0285732 -21.4125612 51 0.4527019 -23.0285732 52 -20.8859038 0.4527019 53 -5.8272320 -20.8859038 54 11.7163536 -5.8272320 55 20.8485183 11.7163536 56 13.2348340 20.8485183 57 5.3830159 13.2348340 58 19.1480482 5.3830159 59 17.0236090 19.1480482 60 25.3587946 17.0236090 61 42.7905355 25.3587946 62 11.2587611 42.7905355 63 7.5358083 11.2587611 64 18.2547677 7.5358083 65 47.9939788 18.2547677 66 12.5286045 47.9939788 67 11.1386994 12.5286045 68 4.4606026 11.1386994 69 -7.0847045 4.4606026 70 6.9646044 -7.0847045 71 3.2975564 6.9646044 72 -26.0817245 3.2975564 73 -44.1938244 -26.0817245 74 -27.5085983 -44.1938244 75 -10.3769317 -27.5085983 76 -28.4880764 -10.3769317 77 -20.2214746 -28.4880764 78 -11.8096490 -20.2214746 79 15.2537791 -11.8096490 80 0.6637988 15.2537791 81 -9.9473771 0.6637988 82 -10.0816761 -9.9473771 83 -27.0101285 -10.0816761 84 8.9367055 -27.0101285 85 -6.8468739 8.9367055 86 5.6103146 -6.8468739 87 1.0408683 5.6103146 88 23.2763735 1.0408683 89 -3.0944475 23.2763735 90 5.0420546 -3.0944475 91 -13.4379388 5.0420546 92 -20.6375285 -13.4379388 93 14.6815664 -20.6375285 94 0.9832717 14.6815664 95 -23.4154730 0.9832717 96 -16.3357439 -23.4154730 97 -4.0806483 -16.3357439 98 5.2594378 -4.0806483 99 16.8102336 5.2594378 100 18.8270423 16.8102336 101 25.8150407 18.8270423 102 4.2315975 25.8150407 103 10.2973338 4.2315975 104 -14.9146601 10.2973338 105 -9.8798107 -14.9146601 106 -16.8353732 -9.8798107 107 -8.2687785 -16.8353732 108 -17.3662332 -8.2687785 109 15.1976751 -17.3662332 110 17.9568001 15.1976751 111 -3.8690042 17.9568001 112 -12.1741902 -3.8690042 113 9.1371518 -12.1741902 114 16.1341356 9.1371518 115 -6.1117254 16.1341356 116 -12.0995039 -6.1117254 117 -8.7957689 -12.0995039 118 13.5236677 -8.7957689 119 50.0675767 13.5236677 120 -13.8268245 50.0675767 121 24.1522210 -13.8268245 122 -8.5758082 24.1522210 123 15.5287303 -8.5758082 124 -7.6928824 15.5287303 125 -2.0716944 -7.6928824 126 -3.8585862 -2.0716944 127 9.0161037 -3.8585862 128 -15.2550316 9.0161037 129 -29.6666463 -15.2550316 130 -20.2265474 -29.6666463 131 -30.5108626 -20.2265474 132 -16.3469588 -30.5108626 133 -21.8177165 -16.3469588 134 4.0263713 -21.8177165 135 -22.7711125 4.0263713 136 -16.2618474 -22.7711125 137 -21.5724806 -16.2618474 138 -19.6798215 -21.5724806 139 -26.5024999 -19.6798215 140 6.3155554 -26.5024999 141 29.9110935 6.3155554 142 16.8565137 29.9110935 143 37.3115699 16.8565137 144 10.3730136 37.3115699 145 57.5605065 10.3730136 146 -10.0332099 57.5605065 147 -20.0080910 -10.0332099 148 -22.4872961 -20.0080910 149 -45.8084689 -22.4872961 150 -13.7492519 -45.8084689 151 -38.2682172 -13.7492519 152 -29.4587150 -38.2682172 153 -5.1545162 -29.4587150 154 -7.3203428 -5.1545162 155 -21.4840254 -7.3203428 156 -10.0672162 -21.4840254 157 5.2570278 -10.0672162 158 -4.9930985 5.2570278 159 -7.5950244 -4.9930985 160 -5.1718532 -7.5950244 161 -23.5276342 -5.1718532 162 -2.6387135 -23.5276342 163 -10.1244117 -2.6387135 164 15.3996989 -10.1244117 165 53.3392963 15.3996989 166 13.7275517 53.3392963 167 26.9853044 13.7275517 168 11.4598315 26.9853044 169 10.7624172 11.4598315 170 14.8976658 10.7624172 171 -27.8055972 14.8976658 172 -26.4011140 -27.8055972 173 -22.1441842 -26.4011140 174 -21.9658268 -22.1441842 175 -21.7760545 -21.9658268 176 -22.7406227 -21.7760545 177 -14.2607259 -22.7406227 178 -17.7427798 -14.2607259 179 -7.9477932 -17.7427798 180 -9.4864564 -7.9477932 181 -9.8419536 -9.4864564 182 -23.1934013 -9.8419536 183 -4.8773660 -23.1934013 184 -4.9908790 -4.8773660 185 -17.2019959 -4.9908790 186 -53.5081870 -17.2019959 187 -65.2923811 -53.5081870 188 -14.5727302 -65.2923811 189 11.8051607 -14.5727302 190 12.0917788 11.8051607 191 31.5066015 12.0917788 192 17.6097187 31.5066015 193 21.1117409 17.6097187 194 45.7549912 21.1117409 > 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/7fvsp1386167226.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/855871386167226.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/9hsmw1386167226.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0)) > plot(mylm, las = 1, sub='Residual Diagnostics') > par(opar) > dev.off() null device 1 > if (n > n25) { + postscript(file="/var/wessaorg/rcomp/tmp/10qov11386167226.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) + plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint') + grid() + dev.off() + } null device 1 > > #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab > load(file="/var/wessaorg/rcomp/createtable") > > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE) > a<-table.row.end(a) > myeq <- colnames(x)[1] > myeq <- paste(myeq, '[t] = ', sep='') > for (i in 1:k){ + if (mysum$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/117pch1386167226.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/12cpuf1386167226.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/137pdb1386167226.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/14x20v1386167226.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/15zwki1386167226.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/16agig1386167226.tab") + } > > try(system("convert tmp/18rfg1386167226.ps tmp/18rfg1386167226.png",intern=TRUE)) character(0) > try(system("convert tmp/2tcf11386167226.ps tmp/2tcf11386167226.png",intern=TRUE)) character(0) > try(system("convert tmp/3y6gy1386167226.ps tmp/3y6gy1386167226.png",intern=TRUE)) character(0) > try(system("convert tmp/43phb1386167226.ps tmp/43phb1386167226.png",intern=TRUE)) character(0) > try(system("convert tmp/5ay3s1386167226.ps tmp/5ay3s1386167226.png",intern=TRUE)) character(0) > try(system("convert tmp/6ng3z1386167226.ps tmp/6ng3z1386167226.png",intern=TRUE)) character(0) > try(system("convert tmp/7fvsp1386167226.ps tmp/7fvsp1386167226.png",intern=TRUE)) character(0) > try(system("convert tmp/855871386167226.ps tmp/855871386167226.png",intern=TRUE)) character(0) > try(system("convert tmp/9hsmw1386167226.ps tmp/9hsmw1386167226.png",intern=TRUE)) character(0) > try(system("convert tmp/10qov11386167226.ps tmp/10qov11386167226.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 15.519 2.999 18.621