R version 3.0.2 (2013-09-25) -- "Frisbee Sailing" Copyright (C) 2013 The R Foundation for Statistical Computing Platform: i686-pc-linux-gnu (32-bit) R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > x <- array(list(1 + ,119.992 + ,157.302 + ,74.997 + ,0.00784 + ,0.00007 + ,0.0037 + ,0.00554 + ,1 + ,122.4 + ,148.65 + ,113.819 + ,0.00968 + ,0.00008 + ,0.00465 + ,0.00696 + ,1 + ,116.682 + ,131.111 + ,111.555 + ,0.0105 + ,0.00009 + ,0.00544 + ,0.00781 + ,1 + ,116.676 + ,137.871 + ,111.366 + ,0.00997 + ,0.00009 + ,0.00502 + ,0.00698 + ,1 + ,116.014 + ,141.781 + ,110.655 + ,0.01284 + ,0.00011 + ,0.00655 + ,0.00908 + ,1 + ,120.552 + ,131.162 + ,113.787 + ,0.00968 + ,0.00008 + ,0.00463 + ,0.0075 + ,1 + ,120.267 + ,137.244 + ,114.82 + ,0.00333 + ,0.00003 + ,0.00155 + ,0.00202 + ,1 + ,107.332 + ,113.84 + ,104.315 + ,0.0029 + ,0.00003 + ,0.00144 + ,0.00182 + ,1 + ,95.73 + ,132.068 + ,91.754 + ,0.00551 + ,0.00006 + ,0.00293 + ,0.00332 + ,1 + ,95.056 + ,120.103 + ,91.226 + ,0.00532 + ,0.00006 + ,0.00268 + ,0.00332 + ,1 + ,88.333 + ,112.24 + ,84.072 + ,0.00505 + ,0.00006 + ,0.00254 + ,0.0033 + ,1 + ,91.904 + ,115.871 + ,86.292 + ,0.0054 + ,0.00006 + ,0.00281 + ,0.00336 + 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+ ,0.00346 + ,0 + ,116.286 + ,177.291 + ,96.983 + ,0.00314 + ,0.00003 + ,0.00134 + ,0.00192 + ,0 + ,116.556 + ,592.03 + ,86.228 + ,0.00496 + ,0.00004 + ,0.00254 + ,0.00263 + ,0 + ,116.342 + ,581.289 + ,94.246 + ,0.00267 + ,0.00002 + ,0.00115 + ,0.00148 + ,0 + ,114.563 + ,119.167 + ,86.647 + ,0.00327 + ,0.00003 + ,0.00146 + ,0.00184 + ,0 + ,201.774 + ,262.707 + ,78.228 + ,0.00694 + ,0.00003 + ,0.00412 + ,0.00396 + ,0 + ,174.188 + ,230.978 + ,94.261 + ,0.00459 + ,0.00003 + ,0.00263 + ,0.00259 + ,0 + ,209.516 + ,253.017 + ,89.488 + ,0.00564 + ,0.00003 + ,0.00331 + ,0.00292 + ,0 + ,174.688 + ,240.005 + ,74.287 + ,0.0136 + ,0.00008 + ,0.00624 + ,0.00564 + ,0 + ,198.764 + ,396.961 + ,74.904 + ,0.0074 + ,0.00004 + ,0.0037 + ,0.0039 + ,0 + ,214.289 + ,260.277 + ,77.973 + ,0.00567 + ,0.00003 + ,0.00295 + ,0.00317) + ,dim=c(8 + ,195) + ,dimnames=list(c('status' + ,'MDVP:Fo(Hz)' + ,'MDVP:Fhi(Hz)' + ,'MDVP:Flo(Hz)' + ,'MDVP:Jitter(%)' + ,'MDVP:Jitter(Abs)' + ,'MDVP:RAP' + ,'MDVP:PPQ') + ,1:195)) > y <- array(NA,dim=c(8,195),dimnames=list(c('status','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 = '8' > par3 <- 'No Linear Trend' > par2 <- 'Do not include Seasonal Dummies' > par1 <- '8' > #'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:PPQ status MDVP:Fo(Hz) MDVP:Fhi(Hz) MDVP:Flo(Hz) MDVP:Jitter(%) 1 0.00554 1 119.992 157.302 74.997 0.00784 2 0.00696 1 122.400 148.650 113.819 0.00968 3 0.00781 1 116.682 131.111 111.555 0.01050 4 0.00698 1 116.676 137.871 111.366 0.00997 5 0.00908 1 116.014 141.781 110.655 0.01284 6 0.00750 1 120.552 131.162 113.787 0.00968 7 0.00202 1 120.267 137.244 114.820 0.00333 8 0.00182 1 107.332 113.840 104.315 0.00290 9 0.00332 1 95.730 132.068 91.754 0.00551 10 0.00332 1 95.056 120.103 91.226 0.00532 11 0.00330 1 88.333 112.240 84.072 0.00505 12 0.00336 1 91.904 115.871 86.292 0.00540 13 0.00153 1 136.926 159.866 131.276 0.00293 14 0.00208 1 139.173 179.139 76.556 0.00390 15 0.00149 1 152.845 163.305 75.836 0.00294 16 0.00203 1 142.167 217.455 83.159 0.00369 17 0.00292 1 144.188 349.259 82.764 0.00544 18 0.00387 1 168.778 232.181 75.603 0.00718 19 0.00432 1 153.046 175.829 68.623 0.00742 20 0.00399 1 156.405 189.398 142.822 0.00768 21 0.00450 1 153.848 165.738 65.782 0.00840 22 0.00267 1 153.880 172.860 78.128 0.00480 23 0.00247 1 167.930 193.221 79.068 0.00442 24 0.00258 1 173.917 192.735 86.180 0.00476 25 0.00390 1 163.656 200.841 76.779 0.00742 26 0.00375 1 104.400 206.002 77.968 0.00633 27 0.00234 1 171.041 208.313 75.501 0.00455 28 0.00275 1 146.845 208.701 81.737 0.00496 29 0.00176 1 155.358 227.383 80.055 0.00310 30 0.00253 1 162.568 198.346 77.630 0.00502 31 0.00168 0 197.076 206.896 192.055 0.00289 32 0.00138 0 199.228 209.512 192.091 0.00241 33 0.00135 0 198.383 215.203 193.104 0.00212 34 0.00107 0 202.266 211.604 197.079 0.00180 35 0.00106 0 203.184 211.526 196.160 0.00178 36 0.00115 0 201.464 210.565 195.708 0.00198 37 0.00241 1 177.876 192.921 168.013 0.00411 38 0.00218 1 176.170 185.604 163.564 0.00369 39 0.00166 1 180.198 201.249 175.456 0.00284 40 0.00182 1 187.733 202.324 173.015 0.00316 41 0.00175 1 186.163 197.724 177.584 0.00298 42 0.00147 1 184.055 196.537 166.977 0.00258 43 0.00182 0 237.226 247.326 225.227 0.00298 44 0.00173 0 241.404 248.834 232.483 0.00281 45 0.00137 0 243.439 250.912 232.435 0.00210 46 0.00139 0 242.852 255.034 227.911 0.00225 47 0.00148 0 245.510 262.090 231.848 0.00235 48 0.00113 0 252.455 261.487 182.786 0.00185 49 0.00203 0 122.188 128.611 115.765 0.00524 50 0.00155 0 122.964 130.049 114.676 0.00428 51 0.00167 0 124.445 135.069 117.495 0.00431 52 0.00169 0 126.344 134.231 112.773 0.00448 53 0.00166 0 128.001 138.052 122.080 0.00436 54 0.00183 0 129.336 139.867 118.604 0.00490 55 0.00486 1 108.807 134.656 102.874 0.00761 56 0.00539 1 109.860 126.358 104.437 0.00874 57 0.00514 1 110.417 131.067 103.370 0.00784 58 0.00469 1 117.274 129.916 110.402 0.00752 59 0.00493 1 116.879 131.897 108.153 0.00788 60 0.00520 1 114.847 271.314 104.680 0.00867 61 0.00152 0 209.144 237.494 109.379 0.00282 62 0.00151 0 223.365 238.987 98.664 0.00264 63 0.00144 0 222.236 231.345 205.495 0.00266 64 0.00155 0 228.832 234.619 223.634 0.00296 65 0.00113 0 229.401 252.221 221.156 0.00205 66 0.00140 0 228.969 239.541 113.201 0.00238 67 0.00440 1 140.341 159.774 67.021 0.00817 68 0.00463 1 136.969 166.607 66.004 0.00923 69 0.00467 1 143.533 162.215 65.809 0.01101 70 0.00354 1 148.090 162.824 67.343 0.00762 71 0.00419 1 142.729 162.408 65.476 0.00831 72 0.00478 1 136.358 176.595 65.750 0.00971 73 0.00220 1 120.080 139.710 111.208 0.00405 74 0.00329 1 112.014 588.518 107.024 0.00533 75 0.00283 1 110.793 128.101 107.316 0.00494 76 0.00289 1 110.707 122.611 105.007 0.00516 77 0.00289 1 112.876 148.826 106.981 0.00500 78 0.00280 1 110.568 125.394 106.821 0.00462 79 0.00332 1 95.385 102.145 90.264 0.00608 80 0.00576 1 100.770 115.697 85.545 0.01038 81 0.00415 1 96.106 108.664 84.510 0.00694 82 0.00371 1 95.605 107.715 87.549 0.00702 83 0.00348 1 100.960 110.019 95.628 0.00606 84 0.00258 1 98.804 102.305 87.804 0.00432 85 0.00420 1 176.858 205.560 75.344 0.00747 86 0.00244 1 180.978 200.125 155.495 0.00406 87 0.00194 1 178.222 202.450 141.047 0.00321 88 0.00312 1 176.281 227.381 125.610 0.00520 89 0.00254 1 173.898 211.350 74.677 0.00448 90 0.00419 1 179.711 225.930 144.878 0.00709 91 0.00453 1 166.605 206.008 78.032 0.00742 92 0.00227 1 151.955 163.335 147.226 0.00419 93 0.00256 1 148.272 164.989 142.299 0.00459 94 0.00226 1 152.125 161.469 76.596 0.00382 95 0.00196 1 157.821 172.975 68.401 0.00358 96 0.00197 1 157.447 163.267 149.605 0.00369 97 0.00184 1 159.116 168.913 144.811 0.00342 98 0.00623 1 125.036 143.946 116.187 0.01280 99 0.00655 1 125.791 140.557 96.206 0.01378 100 0.00990 1 126.512 141.756 99.770 0.01936 101 0.01522 1 125.641 141.068 116.346 0.03316 102 0.00909 1 128.451 150.449 75.632 0.01551 103 0.01628 1 139.224 586.567 66.157 0.03011 104 0.00136 1 150.258 154.609 75.349 0.00248 105 0.00100 1 154.003 160.267 128.621 0.00183 106 0.00134 1 149.689 160.368 133.608 0.00257 107 0.00092 1 155.078 163.736 144.148 0.00168 108 0.00122 1 151.884 157.765 133.751 0.00258 109 0.00096 1 151.989 157.339 132.857 0.00174 110 0.00389 1 193.030 208.900 80.297 0.00766 111 0.00337 1 200.714 223.982 89.686 0.00621 112 0.00339 1 208.519 220.315 199.020 0.00609 113 0.00485 1 204.664 221.300 189.621 0.00841 114 0.00280 1 210.141 232.706 185.258 0.00534 115 0.00246 1 206.327 226.355 92.020 0.00495 116 0.00385 1 151.872 492.892 69.085 0.00856 117 0.00207 1 158.219 442.557 71.948 0.00476 118 0.00261 1 170.756 450.247 79.032 0.00555 119 0.00194 1 178.285 442.824 82.063 0.00462 120 0.00128 1 217.116 233.481 93.978 0.00404 121 0.00314 1 128.940 479.697 88.251 0.00581 122 0.00221 1 176.824 215.293 83.961 0.00460 123 0.00398 1 138.190 203.522 83.340 0.00704 124 0.00449 1 182.018 197.173 79.187 0.00842 125 0.00395 1 156.239 195.107 79.820 0.00694 126 0.00422 1 145.174 198.109 80.637 0.00733 127 0.00327 1 138.145 197.238 81.114 0.00544 128 0.00351 1 166.888 198.966 79.512 0.00638 129 0.00192 1 119.031 127.533 109.216 0.00440 130 0.00135 1 120.078 126.632 105.667 0.00270 131 0.00238 1 120.289 128.143 100.209 0.00492 132 0.00205 1 120.256 125.306 104.773 0.00407 133 0.00170 1 119.056 125.213 86.795 0.00346 134 0.00171 1 118.747 123.723 109.836 0.00331 135 0.00319 1 106.516 112.777 93.105 0.00589 136 0.00315 1 110.453 127.611 105.554 0.00494 137 0.00283 1 113.400 133.344 107.816 0.00451 138 0.00312 1 113.166 130.270 100.673 0.00502 139 0.00290 1 112.239 126.609 104.095 0.00472 140 0.00232 1 116.150 131.731 109.815 0.00381 141 0.00269 1 170.368 268.796 79.543 0.00571 142 0.00428 1 208.083 253.792 91.802 0.00757 143 0.00215 1 198.458 219.290 148.691 0.00376 144 0.00211 1 202.805 231.508 86.232 0.00370 145 0.00133 1 202.544 241.350 164.168 0.00254 146 0.00188 1 223.361 263.872 87.638 0.00352 147 0.00946 1 169.774 191.759 151.451 0.01568 148 0.00819 1 183.520 216.814 161.340 0.01466 149 0.01027 1 188.620 216.302 165.982 0.01719 150 0.00963 1 202.632 565.740 177.258 0.01627 151 0.01154 1 186.695 211.961 149.442 0.01872 152 0.01958 1 192.818 224.429 168.793 0.03107 153 0.01699 1 198.116 233.099 174.478 0.02714 154 0.00332 1 121.345 139.644 98.250 0.00684 155 0.00300 1 119.100 128.442 88.833 0.00692 156 0.00300 1 117.870 127.349 95.654 0.00647 157 0.00339 1 122.336 142.369 94.794 0.00727 158 0.00718 1 117.963 134.209 100.757 0.01813 159 0.00454 1 126.144 154.284 97.543 0.00975 160 0.00318 1 127.930 138.752 112.173 0.00605 161 0.00316 1 114.238 124.393 77.022 0.00581 162 0.00329 1 115.322 135.738 107.802 0.00619 163 0.00340 1 114.554 126.778 91.121 0.00651 164 0.00284 1 112.150 131.669 97.527 0.00519 165 0.00461 1 102.273 142.830 85.902 0.00907 166 0.00153 0 236.200 244.663 102.137 0.00277 167 0.00159 0 237.323 243.709 229.256 0.00303 168 0.00186 0 260.105 264.919 237.303 0.00339 169 0.00448 0 197.569 217.627 90.794 0.00803 170 0.00283 0 240.301 245.135 219.783 0.00517 171 0.00237 0 244.990 272.210 239.170 0.00451 172 0.00190 0 112.547 133.374 105.715 0.00355 173 0.00200 0 110.739 113.597 100.139 0.00356 174 0.00203 0 113.715 116.443 96.913 0.00349 175 0.00218 0 117.004 144.466 99.923 0.00353 176 0.00199 0 115.380 123.109 108.634 0.00332 177 0.00213 0 116.388 129.038 108.970 0.00346 178 0.00162 1 151.737 190.204 129.859 0.00314 179 0.00186 1 148.790 158.359 138.990 0.00309 180 0.00231 1 148.143 155.982 135.041 0.00392 181 0.00233 1 150.440 163.441 144.736 0.00396 182 0.00235 1 148.462 161.078 141.998 0.00397 183 0.00198 1 149.818 163.417 144.786 0.00336 184 0.00270 0 117.226 123.925 106.656 0.00417 185 0.00346 0 116.848 217.552 99.503 0.00531 186 0.00192 0 116.286 177.291 96.983 0.00314 187 0.00263 0 116.556 592.030 86.228 0.00496 188 0.00148 0 116.342 581.289 94.246 0.00267 189 0.00184 0 114.563 119.167 86.647 0.00327 190 0.00396 0 201.774 262.707 78.228 0.00694 191 0.00259 0 174.188 230.978 94.261 0.00459 192 0.00292 0 209.516 253.017 89.488 0.00564 193 0.00564 0 174.688 240.005 74.287 0.01360 194 0.00390 0 198.764 396.961 74.904 0.00740 195 0.00317 0 214.289 260.277 77.973 0.00567 MDVP:Jitter(Abs) MDVP:RAP 1 7.0e-05 0.00370 2 8.0e-05 0.00465 3 9.0e-05 0.00544 4 9.0e-05 0.00502 5 1.1e-04 0.00655 6 8.0e-05 0.00463 7 3.0e-05 0.00155 8 3.0e-05 0.00144 9 6.0e-05 0.00293 10 6.0e-05 0.00268 11 6.0e-05 0.00254 12 6.0e-05 0.00281 13 2.0e-05 0.00118 14 3.0e-05 0.00165 15 2.0e-05 0.00121 16 3.0e-05 0.00157 17 4.0e-05 0.00211 18 4.0e-05 0.00284 19 5.0e-05 0.00364 20 5.0e-05 0.00372 21 5.0e-05 0.00428 22 3.0e-05 0.00232 23 3.0e-05 0.00220 24 3.0e-05 0.00221 25 5.0e-05 0.00380 26 6.0e-05 0.00316 27 3.0e-05 0.00250 28 3.0e-05 0.00250 29 2.0e-05 0.00159 30 3.0e-05 0.00280 31 1.0e-05 0.00166 32 1.0e-05 0.00134 33 1.0e-05 0.00113 34 9.0e-06 0.00093 35 9.0e-06 0.00094 36 1.0e-05 0.00105 37 2.0e-05 0.00233 38 2.0e-05 0.00205 39 2.0e-05 0.00153 40 2.0e-05 0.00168 41 2.0e-05 0.00165 42 1.0e-05 0.00134 43 1.0e-05 0.00169 44 1.0e-05 0.00157 45 9.0e-06 0.00109 46 9.0e-06 0.00117 47 1.0e-05 0.00127 48 7.0e-06 0.00092 49 4.0e-05 0.00169 50 3.0e-05 0.00124 51 3.0e-05 0.00141 52 4.0e-05 0.00131 53 3.0e-05 0.00137 54 4.0e-05 0.00165 55 7.0e-05 0.00349 56 8.0e-05 0.00398 57 7.0e-05 0.00352 58 6.0e-05 0.00299 59 7.0e-05 0.00334 60 8.0e-05 0.00373 61 1.0e-05 0.00147 62 1.0e-05 0.00154 63 1.0e-05 0.00152 64 1.0e-05 0.00175 65 9.0e-06 0.00114 66 1.0e-05 0.00136 67 6.0e-05 0.00430 68 7.0e-05 0.00507 69 8.0e-05 0.00647 70 5.0e-05 0.00467 71 6.0e-05 0.00469 72 7.0e-05 0.00534 73 3.0e-05 0.00180 74 5.0e-05 0.00268 75 4.0e-05 0.00260 76 5.0e-05 0.00277 77 4.0e-05 0.00270 78 4.0e-05 0.00226 79 6.0e-05 0.00331 80 1.0e-04 0.00622 81 7.0e-05 0.00389 82 7.0e-05 0.00428 83 6.0e-05 0.00351 84 4.0e-05 0.00247 85 4.0e-05 0.00418 86 2.0e-05 0.00220 87 2.0e-05 0.00163 88 3.0e-05 0.00287 89 3.0e-05 0.00237 90 4.0e-05 0.00391 91 4.0e-05 0.00387 92 3.0e-05 0.00224 93 3.0e-05 0.00250 94 3.0e-05 0.00191 95 2.0e-05 0.00196 96 2.0e-05 0.00201 97 2.0e-05 0.00178 98 1.0e-04 0.00743 99 1.1e-04 0.00826 100 1.5e-04 0.01159 101 2.6e-04 0.02144 102 1.2e-04 0.00905 103 2.2e-04 0.01854 104 2.0e-05 0.00105 105 1.0e-05 0.00076 106 2.0e-05 0.00116 107 1.0e-05 0.00068 108 2.0e-05 0.00115 109 1.0e-05 0.00075 110 4.0e-05 0.00450 111 3.0e-05 0.00371 112 3.0e-05 0.00368 113 4.0e-05 0.00502 114 3.0e-05 0.00321 115 2.0e-05 0.00302 116 6.0e-05 0.00404 117 3.0e-05 0.00214 118 3.0e-05 0.00244 119 3.0e-05 0.00157 120 2.0e-05 0.00127 121 5.0e-05 0.00241 122 3.0e-05 0.00209 123 5.0e-05 0.00406 124 5.0e-05 0.00506 125 4.0e-05 0.00403 126 5.0e-05 0.00414 127 4.0e-05 0.00294 128 4.0e-05 0.00368 129 4.0e-05 0.00214 130 2.0e-05 0.00116 131 4.0e-05 0.00269 132 3.0e-05 0.00224 133 3.0e-05 0.00169 134 3.0e-05 0.00168 135 6.0e-05 0.00291 136 4.0e-05 0.00244 137 4.0e-05 0.00219 138 4.0e-05 0.00257 139 4.0e-05 0.00238 140 3.0e-05 0.00181 141 3.0e-05 0.00232 142 4.0e-05 0.00428 143 2.0e-05 0.00182 144 2.0e-05 0.00189 145 1.0e-05 0.00100 146 2.0e-05 0.00169 147 9.0e-05 0.00863 148 8.0e-05 0.00849 149 9.0e-05 0.00996 150 8.0e-05 0.00919 151 1.0e-04 0.01075 152 1.6e-04 0.01800 153 1.4e-04 0.01568 154 6.0e-05 0.00388 155 6.0e-05 0.00393 156 5.0e-05 0.00356 157 6.0e-05 0.00415 158 1.5e-04 0.01117 159 8.0e-05 0.00593 160 5.0e-05 0.00321 161 5.0e-05 0.00299 162 5.0e-05 0.00352 163 6.0e-05 0.00366 164 5.0e-05 0.00291 165 9.0e-05 0.00493 166 1.0e-05 0.00154 167 1.0e-05 0.00173 168 1.0e-05 0.00205 169 4.0e-05 0.00490 170 2.0e-05 0.00316 171 2.0e-05 0.00279 172 3.0e-05 0.00166 173 3.0e-05 0.00170 174 3.0e-05 0.00171 175 3.0e-05 0.00176 176 3.0e-05 0.00160 177 3.0e-05 0.00169 178 2.0e-05 0.00135 179 2.0e-05 0.00152 180 3.0e-05 0.00204 181 3.0e-05 0.00206 182 3.0e-05 0.00202 183 2.0e-05 0.00174 184 4.0e-05 0.00186 185 5.0e-05 0.00260 186 3.0e-05 0.00134 187 4.0e-05 0.00254 188 2.0e-05 0.00115 189 3.0e-05 0.00146 190 3.0e-05 0.00412 191 3.0e-05 0.00263 192 3.0e-05 0.00331 193 8.0e-05 0.00624 194 4.0e-05 0.00370 195 3.0e-05 0.00295 > k <- length(x[1,]) > df <- as.data.frame(x) > (mylm <- lm(df)) Call: lm(formula = df) Coefficients: (Intercept) status `MDVP:Fo(Hz)` `MDVP:Fhi(Hz)` -1.305e-04 2.201e-04 -4.523e-06 -2.821e-07 `MDVP:Flo(Hz)` `MDVP:Jitter(%)` `MDVP:Jitter(Abs)` `MDVP:RAP` 5.043e-06 9.026e-01 -1.658e+01 -3.958e-01 > (mysum <- summary(mylm)) Call: lm(formula = df) Residuals: Min 1Q Median 3Q Max -0.0023036 -0.0002231 0.0000137 0.0002383 0.0018899 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -1.305e-04 3.558e-04 -0.367 0.714212 status 2.201e-04 1.092e-04 2.016 0.045236 * `MDVP:Fo(Hz)` -4.523e-06 1.998e-06 -2.263 0.024757 * `MDVP:Fhi(Hz)` -2.821e-07 5.125e-07 -0.550 0.582678 `MDVP:Flo(Hz)` 5.043e-06 1.232e-06 4.092 6.35e-05 *** `MDVP:Jitter(%)` 9.026e-01 6.806e-02 13.262 < 2e-16 *** `MDVP:Jitter(Abs)` -1.658e+01 5.479e+00 -3.025 0.002833 ** `MDVP:RAP` -3.958e-01 1.094e-01 -3.618 0.000382 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 0.0005692 on 187 degrees of freedom Multiple R-squared: 0.959, Adjusted R-squared: 0.9574 F-statistic: 624.3 on 7 and 187 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,] 9.425464e-02 1.885093e-01 9.057454e-01 [2,] 4.346497e-02 8.692993e-02 9.565350e-01 [3,] 1.490966e-02 2.981932e-02 9.850903e-01 [4,] 8.112045e-03 1.622409e-02 9.918880e-01 [5,] 3.859792e-03 7.719585e-03 9.961402e-01 [6,] 1.684101e-02 3.368201e-02 9.831590e-01 [7,] 7.367468e-03 1.473494e-02 9.926325e-01 [8,] 1.099929e-01 2.199857e-01 8.900071e-01 [9,] 7.419463e-02 1.483893e-01 9.258054e-01 [10,] 2.937234e-01 5.874468e-01 7.062766e-01 [11,] 2.251763e-01 4.503526e-01 7.748237e-01 [12,] 1.985648e-01 3.971296e-01 8.014352e-01 [13,] 1.581659e-01 3.163317e-01 8.418341e-01 [14,] 1.158031e-01 2.316062e-01 8.841969e-01 [15,] 9.441937e-02 1.888387e-01 9.055806e-01 [16,] 6.850790e-02 1.370158e-01 9.314921e-01 [17,] 5.792234e-02 1.158447e-01 9.420777e-01 [18,] 7.294268e-02 1.458854e-01 9.270573e-01 [19,] 1.066852e-01 2.133703e-01 8.933148e-01 [20,] 8.000857e-02 1.600171e-01 9.199914e-01 [21,] 5.746910e-02 1.149382e-01 9.425309e-01 [22,] 4.130701e-02 8.261401e-02 9.586930e-01 [23,] 2.855010e-02 5.710019e-02 9.714499e-01 [24,] 2.033025e-02 4.066051e-02 9.796697e-01 [25,] 1.391836e-02 2.783673e-02 9.860816e-01 [26,] 9.352729e-03 1.870546e-02 9.906473e-01 [27,] 6.633598e-03 1.326720e-02 9.933664e-01 [28,] 4.253592e-03 8.507183e-03 9.957464e-01 [29,] 2.844795e-03 5.689590e-03 9.971552e-01 [30,] 1.828440e-03 3.656880e-03 9.981716e-01 [31,] 1.131312e-03 2.262624e-03 9.988687e-01 [32,] 7.756172e-04 1.551234e-03 9.992244e-01 [33,] 4.671993e-04 9.343985e-04 9.995328e-01 [34,] 2.885117e-04 5.770233e-04 9.997115e-01 [35,] 1.816583e-04 3.633166e-04 9.998183e-01 [36,] 1.106926e-04 2.213853e-04 9.998893e-01 [37,] 6.644603e-05 1.328921e-04 9.999336e-01 [38,] 4.266582e-05 8.533164e-05 9.999573e-01 [39,] 9.764320e-04 1.952864e-03 9.990236e-01 [40,] 1.098445e-03 2.196889e-03 9.989016e-01 [41,] 1.046098e-03 2.092196e-03 9.989539e-01 [42,] 1.531758e-03 3.063516e-03 9.984682e-01 [43,] 1.716464e-03 3.432928e-03 9.982835e-01 [44,] 3.405379e-03 6.810758e-03 9.965946e-01 [45,] 3.039223e-03 6.078447e-03 9.969608e-01 [46,] 4.161865e-03 8.323730e-03 9.958381e-01 [47,] 4.208330e-03 8.416659e-03 9.957917e-01 [48,] 3.410704e-03 6.821407e-03 9.965893e-01 [49,] 3.414090e-03 6.828180e-03 9.965859e-01 [50,] 5.876893e-03 1.175379e-02 9.941231e-01 [51,] 6.114638e-03 1.222928e-02 9.938854e-01 [52,] 5.414918e-03 1.082984e-02 9.945851e-01 [53,] 3.928417e-03 7.856834e-03 9.960716e-01 [54,] 3.220952e-03 6.441904e-03 9.967790e-01 [55,] 2.350172e-03 4.700344e-03 9.976498e-01 [56,] 2.036232e-03 4.072464e-03 9.979638e-01 [57,] 4.411346e-03 8.822692e-03 9.955887e-01 [58,] 3.342863e-02 6.685727e-02 9.665714e-01 [59,] 4.862241e-01 9.724482e-01 5.137759e-01 [60,] 4.958263e-01 9.916526e-01 5.041737e-01 [61,] 4.758428e-01 9.516856e-01 5.241572e-01 [62,] 4.829148e-01 9.658296e-01 5.170852e-01 [63,] 4.509029e-01 9.018057e-01 5.490971e-01 [64,] 4.739099e-01 9.478198e-01 5.260901e-01 [65,] 4.403080e-01 8.806159e-01 5.596920e-01 [66,] 4.135192e-01 8.270384e-01 5.864808e-01 [67,] 3.839175e-01 7.678349e-01 6.160825e-01 [68,] 3.527779e-01 7.055557e-01 6.472221e-01 [69,] 3.337799e-01 6.675599e-01 6.662201e-01 [70,] 4.644554e-01 9.289107e-01 5.355446e-01 [71,] 5.024397e-01 9.951206e-01 4.975603e-01 [72,] 4.925998e-01 9.851997e-01 5.074002e-01 [73,] 4.820420e-01 9.640839e-01 5.179580e-01 [74,] 4.731688e-01 9.463376e-01 5.268312e-01 [75,] 4.610071e-01 9.220142e-01 5.389929e-01 [76,] 4.197363e-01 8.394727e-01 5.802637e-01 [77,] 3.877509e-01 7.755017e-01 6.122491e-01 [78,] 3.576075e-01 7.152150e-01 6.423925e-01 [79,] 3.322849e-01 6.645698e-01 6.677151e-01 [80,] 2.991512e-01 5.983025e-01 7.008488e-01 [81,] 3.249795e-01 6.499589e-01 6.750205e-01 [82,] 2.948769e-01 5.897539e-01 7.051231e-01 [83,] 2.607263e-01 5.214527e-01 7.392737e-01 [84,] 2.455075e-01 4.910150e-01 7.544925e-01 [85,] 2.263736e-01 4.527473e-01 7.736264e-01 [86,] 2.058437e-01 4.116875e-01 7.941563e-01 [87,] 1.813315e-01 3.626629e-01 8.186685e-01 [88,] 2.843856e-01 5.687711e-01 7.156144e-01 [89,] 3.817740e-01 7.635481e-01 6.182260e-01 [90,] 3.816159e-01 7.632318e-01 6.183841e-01 [91,] 7.236793e-01 5.526413e-01 2.763207e-01 [92,] 8.757686e-01 2.484629e-01 1.242314e-01 [93,] 9.479948e-01 1.040103e-01 5.200516e-02 [94,] 9.390423e-01 1.219153e-01 6.095766e-02 [95,] 9.281450e-01 1.437099e-01 7.185497e-02 [96,] 9.172104e-01 1.655791e-01 8.278957e-02 [97,] 9.044167e-01 1.911667e-01 9.558334e-02 [98,] 8.970723e-01 2.058554e-01 1.029277e-01 [99,] 8.817535e-01 2.364930e-01 1.182465e-01 [100,] 8.693835e-01 2.612330e-01 1.306165e-01 [101,] 8.485961e-01 3.028078e-01 1.514039e-01 [102,] 8.263786e-01 3.472428e-01 1.736214e-01 [103,] 8.209794e-01 3.580413e-01 1.790206e-01 [104,] 8.062239e-01 3.875522e-01 1.937761e-01 [105,] 7.939156e-01 4.121688e-01 2.060844e-01 [106,] 8.497318e-01 3.005364e-01 1.502682e-01 [107,] 8.502605e-01 2.994790e-01 1.497395e-01 [108,] 8.466004e-01 3.067991e-01 1.533996e-01 [109,] 8.700832e-01 2.598336e-01 1.299168e-01 [110,] 9.324848e-01 1.350304e-01 6.751519e-02 [111,] 9.341848e-01 1.316304e-01 6.581519e-02 [112,] 9.236380e-01 1.527241e-01 7.636203e-02 [113,] 9.116504e-01 1.766991e-01 8.834957e-02 [114,] 8.969148e-01 2.061703e-01 1.030852e-01 [115,] 8.896769e-01 2.206463e-01 1.103231e-01 [116,] 8.759016e-01 2.481968e-01 1.240984e-01 [117,] 8.647117e-01 2.705766e-01 1.352883e-01 [118,] 8.405094e-01 3.189812e-01 1.594906e-01 [119,] 8.395939e-01 3.208121e-01 1.604061e-01 [120,] 8.389630e-01 3.220740e-01 1.610370e-01 [121,] 8.281226e-01 3.437548e-01 1.718774e-01 [122,] 8.365770e-01 3.268460e-01 1.634230e-01 [123,] 8.209113e-01 3.581774e-01 1.790887e-01 [124,] 8.002619e-01 3.994762e-01 1.997381e-01 [125,] 8.227561e-01 3.544877e-01 1.772439e-01 [126,] 8.013719e-01 3.972563e-01 1.986281e-01 [127,] 7.838305e-01 4.323390e-01 2.161695e-01 [128,] 7.557567e-01 4.884866e-01 2.442433e-01 [129,] 7.252057e-01 5.495886e-01 2.747943e-01 [130,] 6.858578e-01 6.282844e-01 3.141422e-01 [131,] 6.910873e-01 6.178254e-01 3.089127e-01 [132,] 6.530308e-01 6.939383e-01 3.469692e-01 [133,] 6.110086e-01 7.779829e-01 3.889914e-01 [134,] 5.761407e-01 8.477185e-01 4.238593e-01 [135,] 5.409041e-01 9.181917e-01 4.590959e-01 [136,] 5.688075e-01 8.623850e-01 4.311925e-01 [137,] 5.957274e-01 8.085452e-01 4.042726e-01 [138,] 6.004282e-01 7.991436e-01 3.995718e-01 [139,] 6.017410e-01 7.965181e-01 3.982590e-01 [140,] 5.823904e-01 8.352192e-01 4.176096e-01 [141,] 5.967849e-01 8.064303e-01 4.032151e-01 [142,] 7.362010e-01 5.275981e-01 2.637990e-01 [143,] 9.999921e-01 1.579687e-05 7.898435e-06 [144,] 9.999848e-01 3.044953e-05 1.522477e-05 [145,] 9.999896e-01 2.071334e-05 1.035667e-05 [146,] 9.999864e-01 2.725978e-05 1.362989e-05 [147,] 9.999790e-01 4.193163e-05 2.096582e-05 [148,] 9.999999e-01 1.523292e-07 7.616461e-08 [149,] 1.000000e+00 2.534516e-08 1.267258e-08 [150,] 1.000000e+00 7.181154e-08 3.590577e-08 [151,] 9.999999e-01 2.000520e-07 1.000260e-07 [152,] 9.999998e-01 4.858193e-07 2.429096e-07 [153,] 9.999996e-01 8.367946e-07 4.183973e-07 [154,] 9.999993e-01 1.425667e-06 7.128337e-07 [155,] 9.999999e-01 1.781348e-07 8.906741e-08 [156,] 9.999999e-01 2.773602e-07 1.386801e-07 [157,] 9.999996e-01 8.687760e-07 4.343880e-07 [158,] 9.999987e-01 2.642938e-06 1.321469e-06 [159,] 9.999967e-01 6.686701e-06 3.343351e-06 [160,] 9.999932e-01 1.352317e-05 6.761585e-06 [161,] 9.999809e-01 3.821079e-05 1.910540e-05 [162,] 9.999565e-01 8.690403e-05 4.345201e-05 [163,] 9.998896e-01 2.208919e-04 1.104459e-04 [164,] 9.997359e-01 5.282792e-04 2.641396e-04 [165,] 9.993315e-01 1.337020e-03 6.685099e-04 [166,] 9.984677e-01 3.064516e-03 1.532258e-03 [167,] 9.963428e-01 7.314440e-03 3.657220e-03 [168,] 9.916489e-01 1.670220e-02 8.351098e-03 [169,] 9.815068e-01 3.698646e-02 1.849323e-02 [170,] 9.609797e-01 7.804055e-02 3.902028e-02 [171,] 9.224499e-01 1.551003e-01 7.755014e-02 [172,] 8.538148e-01 2.923704e-01 1.461852e-01 [173,] 7.410192e-01 5.179617e-01 2.589808e-01 [174,] 6.361457e-01 7.277086e-01 3.638543e-01 > postscript(file="/var/fisher/rcomp/tmp/10uef1386680130.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index') > points(x[,1]-mysum$resid) > grid() > dev.off() null device 1 > postscript(file="/var/fisher/rcomp/tmp/2t66l1386680130.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index') > grid() > dev.off() null device 1 > postscript(file="/var/fisher/rcomp/tmp/3gqoi1386680130.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') > grid() > dev.off() null device 1 > postscript(file="/var/fisher/rcomp/tmp/4t5a11386680130.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') > dev.off() null device 1 > postscript(file="/var/fisher/rcomp/tmp/5whaa1386680130.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 1.207412e-03 1.321009e-03 1.889887e-03 1.374882e-03 1.823128e-03 6 7 8 9 10 1.839964e-03 3.899249e-05 1.714509e-04 4.186211e-04 4.874158e-04 11 12 13 14 15 6.591661e-04 5.160890e-04 -4.034154e-04 -8.560891e-05 -8.799748e-05 16 17 18 19 20 1.332708e-05 -2.484471e-04 -4.657658e-04 1.981207e-04 -6.900814e-04 21 22 23 24 25 -2.380375e-04 1.402537e-05 1.740749e-04 -2.777808e-05 -1.446467e-04 26 27 28 29 30 3.291086e-04 8.178094e-05 -1.906493e-05 1.961380e-04 -8.558779e-05 31 32 33 34 35 5.841665e-06 2.273903e-05 1.640598e-04 7.366623e-05 9.444071e-05 36 37 38 39 40 5.825796e-05 -1.241012e-04 -7.316243e-05 -6.908579e-05 -9.186056e-05 41 42 43 44 45 -4.270631e-05 -2.064821e-04 1.021880e-04 1.008682e-04 1.852102e-04 46 47 48 49 50 1.228039e-04 1.728521e-04 3.645874e-04 -1.232179e-03 -1.180121e-03 51 52 53 54 55 -1.026023e-03 -1.001116e-03 -1.103181e-03 -1.119937e-03 4.543326e-04 56 57 58 59 60 3.186086e-04 5.423748e-04 9.251342e-07 2.303746e-04 1.550744e-04 61 62 63 64 65 3.140038e-04 6.129566e-04 -2.905157e-05 -1.595314e-04 3.886672e-06 66 67 68 69 70 6.185863e-04 -2.578973e-05 -2.902543e-04 -1.107638e-03 -3.744013e-04 71 72 73 74 75 -1.884737e-04 -4.653145e-04 -3.138762e-04 4.117983e-04 -3.048227e-05 76 77 78 79 80 7.368823e-05 3.189448e-05 9.450884e-05 5.203758e-05 4.775032e-04 81 82 83 84 85 5.352141e-04 1.594901e-04 3.096254e-04 2.645843e-04 1.631337e-04 86 87 88 89 90 -2.122486e-05 8.146249e-05 1.978846e-04 3.114495e-04 5.723962e-05 91 92 93 94 95 3.557755e-04 -2.269220e-04 -1.864100e-04 3.228911e-04 1.638845e-04 96 97 98 99 100 -3.195788e-04 -2.635808e-04 -7.947428e-04 -7.618302e-04 -4.818319e-04 101 102 103 104 105 -1.983955e-03 8.136539e-04 4.584895e-04 1.221807e-04 -1.817851e-04 106 107 108 109 110 -2.302859e-04 -2.305214e-04 -3.547970e-04 -1.758071e-04 -1.426103e-04 111 112 113 114 115 1.594170e-04 -2.412779e-04 -1.490025e-04 -2.600937e-04 -3.784654e-05 116 117 118 119 120 -8.949839e-04 -4.942329e-04 -5.254146e-04 -6.836279e-04 -1.048116e-03 121 122 123 124 125 -1.377690e-04 -2.701494e-04 2.337231e-04 1.112748e-04 2.133674e-04 126 127 128 129 130 2.873225e-04 3.681338e-04 1.911178e-04 -6.076064e-04 -3.401573e-04 131 132 133 134 135 -3.480074e-04 -2.786098e-04 -2.104734e-04 -1.870608e-04 -2.575322e-05 136 137 138 139 140 2.334071e-04 2.061260e-04 2.202844e-04 1.733879e-04 1.370495e-05 141 142 143 144 145 -6.928404e-04 2.642834e-04 -7.204821e-05 3.079172e-04 -3.345008e-04 146 147 148 149 150 2.562483e-04 1.829030e-04 -3.682377e-04 1.752078e-04 1.968880e-07 151 152 153 154 155 6.161148e-04 1.306364e-03 1.011662e-03 -3.206400e-04 -6.588820e-04 156 157 158 159 160 -6.051696e-04 -5.092245e-04 -2.303622e-03 -5.549985e-04 -2.191828e-04 161 162 163 164 165 1.672501e-06 -1.486950e-04 -2.823894e-05 9.882155e-05 -1.537175e-04 166 167 168 169 170 5.577569e-04 -1.780129e-04 -3.786514e-05 4.619293e-04 -7.624785e-05 171 172 173 174 175 -1.558877e-04 -5.989102e-06 1.151798e-04 2.428524e-04 3.841372e-04 176 177 178 179 180 2.630607e-04 3.168515e-04 -3.529887e-04 -6.894313e-05 1.976601e-05 181 182 183 184 185 -2.482410e-05 -2.548491e-05 -1.287340e-04 4.930577e-04 7.434841e-04 186 187 188 189 190 3.307745e-04 2.111603e-04 2.020702e-04 2.088644e-04 5.464295e-04 191 192 193 194 195 4.932887e-04 3.347470e-04 -2.226152e-03 1.117950e-04 4.969068e-04 > postscript(file="/var/fisher/rcomp/tmp/6dktn1386680130.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 1.207412e-03 NA 1 1.321009e-03 1.207412e-03 2 1.889887e-03 1.321009e-03 3 1.374882e-03 1.889887e-03 4 1.823128e-03 1.374882e-03 5 1.839964e-03 1.823128e-03 6 3.899249e-05 1.839964e-03 7 1.714509e-04 3.899249e-05 8 4.186211e-04 1.714509e-04 9 4.874158e-04 4.186211e-04 10 6.591661e-04 4.874158e-04 11 5.160890e-04 6.591661e-04 12 -4.034154e-04 5.160890e-04 13 -8.560891e-05 -4.034154e-04 14 -8.799748e-05 -8.560891e-05 15 1.332708e-05 -8.799748e-05 16 -2.484471e-04 1.332708e-05 17 -4.657658e-04 -2.484471e-04 18 1.981207e-04 -4.657658e-04 19 -6.900814e-04 1.981207e-04 20 -2.380375e-04 -6.900814e-04 21 1.402537e-05 -2.380375e-04 22 1.740749e-04 1.402537e-05 23 -2.777808e-05 1.740749e-04 24 -1.446467e-04 -2.777808e-05 25 3.291086e-04 -1.446467e-04 26 8.178094e-05 3.291086e-04 27 -1.906493e-05 8.178094e-05 28 1.961380e-04 -1.906493e-05 29 -8.558779e-05 1.961380e-04 30 5.841665e-06 -8.558779e-05 31 2.273903e-05 5.841665e-06 32 1.640598e-04 2.273903e-05 33 7.366623e-05 1.640598e-04 34 9.444071e-05 7.366623e-05 35 5.825796e-05 9.444071e-05 36 -1.241012e-04 5.825796e-05 37 -7.316243e-05 -1.241012e-04 38 -6.908579e-05 -7.316243e-05 39 -9.186056e-05 -6.908579e-05 40 -4.270631e-05 -9.186056e-05 41 -2.064821e-04 -4.270631e-05 42 1.021880e-04 -2.064821e-04 43 1.008682e-04 1.021880e-04 44 1.852102e-04 1.008682e-04 45 1.228039e-04 1.852102e-04 46 1.728521e-04 1.228039e-04 47 3.645874e-04 1.728521e-04 48 -1.232179e-03 3.645874e-04 49 -1.180121e-03 -1.232179e-03 50 -1.026023e-03 -1.180121e-03 51 -1.001116e-03 -1.026023e-03 52 -1.103181e-03 -1.001116e-03 53 -1.119937e-03 -1.103181e-03 54 4.543326e-04 -1.119937e-03 55 3.186086e-04 4.543326e-04 56 5.423748e-04 3.186086e-04 57 9.251342e-07 5.423748e-04 58 2.303746e-04 9.251342e-07 59 1.550744e-04 2.303746e-04 60 3.140038e-04 1.550744e-04 61 6.129566e-04 3.140038e-04 62 -2.905157e-05 6.129566e-04 63 -1.595314e-04 -2.905157e-05 64 3.886672e-06 -1.595314e-04 65 6.185863e-04 3.886672e-06 66 -2.578973e-05 6.185863e-04 67 -2.902543e-04 -2.578973e-05 68 -1.107638e-03 -2.902543e-04 69 -3.744013e-04 -1.107638e-03 70 -1.884737e-04 -3.744013e-04 71 -4.653145e-04 -1.884737e-04 72 -3.138762e-04 -4.653145e-04 73 4.117983e-04 -3.138762e-04 74 -3.048227e-05 4.117983e-04 75 7.368823e-05 -3.048227e-05 76 3.189448e-05 7.368823e-05 77 9.450884e-05 3.189448e-05 78 5.203758e-05 9.450884e-05 79 4.775032e-04 5.203758e-05 80 5.352141e-04 4.775032e-04 81 1.594901e-04 5.352141e-04 82 3.096254e-04 1.594901e-04 83 2.645843e-04 3.096254e-04 84 1.631337e-04 2.645843e-04 85 -2.122486e-05 1.631337e-04 86 8.146249e-05 -2.122486e-05 87 1.978846e-04 8.146249e-05 88 3.114495e-04 1.978846e-04 89 5.723962e-05 3.114495e-04 90 3.557755e-04 5.723962e-05 91 -2.269220e-04 3.557755e-04 92 -1.864100e-04 -2.269220e-04 93 3.228911e-04 -1.864100e-04 94 1.638845e-04 3.228911e-04 95 -3.195788e-04 1.638845e-04 96 -2.635808e-04 -3.195788e-04 97 -7.947428e-04 -2.635808e-04 98 -7.618302e-04 -7.947428e-04 99 -4.818319e-04 -7.618302e-04 100 -1.983955e-03 -4.818319e-04 101 8.136539e-04 -1.983955e-03 102 4.584895e-04 8.136539e-04 103 1.221807e-04 4.584895e-04 104 -1.817851e-04 1.221807e-04 105 -2.302859e-04 -1.817851e-04 106 -2.305214e-04 -2.302859e-04 107 -3.547970e-04 -2.305214e-04 108 -1.758071e-04 -3.547970e-04 109 -1.426103e-04 -1.758071e-04 110 1.594170e-04 -1.426103e-04 111 -2.412779e-04 1.594170e-04 112 -1.490025e-04 -2.412779e-04 113 -2.600937e-04 -1.490025e-04 114 -3.784654e-05 -2.600937e-04 115 -8.949839e-04 -3.784654e-05 116 -4.942329e-04 -8.949839e-04 117 -5.254146e-04 -4.942329e-04 118 -6.836279e-04 -5.254146e-04 119 -1.048116e-03 -6.836279e-04 120 -1.377690e-04 -1.048116e-03 121 -2.701494e-04 -1.377690e-04 122 2.337231e-04 -2.701494e-04 123 1.112748e-04 2.337231e-04 124 2.133674e-04 1.112748e-04 125 2.873225e-04 2.133674e-04 126 3.681338e-04 2.873225e-04 127 1.911178e-04 3.681338e-04 128 -6.076064e-04 1.911178e-04 129 -3.401573e-04 -6.076064e-04 130 -3.480074e-04 -3.401573e-04 131 -2.786098e-04 -3.480074e-04 132 -2.104734e-04 -2.786098e-04 133 -1.870608e-04 -2.104734e-04 134 -2.575322e-05 -1.870608e-04 135 2.334071e-04 -2.575322e-05 136 2.061260e-04 2.334071e-04 137 2.202844e-04 2.061260e-04 138 1.733879e-04 2.202844e-04 139 1.370495e-05 1.733879e-04 140 -6.928404e-04 1.370495e-05 141 2.642834e-04 -6.928404e-04 142 -7.204821e-05 2.642834e-04 143 3.079172e-04 -7.204821e-05 144 -3.345008e-04 3.079172e-04 145 2.562483e-04 -3.345008e-04 146 1.829030e-04 2.562483e-04 147 -3.682377e-04 1.829030e-04 148 1.752078e-04 -3.682377e-04 149 1.968880e-07 1.752078e-04 150 6.161148e-04 1.968880e-07 151 1.306364e-03 6.161148e-04 152 1.011662e-03 1.306364e-03 153 -3.206400e-04 1.011662e-03 154 -6.588820e-04 -3.206400e-04 155 -6.051696e-04 -6.588820e-04 156 -5.092245e-04 -6.051696e-04 157 -2.303622e-03 -5.092245e-04 158 -5.549985e-04 -2.303622e-03 159 -2.191828e-04 -5.549985e-04 160 1.672501e-06 -2.191828e-04 161 -1.486950e-04 1.672501e-06 162 -2.823894e-05 -1.486950e-04 163 9.882155e-05 -2.823894e-05 164 -1.537175e-04 9.882155e-05 165 5.577569e-04 -1.537175e-04 166 -1.780129e-04 5.577569e-04 167 -3.786514e-05 -1.780129e-04 168 4.619293e-04 -3.786514e-05 169 -7.624785e-05 4.619293e-04 170 -1.558877e-04 -7.624785e-05 171 -5.989102e-06 -1.558877e-04 172 1.151798e-04 -5.989102e-06 173 2.428524e-04 1.151798e-04 174 3.841372e-04 2.428524e-04 175 2.630607e-04 3.841372e-04 176 3.168515e-04 2.630607e-04 177 -3.529887e-04 3.168515e-04 178 -6.894313e-05 -3.529887e-04 179 1.976601e-05 -6.894313e-05 180 -2.482410e-05 1.976601e-05 181 -2.548491e-05 -2.482410e-05 182 -1.287340e-04 -2.548491e-05 183 4.930577e-04 -1.287340e-04 184 7.434841e-04 4.930577e-04 185 3.307745e-04 7.434841e-04 186 2.111603e-04 3.307745e-04 187 2.020702e-04 2.111603e-04 188 2.088644e-04 2.020702e-04 189 5.464295e-04 2.088644e-04 190 4.932887e-04 5.464295e-04 191 3.347470e-04 4.932887e-04 192 -2.226152e-03 3.347470e-04 193 1.117950e-04 -2.226152e-03 194 4.969068e-04 1.117950e-04 195 NA 4.969068e-04 > dum1 <- dum[2:length(myerror),] > dum1 lag(myerror, k = 1) myerror [1,] 1.321009e-03 1.207412e-03 [2,] 1.889887e-03 1.321009e-03 [3,] 1.374882e-03 1.889887e-03 [4,] 1.823128e-03 1.374882e-03 [5,] 1.839964e-03 1.823128e-03 [6,] 3.899249e-05 1.839964e-03 [7,] 1.714509e-04 3.899249e-05 [8,] 4.186211e-04 1.714509e-04 [9,] 4.874158e-04 4.186211e-04 [10,] 6.591661e-04 4.874158e-04 [11,] 5.160890e-04 6.591661e-04 [12,] -4.034154e-04 5.160890e-04 [13,] -8.560891e-05 -4.034154e-04 [14,] -8.799748e-05 -8.560891e-05 [15,] 1.332708e-05 -8.799748e-05 [16,] -2.484471e-04 1.332708e-05 [17,] -4.657658e-04 -2.484471e-04 [18,] 1.981207e-04 -4.657658e-04 [19,] -6.900814e-04 1.981207e-04 [20,] -2.380375e-04 -6.900814e-04 [21,] 1.402537e-05 -2.380375e-04 [22,] 1.740749e-04 1.402537e-05 [23,] -2.777808e-05 1.740749e-04 [24,] -1.446467e-04 -2.777808e-05 [25,] 3.291086e-04 -1.446467e-04 [26,] 8.178094e-05 3.291086e-04 [27,] -1.906493e-05 8.178094e-05 [28,] 1.961380e-04 -1.906493e-05 [29,] -8.558779e-05 1.961380e-04 [30,] 5.841665e-06 -8.558779e-05 [31,] 2.273903e-05 5.841665e-06 [32,] 1.640598e-04 2.273903e-05 [33,] 7.366623e-05 1.640598e-04 [34,] 9.444071e-05 7.366623e-05 [35,] 5.825796e-05 9.444071e-05 [36,] -1.241012e-04 5.825796e-05 [37,] -7.316243e-05 -1.241012e-04 [38,] -6.908579e-05 -7.316243e-05 [39,] -9.186056e-05 -6.908579e-05 [40,] -4.270631e-05 -9.186056e-05 [41,] -2.064821e-04 -4.270631e-05 [42,] 1.021880e-04 -2.064821e-04 [43,] 1.008682e-04 1.021880e-04 [44,] 1.852102e-04 1.008682e-04 [45,] 1.228039e-04 1.852102e-04 [46,] 1.728521e-04 1.228039e-04 [47,] 3.645874e-04 1.728521e-04 [48,] -1.232179e-03 3.645874e-04 [49,] -1.180121e-03 -1.232179e-03 [50,] -1.026023e-03 -1.180121e-03 [51,] -1.001116e-03 -1.026023e-03 [52,] -1.103181e-03 -1.001116e-03 [53,] -1.119937e-03 -1.103181e-03 [54,] 4.543326e-04 -1.119937e-03 [55,] 3.186086e-04 4.543326e-04 [56,] 5.423748e-04 3.186086e-04 [57,] 9.251342e-07 5.423748e-04 [58,] 2.303746e-04 9.251342e-07 [59,] 1.550744e-04 2.303746e-04 [60,] 3.140038e-04 1.550744e-04 [61,] 6.129566e-04 3.140038e-04 [62,] -2.905157e-05 6.129566e-04 [63,] -1.595314e-04 -2.905157e-05 [64,] 3.886672e-06 -1.595314e-04 [65,] 6.185863e-04 3.886672e-06 [66,] -2.578973e-05 6.185863e-04 [67,] -2.902543e-04 -2.578973e-05 [68,] -1.107638e-03 -2.902543e-04 [69,] -3.744013e-04 -1.107638e-03 [70,] -1.884737e-04 -3.744013e-04 [71,] -4.653145e-04 -1.884737e-04 [72,] -3.138762e-04 -4.653145e-04 [73,] 4.117983e-04 -3.138762e-04 [74,] -3.048227e-05 4.117983e-04 [75,] 7.368823e-05 -3.048227e-05 [76,] 3.189448e-05 7.368823e-05 [77,] 9.450884e-05 3.189448e-05 [78,] 5.203758e-05 9.450884e-05 [79,] 4.775032e-04 5.203758e-05 [80,] 5.352141e-04 4.775032e-04 [81,] 1.594901e-04 5.352141e-04 [82,] 3.096254e-04 1.594901e-04 [83,] 2.645843e-04 3.096254e-04 [84,] 1.631337e-04 2.645843e-04 [85,] -2.122486e-05 1.631337e-04 [86,] 8.146249e-05 -2.122486e-05 [87,] 1.978846e-04 8.146249e-05 [88,] 3.114495e-04 1.978846e-04 [89,] 5.723962e-05 3.114495e-04 [90,] 3.557755e-04 5.723962e-05 [91,] -2.269220e-04 3.557755e-04 [92,] -1.864100e-04 -2.269220e-04 [93,] 3.228911e-04 -1.864100e-04 [94,] 1.638845e-04 3.228911e-04 [95,] -3.195788e-04 1.638845e-04 [96,] -2.635808e-04 -3.195788e-04 [97,] -7.947428e-04 -2.635808e-04 [98,] -7.618302e-04 -7.947428e-04 [99,] -4.818319e-04 -7.618302e-04 [100,] -1.983955e-03 -4.818319e-04 [101,] 8.136539e-04 -1.983955e-03 [102,] 4.584895e-04 8.136539e-04 [103,] 1.221807e-04 4.584895e-04 [104,] -1.817851e-04 1.221807e-04 [105,] -2.302859e-04 -1.817851e-04 [106,] -2.305214e-04 -2.302859e-04 [107,] -3.547970e-04 -2.305214e-04 [108,] -1.758071e-04 -3.547970e-04 [109,] -1.426103e-04 -1.758071e-04 [110,] 1.594170e-04 -1.426103e-04 [111,] -2.412779e-04 1.594170e-04 [112,] -1.490025e-04 -2.412779e-04 [113,] -2.600937e-04 -1.490025e-04 [114,] -3.784654e-05 -2.600937e-04 [115,] -8.949839e-04 -3.784654e-05 [116,] -4.942329e-04 -8.949839e-04 [117,] -5.254146e-04 -4.942329e-04 [118,] -6.836279e-04 -5.254146e-04 [119,] -1.048116e-03 -6.836279e-04 [120,] -1.377690e-04 -1.048116e-03 [121,] -2.701494e-04 -1.377690e-04 [122,] 2.337231e-04 -2.701494e-04 [123,] 1.112748e-04 2.337231e-04 [124,] 2.133674e-04 1.112748e-04 [125,] 2.873225e-04 2.133674e-04 [126,] 3.681338e-04 2.873225e-04 [127,] 1.911178e-04 3.681338e-04 [128,] -6.076064e-04 1.911178e-04 [129,] -3.401573e-04 -6.076064e-04 [130,] -3.480074e-04 -3.401573e-04 [131,] -2.786098e-04 -3.480074e-04 [132,] -2.104734e-04 -2.786098e-04 [133,] -1.870608e-04 -2.104734e-04 [134,] -2.575322e-05 -1.870608e-04 [135,] 2.334071e-04 -2.575322e-05 [136,] 2.061260e-04 2.334071e-04 [137,] 2.202844e-04 2.061260e-04 [138,] 1.733879e-04 2.202844e-04 [139,] 1.370495e-05 1.733879e-04 [140,] -6.928404e-04 1.370495e-05 [141,] 2.642834e-04 -6.928404e-04 [142,] -7.204821e-05 2.642834e-04 [143,] 3.079172e-04 -7.204821e-05 [144,] -3.345008e-04 3.079172e-04 [145,] 2.562483e-04 -3.345008e-04 [146,] 1.829030e-04 2.562483e-04 [147,] -3.682377e-04 1.829030e-04 [148,] 1.752078e-04 -3.682377e-04 [149,] 1.968880e-07 1.752078e-04 [150,] 6.161148e-04 1.968880e-07 [151,] 1.306364e-03 6.161148e-04 [152,] 1.011662e-03 1.306364e-03 [153,] -3.206400e-04 1.011662e-03 [154,] -6.588820e-04 -3.206400e-04 [155,] -6.051696e-04 -6.588820e-04 [156,] -5.092245e-04 -6.051696e-04 [157,] -2.303622e-03 -5.092245e-04 [158,] -5.549985e-04 -2.303622e-03 [159,] -2.191828e-04 -5.549985e-04 [160,] 1.672501e-06 -2.191828e-04 [161,] -1.486950e-04 1.672501e-06 [162,] -2.823894e-05 -1.486950e-04 [163,] 9.882155e-05 -2.823894e-05 [164,] -1.537175e-04 9.882155e-05 [165,] 5.577569e-04 -1.537175e-04 [166,] -1.780129e-04 5.577569e-04 [167,] -3.786514e-05 -1.780129e-04 [168,] 4.619293e-04 -3.786514e-05 [169,] -7.624785e-05 4.619293e-04 [170,] -1.558877e-04 -7.624785e-05 [171,] -5.989102e-06 -1.558877e-04 [172,] 1.151798e-04 -5.989102e-06 [173,] 2.428524e-04 1.151798e-04 [174,] 3.841372e-04 2.428524e-04 [175,] 2.630607e-04 3.841372e-04 [176,] 3.168515e-04 2.630607e-04 [177,] -3.529887e-04 3.168515e-04 [178,] -6.894313e-05 -3.529887e-04 [179,] 1.976601e-05 -6.894313e-05 [180,] -2.482410e-05 1.976601e-05 [181,] -2.548491e-05 -2.482410e-05 [182,] -1.287340e-04 -2.548491e-05 [183,] 4.930577e-04 -1.287340e-04 [184,] 7.434841e-04 4.930577e-04 [185,] 3.307745e-04 7.434841e-04 [186,] 2.111603e-04 3.307745e-04 [187,] 2.020702e-04 2.111603e-04 [188,] 2.088644e-04 2.020702e-04 [189,] 5.464295e-04 2.088644e-04 [190,] 4.932887e-04 5.464295e-04 [191,] 3.347470e-04 4.932887e-04 [192,] -2.226152e-03 3.347470e-04 [193,] 1.117950e-04 -2.226152e-03 [194,] 4.969068e-04 1.117950e-04 > z <- as.data.frame(dum1) > z lag(myerror, k = 1) myerror 1 1.321009e-03 1.207412e-03 2 1.889887e-03 1.321009e-03 3 1.374882e-03 1.889887e-03 4 1.823128e-03 1.374882e-03 5 1.839964e-03 1.823128e-03 6 3.899249e-05 1.839964e-03 7 1.714509e-04 3.899249e-05 8 4.186211e-04 1.714509e-04 9 4.874158e-04 4.186211e-04 10 6.591661e-04 4.874158e-04 11 5.160890e-04 6.591661e-04 12 -4.034154e-04 5.160890e-04 13 -8.560891e-05 -4.034154e-04 14 -8.799748e-05 -8.560891e-05 15 1.332708e-05 -8.799748e-05 16 -2.484471e-04 1.332708e-05 17 -4.657658e-04 -2.484471e-04 18 1.981207e-04 -4.657658e-04 19 -6.900814e-04 1.981207e-04 20 -2.380375e-04 -6.900814e-04 21 1.402537e-05 -2.380375e-04 22 1.740749e-04 1.402537e-05 23 -2.777808e-05 1.740749e-04 24 -1.446467e-04 -2.777808e-05 25 3.291086e-04 -1.446467e-04 26 8.178094e-05 3.291086e-04 27 -1.906493e-05 8.178094e-05 28 1.961380e-04 -1.906493e-05 29 -8.558779e-05 1.961380e-04 30 5.841665e-06 -8.558779e-05 31 2.273903e-05 5.841665e-06 32 1.640598e-04 2.273903e-05 33 7.366623e-05 1.640598e-04 34 9.444071e-05 7.366623e-05 35 5.825796e-05 9.444071e-05 36 -1.241012e-04 5.825796e-05 37 -7.316243e-05 -1.241012e-04 38 -6.908579e-05 -7.316243e-05 39 -9.186056e-05 -6.908579e-05 40 -4.270631e-05 -9.186056e-05 41 -2.064821e-04 -4.270631e-05 42 1.021880e-04 -2.064821e-04 43 1.008682e-04 1.021880e-04 44 1.852102e-04 1.008682e-04 45 1.228039e-04 1.852102e-04 46 1.728521e-04 1.228039e-04 47 3.645874e-04 1.728521e-04 48 -1.232179e-03 3.645874e-04 49 -1.180121e-03 -1.232179e-03 50 -1.026023e-03 -1.180121e-03 51 -1.001116e-03 -1.026023e-03 52 -1.103181e-03 -1.001116e-03 53 -1.119937e-03 -1.103181e-03 54 4.543326e-04 -1.119937e-03 55 3.186086e-04 4.543326e-04 56 5.423748e-04 3.186086e-04 57 9.251342e-07 5.423748e-04 58 2.303746e-04 9.251342e-07 59 1.550744e-04 2.303746e-04 60 3.140038e-04 1.550744e-04 61 6.129566e-04 3.140038e-04 62 -2.905157e-05 6.129566e-04 63 -1.595314e-04 -2.905157e-05 64 3.886672e-06 -1.595314e-04 65 6.185863e-04 3.886672e-06 66 -2.578973e-05 6.185863e-04 67 -2.902543e-04 -2.578973e-05 68 -1.107638e-03 -2.902543e-04 69 -3.744013e-04 -1.107638e-03 70 -1.884737e-04 -3.744013e-04 71 -4.653145e-04 -1.884737e-04 72 -3.138762e-04 -4.653145e-04 73 4.117983e-04 -3.138762e-04 74 -3.048227e-05 4.117983e-04 75 7.368823e-05 -3.048227e-05 76 3.189448e-05 7.368823e-05 77 9.450884e-05 3.189448e-05 78 5.203758e-05 9.450884e-05 79 4.775032e-04 5.203758e-05 80 5.352141e-04 4.775032e-04 81 1.594901e-04 5.352141e-04 82 3.096254e-04 1.594901e-04 83 2.645843e-04 3.096254e-04 84 1.631337e-04 2.645843e-04 85 -2.122486e-05 1.631337e-04 86 8.146249e-05 -2.122486e-05 87 1.978846e-04 8.146249e-05 88 3.114495e-04 1.978846e-04 89 5.723962e-05 3.114495e-04 90 3.557755e-04 5.723962e-05 91 -2.269220e-04 3.557755e-04 92 -1.864100e-04 -2.269220e-04 93 3.228911e-04 -1.864100e-04 94 1.638845e-04 3.228911e-04 95 -3.195788e-04 1.638845e-04 96 -2.635808e-04 -3.195788e-04 97 -7.947428e-04 -2.635808e-04 98 -7.618302e-04 -7.947428e-04 99 -4.818319e-04 -7.618302e-04 100 -1.983955e-03 -4.818319e-04 101 8.136539e-04 -1.983955e-03 102 4.584895e-04 8.136539e-04 103 1.221807e-04 4.584895e-04 104 -1.817851e-04 1.221807e-04 105 -2.302859e-04 -1.817851e-04 106 -2.305214e-04 -2.302859e-04 107 -3.547970e-04 -2.305214e-04 108 -1.758071e-04 -3.547970e-04 109 -1.426103e-04 -1.758071e-04 110 1.594170e-04 -1.426103e-04 111 -2.412779e-04 1.594170e-04 112 -1.490025e-04 -2.412779e-04 113 -2.600937e-04 -1.490025e-04 114 -3.784654e-05 -2.600937e-04 115 -8.949839e-04 -3.784654e-05 116 -4.942329e-04 -8.949839e-04 117 -5.254146e-04 -4.942329e-04 118 -6.836279e-04 -5.254146e-04 119 -1.048116e-03 -6.836279e-04 120 -1.377690e-04 -1.048116e-03 121 -2.701494e-04 -1.377690e-04 122 2.337231e-04 -2.701494e-04 123 1.112748e-04 2.337231e-04 124 2.133674e-04 1.112748e-04 125 2.873225e-04 2.133674e-04 126 3.681338e-04 2.873225e-04 127 1.911178e-04 3.681338e-04 128 -6.076064e-04 1.911178e-04 129 -3.401573e-04 -6.076064e-04 130 -3.480074e-04 -3.401573e-04 131 -2.786098e-04 -3.480074e-04 132 -2.104734e-04 -2.786098e-04 133 -1.870608e-04 -2.104734e-04 134 -2.575322e-05 -1.870608e-04 135 2.334071e-04 -2.575322e-05 136 2.061260e-04 2.334071e-04 137 2.202844e-04 2.061260e-04 138 1.733879e-04 2.202844e-04 139 1.370495e-05 1.733879e-04 140 -6.928404e-04 1.370495e-05 141 2.642834e-04 -6.928404e-04 142 -7.204821e-05 2.642834e-04 143 3.079172e-04 -7.204821e-05 144 -3.345008e-04 3.079172e-04 145 2.562483e-04 -3.345008e-04 146 1.829030e-04 2.562483e-04 147 -3.682377e-04 1.829030e-04 148 1.752078e-04 -3.682377e-04 149 1.968880e-07 1.752078e-04 150 6.161148e-04 1.968880e-07 151 1.306364e-03 6.161148e-04 152 1.011662e-03 1.306364e-03 153 -3.206400e-04 1.011662e-03 154 -6.588820e-04 -3.206400e-04 155 -6.051696e-04 -6.588820e-04 156 -5.092245e-04 -6.051696e-04 157 -2.303622e-03 -5.092245e-04 158 -5.549985e-04 -2.303622e-03 159 -2.191828e-04 -5.549985e-04 160 1.672501e-06 -2.191828e-04 161 -1.486950e-04 1.672501e-06 162 -2.823894e-05 -1.486950e-04 163 9.882155e-05 -2.823894e-05 164 -1.537175e-04 9.882155e-05 165 5.577569e-04 -1.537175e-04 166 -1.780129e-04 5.577569e-04 167 -3.786514e-05 -1.780129e-04 168 4.619293e-04 -3.786514e-05 169 -7.624785e-05 4.619293e-04 170 -1.558877e-04 -7.624785e-05 171 -5.989102e-06 -1.558877e-04 172 1.151798e-04 -5.989102e-06 173 2.428524e-04 1.151798e-04 174 3.841372e-04 2.428524e-04 175 2.630607e-04 3.841372e-04 176 3.168515e-04 2.630607e-04 177 -3.529887e-04 3.168515e-04 178 -6.894313e-05 -3.529887e-04 179 1.976601e-05 -6.894313e-05 180 -2.482410e-05 1.976601e-05 181 -2.548491e-05 -2.482410e-05 182 -1.287340e-04 -2.548491e-05 183 4.930577e-04 -1.287340e-04 184 7.434841e-04 4.930577e-04 185 3.307745e-04 7.434841e-04 186 2.111603e-04 3.307745e-04 187 2.020702e-04 2.111603e-04 188 2.088644e-04 2.020702e-04 189 5.464295e-04 2.088644e-04 190 4.932887e-04 5.464295e-04 191 3.347470e-04 4.932887e-04 192 -2.226152e-03 3.347470e-04 193 1.117950e-04 -2.226152e-03 194 4.969068e-04 1.117950e-04 > plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals') > lines(lowess(z)) > abline(lm(z)) > grid() > dev.off() null device 1 > postscript(file="/var/fisher/rcomp/tmp/7hqst1386680130.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/fisher/rcomp/tmp/8oniv1386680130.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/fisher/rcomp/tmp/9imu51386680130.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0)) > plot(mylm, las = 1, sub='Residual Diagnostics') > par(opar) > dev.off() null device 1 > if (n > n25) { + postscript(file="/var/fisher/rcomp/tmp/10qtau1386680130.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) + plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint') + grid() + dev.off() + } null device 1 > > #Note: the /var/fisher/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab > load(file="/var/fisher/rcomp/createtable") > > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE) > a<-table.row.end(a) > myeq <- colnames(x)[1] > myeq <- paste(myeq, '[t] = ', sep='') > for (i in 1:k){ + if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '') + myeq <- paste(myeq, signif(mysum$coefficients[i,1],6), sep=' ') + if (rownames(mysum$coefficients)[i] != '(Intercept)') { + myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='') + if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='') + } + } > myeq <- paste(myeq, ' + e[t]') > a<-table.row.start(a) > a<-table.element(a, myeq) > a<-table.row.end(a) > a<-table.end(a) > table.save(a,file="/var/fisher/rcomp/tmp/119vcu1386680130.tab") > a<-table.start() > a<-table.row.start(a) > a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a,'Variable',header=TRUE) > a<-table.element(a,'Parameter',header=TRUE) > a<-table.element(a,'S.D.',header=TRUE) > a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE) > a<-table.element(a,'2-tail p-value',header=TRUE) > a<-table.element(a,'1-tail p-value',header=TRUE) > a<-table.row.end(a) > for (i in 1:k){ + a<-table.row.start(a) + a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE) + a<-table.element(a,signif(mysum$coefficients[i,1],6)) + a<-table.element(a, signif(mysum$coefficients[i,2],6)) + a<-table.element(a, signif(mysum$coefficients[i,3],4)) + a<-table.element(a, signif(mysum$coefficients[i,4],6)) + a<-table.element(a, signif(mysum$coefficients[i,4]/2,6)) + a<-table.row.end(a) + } > a<-table.end(a) > table.save(a,file="/var/fisher/rcomp/tmp/129nea1386680130.tab") > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Multiple R',1,TRUE) > a<-table.element(a, signif(sqrt(mysum$r.squared),6)) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'R-squared',1,TRUE) > a<-table.element(a, signif(mysum$r.squared,6)) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Adjusted R-squared',1,TRUE) > a<-table.element(a, signif(mysum$adj.r.squared,6)) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'F-TEST (value)',1,TRUE) > a<-table.element(a, signif(mysum$fstatistic[1],6)) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE) > a<-table.element(a, signif(mysum$fstatistic[2],6)) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE) > a<-table.element(a, signif(mysum$fstatistic[3],6)) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'p-value',1,TRUE) > a<-table.element(a, signif(1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]),6)) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Residual Standard Deviation',1,TRUE) > a<-table.element(a, signif(mysum$sigma,6)) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Sum Squared Residuals',1,TRUE) > a<-table.element(a, signif(sum(myerror*myerror),6)) > a<-table.row.end(a) > a<-table.end(a) > table.save(a,file="/var/fisher/rcomp/tmp/13qqc51386680130.tab") > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Time or Index', 1, TRUE) > a<-table.element(a, 'Actuals', 1, TRUE) > a<-table.element(a, 'Interpolation
Forecast', 1, TRUE) > a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE) > a<-table.row.end(a) > for (i in 1:n) { + a<-table.row.start(a) + a<-table.element(a,i, 1, TRUE) + a<-table.element(a,signif(x[i],6)) + a<-table.element(a,signif(x[i]-mysum$resid[i],6)) + a<-table.element(a,signif(mysum$resid[i],6)) + a<-table.row.end(a) + } > a<-table.end(a) > table.save(a,file="/var/fisher/rcomp/tmp/14tt341386680130.tab") > if (n > n25) { + a<-table.start() + a<-table.row.start(a) + a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'p-values',header=TRUE) + a<-table.element(a,'Alternative Hypothesis',3,header=TRUE) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'breakpoint index',header=TRUE) + a<-table.element(a,'greater',header=TRUE) + a<-table.element(a,'2-sided',header=TRUE) + a<-table.element(a,'less',header=TRUE) + a<-table.row.end(a) + for (mypoint in kp3:nmkm3) { + a<-table.row.start(a) + a<-table.element(a,mypoint,header=TRUE) + a<-table.element(a,signif(gqarr[mypoint-kp3+1,1],6)) + a<-table.element(a,signif(gqarr[mypoint-kp3+1,2],6)) + a<-table.element(a,signif(gqarr[mypoint-kp3+1,3],6)) + a<-table.row.end(a) + } + a<-table.end(a) + table.save(a,file="/var/fisher/rcomp/tmp/15y60b1386680131.tab") + a<-table.start() + a<-table.row.start(a) + a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'Description',header=TRUE) + a<-table.element(a,'# significant tests',header=TRUE) + a<-table.element(a,'% significant tests',header=TRUE) + a<-table.element(a,'OK/NOK',header=TRUE) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'1% type I error level',header=TRUE) + a<-table.element(a,signif(numsignificant1,6)) + a<-table.element(a,signif(numsignificant1/numgqtests,6)) + if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK' + a<-table.element(a,dum) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'5% type I error level',header=TRUE) + a<-table.element(a,signif(numsignificant5,6)) + a<-table.element(a,signif(numsignificant5/numgqtests,6)) + if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK' + a<-table.element(a,dum) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'10% type I error level',header=TRUE) + a<-table.element(a,signif(numsignificant10,6)) + a<-table.element(a,signif(numsignificant10/numgqtests,6)) + if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK' + a<-table.element(a,dum) + a<-table.row.end(a) + a<-table.end(a) + table.save(a,file="/var/fisher/rcomp/tmp/16nv7f1386680131.tab") + } > > try(system("convert tmp/10uef1386680130.ps tmp/10uef1386680130.png",intern=TRUE)) character(0) > try(system("convert tmp/2t66l1386680130.ps tmp/2t66l1386680130.png",intern=TRUE)) character(0) > try(system("convert tmp/3gqoi1386680130.ps tmp/3gqoi1386680130.png",intern=TRUE)) character(0) > try(system("convert tmp/4t5a11386680130.ps tmp/4t5a11386680130.png",intern=TRUE)) character(0) > try(system("convert tmp/5whaa1386680130.ps tmp/5whaa1386680130.png",intern=TRUE)) character(0) > try(system("convert tmp/6dktn1386680130.ps tmp/6dktn1386680130.png",intern=TRUE)) character(0) > try(system("convert tmp/7hqst1386680130.ps tmp/7hqst1386680130.png",intern=TRUE)) character(0) > try(system("convert tmp/8oniv1386680130.ps tmp/8oniv1386680130.png",intern=TRUE)) character(0) > try(system("convert tmp/9imu51386680130.ps tmp/9imu51386680130.png",intern=TRUE)) character(0) > try(system("convert tmp/10qtau1386680130.ps tmp/10qtau1386680130.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 20.689 4.216 24.870