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Data X:
1 119.992 157.302 74.997 0.00784 0.00007 0.0037 0.00554 0.04374 0.426 0.02971 1 122.4 148.65 113.819 0.00968 0.00008 0.00465 0.00696 0.06134 0.626 0.04368 1 116.682 131.111 111.555 0.0105 0.00009 0.00544 0.00781 0.05233 0.482 0.0359 1 116.676 137.871 111.366 0.00997 0.00009 0.00502 0.00698 0.05492 0.517 0.03772 1 116.014 141.781 110.655 0.01284 0.00011 0.00655 0.00908 0.06425 0.584 0.04465 1 120.552 131.162 113.787 0.00968 0.00008 0.00463 0.0075 0.04701 0.456 0.03243 1 120.267 137.244 114.82 0.00333 0.00003 0.00155 0.00202 0.01608 0.14 0.01351 1 107.332 113.84 104.315 0.0029 0.00003 0.00144 0.00182 0.01567 0.134 0.01256 1 95.73 132.068 91.754 0.00551 0.00006 0.00293 0.00332 0.02093 0.191 0.01717 1 95.056 120.103 91.226 0.00532 0.00006 0.00268 0.00332 0.02838 0.255 0.02444 1 88.333 112.24 84.072 0.00505 0.00006 0.00254 0.0033 0.02143 0.197 0.01892 1 91.904 115.871 86.292 0.0054 0.00006 0.00281 0.00336 0.02752 0.249 0.02214 1 136.926 159.866 131.276 0.00293 0.00002 0.00118 0.00153 0.01259 0.112 0.0114 1 139.173 179.139 76.556 0.0039 0.00003 0.00165 0.00208 0.01642 0.154 0.01797 1 152.845 163.305 75.836 0.00294 0.00002 0.00121 0.00149 0.01828 0.158 0.01246 1 142.167 217.455 83.159 0.00369 0.00003 0.00157 0.00203 0.01503 0.126 0.01359 1 144.188 349.259 82.764 0.00544 0.00004 0.00211 0.00292 0.02047 0.192 0.02074 1 168.778 232.181 75.603 0.00718 0.00004 0.00284 0.00387 0.03327 0.348 0.0343 1 153.046 175.829 68.623 0.00742 0.00005 0.00364 0.00432 0.05517 0.542 0.05767 1 156.405 189.398 142.822 0.00768 0.00005 0.00372 0.00399 0.03995 0.348 0.0431 1 153.848 165.738 65.782 0.0084 0.00005 0.00428 0.0045 0.0381 0.328 0.04055 1 153.88 172.86 78.128 0.0048 0.00003 0.00232 0.00267 0.04137 0.37 0.04525 1 167.93 193.221 79.068 0.00442 0.00003 0.0022 0.00247 0.04351 0.377 0.04246 1 173.917 192.735 86.18 0.00476 0.00003 0.00221 0.00258 0.04192 0.364 0.03772 1 163.656 200.841 76.779 0.00742 0.00005 0.0038 0.0039 0.01659 0.164 0.01497 1 104.4 206.002 77.968 0.00633 0.00006 0.00316 0.00375 0.03767 0.381 0.0378 1 171.041 208.313 75.501 0.00455 0.00003 0.0025 0.00234 0.01966 0.186 0.01872 1 146.845 208.701 81.737 0.00496 0.00003 0.0025 0.00275 0.01919 0.198 0.01826 1 155.358 227.383 80.055 0.0031 0.00002 0.00159 0.00176 0.01718 0.161 0.01661 1 162.568 198.346 77.63 0.00502 0.00003 0.0028 0.00253 0.01791 0.168 0.01799 0 197.076 206.896 192.055 0.00289 0.00001 0.00166 0.00168 0.01098 0.097 0.00802 0 199.228 209.512 192.091 0.00241 0.00001 0.00134 0.00138 0.01015 0.089 0.00762 0 198.383 215.203 193.104 0.00212 0.00001 0.00113 0.00135 0.01263 0.111 0.00951 0 202.266 211.604 197.079 0.0018 0.000009 0.00093 0.00107 0.00954 0.085 0.00719 0 203.184 211.526 196.16 0.00178 0.000009 0.00094 0.00106 0.00958 0.085 0.00726 0 201.464 210.565 195.708 0.00198 0.00001 0.00105 0.00115 0.01194 0.107 0.00957 1 177.876 192.921 168.013 0.00411 0.00002 0.00233 0.00241 0.02126 0.189 0.01612 1 176.17 185.604 163.564 0.00369 0.00002 0.00205 0.00218 0.01851 0.168 0.01491 1 180.198 201.249 175.456 0.00284 0.00002 0.00153 0.00166 0.01444 0.131 0.0119 1 187.733 202.324 173.015 0.00316 0.00002 0.00168 0.00182 0.01663 0.151 0.01366 1 186.163 197.724 177.584 0.00298 0.00002 0.00165 0.00175 0.01495 0.135 0.01233 1 184.055 196.537 166.977 0.00258 0.00001 0.00134 0.00147 0.01463 0.132 0.01234 0 237.226 247.326 225.227 0.00298 0.00001 0.00169 0.00182 0.01752 0.164 0.01133 0 241.404 248.834 232.483 0.00281 0.00001 0.00157 0.00173 0.0176 0.154 0.01251 0 243.439 250.912 232.435 0.0021 0.000009 0.00109 0.00137 0.01419 0.126 0.01033 0 242.852 255.034 227.911 0.00225 0.000009 0.00117 0.00139 0.01494 0.134 0.01014 0 245.51 262.09 231.848 0.00235 0.00001 0.00127 0.00148 0.01608 0.141 0.01149 0 252.455 261.487 182.786 0.00185 0.000007 0.00092 0.00113 0.01152 0.103 0.0086 0 122.188 128.611 115.765 0.00524 0.00004 0.00169 0.00203 0.01613 0.143 0.01433 0 122.964 130.049 114.676 0.00428 0.00003 0.00124 0.00155 0.01681 0.154 0.014 0 124.445 135.069 117.495 0.00431 0.00003 0.00141 0.00167 0.02184 0.197 0.01685 0 126.344 134.231 112.773 0.00448 0.00004 0.00131 0.00169 0.02033 0.185 0.01614 0 128.001 138.052 122.08 0.00436 0.00003 0.00137 0.00166 0.02297 0.21 0.01677 0 129.336 139.867 118.604 0.0049 0.00004 0.00165 0.00183 0.02498 0.228 0.01947 1 108.807 134.656 102.874 0.00761 0.00007 0.00349 0.00486 0.02719 0.255 0.02067 1 109.86 126.358 104.437 0.00874 0.00008 0.00398 0.00539 0.03209 0.307 0.02454 1 110.417 131.067 103.37 0.00784 0.00007 0.00352 0.00514 0.03715 0.334 0.02802 1 117.274 129.916 110.402 0.00752 0.00006 0.00299 0.00469 0.02293 0.221 0.01948 1 116.879 131.897 108.153 0.00788 0.00007 0.00334 0.00493 0.02645 0.265 0.02137 1 114.847 271.314 104.68 0.00867 0.00008 0.00373 0.0052 0.03225 0.35 0.02519 0 209.144 237.494 109.379 0.00282 0.00001 0.00147 0.00152 0.01861 0.17 0.01382 0 223.365 238.987 98.664 0.00264 0.00001 0.00154 0.00151 0.01906 0.165 0.0134 0 222.236 231.345 205.495 0.00266 0.00001 0.00152 0.00144 0.01643 0.145 0.012 0 228.832 234.619 223.634 0.00296 0.00001 0.00175 0.00155 0.01644 0.145 0.01179 0 229.401 252.221 221.156 0.00205 0.000009 0.00114 0.00113 0.01457 0.129 0.01016 0 228.969 239.541 113.201 0.00238 0.00001 0.00136 0.0014 0.01745 0.154 0.01234 1 140.341 159.774 67.021 0.00817 0.00006 0.0043 0.0044 0.03198 0.313 0.02428 1 136.969 166.607 66.004 0.00923 0.00007 0.00507 0.00463 0.03111 0.308 0.02603 1 143.533 162.215 65.809 0.01101 0.00008 0.00647 0.00467 0.05384 0.478 0.03392 1 148.09 162.824 67.343 0.00762 0.00005 0.00467 0.00354 0.05428 0.497 0.03635 1 142.729 162.408 65.476 0.00831 0.00006 0.00469 0.00419 0.03485 0.365 0.02949 1 136.358 176.595 65.75 0.00971 0.00007 0.00534 0.00478 0.04978 0.483 0.03736 1 120.08 139.71 111.208 0.00405 0.00003 0.0018 0.0022 0.01706 0.152 0.01345 1 112.014 588.518 107.024 0.00533 0.00005 0.00268 0.00329 0.02448 0.226 0.01956 1 110.793 128.101 107.316 0.00494 0.00004 0.0026 0.00283 0.02442 0.216 0.01831 1 110.707 122.611 105.007 0.00516 0.00005 0.00277 0.00289 0.02215 0.206 0.01715 1 112.876 148.826 106.981 0.005 0.00004 0.0027 0.00289 0.03999 0.35 0.02704 1 110.568 125.394 106.821 0.00462 0.00004 0.00226 0.0028 0.02199 0.197 0.01636 1 95.385 102.145 90.264 0.00608 0.00006 0.00331 0.00332 0.03202 0.263 0.02455 1 100.77 115.697 85.545 0.01038 0.0001 0.00622 0.00576 0.03121 0.361 0.02139 1 96.106 108.664 84.51 0.00694 0.00007 0.00389 0.00415 0.04024 0.364 0.02876 1 95.605 107.715 87.549 0.00702 0.00007 0.00428 0.00371 0.03156 0.296 0.0219 1 100.96 110.019 95.628 0.00606 0.00006 0.00351 0.00348 0.02427 0.216 0.01751 1 98.804 102.305 87.804 0.00432 0.00004 0.00247 0.00258 0.02223 0.202 0.01552 1 176.858 205.56 75.344 0.00747 0.00004 0.00418 0.0042 0.04795 0.435 0.0351 1 180.978 200.125 155.495 0.00406 0.00002 0.0022 0.00244 0.03852 0.331 0.02877 1 178.222 202.45 141.047 0.00321 0.00002 0.00163 0.00194 0.03759 0.327 0.02784 1 176.281 227.381 125.61 0.0052 0.00003 0.00287 0.00312 0.06511 0.58 0.04683 1 173.898 211.35 74.677 0.00448 0.00003 0.00237 0.00254 0.06727 0.65 0.04802 1 179.711 225.93 144.878 0.00709 0.00004 0.00391 0.00419 0.04313 0.442 0.03455 1 166.605 206.008 78.032 0.00742 0.00004 0.00387 0.00453 0.0664 0.634 0.05114 1 151.955 163.335 147.226 0.00419 0.00003 0.00224 0.00227 0.07959 0.772 0.0569 1 148.272 164.989 142.299 0.00459 0.00003 0.0025 0.00256 0.0419 0.383 0.03051 1 152.125 161.469 76.596 0.00382 0.00003 0.00191 0.00226 0.05925 0.637 0.04398 1 157.821 172.975 68.401 0.00358 0.00002 0.00196 0.00196 0.03716 0.307 0.02764 1 157.447 163.267 149.605 0.00369 0.00002 0.00201 0.00197 0.03272 0.283 0.02571 1 159.116 168.913 144.811 0.00342 0.00002 0.00178 0.00184 0.03381 0.307 0.02809 1 125.036 143.946 116.187 0.0128 0.0001 0.00743 0.00623 0.03886 0.342 0.03088 1 125.791 140.557 96.206 0.01378 0.00011 0.00826 0.00655 0.04689 0.422 0.03908 1 126.512 141.756 99.77 0.01936 0.00015 0.01159 0.0099 0.06734 0.659 0.05783 1 125.641 141.068 116.346 0.03316 0.00026 0.02144 0.01522 0.09178 0.891 0.06196 1 128.451 150.449 75.632 0.01551 0.00012 0.00905 0.00909 0.0617 0.584 0.05174 1 139.224 586.567 66.157 0.03011 0.00022 0.01854 0.01628 0.09419 0.93 0.06023 1 150.258 154.609 75.349 0.00248 0.00002 0.00105 0.00136 0.01131 0.107 0.01009 1 154.003 160.267 128.621 0.00183 0.00001 0.00076 0.001 0.0103 0.094 0.00871 1 149.689 160.368 133.608 0.00257 0.00002 0.00116 0.00134 0.01346 0.126 0.01059 1 155.078 163.736 144.148 0.00168 0.00001 0.00068 0.00092 0.01064 0.097 0.00928 1 151.884 157.765 133.751 0.00258 0.00002 0.00115 0.00122 0.0145 0.137 0.01267 1 151.989 157.339 132.857 0.00174 0.00001 0.00075 0.00096 0.01024 0.093 0.00993 1 193.03 208.9 80.297 0.00766 0.00004 0.0045 0.00389 0.03044 0.275 0.02084 1 200.714 223.982 89.686 0.00621 0.00003 0.00371 0.00337 0.02286 0.207 0.01852 1 208.519 220.315 199.02 0.00609 0.00003 0.00368 0.00339 0.01761 0.155 0.01307 1 204.664 221.3 189.621 0.00841 0.00004 0.00502 0.00485 0.02378 0.21 0.01767 1 210.141 232.706 185.258 0.00534 0.00003 0.00321 0.0028 0.0168 0.149 0.01301 1 206.327 226.355 92.02 0.00495 0.00002 0.00302 0.00246 0.02105 0.209 0.01604 1 151.872 492.892 69.085 0.00856 0.00006 0.00404 0.00385 0.01843 0.235 0.01271 1 158.219 442.557 71.948 0.00476 0.00003 0.00214 0.00207 0.01458 0.148 0.01312 1 170.756 450.247 79.032 0.00555 0.00003 0.00244 0.00261 0.01725 0.175 0.01652 1 178.285 442.824 82.063 0.00462 0.00003 0.00157 0.00194 0.01279 0.129 0.01151 1 217.116 233.481 93.978 0.00404 0.00002 0.00127 0.00128 0.01299 0.124 0.01075 1 128.94 479.697 88.251 0.00581 0.00005 0.00241 0.00314 0.02008 0.221 0.01734 1 176.824 215.293 83.961 0.0046 0.00003 0.00209 0.00221 0.01169 0.117 0.01104 1 138.19 203.522 83.34 0.00704 0.00005 0.00406 0.00398 0.04479 0.441 0.0322 1 182.018 197.173 79.187 0.00842 0.00005 0.00506 0.00449 0.02503 0.231 0.01931 1 156.239 195.107 79.82 0.00694 0.00004 0.00403 0.00395 0.02343 0.224 0.0172 1 145.174 198.109 80.637 0.00733 0.00005 0.00414 0.00422 0.02362 0.233 0.01944 1 138.145 197.238 81.114 0.00544 0.00004 0.00294 0.00327 0.02791 0.246 0.02259 1 166.888 198.966 79.512 0.00638 0.00004 0.00368 0.00351 0.02857 0.257 0.02301 1 119.031 127.533 109.216 0.0044 0.00004 0.00214 0.00192 0.01033 0.098 0.00811 1 120.078 126.632 105.667 0.0027 0.00002 0.00116 0.00135 0.01022 0.09 0.00903 1 120.289 128.143 100.209 0.00492 0.00004 0.00269 0.00238 0.01412 0.125 0.01194 1 120.256 125.306 104.773 0.00407 0.00003 0.00224 0.00205 0.01516 0.138 0.0131 1 119.056 125.213 86.795 0.00346 0.00003 0.00169 0.0017 0.01201 0.106 0.00915 1 118.747 123.723 109.836 0.00331 0.00003 0.00168 0.00171 0.01043 0.099 0.00903 1 106.516 112.777 93.105 0.00589 0.00006 0.00291 0.00319 0.04932 0.441 0.03651 1 110.453 127.611 105.554 0.00494 0.00004 0.00244 0.00315 0.04128 0.379 0.03316 1 113.4 133.344 107.816 0.00451 0.00004 0.00219 0.00283 0.04879 0.431 0.0437 1 113.166 130.27 100.673 0.00502 0.00004 0.00257 0.00312 0.05279 0.476 0.04134 1 112.239 126.609 104.095 0.00472 0.00004 0.00238 0.0029 0.05643 0.517 0.04451 1 116.15 131.731 109.815 0.00381 0.00003 0.00181 0.00232 0.03026 0.267 0.0277 1 170.368 268.796 79.543 0.00571 0.00003 0.00232 0.00269 0.03273 0.281 0.02824 1 208.083 253.792 91.802 0.00757 0.00004 0.00428 0.00428 0.06725 0.571 0.04464 1 198.458 219.29 148.691 0.00376 0.00002 0.00182 0.00215 0.03527 0.297 0.0253 1 202.805 231.508 86.232 0.0037 0.00002 0.00189 0.00211 0.01997 0.18 0.01506 1 202.544 241.35 164.168 0.00254 0.00001 0.001 0.00133 0.02662 0.228 0.02006 1 223.361 263.872 87.638 0.00352 0.00002 0.00169 0.00188 0.02536 0.225 0.01909 1 169.774 191.759 151.451 0.01568 0.00009 0.00863 0.00946 0.08143 0.821 0.08808 1 183.52 216.814 161.34 0.01466 0.00008 0.00849 0.00819 0.0605 0.618 0.06359 1 188.62 216.302 165.982 0.01719 0.00009 0.00996 0.01027 0.07118 0.722 0.06824 1 202.632 565.74 177.258 0.01627 0.00008 0.00919 0.00963 0.0717 0.833 0.0646 1 186.695 211.961 149.442 0.01872 0.0001 0.01075 0.01154 0.0583 0.784 0.06259 1 192.818 224.429 168.793 0.03107 0.00016 0.018 0.01958 0.11908 1.302 0.13778 1 198.116 233.099 174.478 0.02714 0.00014 0.01568 0.01699 0.08684 1.018 0.08318 1 121.345 139.644 98.25 0.00684 0.00006 0.00388 0.00332 0.02534 0.241 0.02056 1 119.1 128.442 88.833 0.00692 0.00006 0.00393 0.003 0.02682 0.236 0.02018 1 117.87 127.349 95.654 0.00647 0.00005 0.00356 0.003 0.03087 0.276 0.02402 1 122.336 142.369 94.794 0.00727 0.00006 0.00415 0.00339 0.02293 0.223 0.01771 1 117.963 134.209 100.757 0.01813 0.00015 0.01117 0.00718 0.04912 0.438 0.02916 1 126.144 154.284 97.543 0.00975 0.00008 0.00593 0.00454 0.02852 0.266 0.02157 1 127.93 138.752 112.173 0.00605 0.00005 0.00321 0.00318 0.03235 0.339 0.03105 1 114.238 124.393 77.022 0.00581 0.00005 0.00299 0.00316 0.04009 0.406 0.04114 1 115.322 135.738 107.802 0.00619 0.00005 0.00352 0.00329 0.03273 0.325 0.02931 1 114.554 126.778 91.121 0.00651 0.00006 0.00366 0.0034 0.03658 0.369 0.03091 1 112.15 131.669 97.527 0.00519 0.00005 0.00291 0.00284 0.01756 0.155 0.01363 1 102.273 142.83 85.902 0.00907 0.00009 0.00493 0.00461 0.02814 0.272 0.02073 0 236.2 244.663 102.137 0.00277 0.00001 0.00154 0.00153 0.02448 0.217 0.01621 0 237.323 243.709 229.256 0.00303 0.00001 0.00173 0.00159 0.01242 0.116 0.00882 0 260.105 264.919 237.303 0.00339 0.00001 0.00205 0.00186 0.0203 0.197 0.01367 0 197.569 217.627 90.794 0.00803 0.00004 0.0049 0.00448 0.02177 0.189 0.01439 0 240.301 245.135 219.783 0.00517 0.00002 0.00316 0.00283 0.02018 0.212 0.01344 0 244.99 272.21 239.17 0.00451 0.00002 0.00279 0.00237 0.01897 0.181 0.01255 0 112.547 133.374 105.715 0.00355 0.00003 0.00166 0.0019 0.01358 0.129 0.0114 0 110.739 113.597 100.139 0.00356 0.00003 0.0017 0.002 0.01484 0.133 0.01285 0 113.715 116.443 96.913 0.00349 0.00003 0.00171 0.00203 0.01472 0.133 0.01148 0 117.004 144.466 99.923 0.00353 0.00003 0.00176 0.00218 0.01657 0.145 0.01318 0 115.38 123.109 108.634 0.00332 0.00003 0.0016 0.00199 0.01503 0.137 0.01133 0 116.388 129.038 108.97 0.00346 0.00003 0.00169 0.00213 0.01725 0.155 0.01331 1 151.737 190.204 129.859 0.00314 0.00002 0.00135 0.00162 0.01469 0.132 0.0123 1 148.79 158.359 138.99 0.00309 0.00002 0.00152 0.00186 0.01574 0.142 0.01309 1 148.143 155.982 135.041 0.00392 0.00003 0.00204 0.00231 0.0145 0.131 0.01263 1 150.44 163.441 144.736 0.00396 0.00003 0.00206 0.00233 0.02551 0.237 0.02148 1 148.462 161.078 141.998 0.00397 0.00003 0.00202 0.00235 0.01831 0.163 0.01559 1 149.818 163.417 144.786 0.00336 0.00002 0.00174 0.00198 0.02145 0.198 0.01666 0 117.226 123.925 106.656 0.00417 0.00004 0.00186 0.0027 0.01909 0.171 0.01949 0 116.848 217.552 99.503 0.00531 0.00005 0.0026 0.00346 0.01795 0.163 0.01756 0 116.286 177.291 96.983 0.00314 0.00003 0.00134 0.00192 0.01564 0.136 0.01691 0 116.556 592.03 86.228 0.00496 0.00004 0.00254 0.00263 0.0166 0.154 0.01491 0 116.342 581.289 94.246 0.00267 0.00002 0.00115 0.00148 0.013 0.117 0.01144 0 114.563 119.167 86.647 0.00327 0.00003 0.00146 0.00184 0.01185 0.106 0.01095 0 201.774 262.707 78.228 0.00694 0.00003 0.00412 0.00396 0.02574 0.255 0.01758 0 174.188 230.978 94.261 0.00459 0.00003 0.00263 0.00259 0.04087 0.405 0.02745 0 209.516 253.017 89.488 0.00564 0.00003 0.00331 0.00292 0.02751 0.263 0.01879 0 174.688 240.005 74.287 0.0136 0.00008 0.00624 0.00564 0.02308 0.256 0.01667 0 198.764 396.961 74.904 0.0074 0.00004 0.0037 0.0039 0.02296 0.241 0.01588 0 214.289 260.277 77.973 0.00567 0.00003 0.00295 0.00317 0.01884 0.19 0.01373
Names of X columns:
status MDVP:Fo(Hz) MDVP:Fhi(Hz) MDVP:Flo(Hz) MDVP:Jitter(%) MDVP:Jitter(Abs) MDVP:RAP MDVP:PPQ MDVP:Shimmer MDVP:Shimmer(dB) MDVP:APQ
Sample Range:
(leave blank to include all observations)
From:
To:
Column Number of Endogenous Series
(?)
Fixed Seasonal Effects
Do not include Seasonal Dummies
Do not include Seasonal Dummies
Include Seasonal Dummies
Type of Equation
No Linear Trend
No Linear Trend
Linear Trend
First Differences
Seasonal Differences (s)
First and Seasonal Differences (s)
Degree of Predetermination (lagged endogenous variables)
Degree of Seasonal Predetermination
Seasonality
12
1
2
3
4
5
6
7
8
9
10
11
12
Chart options
R Code
library(lattice) library(lmtest) 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 k <- length(x[1,]) df <- as.data.frame(x) (mylm <- lm(df)) (mysum <- summary(mylm)) 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)$p.value } if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1 if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1 if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1 } gqarr } bitmap(file='test0.png') plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index') points(x[,1]-mysum$resid) grid() dev.off() bitmap(file='test1.png') plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index') grid() dev.off() bitmap(file='test2.png') hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') grid() dev.off() bitmap(file='test3.png') densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') dev.off() bitmap(file='test4.png') qqnorm(mysum$resid, main='Residual Normal Q-Q Plot') qqline(mysum$resid) grid() dev.off() (myerror <- as.ts(mysum$resid)) bitmap(file='test5.png') dum <- cbind(lag(myerror,k=1),myerror) dum dum1 <- dum[2:length(myerror),] dum1 z <- as.data.frame(dum1) z 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() bitmap(file='test6.png') acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function') grid() dev.off() bitmap(file='test7.png') pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function') grid() dev.off() bitmap(file='test8.png') opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0)) plot(mylm, las = 1, sub='Residual Diagnostics') par(opar) dev.off() if (n > n25) { bitmap(file='test9.png') plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint') grid() dev.off() } load(file='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$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='') } } myeq <- paste(myeq, ' + e[t]') a<-table.row.start(a) a<-table.element(a, myeq) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable1.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Variable',header=TRUE) a<-table.element(a,'Parameter',header=TRUE) a<-table.element(a,'S.D.',header=TRUE) a<-table.element(a,'T-STAT<br />H0: parameter = 0',header=TRUE) a<-table.element(a,'2-tail p-value',header=TRUE) a<-table.element(a,'1-tail p-value',header=TRUE) a<-table.row.end(a) for (i in 1:k){ a<-table.row.start(a) a<-table.element(a,rownames(mysum$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$coefficients[i,4]/2,6)) a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable2.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Multiple R',1,TRUE) a<-table.element(a, signif(sqrt(mysum$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$sigma,6)) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Sum Squared Residuals',1,TRUE) a<-table.element(a, signif(sum(myerror*myerror),6)) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable3.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Time or Index', 1, TRUE) a<-table.element(a, 'Actuals', 1, TRUE) a<-table.element(a, 'Interpolation<br />Forecast', 1, TRUE) a<-table.element(a, 'Residuals<br />Prediction Error', 1, TRUE) a<-table.row.end(a) for (i in 1:n) { a<-table.row.start(a) a<-table.element(a,i, 1, TRUE) a<-table.element(a,signif(x[i],6)) a<-table.element(a,signif(x[i]-mysum$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='mytable4.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='mytable5.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='mytable6.tab') }
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