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 = '1' > par3 <- 'No Linear Trend' > par2 <- 'Do not include Seasonal Dummies' > par1 <- '1' > #'GNU S' R Code compiled by R2WASP v. 1.2.327 () > #Author: root > #To cite this work: Wessa P., (2013), Multiple Regression (v1.0.29) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_multipleregression.wasp/ > #Source of accompanying publication: Office for Research, Development, and Education > # > library(lattice) > library(lmtest) Loading required package: zoo Attaching package: 'zoo' The following objects are masked from 'package:base': as.Date, as.Date.numeric > n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test > par1 <- as.numeric(par1) > x <- t(y) > k <- length(x[1,]) > n <- length(x[,1]) > x1 <- cbind(x[,par1], x[,1:k!=par1]) > mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1]) > colnames(x1) <- mycolnames #colnames(x)[par1] > x <- x1 > if (par3 == 'First Differences'){ + x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep=''))) + for (i in 1:n-1) { + for (j in 1:k) { + x2[i,j] <- x[i+1,j] - x[i,j] + } + } + x <- x2 + } > if (par2 == 'Include Monthly Dummies'){ + x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep =''))) + for (i in 1:11){ + x2[seq(i,n,12),i] <- 1 + } + x <- cbind(x, x2) + } > if (par2 == 'Include Quarterly Dummies'){ + x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep =''))) + for (i in 1:3){ + x2[seq(i,n,4),i] <- 1 + } + x <- cbind(x, x2) + } > k <- length(x[1,]) > if (par3 == 'Linear Trend'){ + x <- cbind(x, c(1:n)) + colnames(x)[k+1] <- 't' + } > x status MDVP:Fo(Hz) MDVP:Fhi(Hz) MDVP:Flo(Hz) MDVP:Jitter(%) 1 1 119.992 157.302 74.997 0.00784 2 1 122.400 148.650 113.819 0.00968 3 1 116.682 131.111 111.555 0.01050 4 1 116.676 137.871 111.366 0.00997 5 1 116.014 141.781 110.655 0.01284 6 1 120.552 131.162 113.787 0.00968 7 1 120.267 137.244 114.820 0.00333 8 1 107.332 113.840 104.315 0.00290 9 1 95.730 132.068 91.754 0.00551 10 1 95.056 120.103 91.226 0.00532 11 1 88.333 112.240 84.072 0.00505 12 1 91.904 115.871 86.292 0.00540 13 1 136.926 159.866 131.276 0.00293 14 1 139.173 179.139 76.556 0.00390 15 1 152.845 163.305 75.836 0.00294 16 1 142.167 217.455 83.159 0.00369 17 1 144.188 349.259 82.764 0.00544 18 1 168.778 232.181 75.603 0.00718 19 1 153.046 175.829 68.623 0.00742 20 1 156.405 189.398 142.822 0.00768 21 1 153.848 165.738 65.782 0.00840 22 1 153.880 172.860 78.128 0.00480 23 1 167.930 193.221 79.068 0.00442 24 1 173.917 192.735 86.180 0.00476 25 1 163.656 200.841 76.779 0.00742 26 1 104.400 206.002 77.968 0.00633 27 1 171.041 208.313 75.501 0.00455 28 1 146.845 208.701 81.737 0.00496 29 1 155.358 227.383 80.055 0.00310 30 1 162.568 198.346 77.630 0.00502 31 0 197.076 206.896 192.055 0.00289 32 0 199.228 209.512 192.091 0.00241 33 0 198.383 215.203 193.104 0.00212 34 0 202.266 211.604 197.079 0.00180 35 0 203.184 211.526 196.160 0.00178 36 0 201.464 210.565 195.708 0.00198 37 1 177.876 192.921 168.013 0.00411 38 1 176.170 185.604 163.564 0.00369 39 1 180.198 201.249 175.456 0.00284 40 1 187.733 202.324 173.015 0.00316 41 1 186.163 197.724 177.584 0.00298 42 1 184.055 196.537 166.977 0.00258 43 0 237.226 247.326 225.227 0.00298 44 0 241.404 248.834 232.483 0.00281 45 0 243.439 250.912 232.435 0.00210 46 0 242.852 255.034 227.911 0.00225 47 0 245.510 262.090 231.848 0.00235 48 0 252.455 261.487 182.786 0.00185 49 0 122.188 128.611 115.765 0.00524 50 0 122.964 130.049 114.676 0.00428 51 0 124.445 135.069 117.495 0.00431 52 0 126.344 134.231 112.773 0.00448 53 0 128.001 138.052 122.080 0.00436 54 0 129.336 139.867 118.604 0.00490 55 1 108.807 134.656 102.874 0.00761 56 1 109.860 126.358 104.437 0.00874 57 1 110.417 131.067 103.370 0.00784 58 1 117.274 129.916 110.402 0.00752 59 1 116.879 131.897 108.153 0.00788 60 1 114.847 271.314 104.680 0.00867 61 0 209.144 237.494 109.379 0.00282 62 0 223.365 238.987 98.664 0.00264 63 0 222.236 231.345 205.495 0.00266 64 0 228.832 234.619 223.634 0.00296 65 0 229.401 252.221 221.156 0.00205 66 0 228.969 239.541 113.201 0.00238 67 1 140.341 159.774 67.021 0.00817 68 1 136.969 166.607 66.004 0.00923 69 1 143.533 162.215 65.809 0.01101 70 1 148.090 162.824 67.343 0.00762 71 1 142.729 162.408 65.476 0.00831 72 1 136.358 176.595 65.750 0.00971 73 1 120.080 139.710 111.208 0.00405 74 1 112.014 588.518 107.024 0.00533 75 1 110.793 128.101 107.316 0.00494 76 1 110.707 122.611 105.007 0.00516 77 1 112.876 148.826 106.981 0.00500 78 1 110.568 125.394 106.821 0.00462 79 1 95.385 102.145 90.264 0.00608 80 1 100.770 115.697 85.545 0.01038 81 1 96.106 108.664 84.510 0.00694 82 1 95.605 107.715 87.549 0.00702 83 1 100.960 110.019 95.628 0.00606 84 1 98.804 102.305 87.804 0.00432 85 1 176.858 205.560 75.344 0.00747 86 1 180.978 200.125 155.495 0.00406 87 1 178.222 202.450 141.047 0.00321 88 1 176.281 227.381 125.610 0.00520 89 1 173.898 211.350 74.677 0.00448 90 1 179.711 225.930 144.878 0.00709 91 1 166.605 206.008 78.032 0.00742 92 1 151.955 163.335 147.226 0.00419 93 1 148.272 164.989 142.299 0.00459 94 1 152.125 161.469 76.596 0.00382 95 1 157.821 172.975 68.401 0.00358 96 1 157.447 163.267 149.605 0.00369 97 1 159.116 168.913 144.811 0.00342 98 1 125.036 143.946 116.187 0.01280 99 1 125.791 140.557 96.206 0.01378 100 1 126.512 141.756 99.770 0.01936 101 1 125.641 141.068 116.346 0.03316 102 1 128.451 150.449 75.632 0.01551 103 1 139.224 586.567 66.157 0.03011 104 1 150.258 154.609 75.349 0.00248 105 1 154.003 160.267 128.621 0.00183 106 1 149.689 160.368 133.608 0.00257 107 1 155.078 163.736 144.148 0.00168 108 1 151.884 157.765 133.751 0.00258 109 1 151.989 157.339 132.857 0.00174 110 1 193.030 208.900 80.297 0.00766 111 1 200.714 223.982 89.686 0.00621 112 1 208.519 220.315 199.020 0.00609 113 1 204.664 221.300 189.621 0.00841 114 1 210.141 232.706 185.258 0.00534 115 1 206.327 226.355 92.020 0.00495 116 1 151.872 492.892 69.085 0.00856 117 1 158.219 442.557 71.948 0.00476 118 1 170.756 450.247 79.032 0.00555 119 1 178.285 442.824 82.063 0.00462 120 1 217.116 233.481 93.978 0.00404 121 1 128.940 479.697 88.251 0.00581 122 1 176.824 215.293 83.961 0.00460 123 1 138.190 203.522 83.340 0.00704 124 1 182.018 197.173 79.187 0.00842 125 1 156.239 195.107 79.820 0.00694 126 1 145.174 198.109 80.637 0.00733 127 1 138.145 197.238 81.114 0.00544 128 1 166.888 198.966 79.512 0.00638 129 1 119.031 127.533 109.216 0.00440 130 1 120.078 126.632 105.667 0.00270 131 1 120.289 128.143 100.209 0.00492 132 1 120.256 125.306 104.773 0.00407 133 1 119.056 125.213 86.795 0.00346 134 1 118.747 123.723 109.836 0.00331 135 1 106.516 112.777 93.105 0.00589 136 1 110.453 127.611 105.554 0.00494 137 1 113.400 133.344 107.816 0.00451 138 1 113.166 130.270 100.673 0.00502 139 1 112.239 126.609 104.095 0.00472 140 1 116.150 131.731 109.815 0.00381 141 1 170.368 268.796 79.543 0.00571 142 1 208.083 253.792 91.802 0.00757 143 1 198.458 219.290 148.691 0.00376 144 1 202.805 231.508 86.232 0.00370 145 1 202.544 241.350 164.168 0.00254 146 1 223.361 263.872 87.638 0.00352 147 1 169.774 191.759 151.451 0.01568 148 1 183.520 216.814 161.340 0.01466 149 1 188.620 216.302 165.982 0.01719 150 1 202.632 565.740 177.258 0.01627 151 1 186.695 211.961 149.442 0.01872 152 1 192.818 224.429 168.793 0.03107 153 1 198.116 233.099 174.478 0.02714 154 1 121.345 139.644 98.250 0.00684 155 1 119.100 128.442 88.833 0.00692 156 1 117.870 127.349 95.654 0.00647 157 1 122.336 142.369 94.794 0.00727 158 1 117.963 134.209 100.757 0.01813 159 1 126.144 154.284 97.543 0.00975 160 1 127.930 138.752 112.173 0.00605 161 1 114.238 124.393 77.022 0.00581 162 1 115.322 135.738 107.802 0.00619 163 1 114.554 126.778 91.121 0.00651 164 1 112.150 131.669 97.527 0.00519 165 1 102.273 142.830 85.902 0.00907 166 0 236.200 244.663 102.137 0.00277 167 0 237.323 243.709 229.256 0.00303 168 0 260.105 264.919 237.303 0.00339 169 0 197.569 217.627 90.794 0.00803 170 0 240.301 245.135 219.783 0.00517 171 0 244.990 272.210 239.170 0.00451 172 0 112.547 133.374 105.715 0.00355 173 0 110.739 113.597 100.139 0.00356 174 0 113.715 116.443 96.913 0.00349 175 0 117.004 144.466 99.923 0.00353 176 0 115.380 123.109 108.634 0.00332 177 0 116.388 129.038 108.970 0.00346 178 1 151.737 190.204 129.859 0.00314 179 1 148.790 158.359 138.990 0.00309 180 1 148.143 155.982 135.041 0.00392 181 1 150.440 163.441 144.736 0.00396 182 1 148.462 161.078 141.998 0.00397 183 1 149.818 163.417 144.786 0.00336 184 0 117.226 123.925 106.656 0.00417 185 0 116.848 217.552 99.503 0.00531 186 0 116.286 177.291 96.983 0.00314 187 0 116.556 592.030 86.228 0.00496 188 0 116.342 581.289 94.246 0.00267 189 0 114.563 119.167 86.647 0.00327 190 0 201.774 262.707 78.228 0.00694 191 0 174.188 230.978 94.261 0.00459 192 0 209.516 253.017 89.488 0.00564 193 0 174.688 240.005 74.287 0.01360 194 0 198.764 396.961 74.904 0.00740 195 0 214.289 260.277 77.973 0.00567 MDVP:Jitter(Abs) MDVP:RAP MDVP:PPQ 1 7.0e-05 0.00370 0.00554 2 8.0e-05 0.00465 0.00696 3 9.0e-05 0.00544 0.00781 4 9.0e-05 0.00502 0.00698 5 1.1e-04 0.00655 0.00908 6 8.0e-05 0.00463 0.00750 7 3.0e-05 0.00155 0.00202 8 3.0e-05 0.00144 0.00182 9 6.0e-05 0.00293 0.00332 10 6.0e-05 0.00268 0.00332 11 6.0e-05 0.00254 0.00330 12 6.0e-05 0.00281 0.00336 13 2.0e-05 0.00118 0.00153 14 3.0e-05 0.00165 0.00208 15 2.0e-05 0.00121 0.00149 16 3.0e-05 0.00157 0.00203 17 4.0e-05 0.00211 0.00292 18 4.0e-05 0.00284 0.00387 19 5.0e-05 0.00364 0.00432 20 5.0e-05 0.00372 0.00399 21 5.0e-05 0.00428 0.00450 22 3.0e-05 0.00232 0.00267 23 3.0e-05 0.00220 0.00247 24 3.0e-05 0.00221 0.00258 25 5.0e-05 0.00380 0.00390 26 6.0e-05 0.00316 0.00375 27 3.0e-05 0.00250 0.00234 28 3.0e-05 0.00250 0.00275 29 2.0e-05 0.00159 0.00176 30 3.0e-05 0.00280 0.00253 31 1.0e-05 0.00166 0.00168 32 1.0e-05 0.00134 0.00138 33 1.0e-05 0.00113 0.00135 34 9.0e-06 0.00093 0.00107 35 9.0e-06 0.00094 0.00106 36 1.0e-05 0.00105 0.00115 37 2.0e-05 0.00233 0.00241 38 2.0e-05 0.00205 0.00218 39 2.0e-05 0.00153 0.00166 40 2.0e-05 0.00168 0.00182 41 2.0e-05 0.00165 0.00175 42 1.0e-05 0.00134 0.00147 43 1.0e-05 0.00169 0.00182 44 1.0e-05 0.00157 0.00173 45 9.0e-06 0.00109 0.00137 46 9.0e-06 0.00117 0.00139 47 1.0e-05 0.00127 0.00148 48 7.0e-06 0.00092 0.00113 49 4.0e-05 0.00169 0.00203 50 3.0e-05 0.00124 0.00155 51 3.0e-05 0.00141 0.00167 52 4.0e-05 0.00131 0.00169 53 3.0e-05 0.00137 0.00166 54 4.0e-05 0.00165 0.00183 55 7.0e-05 0.00349 0.00486 56 8.0e-05 0.00398 0.00539 57 7.0e-05 0.00352 0.00514 58 6.0e-05 0.00299 0.00469 59 7.0e-05 0.00334 0.00493 60 8.0e-05 0.00373 0.00520 61 1.0e-05 0.00147 0.00152 62 1.0e-05 0.00154 0.00151 63 1.0e-05 0.00152 0.00144 64 1.0e-05 0.00175 0.00155 65 9.0e-06 0.00114 0.00113 66 1.0e-05 0.00136 0.00140 67 6.0e-05 0.00430 0.00440 68 7.0e-05 0.00507 0.00463 69 8.0e-05 0.00647 0.00467 70 5.0e-05 0.00467 0.00354 71 6.0e-05 0.00469 0.00419 72 7.0e-05 0.00534 0.00478 73 3.0e-05 0.00180 0.00220 74 5.0e-05 0.00268 0.00329 75 4.0e-05 0.00260 0.00283 76 5.0e-05 0.00277 0.00289 77 4.0e-05 0.00270 0.00289 78 4.0e-05 0.00226 0.00280 79 6.0e-05 0.00331 0.00332 80 1.0e-04 0.00622 0.00576 81 7.0e-05 0.00389 0.00415 82 7.0e-05 0.00428 0.00371 83 6.0e-05 0.00351 0.00348 84 4.0e-05 0.00247 0.00258 85 4.0e-05 0.00418 0.00420 86 2.0e-05 0.00220 0.00244 87 2.0e-05 0.00163 0.00194 88 3.0e-05 0.00287 0.00312 89 3.0e-05 0.00237 0.00254 90 4.0e-05 0.00391 0.00419 91 4.0e-05 0.00387 0.00453 92 3.0e-05 0.00224 0.00227 93 3.0e-05 0.00250 0.00256 94 3.0e-05 0.00191 0.00226 95 2.0e-05 0.00196 0.00196 96 2.0e-05 0.00201 0.00197 97 2.0e-05 0.00178 0.00184 98 1.0e-04 0.00743 0.00623 99 1.1e-04 0.00826 0.00655 100 1.5e-04 0.01159 0.00990 101 2.6e-04 0.02144 0.01522 102 1.2e-04 0.00905 0.00909 103 2.2e-04 0.01854 0.01628 104 2.0e-05 0.00105 0.00136 105 1.0e-05 0.00076 0.00100 106 2.0e-05 0.00116 0.00134 107 1.0e-05 0.00068 0.00092 108 2.0e-05 0.00115 0.00122 109 1.0e-05 0.00075 0.00096 110 4.0e-05 0.00450 0.00389 111 3.0e-05 0.00371 0.00337 112 3.0e-05 0.00368 0.00339 113 4.0e-05 0.00502 0.00485 114 3.0e-05 0.00321 0.00280 115 2.0e-05 0.00302 0.00246 116 6.0e-05 0.00404 0.00385 117 3.0e-05 0.00214 0.00207 118 3.0e-05 0.00244 0.00261 119 3.0e-05 0.00157 0.00194 120 2.0e-05 0.00127 0.00128 121 5.0e-05 0.00241 0.00314 122 3.0e-05 0.00209 0.00221 123 5.0e-05 0.00406 0.00398 124 5.0e-05 0.00506 0.00449 125 4.0e-05 0.00403 0.00395 126 5.0e-05 0.00414 0.00422 127 4.0e-05 0.00294 0.00327 128 4.0e-05 0.00368 0.00351 129 4.0e-05 0.00214 0.00192 130 2.0e-05 0.00116 0.00135 131 4.0e-05 0.00269 0.00238 132 3.0e-05 0.00224 0.00205 133 3.0e-05 0.00169 0.00170 134 3.0e-05 0.00168 0.00171 135 6.0e-05 0.00291 0.00319 136 4.0e-05 0.00244 0.00315 137 4.0e-05 0.00219 0.00283 138 4.0e-05 0.00257 0.00312 139 4.0e-05 0.00238 0.00290 140 3.0e-05 0.00181 0.00232 141 3.0e-05 0.00232 0.00269 142 4.0e-05 0.00428 0.00428 143 2.0e-05 0.00182 0.00215 144 2.0e-05 0.00189 0.00211 145 1.0e-05 0.00100 0.00133 146 2.0e-05 0.00169 0.00188 147 9.0e-05 0.00863 0.00946 148 8.0e-05 0.00849 0.00819 149 9.0e-05 0.00996 0.01027 150 8.0e-05 0.00919 0.00963 151 1.0e-04 0.01075 0.01154 152 1.6e-04 0.01800 0.01958 153 1.4e-04 0.01568 0.01699 154 6.0e-05 0.00388 0.00332 155 6.0e-05 0.00393 0.00300 156 5.0e-05 0.00356 0.00300 157 6.0e-05 0.00415 0.00339 158 1.5e-04 0.01117 0.00718 159 8.0e-05 0.00593 0.00454 160 5.0e-05 0.00321 0.00318 161 5.0e-05 0.00299 0.00316 162 5.0e-05 0.00352 0.00329 163 6.0e-05 0.00366 0.00340 164 5.0e-05 0.00291 0.00284 165 9.0e-05 0.00493 0.00461 166 1.0e-05 0.00154 0.00153 167 1.0e-05 0.00173 0.00159 168 1.0e-05 0.00205 0.00186 169 4.0e-05 0.00490 0.00448 170 2.0e-05 0.00316 0.00283 171 2.0e-05 0.00279 0.00237 172 3.0e-05 0.00166 0.00190 173 3.0e-05 0.00170 0.00200 174 3.0e-05 0.00171 0.00203 175 3.0e-05 0.00176 0.00218 176 3.0e-05 0.00160 0.00199 177 3.0e-05 0.00169 0.00213 178 2.0e-05 0.00135 0.00162 179 2.0e-05 0.00152 0.00186 180 3.0e-05 0.00204 0.00231 181 3.0e-05 0.00206 0.00233 182 3.0e-05 0.00202 0.00235 183 2.0e-05 0.00174 0.00198 184 4.0e-05 0.00186 0.00270 185 5.0e-05 0.00260 0.00346 186 3.0e-05 0.00134 0.00192 187 4.0e-05 0.00254 0.00263 188 2.0e-05 0.00115 0.00148 189 3.0e-05 0.00146 0.00184 190 3.0e-05 0.00412 0.00396 191 3.0e-05 0.00263 0.00259 192 3.0e-05 0.00331 0.00292 193 8.0e-05 0.00624 0.00564 194 4.0e-05 0.00370 0.00390 195 3.0e-05 0.00295 0.00317 > k <- length(x[1,]) > df <- as.data.frame(x) > (mylm <- lm(df)) Call: lm(formula = df) Coefficients: (Intercept) `MDVP:Fo(Hz)` `MDVP:Fhi(Hz)` `MDVP:Flo(Hz)` 1.529e+00 -2.796e-03 -3.917e-04 -2.778e-03 `MDVP:Jitter(%)` `MDVP:Jitter(Abs)` `MDVP:RAP` `MDVP:PPQ` -8.408e+01 -3.423e+03 1.200e+02 9.662e+01 > (mysum <- summary(mylm)) Call: lm(formula = df) Residuals: Min 1Q Median 3Q Max -0.90159 -0.16077 0.09907 0.24758 0.58709 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 1.529e+00 2.076e-01 7.366 5.46e-12 *** `MDVP:Fo(Hz)` -2.796e-03 1.326e-03 -2.108 0.036320 * `MDVP:Fhi(Hz)` -3.917e-04 3.386e-04 -1.157 0.248792 `MDVP:Flo(Hz)` -2.778e-03 8.277e-04 -3.357 0.000955 *** `MDVP:Jitter(%)` -8.408e+01 6.251e+01 -1.345 0.180216 `MDVP:Jitter(Abs)` -3.423e+03 3.709e+03 -0.923 0.357301 `MDVP:RAP` 1.200e+02 7.444e+01 1.612 0.108634 `MDVP:PPQ` 9.662e+01 4.793e+01 2.016 0.045236 * --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 0.3771 on 187 degrees of freedom Multiple R-squared: 0.2651, Adjusted R-squared: 0.2375 F-statistic: 9.635 on 7 and 187 DF, p-value: 3.24e-10 > if (n > n25) { + kp3 <- k + 3 + nmkm3 <- n - k - 3 + gqarr <- array(NA, dim=c(nmkm3-kp3+1,3)) + numgqtests <- 0 + numsignificant1 <- 0 + numsignificant5 <- 0 + numsignificant10 <- 0 + for (mypoint in kp3:nmkm3) { + j <- 0 + numgqtests <- numgqtests + 1 + for (myalt in c('greater', 'two.sided', 'less')) { + j <- j + 1 + gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value + } + if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1 + if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1 + if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1 + } + gqarr + } [,1] [,2] [,3] [1,] 3.666438e-54 7.332876e-54 1.000000000 [2,] 1.946725e-66 3.893451e-66 1.000000000 [3,] 3.097209e-93 6.194418e-93 1.000000000 [4,] 1.718351e-92 3.436703e-92 1.000000000 [5,] 5.742669e-108 1.148534e-107 1.000000000 [6,] 0.000000e+00 0.000000e+00 1.000000000 [7,] 1.170841e-148 2.341682e-148 1.000000000 [8,] 6.221932e-155 1.244386e-154 1.000000000 [9,] 2.747854e-169 5.495709e-169 1.000000000 [10,] 4.904526e-193 9.809052e-193 1.000000000 [11,] 1.045233e-225 2.090467e-225 1.000000000 [12,] 7.432430e-218 1.486486e-217 1.000000000 [13,] 1.216391e-229 2.432783e-229 1.000000000 [14,] 5.583323e-248 1.116665e-247 1.000000000 [15,] 9.954098e-267 1.990820e-266 1.000000000 [16,] 5.801695e-308 1.160339e-307 1.000000000 [17,] 1.087341e-296 2.174681e-296 1.000000000 [18,] 2.417276e-307 4.834552e-307 1.000000000 [19,] 0.000000e+00 0.000000e+00 1.000000000 [20,] 0.000000e+00 0.000000e+00 1.000000000 [21,] 2.649017e-09 5.298034e-09 0.999999997 [22,] 3.225501e-09 6.451001e-09 0.999999997 [23,] 1.439791e-09 2.879582e-09 0.999999999 [24,] 4.549528e-10 9.099055e-10 1.000000000 [25,] 1.405319e-10 2.810637e-10 1.000000000 [26,] 4.440812e-11 8.881624e-11 1.000000000 [27,] 2.079537e-08 4.159075e-08 0.999999979 [28,] 3.326613e-07 6.653227e-07 0.999999667 [29,] 2.261219e-05 4.522439e-05 0.999977388 [30,] 2.114749e-04 4.229499e-04 0.999788525 [31,] 8.886790e-04 1.777358e-03 0.999111321 [32,] 1.291579e-03 2.583157e-03 0.998708421 [33,] 8.449718e-04 1.689944e-03 0.999155028 [34,] 5.194886e-04 1.038977e-03 0.999480511 [35,] 3.301697e-04 6.603394e-04 0.999669830 [36,] 2.005303e-04 4.010606e-04 0.999799470 [37,] 1.224469e-04 2.448939e-04 0.999877553 [38,] 7.438331e-05 1.487666e-04 0.999925617 [39,] 4.274603e-04 8.549205e-04 0.999572540 [40,] 6.801330e-04 1.360266e-03 0.999319867 [41,] 1.019005e-03 2.038011e-03 0.998980995 [42,] 8.835737e-04 1.767147e-03 0.999116426 [43,] 1.019247e-03 2.038493e-03 0.998980753 [44,] 1.073522e-03 2.147045e-03 0.998926478 [45,] 9.450983e-04 1.890197e-03 0.999054902 [46,] 1.147929e-03 2.295859e-03 0.998852071 [47,] 9.352684e-04 1.870537e-03 0.999064732 [48,] 1.287790e-03 2.575581e-03 0.998712210 [49,] 1.527090e-03 3.054180e-03 0.998472910 [50,] 1.144677e-03 2.289353e-03 0.998855323 [51,] 4.390732e-03 8.781464e-03 0.995609268 [52,] 9.030038e-03 1.806008e-02 0.990969962 [53,] 8.140840e-03 1.628168e-02 0.991859160 [54,] 7.100689e-03 1.420138e-02 0.992899311 [55,] 5.876693e-03 1.175339e-02 0.994123307 [56,] 6.878407e-03 1.375681e-02 0.993121593 [57,] 4.997473e-03 9.994946e-03 0.995002527 [58,] 3.640471e-03 7.280943e-03 0.996359529 [59,] 2.575348e-03 5.150695e-03 0.997424652 [60,] 2.062975e-03 4.125951e-03 0.997937025 [61,] 1.456185e-03 2.912370e-03 0.998543815 [62,] 1.028314e-03 2.056628e-03 0.998971686 [63,] 7.218494e-04 1.443699e-03 0.999278151 [64,] 1.817115e-03 3.634231e-03 0.998182885 [65,] 1.297795e-03 2.595589e-03 0.998702205 [66,] 9.108017e-04 1.821603e-03 0.999089198 [67,] 6.565540e-04 1.313108e-03 0.999343446 [68,] 4.483875e-04 8.967749e-04 0.999551613 [69,] 3.019029e-04 6.038059e-04 0.999698097 [70,] 2.117367e-04 4.234733e-04 0.999788263 [71,] 1.456569e-04 2.913137e-04 0.999854343 [72,] 9.875713e-05 1.975143e-04 0.999901243 [73,] 6.557390e-05 1.311478e-04 0.999934426 [74,] 4.853944e-05 9.707888e-05 0.999951461 [75,] 3.362745e-05 6.725489e-05 0.999966373 [76,] 3.181460e-05 6.362919e-05 0.999968185 [77,] 3.861446e-05 7.722892e-05 0.999961386 [78,] 2.814815e-05 5.629631e-05 0.999971852 [79,] 2.095819e-05 4.191638e-05 0.999979042 [80,] 1.465651e-05 2.931303e-05 0.999985343 [81,] 1.119931e-05 2.239861e-05 0.999988801 [82,] 1.029234e-05 2.058468e-05 0.999989708 [83,] 7.342651e-06 1.468530e-05 0.999992657 [84,] 5.225000e-06 1.045000e-05 0.999994775 [85,] 3.458397e-06 6.916794e-06 0.999996542 [86,] 2.659998e-06 5.319997e-06 0.999997340 [87,] 2.282512e-06 4.565023e-06 0.999997717 [88,] 1.392122e-06 2.784244e-06 0.999998608 [89,] 8.205410e-07 1.641082e-06 0.999999179 [90,] 5.392218e-07 1.078444e-06 0.999999461 [91,] 3.891032e-07 7.782065e-07 0.999999611 [92,] 3.923618e-07 7.847235e-07 0.999999608 [93,] 1.542378e-06 3.084755e-06 0.999998458 [94,] 1.102479e-06 2.204957e-06 0.999998898 [95,] 8.645672e-07 1.729134e-06 0.999999135 [96,] 7.809042e-07 1.561808e-06 0.999999219 [97,] 6.995690e-07 1.399138e-06 0.999999300 [98,] 6.943920e-07 1.388784e-06 0.999999306 [99,] 5.506889e-07 1.101378e-06 0.999999449 [100,] 3.605753e-07 7.211507e-07 0.999999639 [101,] 2.617085e-07 5.234170e-07 0.999999738 [102,] 3.864805e-07 7.729609e-07 0.999999614 [103,] 3.117356e-07 6.234713e-07 0.999999688 [104,] 7.243598e-07 1.448720e-06 0.999999276 [105,] 6.423072e-07 1.284614e-06 0.999999358 [106,] 6.991646e-07 1.398329e-06 0.999999301 [107,] 6.472863e-07 1.294573e-06 0.999999353 [108,] 6.580608e-07 1.316122e-06 0.999999342 [109,] 1.379033e-06 2.758065e-06 0.999998621 [110,] 7.341414e-06 1.468283e-05 0.999992659 [111,] 8.522791e-06 1.704558e-05 0.999991477 [112,] 9.371027e-06 1.874205e-05 0.999990629 [113,] 7.027404e-06 1.405481e-05 0.999992973 [114,] 5.169601e-06 1.033920e-05 0.999994830 [115,] 4.366307e-06 8.732615e-06 0.999995634 [116,] 3.369872e-06 6.739743e-06 0.999996630 [117,] 2.728548e-06 5.457095e-06 0.999997271 [118,] 2.370065e-06 4.740130e-06 0.999997630 [119,] 1.763168e-06 3.526336e-06 0.999998237 [120,] 1.351842e-06 2.703684e-06 0.999998648 [121,] 9.487842e-07 1.897568e-06 0.999999051 [122,] 7.389465e-07 1.477893e-06 0.999999261 [123,] 5.995940e-07 1.199188e-06 0.999999400 [124,] 4.760559e-07 9.521117e-07 0.999999524 [125,] 2.959384e-07 5.918768e-07 0.999999704 [126,] 2.118008e-07 4.236016e-07 0.999999788 [127,] 1.490426e-07 2.980851e-07 0.999999851 [128,] 1.157770e-07 2.315540e-07 0.999999884 [129,] 8.811213e-08 1.762243e-07 0.999999912 [130,] 8.162246e-08 1.632449e-07 0.999999918 [131,] 1.706443e-07 3.412886e-07 0.999999829 [132,] 2.429968e-07 4.859936e-07 0.999999757 [133,] 5.977927e-07 1.195585e-06 0.999999402 [134,] 1.993504e-06 3.987008e-06 0.999998006 [135,] 1.075130e-05 2.150260e-05 0.999989249 [136,] 1.494835e-04 2.989669e-04 0.999850517 [137,] 1.079775e-04 2.159550e-04 0.999892023 [138,] 7.547038e-05 1.509408e-04 0.999924530 [139,] 5.222123e-05 1.044425e-04 0.999947779 [140,] 1.026615e-04 2.053230e-04 0.999897339 [141,] 8.578030e-05 1.715606e-04 0.999914220 [142,] 8.057238e-05 1.611448e-04 0.999919428 [143,] 5.740354e-05 1.148071e-04 0.999942596 [144,] 4.388767e-05 8.777535e-05 0.999956112 [145,] 3.270820e-05 6.541639e-05 0.999967292 [146,] 3.058412e-05 6.116824e-05 0.999969416 [147,] 2.703001e-05 5.406002e-05 0.999972970 [148,] 6.089221e-05 1.217844e-04 0.999939108 [149,] 4.044362e-05 8.088724e-05 0.999959556 [150,] 3.801857e-05 7.603714e-05 0.999961981 [151,] 5.114830e-05 1.022966e-04 0.999948852 [152,] 4.283728e-05 8.567456e-05 0.999957163 [153,] 3.566894e-05 7.133788e-05 0.999964331 [154,] 4.535965e-05 9.071930e-05 0.999954640 [155,] 5.052614e-04 1.010523e-03 0.999494739 [156,] 5.369153e-04 1.073831e-03 0.999463085 [157,] 6.091668e-04 1.218334e-03 0.999390833 [158,] 9.530511e-04 1.906102e-03 0.999046949 [159,] 1.762463e-03 3.524926e-03 0.998237537 [160,] 5.684359e-03 1.136872e-02 0.994315641 [161,] 9.986182e-01 2.763697e-03 0.001381848 [162,] 9.987306e-01 2.538709e-03 0.001269354 [163,] 9.982066e-01 3.586786e-03 0.001793393 [164,] 9.975366e-01 4.926804e-03 0.002463402 [165,] 9.960598e-01 7.880365e-03 0.003940183 [166,] 9.960864e-01 7.827224e-03 0.003913612 [167,] 9.976926e-01 4.614759e-03 0.002307379 [168,] 9.966311e-01 6.737841e-03 0.003368921 [169,] 9.920251e-01 1.594987e-02 0.007974936 [170,] 9.926949e-01 1.461018e-02 0.007305088 [171,] 9.824290e-01 3.514202e-02 0.017571009 [172,] 9.754829e-01 4.903422e-02 0.024517112 [173,] 1.000000e+00 0.000000e+00 0.000000000 [174,] 1.000000e+00 0.000000e+00 0.000000000 > postscript(file="/var/wessaorg/rcomp/tmp/1cucy1386681599.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$resid) > grid() > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/2m9211386681599.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(mysum$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/3tm0s1386681599.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') > grid() > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/4k5t61386681599.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/5tcya1386681599.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > qqnorm(mysum$resid, main='Residual Normal Q-Q Plot') > qqline(mysum$resid) > grid() > dev.off() null device 1 > (myerror <- as.ts(mysum$resid)) Time Series: Start = 1 End = 195 Frequency = 1 1 2 3 4 5 6 -0.004208007 0.044725916 -0.058181860 0.029958720 -0.049081369 -0.017155064 7 8 9 10 11 12 0.181347198 0.103194254 0.041387429 0.047375504 0.001651995 0.010456604 13 14 15 16 17 18 0.306397157 0.174441442 0.199332110 0.212941860 0.299698215 0.269607794 19 20 21 22 23 24 0.099073534 0.364075030 0.077673248 0.155735603 0.207383569 0.260452888 25 26 27 28 29 30 0.182577811 0.056087522 0.199572799 0.144252550 0.184943483 0.163049532 31 32 33 34 35 36 -0.447812235 -0.413640680 -0.407243162 -0.366025085 -0.367957501 -0.376056850 37 38 39 40 41 42 0.412098902 0.412612235 0.504220707 0.512375011 0.514106272 0.474673179 43 44 45 46 47 48 -0.237104729 -0.195869057 -0.160231787 -0.171748510 -0.159479691 -0.253095675 49 50 51 52 53 54 -0.636982287 -0.651839211 -0.667372758 -0.616920676 -0.633551796 -0.609161587 55 56 57 58 59 60 0.104661426 0.127924920 0.097820450 0.182029841 0.174758132 0.241799308 61 62 63 64 65 66 -0.599407980 -0.611396964 -0.309890914 -0.252774192 -0.217324660 -0.544753056 67 68 69 70 71 72 0.063161642 0.062308893 0.090417496 0.045136866 0.051836777 0.057271052 73 74 75 76 77 78 0.184899924 0.291680078 0.095754131 0.113474835 0.096013002 0.109484559 79 80 81 82 83 84 0.026885278 -0.052375058 -0.027793099 -0.018686207 0.019319116 -0.014459221 85 86 87 88 89 90 0.112742786 0.397313876 0.395622200 0.295799733 0.196853220 0.323304968 91 92 93 94 95 96 0.092830681 0.335557578 0.286628998 0.148527571 0.114772028 0.337817001 97 98 99 100 101 102 0.348836567 0.124532487 0.055904766 -0.048926247 -0.164847627 -0.150475951 103 104 105 106 107 108 -0.239554045 0.180423423 0.321823569 0.339249546 0.374045400 0.358400151 109 110 111 112 113 114 0.324311628 0.180557515 0.222940184 0.538671703 0.429585455 0.560173830 115 116 117 118 119 120 0.276608911 0.348759026 0.332551045 0.368548206 0.486060620 0.562519103 121 122 123 124 125 126 0.331478930 0.307949747 0.059765966 0.115041200 0.061022067 0.061258750 127 128 129 130 131 132 0.085245293 0.128884648 0.221569194 0.175571471 0.133574369 0.125242600 133 134 135 136 137 138 0.120433550 0.170622978 0.114655918 0.077998990 0.119534961 0.067089750 139 140 141 142 143 144 0.091404474 0.133941266 0.317931266 0.253350782 0.483193227 0.317022226 145 146 147 148 149 150 0.587087711 0.422174129 0.118168513 0.213412193 0.109941928 0.359989264 151 152 153 154 155 156 0.002263911 -0.325078232 -0.161304202 0.131848604 0.126664104 0.114086446 157 158 159 160 161 162 0.123074625 0.136743835 0.098277385 0.181875157 0.048458594 0.097236738 163 164 165 166 167 168 0.078938461 0.090828353 0.084996424 -0.554637010 -0.205430160 -0.145282126 169 170 171 172 173 174 -0.848066580 -0.300121721 -0.189187072 -0.850160725 -0.892076504 -0.901587404 175 176 177 178 179 180 -0.890181128 -0.858984004 -0.865465541 0.344318572 0.301178989 0.285599123 181 182 183 184 185 186 0.320910337 0.310555493 0.306843809 -0.853103641 -0.869516033 -0.844766571 187 188 189 190 191 192 -0.736789320 -0.702395947 -0.896809709 -0.835590430 -0.767020714 -0.698068780 193 194 195 -0.616790168 -0.671548528 -0.692300844 > postscript(file="/var/wessaorg/rcomp/tmp/6j6041386681599.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > dum <- cbind(lag(myerror,k=1),myerror) > dum Time Series: Start = 0 End = 195 Frequency = 1 lag(myerror, k = 1) myerror 0 -0.004208007 NA 1 0.044725916 -0.004208007 2 -0.058181860 0.044725916 3 0.029958720 -0.058181860 4 -0.049081369 0.029958720 5 -0.017155064 -0.049081369 6 0.181347198 -0.017155064 7 0.103194254 0.181347198 8 0.041387429 0.103194254 9 0.047375504 0.041387429 10 0.001651995 0.047375504 11 0.010456604 0.001651995 12 0.306397157 0.010456604 13 0.174441442 0.306397157 14 0.199332110 0.174441442 15 0.212941860 0.199332110 16 0.299698215 0.212941860 17 0.269607794 0.299698215 18 0.099073534 0.269607794 19 0.364075030 0.099073534 20 0.077673248 0.364075030 21 0.155735603 0.077673248 22 0.207383569 0.155735603 23 0.260452888 0.207383569 24 0.182577811 0.260452888 25 0.056087522 0.182577811 26 0.199572799 0.056087522 27 0.144252550 0.199572799 28 0.184943483 0.144252550 29 0.163049532 0.184943483 30 -0.447812235 0.163049532 31 -0.413640680 -0.447812235 32 -0.407243162 -0.413640680 33 -0.366025085 -0.407243162 34 -0.367957501 -0.366025085 35 -0.376056850 -0.367957501 36 0.412098902 -0.376056850 37 0.412612235 0.412098902 38 0.504220707 0.412612235 39 0.512375011 0.504220707 40 0.514106272 0.512375011 41 0.474673179 0.514106272 42 -0.237104729 0.474673179 43 -0.195869057 -0.237104729 44 -0.160231787 -0.195869057 45 -0.171748510 -0.160231787 46 -0.159479691 -0.171748510 47 -0.253095675 -0.159479691 48 -0.636982287 -0.253095675 49 -0.651839211 -0.636982287 50 -0.667372758 -0.651839211 51 -0.616920676 -0.667372758 52 -0.633551796 -0.616920676 53 -0.609161587 -0.633551796 54 0.104661426 -0.609161587 55 0.127924920 0.104661426 56 0.097820450 0.127924920 57 0.182029841 0.097820450 58 0.174758132 0.182029841 59 0.241799308 0.174758132 60 -0.599407980 0.241799308 61 -0.611396964 -0.599407980 62 -0.309890914 -0.611396964 63 -0.252774192 -0.309890914 64 -0.217324660 -0.252774192 65 -0.544753056 -0.217324660 66 0.063161642 -0.544753056 67 0.062308893 0.063161642 68 0.090417496 0.062308893 69 0.045136866 0.090417496 70 0.051836777 0.045136866 71 0.057271052 0.051836777 72 0.184899924 0.057271052 73 0.291680078 0.184899924 74 0.095754131 0.291680078 75 0.113474835 0.095754131 76 0.096013002 0.113474835 77 0.109484559 0.096013002 78 0.026885278 0.109484559 79 -0.052375058 0.026885278 80 -0.027793099 -0.052375058 81 -0.018686207 -0.027793099 82 0.019319116 -0.018686207 83 -0.014459221 0.019319116 84 0.112742786 -0.014459221 85 0.397313876 0.112742786 86 0.395622200 0.397313876 87 0.295799733 0.395622200 88 0.196853220 0.295799733 89 0.323304968 0.196853220 90 0.092830681 0.323304968 91 0.335557578 0.092830681 92 0.286628998 0.335557578 93 0.148527571 0.286628998 94 0.114772028 0.148527571 95 0.337817001 0.114772028 96 0.348836567 0.337817001 97 0.124532487 0.348836567 98 0.055904766 0.124532487 99 -0.048926247 0.055904766 100 -0.164847627 -0.048926247 101 -0.150475951 -0.164847627 102 -0.239554045 -0.150475951 103 0.180423423 -0.239554045 104 0.321823569 0.180423423 105 0.339249546 0.321823569 106 0.374045400 0.339249546 107 0.358400151 0.374045400 108 0.324311628 0.358400151 109 0.180557515 0.324311628 110 0.222940184 0.180557515 111 0.538671703 0.222940184 112 0.429585455 0.538671703 113 0.560173830 0.429585455 114 0.276608911 0.560173830 115 0.348759026 0.276608911 116 0.332551045 0.348759026 117 0.368548206 0.332551045 118 0.486060620 0.368548206 119 0.562519103 0.486060620 120 0.331478930 0.562519103 121 0.307949747 0.331478930 122 0.059765966 0.307949747 123 0.115041200 0.059765966 124 0.061022067 0.115041200 125 0.061258750 0.061022067 126 0.085245293 0.061258750 127 0.128884648 0.085245293 128 0.221569194 0.128884648 129 0.175571471 0.221569194 130 0.133574369 0.175571471 131 0.125242600 0.133574369 132 0.120433550 0.125242600 133 0.170622978 0.120433550 134 0.114655918 0.170622978 135 0.077998990 0.114655918 136 0.119534961 0.077998990 137 0.067089750 0.119534961 138 0.091404474 0.067089750 139 0.133941266 0.091404474 140 0.317931266 0.133941266 141 0.253350782 0.317931266 142 0.483193227 0.253350782 143 0.317022226 0.483193227 144 0.587087711 0.317022226 145 0.422174129 0.587087711 146 0.118168513 0.422174129 147 0.213412193 0.118168513 148 0.109941928 0.213412193 149 0.359989264 0.109941928 150 0.002263911 0.359989264 151 -0.325078232 0.002263911 152 -0.161304202 -0.325078232 153 0.131848604 -0.161304202 154 0.126664104 0.131848604 155 0.114086446 0.126664104 156 0.123074625 0.114086446 157 0.136743835 0.123074625 158 0.098277385 0.136743835 159 0.181875157 0.098277385 160 0.048458594 0.181875157 161 0.097236738 0.048458594 162 0.078938461 0.097236738 163 0.090828353 0.078938461 164 0.084996424 0.090828353 165 -0.554637010 0.084996424 166 -0.205430160 -0.554637010 167 -0.145282126 -0.205430160 168 -0.848066580 -0.145282126 169 -0.300121721 -0.848066580 170 -0.189187072 -0.300121721 171 -0.850160725 -0.189187072 172 -0.892076504 -0.850160725 173 -0.901587404 -0.892076504 174 -0.890181128 -0.901587404 175 -0.858984004 -0.890181128 176 -0.865465541 -0.858984004 177 0.344318572 -0.865465541 178 0.301178989 0.344318572 179 0.285599123 0.301178989 180 0.320910337 0.285599123 181 0.310555493 0.320910337 182 0.306843809 0.310555493 183 -0.853103641 0.306843809 184 -0.869516033 -0.853103641 185 -0.844766571 -0.869516033 186 -0.736789320 -0.844766571 187 -0.702395947 -0.736789320 188 -0.896809709 -0.702395947 189 -0.835590430 -0.896809709 190 -0.767020714 -0.835590430 191 -0.698068780 -0.767020714 192 -0.616790168 -0.698068780 193 -0.671548528 -0.616790168 194 -0.692300844 -0.671548528 195 NA -0.692300844 > dum1 <- dum[2:length(myerror),] > dum1 lag(myerror, k = 1) myerror [1,] 0.044725916 -0.004208007 [2,] -0.058181860 0.044725916 [3,] 0.029958720 -0.058181860 [4,] -0.049081369 0.029958720 [5,] -0.017155064 -0.049081369 [6,] 0.181347198 -0.017155064 [7,] 0.103194254 0.181347198 [8,] 0.041387429 0.103194254 [9,] 0.047375504 0.041387429 [10,] 0.001651995 0.047375504 [11,] 0.010456604 0.001651995 [12,] 0.306397157 0.010456604 [13,] 0.174441442 0.306397157 [14,] 0.199332110 0.174441442 [15,] 0.212941860 0.199332110 [16,] 0.299698215 0.212941860 [17,] 0.269607794 0.299698215 [18,] 0.099073534 0.269607794 [19,] 0.364075030 0.099073534 [20,] 0.077673248 0.364075030 [21,] 0.155735603 0.077673248 [22,] 0.207383569 0.155735603 [23,] 0.260452888 0.207383569 [24,] 0.182577811 0.260452888 [25,] 0.056087522 0.182577811 [26,] 0.199572799 0.056087522 [27,] 0.144252550 0.199572799 [28,] 0.184943483 0.144252550 [29,] 0.163049532 0.184943483 [30,] -0.447812235 0.163049532 [31,] -0.413640680 -0.447812235 [32,] -0.407243162 -0.413640680 [33,] -0.366025085 -0.407243162 [34,] -0.367957501 -0.366025085 [35,] -0.376056850 -0.367957501 [36,] 0.412098902 -0.376056850 [37,] 0.412612235 0.412098902 [38,] 0.504220707 0.412612235 [39,] 0.512375011 0.504220707 [40,] 0.514106272 0.512375011 [41,] 0.474673179 0.514106272 [42,] -0.237104729 0.474673179 [43,] -0.195869057 -0.237104729 [44,] -0.160231787 -0.195869057 [45,] -0.171748510 -0.160231787 [46,] -0.159479691 -0.171748510 [47,] -0.253095675 -0.159479691 [48,] -0.636982287 -0.253095675 [49,] -0.651839211 -0.636982287 [50,] -0.667372758 -0.651839211 [51,] -0.616920676 -0.667372758 [52,] -0.633551796 -0.616920676 [53,] -0.609161587 -0.633551796 [54,] 0.104661426 -0.609161587 [55,] 0.127924920 0.104661426 [56,] 0.097820450 0.127924920 [57,] 0.182029841 0.097820450 [58,] 0.174758132 0.182029841 [59,] 0.241799308 0.174758132 [60,] -0.599407980 0.241799308 [61,] -0.611396964 -0.599407980 [62,] -0.309890914 -0.611396964 [63,] -0.252774192 -0.309890914 [64,] -0.217324660 -0.252774192 [65,] -0.544753056 -0.217324660 [66,] 0.063161642 -0.544753056 [67,] 0.062308893 0.063161642 [68,] 0.090417496 0.062308893 [69,] 0.045136866 0.090417496 [70,] 0.051836777 0.045136866 [71,] 0.057271052 0.051836777 [72,] 0.184899924 0.057271052 [73,] 0.291680078 0.184899924 [74,] 0.095754131 0.291680078 [75,] 0.113474835 0.095754131 [76,] 0.096013002 0.113474835 [77,] 0.109484559 0.096013002 [78,] 0.026885278 0.109484559 [79,] -0.052375058 0.026885278 [80,] -0.027793099 -0.052375058 [81,] -0.018686207 -0.027793099 [82,] 0.019319116 -0.018686207 [83,] -0.014459221 0.019319116 [84,] 0.112742786 -0.014459221 [85,] 0.397313876 0.112742786 [86,] 0.395622200 0.397313876 [87,] 0.295799733 0.395622200 [88,] 0.196853220 0.295799733 [89,] 0.323304968 0.196853220 [90,] 0.092830681 0.323304968 [91,] 0.335557578 0.092830681 [92,] 0.286628998 0.335557578 [93,] 0.148527571 0.286628998 [94,] 0.114772028 0.148527571 [95,] 0.337817001 0.114772028 [96,] 0.348836567 0.337817001 [97,] 0.124532487 0.348836567 [98,] 0.055904766 0.124532487 [99,] -0.048926247 0.055904766 [100,] -0.164847627 -0.048926247 [101,] -0.150475951 -0.164847627 [102,] -0.239554045 -0.150475951 [103,] 0.180423423 -0.239554045 [104,] 0.321823569 0.180423423 [105,] 0.339249546 0.321823569 [106,] 0.374045400 0.339249546 [107,] 0.358400151 0.374045400 [108,] 0.324311628 0.358400151 [109,] 0.180557515 0.324311628 [110,] 0.222940184 0.180557515 [111,] 0.538671703 0.222940184 [112,] 0.429585455 0.538671703 [113,] 0.560173830 0.429585455 [114,] 0.276608911 0.560173830 [115,] 0.348759026 0.276608911 [116,] 0.332551045 0.348759026 [117,] 0.368548206 0.332551045 [118,] 0.486060620 0.368548206 [119,] 0.562519103 0.486060620 [120,] 0.331478930 0.562519103 [121,] 0.307949747 0.331478930 [122,] 0.059765966 0.307949747 [123,] 0.115041200 0.059765966 [124,] 0.061022067 0.115041200 [125,] 0.061258750 0.061022067 [126,] 0.085245293 0.061258750 [127,] 0.128884648 0.085245293 [128,] 0.221569194 0.128884648 [129,] 0.175571471 0.221569194 [130,] 0.133574369 0.175571471 [131,] 0.125242600 0.133574369 [132,] 0.120433550 0.125242600 [133,] 0.170622978 0.120433550 [134,] 0.114655918 0.170622978 [135,] 0.077998990 0.114655918 [136,] 0.119534961 0.077998990 [137,] 0.067089750 0.119534961 [138,] 0.091404474 0.067089750 [139,] 0.133941266 0.091404474 [140,] 0.317931266 0.133941266 [141,] 0.253350782 0.317931266 [142,] 0.483193227 0.253350782 [143,] 0.317022226 0.483193227 [144,] 0.587087711 0.317022226 [145,] 0.422174129 0.587087711 [146,] 0.118168513 0.422174129 [147,] 0.213412193 0.118168513 [148,] 0.109941928 0.213412193 [149,] 0.359989264 0.109941928 [150,] 0.002263911 0.359989264 [151,] -0.325078232 0.002263911 [152,] -0.161304202 -0.325078232 [153,] 0.131848604 -0.161304202 [154,] 0.126664104 0.131848604 [155,] 0.114086446 0.126664104 [156,] 0.123074625 0.114086446 [157,] 0.136743835 0.123074625 [158,] 0.098277385 0.136743835 [159,] 0.181875157 0.098277385 [160,] 0.048458594 0.181875157 [161,] 0.097236738 0.048458594 [162,] 0.078938461 0.097236738 [163,] 0.090828353 0.078938461 [164,] 0.084996424 0.090828353 [165,] -0.554637010 0.084996424 [166,] -0.205430160 -0.554637010 [167,] -0.145282126 -0.205430160 [168,] -0.848066580 -0.145282126 [169,] -0.300121721 -0.848066580 [170,] -0.189187072 -0.300121721 [171,] -0.850160725 -0.189187072 [172,] -0.892076504 -0.850160725 [173,] -0.901587404 -0.892076504 [174,] -0.890181128 -0.901587404 [175,] -0.858984004 -0.890181128 [176,] -0.865465541 -0.858984004 [177,] 0.344318572 -0.865465541 [178,] 0.301178989 0.344318572 [179,] 0.285599123 0.301178989 [180,] 0.320910337 0.285599123 [181,] 0.310555493 0.320910337 [182,] 0.306843809 0.310555493 [183,] -0.853103641 0.306843809 [184,] -0.869516033 -0.853103641 [185,] -0.844766571 -0.869516033 [186,] -0.736789320 -0.844766571 [187,] -0.702395947 -0.736789320 [188,] -0.896809709 -0.702395947 [189,] -0.835590430 -0.896809709 [190,] -0.767020714 -0.835590430 [191,] -0.698068780 -0.767020714 [192,] -0.616790168 -0.698068780 [193,] -0.671548528 -0.616790168 [194,] -0.692300844 -0.671548528 > z <- as.data.frame(dum1) > z lag(myerror, k = 1) myerror 1 0.044725916 -0.004208007 2 -0.058181860 0.044725916 3 0.029958720 -0.058181860 4 -0.049081369 0.029958720 5 -0.017155064 -0.049081369 6 0.181347198 -0.017155064 7 0.103194254 0.181347198 8 0.041387429 0.103194254 9 0.047375504 0.041387429 10 0.001651995 0.047375504 11 0.010456604 0.001651995 12 0.306397157 0.010456604 13 0.174441442 0.306397157 14 0.199332110 0.174441442 15 0.212941860 0.199332110 16 0.299698215 0.212941860 17 0.269607794 0.299698215 18 0.099073534 0.269607794 19 0.364075030 0.099073534 20 0.077673248 0.364075030 21 0.155735603 0.077673248 22 0.207383569 0.155735603 23 0.260452888 0.207383569 24 0.182577811 0.260452888 25 0.056087522 0.182577811 26 0.199572799 0.056087522 27 0.144252550 0.199572799 28 0.184943483 0.144252550 29 0.163049532 0.184943483 30 -0.447812235 0.163049532 31 -0.413640680 -0.447812235 32 -0.407243162 -0.413640680 33 -0.366025085 -0.407243162 34 -0.367957501 -0.366025085 35 -0.376056850 -0.367957501 36 0.412098902 -0.376056850 37 0.412612235 0.412098902 38 0.504220707 0.412612235 39 0.512375011 0.504220707 40 0.514106272 0.512375011 41 0.474673179 0.514106272 42 -0.237104729 0.474673179 43 -0.195869057 -0.237104729 44 -0.160231787 -0.195869057 45 -0.171748510 -0.160231787 46 -0.159479691 -0.171748510 47 -0.253095675 -0.159479691 48 -0.636982287 -0.253095675 49 -0.651839211 -0.636982287 50 -0.667372758 -0.651839211 51 -0.616920676 -0.667372758 52 -0.633551796 -0.616920676 53 -0.609161587 -0.633551796 54 0.104661426 -0.609161587 55 0.127924920 0.104661426 56 0.097820450 0.127924920 57 0.182029841 0.097820450 58 0.174758132 0.182029841 59 0.241799308 0.174758132 60 -0.599407980 0.241799308 61 -0.611396964 -0.599407980 62 -0.309890914 -0.611396964 63 -0.252774192 -0.309890914 64 -0.217324660 -0.252774192 65 -0.544753056 -0.217324660 66 0.063161642 -0.544753056 67 0.062308893 0.063161642 68 0.090417496 0.062308893 69 0.045136866 0.090417496 70 0.051836777 0.045136866 71 0.057271052 0.051836777 72 0.184899924 0.057271052 73 0.291680078 0.184899924 74 0.095754131 0.291680078 75 0.113474835 0.095754131 76 0.096013002 0.113474835 77 0.109484559 0.096013002 78 0.026885278 0.109484559 79 -0.052375058 0.026885278 80 -0.027793099 -0.052375058 81 -0.018686207 -0.027793099 82 0.019319116 -0.018686207 83 -0.014459221 0.019319116 84 0.112742786 -0.014459221 85 0.397313876 0.112742786 86 0.395622200 0.397313876 87 0.295799733 0.395622200 88 0.196853220 0.295799733 89 0.323304968 0.196853220 90 0.092830681 0.323304968 91 0.335557578 0.092830681 92 0.286628998 0.335557578 93 0.148527571 0.286628998 94 0.114772028 0.148527571 95 0.337817001 0.114772028 96 0.348836567 0.337817001 97 0.124532487 0.348836567 98 0.055904766 0.124532487 99 -0.048926247 0.055904766 100 -0.164847627 -0.048926247 101 -0.150475951 -0.164847627 102 -0.239554045 -0.150475951 103 0.180423423 -0.239554045 104 0.321823569 0.180423423 105 0.339249546 0.321823569 106 0.374045400 0.339249546 107 0.358400151 0.374045400 108 0.324311628 0.358400151 109 0.180557515 0.324311628 110 0.222940184 0.180557515 111 0.538671703 0.222940184 112 0.429585455 0.538671703 113 0.560173830 0.429585455 114 0.276608911 0.560173830 115 0.348759026 0.276608911 116 0.332551045 0.348759026 117 0.368548206 0.332551045 118 0.486060620 0.368548206 119 0.562519103 0.486060620 120 0.331478930 0.562519103 121 0.307949747 0.331478930 122 0.059765966 0.307949747 123 0.115041200 0.059765966 124 0.061022067 0.115041200 125 0.061258750 0.061022067 126 0.085245293 0.061258750 127 0.128884648 0.085245293 128 0.221569194 0.128884648 129 0.175571471 0.221569194 130 0.133574369 0.175571471 131 0.125242600 0.133574369 132 0.120433550 0.125242600 133 0.170622978 0.120433550 134 0.114655918 0.170622978 135 0.077998990 0.114655918 136 0.119534961 0.077998990 137 0.067089750 0.119534961 138 0.091404474 0.067089750 139 0.133941266 0.091404474 140 0.317931266 0.133941266 141 0.253350782 0.317931266 142 0.483193227 0.253350782 143 0.317022226 0.483193227 144 0.587087711 0.317022226 145 0.422174129 0.587087711 146 0.118168513 0.422174129 147 0.213412193 0.118168513 148 0.109941928 0.213412193 149 0.359989264 0.109941928 150 0.002263911 0.359989264 151 -0.325078232 0.002263911 152 -0.161304202 -0.325078232 153 0.131848604 -0.161304202 154 0.126664104 0.131848604 155 0.114086446 0.126664104 156 0.123074625 0.114086446 157 0.136743835 0.123074625 158 0.098277385 0.136743835 159 0.181875157 0.098277385 160 0.048458594 0.181875157 161 0.097236738 0.048458594 162 0.078938461 0.097236738 163 0.090828353 0.078938461 164 0.084996424 0.090828353 165 -0.554637010 0.084996424 166 -0.205430160 -0.554637010 167 -0.145282126 -0.205430160 168 -0.848066580 -0.145282126 169 -0.300121721 -0.848066580 170 -0.189187072 -0.300121721 171 -0.850160725 -0.189187072 172 -0.892076504 -0.850160725 173 -0.901587404 -0.892076504 174 -0.890181128 -0.901587404 175 -0.858984004 -0.890181128 176 -0.865465541 -0.858984004 177 0.344318572 -0.865465541 178 0.301178989 0.344318572 179 0.285599123 0.301178989 180 0.320910337 0.285599123 181 0.310555493 0.320910337 182 0.306843809 0.310555493 183 -0.853103641 0.306843809 184 -0.869516033 -0.853103641 185 -0.844766571 -0.869516033 186 -0.736789320 -0.844766571 187 -0.702395947 -0.736789320 188 -0.896809709 -0.702395947 189 -0.835590430 -0.896809709 190 -0.767020714 -0.835590430 191 -0.698068780 -0.767020714 192 -0.616790168 -0.698068780 193 -0.671548528 -0.616790168 194 -0.692300844 -0.671548528 > 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/7hzv21386681599.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/8hvl01386681599.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/9ik5i1386681599.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0)) > plot(mylm, las = 1, sub='Residual Diagnostics') > par(opar) > dev.off() null device 1 > if (n > n25) { + postscript(file="/var/wessaorg/rcomp/tmp/10htsl1386681599.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) + plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint') + grid() + dev.off() + } null device 1 > > #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab > load(file="/var/wessaorg/rcomp/createtable") > > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE) > a<-table.row.end(a) > myeq <- colnames(x)[1] > myeq <- paste(myeq, '[t] = ', sep='') > for (i in 1:k){ + if (mysum$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/11ufae1386681599.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/128gwz1386681599.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/1346es1386681599.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/14d7qu1386681599.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/150p7c1386681599.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/164u2l1386681599.tab") + } > > try(system("convert tmp/1cucy1386681599.ps tmp/1cucy1386681599.png",intern=TRUE)) character(0) > try(system("convert tmp/2m9211386681599.ps tmp/2m9211386681599.png",intern=TRUE)) character(0) > try(system("convert tmp/3tm0s1386681599.ps tmp/3tm0s1386681599.png",intern=TRUE)) character(0) > try(system("convert tmp/4k5t61386681599.ps tmp/4k5t61386681599.png",intern=TRUE)) character(0) > try(system("convert tmp/5tcya1386681599.ps tmp/5tcya1386681599.png",intern=TRUE)) character(0) > try(system("convert tmp/6j6041386681599.ps tmp/6j6041386681599.png",intern=TRUE)) character(0) > try(system("convert tmp/7hzv21386681599.ps tmp/7hzv21386681599.png",intern=TRUE)) character(0) > try(system("convert tmp/8hvl01386681599.ps tmp/8hvl01386681599.png",intern=TRUE)) character(0) > try(system("convert tmp/9ik5i1386681599.ps tmp/9ik5i1386681599.png",intern=TRUE)) character(0) > try(system("convert tmp/10htsl1386681599.ps tmp/10htsl1386681599.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 14.690 2.695 17.395