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 + ,0.0037 + ,0.00554 + ,0.04374 + ,1 + ,122.4 + ,148.65 + ,113.819 + ,0.00968 + ,0.00465 + ,0.00696 + ,0.06134 + ,1 + ,116.682 + ,131.111 + ,111.555 + ,0.0105 + ,0.00544 + ,0.00781 + ,0.05233 + ,1 + ,116.676 + ,137.871 + ,111.366 + ,0.00997 + ,0.00502 + ,0.00698 + ,0.05492 + ,1 + ,116.014 + ,141.781 + ,110.655 + ,0.01284 + ,0.00655 + ,0.00908 + ,0.06425 + ,1 + ,120.552 + ,131.162 + ,113.787 + ,0.00968 + ,0.00463 + ,0.0075 + ,0.04701 + ,1 + ,120.267 + ,137.244 + ,114.82 + ,0.00333 + ,0.00155 + ,0.00202 + ,0.01608 + ,1 + ,107.332 + ,113.84 + ,104.315 + ,0.0029 + ,0.00144 + ,0.00182 + ,0.01567 + ,1 + ,95.73 + ,132.068 + ,91.754 + ,0.00551 + ,0.00293 + ,0.00332 + ,0.02093 + ,1 + ,95.056 + ,120.103 + ,91.226 + ,0.00532 + ,0.00268 + ,0.00332 + ,0.02838 + ,1 + ,88.333 + ,112.24 + ,84.072 + ,0.00505 + ,0.00254 + ,0.0033 + ,0.02143 + ,1 + ,91.904 + ,115.871 + ,86.292 + ,0.0054 + ,0.00281 + ,0.00336 + ,0.02752 + 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,177.291 + ,96.983 + ,0.00314 + ,0.00134 + ,0.00192 + ,0.01564 + ,0 + ,116.556 + ,592.03 + ,86.228 + ,0.00496 + ,0.00254 + ,0.00263 + ,0.0166 + ,0 + ,116.342 + ,581.289 + ,94.246 + ,0.00267 + ,0.00115 + ,0.00148 + ,0.013 + ,0 + ,114.563 + ,119.167 + ,86.647 + ,0.00327 + ,0.00146 + ,0.00184 + ,0.01185 + ,0 + ,201.774 + ,262.707 + ,78.228 + ,0.00694 + ,0.00412 + ,0.00396 + ,0.02574 + ,0 + ,174.188 + ,230.978 + ,94.261 + ,0.00459 + ,0.00263 + ,0.00259 + ,0.04087 + ,0 + ,209.516 + ,253.017 + ,89.488 + ,0.00564 + ,0.00331 + ,0.00292 + ,0.02751 + ,0 + ,174.688 + ,240.005 + ,74.287 + ,0.0136 + ,0.00624 + ,0.00564 + ,0.02308 + ,0 + ,198.764 + ,396.961 + ,74.904 + ,0.0074 + ,0.0037 + ,0.0039 + ,0.02296 + ,0 + ,214.289 + ,260.277 + ,77.973 + ,0.00567 + ,0.00295 + ,0.00317 + ,0.01884) + ,dim=c(8 + ,195) + ,dimnames=list(c('status' + ,'MDVP:Fo(Hz)' + ,'MDVP:Fhi(Hz)' + ,'MDVP:Flo(Hz)' + ,'MDVP:Jitter(%)' + ,'MDVP:RAP' + ,'MDVP:PPQ' + ,'MDVP:Shimmer') + ,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:RAP','MDVP:PPQ','MDVP:Shimmer'),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(%) MDVP:RAP 1 1 119.992 157.302 74.997 0.00784 0.00370 2 1 122.400 148.650 113.819 0.00968 0.00465 3 1 116.682 131.111 111.555 0.01050 0.00544 4 1 116.676 137.871 111.366 0.00997 0.00502 5 1 116.014 141.781 110.655 0.01284 0.00655 6 1 120.552 131.162 113.787 0.00968 0.00463 7 1 120.267 137.244 114.820 0.00333 0.00155 8 1 107.332 113.840 104.315 0.00290 0.00144 9 1 95.730 132.068 91.754 0.00551 0.00293 10 1 95.056 120.103 91.226 0.00532 0.00268 11 1 88.333 112.240 84.072 0.00505 0.00254 12 1 91.904 115.871 86.292 0.00540 0.00281 13 1 136.926 159.866 131.276 0.00293 0.00118 14 1 139.173 179.139 76.556 0.00390 0.00165 15 1 152.845 163.305 75.836 0.00294 0.00121 16 1 142.167 217.455 83.159 0.00369 0.00157 17 1 144.188 349.259 82.764 0.00544 0.00211 18 1 168.778 232.181 75.603 0.00718 0.00284 19 1 153.046 175.829 68.623 0.00742 0.00364 20 1 156.405 189.398 142.822 0.00768 0.00372 21 1 153.848 165.738 65.782 0.00840 0.00428 22 1 153.880 172.860 78.128 0.00480 0.00232 23 1 167.930 193.221 79.068 0.00442 0.00220 24 1 173.917 192.735 86.180 0.00476 0.00221 25 1 163.656 200.841 76.779 0.00742 0.00380 26 1 104.400 206.002 77.968 0.00633 0.00316 27 1 171.041 208.313 75.501 0.00455 0.00250 28 1 146.845 208.701 81.737 0.00496 0.00250 29 1 155.358 227.383 80.055 0.00310 0.00159 30 1 162.568 198.346 77.630 0.00502 0.00280 31 0 197.076 206.896 192.055 0.00289 0.00166 32 0 199.228 209.512 192.091 0.00241 0.00134 33 0 198.383 215.203 193.104 0.00212 0.00113 34 0 202.266 211.604 197.079 0.00180 0.00093 35 0 203.184 211.526 196.160 0.00178 0.00094 36 0 201.464 210.565 195.708 0.00198 0.00105 37 1 177.876 192.921 168.013 0.00411 0.00233 38 1 176.170 185.604 163.564 0.00369 0.00205 39 1 180.198 201.249 175.456 0.00284 0.00153 40 1 187.733 202.324 173.015 0.00316 0.00168 41 1 186.163 197.724 177.584 0.00298 0.00165 42 1 184.055 196.537 166.977 0.00258 0.00134 43 0 237.226 247.326 225.227 0.00298 0.00169 44 0 241.404 248.834 232.483 0.00281 0.00157 45 0 243.439 250.912 232.435 0.00210 0.00109 46 0 242.852 255.034 227.911 0.00225 0.00117 47 0 245.510 262.090 231.848 0.00235 0.00127 48 0 252.455 261.487 182.786 0.00185 0.00092 49 0 122.188 128.611 115.765 0.00524 0.00169 50 0 122.964 130.049 114.676 0.00428 0.00124 51 0 124.445 135.069 117.495 0.00431 0.00141 52 0 126.344 134.231 112.773 0.00448 0.00131 53 0 128.001 138.052 122.080 0.00436 0.00137 54 0 129.336 139.867 118.604 0.00490 0.00165 55 1 108.807 134.656 102.874 0.00761 0.00349 56 1 109.860 126.358 104.437 0.00874 0.00398 57 1 110.417 131.067 103.370 0.00784 0.00352 58 1 117.274 129.916 110.402 0.00752 0.00299 59 1 116.879 131.897 108.153 0.00788 0.00334 60 1 114.847 271.314 104.680 0.00867 0.00373 61 0 209.144 237.494 109.379 0.00282 0.00147 62 0 223.365 238.987 98.664 0.00264 0.00154 63 0 222.236 231.345 205.495 0.00266 0.00152 64 0 228.832 234.619 223.634 0.00296 0.00175 65 0 229.401 252.221 221.156 0.00205 0.00114 66 0 228.969 239.541 113.201 0.00238 0.00136 67 1 140.341 159.774 67.021 0.00817 0.00430 68 1 136.969 166.607 66.004 0.00923 0.00507 69 1 143.533 162.215 65.809 0.01101 0.00647 70 1 148.090 162.824 67.343 0.00762 0.00467 71 1 142.729 162.408 65.476 0.00831 0.00469 72 1 136.358 176.595 65.750 0.00971 0.00534 73 1 120.080 139.710 111.208 0.00405 0.00180 74 1 112.014 588.518 107.024 0.00533 0.00268 75 1 110.793 128.101 107.316 0.00494 0.00260 76 1 110.707 122.611 105.007 0.00516 0.00277 77 1 112.876 148.826 106.981 0.00500 0.00270 78 1 110.568 125.394 106.821 0.00462 0.00226 79 1 95.385 102.145 90.264 0.00608 0.00331 80 1 100.770 115.697 85.545 0.01038 0.00622 81 1 96.106 108.664 84.510 0.00694 0.00389 82 1 95.605 107.715 87.549 0.00702 0.00428 83 1 100.960 110.019 95.628 0.00606 0.00351 84 1 98.804 102.305 87.804 0.00432 0.00247 85 1 176.858 205.560 75.344 0.00747 0.00418 86 1 180.978 200.125 155.495 0.00406 0.00220 87 1 178.222 202.450 141.047 0.00321 0.00163 88 1 176.281 227.381 125.610 0.00520 0.00287 89 1 173.898 211.350 74.677 0.00448 0.00237 90 1 179.711 225.930 144.878 0.00709 0.00391 91 1 166.605 206.008 78.032 0.00742 0.00387 92 1 151.955 163.335 147.226 0.00419 0.00224 93 1 148.272 164.989 142.299 0.00459 0.00250 94 1 152.125 161.469 76.596 0.00382 0.00191 95 1 157.821 172.975 68.401 0.00358 0.00196 96 1 157.447 163.267 149.605 0.00369 0.00201 97 1 159.116 168.913 144.811 0.00342 0.00178 98 1 125.036 143.946 116.187 0.01280 0.00743 99 1 125.791 140.557 96.206 0.01378 0.00826 100 1 126.512 141.756 99.770 0.01936 0.01159 101 1 125.641 141.068 116.346 0.03316 0.02144 102 1 128.451 150.449 75.632 0.01551 0.00905 103 1 139.224 586.567 66.157 0.03011 0.01854 104 1 150.258 154.609 75.349 0.00248 0.00105 105 1 154.003 160.267 128.621 0.00183 0.00076 106 1 149.689 160.368 133.608 0.00257 0.00116 107 1 155.078 163.736 144.148 0.00168 0.00068 108 1 151.884 157.765 133.751 0.00258 0.00115 109 1 151.989 157.339 132.857 0.00174 0.00075 110 1 193.030 208.900 80.297 0.00766 0.00450 111 1 200.714 223.982 89.686 0.00621 0.00371 112 1 208.519 220.315 199.020 0.00609 0.00368 113 1 204.664 221.300 189.621 0.00841 0.00502 114 1 210.141 232.706 185.258 0.00534 0.00321 115 1 206.327 226.355 92.020 0.00495 0.00302 116 1 151.872 492.892 69.085 0.00856 0.00404 117 1 158.219 442.557 71.948 0.00476 0.00214 118 1 170.756 450.247 79.032 0.00555 0.00244 119 1 178.285 442.824 82.063 0.00462 0.00157 120 1 217.116 233.481 93.978 0.00404 0.00127 121 1 128.940 479.697 88.251 0.00581 0.00241 122 1 176.824 215.293 83.961 0.00460 0.00209 123 1 138.190 203.522 83.340 0.00704 0.00406 124 1 182.018 197.173 79.187 0.00842 0.00506 125 1 156.239 195.107 79.820 0.00694 0.00403 126 1 145.174 198.109 80.637 0.00733 0.00414 127 1 138.145 197.238 81.114 0.00544 0.00294 128 1 166.888 198.966 79.512 0.00638 0.00368 129 1 119.031 127.533 109.216 0.00440 0.00214 130 1 120.078 126.632 105.667 0.00270 0.00116 131 1 120.289 128.143 100.209 0.00492 0.00269 132 1 120.256 125.306 104.773 0.00407 0.00224 133 1 119.056 125.213 86.795 0.00346 0.00169 134 1 118.747 123.723 109.836 0.00331 0.00168 135 1 106.516 112.777 93.105 0.00589 0.00291 136 1 110.453 127.611 105.554 0.00494 0.00244 137 1 113.400 133.344 107.816 0.00451 0.00219 138 1 113.166 130.270 100.673 0.00502 0.00257 139 1 112.239 126.609 104.095 0.00472 0.00238 140 1 116.150 131.731 109.815 0.00381 0.00181 141 1 170.368 268.796 79.543 0.00571 0.00232 142 1 208.083 253.792 91.802 0.00757 0.00428 143 1 198.458 219.290 148.691 0.00376 0.00182 144 1 202.805 231.508 86.232 0.00370 0.00189 145 1 202.544 241.350 164.168 0.00254 0.00100 146 1 223.361 263.872 87.638 0.00352 0.00169 147 1 169.774 191.759 151.451 0.01568 0.00863 148 1 183.520 216.814 161.340 0.01466 0.00849 149 1 188.620 216.302 165.982 0.01719 0.00996 150 1 202.632 565.740 177.258 0.01627 0.00919 151 1 186.695 211.961 149.442 0.01872 0.01075 152 1 192.818 224.429 168.793 0.03107 0.01800 153 1 198.116 233.099 174.478 0.02714 0.01568 154 1 121.345 139.644 98.250 0.00684 0.00388 155 1 119.100 128.442 88.833 0.00692 0.00393 156 1 117.870 127.349 95.654 0.00647 0.00356 157 1 122.336 142.369 94.794 0.00727 0.00415 158 1 117.963 134.209 100.757 0.01813 0.01117 159 1 126.144 154.284 97.543 0.00975 0.00593 160 1 127.930 138.752 112.173 0.00605 0.00321 161 1 114.238 124.393 77.022 0.00581 0.00299 162 1 115.322 135.738 107.802 0.00619 0.00352 163 1 114.554 126.778 91.121 0.00651 0.00366 164 1 112.150 131.669 97.527 0.00519 0.00291 165 1 102.273 142.830 85.902 0.00907 0.00493 166 0 236.200 244.663 102.137 0.00277 0.00154 167 0 237.323 243.709 229.256 0.00303 0.00173 168 0 260.105 264.919 237.303 0.00339 0.00205 169 0 197.569 217.627 90.794 0.00803 0.00490 170 0 240.301 245.135 219.783 0.00517 0.00316 171 0 244.990 272.210 239.170 0.00451 0.00279 172 0 112.547 133.374 105.715 0.00355 0.00166 173 0 110.739 113.597 100.139 0.00356 0.00170 174 0 113.715 116.443 96.913 0.00349 0.00171 175 0 117.004 144.466 99.923 0.00353 0.00176 176 0 115.380 123.109 108.634 0.00332 0.00160 177 0 116.388 129.038 108.970 0.00346 0.00169 178 1 151.737 190.204 129.859 0.00314 0.00135 179 1 148.790 158.359 138.990 0.00309 0.00152 180 1 148.143 155.982 135.041 0.00392 0.00204 181 1 150.440 163.441 144.736 0.00396 0.00206 182 1 148.462 161.078 141.998 0.00397 0.00202 183 1 149.818 163.417 144.786 0.00336 0.00174 184 0 117.226 123.925 106.656 0.00417 0.00186 185 0 116.848 217.552 99.503 0.00531 0.00260 186 0 116.286 177.291 96.983 0.00314 0.00134 187 0 116.556 592.030 86.228 0.00496 0.00254 188 0 116.342 581.289 94.246 0.00267 0.00115 189 0 114.563 119.167 86.647 0.00327 0.00146 190 0 201.774 262.707 78.228 0.00694 0.00412 191 0 174.188 230.978 94.261 0.00459 0.00263 192 0 209.516 253.017 89.488 0.00564 0.00331 193 0 174.688 240.005 74.287 0.01360 0.00624 194 0 198.764 396.961 74.904 0.00740 0.00370 195 0 214.289 260.277 77.973 0.00567 0.00295 MDVP:PPQ MDVP:Shimmer 1 0.00554 0.04374 2 0.00696 0.06134 3 0.00781 0.05233 4 0.00698 0.05492 5 0.00908 0.06425 6 0.00750 0.04701 7 0.00202 0.01608 8 0.00182 0.01567 9 0.00332 0.02093 10 0.00332 0.02838 11 0.00330 0.02143 12 0.00336 0.02752 13 0.00153 0.01259 14 0.00208 0.01642 15 0.00149 0.01828 16 0.00203 0.01503 17 0.00292 0.02047 18 0.00387 0.03327 19 0.00432 0.05517 20 0.00399 0.03995 21 0.00450 0.03810 22 0.00267 0.04137 23 0.00247 0.04351 24 0.00258 0.04192 25 0.00390 0.01659 26 0.00375 0.03767 27 0.00234 0.01966 28 0.00275 0.01919 29 0.00176 0.01718 30 0.00253 0.01791 31 0.00168 0.01098 32 0.00138 0.01015 33 0.00135 0.01263 34 0.00107 0.00954 35 0.00106 0.00958 36 0.00115 0.01194 37 0.00241 0.02126 38 0.00218 0.01851 39 0.00166 0.01444 40 0.00182 0.01663 41 0.00175 0.01495 42 0.00147 0.01463 43 0.00182 0.01752 44 0.00173 0.01760 45 0.00137 0.01419 46 0.00139 0.01494 47 0.00148 0.01608 48 0.00113 0.01152 49 0.00203 0.01613 50 0.00155 0.01681 51 0.00167 0.02184 52 0.00169 0.02033 53 0.00166 0.02297 54 0.00183 0.02498 55 0.00486 0.02719 56 0.00539 0.03209 57 0.00514 0.03715 58 0.00469 0.02293 59 0.00493 0.02645 60 0.00520 0.03225 61 0.00152 0.01861 62 0.00151 0.01906 63 0.00144 0.01643 64 0.00155 0.01644 65 0.00113 0.01457 66 0.00140 0.01745 67 0.00440 0.03198 68 0.00463 0.03111 69 0.00467 0.05384 70 0.00354 0.05428 71 0.00419 0.03485 72 0.00478 0.04978 73 0.00220 0.01706 74 0.00329 0.02448 75 0.00283 0.02442 76 0.00289 0.02215 77 0.00289 0.03999 78 0.00280 0.02199 79 0.00332 0.03202 80 0.00576 0.03121 81 0.00415 0.04024 82 0.00371 0.03156 83 0.00348 0.02427 84 0.00258 0.02223 85 0.00420 0.04795 86 0.00244 0.03852 87 0.00194 0.03759 88 0.00312 0.06511 89 0.00254 0.06727 90 0.00419 0.04313 91 0.00453 0.06640 92 0.00227 0.07959 93 0.00256 0.04190 94 0.00226 0.05925 95 0.00196 0.03716 96 0.00197 0.03272 97 0.00184 0.03381 98 0.00623 0.03886 99 0.00655 0.04689 100 0.00990 0.06734 101 0.01522 0.09178 102 0.00909 0.06170 103 0.01628 0.09419 104 0.00136 0.01131 105 0.00100 0.01030 106 0.00134 0.01346 107 0.00092 0.01064 108 0.00122 0.01450 109 0.00096 0.01024 110 0.00389 0.03044 111 0.00337 0.02286 112 0.00339 0.01761 113 0.00485 0.02378 114 0.00280 0.01680 115 0.00246 0.02105 116 0.00385 0.01843 117 0.00207 0.01458 118 0.00261 0.01725 119 0.00194 0.01279 120 0.00128 0.01299 121 0.00314 0.02008 122 0.00221 0.01169 123 0.00398 0.04479 124 0.00449 0.02503 125 0.00395 0.02343 126 0.00422 0.02362 127 0.00327 0.02791 128 0.00351 0.02857 129 0.00192 0.01033 130 0.00135 0.01022 131 0.00238 0.01412 132 0.00205 0.01516 133 0.00170 0.01201 134 0.00171 0.01043 135 0.00319 0.04932 136 0.00315 0.04128 137 0.00283 0.04879 138 0.00312 0.05279 139 0.00290 0.05643 140 0.00232 0.03026 141 0.00269 0.03273 142 0.00428 0.06725 143 0.00215 0.03527 144 0.00211 0.01997 145 0.00133 0.02662 146 0.00188 0.02536 147 0.00946 0.08143 148 0.00819 0.06050 149 0.01027 0.07118 150 0.00963 0.07170 151 0.01154 0.05830 152 0.01958 0.11908 153 0.01699 0.08684 154 0.00332 0.02534 155 0.00300 0.02682 156 0.00300 0.03087 157 0.00339 0.02293 158 0.00718 0.04912 159 0.00454 0.02852 160 0.00318 0.03235 161 0.00316 0.04009 162 0.00329 0.03273 163 0.00340 0.03658 164 0.00284 0.01756 165 0.00461 0.02814 166 0.00153 0.02448 167 0.00159 0.01242 168 0.00186 0.02030 169 0.00448 0.02177 170 0.00283 0.02018 171 0.00237 0.01897 172 0.00190 0.01358 173 0.00200 0.01484 174 0.00203 0.01472 175 0.00218 0.01657 176 0.00199 0.01503 177 0.00213 0.01725 178 0.00162 0.01469 179 0.00186 0.01574 180 0.00231 0.01450 181 0.00233 0.02551 182 0.00235 0.01831 183 0.00198 0.02145 184 0.00270 0.01909 185 0.00346 0.01795 186 0.00192 0.01564 187 0.00263 0.01660 188 0.00148 0.01300 189 0.00184 0.01185 190 0.00396 0.02574 191 0.00259 0.04087 192 0.00292 0.02751 193 0.00564 0.02308 194 0.00390 0.02296 195 0.00317 0.01884 > 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)` 1.271e+00 -2.239e-03 -2.552e-04 -2.411e-03 `MDVP:Jitter(%)` `MDVP:RAP` `MDVP:PPQ` `MDVP:Shimmer` -8.599e+01 9.102e+01 5.441e+01 6.901e+00 > (mysum <- summary(mylm)) Call: lm(formula = df) Residuals: Min 1Q Median 3Q Max -0.86279 -0.19360 0.08267 0.25589 0.61433 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 1.271e+00 1.402e-01 9.070 < 2e-16 *** `MDVP:Fo(Hz)` -2.239e-03 9.236e-04 -2.424 0.01631 * `MDVP:Fhi(Hz)` -2.552e-04 3.352e-04 -0.761 0.44746 `MDVP:Flo(Hz)` -2.411e-03 8.225e-04 -2.931 0.00380 ** `MDVP:Jitter(%)` -8.599e+01 5.764e+01 -1.492 0.13740 `MDVP:RAP` 9.102e+01 7.215e+01 1.262 0.20867 `MDVP:PPQ` 5.441e+01 4.926e+01 1.105 0.27078 `MDVP:Shimmer` 6.901e+00 2.402e+00 2.873 0.00454 ** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 0.3699 on 187 degrees of freedom Multiple R-squared: 0.2929, Adjusted R-squared: 0.2665 F-statistic: 11.07 on 7 and 187 DF, p-value: 1.111e-11 > if (n > n25) { + kp3 <- k + 3 + nmkm3 <- n - k - 3 + gqarr <- array(NA, dim=c(nmkm3-kp3+1,3)) + numgqtests <- 0 + numsignificant1 <- 0 + numsignificant5 <- 0 + numsignificant10 <- 0 + for (mypoint in kp3:nmkm3) { + j <- 0 + numgqtests <- numgqtests + 1 + for (myalt in c('greater', 'two.sided', 'less')) { + j <- j + 1 + gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value + } + if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1 + if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1 + if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1 + } + gqarr + } [,1] [,2] [,3] [1,] 1.317556e-54 2.635113e-54 1.0000000000 [2,] 1.500739e-66 3.001479e-66 1.0000000000 [3,] 1.956010e-93 3.912021e-93 1.0000000000 [4,] 1.521771e-92 3.043542e-92 1.0000000000 [5,] 5.012060e-108 1.002412e-107 1.0000000000 [6,] 0.000000e+00 0.000000e+00 1.0000000000 [7,] 1.330465e-148 2.660930e-148 1.0000000000 [8,] 6.781184e-155 1.356237e-154 1.0000000000 [9,] 3.168340e-169 6.336680e-169 1.0000000000 [10,] 6.661978e-193 1.332396e-192 1.0000000000 [11,] 1.400411e-225 2.800822e-225 1.0000000000 [12,] 1.040644e-217 2.081288e-217 1.0000000000 [13,] 1.716867e-229 3.433734e-229 1.0000000000 [14,] 7.851214e-248 1.570243e-247 1.0000000000 [15,] 1.598754e-266 3.197508e-266 1.0000000000 [16,] 9.722517e-308 1.944503e-307 1.0000000000 [17,] 1.922852e-296 3.845703e-296 1.0000000000 [18,] 4.253687e-307 8.507375e-307 1.0000000000 [19,] 0.000000e+00 0.000000e+00 1.0000000000 [20,] 0.000000e+00 0.000000e+00 1.0000000000 [21,] 5.926277e-09 1.185255e-08 0.9999999941 [22,] 1.146973e-08 2.293945e-08 0.9999999885 [23,] 7.281364e-09 1.456273e-08 0.9999999927 [24,] 3.008124e-09 6.016248e-09 0.9999999970 [25,] 1.130801e-09 2.261602e-09 0.9999999989 [26,] 4.328271e-10 8.656543e-10 0.9999999996 [27,] 8.221234e-07 1.644247e-06 0.9999991779 [28,] 1.577772e-05 3.155544e-05 0.9999842223 [29,] 1.825880e-04 3.651760e-04 0.9998174120 [30,] 7.811026e-04 1.562205e-03 0.9992188974 [31,] 2.213278e-03 4.426557e-03 0.9977867216 [32,] 3.950001e-03 7.900001e-03 0.9960499995 [33,] 2.967536e-03 5.935072e-03 0.9970324641 [34,] 2.035650e-03 4.071301e-03 0.9979643497 [35,] 1.326961e-03 2.653923e-03 0.9986730387 [36,] 8.702961e-04 1.740592e-03 0.9991297039 [37,] 5.583374e-04 1.116675e-03 0.9994416626 [38,] 3.942077e-04 7.884154e-04 0.9996057923 [39,] 2.486608e-03 4.973216e-03 0.9975133922 [40,] 3.545127e-03 7.090255e-03 0.9964548726 [41,] 5.041862e-03 1.008372e-02 0.9949581381 [42,] 5.385921e-03 1.077184e-02 0.9946140794 [43,] 6.261900e-03 1.252380e-02 0.9937381001 [44,] 8.096544e-03 1.619309e-02 0.9919034557 [45,] 6.851690e-03 1.370338e-02 0.9931483104 [46,] 6.388435e-03 1.277687e-02 0.9936115647 [47,] 5.241458e-03 1.048292e-02 0.9947585422 [48,] 7.712098e-03 1.542420e-02 0.9922879024 [49,] 7.294435e-03 1.458887e-02 0.9927055653 [50,] 5.257427e-03 1.051485e-02 0.9947425731 [51,] 1.798181e-02 3.596361e-02 0.9820181930 [52,] 4.168117e-02 8.336234e-02 0.9583188299 [53,] 3.890483e-02 7.780965e-02 0.9610951741 [54,] 3.505106e-02 7.010213e-02 0.9649489365 [55,] 3.144960e-02 6.289921e-02 0.9685503968 [56,] 4.326177e-02 8.652355e-02 0.9567382272 [57,] 3.483472e-02 6.966945e-02 0.9651652762 [58,] 2.954015e-02 5.908030e-02 0.9704598508 [59,] 2.398842e-02 4.797684e-02 0.9760115816 [60,] 1.873347e-02 3.746693e-02 0.9812665331 [61,] 1.444782e-02 2.889563e-02 0.9855521839 [62,] 1.091015e-02 2.182031e-02 0.9890898470 [63,] 9.251409e-03 1.850282e-02 0.9907485909 [64,] 1.382413e-02 2.764826e-02 0.9861758717 [65,] 1.052246e-02 2.104491e-02 0.9894775425 [66,] 7.970789e-03 1.594158e-02 0.9920292110 [67,] 5.854539e-03 1.170908e-02 0.9941454614 [68,] 4.393575e-03 8.787149e-03 0.9956064253 [69,] 3.266510e-03 6.533021e-03 0.9967334897 [70,] 3.438958e-03 6.877916e-03 0.9965610421 [71,] 2.713251e-03 5.426503e-03 0.9972867487 [72,] 2.145238e-03 4.290475e-03 0.9978547624 [73,] 1.618272e-03 3.236545e-03 0.9983817276 [74,] 1.212994e-03 2.425988e-03 0.9987870060 [75,] 8.577569e-04 1.715514e-03 0.9991422431 [76,] 1.037153e-03 2.074306e-03 0.9989628468 [77,] 1.077597e-03 2.155194e-03 0.9989224032 [78,] 7.812781e-04 1.562556e-03 0.9992187219 [79,] 5.374590e-04 1.074918e-03 0.9994625410 [80,] 4.623961e-04 9.247922e-04 0.9995376039 [81,] 3.157277e-04 6.314555e-04 0.9996842723 [82,] 2.430792e-04 4.861584e-04 0.9997569208 [83,] 1.861853e-04 3.723707e-04 0.9998138147 [84,] 1.249710e-04 2.499421e-04 0.9998750290 [85,] 8.324428e-05 1.664886e-04 0.9999167557 [86,] 7.616972e-05 1.523394e-04 0.9999238303 [87,] 6.781858e-05 1.356372e-04 0.9999321814 [88,] 4.533603e-05 9.067206e-05 0.9999546640 [89,] 2.912963e-05 5.825927e-05 0.9999708704 [90,] 2.040837e-05 4.081673e-05 0.9999795916 [91,] 1.780163e-05 3.560327e-05 0.9999821984 [92,] 1.403120e-05 2.806239e-05 0.9999859688 [93,] 1.844625e-05 3.689249e-05 0.9999815538 [94,] 1.469778e-05 2.939557e-05 0.9999853022 [95,] 1.502954e-05 3.005909e-05 0.9999849705 [96,] 1.501103e-05 3.002207e-05 0.9999849890 [97,] 1.680595e-05 3.361189e-05 0.9999831941 [98,] 1.704247e-05 3.408494e-05 0.9999829575 [99,] 1.717514e-05 3.435027e-05 0.9999828249 [100,] 1.358975e-05 2.717949e-05 0.9999864103 [101,] 1.251953e-05 2.503907e-05 0.9999874805 [102,] 3.464631e-05 6.929262e-05 0.9999653537 [103,] 6.712245e-05 1.342449e-04 0.9999328776 [104,] 1.498929e-04 2.997857e-04 0.9998501071 [105,] 1.497749e-04 2.995499e-04 0.9998502251 [106,] 1.493565e-04 2.987130e-04 0.9998506435 [107,] 1.453228e-04 2.906456e-04 0.9998546772 [108,] 1.617330e-04 3.234660e-04 0.9998382670 [109,] 2.397302e-04 4.794605e-04 0.9997602698 [110,] 5.114630e-04 1.022926e-03 0.9994885370 [111,] 7.216904e-04 1.443381e-03 0.9992783096 [112,] 9.776484e-04 1.955297e-03 0.9990223516 [113,] 6.949288e-04 1.389858e-03 0.9993050712 [114,] 6.535975e-04 1.307195e-03 0.9993464025 [115,] 6.422334e-04 1.284467e-03 0.9993577666 [116,] 6.585041e-04 1.317008e-03 0.9993414959 [117,] 6.008150e-04 1.201630e-03 0.9993991850 [118,] 5.969355e-04 1.193871e-03 0.9994030645 [119,] 5.849708e-04 1.169942e-03 0.9994150292 [120,] 6.041656e-04 1.208331e-03 0.9993958344 [121,] 5.900363e-04 1.180073e-03 0.9994099637 [122,] 5.715308e-04 1.143062e-03 0.9994284692 [123,] 6.549342e-04 1.309868e-03 0.9993450658 [124,] 8.148985e-04 1.629797e-03 0.9991851015 [125,] 5.794450e-04 1.158890e-03 0.9994205550 [126,] 3.920522e-04 7.841044e-04 0.9996079478 [127,] 2.655211e-04 5.310423e-04 0.9997344789 [128,] 1.879027e-04 3.758055e-04 0.9998120973 [129,] 1.604952e-04 3.209903e-04 0.9998395048 [130,] 1.115696e-04 2.231392e-04 0.9998884304 [131,] 1.155145e-04 2.310290e-04 0.9998844855 [132,] 7.979513e-05 1.595903e-04 0.9999202049 [133,] 8.344820e-05 1.668964e-04 0.9999165518 [134,] 2.690972e-04 5.381943e-04 0.9997309028 [135,] 4.987776e-04 9.975553e-04 0.9995012224 [136,] 2.841842e-03 5.683684e-03 0.9971581579 [137,] 2.360320e-03 4.720640e-03 0.9976396802 [138,] 1.851693e-03 3.703387e-03 0.9981483067 [139,] 1.337670e-03 2.675340e-03 0.9986623299 [140,] 1.215388e-03 2.430776e-03 0.9987846121 [141,] 1.322389e-03 2.644777e-03 0.9986776113 [142,] 1.279090e-03 2.558180e-03 0.9987209099 [143,] 8.556020e-04 1.711204e-03 0.9991443980 [144,] 7.526550e-04 1.505310e-03 0.9992473450 [145,] 6.240444e-04 1.248089e-03 0.9993759556 [146,] 4.587775e-04 9.175549e-04 0.9995412225 [147,] 5.423010e-04 1.084602e-03 0.9994576990 [148,] 8.563408e-04 1.712682e-03 0.9991436592 [149,] 5.423030e-04 1.084606e-03 0.9994576970 [150,] 3.829081e-04 7.658162e-04 0.9996170919 [151,] 2.523638e-04 5.047276e-04 0.9997476362 [152,] 1.605915e-04 3.211830e-04 0.9998394085 [153,] 1.039081e-04 2.078163e-04 0.9998960919 [154,] 1.889071e-04 3.778143e-04 0.9998110929 [155,] 3.516431e-04 7.032861e-04 0.9996483569 [156,] 4.170518e-04 8.341035e-04 0.9995829482 [157,] 3.804907e-04 7.609813e-04 0.9996195093 [158,] 6.749509e-04 1.349902e-03 0.9993250491 [159,] 1.725003e-03 3.450005e-03 0.9982749973 [160,] 2.657395e-03 5.314789e-03 0.9973426054 [161,] 9.996312e-01 7.375855e-04 0.0003687928 [162,] 9.996978e-01 6.043535e-04 0.0003021767 [163,] 9.994960e-01 1.008029e-03 0.0005040146 [164,] 9.991697e-01 1.660508e-03 0.0008302542 [165,] 9.984976e-01 3.004817e-03 0.0015024087 [166,] 9.989302e-01 2.139529e-03 0.0010697647 [167,] 9.995027e-01 9.945755e-04 0.0004972877 [168,] 9.993539e-01 1.292132e-03 0.0006460662 [169,] 9.981574e-01 3.685184e-03 0.0018425922 [170,] 9.959863e-01 8.027406e-03 0.0040137028 [171,] 9.896811e-01 2.063779e-02 0.0103188933 [172,] 9.755592e-01 4.888161e-02 0.0244408049 [173,] 1.000000e+00 0.000000e+00 0.0000000000 [174,] 1.000000e+00 0.000000e+00 0.0000000000 > postscript(file="/var/wessaorg/rcomp/tmp/138yh1386668995.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$resid) > grid() > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/2qf9k1386668995.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(mysum$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/3vmo21386668995.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') > grid() > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/4a4mc1386668995.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/5q0js1386668995.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 -0.047579233 -0.077764966 -0.085956373 -0.064765541 -0.138069119 -0.015103432 7 8 9 10 11 12 0.234194556 0.160684110 0.079988076 0.029154653 0.033427515 0.007928464 13 14 15 16 17 18 0.366961155 0.229263988 0.230854523 0.263199748 0.315777553 0.266846263 19 20 21 22 23 24 -0.027383778 0.300552590 0.099019778 0.076496616 0.089770113 0.153511599 25 26 27 28 29 30 0.296956753 -0.004316119 0.247529353 0.224689086 0.235075874 0.246000389 31 32 33 34 35 36 -0.384027640 -0.368555298 -0.387859086 -0.343255811 -0.345798255 -0.364981408 37 38 39 40 41 42 0.444724740 0.449173537 0.521470561 0.522774283 0.531755545 0.512422895 43 44 45 46 47 48 -0.251595168 -0.223715376 -0.192989681 -0.204805750 -0.200830022 -0.264344950 49 50 51 52 53 54 -0.610814889 -0.631507801 -0.674251617 -0.648544186 -0.653789563 -0.660888632 55 56 57 58 59 60 0.139359572 0.133287278 0.076320389 0.251675155 0.207623782 0.207996725 61 62 63 64 65 66 -0.581202316 -0.599228886 -0.320650666 -0.262508498 -0.249691266 -0.540373090 67 68 69 70 71 72 0.096324565 0.102623648 -0.017680839 -0.072859967 0.066776289 -0.017116834 73 74 75 76 77 78 0.248300216 0.254144546 0.133809907 0.142496173 0.028292646 0.153254459 79 80 81 82 83 84 0.005878834 -0.012248361 -0.085440437 -0.024254667 0.058157468 0.040578595 85 86 87 88 89 90 0.061219108 0.310111496 0.282112133 0.051046024 -0.080928638 0.266149700 91 92 93 94 95 96 -0.076506644 -0.050839489 0.184525940 -0.082077188 0.057437190 0.284901752 97 98 99 100 101 102 0.275788431 0.142759314 0.031309665 -0.104832322 -0.234968569 -0.173350904 103 104 105 106 107 108 -0.284491342 0.251851868 0.387169313 0.376479990 0.424283662 0.382195857 109 110 111 112 113 114 0.387886956 0.235137447 0.306645247 0.614329368 0.538811184 0.603919021 115 116 117 118 119 120 0.341898266 0.392760907 0.370608552 0.410538775 0.499255075 0.573450079 121 122 123 124 125 126 0.323388402 0.386348787 0.001132509 0.223891446 0.174082709 0.159571113 127 128 129 130 131 132 0.113540996 0.170332061 0.298871619 0.267213426 0.223600139 0.212447860 133 134 135 136 137 138 0.204782726 0.257631166 -0.051890363 0.009472388 -0.025654829 -0.078299178 139 140 141 142 143 144 -0.094714201 0.114932098 0.278094908 0.045057458 0.384729075 0.343233783 145 146 147 148 149 150 0.510855171 0.378941970 0.009046120 0.208625103 0.127993006 0.297921743 151 152 153 154 155 156 0.162156612 -0.229084022 0.035324612 0.152391623 0.131327827 0.111770757 157 158 159 160 161 162 0.172198477 0.082665746 0.140489463 0.152761324 -0.019242224 0.088443290 163 164 165 166 167 168 0.026441398 0.154238947 0.087422456 -0.587987920 -0.194218578 -0.185645187 169 170 171 172 173 174 -0.704022700 -0.277180285 -0.202734174 -0.773338936 -0.812793583 -0.820916116 175 176 177 178 179 180 -0.821184985 -0.791799251 -0.806310385 0.387639765 0.354852476 0.351394791 181 182 183 184 185 186 0.306360952 0.347831467 0.329675683 -0.809446062 -0.806450365 -0.796248183 187 188 189 190 191 192 -0.713711545 -0.680592952 -0.809091495 -0.735242973 -0.862786464 -0.686933740 193 194 195 -0.504472211 -0.615499545 -0.620575266 > postscript(file="/var/wessaorg/rcomp/tmp/61yez1386668995.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 -0.047579233 NA 1 -0.077764966 -0.047579233 2 -0.085956373 -0.077764966 3 -0.064765541 -0.085956373 4 -0.138069119 -0.064765541 5 -0.015103432 -0.138069119 6 0.234194556 -0.015103432 7 0.160684110 0.234194556 8 0.079988076 0.160684110 9 0.029154653 0.079988076 10 0.033427515 0.029154653 11 0.007928464 0.033427515 12 0.366961155 0.007928464 13 0.229263988 0.366961155 14 0.230854523 0.229263988 15 0.263199748 0.230854523 16 0.315777553 0.263199748 17 0.266846263 0.315777553 18 -0.027383778 0.266846263 19 0.300552590 -0.027383778 20 0.099019778 0.300552590 21 0.076496616 0.099019778 22 0.089770113 0.076496616 23 0.153511599 0.089770113 24 0.296956753 0.153511599 25 -0.004316119 0.296956753 26 0.247529353 -0.004316119 27 0.224689086 0.247529353 28 0.235075874 0.224689086 29 0.246000389 0.235075874 30 -0.384027640 0.246000389 31 -0.368555298 -0.384027640 32 -0.387859086 -0.368555298 33 -0.343255811 -0.387859086 34 -0.345798255 -0.343255811 35 -0.364981408 -0.345798255 36 0.444724740 -0.364981408 37 0.449173537 0.444724740 38 0.521470561 0.449173537 39 0.522774283 0.521470561 40 0.531755545 0.522774283 41 0.512422895 0.531755545 42 -0.251595168 0.512422895 43 -0.223715376 -0.251595168 44 -0.192989681 -0.223715376 45 -0.204805750 -0.192989681 46 -0.200830022 -0.204805750 47 -0.264344950 -0.200830022 48 -0.610814889 -0.264344950 49 -0.631507801 -0.610814889 50 -0.674251617 -0.631507801 51 -0.648544186 -0.674251617 52 -0.653789563 -0.648544186 53 -0.660888632 -0.653789563 54 0.139359572 -0.660888632 55 0.133287278 0.139359572 56 0.076320389 0.133287278 57 0.251675155 0.076320389 58 0.207623782 0.251675155 59 0.207996725 0.207623782 60 -0.581202316 0.207996725 61 -0.599228886 -0.581202316 62 -0.320650666 -0.599228886 63 -0.262508498 -0.320650666 64 -0.249691266 -0.262508498 65 -0.540373090 -0.249691266 66 0.096324565 -0.540373090 67 0.102623648 0.096324565 68 -0.017680839 0.102623648 69 -0.072859967 -0.017680839 70 0.066776289 -0.072859967 71 -0.017116834 0.066776289 72 0.248300216 -0.017116834 73 0.254144546 0.248300216 74 0.133809907 0.254144546 75 0.142496173 0.133809907 76 0.028292646 0.142496173 77 0.153254459 0.028292646 78 0.005878834 0.153254459 79 -0.012248361 0.005878834 80 -0.085440437 -0.012248361 81 -0.024254667 -0.085440437 82 0.058157468 -0.024254667 83 0.040578595 0.058157468 84 0.061219108 0.040578595 85 0.310111496 0.061219108 86 0.282112133 0.310111496 87 0.051046024 0.282112133 88 -0.080928638 0.051046024 89 0.266149700 -0.080928638 90 -0.076506644 0.266149700 91 -0.050839489 -0.076506644 92 0.184525940 -0.050839489 93 -0.082077188 0.184525940 94 0.057437190 -0.082077188 95 0.284901752 0.057437190 96 0.275788431 0.284901752 97 0.142759314 0.275788431 98 0.031309665 0.142759314 99 -0.104832322 0.031309665 100 -0.234968569 -0.104832322 101 -0.173350904 -0.234968569 102 -0.284491342 -0.173350904 103 0.251851868 -0.284491342 104 0.387169313 0.251851868 105 0.376479990 0.387169313 106 0.424283662 0.376479990 107 0.382195857 0.424283662 108 0.387886956 0.382195857 109 0.235137447 0.387886956 110 0.306645247 0.235137447 111 0.614329368 0.306645247 112 0.538811184 0.614329368 113 0.603919021 0.538811184 114 0.341898266 0.603919021 115 0.392760907 0.341898266 116 0.370608552 0.392760907 117 0.410538775 0.370608552 118 0.499255075 0.410538775 119 0.573450079 0.499255075 120 0.323388402 0.573450079 121 0.386348787 0.323388402 122 0.001132509 0.386348787 123 0.223891446 0.001132509 124 0.174082709 0.223891446 125 0.159571113 0.174082709 126 0.113540996 0.159571113 127 0.170332061 0.113540996 128 0.298871619 0.170332061 129 0.267213426 0.298871619 130 0.223600139 0.267213426 131 0.212447860 0.223600139 132 0.204782726 0.212447860 133 0.257631166 0.204782726 134 -0.051890363 0.257631166 135 0.009472388 -0.051890363 136 -0.025654829 0.009472388 137 -0.078299178 -0.025654829 138 -0.094714201 -0.078299178 139 0.114932098 -0.094714201 140 0.278094908 0.114932098 141 0.045057458 0.278094908 142 0.384729075 0.045057458 143 0.343233783 0.384729075 144 0.510855171 0.343233783 145 0.378941970 0.510855171 146 0.009046120 0.378941970 147 0.208625103 0.009046120 148 0.127993006 0.208625103 149 0.297921743 0.127993006 150 0.162156612 0.297921743 151 -0.229084022 0.162156612 152 0.035324612 -0.229084022 153 0.152391623 0.035324612 154 0.131327827 0.152391623 155 0.111770757 0.131327827 156 0.172198477 0.111770757 157 0.082665746 0.172198477 158 0.140489463 0.082665746 159 0.152761324 0.140489463 160 -0.019242224 0.152761324 161 0.088443290 -0.019242224 162 0.026441398 0.088443290 163 0.154238947 0.026441398 164 0.087422456 0.154238947 165 -0.587987920 0.087422456 166 -0.194218578 -0.587987920 167 -0.185645187 -0.194218578 168 -0.704022700 -0.185645187 169 -0.277180285 -0.704022700 170 -0.202734174 -0.277180285 171 -0.773338936 -0.202734174 172 -0.812793583 -0.773338936 173 -0.820916116 -0.812793583 174 -0.821184985 -0.820916116 175 -0.791799251 -0.821184985 176 -0.806310385 -0.791799251 177 0.387639765 -0.806310385 178 0.354852476 0.387639765 179 0.351394791 0.354852476 180 0.306360952 0.351394791 181 0.347831467 0.306360952 182 0.329675683 0.347831467 183 -0.809446062 0.329675683 184 -0.806450365 -0.809446062 185 -0.796248183 -0.806450365 186 -0.713711545 -0.796248183 187 -0.680592952 -0.713711545 188 -0.809091495 -0.680592952 189 -0.735242973 -0.809091495 190 -0.862786464 -0.735242973 191 -0.686933740 -0.862786464 192 -0.504472211 -0.686933740 193 -0.615499545 -0.504472211 194 -0.620575266 -0.615499545 195 NA -0.620575266 > dum1 <- dum[2:length(myerror),] > dum1 lag(myerror, k = 1) myerror [1,] -0.077764966 -0.047579233 [2,] -0.085956373 -0.077764966 [3,] -0.064765541 -0.085956373 [4,] -0.138069119 -0.064765541 [5,] -0.015103432 -0.138069119 [6,] 0.234194556 -0.015103432 [7,] 0.160684110 0.234194556 [8,] 0.079988076 0.160684110 [9,] 0.029154653 0.079988076 [10,] 0.033427515 0.029154653 [11,] 0.007928464 0.033427515 [12,] 0.366961155 0.007928464 [13,] 0.229263988 0.366961155 [14,] 0.230854523 0.229263988 [15,] 0.263199748 0.230854523 [16,] 0.315777553 0.263199748 [17,] 0.266846263 0.315777553 [18,] -0.027383778 0.266846263 [19,] 0.300552590 -0.027383778 [20,] 0.099019778 0.300552590 [21,] 0.076496616 0.099019778 [22,] 0.089770113 0.076496616 [23,] 0.153511599 0.089770113 [24,] 0.296956753 0.153511599 [25,] -0.004316119 0.296956753 [26,] 0.247529353 -0.004316119 [27,] 0.224689086 0.247529353 [28,] 0.235075874 0.224689086 [29,] 0.246000389 0.235075874 [30,] -0.384027640 0.246000389 [31,] -0.368555298 -0.384027640 [32,] -0.387859086 -0.368555298 [33,] -0.343255811 -0.387859086 [34,] -0.345798255 -0.343255811 [35,] -0.364981408 -0.345798255 [36,] 0.444724740 -0.364981408 [37,] 0.449173537 0.444724740 [38,] 0.521470561 0.449173537 [39,] 0.522774283 0.521470561 [40,] 0.531755545 0.522774283 [41,] 0.512422895 0.531755545 [42,] -0.251595168 0.512422895 [43,] -0.223715376 -0.251595168 [44,] -0.192989681 -0.223715376 [45,] -0.204805750 -0.192989681 [46,] -0.200830022 -0.204805750 [47,] -0.264344950 -0.200830022 [48,] -0.610814889 -0.264344950 [49,] -0.631507801 -0.610814889 [50,] -0.674251617 -0.631507801 [51,] -0.648544186 -0.674251617 [52,] -0.653789563 -0.648544186 [53,] -0.660888632 -0.653789563 [54,] 0.139359572 -0.660888632 [55,] 0.133287278 0.139359572 [56,] 0.076320389 0.133287278 [57,] 0.251675155 0.076320389 [58,] 0.207623782 0.251675155 [59,] 0.207996725 0.207623782 [60,] -0.581202316 0.207996725 [61,] -0.599228886 -0.581202316 [62,] -0.320650666 -0.599228886 [63,] -0.262508498 -0.320650666 [64,] -0.249691266 -0.262508498 [65,] -0.540373090 -0.249691266 [66,] 0.096324565 -0.540373090 [67,] 0.102623648 0.096324565 [68,] -0.017680839 0.102623648 [69,] -0.072859967 -0.017680839 [70,] 0.066776289 -0.072859967 [71,] -0.017116834 0.066776289 [72,] 0.248300216 -0.017116834 [73,] 0.254144546 0.248300216 [74,] 0.133809907 0.254144546 [75,] 0.142496173 0.133809907 [76,] 0.028292646 0.142496173 [77,] 0.153254459 0.028292646 [78,] 0.005878834 0.153254459 [79,] -0.012248361 0.005878834 [80,] -0.085440437 -0.012248361 [81,] -0.024254667 -0.085440437 [82,] 0.058157468 -0.024254667 [83,] 0.040578595 0.058157468 [84,] 0.061219108 0.040578595 [85,] 0.310111496 0.061219108 [86,] 0.282112133 0.310111496 [87,] 0.051046024 0.282112133 [88,] -0.080928638 0.051046024 [89,] 0.266149700 -0.080928638 [90,] -0.076506644 0.266149700 [91,] -0.050839489 -0.076506644 [92,] 0.184525940 -0.050839489 [93,] -0.082077188 0.184525940 [94,] 0.057437190 -0.082077188 [95,] 0.284901752 0.057437190 [96,] 0.275788431 0.284901752 [97,] 0.142759314 0.275788431 [98,] 0.031309665 0.142759314 [99,] -0.104832322 0.031309665 [100,] -0.234968569 -0.104832322 [101,] -0.173350904 -0.234968569 [102,] -0.284491342 -0.173350904 [103,] 0.251851868 -0.284491342 [104,] 0.387169313 0.251851868 [105,] 0.376479990 0.387169313 [106,] 0.424283662 0.376479990 [107,] 0.382195857 0.424283662 [108,] 0.387886956 0.382195857 [109,] 0.235137447 0.387886956 [110,] 0.306645247 0.235137447 [111,] 0.614329368 0.306645247 [112,] 0.538811184 0.614329368 [113,] 0.603919021 0.538811184 [114,] 0.341898266 0.603919021 [115,] 0.392760907 0.341898266 [116,] 0.370608552 0.392760907 [117,] 0.410538775 0.370608552 [118,] 0.499255075 0.410538775 [119,] 0.573450079 0.499255075 [120,] 0.323388402 0.573450079 [121,] 0.386348787 0.323388402 [122,] 0.001132509 0.386348787 [123,] 0.223891446 0.001132509 [124,] 0.174082709 0.223891446 [125,] 0.159571113 0.174082709 [126,] 0.113540996 0.159571113 [127,] 0.170332061 0.113540996 [128,] 0.298871619 0.170332061 [129,] 0.267213426 0.298871619 [130,] 0.223600139 0.267213426 [131,] 0.212447860 0.223600139 [132,] 0.204782726 0.212447860 [133,] 0.257631166 0.204782726 [134,] -0.051890363 0.257631166 [135,] 0.009472388 -0.051890363 [136,] -0.025654829 0.009472388 [137,] -0.078299178 -0.025654829 [138,] -0.094714201 -0.078299178 [139,] 0.114932098 -0.094714201 [140,] 0.278094908 0.114932098 [141,] 0.045057458 0.278094908 [142,] 0.384729075 0.045057458 [143,] 0.343233783 0.384729075 [144,] 0.510855171 0.343233783 [145,] 0.378941970 0.510855171 [146,] 0.009046120 0.378941970 [147,] 0.208625103 0.009046120 [148,] 0.127993006 0.208625103 [149,] 0.297921743 0.127993006 [150,] 0.162156612 0.297921743 [151,] -0.229084022 0.162156612 [152,] 0.035324612 -0.229084022 [153,] 0.152391623 0.035324612 [154,] 0.131327827 0.152391623 [155,] 0.111770757 0.131327827 [156,] 0.172198477 0.111770757 [157,] 0.082665746 0.172198477 [158,] 0.140489463 0.082665746 [159,] 0.152761324 0.140489463 [160,] -0.019242224 0.152761324 [161,] 0.088443290 -0.019242224 [162,] 0.026441398 0.088443290 [163,] 0.154238947 0.026441398 [164,] 0.087422456 0.154238947 [165,] -0.587987920 0.087422456 [166,] -0.194218578 -0.587987920 [167,] -0.185645187 -0.194218578 [168,] -0.704022700 -0.185645187 [169,] -0.277180285 -0.704022700 [170,] -0.202734174 -0.277180285 [171,] -0.773338936 -0.202734174 [172,] -0.812793583 -0.773338936 [173,] -0.820916116 -0.812793583 [174,] -0.821184985 -0.820916116 [175,] -0.791799251 -0.821184985 [176,] -0.806310385 -0.791799251 [177,] 0.387639765 -0.806310385 [178,] 0.354852476 0.387639765 [179,] 0.351394791 0.354852476 [180,] 0.306360952 0.351394791 [181,] 0.347831467 0.306360952 [182,] 0.329675683 0.347831467 [183,] -0.809446062 0.329675683 [184,] -0.806450365 -0.809446062 [185,] -0.796248183 -0.806450365 [186,] -0.713711545 -0.796248183 [187,] -0.680592952 -0.713711545 [188,] -0.809091495 -0.680592952 [189,] -0.735242973 -0.809091495 [190,] -0.862786464 -0.735242973 [191,] -0.686933740 -0.862786464 [192,] -0.504472211 -0.686933740 [193,] -0.615499545 -0.504472211 [194,] -0.620575266 -0.615499545 > z <- as.data.frame(dum1) > z lag(myerror, k = 1) myerror 1 -0.077764966 -0.047579233 2 -0.085956373 -0.077764966 3 -0.064765541 -0.085956373 4 -0.138069119 -0.064765541 5 -0.015103432 -0.138069119 6 0.234194556 -0.015103432 7 0.160684110 0.234194556 8 0.079988076 0.160684110 9 0.029154653 0.079988076 10 0.033427515 0.029154653 11 0.007928464 0.033427515 12 0.366961155 0.007928464 13 0.229263988 0.366961155 14 0.230854523 0.229263988 15 0.263199748 0.230854523 16 0.315777553 0.263199748 17 0.266846263 0.315777553 18 -0.027383778 0.266846263 19 0.300552590 -0.027383778 20 0.099019778 0.300552590 21 0.076496616 0.099019778 22 0.089770113 0.076496616 23 0.153511599 0.089770113 24 0.296956753 0.153511599 25 -0.004316119 0.296956753 26 0.247529353 -0.004316119 27 0.224689086 0.247529353 28 0.235075874 0.224689086 29 0.246000389 0.235075874 30 -0.384027640 0.246000389 31 -0.368555298 -0.384027640 32 -0.387859086 -0.368555298 33 -0.343255811 -0.387859086 34 -0.345798255 -0.343255811 35 -0.364981408 -0.345798255 36 0.444724740 -0.364981408 37 0.449173537 0.444724740 38 0.521470561 0.449173537 39 0.522774283 0.521470561 40 0.531755545 0.522774283 41 0.512422895 0.531755545 42 -0.251595168 0.512422895 43 -0.223715376 -0.251595168 44 -0.192989681 -0.223715376 45 -0.204805750 -0.192989681 46 -0.200830022 -0.204805750 47 -0.264344950 -0.200830022 48 -0.610814889 -0.264344950 49 -0.631507801 -0.610814889 50 -0.674251617 -0.631507801 51 -0.648544186 -0.674251617 52 -0.653789563 -0.648544186 53 -0.660888632 -0.653789563 54 0.139359572 -0.660888632 55 0.133287278 0.139359572 56 0.076320389 0.133287278 57 0.251675155 0.076320389 58 0.207623782 0.251675155 59 0.207996725 0.207623782 60 -0.581202316 0.207996725 61 -0.599228886 -0.581202316 62 -0.320650666 -0.599228886 63 -0.262508498 -0.320650666 64 -0.249691266 -0.262508498 65 -0.540373090 -0.249691266 66 0.096324565 -0.540373090 67 0.102623648 0.096324565 68 -0.017680839 0.102623648 69 -0.072859967 -0.017680839 70 0.066776289 -0.072859967 71 -0.017116834 0.066776289 72 0.248300216 -0.017116834 73 0.254144546 0.248300216 74 0.133809907 0.254144546 75 0.142496173 0.133809907 76 0.028292646 0.142496173 77 0.153254459 0.028292646 78 0.005878834 0.153254459 79 -0.012248361 0.005878834 80 -0.085440437 -0.012248361 81 -0.024254667 -0.085440437 82 0.058157468 -0.024254667 83 0.040578595 0.058157468 84 0.061219108 0.040578595 85 0.310111496 0.061219108 86 0.282112133 0.310111496 87 0.051046024 0.282112133 88 -0.080928638 0.051046024 89 0.266149700 -0.080928638 90 -0.076506644 0.266149700 91 -0.050839489 -0.076506644 92 0.184525940 -0.050839489 93 -0.082077188 0.184525940 94 0.057437190 -0.082077188 95 0.284901752 0.057437190 96 0.275788431 0.284901752 97 0.142759314 0.275788431 98 0.031309665 0.142759314 99 -0.104832322 0.031309665 100 -0.234968569 -0.104832322 101 -0.173350904 -0.234968569 102 -0.284491342 -0.173350904 103 0.251851868 -0.284491342 104 0.387169313 0.251851868 105 0.376479990 0.387169313 106 0.424283662 0.376479990 107 0.382195857 0.424283662 108 0.387886956 0.382195857 109 0.235137447 0.387886956 110 0.306645247 0.235137447 111 0.614329368 0.306645247 112 0.538811184 0.614329368 113 0.603919021 0.538811184 114 0.341898266 0.603919021 115 0.392760907 0.341898266 116 0.370608552 0.392760907 117 0.410538775 0.370608552 118 0.499255075 0.410538775 119 0.573450079 0.499255075 120 0.323388402 0.573450079 121 0.386348787 0.323388402 122 0.001132509 0.386348787 123 0.223891446 0.001132509 124 0.174082709 0.223891446 125 0.159571113 0.174082709 126 0.113540996 0.159571113 127 0.170332061 0.113540996 128 0.298871619 0.170332061 129 0.267213426 0.298871619 130 0.223600139 0.267213426 131 0.212447860 0.223600139 132 0.204782726 0.212447860 133 0.257631166 0.204782726 134 -0.051890363 0.257631166 135 0.009472388 -0.051890363 136 -0.025654829 0.009472388 137 -0.078299178 -0.025654829 138 -0.094714201 -0.078299178 139 0.114932098 -0.094714201 140 0.278094908 0.114932098 141 0.045057458 0.278094908 142 0.384729075 0.045057458 143 0.343233783 0.384729075 144 0.510855171 0.343233783 145 0.378941970 0.510855171 146 0.009046120 0.378941970 147 0.208625103 0.009046120 148 0.127993006 0.208625103 149 0.297921743 0.127993006 150 0.162156612 0.297921743 151 -0.229084022 0.162156612 152 0.035324612 -0.229084022 153 0.152391623 0.035324612 154 0.131327827 0.152391623 155 0.111770757 0.131327827 156 0.172198477 0.111770757 157 0.082665746 0.172198477 158 0.140489463 0.082665746 159 0.152761324 0.140489463 160 -0.019242224 0.152761324 161 0.088443290 -0.019242224 162 0.026441398 0.088443290 163 0.154238947 0.026441398 164 0.087422456 0.154238947 165 -0.587987920 0.087422456 166 -0.194218578 -0.587987920 167 -0.185645187 -0.194218578 168 -0.704022700 -0.185645187 169 -0.277180285 -0.704022700 170 -0.202734174 -0.277180285 171 -0.773338936 -0.202734174 172 -0.812793583 -0.773338936 173 -0.820916116 -0.812793583 174 -0.821184985 -0.820916116 175 -0.791799251 -0.821184985 176 -0.806310385 -0.791799251 177 0.387639765 -0.806310385 178 0.354852476 0.387639765 179 0.351394791 0.354852476 180 0.306360952 0.351394791 181 0.347831467 0.306360952 182 0.329675683 0.347831467 183 -0.809446062 0.329675683 184 -0.806450365 -0.809446062 185 -0.796248183 -0.806450365 186 -0.713711545 -0.796248183 187 -0.680592952 -0.713711545 188 -0.809091495 -0.680592952 189 -0.735242973 -0.809091495 190 -0.862786464 -0.735242973 191 -0.686933740 -0.862786464 192 -0.504472211 -0.686933740 193 -0.615499545 -0.504472211 194 -0.620575266 -0.615499545 > 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/7n2dx1386668995.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/8m7aw1386668995.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/96anz1386668995.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0)) > plot(mylm, las = 1, sub='Residual Diagnostics') > par(opar) > dev.off() null device 1 > if (n > n25) { + postscript(file="/var/wessaorg/rcomp/tmp/10i1m41386668995.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) + plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint') + grid() + dev.off() + } null device 1 > > #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab > load(file="/var/wessaorg/rcomp/createtable") > > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE) > a<-table.row.end(a) > myeq <- colnames(x)[1] > myeq <- paste(myeq, '[t] = ', sep='') > for (i in 1:k){ + if (mysum$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/11p0ey1386668995.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/12vvja1386668995.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/13x16e1386668995.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/141oft1386668995.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/15f76s1386668995.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/16tj8a1386668995.tab") + } > > try(system("convert tmp/138yh1386668995.ps tmp/138yh1386668995.png",intern=TRUE)) character(0) > try(system("convert tmp/2qf9k1386668995.ps tmp/2qf9k1386668995.png",intern=TRUE)) character(0) > try(system("convert tmp/3vmo21386668995.ps tmp/3vmo21386668995.png",intern=TRUE)) character(0) > try(system("convert tmp/4a4mc1386668995.ps tmp/4a4mc1386668995.png",intern=TRUE)) character(0) > try(system("convert tmp/5q0js1386668995.ps tmp/5q0js1386668995.png",intern=TRUE)) character(0) > try(system("convert tmp/61yez1386668995.ps tmp/61yez1386668995.png",intern=TRUE)) character(0) > try(system("convert tmp/7n2dx1386668995.ps tmp/7n2dx1386668995.png",intern=TRUE)) character(0) > try(system("convert tmp/8m7aw1386668995.ps tmp/8m7aw1386668995.png",intern=TRUE)) character(0) > try(system("convert tmp/96anz1386668995.ps tmp/96anz1386668995.png",intern=TRUE)) character(0) > try(system("convert tmp/10i1m41386668995.ps tmp/10i1m41386668995.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 15.060 2.604 17.645