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 + ,0.04374 + ,0.426 + ,0.02971 + ,1 + ,122.4 + ,148.65 + ,113.819 + ,0.00968 + ,0.00008 + ,0.00465 + ,0.00696 + ,0.06134 + ,0.626 + ,0.04368 + ,1 + ,116.682 + ,131.111 + ,111.555 + ,0.0105 + ,0.00009 + ,0.00544 + ,0.00781 + ,0.05233 + ,0.482 + ,0.0359 + ,1 + ,116.676 + ,137.871 + ,111.366 + ,0.00997 + ,0.00009 + ,0.00502 + ,0.00698 + ,0.05492 + ,0.517 + ,0.03772 + ,1 + ,116.014 + ,141.781 + ,110.655 + ,0.01284 + ,0.00011 + ,0.00655 + ,0.00908 + ,0.06425 + ,0.584 + ,0.04465 + ,1 + ,120.552 + ,131.162 + ,113.787 + ,0.00968 + ,0.00008 + ,0.00463 + ,0.0075 + ,0.04701 + ,0.456 + ,0.03243 + ,1 + ,120.267 + ,137.244 + ,114.82 + ,0.00333 + ,0.00003 + ,0.00155 + ,0.00202 + ,0.01608 + ,0.14 + ,0.01351 + ,1 + ,107.332 + ,113.84 + ,104.315 + ,0.0029 + ,0.00003 + ,0.00144 + ,0.00182 + ,0.01567 + ,0.134 + ,0.01256 + ,1 + ,95.73 + ,132.068 + ,91.754 + ,0.00551 + ,0.00006 + ,0.00293 + 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,0.142 + ,0.01309 + ,1 + ,148.143 + ,155.982 + ,135.041 + ,0.00392 + ,0.00003 + ,0.00204 + ,0.00231 + ,0.0145 + ,0.131 + ,0.01263 + ,1 + ,150.44 + ,163.441 + ,144.736 + ,0.00396 + ,0.00003 + ,0.00206 + ,0.00233 + ,0.02551 + ,0.237 + ,0.02148 + ,1 + ,148.462 + ,161.078 + ,141.998 + ,0.00397 + ,0.00003 + ,0.00202 + ,0.00235 + ,0.01831 + ,0.163 + ,0.01559 + ,1 + ,149.818 + ,163.417 + ,144.786 + ,0.00336 + ,0.00002 + ,0.00174 + ,0.00198 + ,0.02145 + ,0.198 + ,0.01666 + ,0 + ,117.226 + ,123.925 + ,106.656 + ,0.00417 + ,0.00004 + ,0.00186 + ,0.0027 + ,0.01909 + ,0.171 + ,0.01949 + ,0 + ,116.848 + ,217.552 + ,99.503 + ,0.00531 + ,0.00005 + ,0.0026 + ,0.00346 + ,0.01795 + ,0.163 + ,0.01756 + ,0 + ,116.286 + ,177.291 + ,96.983 + ,0.00314 + ,0.00003 + ,0.00134 + ,0.00192 + ,0.01564 + ,0.136 + ,0.01691 + ,0 + ,116.556 + ,592.03 + ,86.228 + ,0.00496 + ,0.00004 + ,0.00254 + ,0.00263 + ,0.0166 + ,0.154 + ,0.01491 + ,0 + ,116.342 + ,581.289 + ,94.246 + ,0.00267 + ,0.00002 + ,0.00115 + ,0.00148 + ,0.013 + ,0.117 + ,0.01144 + ,0 + ,114.563 + ,119.167 + ,86.647 + ,0.00327 + ,0.00003 + ,0.00146 + ,0.00184 + ,0.01185 + ,0.106 + ,0.01095 + ,0 + ,201.774 + ,262.707 + ,78.228 + ,0.00694 + ,0.00003 + ,0.00412 + ,0.00396 + ,0.02574 + ,0.255 + ,0.01758 + ,0 + ,174.188 + ,230.978 + ,94.261 + ,0.00459 + ,0.00003 + ,0.00263 + ,0.00259 + ,0.04087 + ,0.405 + ,0.02745 + ,0 + ,209.516 + ,253.017 + ,89.488 + ,0.00564 + ,0.00003 + ,0.00331 + ,0.00292 + ,0.02751 + ,0.263 + ,0.01879 + ,0 + ,174.688 + ,240.005 + ,74.287 + ,0.0136 + ,0.00008 + ,0.00624 + ,0.00564 + ,0.02308 + ,0.256 + ,0.01667 + ,0 + ,198.764 + ,396.961 + ,74.904 + ,0.0074 + ,0.00004 + ,0.0037 + ,0.0039 + ,0.02296 + ,0.241 + ,0.01588 + ,0 + ,214.289 + ,260.277 + ,77.973 + ,0.00567 + ,0.00003 + ,0.00295 + ,0.00317 + ,0.01884 + ,0.19 + ,0.01373) + ,dim=c(11 + ,195) + ,dimnames=list(c('status' + ,'MDVP:Fo(Hz)' + ,'MDVP:Fhi(Hz)' + ,'MDVP:Flo(Hz)' + ,'MDVP:Jitter(%)' + ,'MDVP:Jitter(Abs)' + ,'MDVP:RAP' + ,'MDVP:PPQ' + ,'MDVP:Shimmer' + ,'MDVP:Shimmer(dB)' + ,'MDVP:APQ') + ,1:195)) > y <- array(NA,dim=c(11,195),dimnames=list(c('status','MDVP:Fo(Hz)','MDVP:Fhi(Hz)','MDVP:Flo(Hz)','MDVP:Jitter(%)','MDVP:Jitter(Abs)','MDVP:RAP','MDVP:PPQ','MDVP:Shimmer','MDVP:Shimmer(dB)','MDVP:APQ'),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 MDVP:Shimmer MDVP:Shimmer(dB) MDVP:APQ 1 7.0e-05 0.00370 0.00554 0.04374 0.426 0.02971 2 8.0e-05 0.00465 0.00696 0.06134 0.626 0.04368 3 9.0e-05 0.00544 0.00781 0.05233 0.482 0.03590 4 9.0e-05 0.00502 0.00698 0.05492 0.517 0.03772 5 1.1e-04 0.00655 0.00908 0.06425 0.584 0.04465 6 8.0e-05 0.00463 0.00750 0.04701 0.456 0.03243 7 3.0e-05 0.00155 0.00202 0.01608 0.140 0.01351 8 3.0e-05 0.00144 0.00182 0.01567 0.134 0.01256 9 6.0e-05 0.00293 0.00332 0.02093 0.191 0.01717 10 6.0e-05 0.00268 0.00332 0.02838 0.255 0.02444 11 6.0e-05 0.00254 0.00330 0.02143 0.197 0.01892 12 6.0e-05 0.00281 0.00336 0.02752 0.249 0.02214 13 2.0e-05 0.00118 0.00153 0.01259 0.112 0.01140 14 3.0e-05 0.00165 0.00208 0.01642 0.154 0.01797 15 2.0e-05 0.00121 0.00149 0.01828 0.158 0.01246 16 3.0e-05 0.00157 0.00203 0.01503 0.126 0.01359 17 4.0e-05 0.00211 0.00292 0.02047 0.192 0.02074 18 4.0e-05 0.00284 0.00387 0.03327 0.348 0.03430 19 5.0e-05 0.00364 0.00432 0.05517 0.542 0.05767 20 5.0e-05 0.00372 0.00399 0.03995 0.348 0.04310 21 5.0e-05 0.00428 0.00450 0.03810 0.328 0.04055 22 3.0e-05 0.00232 0.00267 0.04137 0.370 0.04525 23 3.0e-05 0.00220 0.00247 0.04351 0.377 0.04246 24 3.0e-05 0.00221 0.00258 0.04192 0.364 0.03772 25 5.0e-05 0.00380 0.00390 0.01659 0.164 0.01497 26 6.0e-05 0.00316 0.00375 0.03767 0.381 0.03780 27 3.0e-05 0.00250 0.00234 0.01966 0.186 0.01872 28 3.0e-05 0.00250 0.00275 0.01919 0.198 0.01826 29 2.0e-05 0.00159 0.00176 0.01718 0.161 0.01661 30 3.0e-05 0.00280 0.00253 0.01791 0.168 0.01799 31 1.0e-05 0.00166 0.00168 0.01098 0.097 0.00802 32 1.0e-05 0.00134 0.00138 0.01015 0.089 0.00762 33 1.0e-05 0.00113 0.00135 0.01263 0.111 0.00951 34 9.0e-06 0.00093 0.00107 0.00954 0.085 0.00719 35 9.0e-06 0.00094 0.00106 0.00958 0.085 0.00726 36 1.0e-05 0.00105 0.00115 0.01194 0.107 0.00957 37 2.0e-05 0.00233 0.00241 0.02126 0.189 0.01612 38 2.0e-05 0.00205 0.00218 0.01851 0.168 0.01491 39 2.0e-05 0.00153 0.00166 0.01444 0.131 0.01190 40 2.0e-05 0.00168 0.00182 0.01663 0.151 0.01366 41 2.0e-05 0.00165 0.00175 0.01495 0.135 0.01233 42 1.0e-05 0.00134 0.00147 0.01463 0.132 0.01234 43 1.0e-05 0.00169 0.00182 0.01752 0.164 0.01133 44 1.0e-05 0.00157 0.00173 0.01760 0.154 0.01251 45 9.0e-06 0.00109 0.00137 0.01419 0.126 0.01033 46 9.0e-06 0.00117 0.00139 0.01494 0.134 0.01014 47 1.0e-05 0.00127 0.00148 0.01608 0.141 0.01149 48 7.0e-06 0.00092 0.00113 0.01152 0.103 0.00860 49 4.0e-05 0.00169 0.00203 0.01613 0.143 0.01433 50 3.0e-05 0.00124 0.00155 0.01681 0.154 0.01400 51 3.0e-05 0.00141 0.00167 0.02184 0.197 0.01685 52 4.0e-05 0.00131 0.00169 0.02033 0.185 0.01614 53 3.0e-05 0.00137 0.00166 0.02297 0.210 0.01677 54 4.0e-05 0.00165 0.00183 0.02498 0.228 0.01947 55 7.0e-05 0.00349 0.00486 0.02719 0.255 0.02067 56 8.0e-05 0.00398 0.00539 0.03209 0.307 0.02454 57 7.0e-05 0.00352 0.00514 0.03715 0.334 0.02802 58 6.0e-05 0.00299 0.00469 0.02293 0.221 0.01948 59 7.0e-05 0.00334 0.00493 0.02645 0.265 0.02137 60 8.0e-05 0.00373 0.00520 0.03225 0.350 0.02519 61 1.0e-05 0.00147 0.00152 0.01861 0.170 0.01382 62 1.0e-05 0.00154 0.00151 0.01906 0.165 0.01340 63 1.0e-05 0.00152 0.00144 0.01643 0.145 0.01200 64 1.0e-05 0.00175 0.00155 0.01644 0.145 0.01179 65 9.0e-06 0.00114 0.00113 0.01457 0.129 0.01016 66 1.0e-05 0.00136 0.00140 0.01745 0.154 0.01234 67 6.0e-05 0.00430 0.00440 0.03198 0.313 0.02428 68 7.0e-05 0.00507 0.00463 0.03111 0.308 0.02603 69 8.0e-05 0.00647 0.00467 0.05384 0.478 0.03392 70 5.0e-05 0.00467 0.00354 0.05428 0.497 0.03635 71 6.0e-05 0.00469 0.00419 0.03485 0.365 0.02949 72 7.0e-05 0.00534 0.00478 0.04978 0.483 0.03736 73 3.0e-05 0.00180 0.00220 0.01706 0.152 0.01345 74 5.0e-05 0.00268 0.00329 0.02448 0.226 0.01956 75 4.0e-05 0.00260 0.00283 0.02442 0.216 0.01831 76 5.0e-05 0.00277 0.00289 0.02215 0.206 0.01715 77 4.0e-05 0.00270 0.00289 0.03999 0.350 0.02704 78 4.0e-05 0.00226 0.00280 0.02199 0.197 0.01636 79 6.0e-05 0.00331 0.00332 0.03202 0.263 0.02455 80 1.0e-04 0.00622 0.00576 0.03121 0.361 0.02139 81 7.0e-05 0.00389 0.00415 0.04024 0.364 0.02876 82 7.0e-05 0.00428 0.00371 0.03156 0.296 0.02190 83 6.0e-05 0.00351 0.00348 0.02427 0.216 0.01751 84 4.0e-05 0.00247 0.00258 0.02223 0.202 0.01552 85 4.0e-05 0.00418 0.00420 0.04795 0.435 0.03510 86 2.0e-05 0.00220 0.00244 0.03852 0.331 0.02877 87 2.0e-05 0.00163 0.00194 0.03759 0.327 0.02784 88 3.0e-05 0.00287 0.00312 0.06511 0.580 0.04683 89 3.0e-05 0.00237 0.00254 0.06727 0.650 0.04802 90 4.0e-05 0.00391 0.00419 0.04313 0.442 0.03455 91 4.0e-05 0.00387 0.00453 0.06640 0.634 0.05114 92 3.0e-05 0.00224 0.00227 0.07959 0.772 0.05690 93 3.0e-05 0.00250 0.00256 0.04190 0.383 0.03051 94 3.0e-05 0.00191 0.00226 0.05925 0.637 0.04398 95 2.0e-05 0.00196 0.00196 0.03716 0.307 0.02764 96 2.0e-05 0.00201 0.00197 0.03272 0.283 0.02571 97 2.0e-05 0.00178 0.00184 0.03381 0.307 0.02809 98 1.0e-04 0.00743 0.00623 0.03886 0.342 0.03088 99 1.1e-04 0.00826 0.00655 0.04689 0.422 0.03908 100 1.5e-04 0.01159 0.00990 0.06734 0.659 0.05783 101 2.6e-04 0.02144 0.01522 0.09178 0.891 0.06196 102 1.2e-04 0.00905 0.00909 0.06170 0.584 0.05174 103 2.2e-04 0.01854 0.01628 0.09419 0.930 0.06023 104 2.0e-05 0.00105 0.00136 0.01131 0.107 0.01009 105 1.0e-05 0.00076 0.00100 0.01030 0.094 0.00871 106 2.0e-05 0.00116 0.00134 0.01346 0.126 0.01059 107 1.0e-05 0.00068 0.00092 0.01064 0.097 0.00928 108 2.0e-05 0.00115 0.00122 0.01450 0.137 0.01267 109 1.0e-05 0.00075 0.00096 0.01024 0.093 0.00993 110 4.0e-05 0.00450 0.00389 0.03044 0.275 0.02084 111 3.0e-05 0.00371 0.00337 0.02286 0.207 0.01852 112 3.0e-05 0.00368 0.00339 0.01761 0.155 0.01307 113 4.0e-05 0.00502 0.00485 0.02378 0.210 0.01767 114 3.0e-05 0.00321 0.00280 0.01680 0.149 0.01301 115 2.0e-05 0.00302 0.00246 0.02105 0.209 0.01604 116 6.0e-05 0.00404 0.00385 0.01843 0.235 0.01271 117 3.0e-05 0.00214 0.00207 0.01458 0.148 0.01312 118 3.0e-05 0.00244 0.00261 0.01725 0.175 0.01652 119 3.0e-05 0.00157 0.00194 0.01279 0.129 0.01151 120 2.0e-05 0.00127 0.00128 0.01299 0.124 0.01075 121 5.0e-05 0.00241 0.00314 0.02008 0.221 0.01734 122 3.0e-05 0.00209 0.00221 0.01169 0.117 0.01104 123 5.0e-05 0.00406 0.00398 0.04479 0.441 0.03220 124 5.0e-05 0.00506 0.00449 0.02503 0.231 0.01931 125 4.0e-05 0.00403 0.00395 0.02343 0.224 0.01720 126 5.0e-05 0.00414 0.00422 0.02362 0.233 0.01944 127 4.0e-05 0.00294 0.00327 0.02791 0.246 0.02259 128 4.0e-05 0.00368 0.00351 0.02857 0.257 0.02301 129 4.0e-05 0.00214 0.00192 0.01033 0.098 0.00811 130 2.0e-05 0.00116 0.00135 0.01022 0.090 0.00903 131 4.0e-05 0.00269 0.00238 0.01412 0.125 0.01194 132 3.0e-05 0.00224 0.00205 0.01516 0.138 0.01310 133 3.0e-05 0.00169 0.00170 0.01201 0.106 0.00915 134 3.0e-05 0.00168 0.00171 0.01043 0.099 0.00903 135 6.0e-05 0.00291 0.00319 0.04932 0.441 0.03651 136 4.0e-05 0.00244 0.00315 0.04128 0.379 0.03316 137 4.0e-05 0.00219 0.00283 0.04879 0.431 0.04370 138 4.0e-05 0.00257 0.00312 0.05279 0.476 0.04134 139 4.0e-05 0.00238 0.00290 0.05643 0.517 0.04451 140 3.0e-05 0.00181 0.00232 0.03026 0.267 0.02770 141 3.0e-05 0.00232 0.00269 0.03273 0.281 0.02824 142 4.0e-05 0.00428 0.00428 0.06725 0.571 0.04464 143 2.0e-05 0.00182 0.00215 0.03527 0.297 0.02530 144 2.0e-05 0.00189 0.00211 0.01997 0.180 0.01506 145 1.0e-05 0.00100 0.00133 0.02662 0.228 0.02006 146 2.0e-05 0.00169 0.00188 0.02536 0.225 0.01909 147 9.0e-05 0.00863 0.00946 0.08143 0.821 0.08808 148 8.0e-05 0.00849 0.00819 0.06050 0.618 0.06359 149 9.0e-05 0.00996 0.01027 0.07118 0.722 0.06824 150 8.0e-05 0.00919 0.00963 0.07170 0.833 0.06460 151 1.0e-04 0.01075 0.01154 0.05830 0.784 0.06259 152 1.6e-04 0.01800 0.01958 0.11908 1.302 0.13778 153 1.4e-04 0.01568 0.01699 0.08684 1.018 0.08318 154 6.0e-05 0.00388 0.00332 0.02534 0.241 0.02056 155 6.0e-05 0.00393 0.00300 0.02682 0.236 0.02018 156 5.0e-05 0.00356 0.00300 0.03087 0.276 0.02402 157 6.0e-05 0.00415 0.00339 0.02293 0.223 0.01771 158 1.5e-04 0.01117 0.00718 0.04912 0.438 0.02916 159 8.0e-05 0.00593 0.00454 0.02852 0.266 0.02157 160 5.0e-05 0.00321 0.00318 0.03235 0.339 0.03105 161 5.0e-05 0.00299 0.00316 0.04009 0.406 0.04114 162 5.0e-05 0.00352 0.00329 0.03273 0.325 0.02931 163 6.0e-05 0.00366 0.00340 0.03658 0.369 0.03091 164 5.0e-05 0.00291 0.00284 0.01756 0.155 0.01363 165 9.0e-05 0.00493 0.00461 0.02814 0.272 0.02073 166 1.0e-05 0.00154 0.00153 0.02448 0.217 0.01621 167 1.0e-05 0.00173 0.00159 0.01242 0.116 0.00882 168 1.0e-05 0.00205 0.00186 0.02030 0.197 0.01367 169 4.0e-05 0.00490 0.00448 0.02177 0.189 0.01439 170 2.0e-05 0.00316 0.00283 0.02018 0.212 0.01344 171 2.0e-05 0.00279 0.00237 0.01897 0.181 0.01255 172 3.0e-05 0.00166 0.00190 0.01358 0.129 0.01140 173 3.0e-05 0.00170 0.00200 0.01484 0.133 0.01285 174 3.0e-05 0.00171 0.00203 0.01472 0.133 0.01148 175 3.0e-05 0.00176 0.00218 0.01657 0.145 0.01318 176 3.0e-05 0.00160 0.00199 0.01503 0.137 0.01133 177 3.0e-05 0.00169 0.00213 0.01725 0.155 0.01331 178 2.0e-05 0.00135 0.00162 0.01469 0.132 0.01230 179 2.0e-05 0.00152 0.00186 0.01574 0.142 0.01309 180 3.0e-05 0.00204 0.00231 0.01450 0.131 0.01263 181 3.0e-05 0.00206 0.00233 0.02551 0.237 0.02148 182 3.0e-05 0.00202 0.00235 0.01831 0.163 0.01559 183 2.0e-05 0.00174 0.00198 0.02145 0.198 0.01666 184 4.0e-05 0.00186 0.00270 0.01909 0.171 0.01949 185 5.0e-05 0.00260 0.00346 0.01795 0.163 0.01756 186 3.0e-05 0.00134 0.00192 0.01564 0.136 0.01691 187 4.0e-05 0.00254 0.00263 0.01660 0.154 0.01491 188 2.0e-05 0.00115 0.00148 0.01300 0.117 0.01144 189 3.0e-05 0.00146 0.00184 0.01185 0.106 0.01095 190 3.0e-05 0.00412 0.00396 0.02574 0.255 0.01758 191 3.0e-05 0.00263 0.00259 0.04087 0.405 0.02745 192 3.0e-05 0.00331 0.00292 0.02751 0.263 0.01879 193 8.0e-05 0.00624 0.00564 0.02308 0.256 0.01667 194 4.0e-05 0.00370 0.00390 0.02296 0.241 0.01588 195 3.0e-05 0.00295 0.00317 0.01884 0.190 0.01373 > 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)` 1.294768 -0.002381 -0.000185 -0.002440 `MDVP:Jitter(%)` `MDVP:Jitter(Abs)` `MDVP:RAP` `MDVP:PPQ` -94.728998 -85.046138 116.984696 40.779554 `MDVP:Shimmer` `MDVP:Shimmer(dB)` `MDVP:APQ` 4.459670 -0.527263 9.492059 > (mysum <- summary(mylm)) Call: lm(formula = df) Residuals: Min 1Q Median 3Q Max -0.84206 -0.17933 0.07661 0.25092 0.61793 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 1.295e+00 2.215e-01 5.844 2.27e-08 *** `MDVP:Fo(Hz)` -2.381e-03 1.406e-03 -1.694 0.09200 . `MDVP:Fhi(Hz)` -1.850e-04 3.425e-04 -0.540 0.58980 `MDVP:Flo(Hz)` -2.440e-03 8.337e-04 -2.927 0.00386 ** `MDVP:Jitter(%)` -9.473e+01 6.627e+01 -1.429 0.15458 `MDVP:Jitter(Abs)` -8.505e+01 4.517e+03 -0.019 0.98500 `MDVP:RAP` 1.170e+02 7.498e+01 1.560 0.12043 `MDVP:PPQ` 4.078e+01 5.267e+01 0.774 0.43978 `MDVP:Shimmer` 4.460e+00 1.136e+01 0.393 0.69497 `MDVP:Shimmer(dB)` -5.273e-01 1.198e+00 -0.440 0.66049 `MDVP:APQ` 9.492e+00 6.868e+00 1.382 0.16861 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 0.3703 on 184 degrees of freedom Multiple R-squared: 0.3027, Adjusted R-squared: 0.2648 F-statistic: 7.988 on 10 and 184 DF, p-value: 1.21e-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,] 4.735597e-49 9.471194e-49 1.000000e+00 [2,] 8.185602e-65 1.637120e-64 1.000000e+00 [3,] 0.000000e+00 0.000000e+00 1.000000e+00 [4,] 1.875720e-99 3.751441e-99 1.000000e+00 [5,] 2.522648e-106 5.045296e-106 1.000000e+00 [6,] 1.707631e-121 3.415262e-121 1.000000e+00 [7,] 1.161392e-144 2.322784e-144 1.000000e+00 [8,] 1.640450e-173 3.280900e-173 1.000000e+00 [9,] 2.032099e-165 4.064198e-165 1.000000e+00 [10,] 1.392806e-183 2.785613e-183 1.000000e+00 [11,] 8.881485e-196 1.776297e-195 1.000000e+00 [12,] 2.090589e-219 4.181177e-219 1.000000e+00 [13,] 2.235231e-256 4.470461e-256 1.000000e+00 [14,] 1.458472e-249 2.916943e-249 1.000000e+00 [15,] 5.398638e-257 1.079728e-256 1.000000e+00 [16,] 1.359553e-280 2.719106e-280 1.000000e+00 [17,] 2.987250e-286 5.974500e-286 1.000000e+00 [18,] 2.757952e-08 5.515905e-08 1.000000e+00 [19,] 1.675411e-08 3.350822e-08 1.000000e+00 [20,] 5.746888e-09 1.149378e-08 1.000000e+00 [21,] 1.729087e-09 3.458174e-09 1.000000e+00 [22,] 5.123662e-10 1.024732e-09 1.000000e+00 [23,] 1.476651e-10 2.953303e-10 1.000000e+00 [24,] 1.717540e-08 3.435081e-08 1.000000e+00 [25,] 3.270280e-07 6.540559e-07 9.999997e-01 [26,] 5.189316e-05 1.037863e-04 9.999481e-01 [27,] 5.439352e-04 1.087870e-03 9.994561e-01 [28,] 2.566751e-03 5.133502e-03 9.974332e-01 [29,] 3.490532e-03 6.981064e-03 9.965095e-01 [30,] 2.386868e-03 4.773735e-03 9.976131e-01 [31,] 1.503049e-03 3.006099e-03 9.984970e-01 [32,] 1.045024e-03 2.090048e-03 9.989550e-01 [33,] 6.532266e-04 1.306453e-03 9.993468e-01 [34,] 4.121505e-04 8.243010e-04 9.995878e-01 [35,] 2.613754e-04 5.227508e-04 9.997386e-01 [36,] 1.510176e-03 3.020352e-03 9.984898e-01 [37,] 2.254793e-03 4.509585e-03 9.977452e-01 [38,] 3.198231e-03 6.396462e-03 9.968018e-01 [39,] 2.893446e-03 5.786891e-03 9.971066e-01 [40,] 3.261921e-03 6.523842e-03 9.967381e-01 [41,] 3.762300e-03 7.524600e-03 9.962377e-01 [42,] 3.257746e-03 6.515491e-03 9.967423e-01 [43,] 3.625447e-03 7.250894e-03 9.963746e-01 [44,] 2.775863e-03 5.551727e-03 9.972241e-01 [45,] 2.481645e-03 4.963291e-03 9.975184e-01 [46,] 2.216013e-03 4.432026e-03 9.977840e-01 [47,] 1.853353e-03 3.706705e-03 9.981466e-01 [48,] 4.314906e-03 8.629812e-03 9.956851e-01 [49,] 6.134057e-03 1.226811e-02 9.938659e-01 [50,] 5.167719e-03 1.033544e-02 9.948323e-01 [51,] 4.220096e-03 8.440192e-03 9.957799e-01 [52,] 3.412796e-03 6.825592e-03 9.965872e-01 [53,] 3.549775e-03 7.099550e-03 9.964502e-01 [54,] 2.813974e-03 5.627949e-03 9.971860e-01 [55,] 1.976172e-03 3.952343e-03 9.980238e-01 [56,] 2.486305e-03 4.972610e-03 9.975137e-01 [57,] 1.762660e-03 3.525320e-03 9.982373e-01 [58,] 1.253944e-03 2.507888e-03 9.987461e-01 [59,] 8.419767e-04 1.683953e-03 9.991580e-01 [60,] 6.274025e-04 1.254805e-03 9.993726e-01 [61,] 1.029393e-03 2.058787e-03 9.989706e-01 [62,] 7.148303e-04 1.429661e-03 9.992852e-01 [63,] 4.955619e-04 9.911238e-04 9.995044e-01 [64,] 3.428242e-04 6.856484e-04 9.996572e-01 [65,] 2.374044e-04 4.748088e-04 9.997626e-01 [66,] 1.570010e-04 3.140020e-04 9.998430e-01 [67,] 1.428930e-04 2.857859e-04 9.998571e-01 [68,] 9.552306e-05 1.910461e-04 9.999045e-01 [69,] 6.526466e-05 1.305293e-04 9.999347e-01 [70,] 4.660398e-05 9.320796e-05 9.999534e-01 [71,] 3.363946e-05 6.727893e-05 9.999664e-01 [72,] 2.116151e-05 4.232301e-05 9.999788e-01 [73,] 2.579975e-05 5.159950e-05 9.999742e-01 [74,] 4.342930e-05 8.685859e-05 9.999566e-01 [75,] 3.392274e-05 6.784548e-05 9.999661e-01 [76,] 2.823220e-05 5.646439e-05 9.999718e-01 [77,] 1.885613e-05 3.771226e-05 9.999811e-01 [78,] 1.305343e-05 2.610685e-05 9.999869e-01 [79,] 1.083292e-05 2.166585e-05 9.999892e-01 [80,] 7.210949e-06 1.442190e-05 9.999928e-01 [81,] 4.460644e-06 8.921288e-06 9.999955e-01 [82,] 2.700907e-06 5.401814e-06 9.999973e-01 [83,] 1.869532e-06 3.739064e-06 9.999981e-01 [84,] 1.295212e-06 2.590424e-06 9.999987e-01 [85,] 7.730405e-07 1.546081e-06 9.999992e-01 [86,] 4.549990e-07 9.099980e-07 9.999995e-01 [87,] 4.163953e-07 8.327905e-07 9.999996e-01 [88,] 3.357894e-07 6.715788e-07 9.999997e-01 [89,] 4.648212e-07 9.296424e-07 9.999995e-01 [90,] 9.152294e-07 1.830459e-06 9.999991e-01 [91,] 7.364721e-07 1.472944e-06 9.999993e-01 [92,] 6.408968e-07 1.281794e-06 9.999994e-01 [93,] 6.499355e-07 1.299871e-06 9.999994e-01 [94,] 6.122305e-07 1.224461e-06 9.999994e-01 [95,] 5.959952e-07 1.191990e-06 9.999994e-01 [96,] 4.701560e-07 9.403120e-07 9.999995e-01 [97,] 3.649475e-07 7.298950e-07 9.999996e-01 [98,] 2.860659e-07 5.721318e-07 9.999997e-01 [99,] 6.176278e-07 1.235256e-06 9.999994e-01 [100,] 9.182165e-07 1.836433e-06 9.999991e-01 [101,] 2.918175e-06 5.836350e-06 9.999971e-01 [102,] 2.367038e-06 4.734075e-06 9.999976e-01 [103,] 3.314612e-06 6.629223e-06 9.999967e-01 [104,] 2.956128e-06 5.912257e-06 9.999970e-01 [105,] 2.945259e-06 5.890519e-06 9.999971e-01 [106,] 7.806247e-06 1.561249e-05 9.999922e-01 [107,] 5.050261e-05 1.010052e-04 9.999495e-01 [108,] 8.205901e-05 1.641180e-04 9.999179e-01 [109,] 1.174210e-04 2.348420e-04 9.998826e-01 [110,] 8.049709e-05 1.609942e-04 9.999195e-01 [111,] 7.476310e-05 1.495262e-04 9.999252e-01 [112,] 7.860117e-05 1.572023e-04 9.999214e-01 [113,] 8.938965e-05 1.787793e-04 9.999106e-01 [114,] 8.920478e-05 1.784096e-04 9.999108e-01 [115,] 8.940896e-05 1.788179e-04 9.999106e-01 [116,] 8.616397e-05 1.723279e-04 9.999138e-01 [117,] 8.108473e-05 1.621695e-04 9.999189e-01 [118,] 7.684012e-05 1.536802e-04 9.999232e-01 [119,] 7.308888e-05 1.461778e-04 9.999269e-01 [120,] 8.619025e-05 1.723805e-04 9.999138e-01 [121,] 1.095404e-04 2.190807e-04 9.998905e-01 [122,] 8.060559e-05 1.612112e-04 9.999194e-01 [123,] 5.365430e-05 1.073086e-04 9.999463e-01 [124,] 3.515507e-05 7.031015e-05 9.999648e-01 [125,] 2.334220e-05 4.668439e-05 9.999767e-01 [126,] 1.971968e-05 3.943936e-05 9.999803e-01 [127,] 1.389377e-05 2.778755e-05 9.999861e-01 [128,] 1.734527e-05 3.469054e-05 9.999827e-01 [129,] 1.167890e-05 2.335780e-05 9.999883e-01 [130,] 1.647673e-05 3.295346e-05 9.999835e-01 [131,] 6.345003e-05 1.269001e-04 9.999365e-01 [132,] 1.726108e-04 3.452216e-04 9.998274e-01 [133,] 1.342250e-03 2.684500e-03 9.986578e-01 [134,] 1.022095e-03 2.044191e-03 9.989779e-01 [135,] 6.819280e-04 1.363856e-03 9.993181e-01 [136,] 5.315424e-04 1.063085e-03 9.994685e-01 [137,] 3.973074e-04 7.946148e-04 9.996027e-01 [138,] 2.681048e-04 5.362096e-04 9.997319e-01 [139,] 7.823537e-04 1.564707e-03 9.992176e-01 [140,] 5.003840e-04 1.000768e-03 9.994996e-01 [141,] 3.947428e-04 7.894855e-04 9.996053e-01 [142,] 2.964300e-04 5.928600e-04 9.997036e-01 [143,] 2.285443e-04 4.570887e-04 9.997715e-01 [144,] 2.263404e-04 4.526808e-04 9.997737e-01 [145,] 4.433537e-04 8.867074e-04 9.995566e-01 [146,] 2.839830e-04 5.679660e-04 9.997160e-01 [147,] 1.954089e-04 3.908179e-04 9.998046e-01 [148,] 1.199163e-04 2.398325e-04 9.998801e-01 [149,] 6.794475e-05 1.358895e-04 9.999321e-01 [150,] 3.986115e-05 7.972231e-05 9.999601e-01 [151,] 6.804058e-05 1.360812e-04 9.999320e-01 [152,] 3.833379e-03 7.666758e-03 9.961666e-01 [153,] 4.357822e-03 8.715644e-03 9.956422e-01 [154,] 4.461329e-03 8.922658e-03 9.955387e-01 [155,] 1.159212e-02 2.318423e-02 9.884079e-01 [156,] 1.670534e-02 3.341067e-02 9.832947e-01 [157,] 1.248862e-02 2.497725e-02 9.875114e-01 [158,] 9.970805e-01 5.839030e-03 2.919515e-03 [159,] 9.992438e-01 1.512368e-03 7.561838e-04 [160,] 9.988294e-01 2.341248e-03 1.170624e-03 [161,] 9.978189e-01 4.362159e-03 2.181080e-03 [162,] 9.978372e-01 4.325696e-03 2.162848e-03 [163,] 9.986612e-01 2.677630e-03 1.338815e-03 [164,] 9.989561e-01 2.087834e-03 1.043917e-03 [165,] 9.999054e-01 1.892602e-04 9.463012e-05 [166,] 9.994165e-01 1.166943e-03 5.834717e-04 [167,] 9.987920e-01 2.416037e-03 1.208018e-03 [168,] 9.931867e-01 1.362663e-02 6.813314e-03 > postscript(file="/var/fisher/rcomp/tmp/1gx7e1386774349.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index') > points(x[,1]-mysum$resid) > grid() > dev.off() null device 1 > postscript(file="/var/fisher/rcomp/tmp/2ppbu1386774349.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index') > grid() > dev.off() null device 1 > postscript(file="/var/fisher/rcomp/tmp/3exsc1386774349.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') > grid() > dev.off() null device 1 > postscript(file="/var/fisher/rcomp/tmp/40lza1386774349.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') > dev.off() null device 1 > postscript(file="/var/fisher/rcomp/tmp/5t4w61386774349.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 0.040434571 0.039765405 0.006933691 0.030111174 -0.035589164 0.093428814 7 8 9 10 11 12 0.225311742 0.152524067 0.074777456 0.012431443 0.021939299 0.005346817 13 14 15 16 17 18 0.354669045 0.188088733 0.245812998 0.242933403 0.280996607 0.237556600 19 20 21 22 23 24 -0.132918021 0.191259051 -0.003387485 -0.047727439 -0.001387602 0.101912431 25 26 27 28 29 30 0.293342198 -0.061601405 0.213625757 0.206213659 0.200733089 0.203725133 31 32 33 34 35 36 -0.380663260 -0.367488364 -0.385056999 -0.340268116 -0.343838775 -0.367574046 37 38 39 40 41 42 0.446419901 0.445174044 0.515407772 0.517904118 0.525457320 0.503028165 43 44 45 46 47 48 -0.222596416 -0.209889957 -0.180149967 -0.185112295 -0.187889758 -0.255852972 49 50 51 52 53 54 -0.610833159 -0.625050113 -0.662467802 -0.634640104 -0.629863355 -0.647616769 55 56 57 58 59 60 0.177600499 0.180175980 0.116308817 0.283667894 0.251388772 0.265611993 61 62 63 64 65 66 -0.576479212 -0.593978381 -0.315838063 -0.256291068 -0.240151458 -0.532705798 67 68 69 70 71 72 0.120994171 0.098186323 0.029958840 -0.037374688 0.064763690 0.007190620 73 74 75 76 77 78 0.250653582 0.227757718 0.137567753 0.145931369 0.055432958 0.165336803 79 80 81 82 83 84 -0.007220431 0.052681114 -0.061085475 -0.007169479 0.065387791 0.052177399 85 86 87 88 89 90 0.081302625 0.311663924 0.287694738 0.076610874 -0.026391891 0.287939187 91 92 93 94 95 96 -0.048003937 0.013349223 0.201988380 -0.012807495 0.045303006 0.270393749 97 98 99 100 101 102 0.255547274 0.124765639 -0.010746702 -0.138507582 -0.178763982 -0.184853397 103 104 105 106 107 108 -0.156532135 0.243989612 0.380874003 0.378015968 0.415029897 0.371538552 109 110 111 112 113 114 0.368308884 0.254799675 0.294183129 0.617930197 0.548143085 0.599517746 115 116 117 118 119 120 0.343968292 0.434026911 0.346555833 0.382889533 0.491030672 0.583732668 121 122 123 124 125 126 0.323348380 0.379392472 0.036280263 0.222018112 0.186740853 0.159496461 127 128 129 130 131 132 0.100820002 0.157225397 0.298920551 0.255275611 0.207188702 0.193657831 133 134 135 136 137 138 0.202413883 0.248672582 -0.034546643 0.007832907 -0.083128876 -0.081363569 139 140 141 142 143 144 -0.097823249 0.074128640 0.251183541 0.094324068 0.397591768 0.349294833 145 146 147 148 149 150 0.514047472 0.384816941 -0.154187302 0.096767659 0.066936636 0.311394139 151 152 153 154 155 156 0.166529464 -0.482011888 0.061419617 0.142389955 0.121142607 0.101041089 157 158 159 160 161 162 0.171423010 0.122554652 0.132004826 0.121117565 -0.091060365 0.059826139 163 164 165 166 167 168 0.016778940 0.148285214 0.108041296 -0.565821607 -0.182506232 -0.157517561 169 170 171 172 173 174 -0.688424310 -0.240394620 -0.179894009 -0.777759279 -0.824412604 -0.820157220 175 176 177 178 179 180 -0.826033966 -0.785813113 -0.803676079 0.381060756 0.349113804 0.341886673 181 182 183 184 185 186 0.295810668 0.337794434 0.331762108 -0.841727415 -0.832294470 -0.842056067 187 188 189 190 191 192 -0.762813588 -0.725386889 -0.822936681 -0.705571997 -0.812510092 -0.662595878 193 194 195 -0.444220679 -0.578853524 -0.595020096 > postscript(file="/var/fisher/rcomp/tmp/6n0pa1386774349.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 0.040434571 NA 1 0.039765405 0.040434571 2 0.006933691 0.039765405 3 0.030111174 0.006933691 4 -0.035589164 0.030111174 5 0.093428814 -0.035589164 6 0.225311742 0.093428814 7 0.152524067 0.225311742 8 0.074777456 0.152524067 9 0.012431443 0.074777456 10 0.021939299 0.012431443 11 0.005346817 0.021939299 12 0.354669045 0.005346817 13 0.188088733 0.354669045 14 0.245812998 0.188088733 15 0.242933403 0.245812998 16 0.280996607 0.242933403 17 0.237556600 0.280996607 18 -0.132918021 0.237556600 19 0.191259051 -0.132918021 20 -0.003387485 0.191259051 21 -0.047727439 -0.003387485 22 -0.001387602 -0.047727439 23 0.101912431 -0.001387602 24 0.293342198 0.101912431 25 -0.061601405 0.293342198 26 0.213625757 -0.061601405 27 0.206213659 0.213625757 28 0.200733089 0.206213659 29 0.203725133 0.200733089 30 -0.380663260 0.203725133 31 -0.367488364 -0.380663260 32 -0.385056999 -0.367488364 33 -0.340268116 -0.385056999 34 -0.343838775 -0.340268116 35 -0.367574046 -0.343838775 36 0.446419901 -0.367574046 37 0.445174044 0.446419901 38 0.515407772 0.445174044 39 0.517904118 0.515407772 40 0.525457320 0.517904118 41 0.503028165 0.525457320 42 -0.222596416 0.503028165 43 -0.209889957 -0.222596416 44 -0.180149967 -0.209889957 45 -0.185112295 -0.180149967 46 -0.187889758 -0.185112295 47 -0.255852972 -0.187889758 48 -0.610833159 -0.255852972 49 -0.625050113 -0.610833159 50 -0.662467802 -0.625050113 51 -0.634640104 -0.662467802 52 -0.629863355 -0.634640104 53 -0.647616769 -0.629863355 54 0.177600499 -0.647616769 55 0.180175980 0.177600499 56 0.116308817 0.180175980 57 0.283667894 0.116308817 58 0.251388772 0.283667894 59 0.265611993 0.251388772 60 -0.576479212 0.265611993 61 -0.593978381 -0.576479212 62 -0.315838063 -0.593978381 63 -0.256291068 -0.315838063 64 -0.240151458 -0.256291068 65 -0.532705798 -0.240151458 66 0.120994171 -0.532705798 67 0.098186323 0.120994171 68 0.029958840 0.098186323 69 -0.037374688 0.029958840 70 0.064763690 -0.037374688 71 0.007190620 0.064763690 72 0.250653582 0.007190620 73 0.227757718 0.250653582 74 0.137567753 0.227757718 75 0.145931369 0.137567753 76 0.055432958 0.145931369 77 0.165336803 0.055432958 78 -0.007220431 0.165336803 79 0.052681114 -0.007220431 80 -0.061085475 0.052681114 81 -0.007169479 -0.061085475 82 0.065387791 -0.007169479 83 0.052177399 0.065387791 84 0.081302625 0.052177399 85 0.311663924 0.081302625 86 0.287694738 0.311663924 87 0.076610874 0.287694738 88 -0.026391891 0.076610874 89 0.287939187 -0.026391891 90 -0.048003937 0.287939187 91 0.013349223 -0.048003937 92 0.201988380 0.013349223 93 -0.012807495 0.201988380 94 0.045303006 -0.012807495 95 0.270393749 0.045303006 96 0.255547274 0.270393749 97 0.124765639 0.255547274 98 -0.010746702 0.124765639 99 -0.138507582 -0.010746702 100 -0.178763982 -0.138507582 101 -0.184853397 -0.178763982 102 -0.156532135 -0.184853397 103 0.243989612 -0.156532135 104 0.380874003 0.243989612 105 0.378015968 0.380874003 106 0.415029897 0.378015968 107 0.371538552 0.415029897 108 0.368308884 0.371538552 109 0.254799675 0.368308884 110 0.294183129 0.254799675 111 0.617930197 0.294183129 112 0.548143085 0.617930197 113 0.599517746 0.548143085 114 0.343968292 0.599517746 115 0.434026911 0.343968292 116 0.346555833 0.434026911 117 0.382889533 0.346555833 118 0.491030672 0.382889533 119 0.583732668 0.491030672 120 0.323348380 0.583732668 121 0.379392472 0.323348380 122 0.036280263 0.379392472 123 0.222018112 0.036280263 124 0.186740853 0.222018112 125 0.159496461 0.186740853 126 0.100820002 0.159496461 127 0.157225397 0.100820002 128 0.298920551 0.157225397 129 0.255275611 0.298920551 130 0.207188702 0.255275611 131 0.193657831 0.207188702 132 0.202413883 0.193657831 133 0.248672582 0.202413883 134 -0.034546643 0.248672582 135 0.007832907 -0.034546643 136 -0.083128876 0.007832907 137 -0.081363569 -0.083128876 138 -0.097823249 -0.081363569 139 0.074128640 -0.097823249 140 0.251183541 0.074128640 141 0.094324068 0.251183541 142 0.397591768 0.094324068 143 0.349294833 0.397591768 144 0.514047472 0.349294833 145 0.384816941 0.514047472 146 -0.154187302 0.384816941 147 0.096767659 -0.154187302 148 0.066936636 0.096767659 149 0.311394139 0.066936636 150 0.166529464 0.311394139 151 -0.482011888 0.166529464 152 0.061419617 -0.482011888 153 0.142389955 0.061419617 154 0.121142607 0.142389955 155 0.101041089 0.121142607 156 0.171423010 0.101041089 157 0.122554652 0.171423010 158 0.132004826 0.122554652 159 0.121117565 0.132004826 160 -0.091060365 0.121117565 161 0.059826139 -0.091060365 162 0.016778940 0.059826139 163 0.148285214 0.016778940 164 0.108041296 0.148285214 165 -0.565821607 0.108041296 166 -0.182506232 -0.565821607 167 -0.157517561 -0.182506232 168 -0.688424310 -0.157517561 169 -0.240394620 -0.688424310 170 -0.179894009 -0.240394620 171 -0.777759279 -0.179894009 172 -0.824412604 -0.777759279 173 -0.820157220 -0.824412604 174 -0.826033966 -0.820157220 175 -0.785813113 -0.826033966 176 -0.803676079 -0.785813113 177 0.381060756 -0.803676079 178 0.349113804 0.381060756 179 0.341886673 0.349113804 180 0.295810668 0.341886673 181 0.337794434 0.295810668 182 0.331762108 0.337794434 183 -0.841727415 0.331762108 184 -0.832294470 -0.841727415 185 -0.842056067 -0.832294470 186 -0.762813588 -0.842056067 187 -0.725386889 -0.762813588 188 -0.822936681 -0.725386889 189 -0.705571997 -0.822936681 190 -0.812510092 -0.705571997 191 -0.662595878 -0.812510092 192 -0.444220679 -0.662595878 193 -0.578853524 -0.444220679 194 -0.595020096 -0.578853524 195 NA -0.595020096 > dum1 <- dum[2:length(myerror),] > dum1 lag(myerror, k = 1) myerror [1,] 0.039765405 0.040434571 [2,] 0.006933691 0.039765405 [3,] 0.030111174 0.006933691 [4,] -0.035589164 0.030111174 [5,] 0.093428814 -0.035589164 [6,] 0.225311742 0.093428814 [7,] 0.152524067 0.225311742 [8,] 0.074777456 0.152524067 [9,] 0.012431443 0.074777456 [10,] 0.021939299 0.012431443 [11,] 0.005346817 0.021939299 [12,] 0.354669045 0.005346817 [13,] 0.188088733 0.354669045 [14,] 0.245812998 0.188088733 [15,] 0.242933403 0.245812998 [16,] 0.280996607 0.242933403 [17,] 0.237556600 0.280996607 [18,] -0.132918021 0.237556600 [19,] 0.191259051 -0.132918021 [20,] -0.003387485 0.191259051 [21,] -0.047727439 -0.003387485 [22,] -0.001387602 -0.047727439 [23,] 0.101912431 -0.001387602 [24,] 0.293342198 0.101912431 [25,] -0.061601405 0.293342198 [26,] 0.213625757 -0.061601405 [27,] 0.206213659 0.213625757 [28,] 0.200733089 0.206213659 [29,] 0.203725133 0.200733089 [30,] -0.380663260 0.203725133 [31,] -0.367488364 -0.380663260 [32,] -0.385056999 -0.367488364 [33,] -0.340268116 -0.385056999 [34,] -0.343838775 -0.340268116 [35,] -0.367574046 -0.343838775 [36,] 0.446419901 -0.367574046 [37,] 0.445174044 0.446419901 [38,] 0.515407772 0.445174044 [39,] 0.517904118 0.515407772 [40,] 0.525457320 0.517904118 [41,] 0.503028165 0.525457320 [42,] -0.222596416 0.503028165 [43,] -0.209889957 -0.222596416 [44,] -0.180149967 -0.209889957 [45,] -0.185112295 -0.180149967 [46,] -0.187889758 -0.185112295 [47,] -0.255852972 -0.187889758 [48,] -0.610833159 -0.255852972 [49,] -0.625050113 -0.610833159 [50,] -0.662467802 -0.625050113 [51,] -0.634640104 -0.662467802 [52,] -0.629863355 -0.634640104 [53,] -0.647616769 -0.629863355 [54,] 0.177600499 -0.647616769 [55,] 0.180175980 0.177600499 [56,] 0.116308817 0.180175980 [57,] 0.283667894 0.116308817 [58,] 0.251388772 0.283667894 [59,] 0.265611993 0.251388772 [60,] -0.576479212 0.265611993 [61,] -0.593978381 -0.576479212 [62,] -0.315838063 -0.593978381 [63,] -0.256291068 -0.315838063 [64,] -0.240151458 -0.256291068 [65,] -0.532705798 -0.240151458 [66,] 0.120994171 -0.532705798 [67,] 0.098186323 0.120994171 [68,] 0.029958840 0.098186323 [69,] -0.037374688 0.029958840 [70,] 0.064763690 -0.037374688 [71,] 0.007190620 0.064763690 [72,] 0.250653582 0.007190620 [73,] 0.227757718 0.250653582 [74,] 0.137567753 0.227757718 [75,] 0.145931369 0.137567753 [76,] 0.055432958 0.145931369 [77,] 0.165336803 0.055432958 [78,] -0.007220431 0.165336803 [79,] 0.052681114 -0.007220431 [80,] -0.061085475 0.052681114 [81,] -0.007169479 -0.061085475 [82,] 0.065387791 -0.007169479 [83,] 0.052177399 0.065387791 [84,] 0.081302625 0.052177399 [85,] 0.311663924 0.081302625 [86,] 0.287694738 0.311663924 [87,] 0.076610874 0.287694738 [88,] -0.026391891 0.076610874 [89,] 0.287939187 -0.026391891 [90,] -0.048003937 0.287939187 [91,] 0.013349223 -0.048003937 [92,] 0.201988380 0.013349223 [93,] -0.012807495 0.201988380 [94,] 0.045303006 -0.012807495 [95,] 0.270393749 0.045303006 [96,] 0.255547274 0.270393749 [97,] 0.124765639 0.255547274 [98,] -0.010746702 0.124765639 [99,] -0.138507582 -0.010746702 [100,] -0.178763982 -0.138507582 [101,] -0.184853397 -0.178763982 [102,] -0.156532135 -0.184853397 [103,] 0.243989612 -0.156532135 [104,] 0.380874003 0.243989612 [105,] 0.378015968 0.380874003 [106,] 0.415029897 0.378015968 [107,] 0.371538552 0.415029897 [108,] 0.368308884 0.371538552 [109,] 0.254799675 0.368308884 [110,] 0.294183129 0.254799675 [111,] 0.617930197 0.294183129 [112,] 0.548143085 0.617930197 [113,] 0.599517746 0.548143085 [114,] 0.343968292 0.599517746 [115,] 0.434026911 0.343968292 [116,] 0.346555833 0.434026911 [117,] 0.382889533 0.346555833 [118,] 0.491030672 0.382889533 [119,] 0.583732668 0.491030672 [120,] 0.323348380 0.583732668 [121,] 0.379392472 0.323348380 [122,] 0.036280263 0.379392472 [123,] 0.222018112 0.036280263 [124,] 0.186740853 0.222018112 [125,] 0.159496461 0.186740853 [126,] 0.100820002 0.159496461 [127,] 0.157225397 0.100820002 [128,] 0.298920551 0.157225397 [129,] 0.255275611 0.298920551 [130,] 0.207188702 0.255275611 [131,] 0.193657831 0.207188702 [132,] 0.202413883 0.193657831 [133,] 0.248672582 0.202413883 [134,] -0.034546643 0.248672582 [135,] 0.007832907 -0.034546643 [136,] -0.083128876 0.007832907 [137,] -0.081363569 -0.083128876 [138,] -0.097823249 -0.081363569 [139,] 0.074128640 -0.097823249 [140,] 0.251183541 0.074128640 [141,] 0.094324068 0.251183541 [142,] 0.397591768 0.094324068 [143,] 0.349294833 0.397591768 [144,] 0.514047472 0.349294833 [145,] 0.384816941 0.514047472 [146,] -0.154187302 0.384816941 [147,] 0.096767659 -0.154187302 [148,] 0.066936636 0.096767659 [149,] 0.311394139 0.066936636 [150,] 0.166529464 0.311394139 [151,] -0.482011888 0.166529464 [152,] 0.061419617 -0.482011888 [153,] 0.142389955 0.061419617 [154,] 0.121142607 0.142389955 [155,] 0.101041089 0.121142607 [156,] 0.171423010 0.101041089 [157,] 0.122554652 0.171423010 [158,] 0.132004826 0.122554652 [159,] 0.121117565 0.132004826 [160,] -0.091060365 0.121117565 [161,] 0.059826139 -0.091060365 [162,] 0.016778940 0.059826139 [163,] 0.148285214 0.016778940 [164,] 0.108041296 0.148285214 [165,] -0.565821607 0.108041296 [166,] -0.182506232 -0.565821607 [167,] -0.157517561 -0.182506232 [168,] -0.688424310 -0.157517561 [169,] -0.240394620 -0.688424310 [170,] -0.179894009 -0.240394620 [171,] -0.777759279 -0.179894009 [172,] -0.824412604 -0.777759279 [173,] -0.820157220 -0.824412604 [174,] -0.826033966 -0.820157220 [175,] -0.785813113 -0.826033966 [176,] -0.803676079 -0.785813113 [177,] 0.381060756 -0.803676079 [178,] 0.349113804 0.381060756 [179,] 0.341886673 0.349113804 [180,] 0.295810668 0.341886673 [181,] 0.337794434 0.295810668 [182,] 0.331762108 0.337794434 [183,] -0.841727415 0.331762108 [184,] -0.832294470 -0.841727415 [185,] -0.842056067 -0.832294470 [186,] -0.762813588 -0.842056067 [187,] -0.725386889 -0.762813588 [188,] -0.822936681 -0.725386889 [189,] -0.705571997 -0.822936681 [190,] -0.812510092 -0.705571997 [191,] -0.662595878 -0.812510092 [192,] -0.444220679 -0.662595878 [193,] -0.578853524 -0.444220679 [194,] -0.595020096 -0.578853524 > z <- as.data.frame(dum1) > z lag(myerror, k = 1) myerror 1 0.039765405 0.040434571 2 0.006933691 0.039765405 3 0.030111174 0.006933691 4 -0.035589164 0.030111174 5 0.093428814 -0.035589164 6 0.225311742 0.093428814 7 0.152524067 0.225311742 8 0.074777456 0.152524067 9 0.012431443 0.074777456 10 0.021939299 0.012431443 11 0.005346817 0.021939299 12 0.354669045 0.005346817 13 0.188088733 0.354669045 14 0.245812998 0.188088733 15 0.242933403 0.245812998 16 0.280996607 0.242933403 17 0.237556600 0.280996607 18 -0.132918021 0.237556600 19 0.191259051 -0.132918021 20 -0.003387485 0.191259051 21 -0.047727439 -0.003387485 22 -0.001387602 -0.047727439 23 0.101912431 -0.001387602 24 0.293342198 0.101912431 25 -0.061601405 0.293342198 26 0.213625757 -0.061601405 27 0.206213659 0.213625757 28 0.200733089 0.206213659 29 0.203725133 0.200733089 30 -0.380663260 0.203725133 31 -0.367488364 -0.380663260 32 -0.385056999 -0.367488364 33 -0.340268116 -0.385056999 34 -0.343838775 -0.340268116 35 -0.367574046 -0.343838775 36 0.446419901 -0.367574046 37 0.445174044 0.446419901 38 0.515407772 0.445174044 39 0.517904118 0.515407772 40 0.525457320 0.517904118 41 0.503028165 0.525457320 42 -0.222596416 0.503028165 43 -0.209889957 -0.222596416 44 -0.180149967 -0.209889957 45 -0.185112295 -0.180149967 46 -0.187889758 -0.185112295 47 -0.255852972 -0.187889758 48 -0.610833159 -0.255852972 49 -0.625050113 -0.610833159 50 -0.662467802 -0.625050113 51 -0.634640104 -0.662467802 52 -0.629863355 -0.634640104 53 -0.647616769 -0.629863355 54 0.177600499 -0.647616769 55 0.180175980 0.177600499 56 0.116308817 0.180175980 57 0.283667894 0.116308817 58 0.251388772 0.283667894 59 0.265611993 0.251388772 60 -0.576479212 0.265611993 61 -0.593978381 -0.576479212 62 -0.315838063 -0.593978381 63 -0.256291068 -0.315838063 64 -0.240151458 -0.256291068 65 -0.532705798 -0.240151458 66 0.120994171 -0.532705798 67 0.098186323 0.120994171 68 0.029958840 0.098186323 69 -0.037374688 0.029958840 70 0.064763690 -0.037374688 71 0.007190620 0.064763690 72 0.250653582 0.007190620 73 0.227757718 0.250653582 74 0.137567753 0.227757718 75 0.145931369 0.137567753 76 0.055432958 0.145931369 77 0.165336803 0.055432958 78 -0.007220431 0.165336803 79 0.052681114 -0.007220431 80 -0.061085475 0.052681114 81 -0.007169479 -0.061085475 82 0.065387791 -0.007169479 83 0.052177399 0.065387791 84 0.081302625 0.052177399 85 0.311663924 0.081302625 86 0.287694738 0.311663924 87 0.076610874 0.287694738 88 -0.026391891 0.076610874 89 0.287939187 -0.026391891 90 -0.048003937 0.287939187 91 0.013349223 -0.048003937 92 0.201988380 0.013349223 93 -0.012807495 0.201988380 94 0.045303006 -0.012807495 95 0.270393749 0.045303006 96 0.255547274 0.270393749 97 0.124765639 0.255547274 98 -0.010746702 0.124765639 99 -0.138507582 -0.010746702 100 -0.178763982 -0.138507582 101 -0.184853397 -0.178763982 102 -0.156532135 -0.184853397 103 0.243989612 -0.156532135 104 0.380874003 0.243989612 105 0.378015968 0.380874003 106 0.415029897 0.378015968 107 0.371538552 0.415029897 108 0.368308884 0.371538552 109 0.254799675 0.368308884 110 0.294183129 0.254799675 111 0.617930197 0.294183129 112 0.548143085 0.617930197 113 0.599517746 0.548143085 114 0.343968292 0.599517746 115 0.434026911 0.343968292 116 0.346555833 0.434026911 117 0.382889533 0.346555833 118 0.491030672 0.382889533 119 0.583732668 0.491030672 120 0.323348380 0.583732668 121 0.379392472 0.323348380 122 0.036280263 0.379392472 123 0.222018112 0.036280263 124 0.186740853 0.222018112 125 0.159496461 0.186740853 126 0.100820002 0.159496461 127 0.157225397 0.100820002 128 0.298920551 0.157225397 129 0.255275611 0.298920551 130 0.207188702 0.255275611 131 0.193657831 0.207188702 132 0.202413883 0.193657831 133 0.248672582 0.202413883 134 -0.034546643 0.248672582 135 0.007832907 -0.034546643 136 -0.083128876 0.007832907 137 -0.081363569 -0.083128876 138 -0.097823249 -0.081363569 139 0.074128640 -0.097823249 140 0.251183541 0.074128640 141 0.094324068 0.251183541 142 0.397591768 0.094324068 143 0.349294833 0.397591768 144 0.514047472 0.349294833 145 0.384816941 0.514047472 146 -0.154187302 0.384816941 147 0.096767659 -0.154187302 148 0.066936636 0.096767659 149 0.311394139 0.066936636 150 0.166529464 0.311394139 151 -0.482011888 0.166529464 152 0.061419617 -0.482011888 153 0.142389955 0.061419617 154 0.121142607 0.142389955 155 0.101041089 0.121142607 156 0.171423010 0.101041089 157 0.122554652 0.171423010 158 0.132004826 0.122554652 159 0.121117565 0.132004826 160 -0.091060365 0.121117565 161 0.059826139 -0.091060365 162 0.016778940 0.059826139 163 0.148285214 0.016778940 164 0.108041296 0.148285214 165 -0.565821607 0.108041296 166 -0.182506232 -0.565821607 167 -0.157517561 -0.182506232 168 -0.688424310 -0.157517561 169 -0.240394620 -0.688424310 170 -0.179894009 -0.240394620 171 -0.777759279 -0.179894009 172 -0.824412604 -0.777759279 173 -0.820157220 -0.824412604 174 -0.826033966 -0.820157220 175 -0.785813113 -0.826033966 176 -0.803676079 -0.785813113 177 0.381060756 -0.803676079 178 0.349113804 0.381060756 179 0.341886673 0.349113804 180 0.295810668 0.341886673 181 0.337794434 0.295810668 182 0.331762108 0.337794434 183 -0.841727415 0.331762108 184 -0.832294470 -0.841727415 185 -0.842056067 -0.832294470 186 -0.762813588 -0.842056067 187 -0.725386889 -0.762813588 188 -0.822936681 -0.725386889 189 -0.705571997 -0.822936681 190 -0.812510092 -0.705571997 191 -0.662595878 -0.812510092 192 -0.444220679 -0.662595878 193 -0.578853524 -0.444220679 194 -0.595020096 -0.578853524 > plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals') > lines(lowess(z)) > abline(lm(z)) > grid() > dev.off() null device 1 > postscript(file="/var/fisher/rcomp/tmp/7550t1386774349.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/fisher/rcomp/tmp/86p8g1386774349.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/fisher/rcomp/tmp/9rx021386774349.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0)) > plot(mylm, las = 1, sub='Residual Diagnostics') > par(opar) > dev.off() null device 1 > if (n > n25) { + postscript(file="/var/fisher/rcomp/tmp/10i5c01386774349.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) + plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint') + grid() + dev.off() + } null device 1 > > #Note: the /var/fisher/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab > load(file="/var/fisher/rcomp/createtable") > > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE) > a<-table.row.end(a) > myeq <- colnames(x)[1] > myeq <- paste(myeq, '[t] = ', sep='') > for (i in 1:k){ + if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '') + myeq <- paste(myeq, signif(mysum$coefficients[i,1],6), sep=' ') + if (rownames(mysum$coefficients)[i] != '(Intercept)') { + myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='') + if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='') + } + } > myeq <- paste(myeq, ' + e[t]') > a<-table.row.start(a) > a<-table.element(a, myeq) > a<-table.row.end(a) > a<-table.end(a) > table.save(a,file="/var/fisher/rcomp/tmp/11s67i1386774349.tab") > a<-table.start() > a<-table.row.start(a) > a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a,'Variable',header=TRUE) > a<-table.element(a,'Parameter',header=TRUE) > a<-table.element(a,'S.D.',header=TRUE) > a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE) > a<-table.element(a,'2-tail p-value',header=TRUE) > a<-table.element(a,'1-tail p-value',header=TRUE) > a<-table.row.end(a) > for (i in 1:k){ + a<-table.row.start(a) + a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE) + a<-table.element(a,signif(mysum$coefficients[i,1],6)) + a<-table.element(a, signif(mysum$coefficients[i,2],6)) + a<-table.element(a, signif(mysum$coefficients[i,3],4)) + a<-table.element(a, signif(mysum$coefficients[i,4],6)) + a<-table.element(a, signif(mysum$coefficients[i,4]/2,6)) + a<-table.row.end(a) + } > a<-table.end(a) > table.save(a,file="/var/fisher/rcomp/tmp/12ww6j1386774349.tab") > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Multiple R',1,TRUE) > a<-table.element(a, signif(sqrt(mysum$r.squared),6)) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'R-squared',1,TRUE) > a<-table.element(a, signif(mysum$r.squared,6)) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Adjusted R-squared',1,TRUE) > a<-table.element(a, signif(mysum$adj.r.squared,6)) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'F-TEST (value)',1,TRUE) > a<-table.element(a, signif(mysum$fstatistic[1],6)) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE) > a<-table.element(a, signif(mysum$fstatistic[2],6)) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE) > a<-table.element(a, signif(mysum$fstatistic[3],6)) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'p-value',1,TRUE) > a<-table.element(a, signif(1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]),6)) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Residual Standard Deviation',1,TRUE) > a<-table.element(a, signif(mysum$sigma,6)) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Sum Squared Residuals',1,TRUE) > a<-table.element(a, signif(sum(myerror*myerror),6)) > a<-table.row.end(a) > a<-table.end(a) > table.save(a,file="/var/fisher/rcomp/tmp/130bdc1386774349.tab") > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Time or Index', 1, TRUE) > a<-table.element(a, 'Actuals', 1, TRUE) > a<-table.element(a, 'Interpolation
Forecast', 1, TRUE) > a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE) > a<-table.row.end(a) > for (i in 1:n) { + a<-table.row.start(a) + a<-table.element(a,i, 1, TRUE) + a<-table.element(a,signif(x[i],6)) + a<-table.element(a,signif(x[i]-mysum$resid[i],6)) + a<-table.element(a,signif(mysum$resid[i],6)) + a<-table.row.end(a) + } > a<-table.end(a) > table.save(a,file="/var/fisher/rcomp/tmp/14v0x71386774349.tab") > if (n > n25) { + a<-table.start() + a<-table.row.start(a) + a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'p-values',header=TRUE) + a<-table.element(a,'Alternative Hypothesis',3,header=TRUE) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'breakpoint index',header=TRUE) + a<-table.element(a,'greater',header=TRUE) + a<-table.element(a,'2-sided',header=TRUE) + a<-table.element(a,'less',header=TRUE) + a<-table.row.end(a) + for (mypoint in kp3:nmkm3) { + a<-table.row.start(a) + a<-table.element(a,mypoint,header=TRUE) + a<-table.element(a,signif(gqarr[mypoint-kp3+1,1],6)) + a<-table.element(a,signif(gqarr[mypoint-kp3+1,2],6)) + a<-table.element(a,signif(gqarr[mypoint-kp3+1,3],6)) + a<-table.row.end(a) + } + a<-table.end(a) + table.save(a,file="/var/fisher/rcomp/tmp/15jt2z1386774349.tab") + a<-table.start() + a<-table.row.start(a) + a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'Description',header=TRUE) + a<-table.element(a,'# significant tests',header=TRUE) + a<-table.element(a,'% significant tests',header=TRUE) + a<-table.element(a,'OK/NOK',header=TRUE) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'1% type I error level',header=TRUE) + a<-table.element(a,signif(numsignificant1,6)) + a<-table.element(a,signif(numsignificant1/numgqtests,6)) + if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK' + a<-table.element(a,dum) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'5% type I error level',header=TRUE) + a<-table.element(a,signif(numsignificant5,6)) + a<-table.element(a,signif(numsignificant5/numgqtests,6)) + if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK' + a<-table.element(a,dum) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'10% type I error level',header=TRUE) + a<-table.element(a,signif(numsignificant10,6)) + a<-table.element(a,signif(numsignificant10/numgqtests,6)) + if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK' + a<-table.element(a,dum) + a<-table.row.end(a) + a<-table.end(a) + table.save(a,file="/var/fisher/rcomp/tmp/169diy1386774349.tab") + } > > try(system("convert tmp/1gx7e1386774349.ps tmp/1gx7e1386774349.png",intern=TRUE)) character(0) > try(system("convert tmp/2ppbu1386774349.ps tmp/2ppbu1386774349.png",intern=TRUE)) character(0) > try(system("convert tmp/3exsc1386774349.ps tmp/3exsc1386774349.png",intern=TRUE)) character(0) > try(system("convert tmp/40lza1386774349.ps tmp/40lza1386774349.png",intern=TRUE)) character(0) > try(system("convert tmp/5t4w61386774349.ps tmp/5t4w61386774349.png",intern=TRUE)) character(0) > try(system("convert tmp/6n0pa1386774349.ps tmp/6n0pa1386774349.png",intern=TRUE)) character(0) > try(system("convert tmp/7550t1386774349.ps tmp/7550t1386774349.png",intern=TRUE)) character(0) > try(system("convert tmp/86p8g1386774349.ps tmp/86p8g1386774349.png",intern=TRUE)) character(0) > try(system("convert tmp/9rx021386774349.ps tmp/9rx021386774349.png",intern=TRUE)) character(0) > try(system("convert tmp/10i5c01386774349.ps tmp/10i5c01386774349.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 22.739 4.059 26.772