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(119.992 + ,157.302 + ,74.997 + ,0.00784 + ,0.00007 + ,0.0037 + ,0.00554 + ,0.01109 + ,0.04374 + ,0.426 + ,0.02182 + ,0.0313 + ,0.02971 + ,0.06545 + ,1 + ,122.4 + ,148.65 + ,113.819 + ,0.00968 + ,0.00008 + ,0.00465 + ,0.00696 + ,0.01394 + ,0.06134 + ,0.626 + ,0.03134 + ,0.04518 + ,0.04368 + ,0.09403 + ,1 + ,116.682 + ,131.111 + ,111.555 + ,0.0105 + ,0.00009 + ,0.00544 + ,0.00781 + ,0.01633 + ,0.05233 + ,0.482 + ,0.02757 + ,0.03858 + ,0.0359 + ,0.0827 + ,1 + ,116.676 + ,137.871 + ,111.366 + ,0.00997 + ,0.00009 + ,0.00502 + ,0.00698 + ,0.01505 + ,0.05492 + ,0.517 + ,0.02924 + ,0.04005 + ,0.03772 + ,0.08771 + ,1 + ,116.014 + ,141.781 + ,110.655 + ,0.01284 + ,0.00011 + ,0.00655 + ,0.00908 + ,0.01966 + ,0.06425 + ,0.584 + ,0.0349 + ,0.04825 + ,0.04465 + ,0.1047 + ,1 + ,120.552 + ,131.162 + ,113.787 + ,0.00968 + ,0.00008 + ,0.00463 + ,0.0075 + ,0.01388 + ,0.04701 + ,0.456 + ,0.02328 + ,0.03526 + ,0.03243 + ,0.06985 + ,1 + ,120.267 + ,137.244 + ,114.82 + 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+ ,0.00748 + ,0.00905 + ,0.01148 + ,0.02245 + ,0 + ,117.004 + ,144.466 + ,99.923 + ,0.00353 + ,0.00003 + ,0.00176 + ,0.00218 + ,0.00528 + ,0.01657 + ,0.145 + ,0.00881 + ,0.01062 + ,0.01318 + ,0.02643 + ,0 + ,115.38 + ,123.109 + ,108.634 + ,0.00332 + ,0.00003 + ,0.0016 + ,0.00199 + ,0.0048 + ,0.01503 + ,0.137 + ,0.00812 + ,0.00933 + ,0.01133 + ,0.02436 + ,0 + ,116.388 + ,129.038 + ,108.97 + ,0.00346 + ,0.00003 + ,0.00169 + ,0.00213 + ,0.00507 + ,0.01725 + ,0.155 + ,0.00874 + ,0.01021 + ,0.01331 + ,0.02623 + ,0 + ,151.737 + ,190.204 + ,129.859 + ,0.00314 + ,0.00002 + ,0.00135 + ,0.00162 + ,0.00406 + ,0.01469 + ,0.132 + ,0.00728 + ,0.00886 + ,0.0123 + ,0.02184 + ,1 + ,148.79 + ,158.359 + ,138.99 + ,0.00309 + ,0.00002 + ,0.00152 + ,0.00186 + ,0.00456 + ,0.01574 + ,0.142 + ,0.00839 + ,0.00956 + ,0.01309 + ,0.02518 + ,1 + ,148.143 + ,155.982 + ,135.041 + ,0.00392 + ,0.00003 + ,0.00204 + ,0.00231 + ,0.00612 + ,0.0145 + ,0.131 + ,0.00725 + ,0.00876 + ,0.01263 + ,0.02175 + ,1 + ,150.44 + ,163.441 + ,144.736 + ,0.00396 + ,0.00003 + ,0.00206 + ,0.00233 + ,0.00619 + ,0.02551 + ,0.237 + ,0.01321 + ,0.01574 + ,0.02148 + ,0.03964 + ,1 + ,148.462 + ,161.078 + ,141.998 + ,0.00397 + ,0.00003 + ,0.00202 + ,0.00235 + ,0.00605 + ,0.01831 + ,0.163 + ,0.0095 + ,0.01103 + ,0.01559 + ,0.02849 + ,1 + ,149.818 + ,163.417 + ,144.786 + ,0.00336 + ,0.00002 + ,0.00174 + ,0.00198 + ,0.00521 + ,0.02145 + ,0.198 + ,0.01155 + ,0.01341 + ,0.01666 + ,0.03464 + ,1 + ,117.226 + ,123.925 + ,106.656 + ,0.00417 + ,0.00004 + ,0.00186 + ,0.0027 + ,0.00558 + ,0.01909 + ,0.171 + ,0.00864 + ,0.01223 + ,0.01949 + ,0.02592 + ,0 + ,116.848 + ,217.552 + ,99.503 + ,0.00531 + ,0.00005 + ,0.0026 + ,0.00346 + ,0.0078 + ,0.01795 + ,0.163 + ,0.0081 + ,0.01144 + ,0.01756 + ,0.02429 + ,0 + ,116.286 + ,177.291 + ,96.983 + ,0.00314 + ,0.00003 + ,0.00134 + ,0.00192 + ,0.00403 + ,0.01564 + ,0.136 + ,0.00667 + ,0.0099 + ,0.01691 + ,0.02001 + ,0 + ,116.556 + ,592.03 + ,86.228 + ,0.00496 + ,0.00004 + ,0.00254 + ,0.00263 + ,0.00762 + ,0.0166 + ,0.154 + ,0.0082 + ,0.00972 + ,0.01491 + ,0.0246 + ,0 + ,116.342 + ,581.289 + ,94.246 + ,0.00267 + ,0.00002 + ,0.00115 + ,0.00148 + ,0.00345 + ,0.013 + ,0.117 + ,0.00631 + ,0.00789 + ,0.01144 + ,0.01892 + ,0 + ,114.563 + ,119.167 + ,86.647 + ,0.00327 + ,0.00003 + ,0.00146 + ,0.00184 + ,0.00439 + ,0.01185 + ,0.106 + ,0.00557 + ,0.00721 + ,0.01095 + ,0.01672 + ,0 + ,201.774 + ,262.707 + ,78.228 + ,0.00694 + ,0.00003 + ,0.00412 + ,0.00396 + ,0.01235 + ,0.02574 + ,0.255 + ,0.01454 + ,0.01582 + ,0.01758 + ,0.04363 + ,0 + ,174.188 + ,230.978 + ,94.261 + ,0.00459 + ,0.00003 + ,0.00263 + ,0.00259 + ,0.0079 + ,0.04087 + ,0.405 + ,0.02336 + ,0.02498 + ,0.02745 + ,0.07008 + ,0 + ,209.516 + ,253.017 + ,89.488 + ,0.00564 + ,0.00003 + ,0.00331 + ,0.00292 + ,0.00994 + ,0.02751 + ,0.263 + ,0.01604 + ,0.01657 + ,0.01879 + ,0.04812 + ,0 + ,174.688 + ,240.005 + ,74.287 + ,0.0136 + ,0.00008 + ,0.00624 + ,0.00564 + ,0.01873 + ,0.02308 + ,0.256 + ,0.01268 + ,0.01365 + ,0.01667 + ,0.03804 + ,0 + ,198.764 + ,396.961 + ,74.904 + ,0.0074 + ,0.00004 + ,0.0037 + ,0.0039 + ,0.01109 + ,0.02296 + ,0.241 + ,0.01265 + ,0.01321 + ,0.01588 + ,0.03794 + ,0 + ,214.289 + ,260.277 + ,77.973 + ,0.00567 + ,0.00003 + ,0.00295 + ,0.00317 + ,0.00885 + ,0.01884 + ,0.19 + ,0.01026 + ,0.01161 + ,0.01373 + ,0.03078 + ,0) + ,dim=c(15 + ,195) + ,dimnames=list(c('MDVP:Fo(Hz)' + ,'MDVP:Fhi(Hz)' + ,'MDVP:Flo(Hz)' + ,'MDVP:Jitter(%)' + ,'MDVP:Jitter(Abs)' + ,'MDVP:RAP' + ,'MDVP:PPQ' + ,'Jitter:DDP' + ,'MDVP:Shimmer' + ,'MDVP:Shimmer(dB)' + ,'Shimmer:APQ3' + ,'Shimmer:APQ5' + ,'MDVP:APQ' + ,'Shimmer:DDA' + ,'status') + ,1:195)) > y <- array(NA,dim=c(15,195),dimnames=list(c('MDVP:Fo(Hz)','MDVP:Fhi(Hz)','MDVP:Flo(Hz)','MDVP:Jitter(%)','MDVP:Jitter(Abs)','MDVP:RAP','MDVP:PPQ','Jitter:DDP','MDVP:Shimmer','MDVP:Shimmer(dB)','Shimmer:APQ3','Shimmer:APQ5','MDVP:APQ','Shimmer:DDA','status'),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 = '15' > par3 <- 'No Linear Trend' > par2 <- 'Do not include Seasonal Dummies' > par1 <- '15' > #'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 Jitter:DDP MDVP:Shimmer MDVP:Shimmer(dB) 1 7.0e-05 0.00370 0.00554 0.01109 0.04374 0.426 2 8.0e-05 0.00465 0.00696 0.01394 0.06134 0.626 3 9.0e-05 0.00544 0.00781 0.01633 0.05233 0.482 4 9.0e-05 0.00502 0.00698 0.01505 0.05492 0.517 5 1.1e-04 0.00655 0.00908 0.01966 0.06425 0.584 6 8.0e-05 0.00463 0.00750 0.01388 0.04701 0.456 7 3.0e-05 0.00155 0.00202 0.00466 0.01608 0.140 8 3.0e-05 0.00144 0.00182 0.00431 0.01567 0.134 9 6.0e-05 0.00293 0.00332 0.00880 0.02093 0.191 10 6.0e-05 0.00268 0.00332 0.00803 0.02838 0.255 11 6.0e-05 0.00254 0.00330 0.00763 0.02143 0.197 12 6.0e-05 0.00281 0.00336 0.00844 0.02752 0.249 13 2.0e-05 0.00118 0.00153 0.00355 0.01259 0.112 14 3.0e-05 0.00165 0.00208 0.00496 0.01642 0.154 15 2.0e-05 0.00121 0.00149 0.00364 0.01828 0.158 16 3.0e-05 0.00157 0.00203 0.00471 0.01503 0.126 17 4.0e-05 0.00211 0.00292 0.00632 0.02047 0.192 18 4.0e-05 0.00284 0.00387 0.00853 0.03327 0.348 19 5.0e-05 0.00364 0.00432 0.01092 0.05517 0.542 20 5.0e-05 0.00372 0.00399 0.01116 0.03995 0.348 21 5.0e-05 0.00428 0.00450 0.01285 0.03810 0.328 22 3.0e-05 0.00232 0.00267 0.00696 0.04137 0.370 23 3.0e-05 0.00220 0.00247 0.00661 0.04351 0.377 24 3.0e-05 0.00221 0.00258 0.00663 0.04192 0.364 25 5.0e-05 0.00380 0.00390 0.01140 0.01659 0.164 26 6.0e-05 0.00316 0.00375 0.00948 0.03767 0.381 27 3.0e-05 0.00250 0.00234 0.00750 0.01966 0.186 28 3.0e-05 0.00250 0.00275 0.00749 0.01919 0.198 29 2.0e-05 0.00159 0.00176 0.00476 0.01718 0.161 30 3.0e-05 0.00280 0.00253 0.00841 0.01791 0.168 31 1.0e-05 0.00166 0.00168 0.00498 0.01098 0.097 32 1.0e-05 0.00134 0.00138 0.00402 0.01015 0.089 33 1.0e-05 0.00113 0.00135 0.00339 0.01263 0.111 34 9.0e-06 0.00093 0.00107 0.00278 0.00954 0.085 35 9.0e-06 0.00094 0.00106 0.00283 0.00958 0.085 36 1.0e-05 0.00105 0.00115 0.00314 0.01194 0.107 37 2.0e-05 0.00233 0.00241 0.00700 0.02126 0.189 38 2.0e-05 0.00205 0.00218 0.00616 0.01851 0.168 39 2.0e-05 0.00153 0.00166 0.00459 0.01444 0.131 40 2.0e-05 0.00168 0.00182 0.00504 0.01663 0.151 41 2.0e-05 0.00165 0.00175 0.00496 0.01495 0.135 42 1.0e-05 0.00134 0.00147 0.00403 0.01463 0.132 43 1.0e-05 0.00169 0.00182 0.00507 0.01752 0.164 44 1.0e-05 0.00157 0.00173 0.00470 0.01760 0.154 45 9.0e-06 0.00109 0.00137 0.00327 0.01419 0.126 46 9.0e-06 0.00117 0.00139 0.00350 0.01494 0.134 47 1.0e-05 0.00127 0.00148 0.00380 0.01608 0.141 48 7.0e-06 0.00092 0.00113 0.00276 0.01152 0.103 49 4.0e-05 0.00169 0.00203 0.00507 0.01613 0.143 50 3.0e-05 0.00124 0.00155 0.00373 0.01681 0.154 51 3.0e-05 0.00141 0.00167 0.00422 0.02184 0.197 52 4.0e-05 0.00131 0.00169 0.00393 0.02033 0.185 53 3.0e-05 0.00137 0.00166 0.00411 0.02297 0.210 54 4.0e-05 0.00165 0.00183 0.00495 0.02498 0.228 55 7.0e-05 0.00349 0.00486 0.01046 0.02719 0.255 56 8.0e-05 0.00398 0.00539 0.01193 0.03209 0.307 57 7.0e-05 0.00352 0.00514 0.01056 0.03715 0.334 58 6.0e-05 0.00299 0.00469 0.00898 0.02293 0.221 59 7.0e-05 0.00334 0.00493 0.01003 0.02645 0.265 60 8.0e-05 0.00373 0.00520 0.01120 0.03225 0.350 61 1.0e-05 0.00147 0.00152 0.00442 0.01861 0.170 62 1.0e-05 0.00154 0.00151 0.00461 0.01906 0.165 63 1.0e-05 0.00152 0.00144 0.00457 0.01643 0.145 64 1.0e-05 0.00175 0.00155 0.00526 0.01644 0.145 65 9.0e-06 0.00114 0.00113 0.00342 0.01457 0.129 66 1.0e-05 0.00136 0.00140 0.00408 0.01745 0.154 67 6.0e-05 0.00430 0.00440 0.01289 0.03198 0.313 68 7.0e-05 0.00507 0.00463 0.01520 0.03111 0.308 69 8.0e-05 0.00647 0.00467 0.01941 0.05384 0.478 70 5.0e-05 0.00467 0.00354 0.01400 0.05428 0.497 71 6.0e-05 0.00469 0.00419 0.01407 0.03485 0.365 72 7.0e-05 0.00534 0.00478 0.01601 0.04978 0.483 73 3.0e-05 0.00180 0.00220 0.00540 0.01706 0.152 74 5.0e-05 0.00268 0.00329 0.00805 0.02448 0.226 75 4.0e-05 0.00260 0.00283 0.00780 0.02442 0.216 76 5.0e-05 0.00277 0.00289 0.00831 0.02215 0.206 77 4.0e-05 0.00270 0.00289 0.00810 0.03999 0.350 78 4.0e-05 0.00226 0.00280 0.00677 0.02199 0.197 79 6.0e-05 0.00331 0.00332 0.00994 0.03202 0.263 80 1.0e-04 0.00622 0.00576 0.01865 0.03121 0.361 81 7.0e-05 0.00389 0.00415 0.01168 0.04024 0.364 82 7.0e-05 0.00428 0.00371 0.01283 0.03156 0.296 83 6.0e-05 0.00351 0.00348 0.01053 0.02427 0.216 84 4.0e-05 0.00247 0.00258 0.00742 0.02223 0.202 85 4.0e-05 0.00418 0.00420 0.01254 0.04795 0.435 86 2.0e-05 0.00220 0.00244 0.00659 0.03852 0.331 87 2.0e-05 0.00163 0.00194 0.00488 0.03759 0.327 88 3.0e-05 0.00287 0.00312 0.00862 0.06511 0.580 89 3.0e-05 0.00237 0.00254 0.00710 0.06727 0.650 90 4.0e-05 0.00391 0.00419 0.01172 0.04313 0.442 91 4.0e-05 0.00387 0.00453 0.01161 0.06640 0.634 92 3.0e-05 0.00224 0.00227 0.00672 0.07959 0.772 93 3.0e-05 0.00250 0.00256 0.00750 0.04190 0.383 94 3.0e-05 0.00191 0.00226 0.00574 0.05925 0.637 95 2.0e-05 0.00196 0.00196 0.00587 0.03716 0.307 96 2.0e-05 0.00201 0.00197 0.00602 0.03272 0.283 97 2.0e-05 0.00178 0.00184 0.00535 0.03381 0.307 98 1.0e-04 0.00743 0.00623 0.02228 0.03886 0.342 99 1.1e-04 0.00826 0.00655 0.02478 0.04689 0.422 100 1.5e-04 0.01159 0.00990 0.03476 0.06734 0.659 101 2.6e-04 0.02144 0.01522 0.06433 0.09178 0.891 102 1.2e-04 0.00905 0.00909 0.02716 0.06170 0.584 103 2.2e-04 0.01854 0.01628 0.05563 0.09419 0.930 104 2.0e-05 0.00105 0.00136 0.00315 0.01131 0.107 105 1.0e-05 0.00076 0.00100 0.00229 0.01030 0.094 106 2.0e-05 0.00116 0.00134 0.00349 0.01346 0.126 107 1.0e-05 0.00068 0.00092 0.00204 0.01064 0.097 108 2.0e-05 0.00115 0.00122 0.00346 0.01450 0.137 109 1.0e-05 0.00075 0.00096 0.00225 0.01024 0.093 110 4.0e-05 0.00450 0.00389 0.01351 0.03044 0.275 111 3.0e-05 0.00371 0.00337 0.01112 0.02286 0.207 112 3.0e-05 0.00368 0.00339 0.01105 0.01761 0.155 113 4.0e-05 0.00502 0.00485 0.01506 0.02378 0.210 114 3.0e-05 0.00321 0.00280 0.00964 0.01680 0.149 115 2.0e-05 0.00302 0.00246 0.00905 0.02105 0.209 116 6.0e-05 0.00404 0.00385 0.01211 0.01843 0.235 117 3.0e-05 0.00214 0.00207 0.00642 0.01458 0.148 118 3.0e-05 0.00244 0.00261 0.00731 0.01725 0.175 119 3.0e-05 0.00157 0.00194 0.00472 0.01279 0.129 120 2.0e-05 0.00127 0.00128 0.00381 0.01299 0.124 121 5.0e-05 0.00241 0.00314 0.00723 0.02008 0.221 122 3.0e-05 0.00209 0.00221 0.00628 0.01169 0.117 123 5.0e-05 0.00406 0.00398 0.01218 0.04479 0.441 124 5.0e-05 0.00506 0.00449 0.01517 0.02503 0.231 125 4.0e-05 0.00403 0.00395 0.01209 0.02343 0.224 126 5.0e-05 0.00414 0.00422 0.01242 0.02362 0.233 127 4.0e-05 0.00294 0.00327 0.00883 0.02791 0.246 128 4.0e-05 0.00368 0.00351 0.01104 0.02857 0.257 129 4.0e-05 0.00214 0.00192 0.00641 0.01033 0.098 130 2.0e-05 0.00116 0.00135 0.00349 0.01022 0.090 131 4.0e-05 0.00269 0.00238 0.00808 0.01412 0.125 132 3.0e-05 0.00224 0.00205 0.00671 0.01516 0.138 133 3.0e-05 0.00169 0.00170 0.00508 0.01201 0.106 134 3.0e-05 0.00168 0.00171 0.00504 0.01043 0.099 135 6.0e-05 0.00291 0.00319 0.00873 0.04932 0.441 136 4.0e-05 0.00244 0.00315 0.00731 0.04128 0.379 137 4.0e-05 0.00219 0.00283 0.00658 0.04879 0.431 138 4.0e-05 0.00257 0.00312 0.00772 0.05279 0.476 139 4.0e-05 0.00238 0.00290 0.00715 0.05643 0.517 140 3.0e-05 0.00181 0.00232 0.00542 0.03026 0.267 141 3.0e-05 0.00232 0.00269 0.00696 0.03273 0.281 142 4.0e-05 0.00428 0.00428 0.01285 0.06725 0.571 143 2.0e-05 0.00182 0.00215 0.00546 0.03527 0.297 144 2.0e-05 0.00189 0.00211 0.00568 0.01997 0.180 145 1.0e-05 0.00100 0.00133 0.00301 0.02662 0.228 146 2.0e-05 0.00169 0.00188 0.00506 0.02536 0.225 147 9.0e-05 0.00863 0.00946 0.02589 0.08143 0.821 148 8.0e-05 0.00849 0.00819 0.02546 0.06050 0.618 149 9.0e-05 0.00996 0.01027 0.02987 0.07118 0.722 150 8.0e-05 0.00919 0.00963 0.02756 0.07170 0.833 151 1.0e-04 0.01075 0.01154 0.03225 0.05830 0.784 152 1.6e-04 0.01800 0.01958 0.05401 0.11908 1.302 153 1.4e-04 0.01568 0.01699 0.04705 0.08684 1.018 154 6.0e-05 0.00388 0.00332 0.01164 0.02534 0.241 155 6.0e-05 0.00393 0.00300 0.01179 0.02682 0.236 156 5.0e-05 0.00356 0.00300 0.01067 0.03087 0.276 157 6.0e-05 0.00415 0.00339 0.01246 0.02293 0.223 158 1.5e-04 0.01117 0.00718 0.03351 0.04912 0.438 159 8.0e-05 0.00593 0.00454 0.01778 0.02852 0.266 160 5.0e-05 0.00321 0.00318 0.00962 0.03235 0.339 161 5.0e-05 0.00299 0.00316 0.00896 0.04009 0.406 162 5.0e-05 0.00352 0.00329 0.01057 0.03273 0.325 163 6.0e-05 0.00366 0.00340 0.01097 0.03658 0.369 164 5.0e-05 0.00291 0.00284 0.00873 0.01756 0.155 165 9.0e-05 0.00493 0.00461 0.01480 0.02814 0.272 166 1.0e-05 0.00154 0.00153 0.00462 0.02448 0.217 167 1.0e-05 0.00173 0.00159 0.00519 0.01242 0.116 168 1.0e-05 0.00205 0.00186 0.00616 0.02030 0.197 169 4.0e-05 0.00490 0.00448 0.01470 0.02177 0.189 170 2.0e-05 0.00316 0.00283 0.00949 0.02018 0.212 171 2.0e-05 0.00279 0.00237 0.00837 0.01897 0.181 172 3.0e-05 0.00166 0.00190 0.00499 0.01358 0.129 173 3.0e-05 0.00170 0.00200 0.00510 0.01484 0.133 174 3.0e-05 0.00171 0.00203 0.00514 0.01472 0.133 175 3.0e-05 0.00176 0.00218 0.00528 0.01657 0.145 176 3.0e-05 0.00160 0.00199 0.00480 0.01503 0.137 177 3.0e-05 0.00169 0.00213 0.00507 0.01725 0.155 178 2.0e-05 0.00135 0.00162 0.00406 0.01469 0.132 179 2.0e-05 0.00152 0.00186 0.00456 0.01574 0.142 180 3.0e-05 0.00204 0.00231 0.00612 0.01450 0.131 181 3.0e-05 0.00206 0.00233 0.00619 0.02551 0.237 182 3.0e-05 0.00202 0.00235 0.00605 0.01831 0.163 183 2.0e-05 0.00174 0.00198 0.00521 0.02145 0.198 184 4.0e-05 0.00186 0.00270 0.00558 0.01909 0.171 185 5.0e-05 0.00260 0.00346 0.00780 0.01795 0.163 186 3.0e-05 0.00134 0.00192 0.00403 0.01564 0.136 187 4.0e-05 0.00254 0.00263 0.00762 0.01660 0.154 188 2.0e-05 0.00115 0.00148 0.00345 0.01300 0.117 189 3.0e-05 0.00146 0.00184 0.00439 0.01185 0.106 190 3.0e-05 0.00412 0.00396 0.01235 0.02574 0.255 191 3.0e-05 0.00263 0.00259 0.00790 0.04087 0.405 192 3.0e-05 0.00331 0.00292 0.00994 0.02751 0.263 193 8.0e-05 0.00624 0.00564 0.01873 0.02308 0.256 194 4.0e-05 0.00370 0.00390 0.01109 0.02296 0.241 195 3.0e-05 0.00295 0.00317 0.00885 0.01884 0.190 Shimmer:APQ3 Shimmer:APQ5 MDVP:APQ Shimmer:DDA 1 0.02182 0.03130 0.02971 0.06545 2 0.03134 0.04518 0.04368 0.09403 3 0.02757 0.03858 0.03590 0.08270 4 0.02924 0.04005 0.03772 0.08771 5 0.03490 0.04825 0.04465 0.10470 6 0.02328 0.03526 0.03243 0.06985 7 0.00779 0.00937 0.01351 0.02337 8 0.00829 0.00946 0.01256 0.02487 9 0.01073 0.01277 0.01717 0.03218 10 0.01441 0.01725 0.02444 0.04324 11 0.01079 0.01342 0.01892 0.03237 12 0.01424 0.01641 0.02214 0.04272 13 0.00656 0.00717 0.01140 0.01968 14 0.00728 0.00932 0.01797 0.02184 15 0.01064 0.00972 0.01246 0.03191 16 0.00772 0.00888 0.01359 0.02316 17 0.00969 0.01200 0.02074 0.02908 18 0.01441 0.01893 0.03430 0.04322 19 0.02471 0.03572 0.05767 0.07413 20 0.01721 0.02374 0.04310 0.05164 21 0.01667 0.02383 0.04055 0.05000 22 0.02021 0.02591 0.04525 0.06062 23 0.02228 0.02540 0.04246 0.06685 24 0.02187 0.02470 0.03772 0.06562 25 0.00738 0.00948 0.01497 0.02214 26 0.01732 0.02245 0.03780 0.05197 27 0.00889 0.01169 0.01872 0.02666 28 0.00883 0.01144 0.01826 0.02650 29 0.00769 0.01012 0.01661 0.02307 30 0.00793 0.01057 0.01799 0.02380 31 0.00563 0.00680 0.00802 0.01689 32 0.00504 0.00641 0.00762 0.01513 33 0.00640 0.00825 0.00951 0.01919 34 0.00469 0.00606 0.00719 0.01407 35 0.00468 0.00610 0.00726 0.01403 36 0.00586 0.00760 0.00957 0.01758 37 0.01154 0.01347 0.01612 0.03463 38 0.00938 0.01160 0.01491 0.02814 39 0.00726 0.00885 0.01190 0.02177 40 0.00829 0.01003 0.01366 0.02488 41 0.00774 0.00941 0.01233 0.02321 42 0.00742 0.00901 0.01234 0.02226 43 0.01035 0.01024 0.01133 0.03104 44 0.01006 0.01038 0.01251 0.03017 45 0.00777 0.00898 0.01033 0.02330 46 0.00847 0.00879 0.01014 0.02542 47 0.00906 0.00977 0.01149 0.02719 48 0.00614 0.00730 0.00860 0.01841 49 0.00855 0.00776 0.01433 0.02566 50 0.00930 0.00802 0.01400 0.02789 51 0.01241 0.01024 0.01685 0.03724 52 0.01143 0.00959 0.01614 0.03429 53 0.01323 0.01072 0.01677 0.03969 54 0.01396 0.01219 0.01947 0.04188 55 0.01483 0.01609 0.02067 0.04450 56 0.01789 0.01992 0.02454 0.05368 57 0.02032 0.02302 0.02802 0.06097 58 0.01189 0.01459 0.01948 0.03568 59 0.01394 0.01625 0.02137 0.04183 60 0.01805 0.01974 0.02519 0.05414 61 0.00975 0.01258 0.01382 0.02925 62 0.01013 0.01296 0.01340 0.03039 63 0.00867 0.01108 0.01200 0.02602 64 0.00882 0.01075 0.01179 0.02647 65 0.00769 0.00957 0.01016 0.02308 66 0.00942 0.01160 0.01234 0.02827 67 0.01830 0.01810 0.02428 0.05490 68 0.01638 0.01759 0.02603 0.04914 69 0.03152 0.02422 0.03392 0.09455 70 0.03357 0.02494 0.03635 0.10070 71 0.01868 0.01906 0.02949 0.05605 72 0.02749 0.02466 0.03736 0.08247 73 0.00974 0.00925 0.01345 0.02921 74 0.01373 0.01375 0.01956 0.04120 75 0.01432 0.01325 0.01831 0.04295 76 0.01284 0.01219 0.01715 0.03851 77 0.02413 0.02231 0.02704 0.07238 78 0.01284 0.01199 0.01636 0.03852 79 0.01803 0.01886 0.02455 0.05408 80 0.01773 0.01783 0.02139 0.05320 81 0.02266 0.02451 0.02876 0.06799 82 0.01792 0.01841 0.02190 0.05377 83 0.01371 0.01421 0.01751 0.04114 84 0.01277 0.01343 0.01552 0.03831 85 0.02679 0.03022 0.03510 0.08037 86 0.02107 0.02493 0.02877 0.06321 87 0.02073 0.02415 0.02784 0.06219 88 0.03671 0.04159 0.04683 0.11012 89 0.03788 0.04254 0.04802 0.11363 90 0.02297 0.02768 0.03455 0.06892 91 0.03650 0.04282 0.05114 0.10949 92 0.04421 0.04962 0.05690 0.13262 93 0.02383 0.02521 0.03051 0.07150 94 0.03341 0.03794 0.04398 0.10024 95 0.02062 0.02321 0.02764 0.06185 96 0.01813 0.01909 0.02571 0.05439 97 0.01806 0.02024 0.02809 0.05417 98 0.02135 0.02174 0.03088 0.06406 99 0.02542 0.02630 0.03908 0.07625 100 0.03611 0.03963 0.05783 0.10833 101 0.05358 0.04791 0.06196 0.16074 102 0.03223 0.03672 0.05174 0.09669 103 0.05551 0.05005 0.06023 0.16654 104 0.00522 0.00659 0.01009 0.01567 105 0.00469 0.00582 0.00871 0.01406 106 0.00660 0.00818 0.01059 0.01979 107 0.00522 0.00632 0.00928 0.01567 108 0.00633 0.00788 0.01267 0.01898 109 0.00455 0.00576 0.00993 0.01364 110 0.01771 0.01815 0.02084 0.05312 111 0.01192 0.01439 0.01852 0.03576 112 0.00952 0.01058 0.01307 0.02855 113 0.01277 0.01483 0.01767 0.03831 114 0.00861 0.01017 0.01301 0.02583 115 0.01107 0.01284 0.01604 0.03320 116 0.00796 0.00832 0.01271 0.02389 117 0.00606 0.00747 0.01312 0.01818 118 0.00757 0.00971 0.01652 0.02270 119 0.00617 0.00744 0.01151 0.01851 120 0.00679 0.00631 0.01075 0.02038 121 0.00849 0.01117 0.01734 0.02548 122 0.00534 0.00630 0.01104 0.01603 123 0.02587 0.02567 0.03220 0.07761 124 0.01372 0.01580 0.01931 0.04115 125 0.01289 0.01420 0.01720 0.03867 126 0.01235 0.01495 0.01944 0.03706 127 0.01484 0.01805 0.02259 0.04451 128 0.01547 0.01859 0.02301 0.04641 129 0.00538 0.00570 0.00811 0.01614 130 0.00476 0.00588 0.00903 0.01428 131 0.00703 0.00820 0.01194 0.02110 132 0.00721 0.00815 0.01310 0.02164 133 0.00633 0.00701 0.00915 0.01898 134 0.00490 0.00621 0.00903 0.01471 135 0.02683 0.03112 0.03651 0.08050 136 0.02229 0.02592 0.03316 0.06688 137 0.02385 0.02973 0.04370 0.07154 138 0.02896 0.03347 0.04134 0.08689 139 0.03070 0.03530 0.04451 0.09211 140 0.01514 0.01812 0.02770 0.04543 141 0.01713 0.01964 0.02824 0.05139 142 0.04016 0.04003 0.04464 0.12047 143 0.02055 0.02076 0.02530 0.06165 144 0.01117 0.01177 0.01506 0.03350 145 0.01475 0.01558 0.02006 0.04426 146 0.01379 0.01478 0.01909 0.04137 147 0.03804 0.05426 0.08808 0.11411 148 0.02865 0.04101 0.06359 0.08595 149 0.03474 0.04580 0.06824 0.10422 150 0.03515 0.04265 0.06460 0.10546 151 0.02699 0.03714 0.06259 0.08096 152 0.05647 0.07940 0.13778 0.16942 153 0.04284 0.05556 0.08318 0.12851 154 0.01340 0.01399 0.02056 0.04019 155 0.01484 0.01405 0.02018 0.04451 156 0.01659 0.01804 0.02402 0.04977 157 0.01205 0.01289 0.01771 0.03615 158 0.02610 0.02161 0.02916 0.07830 159 0.01500 0.01581 0.02157 0.04499 160 0.01360 0.01650 0.03105 0.04079 161 0.01579 0.01994 0.04114 0.04736 162 0.01644 0.01722 0.02931 0.04933 163 0.01864 0.01940 0.03091 0.05592 164 0.00967 0.01033 0.01363 0.02902 165 0.01579 0.01553 0.02073 0.04736 166 0.01410 0.01426 0.01621 0.04231 167 0.00696 0.00747 0.00882 0.02089 168 0.01186 0.01230 0.01367 0.03557 169 0.01279 0.01272 0.01439 0.03836 170 0.01176 0.01191 0.01344 0.03529 171 0.01084 0.01121 0.01255 0.03253 172 0.00664 0.00786 0.01140 0.01992 173 0.00754 0.00950 0.01285 0.02261 174 0.00748 0.00905 0.01148 0.02245 175 0.00881 0.01062 0.01318 0.02643 176 0.00812 0.00933 0.01133 0.02436 177 0.00874 0.01021 0.01331 0.02623 178 0.00728 0.00886 0.01230 0.02184 179 0.00839 0.00956 0.01309 0.02518 180 0.00725 0.00876 0.01263 0.02175 181 0.01321 0.01574 0.02148 0.03964 182 0.00950 0.01103 0.01559 0.02849 183 0.01155 0.01341 0.01666 0.03464 184 0.00864 0.01223 0.01949 0.02592 185 0.00810 0.01144 0.01756 0.02429 186 0.00667 0.00990 0.01691 0.02001 187 0.00820 0.00972 0.01491 0.02460 188 0.00631 0.00789 0.01144 0.01892 189 0.00557 0.00721 0.01095 0.01672 190 0.01454 0.01582 0.01758 0.04363 191 0.02336 0.02498 0.02745 0.07008 192 0.01604 0.01657 0.01879 0.04812 193 0.01268 0.01365 0.01667 0.03804 194 0.01265 0.01321 0.01588 0.03794 195 0.01026 0.01161 0.01373 0.03078 > 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)` 1.273e+00 -2.416e-03 -2.680e-04 -2.383e-03 `MDVP:Jitter(%)` `MDVP:Jitter(Abs)` `MDVP:RAP` `MDVP:PPQ` -1.073e+02 -1.672e+03 2.749e+02 7.898e+01 `Jitter:DDP` `MDVP:Shimmer` `MDVP:Shimmer(dB)` `Shimmer:APQ3` -5.091e+01 6.664e+01 -1.029e+00 3.104e+03 `Shimmer:APQ5` `MDVP:APQ` `Shimmer:DDA` -1.894e+01 -5.577e+00 -1.055e+03 > (mysum <- summary(mylm)) Call: lm(formula = df) Residuals: Min 1Q Median 3Q Max -0.85674 -0.17034 0.09083 0.25570 0.62020 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 1.273e+00 2.282e-01 5.578 8.78e-08 *** `MDVP:Fo(Hz)` -2.416e-03 1.447e-03 -1.670 0.09675 . `MDVP:Fhi(Hz)` -2.680e-04 3.500e-04 -0.766 0.44489 `MDVP:Flo(Hz)` -2.383e-03 8.400e-04 -2.837 0.00508 ** `MDVP:Jitter(%)` -1.073e+02 7.069e+01 -1.518 0.13080 `MDVP:Jitter(Abs)` -1.672e+03 4.683e+03 -0.357 0.72141 `MDVP:RAP` 2.749e+02 1.027e+04 0.027 0.97868 `MDVP:PPQ` 7.898e+01 7.919e+01 0.997 0.31993 `Jitter:DDP` -5.091e+01 3.425e+03 -0.015 0.98816 `MDVP:Shimmer` 6.664e+01 3.747e+01 1.778 0.07704 . `MDVP:Shimmer(dB)` -1.029e+00 1.242e+00 -0.828 0.40855 `Shimmer:APQ3` 3.104e+03 9.961e+03 0.312 0.75570 `Shimmer:APQ5` -1.894e+01 2.172e+01 -0.872 0.38432 `MDVP:APQ` -5.577e+00 1.147e+01 -0.486 0.62735 `Shimmer:DDA` -1.055e+03 3.320e+03 -0.318 0.75093 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 0.3709 on 180 degrees of freedom Multiple R-squared: 0.3156, Adjusted R-squared: 0.2623 F-statistic: 5.928 on 14 and 180 DF, p-value: 1.607e-09 > 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,] 1.309164e-47 2.618328e-47 1.000000000 [2,] 7.052726e-63 1.410545e-62 1.000000000 [3,] 3.331808e-82 6.663615e-82 1.000000000 [4,] 5.545957e-106 1.109191e-105 1.000000000 [5,] 5.507606e-111 1.101521e-110 1.000000000 [6,] 1.743859e-124 3.487719e-124 1.000000000 [7,] 9.396406e-142 1.879281e-141 1.000000000 [8,] 4.103539e-158 8.207078e-158 1.000000000 [9,] 1.942832e-189 3.885664e-189 1.000000000 [10,] 1.591775e-188 3.183550e-188 1.000000000 [11,] 8.247129e-201 1.649426e-200 1.000000000 [12,] 1.799710e-219 3.599421e-219 1.000000000 [13,] 1.129893e-235 2.259785e-235 1.000000000 [14,] 1.366484e-06 2.732967e-06 0.999998634 [15,] 9.987303e-07 1.997461e-06 0.999999001 [16,] 3.169835e-07 6.339670e-07 0.999999683 [17,] 9.807482e-08 1.961496e-07 0.999999902 [18,] 3.204532e-08 6.409064e-08 0.999999968 [19,] 9.688978e-09 1.937796e-08 0.999999990 [20,] 3.884769e-07 7.769539e-07 0.999999612 [21,] 4.174098e-06 8.348197e-06 0.999995826 [22,] 4.223471e-04 8.446941e-04 0.999577653 [23,] 2.628537e-03 5.257074e-03 0.997371463 [24,] 8.533796e-03 1.706759e-02 0.991466204 [25,] 9.603814e-03 1.920763e-02 0.990396186 [26,] 6.977650e-03 1.395530e-02 0.993022350 [27,] 4.631549e-03 9.263097e-03 0.995368451 [28,] 3.195792e-03 6.391585e-03 0.996804208 [29,] 2.056181e-03 4.112362e-03 0.997943819 [30,] 1.331047e-03 2.662095e-03 0.998668953 [31,] 8.620240e-04 1.724048e-03 0.999137976 [32,] 2.763124e-03 5.526248e-03 0.997236876 [33,] 4.018396e-03 8.036791e-03 0.995981604 [34,] 4.638185e-03 9.276370e-03 0.995361815 [35,] 4.116056e-03 8.232113e-03 0.995883944 [36,] 4.782443e-03 9.564885e-03 0.995217557 [37,] 5.730450e-03 1.146090e-02 0.994269550 [38,] 5.447061e-03 1.089412e-02 0.994552939 [39,] 5.727076e-03 1.145415e-02 0.994272924 [40,] 3.885077e-03 7.770153e-03 0.996114923 [41,] 2.838745e-03 5.677489e-03 0.997161255 [42,] 1.973342e-03 3.946683e-03 0.998026658 [43,] 1.310165e-03 2.620331e-03 0.998689835 [44,] 3.640143e-03 7.280286e-03 0.996359857 [45,] 4.147483e-03 8.294966e-03 0.995852517 [46,] 3.298100e-03 6.596200e-03 0.996701900 [47,] 2.552045e-03 5.104089e-03 0.997447955 [48,] 2.045486e-03 4.090973e-03 0.997954514 [49,] 2.036481e-03 4.072961e-03 0.997963519 [50,] 1.371977e-03 2.743954e-03 0.998628023 [51,] 9.257110e-04 1.851422e-03 0.999074289 [52,] 6.308098e-04 1.261620e-03 0.999369190 [53,] 6.518865e-04 1.303773e-03 0.999348114 [54,] 4.444125e-04 8.888250e-04 0.999555587 [55,] 3.232995e-04 6.465989e-04 0.999676701 [56,] 2.222820e-04 4.445639e-04 0.999777718 [57,] 4.598769e-04 9.197537e-04 0.999540123 [58,] 3.103221e-04 6.206443e-04 0.999689678 [59,] 2.028807e-04 4.057614e-04 0.999797119 [60,] 1.289293e-04 2.578585e-04 0.999871071 [61,] 8.105620e-05 1.621124e-04 0.999918944 [62,] 5.112235e-05 1.022447e-04 0.999948878 [63,] 3.900939e-05 7.801877e-05 0.999960991 [64,] 2.549626e-05 5.099253e-05 0.999974504 [65,] 1.670477e-05 3.340954e-05 0.999983295 [66,] 1.129641e-05 2.259281e-05 0.999988704 [67,] 8.265237e-06 1.653047e-05 0.999991735 [68,] 5.599435e-06 1.119887e-05 0.999994401 [69,] 1.343636e-05 2.687272e-05 0.999986564 [70,] 3.801205e-05 7.602410e-05 0.999961988 [71,] 3.364634e-05 6.729267e-05 0.999966354 [72,] 3.216613e-05 6.433226e-05 0.999967834 [73,] 2.225034e-05 4.450069e-05 0.999977750 [74,] 1.594446e-05 3.188893e-05 0.999984056 [75,] 1.355562e-05 2.711123e-05 0.999986444 [76,] 9.154617e-06 1.830923e-05 0.999990845 [77,] 5.905362e-06 1.181072e-05 0.999994095 [78,] 3.703643e-06 7.407286e-06 0.999996296 [79,] 2.553810e-06 5.107619e-06 0.999997446 [80,] 1.661842e-06 3.323684e-06 0.999998338 [81,] 1.164671e-06 2.329341e-06 0.999998835 [82,] 6.647162e-07 1.329432e-06 0.999999335 [83,] 5.417558e-07 1.083512e-06 0.999999458 [84,] 3.387393e-07 6.774785e-07 0.999999661 [85,] 8.952548e-07 1.790510e-06 0.999999105 [86,] 2.279033e-06 4.558065e-06 0.999997721 [87,] 1.952166e-06 3.904333e-06 0.999998048 [88,] 1.589245e-06 3.178491e-06 0.999998411 [89,] 1.562458e-06 3.124917e-06 0.999998438 [90,] 1.546846e-06 3.093693e-06 0.999998453 [91,] 1.344898e-06 2.689797e-06 0.999998655 [92,] 9.666973e-07 1.933395e-06 0.999999033 [93,] 7.897855e-07 1.579571e-06 0.999999210 [94,] 6.184998e-07 1.237000e-06 0.999999382 [95,] 1.159609e-06 2.319217e-06 0.999998840 [96,] 1.474796e-06 2.949591e-06 0.999998525 [97,] 5.330936e-06 1.066187e-05 0.999994669 [98,] 4.430150e-06 8.860301e-06 0.999995570 [99,] 5.555024e-06 1.111005e-05 0.999994445 [100,] 5.037359e-06 1.007472e-05 0.999994963 [101,] 4.277002e-06 8.554004e-06 0.999995723 [102,] 1.232861e-05 2.465722e-05 0.999987671 [103,] 7.391561e-05 1.478312e-04 0.999926084 [104,] 1.416768e-04 2.833537e-04 0.999858323 [105,] 2.699314e-04 5.398629e-04 0.999730069 [106,] 1.851115e-04 3.702229e-04 0.999814889 [107,] 1.558621e-04 3.117243e-04 0.999844138 [108,] 1.641210e-04 3.282419e-04 0.999835879 [109,] 2.434037e-04 4.868074e-04 0.999756596 [110,] 3.336265e-04 6.672530e-04 0.999666374 [111,] 4.746120e-04 9.492241e-04 0.999525388 [112,] 3.794973e-04 7.589947e-04 0.999620503 [113,] 4.149712e-04 8.299425e-04 0.999585029 [114,] 6.569400e-04 1.313880e-03 0.999343060 [115,] 5.452083e-04 1.090417e-03 0.999454792 [116,] 7.848106e-04 1.569621e-03 0.999215189 [117,] 1.319381e-03 2.638761e-03 0.998680619 [118,] 9.342616e-04 1.868523e-03 0.999065738 [119,] 6.279139e-04 1.255828e-03 0.999372086 [120,] 4.396873e-04 8.793745e-04 0.999560313 [121,] 3.058563e-04 6.117126e-04 0.999694144 [122,] 1.973245e-04 3.946490e-04 0.999802676 [123,] 1.255560e-04 2.511119e-04 0.999874444 [124,] 1.227485e-04 2.454971e-04 0.999877251 [125,] 9.162194e-05 1.832439e-04 0.999908378 [126,] 9.295097e-05 1.859019e-04 0.999907049 [127,] 4.761374e-04 9.522749e-04 0.999523863 [128,] 1.412948e-03 2.825896e-03 0.998587052 [129,] 5.217349e-03 1.043470e-02 0.994782651 [130,] 3.858476e-03 7.716952e-03 0.996141524 [131,] 3.058415e-03 6.116830e-03 0.996941585 [132,] 2.919621e-03 5.839241e-03 0.997080379 [133,] 2.131244e-03 4.262489e-03 0.997868756 [134,] 1.537268e-03 3.074537e-03 0.998462732 [135,] 7.461686e-03 1.492337e-02 0.992538314 [136,] 5.292062e-03 1.058412e-02 0.994707938 [137,] 3.532919e-03 7.065837e-03 0.996467081 [138,] 2.662093e-03 5.324186e-03 0.997337907 [139,] 1.790730e-03 3.581459e-03 0.998209270 [140,] 2.684871e-03 5.369742e-03 0.997315129 [141,] 1.926043e-03 3.852086e-03 0.998073957 [142,] 1.241767e-03 2.483535e-03 0.998758233 [143,] 1.054716e-03 2.109431e-03 0.998945284 [144,] 1.185957e-03 2.371914e-03 0.998814043 [145,] 8.033012e-04 1.606602e-03 0.999196699 [146,] 1.760457e-03 3.520914e-03 0.998239543 [147,] 2.544184e-03 5.088369e-03 0.997455816 [148,] 5.630790e-03 1.126158e-02 0.994369210 [149,] 1.072680e-02 2.145361e-02 0.989273196 [150,] 1.179067e-02 2.358134e-02 0.988209329 [151,] 1.387480e-02 2.774960e-02 0.986125201 [152,] 2.576197e-02 5.152393e-02 0.974238034 [153,] 2.867053e-02 5.734106e-02 0.971329470 [154,] 9.640785e-01 7.184309e-02 0.035921547 [155,] 9.862707e-01 2.745851e-02 0.013729256 [156,] 9.770892e-01 4.582152e-02 0.022910760 [157,] 9.570268e-01 8.594641e-02 0.042973207 [158,] 9.657806e-01 6.843886e-02 0.034219428 [159,] 9.528584e-01 9.428317e-02 0.047141585 [160,] 9.909739e-01 1.805213e-02 0.009026064 > postscript(file="/var/wessaorg/rcomp/tmp/10aks1386271488.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$resid) > grid() > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/2uhpz1386271488.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(mysum$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/31v1s1386271488.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') > grid() > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/4ni2x1386271488.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/5lo7s1386271488.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 -0.074147548 -0.009004078 -0.061051763 0.003882241 -0.007213961 -0.027147135 7 8 9 10 11 12 0.193662493 0.161639009 0.076082216 0.022438839 0.043641613 -0.006166807 13 14 15 16 17 18 0.377752174 0.187736997 0.249526997 0.269901734 0.313548659 0.133025328 19 20 21 22 23 24 -0.192360926 0.093936637 -0.097389185 0.007428116 0.027658285 0.112551609 25 26 27 28 29 30 0.248917082 -0.083882164 0.169925638 0.191300092 0.166586882 0.185737328 31 32 33 34 35 36 -0.402972715 -0.374152640 -0.409645231 -0.352106751 -0.366347643 -0.380131511 37 38 39 40 41 42 0.455143713 0.421207682 0.499060293 0.506672167 0.524159839 0.495305127 43 44 45 46 47 48 -0.223286452 -0.216787407 -0.183469404 -0.172373806 -0.167814127 -0.255561473 49 50 51 52 53 54 -0.567141596 -0.599487132 -0.635599689 -0.595487643 -0.616892285 -0.621838373 55 56 57 58 59 60 0.191432676 0.241936298 0.124038472 0.309865013 0.277093790 0.355032404 61 62 63 64 65 66 -0.570710249 -0.596375758 -0.303830652 -0.247203945 -0.232275188 -0.513783541 67 68 69 70 71 72 0.172217132 0.115992568 -0.080882466 -0.027245986 0.103009660 -0.057153835 73 74 75 76 77 78 0.271639914 0.308823005 0.162846794 0.193462402 0.088957732 0.196189320 79 80 81 82 83 84 0.021116334 0.125816380 -0.022638810 0.039551281 0.104642629 0.084231370 85 86 87 88 89 90 0.090831150 0.303782898 0.298118192 0.089551317 0.011746351 0.313083289 91 92 93 94 95 96 -0.028087365 0.004705942 0.236268168 0.115131758 0.037426133 0.271400498 97 98 99 100 101 102 0.248165890 0.161133628 0.019873327 -0.066511602 -0.117486717 -0.235209492 103 104 105 106 107 108 -0.168303007 0.245387749 0.338594687 0.356060442 0.425549199 0.304024126 109 110 111 112 113 114 0.337239901 0.271280940 0.284250889 0.587829739 0.505506355 0.579830844 115 116 117 118 119 120 0.324650678 0.355785477 0.305008824 0.353206601 0.540032505 0.620204103 121 122 123 124 125 126 0.274632385 0.389067484 0.076081431 0.235972527 0.184707864 0.185015944 127 128 129 130 131 132 0.108103956 0.194580793 0.332456734 0.245506397 0.223943105 0.166783654 133 134 135 136 137 138 0.209289644 0.261879973 -0.009997143 0.032079371 -0.183886650 -0.042583000 139 140 141 142 143 144 -0.069763220 0.060717418 0.263174218 0.119624167 0.423695379 0.358884049 145 146 147 148 149 150 0.524390671 0.381901720 -0.141519148 0.141299074 -0.002806019 0.216185958 151 152 153 154 155 156 0.173607496 -0.311411972 -0.013171765 0.146206764 0.127327547 0.106959051 157 158 159 160 161 162 0.181706403 -0.085210884 0.111233395 -0.061946131 -0.370019052 0.026674545 163 164 165 166 167 168 -0.028753632 0.195359606 0.154487444 -0.554188320 -0.160158840 -0.143073289 169 170 171 172 173 174 -0.700266478 -0.212775751 -0.164125805 -0.784234907 -0.815042826 -0.816142275 175 176 177 178 179 180 -0.807763578 -0.767632027 -0.822677162 0.375104547 0.372747415 0.340448864 181 182 183 184 185 186 0.307685537 0.330332858 0.326629150 -0.850573476 -0.856737335 -0.849573868 187 188 189 190 191 192 -0.720054663 -0.702413354 -0.808257564 -0.696701583 -0.785411719 -0.617762821 193 194 195 -0.363408746 -0.579766277 -0.581500764 > postscript(file="/var/wessaorg/rcomp/tmp/61n7t1386271488.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 -0.074147548 NA 1 -0.009004078 -0.074147548 2 -0.061051763 -0.009004078 3 0.003882241 -0.061051763 4 -0.007213961 0.003882241 5 -0.027147135 -0.007213961 6 0.193662493 -0.027147135 7 0.161639009 0.193662493 8 0.076082216 0.161639009 9 0.022438839 0.076082216 10 0.043641613 0.022438839 11 -0.006166807 0.043641613 12 0.377752174 -0.006166807 13 0.187736997 0.377752174 14 0.249526997 0.187736997 15 0.269901734 0.249526997 16 0.313548659 0.269901734 17 0.133025328 0.313548659 18 -0.192360926 0.133025328 19 0.093936637 -0.192360926 20 -0.097389185 0.093936637 21 0.007428116 -0.097389185 22 0.027658285 0.007428116 23 0.112551609 0.027658285 24 0.248917082 0.112551609 25 -0.083882164 0.248917082 26 0.169925638 -0.083882164 27 0.191300092 0.169925638 28 0.166586882 0.191300092 29 0.185737328 0.166586882 30 -0.402972715 0.185737328 31 -0.374152640 -0.402972715 32 -0.409645231 -0.374152640 33 -0.352106751 -0.409645231 34 -0.366347643 -0.352106751 35 -0.380131511 -0.366347643 36 0.455143713 -0.380131511 37 0.421207682 0.455143713 38 0.499060293 0.421207682 39 0.506672167 0.499060293 40 0.524159839 0.506672167 41 0.495305127 0.524159839 42 -0.223286452 0.495305127 43 -0.216787407 -0.223286452 44 -0.183469404 -0.216787407 45 -0.172373806 -0.183469404 46 -0.167814127 -0.172373806 47 -0.255561473 -0.167814127 48 -0.567141596 -0.255561473 49 -0.599487132 -0.567141596 50 -0.635599689 -0.599487132 51 -0.595487643 -0.635599689 52 -0.616892285 -0.595487643 53 -0.621838373 -0.616892285 54 0.191432676 -0.621838373 55 0.241936298 0.191432676 56 0.124038472 0.241936298 57 0.309865013 0.124038472 58 0.277093790 0.309865013 59 0.355032404 0.277093790 60 -0.570710249 0.355032404 61 -0.596375758 -0.570710249 62 -0.303830652 -0.596375758 63 -0.247203945 -0.303830652 64 -0.232275188 -0.247203945 65 -0.513783541 -0.232275188 66 0.172217132 -0.513783541 67 0.115992568 0.172217132 68 -0.080882466 0.115992568 69 -0.027245986 -0.080882466 70 0.103009660 -0.027245986 71 -0.057153835 0.103009660 72 0.271639914 -0.057153835 73 0.308823005 0.271639914 74 0.162846794 0.308823005 75 0.193462402 0.162846794 76 0.088957732 0.193462402 77 0.196189320 0.088957732 78 0.021116334 0.196189320 79 0.125816380 0.021116334 80 -0.022638810 0.125816380 81 0.039551281 -0.022638810 82 0.104642629 0.039551281 83 0.084231370 0.104642629 84 0.090831150 0.084231370 85 0.303782898 0.090831150 86 0.298118192 0.303782898 87 0.089551317 0.298118192 88 0.011746351 0.089551317 89 0.313083289 0.011746351 90 -0.028087365 0.313083289 91 0.004705942 -0.028087365 92 0.236268168 0.004705942 93 0.115131758 0.236268168 94 0.037426133 0.115131758 95 0.271400498 0.037426133 96 0.248165890 0.271400498 97 0.161133628 0.248165890 98 0.019873327 0.161133628 99 -0.066511602 0.019873327 100 -0.117486717 -0.066511602 101 -0.235209492 -0.117486717 102 -0.168303007 -0.235209492 103 0.245387749 -0.168303007 104 0.338594687 0.245387749 105 0.356060442 0.338594687 106 0.425549199 0.356060442 107 0.304024126 0.425549199 108 0.337239901 0.304024126 109 0.271280940 0.337239901 110 0.284250889 0.271280940 111 0.587829739 0.284250889 112 0.505506355 0.587829739 113 0.579830844 0.505506355 114 0.324650678 0.579830844 115 0.355785477 0.324650678 116 0.305008824 0.355785477 117 0.353206601 0.305008824 118 0.540032505 0.353206601 119 0.620204103 0.540032505 120 0.274632385 0.620204103 121 0.389067484 0.274632385 122 0.076081431 0.389067484 123 0.235972527 0.076081431 124 0.184707864 0.235972527 125 0.185015944 0.184707864 126 0.108103956 0.185015944 127 0.194580793 0.108103956 128 0.332456734 0.194580793 129 0.245506397 0.332456734 130 0.223943105 0.245506397 131 0.166783654 0.223943105 132 0.209289644 0.166783654 133 0.261879973 0.209289644 134 -0.009997143 0.261879973 135 0.032079371 -0.009997143 136 -0.183886650 0.032079371 137 -0.042583000 -0.183886650 138 -0.069763220 -0.042583000 139 0.060717418 -0.069763220 140 0.263174218 0.060717418 141 0.119624167 0.263174218 142 0.423695379 0.119624167 143 0.358884049 0.423695379 144 0.524390671 0.358884049 145 0.381901720 0.524390671 146 -0.141519148 0.381901720 147 0.141299074 -0.141519148 148 -0.002806019 0.141299074 149 0.216185958 -0.002806019 150 0.173607496 0.216185958 151 -0.311411972 0.173607496 152 -0.013171765 -0.311411972 153 0.146206764 -0.013171765 154 0.127327547 0.146206764 155 0.106959051 0.127327547 156 0.181706403 0.106959051 157 -0.085210884 0.181706403 158 0.111233395 -0.085210884 159 -0.061946131 0.111233395 160 -0.370019052 -0.061946131 161 0.026674545 -0.370019052 162 -0.028753632 0.026674545 163 0.195359606 -0.028753632 164 0.154487444 0.195359606 165 -0.554188320 0.154487444 166 -0.160158840 -0.554188320 167 -0.143073289 -0.160158840 168 -0.700266478 -0.143073289 169 -0.212775751 -0.700266478 170 -0.164125805 -0.212775751 171 -0.784234907 -0.164125805 172 -0.815042826 -0.784234907 173 -0.816142275 -0.815042826 174 -0.807763578 -0.816142275 175 -0.767632027 -0.807763578 176 -0.822677162 -0.767632027 177 0.375104547 -0.822677162 178 0.372747415 0.375104547 179 0.340448864 0.372747415 180 0.307685537 0.340448864 181 0.330332858 0.307685537 182 0.326629150 0.330332858 183 -0.850573476 0.326629150 184 -0.856737335 -0.850573476 185 -0.849573868 -0.856737335 186 -0.720054663 -0.849573868 187 -0.702413354 -0.720054663 188 -0.808257564 -0.702413354 189 -0.696701583 -0.808257564 190 -0.785411719 -0.696701583 191 -0.617762821 -0.785411719 192 -0.363408746 -0.617762821 193 -0.579766277 -0.363408746 194 -0.581500764 -0.579766277 195 NA -0.581500764 > dum1 <- dum[2:length(myerror),] > dum1 lag(myerror, k = 1) myerror [1,] -0.009004078 -0.074147548 [2,] -0.061051763 -0.009004078 [3,] 0.003882241 -0.061051763 [4,] -0.007213961 0.003882241 [5,] -0.027147135 -0.007213961 [6,] 0.193662493 -0.027147135 [7,] 0.161639009 0.193662493 [8,] 0.076082216 0.161639009 [9,] 0.022438839 0.076082216 [10,] 0.043641613 0.022438839 [11,] -0.006166807 0.043641613 [12,] 0.377752174 -0.006166807 [13,] 0.187736997 0.377752174 [14,] 0.249526997 0.187736997 [15,] 0.269901734 0.249526997 [16,] 0.313548659 0.269901734 [17,] 0.133025328 0.313548659 [18,] -0.192360926 0.133025328 [19,] 0.093936637 -0.192360926 [20,] -0.097389185 0.093936637 [21,] 0.007428116 -0.097389185 [22,] 0.027658285 0.007428116 [23,] 0.112551609 0.027658285 [24,] 0.248917082 0.112551609 [25,] -0.083882164 0.248917082 [26,] 0.169925638 -0.083882164 [27,] 0.191300092 0.169925638 [28,] 0.166586882 0.191300092 [29,] 0.185737328 0.166586882 [30,] -0.402972715 0.185737328 [31,] -0.374152640 -0.402972715 [32,] -0.409645231 -0.374152640 [33,] -0.352106751 -0.409645231 [34,] -0.366347643 -0.352106751 [35,] -0.380131511 -0.366347643 [36,] 0.455143713 -0.380131511 [37,] 0.421207682 0.455143713 [38,] 0.499060293 0.421207682 [39,] 0.506672167 0.499060293 [40,] 0.524159839 0.506672167 [41,] 0.495305127 0.524159839 [42,] -0.223286452 0.495305127 [43,] -0.216787407 -0.223286452 [44,] -0.183469404 -0.216787407 [45,] -0.172373806 -0.183469404 [46,] -0.167814127 -0.172373806 [47,] -0.255561473 -0.167814127 [48,] -0.567141596 -0.255561473 [49,] -0.599487132 -0.567141596 [50,] -0.635599689 -0.599487132 [51,] -0.595487643 -0.635599689 [52,] -0.616892285 -0.595487643 [53,] -0.621838373 -0.616892285 [54,] 0.191432676 -0.621838373 [55,] 0.241936298 0.191432676 [56,] 0.124038472 0.241936298 [57,] 0.309865013 0.124038472 [58,] 0.277093790 0.309865013 [59,] 0.355032404 0.277093790 [60,] -0.570710249 0.355032404 [61,] -0.596375758 -0.570710249 [62,] -0.303830652 -0.596375758 [63,] -0.247203945 -0.303830652 [64,] -0.232275188 -0.247203945 [65,] -0.513783541 -0.232275188 [66,] 0.172217132 -0.513783541 [67,] 0.115992568 0.172217132 [68,] -0.080882466 0.115992568 [69,] -0.027245986 -0.080882466 [70,] 0.103009660 -0.027245986 [71,] -0.057153835 0.103009660 [72,] 0.271639914 -0.057153835 [73,] 0.308823005 0.271639914 [74,] 0.162846794 0.308823005 [75,] 0.193462402 0.162846794 [76,] 0.088957732 0.193462402 [77,] 0.196189320 0.088957732 [78,] 0.021116334 0.196189320 [79,] 0.125816380 0.021116334 [80,] -0.022638810 0.125816380 [81,] 0.039551281 -0.022638810 [82,] 0.104642629 0.039551281 [83,] 0.084231370 0.104642629 [84,] 0.090831150 0.084231370 [85,] 0.303782898 0.090831150 [86,] 0.298118192 0.303782898 [87,] 0.089551317 0.298118192 [88,] 0.011746351 0.089551317 [89,] 0.313083289 0.011746351 [90,] -0.028087365 0.313083289 [91,] 0.004705942 -0.028087365 [92,] 0.236268168 0.004705942 [93,] 0.115131758 0.236268168 [94,] 0.037426133 0.115131758 [95,] 0.271400498 0.037426133 [96,] 0.248165890 0.271400498 [97,] 0.161133628 0.248165890 [98,] 0.019873327 0.161133628 [99,] -0.066511602 0.019873327 [100,] -0.117486717 -0.066511602 [101,] -0.235209492 -0.117486717 [102,] -0.168303007 -0.235209492 [103,] 0.245387749 -0.168303007 [104,] 0.338594687 0.245387749 [105,] 0.356060442 0.338594687 [106,] 0.425549199 0.356060442 [107,] 0.304024126 0.425549199 [108,] 0.337239901 0.304024126 [109,] 0.271280940 0.337239901 [110,] 0.284250889 0.271280940 [111,] 0.587829739 0.284250889 [112,] 0.505506355 0.587829739 [113,] 0.579830844 0.505506355 [114,] 0.324650678 0.579830844 [115,] 0.355785477 0.324650678 [116,] 0.305008824 0.355785477 [117,] 0.353206601 0.305008824 [118,] 0.540032505 0.353206601 [119,] 0.620204103 0.540032505 [120,] 0.274632385 0.620204103 [121,] 0.389067484 0.274632385 [122,] 0.076081431 0.389067484 [123,] 0.235972527 0.076081431 [124,] 0.184707864 0.235972527 [125,] 0.185015944 0.184707864 [126,] 0.108103956 0.185015944 [127,] 0.194580793 0.108103956 [128,] 0.332456734 0.194580793 [129,] 0.245506397 0.332456734 [130,] 0.223943105 0.245506397 [131,] 0.166783654 0.223943105 [132,] 0.209289644 0.166783654 [133,] 0.261879973 0.209289644 [134,] -0.009997143 0.261879973 [135,] 0.032079371 -0.009997143 [136,] -0.183886650 0.032079371 [137,] -0.042583000 -0.183886650 [138,] -0.069763220 -0.042583000 [139,] 0.060717418 -0.069763220 [140,] 0.263174218 0.060717418 [141,] 0.119624167 0.263174218 [142,] 0.423695379 0.119624167 [143,] 0.358884049 0.423695379 [144,] 0.524390671 0.358884049 [145,] 0.381901720 0.524390671 [146,] -0.141519148 0.381901720 [147,] 0.141299074 -0.141519148 [148,] -0.002806019 0.141299074 [149,] 0.216185958 -0.002806019 [150,] 0.173607496 0.216185958 [151,] -0.311411972 0.173607496 [152,] -0.013171765 -0.311411972 [153,] 0.146206764 -0.013171765 [154,] 0.127327547 0.146206764 [155,] 0.106959051 0.127327547 [156,] 0.181706403 0.106959051 [157,] -0.085210884 0.181706403 [158,] 0.111233395 -0.085210884 [159,] -0.061946131 0.111233395 [160,] -0.370019052 -0.061946131 [161,] 0.026674545 -0.370019052 [162,] -0.028753632 0.026674545 [163,] 0.195359606 -0.028753632 [164,] 0.154487444 0.195359606 [165,] -0.554188320 0.154487444 [166,] -0.160158840 -0.554188320 [167,] -0.143073289 -0.160158840 [168,] -0.700266478 -0.143073289 [169,] -0.212775751 -0.700266478 [170,] -0.164125805 -0.212775751 [171,] -0.784234907 -0.164125805 [172,] -0.815042826 -0.784234907 [173,] -0.816142275 -0.815042826 [174,] -0.807763578 -0.816142275 [175,] -0.767632027 -0.807763578 [176,] -0.822677162 -0.767632027 [177,] 0.375104547 -0.822677162 [178,] 0.372747415 0.375104547 [179,] 0.340448864 0.372747415 [180,] 0.307685537 0.340448864 [181,] 0.330332858 0.307685537 [182,] 0.326629150 0.330332858 [183,] -0.850573476 0.326629150 [184,] -0.856737335 -0.850573476 [185,] -0.849573868 -0.856737335 [186,] -0.720054663 -0.849573868 [187,] -0.702413354 -0.720054663 [188,] -0.808257564 -0.702413354 [189,] -0.696701583 -0.808257564 [190,] -0.785411719 -0.696701583 [191,] -0.617762821 -0.785411719 [192,] -0.363408746 -0.617762821 [193,] -0.579766277 -0.363408746 [194,] -0.581500764 -0.579766277 > z <- as.data.frame(dum1) > z lag(myerror, k = 1) myerror 1 -0.009004078 -0.074147548 2 -0.061051763 -0.009004078 3 0.003882241 -0.061051763 4 -0.007213961 0.003882241 5 -0.027147135 -0.007213961 6 0.193662493 -0.027147135 7 0.161639009 0.193662493 8 0.076082216 0.161639009 9 0.022438839 0.076082216 10 0.043641613 0.022438839 11 -0.006166807 0.043641613 12 0.377752174 -0.006166807 13 0.187736997 0.377752174 14 0.249526997 0.187736997 15 0.269901734 0.249526997 16 0.313548659 0.269901734 17 0.133025328 0.313548659 18 -0.192360926 0.133025328 19 0.093936637 -0.192360926 20 -0.097389185 0.093936637 21 0.007428116 -0.097389185 22 0.027658285 0.007428116 23 0.112551609 0.027658285 24 0.248917082 0.112551609 25 -0.083882164 0.248917082 26 0.169925638 -0.083882164 27 0.191300092 0.169925638 28 0.166586882 0.191300092 29 0.185737328 0.166586882 30 -0.402972715 0.185737328 31 -0.374152640 -0.402972715 32 -0.409645231 -0.374152640 33 -0.352106751 -0.409645231 34 -0.366347643 -0.352106751 35 -0.380131511 -0.366347643 36 0.455143713 -0.380131511 37 0.421207682 0.455143713 38 0.499060293 0.421207682 39 0.506672167 0.499060293 40 0.524159839 0.506672167 41 0.495305127 0.524159839 42 -0.223286452 0.495305127 43 -0.216787407 -0.223286452 44 -0.183469404 -0.216787407 45 -0.172373806 -0.183469404 46 -0.167814127 -0.172373806 47 -0.255561473 -0.167814127 48 -0.567141596 -0.255561473 49 -0.599487132 -0.567141596 50 -0.635599689 -0.599487132 51 -0.595487643 -0.635599689 52 -0.616892285 -0.595487643 53 -0.621838373 -0.616892285 54 0.191432676 -0.621838373 55 0.241936298 0.191432676 56 0.124038472 0.241936298 57 0.309865013 0.124038472 58 0.277093790 0.309865013 59 0.355032404 0.277093790 60 -0.570710249 0.355032404 61 -0.596375758 -0.570710249 62 -0.303830652 -0.596375758 63 -0.247203945 -0.303830652 64 -0.232275188 -0.247203945 65 -0.513783541 -0.232275188 66 0.172217132 -0.513783541 67 0.115992568 0.172217132 68 -0.080882466 0.115992568 69 -0.027245986 -0.080882466 70 0.103009660 -0.027245986 71 -0.057153835 0.103009660 72 0.271639914 -0.057153835 73 0.308823005 0.271639914 74 0.162846794 0.308823005 75 0.193462402 0.162846794 76 0.088957732 0.193462402 77 0.196189320 0.088957732 78 0.021116334 0.196189320 79 0.125816380 0.021116334 80 -0.022638810 0.125816380 81 0.039551281 -0.022638810 82 0.104642629 0.039551281 83 0.084231370 0.104642629 84 0.090831150 0.084231370 85 0.303782898 0.090831150 86 0.298118192 0.303782898 87 0.089551317 0.298118192 88 0.011746351 0.089551317 89 0.313083289 0.011746351 90 -0.028087365 0.313083289 91 0.004705942 -0.028087365 92 0.236268168 0.004705942 93 0.115131758 0.236268168 94 0.037426133 0.115131758 95 0.271400498 0.037426133 96 0.248165890 0.271400498 97 0.161133628 0.248165890 98 0.019873327 0.161133628 99 -0.066511602 0.019873327 100 -0.117486717 -0.066511602 101 -0.235209492 -0.117486717 102 -0.168303007 -0.235209492 103 0.245387749 -0.168303007 104 0.338594687 0.245387749 105 0.356060442 0.338594687 106 0.425549199 0.356060442 107 0.304024126 0.425549199 108 0.337239901 0.304024126 109 0.271280940 0.337239901 110 0.284250889 0.271280940 111 0.587829739 0.284250889 112 0.505506355 0.587829739 113 0.579830844 0.505506355 114 0.324650678 0.579830844 115 0.355785477 0.324650678 116 0.305008824 0.355785477 117 0.353206601 0.305008824 118 0.540032505 0.353206601 119 0.620204103 0.540032505 120 0.274632385 0.620204103 121 0.389067484 0.274632385 122 0.076081431 0.389067484 123 0.235972527 0.076081431 124 0.184707864 0.235972527 125 0.185015944 0.184707864 126 0.108103956 0.185015944 127 0.194580793 0.108103956 128 0.332456734 0.194580793 129 0.245506397 0.332456734 130 0.223943105 0.245506397 131 0.166783654 0.223943105 132 0.209289644 0.166783654 133 0.261879973 0.209289644 134 -0.009997143 0.261879973 135 0.032079371 -0.009997143 136 -0.183886650 0.032079371 137 -0.042583000 -0.183886650 138 -0.069763220 -0.042583000 139 0.060717418 -0.069763220 140 0.263174218 0.060717418 141 0.119624167 0.263174218 142 0.423695379 0.119624167 143 0.358884049 0.423695379 144 0.524390671 0.358884049 145 0.381901720 0.524390671 146 -0.141519148 0.381901720 147 0.141299074 -0.141519148 148 -0.002806019 0.141299074 149 0.216185958 -0.002806019 150 0.173607496 0.216185958 151 -0.311411972 0.173607496 152 -0.013171765 -0.311411972 153 0.146206764 -0.013171765 154 0.127327547 0.146206764 155 0.106959051 0.127327547 156 0.181706403 0.106959051 157 -0.085210884 0.181706403 158 0.111233395 -0.085210884 159 -0.061946131 0.111233395 160 -0.370019052 -0.061946131 161 0.026674545 -0.370019052 162 -0.028753632 0.026674545 163 0.195359606 -0.028753632 164 0.154487444 0.195359606 165 -0.554188320 0.154487444 166 -0.160158840 -0.554188320 167 -0.143073289 -0.160158840 168 -0.700266478 -0.143073289 169 -0.212775751 -0.700266478 170 -0.164125805 -0.212775751 171 -0.784234907 -0.164125805 172 -0.815042826 -0.784234907 173 -0.816142275 -0.815042826 174 -0.807763578 -0.816142275 175 -0.767632027 -0.807763578 176 -0.822677162 -0.767632027 177 0.375104547 -0.822677162 178 0.372747415 0.375104547 179 0.340448864 0.372747415 180 0.307685537 0.340448864 181 0.330332858 0.307685537 182 0.326629150 0.330332858 183 -0.850573476 0.326629150 184 -0.856737335 -0.850573476 185 -0.849573868 -0.856737335 186 -0.720054663 -0.849573868 187 -0.702413354 -0.720054663 188 -0.808257564 -0.702413354 189 -0.696701583 -0.808257564 190 -0.785411719 -0.696701583 191 -0.617762821 -0.785411719 192 -0.363408746 -0.617762821 193 -0.579766277 -0.363408746 194 -0.581500764 -0.579766277 > 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/7idrg1386271488.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/8w27k1386271488.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/96nne1386271488.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0)) > plot(mylm, las = 1, sub='Residual Diagnostics') > par(opar) > dev.off() null device 1 > if (n > n25) { + postscript(file="/var/wessaorg/rcomp/tmp/100got1386271488.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) + plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint') + grid() + dev.off() + } null device 1 > > #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab > load(file="/var/wessaorg/rcomp/createtable") > > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE) > a<-table.row.end(a) > myeq <- colnames(x)[1] > myeq <- paste(myeq, '[t] = ', sep='') > for (i in 1:k){ + if (mysum$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/11gexm1386271488.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/12zekm1386271488.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/13e8s11386271488.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/14ghof1386271488.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/15gec01386271488.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/167f6e1386271488.tab") + } > > try(system("convert tmp/10aks1386271488.ps tmp/10aks1386271488.png",intern=TRUE)) character(0) > try(system("convert tmp/2uhpz1386271488.ps tmp/2uhpz1386271488.png",intern=TRUE)) character(0) > try(system("convert tmp/31v1s1386271488.ps tmp/31v1s1386271488.png",intern=TRUE)) character(0) > try(system("convert tmp/4ni2x1386271488.ps tmp/4ni2x1386271488.png",intern=TRUE)) character(0) > try(system("convert tmp/5lo7s1386271488.ps tmp/5lo7s1386271488.png",intern=TRUE)) character(0) > try(system("convert tmp/61n7t1386271488.ps tmp/61n7t1386271488.png",intern=TRUE)) character(0) > try(system("convert tmp/7idrg1386271488.ps tmp/7idrg1386271488.png",intern=TRUE)) character(0) > try(system("convert tmp/8w27k1386271488.ps tmp/8w27k1386271488.png",intern=TRUE)) character(0) > try(system("convert tmp/96nne1386271488.ps tmp/96nne1386271488.png",intern=TRUE)) character(0) > try(system("convert tmp/100got1386271488.ps tmp/100got1386271488.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 23.441 4.379 27.823