R version 2.15.2 (2012-10-26) -- "Trick or Treat" Copyright (C) 2012 The R Foundation for Statistical Computing ISBN 3-900051-07-0 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(56 + ,30 + ,112285 + ,210907 + ,56 + ,28 + ,84786 + ,120982 + ,54 + ,38 + ,83123 + ,176508 + ,89 + ,30 + ,101193 + ,179321 + ,40 + ,22 + ,38361 + ,123185 + ,25 + ,26 + ,68504 + ,52746 + ,92 + ,25 + ,119182 + ,385534 + ,18 + ,18 + ,22807 + ,33170 + ,63 + ,11 + ,17140 + ,101645 + ,44 + ,26 + ,116174 + ,149061 + ,33 + ,25 + ,57635 + ,165446 + ,84 + ,38 + ,66198 + ,237213 + ,88 + ,44 + ,71701 + ,173326 + ,55 + ,30 + ,57793 + ,133131 + ,60 + ,40 + ,80444 + ,258873 + ,66 + ,34 + ,53855 + ,180083 + ,154 + ,47 + ,97668 + ,324799 + ,53 + ,30 + ,133824 + ,230964 + ,119 + ,31 + ,101481 + ,236785 + ,41 + ,23 + ,99645 + ,135473 + ,61 + ,36 + ,114789 + ,202925 + ,58 + ,36 + ,99052 + ,215147 + ,75 + ,30 + ,67654 + ,344297 + ,33 + ,25 + ,65553 + ,153935 + ,40 + ,39 + ,97500 + ,132943 + ,92 + ,34 + ,69112 + ,174724 + ,100 + ,31 + ,82753 + ,174415 + ,112 + ,31 + ,85323 + ,225548 + ,73 + ,33 + ,72654 + ,223632 + ,40 + ,25 + ,30727 + ,124817 + ,45 + ,33 + ,77873 + 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+ ,12 + ,19474 + ,135458 + ,35 + ,7 + ,35130 + ,73504 + ,43 + ,17 + ,39067 + ,63123 + ,46 + ,14 + ,13310 + ,61254 + ,30 + ,23 + ,65892 + ,74914 + ,23 + ,17 + ,4143 + ,31774 + ,38 + ,14 + ,28579 + ,81437 + ,54 + ,15 + ,51776 + ,87186 + ,20 + ,17 + ,21152 + ,50090 + ,53 + ,21 + ,38084 + ,65745 + ,45 + ,18 + ,27717 + ,56653 + ,39 + ,18 + ,32928 + ,158399 + ,20 + ,17 + ,11342 + ,46455 + ,24 + ,17 + ,19499 + ,73624 + ,31 + ,16 + ,16380 + ,38395 + ,35 + ,15 + ,36874 + ,91899 + ,151 + ,21 + ,48259 + ,139526 + ,52 + ,16 + ,16734 + ,52164 + ,30 + ,14 + ,28207 + ,51567 + ,31 + ,15 + ,30143 + ,70551 + ,29 + ,17 + ,41369 + ,84856 + ,57 + ,15 + ,45833 + ,102538 + ,40 + ,15 + ,29156 + ,86678 + ,44 + ,10 + ,35944 + ,85709 + ,25 + ,6 + ,36278 + ,34662 + ,77 + ,22 + ,45588 + ,150580 + ,35 + ,21 + ,45097 + ,99611 + ,11 + ,1 + ,3895 + ,19349 + ,63 + ,18 + ,28394 + ,99373 + ,44 + ,17 + ,18632 + ,86230 + ,19 + ,4 + ,2325 + ,30837 + ,13 + ,10 + ,25139 + ,31706 + ,42 + ,16 + ,27975 + ,89806 + ,38 + ,16 + ,14483 + ,62088 + ,29 + ,9 + ,13127 + ,40151 + ,20 + ,16 + ,5839 + ,27634 + ,27 + ,17 + ,24069 + ,76990 + ,20 + ,7 + ,3738 + ,37460 + ,19 + ,15 + ,18625 + ,54157 + ,37 + ,14 + ,36341 + ,49862 + ,26 + ,14 + ,24548 + ,84337 + ,42 + ,18 + ,21792 + ,64175 + ,49 + ,12 + ,26263 + ,59382 + ,30 + ,16 + ,23686 + ,119308 + ,49 + ,21 + ,49303 + ,76702 + ,67 + ,19 + ,25659 + ,103425 + ,28 + ,16 + ,28904 + ,70344 + ,19 + ,1 + ,2781 + ,43410 + ,49 + ,16 + ,29236 + ,104838 + ,27 + ,10 + ,19546 + ,62215 + ,30 + ,19 + ,22818 + ,69304 + ,22 + ,12 + ,32689 + ,53117 + ,12 + ,2 + ,5752 + ,19764 + ,31 + ,14 + ,22197 + ,86680 + ,20 + ,17 + ,20055 + ,84105 + ,20 + ,19 + ,25272 + ,77945 + ,39 + ,14 + ,82206 + ,89113 + ,29 + ,11 + ,32073 + ,91005 + ,16 + ,4 + ,5444 + ,40248 + ,27 + ,16 + ,20154 + ,64187 + ,21 + ,20 + ,36944 + ,50857 + ,19 + ,12 + ,8019 + ,56613 + ,35 + ,15 + ,30884 + ,62792 + ,14 + ,16 + ,19540 + ,72535) + ,dim=c(4 + ,289) + ,dimnames=list(c('#logins' + ,'totaal#peer_reviews' + ,'totaal#karakterscompendium' + ,'AantalurenRFC') + ,1:289)) > y <- array(NA,dim=c(4,289),dimnames=list(c('#logins','totaal#peer_reviews','totaal#karakterscompendium','AantalurenRFC'),1:289)) > 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 = '4' > par3 <- 'No Linear Trend' > par2 <- 'Do not include Seasonal Dummies' > par1 <- '4' > #'GNU S' R Code compiled by R2WASP v. 1.0.44 () > #Author: Prof. Dr. P. Wessa > #To cite this work: AUTHOR(S), (YEAR), YOUR SOFTWARE TITLE (vNUMBER) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/ > #Source of accompanying publication: Office for Research, Development, and Education > #Technical description: Write here your technical program description (don't use hard returns!) > library(lattice) > library(lmtest) Loading required package: zoo Attaching package: 'zoo' The following object(s) 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 AantalurenRFC #logins totaal#peer_reviews totaal#karakterscompendium 1 210907 56 30 112285 2 120982 56 28 84786 3 176508 54 38 83123 4 179321 89 30 101193 5 123185 40 22 38361 6 52746 25 26 68504 7 385534 92 25 119182 8 33170 18 18 22807 9 101645 63 11 17140 10 149061 44 26 116174 11 165446 33 25 57635 12 237213 84 38 66198 13 173326 88 44 71701 14 133131 55 30 57793 15 258873 60 40 80444 16 180083 66 34 53855 17 324799 154 47 97668 18 230964 53 30 133824 19 236785 119 31 101481 20 135473 41 23 99645 21 202925 61 36 114789 22 215147 58 36 99052 23 344297 75 30 67654 24 153935 33 25 65553 25 132943 40 39 97500 26 174724 92 34 69112 27 174415 100 31 82753 28 225548 112 31 85323 29 223632 73 33 72654 30 124817 40 25 30727 31 221698 45 33 77873 32 210767 60 35 117478 33 170266 62 42 74007 34 260561 75 43 90183 35 84853 31 30 61542 36 294424 77 33 101494 37 101011 34 13 27570 38 215641 46 32 55813 39 325107 99 36 79215 40 7176 17 0 1423 41 167542 66 28 55461 42 106408 30 14 31081 43 96560 76 17 22996 44 265769 146 32 83122 45 269651 67 30 70106 46 149112 56 35 60578 47 175824 107 20 39992 48 152871 58 28 79892 49 111665 34 28 49810 50 116408 61 39 71570 51 362301 119 34 100708 52 78800 42 26 33032 53 183167 66 39 82875 54 277965 89 39 139077 55 150629 44 33 71595 56 168809 66 28 72260 57 24188 24 4 5950 58 329267 259 39 115762 59 65029 17 18 32551 60 101097 64 14 31701 61 218946 41 29 80670 62 244052 68 44 143558 63 341570 168 21 117105 64 103597 43 16 23789 65 233328 132 28 120733 66 256462 105 35 105195 67 206161 71 28 73107 68 311473 112 38 132068 69 235800 94 23 149193 70 177939 82 36 46821 71 207176 70 32 87011 72 196553 57 29 95260 73 174184 53 25 55183 74 143246 103 27 106671 75 187559 121 36 73511 76 187681 62 28 92945 77 119016 52 23 78664 78 182192 52 40 70054 79 73566 32 23 22618 80 194979 62 40 74011 81 167488 45 28 83737 82 143756 46 34 69094 83 275541 63 33 93133 84 243199 75 28 95536 85 182999 88 34 225920 86 135649 46 30 62133 87 152299 53 33 61370 88 120221 37 22 43836 89 346485 90 38 106117 90 145790 63 26 38692 91 193339 78 35 84651 92 80953 25 8 56622 93 122774 45 24 15986 94 130585 46 29 95364 95 112611 41 20 26706 96 286468 144 29 89691 97 241066 82 45 67267 98 148446 91 37 126846 99 204713 71 33 41140 100 182079 63 33 102860 101 140344 53 25 51715 102 220516 62 32 55801 103 243060 63 29 111813 104 162765 32 28 120293 105 182613 39 28 138599 106 232138 62 31 161647 107 265318 117 52 115929 108 85574 34 21 24266 109 310839 92 24 162901 110 225060 93 41 109825 111 232317 54 33 129838 112 144966 144 32 37510 113 43287 14 19 43750 114 155754 61 20 40652 115 164709 109 31 87771 116 201940 38 31 85872 117 235454 73 32 89275 118 220801 75 18 44418 119 99466 50 23 192565 120 92661 61 17 35232 121 133328 55 20 40909 122 61361 77 12 13294 123 125930 75 17 32387 124 100750 72 30 140867 125 224549 50 31 120662 126 82316 32 10 21233 127 102010 53 13 44332 128 101523 42 22 61056 129 243511 71 42 101338 130 22938 10 1 1168 131 41566 35 9 13497 132 152474 65 32 65567 133 61857 25 11 25162 134 99923 66 25 32334 135 132487 41 36 40735 136 317394 86 31 91413 137 21054 16 0 855 138 209641 42 24 97068 139 22648 19 13 44339 140 31414 19 8 14116 141 46698 45 13 10288 142 131698 65 19 65622 143 91735 35 18 16563 144 244749 95 33 76643 145 184510 49 40 110681 146 79863 37 22 29011 147 128423 64 38 92696 148 97839 38 24 94785 149 38214 34 8 8773 150 151101 32 35 83209 151 272458 65 43 93815 152 172494 52 43 86687 153 108043 62 14 34553 154 328107 65 41 105547 155 250579 83 38 103487 156 351067 95 45 213688 157 158015 29 31 71220 158 98866 18 13 23517 159 85439 33 28 56926 160 229242 247 31 91721 161 351619 139 40 115168 162 84207 29 30 111194 163 120445 118 16 51009 164 324598 110 37 135777 165 131069 67 30 51513 166 204271 42 35 74163 167 165543 65 32 51633 168 141722 94 27 75345 169 116048 64 20 33416 170 250047 81 18 83305 171 299775 95 31 98952 172 195838 67 31 102372 173 173260 63 21 37238 174 254488 83 39 103772 175 104389 45 41 123969 176 136084 30 13 27142 177 199476 70 32 135400 178 92499 32 18 21399 179 224330 83 39 130115 180 135781 31 14 24874 181 74408 67 7 34988 182 81240 66 17 45549 183 14688 10 0 6023 184 181633 70 30 64466 185 271856 103 37 54990 186 7199 5 0 1644 187 46660 20 5 6179 188 17547 5 1 3926 189 133368 36 16 32755 190 95227 34 32 34777 191 152601 48 24 73224 192 98146 40 17 27114 193 79619 43 11 20760 194 59194 31 24 37636 195 139942 42 22 65461 196 118612 46 12 30080 197 72880 33 19 24094 198 65475 18 13 69008 199 99643 55 17 54968 200 71965 35 15 46090 201 77272 59 16 27507 202 49289 19 24 10672 203 135131 66 15 34029 204 108446 60 17 46300 205 89746 36 18 24760 206 44296 25 20 18779 207 77648 47 16 21280 208 181528 54 16 40662 209 134019 53 18 28987 210 124064 40 22 22827 211 92630 40 8 18513 212 121848 39 17 30594 213 52915 14 18 24006 214 81872 45 16 27913 215 58981 36 23 42744 216 53515 28 22 12934 217 60812 44 13 22574 218 56375 30 13 41385 219 65490 22 16 18653 220 80949 17 16 18472 221 76302 31 20 30976 222 104011 55 22 63339 223 98104 54 17 25568 224 67989 21 18 33747 225 30989 14 17 4154 226 135458 81 12 19474 227 73504 35 7 35130 228 63123 43 17 39067 229 61254 46 14 13310 230 74914 30 23 65892 231 31774 23 17 4143 232 81437 38 14 28579 233 87186 54 15 51776 234 50090 20 17 21152 235 65745 53 21 38084 236 56653 45 18 27717 237 158399 39 18 32928 238 46455 20 17 11342 239 73624 24 17 19499 240 38395 31 16 16380 241 91899 35 15 36874 242 139526 151 21 48259 243 52164 52 16 16734 244 51567 30 14 28207 245 70551 31 15 30143 246 84856 29 17 41369 247 102538 57 15 45833 248 86678 40 15 29156 249 85709 44 10 35944 250 34662 25 6 36278 251 150580 77 22 45588 252 99611 35 21 45097 253 19349 11 1 3895 254 99373 63 18 28394 255 86230 44 17 18632 256 30837 19 4 2325 257 31706 13 10 25139 258 89806 42 16 27975 259 62088 38 16 14483 260 40151 29 9 13127 261 27634 20 16 5839 262 76990 27 17 24069 263 37460 20 7 3738 264 54157 19 15 18625 265 49862 37 14 36341 266 84337 26 14 24548 267 64175 42 18 21792 268 59382 49 12 26263 269 119308 30 16 23686 270 76702 49 21 49303 271 103425 67 19 25659 272 70344 28 16 28904 273 43410 19 1 2781 274 104838 49 16 29236 275 62215 27 10 19546 276 69304 30 19 22818 277 53117 22 12 32689 278 19764 12 2 5752 279 86680 31 14 22197 280 84105 20 17 20055 281 77945 20 19 25272 282 89113 39 14 82206 283 91005 29 11 32073 284 40248 16 4 5444 285 64187 27 16 20154 286 50857 21 20 36944 287 56613 19 12 8019 288 62792 35 15 30884 289 72535 14 16 19540 > k <- length(x[1,]) > df <- as.data.frame(x) > (mylm <- lm(df)) Call: lm(formula = df) Coefficients: (Intercept) `#logins` -7081.372 902.659 `totaal#peer_reviews` `totaal#karakterscompendium` 2572.502 0.619 > (mysum <- summary(mylm)) Call: lm(formula = df) Residuals: Min 1Q Median 3Q Max -123153 -22381 -1671 18618 171489 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -7.081e+03 6.171e+03 -1.147 0.252 `#logins` 9.027e+02 8.976e+01 10.057 < 2e-16 *** `totaal#peer_reviews` 2.573e+03 3.568e+02 7.209 5.07e-12 *** `totaal#karakterscompendium` 6.190e-01 9.264e-02 6.681 1.24e-10 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 41920 on 285 degrees of freedom Multiple R-squared: 0.7435, Adjusted R-squared: 0.7408 F-statistic: 275.4 on 3 and 285 DF, p-value: < 2.2e-16 > 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,] 0.9899473 2.010541e-02 1.005271e-02 [2,] 0.9771043 4.579146e-02 2.289573e-02 [3,] 0.9625446 7.491085e-02 3.745542e-02 [4,] 0.9543930 9.121410e-02 4.560705e-02 [5,] 0.9766615 4.667697e-02 2.333848e-02 [6,] 0.9650538 6.989240e-02 3.494620e-02 [7,] 0.9618477 7.630465e-02 3.815233e-02 [8,] 0.9406172 1.187655e-01 5.938275e-02 [9,] 0.9777685 4.446303e-02 2.223152e-02 [10,] 0.9665556 6.688880e-02 3.344440e-02 [11,] 0.9627130 7.457401e-02 3.728701e-02 [12,] 0.9458973 1.082054e-01 5.410272e-02 [13,] 0.9500246 9.995089e-02 4.997544e-02 [14,] 0.9379059 1.241882e-01 6.209412e-02 [15,] 0.9159830 1.680340e-01 8.401702e-02 [16,] 0.8928338 2.143323e-01 1.071662e-01 [17,] 0.9974658 5.068380e-03 2.534190e-03 [18,] 0.9965850 6.830040e-03 3.415020e-03 [19,] 0.9960781 7.843896e-03 3.921948e-03 [20,] 0.9959267 8.146653e-03 4.073326e-03 [21,] 0.9970174 5.965252e-03 2.982626e-03 [22,] 0.9960208 7.958466e-03 3.979233e-03 [23,] 0.9951397 9.720681e-03 4.860341e-03 [24,] 0.9932687 1.346265e-02 6.731325e-03 [25,] 0.9946708 1.065849e-02 5.329247e-03 [26,] 0.9923256 1.534877e-02 7.674384e-03 [27,] 0.9898099 2.038022e-02 1.019011e-02 [28,] 0.9888123 2.237533e-02 1.118767e-02 [29,] 0.9884455 2.310893e-02 1.155446e-02 [30,] 0.9929096 1.418070e-02 7.090351e-03 [31,] 0.9905751 1.884978e-02 9.424889e-03 [32,] 0.9938627 1.227462e-02 6.137311e-03 [33,] 0.9975376 4.924799e-03 2.462400e-03 [34,] 0.9966470 6.705988e-03 3.352994e-03 [35,] 0.9952784 9.443182e-03 4.721591e-03 [36,] 0.9940030 1.199392e-02 5.996960e-03 [37,] 0.9937039 1.259227e-02 6.296133e-03 [38,] 0.9924886 1.502287e-02 7.511436e-03 [39,] 0.9969278 6.144397e-03 3.072198e-03 [40,] 0.9960183 7.963353e-03 3.981677e-03 [41,] 0.9947450 1.051005e-02 5.255023e-03 [42,] 0.9934379 1.312410e-02 6.562050e-03 [43,] 0.9913225 1.735499e-02 8.677493e-03 [44,] 0.9947698 1.046036e-02 5.230180e-03 [45,] 0.9980410 3.918024e-03 1.959012e-03 [46,] 0.9977233 4.553403e-03 2.276701e-03 [47,] 0.9970425 5.914905e-03 2.957452e-03 [48,] 0.9961285 7.742964e-03 3.871482e-03 [49,] 0.9947849 1.043015e-02 5.215074e-03 [50,] 0.9931360 1.372803e-02 6.864015e-03 [51,] 0.9910236 1.795286e-02 8.976430e-03 [52,] 0.9980432 3.913685e-03 1.956843e-03 [53,] 0.9973722 5.255693e-03 2.627846e-03 [54,] 0.9965040 6.991963e-03 3.495982e-03 [55,] 0.9971655 5.669057e-03 2.834529e-03 [56,] 0.9965727 6.854615e-03 3.427308e-03 [57,] 0.9969368 6.126396e-03 3.063198e-03 [58,] 0.9959886 8.022814e-03 4.011407e-03 [59,] 0.9962249 7.550182e-03 3.775091e-03 [60,] 0.9950706 9.858712e-03 4.929356e-03 [61,] 0.9941459 1.170816e-02 5.854080e-03 [62,] 0.9932679 1.346420e-02 6.732099e-03 [63,] 0.9923583 1.528341e-02 7.641703e-03 [64,] 0.9901275 1.974503e-02 9.872517e-03 [65,] 0.9875362 2.492761e-02 1.246381e-02 [66,] 0.9844941 3.101189e-02 1.550595e-02 [67,] 0.9826734 3.465318e-02 1.732659e-02 [68,] 0.9922413 1.551740e-02 7.758698e-03 [69,] 0.9930349 1.393014e-02 6.965071e-03 [70,] 0.9910947 1.781057e-02 8.905284e-03 [71,] 0.9904429 1.911416e-02 9.557078e-03 [72,] 0.9877795 2.444100e-02 1.222050e-02 [73,] 0.9853073 2.938550e-02 1.469275e-02 [74,] 0.9814844 3.703128e-02 1.851564e-02 [75,] 0.9770347 4.593063e-02 2.296531e-02 [76,] 0.9729376 5.412489e-02 2.706245e-02 [77,] 0.9841463 3.170744e-02 1.585372e-02 [78,] 0.9847185 3.056308e-02 1.528154e-02 [79,] 0.9979143 4.171362e-03 2.085681e-03 [80,] 0.9973460 5.308079e-03 2.654040e-03 [81,] 0.9965884 6.823117e-03 3.411558e-03 [82,] 0.9955804 8.839131e-03 4.419565e-03 [83,] 0.9990235 1.953090e-03 9.765452e-04 [84,] 0.9986937 2.612584e-03 1.306292e-03 [85,] 0.9983089 3.382295e-03 1.691148e-03 [86,] 0.9977744 4.451290e-03 2.225645e-03 [87,] 0.9971698 5.660473e-03 2.830236e-03 [88,] 0.9970240 5.951935e-03 2.975968e-03 [89,] 0.9961983 7.603388e-03 3.801694e-03 [90,] 0.9958217 8.356553e-03 4.178276e-03 [91,] 0.9948702 1.025964e-02 5.129820e-03 [92,] 0.9984037 3.192647e-03 1.596324e-03 [93,] 0.9982853 3.429341e-03 1.714671e-03 [94,] 0.9978197 4.360689e-03 2.180344e-03 [95,] 0.9971562 5.687528e-03 2.843764e-03 [96,] 0.9976051 4.789766e-03 2.394883e-03 [97,] 0.9978416 4.316843e-03 2.158421e-03 [98,] 0.9971836 5.632833e-03 2.816416e-03 [99,] 0.9963406 7.318888e-03 3.659444e-03 [100,] 0.9952969 9.406212e-03 4.703106e-03 [101,] 0.9948346 1.033070e-02 5.165352e-03 [102,] 0.9935388 1.292241e-02 6.461204e-03 [103,] 0.9961022 7.795607e-03 3.897804e-03 [104,] 0.9953195 9.361093e-03 4.680546e-03 [105,] 0.9946465 1.070691e-02 5.353457e-03 [106,] 0.9974889 5.022293e-03 2.511147e-03 [107,] 0.9974743 5.051460e-03 2.525730e-03 [108,] 0.9971062 5.787608e-03 2.893804e-03 [109,] 0.9977776 4.444883e-03 2.222441e-03 [110,] 0.9977935 4.413011e-03 2.206505e-03 [111,] 0.9977662 4.467638e-03 2.233819e-03 [112,] 0.9990445 1.910985e-03 9.554926e-04 [113,] 0.9998743 2.513539e-04 1.256770e-04 [114,] 0.9998446 3.107481e-04 1.553740e-04 [115,] 0.9997933 4.134807e-04 2.067403e-04 [116,] 0.9998002 3.995061e-04 1.997531e-04 [117,] 0.9997288 5.424875e-04 2.712438e-04 [118,] 0.9999805 3.909969e-05 1.954985e-05 [119,] 0.9999767 4.651626e-05 2.325813e-05 [120,] 0.9999691 6.188377e-05 3.094189e-05 [121,] 0.9999561 8.776045e-05 4.388023e-05 [122,] 0.9999455 1.089579e-04 5.447893e-05 [123,] 0.9999288 1.424983e-04 7.124917e-05 [124,] 0.9999032 1.935745e-04 9.678725e-05 [125,] 0.9998736 2.528111e-04 1.264056e-04 [126,] 0.9998380 3.239008e-04 1.619504e-04 [127,] 0.9997772 4.455915e-04 2.227957e-04 [128,] 0.9997580 4.839560e-04 2.419780e-04 [129,] 0.9996793 6.413243e-04 3.206621e-04 [130,] 0.9999636 7.276843e-05 3.638421e-05 [131,] 0.9999492 1.016633e-04 5.083164e-05 [132,] 0.9999627 7.457778e-05 3.728889e-05 [133,] 0.9999696 6.076172e-05 3.038086e-05 [134,] 0.9999577 8.453410e-05 4.226705e-05 [135,] 0.9999486 1.028924e-04 5.144622e-05 [136,] 0.9999286 1.428075e-04 7.140375e-05 [137,] 0.9999029 1.942417e-04 9.712083e-05 [138,] 0.9999018 1.964063e-04 9.820314e-05 [139,] 0.9998743 2.513122e-04 1.256561e-04 [140,] 0.9998384 3.231039e-04 1.615520e-04 [141,] 0.9999257 1.486974e-04 7.434868e-05 [142,] 0.9999411 1.177356e-04 5.886780e-05 [143,] 0.9999195 1.609710e-04 8.048552e-05 [144,] 0.9998904 2.191939e-04 1.095969e-04 [145,] 0.9999215 1.569318e-04 7.846590e-05 [146,] 0.9999052 1.895684e-04 9.478418e-05 [147,] 0.9998681 2.637973e-04 1.318986e-04 [148,] 0.9999881 2.382869e-05 1.191434e-05 [149,] 0.9999860 2.806634e-05 1.403317e-05 [150,] 0.9999837 3.257177e-05 1.628589e-05 [151,] 0.9999788 4.240861e-05 2.120430e-05 [152,] 0.9999793 4.148120e-05 2.074060e-05 [153,] 0.9999792 4.162796e-05 2.081398e-05 [154,] 0.9999994 1.246772e-06 6.233862e-07 [155,] 0.9999997 5.788306e-07 2.894153e-07 [156,] 0.9999999 1.829092e-07 9.145459e-08 [157,] 1.0000000 9.415688e-08 4.707844e-08 [158,] 1.0000000 4.527018e-08 2.263509e-08 [159,] 1.0000000 6.095488e-08 3.047744e-08 [160,] 1.0000000 3.423980e-08 1.711990e-08 [161,] 1.0000000 5.261332e-08 2.630666e-08 [162,] 1.0000000 3.651903e-08 1.825951e-08 [163,] 1.0000000 5.974187e-08 2.987093e-08 [164,] 1.0000000 6.463137e-09 3.231569e-09 [165,] 1.0000000 2.610830e-10 1.305415e-10 [166,] 1.0000000 3.685311e-10 1.842656e-10 [167,] 1.0000000 1.882911e-10 9.414554e-11 [168,] 1.0000000 8.111219e-11 4.055609e-11 [169,] 1.0000000 6.488127e-12 3.244064e-12 [170,] 1.0000000 1.029214e-12 5.146069e-13 [171,] 1.0000000 1.906864e-12 9.534320e-13 [172,] 1.0000000 3.234438e-12 1.617219e-12 [173,] 1.0000000 5.625792e-12 2.812896e-12 [174,] 1.0000000 8.920633e-13 4.460317e-13 [175,] 1.0000000 1.347269e-12 6.736345e-13 [176,] 1.0000000 1.114220e-12 5.571098e-13 [177,] 1.0000000 2.219317e-12 1.109659e-12 [178,] 1.0000000 2.716543e-12 1.358272e-12 [179,] 1.0000000 2.974646e-14 1.487323e-14 [180,] 1.0000000 5.563017e-14 2.781509e-14 [181,] 1.0000000 1.139977e-13 5.699884e-14 [182,] 1.0000000 2.313338e-13 1.156669e-13 [183,] 1.0000000 6.649080e-14 3.324540e-14 [184,] 1.0000000 1.254419e-13 6.272097e-14 [185,] 1.0000000 1.110835e-13 5.554177e-14 [186,] 1.0000000 1.897118e-13 9.485588e-14 [187,] 1.0000000 3.939298e-13 1.969649e-13 [188,] 1.0000000 4.566605e-13 2.283302e-13 [189,] 1.0000000 3.928280e-13 1.964140e-13 [190,] 1.0000000 3.064611e-13 1.532306e-13 [191,] 1.0000000 6.326824e-13 3.163412e-13 [192,] 1.0000000 1.212269e-12 6.061347e-13 [193,] 1.0000000 2.403311e-12 1.201655e-12 [194,] 1.0000000 4.496488e-12 2.248244e-12 [195,] 1.0000000 7.078172e-12 3.539086e-12 [196,] 1.0000000 1.211886e-11 6.059431e-12 [197,] 1.0000000 1.212581e-11 6.062903e-12 [198,] 1.0000000 2.430032e-11 1.215016e-11 [199,] 1.0000000 4.443165e-11 2.221582e-11 [200,] 1.0000000 5.813169e-11 2.906584e-11 [201,] 1.0000000 1.145212e-10 5.726059e-11 [202,] 1.0000000 8.054944e-13 4.027472e-13 [203,] 1.0000000 3.524510e-13 1.762255e-13 [204,] 1.0000000 1.611436e-13 8.057179e-14 [205,] 1.0000000 1.737372e-13 8.686859e-14 [206,] 1.0000000 6.078063e-14 3.039031e-14 [207,] 1.0000000 1.368012e-13 6.840060e-14 [208,] 1.0000000 3.133732e-13 1.566866e-13 [209,] 1.0000000 2.841564e-13 1.420782e-13 [210,] 1.0000000 4.869957e-13 2.434978e-13 [211,] 1.0000000 9.466356e-13 4.733178e-13 [212,] 1.0000000 1.700602e-12 8.503012e-13 [213,] 1.0000000 3.832986e-12 1.916493e-12 [214,] 1.0000000 5.037279e-12 2.518640e-12 [215,] 1.0000000 1.129853e-11 5.649267e-12 [216,] 1.0000000 2.319850e-11 1.159925e-11 [217,] 1.0000000 4.592853e-11 2.296427e-11 [218,] 1.0000000 1.014001e-10 5.070006e-11 [219,] 1.0000000 1.615051e-10 8.075257e-11 [220,] 1.0000000 6.922730e-11 3.461365e-11 [221,] 1.0000000 1.410358e-10 7.051790e-11 [222,] 1.0000000 1.960134e-10 9.800669e-11 [223,] 1.0000000 4.031483e-10 2.015742e-10 [224,] 1.0000000 4.385276e-10 2.192638e-10 [225,] 1.0000000 5.149181e-10 2.574591e-10 [226,] 1.0000000 1.081216e-09 5.406081e-10 [227,] 1.0000000 2.220270e-09 1.110135e-09 [228,] 1.0000000 3.896877e-09 1.948438e-09 [229,] 1.0000000 2.801935e-09 1.400967e-09 [230,] 1.0000000 2.733969e-09 1.366985e-09 [231,] 1.0000000 1.132560e-11 5.662802e-12 [232,] 1.0000000 2.161369e-11 1.080684e-11 [233,] 1.0000000 5.343023e-11 2.671512e-11 [234,] 1.0000000 3.912031e-11 1.956015e-11 [235,] 1.0000000 7.548995e-11 3.774498e-11 [236,] 1.0000000 6.686227e-11 3.343114e-11 [237,] 1.0000000 2.848078e-11 1.424039e-11 [238,] 1.0000000 4.865984e-11 2.432992e-11 [239,] 1.0000000 1.329536e-10 6.647682e-11 [240,] 1.0000000 3.282544e-10 1.641272e-10 [241,] 1.0000000 8.544767e-10 4.272384e-10 [242,] 1.0000000 2.097188e-09 1.048594e-09 [243,] 1.0000000 4.553665e-09 2.276833e-09 [244,] 1.0000000 8.095559e-09 4.047779e-09 [245,] 1.0000000 7.232135e-09 3.616067e-09 [246,] 1.0000000 1.588716e-08 7.943579e-09 [247,] 1.0000000 3.832605e-08 1.916303e-08 [248,] 1.0000000 9.700223e-08 4.850112e-08 [249,] 0.9999999 2.312373e-07 1.156186e-07 [250,] 0.9999997 5.399013e-07 2.699507e-07 [251,] 0.9999996 8.323345e-07 4.161673e-07 [252,] 0.9999991 1.720237e-06 8.601183e-07 [253,] 0.9999981 3.717006e-06 1.858503e-06 [254,] 0.9999968 6.464127e-06 3.232064e-06 [255,] 0.9999984 3.262161e-06 1.631081e-06 [256,] 0.9999960 8.047741e-06 4.023870e-06 [257,] 0.9999918 1.641586e-05 8.207929e-06 [258,] 0.9999829 3.417352e-05 1.708676e-05 [259,] 0.9999816 3.680206e-05 1.840103e-05 [260,] 0.9999669 6.620903e-05 3.310452e-05 [261,] 0.9999521 9.580653e-05 4.790326e-05 [262,] 0.9999390 1.220725e-04 6.103624e-05 [263,] 0.9999945 1.102734e-05 5.513671e-06 [264,] 0.9999946 1.080066e-05 5.400329e-06 [265,] 0.9999890 2.207966e-05 1.103983e-05 [266,] 0.9999658 6.842003e-05 3.421002e-05 [267,] 0.9998975 2.049568e-04 1.024784e-04 [268,] 0.9997178 5.643016e-04 2.821508e-04 [269,] 0.9991801 1.639790e-03 8.198948e-04 [270,] 0.9982126 3.574724e-03 1.787362e-03 [271,] 0.9956564 8.687176e-03 4.343588e-03 [272,] 0.9931286 1.374279e-02 6.871393e-03 [273,] 0.9867322 2.653555e-02 1.326777e-02 [274,] 0.9780793 4.384149e-02 2.192075e-02 [275,] 0.9534419 9.311620e-02 4.655810e-02 [276,] 0.8804682 2.390637e-01 1.195318e-01 > postscript(file="/var/wessaorg/rcomp/tmp/1gn621352127979.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$resid) > grid() > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/2za5b1352127979.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(mysum$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/373gv1352127979.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') > grid() > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/4r3iw1352127979.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/5cqmp1352127979.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 = 289 Frequency = 1 1 2 3 4 5 2.076381e+04 -4.699524e+04 -1.435960e+04 -3.374436e+04 1.382081e+04 6 7 8 9 10 -7.202580e+04 1.714886e+05 -3.641829e+04 1.295226e+04 -2.236743e+04 11 12 13 14 15 4.275297e+04 2.974163e+04 -5.659719e+04 -2.238083e+04 5.910265e+04 16 17 18 19 20 6.789396e+03 1.151013e+04 3.019688e+04 -6.110887e+03 -1.529906e+04 21 22 23 24 25 -8.716384e+03 1.595426e+04 1.646284e+05 2.634100e+04 -5.675875e+04 26 27 28 29 30 -3.148230e+04 -3.973838e+04 -1.028024e+03 3.495635e+04 1.246049e+04 31 32 33 34 35 5.506641e+04 9.363774e+02 -3.247039e+04 3.350515e+04 -5.131553e+04 36 37 38 39 40 8.428673e+04 2.689455e+04 6.433364e+04 1.011837e+05 -1.968617e+03 41 42 43 44 45 8.689351e+03 3.115649e+04 -2.292698e+04 7.292417e+03 9.568593e+04 46 47 48 49 50 -2.188883e+04 1.011714e+04 -1.388233e+04 -1.480479e+04 -7.619980e+04 51 52 53 54 55 1.121661e+05 -3.936104e+04 -2.095151e+04 1.829822e+04 -1.121406e+04 56 57 58 59 60 -4.416575e+02 -4.367297e+03 -6.942060e+04 -9.687831e+03 -5.228667e+03 61 62 63 64 65 6.448381e+04 -1.229505e+04 7.049813e+04 1.597942e+04 -2.550128e+04 66 67 68 69 70 1.361449e+04 3.187278e+04 3.795590e+04 6.518501e+03 -1.058834e+04 71 72 73 74 75 1.489433e+04 1.861755e+04 3.495550e+04 -7.812977e+04 -5.269224e+04 76 77 78 79 80 9.237668e+03 -2.869878e+04 -3.926024e+03 -2.140504e+04 -2.614860e+03 81 82 83 84 85 1.008931e+04 -2.091685e+04 8.321613e+04 5.141736e+04 -1.166555e+05 86 87 88 89 90 -1.442522e+04 -1.133907e+04 1.017595e+04 1.088892e+05 5.169772e+03 91 92 93 94 95 -1.242069e+04 9.840793e+03 1.760088e+04 -3.748557e+04 1.470321e+04 96 97 98 99 100 3.344825e+04 1.673076e+04 -1.003105e+05 3.734876e+04 -1.626655e+04 101 102 103 104 105 3.262070e+03 5.477353e+04 4.946286e+04 -5.526047e+03 -3.327449e+03 106 107 108 109 110 3.452963e+03 -3.873794e+04 -7.077406e+03 7.230553e+04 -2.525643e+04 111 112 113 114 115 2.539692e+04 -8.347299e+04 -3.822616e+04 3.116093e+04 -6.067428e+04 116 117 118 119 120 4.182092e+04 3.906302e+04 8.638469e+04 -1.169443e+05 -2.085977e+04 121 122 123 124 125 1.399181e+04 -4.016092e+04 1.532968e+03 -1.215270e+05 3.206419e+04 126 127 128 129 130 2.164476e+04 3.679241e+02 -2.369394e+04 1.573372e+04 1.769733e+04 131 132 133 134 135 -1.445239e+04 -2.202127e+04 2.499954e+03 -3.689732e+04 -1.526430e+04 136 137 138 139 140 1.105176e+05 1.316361e+04 5.698886e+04 -4.830801e+04 -7.972488e+03 141 142 143 144 145 -2.665073e+04 -9.388781e+03 1.066634e+04 3.374580e+04 -2.404678e+04 146 147 148 149 150 -2.100588e+04 -7.739655e+04 -4.978941e+04 -1.140524e+04 -1.224383e+04 151 152 153 154 155 5.218065e+04 -3.163679e+04 1.757360e+03 1.057130e+05 2.092967e+04 156 157 158 159 160 2.436758e+04 1.508894e+04 4.170076e+04 -4.453269e+04 -1.231531e+05 161 162 163 164 165 5.904563e+04 -8.088910e+04 -5.172024e+04 5.316297e+04 -3.138764e+04 166 167 168 169 170 3.749875e+04 -3.275980e+02 -5.214011e+04 -6.774208e+03 8.614500e+04 171 172 173 174 175 8.010829e+04 -6.711257e+02 4.640226e+04 2.208976e+04 -1.113545e+05 176 177 178 179 180 6.584310e+04 -2.275681e+04 1.114499e+04 -2.437366e+04 6.346876e+04 181 182 183 184 185 -1.865267e+04 -4.317993e+04 9.014752e+03 8.450923e+03 5.674398e+04 186 187 188 189 190 8.749498e+03 1.900109e+04 1.511252e+04 4.651939e+04 -3.222788e+04 191 192 193 194 195 9.291528e+03 8.605835e+03 6.738782e+03 -4.674251e+04 1.199852e+04 196 197 198 199 200 3.468254e+04 -1.361728e+04 -1.984762e+04 -2.067772e+04 -1.966236e+04 201 202 203 204 205 -2.708943e+04 -2.912581e+04 2.298656e+04 -1.102282e+04 2.701014e+03 206 207 208 209 210 -3.426271e+04 -1.202723e+04 7.353736e+04 2.901244e+04 2.431483e+04 211 212 213 214 215 3.156608e+04 3.105649e+04 -1.380479e+04 -1.010351e+04 -5.205798e+04 216 217 218 219 220 -2.927883e+04 -1.923869e+04 -2.268183e+04 7.267538e+00 2.009159e+04 221 222 223 224 225 -1.522219e+04 -3.435360e+04 -3.116468e+03 -1.107875e+04 -2.087058e+04 226 227 228 229 230 2.650023e+04 9.240522e+03 -3.652364e+04 -1.744041e+04 -4.503685e+04 231 232 233 234 235 -2.820270e+04 5.128748e+02 -2.511132e+04 -1.770671e+04 -5.260980e+04 236 237 238 239 240 -4.034620e+04 6.359032e+04 -1.526966e+04 3.239802e+03 -3.380475e+04 241 242 243 244 245 5.976027e+03 -7.358734e+04 -3.921070e+04 -2.190560e+04 -7.595078e+03 246 247 248 249 250 -3.578277e+03 -8.788784e+03 1.018911e+03 5.100247e+03 -1.871296e+04 251 252 253 254 255 3.344169e+03 -6.836745e+03 1.151775e+04 -1.429310e+04 -1.670731e+03 256 257 258 259 260 9.038749e+03 -1.423240e+04 5.000900e+02 -1.525619e+04 -1.022242e+04 261 262 263 264 265 -2.811199e+04 1.069151e+03 6.166984e+03 -6.027922e+03 -3.496388e+04 266 267 268 269 270 1.673983e+04 -2.644885e+04 -2.489284e+04 4.348874e+04 -4.498634e+04 271 272 273 274 275 -1.473136e+04 -6.899705e+03 2.904701e+04 8.432962e+03 7.101251e+03 276 277 278 279 280 -1.369550e+04 -1.076353e+04 7.308169e+03 1.602473e+04 1.698729e+04 281 282 283 284 285 2.453143e+03 -2.590707e+04 2.375964e+04 1.922717e+04 -6.738095e+03 286 287 288 289 -3.533459e+04 1.071034e+04 -1.942337e+04 1.372452e+04 > postscript(file="/var/wessaorg/rcomp/tmp/6khea1352127979.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 = 289 Frequency = 1 lag(myerror, k = 1) myerror 0 2.076381e+04 NA 1 -4.699524e+04 2.076381e+04 2 -1.435960e+04 -4.699524e+04 3 -3.374436e+04 -1.435960e+04 4 1.382081e+04 -3.374436e+04 5 -7.202580e+04 1.382081e+04 6 1.714886e+05 -7.202580e+04 7 -3.641829e+04 1.714886e+05 8 1.295226e+04 -3.641829e+04 9 -2.236743e+04 1.295226e+04 10 4.275297e+04 -2.236743e+04 11 2.974163e+04 4.275297e+04 12 -5.659719e+04 2.974163e+04 13 -2.238083e+04 -5.659719e+04 14 5.910265e+04 -2.238083e+04 15 6.789396e+03 5.910265e+04 16 1.151013e+04 6.789396e+03 17 3.019688e+04 1.151013e+04 18 -6.110887e+03 3.019688e+04 19 -1.529906e+04 -6.110887e+03 20 -8.716384e+03 -1.529906e+04 21 1.595426e+04 -8.716384e+03 22 1.646284e+05 1.595426e+04 23 2.634100e+04 1.646284e+05 24 -5.675875e+04 2.634100e+04 25 -3.148230e+04 -5.675875e+04 26 -3.973838e+04 -3.148230e+04 27 -1.028024e+03 -3.973838e+04 28 3.495635e+04 -1.028024e+03 29 1.246049e+04 3.495635e+04 30 5.506641e+04 1.246049e+04 31 9.363774e+02 5.506641e+04 32 -3.247039e+04 9.363774e+02 33 3.350515e+04 -3.247039e+04 34 -5.131553e+04 3.350515e+04 35 8.428673e+04 -5.131553e+04 36 2.689455e+04 8.428673e+04 37 6.433364e+04 2.689455e+04 38 1.011837e+05 6.433364e+04 39 -1.968617e+03 1.011837e+05 40 8.689351e+03 -1.968617e+03 41 3.115649e+04 8.689351e+03 42 -2.292698e+04 3.115649e+04 43 7.292417e+03 -2.292698e+04 44 9.568593e+04 7.292417e+03 45 -2.188883e+04 9.568593e+04 46 1.011714e+04 -2.188883e+04 47 -1.388233e+04 1.011714e+04 48 -1.480479e+04 -1.388233e+04 49 -7.619980e+04 -1.480479e+04 50 1.121661e+05 -7.619980e+04 51 -3.936104e+04 1.121661e+05 52 -2.095151e+04 -3.936104e+04 53 1.829822e+04 -2.095151e+04 54 -1.121406e+04 1.829822e+04 55 -4.416575e+02 -1.121406e+04 56 -4.367297e+03 -4.416575e+02 57 -6.942060e+04 -4.367297e+03 58 -9.687831e+03 -6.942060e+04 59 -5.228667e+03 -9.687831e+03 60 6.448381e+04 -5.228667e+03 61 -1.229505e+04 6.448381e+04 62 7.049813e+04 -1.229505e+04 63 1.597942e+04 7.049813e+04 64 -2.550128e+04 1.597942e+04 65 1.361449e+04 -2.550128e+04 66 3.187278e+04 1.361449e+04 67 3.795590e+04 3.187278e+04 68 6.518501e+03 3.795590e+04 69 -1.058834e+04 6.518501e+03 70 1.489433e+04 -1.058834e+04 71 1.861755e+04 1.489433e+04 72 3.495550e+04 1.861755e+04 73 -7.812977e+04 3.495550e+04 74 -5.269224e+04 -7.812977e+04 75 9.237668e+03 -5.269224e+04 76 -2.869878e+04 9.237668e+03 77 -3.926024e+03 -2.869878e+04 78 -2.140504e+04 -3.926024e+03 79 -2.614860e+03 -2.140504e+04 80 1.008931e+04 -2.614860e+03 81 -2.091685e+04 1.008931e+04 82 8.321613e+04 -2.091685e+04 83 5.141736e+04 8.321613e+04 84 -1.166555e+05 5.141736e+04 85 -1.442522e+04 -1.166555e+05 86 -1.133907e+04 -1.442522e+04 87 1.017595e+04 -1.133907e+04 88 1.088892e+05 1.017595e+04 89 5.169772e+03 1.088892e+05 90 -1.242069e+04 5.169772e+03 91 9.840793e+03 -1.242069e+04 92 1.760088e+04 9.840793e+03 93 -3.748557e+04 1.760088e+04 94 1.470321e+04 -3.748557e+04 95 3.344825e+04 1.470321e+04 96 1.673076e+04 3.344825e+04 97 -1.003105e+05 1.673076e+04 98 3.734876e+04 -1.003105e+05 99 -1.626655e+04 3.734876e+04 100 3.262070e+03 -1.626655e+04 101 5.477353e+04 3.262070e+03 102 4.946286e+04 5.477353e+04 103 -5.526047e+03 4.946286e+04 104 -3.327449e+03 -5.526047e+03 105 3.452963e+03 -3.327449e+03 106 -3.873794e+04 3.452963e+03 107 -7.077406e+03 -3.873794e+04 108 7.230553e+04 -7.077406e+03 109 -2.525643e+04 7.230553e+04 110 2.539692e+04 -2.525643e+04 111 -8.347299e+04 2.539692e+04 112 -3.822616e+04 -8.347299e+04 113 3.116093e+04 -3.822616e+04 114 -6.067428e+04 3.116093e+04 115 4.182092e+04 -6.067428e+04 116 3.906302e+04 4.182092e+04 117 8.638469e+04 3.906302e+04 118 -1.169443e+05 8.638469e+04 119 -2.085977e+04 -1.169443e+05 120 1.399181e+04 -2.085977e+04 121 -4.016092e+04 1.399181e+04 122 1.532968e+03 -4.016092e+04 123 -1.215270e+05 1.532968e+03 124 3.206419e+04 -1.215270e+05 125 2.164476e+04 3.206419e+04 126 3.679241e+02 2.164476e+04 127 -2.369394e+04 3.679241e+02 128 1.573372e+04 -2.369394e+04 129 1.769733e+04 1.573372e+04 130 -1.445239e+04 1.769733e+04 131 -2.202127e+04 -1.445239e+04 132 2.499954e+03 -2.202127e+04 133 -3.689732e+04 2.499954e+03 134 -1.526430e+04 -3.689732e+04 135 1.105176e+05 -1.526430e+04 136 1.316361e+04 1.105176e+05 137 5.698886e+04 1.316361e+04 138 -4.830801e+04 5.698886e+04 139 -7.972488e+03 -4.830801e+04 140 -2.665073e+04 -7.972488e+03 141 -9.388781e+03 -2.665073e+04 142 1.066634e+04 -9.388781e+03 143 3.374580e+04 1.066634e+04 144 -2.404678e+04 3.374580e+04 145 -2.100588e+04 -2.404678e+04 146 -7.739655e+04 -2.100588e+04 147 -4.978941e+04 -7.739655e+04 148 -1.140524e+04 -4.978941e+04 149 -1.224383e+04 -1.140524e+04 150 5.218065e+04 -1.224383e+04 151 -3.163679e+04 5.218065e+04 152 1.757360e+03 -3.163679e+04 153 1.057130e+05 1.757360e+03 154 2.092967e+04 1.057130e+05 155 2.436758e+04 2.092967e+04 156 1.508894e+04 2.436758e+04 157 4.170076e+04 1.508894e+04 158 -4.453269e+04 4.170076e+04 159 -1.231531e+05 -4.453269e+04 160 5.904563e+04 -1.231531e+05 161 -8.088910e+04 5.904563e+04 162 -5.172024e+04 -8.088910e+04 163 5.316297e+04 -5.172024e+04 164 -3.138764e+04 5.316297e+04 165 3.749875e+04 -3.138764e+04 166 -3.275980e+02 3.749875e+04 167 -5.214011e+04 -3.275980e+02 168 -6.774208e+03 -5.214011e+04 169 8.614500e+04 -6.774208e+03 170 8.010829e+04 8.614500e+04 171 -6.711257e+02 8.010829e+04 172 4.640226e+04 -6.711257e+02 173 2.208976e+04 4.640226e+04 174 -1.113545e+05 2.208976e+04 175 6.584310e+04 -1.113545e+05 176 -2.275681e+04 6.584310e+04 177 1.114499e+04 -2.275681e+04 178 -2.437366e+04 1.114499e+04 179 6.346876e+04 -2.437366e+04 180 -1.865267e+04 6.346876e+04 181 -4.317993e+04 -1.865267e+04 182 9.014752e+03 -4.317993e+04 183 8.450923e+03 9.014752e+03 184 5.674398e+04 8.450923e+03 185 8.749498e+03 5.674398e+04 186 1.900109e+04 8.749498e+03 187 1.511252e+04 1.900109e+04 188 4.651939e+04 1.511252e+04 189 -3.222788e+04 4.651939e+04 190 9.291528e+03 -3.222788e+04 191 8.605835e+03 9.291528e+03 192 6.738782e+03 8.605835e+03 193 -4.674251e+04 6.738782e+03 194 1.199852e+04 -4.674251e+04 195 3.468254e+04 1.199852e+04 196 -1.361728e+04 3.468254e+04 197 -1.984762e+04 -1.361728e+04 198 -2.067772e+04 -1.984762e+04 199 -1.966236e+04 -2.067772e+04 200 -2.708943e+04 -1.966236e+04 201 -2.912581e+04 -2.708943e+04 202 2.298656e+04 -2.912581e+04 203 -1.102282e+04 2.298656e+04 204 2.701014e+03 -1.102282e+04 205 -3.426271e+04 2.701014e+03 206 -1.202723e+04 -3.426271e+04 207 7.353736e+04 -1.202723e+04 208 2.901244e+04 7.353736e+04 209 2.431483e+04 2.901244e+04 210 3.156608e+04 2.431483e+04 211 3.105649e+04 3.156608e+04 212 -1.380479e+04 3.105649e+04 213 -1.010351e+04 -1.380479e+04 214 -5.205798e+04 -1.010351e+04 215 -2.927883e+04 -5.205798e+04 216 -1.923869e+04 -2.927883e+04 217 -2.268183e+04 -1.923869e+04 218 7.267538e+00 -2.268183e+04 219 2.009159e+04 7.267538e+00 220 -1.522219e+04 2.009159e+04 221 -3.435360e+04 -1.522219e+04 222 -3.116468e+03 -3.435360e+04 223 -1.107875e+04 -3.116468e+03 224 -2.087058e+04 -1.107875e+04 225 2.650023e+04 -2.087058e+04 226 9.240522e+03 2.650023e+04 227 -3.652364e+04 9.240522e+03 228 -1.744041e+04 -3.652364e+04 229 -4.503685e+04 -1.744041e+04 230 -2.820270e+04 -4.503685e+04 231 5.128748e+02 -2.820270e+04 232 -2.511132e+04 5.128748e+02 233 -1.770671e+04 -2.511132e+04 234 -5.260980e+04 -1.770671e+04 235 -4.034620e+04 -5.260980e+04 236 6.359032e+04 -4.034620e+04 237 -1.526966e+04 6.359032e+04 238 3.239802e+03 -1.526966e+04 239 -3.380475e+04 3.239802e+03 240 5.976027e+03 -3.380475e+04 241 -7.358734e+04 5.976027e+03 242 -3.921070e+04 -7.358734e+04 243 -2.190560e+04 -3.921070e+04 244 -7.595078e+03 -2.190560e+04 245 -3.578277e+03 -7.595078e+03 246 -8.788784e+03 -3.578277e+03 247 1.018911e+03 -8.788784e+03 248 5.100247e+03 1.018911e+03 249 -1.871296e+04 5.100247e+03 250 3.344169e+03 -1.871296e+04 251 -6.836745e+03 3.344169e+03 252 1.151775e+04 -6.836745e+03 253 -1.429310e+04 1.151775e+04 254 -1.670731e+03 -1.429310e+04 255 9.038749e+03 -1.670731e+03 256 -1.423240e+04 9.038749e+03 257 5.000900e+02 -1.423240e+04 258 -1.525619e+04 5.000900e+02 259 -1.022242e+04 -1.525619e+04 260 -2.811199e+04 -1.022242e+04 261 1.069151e+03 -2.811199e+04 262 6.166984e+03 1.069151e+03 263 -6.027922e+03 6.166984e+03 264 -3.496388e+04 -6.027922e+03 265 1.673983e+04 -3.496388e+04 266 -2.644885e+04 1.673983e+04 267 -2.489284e+04 -2.644885e+04 268 4.348874e+04 -2.489284e+04 269 -4.498634e+04 4.348874e+04 270 -1.473136e+04 -4.498634e+04 271 -6.899705e+03 -1.473136e+04 272 2.904701e+04 -6.899705e+03 273 8.432962e+03 2.904701e+04 274 7.101251e+03 8.432962e+03 275 -1.369550e+04 7.101251e+03 276 -1.076353e+04 -1.369550e+04 277 7.308169e+03 -1.076353e+04 278 1.602473e+04 7.308169e+03 279 1.698729e+04 1.602473e+04 280 2.453143e+03 1.698729e+04 281 -2.590707e+04 2.453143e+03 282 2.375964e+04 -2.590707e+04 283 1.922717e+04 2.375964e+04 284 -6.738095e+03 1.922717e+04 285 -3.533459e+04 -6.738095e+03 286 1.071034e+04 -3.533459e+04 287 -1.942337e+04 1.071034e+04 288 1.372452e+04 -1.942337e+04 289 NA 1.372452e+04 > dum1 <- dum[2:length(myerror),] > dum1 lag(myerror, k = 1) myerror [1,] -4.699524e+04 2.076381e+04 [2,] -1.435960e+04 -4.699524e+04 [3,] -3.374436e+04 -1.435960e+04 [4,] 1.382081e+04 -3.374436e+04 [5,] -7.202580e+04 1.382081e+04 [6,] 1.714886e+05 -7.202580e+04 [7,] -3.641829e+04 1.714886e+05 [8,] 1.295226e+04 -3.641829e+04 [9,] -2.236743e+04 1.295226e+04 [10,] 4.275297e+04 -2.236743e+04 [11,] 2.974163e+04 4.275297e+04 [12,] -5.659719e+04 2.974163e+04 [13,] -2.238083e+04 -5.659719e+04 [14,] 5.910265e+04 -2.238083e+04 [15,] 6.789396e+03 5.910265e+04 [16,] 1.151013e+04 6.789396e+03 [17,] 3.019688e+04 1.151013e+04 [18,] -6.110887e+03 3.019688e+04 [19,] -1.529906e+04 -6.110887e+03 [20,] -8.716384e+03 -1.529906e+04 [21,] 1.595426e+04 -8.716384e+03 [22,] 1.646284e+05 1.595426e+04 [23,] 2.634100e+04 1.646284e+05 [24,] -5.675875e+04 2.634100e+04 [25,] -3.148230e+04 -5.675875e+04 [26,] -3.973838e+04 -3.148230e+04 [27,] -1.028024e+03 -3.973838e+04 [28,] 3.495635e+04 -1.028024e+03 [29,] 1.246049e+04 3.495635e+04 [30,] 5.506641e+04 1.246049e+04 [31,] 9.363774e+02 5.506641e+04 [32,] -3.247039e+04 9.363774e+02 [33,] 3.350515e+04 -3.247039e+04 [34,] -5.131553e+04 3.350515e+04 [35,] 8.428673e+04 -5.131553e+04 [36,] 2.689455e+04 8.428673e+04 [37,] 6.433364e+04 2.689455e+04 [38,] 1.011837e+05 6.433364e+04 [39,] -1.968617e+03 1.011837e+05 [40,] 8.689351e+03 -1.968617e+03 [41,] 3.115649e+04 8.689351e+03 [42,] -2.292698e+04 3.115649e+04 [43,] 7.292417e+03 -2.292698e+04 [44,] 9.568593e+04 7.292417e+03 [45,] -2.188883e+04 9.568593e+04 [46,] 1.011714e+04 -2.188883e+04 [47,] -1.388233e+04 1.011714e+04 [48,] -1.480479e+04 -1.388233e+04 [49,] -7.619980e+04 -1.480479e+04 [50,] 1.121661e+05 -7.619980e+04 [51,] -3.936104e+04 1.121661e+05 [52,] -2.095151e+04 -3.936104e+04 [53,] 1.829822e+04 -2.095151e+04 [54,] -1.121406e+04 1.829822e+04 [55,] -4.416575e+02 -1.121406e+04 [56,] -4.367297e+03 -4.416575e+02 [57,] -6.942060e+04 -4.367297e+03 [58,] -9.687831e+03 -6.942060e+04 [59,] -5.228667e+03 -9.687831e+03 [60,] 6.448381e+04 -5.228667e+03 [61,] -1.229505e+04 6.448381e+04 [62,] 7.049813e+04 -1.229505e+04 [63,] 1.597942e+04 7.049813e+04 [64,] -2.550128e+04 1.597942e+04 [65,] 1.361449e+04 -2.550128e+04 [66,] 3.187278e+04 1.361449e+04 [67,] 3.795590e+04 3.187278e+04 [68,] 6.518501e+03 3.795590e+04 [69,] -1.058834e+04 6.518501e+03 [70,] 1.489433e+04 -1.058834e+04 [71,] 1.861755e+04 1.489433e+04 [72,] 3.495550e+04 1.861755e+04 [73,] -7.812977e+04 3.495550e+04 [74,] -5.269224e+04 -7.812977e+04 [75,] 9.237668e+03 -5.269224e+04 [76,] -2.869878e+04 9.237668e+03 [77,] -3.926024e+03 -2.869878e+04 [78,] -2.140504e+04 -3.926024e+03 [79,] -2.614860e+03 -2.140504e+04 [80,] 1.008931e+04 -2.614860e+03 [81,] -2.091685e+04 1.008931e+04 [82,] 8.321613e+04 -2.091685e+04 [83,] 5.141736e+04 8.321613e+04 [84,] -1.166555e+05 5.141736e+04 [85,] -1.442522e+04 -1.166555e+05 [86,] -1.133907e+04 -1.442522e+04 [87,] 1.017595e+04 -1.133907e+04 [88,] 1.088892e+05 1.017595e+04 [89,] 5.169772e+03 1.088892e+05 [90,] -1.242069e+04 5.169772e+03 [91,] 9.840793e+03 -1.242069e+04 [92,] 1.760088e+04 9.840793e+03 [93,] -3.748557e+04 1.760088e+04 [94,] 1.470321e+04 -3.748557e+04 [95,] 3.344825e+04 1.470321e+04 [96,] 1.673076e+04 3.344825e+04 [97,] -1.003105e+05 1.673076e+04 [98,] 3.734876e+04 -1.003105e+05 [99,] -1.626655e+04 3.734876e+04 [100,] 3.262070e+03 -1.626655e+04 [101,] 5.477353e+04 3.262070e+03 [102,] 4.946286e+04 5.477353e+04 [103,] -5.526047e+03 4.946286e+04 [104,] -3.327449e+03 -5.526047e+03 [105,] 3.452963e+03 -3.327449e+03 [106,] -3.873794e+04 3.452963e+03 [107,] -7.077406e+03 -3.873794e+04 [108,] 7.230553e+04 -7.077406e+03 [109,] -2.525643e+04 7.230553e+04 [110,] 2.539692e+04 -2.525643e+04 [111,] -8.347299e+04 2.539692e+04 [112,] -3.822616e+04 -8.347299e+04 [113,] 3.116093e+04 -3.822616e+04 [114,] -6.067428e+04 3.116093e+04 [115,] 4.182092e+04 -6.067428e+04 [116,] 3.906302e+04 4.182092e+04 [117,] 8.638469e+04 3.906302e+04 [118,] -1.169443e+05 8.638469e+04 [119,] -2.085977e+04 -1.169443e+05 [120,] 1.399181e+04 -2.085977e+04 [121,] -4.016092e+04 1.399181e+04 [122,] 1.532968e+03 -4.016092e+04 [123,] -1.215270e+05 1.532968e+03 [124,] 3.206419e+04 -1.215270e+05 [125,] 2.164476e+04 3.206419e+04 [126,] 3.679241e+02 2.164476e+04 [127,] -2.369394e+04 3.679241e+02 [128,] 1.573372e+04 -2.369394e+04 [129,] 1.769733e+04 1.573372e+04 [130,] -1.445239e+04 1.769733e+04 [131,] -2.202127e+04 -1.445239e+04 [132,] 2.499954e+03 -2.202127e+04 [133,] -3.689732e+04 2.499954e+03 [134,] -1.526430e+04 -3.689732e+04 [135,] 1.105176e+05 -1.526430e+04 [136,] 1.316361e+04 1.105176e+05 [137,] 5.698886e+04 1.316361e+04 [138,] -4.830801e+04 5.698886e+04 [139,] -7.972488e+03 -4.830801e+04 [140,] -2.665073e+04 -7.972488e+03 [141,] -9.388781e+03 -2.665073e+04 [142,] 1.066634e+04 -9.388781e+03 [143,] 3.374580e+04 1.066634e+04 [144,] -2.404678e+04 3.374580e+04 [145,] -2.100588e+04 -2.404678e+04 [146,] -7.739655e+04 -2.100588e+04 [147,] -4.978941e+04 -7.739655e+04 [148,] -1.140524e+04 -4.978941e+04 [149,] -1.224383e+04 -1.140524e+04 [150,] 5.218065e+04 -1.224383e+04 [151,] -3.163679e+04 5.218065e+04 [152,] 1.757360e+03 -3.163679e+04 [153,] 1.057130e+05 1.757360e+03 [154,] 2.092967e+04 1.057130e+05 [155,] 2.436758e+04 2.092967e+04 [156,] 1.508894e+04 2.436758e+04 [157,] 4.170076e+04 1.508894e+04 [158,] -4.453269e+04 4.170076e+04 [159,] -1.231531e+05 -4.453269e+04 [160,] 5.904563e+04 -1.231531e+05 [161,] -8.088910e+04 5.904563e+04 [162,] -5.172024e+04 -8.088910e+04 [163,] 5.316297e+04 -5.172024e+04 [164,] -3.138764e+04 5.316297e+04 [165,] 3.749875e+04 -3.138764e+04 [166,] -3.275980e+02 3.749875e+04 [167,] -5.214011e+04 -3.275980e+02 [168,] -6.774208e+03 -5.214011e+04 [169,] 8.614500e+04 -6.774208e+03 [170,] 8.010829e+04 8.614500e+04 [171,] -6.711257e+02 8.010829e+04 [172,] 4.640226e+04 -6.711257e+02 [173,] 2.208976e+04 4.640226e+04 [174,] -1.113545e+05 2.208976e+04 [175,] 6.584310e+04 -1.113545e+05 [176,] -2.275681e+04 6.584310e+04 [177,] 1.114499e+04 -2.275681e+04 [178,] -2.437366e+04 1.114499e+04 [179,] 6.346876e+04 -2.437366e+04 [180,] -1.865267e+04 6.346876e+04 [181,] -4.317993e+04 -1.865267e+04 [182,] 9.014752e+03 -4.317993e+04 [183,] 8.450923e+03 9.014752e+03 [184,] 5.674398e+04 8.450923e+03 [185,] 8.749498e+03 5.674398e+04 [186,] 1.900109e+04 8.749498e+03 [187,] 1.511252e+04 1.900109e+04 [188,] 4.651939e+04 1.511252e+04 [189,] -3.222788e+04 4.651939e+04 [190,] 9.291528e+03 -3.222788e+04 [191,] 8.605835e+03 9.291528e+03 [192,] 6.738782e+03 8.605835e+03 [193,] -4.674251e+04 6.738782e+03 [194,] 1.199852e+04 -4.674251e+04 [195,] 3.468254e+04 1.199852e+04 [196,] -1.361728e+04 3.468254e+04 [197,] -1.984762e+04 -1.361728e+04 [198,] -2.067772e+04 -1.984762e+04 [199,] -1.966236e+04 -2.067772e+04 [200,] -2.708943e+04 -1.966236e+04 [201,] -2.912581e+04 -2.708943e+04 [202,] 2.298656e+04 -2.912581e+04 [203,] -1.102282e+04 2.298656e+04 [204,] 2.701014e+03 -1.102282e+04 [205,] -3.426271e+04 2.701014e+03 [206,] -1.202723e+04 -3.426271e+04 [207,] 7.353736e+04 -1.202723e+04 [208,] 2.901244e+04 7.353736e+04 [209,] 2.431483e+04 2.901244e+04 [210,] 3.156608e+04 2.431483e+04 [211,] 3.105649e+04 3.156608e+04 [212,] -1.380479e+04 3.105649e+04 [213,] -1.010351e+04 -1.380479e+04 [214,] -5.205798e+04 -1.010351e+04 [215,] -2.927883e+04 -5.205798e+04 [216,] -1.923869e+04 -2.927883e+04 [217,] -2.268183e+04 -1.923869e+04 [218,] 7.267538e+00 -2.268183e+04 [219,] 2.009159e+04 7.267538e+00 [220,] -1.522219e+04 2.009159e+04 [221,] -3.435360e+04 -1.522219e+04 [222,] -3.116468e+03 -3.435360e+04 [223,] -1.107875e+04 -3.116468e+03 [224,] -2.087058e+04 -1.107875e+04 [225,] 2.650023e+04 -2.087058e+04 [226,] 9.240522e+03 2.650023e+04 [227,] -3.652364e+04 9.240522e+03 [228,] -1.744041e+04 -3.652364e+04 [229,] -4.503685e+04 -1.744041e+04 [230,] -2.820270e+04 -4.503685e+04 [231,] 5.128748e+02 -2.820270e+04 [232,] -2.511132e+04 5.128748e+02 [233,] -1.770671e+04 -2.511132e+04 [234,] -5.260980e+04 -1.770671e+04 [235,] -4.034620e+04 -5.260980e+04 [236,] 6.359032e+04 -4.034620e+04 [237,] -1.526966e+04 6.359032e+04 [238,] 3.239802e+03 -1.526966e+04 [239,] -3.380475e+04 3.239802e+03 [240,] 5.976027e+03 -3.380475e+04 [241,] -7.358734e+04 5.976027e+03 [242,] -3.921070e+04 -7.358734e+04 [243,] -2.190560e+04 -3.921070e+04 [244,] -7.595078e+03 -2.190560e+04 [245,] -3.578277e+03 -7.595078e+03 [246,] -8.788784e+03 -3.578277e+03 [247,] 1.018911e+03 -8.788784e+03 [248,] 5.100247e+03 1.018911e+03 [249,] -1.871296e+04 5.100247e+03 [250,] 3.344169e+03 -1.871296e+04 [251,] -6.836745e+03 3.344169e+03 [252,] 1.151775e+04 -6.836745e+03 [253,] -1.429310e+04 1.151775e+04 [254,] -1.670731e+03 -1.429310e+04 [255,] 9.038749e+03 -1.670731e+03 [256,] -1.423240e+04 9.038749e+03 [257,] 5.000900e+02 -1.423240e+04 [258,] -1.525619e+04 5.000900e+02 [259,] -1.022242e+04 -1.525619e+04 [260,] -2.811199e+04 -1.022242e+04 [261,] 1.069151e+03 -2.811199e+04 [262,] 6.166984e+03 1.069151e+03 [263,] -6.027922e+03 6.166984e+03 [264,] -3.496388e+04 -6.027922e+03 [265,] 1.673983e+04 -3.496388e+04 [266,] -2.644885e+04 1.673983e+04 [267,] -2.489284e+04 -2.644885e+04 [268,] 4.348874e+04 -2.489284e+04 [269,] -4.498634e+04 4.348874e+04 [270,] -1.473136e+04 -4.498634e+04 [271,] -6.899705e+03 -1.473136e+04 [272,] 2.904701e+04 -6.899705e+03 [273,] 8.432962e+03 2.904701e+04 [274,] 7.101251e+03 8.432962e+03 [275,] -1.369550e+04 7.101251e+03 [276,] -1.076353e+04 -1.369550e+04 [277,] 7.308169e+03 -1.076353e+04 [278,] 1.602473e+04 7.308169e+03 [279,] 1.698729e+04 1.602473e+04 [280,] 2.453143e+03 1.698729e+04 [281,] -2.590707e+04 2.453143e+03 [282,] 2.375964e+04 -2.590707e+04 [283,] 1.922717e+04 2.375964e+04 [284,] -6.738095e+03 1.922717e+04 [285,] -3.533459e+04 -6.738095e+03 [286,] 1.071034e+04 -3.533459e+04 [287,] -1.942337e+04 1.071034e+04 [288,] 1.372452e+04 -1.942337e+04 > z <- as.data.frame(dum1) > z lag(myerror, k = 1) myerror 1 -4.699524e+04 2.076381e+04 2 -1.435960e+04 -4.699524e+04 3 -3.374436e+04 -1.435960e+04 4 1.382081e+04 -3.374436e+04 5 -7.202580e+04 1.382081e+04 6 1.714886e+05 -7.202580e+04 7 -3.641829e+04 1.714886e+05 8 1.295226e+04 -3.641829e+04 9 -2.236743e+04 1.295226e+04 10 4.275297e+04 -2.236743e+04 11 2.974163e+04 4.275297e+04 12 -5.659719e+04 2.974163e+04 13 -2.238083e+04 -5.659719e+04 14 5.910265e+04 -2.238083e+04 15 6.789396e+03 5.910265e+04 16 1.151013e+04 6.789396e+03 17 3.019688e+04 1.151013e+04 18 -6.110887e+03 3.019688e+04 19 -1.529906e+04 -6.110887e+03 20 -8.716384e+03 -1.529906e+04 21 1.595426e+04 -8.716384e+03 22 1.646284e+05 1.595426e+04 23 2.634100e+04 1.646284e+05 24 -5.675875e+04 2.634100e+04 25 -3.148230e+04 -5.675875e+04 26 -3.973838e+04 -3.148230e+04 27 -1.028024e+03 -3.973838e+04 28 3.495635e+04 -1.028024e+03 29 1.246049e+04 3.495635e+04 30 5.506641e+04 1.246049e+04 31 9.363774e+02 5.506641e+04 32 -3.247039e+04 9.363774e+02 33 3.350515e+04 -3.247039e+04 34 -5.131553e+04 3.350515e+04 35 8.428673e+04 -5.131553e+04 36 2.689455e+04 8.428673e+04 37 6.433364e+04 2.689455e+04 38 1.011837e+05 6.433364e+04 39 -1.968617e+03 1.011837e+05 40 8.689351e+03 -1.968617e+03 41 3.115649e+04 8.689351e+03 42 -2.292698e+04 3.115649e+04 43 7.292417e+03 -2.292698e+04 44 9.568593e+04 7.292417e+03 45 -2.188883e+04 9.568593e+04 46 1.011714e+04 -2.188883e+04 47 -1.388233e+04 1.011714e+04 48 -1.480479e+04 -1.388233e+04 49 -7.619980e+04 -1.480479e+04 50 1.121661e+05 -7.619980e+04 51 -3.936104e+04 1.121661e+05 52 -2.095151e+04 -3.936104e+04 53 1.829822e+04 -2.095151e+04 54 -1.121406e+04 1.829822e+04 55 -4.416575e+02 -1.121406e+04 56 -4.367297e+03 -4.416575e+02 57 -6.942060e+04 -4.367297e+03 58 -9.687831e+03 -6.942060e+04 59 -5.228667e+03 -9.687831e+03 60 6.448381e+04 -5.228667e+03 61 -1.229505e+04 6.448381e+04 62 7.049813e+04 -1.229505e+04 63 1.597942e+04 7.049813e+04 64 -2.550128e+04 1.597942e+04 65 1.361449e+04 -2.550128e+04 66 3.187278e+04 1.361449e+04 67 3.795590e+04 3.187278e+04 68 6.518501e+03 3.795590e+04 69 -1.058834e+04 6.518501e+03 70 1.489433e+04 -1.058834e+04 71 1.861755e+04 1.489433e+04 72 3.495550e+04 1.861755e+04 73 -7.812977e+04 3.495550e+04 74 -5.269224e+04 -7.812977e+04 75 9.237668e+03 -5.269224e+04 76 -2.869878e+04 9.237668e+03 77 -3.926024e+03 -2.869878e+04 78 -2.140504e+04 -3.926024e+03 79 -2.614860e+03 -2.140504e+04 80 1.008931e+04 -2.614860e+03 81 -2.091685e+04 1.008931e+04 82 8.321613e+04 -2.091685e+04 83 5.141736e+04 8.321613e+04 84 -1.166555e+05 5.141736e+04 85 -1.442522e+04 -1.166555e+05 86 -1.133907e+04 -1.442522e+04 87 1.017595e+04 -1.133907e+04 88 1.088892e+05 1.017595e+04 89 5.169772e+03 1.088892e+05 90 -1.242069e+04 5.169772e+03 91 9.840793e+03 -1.242069e+04 92 1.760088e+04 9.840793e+03 93 -3.748557e+04 1.760088e+04 94 1.470321e+04 -3.748557e+04 95 3.344825e+04 1.470321e+04 96 1.673076e+04 3.344825e+04 97 -1.003105e+05 1.673076e+04 98 3.734876e+04 -1.003105e+05 99 -1.626655e+04 3.734876e+04 100 3.262070e+03 -1.626655e+04 101 5.477353e+04 3.262070e+03 102 4.946286e+04 5.477353e+04 103 -5.526047e+03 4.946286e+04 104 -3.327449e+03 -5.526047e+03 105 3.452963e+03 -3.327449e+03 106 -3.873794e+04 3.452963e+03 107 -7.077406e+03 -3.873794e+04 108 7.230553e+04 -7.077406e+03 109 -2.525643e+04 7.230553e+04 110 2.539692e+04 -2.525643e+04 111 -8.347299e+04 2.539692e+04 112 -3.822616e+04 -8.347299e+04 113 3.116093e+04 -3.822616e+04 114 -6.067428e+04 3.116093e+04 115 4.182092e+04 -6.067428e+04 116 3.906302e+04 4.182092e+04 117 8.638469e+04 3.906302e+04 118 -1.169443e+05 8.638469e+04 119 -2.085977e+04 -1.169443e+05 120 1.399181e+04 -2.085977e+04 121 -4.016092e+04 1.399181e+04 122 1.532968e+03 -4.016092e+04 123 -1.215270e+05 1.532968e+03 124 3.206419e+04 -1.215270e+05 125 2.164476e+04 3.206419e+04 126 3.679241e+02 2.164476e+04 127 -2.369394e+04 3.679241e+02 128 1.573372e+04 -2.369394e+04 129 1.769733e+04 1.573372e+04 130 -1.445239e+04 1.769733e+04 131 -2.202127e+04 -1.445239e+04 132 2.499954e+03 -2.202127e+04 133 -3.689732e+04 2.499954e+03 134 -1.526430e+04 -3.689732e+04 135 1.105176e+05 -1.526430e+04 136 1.316361e+04 1.105176e+05 137 5.698886e+04 1.316361e+04 138 -4.830801e+04 5.698886e+04 139 -7.972488e+03 -4.830801e+04 140 -2.665073e+04 -7.972488e+03 141 -9.388781e+03 -2.665073e+04 142 1.066634e+04 -9.388781e+03 143 3.374580e+04 1.066634e+04 144 -2.404678e+04 3.374580e+04 145 -2.100588e+04 -2.404678e+04 146 -7.739655e+04 -2.100588e+04 147 -4.978941e+04 -7.739655e+04 148 -1.140524e+04 -4.978941e+04 149 -1.224383e+04 -1.140524e+04 150 5.218065e+04 -1.224383e+04 151 -3.163679e+04 5.218065e+04 152 1.757360e+03 -3.163679e+04 153 1.057130e+05 1.757360e+03 154 2.092967e+04 1.057130e+05 155 2.436758e+04 2.092967e+04 156 1.508894e+04 2.436758e+04 157 4.170076e+04 1.508894e+04 158 -4.453269e+04 4.170076e+04 159 -1.231531e+05 -4.453269e+04 160 5.904563e+04 -1.231531e+05 161 -8.088910e+04 5.904563e+04 162 -5.172024e+04 -8.088910e+04 163 5.316297e+04 -5.172024e+04 164 -3.138764e+04 5.316297e+04 165 3.749875e+04 -3.138764e+04 166 -3.275980e+02 3.749875e+04 167 -5.214011e+04 -3.275980e+02 168 -6.774208e+03 -5.214011e+04 169 8.614500e+04 -6.774208e+03 170 8.010829e+04 8.614500e+04 171 -6.711257e+02 8.010829e+04 172 4.640226e+04 -6.711257e+02 173 2.208976e+04 4.640226e+04 174 -1.113545e+05 2.208976e+04 175 6.584310e+04 -1.113545e+05 176 -2.275681e+04 6.584310e+04 177 1.114499e+04 -2.275681e+04 178 -2.437366e+04 1.114499e+04 179 6.346876e+04 -2.437366e+04 180 -1.865267e+04 6.346876e+04 181 -4.317993e+04 -1.865267e+04 182 9.014752e+03 -4.317993e+04 183 8.450923e+03 9.014752e+03 184 5.674398e+04 8.450923e+03 185 8.749498e+03 5.674398e+04 186 1.900109e+04 8.749498e+03 187 1.511252e+04 1.900109e+04 188 4.651939e+04 1.511252e+04 189 -3.222788e+04 4.651939e+04 190 9.291528e+03 -3.222788e+04 191 8.605835e+03 9.291528e+03 192 6.738782e+03 8.605835e+03 193 -4.674251e+04 6.738782e+03 194 1.199852e+04 -4.674251e+04 195 3.468254e+04 1.199852e+04 196 -1.361728e+04 3.468254e+04 197 -1.984762e+04 -1.361728e+04 198 -2.067772e+04 -1.984762e+04 199 -1.966236e+04 -2.067772e+04 200 -2.708943e+04 -1.966236e+04 201 -2.912581e+04 -2.708943e+04 202 2.298656e+04 -2.912581e+04 203 -1.102282e+04 2.298656e+04 204 2.701014e+03 -1.102282e+04 205 -3.426271e+04 2.701014e+03 206 -1.202723e+04 -3.426271e+04 207 7.353736e+04 -1.202723e+04 208 2.901244e+04 7.353736e+04 209 2.431483e+04 2.901244e+04 210 3.156608e+04 2.431483e+04 211 3.105649e+04 3.156608e+04 212 -1.380479e+04 3.105649e+04 213 -1.010351e+04 -1.380479e+04 214 -5.205798e+04 -1.010351e+04 215 -2.927883e+04 -5.205798e+04 216 -1.923869e+04 -2.927883e+04 217 -2.268183e+04 -1.923869e+04 218 7.267538e+00 -2.268183e+04 219 2.009159e+04 7.267538e+00 220 -1.522219e+04 2.009159e+04 221 -3.435360e+04 -1.522219e+04 222 -3.116468e+03 -3.435360e+04 223 -1.107875e+04 -3.116468e+03 224 -2.087058e+04 -1.107875e+04 225 2.650023e+04 -2.087058e+04 226 9.240522e+03 2.650023e+04 227 -3.652364e+04 9.240522e+03 228 -1.744041e+04 -3.652364e+04 229 -4.503685e+04 -1.744041e+04 230 -2.820270e+04 -4.503685e+04 231 5.128748e+02 -2.820270e+04 232 -2.511132e+04 5.128748e+02 233 -1.770671e+04 -2.511132e+04 234 -5.260980e+04 -1.770671e+04 235 -4.034620e+04 -5.260980e+04 236 6.359032e+04 -4.034620e+04 237 -1.526966e+04 6.359032e+04 238 3.239802e+03 -1.526966e+04 239 -3.380475e+04 3.239802e+03 240 5.976027e+03 -3.380475e+04 241 -7.358734e+04 5.976027e+03 242 -3.921070e+04 -7.358734e+04 243 -2.190560e+04 -3.921070e+04 244 -7.595078e+03 -2.190560e+04 245 -3.578277e+03 -7.595078e+03 246 -8.788784e+03 -3.578277e+03 247 1.018911e+03 -8.788784e+03 248 5.100247e+03 1.018911e+03 249 -1.871296e+04 5.100247e+03 250 3.344169e+03 -1.871296e+04 251 -6.836745e+03 3.344169e+03 252 1.151775e+04 -6.836745e+03 253 -1.429310e+04 1.151775e+04 254 -1.670731e+03 -1.429310e+04 255 9.038749e+03 -1.670731e+03 256 -1.423240e+04 9.038749e+03 257 5.000900e+02 -1.423240e+04 258 -1.525619e+04 5.000900e+02 259 -1.022242e+04 -1.525619e+04 260 -2.811199e+04 -1.022242e+04 261 1.069151e+03 -2.811199e+04 262 6.166984e+03 1.069151e+03 263 -6.027922e+03 6.166984e+03 264 -3.496388e+04 -6.027922e+03 265 1.673983e+04 -3.496388e+04 266 -2.644885e+04 1.673983e+04 267 -2.489284e+04 -2.644885e+04 268 4.348874e+04 -2.489284e+04 269 -4.498634e+04 4.348874e+04 270 -1.473136e+04 -4.498634e+04 271 -6.899705e+03 -1.473136e+04 272 2.904701e+04 -6.899705e+03 273 8.432962e+03 2.904701e+04 274 7.101251e+03 8.432962e+03 275 -1.369550e+04 7.101251e+03 276 -1.076353e+04 -1.369550e+04 277 7.308169e+03 -1.076353e+04 278 1.602473e+04 7.308169e+03 279 1.698729e+04 1.602473e+04 280 2.453143e+03 1.698729e+04 281 -2.590707e+04 2.453143e+03 282 2.375964e+04 -2.590707e+04 283 1.922717e+04 2.375964e+04 284 -6.738095e+03 1.922717e+04 285 -3.533459e+04 -6.738095e+03 286 1.071034e+04 -3.533459e+04 287 -1.942337e+04 1.071034e+04 288 1.372452e+04 -1.942337e+04 > 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/7x3w51352127979.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/8rf5b1352127979.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function') > grid() > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/9rlfh1352127979.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0)) > plot(mylm, las = 1, sub='Residual Diagnostics') > par(opar) > dev.off() null device 1 > if (n > n25) { + postscript(file="/var/wessaorg/rcomp/tmp/10bh0b1352127979.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) + plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint') + grid() + dev.off() + } null device 1 > > #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab > load(file="/var/wessaorg/rcomp/createtable") > > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE) > a<-table.row.end(a) > myeq <- colnames(x)[1] > myeq <- paste(myeq, '[t] = ', sep='') > for (i in 1:k){ + if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '') + myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ') + if (rownames(mysum$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/11v88n1352127979.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$coefficients)[i],header=TRUE) + a<-table.element(a,mysum$coefficients[i,1]) + a<-table.element(a, round(mysum$coefficients[i,2],6)) + a<-table.element(a, round(mysum$coefficients[i,3],4)) + a<-table.element(a, round(mysum$coefficients[i,4],6)) + a<-table.element(a, round(mysum$coefficients[i,4]/2,6)) + a<-table.row.end(a) + } > a<-table.end(a) > table.save(a,file="/var/wessaorg/rcomp/tmp/121hn71352127979.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, sqrt(mysum$r.squared)) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'R-squared',1,TRUE) > a<-table.element(a, mysum$r.squared) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Adjusted R-squared',1,TRUE) > a<-table.element(a, mysum$adj.r.squared) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'F-TEST (value)',1,TRUE) > a<-table.element(a, mysum$fstatistic[1]) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE) > a<-table.element(a, mysum$fstatistic[2]) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE) > a<-table.element(a, mysum$fstatistic[3]) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'p-value',1,TRUE) > a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3])) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Residual Standard Deviation',1,TRUE) > a<-table.element(a, mysum$sigma) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Sum Squared Residuals',1,TRUE) > a<-table.element(a, sum(myerror*myerror)) > a<-table.row.end(a) > a<-table.end(a) > table.save(a,file="/var/wessaorg/rcomp/tmp/133xnr1352127979.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,x[i]) + a<-table.element(a,x[i]-mysum$resid[i]) + a<-table.element(a,mysum$resid[i]) + a<-table.row.end(a) + } > a<-table.end(a) > table.save(a,file="/var/wessaorg/rcomp/tmp/14hw6m1352127979.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,gqarr[mypoint-kp3+1,1]) + a<-table.element(a,gqarr[mypoint-kp3+1,2]) + a<-table.element(a,gqarr[mypoint-kp3+1,3]) + a<-table.row.end(a) + } + a<-table.end(a) + table.save(a,file="/var/wessaorg/rcomp/tmp/15885c1352127979.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,numsignificant1) + a<-table.element(a,numsignificant1/numgqtests) + 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,numsignificant5) + a<-table.element(a,numsignificant5/numgqtests) + 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,numsignificant10) + a<-table.element(a,numsignificant10/numgqtests) + 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/1661k21352127979.tab") + } > > try(system("convert tmp/1gn621352127979.ps tmp/1gn621352127979.png",intern=TRUE)) character(0) > try(system("convert tmp/2za5b1352127979.ps tmp/2za5b1352127979.png",intern=TRUE)) character(0) > try(system("convert tmp/373gv1352127979.ps tmp/373gv1352127979.png",intern=TRUE)) character(0) > try(system("convert tmp/4r3iw1352127979.ps tmp/4r3iw1352127979.png",intern=TRUE)) character(0) > try(system("convert tmp/5cqmp1352127979.ps tmp/5cqmp1352127979.png",intern=TRUE)) character(0) > try(system("convert tmp/6khea1352127979.ps tmp/6khea1352127979.png",intern=TRUE)) character(0) > try(system("convert tmp/7x3w51352127979.ps tmp/7x3w51352127979.png",intern=TRUE)) character(0) > try(system("convert tmp/8rf5b1352127979.ps tmp/8rf5b1352127979.png",intern=TRUE)) character(0) > try(system("convert tmp/9rlfh1352127979.ps tmp/9rlfh1352127979.png",intern=TRUE)) character(0) > try(system("convert tmp/10bh0b1352127979.ps tmp/10bh0b1352127979.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 14.677 1.156 15.837