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. 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,44 + ,0 + ,20 + ,0 + ,55 + ,0) + ,dim=c(6 + ,269) + ,dimnames=list(c('hours_blogs' + ,'hours_uk' + ,'hours_spr' + ,'blogs_uk' + ,'blogs_spr' + ,'uk_spr') + ,1:269)) > y <- array(NA,dim=c(6,269),dimnames=list(c('hours_blogs','hours_uk','hours_spr','blogs_uk','blogs_spr','uk_spr'),1:269)) > 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 = '2' > 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 hours_uk hours_blogs hours_spr blogs_uk blogs_spr uk_spr 1 102 8976 918 88 792 9 2 99 10494 297 106 318 3 3 97 6790 194 70 140 2 4 82 5740 1722 70 1470 21 5 77 4312 770 56 560 10 6 65 3250 130 50 100 2 7 64 3072 256 48 192 4 8 62 4402 248 71 284 4 9 62 3782 248 61 244 4 10 62 4092 310 66 330 5 11 61 4880 122 80 160 2 12 59 2183 177 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160 0 265 0 186 0 1920 224 0 420 0 187 0 1088 448 0 476 0 188 0 576 800 0 450 0 189 0 1568 192 0 294 0 190 0 837 124 0 108 0 191 0 1395 93 0 135 0 192 0 279 1922 0 558 0 193 0 690 150 0 115 0 194 0 1830 300 0 610 0 195 0 1943 116 0 268 0 196 0 2088 145 0 360 0 197 0 1682 145 0 290 0 198 0 1540 112 0 220 0 199 0 924 280 0 330 0 200 0 1120 140 0 200 0 201 0 1596 84 0 171 0 202 0 1708 84 0 183 0 203 0 2436 476 0 1479 0 204 0 1755 108 0 260 0 205 0 2295 162 0 510 0 206 0 2295 81 0 255 0 207 0 1404 104 0 216 0 208 0 624 208 0 192 0 209 0 806 78 0 93 0 210 0 1664 104 0 256 0 211 0 1750 100 0 280 0 212 0 50 1175 0 94 0 213 0 648 192 0 216 0 214 0 696 72 0 87 0 215 0 1632 120 0 340 0 216 0 1008 72 0 126 0 217 0 1872 96 0 312 0 218 0 312 720 0 390 0 219 0 1248 96 0 208 0 220 0 575 276 0 300 0 221 0 874 184 0 304 0 222 0 920 69 0 120 0 223 0 966 184 0 336 0 224 0 920 92 0 160 0 225 0 1702 69 0 222 0 226 0 1679 92 0 292 0 227 0 1232 88 0 224 0 228 0 66 462 0 63 0 229 0 189 252 0 108 0 230 0 1428 63 0 204 0 231 0 588 63 0 84 0 232 0 756 84 0 144 0 233 0 760 120 0 228 0 234 0 1100 340 0 935 0 235 0 720 60 0 108 0 236 0 340 360 0 306 0 237 0 1080 100 0 270 0 238 0 1083 95 0 285 0 239 0 570 304 0 480 0 240 0 760 190 0 400 0 241 0 666 90 0 185 0 242 0 828 72 0 184 0 243 0 576 54 0 96 0 244 0 612 90 0 170 0 245 0 396 72 0 88 0 246 0 1003 85 0 295 0 247 0 544 170 0 320 0 248 0 288 192 0 216 0 249 0 448 64 0 112 0 250 0 510 135 0 306 0 251 0 435 180 0 348 0 252 0 360 150 0 240 0 253 0 360 135 0 216 0 254 0 322 238 0 391 0 255 0 559 78 0 258 0 256 0 364 39 0 84 0 257 0 228 48 0 76 0 258 0 176 209 0 304 0 259 0 440 33 0 120 0 260 0 140 90 0 126 0 261 0 190 70 0 133 0 262 0 220 40 0 88 0 263 0 80 30 0 24 0 264 0 279 414 0 1426 0 265 0 72 248 0 279 0 266 0 144 168 0 378 0 267 0 63 49 0 63 0 268 0 35 203 0 145 0 269 0 44 20 0 55 0 > k <- length(x[1,]) > df <- as.data.frame(x) > (mylm <- lm(df)) Call: lm(formula = df) Coefficients: (Intercept) hours_blogs hours_spr blogs_uk blogs_spr uk_spr 1.4011664 0.0005169 0.0110944 0.8573112 -0.0144429 0.2790182 > (mysum <- summary(mylm)) Call: lm(formula = df) Residuals: Min 1Q Median 3Q Max -33.297 -2.054 -0.560 1.398 31.389 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 1.4011664 0.7816842 1.792 0.07420 . hours_blogs 0.0005169 0.0002370 2.181 0.03009 * hours_spr 0.0110944 0.0028420 3.904 0.00012 *** blogs_uk 0.8573112 0.0252008 34.019 < 2e-16 *** blogs_spr -0.0144429 0.0028929 -4.992 1.09e-06 *** uk_spr 0.2790182 0.1574969 1.772 0.07762 . --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 7.21 on 263 degrees of freedom Multiple R-squared: 0.8984, Adjusted R-squared: 0.8965 F-statistic: 465.3 on 5 and 263 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,] 0.27616993 5.523399e-01 7.238301e-01 [2,] 0.14857053 2.971411e-01 8.514295e-01 [3,] 0.08082894 1.616579e-01 9.191711e-01 [4,] 0.10070335 2.014067e-01 8.992966e-01 [5,] 0.06585624 1.317125e-01 9.341438e-01 [6,] 0.06894028 1.378806e-01 9.310597e-01 [7,] 0.06529888 1.305978e-01 9.347011e-01 [8,] 0.03743845 7.487690e-02 9.625615e-01 [9,] 0.03262954 6.525908e-02 9.673705e-01 [10,] 0.02718300 5.436601e-02 9.728170e-01 [11,] 0.03111871 6.223742e-02 9.688813e-01 [12,] 0.01843788 3.687576e-02 9.815621e-01 [13,] 0.01304996 2.609992e-02 9.869500e-01 [14,] 0.06489699 1.297940e-01 9.351030e-01 [15,] 0.06743819 1.348764e-01 9.325618e-01 [16,] 0.05204120 1.040824e-01 9.479588e-01 [17,] 0.04870027 9.740055e-02 9.512997e-01 [18,] 0.03749328 7.498657e-02 9.625067e-01 [19,] 0.03054574 6.109148e-02 9.694543e-01 [20,] 0.04709563 9.419126e-02 9.529044e-01 [21,] 0.04817286 9.634572e-02 9.518271e-01 [22,] 0.04405780 8.811560e-02 9.559422e-01 [23,] 0.03892117 7.784234e-02 9.610788e-01 [24,] 0.03508601 7.017201e-02 9.649140e-01 [25,] 0.02833039 5.666078e-02 9.716696e-01 [26,] 0.02250590 4.501181e-02 9.774941e-01 [27,] 0.02161873 4.323747e-02 9.783813e-01 [28,] 0.02292254 4.584508e-02 9.770775e-01 [29,] 0.04433099 8.866198e-02 9.556690e-01 [30,] 0.03785099 7.570198e-02 9.621490e-01 [31,] 0.03617603 7.235206e-02 9.638240e-01 [32,] 0.04327663 8.655326e-02 9.567234e-01 [33,] 0.05349840 1.069968e-01 9.465016e-01 [34,] 0.10948379 2.189676e-01 8.905162e-01 [35,] 0.17980272 3.596054e-01 8.201973e-01 [36,] 0.16561195 3.312239e-01 8.343881e-01 [37,] 0.13597719 2.719544e-01 8.640228e-01 [38,] 0.17822319 3.564464e-01 8.217768e-01 [39,] 0.16298346 3.259669e-01 8.370165e-01 [40,] 0.14305262 2.861052e-01 8.569474e-01 [41,] 0.11916939 2.383388e-01 8.808306e-01 [42,] 0.11918364 2.383673e-01 8.808164e-01 [43,] 0.24630622 4.926124e-01 7.536938e-01 [44,] 0.31652095 6.330419e-01 6.834790e-01 [45,] 0.29686499 5.937300e-01 7.031350e-01 [46,] 0.26969341 5.393868e-01 7.303066e-01 [47,] 0.29546656 5.909331e-01 7.045334e-01 [48,] 0.27205962 5.441192e-01 7.279404e-01 [49,] 0.35562504 7.112501e-01 6.443750e-01 [50,] 0.33455026 6.691005e-01 6.654497e-01 [51,] 0.34765288 6.953058e-01 6.523471e-01 [52,] 0.33030775 6.606155e-01 6.696922e-01 [53,] 0.39087870 7.817574e-01 6.091213e-01 [54,] 0.50390812 9.921838e-01 4.960919e-01 [55,] 0.69204113 6.159177e-01 3.079589e-01 [56,] 0.76674080 4.665184e-01 2.332592e-01 [57,] 0.77205965 4.558807e-01 2.279404e-01 [58,] 0.77457277 4.508545e-01 2.254272e-01 [59,] 0.77528542 4.494292e-01 2.247146e-01 [60,] 0.86137234 2.772553e-01 1.386277e-01 [61,] 0.91068152 1.786370e-01 8.931848e-02 [62,] 0.93213866 1.357227e-01 6.786134e-02 [63,] 0.92028393 1.594321e-01 7.971607e-02 [64,] 0.95140905 9.718191e-02 4.859095e-02 [65,] 0.95931302 8.137397e-02 4.068698e-02 [66,] 0.99381731 1.236538e-02 6.182688e-03 [67,] 0.99722430 5.551404e-03 2.775702e-03 [68,] 0.99759217 4.815669e-03 2.407835e-03 [69,] 0.99870442 2.591168e-03 1.295584e-03 [70,] 0.99940541 1.189182e-03 5.945911e-04 [71,] 0.99980837 3.832526e-04 1.916263e-04 [72,] 0.99982966 3.406731e-04 1.703365e-04 [73,] 0.99997912 4.176437e-05 2.088219e-05 [74,] 0.99998289 3.422989e-05 1.711494e-05 [75,] 0.99998546 2.908757e-05 1.454378e-05 [76,] 1.00000000 4.751820e-09 2.375910e-09 [77,] 1.00000000 2.457679e-09 1.228839e-09 [78,] 1.00000000 4.090773e-11 2.045387e-11 [79,] 1.00000000 4.066199e-12 2.033100e-12 [80,] 1.00000000 4.669614e-16 2.334807e-16 [81,] 1.00000000 1.970909e-16 9.854543e-17 [82,] 1.00000000 7.355071e-18 3.677535e-18 [83,] 1.00000000 2.878595e-18 1.439298e-18 [84,] 1.00000000 1.729386e-20 8.646931e-21 [85,] 1.00000000 1.373106e-23 6.865530e-24 [86,] 1.00000000 6.481563e-29 3.240782e-29 [87,] 1.00000000 2.311108e-41 1.155554e-41 [88,] 1.00000000 1.518593e-42 7.592965e-43 [89,] 1.00000000 5.840030e-43 2.920015e-43 [90,] 1.00000000 2.750006e-48 1.375003e-48 [91,] 1.00000000 1.273447e-48 6.367233e-49 [92,] 1.00000000 7.848998e-49 3.924499e-49 [93,] 1.00000000 6.206674e-51 3.103337e-51 [94,] 1.00000000 9.041811e-54 4.520906e-54 [95,] 1.00000000 9.110855e-57 4.555428e-57 [96,] 1.00000000 1.647442e-62 8.237208e-63 [97,] 1.00000000 1.077600e-98 5.387999e-99 [98,] 1.00000000 1.484684e-99 7.423419e-100 [99,] 1.00000000 1.512043e-99 7.560217e-100 [100,] 1.00000000 1.188369e-140 5.941843e-141 [101,] 1.00000000 8.889657e-312 4.444829e-312 [102,] 1.00000000 0.000000e+00 0.000000e+00 [103,] 1.00000000 0.000000e+00 0.000000e+00 [104,] 1.00000000 0.000000e+00 0.000000e+00 [105,] 1.00000000 0.000000e+00 0.000000e+00 [106,] 1.00000000 0.000000e+00 0.000000e+00 [107,] 1.00000000 0.000000e+00 0.000000e+00 [108,] 1.00000000 0.000000e+00 0.000000e+00 [109,] 1.00000000 0.000000e+00 0.000000e+00 [110,] 1.00000000 0.000000e+00 0.000000e+00 [111,] 1.00000000 0.000000e+00 0.000000e+00 [112,] 1.00000000 0.000000e+00 0.000000e+00 [113,] 1.00000000 0.000000e+00 0.000000e+00 [114,] 1.00000000 0.000000e+00 0.000000e+00 [115,] 1.00000000 0.000000e+00 0.000000e+00 [116,] 1.00000000 0.000000e+00 0.000000e+00 [117,] 1.00000000 0.000000e+00 0.000000e+00 [118,] 1.00000000 0.000000e+00 0.000000e+00 [119,] 1.00000000 0.000000e+00 0.000000e+00 [120,] 1.00000000 0.000000e+00 0.000000e+00 [121,] 1.00000000 0.000000e+00 0.000000e+00 [122,] 1.00000000 0.000000e+00 0.000000e+00 [123,] 1.00000000 0.000000e+00 0.000000e+00 [124,] 1.00000000 0.000000e+00 0.000000e+00 [125,] 1.00000000 0.000000e+00 0.000000e+00 [126,] 1.00000000 0.000000e+00 0.000000e+00 [127,] 1.00000000 0.000000e+00 0.000000e+00 [128,] 1.00000000 0.000000e+00 0.000000e+00 [129,] 1.00000000 0.000000e+00 0.000000e+00 [130,] 1.00000000 0.000000e+00 0.000000e+00 [131,] 1.00000000 0.000000e+00 0.000000e+00 [132,] 1.00000000 0.000000e+00 0.000000e+00 [133,] 1.00000000 0.000000e+00 0.000000e+00 [134,] 1.00000000 0.000000e+00 0.000000e+00 [135,] 1.00000000 0.000000e+00 0.000000e+00 [136,] 1.00000000 0.000000e+00 0.000000e+00 [137,] 1.00000000 0.000000e+00 0.000000e+00 [138,] 1.00000000 0.000000e+00 0.000000e+00 [139,] 1.00000000 0.000000e+00 0.000000e+00 [140,] 1.00000000 0.000000e+00 0.000000e+00 [141,] 1.00000000 0.000000e+00 0.000000e+00 [142,] 1.00000000 0.000000e+00 0.000000e+00 [143,] 1.00000000 0.000000e+00 0.000000e+00 [144,] 1.00000000 0.000000e+00 0.000000e+00 [145,] 1.00000000 0.000000e+00 0.000000e+00 [146,] 1.00000000 0.000000e+00 0.000000e+00 [147,] 1.00000000 0.000000e+00 0.000000e+00 [148,] 1.00000000 0.000000e+00 0.000000e+00 [149,] 1.00000000 0.000000e+00 0.000000e+00 [150,] 1.00000000 0.000000e+00 0.000000e+00 [151,] 1.00000000 0.000000e+00 0.000000e+00 [152,] 1.00000000 0.000000e+00 0.000000e+00 [153,] 1.00000000 0.000000e+00 0.000000e+00 [154,] 1.00000000 0.000000e+00 0.000000e+00 [155,] 1.00000000 0.000000e+00 0.000000e+00 [156,] 1.00000000 0.000000e+00 0.000000e+00 [157,] 1.00000000 0.000000e+00 0.000000e+00 [158,] 1.00000000 0.000000e+00 0.000000e+00 [159,] 1.00000000 0.000000e+00 0.000000e+00 [160,] 1.00000000 0.000000e+00 0.000000e+00 [161,] 1.00000000 0.000000e+00 0.000000e+00 [162,] 1.00000000 0.000000e+00 0.000000e+00 [163,] 1.00000000 0.000000e+00 0.000000e+00 [164,] 1.00000000 0.000000e+00 0.000000e+00 [165,] 1.00000000 0.000000e+00 0.000000e+00 [166,] 1.00000000 0.000000e+00 0.000000e+00 [167,] 1.00000000 0.000000e+00 0.000000e+00 [168,] 1.00000000 0.000000e+00 0.000000e+00 [169,] 1.00000000 0.000000e+00 0.000000e+00 [170,] 1.00000000 0.000000e+00 0.000000e+00 [171,] 1.00000000 0.000000e+00 0.000000e+00 [172,] 1.00000000 0.000000e+00 0.000000e+00 [173,] 1.00000000 0.000000e+00 0.000000e+00 [174,] 1.00000000 0.000000e+00 0.000000e+00 [175,] 1.00000000 0.000000e+00 0.000000e+00 [176,] 1.00000000 0.000000e+00 0.000000e+00 [177,] 1.00000000 0.000000e+00 0.000000e+00 [178,] 1.00000000 0.000000e+00 0.000000e+00 [179,] 1.00000000 0.000000e+00 0.000000e+00 [180,] 1.00000000 0.000000e+00 0.000000e+00 [181,] 1.00000000 0.000000e+00 0.000000e+00 [182,] 1.00000000 0.000000e+00 0.000000e+00 [183,] 1.00000000 0.000000e+00 0.000000e+00 [184,] 1.00000000 0.000000e+00 0.000000e+00 [185,] 1.00000000 0.000000e+00 0.000000e+00 [186,] 1.00000000 0.000000e+00 0.000000e+00 [187,] 1.00000000 0.000000e+00 0.000000e+00 [188,] 1.00000000 0.000000e+00 0.000000e+00 [189,] 1.00000000 0.000000e+00 0.000000e+00 [190,] 1.00000000 0.000000e+00 0.000000e+00 [191,] 1.00000000 0.000000e+00 0.000000e+00 [192,] 1.00000000 0.000000e+00 0.000000e+00 [193,] 1.00000000 0.000000e+00 0.000000e+00 [194,] 1.00000000 0.000000e+00 0.000000e+00 [195,] 1.00000000 0.000000e+00 0.000000e+00 [196,] 1.00000000 0.000000e+00 0.000000e+00 [197,] 1.00000000 0.000000e+00 0.000000e+00 [198,] 1.00000000 0.000000e+00 0.000000e+00 [199,] 1.00000000 0.000000e+00 0.000000e+00 [200,] 1.00000000 0.000000e+00 0.000000e+00 [201,] 1.00000000 0.000000e+00 0.000000e+00 [202,] 1.00000000 0.000000e+00 0.000000e+00 [203,] 1.00000000 0.000000e+00 0.000000e+00 [204,] 1.00000000 0.000000e+00 0.000000e+00 [205,] 1.00000000 0.000000e+00 0.000000e+00 [206,] 1.00000000 0.000000e+00 0.000000e+00 [207,] 1.00000000 0.000000e+00 0.000000e+00 [208,] 1.00000000 0.000000e+00 0.000000e+00 [209,] 1.00000000 0.000000e+00 0.000000e+00 [210,] 1.00000000 0.000000e+00 0.000000e+00 [211,] 1.00000000 0.000000e+00 0.000000e+00 [212,] 1.00000000 0.000000e+00 0.000000e+00 [213,] 1.00000000 0.000000e+00 0.000000e+00 [214,] 1.00000000 0.000000e+00 0.000000e+00 [215,] 1.00000000 0.000000e+00 0.000000e+00 [216,] 1.00000000 0.000000e+00 0.000000e+00 [217,] 1.00000000 0.000000e+00 0.000000e+00 [218,] 1.00000000 0.000000e+00 0.000000e+00 [219,] 1.00000000 0.000000e+00 0.000000e+00 [220,] 1.00000000 0.000000e+00 0.000000e+00 [221,] 1.00000000 0.000000e+00 0.000000e+00 [222,] 1.00000000 0.000000e+00 0.000000e+00 [223,] 1.00000000 0.000000e+00 0.000000e+00 [224,] 1.00000000 0.000000e+00 0.000000e+00 [225,] 1.00000000 0.000000e+00 0.000000e+00 [226,] 1.00000000 0.000000e+00 0.000000e+00 [227,] 1.00000000 0.000000e+00 0.000000e+00 [228,] 1.00000000 0.000000e+00 0.000000e+00 [229,] 1.00000000 0.000000e+00 0.000000e+00 [230,] 1.00000000 0.000000e+00 0.000000e+00 [231,] 1.00000000 0.000000e+00 0.000000e+00 [232,] 1.00000000 0.000000e+00 0.000000e+00 [233,] 1.00000000 0.000000e+00 0.000000e+00 [234,] 1.00000000 0.000000e+00 0.000000e+00 [235,] 1.00000000 0.000000e+00 0.000000e+00 [236,] 1.00000000 0.000000e+00 0.000000e+00 [237,] 1.00000000 0.000000e+00 0.000000e+00 [238,] 1.00000000 0.000000e+00 0.000000e+00 [239,] 1.00000000 0.000000e+00 0.000000e+00 [240,] 1.00000000 0.000000e+00 0.000000e+00 [241,] 1.00000000 0.000000e+00 0.000000e+00 [242,] 1.00000000 0.000000e+00 0.000000e+00 [243,] 1.00000000 0.000000e+00 0.000000e+00 [244,] 1.00000000 0.000000e+00 0.000000e+00 [245,] 1.00000000 0.000000e+00 0.000000e+00 [246,] 1.00000000 0.000000e+00 0.000000e+00 [247,] 1.00000000 0.000000e+00 0.000000e+00 [248,] 1.00000000 0.000000e+00 0.000000e+00 [249,] 1.00000000 0.000000e+00 0.000000e+00 [250,] 1.00000000 0.000000e+00 0.000000e+00 [251,] 1.00000000 0.000000e+00 0.000000e+00 [252,] 1.00000000 0.000000e+00 0.000000e+00 > postscript(file="/var/wessaorg/rcomp/tmp/1uwd41358158465.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/2gfl81358158465.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index') > grid() > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/3ryld1358158465.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') > grid() > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/4ii2o1358158465.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/5wvl11358158465.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > qqnorm(mysum$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 = 269 Frequency = 1 1 2 3 4 5 6 19.25902714 1.76058999 31.38917584 13.88741814 22.11585458 18.49743683 7 8 9 10 11 12 18.67688311 -2.31120903 6.00464589 1.83308131 -11.10906711 23.55240031 13 14 15 16 17 18 8.16210994 18.01268003 16.28875590 0.26679796 13.65996054 16.48959833 19 20 21 22 23 24 16.81200061 -0.85678425 13.65131354 -33.29698031 9.98510914 -14.98907453 25 26 27 28 29 30 1.55844667 0.53319023 2.24009079 17.32284049 8.09612593 -1.19613989 31 32 33 34 35 36 1.28370677 0.34283324 -4.68371774 -3.13534664 1.91368459 5.92823016 37 38 39 40 41 42 16.03230614 -5.80077805 4.09992284 4.10055833 7.23272844 17.85552344 43 44 45 46 47 48 15.09018679 -12.47722666 -7.51811272 8.16607792 -8.47693272 -2.02122400 49 50 51 52 53 54 -3.63109984 2.51459091 -5.24326834 7.21745137 -6.97172253 -8.46966530 55 56 57 58 59 60 2.60893515 -6.94348438 6.66168602 -7.05253230 -1.42834935 -2.28878028 61 62 63 64 65 66 0.92517571 5.08812478 8.85353972 2.77760497 -5.07800441 -6.67848382 67 68 69 70 71 72 -10.94764914 5.06041289 3.18880421 -1.08501293 -9.60079266 2.23914392 73 74 75 76 77 78 -4.52417277 11.25575174 1.44690565 -6.36178948 -1.32018761 -22.75130448 79 80 81 82 83 84 1.52150487 -10.60178165 5.19658330 -8.19127115 -13.79639360 14.31914854 85 86 87 88 89 90 -5.18775829 4.57418300 -0.14706581 8.50831782 -7.01194693 -19.08397558 91 92 93 94 95 96 -5.35813840 1.95607529 3.01000172 -22.73215687 -10.97684574 -6.35957271 97 98 99 100 101 102 -3.38768095 -15.34245205 -7.32084423 -9.84023390 -1.60428997 5.87013288 103 104 105 106 107 108 2.31655069 1.30094573 6.56026028 -8.17413294 -8.84157411 2.79579421 109 110 111 112 113 114 -4.34450576 -4.95824243 -7.18337990 -2.05364234 -10.86148653 -2.96442216 115 116 117 118 119 120 -6.03027213 -5.23050250 -3.69896115 -3.55803071 -3.29908536 -3.28752642 121 122 123 124 125 126 -2.19916029 -2.21191572 -3.05178926 -2.22786084 -2.05732381 1.98905815 127 128 129 130 131 132 -2.47389406 -0.48471991 -1.65124685 -1.12009893 -0.90021979 -1.96374698 133 134 135 136 137 138 0.48888330 -0.75294121 -0.96025345 -0.37337865 -1.17686932 -1.62215296 139 140 141 142 143 144 -0.52934550 0.84688907 1.37260361 1.81639174 -2.14401305 -1.38339407 145 146 147 148 149 150 -1.65101840 -1.83576494 -1.24771280 1.16810251 -1.23831264 -0.58981249 151 152 153 154 155 156 -0.72787066 -1.07724789 -0.68699367 -0.53653878 -1.56383527 -1.03090623 157 158 159 160 161 162 -0.34847017 0.70407718 -1.22097332 -0.34210569 1.22958216 -0.14439060 163 164 165 166 167 168 -2.38915283 -1.38309812 1.16523887 -1.46217077 0.13992650 -0.76638675 169 170 171 172 173 174 0.35044532 -0.74524923 -1.73410652 -1.22844367 -1.05177556 -3.07788890 175 176 177 178 179 180 -0.25692638 -1.31556365 -0.47237704 -0.91021491 -1.29099378 -2.12857831 181 182 183 184 185 186 -0.58164879 -0.54487005 1.49887283 -0.01557594 -0.22550426 1.18732732 187 188 189 190 191 192 -0.05897163 -4.07506311 -0.09552052 -1.64965229 -1.20418156 -14.80958607 193 194 195 196 197 198 -1.76102562 3.13482456 0.17830959 1.11037377 0.30921958 -0.26227410 199 200 201 202 203 204 -0.21901641 -0.64468930 -0.68827864 -0.57285314 13.41987180 0.24869247 205 206 207 208 209 210 2.98121228 0.19691677 -0.16099678 -1.25828196 -1.33993250 0.28233330 211 212 213 214 215 216 0.62888954 -13.10524390 -0.74654765 -1.30316825 1.33456659 -0.90115821 217 218 219 220 221 222 1.07238168 -3.91763528 -0.10715368 -0.42753886 0.49636974 -0.90904812 223 224 225 226 227 228 0.91099051 -0.58650247 0.15993629 0.92765675 0.22095737 -5.65096896 229 230 231 232 233 234 -2.73479908 0.10815217 -1.19082599 -0.64406758 0.16767136 7.76230496 235 236 237 238 239 240 -0.87914005 -1.15134324 0.83076223 1.10132682 1.86412464 1.87524449 241 242 243 244 245 246 -0.07195689 0.02956596 -0.91145965 -0.26068943 -1.13366492 1.39804877 247 248 249 250 251 252 1.05334350 -0.56047513 -0.72515775 1.25701888 1.40313964 0.21490273 253 254 255 256 257 258 0.03468856 1.43911767 1.17081158 -0.80878303 -0.95388131 0.57978483 259 260 261 262 263 264 -0.26155467 -0.65221505 -0.35507111 -0.68767680 -1.42871699 14.45713288 265 266 267 268 269 -0.16021307 2.11996736 -1.06745006 -1.57719092 -0.85143645 > postscript(file="/var/wessaorg/rcomp/tmp/6s95t1358158465.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 = 269 Frequency = 1 lag(myerror, k = 1) myerror 0 19.25902714 NA 1 1.76058999 19.25902714 2 31.38917584 1.76058999 3 13.88741814 31.38917584 4 22.11585458 13.88741814 5 18.49743683 22.11585458 6 18.67688311 18.49743683 7 -2.31120903 18.67688311 8 6.00464589 -2.31120903 9 1.83308131 6.00464589 10 -11.10906711 1.83308131 11 23.55240031 -11.10906711 12 8.16210994 23.55240031 13 18.01268003 8.16210994 14 16.28875590 18.01268003 15 0.26679796 16.28875590 16 13.65996054 0.26679796 17 16.48959833 13.65996054 18 16.81200061 16.48959833 19 -0.85678425 16.81200061 20 13.65131354 -0.85678425 21 -33.29698031 13.65131354 22 9.98510914 -33.29698031 23 -14.98907453 9.98510914 24 1.55844667 -14.98907453 25 0.53319023 1.55844667 26 2.24009079 0.53319023 27 17.32284049 2.24009079 28 8.09612593 17.32284049 29 -1.19613989 8.09612593 30 1.28370677 -1.19613989 31 0.34283324 1.28370677 32 -4.68371774 0.34283324 33 -3.13534664 -4.68371774 34 1.91368459 -3.13534664 35 5.92823016 1.91368459 36 16.03230614 5.92823016 37 -5.80077805 16.03230614 38 4.09992284 -5.80077805 39 4.10055833 4.09992284 40 7.23272844 4.10055833 41 17.85552344 7.23272844 42 15.09018679 17.85552344 43 -12.47722666 15.09018679 44 -7.51811272 -12.47722666 45 8.16607792 -7.51811272 46 -8.47693272 8.16607792 47 -2.02122400 -8.47693272 48 -3.63109984 -2.02122400 49 2.51459091 -3.63109984 50 -5.24326834 2.51459091 51 7.21745137 -5.24326834 52 -6.97172253 7.21745137 53 -8.46966530 -6.97172253 54 2.60893515 -8.46966530 55 -6.94348438 2.60893515 56 6.66168602 -6.94348438 57 -7.05253230 6.66168602 58 -1.42834935 -7.05253230 59 -2.28878028 -1.42834935 60 0.92517571 -2.28878028 61 5.08812478 0.92517571 62 8.85353972 5.08812478 63 2.77760497 8.85353972 64 -5.07800441 2.77760497 65 -6.67848382 -5.07800441 66 -10.94764914 -6.67848382 67 5.06041289 -10.94764914 68 3.18880421 5.06041289 69 -1.08501293 3.18880421 70 -9.60079266 -1.08501293 71 2.23914392 -9.60079266 72 -4.52417277 2.23914392 73 11.25575174 -4.52417277 74 1.44690565 11.25575174 75 -6.36178948 1.44690565 76 -1.32018761 -6.36178948 77 -22.75130448 -1.32018761 78 1.52150487 -22.75130448 79 -10.60178165 1.52150487 80 5.19658330 -10.60178165 81 -8.19127115 5.19658330 82 -13.79639360 -8.19127115 83 14.31914854 -13.79639360 84 -5.18775829 14.31914854 85 4.57418300 -5.18775829 86 -0.14706581 4.57418300 87 8.50831782 -0.14706581 88 -7.01194693 8.50831782 89 -19.08397558 -7.01194693 90 -5.35813840 -19.08397558 91 1.95607529 -5.35813840 92 3.01000172 1.95607529 93 -22.73215687 3.01000172 94 -10.97684574 -22.73215687 95 -6.35957271 -10.97684574 96 -3.38768095 -6.35957271 97 -15.34245205 -3.38768095 98 -7.32084423 -15.34245205 99 -9.84023390 -7.32084423 100 -1.60428997 -9.84023390 101 5.87013288 -1.60428997 102 2.31655069 5.87013288 103 1.30094573 2.31655069 104 6.56026028 1.30094573 105 -8.17413294 6.56026028 106 -8.84157411 -8.17413294 107 2.79579421 -8.84157411 108 -4.34450576 2.79579421 109 -4.95824243 -4.34450576 110 -7.18337990 -4.95824243 111 -2.05364234 -7.18337990 112 -10.86148653 -2.05364234 113 -2.96442216 -10.86148653 114 -6.03027213 -2.96442216 115 -5.23050250 -6.03027213 116 -3.69896115 -5.23050250 117 -3.55803071 -3.69896115 118 -3.29908536 -3.55803071 119 -3.28752642 -3.29908536 120 -2.19916029 -3.28752642 121 -2.21191572 -2.19916029 122 -3.05178926 -2.21191572 123 -2.22786084 -3.05178926 124 -2.05732381 -2.22786084 125 1.98905815 -2.05732381 126 -2.47389406 1.98905815 127 -0.48471991 -2.47389406 128 -1.65124685 -0.48471991 129 -1.12009893 -1.65124685 130 -0.90021979 -1.12009893 131 -1.96374698 -0.90021979 132 0.48888330 -1.96374698 133 -0.75294121 0.48888330 134 -0.96025345 -0.75294121 135 -0.37337865 -0.96025345 136 -1.17686932 -0.37337865 137 -1.62215296 -1.17686932 138 -0.52934550 -1.62215296 139 0.84688907 -0.52934550 140 1.37260361 0.84688907 141 1.81639174 1.37260361 142 -2.14401305 1.81639174 143 -1.38339407 -2.14401305 144 -1.65101840 -1.38339407 145 -1.83576494 -1.65101840 146 -1.24771280 -1.83576494 147 1.16810251 -1.24771280 148 -1.23831264 1.16810251 149 -0.58981249 -1.23831264 150 -0.72787066 -0.58981249 151 -1.07724789 -0.72787066 152 -0.68699367 -1.07724789 153 -0.53653878 -0.68699367 154 -1.56383527 -0.53653878 155 -1.03090623 -1.56383527 156 -0.34847017 -1.03090623 157 0.70407718 -0.34847017 158 -1.22097332 0.70407718 159 -0.34210569 -1.22097332 160 1.22958216 -0.34210569 161 -0.14439060 1.22958216 162 -2.38915283 -0.14439060 163 -1.38309812 -2.38915283 164 1.16523887 -1.38309812 165 -1.46217077 1.16523887 166 0.13992650 -1.46217077 167 -0.76638675 0.13992650 168 0.35044532 -0.76638675 169 -0.74524923 0.35044532 170 -1.73410652 -0.74524923 171 -1.22844367 -1.73410652 172 -1.05177556 -1.22844367 173 -3.07788890 -1.05177556 174 -0.25692638 -3.07788890 175 -1.31556365 -0.25692638 176 -0.47237704 -1.31556365 177 -0.91021491 -0.47237704 178 -1.29099378 -0.91021491 179 -2.12857831 -1.29099378 180 -0.58164879 -2.12857831 181 -0.54487005 -0.58164879 182 1.49887283 -0.54487005 183 -0.01557594 1.49887283 184 -0.22550426 -0.01557594 185 1.18732732 -0.22550426 186 -0.05897163 1.18732732 187 -4.07506311 -0.05897163 188 -0.09552052 -4.07506311 189 -1.64965229 -0.09552052 190 -1.20418156 -1.64965229 191 -14.80958607 -1.20418156 192 -1.76102562 -14.80958607 193 3.13482456 -1.76102562 194 0.17830959 3.13482456 195 1.11037377 0.17830959 196 0.30921958 1.11037377 197 -0.26227410 0.30921958 198 -0.21901641 -0.26227410 199 -0.64468930 -0.21901641 200 -0.68827864 -0.64468930 201 -0.57285314 -0.68827864 202 13.41987180 -0.57285314 203 0.24869247 13.41987180 204 2.98121228 0.24869247 205 0.19691677 2.98121228 206 -0.16099678 0.19691677 207 -1.25828196 -0.16099678 208 -1.33993250 -1.25828196 209 0.28233330 -1.33993250 210 0.62888954 0.28233330 211 -13.10524390 0.62888954 212 -0.74654765 -13.10524390 213 -1.30316825 -0.74654765 214 1.33456659 -1.30316825 215 -0.90115821 1.33456659 216 1.07238168 -0.90115821 217 -3.91763528 1.07238168 218 -0.10715368 -3.91763528 219 -0.42753886 -0.10715368 220 0.49636974 -0.42753886 221 -0.90904812 0.49636974 222 0.91099051 -0.90904812 223 -0.58650247 0.91099051 224 0.15993629 -0.58650247 225 0.92765675 0.15993629 226 0.22095737 0.92765675 227 -5.65096896 0.22095737 228 -2.73479908 -5.65096896 229 0.10815217 -2.73479908 230 -1.19082599 0.10815217 231 -0.64406758 -1.19082599 232 0.16767136 -0.64406758 233 7.76230496 0.16767136 234 -0.87914005 7.76230496 235 -1.15134324 -0.87914005 236 0.83076223 -1.15134324 237 1.10132682 0.83076223 238 1.86412464 1.10132682 239 1.87524449 1.86412464 240 -0.07195689 1.87524449 241 0.02956596 -0.07195689 242 -0.91145965 0.02956596 243 -0.26068943 -0.91145965 244 -1.13366492 -0.26068943 245 1.39804877 -1.13366492 246 1.05334350 1.39804877 247 -0.56047513 1.05334350 248 -0.72515775 -0.56047513 249 1.25701888 -0.72515775 250 1.40313964 1.25701888 251 0.21490273 1.40313964 252 0.03468856 0.21490273 253 1.43911767 0.03468856 254 1.17081158 1.43911767 255 -0.80878303 1.17081158 256 -0.95388131 -0.80878303 257 0.57978483 -0.95388131 258 -0.26155467 0.57978483 259 -0.65221505 -0.26155467 260 -0.35507111 -0.65221505 261 -0.68767680 -0.35507111 262 -1.42871699 -0.68767680 263 14.45713288 -1.42871699 264 -0.16021307 14.45713288 265 2.11996736 -0.16021307 266 -1.06745006 2.11996736 267 -1.57719092 -1.06745006 268 -0.85143645 -1.57719092 269 NA -0.85143645 > dum1 <- dum[2:length(myerror),] > dum1 lag(myerror, k = 1) myerror [1,] 1.76058999 19.25902714 [2,] 31.38917584 1.76058999 [3,] 13.88741814 31.38917584 [4,] 22.11585458 13.88741814 [5,] 18.49743683 22.11585458 [6,] 18.67688311 18.49743683 [7,] -2.31120903 18.67688311 [8,] 6.00464589 -2.31120903 [9,] 1.83308131 6.00464589 [10,] -11.10906711 1.83308131 [11,] 23.55240031 -11.10906711 [12,] 8.16210994 23.55240031 [13,] 18.01268003 8.16210994 [14,] 16.28875590 18.01268003 [15,] 0.26679796 16.28875590 [16,] 13.65996054 0.26679796 [17,] 16.48959833 13.65996054 [18,] 16.81200061 16.48959833 [19,] -0.85678425 16.81200061 [20,] 13.65131354 -0.85678425 [21,] -33.29698031 13.65131354 [22,] 9.98510914 -33.29698031 [23,] -14.98907453 9.98510914 [24,] 1.55844667 -14.98907453 [25,] 0.53319023 1.55844667 [26,] 2.24009079 0.53319023 [27,] 17.32284049 2.24009079 [28,] 8.09612593 17.32284049 [29,] -1.19613989 8.09612593 [30,] 1.28370677 -1.19613989 [31,] 0.34283324 1.28370677 [32,] -4.68371774 0.34283324 [33,] -3.13534664 -4.68371774 [34,] 1.91368459 -3.13534664 [35,] 5.92823016 1.91368459 [36,] 16.03230614 5.92823016 [37,] -5.80077805 16.03230614 [38,] 4.09992284 -5.80077805 [39,] 4.10055833 4.09992284 [40,] 7.23272844 4.10055833 [41,] 17.85552344 7.23272844 [42,] 15.09018679 17.85552344 [43,] -12.47722666 15.09018679 [44,] -7.51811272 -12.47722666 [45,] 8.16607792 -7.51811272 [46,] -8.47693272 8.16607792 [47,] -2.02122400 -8.47693272 [48,] -3.63109984 -2.02122400 [49,] 2.51459091 -3.63109984 [50,] -5.24326834 2.51459091 [51,] 7.21745137 -5.24326834 [52,] -6.97172253 7.21745137 [53,] -8.46966530 -6.97172253 [54,] 2.60893515 -8.46966530 [55,] -6.94348438 2.60893515 [56,] 6.66168602 -6.94348438 [57,] -7.05253230 6.66168602 [58,] -1.42834935 -7.05253230 [59,] -2.28878028 -1.42834935 [60,] 0.92517571 -2.28878028 [61,] 5.08812478 0.92517571 [62,] 8.85353972 5.08812478 [63,] 2.77760497 8.85353972 [64,] -5.07800441 2.77760497 [65,] -6.67848382 -5.07800441 [66,] -10.94764914 -6.67848382 [67,] 5.06041289 -10.94764914 [68,] 3.18880421 5.06041289 [69,] -1.08501293 3.18880421 [70,] -9.60079266 -1.08501293 [71,] 2.23914392 -9.60079266 [72,] -4.52417277 2.23914392 [73,] 11.25575174 -4.52417277 [74,] 1.44690565 11.25575174 [75,] -6.36178948 1.44690565 [76,] -1.32018761 -6.36178948 [77,] -22.75130448 -1.32018761 [78,] 1.52150487 -22.75130448 [79,] -10.60178165 1.52150487 [80,] 5.19658330 -10.60178165 [81,] -8.19127115 5.19658330 [82,] -13.79639360 -8.19127115 [83,] 14.31914854 -13.79639360 [84,] -5.18775829 14.31914854 [85,] 4.57418300 -5.18775829 [86,] -0.14706581 4.57418300 [87,] 8.50831782 -0.14706581 [88,] -7.01194693 8.50831782 [89,] -19.08397558 -7.01194693 [90,] -5.35813840 -19.08397558 [91,] 1.95607529 -5.35813840 [92,] 3.01000172 1.95607529 [93,] -22.73215687 3.01000172 [94,] -10.97684574 -22.73215687 [95,] -6.35957271 -10.97684574 [96,] -3.38768095 -6.35957271 [97,] -15.34245205 -3.38768095 [98,] -7.32084423 -15.34245205 [99,] -9.84023390 -7.32084423 [100,] -1.60428997 -9.84023390 [101,] 5.87013288 -1.60428997 [102,] 2.31655069 5.87013288 [103,] 1.30094573 2.31655069 [104,] 6.56026028 1.30094573 [105,] -8.17413294 6.56026028 [106,] -8.84157411 -8.17413294 [107,] 2.79579421 -8.84157411 [108,] -4.34450576 2.79579421 [109,] -4.95824243 -4.34450576 [110,] -7.18337990 -4.95824243 [111,] -2.05364234 -7.18337990 [112,] -10.86148653 -2.05364234 [113,] -2.96442216 -10.86148653 [114,] -6.03027213 -2.96442216 [115,] -5.23050250 -6.03027213 [116,] -3.69896115 -5.23050250 [117,] -3.55803071 -3.69896115 [118,] -3.29908536 -3.55803071 [119,] -3.28752642 -3.29908536 [120,] -2.19916029 -3.28752642 [121,] -2.21191572 -2.19916029 [122,] -3.05178926 -2.21191572 [123,] -2.22786084 -3.05178926 [124,] -2.05732381 -2.22786084 [125,] 1.98905815 -2.05732381 [126,] -2.47389406 1.98905815 [127,] -0.48471991 -2.47389406 [128,] -1.65124685 -0.48471991 [129,] -1.12009893 -1.65124685 [130,] -0.90021979 -1.12009893 [131,] -1.96374698 -0.90021979 [132,] 0.48888330 -1.96374698 [133,] -0.75294121 0.48888330 [134,] -0.96025345 -0.75294121 [135,] -0.37337865 -0.96025345 [136,] -1.17686932 -0.37337865 [137,] -1.62215296 -1.17686932 [138,] -0.52934550 -1.62215296 [139,] 0.84688907 -0.52934550 [140,] 1.37260361 0.84688907 [141,] 1.81639174 1.37260361 [142,] -2.14401305 1.81639174 [143,] -1.38339407 -2.14401305 [144,] -1.65101840 -1.38339407 [145,] -1.83576494 -1.65101840 [146,] -1.24771280 -1.83576494 [147,] 1.16810251 -1.24771280 [148,] -1.23831264 1.16810251 [149,] -0.58981249 -1.23831264 [150,] -0.72787066 -0.58981249 [151,] -1.07724789 -0.72787066 [152,] -0.68699367 -1.07724789 [153,] -0.53653878 -0.68699367 [154,] -1.56383527 -0.53653878 [155,] -1.03090623 -1.56383527 [156,] -0.34847017 -1.03090623 [157,] 0.70407718 -0.34847017 [158,] -1.22097332 0.70407718 [159,] -0.34210569 -1.22097332 [160,] 1.22958216 -0.34210569 [161,] -0.14439060 1.22958216 [162,] -2.38915283 -0.14439060 [163,] -1.38309812 -2.38915283 [164,] 1.16523887 -1.38309812 [165,] -1.46217077 1.16523887 [166,] 0.13992650 -1.46217077 [167,] -0.76638675 0.13992650 [168,] 0.35044532 -0.76638675 [169,] -0.74524923 0.35044532 [170,] -1.73410652 -0.74524923 [171,] -1.22844367 -1.73410652 [172,] -1.05177556 -1.22844367 [173,] -3.07788890 -1.05177556 [174,] -0.25692638 -3.07788890 [175,] -1.31556365 -0.25692638 [176,] -0.47237704 -1.31556365 [177,] -0.91021491 -0.47237704 [178,] -1.29099378 -0.91021491 [179,] -2.12857831 -1.29099378 [180,] -0.58164879 -2.12857831 [181,] -0.54487005 -0.58164879 [182,] 1.49887283 -0.54487005 [183,] -0.01557594 1.49887283 [184,] -0.22550426 -0.01557594 [185,] 1.18732732 -0.22550426 [186,] -0.05897163 1.18732732 [187,] -4.07506311 -0.05897163 [188,] -0.09552052 -4.07506311 [189,] -1.64965229 -0.09552052 [190,] -1.20418156 -1.64965229 [191,] -14.80958607 -1.20418156 [192,] -1.76102562 -14.80958607 [193,] 3.13482456 -1.76102562 [194,] 0.17830959 3.13482456 [195,] 1.11037377 0.17830959 [196,] 0.30921958 1.11037377 [197,] -0.26227410 0.30921958 [198,] -0.21901641 -0.26227410 [199,] -0.64468930 -0.21901641 [200,] -0.68827864 -0.64468930 [201,] -0.57285314 -0.68827864 [202,] 13.41987180 -0.57285314 [203,] 0.24869247 13.41987180 [204,] 2.98121228 0.24869247 [205,] 0.19691677 2.98121228 [206,] -0.16099678 0.19691677 [207,] -1.25828196 -0.16099678 [208,] -1.33993250 -1.25828196 [209,] 0.28233330 -1.33993250 [210,] 0.62888954 0.28233330 [211,] -13.10524390 0.62888954 [212,] -0.74654765 -13.10524390 [213,] -1.30316825 -0.74654765 [214,] 1.33456659 -1.30316825 [215,] -0.90115821 1.33456659 [216,] 1.07238168 -0.90115821 [217,] -3.91763528 1.07238168 [218,] -0.10715368 -3.91763528 [219,] -0.42753886 -0.10715368 [220,] 0.49636974 -0.42753886 [221,] -0.90904812 0.49636974 [222,] 0.91099051 -0.90904812 [223,] -0.58650247 0.91099051 [224,] 0.15993629 -0.58650247 [225,] 0.92765675 0.15993629 [226,] 0.22095737 0.92765675 [227,] -5.65096896 0.22095737 [228,] -2.73479908 -5.65096896 [229,] 0.10815217 -2.73479908 [230,] -1.19082599 0.10815217 [231,] -0.64406758 -1.19082599 [232,] 0.16767136 -0.64406758 [233,] 7.76230496 0.16767136 [234,] -0.87914005 7.76230496 [235,] -1.15134324 -0.87914005 [236,] 0.83076223 -1.15134324 [237,] 1.10132682 0.83076223 [238,] 1.86412464 1.10132682 [239,] 1.87524449 1.86412464 [240,] -0.07195689 1.87524449 [241,] 0.02956596 -0.07195689 [242,] -0.91145965 0.02956596 [243,] -0.26068943 -0.91145965 [244,] -1.13366492 -0.26068943 [245,] 1.39804877 -1.13366492 [246,] 1.05334350 1.39804877 [247,] -0.56047513 1.05334350 [248,] -0.72515775 -0.56047513 [249,] 1.25701888 -0.72515775 [250,] 1.40313964 1.25701888 [251,] 0.21490273 1.40313964 [252,] 0.03468856 0.21490273 [253,] 1.43911767 0.03468856 [254,] 1.17081158 1.43911767 [255,] -0.80878303 1.17081158 [256,] -0.95388131 -0.80878303 [257,] 0.57978483 -0.95388131 [258,] -0.26155467 0.57978483 [259,] -0.65221505 -0.26155467 [260,] -0.35507111 -0.65221505 [261,] -0.68767680 -0.35507111 [262,] -1.42871699 -0.68767680 [263,] 14.45713288 -1.42871699 [264,] -0.16021307 14.45713288 [265,] 2.11996736 -0.16021307 [266,] -1.06745006 2.11996736 [267,] -1.57719092 -1.06745006 [268,] -0.85143645 -1.57719092 > z <- as.data.frame(dum1) > z lag(myerror, k = 1) myerror 1 1.76058999 19.25902714 2 31.38917584 1.76058999 3 13.88741814 31.38917584 4 22.11585458 13.88741814 5 18.49743683 22.11585458 6 18.67688311 18.49743683 7 -2.31120903 18.67688311 8 6.00464589 -2.31120903 9 1.83308131 6.00464589 10 -11.10906711 1.83308131 11 23.55240031 -11.10906711 12 8.16210994 23.55240031 13 18.01268003 8.16210994 14 16.28875590 18.01268003 15 0.26679796 16.28875590 16 13.65996054 0.26679796 17 16.48959833 13.65996054 18 16.81200061 16.48959833 19 -0.85678425 16.81200061 20 13.65131354 -0.85678425 21 -33.29698031 13.65131354 22 9.98510914 -33.29698031 23 -14.98907453 9.98510914 24 1.55844667 -14.98907453 25 0.53319023 1.55844667 26 2.24009079 0.53319023 27 17.32284049 2.24009079 28 8.09612593 17.32284049 29 -1.19613989 8.09612593 30 1.28370677 -1.19613989 31 0.34283324 1.28370677 32 -4.68371774 0.34283324 33 -3.13534664 -4.68371774 34 1.91368459 -3.13534664 35 5.92823016 1.91368459 36 16.03230614 5.92823016 37 -5.80077805 16.03230614 38 4.09992284 -5.80077805 39 4.10055833 4.09992284 40 7.23272844 4.10055833 41 17.85552344 7.23272844 42 15.09018679 17.85552344 43 -12.47722666 15.09018679 44 -7.51811272 -12.47722666 45 8.16607792 -7.51811272 46 -8.47693272 8.16607792 47 -2.02122400 -8.47693272 48 -3.63109984 -2.02122400 49 2.51459091 -3.63109984 50 -5.24326834 2.51459091 51 7.21745137 -5.24326834 52 -6.97172253 7.21745137 53 -8.46966530 -6.97172253 54 2.60893515 -8.46966530 55 -6.94348438 2.60893515 56 6.66168602 -6.94348438 57 -7.05253230 6.66168602 58 -1.42834935 -7.05253230 59 -2.28878028 -1.42834935 60 0.92517571 -2.28878028 61 5.08812478 0.92517571 62 8.85353972 5.08812478 63 2.77760497 8.85353972 64 -5.07800441 2.77760497 65 -6.67848382 -5.07800441 66 -10.94764914 -6.67848382 67 5.06041289 -10.94764914 68 3.18880421 5.06041289 69 -1.08501293 3.18880421 70 -9.60079266 -1.08501293 71 2.23914392 -9.60079266 72 -4.52417277 2.23914392 73 11.25575174 -4.52417277 74 1.44690565 11.25575174 75 -6.36178948 1.44690565 76 -1.32018761 -6.36178948 77 -22.75130448 -1.32018761 78 1.52150487 -22.75130448 79 -10.60178165 1.52150487 80 5.19658330 -10.60178165 81 -8.19127115 5.19658330 82 -13.79639360 -8.19127115 83 14.31914854 -13.79639360 84 -5.18775829 14.31914854 85 4.57418300 -5.18775829 86 -0.14706581 4.57418300 87 8.50831782 -0.14706581 88 -7.01194693 8.50831782 89 -19.08397558 -7.01194693 90 -5.35813840 -19.08397558 91 1.95607529 -5.35813840 92 3.01000172 1.95607529 93 -22.73215687 3.01000172 94 -10.97684574 -22.73215687 95 -6.35957271 -10.97684574 96 -3.38768095 -6.35957271 97 -15.34245205 -3.38768095 98 -7.32084423 -15.34245205 99 -9.84023390 -7.32084423 100 -1.60428997 -9.84023390 101 5.87013288 -1.60428997 102 2.31655069 5.87013288 103 1.30094573 2.31655069 104 6.56026028 1.30094573 105 -8.17413294 6.56026028 106 -8.84157411 -8.17413294 107 2.79579421 -8.84157411 108 -4.34450576 2.79579421 109 -4.95824243 -4.34450576 110 -7.18337990 -4.95824243 111 -2.05364234 -7.18337990 112 -10.86148653 -2.05364234 113 -2.96442216 -10.86148653 114 -6.03027213 -2.96442216 115 -5.23050250 -6.03027213 116 -3.69896115 -5.23050250 117 -3.55803071 -3.69896115 118 -3.29908536 -3.55803071 119 -3.28752642 -3.29908536 120 -2.19916029 -3.28752642 121 -2.21191572 -2.19916029 122 -3.05178926 -2.21191572 123 -2.22786084 -3.05178926 124 -2.05732381 -2.22786084 125 1.98905815 -2.05732381 126 -2.47389406 1.98905815 127 -0.48471991 -2.47389406 128 -1.65124685 -0.48471991 129 -1.12009893 -1.65124685 130 -0.90021979 -1.12009893 131 -1.96374698 -0.90021979 132 0.48888330 -1.96374698 133 -0.75294121 0.48888330 134 -0.96025345 -0.75294121 135 -0.37337865 -0.96025345 136 -1.17686932 -0.37337865 137 -1.62215296 -1.17686932 138 -0.52934550 -1.62215296 139 0.84688907 -0.52934550 140 1.37260361 0.84688907 141 1.81639174 1.37260361 142 -2.14401305 1.81639174 143 -1.38339407 -2.14401305 144 -1.65101840 -1.38339407 145 -1.83576494 -1.65101840 146 -1.24771280 -1.83576494 147 1.16810251 -1.24771280 148 -1.23831264 1.16810251 149 -0.58981249 -1.23831264 150 -0.72787066 -0.58981249 151 -1.07724789 -0.72787066 152 -0.68699367 -1.07724789 153 -0.53653878 -0.68699367 154 -1.56383527 -0.53653878 155 -1.03090623 -1.56383527 156 -0.34847017 -1.03090623 157 0.70407718 -0.34847017 158 -1.22097332 0.70407718 159 -0.34210569 -1.22097332 160 1.22958216 -0.34210569 161 -0.14439060 1.22958216 162 -2.38915283 -0.14439060 163 -1.38309812 -2.38915283 164 1.16523887 -1.38309812 165 -1.46217077 1.16523887 166 0.13992650 -1.46217077 167 -0.76638675 0.13992650 168 0.35044532 -0.76638675 169 -0.74524923 0.35044532 170 -1.73410652 -0.74524923 171 -1.22844367 -1.73410652 172 -1.05177556 -1.22844367 173 -3.07788890 -1.05177556 174 -0.25692638 -3.07788890 175 -1.31556365 -0.25692638 176 -0.47237704 -1.31556365 177 -0.91021491 -0.47237704 178 -1.29099378 -0.91021491 179 -2.12857831 -1.29099378 180 -0.58164879 -2.12857831 181 -0.54487005 -0.58164879 182 1.49887283 -0.54487005 183 -0.01557594 1.49887283 184 -0.22550426 -0.01557594 185 1.18732732 -0.22550426 186 -0.05897163 1.18732732 187 -4.07506311 -0.05897163 188 -0.09552052 -4.07506311 189 -1.64965229 -0.09552052 190 -1.20418156 -1.64965229 191 -14.80958607 -1.20418156 192 -1.76102562 -14.80958607 193 3.13482456 -1.76102562 194 0.17830959 3.13482456 195 1.11037377 0.17830959 196 0.30921958 1.11037377 197 -0.26227410 0.30921958 198 -0.21901641 -0.26227410 199 -0.64468930 -0.21901641 200 -0.68827864 -0.64468930 201 -0.57285314 -0.68827864 202 13.41987180 -0.57285314 203 0.24869247 13.41987180 204 2.98121228 0.24869247 205 0.19691677 2.98121228 206 -0.16099678 0.19691677 207 -1.25828196 -0.16099678 208 -1.33993250 -1.25828196 209 0.28233330 -1.33993250 210 0.62888954 0.28233330 211 -13.10524390 0.62888954 212 -0.74654765 -13.10524390 213 -1.30316825 -0.74654765 214 1.33456659 -1.30316825 215 -0.90115821 1.33456659 216 1.07238168 -0.90115821 217 -3.91763528 1.07238168 218 -0.10715368 -3.91763528 219 -0.42753886 -0.10715368 220 0.49636974 -0.42753886 221 -0.90904812 0.49636974 222 0.91099051 -0.90904812 223 -0.58650247 0.91099051 224 0.15993629 -0.58650247 225 0.92765675 0.15993629 226 0.22095737 0.92765675 227 -5.65096896 0.22095737 228 -2.73479908 -5.65096896 229 0.10815217 -2.73479908 230 -1.19082599 0.10815217 231 -0.64406758 -1.19082599 232 0.16767136 -0.64406758 233 7.76230496 0.16767136 234 -0.87914005 7.76230496 235 -1.15134324 -0.87914005 236 0.83076223 -1.15134324 237 1.10132682 0.83076223 238 1.86412464 1.10132682 239 1.87524449 1.86412464 240 -0.07195689 1.87524449 241 0.02956596 -0.07195689 242 -0.91145965 0.02956596 243 -0.26068943 -0.91145965 244 -1.13366492 -0.26068943 245 1.39804877 -1.13366492 246 1.05334350 1.39804877 247 -0.56047513 1.05334350 248 -0.72515775 -0.56047513 249 1.25701888 -0.72515775 250 1.40313964 1.25701888 251 0.21490273 1.40313964 252 0.03468856 0.21490273 253 1.43911767 0.03468856 254 1.17081158 1.43911767 255 -0.80878303 1.17081158 256 -0.95388131 -0.80878303 257 0.57978483 -0.95388131 258 -0.26155467 0.57978483 259 -0.65221505 -0.26155467 260 -0.35507111 -0.65221505 261 -0.68767680 -0.35507111 262 -1.42871699 -0.68767680 263 14.45713288 -1.42871699 264 -0.16021307 14.45713288 265 2.11996736 -0.16021307 266 -1.06745006 2.11996736 267 -1.57719092 -1.06745006 268 -0.85143645 -1.57719092 > 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/7ss001358158465.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/802m11358158465.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/941u01358158465.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/105zzg1358158465.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$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='') + } + } > myeq <- paste(myeq, ' + e[t]') > a<-table.row.start(a) > a<-table.element(a, myeq) > a<-table.row.end(a) > a<-table.end(a) > table.save(a,file="/var/wessaorg/rcomp/tmp/11yy031358158465.tab") > a<-table.start() > a<-table.row.start(a) > a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a,'Variable',header=TRUE) > a<-table.element(a,'Parameter',header=TRUE) > a<-table.element(a,'S.D.',header=TRUE) > a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE) > a<-table.element(a,'2-tail p-value',header=TRUE) > a<-table.element(a,'1-tail p-value',header=TRUE) > a<-table.row.end(a) > for (i in 1:k){ + a<-table.row.start(a) + a<-table.element(a,rownames(mysum$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$coefficients[i,4]/2,6)) + a<-table.row.end(a) + } > a<-table.end(a) > table.save(a,file="/var/wessaorg/rcomp/tmp/12xvt61358158465.tab") > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Multiple R',1,TRUE) > a<-table.element(a, sqrt(mysum$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$sigma) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Sum Squared Residuals',1,TRUE) > a<-table.element(a, sum(myerror*myerror)) > a<-table.row.end(a) > a<-table.end(a) > table.save(a,file="/var/wessaorg/rcomp/tmp/13wp9v1358158465.tab") > a<-table.start() > a<-table.row.start(a) > a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a, 'Time or Index', 1, TRUE) > a<-table.element(a, 'Actuals', 1, TRUE) > a<-table.element(a, 'Interpolation
Forecast', 1, TRUE) > a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE) > a<-table.row.end(a) > for (i in 1:n) { + a<-table.row.start(a) + a<-table.element(a,i, 1, TRUE) + a<-table.element(a,x[i]) + a<-table.element(a,x[i]-mysum$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/14mtog1358158465.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/15h30e1358158465.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/16l8rs1358158465.tab") + } > > try(system("convert tmp/1uwd41358158465.ps tmp/1uwd41358158465.png",intern=TRUE)) character(0) > try(system("convert tmp/2gfl81358158465.ps tmp/2gfl81358158465.png",intern=TRUE)) character(0) > try(system("convert tmp/3ryld1358158465.ps tmp/3ryld1358158465.png",intern=TRUE)) character(0) > try(system("convert tmp/4ii2o1358158465.ps tmp/4ii2o1358158465.png",intern=TRUE)) character(0) > try(system("convert tmp/5wvl11358158465.ps tmp/5wvl11358158465.png",intern=TRUE)) character(0) > try(system("convert tmp/6s95t1358158465.ps tmp/6s95t1358158465.png",intern=TRUE)) character(0) > try(system("convert tmp/7ss001358158465.ps tmp/7ss001358158465.png",intern=TRUE)) character(0) > try(system("convert tmp/802m11358158465.ps tmp/802m11358158465.png",intern=TRUE)) character(0) > try(system("convert tmp/941u01358158465.ps tmp/941u01358158465.png",intern=TRUE)) character(0) > try(system("convert tmp/105zzg1358158465.ps tmp/105zzg1358158465.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 10.628 0.879 11.562