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 <- c(8.64 + ,8.89 + ,8.87 + ,8.81 + ,8.87 + ,9.06 + ,9.12 + ,8.66 + ,8.17 + ,8.04 + ,7.71 + ,7.55 + ,7.52 + ,7.38 + ,7.52 + ,7.31 + ,6.92 + ,7.09 + ,7.05 + ,7.37 + ,7.05 + ,6.79 + ,6.35 + ,6.44 + ,6.89 + ,7.16 + ,7.46 + ,7.91 + ,7.86 + ,8.02 + ,8.38 + ,8.50 + ,8.40 + ,8.24 + ,8.33 + ,8.28 + ,8.15 + ,8.06 + ,7.79 + ,7.28 + ,7.52 + ,7.23 + ,7.13 + ,7.21 + ,6.99 + ,6.77 + ,6.69 + ,6.39 + ,6.85 + ,6.74 + ,6.56 + ,6.62 + ,6.71 + ,6.67 + ,6.54 + ,6.14 + ,6.13 + ,5.86 + ,5.88 + ,5.75 + ,5.53 + ,5.86 + ,5.90 + ,5.95 + ,5.69 + ,5.53 + ,5.71 + ,5.60 + ,5.73 + ,5.60 + ,5.41 + ,5.13 + ,5.00 + ,5.04 + ,5.10 + ,4.96 + ,4.90 + ,4.80 + ,4.48 + ,4.29 + ,4.27 + ,4.18 + ,4.02 + ,3.82 + ,4.13 + ,4.16 + ,3.98 + ,4.26 + ,4.70 + ,4.96 + ,5.13 + ,5.35 + ,5.41 + ,5.42 + ,5.51 + ,5.75 + ,5.67 + ,5.46 + ,5.56 + ,5.56 + ,5.54 + ,5.53 + ,5.65 + ,5.58 + ,5.57 + ,5.36 + ,5.23 + ,5.11 + ,5.07 + ,5.04 + ,5.34 + ,5.43 + ,5.31 + ,5.12 + ,4.97 + ,5.00 + ,4.64 + ,4.80 + ,5.10 + ,5.11 + ,5.12 + ,5.36 + ,5.26 + ,5.27 + ,5.10 + ,4.94 + ,4.68 + ,4.41 + ,4.60 + ,4.53 + ,4.18 + ,4.00 + ,3.87 + ,4.09 + ,4.13 + ,3.74 + ,3.81 + ,4.11 + ,4.14 + ,3.99 + ,4.28 + ,4.37 + ,4.24 + ,4.19 + ,4.01 + ,3.95 + ,4.30 + ,4.37 + ,4.40 + ,4.29 + ,4.12 + ,4.07 + ,3.93 + ,3.79 + ,3.67 + ,3.53 + ,3.69 + ,3.69 + ,3.48 + ,3.31 + ,3.16 + ,3.25 + ,3.14 + ,3.19 + ,3.43 + ,3.45 + ,3.31 + ,3.51 + ,3.53 + ,3.83 + ,4.02 + ,3.99 + ,4.11 + ,3.96 + ,3.83 + ,3.71 + ,3.81 + ,3.73 + ,3.99 + ,4.17 + ,4.00 + ,4.10 + ,4.24 + ,4.45 + ,4.62 + ,4.49 + ,4.45 + ,4.49 + ,4.36 + ,4.32 + ,4.45 + ,4.13 + ,4.14 + ,4.30 + ,4.42 + ,4.67 + ,4.96 + ,4.73 + ,4.52 + ,4.36 + ,4.15 + ,3.92 + ,3.88 + ,4.20 + ,3.95 + ,3.78 + ,3.69 + ,3.77 + ,3.66 + ,3.53 + ,3.50 + ,3.14 + ,3.42 + ,3.30 + ,2.81 + ,3.15 + ,3.37 + ,4.05 + ,4.00 + ,4.20 + ,4.21 + ,4.24 + ,4.24 + ,4.17 + ,4.12 + ,4.35 + ,3.98 + ,3.62 + ,4.39 + ,5.01 + ,4.07 + ,3.70 + ,3.59 + ,3.44 + ,3.33 + ,2.98 + ,3.14 + ,2.55 + ,2.49 + ,2.53 + ,2.43) > par8 = 'FALSE' > par7 = '1' > par6 = '' > par5 = '1' > par4 = '' > par3 = '0' > par2 = 'periodic' > par1 = '12' > main = 'Seasonal Decomposition by Loess' > par1 <- as.numeric(par1) #seasonal period > if (par2 != 'periodic') par2 <- as.numeric(par2) #s.window > par3 <- as.numeric(par3) #s.degree > if (par4 == '') par4 <- NULL else par4 <- as.numeric(par4)#t.window > par5 <- as.numeric(par5)#t.degree > if (par6 != '') par6 <- as.numeric(par6)#l.window > par7 <- as.numeric(par7)#l.degree > if (par8 == 'FALSE') par8 <- FALSE else par9 <- TRUE #robust > nx <- length(x) > x <- ts(x,frequency=par1) > if (par6 != '') { + m <- stl(x,s.window=par2, s.degree=par3, t.window=par4, t.degre=par5, l.window=par6, l.degree=par7, robust=par8) + } else { + m <- stl(x,s.window=par2, s.degree=par3, t.window=par4, t.degre=par5, l.degree=par7, robust=par8) + } > m$time.series seasonal trend remainder Jan 1 -0.103733903 9.088335 -0.3446015014 Feb 1 0.046012634 8.990846 -0.1468588157 Mar 1 0.064137317 8.893357 -0.0874942748 Apr 1 0.085444615 8.788803 -0.0642477843 May 1 0.117251926 8.684249 0.0684986930 Jun 1 0.106816127 8.575714 0.3774700274 Jul 1 0.084880308 8.467178 0.5679413829 Aug 1 0.011127916 8.358606 0.2902661638 Sep 1 0.001875561 8.250034 -0.0819090925 Oct 1 -0.081778111 8.113843 0.0079355496 Nov 1 -0.160931771 7.977652 -0.1067198211 Dec 1 -0.171102649 7.820721 -0.0996181112 Jan 2 -0.103733903 7.663790 -0.0400560253 Feb 2 0.046012634 7.534525 -0.2005372831 Mar 2 0.064137317 7.405259 0.0506033141 Apr 2 0.085444615 7.306103 -0.0815471882 May 2 0.117251926 7.206946 -0.4041977037 Jun 2 0.106816127 7.130532 -0.1473483167 Jul 2 0.084880308 7.054119 -0.0889989088 Aug 2 0.011127916 7.024733 0.3341387812 Sep 2 0.001875561 6.995348 0.0527764340 Oct 2 -0.081778111 7.026348 -0.1545696951 Nov 2 -0.160931771 7.057348 -0.5464158369 Dec 2 -0.171102649 7.137401 -0.5262987842 Jan 3 -0.103733903 7.217455 -0.2237213556 Feb 3 0.046012634 7.333831 -0.2198435199 Mar 3 0.064137317 7.450207 -0.0543438291 Apr 3 0.085444615 7.595128 0.2294270232 May 3 0.117251926 7.740050 0.0026978623 Jun 3 0.106816127 7.875610 0.0375734415 Jul 3 0.084880308 8.011171 0.2839490417 Aug 3 0.011127916 8.082267 0.4066054259 Sep 3 0.001875561 8.153363 0.2447617730 Oct 3 -0.081778111 8.141668 0.1801102651 Nov 3 -0.160931771 8.129973 0.3609587445 Dec 3 -0.171102649 8.051917 0.3991859617 Jan 4 -0.103733903 7.973860 0.2798735548 Feb 4 0.046012634 7.855680 0.1583076674 Mar 4 0.064137317 7.737499 -0.0116363647 Apr 4 0.085444615 7.602363 -0.4078079909 May 4 0.117251926 7.467228 -0.0644796302 Jun 4 0.106816127 7.337652 -0.2144678617 Jul 4 0.084880308 7.208076 -0.1629560721 Aug 4 0.011127916 7.106165 0.0927069226 Sep 4 0.001875561 7.004255 -0.0161301199 Oct 4 -0.081778111 6.931961 -0.0801825676 Nov 4 -0.160931771 6.859667 -0.0087350280 Dec 4 -0.171102649 6.799295 -0.2381924793 Jan 5 -0.103733903 6.738923 0.2148104454 Feb 5 0.046012634 6.674580 0.0194068710 Mar 5 0.064137317 6.610238 -0.1143748482 Apr 5 0.085444615 6.539877 -0.0053211180 May 5 0.117251926 6.469515 0.1232325991 Jun 5 0.106816127 6.393325 0.1698592635 Jul 5 0.084880308 6.317134 0.1379859490 Aug 5 0.011127916 6.239864 -0.1109914498 Sep 5 0.001875561 6.162593 -0.0344688858 Oct 5 -0.081778111 6.088100 -0.1463218332 Nov 5 -0.160931771 6.013607 0.0273252068 Dec 5 -0.171102649 5.942229 -0.0211267676 Jan 6 -0.103733903 5.870852 -0.2371183660 Feb 6 0.046012634 5.818505 -0.0045175306 Mar 6 0.064137317 5.766158 0.0697051598 Apr 6 0.085444615 5.730919 0.1336366971 May 6 0.117251926 5.695680 -0.1229317789 Jun 6 0.106816127 5.654885 -0.2317012707 Jul 6 0.084880308 5.614090 0.0110292586 Aug 6 0.011127916 5.557467 0.0314055456 Sep 6 0.001875561 5.500843 0.2272817954 Oct 6 -0.081778111 5.430878 0.2508997706 Nov 6 -0.160931771 5.360914 0.2100177330 Dec 6 -0.171102649 5.275911 0.0251920522 Jan 7 -0.103733903 5.190907 -0.0871732526 Feb 7 0.046012634 5.081689 -0.0877011528 Mar 7 0.064137317 4.972470 0.0633928022 Apr 7 0.085444615 4.856181 0.0183747036 May 7 0.117251926 4.739891 0.0428565917 Jun 7 0.106816127 4.638496 0.0546883204 Jul 7 0.084880308 4.537100 -0.1419799299 Aug 7 0.011127916 4.453781 -0.1749094098 Sep 7 0.001875561 4.370463 -0.1023389270 Oct 7 -0.081778111 4.318978 -0.0572002514 Nov 7 -0.160931771 4.267493 -0.0865615885 Dec 7 -0.171102649 4.276463 -0.2853602540 Jan 8 -0.103733903 4.285432 -0.0516985436 Feb 8 0.046012634 4.358324 -0.2443363129 Mar 8 0.064137317 4.431215 -0.5153522271 Apr 8 0.085444615 4.549358 -0.3748021570 May 8 0.117251926 4.667500 -0.0847521001 Jun 8 0.106816127 4.812175 0.0410085266 Jul 8 0.084880308 4.956851 0.0882691743 Aug 8 0.011127916 5.093208 0.2456636092 Sep 8 0.001875561 5.229566 0.1785580068 Oct 8 -0.081778111 5.327421 0.1743571638 Nov 8 -0.160931771 5.425275 0.2456563081 Dec 8 -0.171102649 5.474157 0.4469451907 Jan 9 -0.103733903 5.523039 0.2506944492 Feb 9 0.046012634 5.537734 -0.1237463937 Mar 9 0.064137317 5.552428 -0.0565653815 Apr 9 0.085444615 5.541038 -0.0664828528 May 9 0.117251926 5.529648 -0.1069003374 Jun 9 0.106816127 5.499558 -0.0763737297 Jul 9 0.084880308 5.469467 0.0956528990 Aug 9 0.011127916 5.438996 0.1298759544 Sep 9 0.001875561 5.408525 0.1595989727 Oct 9 -0.081778111 5.382044 0.0597344819 Nov 9 -0.160931771 5.355562 0.0353699784 Dec 9 -0.171102649 5.318120 -0.0370173083 Jan 10 -0.103733903 5.280678 -0.1069442190 Feb 10 0.046012634 5.228933 -0.2349458410 Mar 10 0.064137317 5.177188 0.0986743921 Apr 10 0.085444615 5.136345 0.2082103939 May 10 0.117251926 5.095502 0.0972463825 Jun 10 0.106816127 5.082888 -0.0697044848 Jul 10 0.084880308 5.070275 -0.1851553311 Aug 10 0.011127916 5.073501 -0.0846286942 Sep 10 0.001875561 5.076727 -0.4386020944 Oct 10 -0.081778111 5.077635 -0.1958568119 Nov 10 -0.160931771 5.078543 0.1823884579 Dec 10 -0.171102649 5.070699 0.2104036191 Jan 11 -0.103733903 5.062855 0.1608791563 Feb 11 0.046012634 5.037665 0.2763226984 Mar 11 0.064137317 5.012475 0.1833880955 Apr 11 0.085444615 4.959655 0.2249007281 May 11 0.117251926 4.906835 0.0759133475 Jun 11 0.106816127 4.819037 0.0141464472 Jul 11 0.084880308 4.731240 -0.1361204320 Aug 11 0.011127916 4.626757 -0.2278848189 Sep 11 0.001875561 4.522274 0.0758507569 Oct 11 -0.081778111 4.419235 0.1925429544 Nov 11 -0.160931771 4.316197 0.0247351391 Dec 11 -0.171102649 4.237273 -0.0661700508 Jan 12 -0.103733903 4.158349 -0.1846148646 Feb 12 0.046012634 4.112448 -0.0684609425 Mar 12 0.064137317 4.066548 -0.0006851653 Apr 12 0.085444615 4.055191 -0.4006351709 May 12 0.117251926 4.043833 -0.3510851897 Jun 12 0.106816127 4.057711 -0.0545274379 Jul 12 0.084880308 4.071589 -0.0164696651 Aug 12 0.011127916 4.095143 -0.1162707497 Sep 12 0.001875561 4.118696 0.1594281285 Oct 12 -0.081778111 4.147662 0.3041165229 Nov 12 -0.160931771 4.176627 0.2243049046 Dec 12 -0.171102649 4.193926 0.1671764203 Jan 13 -0.103733903 4.211226 -0.0974916880 Feb 13 0.046012634 4.199774 -0.2957869950 Mar 13 0.064137317 4.188323 0.0475395532 Apr 13 0.085444615 4.154037 0.1305188469 May 13 0.117251926 4.119750 0.1629981275 Jun 13 0.106816127 4.079537 0.1036466660 Jul 13 0.084880308 4.039324 -0.0042047743 Aug 13 0.011127916 3.989445 0.0694274012 Sep 13 0.001875561 3.939565 -0.0114404605 Oct 13 -0.081778111 3.863149 0.0086295580 Nov 13 -0.160931771 3.786732 0.0441995638 Dec 13 -0.171102649 3.699005 0.0020976014 Jan 14 -0.103733903 3.611278 0.1824560150 Feb 14 0.046012634 3.539085 0.1049021319 Mar 14 0.064137317 3.466893 -0.0510298961 Apr 14 0.085444615 3.423247 -0.1986911284 May 14 0.117251926 3.379600 -0.3368523738 Jun 14 0.106816127 3.367364 -0.2241797730 Jul 14 0.084880308 3.355127 -0.3000071512 Aug 14 0.011127916 3.380301 -0.2014289091 Sep 14 0.001875561 3.405475 0.0226492959 Oct 14 -0.081778111 3.465856 0.0659216724 Nov 14 -0.160931771 3.526238 -0.0553059638 Dec 14 -0.171102649 3.591860 0.0892427150 Jan 15 -0.103733903 3.657482 -0.0237482303 Feb 15 0.046012634 3.703866 0.0801213787 Mar 15 0.064137317 3.750250 0.2056128429 Apr 15 0.085444615 3.785125 0.1194304282 May 15 0.117251926 3.820000 0.1727480003 Jun 15 0.106816127 3.855518 -0.0023339744 Jul 15 0.084880308 3.891036 -0.1459159280 Aug 15 0.011127916 3.922230 -0.2233578469 Sep 15 0.001875561 3.953424 -0.1452998029 Oct 15 -0.081778111 3.990392 -0.1786134000 Nov 15 -0.160931771 4.027359 0.1235729902 Dec 15 -0.171102649 4.078511 0.2625916584 Jan 16 -0.103733903 4.129663 -0.0259292975 Feb 16 0.046012634 4.183195 -0.1292077017 Mar 16 0.064137317 4.236727 -0.0608642507 Apr 16 0.085444615 4.276951 0.0876045714 May 16 0.117251926 4.317175 0.1855733803 Jun 16 0.106816127 4.339232 0.0439520956 Jul 16 0.084880308 4.361289 0.0038308319 Aug 16 0.011127916 4.373348 0.1055242412 Sep 16 0.001875561 4.385407 -0.0272823868 Oct 16 -0.081778111 4.398031 0.0037473407 Nov 16 -0.160931771 4.410655 0.2002770555 Dec 16 -0.171102649 4.424038 -0.1229351718 Jan 17 -0.103733903 4.437421 -0.1936870231 Feb 17 0.046012634 4.436998 -0.1830106246 Mar 17 0.064137317 4.436575 -0.0807123709 Apr 17 0.085444615 4.418557 0.1659982355 May 17 0.117251926 4.400539 0.4422088287 Jun 17 0.106816127 4.375846 0.2473378974 Jul 17 0.084880308 4.351153 0.0839669871 Aug 17 0.011127916 4.304541 0.0443314373 Sep 17 0.001875561 4.257929 -0.1098041497 Oct 17 -0.081778111 4.178524 -0.1767462424 Nov 17 -0.160931771 4.099120 -0.0581883478 Dec 17 -0.171102649 4.006810 0.3642923718 Jan 18 -0.103733903 3.914500 0.1392334673 Feb 18 0.046012634 3.830439 -0.0964520395 Mar 18 0.064137317 3.746378 -0.1205156912 Apr 18 0.085444615 3.667457 0.0170984466 May 18 0.117251926 3.588536 -0.0457874288 Jun 18 0.106816127 3.526474 -0.1032905150 Jul 18 0.084880308 3.464413 -0.0492935800 Aug 18 0.011127916 3.451700 -0.3228282710 Sep 18 0.001875561 3.438987 -0.0208629991 Oct 18 -0.081778111 3.475483 -0.0937051573 Nov 18 -0.160931771 3.511979 -0.5410473282 Dec 18 -0.171102649 3.577518 -0.2564150116 Jan 19 -0.103733903 3.643056 -0.1693223191 Feb 19 0.046012634 3.724745 0.2792426928 Mar 19 0.064137317 3.806433 0.1294295599 Apr 19 0.085444615 3.888236 0.2263197362 May 19 0.117251926 3.970038 0.1227098993 Jun 19 0.106816127 4.037040 0.0961438341 Jul 19 0.084880308 4.104042 0.0510777899 Aug 19 0.011127916 4.142231 0.0166411526 Sep 19 0.001875561 4.180420 -0.0622955218 Oct 19 -0.081778111 4.170923 0.2608554315 Nov 19 -0.160931771 4.161425 -0.0204936279 Dec 19 -0.171102649 4.103136 -0.3120333027 Jan 20 -0.103733903 4.044847 0.4488873984 Feb 20 0.046012634 3.949955 1.0140320957 Mar 20 0.064137317 3.855064 0.1507986481 Apr 20 0.085444615 3.722524 -0.1079682846 May 20 0.117251926 3.589983 -0.1172352304 Jun 20 0.106816127 3.444684 -0.1115005920 Jul 20 0.084880308 3.299386 -0.0542659324 Aug 20 0.011127916 3.151094 -0.1822218822 Sep 20 0.001875561 3.002802 0.1353221308 Oct 20 -0.081778111 2.853178 -0.2213993949 Nov 20 -0.160931771 2.703553 -0.0526209333 Dec 20 -0.171102649 2.554688 0.1464142434 Jan 21 -0.103733903 2.405824 0.1279097961 > m$win s t l 2411 19 13 > m$deg s t l 0 1 1 > m$jump s t l 242 2 2 > m$inner [1] 2 > m$outer [1] 0 > postscript(file="/var/wessaorg/rcomp/tmp/1c1h81353593980.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(m,main=main) > dev.off() null device 1 > mylagmax <- nx/2 > postscript(file="/var/wessaorg/rcomp/tmp/2akgz1353593980.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > op <- par(mfrow = c(2,2)) > acf(as.numeric(x),lag.max = mylagmax,main='Observed') > acf(as.numeric(m$time.series[,'trend']),na.action=na.pass,lag.max = mylagmax,main='Trend') > acf(as.numeric(m$time.series[,'seasonal']),na.action=na.pass,lag.max = mylagmax,main='Seasonal') > acf(as.numeric(m$time.series[,'remainder']),na.action=na.pass,lag.max = mylagmax,main='Remainder') > par(op) > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/3j9n11353593980.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > op <- par(mfrow = c(2,2)) > spectrum(as.numeric(x),main='Observed') > spectrum(as.numeric(m$time.series[!is.na(m$time.series[,'trend']),'trend']),main='Trend') > spectrum(as.numeric(m$time.series[!is.na(m$time.series[,'seasonal']),'seasonal']),main='Seasonal') > spectrum(as.numeric(m$time.series[!is.na(m$time.series[,'remainder']),'remainder']),main='Remainder') > par(op) > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/456t91353593980.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > op <- par(mfrow = c(2,2)) > cpgram(as.numeric(x),main='Observed') > cpgram(as.numeric(m$time.series[!is.na(m$time.series[,'trend']),'trend']),main='Trend') > cpgram(as.numeric(m$time.series[!is.na(m$time.series[,'seasonal']),'seasonal']),main='Seasonal') > cpgram(as.numeric(m$time.series[!is.na(m$time.series[,'remainder']),'remainder']),main='Remainder') > par(op) > dev.off() null device 1 > > #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab > load(file="/var/wessaorg/rcomp/createtable") > > a<-table.start() > a<-table.row.start(a) > a<-table.element(a,'Seasonal Decomposition by Loess - Parameters',4,TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a,'Component',header=TRUE) > a<-table.element(a,'Window',header=TRUE) > a<-table.element(a,'Degree',header=TRUE) > a<-table.element(a,'Jump',header=TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a,'Seasonal',header=TRUE) > a<-table.element(a,m$win['s']) > a<-table.element(a,m$deg['s']) > a<-table.element(a,m$jump['s']) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a,'Trend',header=TRUE) > a<-table.element(a,m$win['t']) > a<-table.element(a,m$deg['t']) > a<-table.element(a,m$jump['t']) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a,'Low-pass',header=TRUE) > a<-table.element(a,m$win['l']) > a<-table.element(a,m$deg['l']) > a<-table.element(a,m$jump['l']) > a<-table.row.end(a) > a<-table.end(a) > table.save(a,file="/var/wessaorg/rcomp/tmp/5df5m1353593980.tab") > a<-table.start() > a<-table.row.start(a) > a<-table.element(a,'Seasonal Decomposition by Loess - Time Series Components',6,TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a,'t',header=TRUE) > a<-table.element(a,'Observed',header=TRUE) > a<-table.element(a,'Fitted',header=TRUE) > a<-table.element(a,'Seasonal',header=TRUE) > a<-table.element(a,'Trend',header=TRUE) > a<-table.element(a,'Remainder',header=TRUE) > a<-table.row.end(a) > for (i in 1:nx) { + a<-table.row.start(a) + a<-table.element(a,i,header=TRUE) + a<-table.element(a,x[i]) + a<-table.element(a,x[i]+m$time.series[i,'remainder']) + a<-table.element(a,m$time.series[i,'seasonal']) + a<-table.element(a,m$time.series[i,'trend']) + a<-table.element(a,m$time.series[i,'remainder']) + a<-table.row.end(a) + } > a<-table.end(a) > table.save(a,file="/var/wessaorg/rcomp/tmp/6iix51353593980.tab") > > try(system("convert tmp/1c1h81353593980.ps tmp/1c1h81353593980.png",intern=TRUE)) character(0) > try(system("convert tmp/2akgz1353593980.ps tmp/2akgz1353593980.png",intern=TRUE)) character(0) > try(system("convert tmp/3j9n11353593980.ps tmp/3j9n11353593980.png",intern=TRUE)) character(0) > try(system("convert tmp/456t91353593980.ps tmp/456t91353593980.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 5.613 0.687 6.265