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Type 'q()' to quit R. > x <- c(413491 + ,399153 + ,385939 + ,373917 + ,364635 + ,364696 + ,418358 + ,428212 + ,423730 + ,420677 + ,417428 + ,423245 + ,423113 + ,418873 + ,405733 + ,397812 + ,389918 + ,391116 + ,443814 + ,460373 + ,455422 + ,456288 + ,452233 + ,459256 + ,461146 + ,451391 + ,443101 + ,438810 + ,430457 + ,435721 + ,488280 + ,505814 + ,502338 + ,500910 + ,501434 + ,515476 + ,520862 + ,519517 + ,511805 + ,508607 + ,505327 + ,511435 + ,570158 + ,591665 + ,593572 + ,586346 + ,586063 + ,591504 + ,594033 + ,585597 + ,572450 + ,562917 + ,554675 + ,553997 + ,601310 + ,622255 + ,616735 + ,606480 + ,595079 + ,598588 + ,599917 + ,591573 + ,575489 + ,567223 + ,555338 + ,555252 + ,608249 + ,630859 + ,628632 + ,624435 + ,609670 + ,615830 + ,621170 + ,604212 + ,584348 + ,573717 + ,555234 + ,544897 + ,598866 + ,620081 + ,607699 + ,589960 + ,578665 + ,580166 + ,579457 + ,571560 + ,560460 + ,551397 + ,536763 + ,540562 + ,588184 + ,607049 + ,598968 + ,577644 + ,562640 + ,565867 + ,561274 + ,554144 + ,539900 + ,526271 + ,511841 + ,505282 + ,554083 + ,584225 + ,568858 + ,539516 + ,521612 + ,525562 + ,526519 + ,515713 + ,503454 + ,489301 + ,479020 + ,475102 + ,523682 + ,551528 + ,531626 + ,511037 + ,492417 + ,492188 + ,492865 + ,480961 + ,461935 + ,456608 + ,441977 + ,439148 + ,488180 + ,520564 + ,501492 + ,485025 + ,464196 + ,460170 + ,467037 + ,460070 + ,447988 + ,442867 + ,436087 + ,431328 + ,484015 + ,509673 + ,512927 + ,502831 + ,470984 + ,471067 + ,476049 + ,474605 + ,470439 + ,461251 + ,454724 + ,455626 + ,516847 + ,525192 + ,522975 + ,518585 + ,509239 + ,512238 + ,519164 + ,517009 + ,509933 + ,509127 + ,500857 + ,506971 + ,569323 + ,579714 + ,577992 + ,565464 + ,547344 + ,554788 + ,562325 + ,560854 + ,555332 + ,543599 + ,536662 + ,542722 + ,593530 + ,610763 + ,612613 + ,611324 + ,594167 + ,595454 + ,590865 + ,589379 + ,584428 + ,573100 + ,567456 + ,569028 + ,620735 + ,628884 + ,628232 + ,612117 + ,595404 + ,597141 + ,593408 + ,590072 + ,579799 + ,574205 + ,572775 + ,572942 + ,619567 + ,625809 + ,619916 + ,587625 + ,565742 + ,557274 + ,560576 + ,548854 + ,531673 + ,525919 + ,511038 + ,498662 + ,555362 + ,564591 + ,541657 + ,527070 + ,509846 + ,514258 + ,516922 + ,507561 + ,492622 + ,490243 + ,469357 + ,477580 + ,528379 + ,533590 + ,517945 + ,506174 + ,501866 + ,516141 + ,528222 + ,532638 + ,536322 + ,536535 + ,523597 + ,536214 + ,586570 + ,596594 + ,580523 + ,564478 + ,557560 + ,575093 + ,580112 + ,574761 + ,563250 + ,551531 + ,537034 + ,544686 + ,600991 + ,604378 + ,586111 + ,563668 + ,548604 + ,551174 + ,555654) > par8 = 'FALSE' > par7 = '1' > par6 = '' > par5 = '1' > par4 = '' > par3 = '0' > par2 = 'periodic' > par1 = '12' > main = 'Seasonal Decomposition by Loess' > #'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$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/ > #Source of accompanying publication: Office for Research, Development, and Education > #Technical description: Write here your technical program description (don't use hard returns!) > par1 <- as.numeric(par1) #seasonal period > if (par2 != 'periodic') par2 <- as.numeric(par2) #s.window > par3 <- as.numeric(par3) #s.degree > if (par4 == '') par4 <- NULL else par4 <- as.numeric(par4)#t.window > par5 <- as.numeric(par5)#t.degree > if (par6 != '') par6 <- as.numeric(par6)#l.window > par7 <- as.numeric(par7)#l.degree > if (par8 == 'FALSE') par8 <- FALSE else par9 <- TRUE #robust > nx <- length(x) > x <- ts(x,frequency=par1) > if (par6 != '') { + m <- stl(x,s.window=par2, s.degree=par3, t.window=par4, t.degre=par5, l.window=par6, l.degree=par7, robust=par8) + } else { + m <- stl(x,s.window=par2, s.degree=par3, t.window=par4, t.degre=par5, l.degree=par7, robust=par8) + } > m$time.series seasonal trend remainder Jan 1 5642.4147 396297.1 11551.49744 Feb 1 -1819.8947 397343.3 3629.62563 Mar 1 -13650.3735 398389.5 1199.92325 Apr 1 -21931.8567 399614.3 -3765.42174 May 1 -33010.5765 400839.1 -3193.53011 Jun 1 -32614.8604 402168.4 -4857.52480 Jul 1 19558.0493 403497.7 -4697.71306 Aug 1 35414.8310 404862.4 -12065.18156 Sep 1 26739.2265 406227.0 -9236.26381 Oct 1 13161.9272 408193.2 -678.09091 Nov 1 -565.4712 410159.3 7834.18118 Dec 1 3076.5824 412531.5 7636.89276 Jan 2 5642.4147 414903.8 2566.82557 Feb 2 -1819.8947 417251.3 3441.63010 Mar 2 -13650.3735 419598.8 -215.39594 Apr 2 -21931.8567 422077.5 -2333.67200 May 2 -33010.5765 424556.3 -1627.71144 Jun 2 -32614.8604 427430.7 -3699.85726 Jul 2 19558.0493 430305.1 -6049.19665 Aug 2 35414.8310 433596.1 -8637.96144 Sep 2 26739.2265 436887.1 -8204.33997 Oct 2 13161.9272 440430.6 2695.42726 Nov 2 -565.4712 443974.2 8824.29367 Dec 2 3076.5824 447679.3 8500.12843 Jan 3 5642.4147 451384.4 4119.18441 Feb 3 -1819.8947 454964.5 -1753.58656 Mar 3 -13650.3735 458544.6 -1793.18809 Apr 3 -21931.8567 462205.3 -1463.43221 May 3 -33010.5765 465866.0 -2398.43971 Jun 3 -32614.8604 470344.2 -2008.31801 Jul 3 19558.0493 474822.3 -6100.38989 Aug 3 35414.8310 480365.6 -9966.47462 Sep 3 26739.2265 485908.9 -10310.17310 Oct 3 13161.9272 492107.5 -4359.47265 Nov 3 -565.4712 498306.1 3693.32698 Dec 3 3076.5824 504995.1 7404.36701 Jan 4 5642.4147 511684.0 3535.62827 Feb 4 -1819.8947 518660.5 2676.40886 Mar 4 -13650.3735 525637.0 -181.64111 Apr 4 -21931.8567 532517.4 -1978.53123 May 4 -33010.5765 539397.8 -1060.18474 Jun 4 -32614.8604 545892.8 -1842.90048 Jul 4 19558.0493 552387.8 -1787.80981 Aug 4 35414.8310 558236.0 -1985.87675 Sep 4 26739.2265 564084.3 2748.44255 Oct 4 13161.9272 568876.4 4307.63511 Nov 4 -565.4712 573668.5 12959.92684 Dec 4 3076.5824 577123.7 11303.74538 Jan 5 5642.4147 580578.8 7811.78514 Feb 5 -1819.8947 582721.3 4695.55928 Mar 5 -13650.3735 584863.9 1236.50285 Apr 5 -21931.8567 586024.5 -1175.68991 May 5 -33010.5765 587185.2 500.35395 Jun 5 -32614.8604 587893.0 -1281.18072 Jul 5 19558.0493 588600.9 -6848.90896 Aug 5 35414.8310 589193.1 -2352.94669 Sep 5 26739.2265 589785.4 210.40183 Oct 5 13161.9272 590221.8 3096.31156 Nov 5 -565.4712 590658.2 4986.32046 Dec 5 3076.5824 590950.3 4561.16716 Jan 6 5642.4147 591242.4 3032.23509 Feb 6 -1819.8947 591708.7 1684.16086 Mar 6 -13650.3735 592175.1 -3035.74393 Apr 6 -21931.8567 593115.6 -3960.70853 May 6 -33010.5765 594056.0 -5707.43652 Jun 6 -32614.8604 595564.2 -7697.37915 Jul 6 19558.0493 597072.5 -8381.51537 Aug 6 35414.8310 598619.4 -3175.23447 Sep 6 26739.2265 600166.3 1726.43267 Oct 6 13161.9272 600991.4 10281.68333 Nov 6 -565.4712 601816.4 8419.03318 Dec 6 3076.5824 601363.1 11390.27277 Jan 7 5642.4147 600909.9 14617.73359 Feb 7 -1819.8947 599179.8 6852.08077 Mar 7 -13650.3735 597449.8 548.59739 Apr 7 -21931.8567 594721.3 927.56646 May 7 -33010.5765 591992.8 -3748.22785 Jun 7 -32614.8604 588995.5 -11483.68825 Jul 7 19558.0493 585998.3 -6690.34222 Aug 7 35414.8310 583510.1 1156.09509 Sep 7 26739.2265 581021.9 -62.08135 Oct 7 13161.9272 579403.5 -2605.38029 Nov 7 -565.4712 577785.1 1445.41994 Dec 7 3076.5824 576732.2 357.26505 Jan 8 5642.4147 575679.3 -1864.66862 Feb 8 -1819.8947 574770.0 -1390.14871 Mar 8 -13650.3735 573860.8 249.54063 Apr 8 -21931.8567 572869.3 459.58029 May 8 -33010.5765 571877.7 -2104.14344 Jun 8 -32614.8604 570664.5 2512.38721 Jul 8 19558.0493 569451.2 -825.27573 Aug 8 35414.8310 567905.7 3728.44475 Sep 8 26739.2265 566360.2 5868.55147 Oct 8 13161.9272 564322.6 159.42485 Nov 8 -565.4712 562285.1 920.39740 Dec 8 3076.5824 559738.7 3051.66860 Jan 9 5642.4147 557192.4 -1560.83897 Feb 9 -1819.8947 554512.1 1451.82540 Mar 9 -13650.3735 551831.7 1718.65921 Apr 9 -21931.8567 548930.4 -727.51577 May 9 -33010.5765 546029.0 -1177.45413 Jun 9 -32614.8604 542909.0 -5012.12818 Jul 9 19558.0493 539788.9 -5263.99581 Aug 9 35414.8310 536669.1 12141.03554 Sep 9 26739.2265 533549.3 8569.45314 Oct 9 13161.9272 530590.5 -4236.44105 Nov 9 -565.4712 527631.7 -5454.23607 Dec 9 3076.5824 524778.5 -2293.07180 Jan 10 5642.4147 521925.3 -1048.68631 Feb 10 -1819.8947 519254.2 -1721.34603 Mar 10 -13650.3735 516583.2 521.16367 Apr 10 -21931.8567 514099.3 -2866.47549 May 10 -33010.5765 511615.5 415.12195 Jun 10 -32614.8604 509040.4 -1323.58043 Jul 10 19558.0493 506465.4 -2341.47639 Aug 10 35414.8310 503519.6 12593.61295 Sep 10 26739.2265 500573.7 4313.08854 Oct 10 13161.9272 497442.7 432.35399 Nov 10 -565.4712 494311.8 -1329.28138 Dec 10 3076.5824 491139.6 -2028.22434 Jan 11 5642.4147 487967.5 -744.94607 Feb 11 -1819.8947 485201.5 -2420.55736 Mar 11 -13650.3735 482435.4 -6849.99922 Apr 11 -21931.8567 480152.8 -1612.95966 May 11 -33010.5765 477870.3 -2882.68349 Jun 11 -32614.8604 475826.2 -4063.34466 Jul 11 19558.0493 473782.2 -5160.19941 Aug 11 35414.8310 472055.7 13093.44451 Sep 11 26739.2265 470329.3 4423.47469 Oct 11 13161.9272 469145.3 2717.82207 Nov 11 -565.4712 467961.2 -3199.73138 Dec 11 3076.5824 467130.4 -10036.96364 Jan 12 5642.4147 466299.6 -4904.97467 Feb 12 -1819.8947 466230.3 -4340.40372 Mar 12 -13650.3735 466161.0 -4522.66333 Apr 12 -21931.8567 467092.7 -2293.86794 May 12 -33010.5765 468024.4 1073.16408 Jun 12 -32614.8604 469293.5 -5350.66522 Jul 12 19558.0493 470562.6 -6105.68809 Aug 12 35414.8310 471830.0 2428.12473 Sep 12 26739.2265 473097.4 13090.32380 Oct 12 13161.9272 474600.6 15068.42406 Nov 12 -565.4712 476103.8 -4554.37651 Dec 12 3076.5824 477761.4 -9771.01501 Jan 13 5642.4147 479419.0 -9012.43227 Feb 13 -1819.8947 481025.7 -4600.82371 Mar 13 -13650.3735 482632.4 1456.95429 Apr 13 -21931.8567 484785.1 -1602.24455 May 13 -33010.5765 486937.8 796.79322 Jun 13 -32614.8604 490069.7 -1828.86941 Jul 13 19558.0493 493201.7 4087.27439 Aug 13 35414.8310 496696.6 -6919.38378 Sep 13 26739.2265 500191.4 -3955.65571 Oct 13 13161.9272 503940.0 1483.09381 Nov 13 -565.4712 507688.5 2115.94251 Dec 13 3076.5824 511913.6 -2752.17262 Jan 14 5642.4147 516138.7 -2617.06652 Feb 14 -1819.8947 520557.6 -1728.68720 Mar 14 -13650.3735 524976.5 -1393.13844 Apr 14 -21931.8567 529083.2 1975.60958 May 14 -33010.5765 533190.0 677.59421 Jun 14 -32614.8604 536839.5 2746.34549 Jul 14 19558.0493 540489.0 9275.90319 Aug 14 35414.8310 543888.5 410.70958 Sep 14 26739.2265 547287.9 3964.90222 Oct 14 13161.9272 550327.6 1974.50744 Nov 14 -565.4712 553367.3 -5457.78817 Dec 14 3076.5824 556068.5 -4357.11131 Jan 15 5642.4147 558769.8 -2087.21322 Feb 15 -1819.8947 561560.8 1113.09021 Mar 15 -13650.3735 564351.8 4630.56307 Apr 15 -21931.8567 567689.9 -2159.07983 May 15 -33010.5765 571028.1 -1355.48612 Jun 15 -32614.8604 574288.7 1048.13299 Jul 15 19558.0493 577549.4 -3577.44147 Aug 15 35414.8310 580260.5 -4912.28331 Sep 15 26739.2265 582971.5 2902.26110 Oct 15 13161.9272 585405.1 12756.98855 Nov 15 -565.4712 587838.7 6893.81517 Dec 15 3076.5824 589998.3 2379.06829 Jan 16 5642.4147 592158.0 -6935.45736 Feb 16 -1819.8947 593597.3 -2398.39859 Mar 16 -13650.3735 595036.5 3041.82961 Apr 16 -21931.8567 595724.7 -692.85293 May 16 -33010.5765 596412.9 4053.70115 Jun 16 -32614.8604 596664.2 4978.66418 Jul 16 19558.0493 596915.5 4261.43364 Aug 16 35414.8310 596821.4 -3352.21521 Sep 16 26739.2265 596727.3 4765.52218 Oct 16 13161.9272 596559.8 2395.23517 Nov 16 -565.4712 596392.4 -422.95267 Dec 16 3076.5824 596429.2 -2364.82419 Jan 17 5642.4147 596466.1 -8700.47449 Feb 17 -1819.8947 596234.2 -4342.26872 Mar 17 -13650.3735 596002.3 -2552.89352 Apr 17 -21931.8567 594722.6 1414.20889 May 17 -33010.5765 593443.0 12342.54792 Jun 17 -32614.8604 590854.4 14702.44417 Jul 17 19558.0493 588265.8 11743.14684 Aug 17 35414.8310 584411.4 5982.71908 Sep 17 26739.2265 580557.1 12619.67755 Oct 17 13161.9272 575603.2 -1140.15706 Nov 17 -565.4712 570649.4 -4341.89251 Dec 17 3076.5824 565112.4 -10914.98880 Jan 18 5642.4147 559575.4 -4641.86386 Feb 18 -1819.8947 554178.3 -3504.38620 Mar 18 -13650.3735 548781.1 -3457.73910 Apr 18 -21931.8567 543910.8 3940.02281 May 18 -33010.5765 539040.6 5008.02132 Jun 18 -32614.8604 534888.6 -3611.71688 Jul 18 19558.0493 530736.6 5067.35134 Aug 18 35414.8310 527073.7 2102.49174 Sep 18 26739.2265 523410.8 -8492.98162 Oct 18 13161.9272 520253.0 -6344.93413 Nov 18 -565.4712 517095.3 -6683.78745 Dec 18 3076.5824 514671.3 -3489.92927 Jan 19 5642.4147 512247.4 -967.84985 Feb 19 -1819.8947 510345.5 -964.58388 Mar 19 -13650.3735 508443.5 -2171.14847 Apr 19 -21931.8567 507089.5 5085.32880 May 19 -33010.5765 505735.5 -3367.95732 Jun 19 -32614.8604 505513.5 4681.32146 Jul 19 19558.0493 505291.5 3529.40666 Aug 19 35414.8310 507014.9 -8839.68844 Sep 19 26739.2265 508738.2 -17532.39731 Oct 19 13161.9272 512489.7 -19477.58932 Nov 19 -565.4712 516241.2 -13809.68215 Dec 19 3076.5824 521537.9 -8473.45441 Jan 20 5642.4147 526834.6 -4255.00543 Feb 20 -1819.8947 532648.3 1809.57210 Mar 20 -13650.3735 538462.1 11510.31907 Apr 20 -21931.8567 543668.6 14798.29694 May 20 -33010.5765 548875.1 7732.51142 Jun 20 -32614.8604 552913.5 15915.32274 Jul 20 19558.0493 556952.0 10059.94049 Aug 20 35414.8310 559759.8 1419.39162 Sep 20 26739.2265 562567.5 -8783.77100 Oct 20 13161.9272 564216.0 -12899.92453 Nov 20 -565.4712 565864.5 -7738.97888 Dec 20 3076.5824 567138.6 4877.80906 Jan 21 5642.4147 568412.8 6056.81823 Feb 21 -1819.8947 569391.3 7189.57033 Mar 21 -13650.3735 570369.9 6530.49187 Apr 21 -21931.8567 569543.9 3918.96354 May 21 -33010.5765 568717.9 1326.67183 Jun 21 -32614.8604 566726.8 10574.09495 Jul 21 19558.0493 564735.6 16697.32450 Aug 21 35414.8310 562661.5 6301.67186 Sep 21 26739.2265 560587.4 -1215.59454 Oct 21 13161.9272 558271.2 -7765.14564 Nov 21 -565.4712 555955.1 -6785.59756 Dec 21 3076.5824 553380.2 -5282.79983 Jan 22 5642.4147 550805.4 -793.78087 > m$win s t l 2531 19 13 > m$deg s t l 0 1 1 > m$jump s t l 254 2 2 > m$inner [1] 2 > m$outer [1] 0 > postscript(file="/var/www/rcomp/tmp/1mnht1322521176.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(m,main=main) > dev.off() null device 1 > mylagmax <- nx/2 > postscript(file="/var/www/rcomp/tmp/2r3pj1322521176.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > op <- par(mfrow = c(2,2)) > acf(as.numeric(x),lag.max = mylagmax,main='Observed') > acf(as.numeric(m$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/www/rcomp/tmp/331wi1322521176.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/www/rcomp/tmp/4r2ve1322521176.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/www/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab > load(file="/var/www/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$jump['l']) > a<-table.row.end(a) > a<-table.end(a) > table.save(a,file="/var/www/rcomp/tmp/5qvmg1322521176.tab") > a<-table.start() > a<-table.row.start(a) > a<-table.element(a,'Seasonal Decomposition by Loess - Time Series Components',6,TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a,'t',header=TRUE) > a<-table.element(a,'Observed',header=TRUE) > a<-table.element(a,'Fitted',header=TRUE) > a<-table.element(a,'Seasonal',header=TRUE) > a<-table.element(a,'Trend',header=TRUE) > a<-table.element(a,'Remainder',header=TRUE) > a<-table.row.end(a) > for (i in 1:nx) { + a<-table.row.start(a) + a<-table.element(a,i,header=TRUE) + a<-table.element(a,x[i]) + a<-table.element(a,x[i]+m$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/www/rcomp/tmp/63nm61322521176.tab") > > try(system("convert tmp/1mnht1322521176.ps tmp/1mnht1322521176.png",intern=TRUE)) character(0) > try(system("convert tmp/2r3pj1322521176.ps tmp/2r3pj1322521176.png",intern=TRUE)) character(0) > try(system("convert tmp/331wi1322521176.ps tmp/331wi1322521176.png",intern=TRUE)) character(0) > try(system("convert tmp/4r2ve1322521176.ps tmp/4r2ve1322521176.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 2.890 0.140 3.027