R version 2.13.0 (2011-04-13)
Copyright (C) 2011 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: i486-pc-linux-gnu (32-bit)

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> x <- c(135094,135411,135698,135880,135891,135971,136173,136358,136514,136506,136711,136891,137094,137182,137400,137479,137620,137687,137638,137612,137681,137772,137899,137983,137996,137913,137841,137656,137423,137245,137014,136747,136313,135804,135002,134383,133563,132837,132041,131381,130995,130493,130193,129962,129726,129505,129450,129320,129281,129246,129438,129715,130173,129981,129932,129873,129844,130015,130108,130260)
> 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  11.7529679 135241.0 -158.784024
Feb 1   0.9119472 135395.8   14.288758
Mar 1  43.6708856 135550.6  103.761581
Apr 1  45.4199428 135704.8  129.761066
May 1 106.7690288 135859.1  -74.839478
Jun 1  22.2737536 136012.7  -63.960656
Jul 1  -2.6215933 136166.3    9.318239
Aug 1 -16.2672706 136318.2   56.055366
Sep 1 -45.1129801 136470.1   88.992525
Oct 1 -59.6061236 136614.5  -48.881113
Nov 1 -65.2993525 136758.9   17.445335
Dec 1 -41.8911459 136891.9   40.948858
Jan 2  11.7529679 137025.0   57.216475
Feb 2   0.9119472 137139.2   41.898023
Mar 2  43.6708856 137253.3  102.979613
Apr 2  45.4199428 137352.4   81.134968
May 2 106.7690288 137451.5   61.690294
Jun 2  22.2737536 137535.8  128.935395
Jul 2  -2.6215933 137620.0   20.580568
Aug 2 -16.2672706 137678.4  -50.146694
Sep 2 -45.1129801 137736.8  -10.673924
Oct 2 -59.6061236 137755.2   76.434063
Nov 2 -65.2993525 137773.6  190.742137
Dec 2 -41.8911459 137745.4  279.501701
Jan 3  11.7529679 137717.2  267.025358
Feb 3   0.9119472 137631.7  280.370994
Mar 3  43.6708856 137546.2  251.116672
Apr 3  45.4199428 137372.2  238.337075
May 3 106.7690288 137198.3  117.957450
Jun 3  22.2737536 136906.1  316.627212
Jul 3  -2.6215933 136613.9  402.697046
Aug 3 -16.2672706 136198.8  564.505295
Sep 3 -45.1129801 135783.6  574.513576
Oct 3 -59.6061236 135265.9  597.720980
Nov 3 -65.2993525 134748.2  319.128470
Dec 3 -41.8911459 134171.0  253.866704
Jan 4  11.7529679 133593.9  -42.630968
Feb 4   0.9119472 133015.6 -179.470335
Mar 4  43.6708856 132437.2 -439.909661
Apr 4  45.4199428 131921.2 -585.585083
May 4 106.7690288 131405.1 -516.860535
Jun 4  22.2737536 130996.7 -526.021998
Jul 4  -2.6215933 130588.4 -392.783389
Aug 4 -16.2672706 130308.1 -329.803177
Sep 4 -45.1129801 130027.7 -256.622933
Oct 4 -59.6061236 129877.9 -313.328240
Nov 4 -65.2993525 129728.1 -212.833462
Dec 4 -41.8911459 129677.0 -315.099293
Jan 5  11.7529679 129625.8 -356.601030
Feb 5   0.9119472 129632.2 -387.080592
Mar 5  43.6708856 129638.5 -244.160113
Apr 5  45.4199428 129696.6  -27.024557
May 5 106.7690288 129754.7  311.510971
Jun 5  22.2737536 129820.3  138.435071
Jul 5  -2.6215933 129885.9   48.759243
Aug 5 -16.2672706 129954.7  -65.471853
Sep 5 -45.1129801 130023.6 -134.502916
Oct 5 -59.6061236 130093.6  -18.985621
Nov 5 -65.2993525 130163.6    9.731760
Dec 5 -41.8911459 130234.0   67.890192
> m$win
  s   t   l 
601  19  13 
> m$deg
s t l 
0 1 1 
> m$jump
 s  t  l 
61  2  2 
> m$inner
[1] 2
> m$outer
[1] 0
> postscript(file="/var/wessaorg/rcomp/tmp/1l5eq1322146010.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) 
> plot(m,main=main)
> dev.off()
null device 
          1 
> mylagmax <- nx/2
> postscript(file="/var/wessaorg/rcomp/tmp/22lsu1322146010.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) 
> op <- par(mfrow = c(2,2))
> acf(as.numeric(x),lag.max = mylagmax,main='Observed')
> acf(as.numeric(m$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/3zdb31322146010.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/47wtu1322146010.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$jump['l'])
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/5jeub1322146010.tab") 
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,'Seasonal Decomposition by Loess - Time Series Components',6,TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'t',header=TRUE)
> a<-table.element(a,'Observed',header=TRUE)
> a<-table.element(a,'Fitted',header=TRUE)
> a<-table.element(a,'Seasonal',header=TRUE)
> a<-table.element(a,'Trend',header=TRUE)
> a<-table.element(a,'Remainder',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:nx) {
+ a<-table.row.start(a)
+ a<-table.element(a,i,header=TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]+m$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/6ptbe1322146010.tab") 
> 
> try(system("convert tmp/1l5eq1322146010.ps tmp/1l5eq1322146010.png",intern=TRUE))
character(0)
> try(system("convert tmp/22lsu1322146010.ps tmp/22lsu1322146010.png",intern=TRUE))
character(0)
> try(system("convert tmp/3zdb31322146010.ps tmp/3zdb31322146010.png",intern=TRUE))
character(0)
> try(system("convert tmp/47wtu1322146010.ps tmp/47wtu1322146010.png",intern=TRUE))
character(0)
> 
> 
> proc.time()
   user  system elapsed 
  1.258   0.222   1.515