R version 2.12.1 (2010-12-16)
Copyright (C) 2010 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: i486-pc-linux-gnu (32-bit)

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> x <- c(16111,15554,15220,14807,14291,14653,17006,18032,16558,16102,15055,15484,14596,14609,13923,14226,14056,14278,16142,16509,15680,14086,13129,13086,13096,12280,11534,11135,10903,10926,13220,13581,11788,11088,10434,11061,10828,10270,10360,9899,9395,9944,12117,12474,11106,10643,10227,11273,11516,11583,11605,11414,11181,12000,14007,14582,13251,12806,12645,13869,13342,13079,12513,12331,11882,12388,14394,14635,13218,12554,12031,12429)
> 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   -28.753646 15949.52  190.229556
Feb 1  -330.723341 15902.18  -17.456412
Mar 1  -650.359571 15854.84   15.524153
Apr 1  -824.444562 15803.57 -172.124747
May 1 -1125.362789 15752.30 -335.940410
Jun 1  -674.813278 15693.88 -366.062609
Jul 1  1478.402950 15635.45 -107.851524
Aug 1  2001.988842 15573.00  457.006509
Sep 1   669.074849 15510.56  378.364429
Oct 1    -6.045515 15452.77  655.279924
Nov 1  -587.165691 15394.97  247.195232
Dec 1    78.201435 15324.07   81.732637
Jan 2   -28.753646 15253.16 -628.407751
Feb 2  -330.723341 15154.90 -215.176757
Mar 2  -650.359571 15056.64 -483.279230
Apr 2  -824.444562 14932.75  117.693448
May 2 -1125.362789 14808.86  372.499363
Jun 2  -674.813278 14661.71  291.102737
Jul 2  1478.402950 14514.56  149.039395
Aug 2  2001.988842 14321.97  185.038430
Sep 2   669.074849 14129.39  881.537350
Oct 2    -6.045515 13879.86  212.186862
Nov 2  -587.165691 13630.33   85.836187
Dec 2    78.201435 13352.27 -344.470690
Jan 3   -28.753646 13074.21   50.544641
Feb 3  -330.723341 12800.79 -190.067768
Mar 3  -650.359571 12527.37 -343.013643
Apr 3  -824.444562 12282.74 -323.295412
May 3 -1125.362789 12038.11   -9.743944
Jun 3  -674.813278 11845.02 -244.209676
Jul 3  1478.402950 11651.94   89.657876
Aug 3  2001.988842 11503.43   75.577102
Sep 3   669.074849 11354.93 -236.003787
Oct 3    -6.045515 11239.33 -145.284440
Nov 3  -587.165691 11123.73 -102.565280
Dec 3    78.201435 11028.44  -45.642973
Jan 4   -28.753646 10933.15  -76.398459
Feb 4  -330.723341 10859.40 -258.679800
Mar 4  -650.359571 10785.65  224.705393
Apr 4  -824.444562 10746.65  -23.204632
May 4 -1125.362789 10707.64 -187.281421
Jun 4  -674.813278 10720.42 -101.604527
Jul 4  1478.402950 10733.19  -94.594349
Aug 4  2001.988842 10812.53 -340.521371
Sep 4   669.074849 10891.87 -454.948507
Oct 4    -6.045515 11026.96 -377.918737
Nov 4  -587.165691 11162.05 -347.889154
Dec 4    78.201435 11335.77 -140.969472
Jan 5   -28.753646 11509.48   35.272417
Feb 5  -330.723341 11695.09  218.634737
Mar 5  -650.359571 11880.70  374.663589
Apr 5  -824.444562 12064.77  173.677857
May 5 -1125.362789 12248.84   57.525361
Jun 5  -674.813278 12416.43  258.385465
Jul 5  1478.402950 12584.02  -55.421147
Aug 5  2001.988842 12712.16 -132.144066
Sep 5   669.074849 12840.29 -258.367100
Oct 5    -6.045515 12924.11 -112.060035
Nov 5  -587.165691 13007.92  224.246844
Dec 5    78.201435 13055.73  735.065687
Jan 6   -28.753646 13103.55  267.206736
Feb 6  -330.723341 13109.28  300.448209
Mar 6  -650.359571 13115.00   48.356215
Apr 6  -824.444562 13048.64  106.807419
May 6 -1125.362789 12982.27   25.091859
Jun 6  -674.813278 12911.14  151.677972
Jul 6  1478.402950 12840.00   75.597368
Aug 6  2001.988842 12763.54 -130.529550
Sep 6   669.074849 12687.08 -138.156582
Oct 6    -6.045515 12606.15  -46.100126
Nov 6  -587.165691 12525.21   92.956144
Dec 6    78.201435 12441.97  -91.171683
> m$win
  s   t   l 
721  19  13 
> m$deg
s t l 
0 1 1 
> m$jump
 s  t  l 
73  2  2 
> m$inner
[1] 2
> m$outer
[1] 0
> postscript(file="/var/www/rcomp/tmp/1ziba1322504351.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/2k5g01322504351.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$time.series[,'remainder']),na.action=na.pass,lag.max = mylagmax,main='Remainder')
> par(op)
> dev.off()
null device 
          1 
> postscript(file="/var/www/rcomp/tmp/3utrj1322504351.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) 
> op <- par(mfrow = c(2,2))
> spectrum(as.numeric(x),main='Observed')
> spectrum(as.numeric(m$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$time.series[,'remainder']),'remainder']),main='Remainder')
> par(op)
> dev.off()
null device 
          1 
> postscript(file="/var/www/rcomp/tmp/4ecoz1322504351.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) 
> op <- par(mfrow = c(2,2))
> cpgram(as.numeric(x),main='Observed')
> cpgram(as.numeric(m$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/5yin21322504351.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/63g0k1322504351.tab") 
> 
> try(system("convert tmp/1ziba1322504351.ps tmp/1ziba1322504351.png",intern=TRUE))
character(0)
> try(system("convert tmp/2k5g01322504351.ps tmp/2k5g01322504351.png",intern=TRUE))
character(0)
> try(system("convert tmp/3utrj1322504351.ps tmp/3utrj1322504351.png",intern=TRUE))
character(0)
> try(system("convert tmp/4ecoz1322504351.ps tmp/4ecoz1322504351.png",intern=TRUE))
character(0)
> 
> 
> proc.time()
   user  system elapsed 
  1.804   0.304   2.093