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)

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> x <- c(37,30,47,35,30,43,82,40,47,19,52,136,80,42,54,66,81,63,137,72,107,58,36,52,79,77,54,84,48,96,83,66,61,53,30,74,69,59,42,65,70,100,63,105,82,81,75,102,121,98,76,77,63,37,35,23,40,29,37,51,20,28,13,22,25,13,16,13,16,17,9,17,25,14,8,7,10,7,10,3)
> par8 = 'TRUE'
> 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  9.6800417 27.233988   0.08597015
Feb 1 -0.3438541 29.739676   0.60417828
Mar 1 -9.7456700 32.245364  24.50030639
Apr 1  2.9160997 34.768022  -2.68412159
May 1  1.9021097 37.290680  -9.19278993
Jun 1 -5.3175616 39.816646   8.50091585
Jul 1 -3.8556116 42.342611  43.51300047
Aug 1 -4.2221224 44.878224  -0.65610205
Sep 1  0.1629425 47.413838  -0.57678018
Oct 1 -6.0556588 50.050920 -24.99526137
Nov 1 -1.0385484 52.688003   0.35054592
Dec 1 15.9178320 55.363017  64.71915072
Jan 2  9.6800417 58.038032  12.28192627
Feb 2 -0.3438541 60.548931 -18.20507693
Mar 2 -9.7456700 63.059830   0.68583984
Apr 2  2.9160997 64.929413  -1.84551293
May 2  1.9021097 66.798996  12.29889394
Jun 2 -5.3175616 67.671264   0.64629780
Jul 2 -3.8556116 68.543531  72.31208050
Aug 2 -4.2221224 69.164833   7.05728952
Sep 2  0.1629425 69.786135  37.05092293
Oct 2 -6.0556588 70.213540  -6.15788144
Nov 2 -1.0385484 70.640946 -33.60239733
Dec 2 15.9178320 70.666411 -34.58424290
Jan 3  9.6800417 70.691876  -1.37191772
Feb 3 -0.3438541 70.467983   6.87587109
Mar 3 -9.7456700 70.244090  -6.49842011
Apr 3  2.9160997 69.673714  11.41018625
May 3  1.9021097 69.103338 -23.00544775
Jun 3 -5.3175616 68.010979  33.30658243
Jul 3 -3.8556116 66.918620  19.93699146
Aug 3 -4.2221224 65.772156   4.44996662
Sep 3  0.1629425 64.625691  -3.78863384
Oct 3 -6.0556588 63.546931  -4.49127269
Nov 3 -1.0385484 62.468171 -31.42962304
Dec 3 15.9178320 61.868351  -3.78618272
Jan 4  9.6800417 61.268530  -1.94857163
Feb 4 -0.3438541 61.986288  -2.64243425
Mar 4 -9.7456700 62.704047 -10.95837688
Apr 4  2.9160997 64.613473  -2.52957282
May 4  1.9021097 66.522899   1.57499086
Jun 4 -5.3175616 68.989441  36.32812028
Jul 4 -3.8556116 71.455983  -4.60037146
Aug 4 -4.2221224 73.719351  35.50277098
Sep 4  0.1629425 75.982720   5.85433780
Oct 4 -6.0556588 76.127672  10.92798694
Nov 4 -1.0385484 76.272624  -0.23407544
Dec 4 15.9178320 74.372753  11.70941490
Jan 5  9.6800417 72.472882  38.84707599
Feb 5 -0.3438541 68.990162  29.35369171
Mar 5 -9.7456700 65.507443  20.23822741
Apr 5  2.9160997 61.631653  12.45224736
May 5  1.9021097 57.755863   3.34202694
Jun 5 -5.3175616 53.446672 -11.12911091
Jul 5 -3.8556116 49.137482 -10.28186991
Aug 5 -4.2221224 44.805136 -17.58301331
Sep 5  0.1629425 40.472790  -0.63573233
Oct 5 -6.0556588 37.028872  -1.97321360
Nov 5 -1.0385484 33.584955   4.45359362
Dec 5 15.9178320 31.184379   3.89778861
Jan 6  9.6800417 28.783804 -18.46384565
Feb 6 -0.3438541 27.005811   1.33804287
Mar 6 -9.7456700 25.227819  -2.48214864
Apr 6  2.9160997 23.476928  -4.39302724
May 6  1.9021097 21.726037   1.37185378
Jun 6 -5.3175616 20.200032  -1.88247035
Jul 6 -3.8556116 18.674027   1.18158436
Aug 6 -4.2221224 17.632630  -0.41050733
Sep 6  0.1629425 16.591232  -0.75417464
Oct 6 -6.0556588 15.667595   7.38806362
Nov 6 -1.0385484 14.743958  -4.70540964
Dec 6 15.9178320 14.048869 -12.96670101
Jan 7  9.6800417 13.353780   1.96617838
Feb 7 -0.3438541 12.669451   1.67440293
Mar 7 -9.7456700 11.985123   5.76054746
Apr 7  2.9160997 11.323582  -7.23968188
May 7  1.9021097 10.662042  -2.56415159
Jun 7 -5.3175616 10.027909   2.28965206
Jul 7 -3.8556116  9.393777   4.46183456
Aug 7 -4.2221224  8.786751  -1.56462868
> m$win
  s   t   l 
801  19  13 
> m$deg
s t l 
0 1 1 
> m$jump
 s  t  l 
81  2  2 
> m$inner
[1] 1
> m$outer
[1] 15
> postscript(file="/var/fisher/rcomp/tmp/17qwd1353683906.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/fisher/rcomp/tmp/2juhj1353683906.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$time.series[,'remainder']),na.action=na.pass,lag.max = mylagmax,main='Remainder')
> par(op)
> dev.off()
null device 
          1 
> postscript(file="/var/fisher/rcomp/tmp/3m6891353683906.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) 
> op <- par(mfrow = c(2,2))
> spectrum(as.numeric(x),main='Observed')
> spectrum(as.numeric(m$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$time.series[,'remainder']),'remainder']),main='Remainder')
> par(op)
> dev.off()
null device 
          1 
> postscript(file="/var/fisher/rcomp/tmp/46foj1353683906.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) 
> op <- par(mfrow = c(2,2))
> cpgram(as.numeric(x),main='Observed')
> cpgram(as.numeric(m$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$time.series[,'remainder']),'remainder']),main='Remainder')
> par(op)
> dev.off()
null device 
          1 
> 
> #Note: the /var/fisher/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/fisher/rcomp/createtable")
> 
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,'Seasonal Decomposition by Loess - Parameters',4,TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Component',header=TRUE)
> a<-table.element(a,'Window',header=TRUE)
> a<-table.element(a,'Degree',header=TRUE)
> a<-table.element(a,'Jump',header=TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Seasonal',header=TRUE)
> a<-table.element(a,m$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/fisher/rcomp/tmp/5wpd51353683906.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/fisher/rcomp/tmp/61eke1353683906.tab") 
> 
> try(system("convert tmp/17qwd1353683906.ps tmp/17qwd1353683906.png",intern=TRUE))
character(0)
> try(system("convert tmp/2juhj1353683906.ps tmp/2juhj1353683906.png",intern=TRUE))
character(0)
> try(system("convert tmp/3m6891353683906.ps tmp/3m6891353683906.png",intern=TRUE))
character(0)
> try(system("convert tmp/46foj1353683906.ps tmp/46foj1353683906.png",intern=TRUE))
character(0)
> 
> 
> proc.time()
   user  system elapsed 
  2.502   0.582   3.071