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(46,62,66,59,58,61,41,27,58,70,49,59,44,36,72,45,56,54,53,35,61,52,47,51,52,63,74,45,51,64,36,30,55,64,39,40,63,45,59,55,40,64,27,28,45,57,45,69,60,56,58,50,51,53,37,22,55,70,62,58,39,49,58,47,42,62,39,40,72,70,54,65)
> 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  -1.06327449 56.64134  -9.57806473
Feb 1   0.09949927 56.20741   5.69308642
Mar 1  12.76228583 55.77349  -2.53577523
Apr 1  -1.64060804 55.31517   5.32544196
May 1  -2.21016421 54.85684   5.35332145
Jun 1   7.74803404 54.39494  -1.14297716
Jul 1 -13.12710605 53.93304   0.19406257
Aug 1 -21.68003658 53.40800  -4.72796576
Sep 1   5.60036095 52.88296  -0.48332215
Oct 1  11.70944271 52.22164   6.06891901
Nov 1  -2.84814305 51.56032   0.28782768
Dec 1   4.64971234 51.48097   2.86932054
Jan 2  -1.06327449 51.40162  -6.33834438
Feb 2   0.09949927 51.60816 -15.70765671
Mar 2  12.76228583 51.81470   7.42301816
Apr 2  -1.64060804 51.76061  -5.11999769
May 2  -2.21016421 51.70652   6.50364875
Jun 2   7.74803404 51.79417  -5.54220086
Jul 2 -13.12710605 51.88182  14.24528786
Aug 2 -21.68003658 52.36742   4.31261835
Sep 2   5.60036095 52.85302   2.54662078
Oct 2  11.70944271 53.03839 -12.74783172
Nov 2  -2.84814305 53.22376  -3.37561671
Dec 2   4.64971234 53.07283  -6.72254499
Jan 3  -1.06327449 52.92191   0.14136896
Feb 3   0.09949927 52.75729  10.14320927
Mar 3  12.76228583 52.59268   8.64503677
Apr 3  -1.64060804 52.45997  -5.81936168
May 3  -2.21016421 52.32726   0.88290217
Jun 3   7.74803404 51.86026   4.39170698
Jul 3 -13.12710605 51.39326  -2.26614987
Aug 3 -21.68003658 50.73533   0.94470794
Sep 3   5.60036095 50.07740  -0.67776231
Oct 3  11.70944271 49.66398   2.62657553
Nov 3  -2.84814305 49.25056  -7.40241912
Dec 3   4.64971234 48.94970 -13.59941070
Jan 4  -1.06327449 48.64883  15.41443994
Feb 4   0.09949927 48.33299  -3.43249021
Mar 4  12.76228583 48.01715  -1.77943317
Apr 4  -1.64060804 47.94244   8.69816592
May 4  -2.21016421 47.86774  -5.65757271
Jun 4   7.74803404 48.35088   7.90108848
Jul 4 -13.12710605 48.83402  -8.70691200
Aug 4 -21.68003658 49.30869   0.37134810
Sep 4   5.60036095 49.78336 -10.38371987
Oct 4  11.70944271 50.12449  -4.83393371
Nov 4  -2.84814305 50.46562  -2.61748004
Dec 4   4.64971234 50.69762  13.65266604
Jan 5  -1.06327449 50.92962  10.13365434
Feb 5   0.09949927 51.17208   4.72842101
Mar 5  12.76228583 51.41454  -6.17682514
Apr 5  -1.64060804 51.65577  -0.01515903
May 5  -2.21016421 51.89699   1.31316938
Jun 5   7.74803404 51.69705  -6.44508592
Jul 5 -13.12710605 51.49711  -1.37000288
Aug 5 -21.68003658 51.09890  -7.41886557
Sep 5   5.60036095 50.70070  -1.30105632
Oct 5  11.70944271 50.47210   7.81845276
Nov 5  -2.84814305 50.24351  14.60462934
Dec 5   4.64971234 50.34051   3.00978182
Jan 6  -1.06327449 50.43750 -10.37422347
Feb 6   0.09949927 50.91914  -2.01864081
Mar 6  12.76228583 51.40079  -6.16307095
Apr 6  -1.64060804 52.33994  -3.69932922
May 6  -2.21016421 53.27909  -9.06892520
Jun 6   7.74803404 54.16999   0.08197427
Jul 6 -13.12710605 55.06089  -2.93378792
Aug 6 -21.68003658 56.07902   5.60101954
Sep 6   5.60036095 57.09714   9.30249894
Oct 6  11.70944271 58.24685   0.04371157
Nov 6  -2.84814305 59.39655  -2.54840829
Dec 6   4.64971234 60.60692  -0.25663085
> 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$outer
[1] 0
> postscript(file="/var/wessaorg/rcomp/tmp/1jnu51324637025.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) 
> plot(m,main=main)
> dev.off()
null device 
          1 
> mylagmax <- nx/2
> postscript(file="/var/wessaorg/rcomp/tmp/2khtd1324637025.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) 
> op <- par(mfrow = c(2,2))
> acf(as.numeric(x),lag.max = mylagmax,main='Observed')
> acf(as.numeric(m$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/3azda1324637025.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/4b9vn1324637026.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$jump['l'])
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/5ycm51324637026.tab") 
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,'Seasonal Decomposition by Loess - Time Series Components',6,TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'t',header=TRUE)
> a<-table.element(a,'Observed',header=TRUE)
> a<-table.element(a,'Fitted',header=TRUE)
> a<-table.element(a,'Seasonal',header=TRUE)
> a<-table.element(a,'Trend',header=TRUE)
> a<-table.element(a,'Remainder',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:nx) {
+ a<-table.row.start(a)
+ a<-table.element(a,i,header=TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]+m$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/69gtd1324637026.tab") 
> 
> try(system("convert tmp/1jnu51324637025.ps tmp/1jnu51324637025.png",intern=TRUE))
character(0)
> try(system("convert tmp/2khtd1324637025.ps tmp/2khtd1324637025.png",intern=TRUE))
character(0)
> try(system("convert tmp/3azda1324637025.ps tmp/3azda1324637025.png",intern=TRUE))
character(0)
> try(system("convert tmp/4b9vn1324637026.ps tmp/4b9vn1324637026.png",intern=TRUE))
character(0)
> 
> 
> proc.time()
   user  system elapsed 
  1.267   0.242   1.545