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(283495,279998,287224,296369,300653,302686,277891,277537,285383,292213,298522,300431,297584,286445,288576,293299,295881,292710,271993,267430,273963,273046,268347,264319,255765,246263,245098,246969,248333,247934,226839,225554,237085,237080,245039,248541,247105,243422,250643,254663,260993,258556,235372,246057,253353,255198,264176,269034,265861,269826,278506,292300,290726,289802,271311,274352,275216,276836,280408,280190)
> 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    492.7436 285320.5 -2318.21096
Feb 1  -4272.5698 286167.2 -1896.66189
Mar 1    551.9182 287014.0  -341.91413
Apr 1   7452.7139 287811.3  1104.97301
May 1  10240.1149 288608.6  1804.25497
Jun 1   9362.9436 289362.0  3961.10055
Jul 1 -12191.0265 290115.3   -33.25505
Aug 1 -10757.5752 290800.7 -2506.08167
Sep 1  -4014.9211 291486.0 -2088.11106
Oct 1  -2240.2234 291640.9  2812.27552
Nov 1   2083.6765 291795.9  4642.45996
Dec 1   3292.1997 291253.9  5884.86050
Jan 2    492.7436 290712.0  6379.24035
Feb 2  -4272.5698 289708.2  1009.41887
Mar 2    551.9182 288704.3  -680.20390
Apr 2   7452.7139 286893.3 -1046.96967
May 2  10240.1149 285082.2   558.65938
Jun 2   9362.9436 282358.7   988.35266
Jul 2 -12191.0265 279635.2  4548.84476
Aug 2 -10757.5752 276252.0  1935.55981
Sep 2  -4014.9211 272868.8  5109.07207
Oct 2  -2240.2234 269003.6  6282.59277
Nov 2   2083.6765 265138.4  1124.91131
Dec 2   3292.1997 261117.3   -90.52651
Jan 3    492.7436 257096.2 -1823.98503
Feb 3  -4272.5698 253473.1 -2937.57295
Mar 3    551.9182 249850.0 -5303.96217
Apr 3   7452.7139 247188.0 -7671.73862
May 3  10240.1149 244526.0 -6433.12025
Jun 3   9362.9436 243205.6 -4634.55203
Jul 3 -12191.0265 241885.2 -2855.18499
Aug 3 -10757.5752 241815.2 -5503.61156
Sep 3  -4014.9211 241745.2  -645.24091
Oct 3  -2240.2234 242478.5 -3158.26506
Nov 3   2083.6765 243211.8  -256.49135
Dec 3   3292.1997 244249.9   998.93624
Jan 4    492.7436 245287.9  1324.34314
Feb 4  -4272.5698 246524.2  1170.32161
Mar 4    551.9182 247760.6  2330.49876
Apr 4   7452.7139 249176.0 -1965.66513
May 4  10240.1149 250591.3   161.56580
Jun 4   9362.9436 252218.0 -3024.95342
Jul 4 -12191.0265 253844.7 -6281.67383
Aug 4 -10757.5752 255951.0   863.61668
Sep 4  -4014.9211 258057.2  -689.29560
Oct 4  -2240.2234 260720.2 -3281.97491
Nov 4   2083.6765 263383.2 -1290.85637
Dec 4   3292.1997 266205.8  -463.96012
Jan 5    492.7436 269028.3 -3660.08457
Feb 5  -4272.5698 271508.1  2590.46424
Mar 5    551.9182 273987.9  3966.21175
Apr 5   7452.7139 275231.1  9616.16378
May 5  10240.1149 276474.4  4011.51064
Jun 5   9362.9436 277570.1  2868.92497
Jul 5 -12191.0265 278665.9  4836.13811
Aug 5 -10757.5752 279659.8  5449.77811
Sep 5  -4014.9211 280653.7 -1422.78468
Oct 5  -2240.2234 281491.5 -2415.27552
Nov 5   2083.6765 282329.3 -4004.96851
Dec 5   3292.1997 283034.7 -6136.93471
> 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$outer
[1] 0
> postscript(file="/var/wessaorg/rcomp/tmp/16atc1324325877.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) 
> plot(m,main=main)
> dev.off()
null device 
          1 
> mylagmax <- nx/2
> postscript(file="/var/wessaorg/rcomp/tmp/2n1h51324325877.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) 
> op <- par(mfrow = c(2,2))
> acf(as.numeric(x),lag.max = mylagmax,main='Observed')
> acf(as.numeric(m$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/37hqf1324325877.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/4afat1324325877.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$jump['l'])
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/5tlzh1324325877.tab") 
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,'Seasonal Decomposition by Loess - Time Series Components',6,TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'t',header=TRUE)
> a<-table.element(a,'Observed',header=TRUE)
> a<-table.element(a,'Fitted',header=TRUE)
> a<-table.element(a,'Seasonal',header=TRUE)
> a<-table.element(a,'Trend',header=TRUE)
> a<-table.element(a,'Remainder',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:nx) {
+ a<-table.row.start(a)
+ a<-table.element(a,i,header=TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]+m$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/6uwx11324325877.tab") 
> 
> try(system("convert tmp/16atc1324325877.ps tmp/16atc1324325877.png",intern=TRUE))
character(0)
> try(system("convert tmp/2n1h51324325877.ps tmp/2n1h51324325877.png",intern=TRUE))
character(0)
> try(system("convert tmp/37hqf1324325877.ps tmp/37hqf1324325877.png",intern=TRUE))
character(0)
> try(system("convert tmp/4afat1324325877.ps tmp/4afat1324325877.png",intern=TRUE))
character(0)
> 
> 
> proc.time()
   user  system elapsed 
  1.344   0.260   1.670