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(467037,460070,447988,442867,436087,431328,484015,509673,512927,502831,470984,471067,476049,474605,470439,461251,454724,455626,516847,525192,522975,518585,509239,512238,519164,517009,509933,509127,500857,506971,569323,579714,577992,565464,547344,554788,562325,560854,555332,543599,536662,542722,593530,610763,612613,611324,594167,595454,590865,589379,584428,573100,567456,569028,620735,628884,628232,612117,595404,597141)
> par8 = 'TRUE'
> 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   1810.0918 461536.5  3690.41287
Feb 1  -3306.3172 462738.7   637.57436
Mar 1 -12375.5813 463941.0 -3577.40913
Apr 1 -22318.6437 465159.9    25.75418
May 1 -31408.0302 466378.8  1116.24159
Jun 1 -31654.5947 467601.6 -4619.04735
Jul 1  21709.2756 468824.5 -6518.77110
Aug 1  33220.5707 470062.3  6390.14724
Sep 1  30320.5184 471300.1 11306.41294
Oct 1  16448.2321 472810.6 13572.11794
Nov 1  -1759.3757 474321.2 -1577.85549
Dec 1   -686.1453 476066.9 -4313.71646
Jan 2   1810.0918 477812.5 -3573.58417
Feb 2  -3306.3172 479724.3 -1813.02795
Mar 2 -12375.5813 481636.2  1178.38329
Apr 2 -22318.6437 484239.1  -669.49197
May 2 -31408.0302 486842.1  -710.04313
Jun 2 -31654.5947 490137.4 -2856.76898
Jul 2  21709.2756 493432.7  1705.07034
Aug 2  33220.5707 497010.1 -5038.62801
Sep 2  30320.5184 500587.5 -7932.97900
Oct 2  16448.2321 504382.4 -2245.63747
Nov 2  -1759.3757 508177.4  2821.02563
Dec 2   -686.1453 512326.6   597.57089
Jan 3   1810.0918 516475.8   878.10942
Feb 3  -3306.3172 520746.6  -431.26194
Mar 3 -12375.5813 525017.4 -2708.77828
Apr 3 -22318.6437 528918.2  2527.46226
May 3 -31408.0302 532819.0  -553.97311
Jun 3 -31654.5947 536387.9  2237.71917
Jul 3  21709.2756 539956.7  7656.97663
Aug 3  33220.5707 543438.5  3054.96209
Sep 3  30320.5184 546920.2   751.29492
Oct 3  16448.2321 550099.9 -1084.16959
Nov 3  -1759.3757 553279.7 -4176.31252
Dec 3   -686.1453 556087.2  -613.03995
Jan 4   1810.0918 558894.7  1620.22589
Feb 4  -3306.3172 561655.1  2505.23850
Mar 4 -12375.5813 564415.5  3292.10612
Apr 4 -22318.6437 567453.4 -1535.74228
May 4 -31408.0302 570491.3 -2421.26658
Jun 4 -31654.5947 573460.1   916.44734
Jul 4  21709.2756 576429.0 -4608.27357
Aug 4  33220.5707 579020.4 -1477.97682
Sep 4  30320.5184 581611.8   680.66728
Oct 4  16448.2321 584070.9 10804.89218
Nov 4  -1759.3757 586529.9  9396.43866
Dec 4   -686.1453 588816.6  7323.57488
Jan 5   1810.0918 591103.2 -2048.29563
Feb 5  -3306.3172 592797.2  -111.84156
Mar 5 -12375.5813 594491.1  2312.46752
Apr 5 -22318.6437 595078.3   340.35099
May 5 -31408.0302 595665.5  3198.55856
Jun 5 -31654.5947 596162.5  4520.09363
Jul 5  21709.2756 596659.5  2366.19387
Aug 5  33220.5707 597101.1 -1437.70662
Sep 5  30320.5184 597542.7   368.74024
Oct 5  16448.2321 597918.5 -2249.76864
Nov 5  -1759.3757 598294.3 -1130.95594
Dec 5   -686.1453 598622.9  -795.80102
> 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] 1
> m$outer
[1] 15
> postscript(file="/var/wessaorg/rcomp/tmp/1es1i1322561175.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) 
> plot(m,main=main)
> dev.off()
null device 
          1 
> mylagmax <- nx/2
> postscript(file="/var/wessaorg/rcomp/tmp/2biik1322561175.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) 
> op <- par(mfrow = c(2,2))
> acf(as.numeric(x),lag.max = mylagmax,main='Observed')
> acf(as.numeric(m$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/3s79r1322561175.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/46s5m1322561175.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$jump['l'])
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/52a0u1322561175.tab") 
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,'Seasonal Decomposition by Loess - Time Series Components',6,TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'t',header=TRUE)
> a<-table.element(a,'Observed',header=TRUE)
> a<-table.element(a,'Fitted',header=TRUE)
> a<-table.element(a,'Seasonal',header=TRUE)
> a<-table.element(a,'Trend',header=TRUE)
> a<-table.element(a,'Remainder',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:nx) {
+ a<-table.row.start(a)
+ a<-table.element(a,i,header=TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]+m$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/64o9n1322561175.tab") 
> 
> try(system("convert tmp/1es1i1322561175.ps tmp/1es1i1322561175.png",intern=TRUE))
character(0)
> try(system("convert tmp/2biik1322561175.ps tmp/2biik1322561175.png",intern=TRUE))
character(0)
> try(system("convert tmp/3s79r1322561175.ps tmp/3s79r1322561175.png",intern=TRUE))
character(0)
> try(system("convert tmp/46s5m1322561175.ps tmp/46s5m1322561175.png",intern=TRUE))
character(0)
> 
> 
> proc.time()
   user  system elapsed 
  1.321   0.214   1.553