R version 2.9.0 (2009-04-17)
Copyright (C) 2009 The R Foundation for Statistical Computing
ISBN 3-900051-07-0

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> x <- c(41086,39690,43129,37863,35953,29133,24693,22205,21725,27192,21790,13253,37702,30364,32609,30212,29965,28352,25814,22414,20506,28806,22228,13971,36845,35338,35022,34777,26887,23970,22780,17351,21382,24561,17409,11514,31514,27071,29462,26105,22397,23843,21705,18089,20764,25316,17704,15548,28029,29383,36438,32034,22679,24319,18004,17537,20366,22782,19169,13807,29743,25591,29096,26482,22405,27044,17970,18730,19684,19785,18479,10698)
> 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   8106.6521 34441.92 -1462.56763
Feb 1   5114.1533 33619.33   956.51839
Mar 1   8230.8769 32796.74  2101.38193
Apr 1   5525.9447 32009.75   327.30719
May 1   1087.1584 31222.75  3643.08670
Jun 1    638.9884 30473.50 -1979.48889
Jul 1  -3282.5715 29724.25 -1748.67448
Aug 1  -5559.2879 28974.69 -1210.40117
Sep 1  -3873.7462 28225.13 -2626.38598
Oct 1    216.8232 27598.45  -623.27720
Nov 1  -4965.8237 26971.78  -215.95203
Dec 1 -11239.1658 26802.93 -2310.76819
Jan 2   8106.6521 26634.09  2961.25566
Feb 2   5114.1533 26675.37 -1425.52409
Mar 2   8230.8769 26716.65 -2338.52632
Apr 2   5525.9447 26786.82 -2100.76757
May 2   1087.1584 26857.00  2020.84543
Jun 2    638.9884 26956.51   756.49832
Jul 2  -3282.5715 27056.03  2040.54123
Aug 2  -5559.2879 27214.25   759.03673
Sep 2  -3873.7462 27372.47 -2992.72589
Oct 2    216.8232 27433.93  1155.24861
Nov 2  -4965.8237 27495.38  -301.56050
Dec 2 -11239.1658 27326.99 -2116.82332
Jan 3   8106.6521 27158.59  1579.75388
Feb 3   5114.1533 26909.52  3314.32528
Mar 3   8230.8769 26660.45   130.67421
Apr 3   5525.9447 26362.74  2888.31316
May 3   1087.1584 26065.04  -265.19364
Jun 3    638.9884 25657.11 -2326.09418
Jul 3  -3282.5715 25249.18   813.39529
Aug 3  -5559.2879 24741.83 -1831.54166
Sep 3  -3873.7462 24234.48  1021.26328
Oct 3    216.8232 23787.18   556.99693
Nov 3  -4965.8237 23339.88  -965.05302
Dec 3 -11239.1658 23103.64  -350.47704
Jan 4   8106.6521 22867.41   539.93895
Feb 4   5114.1533 22823.66  -866.80949
Mar 4   8230.8769 22779.90 -1548.78042
Apr 4   5525.9447 22855.84 -2276.78728
May 4   1087.1584 22931.78 -1621.93990
Jun 4    638.9884 23087.57   116.44506
Jul 4  -3282.5715 23243.35  1744.22002
Aug 4  -5559.2879 23493.80   154.48757
Sep 4  -3873.7462 23744.25   893.49701
Oct 4    216.8232 23967.34  1131.83563
Nov 4  -4965.8237 24190.43 -1520.60935
Dec 4 -11239.1658 24196.18  2590.98393
Jan 5   8106.6521 24201.93 -4279.58279
Feb 5   5114.1533 24089.83   179.01595
Mar 5   8230.8769 23977.73  4229.39221
Apr 5   5525.9447 23893.50  2614.55366
May 5   1087.1584 23809.27 -2217.43065
Jun 5    638.9884 23707.32   -27.31149
Jul 5  -3282.5715 23605.37 -2318.80232
Aug 5  -5559.2879 23377.61  -281.31799
Sep 5  -3873.7462 23149.84  1089.90822
Oct 5    216.8232 22904.34  -339.16302
Nov 5  -4965.8237 22658.84  1475.98215
Dec 5 -11239.1658 22572.63  2473.53300
Jan 6   8106.6521 22486.42  -850.07613
Feb 6   5114.1533 22450.96 -1974.11445
Mar 6   8230.8769 22415.50 -1550.37525
Apr 6   5525.9447 22393.04 -1436.98940
May 6   1087.1584 22370.59 -1052.74930
Jun 6    638.9884 22350.15  4054.86619
Jul 6  -3282.5715 22329.70 -1077.12832
Aug 6  -5559.2879 22332.94  1956.34971
Sep 6  -3873.7462 22336.18  1221.56962
Oct 6    216.8232 22353.58 -2785.40512
Nov 6  -4965.8237 22370.99  1073.83654
Dec 6 -11239.1658 22392.11  -454.94447
> 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] 1
> m$outer
[1] 15
> postscript(file="/var/www/html/rcomp/tmp/1avqa1243876019.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) 
> plot(m,main=main)
> dev.off()
null device 
          1 
> mylagmax <- nx/2
> postscript(file="/var/www/html/rcomp/tmp/2xqwk1243876019.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) 
> op <- par(mfrow = c(2,2))
> acf(as.numeric(x),lag.max = mylagmax,main='Observed')
> acf(as.numeric(m$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/html/rcomp/tmp/38vhg1243876019.ps",horizontal=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/html/rcomp/tmp/47jub1243876019.ps",horizontal=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/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/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$jump['l'])
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/5ys201243876019.tab") 
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,'Seasonal Decomposition by Loess - Time Series Components',6,TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'t',header=TRUE)
> a<-table.element(a,'Observed',header=TRUE)
> a<-table.element(a,'Fitted',header=TRUE)
> a<-table.element(a,'Seasonal',header=TRUE)
> a<-table.element(a,'Trend',header=TRUE)
> a<-table.element(a,'Remainder',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:nx) {
+ a<-table.row.start(a)
+ a<-table.element(a,i,header=TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]+m$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/html/rcomp/tmp/6k1wn1243876019.tab") 
> 
> system("convert tmp/1avqa1243876019.ps tmp/1avqa1243876019.png")
> system("convert tmp/2xqwk1243876019.ps tmp/2xqwk1243876019.png")
> system("convert tmp/38vhg1243876019.ps tmp/38vhg1243876019.png")
> system("convert tmp/47jub1243876019.ps tmp/47jub1243876019.png")
> 
> 
> proc.time()
   user  system elapsed 
  1.019   0.624   1.541