R version 2.12.0 (2010-10-15)
Copyright (C) 2010 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: i486-pc-linux-gnu (32-bit)

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> x <- c(508643,527568,520008,498484,523917,553522,558901,548933,567013,551085,588245,605010,631572,639180,653847,657073,626291,625616,633352,672820,691369,702595,692241,718722,732297,721798,766192,788456,806132,813944,788025,765985,702684,730159,678942,672527,594783,594575,576299,530770,524491,456590,428448,444937,372206,317272,297604,288561,289287,258923,255493,277992,295474,291680,318736,338463,351963,347240,347081,383486)
> 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  -1583.8138 496313.2  13913.64455
Feb 1  -2637.3909 505397.6  24807.75415
Mar 1   5175.6448 514482.1    350.25095
Apr 1   1945.7062 523861.1 -27322.77608
May 1   7234.5561 533240.0 -16557.59163
Jun 1   1856.5800 542785.4   8880.01078
Jul 1    691.1996 552330.8   5879.01744
Aug 1  11839.7927 562038.1 -24944.85393
Sep 1  -2927.4450 571745.3  -1804.89442
Oct 1  -7896.7708 582210.9 -23229.08799
Nov 1 -14336.9304 592676.4   9905.55229
Dec 1    638.8990 601605.0   2766.11276
Jan 2  -1583.8138 610533.6  22622.21531
Feb 2  -2637.3909 619194.6  22622.75081
Mar 2   5175.6448 627855.7  20815.67352
Apr 2   1945.7062 637086.6  18040.68925
May 2   7234.5561 646317.5 -27261.08354
Jun 2   1856.5800 655086.3 -31326.84195
Jul 2    691.1996 663855.0 -31194.19612
Aug 2  11839.7927 673105.9 -12125.71319
Sep 2  -2927.4450 682356.8  11939.60062
Oct 2  -7896.7708 694418.9  16072.89015
Nov 2 -14336.9304 706480.9     97.01353
Dec 2    638.8990 719670.7  -1587.57297
Jan 3  -1583.8138 732860.4   1020.38260
Feb 3  -2637.3909 741496.3 -17060.88554
Mar 3   5175.6448 750132.1  10884.23352
Apr 3   1945.7062 752273.7  34236.61154
May 3   7234.5561 754415.2  44482.20104
Jun 3   1856.5800 749494.9  62592.47502
Jul 3    691.1996 744574.6  42759.15325
Aug 3  11839.7927 731322.4  22822.79640
Sep 3  -2927.4450 718070.2 -12458.72956
Oct 3  -7896.7708 696621.0  41434.77035
Nov 3 -14336.9304 675171.8  18107.10411
Dec 3    638.8990 648190.9  23697.16076
Jan 4  -1583.8138 621210.1 -24843.24050
Feb 4  -2637.3909 591876.9   5335.47669
Mar 4   5175.6448 562543.8   8579.58109
Apr 4   1945.7062 531710.6  -2886.33173
May 4   7234.5561 500877.5  16378.96692
Jun 4   1856.5800 470520.3 -15786.88100
Jul 4    691.1996 440163.1 -12406.32467
Aug 4  11839.7927 412654.9  20442.32535
Sep 4  -2927.4450 385146.6 -10013.19375
Oct 4  -7896.7708 362674.4 -37505.65214
Nov 4 -14336.9304 340202.2 -28261.27668
Dec 4    638.8990 325288.8 -37366.71315
Jan 5  -1583.8138 310375.4 -19504.60755
Feb 5  -2637.3909 304158.4 -42598.00985
Mar 5   5175.6448 297941.4 -47624.02496
Apr 5   1945.7062 303633.0 -27586.72630
May 5   7234.5561 309324.7 -21085.21617
Jun 5   1856.5800 315815.2 -25991.75095
Jul 5    691.1996 322305.7  -4260.88148
Aug 5  11839.7927 329698.3  -3075.05111
Sep 5  -2927.4450 337090.8  17799.61014
Oct 5  -7896.7708 345426.8   9709.94218
Nov 5 -14336.9304 353762.8   7655.10807
Dec 5    638.8990 362717.2  20129.89461
> 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$outer
[1] 0
> postscript(file="/var/www/rcomp/tmp/1aik31293388760.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) 
> plot(m,main=main)
> dev.off()
null device 
          1 
> mylagmax <- nx/2
> postscript(file="/var/www/rcomp/tmp/2krj51293388760.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) 
> op <- par(mfrow = c(2,2))
> acf(as.numeric(x),lag.max = mylagmax,main='Observed')
> acf(as.numeric(m$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/www/rcomp/tmp/3krj51293388760.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/www/rcomp/tmp/4oa0t1293388760.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/www/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/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$jump['l'])
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/rcomp/tmp/59syz1293388760.tab") 
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,'Seasonal Decomposition by Loess - Time Series Components',6,TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'t',header=TRUE)
> a<-table.element(a,'Observed',header=TRUE)
> a<-table.element(a,'Fitted',header=TRUE)
> a<-table.element(a,'Seasonal',header=TRUE)
> a<-table.element(a,'Trend',header=TRUE)
> a<-table.element(a,'Remainder',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:nx) {
+ a<-table.row.start(a)
+ a<-table.element(a,i,header=TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]+m$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/rcomp/tmp/6n2w81293388760.tab") 
> 
> try(system("convert tmp/1aik31293388760.ps tmp/1aik31293388760.png",intern=TRUE))
character(0)
> try(system("convert tmp/2krj51293388760.ps tmp/2krj51293388760.png",intern=TRUE))
character(0)
> try(system("convert tmp/3krj51293388760.ps tmp/3krj51293388760.png",intern=TRUE))
character(0)
> try(system("convert tmp/4oa0t1293388760.ps tmp/4oa0t1293388760.png",intern=TRUE))
character(0)
> 
> 
> proc.time()
   user  system elapsed 
  1.120   0.410   1.514