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(17409,11514,31514,27071,29462,26105,22397,23843,21705,18089,20764,25316,17704,15548,28029,29383,36438,32034,22679,24319,18004,17537,20366,22782,19169,13807,29743,25591,29096,26482,22405,27044,17970,18730,19684,19785,18479,10698,31956,29506,34506,27165,26736,23691,18157,17328,18205,20995,17382,9367)
> 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  -4707.4348 21761.02   355.417366
Feb 1 -10537.7498 21937.44   114.309357
Mar 1   7201.9229 22113.86  2198.213688
Apr 1   4773.5034 22301.81    -4.313719
May 1   9255.5837 22489.76 -2283.340928
Jun 1   4866.2653 22672.16 -1433.426642
Jul 1    513.6941 22854.57  -971.259587
Aug 1   1708.8009 23019.35  -885.148009
Sep 1  -4031.3457 23184.13  2552.216898
Oct 1  -5065.0697 23413.09  -259.016043
Nov 1  -3227.0448 23642.04   349.001979
Dec 1   -751.1268 23897.21  2169.917332
Jan 2  -4707.4348 24152.38 -1740.941387
Feb 2 -10537.7498 24192.66  1893.092535
Mar 2   7201.9229 24232.94 -3405.861202
Apr 2   4773.5034 24121.62   487.881027
May 2   9255.5837 24010.29  3172.123454
Jun 2   4866.2653 23925.26  3242.471003
Jul 2    513.6941 23840.23 -1674.928678
Aug 2   1708.8009 23749.12 -1138.925397
Sep 2  -4031.3457 23658.01 -1622.668788
Oct 2  -5065.0697 23422.41  -820.340243
Nov 2  -3227.0448 23186.81   406.239265
Dec 2   -751.1268 22940.00   593.128202
Jan 3  -4707.4348 22693.19  1183.243069
Feb 3 -10537.7498 22644.96  1699.789827
Mar 3   7201.9229 22596.73   -55.651074
Apr 3   4773.5034 22579.96 -1762.462667
May 3   9255.5837 22563.19 -2722.774062
Jun 3   4866.2653 22482.68  -866.945890
Jul 3    513.6941 22402.17  -510.864949
Aug 3   1708.8009 22413.65  2921.544346
Sep 3  -4031.3457 22425.14  -423.793031
Oct 3  -5065.0697 22616.03  1179.041724
Nov 3  -3227.0448 22806.92   104.127440
Dec 3   -751.1268 22999.39 -2463.266611
Jan 4  -4707.4348 23191.87    -5.434733
Feb 4 -10537.7498 23242.87 -2007.115442
Mar 4   7201.9229 23293.86  1460.216190
Apr 4   4773.5034 23276.10  1456.398239
May 4   9255.5837 23258.34  1992.080486
Jun 4   4866.2653 23081.44  -782.702238
Jul 4    513.6941 22904.54  3317.767807
Aug 4   1708.8009 22700.31  -718.108922
Sep 4  -4031.3457 22496.08  -307.732322
Oct 4  -5065.0697 22261.96   131.109462
Nov 4  -3227.0448 22027.84  -595.797791
Dec 4   -751.1268 21765.67   -19.544858
Jan 5  -4707.4348 21503.50   585.934004
Feb 5 -10537.7498 21223.51 -1318.763133
> m$win
  s   t   l 
501  19  13 
> m$deg
s t l 
0 1 1 
> m$jump
 s  t  l 
51  2  2 
> m$inner
[1] 2
> m$outer
[1] 0
> postscript(file="/var/www/html/rcomp/tmp/1nidb1259784338.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) 
> plot(m,main=main)
> dev.off()
null device 
          1 
> mylagmax <- nx/2
> postscript(file="/var/www/html/rcomp/tmp/24u0l1259784338.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) 
> op <- par(mfrow = c(2,2))
> acf(as.numeric(x),lag.max = mylagmax,main='Observed')
> acf(as.numeric(m$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/3c2hu1259784338.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/4jlj31259784338.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$jump['l'])
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/5ia341259784338.tab") 
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,'Seasonal Decomposition by Loess - Time Series Components',6,TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'t',header=TRUE)
> a<-table.element(a,'Observed',header=TRUE)
> a<-table.element(a,'Fitted',header=TRUE)
> a<-table.element(a,'Seasonal',header=TRUE)
> a<-table.element(a,'Trend',header=TRUE)
> a<-table.element(a,'Remainder',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:nx) {
+ a<-table.row.start(a)
+ a<-table.element(a,i,header=TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]+m$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/6xdsy1259784338.tab") 
> system("convert tmp/1nidb1259784338.ps tmp/1nidb1259784338.png")
> system("convert tmp/24u0l1259784338.ps tmp/24u0l1259784338.png")
> system("convert tmp/3c2hu1259784338.ps tmp/3c2hu1259784338.png")
> system("convert tmp/4jlj31259784338.ps tmp/4jlj31259784338.png")
> 
> 
> proc.time()
   user  system elapsed 
  0.926   0.624   2.150