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(115.47,103.34,102.60,100.69,105.67,123.61,113.08,106.46,123.38,109.87,95.74,123.06,123.39,120.28,115.33,110.4,114.49,132.03,123.16,118.82,128.32,112.24,104.53,132.57,122.52,131.8,124.55,120.96,122.6,145.52,118.57,134.25,136.7,121.37,111.63,134.42,137.65,137.86,119.77,130.69,128.28,147.45,128.42,136.9,143.95,135.64,122.48,136.83,153.04,142.71,123.46,144.37,146.15,147.61,158.51,147.4,165.05,154.64,126.2,157.36,154.15,123.21,113.07,110.45,113.57,122.44,114.93,111.85,126.04,121.34)
> par8 = 'FALSE'
> par7 = '1'
> par6 = ''
> par5 = '1'
> par4 = ''
> par3 = '1'
> 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   8.7783765 106.2964   0.3952048
Feb 1   0.6956556 107.0463  -4.4019935
Mar 1  -9.6204164 107.7963   4.4241594
Apr 1  -6.6964534 108.5645  -1.1780456
May 1  -4.7024964 109.3327   1.0397556
Jun 1   9.9612133 110.1426   3.5061435
Jul 1  -0.3567520 110.9525   2.4842064
Aug 1  -0.2752196 111.7981  -5.0628491
Sep 1  11.2646335 112.6436  -0.5282253
Oct 1   0.2192008 113.4855  -3.8347107
Nov 1 -16.9540216 114.3274  -1.6334062
Dec 1   7.6862844 115.2454   0.1283578
Jan 2   8.7783765 116.1633  -1.5516642
Feb 2   0.6956556 116.9788   2.6055248
Mar 2  -9.6204164 117.7944   7.1560650
Apr 2  -6.6964534 118.3530  -1.2565717
May 2  -4.7024964 118.9117   0.2807977
Jun 2   9.9612133 119.2560   2.8127581
Jul 2  -0.3567520 119.6004   3.9163935
Aug 2  -0.2752196 120.1047  -1.0094944
Sep 2  11.2646335 120.6091  -3.5537031
Oct 2   0.2192008 121.3515  -9.3306597
Nov 2 -16.9540216 122.0938  -0.6098264
Dec 2   7.6862844 122.9481   1.9356570
Jan 3   8.7783765 123.8023 -10.0606455
Feb 3   0.6956556 124.6557   6.4486260
Mar 3  -9.6204164 125.5092   8.6612485
Apr 3  -6.6964534 126.2240   1.4324684
May 3  -4.7024964 126.9388   0.3636943
Jun 3   9.9612133 127.3563   8.2024743
Jul 3  -0.3567520 127.7738  -8.8470707
Aug 3  -0.2752196 128.0747   6.4504868
Sep 3  11.2646335 128.3756  -2.9402765
Oct 3   0.2192008 128.7502  -7.5993964
Nov 3 -16.9540216 129.1247  -0.5407265
Dec 3   7.6862844 129.6932  -2.9594846
Jan 4   8.7783765 130.2617  -1.3900286
Feb 4   0.6956556 130.9599   6.2044790
Mar 4  -9.6204164 131.6581  -2.2676623
Apr 4  -6.6964534 132.4058   4.9806061
May 4  -4.7024964 133.1536  -0.1711195
Jun 4   9.9612133 133.8095   3.6793260
Jul 4  -0.3567520 134.4653  -5.6885535
Aug 4  -0.2752196 135.0551   2.1200726
Sep 4  11.2646335 135.6450  -2.9596219
Oct 4   0.2192008 136.4925  -1.0716686
Nov 4 -16.9540216 137.3399   2.0940747
Dec 4   7.6862844 138.5431  -9.3993847
Jan 5   8.7783765 139.7463   4.5153700
Feb 5   0.6956556 141.2343   0.7800940
Mar 5  -9.6204164 142.7222  -9.6418308
Apr 5  -6.6964534 144.1012   6.9652155
May 5  -4.7024964 145.4802   5.3722679
Jun 5   9.9612133 146.2558  -8.6070330
Jul 5  -0.3567520 147.0314  11.8353411
Aug 5  -0.2752196 146.4106   1.2646112
Sep 5  11.2646335 145.7898   7.9955606
Oct 5   0.2192008 143.6968  10.7240413
Nov 5 -16.9540216 141.6037   1.5503117
Dec 5   7.6862844 138.5045  11.1691914
Jan 6   8.7783765 135.4053   9.9662851
Feb 6   0.6956556 132.2133  -9.6989201
Mar 6  -9.6204164 129.0212  -6.3307741
Apr 6  -6.6964534 126.0001  -8.8536911
May 6  -4.7024964 122.9791  -4.7066019
Jun 6   9.9612133 119.9458  -7.4669888
Jul 6  -0.3567520 116.9125  -1.6257008
Aug 6  -0.2752196 113.9641  -1.8389224
Sep 6  11.2646335 111.0158   3.7595352
Oct 6   0.2192008 108.1897  12.9311283
> m$win
  s   t   l 
701  19  13 
> m$deg
s t l 
0 1 1 
> m$jump
 s  t  l 
71  2  2 
> m$inner
[1] 2
> m$outer
[1] 0
> postscript(file="/var/www/html/rcomp/tmp/1habg1259862436.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) 
> plot(m,main=main)
> dev.off()
null device 
          1 
> mylagmax <- nx/2
> postscript(file="/var/www/html/rcomp/tmp/2u6uc1259862436.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) 
> op <- par(mfrow = c(2,2))
> acf(as.numeric(x),lag.max = mylagmax,main='Observed')
> acf(as.numeric(m$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/3iuvv1259862436.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/4mo5v1259862436.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$jump['l'])
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/5rrz31259862436.tab") 
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,'Seasonal Decomposition by Loess - Time Series Components',6,TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'t',header=TRUE)
> a<-table.element(a,'Observed',header=TRUE)
> a<-table.element(a,'Fitted',header=TRUE)
> a<-table.element(a,'Seasonal',header=TRUE)
> a<-table.element(a,'Trend',header=TRUE)
> a<-table.element(a,'Remainder',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:nx) {
+ a<-table.row.start(a)
+ a<-table.element(a,i,header=TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]+m$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/6rm8u1259862436.tab") 
> 
> system("convert tmp/1habg1259862436.ps tmp/1habg1259862436.png")
> system("convert tmp/2u6uc1259862436.ps tmp/2u6uc1259862436.png")
> system("convert tmp/3iuvv1259862436.ps tmp/3iuvv1259862436.png")
> system("convert tmp/4mo5v1259862436.ps tmp/4mo5v1259862436.png")
> 
> 
> proc.time()
   user  system elapsed 
  0.979   0.632   1.188