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(8.4,8.4,8.4,8.6,8.9,8.8,8.3,7.5,7.2,7.4,8.8,9.3,9.3,8.7,8.2,8.3,8.5,8.6,8.5,8.2,8.1,7.9,8.6,8.7,8.7,8.5,8.4,8.5,8.7,8.7,8.6,8.5,8.3,8,8.2,8.1,8.1,8,7.9,7.9,8,8,7.9,8,7.7,7.2,7.5,7.3,7,7,7,7.2,7.3,7.1,6.8,6.4,6.1,6.5,7.7,7.9,7.5,6.9,6.6,6.9,7.7,8,8,7.7,7.3,7.4,8.1,8.3,8.2)
> 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  0.20399943 8.377759 -0.181758174
Feb 1 -0.02650082 8.370189  0.056311849
Mar 1 -0.18830864 8.362619  0.225689440
Apr 1 -0.03683143 8.361718  0.275112994
May 1  0.24797900 8.360818  0.291203327
Jun 1  0.26854913 8.367408  0.164043244
Jul 1  0.08911938 8.373998 -0.163116960
Aug 1 -0.20339006 8.379910 -0.676519961
Sep 1 -0.46256635 8.385822 -0.723256109
Oct 1 -0.50645582 8.382071 -0.475615180
Nov 1  0.24965460 8.378320  0.172025855
Dec 1  0.36475180 8.391694  0.543554179
Jan 2  0.20399943 8.405068  0.690932073
Feb 2 -0.02650082 8.437677  0.288824262
Mar 2 -0.18830864 8.470285 -0.081975982
Apr 2 -0.03683143 8.478818 -0.141986176
May 2  0.24797900 8.487351 -0.235329592
Jun 2  0.26854913 8.462635 -0.131184235
Jul 2  0.08911938 8.437920 -0.027039000
Aug 2 -0.20339006 8.426759 -0.023368825
Sep 2 -0.46256635 8.415598  0.146968203
Oct 2 -0.50645582 8.427623 -0.021167274
Nov 2  0.24965460 8.439648 -0.089302644
Dec 2  0.36475180 8.452721 -0.117473157
Jan 3  0.20399943 8.465795  0.030205899
Feb 3 -0.02650082 8.479679  0.046821337
Mar 3 -0.18830864 8.493564  0.094744341
Apr 3 -0.03683143 8.494023  0.042808406
May 3  0.24797900 8.494482 -0.042460751
Jun 3  0.26854913 8.463631 -0.032179951
Jul 3  0.08911938 8.432780  0.078100727
Aug 3 -0.20339006 8.382412  0.320977934
Sep 3 -0.46256635 8.332044  0.430521996
Oct 3 -0.50645582 8.274794  0.231661381
Nov 3  0.24965460 8.217545 -0.267199128
Dec 3  0.36475180 8.154499 -0.419250457
Jan 4  0.20399943 8.091453 -0.195452216
Feb 4 -0.02650082 8.037780 -0.011278924
Mar 4 -0.18830864 7.984107  0.104201934
Apr 4 -0.03683143 7.938441 -0.001609908
May 4  0.24797900 7.892776 -0.140754973
Jun 4  0.26854913 7.830686 -0.099235075
Jul 4  0.08911938 7.768596  0.042284701
Aug 4 -0.20339006 7.687129  0.516260698
Sep 4 -0.46256635 7.605663  0.556903549
Oct 4 -0.50645582 7.525567  0.180889223
Nov 4  0.24965460 7.445470 -0.195124996
Dec 4  0.36475180 7.355956 -0.420708052
Jan 5  0.20399943 7.266442 -0.470441538
Feb 5 -0.02650082 7.167190 -0.140688832
Mar 5 -0.18830864 7.067937  0.120371441
Apr 5 -0.03683143 7.016634  0.220197385
May 5  0.24797900 6.965331  0.086690106
Jun 5  0.26854913 6.973509 -0.142057667
Jul 5  0.08911938 6.981686 -0.270805562
Aug 5 -0.20339006 6.991201 -0.387811017
Sep 5 -0.46256635 7.000716 -0.438149619
Oct 5 -0.50645582 7.013303 -0.006847452
Nov 5  0.24965460 7.025891  0.424454821
Dec 5  0.36475180 7.084231  0.451017500
Jan 6  0.20399943 7.142571  0.153429749
Feb 6 -0.02650082 7.227866 -0.301364818
Mar 6 -0.18830864 7.313160 -0.524851819
Apr 6 -0.03683143 7.388754 -0.451922726
May 6  0.24797900 7.464348 -0.012326855
Jun 6  0.26854913 7.537033  0.194417580
Jul 6  0.08911938 7.609719  0.301161894
Aug 6 -0.20339006 7.687151  0.216239162
Sep 6 -0.46256635 7.764583 -0.002016715
Oct 6 -0.50645582 7.844400  0.062055796
Nov 6  0.24965460 7.924217 -0.073871588
Dec 6  0.36475180 8.003746 -0.068497911
Jan 7  0.20399943 8.083275 -0.087274666
> m$win
  s   t   l 
731  19  13 
> m$deg
s t l 
0 1 1 
> m$jump
 s  t  l 
74  2  2 
> m$inner
[1] 2
> m$outer
[1] 0
> postscript(file="/var/www/html/rcomp/tmp/18wbn1259697099.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) 
> plot(m,main=main)
> dev.off()
null device 
          1 
> mylagmax <- nx/2
> postscript(file="/var/www/html/rcomp/tmp/2swfo1259697099.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) 
> op <- par(mfrow = c(2,2))
> acf(as.numeric(x),lag.max = mylagmax,main='Observed')
> acf(as.numeric(m$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/358ti1259697099.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/4w8j81259697099.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$jump['l'])
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/5qepq1259697099.tab") 
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,'Seasonal Decomposition by Loess - Time Series Components',6,TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'t',header=TRUE)
> a<-table.element(a,'Observed',header=TRUE)
> a<-table.element(a,'Fitted',header=TRUE)
> a<-table.element(a,'Seasonal',header=TRUE)
> a<-table.element(a,'Trend',header=TRUE)
> a<-table.element(a,'Remainder',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:nx) {
+ a<-table.row.start(a)
+ a<-table.element(a,i,header=TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]+m$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/6m5lf1259697099.tab") 
> system("convert tmp/18wbn1259697099.ps tmp/18wbn1259697099.png")
> system("convert tmp/2swfo1259697099.ps tmp/2swfo1259697099.png")
> system("convert tmp/358ti1259697099.ps tmp/358ti1259697099.png")
> system("convert tmp/4w8j81259697099.ps tmp/4w8j81259697099.png")
> 
> 
> proc.time()
   user  system elapsed 
  1.017   0.609   1.191