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(8.3,8.2,8,7.9,7.6,7.6,8.3,8.4,8.4,8.4,8.4,8.6,8.9,8.8,8.3,7.5,7.2,7.4,8.8,9.3,9.3,8.7,8.2,8.3,8.5,8.6,8.5,8.2,8.1,7.9,8.6,8.7,8.7,8.5,8.4,8.5,8.7,8.7,8.6,8.5,8.3,8,8.2,8.1,8.1,8,7.9,7.9,8,8,7.9,8,7.7,7.2,7.5,7.3,7,7,7,7.2,7.3,7.1,6.8,6.4,6.1,6.5,7.7,7.9,7.5,6.9,6.6,6.9)
> 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  0.277639294 7.940665  0.081696160
Feb 1  0.234155554 7.980814 -0.014969257
Mar 1  0.024005110 8.020963 -0.044967970
Apr 1 -0.232841275 8.061151  0.071690415
May 1 -0.473020941 8.101339 -0.028317918
Jun 1 -0.527791762 8.140191 -0.012399464
Jul 1  0.234104089 8.179044 -0.113147681
Aug 1  0.346806920 8.214565 -0.161371639
Sep 1  0.242843237 8.250086 -0.092929082
Oct 1  0.006036023 8.266372  0.127591940
Nov 1 -0.147437911 8.282658  0.264779682
Dec 1  0.015501563 8.282885  0.301613910
Jan 2  0.277639294 8.283111  0.339249881
Feb 2  0.234155554 8.308286  0.257557997
Mar 2  0.024005110 8.333462 -0.057467184
Apr 2 -0.232841275 8.355430 -0.622588803
May 2 -0.473020941 8.377398 -0.704377141
Jun 2 -0.527791762 8.378337 -0.450544902
Jul 2  0.234104089 8.379275  0.186620664
Aug 2  0.346806920 8.390757  0.562435752
Sep 2  0.242843237 8.402239  0.654917354
Oct 2  0.006036023 8.434444  0.259519583
Nov 2 -0.147437911 8.466649 -0.119211470
Dec 2  0.015501563 8.476514 -0.192015938
Jan 3  0.277639294 8.486379 -0.264018664
Feb 3  0.234155554 8.463350 -0.097505828
Mar 3  0.024005110 8.440321  0.035673712
Apr 3 -0.232841275 8.430007  0.002834232
May 3 -0.473020941 8.419693  0.153328033
Jun 3 -0.527791762 8.430148 -0.002356562
Jul 3  0.234104089 8.440604 -0.074707830
Aug 3  0.346806920 8.451785 -0.098591576
Sep 3  0.242843237 8.462966 -0.005808810
Oct 3  0.006036023 8.476447  0.017516665
Nov 3 -0.147437911 8.489929  0.057508859
Dec 3  0.015501563 8.491720 -0.007221355
Jan 4  0.277639294 8.493511 -0.071149827
Feb 4  0.234155554 8.464346  0.001498450
Mar 4  0.024005110 8.435181  0.140813431
Apr 4 -0.232841275 8.385660  0.347180986
May 4 -0.473020941 8.336139  0.436881822
Jun 4 -0.527791762 8.277320  0.250472092
Jul 4  0.234104089 8.218500 -0.252604310
Aug 4  0.346806920 8.153562 -0.400368870
Sep 4  0.242843237 8.088624 -0.231466916
Oct 4  0.006036023 8.034548 -0.040583589
Nov 4 -0.147437911 7.980471  0.066966457
Dec 4  0.015501563 7.936138 -0.051639669
Jan 5  0.277639294 7.891805 -0.169444053
Feb 5  0.234155554 7.831401 -0.065556680
Mar 5  0.024005110 7.770997  0.104997397
Apr 5 -0.232841275 7.690378  0.542463743
May 5 -0.473020941 7.609758  0.563263370
Jun 5 -0.527791762 7.528092  0.199699933
Jul 5  0.234104089 7.446426 -0.180530175
Aug 5  0.346806920 7.355020 -0.401826459
Sep 5  0.242843237 7.263613 -0.506456229
Oct 5  0.006036023 7.163957 -0.169993490
Nov 5 -0.147437911 7.064302  0.083135969
Dec 5  0.015501563 7.014331  0.170167625
Jan 6  0.277639294 6.964360  0.058001022
Feb 6  0.234155554 6.974224 -0.108379278
Mar 6  0.024005110 6.984088 -0.208092874
Apr 6 -0.232841275 6.993179 -0.360338185
May 6 -0.473020941 7.002271 -0.429250215
Jun 6 -0.527791762 7.003274  0.024517470
Jul 6  0.234104089 7.004277  0.461618482
Aug 6  0.346806920 7.009600  0.543593141
Sep 6  0.242843237 7.014922  0.242234314
Oct 6  0.006036023 7.022261 -0.128297166
Nov 6 -0.147437911 7.029600 -0.282161927
Dec 6  0.015501563 7.035861 -0.151362149
> 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] 2
> m$outer
[1] 0
> postscript(file="/var/www/html/rcomp/tmp/1t6ei1260547035.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) 
> plot(m,main=main)
> dev.off()
null device 
          1 
> mylagmax <- nx/2
> postscript(file="/var/www/html/rcomp/tmp/2cdba1260547035.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) 
> op <- par(mfrow = c(2,2))
> acf(as.numeric(x),lag.max = mylagmax,main='Observed')
> acf(as.numeric(m$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/3ouc21260547035.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/4umsi1260547035.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$jump['l'])
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/50elm1260547035.tab") 
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,'Seasonal Decomposition by Loess - Time Series Components',6,TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'t',header=TRUE)
> a<-table.element(a,'Observed',header=TRUE)
> a<-table.element(a,'Fitted',header=TRUE)
> a<-table.element(a,'Seasonal',header=TRUE)
> a<-table.element(a,'Trend',header=TRUE)
> a<-table.element(a,'Remainder',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:nx) {
+ a<-table.row.start(a)
+ a<-table.element(a,i,header=TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]+m$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/6dyhz1260547035.tab") 
> system("convert tmp/1t6ei1260547035.ps tmp/1t6ei1260547035.png")
> system("convert tmp/2cdba1260547035.ps tmp/2cdba1260547035.png")
> system("convert tmp/3ouc21260547035.ps tmp/3ouc21260547035.png")
> system("convert tmp/4umsi1260547035.ps tmp/4umsi1260547035.png")
> 
> 
> proc.time()
   user  system elapsed 
  0.983   0.591   1.169