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(3.42,3.42,3.43,3.47,3.51,3.52,3.52,3.52,3.52,3.52,3.52,3.52,3.52,3.52,3.58,3.6,3.61,3.61,3.61,3.63,3.68,3.69,3.69,3.69,3.69,3.69,3.69,3.69,3.69,3.78,3.79,3.79,3.8,3.8,3.8,3.8,3.81,3.95,3.99,4,4.06,4.16,4.19,4.2,4.2,4.2,4.2,4.2,4.23,4.38,4.43,4.44,4.44,4.44,4.44,4.44,4.45,4.45,4.45,4.45,4.45,4.45,4.45,4.45,4.46,4.46,4.46,4.48,4.58,4.67,4.68,4.68)
> par8 = 'TRUE'
> 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 -0.026437103 3.443667  0.002770361
Feb 1 -0.023614209 3.452247 -0.008632757
Mar 1 -0.017048770 3.460827 -0.013778420
Apr 1 -0.019982837 3.469490  0.020492355
May 1  0.006205068 3.478154  0.025641158
Jun 1  0.041309477 3.487021 -0.008330513
Jul 1  0.031711049 3.495888 -0.007599347
Aug 1  0.018577597 3.504913 -0.003490754
Sep 1  0.009130068 3.513938 -0.003068084
Oct 1  0.007058847 3.523282 -0.010340607
Nov 1 -0.006696195 3.532626 -0.005929308
Dec 1 -0.020212979 3.542225 -0.002012358
Jan 2 -0.026437103 3.551825 -0.005388069
Feb 2 -0.023614209 3.562351 -0.018737153
Mar 2 -0.017048770 3.572878  0.024171217
Apr 2 -0.019982837 3.585704  0.034278557
May 2  0.006205068 3.598531  0.005263925
Jun 2  0.041309477 3.612452 -0.043761041
Jul 2  0.031711049 3.626372 -0.048083170
Aug 2  0.018577597 3.639019 -0.027596659
Sep 2  0.009130068 3.651666  0.019203930
Oct 2  0.007058847 3.662830  0.020110717
Nov 2 -0.006696195 3.673995  0.022701326
Dec 2 -0.020212979 3.685263  0.024949540
Jan 3 -0.026437103 3.696532  0.019905094
Feb 3 -0.023614209 3.707144  0.006469969
Mar 3 -0.017048770 3.717756 -0.010707701
Apr 3 -0.019982837 3.727046 -0.017063514
May 3  0.006205068 3.736336 -0.052541299
Jun 3  0.041309477 3.747905 -0.009214334
Jul 3  0.031711049 3.759473 -0.001184533
Aug 3  0.018577597 3.780484 -0.009061147
Sep 3  0.009130068 3.801494 -0.010623684
Oct 3  0.007058847 3.829346 -0.036405318
Nov 3 -0.006696195 3.857199 -0.050503130
Dec 3 -0.020212979 3.888806 -0.068593196
Jan 4 -0.026437103 3.920413 -0.083975923
Feb 4 -0.023614209 3.953979  0.019634781
Mar 4 -0.017048770 3.987546  0.019502940
Apr 4 -0.019982837 4.022021 -0.002038196
May 4  0.006205068 4.056496 -0.002701303
Jun 4  0.041309477 4.087594  0.031096457
Jul 4  0.031711049 4.118692  0.039597053
Aug 4  0.018577597 4.145070  0.036352828
Sep 4  0.009130068 4.171447  0.019422681
Oct 4  0.007058847 4.195867 -0.002926321
Nov 4 -0.006696195 4.220288 -0.013591502
Dec 4 -0.020212979 4.244457 -0.024244514
Jan 5 -0.026437103 4.268627 -0.012190186
Feb 5 -0.023614209 4.290221  0.113393683
Mar 5 -0.017048770 4.311814  0.135235006
Apr 5 -0.019982837 4.332981  0.127001941
May 5  0.006205068 4.354148  0.079646903
Jun 5  0.041309477 4.377010  0.021680204
Jul 5  0.031711049 4.399873  0.008416341
Aug 5  0.018577597 4.416815  0.004607251
Sep 5  0.009130068 4.433758  0.007112238
Oct 5  0.007058847 4.441803  0.001138585
Nov 5 -0.006696195 4.449847  0.006848754
Dec 5 -0.020212979 4.456668  0.013544848
Jan 6 -0.026437103 4.463489  0.012948281
Feb 6 -0.023614209 4.475720 -0.002105922
Mar 6 -0.017048770 4.487951 -0.020902670
Apr 6 -0.019982837 4.513543 -0.043560658
May 6  0.006205068 4.539136 -0.085340619
Jun 6  0.041309477 4.559778 -0.141087047
Jul 6  0.031711049 4.580420 -0.152130638
Aug 6  0.018577597 4.600861 -0.139438541
Sep 6  0.009130068 4.621302 -0.050432365
Oct 6  0.007058847 4.642260  0.020681087
Nov 6 -0.006696195 4.663218  0.023478361
Dec 6 -0.020212979 4.684692  0.015521291
> 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] 1
> m$outer
[1] 15
> postscript(file="/var/www/html/rcomp/tmp/1pj9a1243879353.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) 
> plot(m,main=main)
> dev.off()
null device 
          1 
> mylagmax <- nx/2
> postscript(file="/var/www/html/rcomp/tmp/2ye0o1243879353.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) 
> op <- par(mfrow = c(2,2))
> acf(as.numeric(x),lag.max = mylagmax,main='Observed')
> acf(as.numeric(m$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/3sdz61243879353.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/46ant1243879353.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$jump['l'])
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/5c41a1243879353.tab") 
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,'Seasonal Decomposition by Loess - Time Series Components',6,TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'t',header=TRUE)
> a<-table.element(a,'Observed',header=TRUE)
> a<-table.element(a,'Fitted',header=TRUE)
> a<-table.element(a,'Seasonal',header=TRUE)
> a<-table.element(a,'Trend',header=TRUE)
> a<-table.element(a,'Remainder',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:nx) {
+ a<-table.row.start(a)
+ a<-table.element(a,i,header=TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]+m$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/64c1r1243879354.tab") 
> 
> system("convert tmp/1pj9a1243879353.ps tmp/1pj9a1243879353.png")
> system("convert tmp/2ye0o1243879353.ps tmp/2ye0o1243879353.png")
> system("convert tmp/3sdz61243879353.ps tmp/3sdz61243879353.png")
> system("convert tmp/46ant1243879353.ps tmp/46ant1243879353.png")
> 
> 
> proc.time()
   user  system elapsed 
  0.970   0.597   1.245