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(22,22,20,21,20,21,21,21,19,21,21,22,19,24,22,22,22,24,22,23,24,21,20,22,23,23,22,20,21,21,20,20,17,18,19,19,20,21,20,21,19,22,20,18,16,17,18,19,18,20,21,18,19,19,19,21,19,19,17,16,16,17,16,15,16,16,16,18,19,16,16,16)
> 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 -0.17995847 20.63534  1.544621579
Feb 1  1.36098354 20.66085 -0.021833145
Mar 1  0.40192556 20.68636 -1.088287868
Apr 1 -0.24744238 20.72221  0.525231570
May 1 -0.23014343 20.75806 -0.527915870
Jun 1  0.81840273 20.80003 -0.618428003
Jul 1  0.03361434 20.84199  0.124394410
Aug 1  0.59339709 20.89278 -0.486180014
Sep 1 -0.51348688 20.94357 -1.430087717
Oct 1 -0.78848675 21.06518  0.723309252
Nov 1 -0.89682021 21.18678  0.710039817
Dec 1 -0.35198467 21.36302  0.988964068
Jan 2 -0.17995847 21.53926 -2.359302334
Feb 2  1.36098354 21.72340  0.915619886
Mar 2  0.40192556 21.90753 -0.309457894
Apr 2 -0.24744238 22.02956  0.217878105
May 2 -0.23014343 22.15160  0.078547227
Jun 2  0.81840273 22.22316  0.958441318
Jul 2  0.03361434 22.29472 -0.328330047
Aug 2  0.59339709 22.31266  0.093947209
Sep 2 -0.51348688 22.33060  2.182891184
Oct 2 -0.78848675 22.23778 -0.449292649
Nov 2 -0.89682021 22.14496 -1.248142885
Dec 2 -0.35198467 21.94955  0.402432538
Jan 3 -0.17995847 21.75414  1.425817307
Feb 3  1.36098354 21.46630  0.172713237
Mar 3  0.40192556 21.17847  0.419609166
Apr 3 -0.24744238 20.87005 -0.622606964
May 3 -0.23014343 20.56163  0.668510028
Jun 3  0.81840273 20.31240 -0.130798366
Jul 3  0.03361434 20.06316 -0.096772214
Aug 3  0.59339709 19.89148 -0.484879604
Sep 3 -0.51348688 19.71981 -2.206320274
Oct 3 -0.78848675 19.66102 -0.872529219
Nov 3 -0.89682021 19.60222  0.294595433
Dec 3 -0.35198467 19.61581 -0.263828781
Jan 4 -0.17995847 19.62940  0.550556351
Feb 4  1.36098354 19.60303  0.035987815
Mar 4  0.40192556 19.57666  0.021419280
Apr 4 -0.24744238 19.48316  1.764285227
May 4 -0.23014343 19.38966 -0.159515703
Jun 4  0.81840273 19.26317  1.918427746
Jul 4  0.03361434 19.13668  0.829705741
Aug 4  0.59339709 19.03130 -1.624692448
Sep 4 -0.51348688 18.92591 -2.412423918
Oct 4 -0.78848675 18.83062 -1.042136984
Nov 4 -0.89682021 18.73534  0.161483546
Dec 4 -0.35198467 18.70714  0.644845201
Jan 5 -0.17995847 18.67894 -0.498983798
Feb 5  1.36098354 18.77698 -0.137967261
Mar 5  0.40192556 18.87503  1.723049277
Apr 5 -0.24744238 18.95650 -0.709058127
May 5 -0.23014343 19.03798  0.192167592
Jun 5  0.81840273 18.93067 -0.749070734
Jul 5  0.03361434 18.82336  0.143025485
Aug 5  0.59339709 18.56914  1.837465172
Sep 5 -0.51348688 18.31492  1.198571579
Oct 5 -0.78848675 17.99779  1.790700658
Nov 5 -0.89682021 17.68066  0.216163334
Dec 5 -0.35198467 17.36569 -1.013709815
Jan 6 -0.17995847 17.05073 -0.870773617
Feb 6  1.36098354 16.82111 -1.182093708
Mar 6  0.40192556 16.59149 -0.993413799
Apr 6 -0.24744238 16.61098 -1.363535537
May 6 -0.23014343 16.63047 -0.400324153
Jun 6  0.81840273 16.63803 -1.456427794
Jul 6  0.03361434 16.64558 -0.679196890
Aug 6  0.59339709 16.68182  0.724783807
Sep 6 -0.51348688 16.71806  2.795431223
Oct 6 -0.78848675 16.78659  0.001901372
Nov 6 -0.89682021 16.85512  0.041705118
Dec 6 -0.35198467 16.93909 -0.587100512
> 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$outer
[1] 0
> postscript(file="/var/www/html/rcomp/tmp/1epnd1259858408.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) 
> plot(m,main=main)
> dev.off()
null device 
          1 
> mylagmax <- nx/2
> postscript(file="/var/www/html/rcomp/tmp/26mrq1259858408.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) 
> op <- par(mfrow = c(2,2))
> acf(as.numeric(x),lag.max = mylagmax,main='Observed')
> acf(as.numeric(m$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/3pncx1259858408.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/4b5751259858408.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$jump['l'])
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/5bur11259858408.tab") 
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,'Seasonal Decomposition by Loess - Time Series Components',6,TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'t',header=TRUE)
> a<-table.element(a,'Observed',header=TRUE)
> a<-table.element(a,'Fitted',header=TRUE)
> a<-table.element(a,'Seasonal',header=TRUE)
> a<-table.element(a,'Trend',header=TRUE)
> a<-table.element(a,'Remainder',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:nx) {
+ a<-table.row.start(a)
+ a<-table.element(a,i,header=TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]+m$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/6uv821259858408.tab") 
> 
> system("convert tmp/1epnd1259858408.ps tmp/1epnd1259858408.png")
> system("convert tmp/26mrq1259858408.ps tmp/26mrq1259858408.png")
> system("convert tmp/3pncx1259858408.ps tmp/3pncx1259858408.png")
> system("convert tmp/4b5751259858408.ps tmp/4b5751259858408.png")
> 
> 
> proc.time()
   user  system elapsed 
  1.000   0.617   1.208