R version 2.11.1 (2010-05-31)
Copyright (C) 2010 The R Foundation for Statistical Computing
ISBN 3-900051-07-0

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> x <- c(94.6,95.9,104.7,102.8,98.1,113.9,80.9,95.7,113.2,105.9,108.8,102.3,99,100.7,115.5,100.7,109.9,114.6,85.4,100.5,114.8,116.5,112.9,102,106,105.3,118.8,106.1,109.3,117.2,92.5,104.2,112.5,122.4,113.3,100,110.7,112.8,109.8,117.3,109.1,115.9,96,99.8,116.8,115.7,99.4,94.3,91,93.2,103.1,94.1,91.8,102.7,82.6,89.1,104.5,105.1,95.1,88.7,86.3,91.8,111.5,99.7,97.5,111.7,86.2,95.4)
> 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  -5.0179561  98.49831  1.11964526
Feb 1  -3.1037727  98.98142  0.02235320
Mar 1   7.4104075  99.46453 -2.17493565
Apr 1   0.2241794  99.95924  2.61658433
May 1  -0.6787156 100.45394 -1.67522886
Jun 1   9.3262288 100.95961  3.61416482
Jul 1 -16.1188181 101.46527 -4.44645028
Aug 1  -6.0181268 101.97133 -0.25320780
Sep 1   8.6581110 102.47740  2.06448830
Oct 1   9.4156223 102.96782 -6.48344065
Nov 1   2.1931285 103.45824  3.14863559
Dec 1  -6.2902874 103.86961  4.72067678
Jan 2  -5.0179561 104.28099 -0.26302925
Feb 2  -3.1037727 104.63512 -0.83134373
Mar 2   7.4104075 104.98925  3.10034500
Apr 2   0.2241794 105.30777 -4.83195377
May 2  -0.6787156 105.62630  4.95241427
Jun 2   9.3262288 105.96120 -0.68743218
Jul 2 -16.1188181 106.29611 -4.77728742
Aug 2  -6.0181268 106.68465 -0.16652064
Sep 2   8.6581110 107.07319 -0.93130024
Oct 2   9.4156223 107.42181 -0.33743683
Nov 2   2.1931285 107.77044  2.93643178
Dec 2  -6.2902874 108.07292  0.21736974
Jan 3  -5.0179561 108.37540  2.64256048
Feb 3  -3.1037727 108.58408 -0.18030367
Mar 3   7.4104075 108.79276  2.59683539
Apr 3   0.2241794 108.87080 -2.99497765
May 3  -0.6787156 108.94884  1.02987613
Jun 3   9.3262288 109.06095 -1.18717944
Jul 3 -16.1188181 109.17306 -0.55424379
Aug 3  -6.0181268 109.37326  0.84486695
Sep 3   8.6581110 109.57346 -5.73156870
Oct 3   9.4156223 109.80124  3.18313639
Nov 3   2.1931285 110.02902  1.07784668
Dec 3  -6.2902874 110.16807 -3.87778069
Jan 4  -5.0179561 110.30711  5.41084472
Feb 4  -3.1037727 110.26601  5.63776088
Mar 4   7.4104075 110.22491 -7.83531975
Apr 4   0.2241794 109.76619  7.30963106
May 4  -0.6787156 109.30747  0.47124869
Jun 4   9.3262288 108.32088 -1.74710496
Jul 4 -16.1188181 107.33429  4.78453261
Aug 4  -6.0181268 106.00143 -0.18330113
Sep 4   8.6581110 104.66857  3.47331874
Oct 4   9.4156223 103.22352  3.06085911
Nov 4   2.1931285 101.77847 -4.57159532
Dec 4  -6.2902874 100.42754  0.16274700
Jan 5  -5.0179561  99.07661 -3.05865790
Feb 5  -3.1037727  98.04235 -1.73857990
Mar 5   7.4104075  97.00809 -1.31849869
Apr 5   0.2241794  96.35055 -2.47472891
May 5  -0.6787156  95.69301 -3.21429230
Jun 5   9.3262288  95.34916 -1.97538658
Jul 5 -16.1188181  95.00531  3.71351037
Aug 5  -6.0181268  95.03754  0.08058989
Sep 5   8.6581110  95.06977  0.77212302
Oct 5   9.4156223  95.44593  0.23844859
Nov 5   2.1931285  95.82209 -2.91522066
Dec 5  -6.2902874  96.47661 -1.48632611
Jan 6  -5.0179561  97.13113 -5.81317878
Feb 6  -3.1037727  97.76512 -2.86134350
Mar 6   7.4104075  98.39910  5.69049499
Apr 6   0.2241794  99.07568  0.40013750
May 6  -0.6787156  99.75227 -1.57355316
Jun 6   9.3262288 100.47617  1.89760184
Jul 6 -16.1188181 101.20007  1.11874807
Aug 6  -6.0181268 101.95328 -0.53515631
> m$win
  s   t   l 
681  19  13 
> m$deg
s t l 
0 1 1 
> m$jump
 s  t  l 
69  2  2 
> m$inner
[1] 2
> m$outer
[1] 0
> postscript(file="/var/www/rcomp/tmp/1qyid1290882401.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) 
> plot(m,main=main)
> dev.off()
null device 
          1 
> mylagmax <- nx/2
> postscript(file="/var/www/rcomp/tmp/2qyid1290882401.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) 
> op <- par(mfrow = c(2,2))
> acf(as.numeric(x),lag.max = mylagmax,main='Observed')
> acf(as.numeric(m$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/rcomp/tmp/30phy1290882401.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/rcomp/tmp/40phy1290882401.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/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/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$jump['l'])
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/rcomp/tmp/5pqe91290882401.tab") 
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,'Seasonal Decomposition by Loess - Time Series Components',6,TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'t',header=TRUE)
> a<-table.element(a,'Observed',header=TRUE)
> a<-table.element(a,'Fitted',header=TRUE)
> a<-table.element(a,'Seasonal',header=TRUE)
> a<-table.element(a,'Trend',header=TRUE)
> a<-table.element(a,'Remainder',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:nx) {
+ a<-table.row.start(a)
+ a<-table.element(a,i,header=TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]+m$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/rcomp/tmp/6srdx1290882401.tab") 
> 
> try(system("convert tmp/1qyid1290882401.ps tmp/1qyid1290882401.png",intern=TRUE))
character(0)
> try(system("convert tmp/2qyid1290882401.ps tmp/2qyid1290882401.png",intern=TRUE))
character(0)
> try(system("convert tmp/30phy1290882401.ps tmp/30phy1290882401.png",intern=TRUE))
character(0)
> try(system("convert tmp/40phy1290882401.ps tmp/40phy1290882401.png",intern=TRUE))
character(0)
> 
> 
> proc.time()
   user  system elapsed 
  1.430   0.480   1.873