R version 2.12.0 (2010-10-15)
Copyright (C) 2010 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: i486-pc-linux-gnu (32-bit)

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> x <- c(101.5,100.7,110.6,96.8,100,104.8,86.8,92,100.2,106.6,102.1,93.7,97.6,96.9,105.6,102.8,101.7,104.2,92.7,91.9,106.5,112.3,102.8,96.5,101,98.9,105.1,103,99,104.3,94.6,90.4,108.9,111.4,100.8,102.5,98.2,98.7,113.3,104.6,99.3,111.8,97.3,97.7,115.6,111.9,107,107.1,100.6,99.2,108.4,103,99.8,115,90.8,95.9,114.4,108.2,112.6,109.1,105,105,118.5,103.7,112.5,116.6,96.6,101.9,116.5,119.3,115.4,108.5,111.5,108.8,121.8,109.6,112.2,119.6,104.1,105.3,115,124.1,116.8,107.5,115.6,116.2,116.3,119,111.9,118.6,106.9,103.2,118.6,118.7,102.8,100.6,94.9,94.5,102.9,95.3,92.5,102.7,91.5,89.5,104.2,105.2,99,95.5,90.5,96.1,113,101.9,101.4,113.6,96.6,97.8,114.9,112.5,108.4,107,103.5,107.5,122.3)
> 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 -3.3630559 102.51905  2.344003e+00
Feb  1 -3.2235376 102.00981  1.913731e+00
Mar  1  7.1614357 101.50056  1.938005e+00
Apr  1 -0.8368153 101.05490 -3.418089e+00
May  1 -1.8208866 100.60925  1.211638e+00
Jun  1  6.2265718 100.21232 -1.638893e+00
Jul  1 -9.1459717  99.81539 -3.869422e+00
Aug  1 -8.4223968  99.44851  9.738907e-01
Sep  1  6.4511793  99.08162 -5.332798e+00
Oct  1  7.9104471  99.02053 -3.309770e-01
Nov  1  1.5797168  98.95944  1.560842e+00
Dec  1 -2.5166866  99.21661 -2.999922e+00
Jan  2 -3.3630559  99.47378  1.489279e+00
Feb  2 -3.2235376  99.78710  3.364384e-01
Mar  2  7.1614357 100.10042 -1.661857e+00
Apr  2 -0.8368153 100.39237  3.244443e+00
May  2 -1.8208866 100.68432  2.836563e+00
Jun  2  6.2265718 100.89860 -2.925174e+00
Jul  2 -9.1459717 101.11288  7.330915e-01
Aug  2 -8.4223968 101.21192 -8.895202e-01
Sep  2  6.4511793 101.31095 -1.262133e+00
Oct  2  7.9104471 101.31585  3.073701e+00
Nov  2  1.5797168 101.32075 -1.004676e-01
Dec  2 -2.5166866 101.30411 -2.287426e+00
Jan  3 -3.3630559 101.28747  3.075582e+00
Feb  3 -3.2235376 101.28475  8.387859e-01
Mar  3  7.1614357 101.28203 -3.343465e+00
Apr  3 -0.8368153 101.28367  2.553150e+00
May  3 -1.8208866 101.28530 -4.644142e-01
Jun  3  6.2265718 101.34660 -3.273175e+00
Jul  3 -9.1459717 101.40791  2.338066e+00
Aug  3 -8.4223968 101.57549 -2.753095e+00
Sep  3  6.4511793 101.74308  7.057427e-01
Oct  3  7.9104471 102.02027  1.469281e+00
Nov  3  1.5797168 102.29747 -3.077182e+00
Dec  3 -2.5166866 102.65030  2.366382e+00
Jan  4 -3.3630559 103.00314 -1.440089e+00
Feb  4 -3.2235376 103.42145 -1.497911e+00
Mar  4  7.1614357 103.83975  2.298812e+00
Apr  4 -0.8368153 104.24496  1.191859e+00
May  4 -1.8208866 104.65016 -3.529274e+00
Jun  4  6.2265718 104.95721  6.162218e-01
Jul  4 -9.1459717 105.26425  1.181719e+00
Aug  4 -8.4223968 105.32553  7.968636e-01
Sep  4  6.4511793 105.38681  3.762007e+00
Oct  4  7.9104471 105.27134 -1.281784e+00
Nov  4  1.5797168 105.15586  2.644239e-01
Dec  4 -2.5166866 104.97678  4.639907e+00
Jan  5 -3.3630559 104.79770 -8.346438e-01
Feb  5 -3.2235376 104.58171 -2.158177e+00
Mar  5  7.1614357 104.36573 -3.127165e+00
Apr  5 -0.8368153 104.32083 -4.840133e-01
May  5 -1.8208866 104.27593 -2.655041e+00
Jun  5  6.2265718 104.56754  4.205884e+00
Jul  5 -9.1459717 104.85916 -4.913189e+00
Aug  5 -8.4223968 105.39145 -1.069053e+00
Sep  5  6.4511793 105.92374  2.025083e+00
Oct  5  7.9104471 106.50712 -6.217569e+00
Nov  5  1.5797168 107.09051  3.929777e+00
Dec  5 -2.5166866 107.59962  4.017067e+00
Jan  6 -3.3630559 108.10873  2.543235e-01
Feb  6 -3.2235376 108.53632 -3.127867e-01
Mar  6  7.1614357 108.96392  2.374648e+00
Apr  6 -0.8368153 109.29510 -4.758285e+00
May  6 -1.8208866 109.62628  4.694603e+00
Jun  6  6.2265718 109.92483  4.485966e-01
Jul  6 -9.1459717 110.22338 -4.477408e+00
Aug  6 -8.4223968 110.56956 -2.471612e-01
Sep  6  6.4511793 110.91574 -8.669159e-01
Oct  6  7.9104471 111.25163  1.379270e-01
Nov  6  1.5797168 111.58752  2.232768e+00
Dec  6 -2.5166866 111.90893 -8.922474e-01
Jan  7 -3.3630559 112.23035  2.632703e+00
Feb  7 -3.2235376 112.47758 -4.540431e-01
Mar  7  7.1614357 112.72481  1.913756e+00
Apr  7 -0.8368153 112.84392 -2.407105e+00
May  7 -1.8208866 112.96303  1.057856e+00
Jun  7  6.2265718 113.10447  2.689569e-01
Jul  7 -9.1459717 113.24591  6.012243e-05
Aug  7 -8.4223968 113.46672  2.556730e-01
Sep  7  6.4511793 113.68754 -5.138715e+00
Oct  7  7.9104471 113.91424  2.275309e+00
Nov  7  1.5797168 114.14095  1.079332e+00
Dec  7 -2.5166866 114.28964 -4.272957e+00
Jan  8 -3.3630559 114.43834  4.524720e+00
Feb  8 -3.2235376 114.45094  4.972602e+00
Mar  8  7.1614357 114.46353 -5.324971e+00
Apr  8 -0.8368153 114.05428  5.782538e+00
May  8 -1.8208866 113.64502  7.586596e-02
Jun  8  6.2265718 112.60477 -2.313429e-01
Jul  8 -9.1459717 111.56452  4.481450e+00
Aug  8 -8.4223968 110.02855  1.593842e+00
Sep  8  6.4511793 108.49259  3.656234e+00
Oct  8  7.9104471 106.77898  4.010573e+00
Nov  8  1.5797168 105.06537 -3.845089e+00
Dec  8 -2.5166866 103.46913 -3.524436e-01
Jan  9 -3.3630559 101.87289 -3.609832e+00
Feb  9 -3.2235376 100.62834 -2.904807e+00
Mar  9  7.1614357  99.38380 -3.645237e+00
Apr  9 -0.8368153  98.62475 -2.487937e+00
May  9 -1.8208866  97.86570 -3.544817e+00
Jun  9  6.2265718  97.57477 -1.101344e+00
Jul  9 -9.1459717  97.28384  3.362130e+00
Aug  9 -8.4223968  97.45589  4.665037e-01
Sep  9  6.4511793  97.62794  1.208758e-01
Oct  9  7.9104471  98.15857 -8.690187e-01
Nov  9  1.5797168  98.68920 -1.268915e+00
Dec  9 -2.5166866  99.38489 -1.368205e+00
Jan 10 -3.3630559 100.08059 -6.217530e+00
Feb 10 -3.2235376 100.85706 -1.533518e+00
Mar 10  7.1614357 101.63352  4.205040e+00
Apr 10 -0.8368153 102.51128  2.255392e-01
May 10 -1.8208866 103.38903 -1.681408e-01
Jun 10  6.2265718 104.21021  3.163221e+00
Jul 10 -9.1459717 105.03139  7.145848e-01
Aug 10 -8.4223968 105.88042  3.419724e-01
Sep 10  6.4511793 106.72946  1.719359e+00
Oct 10  7.9104471 107.57288 -2.983324e+00
Nov 10  1.5797168 108.41629 -1.596009e+00
Dec 10 -2.5166866 109.23062  2.860693e-01
Jan 11 -3.3630559 110.04494 -3.181886e+00
Feb 11 -3.2235376 110.84858 -1.250399e-01
Mar 11  7.1614357 111.65221  3.486351e+00
> m$win
   s    t    l 
1231   19   13 
> m$deg
s t l 
0 1 1 
> m$jump
  s   t   l 
124   2   2 
> m$inner
[1] 2
> m$outer
[1] 0
> postscript(file="/var/www/rcomp/tmp/1c0vy1322764677.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) 
> plot(m,main=main)
> dev.off()
null device 
          1 
> mylagmax <- nx/2
> postscript(file="/var/www/rcomp/tmp/2kyt51322764677.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) 
> op <- par(mfrow = c(2,2))
> acf(as.numeric(x),lag.max = mylagmax,main='Observed')
> acf(as.numeric(m$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/3835i1322764677.ps",horizontal=F,onefile=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/46l701322764677.ps",horizontal=F,onefile=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$jump['l'])
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/rcomp/tmp/5eqkc1322764677.tab") 
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,'Seasonal Decomposition by Loess - Time Series Components',6,TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'t',header=TRUE)
> a<-table.element(a,'Observed',header=TRUE)
> a<-table.element(a,'Fitted',header=TRUE)
> a<-table.element(a,'Seasonal',header=TRUE)
> a<-table.element(a,'Trend',header=TRUE)
> a<-table.element(a,'Remainder',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:nx) {
+ a<-table.row.start(a)
+ a<-table.element(a,i,header=TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]+m$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/654my1322764677.tab") 
> 
> try(system("convert tmp/1c0vy1322764677.ps tmp/1c0vy1322764677.png",intern=TRUE))
character(0)
> try(system("convert tmp/2kyt51322764677.ps tmp/2kyt51322764677.png",intern=TRUE))
character(0)
> try(system("convert tmp/3835i1322764677.ps tmp/3835i1322764677.png",intern=TRUE))
character(0)
> try(system("convert tmp/46l701322764677.ps tmp/46l701322764677.png",intern=TRUE))
character(0)
> 
> 
> proc.time()
   user  system elapsed 
  1.770   0.080   1.844