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(49915,47469,45652,43492,41087,42931,67256,72316,65624,59450,52851,51214,44092,43752,40320,40551,38329,39530,59648,61031,55560,43877,38510,36085,35994,32617,30001,27894,26083,28817,48742,49915,40264,34276,30426,30793,29855,28081,26820,25782,22654,27373,43675,45096,38145,34017,31537,33814,36531,36935,36497,35110,33137,37407,53963,56602,49694,43957,41723,45599,42503,42153,39098,37449,34748,36548,53639,55289,47774,42156,38019)
> 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 -2735.807 51889.04   761.76920
Feb 1 -3863.296 52064.28  -731.98906
Mar 1 -5780.117 52239.53  -807.41462
Apr 1 -6937.674 52343.46 -1913.78440
May 1 -9116.898 52447.38 -2243.48730
Jun 1 -6218.484 52456.41 -3306.92359
Jul 1 12971.428 52465.43  1819.14246
Aug 1 15298.746 52422.96  4594.29336
Sep 1  8207.065 52380.49  5036.44362
Oct 1  1800.013 52173.67  5476.31508
Nov 1 -2163.538 51966.85  3047.68546
Dec 1 -1461.443 51346.33  1329.10841
Jan 2 -2735.807 50725.82 -3898.01018
Feb 2 -3863.296 49829.01 -2213.71482
Mar 2 -5780.117 48932.20 -2832.08676
Apr 2 -6937.674 47932.87  -444.19332
May 2 -9116.898 46933.53   512.36698
Jun 2 -6218.484 45993.30  -244.81695
Jul 2 12971.428 45053.07  1623.50146
Aug 2 15298.746 44120.44  1611.80971
Sep 2  8207.065 43187.82  4165.11733
Oct 2  1800.013 42155.63   -78.64351
Nov 2 -2163.538 41123.44  -449.90542
Dec 2 -1461.443 40074.87 -2528.42424
Jan 3 -2735.807 39026.29  -296.48461
Feb 3 -3863.296 38051.83 -1571.53116
Mar 3 -5780.117 37077.36 -1296.24502
Apr 3 -6937.674 36288.34 -1456.66320
May 3 -9116.898 35499.31  -299.41451
Jun 3 -6218.484 34955.86    79.62610
Jul 3 12971.428 34412.40  1358.16905
Aug 3 15298.746 34032.85   583.40544
Sep 3  8207.065 33653.29 -1596.35880
Oct 3  1800.013 33375.91  -899.92046
Nov 3 -2163.538 33098.52  -508.98321
Dec 3 -1461.443 32851.02  -596.58044
Jan 4 -2735.807 32603.53   -12.71921
Feb 4 -3863.296 32378.92  -434.62093
Mar 4 -5780.117 32154.31   445.81004
Apr 4 -6937.674 32081.07   638.59983
May 4 -9116.898 32007.84  -236.94351
Jun 4 -6218.484 32227.68  1363.79926
Jul 4 12971.428 32447.53 -1743.95563
Aug 4 15298.746 33040.51 -3243.25639
Sep 4  8207.065 33633.49 -3695.55778
Oct 4  1800.013 34502.77 -2285.78195
Nov 4 -2163.538 35372.04 -1671.50720
Dec 4 -1461.443 36382.42 -1106.97375
Jan 5 -2735.807 37392.79  1874.01815
Feb 5 -3863.296 38387.29  2411.00960
Mar 5 -5780.117 39381.78  2895.33375
Apr 5 -6937.674 40217.98  1829.69317
May 5 -9116.898 41054.18  1199.71947
Jun 5 -6218.484 41686.32  1939.16301
Jul 5 12971.428 42318.46 -1326.89111
Aug 5 15298.746 42734.58 -1431.32528
Sep 5  8207.065 43150.70 -1663.76008
Oct 5  1800.013 43401.87 -1244.88766
Nov 5 -2163.538 43653.05   233.48368
Dec 5 -1461.443 43752.84  3307.60756
Jan 6 -2735.807 43852.62  1386.18991
Feb 6 -3863.296 43636.12  2380.17197
Mar 6 -5780.117 43419.63  1458.48673
Apr 6 -6937.674 43035.17  1351.50843
May 6 -9116.898 42650.70  1214.19699
Jun 6 -6218.484 42236.46   530.02158
Jul 6 12971.428 41822.22 -1154.65150
Aug 6 15298.746 41356.79 -1366.53968
Sep 6  8207.065 40891.36 -1324.42850
Oct 6  1800.013 40384.47   -28.48616
Nov 6 -2163.538 39877.58   304.95509
> m$win
  s   t   l 
711  19  13 
> m$deg
s t l 
0 1 1 
> m$jump
 s  t  l 
72  2  2 
> m$inner
[1] 2
> m$outer
[1] 0
> postscript(file="/var/www/html/rcomp/tmp/1xxfp1293563490.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) 
> plot(m,main=main)
> dev.off()
null device 
          1 
> mylagmax <- nx/2
> postscript(file="/var/www/html/rcomp/tmp/286xa1293563490.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) 
> op <- par(mfrow = c(2,2))
> acf(as.numeric(x),lag.max = mylagmax,main='Observed')
> acf(as.numeric(m$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/html/rcomp/tmp/386xa1293563490.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/html/rcomp/tmp/4ifev1293563490.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/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$jump['l'])
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/5w7t41293563490.tab") 
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,'Seasonal Decomposition by Loess - Time Series Components',6,TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'t',header=TRUE)
> a<-table.element(a,'Observed',header=TRUE)
> a<-table.element(a,'Fitted',header=TRUE)
> a<-table.element(a,'Seasonal',header=TRUE)
> a<-table.element(a,'Trend',header=TRUE)
> a<-table.element(a,'Remainder',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:nx) {
+ a<-table.row.start(a)
+ a<-table.element(a,i,header=TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]+m$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/6i8as1293563490.tab") 
> 
> try(system("convert tmp/1xxfp1293563490.ps tmp/1xxfp1293563490.png",intern=TRUE))
character(0)
> try(system("convert tmp/286xa1293563490.ps tmp/286xa1293563490.png",intern=TRUE))
character(0)
> try(system("convert tmp/386xa1293563490.ps tmp/386xa1293563490.png",intern=TRUE))
character(0)
> try(system("convert tmp/4ifev1293563490.ps tmp/4ifev1293563490.png",intern=TRUE))
character(0)
> 
> 
> proc.time()
   user  system elapsed 
  1.005   0.671   4.260