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(235243,230354,227184,221678,217142,219452,256446,265845,248624,241114,229245,231805,219277,219313,212610,214771,211142,211457,240048,240636,230580,208795,197922,194596,194581,185686,178106,172608,167302,168053,202300,202388,182516,173476,166444,171297,169701,164182,161914,159612,151001,158114,186530,187069,174330,169362,166827,178037,186413,189226,191563,188906,186005,195309,223532,226899,214126,206903,204442,220375,214320,212588,205816,202196,195722,198563,229139,229527,211868,203555,195770)
> 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  -1294.6913 235593.4   944.29251
Feb 1  -3809.2186 235420.9 -1257.65671
Mar 1  -7318.7443 235248.4  -745.60745
Apr 1  -9722.8344 234964.7 -3563.83160
May 1 -14466.0922 234681.0 -3072.88793
Jun 1 -10350.8923 234252.5 -4449.57818
Jul 1  21166.4717 233824.0  1455.56741
Aug 1  23856.0974 233320.4  8668.52622
Sep 1   9097.5600 232816.8  6709.64820
Oct 1   -293.0042 232123.4  9283.62112
Nov 1  -6969.5655 231430.0  4784.59115
Dec 1    104.9066 230177.4  1522.72763
Jan 2  -1294.6913 228924.8 -8353.06583
Feb 2  -3809.2186 227166.6 -4044.33247
Mar 2  -7318.7443 225408.3 -5479.60063
Apr 2  -9722.8344 223295.1  1198.76048
May 2 -14466.0922 221181.8  4426.28941
Jun 2 -10350.8923 218808.1  2999.77316
Jul 2  21166.4717 216434.4  2447.09277
Aug 2  23856.0974 213584.5  3195.42260
Sep 2   9097.5600 210734.5 10747.91561
Oct 2   -293.0042 207263.8  1824.23573
Nov 2  -6969.5655 203793.0  1098.55296
Dec 2    104.9066 200103.6 -5612.53482
Jan 3  -1294.6913 196414.2  -538.55255
Feb 3  -3809.2186 192950.3 -3455.08838
Mar 3  -7318.7443 189486.4 -4061.62572
Apr 3  -9722.8344 186568.1 -4237.21748
May 3 -14466.0922 183649.7 -1881.64140
Jun 3 -10350.8923 181469.0 -3065.09468
Jul 3  21166.4717 179288.2  1845.28788
Aug 3  23856.0974 177636.8   895.10820
Sep 3   9097.5600 175985.3 -2566.90832
Oct 3   -293.0042 174679.1  -910.11997
Nov 3  -6969.5655 173372.9    40.66549
Dec 3    104.9066 172230.7 -1038.59586
Jan 4  -1294.6913 171088.5   -92.78716
Feb 4  -3809.2186 170128.3 -2137.05581
Mar 4  -7318.7443 169168.1    64.67401
Apr 4  -9722.8344 168749.6   585.22426
May 4 -14466.0922 168331.1 -2864.05766
Jun 4 -10350.8923 168868.4  -403.52096
Jul 4  21166.4717 169405.7 -4042.14841
Aug 4  23856.0974 171159.0 -7946.12508
Sep 4   9097.5600 172912.4 -7679.93858
Oct 4   -293.0042 175645.5 -5990.44995
Nov 4  -6969.5655 178378.5 -4581.96422
Dec 4    104.9066 181702.4 -3770.26676
Jan 5  -1294.6913 185026.2  2681.50076
Feb 5  -3809.2186 188456.4  4578.81627
Mar 5  -7318.7443 191886.6  6995.13026
Apr 5  -9722.8344 195046.5  3582.36822
May 5 -14466.0922 198206.3  2264.77400
Jun 5 -10350.8923 200875.0  4784.90939
Jul 5  21166.4717 203543.6 -1178.11937
Aug 5  23856.0974 205484.4 -2441.50712
Sep 5   9097.5600 207425.2 -2396.73170
Oct 5   -293.0042 208651.4 -1455.39283
Nov 5  -6969.5655 209877.6  1533.94314
Dec 5    104.9066 210539.9  9730.22308
Jan 6  -1294.6913 211202.1  4412.57307
Feb 6  -3809.2186 210981.1  5416.13486
Mar 6  -7318.7443 210760.0  2374.69513
Apr 6  -9722.8344 210003.9  1914.92959
May 6 -14466.0922 209247.8   940.33188
Jun 6 -10350.8923 208400.8   513.11080
Jul 6  21166.4717 207553.8   418.72557
Aug 6  23856.0974 206598.5  -927.62991
Sep 6   9097.5600 205643.3 -2872.82222
Oct 6   -293.0042 204595.4  -747.37306
Nov 6  -6969.5655 203547.5  -807.92678
> 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$outer
[1] 0
> postscript(file="/var/www/html/rcomp/tmp/18pgx1293569253.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) 
> plot(m,main=main)
> dev.off()
null device 
          1 
> mylagmax <- nx/2
> postscript(file="/var/www/html/rcomp/tmp/28pgx1293569253.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) 
> op <- par(mfrow = c(2,2))
> acf(as.numeric(x),lag.max = mylagmax,main='Observed')
> acf(as.numeric(m$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/3ojn71293569253.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/4ojn71293569253.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$jump['l'])
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/5f8dr1293569253.tab") 
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,'Seasonal Decomposition by Loess - Time Series Components',6,TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'t',header=TRUE)
> a<-table.element(a,'Observed',header=TRUE)
> a<-table.element(a,'Fitted',header=TRUE)
> a<-table.element(a,'Seasonal',header=TRUE)
> a<-table.element(a,'Trend',header=TRUE)
> a<-table.element(a,'Remainder',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:nx) {
+ a<-table.row.start(a)
+ a<-table.element(a,i,header=TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]+m$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/60qcf1293569253.tab") 
> 
> try(system("convert tmp/18pgx1293569253.ps tmp/18pgx1293569253.png",intern=TRUE))
character(0)
> try(system("convert tmp/28pgx1293569253.ps tmp/28pgx1293569253.png",intern=TRUE))
character(0)
> try(system("convert tmp/3ojn71293569253.ps tmp/3ojn71293569253.png",intern=TRUE))
character(0)
> try(system("convert tmp/4ojn71293569253.ps tmp/4ojn71293569253.png",intern=TRUE))
character(0)
> 
> 
> proc.time()
   user  system elapsed 
  0.991   0.628   4.296