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(216234,213587,209465,204045,200237,203666,241476,260307,243324,244460,233575,237217,235243,230354,227184,221678,217142,219452,256446,265845,248624,241114,229245,231805,219277,219313,212610,214771,211142,211457,240048,240636,230580,208795,197922,194596,194581,185686,178106,172608,167302,168053,202300,202388,182516,173476,166444,171297,169701,164182,161914,159612,151001,158114,186530,187069,174330,169362,166827,178037,186412,189226,191563,188906,186005,195309,223532,226899,214126)
> 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  -1293.7924 213193.7  4334.0639325
Feb 1  -4443.1085 215325.9  2704.2550510
Mar 1  -7993.4250 217458.0     0.4465862
Apr 1 -11166.6316 219559.0 -4347.3698779
May 1 -15934.6735 221660.0 -5488.3509401
Jun 1 -12163.1779 223648.8 -7819.6038423
Jul 1  20118.1533 225637.5 -4279.6922663
Aug 1  25357.0794 227532.7  7417.2117697
Sep 1  10186.6781 229427.9  3709.4433078
Oct 1   3352.9799 231200.5  9906.5166924
Nov 1  -5049.7455 232973.1  5651.6173378
Dec 1   -970.3402 233994.3  4193.0887798
Jan 2  -1293.7924 235015.4  1521.4176945
Feb 2  -4443.1085 235401.6  -604.5081612
Mar 2  -7993.4250 235787.9  -610.4336001
Apr 2 -11166.6316 235732.8 -2888.1266491
May 2 -15934.6735 235677.7 -2600.9842962
Jun 2 -12163.1779 235227.1 -3611.9690254
Jul 2  20118.1533 234776.6  1551.2107236
Aug 2  25357.0794 233962.4  6525.5367467
Sep 2  10186.6781 233148.1  5289.1902718
Oct 2   3352.9799 232212.4  5548.6198792
Nov 2  -5049.7455 231276.7  3018.0767472
Dec 2   -970.3402 230090.3  2685.0880745
Jan 3  -1293.7924 228903.8 -8333.0431255
Feb 3  -4443.1085 227233.2 -3477.1374653
Mar 3  -7993.4250 225562.7 -4959.2313883
Apr 3 -11166.6316 223447.4  2490.2211276
May 3 -15934.6735 221332.2  5744.5090453
Jun 3 -12163.1779 218883.6  4736.5802815
Jul 3  20118.1533 216435.0  3494.8159959
Aug 3  25357.0794 213519.2  1759.7455679
Sep 3  10186.6781 210603.3  9790.0026421
Oct 3   3352.9799 207121.5 -1679.4931565
Nov 3  -5049.7455 203639.7  -667.9616944
Dec 3   -970.3402 200016.5 -4450.1745827
Jan 4  -1293.7924 196393.3  -518.5299983
Feb 4  -4443.1085 193017.0 -2887.8934120
Mar 4  -7993.4250 189640.7 -3541.2564090
Apr 4 -11166.6316 186720.4 -2945.7566790
May 4 -15934.6735 183800.1  -563.4215472
Jun 4 -12163.1779 181544.5 -1328.2873730
Jul 4  20118.1533 179288.8  2893.0112793
Aug 4  25357.0794 177571.5  -540.5687827
Sep 4  10186.6781 175854.1 -3524.8213425
Oct 4   3352.9799 174536.9 -4413.8490057
Nov 4  -5049.7455 173219.6 -1725.8494083
Dec 4   -970.3402 172143.6   123.7641803
Jan 5  -1293.7924 171067.6   -72.7647583
Feb 5  -4443.1085 170195.0 -1569.8608885
Mar 5  -7993.4250 169322.4   585.0433980
Apr 5 -11166.6316 168901.9  1876.6864769
May 5 -15934.6735 168481.5 -1545.8350424
Jun 5 -12163.1779 168943.9  1333.3045628
Jul 5  20118.1533 169406.2 -2994.3913538
Aug 5  25357.0794 171093.7 -9381.7487806
Sep 5  10186.6781 172781.1 -8637.7787052
Oct 5   3352.9799 175503.1 -9494.0961472
Nov 5  -5049.7455 178225.1 -6348.3863286
Dec 5   -970.3402 181628.0 -2620.6620633
Jan 6  -1293.7924 185030.9  2674.9196747
Feb 6  -4443.1085 188311.3  5357.8448809
Mar 6  -7993.4250 191591.7  7964.7705039
Apr 6 -11166.6316 194920.0  5152.5832648
May 6 -15934.6735 198248.4  3691.2314275
Jun 6 -12163.1779 201559.0  5913.1743871
Jul 6  20118.1533 204869.6 -1455.7181751
Aug 6  25357.0794 208069.1 -6527.2234356
Sep 6  10186.6781 211268.7 -7329.4011941
> m$win
  s   t   l 
691  19  13 
> m$deg
s t l 
0 1 1 
> m$jump
 s  t  l 
70  2  2 
> m$inner
[1] 2
> m$outer
[1] 0
> postscript(file="/var/www/html/rcomp/tmp/11bga1259790387.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) 
> plot(m,main=main)
> dev.off()
null device 
          1 
> mylagmax <- nx/2
> postscript(file="/var/www/html/rcomp/tmp/2vugs1259790387.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) 
> op <- par(mfrow = c(2,2))
> acf(as.numeric(x),lag.max = mylagmax,main='Observed')
> acf(as.numeric(m$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/3xz2b1259790387.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/4613y1259790387.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$jump['l'])
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/5rmy51259790387.tab") 
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,'Seasonal Decomposition by Loess - Time Series Components',6,TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'t',header=TRUE)
> a<-table.element(a,'Observed',header=TRUE)
> a<-table.element(a,'Fitted',header=TRUE)
> a<-table.element(a,'Seasonal',header=TRUE)
> a<-table.element(a,'Trend',header=TRUE)
> a<-table.element(a,'Remainder',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:nx) {
+ a<-table.row.start(a)
+ a<-table.element(a,i,header=TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]+m$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/6sgld1259790387.tab") 
> system("convert tmp/11bga1259790387.ps tmp/11bga1259790387.png")
> system("convert tmp/2vugs1259790387.ps tmp/2vugs1259790387.png")
> system("convert tmp/3xz2b1259790387.ps tmp/3xz2b1259790387.png")
> system("convert tmp/4613y1259790387.ps tmp/4613y1259790387.png")
> 
> 
> proc.time()
   user  system elapsed 
  0.987   0.591   1.196