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(220206,220115,218444,214912,210705,209673,237041,242081,241878,242621,238545,240337,244752,244576,241572,240541,236089,236997,264579,270349,269645,267037,258113,262813,267413,267366,264777,258863,254844,254868,277267,285351,286602,283042,276687,277915,277128,277103,275037,270150,267140,264993,287259,291186,292300,288186,281477,282656,280190,280408,276836,275216,274352,271311,289802,290726,292300,278506,269826,265861,269034,264176,255198,253353,246057,235372,258556,260993,254663,250643,243422,247105,248541,245039,237080,237085,225554,226839,247934,248333,246969,245098,246263,255765,264319,268347,273046,273963,267430,271993,292710,295881,293299,288576)
> 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   2191.2067 216081.4  1933.36177
Feb 1    869.8936 218219.0  1026.11875
Mar 1  -3037.2958 220356.5  1124.75205
Apr 1  -5955.9043 222507.2 -1639.25833
May 1 -11875.1351 224657.8 -2077.64648
Jun 1 -13869.4003 226791.9 -3249.49696
Jul 1   8289.7110 228926.0  -174.72395
Aug 1  11142.3402 231038.5   -99.84565
Sep 1   9370.4705 233151.0  -643.46839
Oct 1   4100.9753 235317.5  3202.55310
Nov 1  -1510.3912 237483.9  2571.44583
Dec 1    283.5261 239650.4   403.07624
Jan 2   2191.2067 241816.8   743.94342
Feb 2    869.8936 243977.5  -271.37295
Mar 2  -3037.2958 246138.1 -1528.81299
Apr 2  -5955.9043 248196.0 -1699.13525
May 2 -11875.1351 250254.0 -2289.83527
Jun 2 -13869.4003 252208.9 -1342.51429
Jul 2   8289.7110 254163.9  2125.43021
Aug 2  11142.3402 256062.9  3143.80859
Sep 2   9370.4705 257961.8  2312.68594
Oct 2   4100.9753 259624.6  3311.42369
Nov 2  -1510.3912 261287.4 -1663.96733
Dec 2    283.5261 262606.4   -76.94697
Jan 3   2191.2067 263925.5  1296.31016
Feb 3    869.8936 265138.6  1357.50918
Mar 3  -3037.2958 266351.7  1462.58453
Apr 3  -5955.9043 267682.5 -2863.63748
May 3 -11875.1351 269013.4 -2294.23726
Jun 3 -13869.4003 270265.8 -1528.41786
Jul 3   8289.7110 271518.3 -2540.97496
Aug 3  11142.3402 272552.8  1655.89717
Sep 3   9370.4705 273587.3  3644.26827
Oct 3   4100.9753 274512.3  4428.72687
Nov 3  -1510.3912 275437.3  2760.05672
Dec 3    283.5261 276176.1  1455.41127
Jan 4   2191.2067 276914.8 -1977.99741
Feb 4    869.8936 277416.8 -1183.73207
Mar 4  -3037.2958 277918.9   155.40960
Apr 4  -5955.9043 278366.9 -2261.01814
May 4 -11875.1351 278815.0   200.17636
Jun 4 -13869.4003 279264.0  -401.61353
Jul 4   8289.7110 279713.1  -743.77992
Aug 4  11142.3402 280059.3   -15.61528
Sep 4   9370.4705 280405.5  2524.04833
Oct 4   4100.9753 280735.2  3349.83285
Nov 4  -1510.3912 281064.9  1922.48861
Dec 4    283.5261 281361.7  1010.74946
Jan 5   2191.2067 281658.5 -3659.75293
Feb 5    869.8936 281687.5 -2149.42403
Mar 5  -3037.2958 281716.5 -1843.21880
Apr 5  -5955.9043 281251.9   -79.97705
May 5 -11875.1351 280787.2  5439.88694
Jun 5 -13869.4003 279823.5  5356.92555
Jul 5   8289.7110 278859.7  2652.58767
Aug 5  11142.3402 277330.9  2252.76849
Sep 5   9370.4705 275802.1  7127.44828
Oct 5   4100.9753 273644.3   760.74440
Nov 5  -1510.3912 271486.5  -150.08824
Dec 5    283.5261 268848.9 -3271.45075
Jan 6   2191.2067 266211.4   631.42351
Feb 6    869.8936 263509.6  -203.52254
Mar 6  -3037.2958 260807.9 -2572.59227
Apr 6  -5955.9043 258386.1   922.78536
May 6 -11875.1351 255964.3  1967.78522
Jun 6 -13869.4003 254048.8 -4807.39107
Jul 6   8289.7110 252133.2 -1866.94386
Aug 6  11142.3402 250550.5  -699.79965
Sep 6   9370.4705 248967.7 -3675.15646
Oct 6   4100.9753 247637.4 -1095.34737
Nov 6  -1510.3912 246307.1 -1374.66702
Dec 6    283.5261 245248.0  1573.47267
Jan 7   2191.2067 244188.9  2160.84913
Feb 7    869.8936 243360.4   808.74637
Mar 7  -3037.2958 242531.8 -2414.48006
Apr 7  -5955.9043 242130.6   910.34924
May 7 -11875.1351 241729.3 -4300.19922
Jun 7 -13869.4003 242286.2 -1577.81636
Jul 7   8289.7110 242843.1 -3198.80998
Aug 7  11142.3402 244781.8 -7591.15198
Sep 7   9370.4705 246720.5 -9121.99500
Oct 7   4100.9753 249925.8 -8928.76282
Nov 7  -1510.3912 253131.1 -5357.65939
Dec 7    283.5261 257106.6 -1625.11378
Jan 8   2191.2067 261082.1  1045.66860
Feb 8    869.8936 264802.0  2675.06747
Mar 8  -3037.2958 268522.0  7561.34265
Apr 8  -5955.9043 272098.2  7820.66448
May 8 -11875.1351 275674.5  3630.60853
Jun 8 -13869.4003 279212.6  6649.79452
Jul 8   8289.7110 282750.7  1669.60400
Aug 8  11142.3402 286165.0 -1426.30336
Sep 8   9370.4705 289579.2 -5650.71174
Oct 8   4100.9753 292860.5 -8385.44386
> m$win
  s   t   l 
941  19  13 
> m$deg
s t l 
0 1 1 
> m$jump
 s  t  l 
95  2  2 
> m$inner
[1] 2
> m$outer
[1] 0
> postscript(file="/var/www/html/rcomp/tmp/1gveu1260225950.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) 
> plot(m,main=main)
> dev.off()
null device 
          1 
> mylagmax <- nx/2
> postscript(file="/var/www/html/rcomp/tmp/281r51260225950.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) 
> op <- par(mfrow = c(2,2))
> acf(as.numeric(x),lag.max = mylagmax,main='Observed')
> acf(as.numeric(m$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/32l8u1260225950.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/431r91260225950.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$jump['l'])
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/5i3cd1260225950.tab") 
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,'Seasonal Decomposition by Loess - Time Series Components',6,TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'t',header=TRUE)
> a<-table.element(a,'Observed',header=TRUE)
> a<-table.element(a,'Fitted',header=TRUE)
> a<-table.element(a,'Seasonal',header=TRUE)
> a<-table.element(a,'Trend',header=TRUE)
> a<-table.element(a,'Remainder',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:nx) {
+ a<-table.row.start(a)
+ a<-table.element(a,i,header=TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]+m$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/65hkd1260225951.tab") 
> system("convert tmp/1gveu1260225950.ps tmp/1gveu1260225950.png")
> system("convert tmp/281r51260225950.ps tmp/281r51260225950.png")
> system("convert tmp/32l8u1260225950.ps tmp/32l8u1260225950.png")
> system("convert tmp/431r91260225950.ps tmp/431r91260225950.png")
> 
> 
> proc.time()
   user  system elapsed 
  1.076   0.628   1.725