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(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)
> 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   2145.2554 263916.7  1351.0161
Feb 1   1151.0189 265177.1  1037.8939
Mar 1  -2556.3860 266437.4   895.9400
Apr 1  -5104.6274 267664.0 -3696.4040
May 1 -10971.0354 268890.6 -3075.5812
Jun 1 -13145.9044 270046.7 -2032.8445
Jul 1   7704.7309 271202.9 -1640.6121
Aug 1  10166.3014 272309.2  2875.4638
Sep 1   8414.7067 273415.6  4771.7048
Oct 1   3928.3515 274454.7  4658.9205
Nov 1  -1895.5990 275493.9  3088.7315
Dec 1    163.1840 276195.5  1556.2895
Jan 2   2145.2554 276897.2 -1914.4407
Feb 2   1151.0189 277373.2 -1421.2259
Mar 2  -2556.3860 277849.2  -255.8426
Apr 2  -5104.6274 278308.8 -3054.1433
May 2 -10971.0354 278768.3  -657.2773
Jun 2 -13145.9044 279246.5 -1107.6436
Jul 2   7704.7309 279724.8  -170.5142
Aug 2  10166.3014 280097.7   922.0001
Sep 2   8414.7067 280470.6  3414.6795
Oct 2   3928.3515 280796.0  3461.6238
Nov 2  -1895.5990 281121.4  2251.1634
Dec 2    163.1840 281381.2  1111.6278
Jan 3   2145.2554 281640.9 -3596.1962
Feb 3   1151.0189 281643.9 -2386.9177
Mar 3  -2556.3860 281646.9 -2254.4709
Apr 3  -5104.6274 281193.7  -873.1021
May 3 -10971.0354 280740.6  4582.4333
Jun 3 -13145.9044 279806.0  4650.8955
Jul 3   7704.7309 278871.4  3225.8533
Aug 3  10166.3014 277369.3  3190.3838
Sep 3   8414.7067 275867.2  8018.0793
Oct 3   3928.3515 273705.1   872.5353
Nov 3  -1895.5990 271543.0   178.5865
Dec 3    163.1840 268868.4 -3170.5724
Jan 4   2145.2554 266193.8   694.9803
Feb 4   1151.0189 263466.0  -441.0161
Mar 4  -2556.3860 260738.2 -2983.8442
Apr 4  -5104.6274 258328.0   129.6604
May 4 -10971.0354 255917.7  1110.3317
Jun 4 -13145.9044 254031.3 -5513.4211
Jul 4   7704.7309 252144.9 -1293.6783
Aug 4  10166.3014 250588.9   237.8156
Sep 4   8414.7067 249032.8 -2784.5255
Oct 4   3928.3515 247698.2  -983.5566
Nov 4  -1895.5990 246363.6 -1045.9923
Dec 4    163.1840 245267.5  1674.3510
Jan 5   2145.2554 244171.3  2224.4060
Feb 5   1151.0189 243316.7   571.2529
Mar 5  -2556.3860 242462.1 -2825.7319
Apr 5  -5104.6274 242072.4   117.2243
May 5 -10971.0354 241682.7 -5157.6527
Jun 5 -13145.9044 242268.8 -2283.8464
Jul 5   7704.7309 242854.8 -2625.5445
Aug 5  10166.3014 244820.2 -6653.5368
Sep 5   8414.7067 246785.7 -8231.3641
Oct 5   3928.3515 249986.6 -8816.9720
Nov 5  -1895.5990 253187.6 -5028.9847
Dec 5    163.1840 257169.8 -1567.9553
Jan 6   2145.2554 261152.0  1021.7858
Feb 6   1151.0189 265080.0  2115.9926
Mar 6  -2556.3860 269008.0  6594.3677
Apr 6  -5104.6274 272972.4  6095.2751
May 6 -10971.0354 276936.7  1464.3491
Jun 6 -13145.9044 280900.3  4238.6494
Jul 6   7704.7309 284863.8   141.4452
Aug 6  10166.3014 288753.3 -3038.6187
Sep 6   8414.7067 292642.8 -7758.5175
> 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$outer
[1] 0
> postscript(file="/var/www/html/rcomp/tmp/19pn31259945620.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) 
> plot(m,main=main)
> dev.off()
null device 
          1 
> mylagmax <- nx/2
> postscript(file="/var/www/html/rcomp/tmp/2kzgg1259945620.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) 
> op <- par(mfrow = c(2,2))
> acf(as.numeric(x),lag.max = mylagmax,main='Observed')
> acf(as.numeric(m$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/3o4zc1259945620.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/4n7px1259945620.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$jump['l'])
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/5544s1259945620.tab") 
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,'Seasonal Decomposition by Loess - Time Series Components',6,TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'t',header=TRUE)
> a<-table.element(a,'Observed',header=TRUE)
> a<-table.element(a,'Fitted',header=TRUE)
> a<-table.element(a,'Seasonal',header=TRUE)
> a<-table.element(a,'Trend',header=TRUE)
> a<-table.element(a,'Remainder',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:nx) {
+ a<-table.row.start(a)
+ a<-table.element(a,i,header=TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]+m$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/654og1259945620.tab") 
> system("convert tmp/19pn31259945620.ps tmp/19pn31259945620.png")
> system("convert tmp/2kzgg1259945620.ps tmp/2kzgg1259945620.png")
> system("convert tmp/3o4zc1259945620.ps tmp/3o4zc1259945620.png")
> system("convert tmp/4n7px1259945620.ps tmp/4n7px1259945620.png")
> 
> 
> proc.time()
   user  system elapsed 
  1.000   0.609   1.187