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(286602,283042,276687,277915,277128,277103,275037,270150,267140,264993,287259,291186,292300,288186,281477,282656,280190,280408,276836,275216,274352,271311,289802,290726,292300,278506,269826,265861,269034,264176,255198,253353,246057,235372,258556,260993,254663,250643,243422,247105,248541,245039,237080,237085,225554,226839,247934,248333,246969,245098,246263,255765,264319,268347,273046,273963,267430,271993,292710,295881,294563)
> 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   8072.74998 276969.7  1559.5179
Feb 1   3522.40906 277136.6  2383.0071
Mar 1  -2114.65148 277303.4  1498.2159
Apr 1     55.40003 277535.5   324.1288
May 1   1882.05333 277767.5 -2521.5601
Jun 1    964.91452 278042.5 -1904.3702
Jul 1  -2699.62600 278317.4  -580.7787
Aug 1  -4329.99328 278600.2 -4120.1683
Sep 1 -10321.16186 278882.9 -1421.7565
Oct 1 -12704.84603 279294.7 -1596.8062
Nov 1   8067.07327 279706.4  -514.4594
Dec 1   9605.68017 280100.1  1480.1850
Jan 2   8072.74998 280493.9  3733.3665
Feb 2   3522.40906 280831.0  3832.5997
Mar 2  -2114.65148 281168.1  2423.5526
Apr 2     55.40003 281412.6  1187.9895
May 2   1882.05333 281657.1 -3349.1754
Jun 2    964.91452 281637.8 -2194.7157
Jul 2  -2699.62600 281618.5 -2082.8543
Aug 2  -4329.99328 281159.9 -1613.8978
Sep 2 -10321.16186 280701.3  3971.8600
Oct 2 -12704.84603 279777.2  4238.6858
Nov 2   8067.07327 278853.0  2881.9081
Dec 2   9605.68017 277371.8  3748.5686
Jan 3   8072.74998 275890.5  8336.7663
Feb 3   3522.40906 273740.1  1243.5112
Mar 3  -2114.65148 271589.7   350.9757
Apr 3     55.40003 268899.8 -3094.2107
May 3   1882.05333 266209.9   942.0012
Jun 3    964.91452 263459.9  -248.8141
Jul 3  -2699.62600 260709.9 -2812.2276
Aug 3  -4329.99328 258294.1  -611.1353
Sep 3 -10321.16186 255878.4   499.7583
Oct 3 -12704.84603 254002.5 -5925.6309
Nov 3   8067.07327 252126.6 -1637.6235
Dec 3   9605.68017 250591.3   796.0004
Jan 4   8072.74998 249056.1 -2465.8386
Feb 4   3522.40906 247733.2  -612.5807
Mar 4  -2114.65148 246410.3  -873.6031
Apr 4     55.40003 245298.9  1750.7128
May 4   1882.05333 244187.5  2471.4269
Jun 4    964.91452 243310.6   763.4550
Jul 4  -2699.62600 242433.7 -2654.1153
Aug 4  -4329.99328 242038.6  -623.5713
Sep 4 -10321.16186 241643.4 -5768.2261
Oct 4 -12704.84603 242239.9 -2696.0562
Nov 4   8067.07327 242836.4 -2969.4898
Dec 4   9605.68017 244822.7 -6095.3521
Jan 5   8072.74998 246808.9 -7912.6773
Feb 5   3522.40906 250021.6 -8445.9962
Mar 5  -2114.65148 253234.2 -4856.5955
Apr 5     55.40003 257200.5 -1490.9491
May 5   1882.05333 261166.9  1270.0955
Jun 5    964.91452 265120.2  2261.9348
Jul 5  -2699.62600 269073.5  6672.1758
Aug 5  -4329.99328 273066.3  5226.6553
Sep 5 -10321.16186 277059.2   691.9362
Oct 5 -12704.84603 281048.6  3649.2568
Nov 5   8067.07327 285038.0  -395.0260
Dec 5   9605.68017 288959.0 -2683.6345
Jan 6   8072.74998 292880.0 -6389.7058
> m$win
  s   t   l 
611  19  13 
> m$deg
s t l 
0 1 1 
> m$jump
 s  t  l 
62  2  2 
> m$inner
[1] 2
> m$outer
[1] 0
> postscript(file="/var/www/html/rcomp/tmp/1xttw1291924025.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) 
> plot(m,main=main)
> dev.off()
null device 
          1 
> mylagmax <- nx/2
> postscript(file="/var/www/html/rcomp/tmp/28ksz1291924025.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) 
> op <- par(mfrow = c(2,2))
> acf(as.numeric(x),lag.max = mylagmax,main='Observed')
> acf(as.numeric(m$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/38ksz1291924025.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/4jt9k1291924025.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$jump['l'])
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/5flpb1291924025.tab") 
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,'Seasonal Decomposition by Loess - Time Series Components',6,TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'t',header=TRUE)
> a<-table.element(a,'Observed',header=TRUE)
> a<-table.element(a,'Fitted',header=TRUE)
> a<-table.element(a,'Seasonal',header=TRUE)
> a<-table.element(a,'Trend',header=TRUE)
> a<-table.element(a,'Remainder',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:nx) {
+ a<-table.row.start(a)
+ a<-table.element(a,i,header=TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]+m$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/60l5z1291924025.tab") 
> 
> try(system("convert tmp/1xttw1291924025.ps tmp/1xttw1291924025.png",intern=TRUE))
character(0)
> try(system("convert tmp/28ksz1291924025.ps tmp/28ksz1291924025.png",intern=TRUE))
character(0)
> try(system("convert tmp/38ksz1291924025.ps tmp/38ksz1291924025.png",intern=TRUE))
character(0)
> try(system("convert tmp/4jt9k1291924025.ps tmp/4jt9k1291924025.png",intern=TRUE))
character(0)
> 
> 
> proc.time()
   user  system elapsed 
  0.956   0.619   2.229