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(10284.5,12792,12823.61538,13845.66667,15335.63636,11188.5,13633.25,12298.46667,15353.63636,12696.15385,12213.93333,13683.72727,11214.14286,13950.23077,11179.13333,11801.875,11188.82353,16456.27273,11110.0625,16530.69231,10038.41176,11681.25,11148.88235,8631,9386.444444,9764.736842,12043.75,12948.06667,10987.125,11648.3125,10633.35294,10219.3,9037.6,10296.31579,11705.41176,10681.94444,9362.947368,11306.35294,10984.45,10062.61905,8118.583333,8867.48,8346.72,8529.307692,10697.18182,8591.84,8695.607143,8125.571429,7009.758621,7883.466667,7527.645161,6763.758621,6682.333333,7855.681818,6738.88,7895.434783,6361.884615,6935.956522,8344.454545,9107.944444)
> 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 -1468.57422 12746.397  -993.32299
Feb 1   312.43593 12793.733  -314.16862
Mar 1   178.00907 12841.068  -195.46186
Apr 1   408.82610 12877.435   559.40529
May 1  -154.93354 12913.802  2576.76749
Jun 1   641.63819 12942.467 -2395.60503
Jul 1  -413.33862 12971.131  1075.45734
Aug 1   652.24217 12984.510 -1338.28515
Sep 1   -81.26357 12997.888  2437.01190
Oct 1  -247.81602 12939.819     4.15059
Nov 1   224.42506 12881.751  -892.24226
Dec 1   -51.65037 12843.843   891.53482
Jan 2 -1468.57422 12805.935  -123.21801
Feb 2   312.43593 12781.591   856.20356
Mar 2   178.00907 12757.247 -1756.12322
Apr 2   408.82610 12638.052 -1245.00280
May 2  -154.93354 12518.856 -1175.09885
Jun 2   641.63819 12322.328  3492.30622
Jul 2  -413.33862 12125.801  -602.39960
Aug 2   652.24217 11948.457  3929.99277
Sep 2   -81.26357 11771.114 -1651.43869
Oct 2  -247.81602 11645.875   283.19063
Nov 2   224.42506 11520.637  -596.17947
Dec 2   -51.65037 11349.259 -2666.60816
Jan 3 -1468.57422 11177.880  -322.86162
Feb 3   312.43593 11002.688 -1550.38750
Mar 3   178.00907 10827.497  1038.24438
Apr 3   408.82610 10769.699  1769.54156
May 3  -154.93354 10711.901   430.15706
Jun 3   641.63819 10733.071   273.60296
Jul 3  -413.33862 10754.241   292.45034
Aug 3   652.24217 10737.763 -1170.70502
Sep 3   -81.26357 10721.284 -1602.42093
Oct 3  -247.81602 10596.610   -52.47794
Nov 3   224.42506 10471.935  1009.05169
Dec 3   -51.65037 10309.159   424.43625
Jan 4 -1468.57422 10146.382   685.13949
Feb 4   312.43593 10008.691   985.22619
Mar 4   178.00907  9871.000   935.44137
Apr 4   408.82610  9716.765   -62.97171
May 4  -154.93354  9562.530 -1289.01289
Jun 4   641.63819  9352.729 -1126.88753
Jul 4  -413.33862  9142.929  -382.87029
Aug 4   652.24217  8922.225 -1045.15920
Sep 4   -81.26357  8701.521  2076.92485
Oct 4  -247.81602  8508.635   331.02080
Nov 4   224.42506  8315.750   155.43219
Dec 4   -51.65037  8153.138    24.08342
Jan 5 -1468.57422  7990.527   487.80599
Feb 5   312.43593  7810.391  -239.36051
Mar 5   178.00907  7630.256  -280.61957
Apr 5   408.82610  7571.841 -1216.90824
May 5  -154.93354  7513.426  -676.15900
Jun 5   641.63819  7495.899  -281.85584
Jul 5  -413.33862  7478.373  -326.15445
Aug 5   652.24217  7475.301  -232.10869
Sep 5   -81.26357  7472.230 -1029.08136
Oct 5  -247.81602  7486.865  -303.09245
Nov 5   224.42506  7501.500   618.52904
Dec 5   -51.65037  7533.750  1625.84523
> m$win
  s   t   l 
601  19  13 
> m$deg
s t l 
0 1 1 
> m$jump
 s  t  l 
61  2  2 
> m$inner
[1] 2
> m$outer
[1] 0
> postscript(file="/var/www/html/rcomp/tmp/1ezqj1259888078.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) 
> plot(m,main=main)
> dev.off()
null device 
          1 
> mylagmax <- nx/2
> postscript(file="/var/www/html/rcomp/tmp/22dmn1259888078.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) 
> op <- par(mfrow = c(2,2))
> acf(as.numeric(x),lag.max = mylagmax,main='Observed')
> acf(as.numeric(m$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/31q731259888078.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/4ptnn1259888078.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$jump['l'])
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/596ng1259888078.tab") 
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,'Seasonal Decomposition by Loess - Time Series Components',6,TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'t',header=TRUE)
> a<-table.element(a,'Observed',header=TRUE)
> a<-table.element(a,'Fitted',header=TRUE)
> a<-table.element(a,'Seasonal',header=TRUE)
> a<-table.element(a,'Trend',header=TRUE)
> a<-table.element(a,'Remainder',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:nx) {
+ a<-table.row.start(a)
+ a<-table.element(a,i,header=TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]+m$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/6515k1259888078.tab") 
> system("convert tmp/1ezqj1259888078.ps tmp/1ezqj1259888078.png")
> system("convert tmp/22dmn1259888078.ps tmp/22dmn1259888078.png")
> system("convert tmp/31q731259888078.ps tmp/31q731259888078.png")
> system("convert tmp/4ptnn1259888078.ps tmp/4ptnn1259888078.png")
> 
> 
> proc.time()
   user  system elapsed 
  0.947   0.608   1.155