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(519,517,510,509,501,507,569,580,578,565,547,555,562,561,555,544,537,543,594,611,613,611,594,595,591,589,584,573,567,569,621,629,628,612,595,597,593,590,580,574,573,573,620,626,620,588,566,557,561,549,532,526,511,499,555,565,542,527,510,514,517,508,493,490,469,478,528,534,518,506,502,516)
> 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   0.3144649 518.7106  -0.02503044
Feb 1  -4.4539711 522.2917  -0.83777506
Mar 1 -14.3890735 525.8729  -1.48385329
Apr 1 -20.6640125 529.4122   0.25183237
May 1 -30.2722763 532.9514  -1.67915708
Jun 1 -28.3537571 536.4516  -1.09784028
Jul 1  24.7314296 539.9518   4.31680901
Aug 1  34.6530482 543.4504   1.89655465
Sep 1  27.2413358 546.9490   3.80963136
Oct 1  12.6755664 550.2662   2.05825177
Nov 1  -2.7235361 553.5833  -3.85979471
Dec 1   1.2407802 556.3318  -2.57255559
Jan 2   0.3144649 559.0802   2.60531503
Feb 2  -4.4539711 561.7815   3.67245640
Mar 2 -14.3890735 564.4828   4.90626418
Apr 2 -20.6640125 567.6892  -3.02517281
May 2 -30.2722763 570.8956  -3.62328489
Jun 2 -28.3537571 574.1164  -2.76267169
Jul 2  24.7314296 577.3373  -8.06872599
Aug 2  34.6530482 580.1408  -3.79382310
Sep 2  27.2413358 582.9443   2.81441086
Oct 2  12.6755664 585.5065  12.81791853
Nov 2  -2.7235361 588.0688   8.65475932
Dec 2   1.2407802 590.2074   3.55177680
Jan 3   0.3144649 592.3461  -1.66057420
Feb 3  -4.4539711 593.6433  -0.18932337
Mar 3 -14.3890735 594.9405   3.44859387
Apr 3 -20.6640125 595.4757  -1.81172796
May 3 -30.2722763 596.0110   1.26127510
Jun 3 -28.3537571 596.2279   1.12588402
Jul 3  24.7314296 596.4447  -0.17617458
Aug 3  34.6530482 596.4784  -2.13143447
Sep 3  27.2413358 596.5120   4.24663670
Oct 3  12.6755664 596.5342   2.79018747
Nov 3  -2.7235361 596.5565   1.16707137
Dec 3   1.2407802 596.6299  -0.87066235
Jan 4   0.3144649 596.7033  -4.01776455
Feb 4  -4.4539711 596.3715  -1.91748879
Mar 4 -14.3890735 596.0396  -1.65054664
Apr 4 -20.6640125 594.6472   0.01683113
May 4 -30.2722763 593.2547  10.01753381
Jun 4 -28.3537571 590.6638  10.68993421
Jul 4  24.7314296 588.0729   7.19566709
Aug 4  34.6530482 584.3603   6.98669105
Sep 4  27.2413358 580.6476  12.11104608
Oct 4  12.6755664 575.8742  -0.54980497
Nov 4  -2.7235361 571.1009  -2.37732289
Dec 4   1.2407802 565.5752  -9.81600444
Jan 5   0.3144649 560.0496   0.63594553
Feb 5  -4.4539711 554.5185  -1.06453518
Mar 5 -14.3890735 548.9874  -2.59834950
Apr 5 -20.6640125 543.9588   2.70521907
May 5 -30.2722763 538.9302   2.34211253
Jun 5 -28.3537571 534.7329  -7.37918040
Jul 5  24.7314296 530.5357  -0.26714085
Aug 5  34.6530482 526.9875   3.35946940
Sep 5  27.2413358 523.4393  -8.68058929
Oct 5  12.6755664 520.4501  -6.12564155
Nov 5  -2.7235361 517.4609  -4.73736069
Dec 5   1.2407802 515.0477  -2.28847633
Jan 6   0.3144649 512.6345   4.05103954
Feb 6  -4.4539711 510.5992   1.85476354
Mar 6 -14.3890735 508.5639  -1.17484607
Apr 6 -20.6640125 507.2532   3.41083798
May 6 -30.2722763 505.9424  -6.67015307
Jun 6 -28.3537571 505.0030   1.35071607
Jul 6  24.7314296 504.0637  -0.79508230
Aug 6  34.6530482 503.1419  -3.79495382
Sep 6  27.2413358 502.2202 -11.46149428
Oct 6  12.6755664 501.3192  -7.99477730
Nov 6  -2.7235361 500.4183   4.30527279
Dec 6   1.2407802 499.5894  15.16980125
> m$win
  s   t   l 
721  19  13 
> m$deg
s t l 
0 1 1 
> m$jump
 s  t  l 
73  2  2 
> m$inner
[1] 2
> m$outer
[1] 0
> postscript(file="/var/www/html/rcomp/tmp/1spio1259786903.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) 
> plot(m,main=main)
> dev.off()
null device 
          1 
> mylagmax <- nx/2
> postscript(file="/var/www/html/rcomp/tmp/2jb8h1259786903.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) 
> op <- par(mfrow = c(2,2))
> acf(as.numeric(x),lag.max = mylagmax,main='Observed')
> acf(as.numeric(m$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/3grs81259786903.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/4wep41259786903.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$jump['l'])
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/5g0pi1259786903.tab") 
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,'Seasonal Decomposition by Loess - Time Series Components',6,TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'t',header=TRUE)
> a<-table.element(a,'Observed',header=TRUE)
> a<-table.element(a,'Fitted',header=TRUE)
> a<-table.element(a,'Seasonal',header=TRUE)
> a<-table.element(a,'Trend',header=TRUE)
> a<-table.element(a,'Remainder',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:nx) {
+ a<-table.row.start(a)
+ a<-table.element(a,i,header=TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]+m$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/6jevb1259786903.tab") 
> 
> system("convert tmp/1spio1259786903.ps tmp/1spio1259786903.png")
> system("convert tmp/2jb8h1259786903.ps tmp/2jb8h1259786903.png")
> system("convert tmp/3grs81259786903.ps tmp/3grs81259786903.png")
> system("convert tmp/4wep41259786903.ps tmp/4wep41259786903.png")
> 
> 
> proc.time()
   user  system elapsed 
  0.990   0.586   1.193