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 = 'TRUE'
> 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   1.191953 518.1030  -0.2949334
Feb 1  -3.693793 521.7488  -1.0550527
Mar 1 -13.782440 525.3947  -1.6122704
Apr 1 -19.617221 528.9967  -0.3794433
May 1 -31.810130 532.5986   0.2115114
Jun 1 -28.928090 536.1640  -0.2359191
Jul 1  24.378971 539.7294   4.8916289
Aug 1  34.705525 543.2884   2.0060706
Sep 1  29.915924 546.8474   1.2366674
Oct 1  13.952958 550.1732   0.8738863
Nov 1  -4.843941 553.4989  -1.6549624
Dec 1  -1.469719 556.2798   0.1899324
Jan 2   1.191953 559.0607   1.7473778
Feb 2  -3.693793 561.6576   3.0362409
Mar 2 -13.782440 564.2544   4.5280056
Apr 2 -19.617221 566.8844  -3.2671465
May 2 -31.810130 569.5143  -0.7041710
Jun 2 -28.928090 572.1855  -0.2574115
Jul 2  24.378971 574.8567  -5.2356735
Aug 2  34.705525 577.4014  -1.1069360
Sep 2  29.915924 579.9461   3.1379567
Oct 2  13.952958 582.3700  14.6770338
Nov 2  -4.843941 584.7939  14.0500432
Dec 2  -1.469719 587.0974   9.3723539
Jan 3   1.191953 589.4008   0.4072151
Feb 3  -3.693793 591.3712   1.3226144
Mar 3 -13.782440 593.3415   4.4409154
Apr 3 -19.617221 594.4086  -1.7913365
May 3 -31.810130 595.4756   3.3345392
Jun 3 -28.928090 595.8992   2.0288677
Jul 3  24.378971 596.3229   0.2981747
Aug 3  34.705525 596.3954  -2.1009739
Sep 3  29.915924 596.4680   1.6160327
Oct 3  13.952958 596.2495   1.7975431
Nov 3  -4.843941 596.0310   3.8129859
Dec 3  -1.469719 595.6144   2.8553650
Jan 4   1.191953 595.1978  -3.3897053
Feb 4  -3.693793 593.8322  -0.1384218
Mar 4 -13.782440 592.4667   1.3157634
Apr 4 -19.617221 589.4577   4.1594830
May 4 -31.810130 586.4488  18.3613302
Jun 4 -28.928090 583.3413  18.5868240
Jul 4  24.378971 580.2337  15.3872963
Aug 4  34.705525 577.0867  14.2077369
Sep 4  29.915924 573.9397  16.1443326
Oct 4  13.952958 570.6921   3.3549005
Nov 4  -4.843941 567.4445   3.3994008
Dec 4  -1.469719 562.9627  -4.4929349
Jan 5   1.191953 558.4808   1.3272801
Feb 5  -3.693793 553.8118  -1.1179998
Mar 5 -13.782440 549.1428  -3.3603781
Apr 5 -19.617221 544.7189   0.8983421
May 5 -31.810130 540.2949   2.5151900
Jun 5 -28.928090 536.4470  -8.5188792
Jul 5  24.378971 532.5990  -1.9779699
Aug 5  34.705525 529.1579   1.1365924
Sep 5  29.915924 525.7168 -13.6326901
Oct 5  13.952958 522.5563  -9.5092196
Nov 5  -4.843941 519.3958  -4.5518166
Dec 5  -1.469719 516.6492  -1.1794649
Jan 6   1.191953 513.9026   1.9054374
Feb 6  -3.693793 511.5585   0.1352931
Mar 6 -13.782440 509.2144  -2.4319495
Apr 6 -19.617221 506.9275   2.6897263
May 6 -31.810130 504.6406  -3.8304702
Jun 6 -28.928090 502.3262   4.6018616
Jul 6  24.378971 500.0119   3.6091719
Aug 6  34.705525 497.6877   1.6068035
Sep 6  29.915924 495.3635  -7.2794098
Oct 6  13.952958 492.9930  -0.9459258
Nov 6  -4.843941 490.6225  16.2214906
Dec 6  -1.469719 488.2149  29.2548517
> 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] 1
> m$outer
[1] 15
> postscript(file="/var/www/html/rcomp/tmp/1gnzy1243878181.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) 
> plot(m,main=main)
> dev.off()
null device 
          1 
> mylagmax <- nx/2
> postscript(file="/var/www/html/rcomp/tmp/2jfy21243878181.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) 
> op <- par(mfrow = c(2,2))
> acf(as.numeric(x),lag.max = mylagmax,main='Observed')
> acf(as.numeric(m$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/3ppg41243878181.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/42dym1243878181.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$jump['l'])
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/5y4h21243878181.tab") 
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,'Seasonal Decomposition by Loess - Time Series Components',6,TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'t',header=TRUE)
> a<-table.element(a,'Observed',header=TRUE)
> a<-table.element(a,'Fitted',header=TRUE)
> a<-table.element(a,'Seasonal',header=TRUE)
> a<-table.element(a,'Trend',header=TRUE)
> a<-table.element(a,'Remainder',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:nx) {
+ a<-table.row.start(a)
+ a<-table.element(a,i,header=TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]+m$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/6vwrj1243878181.tab") 
> 
> system("convert tmp/1gnzy1243878181.ps tmp/1gnzy1243878181.png")
> system("convert tmp/2jfy21243878181.ps tmp/2jfy21243878181.png")
> system("convert tmp/3ppg41243878181.ps tmp/3ppg41243878181.png")
> system("convert tmp/42dym1243878181.ps tmp/42dym1243878181.png")
> 
> 
> proc.time()
   user  system elapsed 
  0.990   0.603   1.188