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(376.974,377.632,378.205,370.861,369.167,371.551,382.842,381.903,384.502,392.058,384.359,388.884,386.586,387.495,385.705,378.67,377.367,376.911,389.827,387.82,387.267,380.575,372.402,376.74,377.795,376.126,370.804,367.98,367.866,366.121,379.421,378.519,372.423,355.072,344.693,342.892,344.178,337.606,327.103,323.953,316.532,306.307,327.225,329.573,313.761,307.836,300.074,304.198,306.122,300.414,292.133,290.616,280.244,285.179,305.486,305.957,293.886,289.441,288.776,299.149,306.532,309.914,313.468,314.901,309.16,316.15,336.544,339.196,326.738,320.838,318.62,331.533,335.378)
> 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  2.4379925 373.3639  1.1720703
Feb 1  1.8257604 374.4114  1.3948314
Mar 1 -1.2137108 375.4589  3.9598316
Apr 1 -4.0016360 376.5453 -1.6827075
May 1 -7.8242280 377.6318 -0.6405799
Jun 1 -6.9850485 378.7079 -0.1718580
Jul 1 10.0613029 379.7840 -7.0033079
Aug 1 10.7613433 380.7715 -9.6298641
Sep 1  3.7923814 381.7590 -1.0494181
Oct 1 -1.2197900 382.6523 10.6254932
Nov 1 -6.9217907 383.5456  7.7352337
Dec 1 -0.7125794 384.1549  5.4417234
Jan 2  2.4379925 384.7642 -0.6161477
Feb 2  1.8257604 384.8197  0.8495778
Mar 2 -1.2137108 384.8752  2.0435422
Apr 2 -4.0016360 384.2660 -1.5943142
May 2 -7.8242280 383.6567  1.5344961
Jun 2 -6.9850485 382.7931  1.1029267
Jul 2 10.0613029 381.9295 -2.1638146
Aug 2 10.7613433 380.9948 -3.9361731
Sep 2  3.7923814 380.0601  3.4144707
Oct 2 -1.2197900 379.1049  2.6899148
Nov 2 -6.9217907 378.1496  1.1741882
Dec 2 -0.7125794 377.2404  0.2121607
Jan 3  2.4379925 376.3312 -0.9742274
Feb 3  1.8257604 375.2500 -0.9498090
Mar 3 -1.2137108 374.1689 -2.1511517
Apr 3 -4.0016360 372.4532 -0.4715496
May 3 -7.8242280 370.7375  4.9527193
Jun 3 -6.9850485 368.2626  4.8434443
Jul 3 10.0613029 365.7877  3.5719975
Aug 3 10.7613433 362.4971  5.2605735
Sep 3  3.7923814 359.2065  9.4241520
Oct 3 -1.2197900 355.1355  1.1562416
Nov 3 -6.9217907 351.0646  0.5501605
Dec 3 -0.7125794 346.4685 -2.8638810
Jan 4  2.4379925 341.8723 -0.1322833
Feb 4  1.8257604 337.3680 -1.5877682
Mar 4 -1.2137108 332.8637 -4.5470142
Apr 4 -4.0016360 328.8564 -0.9018042
May 4 -7.8242280 324.8492 -0.4929275
Jun 4 -6.9850485 321.4798 -8.1877801
Jul 4 10.0613029 318.1105 -0.9468047
Aug 4 10.7613433 315.1320  3.6796155
Sep 4  3.7923814 312.1536 -2.1849620
Oct 4 -1.2197900 309.4407 -0.3849264
Nov 4 -6.9217907 306.7279  0.2679385
Dec 4 -0.7125794 304.3990  0.5115810
Jan 5  2.4379925 302.0701  1.6138627
Feb 5  1.8257604 300.1715 -1.5832337
Mar 5 -1.2137108 298.2728 -4.9260912
Apr 5 -4.0016360 296.9223 -2.3047013
May 5 -7.8242280 295.5719 -7.5036446
Jun 5 -6.9850485 295.1252 -2.9611494
Jul 5 10.0613029 294.6785  0.7461740
Aug 5 10.7613433 295.5067 -0.3110198
Sep 5  3.7923814 296.3348 -6.2412111
Oct 5 -1.2197900 298.2852 -7.6243971
Nov 5 -6.9217907 300.2355 -4.5377538
Dec 5 -0.7125794 302.8722 -3.0105957
Jan 6  2.4379925 305.5088 -1.4147983
Feb 6  1.8257604 308.4119 -0.3236697
Mar 6 -1.2137108 311.3150  3.3666978
Apr 6 -4.0016360 313.8537  5.0489692
May 6 -7.8242280 316.3923  0.5919073
Jun 6 -6.9850485 318.6387  4.4963307
Jul 6 10.0613029 320.8851  5.5975823
Aug 6 10.7613433 323.1043  5.3303401
Sep 6  3.7923814 325.3235 -2.3778997
Oct 6 -1.2197900 327.4335 -5.3757201
Nov 6 -6.9217907 329.5435 -4.0017112
Dec 6 -0.7125794 331.5429  0.7026693
Jan 7  2.4379925 333.5423 -0.6023110
> m$win
  s   t   l 
731  19  13 
> m$deg
s t l 
0 1 1 
> m$jump
 s  t  l 
74  2  2 
> m$inner
[1] 2
> m$outer
[1] 0
> postscript(file="/var/www/html/rcomp/tmp/1jzd71290972783.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) 
> plot(m,main=main)
> dev.off()
null device 
          1 
> mylagmax <- nx/2
> postscript(file="/var/www/html/rcomp/tmp/2jzd71290972783.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) 
> op <- par(mfrow = c(2,2))
> acf(as.numeric(x),lag.max = mylagmax,main='Observed')
> acf(as.numeric(m$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/3jzd71290972783.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/4u9us1290972783.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$jump['l'])
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/5q1ai1290972783.tab") 
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,'Seasonal Decomposition by Loess - Time Series Components',6,TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'t',header=TRUE)
> a<-table.element(a,'Observed',header=TRUE)
> a<-table.element(a,'Fitted',header=TRUE)
> a<-table.element(a,'Seasonal',header=TRUE)
> a<-table.element(a,'Trend',header=TRUE)
> a<-table.element(a,'Remainder',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:nx) {
+ a<-table.row.start(a)
+ a<-table.element(a,i,header=TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]+m$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/6b19o1290972783.tab") 
> 
> try(system("convert tmp/1jzd71290972783.ps tmp/1jzd71290972783.png",intern=TRUE))
character(0)
> try(system("convert tmp/2jzd71290972783.ps tmp/2jzd71290972783.png",intern=TRUE))
character(0)
> try(system("convert tmp/3jzd71290972783.ps tmp/3jzd71290972783.png",intern=TRUE))
character(0)
> try(system("convert tmp/4u9us1290972783.ps tmp/4u9us1290972783.png",intern=TRUE))
character(0)
> 
> 
> proc.time()
   user  system elapsed 
  0.996   0.637   2.647