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 = '6'
> 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  1.3966622 375.6985 -0.12115882
Feb 1  1.1899868 376.4215  0.02050500
Mar 1 -1.4812374 377.1445  2.54171771
Apr 1 -6.1141304 377.8675 -0.89240086
May 1 -7.3178852 378.5399 -2.05505858
Jun 1 -7.3444209 379.2124 -0.31693538
Jul 1  7.6812035 379.8848 -4.72397233
Aug 1  8.2522458 380.4718 -6.82101534
Sep 1  6.0213974 381.0588 -2.57816761
Oct 1  2.4329477 381.6458  7.97928137
Nov 1 -4.5504790 382.2071  6.70233054
Dec 1 -0.1944248 382.7685  6.30989891
Jan 2  1.5394267 383.3299  1.71666994
Feb 2  1.1672692 383.3412  2.98656854
Mar 2 -1.8395492 383.3524  4.19212804
Apr 2 -5.9524944 383.3637  1.25881431
May 2 -7.3072902 382.8277  1.84659645
Jun 2 -7.5307117 382.2917  2.15000435
Jul 2  8.1712597 381.7557 -0.09998060
Aug 2  9.0574076 380.8766 -2.11400401
Sep 2  6.0413101 379.9975  1.22821802
Oct 2  1.9565575 379.1183 -0.49990497
Nov 2 -4.8147438 378.0511 -0.83431534
Dec 2 -0.3208139 376.9838  0.07704299
Jan 3  1.8310970 375.9165  0.04742041
Feb 3  1.0909800 374.2685  0.76650693
Mar 3 -2.8158581 372.6205  0.99931466
Apr 3 -5.5706329 370.9726  2.57805901
May 3 -7.2157725 368.5130  6.56880996
Jun 3 -7.8813247 366.0534  7.94897345
Jul 3  9.6150714 363.5937  6.21218873
Aug 3 11.2932036 360.3170  6.90880485
Sep 3  6.2959665 357.0402  9.08679031
Oct 3  0.2993681 353.7635  1.00913696
Nov 3 -5.8496986 349.8769  0.66582409
Dec 3 -0.9462016 345.9903 -2.15205248
Jan 4  2.4560460 342.1036 -0.38167960
Feb 4  0.8633235 338.1119 -1.36918953
Mar 4 -3.7071643 334.1201 -3.30993419
Apr 4 -4.5394986 330.1283 -1.63583231
May 4 -8.3566797 326.5162 -1.62750139
Jun 4 -8.1278087 322.9040 -8.46922253
Jul 4 11.5203940 319.2919 -3.58727536
Aug 4 13.5496105 316.2250 -0.20156653
Sep 4  4.4511196 313.1580 -3.84815039
Oct 4 -2.7126592 310.0911  0.45755369
Nov 4 -7.7907628 307.7114  0.15338397
Dec 4 -1.6755226 305.3317  0.54187045
Jan 5  2.7118701 302.9519  0.45820450
Feb 5  1.3911736 301.4742 -2.45134185
Mar 5 -2.5272278 299.9964 -5.33618328
Apr 5 -3.1558597 298.5187 -4.74679411
May 5 -8.3925766 298.1692 -9.53263044
Jun 5 -7.3032829 297.8198 -5.33747731
Jul 5 12.4889597 297.4703 -4.47327309
Aug 5 14.2018962 298.4141 -6.65896811
Sep 5  3.3247612 299.3578 -8.79659166
Oct 5 -3.6855610 300.3016 -7.17502799
Nov 5 -8.4448716 302.2497 -5.02882076
Dec 5 -1.2805946 304.1978 -3.76820124
Jan 6  2.8161156 306.1459 -2.43001477
Feb 6  1.5129596 308.5311 -0.13006088
Mar 6 -2.2737152 310.9163  4.82541174
Apr 6 -2.7904107 313.3015  4.38990507
May 6 -8.6208032 315.7248  2.05596773
Jun 6 -7.1800635 318.1482  5.18189808
Jul 6 12.7480617 320.5715  3.22444299
Aug 6 14.3247097 323.0020  1.86926700
Sep 6  2.7679463 325.4326 -1.46249769
Oct 6 -4.0833235 327.8631 -2.94175595
Nov 6 -8.6799987 330.2508 -2.95077213
Dec 6 -1.1270478 332.6385  0.02158571
Jan 7  2.8908130 335.0262 -2.53896649
> m$win
 s  t  l 
 6 25 13 
> m$deg
s t l 
0 1 1 
> m$jump
s t l 
1 3 2 
> m$inner
[1] 2
> m$outer
[1] 0
> postscript(file="/var/www/html/rcomp/tmp/1i2601290973088.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) 
> plot(m,main=main)
> dev.off()
null device 
          1 
> mylagmax <- nx/2
> postscript(file="/var/www/html/rcomp/tmp/2i2601290973088.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) 
> op <- par(mfrow = c(2,2))
> acf(as.numeric(x),lag.max = mylagmax,main='Observed')
> acf(as.numeric(m$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/3btn31290973088.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/4btn31290973088.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$jump['l'])
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/5hc2x1290973088.tab") 
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,'Seasonal Decomposition by Loess - Time Series Components',6,TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'t',header=TRUE)
> a<-table.element(a,'Observed',header=TRUE)
> a<-table.element(a,'Fitted',header=TRUE)
> a<-table.element(a,'Seasonal',header=TRUE)
> a<-table.element(a,'Trend',header=TRUE)
> a<-table.element(a,'Remainder',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:nx) {
+ a<-table.row.start(a)
+ a<-table.element(a,i,header=TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]+m$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/63v021290973088.tab") 
> 
> try(system("convert tmp/1i2601290973088.ps tmp/1i2601290973088.png",intern=TRUE))
character(0)
> try(system("convert tmp/2i2601290973088.ps tmp/2i2601290973088.png",intern=TRUE))
character(0)
> try(system("convert tmp/3btn31290973088.ps tmp/3btn31290973088.png",intern=TRUE))
character(0)
> try(system("convert tmp/4btn31290973088.ps tmp/4btn31290973088.png",intern=TRUE))
character(0)
> 
> 
> proc.time()
   user  system elapsed 
  1.015   0.658   2.831