R version 2.12.0 (2010-10-15)
Copyright (C) 2010 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: i486-pc-linux-gnu (32-bit)

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> x <- c(183.046,175.714,175.768,171.029,170.465,170.102,156.389,124.291,99.360,86.675,85.056,128.236,164.257,162.401,152.779,156.005,153.387,153.190,148.840,144.211,145.953,145.542,150.271,147.489,143.824,134.754,131.736,126.304,125.511,125.495,130.133,126.257,110.323,98.417,105.749,120.665,124.075,127.245,146.731,144.979,148.210,144.670,142.970,142.524,146.142,146.522,148.128,148.798,150.181,152.388,155.694,160.662,155.520,158.262,154.338,158.196,160.371,154.856,150.636,145.899,141.242,140.834,141.119,139.104,134.437,129.425,123.155,119.273,120.472,121.523,121.983,123.658,124.794,124.827,120.382,117.395,115.790,114.283,117.271,117.448,118.764,120.550,123.554,125.412,124.182,119.828,115.361,114.226,115.214,115.864,114.276,113.469,114.883,114.172,111.225,112.149,115.618,118.002,121.382,120.663,128.049)
> 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  4.9740391 176.7330   1.33895995
Feb 1  4.0241104 171.3791   0.31077123
Mar 1  5.3169609 166.0252   4.42580328
Apr 1  4.5643238 161.0871   5.37753515
May 1  4.5673490 156.1490   9.74860472
Jun 1  3.4139027 151.8027  14.88535345
Jul 1  0.9052478 147.4564   8.02731086
Aug 1 -3.6317032 143.5979 -15.67524496
Sep 1 -6.6309067 139.7395 -33.74854832
Oct 1 -9.4536530 137.3094 -41.18079378
Nov 1 -7.7318993 134.8794 -42.09153925
Dec 1 -0.3177680 135.1662  -6.61239856
Jan 2  4.9740391 135.4529  23.83006636
Feb 2  4.0241104 138.0336  20.34329915
Mar 2  5.3169609 140.6143   6.84775270
Apr 2  4.5643238 144.0521   7.38859176
May 2  4.5673490 147.4899   1.32976853
Jun 2  3.4139027 148.8173   0.95876273
Jul 2  0.9052478 150.1448  -2.21003438
Aug 2 -3.6317032 148.9644  -1.12171501
Sep 2 -6.6309067 147.7840   4.79985683
Oct 2 -9.4536530 145.6573   9.33830651
Nov 2 -7.7318993 143.5306  14.47225617
Dec 2 -0.3177680 141.1267   6.68007262
Jan 3  4.9740391 138.7227   0.12721330
Feb 3  4.0241104 136.0463  -5.31644557
Mar 3  5.3169609 133.3699  -6.95088367
Apr 3  4.5643238 130.3508  -8.61113500
May 3  4.5673490 127.3317  -6.38804863
Jun 3  3.4139027 125.0510  -2.96985620
Jul 3  0.9052478 122.7702   6.45754492
Aug 3 -3.6317032 122.2674   7.62133329
Sep 3 -6.6309067 121.7645  -4.81062586
Oct 3 -9.4536530 122.7573 -14.88665001
Nov 3 -7.7318993 123.7501 -10.26917417
Dec 3 -0.3177680 125.4483  -4.46551620
Jan 4  4.9740391 127.1465  -8.04553400
Feb 4  4.0241104 129.5718  -6.35094432
Mar 4  5.3169609 131.9972   9.41686612
Apr 4  4.5643238 135.2154   5.19928577
May 4  4.5673490 138.4336   5.20904312
Jun 4  3.4139027 141.1967   0.05937584
Jul 4  0.9052478 143.9598  -1.89508275
Aug 4 -3.6317032 145.6704   0.48529238
Sep 4 -6.6309067 147.3810   5.39191998
Oct 4 -9.4536530 148.4081   7.56757104
Nov 4 -7.7318993 149.4352   6.42472209
Dec 4 -0.3177680 150.2975  -1.18177221
Jan 5  4.9740391 151.1599  -5.95294228
Feb 5  4.0241104 152.0337  -3.66976300
Mar 5  5.3169609 152.9074  -2.53036294
Apr 5  4.5643238 153.6879   2.40978190
May 5  4.5673490 154.4684  -3.51573556
Jun 5  3.4139027 154.6550   0.19313638
Jul 5  0.9052478 154.8415  -1.40878299
Aug 5 -3.6317032 154.0364   7.79129402
Sep 5 -6.6309067 153.2313  13.77062350
Oct 5 -9.4536530 151.4490  12.86061534
Nov 5 -7.7318993 149.6668   8.70110717
Dec 5 -0.3177680 147.0033  -0.78651596
Jan 6  4.9740391 144.3398  -8.07181486
Feb 6  4.0241104 141.1284  -4.31855221
Mar 6  5.3169609 137.9171  -2.11506880
Apr 6  4.5643238 135.1736  -0.63390971
May 6  4.5673490 132.4301  -2.56041291
Jun 6  3.4139027 130.6330  -4.62186827
Jul 6  0.9052478 128.8359  -6.58611494
Aug 6 -3.6317032 127.4385  -4.53378145
Sep 6 -6.6309067 126.0411   1.06180450
Oct 6 -9.4536530 124.6251   6.35158508
Nov 6 -7.7318993 123.2090   6.50586566
Dec 6 -0.3177680 122.0252   1.95052382
Jan 7  4.9740391 120.8415  -1.02149378
Feb 7  4.0241104 120.1114   0.69148215
Mar 7  5.3169609 119.3814  -4.31632117
Apr 7  4.5643238 119.2228  -6.39213284
May 7  4.5673490 119.0643  -7.84160680
Jun 7  3.4139027 119.3288  -8.45970420
Jul 7  0.9052478 119.5933  -3.22759291
Aug 7 -3.6317032 119.8069   1.27275458
Sep 7 -6.6309067 120.0206   5.37435455
Oct 7 -9.4536530 119.9348  10.06887148
Nov 7 -7.7318993 119.8490  11.43688840
Dec 7 -0.3177680 119.4197   6.31010168
Jan 8  4.9740391 118.9903   0.21763919
Feb 8  4.0241104 118.2799  -2.47603655
Mar 8  5.3169609 117.5695  -7.52549154
Apr 8  4.5643238 116.8717  -7.21001935
May 8  4.5673490 116.1739  -5.52720945
Jun 8  3.4139027 115.7336  -3.28346178
Jul 8  0.9052478 115.2933  -1.92250540
Aug 8 -3.6317032 115.3113   1.78941502
Sep 8 -6.6309067 115.3293   6.18458792
Oct 8 -9.4536530 115.6896   7.93602139
Nov 8 -7.7318993 116.0499   2.90695485
Dec 8 -0.3177680 116.4356  -3.96882241
Jan 9  4.9740391 116.8212  -6.17727544
Feb 9  4.0241104 117.1390  -3.16106272
Mar 9  5.3169609 117.4567  -1.39162925
Apr 9  4.5643238 117.7322  -1.63352418
May 9  4.5673490 118.0077   5.47391860
> m$win
   s    t    l 
1011   19   13 
> m$deg
s t l 
0 1 1 
> m$jump
  s   t   l 
102   2   2 
> m$inner
[1] 2
> m$outer
[1] 0
> postscript(file="/var/www/rcomp/tmp/18fup1324139432.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) 
> plot(m,main=main)
> dev.off()
null device 
          1 
> mylagmax <- nx/2
> postscript(file="/var/www/rcomp/tmp/2zye51324139432.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) 
> op <- par(mfrow = c(2,2))
> acf(as.numeric(x),lag.max = mylagmax,main='Observed')
> acf(as.numeric(m$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/rcomp/tmp/3xijp1324139432.ps",horizontal=F,onefile=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/rcomp/tmp/4x1fr1324139432.ps",horizontal=F,onefile=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/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/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$jump['l'])
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/rcomp/tmp/5qj001324139432.tab") 
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,'Seasonal Decomposition by Loess - Time Series Components',6,TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'t',header=TRUE)
> a<-table.element(a,'Observed',header=TRUE)
> a<-table.element(a,'Fitted',header=TRUE)
> a<-table.element(a,'Seasonal',header=TRUE)
> a<-table.element(a,'Trend',header=TRUE)
> a<-table.element(a,'Remainder',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:nx) {
+ a<-table.row.start(a)
+ a<-table.element(a,i,header=TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]+m$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/rcomp/tmp/6sf2y1324139432.tab") 
> 
> try(system("convert tmp/18fup1324139432.ps tmp/18fup1324139432.png",intern=TRUE))
character(0)
> try(system("convert tmp/2zye51324139432.ps tmp/2zye51324139432.png",intern=TRUE))
character(0)
> try(system("convert tmp/3xijp1324139432.ps tmp/3xijp1324139432.png",intern=TRUE))
character(0)
> try(system("convert tmp/4x1fr1324139432.ps tmp/4x1fr1324139432.png",intern=TRUE))
character(0)
> 
> 
> proc.time()
   user  system elapsed 
  1.450   0.120   1.573