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(7.3,7.6,7.5,7.6,7.9,7.9,8.1,8.2,8,7.5,6.8,6.5,6.6,7.6,8,8.1,7.7,7.5,7.6,7.8,7.8,7.8,7.5,7.5,7.1,7.5,7.5,7.6,7.7,7.7,7.9,8.1,8.2,8.2,8.2,7.9,7.3,6.9,6.6,6.7,6.9,7,7.1,7.2,7.1,6.9,7,6.8,6.4,6.7,6.6,6.4,6.3,6.2,6.5,6.8,6.8,6.4,6.1,5.8,6.1,7.2,7.3,6.9,6.1,5.8,6.2,7.1,7.7,7.9,7.7,7.4,7.5)
> 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 -0.40998337 7.822346 -0.112362174
Feb 1  0.01797253 7.789033 -0.207005476
Mar 1  0.03625113 7.755720 -0.291971475
Apr 1  0.00546667 7.718518 -0.123984621
May 1 -0.10865107 7.681316  0.327335515
Jun 1 -0.19704022 7.645888  0.451152140
Jul 1  0.01457047 7.610461  0.474968916
Aug 1  0.30914933 7.583763  0.307087634
Sep 1  0.37039492 7.557065  0.072539619
Oct 1  0.22037539 7.539057 -0.259432878
Nov 1 -0.01297728 7.521050 -0.708072244
Dec 1 -0.24552840 7.508511 -0.762982354
Jan 2 -0.40998337 7.495972 -0.485988605
Feb 2  0.01797253 7.497188  0.084839783
Mar 2  0.03625113 7.498403  0.465345474
Apr 2  0.00546667 7.530727  0.563806557
May 2 -0.10865107 7.563050  0.245600923
Jun 2 -0.19704022 7.596441  0.100598885
Jul 2  0.01457047 7.629833 -0.044403002
Aug 2  0.30914933 7.623985 -0.133134049
Sep 2  0.37039492 7.618137 -0.188531829
Oct 2  0.22037539 7.599492 -0.019867271
Nov 2 -0.01297728 7.580847 -0.067869581
Dec 2 -0.24552840 7.589604  0.155924260
Jan 3 -0.40998337 7.598361 -0.088378040
Feb 3  0.01797253 7.626781 -0.144754022
Mar 3  0.03625113 7.655202 -0.191452699
Apr 3  0.00546667 7.693503 -0.098969400
May 3 -0.10865107 7.731804  0.076847182
Jun 3 -0.19704022 7.763206  0.133834446
Jul 3  0.01457047 7.794608  0.090821861
Aug 3  0.30914933 7.777535  0.013315945
Sep 3  0.37039492 7.760462  0.069143296
Oct 3  0.22037539 7.692955  0.286669901
Nov 3 -0.01297728 7.625448  0.587529636
Dec 3 -0.24552840 7.540975  0.604553505
Jan 4 -0.40998337 7.456502  0.253481232
Feb 4  0.01797253 7.362925 -0.480897870
Mar 4  0.03625113 7.269349 -0.705599668
Apr 4  0.00546667 7.171051 -0.476517974
May 4 -0.10865107 7.072754 -0.064102997
Jun 4 -0.19704022 7.003872  0.193168611
Jul 4  0.01457047 6.934989  0.150440370
Aug 4  0.30914933 6.905712 -0.014861049
Sep 4  0.37039492 6.876434 -0.146829200
Oct 4  0.22037539 6.844642 -0.165017063
Nov 4 -0.01297728 6.812849  0.200128205
Dec 4 -0.24552840 6.763964  0.281564415
Jan 5 -0.40998337 6.715079  0.094904484
Feb 5  0.01797253 6.669622  0.012405109
Mar 5  0.03625113 6.624166 -0.060416962
Apr 5  0.00546667 6.572758 -0.178224920
May 5 -0.10865107 6.521351 -0.112699596
Jun 5 -0.19704022 6.475346 -0.078306058
Jul 5  0.01457047 6.429342  0.056087632
Aug 5  0.30914933 6.433742  0.057108621
Sep 5  0.37039492 6.438142 -0.008537122
Oct 5  0.22037539 6.463691 -0.284066499
Nov 5 -0.01297728 6.489240 -0.376262746
Dec 5 -0.24552840 6.496257 -0.450728416
Jan 6 -0.40998337 6.503274  0.006709773
Feb 6  0.01797253 6.531849  0.650178028
Mar 6  0.03625113 6.560425  0.703323587
Apr 6  0.00546667 6.656216  0.238316916
May 6 -0.10865107 6.752008 -0.543356473
Jun 6 -0.19704022 6.867886 -0.870845418
Jul 6  0.01457047 6.983764 -0.798334212
Aug 6  0.30914933 7.095414 -0.304563549
Sep 6  0.37039492 7.207065  0.122540382
Oct 6  0.22037539 7.324994  0.354630296
Nov 6 -0.01297728 7.442924  0.270053340
Dec 6 -0.24552840 7.570534  0.074994624
Jan 7 -0.40998337 7.698144  0.211839766
> 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$outer
[1] 0
> postscript(file="/var/www/html/rcomp/tmp/1f50l1259954670.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) 
> plot(m,main=main)
> dev.off()
null device 
          1 
> mylagmax <- nx/2
> postscript(file="/var/www/html/rcomp/tmp/2t81b1259954670.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) 
> op <- par(mfrow = c(2,2))
> acf(as.numeric(x),lag.max = mylagmax,main='Observed')
> acf(as.numeric(m$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/3axw31259954670.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/4o4mh1259954670.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$jump['l'])
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/514zh1259954670.tab") 
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,'Seasonal Decomposition by Loess - Time Series Components',6,TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'t',header=TRUE)
> a<-table.element(a,'Observed',header=TRUE)
> a<-table.element(a,'Fitted',header=TRUE)
> a<-table.element(a,'Seasonal',header=TRUE)
> a<-table.element(a,'Trend',header=TRUE)
> a<-table.element(a,'Remainder',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:nx) {
+ a<-table.row.start(a)
+ a<-table.element(a,i,header=TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]+m$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/6dg421259954670.tab") 
> system("convert tmp/1f50l1259954670.ps tmp/1f50l1259954670.png")
> system("convert tmp/2t81b1259954670.ps tmp/2t81b1259954670.png")
> system("convert tmp/3axw31259954670.ps tmp/3axw31259954670.png")
> system("convert tmp/4o4mh1259954670.ps tmp/4o4mh1259954670.png")
> 
> 
> proc.time()
   user  system elapsed 
  0.999   0.621   1.171