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(14.2,13.5,11.9,14.6,15.6,14.1,14.9,14.2,14.6,17.2,15.4,14.3,17.5,14.5,14.4,16.6,16.7,16.6,16.9,15.7,16.4,18.4,16.9,16.5,18.3,15.1,15.7,18.1,16.8,18.9,19,18.1,17.8,21.5,17.1,18.7,19,16.4,16.9,18.6,19.3,19.4,17.6,18.6,18.1,20.4,18.1,19.6,19.9,19.2,17.8,19.2,22,21.1,19.5,22.2,20.9,22.2,23.5,21.5,24.3,22.8,20.3,23.7,23.3,19.6,18,17.3,16.8,18.2,16.5,16,18.4)
> 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  1.3325977 13.50913 -0.641726757
Feb 1 -0.9057133 13.70317  0.702540580
Mar 1 -1.7268289 13.89722 -0.270387495
Apr 1  0.5355348 14.08769 -0.023227526
May 1  0.9812323 14.27817  0.340598705
Jun 1  0.2777411 14.46457 -0.642311221
Jul 1 -0.3924188 14.65097  0.641447436
Aug 1 -0.3919299 14.83208 -0.240146772
Sep 1 -0.6747749 15.01318  0.261592971
Oct 1  1.5283432 15.17651  0.495144105
Nov 1 -0.2185396 15.33984  0.278696088
Dec 1 -0.3452442 15.48984 -0.844594754
Jan 2  1.3325977 15.63983  0.527567873
Feb 2 -0.9057133 15.78041 -0.374699010
Mar 2 -1.7268289 15.92099  0.205838695
Apr 2  0.5355348 16.05826  0.006202472
May 2  0.9812323 16.19554 -0.476767489
Jun 2  0.2777411 16.31953  0.002723960
Jul 2 -0.3924188 16.44353  0.848883992
Aug 2 -0.3919299 16.54127 -0.449344341
Sep 2 -0.6747749 16.63901  0.435761277
Oct 2  1.5283432 16.72081  0.150849019
Nov 2 -0.2185396 16.80260  0.315937611
Dec 2 -0.3452442 16.91897 -0.073724629
Jan 3  1.3325977 17.03534 -0.067933399
Feb 3 -0.9057133 17.19428 -1.188570995
Mar 3 -1.7268289 17.35323  0.073595997
Apr 3  0.5355348 17.52812  0.036348305
May 3  0.9812323 17.70300 -1.884233125
Jun 3  0.2777411 17.85024  0.772018700
Jul 3 -0.3924188 17.99748  1.394939109
Aug 3 -0.3919299 18.10761  0.384322795
Sep 3 -0.6747749 18.21773  0.257040432
Oct 3  1.5283432 18.28366  1.687998597
Nov 3 -0.2185396 18.34958 -1.031042389
Dec 3 -0.3452442 18.36624  0.679006103
Jan 4  1.3325977 18.38289 -0.715491936
Feb 4 -0.9057133 18.37231 -1.066592196
Mar 4 -1.7268289 18.36172  0.265112133
Apr 4  0.5355348 18.38604 -0.321574932
May 4  0.9812323 18.41036 -0.091595735
Jun 4  0.2777411 18.49288  0.629374658
Jul 4 -0.3924188 18.57541 -0.582986367
Aug 4 -0.3919299 18.69071  0.301216933
Sep 4 -0.6747749 18.80602 -0.031245818
Oct 4  1.5283432 18.93186 -0.060207629
Nov 4 -0.2185396 19.05771 -0.739168591
Dec 4 -0.3452442 19.22091  0.724329367
Jan 5  1.3325977 19.38412 -0.816719206
Feb 5 -0.9057133 19.60461  0.501101807
Mar 5 -1.7268289 19.82510 -0.298272593
Apr 5  0.5355348 20.08901 -1.424548604
May 5  0.9812323 20.35293  0.665841646
Jun 5  0.2777411 20.65021  0.172053807
Jul 5 -0.3924188 20.94748 -1.055065448
Aug 5 -0.3919299 21.24785  1.344080829
Sep 5 -0.6747749 21.54821  0.026561055
Oct 5  1.5283432 21.77156 -1.099898658
Nov 5 -0.2185396 21.99490  1.723642478
Dec 5 -0.3452442 21.98957 -0.144324715
Jan 6  1.3325977 21.98424  0.983161561
Feb 6 -0.9057133 21.72027  1.985444527
Mar 6 -1.7268289 21.45630  0.570532082
Apr 6  0.5355348 20.94533  2.219134088
May 6  0.9812323 20.43437  1.884402356
Jun 6  0.2777411 19.87140 -0.549138206
Jul 6 -0.3924188 19.30843 -0.916010184
Aug 6 -0.3919299 18.72525 -1.033321079
Sep 6 -0.6747749 18.14207 -0.667298025
Oct 6  1.5283432 17.53743 -0.865771912
Nov 6 -0.2185396 16.93278 -0.214244950
Dec 6 -0.3452442 16.32232  0.022926248
Jan 7  1.3325977 15.71185  1.355550915
> 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$outer
[1] 0
> postscript(file="/var/www/html/rcomp/tmp/1z47o1259922071.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) 
> plot(m,main=main)
> dev.off()
null device 
          1 
> mylagmax <- nx/2
> postscript(file="/var/www/html/rcomp/tmp/26ng01259922071.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) 
> op <- par(mfrow = c(2,2))
> acf(as.numeric(x),lag.max = mylagmax,main='Observed')
> acf(as.numeric(m$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/3qnfp1259922071.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/405wa1259922071.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$jump['l'])
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/5ca0n1259922071.tab") 
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,'Seasonal Decomposition by Loess - Time Series Components',6,TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'t',header=TRUE)
> a<-table.element(a,'Observed',header=TRUE)
> a<-table.element(a,'Fitted',header=TRUE)
> a<-table.element(a,'Seasonal',header=TRUE)
> a<-table.element(a,'Trend',header=TRUE)
> a<-table.element(a,'Remainder',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:nx) {
+ a<-table.row.start(a)
+ a<-table.element(a,i,header=TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]+m$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/6ccd81259922071.tab") 
> system("convert tmp/1z47o1259922071.ps tmp/1z47o1259922071.png")
> system("convert tmp/26ng01259922071.ps tmp/26ng01259922071.png")
> system("convert tmp/3qnfp1259922071.ps tmp/3qnfp1259922071.png")
> system("convert tmp/405wa1259922071.ps tmp/405wa1259922071.png")
> 
> 
> proc.time()
   user  system elapsed 
  1.008   0.597   1.200