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(631923,654294,671833,586840,600969,625568,558110,630577,628654,603184,656255,600730,670326,678423,641502,625311,628177,589767,582471,636248,599885,621694,637406,595994,696308,674201,648861,649605,672392,598396,613177,638104,615632,634465,638686,604243,706669,677185,644328,644825,605707,600136,612166,599659,634210,618234,613576,627200,668973,651479,619661,644260,579936,601752,595376,588902,634341,594305,606200,610926,633685,639696,659451,593248,606677,599434,569578,629873,613438,604172,658328,612633,707372,739770,777535,685030,730234,714154,630872,719492,677023,679272,718317,645672)
> 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  42765.588 605046.4 -15888.97962
Feb 1  41422.349 607771.1   5100.52284
Mar 1  32697.821 610495.9  28639.31451
Apr 1  -1346.464 613106.8 -24920.35311
May 1  -2672.970 615717.8 -12075.79923
Jun 1 -17022.968 618141.8  24449.19821
Jul 1 -41740.342 620565.8 -20715.42921
Aug 1  -2797.382 622740.2  10634.16233
Sep 1  -9679.644 624914.7  13418.97696
Oct 1 -17977.662 626239.4  -5077.74078
Nov 1   5338.441 627564.1  23352.42001
Dec 1 -28986.777 627737.7   1979.05721
Jan 2  42765.588 627911.3   -350.88998
Feb 2  41422.349 627443.9   9556.74909
Mar 2  32697.821 626976.5 -18172.32261
Apr 2  -1346.464 626443.0    214.46843
May 2  -2672.970 625909.5   4940.48097
Jun 2 -17022.968 626098.1 -19308.14590
Jul 2 -41740.342 626286.7  -2075.39763
Aug 2  -2797.382 627214.8  11830.58999
Sep 2  -9679.644 628142.8 -18578.19932
Oct 2 -17977.662 629912.6   9759.08903
Nov 2   5338.441 631682.3    385.25589
Dec 2 -28986.777 633604.3  -8623.48329
Jan 3  42765.588 635526.2  18016.19314
Feb 3  41422.349 636925.8  -4147.11649
Mar 3  32697.821 638325.3 -22162.13690
Apr 3  -1346.464 639094.3  11857.21138
May 3  -2672.970 639863.2  35201.78117
Jun 3 -17022.968 640415.0 -24996.04258
Jul 3 -41740.342 640966.8  13950.50881
Aug 3  -2797.382 640884.6     16.80415
Sep 3  -9679.644 640802.3 -15490.67744
Oct 3 -17977.662 639605.4  12837.27303
Nov 3   5338.441 638408.5  -5060.89798
Dec 3 -28986.777 636799.0  -3569.24116
Jan 4  42765.588 635189.6  28713.83129
Feb 4  41422.349 634053.8   1708.80418
Mar 4  32697.821 632918.1 -21287.93371
Apr 4  -1346.464 631931.0  14240.48226
May 4  -2672.970 630943.9 -22563.88027
Jun 4 -17022.968 630033.6 -12874.60247
Jul 4 -41740.342 629123.3  24783.05047
Aug 4  -2797.382 627959.1 -25502.70272
Sep 4  -9679.644 626794.9  17094.76717
Oct 4 -17977.662 625579.7  10631.94291
Nov 4   5338.441 624364.6 -16127.00284
Dec 4 -28986.777 623089.9  33096.90106
Jan 5  42765.588 621815.2   4392.22059
Feb 5  41422.349 620626.1 -10569.43062
Mar 5  32697.821 619437.0 -32473.79260
Apr 5  -1346.464 618323.0  27283.43454
May 5  -2672.970 617209.1 -34600.11681
Jun 5 -17022.968 616245.4   2529.54063
Jul 5 -41740.342 615281.8  21834.57322
Aug 5  -2797.382 614765.6 -23066.17997
Sep 5  -9679.644 614249.4  29771.28992
Oct 5 -17977.662 613852.7  -1570.05157
Nov 5   5338.441 613456.1 -12594.51455
Dec 5 -28986.777 613047.0  26865.72820
Jan 6  42765.588 612638.0 -21718.61343
Feb 6  41422.349 612698.8 -14425.11435
Mar 6  32697.821 612759.5  13993.67394
Apr 6  -1346.464 614080.6 -19486.12375
May 6  -2672.970 615401.7  -6051.69995
Jun 6 -17022.968 618912.9  -2455.91295
Jul 6 -41740.342 622424.1 -11105.75082
Aug 6  -2797.382 629283.1   3387.31534
Sep 6  -9679.644 636142.0 -13024.39543
Oct 6 -17977.662 645255.5 -23105.80900
Nov 6   5338.441 654368.9  -1379.34406
Dec 6 -28986.777 663493.0 -21873.21202
Jan 7  42765.588 672617.1  -8010.66436
Feb 7  41422.349 680116.8  18230.88189
Mar 7  32697.821 687616.5  57220.71736
Apr 7  -1346.464 690718.5  -4342.01439
May 7  -2672.970 693820.5  39086.47536
Jun 7 -17022.968 696355.1  34821.88395
Jul 7 -41740.342 698889.7 -26277.33231
Aug 7  -2797.382 700834.0  21455.42986
Sep 7  -9679.644 702778.2 -16075.58490
Oct 7 -17977.662 704048.8  -6799.10942
Nov 7   5338.441 705319.3   7659.24457
Dec 7 -28986.777 706109.7 -31450.88551
> m$win
  s   t   l 
841  19  13 
> m$deg
s t l 
0 1 1 
> m$jump
 s  t  l 
85  2  2 
> m$inner
[1] 2
> m$outer
[1] 0
> postscript(file="/var/www/html/rcomp/tmp/1875d1292792886.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) 
> plot(m,main=main)
> dev.off()
null device 
          1 
> mylagmax <- nx/2
> postscript(file="/var/www/html/rcomp/tmp/2875d1292792886.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) 
> op <- par(mfrow = c(2,2))
> acf(as.numeric(x),lag.max = mylagmax,main='Observed')
> acf(as.numeric(m$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/html/rcomp/tmp/3tqpb1292792887.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/html/rcomp/tmp/4tqpb1292792887.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/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$jump['l'])
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/5pim21292792887.tab") 
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,'Seasonal Decomposition by Loess - Time Series Components',6,TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'t',header=TRUE)
> a<-table.element(a,'Observed',header=TRUE)
> a<-table.element(a,'Fitted',header=TRUE)
> a<-table.element(a,'Seasonal',header=TRUE)
> a<-table.element(a,'Trend',header=TRUE)
> a<-table.element(a,'Remainder',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:nx) {
+ a<-table.row.start(a)
+ a<-table.element(a,i,header=TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]+m$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/6bj3q1292792887.tab") 
> 
> try(system("convert tmp/1875d1292792886.ps tmp/1875d1292792886.png",intern=TRUE))
character(0)
> try(system("convert tmp/2875d1292792886.ps tmp/2875d1292792886.png",intern=TRUE))
character(0)
> try(system("convert tmp/3tqpb1292792887.ps tmp/3tqpb1292792887.png",intern=TRUE))
character(0)
> try(system("convert tmp/4tqpb1292792887.ps tmp/4tqpb1292792887.png",intern=TRUE))
character(0)
> 
> 
> proc.time()
   user  system elapsed 
  1.064   0.672   3.879