R version 2.15.2 (2012-10-26) -- "Trick or Treat"
Copyright (C) 2012 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: i686-pc-linux-gnu (32-bit)

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> x <- c(41,39,50,40,43,38,44,35,39,35,29,49,50,59,63,32,39,47,53,60,57,52,70,90,74,62,55,84,94,70,108,139,120,97,126,149,158,124,140,109,114,77,120,133,110,92,97,78,99,107,112,90,98,125,155,190,236,189,174,178,136,161,171,149,184,155,276,224,213,279,268,287,238,213,257,293,212,246,353,339,308,247,257,322,298,273,312,249,286,279,309,401,309,328,353,354,327,324,285,243,241,287,355,460,364,487,452,391,500,451,375,372,302,316,398,394,431,431)
> par8 = 'FALSE'
> par7 = '1'
> par6 = ''
> par5 = '1'
> par4 = ''
> par3 = '0'
> par2 = 'periodic'
> par1 = '12'
> main = 'Seasonal Decomposition by Loess'
> par8 <- 'FALSE'
> par7 <- '1'
> par6 <- ''
> par5 <- '1'
> par4 <- ''
> par3 <- '0'
> par2 <- 'periodic'
> par1 <- '12'
> #'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   6.795182  53.14542 -18.9406054
Feb  1  -6.581742  51.18324  -5.6014961
Mar  1  -8.458645  49.22105   9.2375931
Apr  1 -27.009457  47.56188  19.4475762
May  1 -34.460255  45.90271  31.5575457
Jun  1 -34.257676  44.79473  27.4629504
Jul  1  16.344927  43.68674 -16.0316703
Aug  1  34.246572  42.79296 -42.0395280
Sep  1  12.948213  41.89917 -15.8473824
Oct  1  15.571660  41.38137 -21.9530312
Nov  1  10.072214  40.86357 -21.9357865
Dec  1  14.789013  42.63180  -8.4208123
Jan  2   6.795182  44.40003  -1.1952087
Feb  2  -6.581742  46.76404  18.8176976
Mar  2  -8.458645  49.12806  22.3305838
Apr  2 -27.009457  50.95435   8.0551044
May  2 -34.460255  52.78064  20.6796113
Jun  2 -34.257676  54.13202  27.1256567
Jul  2  16.344927  55.48340 -18.8283233
Aug  2  34.246572  56.36611 -30.6126858
Sep  2  12.948213  57.24883 -13.1970449
Oct  2  15.571660  59.69693 -23.2685887
Nov  2  10.072214  62.14502  -2.2172390
Dec  2  14.789013  66.76258   8.4484047
Jan  3   6.795182  71.38014  -4.1753222
Feb  3  -6.581742  76.96848  -8.3867374
Mar  3  -8.458645  82.55682 -19.0981727
Apr  3 -27.009457  87.40553  23.6039300
May  3 -34.460255  92.25424  36.2060190
Jun  3 -34.257676  97.16745   7.0902257
Jul  3  16.344927 102.08067 -10.4255930
Aug  3  34.246572 107.01026  -2.2568309
Sep  3  12.948213 111.93985  -4.8880655
Oct  3  15.571660 115.66810 -34.2397645
Nov  3  10.072214 119.39636  -3.4685700
Dec  3  14.789013 121.42771  12.7832759
Jan  4   6.795182 123.45907  27.7457512
Feb  4  -6.581742 123.89786   6.6838798
Mar  4  -8.458645 124.33666  24.1219884
Apr  4 -27.009457 122.13410  13.8753552
May  4 -34.460255 119.93155  28.5287084
Jun  4 -34.257676 115.49988  -4.2422044
Jul  4  16.344927 111.06822  -7.4131425
Aug  4  34.246572 107.10818  -8.3547484
Sep  4  12.948213 103.14814  -6.0963510
Oct  4  15.571660 101.88402 -25.4556829
Nov  4  10.072214 100.61991 -13.6921213
Dec  4  14.789013 102.94997 -39.7389826
Jan  5   6.795182 105.28003 -13.0752145
Feb  5  -6.581742 111.61392   1.9678219
Mar  5  -8.458645 117.94781   2.5108382
Apr  5 -27.009457 126.26376  -9.2543065
May  5 -34.460255 134.57972  -2.1194649
Jun  5 -34.257676 141.57800  17.6796777
Jul  5  16.344927 148.57628  -9.9212050
Aug  5  34.246572 153.39384   2.3595863
Sep  5  12.948213 158.21141  64.8403810
Oct  5  15.571660 162.57463  10.8537093
Nov  5  10.072214 166.93785  -3.0100689
Dec  5  14.789013 171.49844  -8.2874574
Jan  6   6.795182 176.05903 -46.8542164
Feb  6  -6.581742 180.25100 -12.6692616
Mar  6  -8.458645 184.44297  -4.9843269
Apr  6 -27.009457 190.34372 -14.3342673
May  6 -34.460255 196.24448  22.2157787
Jun  6 -34.257676 204.23438 -14.9767025
Jul  6  16.344927 212.22428  47.4307909
Aug  6  34.246572 219.78799 -30.0345645
Sep  6  12.948213 227.35170 -27.2999166
Oct  6  15.571660 233.97095  29.4573857
Nov  6  10.072214 240.59020  17.3375816
Dec  6  14.789013 247.48110  24.7298889
Jan  7   6.795182 254.37199 -23.1671744
Feb  7  -6.581742 260.70619 -41.1244437
Mar  7  -8.458645 267.04038  -1.5817331
Apr  7 -27.009457 270.55989  49.4495701
May  7 -34.460255 274.07940 -27.6191404
Jun  7 -34.257676 276.42375   3.8339222
Jul  7  16.344927 278.76811  57.8869593
Aug  7  34.246572 281.47189  23.2815368
Sep  7  12.948213 284.17567  10.8761176
Oct  7  15.571660 285.81417 -54.3858263
Nov  7  10.072214 287.45266 -40.5248768
Dec  7  14.789013 288.99949  18.2114999
Jan  8   6.795182 290.54631   0.6585060
Feb  8  -6.581742 293.37440 -13.7926600
Mar  8  -8.458645 296.20249  24.2561539
Apr  8 -27.009457 300.87801 -24.8685545
May  8 -34.460255 305.55353  14.9067233
Jun  8 -34.257676 309.72862   3.5290613
Jul  8  16.344927 313.90370 -21.2486260
Aug  8  34.246572 315.94328  50.8101464
Sep  8  12.948213 317.98286 -21.9310778
Oct  8  15.571660 317.47320  -5.0448575
Nov  8  10.072214 316.96353  25.9642562
Dec  8  14.789013 316.69582  22.5151627
Jan  9   6.795182 316.42812   3.7766987
Feb  9  -6.581742 319.72125  10.8604894
Mar  9  -8.458645 323.01438 -29.5557400
Apr  9 -27.009457 330.41729 -60.4078358
May  9 -34.460255 337.82020 -62.3599454
Jun  9 -34.257676 348.07192 -26.8142440
Jul  9  16.344927 358.32364 -19.6685680
Aug  9  34.246572 370.29951  55.4539174
Sep  9  12.948213 382.27538 -31.2235938
Oct  9  15.571660 391.45528  79.9730550
Nov  9  10.072214 400.63519  41.2925974
Dec  9  14.789013 403.53626 -27.3252707
Jan 10   6.795182 406.43733  86.7674905
Feb 10  -6.581742 403.59011  53.9916330
Mar 10  -8.458645 400.74289 -17.2842445
Apr 10 -27.009457 398.50122   0.5082332
May 10 -34.460255 396.25956 -59.7993027
Jun 10 -34.257676 393.28297 -43.0252888
Jul 10  16.344927 390.30637  -8.6513003
Aug 10  34.246572 386.99954 -27.2461112
Sep 10  12.948213 383.69271  34.3590813
Oct 10  15.571660 380.49498  34.9333593
> m$win
   s    t    l 
1181   19   13 
> m$deg
s t l 
0 1 1 
> m$jump
  s   t   l 
119   2   2 
> m$inner
[1] 2
> m$outer
[1] 0
> postscript(file="/var/wessaorg/rcomp/tmp/1zifa1356019388.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) 
> plot(m,main=main)
> dev.off()
null device 
          1 
> mylagmax <- nx/2
> postscript(file="/var/wessaorg/rcomp/tmp/2eelq1356019388.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) 
> op <- par(mfrow = c(2,2))
> acf(as.numeric(x),lag.max = mylagmax,main='Observed')
> acf(as.numeric(m$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/wessaorg/rcomp/tmp/3lkys1356019388.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/wessaorg/rcomp/tmp/4se5p1356019388.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/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/wessaorg/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$jump['l'])
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/51veo1356019388.tab") 
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,'Seasonal Decomposition by Loess - Time Series Components',6,TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'t',header=TRUE)
> a<-table.element(a,'Observed',header=TRUE)
> a<-table.element(a,'Fitted',header=TRUE)
> a<-table.element(a,'Seasonal',header=TRUE)
> a<-table.element(a,'Trend',header=TRUE)
> a<-table.element(a,'Remainder',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:nx) {
+ a<-table.row.start(a)
+ a<-table.element(a,i,header=TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]+m$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/wessaorg/rcomp/tmp/680dm1356019388.tab") 
> 
> try(system("convert tmp/1zifa1356019388.ps tmp/1zifa1356019388.png",intern=TRUE))
character(0)
> try(system("convert tmp/2eelq1356019388.ps tmp/2eelq1356019388.png",intern=TRUE))
character(0)
> try(system("convert tmp/3lkys1356019388.ps tmp/3lkys1356019388.png",intern=TRUE))
character(0)
> try(system("convert tmp/4se5p1356019388.ps tmp/4se5p1356019388.png",intern=TRUE))
character(0)
> 
> 
> proc.time()
   user  system elapsed 
  2.888   0.403   3.452