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(3464,2582,3015,2629,1881,2654,2518,1612,1631,1980,2320,988,3395,3017,3631,2570,2403,2465,1711,1541,1751,2103,1410,823,3471,2207,2483,2334,2273,2247,1667,1692,1779,2000,1934,851,3314,3178,3360,2787,2474,2563,2528,1649,1902,2141,1471,1178,3038,2633,2636,2708,2252,2579,2321,1490,2076,2333,1566,961,3683,2904,3097,3269,2491,2692,2293,1994,2069,2540,2091,1109,4761,3051,3737,3505,2837,3419,2658,2133,2276,2403,1975,996,4505,3890,4352,3288,3314,2830,2414,2049,1981,2527,1813,926,4277,3301,3773,3175,3045,2933,2544,2324,2229,2844,2175,1055)
> 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  1344.52855 2052.612   66.859046
Feb 1   541.62184 2088.509  -48.130553
Mar 1   901.60377 2124.405  -11.008791
Apr 1   471.46820 2158.790   -1.258447
May 1    99.55451 2193.175 -411.729978
Jun 1   251.83216 2225.630  176.537616
Jul 1  -167.00130 2258.085  426.916327
Aug 1  -636.38595 2290.527  -42.141142
Sep 1  -507.99260 2322.969 -183.976601
Oct 1  -162.81861 2346.652 -203.833634
Nov 1  -627.97800 2370.335  577.642716
Dec 1 -1508.43279 2364.633  131.799433
Jan 2  1344.52855 2358.931 -308.459972
Feb 2   541.62184 2338.862  136.516216
Mar 2   901.60377 2318.792  410.603766
Apr 2   471.46820 2297.363 -198.831174
May 2    99.55451 2275.933   27.512011
Jun 2   251.83216 2247.303  -34.135280
Jul 2  -167.00130 2218.673 -340.671453
Aug 2  -636.38595 2179.516   -2.130429
Sep 2  -507.99260 2140.360  118.632605
Oct 2  -162.81861 2103.447  162.371817
Nov 2  -627.97800 2066.534  -28.555589
Dec 2 -1508.43279 2047.270  284.163157
Jan 3  1344.52855 2028.006   98.465780
Feb 3   541.62184 2022.941 -357.563275
Mar 3   901.60377 2017.877 -436.480969
Apr 3   471.46820 2026.388 -163.856264
May 3    99.55451 2034.899  138.546566
Jun 3   251.83216 2062.455  -67.287052
Jul 3  -167.00130 2090.011 -256.009554
Aug 3  -636.38595 2135.856  192.529516
Sep 3  -507.99260 2181.702  105.290596
Oct 3  -162.81861 2225.964  -63.145147
Nov 3  -627.97800 2270.226  291.752493
Dec 3 -1508.43279 2302.312   57.121108
Jan 4  1344.52855 2334.398 -364.926398
Feb 4   541.62184 2353.565  282.812697
Mar 4   901.60377 2372.733   85.663155
Apr 4   471.46820 2376.416  -60.883837
May 4    99.55451 2380.098   -5.652704
Jun 4   251.83216 2369.302  -58.134533
Jul 4  -167.00130 2358.507  336.494756
Aug 4  -636.38595 2329.937  -44.550754
Sep 4  -507.99260 2301.367  108.625747
Oct 4  -162.81861 2268.934   34.884595
Nov 4  -627.97800 2236.501 -137.523174
Dec 4 -1508.43279 2217.494  468.939118
Jan 5  1344.52855 2198.486 -505.014712
Feb 5   541.62184 2194.925 -103.547140
Mar 5   901.60377 2191.364 -456.968205
Apr 5   471.46820 2203.369   33.162382
May 5    99.55451 2215.374  -62.928907
Jun 5   251.83216 2239.485   87.683238
Jul 5  -167.00130 2263.595  224.406500
Aug 5  -636.38595 2294.918 -168.532317
Sep 5  -507.99260 2326.242  257.750876
Oct 5  -162.81861 2352.163  143.655708
Nov 5  -627.97800 2378.084 -184.106078
Dec 5 -1508.43279 2393.216   76.217211
Jan 6  1344.52855 2408.347  -69.875622
Feb 6   541.62184 2423.350  -60.972331
Mar 6   901.60377 2438.354 -242.957677
Apr 6   471.46820 2461.255  336.276583
May 6    99.55451 2484.157  -92.711032
Jun 6   251.83216 2522.360  -82.191713
Jul 6  -167.00130 2560.563 -100.561276
Aug 6  -636.38595 2601.946   28.440276
Sep 6  -507.99260 2643.329  -66.336162
Oct 6  -162.81861 2682.379   20.439786
Nov 6  -627.97800 2721.429   -2.450882
Dec 6 -1508.43279 2757.811 -140.377725
Jan 7  1344.52855 2794.192  622.279310
Feb 7   541.62184 2816.619 -307.240787
Mar 7   901.60377 2839.046   -3.649523
Apr 7   471.46820 2837.500  196.031890
May 7    99.55451 2835.954  -98.508573
Jun 7   251.83216 2829.368  337.800172
Jul 7  -167.00130 2822.781    2.220035
Aug 7  -636.38595 2836.074  -66.687918
Sep 7  -507.99260 2849.366  -65.373861
Oct 7  -162.81861 2868.919 -303.100585
Nov 7  -627.97800 2888.472 -285.493927
Dec 7 -1508.43279 2891.064 -386.630743
Jan 8  1344.52855 2893.655  266.816318
Feb 8   541.62184 2887.290  461.088135
Mar 8   901.60377 2880.925  569.471314
Apr 8   471.46820 2866.469  -49.937658
May 8    99.55451 2852.014  362.431494
Jun 8   251.83216 2821.498 -243.330624
Jul 8  -167.00130 2790.983 -209.981625
Aug 8  -636.38595 2752.995  -67.608567
Sep 8  -507.99260 2715.006 -226.013500
Oct 8  -162.81861 2697.852   -8.033588
Nov 8  -627.97800 2680.698 -239.720293
Dec 8 -1508.43279 2687.517 -253.084611
Jan 9  1344.52855 2694.337  238.134948
Feb 9   541.62184 2715.354   44.024121
Mar 9   901.60377 2736.372  135.024657
Apr 9   471.46820 2751.091  -47.559181
May 9    99.55451 2765.810  179.635106
Jun 9   251.83216 2774.572  -93.403884
Jul 9  -167.00130 2783.333  -72.331756
Aug 9  -636.38595 2790.162  170.223946
Sep 9  -507.99260 2796.991  -59.998342
Oct 9  -162.81861 2802.372  204.446470
Nov 9  -627.97800 2807.753   -4.775336
Dec 9 -1508.43279 2811.732 -248.299474
> m$win
   s    t    l 
1081   19   13 
> m$deg
s t l 
0 1 1 
> m$jump
  s   t   l 
109   2   2 
> m$inner
[1] 2
> m$outer
[1] 0
> postscript(file="/var/fisher/rcomp/tmp/1ihty1355413952.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) 
> plot(m,main=main)
> dev.off()
null device 
          1 
> mylagmax <- nx/2
> postscript(file="/var/fisher/rcomp/tmp/2kt1m1355413952.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) 
> op <- par(mfrow = c(2,2))
> acf(as.numeric(x),lag.max = mylagmax,main='Observed')
> acf(as.numeric(m$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/fisher/rcomp/tmp/3a3nn1355413952.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/fisher/rcomp/tmp/4dxru1355413952.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/fisher/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/fisher/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$jump['l'])
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/fisher/rcomp/tmp/5lit21355413952.tab") 
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,'Seasonal Decomposition by Loess - Time Series Components',6,TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'t',header=TRUE)
> a<-table.element(a,'Observed',header=TRUE)
> a<-table.element(a,'Fitted',header=TRUE)
> a<-table.element(a,'Seasonal',header=TRUE)
> a<-table.element(a,'Trend',header=TRUE)
> a<-table.element(a,'Remainder',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:nx) {
+ a<-table.row.start(a)
+ a<-table.element(a,i,header=TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]+m$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/fisher/rcomp/tmp/6fomf1355413952.tab") 
> 
> try(system("convert tmp/1ihty1355413952.ps tmp/1ihty1355413952.png",intern=TRUE))
character(0)
> try(system("convert tmp/2kt1m1355413952.ps tmp/2kt1m1355413952.png",intern=TRUE))
character(0)
> try(system("convert tmp/3a3nn1355413952.ps tmp/3a3nn1355413952.png",intern=TRUE))
character(0)
> try(system("convert tmp/4dxru1355413952.ps tmp/4dxru1355413952.png",intern=TRUE))
character(0)
> 
> 
> proc.time()
   user  system elapsed 
  2.633   0.645   3.265