R version 2.12.0 (2010-10-15)
Copyright (C) 2010 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: i486-pc-linux-gnu (32-bit)

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> x <- c(13328,12873,14000,13477,14237,13674,13529,14058,12975,14326,14008,16193,14483,14011,15057,14884,15414,14440,14900,15074,14442,15307,14938,17193,15528,14765,15838,15723,16150,15486,15986,15983,15692,16490,15686,18897,16316,15636,17163,16534,16518,16375,16290,16352,15943,16362,16393,19051,16747,16320,17910,16961,17480,17049,16879,17473,16998,17307,17418,20169,17871,17226,19062,17804,19100,18522,18060,18869,18127,18871,18890,21263,19547,18450,20254,19240,20216,19420,19415,20018,18652,19978,19509,21971)
> par8 = 'FALSE'
> par7 = '1'
> par6 = ''
> par5 = '1'
> par4 = ''
> par3 = '0'
> par2 = '6'
> par1 = '12'
> main = 'Seasonal Decomposition by Loess'
> 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  -81.011651 13404.32    4.6956181
Feb 1 -722.806028 13496.04   99.7612221
Mar 1  419.097786 13587.77   -6.8713656
Apr 1  -68.648345 13679.50 -133.8540068
May 1  338.457074 13772.07  126.4753653
Jun 1 -346.167977 13864.63  155.5352067
Jul 1 -273.031996 13957.20 -155.1659827
Aug 1  -95.099412 14050.14  102.9605205
Sep 1 -790.226982 14143.08 -377.8528221
Oct 1  -19.768427 14236.02  109.7477098
Nov 1 -426.859504 14328.59  106.2725715
Dec 1 2069.083569 14421.15 -297.2367179
Jan 2  -83.180783 14513.72   52.4614179
Feb 2 -728.409827 14603.39  136.0167511
Mar 2  439.585156 14693.07  -75.6519428
Apr 2  -72.033135 14782.74  173.2926387
May 2  324.474965 14866.03  223.4910207
Jun 2 -340.800427 14949.33 -168.5271051
Jul 2 -281.870133 15032.62  149.2490837
Aug 2 -101.709997 15110.86   64.8516685
Sep 2 -777.830286 15189.10   30.7346778
Oct 2  -43.272187 15267.33   82.9392994
Nov 2 -430.179108 15348.89   19.2845399
Dec 2 2085.667483 15430.46 -323.1237330
Jan 3  -82.788820 15512.02   98.7708886
Feb 3 -735.830235 15598.23  -97.3951326
Mar 3  475.564957 15684.43 -321.9977607
Apr 3  -72.091486 15770.64   24.4512457
May 3  305.222849 15854.15   -9.3717103
Jun 3 -337.929649 15937.66 -113.7278346
Jul 3 -291.013526 16021.17  255.8474212
Aug 3 -112.711607 16095.17    0.5465145
Sep 3 -745.489607 16169.16  268.3255281
Oct 3  -83.642981 16243.16  330.4799141
Nov 3 -438.673302 16299.12 -174.4479074
Dec 3 2116.385167 16355.08  425.5354799
Jan 4  -86.137635 16411.04   -8.8998612
Feb 4 -768.617685 16449.29  -44.6709289
Mar 4  570.547562 16487.54  104.9127070
Apr 4  -96.754890 16525.79  104.9640407
May 4  245.406894 16562.22 -289.6228505
Jun 4 -310.488704 16598.64   86.8476394
Jul 4 -329.298194 16635.07  -15.7679792
Aug 4 -140.153282 16681.10 -188.9423697
Sep 4 -669.984754 16727.13 -114.1403770
Oct 4 -183.459639 16773.15 -227.6949719
Nov 4 -459.548308 16833.00   19.5520747
Dec 4 2223.134441 16892.84  -64.9722961
Jan 5  -66.836945 16952.68 -138.8425332
Feb 5 -822.518156 17028.28  114.2371750
Mar 5  663.616170 17103.88  142.5013448
Apr 5 -206.698240 17179.48  -11.7857486
May 5  338.804738 17266.69 -125.4932756
Jun 5 -256.494465 17353.89  -48.3986214
Jul 5 -398.324883 17441.10 -163.7727535
Aug 5  -59.307712 17535.88   -3.5699069
Sep 5 -770.882791 17630.66  138.2251881
Oct 5 -173.211934 17725.44 -245.2256517
Nov 5 -417.314847 17828.49    6.8254189
Dec 5 2165.071864 17931.54   72.3868652
Jan 6  -58.813089 18034.59 -104.7800252
Feb 6 -843.539052 18145.87  -76.3342889
Mar 6  694.773428 18257.15  110.0730037
Apr 6 -239.210711 18368.43 -325.2230852
May 6  369.581656 18479.63  250.7919871
Jun 6 -242.415670 18590.82  173.5967529
Jul 6 -415.543156 18702.01 -226.4683225
Aug 6  -26.413735 18808.27   87.1476134
Sep 6 -812.284720 18914.52   24.7639554
Oct 6 -166.475642 19020.78   16.7002343
Nov 6 -405.995104 19112.21  183.7886338
Dec 6 2142.805352 19203.64  -83.4428857
Jan 7  -57.231692 19295.07  309.1630961
Feb 7 -852.795925 19369.05  -66.2559544
Mar 7  713.146445 19443.04   97.8183924
Apr 7 -259.966913 19517.02  -17.0515331
May 7  388.572291 19589.04  238.3924190
Jun 7 -233.209240 19661.05   -7.8428943
Jul 7 -429.503136 19733.07  111.4341574
Aug 7   -9.835812 19802.96  224.8779231
Sep 7 -831.083869 19872.85 -389.7629309
Oct 7 -166.125958 19942.74  201.3902470
Nov 7 -398.553113 20010.29 -102.7408696
Dec 7 2129.403190 20077.85 -236.2554431
> m$win
 s  t  l 
 6 25 13 
> m$deg
s t l 
0 1 1 
> m$jump
s t l 
1 3 2 
> m$inner
[1] 2
> m$outer
[1] 0
> postscript(file="/var/www/rcomp/tmp/1p6li1322560915.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/rcomp/tmp/2fa9b1322560915.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$time.series[,'remainder']),na.action=na.pass,lag.max = mylagmax,main='Remainder')
> par(op)
> dev.off()
null device 
          1 
> postscript(file="/var/www/rcomp/tmp/3er7g1322560915.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) 
> op <- par(mfrow = c(2,2))
> spectrum(as.numeric(x),main='Observed')
> spectrum(as.numeric(m$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$time.series[,'remainder']),'remainder']),main='Remainder')
> par(op)
> dev.off()
null device 
          1 
> postscript(file="/var/www/rcomp/tmp/4biwg1322560915.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) 
> op <- par(mfrow = c(2,2))
> cpgram(as.numeric(x),main='Observed')
> cpgram(as.numeric(m$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$time.series[,'remainder']),'remainder']),main='Remainder')
> par(op)
> dev.off()
null device 
          1 
> 
> #Note: the /var/www/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/rcomp/createtable")
> 
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,'Seasonal Decomposition by Loess - Parameters',4,TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Component',header=TRUE)
> a<-table.element(a,'Window',header=TRUE)
> a<-table.element(a,'Degree',header=TRUE)
> a<-table.element(a,'Jump',header=TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Seasonal',header=TRUE)
> a<-table.element(a,m$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/rcomp/tmp/5r2ji1322560915.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/rcomp/tmp/6ay6z1322560915.tab") 
> 
> try(system("convert tmp/1p6li1322560915.ps tmp/1p6li1322560915.png",intern=TRUE))
character(0)
> try(system("convert tmp/2fa9b1322560915.ps tmp/2fa9b1322560915.png",intern=TRUE))
character(0)
> try(system("convert tmp/3er7g1322560915.ps tmp/3er7g1322560915.png",intern=TRUE))
character(0)
> try(system("convert tmp/4biwg1322560915.ps tmp/4biwg1322560915.png",intern=TRUE))
character(0)
> 
> 
> proc.time()
   user  system elapsed 
  1.500   0.060   1.559