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(21790,13253,37702,30364,32609,30212,29965,28352,25814,22414,20506,28806,22228,13971,36845,35338,35022,34777,26887,23970,22780,17351,21382,24561,17409,11514,31514,27071,29462,26105,22397,23843,21705,18089,20764,25316,17704,15548,28029,29383,36438,32034,22679,24319,18004,17537,20366,22782,19169,13807,29743,25591,29096,26482,22405,27044,17970,18730,19684,19785,18479,10698,31956,29506,34506,27165,26736,23691,18157,17328,18205,20995,17382,9367,31124,26551,30651,25859,25100,25778,20418,18688,20424,24776,19814,12738,31566,30111,30019,31934,25826,26835,20205,17789,20520,22518,15572,11509,25447,24090,27786,26195,20516,22759,19028,16971,20036,22485,18730)
> 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  -4969.104425 26269.65   489.45142
Feb  1 -11364.748378 26364.19 -1746.44079
Mar  1   7738.466697 26458.73  3504.80797
Apr  1   4879.487155 26554.50 -1069.98764
May  1   7965.508202 26650.28 -2006.78383
Jun  1   5219.757950 26740.98 -1748.73594
Jul  1    982.231510 26831.68  2151.08813
Aug  1   1469.023974 26925.16   -42.18361
Sep  1  -3220.852600 27018.64  2016.21369
Oct  1  -5311.681678 27168.50   557.18224
Nov  1  -3383.178393 27318.36 -3429.18157
Dec  1     -4.909644 27415.45  1395.45614
Jan  2  -4969.104425 27512.55  -315.44262
Feb  2 -11364.748378 27371.18 -2035.43457
Mar  2   7738.466697 27229.82  1876.71445
Apr  2   4879.487155 26984.90  3473.61080
May  2   7965.508202 26739.99   316.50657
Jun  2   5219.757950 26428.02  3129.21873
Jul  2    982.231510 26116.06  -211.29291
Aug  2   1469.023974 25682.18 -3181.20494
Sep  2  -3220.852600 25248.30   752.55207
Oct  2  -5311.681678 24729.14 -2066.46129
Nov  2  -3383.178393 24209.99   555.19299
Dec  2     -4.909644 23768.21   797.70086
Jan  3  -4969.104425 23326.43  -948.32775
Feb  3 -11364.748378 23100.86  -222.10960
Mar  3   7738.466697 22875.28   900.24951
Apr  3   4879.487155 22823.52  -632.00443
May  3   7965.508202 22771.75 -1275.25896
Jun  3   5219.757950 22832.97 -1947.72850
Jul  3    982.231510 22894.19 -1479.42185
Aug  3   1469.023974 23018.97  -644.99313
Sep  3  -3220.852600 23143.75  1782.10463
Oct  3  -5311.681678 23386.19    14.49079
Nov  3  -3383.178393 23628.63   518.54459
Dec  3     -4.909644 23893.78  1427.12778
Jan  4  -4969.104425 24158.93 -1485.82550
Feb  4 -11364.748378 24207.42  2705.32421
Mar  4   7738.466697 24255.92 -3965.38510
Apr  4   4879.487155 24139.45   364.06339
May  4   7965.508202 24022.98  4449.51129
Jun  4   5219.757950 23929.17  2885.06996
Jul  4    982.231510 23835.36 -2138.59518
Aug  4   1469.023974 23734.77  -884.79080
Sep  4  -3220.852600 23634.17 -2409.31738
Oct  4  -5311.681678 23403.78  -555.10154
Nov  4  -3383.178393 23173.40   575.78195
Dec  4     -4.909644 22936.57  -149.66129
Jan  5  -4969.104425 22699.75  1438.35901
Feb  5 -11364.748378 22659.73  2512.02153
Mar  5   7738.466697 22619.71  -615.17499
Apr  5   4879.487155 22597.79 -1886.28035
May  5   7965.508202 22575.88 -1445.38630
Jun  5   5219.757950 22486.59 -1224.34701
Jul  5    982.231510 22397.30  -974.53152
Aug  5   1469.023974 22399.30  3175.67891
Sep  5  -3220.852600 22401.29 -1210.44162
Oct  5  -5311.681678 22597.40  1444.28048
Nov  5  -3383.178393 22793.51   273.67021
Dec  5     -4.909644 22995.97 -3206.05602
Jan  6  -4969.104425 23198.42   249.68128
Feb  6 -11364.748378 23257.63 -1194.88371
Mar  6   7738.466697 23316.84   900.69227
Apr  6   4879.487155 23293.93  1332.58051
May  6   7965.508202 23271.02  3269.46817
Jun  6   5219.757950 23194.35 -1249.11112
Jul  6    982.231510 23117.68  2636.08577
Aug  6   1469.023974 22956.60  -734.62330
Sep  6  -3220.852600 22795.52 -1417.66333
Oct  6  -5311.681678 22582.07    57.60750
Nov  6  -3383.178393 22368.63  -780.45403
Dec  6     -4.909644 22244.62 -1244.70800
Jan  7  -4969.104425 22120.60   230.50155
Feb  7 -11364.748378 22179.98 -1448.23123
Mar  7   7738.466697 22239.36  1146.17696
Apr  7   4879.487155 22429.42  -757.90695
May  7   7965.508202 22619.48    66.00854
Jun  7   5219.757950 22856.08 -2216.84111
Jul  7    982.231510 23092.68  1025.08543
Aug  7   1469.023974 23317.31   991.67072
Sep  7  -3220.852600 23541.93    96.92504
Oct  7  -5311.681678 23724.63   275.05481
Nov  7  -3383.178393 23907.33  -100.14778
Dec  7     -4.909644 24067.25   713.65475
Jan  8  -4969.104425 24227.18   555.92080
Feb  8 -11364.748378 24306.09  -203.34001
Mar  8   7738.466697 24384.99  -557.45984
Apr  8   4879.487155 24366.01   865.50218
May  8   7965.508202 24347.03 -2293.53640
Jun  8   5219.757950 24203.49  2510.74838
Jul  8    982.231510 24059.96   783.80934
Aug  8   1469.023974 23772.50  1593.47163
Sep  8  -3220.852600 23485.05   -59.19705
Oct  8  -5311.681678 23070.89    29.79264
Nov  8  -3383.178393 22656.73  1246.44996
Dec  8     -4.909644 22213.48   309.43222
Jan  9  -4969.104425 21770.23 -1229.12198
Feb  9 -11364.748378 21446.56  1427.18946
Mar  9   7738.466697 21122.89 -3414.35813
Apr  9   4879.487155 21111.49 -1900.97284
May  9   7965.508202 21100.08 -1279.58815
Jun  9   5219.757950 21264.51  -289.27157
Jul  9    982.231510 21428.95 -1895.17880
Aug  9   1469.023974 21620.10  -330.12081
Sep  9  -3220.852600 21811.25   437.60621
Oct  9  -5311.681678 22042.91   239.76770
Nov  9  -3383.178393 22274.58  1144.59683
Dec  9     -4.909644 22544.81   -54.89670
Jan 10  -4969.104425 22815.03   884.07331
> m$win
   s    t    l 
1091   19   13 
> m$deg
s t l 
0 1 1 
> m$jump
  s   t   l 
110   2   2 
> m$inner
[1] 2
> m$outer
[1] 0
> postscript(file="/var/www/html/rcomp/tmp/1bisu1260549846.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) 
> plot(m,main=main)
> dev.off()
null device 
          1 
> mylagmax <- nx/2
> postscript(file="/var/www/html/rcomp/tmp/2km3f1260549846.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) 
> op <- par(mfrow = c(2,2))
> acf(as.numeric(x),lag.max = mylagmax,main='Observed')
> acf(as.numeric(m$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/336o81260549846.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/45sqe1260549846.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$jump['l'])
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/5n8kh1260549846.tab") 
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,'Seasonal Decomposition by Loess - Time Series Components',6,TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'t',header=TRUE)
> a<-table.element(a,'Observed',header=TRUE)
> a<-table.element(a,'Fitted',header=TRUE)
> a<-table.element(a,'Seasonal',header=TRUE)
> a<-table.element(a,'Trend',header=TRUE)
> a<-table.element(a,'Remainder',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:nx) {
+ a<-table.row.start(a)
+ a<-table.element(a,i,header=TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]+m$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/6lams1260549846.tab") 
> system("convert tmp/1bisu1260549846.ps tmp/1bisu1260549846.png")
> system("convert tmp/2km3f1260549846.ps tmp/2km3f1260549846.png")
> system("convert tmp/336o81260549846.ps tmp/336o81260549846.png")
> system("convert tmp/45sqe1260549846.ps tmp/45sqe1260549846.png")
> 
> 
> proc.time()
   user  system elapsed 
  1.198   0.652   1.886