R version 2.10.1 (2009-12-14)
Copyright (C) 2009 The R Foundation for Statistical Computing
ISBN 3-900051-07-0

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> x <- c(20503,22885,26217,26583,27751,28158,27373,28367,26851,26733,26849,26733,27951,29781,32914,33488,35652,36488,35387,35676,34844,32447,31068,29010,29812,30951,32974,32936,34012,32946,31948,30599,27691,25073,23406,22248,22896,25317,26558,26471,27543,26198,24725,25005,23462,20780,19815,19761,21454,23899,24939,23580,24562,24696,23785,23812,21917,19713,19282,18788,21453,24482,27474,27264,27349,30632,29429,30084,26290,24379,23335,21346,21106,24514,28353,30805,31348,34556,33855,34787,32529,29998,29257,28155,30466,35704,39327,39351,42234,43630,43722,43121,37985,37135,34646,33026,35087,38846,42013,43908,42868,44423,44167,43636,44382,42142,43452,36912,42413,45344,44873,47510,49554,47369,45998,48140,48441,44928,40454,38661,37246,36843,36424,37594,38144,38737,34560,36080,33508,35462,33374,32110,35533,35532,37903,36763,40399,44164,44496,43110,43880,43930,44327)
> 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 -3122.8661 23088.23   537.63232
Feb  1  -947.8728 23643.94   188.92845
Mar  1  1034.8694 24199.65   982.47575
Apr  1  1405.4382 24777.79   399.76841
May  1  2515.9227 25355.93  -120.85461
Jun  1  3267.3838 25948.21 -1057.59751
Jul  1  2091.0950 26540.50 -1258.59051
Aug  1  2204.0588 27127.70  -964.76337
Sep  1   349.6048 27714.91 -1213.51842
Oct  1 -1406.3513 28342.38  -203.02746
Nov  1 -2695.2233 28969.84   574.37932
Dec  1 -4696.0588 29649.42  1779.63587
Jan  2 -3122.8661 30329.00   744.86425
Feb  2  -947.8728 30947.06  -218.18364
Mar  2  1034.8694 31565.11   314.01964
Apr  2  1405.4382 32022.03    60.52835
May  2  2515.9227 32478.96   657.12137
Jun  2  3267.3838 32745.93   474.68363
Jul  2  2091.0950 33012.91   282.99579
Aug  2  2204.0588 33110.45   361.49519
Sep  2   349.6048 33207.98  1286.41240
Oct  2 -1406.3513 33134.13   719.22013
Nov  2 -2695.2233 33060.28   702.94368
Dec  2 -4696.0588 32803.86   902.19482
Jan  3 -3122.8661 32547.45   387.41778
Feb  3  -947.8728 32104.60  -205.72291
Mar  3  1034.8694 31661.74   277.38757
Apr  3  1405.4382 31075.72   454.84533
May  3  2515.9227 30489.69  1006.38740
Jun  3  3267.3838 29884.94  -206.32402
Jul  3  2091.0950 29280.19   576.71446
Aug  3  2204.0588 28717.46  -322.51602
Sep  3   349.6048 28154.72  -813.32869
Oct  3 -1406.3513 27622.05 -1142.70223
Nov  3 -2695.2233 27089.38  -988.15996
Dec  3 -4696.0588 26576.39   367.67342
Jan  4 -3122.8661 26063.39   -44.52138
Feb  4  -947.8728 25612.61   652.26230
Mar  4  1034.8694 25161.83   361.29715
Apr  4  1405.4382 24791.24   274.32554
May  4  2515.9227 24420.64   606.43824
Jun  4  3267.3838 24159.21 -1228.59098
Jul  4  2091.0950 23897.78 -1263.87030
Aug  4  2204.0588 23741.10  -940.16290
Sep  4   349.6048 23584.43  -472.03769
Oct  4 -1406.3513 23466.75 -1280.40305
Nov  4 -2695.2233 23349.08  -838.85259
Dec  4 -4696.0588 23240.54  1216.52185
Jan  5 -3122.8661 23132.00  1444.86813
Feb  5  -947.8728 23017.93  1828.93950
Mar  5  1034.8694 22903.87  1000.26204
Apr  5  1405.4382 22760.55  -585.98449
May  5  2515.9227 22617.22  -571.14670
Jun  5  3267.3838 22520.87 -1092.25863
Jul  5  2091.0950 22424.53  -730.62067
Aug  5  2204.0588 22509.22  -901.28275
Sep  5   349.6048 22593.92 -1026.52702
Oct  5 -1406.3513 22891.68 -1772.32678
Nov  5 -2695.2233 23189.43 -1212.21071
Dec  5 -4696.0588 23636.42  -152.35915
Jan  6 -3122.8661 24083.40   492.46423
Feb  6  -947.8728 24565.89   863.98518
Mar  6  1034.8694 25048.37  1390.75730
Apr  6  1405.4382 25427.57   430.99314
May  6  2515.9227 25806.76  -973.68670
Jun  6  3267.3838 25974.55  1390.07008
Jul  6  2091.0950 26142.33  1195.57677
Aug  6  2204.0588 26197.49  1682.45039
Sep  6   349.6048 26252.65  -312.25817
Oct  6 -1406.3513 26391.29  -605.94144
Nov  6 -2695.2233 26529.93  -499.70889
Dec  6 -4696.0588 26833.95  -791.89256
Jan  7 -3122.8661 27137.97 -2909.10440
Feb  7  -947.8728 27595.44 -2133.56510
Mar  7  1034.8694 28052.91  -734.77463
Apr  7  1405.4382 28632.15   767.41278
May  7  2515.9227 29211.39  -379.31550
Jun  7  3267.3838 29872.43  1416.18393
Jul  7  2091.0950 30533.47  1230.43326
Aug  7  2204.0588 31288.66  1294.27702
Sep  7   349.6048 32043.86   135.53859
Oct  7 -1406.3513 32825.46 -1421.10462
Nov  7 -2695.2233 33607.06 -1654.83201
Dec  7 -4696.0588 34413.11 -1562.05373
Jan  8 -3122.8661 35219.17 -1630.30362
Feb  8  -947.8728 35976.54   675.32816
Mar  8  1034.8694 36733.92  1558.21111
Apr  8  1405.4382 37341.12   604.44083
May  8  2515.9227 37948.32  1769.75487
Jun  8  3267.3838 38328.79  2033.82789
Jul  8  2091.0950 38709.25  2921.65080
Aug  8  2204.0588 38922.22  1994.72513
Sep  8   349.6048 39135.18 -1499.78273
Oct  8 -1406.3513 39267.34  -725.99354
Nov  8 -2695.2233 39399.51 -2058.28853
Dec  8 -4696.0588 39524.55 -1802.48677
Jan  9 -3122.8661 39649.58 -1439.71319
Feb  9  -947.8728 39914.24  -120.37041
Mar  9  1034.8694 40178.91   799.22354
Apr  9  1405.4382 40655.50  1847.06619
May  9  2515.9227 41132.08  -780.00685
Jun  9  3267.3838 41643.16  -487.54425
Jul  9  2091.0950 42154.24   -78.33175
Aug  9  2204.0588 42593.18 -1161.24163
Sep  9   349.6048 43032.13  1000.26631
Oct  9 -1406.3513 43423.88   124.47105
Nov  9 -2695.2233 43815.63  2331.59162
Dec  9 -4696.0588 44144.72 -2536.66408
Jan 10 -3122.8661 44473.81  1062.05205
Feb 10  -947.8728 44732.78  1559.08892
Mar 10  1034.8694 44991.75 -1153.62304
Apr 10  1405.4382 45129.44   975.12286
May 10  2515.9227 45267.12  1770.95307
Jun 10  3267.3838 45151.52 -1049.90388
Jul 10  2091.0950 45035.92 -1129.01093
Aug 10  2204.0588 44523.06  1412.88488
Sep 10   349.6048 44010.20  4081.19849
Oct 10 -1406.3513 43205.77  3128.58383
Nov 10 -2695.2233 42401.34   747.88499
Dec 10 -4696.0588 41412.66  1944.40052
Jan 11 -3122.8661 40423.98   -55.11211
Feb 11  -947.8728 39353.48 -1562.60482
Mar 11  1034.8694 38282.98 -2893.84636
Apr 11  1405.4382 37399.72 -1211.15539
May 11  2515.9227 36516.46  -888.38010
Jun 11  3267.3838 36092.55  -622.93118
Jul 11  2091.0950 35668.64 -3199.73235
Aug 11  2204.0588 35610.78 -1734.83780
Sep 11   349.6048 35552.92 -2394.52544
Oct 11 -1406.3513 35720.52  1147.83255
Nov 11 -2695.2233 35888.12   181.10637
Dec 11 -4696.0588 36315.37   490.69266
Jan 12 -3122.8661 36742.62  1913.25077
Feb 12  -947.8728 37446.94  -967.06767
Mar 12  1034.8694 38151.27 -1283.13495
Apr 12  1405.4382 38996.46 -3638.90134
May 12  2515.9227 39841.66 -1958.58342
Jun 12  3267.3838 40696.66   199.95584
Jul 12  2091.0950 41551.66   853.24500
Aug 12  2204.0588 42435.93 -1529.99345
Sep 12   349.6048 43320.21   210.18591
Oct 12 -1406.3513 44243.69  1092.66547
Nov 12 -2695.2233 45167.16  1855.06085
> m$win
   s    t    l 
1431   19   13 
> m$deg
s t l 
0 1 1 
> m$jump
  s   t   l 
144   2   2 
> m$inner
[1] 2
> m$outer
[1] 0
> postscript(file="/var/yougetitorg/rcomp/tmp/1v1k51293659559.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) 
> plot(m,main=main)
> dev.off()
null device 
          1 
> mylagmax <- nx/2
> postscript(file="/var/yougetitorg/rcomp/tmp/2v1k51293659559.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) 
> op <- par(mfrow = c(2,2))
> acf(as.numeric(x),lag.max = mylagmax,main='Observed')
> acf(as.numeric(m$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/yougetitorg/rcomp/tmp/3v1k51293659559.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/yougetitorg/rcomp/tmp/46s1q1293659559.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/yougetitorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/yougetitorg/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$jump['l'])
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/yougetitorg/rcomp/tmp/5kkzg1293659559.tab") 
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,'Seasonal Decomposition by Loess - Time Series Components',6,TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'t',header=TRUE)
> a<-table.element(a,'Observed',header=TRUE)
> a<-table.element(a,'Fitted',header=TRUE)
> a<-table.element(a,'Seasonal',header=TRUE)
> a<-table.element(a,'Trend',header=TRUE)
> a<-table.element(a,'Remainder',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:nx) {
+ a<-table.row.start(a)
+ a<-table.element(a,i,header=TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]+m$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/yougetitorg/rcomp/tmp/653f41293659559.tab") 
> 
> try(system("convert tmp/1v1k51293659559.ps tmp/1v1k51293659559.png",intern=TRUE))
character(0)
> try(system("convert tmp/2v1k51293659559.ps tmp/2v1k51293659559.png",intern=TRUE))
character(0)
> try(system("convert tmp/3v1k51293659559.ps tmp/3v1k51293659559.png",intern=TRUE))
character(0)
> try(system("convert tmp/46s1q1293659559.ps tmp/46s1q1293659559.png",intern=TRUE))
character(0)
> 
> 
> proc.time()
   user  system elapsed 
  1.970   0.810   2.291