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(106370,109375,116476,123297,114813,117925,126466,131235,120546,123791,129813,133463,122987,125418,130199,133016,121454,122044,128313,131556,120027,123001,130111,132524,123742,124931,133646,136557,127509,128945,137191,139716,129083,131604,139413,143125,133948,137116,144864,149277,138796,143258,150034,154708,144888,148762,156500,161088,152772,158011,163318,169969,162269,165765,170600,174681,166364,170240,176150,182056,172218,177856,182253,188090,176863,183273,187969,194650,183036,189516,193805,200499,188142,193732,197126,205140,191751,196700,199784,207360,196101,200824,205743,212489,200810,203683,207286,210910,194915,217920)
> par8 = 'FALSE'
> par7 = '1'
> par6 = ''
> par5 = '1'
> par4 = ''
> par3 = '0'
> par2 = 'periodic'
> par1 = '4'
> 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
 1 Q1 -5726.950 110945.6  1151.349194
 1 Q2 -2377.862 112957.6 -1204.782744
 1 Q3  2198.458 114995.4  -717.832116
 1 Q4  5906.357 117127.2   263.425110
 2 Q1 -5726.950 119418.1  1121.841930
 2 Q2 -2377.862 121591.6 -1288.746049
 2 Q3  2198.458 123350.8   916.742379
 2 Q4  5906.357 124879.9   448.730330
 3 Q1 -5726.950 125937.7   335.211421
 3 Q2 -2377.862 126621.0  -452.126778
 3 Q3  2198.458 127204.8   409.723051
 3 Q4  5906.357 127753.3  -196.639693
 4 Q1 -5726.950 128000.5   713.481370
 4 Q2 -2377.862 127991.1  -195.270414
 4 Q3  2198.458 127709.0   291.510748
 4 Q4  5906.357 127156.1   -46.428633
 5 Q1 -5726.950 126430.6   750.367909
 5 Q2 -2377.862 125955.0 -1533.139037
 5 Q3  2198.458 125632.0   482.548749
 5 Q4  5906.357 125645.6     4.003220
 6 Q1 -5726.950 125883.1  -129.143902
 6 Q2 -2377.862 126275.7  -896.806020
 6 Q3  2198.458 126870.7  1041.839984
 6 Q4  5906.357 127625.9 -1008.239821
 7 Q1 -5726.950 128226.3  1242.608638
 7 Q2 -2377.862 129189.0 -1880.114464
 7 Q3  2198.458 130163.1  1284.436924
 7 Q4  5906.357 131250.7  -600.047243
 8 Q1 -5726.950 132079.4  1156.507299
 8 Q2 -2377.862 132935.8 -1612.897051
 8 Q3  2198.458 133554.4  1438.151670
 8 Q4  5906.357 134137.8  -328.160632
 9 Q1 -5726.950 134600.0   209.916425
 9 Q2 -2377.862 135329.6 -1347.745456
 9 Q3  2198.458 136392.4   822.174179
 9 Q4  5906.357 137751.1  -532.501904
10 Q1 -5726.950 139051.8   623.143328
10 Q2 -2377.862 140513.7 -1019.845287
10 Q3  2198.458 141919.8   745.772899
10 Q4  5906.357 143324.1    46.585719
11 Q1 -5726.950 144658.4  -135.496354
11 Q2 -2377.862 146004.6  -368.737527
11 Q3  2198.458 147465.9   369.617458
11 Q4  5906.357 148936.1  -134.411352
12 Q1 -5726.950 150375.4   239.558755
12 Q2 -2377.862 151986.3  -846.478278
12 Q3  2198.458 153767.7   533.853538
12 Q4  5906.357 155923.1  -741.472504
13 Q1 -5726.950 157975.2   523.730372
13 Q2 -2377.862 159938.2   450.636280
13 Q3  2198.458 162112.6  -993.044992
13 Q4  5906.357 164357.2  -294.542146
14 Q1 -5726.950 166377.7  1618.208029
14 Q2 -2377.862 167826.5   316.373801
14 Q3  2198.458 168759.4  -357.895254
14 Q4  5906.357 169850.6 -1075.915087
15 Q1 -5726.950 171192.8   898.141028
15 Q2 -2377.862 172793.5  -175.683260
15 Q3  2198.458 174386.6  -435.021958
15 Q4  5906.357 176101.6    47.994017
16 Q1 -5726.950 177883.0    61.930124
16 Q2 -2377.862 179387.5   846.378098
16 Q3  2198.458 180689.0  -634.450239
16 Q4  5906.357 181893.8   289.870358
17 Q1 -5726.950 183335.1  -745.155637
17 Q2 -2377.862 184859.3   791.553704
17 Q3  2198.458 186483.3  -712.746885
17 Q4  5906.357 187992.1   751.559268
18 Q1 -5726.950 189545.8  -782.881034
18 Q2 -2377.862 190972.3   921.554000
18 Q3  2198.458 192387.6  -781.107263
18 Q4  5906.357 193523.3  1069.353103
19 Q1 -5726.950 194508.6  -639.635459
19 Q2 -2377.862 195392.9   716.925075
19 Q3  2198.458 196484.0 -1556.412836
19 Q4  5906.357 197351.4  1882.284672
20 Q1 -5726.950 198093.9  -615.915390
20 Q2 -2377.862 198538.3   539.511749
20 Q3  2198.458 199373.6 -1788.037783
20 Q4  5906.357 200495.1   958.542863
21 Q1 -5726.950 201831.5    -3.566553
21 Q2 -2377.862 203100.8   101.049833
21 Q3  2198.458 204363.3  -818.715579
21 Q4  5906.357 205401.5  1181.101014
22 Q1 -5726.950 205982.2   554.768188
22 Q2 -2377.862 205879.8   181.057208
22 Q3  2198.458 205015.8    71.747769
22 Q4  5906.357 206835.8 -1832.121710
23 Q1 -5726.950 209662.9 -9020.974819
23 Q2 -2377.862 212716.7  7581.204047
> m$win
  s   t   l 
901   7   5 
> m$deg
s t l 
0 1 1 
> m$jump
 s  t  l 
91  1  1 
> m$inner
[1] 2
> m$outer
[1] 0
> postscript(file="/var/www/html/rcomp/tmp/1yq6c1259938384.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) 
> plot(m,main=main)
> dev.off()
null device 
          1 
> mylagmax <- nx/2
> postscript(file="/var/www/html/rcomp/tmp/206q01259938384.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) 
> op <- par(mfrow = c(2,2))
> acf(as.numeric(x),lag.max = mylagmax,main='Observed')
> acf(as.numeric(m$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/3712o1259938384.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/44x821259938384.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$jump['l'])
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/5kktq1259938384.tab") 
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,'Seasonal Decomposition by Loess - Time Series Components',6,TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'t',header=TRUE)
> a<-table.element(a,'Observed',header=TRUE)
> a<-table.element(a,'Fitted',header=TRUE)
> a<-table.element(a,'Seasonal',header=TRUE)
> a<-table.element(a,'Trend',header=TRUE)
> a<-table.element(a,'Remainder',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:nx) {
+ a<-table.row.start(a)
+ a<-table.element(a,i,header=TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]+m$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/63c7g1259938384.tab") 
> 
> system("convert tmp/1yq6c1259938384.ps tmp/1yq6c1259938384.png")
> system("convert tmp/206q01259938384.ps tmp/206q01259938384.png")
> system("convert tmp/3712o1259938384.ps tmp/3712o1259938384.png")
> system("convert tmp/44x821259938384.ps tmp/44x821259938384.png")
> 
> 
> proc.time()
   user  system elapsed 
  1.081   0.636   1.263