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(11.73
+ ,11.75
+ ,11.39
+ ,11.54
+ ,9.62
+ ,9.82
+ ,9.94
+ ,9.9
+ ,9.8
+ ,9.86
+ ,10.5
+ ,10.33
+ ,10.16
+ ,9.91
+ ,9.96
+ ,10.03
+ ,9.55
+ ,9.51
+ ,9.8
+ ,10.08
+ ,10.2
+ ,10.23
+ ,10.2
+ ,10.07
+ ,10.01
+ ,10.05
+ ,9.92
+ ,10.03
+ ,10.18
+ ,10.1
+ ,10.16
+ ,10.15
+ ,10.13
+ ,10.09
+ ,10.18
+ ,10.06
+ ,9.65
+ ,9.74
+ ,9.53
+ ,9.5
+ ,9
+ ,9.15
+ ,9.32
+ ,9.62
+ ,9.59
+ ,9.37
+ ,9.35
+ ,9.32
+ ,9.49
+ ,9.52
+ ,9.59
+ ,9.35
+ ,9.2
+ ,9.57
+ ,9.78
+ ,9.79
+ ,9.57
+ ,9.53
+ ,9.65
+ ,9.36
+ ,9.4
+ ,9.32
+ ,9.31
+ ,9.19
+ ,9.39
+ ,9.28
+ ,9.28
+ ,9.31
+ ,9.28
+ ,9.31
+ ,9.35
+ ,9.19
+ ,9.07
+ ,8.96
+ ,8.69
+ ,8.58
+ ,8.56
+ ,8.47
+ ,8.46
+ ,8.75
+ ,8.95
+ ,9.33
+ ,9.51
+ ,9.561
+ ,9.94
+ ,9.9
+ ,9.275
+ ,9.56
+ ,9.779
+ ,9.746
+ ,9.991
+ ,9.98
+ ,10.195
+ ,10.31
+ ,10.25
+ ,9.871
+ ,10.06
+ ,9.894
+ ,9.59
+ ,9.64
+ ,9.89
+ ,9.53
+ ,9.388
+ ,9.16
+ ,9.418
+ ,9.57
+ ,9.857
+ ,9.877
+ ,9.76
+ ,9.76
+ ,9.695
+ ,9.475
+ ,9.262
+ ,9.097
+ ,8.55
+ ,8.16
+ ,7.532
+ ,7.325
+ ,6.749
+ ,7.13
+ ,6.995
+ ,7.346
+ ,7.73
+ ,7.837
+ ,7.514
+ ,7.58
+ ,6.83
+ ,6.617
+ ,6.715
+ ,6.63
+ ,6.891
+ ,7.002
+ ,7.09
+ ,7.36
+ ,7.477
+ ,7.826
+ ,7.79
+ ,7.578
+ ,7.204
+ ,7.198
+ ,7.685
+ ,7.795
+ ,7.46
+ ,7.274
+ ,7.33
+ ,7.655
+ ,7.767
+ ,7.84
+ ,7.424
+ ,7.54
+ ,7.351
+ ,6.735
+ ,6.777
+ ,6.679
+ ,7.34
+ ,6.978
+ ,6.92
+ ,6.628
+ ,6.385
+ ,5.984
+ ,6.268
+ ,6.596
+ ,6.395
+ ,6.715
+ ,6.804
+ ,6.929
+ ,6.846
+ ,6.992
+ ,6.774
+ ,6.75
+ ,6.485
+ ,6.27
+ ,6.47
+ ,6.78
+ ,6.71
+ ,6.141
+ ,6.72
+ ,6.68
+ ,6.371
+ ,6.097
+ ,6.27
+ ,6.447
+ ,6.37
+ ,6.446
+ ,6.54
+ ,6.374
+ ,6.33
+ ,6.63
+ ,6.498
+ ,6.485
+ ,6.36)
> par8 = 'TRUE'
> par7 = '1'
> par6 = ''
> par5 = '1'
> par4 = ''
> par3 = '0'
> par2 = 'periodic'
> par1 = '5'
> 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
Time Series:
Start = c(1, 1) 
End = c(39, 1) 
Frequency = 5 
         seasonal     trend    remainder
 1.0  0.043517418 11.895171 -0.208688509
 1.2 -0.008027948 11.611413  0.146615120
 1.4 -0.014274898 11.316809  0.087465861
 1.6 -0.029967442 11.017968  0.551999137
 1.8  0.008752855 10.711468 -1.100221048
 2.0  0.043517418 10.373767 -0.597284411
 2.2 -0.008027948  9.950097 -0.002068603
 2.4 -0.014274898  9.905987  0.008287661
 2.6 -0.029967442  9.931844 -0.101876156
 2.8  0.008752855 10.012005 -0.160757819
 3.0  0.043517418 10.057952  0.398530441
 3.2 -0.008027948 10.092638  0.245389757
 3.4 -0.014274898 10.119497  0.054778269
 3.6 -0.029967442 10.056209 -0.116241978
 3.8  0.008752855  9.937517  0.013729939
 4.0  0.043517418  9.840923  0.145559374
 4.2 -0.008027948  9.817205 -0.259177334
 4.4 -0.014274898  9.831305 -0.307030153
 4.6 -0.029967442  9.871494 -0.041526259
 4.8  0.008752855  9.980740  0.090507181
 5.0  0.043517418 10.093634  0.062848225
 5.2 -0.008027948 10.144293  0.093735421
 5.4 -0.014274898 10.141385  0.072889566
 5.6 -0.029967442 10.104632 -0.004664135
 5.8  0.008752855 10.054612 -0.053364465
 6.0  0.043517418 10.025596 -0.019113405
 6.2 -0.008027948 10.033223 -0.105195153
 6.4 -0.014274898 10.055919 -0.011644258
 6.6 -0.029967442 10.084740  0.125227750
 6.8  0.008752855 10.117320 -0.026072510
 7.0  0.043517418 10.133355 -0.016872792
 7.2 -0.008027948 10.133724  0.024303708
 7.4 -0.014274898 10.133947  0.010327967
 7.6 -0.029967442 10.106779  0.013188329
 7.8  0.008752855 10.034039  0.137207848
 8.0  0.043517418  9.940467  0.076015135
 8.2 -0.008027948  9.828529 -0.170500751
 8.4 -0.014274898  9.696599  0.057676234
 8.6 -0.029967442  9.565409 -0.005441077
 8.8  0.008752855  9.458748  0.032499155
 9.0  0.043517418  9.399226 -0.442743813
 9.2 -0.008027948  9.382494 -0.224466408
 9.4 -0.014274898  9.380875 -0.046600147
 9.6 -0.029967442  9.412607  0.237360309
 9.8  0.008752855  9.435118  0.146129096
10.0  0.043517418  9.434411 -0.107928829
10.2 -0.008027948  9.417057 -0.059029219
10.4 -0.014274898  9.420149 -0.085873900
10.6 -0.029967442  9.446245  0.073722195
10.8  0.008752855  9.460037  0.051209849
11.0  0.043517418  9.456403  0.090080048
11.2 -0.008027948  9.475199 -0.117171346
11.4 -0.014274898  9.516613 -0.302337652
11.6 -0.029967442  9.561945  0.038022925
11.8  0.008752855  9.611840  0.159407578
12.0  0.043517418  9.646070  0.100412803
12.2 -0.008027948  9.635020 -0.056992106
12.4 -0.014274898  9.576340 -0.032064908
12.6 -0.029967442  9.505035  0.174932249
12.8  0.008752855  9.447809 -0.096562260
13.0  0.043517418  9.390456 -0.033973474
13.2 -0.008027948  9.336281 -0.008252910
13.4 -0.014274898  9.313966  0.010308458
13.6 -0.029967442  9.301083 -0.081115943
13.8  0.008752855  9.291825  0.089422344
14.0  0.043517418  9.293600 -0.057117441
14.2 -0.008027948  9.298937 -0.010909002
14.4 -0.014274898  9.299418  0.024857108
14.6 -0.029967442  9.299734  0.010233090
14.8  0.008752855  9.282691  0.018556150
15.0  0.043517418  9.238774  0.067708959
15.2 -0.008027948  9.164948  0.033079936
15.4 -0.014274898  9.048698  0.035576597
15.6 -0.029967442  8.906654  0.083313101
15.8  0.008752855  8.771836 -0.090589354
16.0  0.043517418  8.652372 -0.115889069
16.2 -0.008027948  8.575217 -0.007189313
16.4 -0.014274898  8.576748 -0.092472734
16.6 -0.029967442  8.653750 -0.163782065
16.8  0.008752855  8.800954 -0.059706460
17.0  0.043517418  9.001102 -0.094619637
17.2 -0.008027948  9.226258  0.111770223
17.4 -0.014274898  9.447795  0.076480220
17.6 -0.029967442  9.633969 -0.043001641
17.8  0.008752855  9.725725  0.205522556
18.0  0.043517418  9.738807  0.117675374
18.2 -0.008027948  9.757665 -0.474637193
18.4 -0.014274898  9.751454 -0.177179495
18.6 -0.029967442  9.747436  0.061531393
18.8  0.008752855  9.819098 -0.081851266
19.0  0.043517418  9.930825  0.016657207
19.2 -0.008027948 10.041532 -0.053504464
19.4 -0.014274898 10.122958  0.086316458
19.6 -0.029967442 10.144857  0.195110939
19.8  0.008752855 10.130394  0.110853019
20.0  0.043517418 10.068976 -0.241493870
20.2 -0.008027948  9.955087  0.112940752
20.4 -0.014274898  9.849874  0.058401074
20.6 -0.029967442  9.780249 -0.160281644
20.8  0.008752855  9.697759 -0.066511446
21.0  0.043517418  9.605851  0.240631713
21.2 -0.008027948  9.528915  0.009112678
21.4 -0.014274898  9.471233 -0.068957608
21.6 -0.029967442  9.445229 -0.255261906
21.8  0.008752855  9.488973 -0.079726269
22.0  0.043517418  9.581807 -0.055324511
22.2 -0.008027948  9.684385  0.180643261
22.4 -0.014274898  9.753441  0.137833470
22.6 -0.029967442  9.767812  0.022155934
22.8  0.008752855  9.706333  0.044913758
23.0  0.043517418  9.592875  0.058607277
23.2 -0.008027948  9.432720  0.050307693
23.4 -0.014274898  9.192758  0.083516989
23.6 -0.029967442  8.882907  0.244059947
23.8  0.008752855  8.516325  0.024922634
24.0  0.043517418  8.110047  0.006435783
24.2 -0.008027948  7.738785 -0.198756764
24.4 -0.014274898  7.477497 -0.138221756
24.6 -0.029967442  7.301269 -0.522301771
24.8  0.008752855  7.236298 -0.115050810
25.0  0.043517418  7.256564 -0.305081356
25.2 -0.008027948  7.370251 -0.016223257
25.4 -0.014274898  7.468084  0.276191017
25.6 -0.029967442  7.512973  0.353994686
25.8  0.008752855  7.442752  0.062495616
26.0  0.043517418  7.270198  0.266284703
26.2 -0.008027948  7.058388 -0.220359754
26.4 -0.014274898  6.880042 -0.248766736
26.6 -0.029967442  6.770232 -0.025264262
26.8  0.008752855  6.782250 -0.161003228
27.0  0.043517418  6.872692 -0.025209154
27.2 -0.008027948  7.004489  0.005538495
27.4 -0.014274898  7.166656 -0.062381583
27.6 -0.029967442  7.341442  0.048525389
27.8  0.008752855  7.490576 -0.022328429
28.0  0.043517418  7.579487  0.202996004
28.2 -0.008027948  7.592204  0.205824206
28.4 -0.014274898  7.556410  0.035864846
28.6 -0.029967442  7.536944 -0.302976093
28.8  0.008752855  7.524463 -0.335215756
29.0  0.043517418  7.504618  0.136864548
29.2 -0.008027948  7.509314  0.293713484
29.4 -0.014274898  7.497391 -0.023116149
29.6 -0.029967442  7.482908 -0.178940334
29.8  0.008752855  7.505278 -0.184030455
30.0  0.043517418  7.563659  0.047823606
30.2 -0.008027948  7.604690  0.170338316
30.4 -0.014274898  7.611819  0.242455593
30.6 -0.029967442  7.548143 -0.094175935
30.8  0.008752855  7.391231  0.140016502
31.0  0.043517418  7.198895  0.108587995
31.2 -0.008027948  7.015806 -0.272778003
31.4 -0.014274898  6.872271 -0.080996362
31.6 -0.029967442  6.830345 -0.121378038
31.8  0.008752855  6.824217  0.507029712
32.0  0.043517418  6.808865  0.125618018
32.2 -0.008027948  6.776803  0.151224907
32.4 -0.014274898  6.645929 -0.003654029
32.6 -0.029967442  6.531712 -0.116744854
32.8  0.008752855  6.473253 -0.498005444
33.0  0.043517418  6.444885 -0.220402275
33.2 -0.008027948  6.465062  0.138966150
33.4 -0.014274898  6.563447 -0.154172469
33.6 -0.029967442  6.672816  0.072151360
33.8  0.008752855  6.759640  0.035607625
34.0  0.043517418  6.834180  0.051302818
34.2 -0.008027948  6.862564 -0.008535773
34.4 -0.014274898  6.840878  0.165397086
34.6 -0.029967442  6.769838  0.034129464
34.8  0.008752855  6.665066  0.076181040
35.0  0.043517418  6.585269 -0.143786630
35.2 -0.008027948  6.560749 -0.282721199
35.4 -0.014274898  6.570984 -0.086709151
35.6 -0.029967442  6.628783  0.181184604
35.8  0.008752855  6.682671  0.018575992
36.0  0.043517418  6.696420 -0.598937176
36.2 -0.008027948  6.650399  0.077628799
36.4 -0.014274898  6.566850  0.127424446
36.6 -0.029967442  6.462371 -0.061403558
36.8  0.008752855  6.391621 -0.303374154
37.0  0.043517418  6.348612 -0.122129814
37.2 -0.008027948  6.354899  0.100128786
37.4 -0.014274898  6.405504 -0.021228887
37.6 -0.029967442  6.429020  0.046947711
37.8  0.008752855  6.429891  0.101356436
38.0  0.043517418  6.447922 -0.117439745
38.2 -0.008027948  6.462932 -0.124904103
38.4 -0.014274898  6.458057  0.186217796
38.6 -0.029967442  6.449573  0.078394755
38.8  0.008752855  6.441992  0.034254655
39.0  0.043517418  6.430872 -0.114389049
> m$win
   s    t    l 
1911    9    5 
> m$deg
s t l 
0 1 1 
> m$jump
  s   t   l 
192   1   1 
> m$inner
[1] 1
> m$outer
[1] 15
> postscript(file="/var/www/rcomp/tmp/1mdz61322232436.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) 
> plot(m,main=main)
> dev.off()
null device 
          1 
> mylagmax <- nx/2
> postscript(file="/var/www/rcomp/tmp/2hos11322232436.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) 
> op <- par(mfrow = c(2,2))
> acf(as.numeric(x),lag.max = mylagmax,main='Observed')
> acf(as.numeric(m$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/3iid31322232436.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/4k0ge1322232436.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$jump['l'])
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/rcomp/tmp/5mkiq1322232436.tab") 
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,'Seasonal Decomposition by Loess - Time Series Components',6,TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'t',header=TRUE)
> a<-table.element(a,'Observed',header=TRUE)
> a<-table.element(a,'Fitted',header=TRUE)
> a<-table.element(a,'Seasonal',header=TRUE)
> a<-table.element(a,'Trend',header=TRUE)
> a<-table.element(a,'Remainder',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:nx) {
+ a<-table.row.start(a)
+ a<-table.element(a,i,header=TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]+m$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/6l9ou1322232436.tab") 
> 
> try(system("convert tmp/1mdz61322232436.ps tmp/1mdz61322232436.png",intern=TRUE))
character(0)
> try(system("convert tmp/2hos11322232436.ps tmp/2hos11322232436.png",intern=TRUE))
character(0)
> try(system("convert tmp/3iid31322232436.ps tmp/3iid31322232436.png",intern=TRUE))
character(0)
> try(system("convert tmp/4k0ge1322232436.ps tmp/4k0ge1322232436.png",intern=TRUE))
character(0)
> 
> 
> proc.time()
   user  system elapsed 
  2.260   0.130   2.356