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)

R is free software and comes with ABSOLUTELY NO WARRANTY.
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Type 'license()' or 'licence()' for distribution details.

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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(42.33600
+ ,42.14710
+ ,40.25640
+ ,39.18980
+ ,39.13170
+ ,38.15070
+ ,38.27070
+ ,39.13350
+ ,40.12190
+ ,41.28450
+ ,42.57490
+ ,43.90190
+ ,43.18350
+ ,43.61880
+ ,44.76240
+ ,45.19720
+ ,44.38810
+ ,43.55520
+ ,43.56780
+ ,44.21350
+ ,45.14510
+ ,45.80790
+ ,42.32820
+ ,37.89990
+ ,34.79640
+ ,35.21440
+ ,36.37270
+ ,36.25020
+ ,36.82610
+ ,36.77230
+ ,36.90420
+ ,37.04940
+ ,36.82590
+ ,36.13570
+ ,36.03000
+ ,35.79270
+ ,35.91740
+ ,35.40080
+ ,35.17230
+ ,34.92110
+ ,35.02920
+ ,34.77390
+ ,34.89990
+ ,34.90540
+ ,34.56800
+ ,34.40600
+ ,34.45780
+ ,34.73160
+ ,34.26020
+ ,33.88490
+ ,34.05490
+ ,34.27550
+ ,34.13930
+ ,34.15870
+ ,34.53860
+ ,33.79870
+ ,33.49730
+ ,33.68020
+ ,34.32840
+ ,34.15380
+ ,33.91840
+ ,34.32620
+ ,34.77500
+ ,35.01190
+ ,34.55130
+ ,34.69510
+ ,35.47300
+ ,35.97940
+ ,36.47890
+ ,36.39100
+ ,36.67040
+ ,37.41620
+ ,37.11850
+ ,36.30010
+ ,35.70020
+ ,35.58590
+ ,35.67770
+ ,35.24080
+ ,34.80160
+ ,34.43890
+ ,34.98810
+ ,36.06800
+ ,36.35660
+ ,36.12540
+ ,34.87100
+ ,35.21990
+ ,34.33900
+ ,33.82340
+ ,34.51990
+ ,35.53000
+ ,35.79660
+ ,33.84840
+ ,33.98710
+ ,34.11610
+ ,33.82350
+ ,32.45400
+ ,31.87740
+ ,31.11500
+ ,31.04360
+ ,30.88680
+ ,31.30010
+ ,30.05070
+ ,28.67990
+ ,27.64380
+ ,27.23940
+ ,26.85490
+ ,27.01580
+ ,26.91880
+ ,26.49660
+ ,26.78500
+ ,26.83980
+ ,26.44780
+ ,25.17280
+ ,24.87840
+ ,25.40150
+ ,25.77160
+ ,26.11810
+ ,26.3969
+ ,26.65710
+ ,25.18390
+ ,23.83940
+ ,23.86190
+ ,24.25810
+ ,25.10980
+ ,26.16170
+ ,26.80870
+ ,25.65770
+ ,27.09820
+ ,27.46650
+ ,28.28900
+ ,28.69330
+ ,27.24010
+ ,27.27980
+ ,27.64040
+ ,26.83100
+ ,26.24670
+ ,25.22120
+ ,25.36530
+ ,26.25920
+ ,27.22790
+ ,26.33150
+ ,25.89810
+ ,26.68980)
> par8 = 'FALSE'
> par7 = '1'
> par6 = ''
> par5 = '1'
> par4 = ''
> par3 = '0'
> par2 = 'periodic'
> par1 = '12'
> main = 'Seasonal Decomposition by Loess'
> par1 <- 5
> 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(29, 3) 
Frequency = 5 
        seasonal    trend     remainder
 1.0  0.07224993 42.28696 -0.0232114226
 1.2  0.07358595 41.45491  0.6186060135
 1.4 -0.05486548 40.65578 -0.3445164720
 1.6 -0.06326401 39.88143 -0.6283666937
 1.8 -0.02770644 39.10631  0.0530998651
 2.0  0.07224993 38.83659 -0.7581410503
 2.2  0.07358595 38.97717 -0.7800536418
 2.4 -0.05486548 39.47308 -0.2847107536
 2.6 -0.06326401 40.32366 -0.1385002141
 2.8 -0.02770644 41.33239 -0.0201840429
 3.0  0.07224993 42.21279  0.2898562200
 3.2  0.07358595 42.93731  0.8909997361
 3.4 -0.05486548 43.56745 -0.3290833595
 3.6 -0.06326401 44.02574 -0.3436763831
 3.8 -0.02770644 44.24856  0.5415467437
 4.0  0.07224993 44.32066  0.8042935884
 4.2  0.07358595 44.27685  0.0376616742
 4.4 -0.05486548 44.17983 -0.5697667636
 4.6 -0.06326401 44.20956 -0.5784976741
 4.8 -0.02770644 44.33699 -0.0957823938
 5.0  0.07224993 44.08308  0.9897661359
 5.2  0.07358595 43.02642  2.7078981671
 5.4 -0.05486548 41.23473  1.1483371350
 5.6 -0.06326401 39.16950 -1.2063395041
 5.8 -0.02770644 37.35840 -2.5342919731
 6.0  0.07224993 36.26556 -1.1234062968
 6.2  0.07358595 36.02456  0.2745562912
 6.4 -0.05486548 36.28776  0.0173036684
 6.6 -0.06326401 36.58782  0.3015439389
 6.8 -0.02770644 36.76132  0.0386857705
 7.0  0.07224993 36.82198  0.0099688839
 7.2  0.07358595 36.74703  0.2287874397
 7.4 -0.05486548 36.58220  0.2985697224
 7.6 -0.06326401 36.36494 -0.1659790261
 7.8 -0.02770644 36.11861 -0.0609005877
 8.0  0.07224993 35.87433 -0.1538749585
 8.2  0.07358595 35.65900  0.1848105310
 8.4 -0.05486548 35.45957 -0.0039027884
 8.6 -0.06326401 35.26740 -0.0318340445
 8.8 -0.02770644 35.08443 -0.1356212595
 9.0  0.07224993 34.96369 -0.0067443698
 9.2  0.07358595 34.89819 -0.1978757761
 9.4 -0.05486548 34.82752  0.1272504331
 9.6 -0.06326401 34.73359  0.2350698580
 9.8 -0.02770644 34.65220 -0.0564977064
10.0  0.07224993 34.58076 -0.2470086911
10.2  0.07358595 34.48312 -0.0989085719
10.4 -0.05486548 34.37511  0.4113515495
10.6 -0.06326401 34.28907  0.0343940692
10.8 -0.02770644 34.21115 -0.2985480085
11.0  0.07224993 34.13671 -0.1540594146
11.2  0.07358595 34.13652  0.0653910569
11.4 -0.05486548 34.19346  0.0007049325
11.6 -0.06326401 34.16932  0.0526402702
11.8 -0.02770644 34.05372  0.5125875036
12.0  0.07224993 33.95713 -0.2306802790
12.2  0.07358595 33.92011 -0.4963991887
12.4 -0.05486548 33.90872 -0.1736522717
12.6 -0.06326401 33.96092  0.4307433184
12.8 -0.02770644 34.09691  0.0845942838
13.0  0.07224993 34.26654 -0.4203850436
13.2  0.07358595 34.41581 -0.1631956524
13.4 -0.05486548 34.54739  0.2824737168
13.6 -0.06326401 34.70052  0.3746488546
13.8 -0.02770644 34.89210 -0.3130929766
14.0  0.07224993 35.13177 -0.5089170999
14.2  0.07358595 35.44583 -0.0464115369
14.4 -0.05486548 35.82250  0.2117660063
14.6 -0.06326401 36.20844  0.3337205641
14.8 -0.02770644 36.54801 -0.1293029376
15.0  0.07224993 36.75621 -0.1580634353
15.2  0.07358595 36.77870  0.5639154774
15.4 -0.05486548 36.64868  0.5246864671
15.6 -0.06326401 36.39615 -0.0327854662
15.8 -0.02770644 36.05498 -0.3270738115
16.0  0.07224993 35.70985 -0.1962031766
16.2  0.07358595 35.41726  0.1868582602
16.4 -0.05486548 35.18433  0.1113398749
16.6 -0.06326401 35.05996 -0.1950923554
16.8 -0.02770644 35.11031 -0.6436986984
17.0  0.07224993 35.32314 -0.4072935672
17.2  0.07358595 35.57267  0.4217396204
17.4 -0.05486548 35.71106  0.7004098239
17.6 -0.06326401 35.65716  0.5315067945
17.8 -0.02770644 35.34575 -0.4470441223
18.0  0.07224993 34.91096  0.2366941381
18.2  0.07358595 34.63889 -0.3734765838
18.4 -0.05486548 34.64888 -0.7706129500
18.6 -0.06326401 34.74754 -0.1643756097
18.8 -0.02770644 34.77378  0.7839216555
19.0  0.07224993 34.73917  0.9851827168
19.2  0.07358595 34.58422 -0.8094010413
19.4 -0.05486548 34.22487 -0.1829068859
19.6 -0.06326401 33.73054  0.4488209557
19.8 -0.02770644 33.23289  0.6183201398
20.0  0.07224993 32.66600 -0.2842529549
20.2  0.07358595 32.04949 -0.2456719374
20.4 -0.05486548 31.53989 -0.3700269425
20.6 -0.06326401 31.20454 -0.0976713879
20.8 -0.02770644 30.86619  0.0483145840
21.0  0.07224993 30.37877  0.8490794781
21.2  0.07358595 29.72029  0.2568237027
21.4 -0.05486548 28.93668 -0.2019161775
21.6 -0.06326401 28.13749 -0.4304291040
21.8 -0.02770644 27.52392 -0.2568106803
22.0  0.07224993 27.14858 -0.3659262922
22.2  0.07358595 26.93403  0.0081844984
22.4 -0.05486548 26.83477  0.1388924065
22.6 -0.06326401 26.78393 -0.2240683020
22.8 -0.02770644 26.65055  0.1621536729
23.0  0.07224993 26.37570  0.3918489651
23.2  0.07358595 26.04380  0.3304101770
23.4 -0.05486548 25.74231 -0.5146453208
23.6 -0.06326401 25.53964 -0.5979799783
23.8 -0.02770644 25.52632 -0.0971100703
24.0  0.07224993 25.73694 -0.0375858254
24.2  0.07358595 25.98533  0.0591833771
24.4 -0.05486548 25.98059  0.4711791538
24.6 -0.06326401 25.66504  1.0553214768
24.8 -0.02770644 25.19563  0.0159805207
25.0  0.07224993 24.74524 -0.9780875461
25.2  0.07358595 24.52028 -0.7319644074
25.4 -0.05486548 24.71290 -0.3999357908
25.6 -0.06326401 25.18957 -0.0165021311
25.8 -0.02770644 25.67409  0.5153127247
26.0  0.07224993 26.15289  0.5835552400
26.2  0.07358595 26.60984 -1.0257278928
26.4 -0.05486548 27.03668  0.1163868084
26.6 -0.06326401 27.44247  0.0872947928
26.8 -0.02770644 27.72798  0.5887309933
27.0  0.07224993 27.81581  0.8052394591
27.2  0.07358595 27.74677 -0.5802561914
27.4 -0.05486548 27.48574 -0.1510731009
27.6 -0.06326401 27.08673  0.6169338148
27.8 -0.02770644 26.66608  0.1926296045
28.0  0.07224993 26.27051 -0.0960629184
28.2  0.07358595 26.02677 -0.8791529469
28.4 -0.05486548 26.01915 -0.5989847940
28.6 -0.06326401 26.12289  0.1995692472
28.8 -0.02770644 26.21931  1.0363003650
29.0  0.07224993 26.35972 -0.1004683886
29.2  0.07358595 26.47909 -0.6545732956
29.4 -0.05486548 26.56818  0.1764888535
> m$win
   s    t    l 
1431    9    5 
> m$deg
s t l 
0 1 1 
> m$jump
  s   t   l 
144   1   1 
> m$inner
[1] 2
> m$outer
[1] 0
> postscript(file="/var/www/rcomp/tmp/1zq301292010560.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/2rz2l1292010560.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$time.series[,'remainder']),na.action=na.pass,lag.max = mylagmax,main='Remainder')
> par(op)
> dev.off()
null device 
          1 
> postscript(file="/var/www/rcomp/tmp/3rz2l1292010560.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) 
> op <- par(mfrow = c(2,2))
> spectrum(as.numeric(x),main='Observed')
> spectrum(as.numeric(m$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$time.series[,'remainder']),'remainder']),main='Remainder')
> par(op)
> dev.off()
null device 
          1 
> postscript(file="/var/www/rcomp/tmp/4k91o1292010560.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) 
> op <- par(mfrow = c(2,2))
> cpgram(as.numeric(x),main='Observed')
> cpgram(as.numeric(m$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/5g0hf1292010560.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/6jjyk1292010560.tab") 
> 
> try(system("convert tmp/1zq301292010560.ps tmp/1zq301292010560.png",intern=TRUE))
character(0)
> try(system("convert tmp/2rz2l1292010560.ps tmp/2rz2l1292010560.png",intern=TRUE))
character(0)
> try(system("convert tmp/3rz2l1292010560.ps tmp/3rz2l1292010560.png",intern=TRUE))
character(0)
> try(system("convert tmp/4k91o1292010560.ps tmp/4k91o1292010560.png",intern=TRUE))
character(0)
> 
> 
> proc.time()
   user  system elapsed 
  1.670   0.370   2.023