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(7291,6820,8031,7862,7357,7213,7079,7012,7319,8148,7599,6908,7878,7407,7911,7323,7179,6758,6934,6696,7688,8296,7697,7907,7592,7710,9011,8225,7733,8062,7859,8221,8330,8868,9053,8811,8120,7953,8878,8601,8361,9116,9310,9891,10147,10317,10682,10276,10614,9413,11068,9772,10350,10541,10049,10714,10759,11684,11462,10485,11056,10184,11082,10554,11315,10847,11104,11026,11073,12073,12328,11172)
> 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   -7.257678  7529.961 -231.703553
Feb 1 -570.637833  7513.974 -123.336153
Mar 1  458.981784  7497.987   74.031476
Apr 1 -195.968594  7484.838  573.131047
May 1 -250.585697  7471.688  135.897343
Jun 1 -252.319662  7463.532    1.787388
Jul 1 -328.054056  7455.376  -48.322139
Aug 1 -166.289313  7448.117 -269.827350
Sep 1   83.975208  7440.857 -205.832339
Oct 1  709.386448  7426.633   11.980460
Nov 1  562.298089  7412.409 -375.707144
Dec 1  -43.528436  7401.311 -449.782882
Jan 2   -7.257678  7390.214  495.044095
Feb 2 -570.637833  7387.073  590.565211
Mar 2  458.981784  7383.932   68.086555
Apr 2 -195.968594  7389.858  129.110186
May 2 -250.585697  7395.785   33.800544
Jun 2 -252.319662  7409.444 -399.123841
Jul 2 -328.054056  7423.102 -161.047798
Aug 2 -166.289313  7461.250 -598.960442
Sep 2   83.975208  7499.398  104.627135
Oct 2  709.386448  7578.514    8.099087
Nov 2  562.298089  7657.631 -522.929363
Dec 2  -43.528436  7754.972  195.556118
Jan 3   -7.257678  7852.313 -253.055686
Feb 3 -570.637833  7942.953  337.684632
Mar 3  458.981784  8033.593  518.425178
Apr 3 -195.968594  8108.002  312.966283
May 3 -250.585697  8182.412 -198.825886
Jun 3 -252.319662  8236.475   77.844678
Jul 3 -328.054056  8290.538 -103.484329
Aug 3 -166.289313  8318.791   68.498101
Sep 3   83.975208  8347.044 -101.019248
Oct 3  709.386448  8379.820 -221.206567
Nov 3  562.298089  8412.596   78.105713
Dec 3  -43.528436  8490.734  363.794298
Jan 4   -7.257678  8568.872 -441.614401
Feb 4 -570.637833  8691.150 -167.511912
Mar 4  458.981784  8813.427 -394.409196
Apr 4 -195.968594  8957.795 -160.826124
May 4 -250.585697  9102.162 -490.576327
Jun 4 -252.319662  9261.093  107.226621
Jul 4 -328.054056  9420.024  218.029998
Aug 4 -166.289313  9582.509  474.779872
Sep 4   83.975208  9744.995  318.029968
Oct 4  709.386448  9881.553 -273.939767
Nov 4  562.298089 10018.112  101.590096
Dec 4  -43.528436 10117.558  201.970481
Jan 5   -7.257678 10217.004  404.253583
Feb 5 -570.637833 10291.690 -308.051972
Mar 5  458.981784 10366.376  242.642702
Apr 5 -195.968594 10433.280 -465.311462
May 5 -250.585697 10500.185  100.401100
Jun 5 -252.319662 10553.608  239.711618
Jul 5 -328.054056 10607.031 -229.977436
Aug 5 -166.289313 10649.547  230.741961
Sep 5   83.975208 10692.063  -17.038421
Oct 5  709.386448 10736.930  237.683624
Nov 5  562.298089 10781.797  117.905266
Dec 5  -43.528436 10830.084 -301.555485
Jan 6   -7.257678 10878.371  184.886479
Feb 6 -570.637833 10923.601 -168.962782
Mar 6  458.981784 10968.830 -345.811814
Apr 6 -195.968594 11021.227 -271.257953
May 6 -250.585697 11073.623  491.962634
Jun 6 -252.319662 11127.545  -28.225245
Jul 6 -328.054056 11181.467  250.587305
Aug 6 -166.289313 11236.839  -44.549562
Sep 6   83.975208 11292.211 -303.186207
Oct 6  709.386448 11347.836   15.777413
Nov 6  562.298089 11403.461  362.240632
Dec 6  -43.528436 11459.182 -243.653644
> m$win
  s   t   l 
721  19  13 
> m$deg
s t l 
0 1 1 
> m$jump
 s  t  l 
73  2  2 
> m$inner
[1] 2
> m$outer
[1] 0
> postscript(file="/var/www/html/rcomp/tmp/1ruv91259693983.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) 
> plot(m,main=main)
> dev.off()
null device 
          1 
> mylagmax <- nx/2
> postscript(file="/var/www/html/rcomp/tmp/2xru81259693983.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) 
> op <- par(mfrow = c(2,2))
> acf(as.numeric(x),lag.max = mylagmax,main='Observed')
> acf(as.numeric(m$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/35ekc1259693983.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/4urfg1259693983.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$jump['l'])
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/5ozd61259693983.tab") 
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,'Seasonal Decomposition by Loess - Time Series Components',6,TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'t',header=TRUE)
> a<-table.element(a,'Observed',header=TRUE)
> a<-table.element(a,'Fitted',header=TRUE)
> a<-table.element(a,'Seasonal',header=TRUE)
> a<-table.element(a,'Trend',header=TRUE)
> a<-table.element(a,'Remainder',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:nx) {
+ a<-table.row.start(a)
+ a<-table.element(a,i,header=TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]+m$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/6vgl11259693983.tab") 
> 
> system("convert tmp/1ruv91259693983.ps tmp/1ruv91259693983.png")
> system("convert tmp/2xru81259693983.ps tmp/2xru81259693983.png")
> system("convert tmp/35ekc1259693983.ps tmp/35ekc1259693983.png")
> system("convert tmp/4urfg1259693983.ps tmp/4urfg1259693983.png")
> 
> 
> proc.time()
   user  system elapsed 
  0.993   0.602   1.179