R version 2.12.1 (2010-12-16)
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(9700,9081,9084,9743,8587,9731,9563,9998,9437,10038,9918,9252,9737,9035,9133,9487,8700,9627,8947,9283,8829,9947,9628,9318,9605,8640,9214,9567,8547,9185,9470,9123,9278,10170,9434,9655,9429,8739,9552,9687,9019,9672,9206,9069,9788,10312,10105,9863,9656,9295,9946,9701,9049,10190,9706,9765,9893,9994,10433,10073,10112,9266,9820,10097,9115,10411,9678,10408,10153,10368,10581,10597,10680,9738,9556)
> par8 = 'FALSE'
> par7 = '1'
> par6 = ''
> par5 = '1'
> par4 = ''
> par3 = '0'
> par2 = '6'
> 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  194.11396  9557.096  -51.210362
Feb 1 -515.86657  9549.139   47.727321
Mar 1 -156.08156  9541.182 -301.100524
Apr 1  198.58128  9533.225   11.193804
May 1 -730.20129  9522.906 -205.704727
Jun 1  161.03909  9512.587   57.373796
Jul 1  -96.19507  9502.268  156.926854
Aug 1   25.17304  9491.152  481.674483
Sep 1 -128.92519  9480.037   85.888444
Oct 1  660.85193  9468.921  -91.772932
Nov 1  337.05131  9457.377  123.572027
Dec 1   53.84131  9445.832 -247.673642
Jan 2  184.56515  9434.288  118.146859
Feb 2 -522.31619  9409.629  147.686996
Mar 2 -132.66236  9384.970 -119.308030
Apr 2  196.45726  9360.312  -69.768856
May 2 -715.16519  9341.720   73.445511
Jun 2  155.58277  9323.128  148.289471
Jul 2 -103.06854  9304.536 -254.467291
Aug 2  -17.02634  9294.827    5.199560
Sep 2 -109.83428  9285.118 -346.283440
Oct 2  658.73545  9275.409   12.855895
Nov 2  337.26585  9273.489   17.244771
Dec 2   75.08527  9271.570  -28.655361
Jan 3  173.60802  9269.651  161.741169
Feb 3 -530.13857  9276.465 -106.326210
Mar 3  -85.23625  9283.279   15.957513
Apr 3  188.36877  9290.093   88.538528
May 3 -662.94331  9301.313  -91.369577
Jun 3  149.25559  9312.533 -276.788651
Jul 3 -132.66478  9323.753  278.911542
Aug 3 -141.06881  9337.181  -73.112411
Sep 3  -81.16672  9350.609    8.557523
Oct 3  656.60145  9364.037  149.361374
Nov 3  330.47773  9380.206 -276.683578
Dec 3  137.63529  9396.375  120.990185
Jan 4  133.56915  9412.543 -117.112342
Feb 4 -572.23047  9433.165 -121.934681
Mar 4   27.67098  9453.787   70.541918
Apr 4  144.54344  9474.409   68.047499
May 4 -667.19778  9499.157  187.041266
Jun 4  184.69169  9523.904  -36.595647
Jul 4 -155.63406  9548.651 -187.017353
Aug 4 -178.96780  9575.617 -327.649678
Sep 4   12.27432  9602.584  173.142136
Oct 4  533.83602  9629.550  148.614370
Nov 4  369.42116  9654.124   81.455255
Dec 4  216.64286  9678.698  -32.340431
Jan 5  178.16700  9703.272 -225.438544
Feb 5 -568.06619  9724.217  138.849634
Mar 5  -81.23401  9745.162  282.072446
Apr 5  122.19804  9766.107 -187.304611
May 5 -706.39316  9782.350  -26.956551
Jun 5  233.23923  9798.593  158.167926
Jul 5 -199.42395  9814.836   90.587971
Aug 5  -96.69047  9829.622   32.068094
Sep 5   40.41327  9844.409    8.177964
Oct 5  451.52713  9859.195 -316.722291
Nov 5  403.08167  9877.293  152.625795
Dec 5  254.66536  9895.390  -77.055265
Jan 6  195.15825  9913.487    3.354468
Feb 6 -561.76235  9938.029 -110.266156
Mar 6 -129.59822  9962.570  -12.971515
Apr 6  107.72284  9987.111    2.166199
May 6 -719.53700 10009.689 -175.152208
Jun 6  253.18095 10032.267  125.551593
Jul 6 -214.67858 10054.846 -162.167126
Aug 6  -72.19758 10075.919  404.278703
Sep 6   50.95674 10096.992    5.051219
Oct 6  416.16835 10118.065 -166.233566
Nov 6  420.27056 10139.421   21.308147
Dec 6  264.26480 10160.777  171.957839
Jan 7  202.24222 10182.133  295.624340
Feb 7 -558.77230 10203.253   93.518855
Mar 7 -149.85299 10224.373 -518.520463
> m$win
 s  t  l 
 6 25 13 
> m$deg
s t l 
0 1 1 
> m$jump
s t l 
1 3 2 
> m$inner
[1] 2
> m$outer
[1] 0
> postscript(file="/var/www/rcomp/tmp/1mqzc1322664860.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) 
> plot(m,main=main)
> dev.off()
null device 
          1 
> mylagmax <- nx/2
> postscript(file="/var/www/rcomp/tmp/2d21p1322664860.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) 
> op <- par(mfrow = c(2,2))
> acf(as.numeric(x),lag.max = mylagmax,main='Observed')
> acf(as.numeric(m$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/3tbjk1322664860.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/4f0bg1322664860.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$jump['l'])
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/rcomp/tmp/5sxwl1322664860.tab") 
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,'Seasonal Decomposition by Loess - Time Series Components',6,TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'t',header=TRUE)
> a<-table.element(a,'Observed',header=TRUE)
> a<-table.element(a,'Fitted',header=TRUE)
> a<-table.element(a,'Seasonal',header=TRUE)
> a<-table.element(a,'Trend',header=TRUE)
> a<-table.element(a,'Remainder',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:nx) {
+ a<-table.row.start(a)
+ a<-table.element(a,i,header=TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]+m$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/6nmfu1322664861.tab") 
> 
> try(system("convert tmp/1mqzc1322664860.ps tmp/1mqzc1322664860.png",intern=TRUE))
character(0)
> try(system("convert tmp/2d21p1322664860.ps tmp/2d21p1322664860.png",intern=TRUE))
character(0)
> try(system("convert tmp/3tbjk1322664860.ps tmp/3tbjk1322664860.png",intern=TRUE))
character(0)
> try(system("convert tmp/4f0bg1322664860.ps tmp/4f0bg1322664860.png",intern=TRUE))
character(0)
> 
> 
> proc.time()
   user  system elapsed 
  1.824   0.324   2.125