R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

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> x <- c(700,700,620,680,700,670,660,730,680,680,650,800,660,710,660,590,660,710,620,700,690,680,640,810,620,700,720,620,630,680,670,720,660,630,620,810,540,690,720,620,650,690,660,700,630,590,570,760,500,660,750,680,710,620,640,720,680,580,530,740,480,640,690,600,640,580,690,690,720,550,510,680,450,560,730,650,680,580,750,670,670,590,480,810,350,570,710,650,710,510,800,680,660,620,580,830,480,550,720,620,730,520,870,660,650,620,560,820)
> par2 = '12'
> par1 = 'additive'
> par2 <- as.numeric(par2)
> x <- ts(x,freq=par2)
> m <- decompose(x,type=par1)
> m$figure
 [1] -140.04774  -15.77691   62.24392  -21.03733   27.24392  -37.39149
 [7]   33.75434   50.68142   23.44184  -35.51649  -77.86024  130.26476
> postscript(file="/var/wessaorg/rcomp/tmp/1dd371471299957.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) 
> plot(m)
> dev.off()
null device 
          1 
> mylagmax <- length(x)/2
> postscript(file="/var/wessaorg/rcomp/tmp/2oiob1471299957.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) 
> op <- par(mfrow = c(2,2))
> acf(as.numeric(x),lag.max = mylagmax,main='Observed')
> acf(as.numeric(m$trend),na.action=na.pass,lag.max = mylagmax,main='Trend')
> acf(as.numeric(m$seasonal),na.action=na.pass,lag.max = mylagmax,main='Seasonal')
> acf(as.numeric(m$random),na.action=na.pass,lag.max = mylagmax,main='Random')
> par(op)
> dev.off()
null device 
          1 
> postscript(file="/var/wessaorg/rcomp/tmp/3gif81471299957.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$trend[!is.na(m$trend)]),main='Trend')
> spectrum(as.numeric(m$seasonal[!is.na(m$seasonal)]),main='Seasonal')
> spectrum(as.numeric(m$random[!is.na(m$random)]),main='Random')
> par(op)
> dev.off()
null device 
          1 
> postscript(file="/var/wessaorg/rcomp/tmp/4nb331471299957.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$trend[!is.na(m$trend)]),main='Trend')
> cpgram(as.numeric(m$seasonal[!is.na(m$seasonal)]),main='Seasonal')
> cpgram(as.numeric(m$random[!is.na(m$random)]),main='Random')
> par(op)
> dev.off()
null device 
          1 
> 
> #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/wessaorg/rcomp/createtable")
> 
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,'Classical Decomposition by Moving Averages',6,TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'t',header=TRUE)
> a<-table.element(a,'Observations',header=TRUE)
> a<-table.element(a,'Fit',header=TRUE)
> a<-table.element(a,'Trend',header=TRUE)
> a<-table.element(a,'Seasonal',header=TRUE)
> a<-table.element(a,'Random',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:length(m$trend)) {
+ a<-table.row.start(a)
+ a<-table.element(a,i,header=TRUE)
+ a<-table.element(a,x[i])
+ if (par1 == 'additive') a<-table.element(a,signif(m$trend[i]+m$seasonal[i],6)) else a<-table.element(a,signif(m$trend[i]*m$seasonal[i],6))
+ a<-table.element(a,signif(m$trend[i],6))
+ a<-table.element(a,signif(m$seasonal[i],6))
+ a<-table.element(a,signif(m$random[i],6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/5ugvb1471299957.tab") 
> 
> try(system("convert tmp/1dd371471299957.ps tmp/1dd371471299957.png",intern=TRUE))
character(0)
> try(system("convert tmp/2oiob1471299957.ps tmp/2oiob1471299957.png",intern=TRUE))
character(0)
> try(system("convert tmp/3gif81471299957.ps tmp/3gif81471299957.png",intern=TRUE))
character(0)
> try(system("convert tmp/4nb331471299957.ps tmp/4nb331471299957.png",intern=TRUE))
character(0)
> 
> 
> proc.time()
   user  system elapsed 
  2.005   0.125   2.155