Maandelijkse Australische bierproductie

R Software Module: rwasp_decompose.wasp (opens new window with default values)

Title produced by software: Classical Decomposition

Date of computation: Mon, 19 May 2008 13:34:47 -0600

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2008/May/19/t1211225908sm8bk99mvf4uwxe.htm/, Retrieved Mon, 19 May 2008 21:38:28 +0200

User-defined keywords:

Dataseries X:

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164 148 152 144 155 125 153 146 138 190 192 192 147 133 163 150 129 131 145 137 138 168 176 188 139 143 150 154 137 129 128 140 143 151 177 184 151 134 164 126 131 125 127 143 143 160 190 182 138 136 152 127 151 130 119 153

Text written by user:

Output produced by software:

Summary of computational transaction Raw Input view raw input (R code) Raw Output view raw output of R engine Computing time 2 seconds R Server 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 Classical Decomposition by Moving Averages t Observations Fit Trend Seasonal Random 1 164 NA NA 0.943176610177838 NA 2 148 NA NA 0.915088331493679 NA 3 152 NA NA 1.03653212181376 NA 4 144 NA NA 1.01222559485875 NA 5 155 NA NA 0.892411024621307 NA 6 125 NA NA 0.874703290486576 NA 7 153 144.726467721348 157.541666666667 0.91865517728441 1.05716668422103 8 146 145.561583205721 156.208333333333 0.931842623882985 1.00301189905072 9 138 147.46591242336 156.041666666667 0.945041895369998 0.935809487984014 10 190 168.651834446503 156.75 1.07592876839875 1.12658128281592 11 192 186.28420804226 155.916666666667 1.19476776937847 1.03068318038233 12 192 195.347121695543 155.083333333333 1.25962679223348 0.982865774184483 13 147 146.192374577565 155 0.943176610177838 1.00552440183538 14 133 141.190503813379 154.291666666667 0.915088331493679 0.941989697662636 15 163 159.539569082501 153.916666666667 1.03653212181376 1.02169011071924 16 150 154.870516013389 153 1.01222559485875 0.968551044196382 17 129 135.125902644743 151.416666666667 0.892411024621307 0.954665223137504 18 131 131.715737159104 150.583333333333 0.874703290486576 0.994566046741712 19 145 137.874831190769 150.083333333333 0.91865517728441 1.05167853151800 20 137 139.931700686428 150.166666666667 0.931842623882985 0.979049059848147 21 138 141.79566105114 150.041666666667 0.945041895369998 0.973231472507673 22 168 161.030672337012 149.666666666667 1.07592876839875 1.04327950422018 23 176 179.414293368334 150.166666666667 1.19476776937847 0.98096978058864 24 188 189.468863331786 150.416666666667 1.25962679223348 0.992247468497163 25 139 141.122800297859 149.625 0.943176610177838 0.984957779371026 26 143 136.386290073037 149.041666666667 0.915088331493679 1.04849248354377 27 150 154.83198569593 149.375 1.03653212181376 0.96879207048717 28 154 150.695085434596 148.875 1.01222559485875 1.02193113701003 29 137 132.262750607416 148.208333333333 0.892411024621307 1.03581695806890 30 129 129.528978932887 148.083333333333 0.874703290486576 0.995916134464696 31 128 136.343739228628 148.416666666667 0.91865517728441 0.938803649688405 32 140 138.417456422618 148.541666666667 0.931842623882985 1.01143312135826 33 143 140.574981936287 148.75 0.945041895369998 1.01725070869873 34 151 159.416779184414 148.166666666667 1.07592876839875 0.947202676986231 35 177 175.332170156291 146.75 1.19476776937847 1.00951240061777 36 184 184.325387263500 146.333333333333 1.25962679223348 0.998234712709246 37 151 137.821682162237 146.125 0.943176610177838 1.09561861117216 38 134 133.793539800472 146.208333333333 0.915088331493679 1.00154312532456 39 164 151.67920049208 146.333333333333 1.03653212181376 1.08122932787059 40 126 148.501929979069 146.708333333333 1.01222559485875 0.848473821301578 41 131 131.742177509720 147.625 0.892411024621307 0.99436643963422 42 125 129.528978932887 148.083333333333 0.874703290486576 0.965035014016178 43 127 135.463361350397 147.458333333333 0.91865517728441 0.937522875070956 44 143 136.980865710799 147 0.931842623882985 1.04394142392054 45 143 138.527391162986 146.583333333333 0.945041895369998 1.03228681923095 46 160 157.220091282267 146.125 1.07592876839875 1.01768163785596 47 190 175.630862098635 147 1.19476776937847 1.08181442446769 48 182 186.477249700232 148.041666666667 1.25962679223348 0.975990370367274 49 138 NA 147.916666666667 NA NA 50 136 NA 148 NA NA 51 152 NA NA NA NA 52 127 NA NA NA NA 53 151 NA NA NA NA 54 130 NA NA NA NA 55 119 NA NA NA NA 56 153 NA NA NA NA

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/19/t1211225908sm8bk99mvf4uwxe/1hutp1211225682.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/19/t1211225908sm8bk99mvf4uwxe/1hutp1211225682.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/19/t1211225908sm8bk99mvf4uwxe/22swo1211225682.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/19/t1211225908sm8bk99mvf4uwxe/22swo1211225682.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/19/t1211225908sm8bk99mvf4uwxe/3b8s21211225682.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/19/t1211225908sm8bk99mvf4uwxe/3b8s21211225682.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/19/t1211225908sm8bk99mvf4uwxe/42r961211225683.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/19/t1211225908sm8bk99mvf4uwxe/42r961211225683.ps (open in new window)

Parameters (Session):

par1 = multiplicative ; par2 = 12 ;

Parameters (R input):

par1 = multiplicative ; par2 = 12 ;

R code (references can be found in the software module):

par2 <- as.numeric(par2) x <- ts(x,freq=par2) m <- decompose(x,type=par1) m$figure bitmap(file='test1.png') plot(m) dev.off() mylagmax <- length(x)/2 bitmap(file='test2.png') op <- par(mfrow = c(2,2)) acf(as.numeric(x),lag.max = mylagmax,main='Observed') acf(as.numeric(m$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() bitmap(file='test3.png') 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() bitmap(file='test4.png') 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() load(file='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$trend)) { a<-table.row.start(a) a<-table.element(a,i,header=TRUE) a<-table.element(a,x[i]) if (par1 == 'additive') a<-table.element(a,m$trend[i]+m$seasonal[i]) else a<-table.element(a,m$trend[i]*m$seasonal[i]) a<-table.element(a,m$trend[i]) a<-table.element(a,m$seasonal[i]) a<-table.element(a,m$random[i]) a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable.tab')

Copyright

This work is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 3.0 License.

Software written by Ed van Stee & Patrick Wessa

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