Classical decomposition

*The author of this computation has been verified*

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

Title produced by software: Classical Decomposition

Date of computation: Wed, 02 Dec 2009 08:59:32 -0700

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2009/Dec/02/t12597697662m17foyfkrc0b1z.htm/, Retrieved Wed, 02 Dec 2009 17:02:51 +0100

BibTeX entries for LaTeX users:

@Manual{KEY, author = {{YOUR NAME}}, publisher = {Office for Research Development and Education}, title = {Statistical Computations at FreeStatistics.org, URL http://www.freestatistics.org/blog/date/2009/Dec/02/t12597697662m17foyfkrc0b1z.htm/}, year = {2009}, } @Manual{R, title = {R: A Language and Environment for Statistical Computing}, author = {{R Development Core Team}}, organization = {R Foundation for Statistical Computing}, address = {Vienna, Austria}, year = {2009}, note = {{ISBN} 3-900051-07-0}, url = {http://www.R-project.org}, }

Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords:

Dataseries X:

» Textbox « » Textfile « » CSV «

108.5 112.3 116.6 115.5 120.1 132.9 128.1 129.3 132.5 131 124.9 120.8 122 122.1 127.4 135.2 137.3 135 136 138.4 134.7 138.4 133.9 133.6 141.2 151.8 155.4 156.6 161.6 160.7 156 159.5 168.7 169.9 169.9 185.9 190.8 195.8 211.9 227.1 251.3 256.7 251.9 251.2 270.3 267.2 243 229.9 187.2 178.2 175.2 192.4 187 184 194.1 212.7 217.5 200.5 205.9 196.5 206.3

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 3 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Classical Decomposition by Moving Averages t Observations Fit Trend Seasonal Random 1 108.5 NA NA 0.937224522886773 NA 2 112.3 NA NA 0.941763741248138 NA 3 116.6 NA NA 0.963321605840951 NA 4 115.5 NA NA 1.00921056998898 NA 5 120.1 NA NA 1.03211747816283 NA 6 132.9 NA NA 1.01830923694087 NA 7 128.1 124.569972229460 123.270833333333 1.01053889927566 1.02833771018298 8 129.3 126.990738457946 124.241666666667 1.0221268103128 1.01818448786183 9 132.5 130.354952368196 125.1 1.04200601413426 1.01645543642826 10 131 131.975287161921 126.370833333333 1.04434926700060 0.992610077364523 11 124.9 127.549677178707 127.908333333333 0.997195990712417 0.979226312152911 12 120.8 126.374548580194 128.7125 0.98183586349573 0.955888676613893 13 122 121.023021619933 129.129166666667 0.937224522886773 1.00807266557213 14 122.1 122.276249754305 129.8375 0.941763741248138 0.998558593719882 15 127.4 125.528832921125 130.308333333333 0.963321605840951 1.01490627320698 16 135.2 131.912231585643 130.708333333333 1.00921056998898 1.02492390868411 17 137.3 135.611635651611 131.391666666667 1.03211747816283 1.01244999619890 18 135 134.722312047278 132.3 1.01830923694087 1.00206118755314 19 136 135.041681573204 133.633333333333 1.01053889927566 1.00709646396307 20 138.4 138.672796127480 135.670833333333 1.0221268103128 0.998032807190036 21 134.7 143.874980401588 138.075 1.04200601413426 0.936229493300516 22 138.4 146.348143949017 140.133333333333 1.04434926700060 0.945690162276428 23 133.9 141.639225530815 142.0375 0.997195990712417 0.945359588759321 24 133.6 141.503002843558 144.120833333333 0.98183586349573 0.94414957502849 25 141.2 136.858210954541 146.025 0.937224522886773 1.03172472455380 26 151.8 139.133820722647 147.7375 0.941763741248138 1.09103594806472 27 155.4 144.530351596337 150.033333333333 0.963321605840951 1.07520668346550 28 156.6 154.169529697941 152.7625 1.00921056998898 1.01576491999957 29 161.6 160.571676665182 155.575 1.03211747816283 1.00640413898749 30 160.7 162.169988937988 159.254166666667 1.01830923694087 0.990935505714622 31 156 165.22311003157 163.5 1.01053889927566 0.94417784515854 32 159.5 171.104028046363 167.4 1.0221268103128 0.932181444359576 33 168.7 178.795206950262 171.5875 1.04200601413426 0.943537597442026 34 169.9 184.72362805601 176.879166666667 1.04434926700060 0.919752398694144 35 169.9 183.039479078559 183.554166666667 0.997195990712417 0.928215054234724 36 185.9 187.817018721204 191.291666666667 0.98183586349573 0.989793157541012 37 190.8 186.777132104798 199.2875 0.937224522886773 1.02153833207453 38 195.8 195.043194828078 207.104166666667 0.941763741248138 1.00388019265471 39 211.9 207.266671176729 215.158333333333 0.963321605840951 1.02235443256248 40 227.1 225.503896819995 223.445833333333 1.00921056998898 1.00707794057004 41 251.3 237.950384100947 230.545833333333 1.03211747816283 1.05610251880657 42 256.7 239.735452106805 235.425 1.01830923694087 1.07076361774660 43 251.9 239.607194175752 237.108333333333 1.01053889927566 1.05130399304802 44 251.2 241.451905766141 236.225 1.0221268103128 1.04037281960118 45 270.3 243.790332081886 233.9625 1.04200601413426 1.10873961937592 46 267.2 241.231626311301 230.9875 1.04434926700060 1.10764912580404 47 243 226.226375442996 226.8625 0.997195990712417 1.0741453092026 48 229.9 217.137092194845 221.154166666667 0.98183586349573 1.05877810960875 49 187.2 202.174949995392 215.716666666667 0.937224522886773 0.925930734763465 50 178.2 199.375308037819 211.704166666667 0.941763741248138 0.893791722524624 51 175.2 200.274561854334 207.9 0.963321605840951 0.874799067728975 52 192.4 204.789849870972 202.920833333333 1.00921056998898 0.939499687710217 53 187 204.974230673645 198.595833333333 1.03211747816283 0.912309802970975 54 184 199.24068811779 195.658333333333 1.01830923694087 0.923506145949568 55 194.1 197.118244039958 195.0625 1.01053889927566 0.984688154794307 56 212.7 NA NA 1.0221268103128 NA 57 217.5 NA NA 1.04200601413426 NA 58 200.5 NA NA 1.04434926700060 NA 59 205.9 NA NA 0.997195990712417 NA 60 196.5 NA NA 0.98183586349573 NA 61 206.3 NA NA NA NA

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Dec/02/t12597697662m17foyfkrc0b1z/18cv21259769569.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/02/t12597697662m17foyfkrc0b1z/18cv21259769569.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/02/t12597697662m17foyfkrc0b1z/29jbf1259769569.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/02/t12597697662m17foyfkrc0b1z/29jbf1259769569.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/02/t12597697662m17foyfkrc0b1z/3pze71259769569.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/02/t12597697662m17foyfkrc0b1z/3pze71259769569.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/02/t12597697662m17foyfkrc0b1z/47ra61259769569.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/02/t12597697662m17foyfkrc0b1z/47ra61259769569.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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