Ws 9: 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: Fri, 04 Dec 2009 08:11:47 -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/04/t1259939563ijg9phsnhslrkn5.htm/, Retrieved Fri, 04 Dec 2009 16:12:48 +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/04/t1259939563ijg9phsnhslrkn5.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 «

79.8 83.4 113.6 112.9 104 109.9 99 106.3 128.9 111.1 102.9 130 87 87.5 117.6 103.4 110.8 112.6 102.5 112.4 135.6 105.1 127.7 137 91 90.5 122.4 123.3 124.3 120 118.1 119 142.7 123.6 129.6 151.6 110.4 99.2 130.5 136.2 129.7 128 121.6 135.8 143.8 147.5 136.2 156.6 123.3 104.5 139.8 136.5 112.1 118.5 94.4 102.3 111.4 99.2 87.8 115.8

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 1 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Classical Decomposition by Moving Averages t Observations Fit Trend Seasonal Random 1 79.8 NA NA 0.845455037396441 NA 2 83.4 NA NA 0.788096489013404 NA 3 113.6 NA NA 1.05599690966033 NA 4 112.9 NA NA 1.03304502569662 NA 5 104 NA NA 0.991406386781802 NA 6 109.9 NA NA 0.9997050446317 NA 7 99 99.3008748374767 107.116666666667 0.927034773650008 0.996970068612496 8 106.3 106.413368376655 107.5875 0.989086728259834 0.998934641592646 9 128.9 124.203307476908 107.925 1.15082981215574 1.03781455275630 10 111.1 108.440480666374 107.695833333333 1.00691435601539 1.02452515257478 11 102.9 110.283413800911 107.583333333333 1.02509757212311 0.933050550881208 12 130 128.207105297975 107.979166666667 1.18733186461563 1.01398436301840 13 87 91.5099396101973 108.2375 0.845455037396441 0.950716396170643 14 87.5 85.6168323251937 108.6375 0.788096489013404 1.02199529722910 15 117.6 115.284062625043 109.170833333333 1.05599690966033 1.02008896392288 16 103.4 112.808516806071 109.2 1.03304502569662 0.91659746025874 17 110.8 109.038179106218 109.983333333333 0.991406386781802 1.01615783488153 18 112.6 111.27550234288 111.308333333333 0.9997050446317 1.01190286836934 19 102.5 103.611586534949 111.766666666667 0.927034773650008 0.989271600096826 20 112.4 110.835410290917 112.058333333333 0.989086728259834 1.01411633434637 21 135.6 129.334090389436 112.383333333333 1.15082981215574 1.0484474711323 22 105.1 114.196674401596 113.4125 1.00691435601539 0.920342037548263 23 127.7 117.685472519616 114.804166666667 1.02509757212311 1.08509569844073 24 137 137.344613439413 115.675 1.18733186461563 0.997490884929646 25 91 98.608239195005 116.633333333333 0.845455037396441 0.922843777993449 26 90.5 92.6473097542674 117.558333333333 0.788096489013404 0.97682275114126 27 122.4 124.74403494075 118.129166666667 1.05599690966033 0.981209242254644 28 123.3 123.134662708763 119.195833333333 1.03304502569662 1.00134273556771 29 124.3 119.014205873210 120.045833333333 0.991406386781802 1.04441313612949 30 120 120.697722388534 120.733333333333 0.9997050446317 0.994219258037962 31 118.1 113.237297601348 122.15 0.927034773650008 1.04294258607063 32 119 121.974999567943 123.320833333333 0.989086728259834 0.975609759553344 33 142.7 142.726872328398 124.020833333333 1.15082981215574 0.999811722011703 34 123.6 125.759407589839 124.895833333333 1.00691435601539 0.98282905723537 35 129.6 128.812052417036 125.658333333333 1.02509757212311 1.00611703305847 36 151.6 149.861070178903 126.216666666667 1.18733186461563 1.01160361272625 37 110.4 107.115630508807 126.695833333333 0.845455037396441 1.03066190690931 38 99.2 100.515139702918 127.541666666667 0.788096489013404 0.986916003829822 39 130.5 135.471203548049 128.2875 1.05599690966033 0.963304352380052 40 136.2 133.602852302489 129.329166666667 1.03304502569662 1.01943931325381 41 129.7 129.477674113703 130.6 0.991406386781802 1.00171709823967 42 128 131.044669600472 131.083333333333 0.9997050446317 0.976766169812519 43 121.6 122.210221681303 131.829166666667 0.927034773650008 0.99500678688814 44 135.8 131.140536583151 132.5875 0.989086728259834 1.03553030617573 45 143.8 153.285735854927 133.195833333333 1.15082981215574 0.938117295767792 46 147.5 134.519562487173 133.595833333333 1.00691435601539 1.09649479430967 47 136.2 136.209839895858 132.875 1.02509757212311 0.99992775928769 48 156.6 156.426025947007 131.745833333333 1.18733186461563 1.00111218099380 49 123.3 110.092336786307 130.216666666667 0.845455037396441 1.11996896059469 50 104.5 100.630070440899 127.6875 0.788096489013404 1.03845698946791 51 139.8 131.938013887811 124.941666666667 1.05599690966033 1.05958848311052 52 136.5 125.596753353340 121.579166666667 1.03304502569662 1.08681153258783 53 112.1 116.539820766201 117.55 0.991406386781802 0.961902972417404 54 118.5 113.799757580575 113.833333333333 0.9997050446317 1.04130274544827 55 94.4 NA NA 0.927034773650008 NA 56 102.3 NA NA 0.989086728259834 NA 57 111.4 NA NA 1.15082981215574 NA 58 99.2 NA NA 1.00691435601539 NA 59 87.8 NA NA 1.02509757212311 NA 60 115.8 NA NA 1.18733186461563 NA

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Dec/04/t1259939563ijg9phsnhslrkn5/1e3q71259939505.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/04/t1259939563ijg9phsnhslrkn5/1e3q71259939505.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/04/t1259939563ijg9phsnhslrkn5/2b24z1259939505.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/04/t1259939563ijg9phsnhslrkn5/2b24z1259939505.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/04/t1259939563ijg9phsnhslrkn5/3gqg31259939505.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/04/t1259939563ijg9phsnhslrkn5/3gqg31259939505.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/04/t1259939563ijg9phsnhslrkn5/4g6zu1259939505.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/04/t1259939563ijg9phsnhslrkn5/4g6zu1259939505.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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