Decompositie Gemiddelde prijs pilsbieren

*Unverified author*

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

Title produced by software: Classical Decomposition

Date of computation: Tue, 01 Jun 2010 17:09:04 +0000

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2010/Jun/01/t1275412442qgg0rey9fim2le7.htm/, Retrieved Tue, 01 Jun 2010 19:14:03 +0200

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/2010/Jun/01/t1275412442qgg0rey9fim2le7.htm/}, year = {2010}, } @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 = {2010}, 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:

KDGP2W52

Dataseries X:

» Textbox « » Textfile « » CSV «

0,47 0,47 0,47 0,47 0,47 0,47 0,47 0,47 0,47 0,47 0,47 0,46 0,46 0,46 0,45 0,45 0,46 0,46 0,46 0,46 0,46 0,44 0,44 0,43 0,44 0,44 0,44 0,44 0,44 0,44 0,44 0,44 0,44 0,44 0,44 0,43 0,43 0,42 0,42 0,42 0,42 0,42 0,42 0,42 0,42 0,42 0,42 0,42 0,42 0,42 0,42 0,41 0,41 0,41 0,41 0,41 0,41 0,41 0,41 0,41 0,41 0,41 0,41 0,41 0,41 0,41 0,41 0,41 0,41 0,41 0,41 0,41

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 12 seconds R Server 'RServer@AstonUniversity' @ vre.aston.ac.uk Classical Decomposition by Moving Averages t Observations Fit Trend Seasonal Random 1 0.47 NA NA 0.999435206936909 NA 2 0.47 NA NA 0.99704990791683 NA 3 0.47 NA NA 0.994995496957232 NA 4 0.47 NA NA 0.992442453680009 NA 5 0.47 NA NA 0.999080931115287 NA 6 0.47 NA NA 1.00118196391945 NA 7 0.47 0.470455212659201 0.46875 1.00363778700629 0.99903239958459 8 0.47 0.470688580812361 0.467916666666667 1.00592394830781 0.998537077718834 9 0.47 0.470495993043849 0.466666666666667 1.00820569937968 0.998945808144635 10 0.47 0.465723189580292 0.465 1.00155524640923 1.00918315968668 11 0.47 0.465525945044297 0.46375 1.00382953109282 1.00961075317784 12 0.46 0.459519704210982 0.462916666666667 0.99266182727845 1.0010452126092 13 0.46 0.461822351872097 0.462083333333333 0.999435206936909 0.996053998112674 14 0.46 0.459889270026638 0.46125 0.99704990791683 1.00024077529218 15 0.45 0.458112510057392 0.460416666666667 0.994995496957232 0.982291446141963 16 0.45 0.455282975625704 0.45875 0.992442453680009 0.988396281195352 17 0.46 0.45583067482135 0.45625 0.999080931115287 1.00914665337142 18 0.46 0.454286316128451 0.45375 1.00118196391945 1.01257727487863 19 0.46 0.453309733797843 0.451666666666667 1.00363778700629 1.01475870845769 20 0.46 0.452665776738513 0.45 1.00592394830781 1.01620229237194 21 0.46 0.45243230759663 0.44875 1.00820569937968 1.01672668435986 22 0.44 0.448613287454135 0.447916666666667 1.00155524640923 0.98080019541326 23 0.44 0.448377190554795 0.446666666666667 1.00382953109282 0.981316644264554 24 0.43 0.44173451313891 0.445 0.99266182727845 0.973435371722426 25 0.44 0.44308294174203 0.443333333333333 0.999435206936909 0.99304206627791 26 0.44 0.440363709329934 0.441666666666667 0.99704990791683 0.999174070609753 27 0.44 0.437798018661182 0.44 0.994995496957232 1.00502967406192 28 0.44 0.435847644241137 0.439166666666667 0.992442453680009 1.00952708088188 29 0.44 0.43876304224813 0.439166666666667 0.999080931115287 1.00281919312422 30 0.44 0.439685745821293 0.439166666666667 1.00118196391945 1.0007147245088 31 0.44 0.440346079049012 0.43875 1.00363778700629 0.99921407487093 32 0.44 0.440091727384665 0.4375 1.00592394830781 0.999791572122452 33 0.44 0.439409650646309 0.435833333333333 1.00820569937968 1.00134350566225 34 0.44 0.434841902816008 0.434166666666667 1.00155524640923 1.01186200582462 35 0.44 0.434156272197647 0.4325 1.00382953109282 1.01345996401888 36 0.43 0.427671803919132 0.430833333333333 0.99266182727845 1.00544388491253 37 0.43 0.428924276310423 0.429166666666667 0.999435206936909 1.00250795711269 38 0.42 0.426238835634445 0.4275 0.99704990791683 0.985363052089895 39 0.42 0.423702249120954 0.425833333333333 0.994995496957232 0.991262144280245 40 0.42 0.420961007435937 0.424166666666667 0.992442453680009 0.997717110566153 41 0.42 0.422111693396209 0.4225 0.999080931115287 0.99499731130588 42 0.42 0.421747902301069 0.42125 1.00118196391945 0.995855575590223 43 0.42 0.421946052953896 0.420416666666667 1.00363778700629 0.995387910515402 44 0.42 0.422488058289279 0.42 1.00592394830781 0.994110938189938 45 0.42 0.423446393739464 0.42 1.00820569937968 0.991861086101056 46 0.42 0.420235888805873 0.419583333333333 1.00155524640923 0.999438675248458 47 0.42 0.42035361614512 0.41875 1.00382953109282 0.999158765069365 48 0.42 0.414849921983452 0.417916666666667 0.99266182727845 1.01241431598185 49 0.42 0.416847767559936 0.417083333333333 0.999435206936909 1.00756207106138 50 0.42 0.415022024170381 0.41625 0.99704990791683 1.01199448593016 51 0.42 0.413337712694317 0.415416666666667 0.994995496957232 1.01611826625317 52 0.41 0.41145010058817 0.414583333333333 0.992442453680009 0.996475634381673 53 0.41 0.41336973524895 0.41375 0.999080931115287 0.991848132648317 54 0.41 0.413404719268407 0.412916666666667 1.00118196391945 0.9917641983516 55 0.41 0.413582404728844 0.412083333333333 1.00363778700629 0.991338111370592 56 0.41 0.413686223741585 0.41125 1.00592394830781 0.991089324396047 57 0.41 0.413784422453742 0.410416666666667 1.00820569937968 0.99085412053141 58 0.41 0.410637651027785 0.41 1.00155524640923 0.998447168626187 59 0.41 0.411570107748058 0.41 1.00382953109282 0.996185078268563 60 0.41 0.406991349184165 0.41 0.99266182727845 1.00739241957321 61 0.41 0.409768434844133 0.41 0.999435206936909 1.00056511223456 62 0.41 0.408790462245901 0.41 0.99704990791683 1.00295882087721 63 0.41 0.407948153752465 0.41 0.994995496957232 1.00502967406192 64 0.41 0.406901406008804 0.41 0.992442453680009 1.00761509777415 65 0.41 0.409623181757268 0.41 0.999080931115287 1.00091991434937 66 0.41 0.410484605206975 0.41 1.00118196391945 0.998819431469955 67 0.41 NA NA 1.00363778700629 NA 68 0.41 NA NA 1.00592394830781 NA 69 0.41 NA NA 1.00820569937968 NA 70 0.41 NA NA 1.00155524640923 NA 71 0.41 NA NA 1.00382953109282 NA 72 0.41 NA NA 0.99266182727845 NA

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Jun/01/t1275412442qgg0rey9fim2le7/13r4o1275412131.png (open in new window) http://www.freestatistics.org/blog/date/2010/Jun/01/t1275412442qgg0rey9fim2le7/13r4o1275412131.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Jun/01/t1275412442qgg0rey9fim2le7/23r4o1275412131.png (open in new window) http://www.freestatistics.org/blog/date/2010/Jun/01/t1275412442qgg0rey9fim2le7/23r4o1275412131.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Jun/01/t1275412442qgg0rey9fim2le7/3dil91275412131.png (open in new window) http://www.freestatistics.org/blog/date/2010/Jun/01/t1275412442qgg0rey9fim2le7/3dil91275412131.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Jun/01/t1275412442qgg0rey9fim2le7/4dil91275412131.png (open in new window) http://www.freestatistics.org/blog/date/2010/Jun/01/t1275412442qgg0rey9fim2le7/4dil91275412131.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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