*Unverified author*

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

Title produced by software: Classical Decomposition

Date of computation: Tue, 17 May 2011 15:06:31 +0000

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2011/May/17/t1305644604gklpdaacccwjfzv.htm/, Retrieved Tue, 17 May 2011 17:03:30 +0200

Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords:

KDGP2W92

Dataseries X:

» Textbox « » Textfile « » CSV «

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 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 0,43 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 0,44 0,44 0,46 0,46 0,46 0,46 0,46 0,45 0,45 0,46 0,46 0,46 0,47 0,47 0,47 0,47 0,47 0,47 0,47 0,47 0,47 0,47 0,47

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 'George Udny Yule' @ 216.218.223.82 Classical Decomposition by Moving Averages t Observations Fit Trend Seasonal Random 1 0.41 NA NA 0.99266182727845 NA 2 0.41 NA NA 1.00382953109282 NA 3 0.41 NA NA 1.00155524640923 NA 4 0.41 NA NA 1.00820569937968 NA 5 0.41 NA NA 1.00592394830781 NA 6 0.41 NA NA 1.00363778700629 NA 7 0.41 0.410484605206975 0.41 1.00118196391945 0.998819431469955 8 0.41 0.409623181757268 0.41 0.999080931115287 1.00091991434937 9 0.41 0.406901406008804 0.41 0.992442453680009 1.00761509777415 10 0.41 0.407948153752465 0.41 0.994995496957232 1.00502967406192 11 0.41 0.4087904622459 0.41 0.99704990791683 1.00295882087721 12 0.41 0.409768434844133 0.41 0.999435206936909 1.00056511223456 13 0.41 0.406991349184164 0.41 0.99266182727845 1.00739241957321 14 0.41 0.411570107748058 0.41 1.00382953109282 0.996185078268563 15 0.41 0.410637651027785 0.41 1.00155524640923 0.998447168626187 16 0.41 0.413784422453742 0.410416666666667 1.00820569937968 0.99085412053141 17 0.41 0.413686223741585 0.41125 1.00592394830781 0.991089324396048 18 0.41 0.413582404728844 0.412083333333333 1.00363778700629 0.991338111370592 19 0.41 0.413404719268407 0.412916666666667 1.00118196391945 0.9917641983516 20 0.41 0.41336973524895 0.41375 0.999080931115287 0.991848132648317 21 0.41 0.41145010058817 0.414583333333333 0.992442453680009 0.996475634381672 22 0.42 0.413337712694317 0.415416666666667 0.994995496957232 1.01611826625317 23 0.42 0.415022024170381 0.41625 0.99704990791683 1.01199448593016 24 0.42 0.416847767559936 0.417083333333333 0.999435206936909 1.00756207106138 25 0.42 0.414849921983452 0.417916666666667 0.99266182727845 1.01241431598185 26 0.42 0.42035361614512 0.41875 1.00382953109282 0.999158765069365 27 0.42 0.420235888805873 0.419583333333333 1.00155524640923 0.999438675248458 28 0.42 0.423446393739464 0.42 1.00820569937968 0.991861086101056 29 0.42 0.422488058289279 0.42 1.00592394830781 0.994110938189938 30 0.42 0.421946052953896 0.420416666666667 1.00363778700629 0.995387910515402 31 0.42 0.421747902301069 0.42125 1.00118196391945 0.995855575590223 32 0.42 0.422111693396209 0.4225 0.999080931115287 0.99499731130588 33 0.42 0.420961007435937 0.424166666666667 0.992442453680009 0.997717110566153 34 0.42 0.423702249120954 0.425833333333333 0.994995496957232 0.991262144280246 35 0.42 0.426238835634445 0.4275 0.99704990791683 0.985363052089896 36 0.43 0.428924276310423 0.429166666666667 0.999435206936909 1.00250795711269 37 0.43 0.427671803919132 0.430833333333333 0.99266182727845 1.00544388491253 38 0.44 0.434156272197646 0.4325 1.00382953109282 1.01345996401889 39 0.44 0.434841902816008 0.434166666666667 1.00155524640923 1.01186200582462 40 0.44 0.439409650646309 0.435833333333333 1.00820569937968 1.00134350566225 41 0.44 0.440091727384665 0.4375 1.00592394830781 0.999791572122452 42 0.44 0.440346079049012 0.43875 1.00363778700629 0.99921407487093 43 0.44 0.439685745821292 0.439166666666667 1.00118196391945 1.0007147245088 44 0.44 0.43876304224813 0.439166666666667 0.999080931115287 1.00281919312422 45 0.44 0.435847644241137 0.439166666666667 0.992442453680009 1.00952708088188 46 0.44 0.437798018661182 0.44 0.994995496957232 1.00502967406192 47 0.44 0.440363709329933 0.441666666666667 0.99704990791683 0.999174070609754 48 0.44 0.44308294174203 0.443333333333333 0.999435206936909 0.99304206627791 49 0.43 0.44173451313891 0.445 0.99266182727845 0.973435371722426 50 0.44 0.448377190554795 0.446666666666667 1.00382953109282 0.981316644264554 51 0.44 0.448613287454135 0.447916666666667 1.00155524640923 0.98080019541326 52 0.46 0.45243230759663 0.44875 1.00820569937968 1.01672668435986 53 0.46 0.452665776738513 0.45 1.00592394830781 1.01620229237194 54 0.46 0.453309733797843 0.451666666666667 1.00363778700629 1.01475870845769 55 0.46 0.454286316128451 0.45375 1.00118196391945 1.01257727487863 56 0.46 0.45583067482135 0.45625 0.999080931115287 1.00914665337142 57 0.45 0.455282975625704 0.45875 0.992442453680009 0.988396281195352 58 0.45 0.458112510057392 0.460416666666667 0.994995496957232 0.982291446141963 59 0.46 0.459889270026638 0.46125 0.99704990791683 1.00024077529218 60 0.46 0.461822351872097 0.462083333333333 0.999435206936909 0.996053998112674 61 0.46 0.459519704210982 0.462916666666667 0.99266182727845 1.0010452126092 62 0.47 0.465525945044297 0.46375 1.00382953109282 1.00961075317784 63 0.47 0.465723189580292 0.465 1.00155524640923 1.00918315968668 64 0.47 0.470495993043849 0.466666666666667 1.00820569937968 0.998945808144635 65 0.47 0.470688580812361 0.467916666666667 1.00592394830781 0.998537077718834 66 0.47 0.470455212659201 0.46875 1.00363778700629 0.99903239958459 67 0.47 NA NA 1.00118196391945 NA 68 0.47 NA NA 0.999080931115287 NA 69 0.47 NA NA 0.992442453680009 NA 70 0.47 NA NA 0.994995496957232 NA 71 0.47 NA NA 0.99704990791683 NA 72 0.47 NA NA 0.999435206936909 NA

Charts produced by software:

http://www.freestatistics.org/blog/date/2011/May/17/t1305644604gklpdaacccwjfzv/1n8s71305644788.png (open in new window) http://www.freestatistics.org/blog/date/2011/May/17/t1305644604gklpdaacccwjfzv/1n8s71305644788.ps (open in new window) http://www.freestatistics.org/blog/date/2011/May/17/t1305644604gklpdaacccwjfzv/2165n1305644788.png (open in new window) http://www.freestatistics.org/blog/date/2011/May/17/t1305644604gklpdaacccwjfzv/2165n1305644788.ps (open in new window) http://www.freestatistics.org/blog/date/2011/May/17/t1305644604gklpdaacccwjfzv/3yto01305644788.png (open in new window) http://www.freestatistics.org/blog/date/2011/May/17/t1305644604gklpdaacccwjfzv/3yto01305644788.ps (open in new window) http://www.freestatistics.org/blog/date/2011/May/17/t1305644604gklpdaacccwjfzv/4oilu1305644788.png (open in new window) http://www.freestatistics.org/blog/date/2011/May/17/t1305644604gklpdaacccwjfzv/4oilu1305644788.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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