ws 9 classical

*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: Sun, 29 Nov 2009 06:41:35 -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/Nov/29/t1259502133cak2eqea4d0xny6.htm/, Retrieved Sun, 29 Nov 2009 14:42:18 +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/Nov/29/t1259502133cak2eqea4d0xny6.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 «

103,63 103,64 103,66 103,77 103,88 103,91 103,91 103,92 104,05 104,23 104,30 104,31 104,31 104,34 104,55 104,65 104,73 104,75 104,75 104,76 104,94 105,29 105,38 105,43 105,43 105,42 105,52 105,69 105,72 105,74 105,74 105,74 105,95 106,17 106,34 106,37 106,37 106,36 106,44 106,29 106,23 106,23 106,23 106,23 106,34 106,44 106,44 106,48 106,50 106,57 106,40 106,37 106,25 106,21 106,21 106,24 106,19 106,08 106,13 106,09

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 'Gwilym Jenkins' @ 72.249.127.135 Classical Decomposition by Moving Averages t Observations Fit Trend Seasonal Random 1 103.63 NA NA 1.00055541275226 NA 2 103.64 NA NA 1.00028715838792 NA 3 103.66 NA NA 1.00037376459922 NA 4 103.77 NA NA 1.00019650002369 NA 5 103.88 NA NA 0.999670231989992 NA 6 103.91 NA NA 0.99931323068568 NA 7 103.91 103.851834036343 103.9625 0.998935520368816 1.00056008605141 8 103.92 103.854535605669 104.02 0.998409302111791 1.00063034699399 9 104.05 104.017831576674 104.08625 0.9993426756817 1.00030925873803 10 104.23 104.246596397299 104.16 1.00083137862231 0.99984079674663 11 104.3 104.348868191934 104.232083333333 1.00112043101189 0.999531684504293 12 104.31 104.403088680647 104.3025 1.00096439376474 0.999108372349674 13 104.31 104.430469817485 104.3725 1.00055541275226 0.99884641122753 14 104.34 104.47249153993 104.4425 1.00028715838792 0.998731804535557 15 104.55 104.553647184686 104.514583333333 1.00037376459922 0.99996511661923 16 104.65 104.616386417061 104.595833333333 1.00019650002369 1.00032130323069 17 104.73 104.650478235872 104.685 0.999670231989992 1.0007598796056 18 104.75 104.704709267143 104.776666666667 0.99931323068568 1.00043255678922 19 104.75 104.758368021078 104.87 0.998935520368816 0.99992012073846 20 104.76 104.794704365157 104.961666666667 0.998409302111791 0.99966883474344 21 104.94 104.978033330892 105.047083333333 0.9993426756817 0.999637702005981 22 105.29 105.218236860712 105.130833333333 1.00083137862231 1.00068204088406 23 105.38 105.333303282429 105.215416666667 1.00112043101189 1.00044332339456 24 105.43 105.399465320940 105.297916666667 1.00096439376474 1.00028970430701 25 105.43 105.438946293922 105.380416666667 1.00055541275226 0.999915151903199 26 105.42 105.492784441486 105.4625 1.00028715838792 0.999310052892518 27 105.52 105.584865807027 105.545416666667 1.00037376459922 0.999385652417788 28 105.69 105.644921817919 105.624166666667 1.00019650002369 1.00042669520982 29 105.72 105.665976579869 105.700833333333 0.999670231989992 1.00051126599006 30 105.74 105.707353541931 105.78 0.99931323068568 1.00030883809853 31 105.74 105.745649293709 105.858333333333 0.998935520368816 0.999946576584978 32 105.74 105.768153434716 105.936666666667 0.998409302111791 0.999733819360536 33 105.95 105.944480976832 106.014166666667 0.9993426756817 1.00005209354104 34 106.17 106.165690565808 106.0775 1.00083137862231 1.00004059159008 35 106.34 106.242654340598 106.12375 1.00112043101189 1.00091625778748 36 106.37 106.267801932531 106.165416666667 1.00096439376474 1.00096170303338 37 106.37 106.265238305620 106.20625 1.00055541275226 1.00098585102759 38 106.36 106.277593074504 106.247083333333 1.00028715838792 1.00077539322365 39 106.44 106.323475103222 106.28375 1.00037376459922 1.00109594703018 40 106.29 106.332140163143 106.31125 1.00019650002369 0.999603693078324 41 106.23 106.291603533389 106.326666666667 0.999670231989992 0.999420428977066 42 106.23 106.262388765475 106.335416666667 0.99931323068568 0.999695200099952 43 106.23 106.232214136755 106.345416666667 0.998935520368816 0.999979157576887 44 106.23 106.190397368734 106.359583333333 0.998409302111791 1.00037293985376 45 106.34 106.29674927001 106.366666666667 0.9993426756817 1.00040688666669 46 106.44 106.456765691757 106.368333333333 1.00083137862231 0.999842511730956 47 106.44 106.491683047812 106.3725 1.00112043101189 0.999514675265402 48 106.48 106.475084975740 106.3725 1.00096439376474 1.00004616126168 49 106.5 106.429913050635 106.370833333333 1.00055541275226 1.00065852679342 50 106.57 106.400961824039 106.370416666667 1.00028715838792 1.00158869030001 51 106.4 106.404338649194 106.364583333333 1.00037376459922 0.999959224884537 52 106.37 106.364229800852 106.343333333333 1.00019650002369 1.0000542494329 53 106.25 106.280357243279 106.315416666667 0.999670231989992 0.999714366379012 54 106.21 106.213255864966 106.28625 0.99931323068568 0.99996934596403 55 106.21 NA NA 0.998935520368816 NA 56 106.24 NA NA 0.998409302111791 NA 57 106.19 NA NA 0.9993426756817 NA 58 106.08 NA NA 1.00083137862231 NA 59 106.13 NA NA 1.00112043101189 NA 60 106.09 NA NA 1.00096439376474 NA

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/29/t1259502133cak2eqea4d0xny6/147yu1259502093.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/29/t1259502133cak2eqea4d0xny6/147yu1259502093.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/29/t1259502133cak2eqea4d0xny6/2c8c91259502093.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/29/t1259502133cak2eqea4d0xny6/2c8c91259502093.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/29/t1259502133cak2eqea4d0xny6/3mh7i1259502093.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/29/t1259502133cak2eqea4d0xny6/3mh7i1259502093.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/29/t1259502133cak2eqea4d0xny6/4uc8t1259502093.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/29/t1259502133cak2eqea4d0xny6/4uc8t1259502093.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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