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

Title produced by software: Classical Decomposition

Date of computation: Tue, 14 Dec 2010 19:37:04 +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/Dec/14/t12923553563eugo8ztd0lfi9n.htm/, Retrieved Tue, 14 Dec 2010 20:35:58 +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/2010/Dec/14/t12923553563eugo8ztd0lfi9n.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:

KDGP2W92

Dataseries X:

» Textbox « » Textfile « » CSV «

101,02 101,15 101,51 101,75 101,8 101,8 101,8 101,82 101,99 102,25 102,34 102,35 102,35 102,39 102,49 102,67 102,68 102,7 102,71 102,72 102,83 102,92 103,04 103,08 103,09 103,11 103,18 103,18 103,22 103,25 103,25 103,25 103,47 103,57 103,66 103,7 103,7 103,75 103,85 104,02 104,13 104,17 104,18 104,2 104,5 104,78 104,88 104,89 104,9 104,95 105,24 105,35 105,44 105,46 105,47 105,48 105,75 106,1 106,19 106,23 106,24 106,25 106,35 106,48 106,52 106,55 106,55 106,56 106,89 107,09 107,24 107,28 107,3 107,31 107,47 107,35 107,31 107,32 107,32 107,34 107,53 107,72 107,75 107,79 107,81 107,9 107,8 107,86 107,8 107,74 107,75 107,83 107,8 107,81 107,86 107,83

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 'RServer@AstonUniversity' @ vre.aston.ac.uk Classical Decomposition by Moving Averages t Observations Fit Trend Seasonal Random 1 101.02 NA NA 0.0258978174603173 NA 2 101.15 NA NA -0.00672123015872303 NA 3 101.51 NA NA 0.0257787698412637 NA 4 101.75 NA NA 0.0338144841269788 NA 5 101.8 NA NA -0.00499503968253818 NA 6 101.8 NA NA -0.0576140873015867 NA 7 101.8 101.73845734127 101.85375 -0.11529265873015 0.0615426587301755 8 101.82 101.777802579365 101.960833333333 -0.183030753968253 0.0421974206349205 9 101.99 102.019826388889 102.053333333333 -0.0335069444444475 -0.0298263888888926 10 102.25 102.235183531746 102.1325 0.102683531746031 0.0148164682539829 11 102.34 102.333814484127 102.2075 0.126314484126984 0.00618551587304239 12 102.35 102.368338293651 102.281666666667 0.0866716269841243 -0.0183382936507854 13 102.35 102.382981150794 102.357083333333 0.0258978174603173 -0.0329811507936455 14 102.39 102.425778769841 102.4325 -0.00672123015872303 -0.0357787698412864 15 102.49 102.530778769841 102.505 0.0257787698412637 -0.0407787698412676 16 102.67 102.601731150794 102.567916666667 0.0338144841269788 0.0682688492063477 17 102.68 102.620004960317 102.625 -0.00499503968253818 0.0599950396825619 18 102.7 102.626969246032 102.684583333333 -0.0576140873015867 0.0730307539682684 19 102.71 102.630540674603 102.745833333333 -0.11529265873015 0.0794593253968259 20 102.72 102.623635912698 102.806666666667 -0.183030753968253 0.0963640873015947 21 102.83 102.831909722222 102.865416666667 -0.0335069444444475 -0.00190972222220864 22 102.92 103.018100198413 102.915416666667 0.102683531746031 -0.098100198412709 23 103.04 103.085481150794 102.959166666667 0.126314484126984 -0.0454811507936341 24 103.08 103.091254960317 103.004583333333 0.0866716269841243 -0.0112549603174728 25 103.09 103.07589781746 103.05 0.0258978174603173 0.0141021825396876 26 103.11 103.087862103175 103.094583333333 -0.00672123015872303 0.0221378968253987 27 103.18 103.169112103175 103.143333333333 0.0257787698412637 0.0108878968254089 28 103.18 103.23089781746 103.197083333333 0.0338144841269788 -0.0508978174602959 29 103.22 103.245004960317 103.25 -0.00499503968253818 -0.0250049603174602 30 103.25 103.244052579365 103.301666666667 -0.0576140873015867 0.00594742063492504 31 103.25 103.237624007936 103.352916666667 -0.11529265873015 0.0123759920635109 32 103.25 103.221969246032 103.405 -0.183030753968253 0.0280307539682667 33 103.47 103.426076388889 103.459583333333 -0.0335069444444475 0.0439236111111256 34 103.57 103.625183531746 103.5225 0.102683531746031 -0.0551835317460245 35 103.66 103.721731150794 103.595416666667 0.126314484126984 -0.0617311507936478 36 103.7 103.758338293651 103.671666666667 0.0866716269841243 -0.0583382936507917 37 103.7 103.77464781746 103.74875 0.0258978174603173 -0.0746478174603027 38 103.75 103.820362103175 103.827083333333 -0.00672123015872303 -0.0703621031746025 39 103.85 103.935362103175 103.909583333333 0.0257787698412637 -0.085362103174603 40 104.02 104.036731150794 104.002916666667 0.0338144841269788 -0.0167311507936461 41 104.13 104.099171626984 104.104166666667 -0.00499503968253818 0.0308283730158792 42 104.17 104.146969246032 104.204583333333 -0.0576140873015867 0.0230307539682713 43 104.18 104.188874007936 104.304166666667 -0.11529265873015 -0.00887400793648396 44 104.2 104.221135912698 104.404166666667 -0.183030753968253 -0.0211359126984263 45 104.5 104.478576388889 104.512083333333 -0.0335069444444475 0.0214236111111177 46 104.78 104.728100198413 104.625416666667 0.102683531746031 0.0518998015873109 47 104.88 104.861731150794 104.735416666667 0.126314484126984 0.0182688492063363 48 104.89 104.930421626984 104.84375 0.0866716269841243 -0.0404216269841271 49 104.9 104.97714781746 104.95125 0.0258978174603173 -0.0771478174603146 50 104.95 105.051612103175 105.058333333333 -0.00672123015872303 -0.101612103174588 51 105.24 105.189528769841 105.16375 0.0257787698412637 0.0504712301587347 52 105.35 105.30464781746 105.270833333333 0.0338144841269788 0.0453521825396876 53 105.44 105.375421626984 105.380416666667 -0.00499503968253818 0.0645783730158627 54 105.46 105.433219246032 105.490833333333 -0.0576140873015867 0.0267807539682394 55 105.47 105.48720734127 105.6025 -0.11529265873015 -0.0172073412698381 56 105.48 105.529469246032 105.7125 -0.183030753968253 -0.0494692460317196 57 105.75 105.779409722222 105.812916666667 -0.0335069444444475 -0.0294097222221978 58 106.1 106.008933531746 105.90625 0.102683531746031 0.0910664682539846 59 106.19 106.12464781746 105.998333333333 0.126314484126984 0.0653521825396979 60 106.23 106.175421626984 106.08875 0.0866716269841243 0.054578373015886 61 106.24 106.205064484127 106.179166666667 0.0258978174603173 0.0349355158730162 62 106.25 106.262445436508 106.269166666667 -0.00672123015872303 -0.0124454365079174 63 106.35 106.387445436508 106.361666666667 0.0257787698412637 -0.0374454365079231 64 106.48 106.484231150794 106.450416666667 0.0338144841269788 -0.0042311507936148 65 106.52 106.530421626984 106.535416666667 -0.00499503968253818 -0.0104216269841118 66 106.55 106.565302579365 106.622916666667 -0.0576140873015867 -0.0153025793650698 67 106.55 106.595540674603 106.710833333333 -0.11529265873015 -0.0455406746031599 68 106.56 106.616135912698 106.799166666667 -0.183030753968253 -0.0561359126983945 69 106.89 106.856493055556 106.89 -0.0335069444444475 0.0335069444444684 70 107.09 107.075600198413 106.972916666667 0.102683531746031 0.0143998015873308 71 107.24 107.16839781746 107.042083333333 0.126314484126984 0.0716021825397064 72 107.28 107.193754960317 107.107083333333 0.0866716269841243 0.0862450396825665 73 107.3 107.19714781746 107.17125 0.0258978174603173 0.102852182539706 74 107.31 107.229112103175 107.235833333333 -0.00672123015872303 0.0808878968254021 75 107.47 107.320778769841 107.295 0.0257787698412637 0.149221230158744 76 107.35 107.381731150794 107.347916666667 0.0338144841269788 -0.0317311507936466 77 107.31 107.390421626984 107.395416666667 -0.00499503968253818 -0.0804216269840907 78 107.32 107.380302579365 107.437916666667 -0.0576140873015867 -0.0603025793650716 79 107.32 107.365124007936 107.480416666667 -0.11529265873015 -0.0451240079364936 80 107.34 107.343219246032 107.52625 -0.183030753968253 -0.00321924603173329 81 107.53 107.531076388889 107.564583333333 -0.0335069444444475 -0.00107638888886186 82 107.72 107.702266865079 107.599583333333 0.102683531746031 0.0177331349206753 83 107.75 107.767564484127 107.64125 0.126314484126984 -0.0175644841269644 84 107.79 107.765838293651 107.679166666667 0.0866716269841243 0.0241617063492043 85 107.81 107.740481150794 107.714583333333 0.0258978174603173 0.0695188492063465 86 107.9 107.746195436508 107.752916666667 -0.00672123015872303 0.153804563492059 87 107.8 107.810362103175 107.784583333333 0.0257787698412637 -0.0103621031746144 88 107.86 107.83339781746 107.799583333333 0.0338144841269788 0.0266021825396763 89 107.8 107.802921626984 107.807916666667 -0.00499503968253818 -0.00292162698411857 90 107.74 107.756552579365 107.814166666667 -0.0576140873015867 -0.0165525793650545 91 107.75 NA NA -0.11529265873015 NA 92 107.83 NA NA -0.183030753968253 NA 93 107.8 NA NA -0.0335069444444475 NA 94 107.81 NA NA 0.102683531746031 NA 95 107.86 NA NA 0.126314484126984 NA 96 107.83 NA NA 0.0866716269841243 NA

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/14/t12923553563eugo8ztd0lfi9n/14r1f1292355420.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t12923553563eugo8ztd0lfi9n/14r1f1292355420.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t12923553563eugo8ztd0lfi9n/2wjii1292355420.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t12923553563eugo8ztd0lfi9n/2wjii1292355420.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t12923553563eugo8ztd0lfi9n/3wjii1292355420.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t12923553563eugo8ztd0lfi9n/3wjii1292355420.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t12923553563eugo8ztd0lfi9n/4psi31292355420.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t12923553563eugo8ztd0lfi9n/4psi31292355420.ps (open in new window)

Parameters (Session):

par1 = additive ; par2 = 12 ;

Parameters (R input):

par1 = additive ; 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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