classical decompostition - sigaretten - caroline thys

*Unverified author*

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

Title produced by software: Classical Decomposition

Date of computation: Fri, 05 Jun 2009 02:03:27 -0600

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2009/Jun/05/t1244189258qixd28ihy22yzr8.htm/, Retrieved Fri, 05 Jun 2009 10:07:44 +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/2009/Jun/05/t1244189258qixd28ihy22yzr8.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 «

106.07 106.07 106.07 106.07 106.07 106.2 107.5 108.31 108.53 108.61 108.62 108.62 108.62 108.62 110.1 110.74 110.77 110.77 110.78 110.78 110.78 110.84 110.84 110.84 110.84 110.84 111.01 112.66 114.04 114.16 114.2 114.2 114.23 114.23 114.23 114.23 114.23 114.23 115.97 116.96 117.08 117.08 117.08 117.63 119.12 119.47 119.5 119.52 119.49 119.49 119.5 119.5 119.56 122.35 122.92 122.92 123.04 123.04 123.04 123.06 123.33 128.21 129.57 129.79 131.66 135.01 136.01 136.31 136.37 136.4 136.4 136.4 137.34 142.18 143.79 144.08 144.08 144.09 144.09 144.11 144.11 144.15 144.15 144.16 144.2 144.38 144.38 144.28 144.46 144.53 144.53 145.34 147.98 150.42 150.53 150.64

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 'George Udny Yule' @ 72.249.76.132 Classical Decomposition by Moving Averages t Observations Fit Trend Seasonal Random 1 106.07 NA NA -1.22211805555555 NA 2 106.07 NA NA -0.248665674603176 NA 3 106.07 NA NA 0.206096230158729 NA 4 106.07 NA NA 0.249548611111105 NA 5 106.07 NA NA 0.271215277777774 NA 6 106.2 NA NA 0.677346230158727 NA 7 107.5 108.058774801587 107.334583333333 0.724191468253967 -0.558774801587305 8 108.31 108.056274801587 107.547083333333 0.50919146825397 0.253725198412695 9 108.53 108.148655753968 107.82125 0.327405753968256 0.381344246031745 10 108.61 108.135679563492 108.18375 -0.0480704365079317 0.474320436507938 11 108.62 108.075858134921 108.574166666667 -0.49830853174603 0.544141865079396 12 108.62 108.012584325397 108.960416666667 -0.947832341269836 0.607415674603189 13 108.62 108.065381944444 109.2875 -1.22211805555555 0.554618055555565 14 108.62 109.278417658730 109.527083333333 -0.248665674603176 -0.658417658730144 15 110.1 109.929846230159 109.72375 0.206096230158729 0.170153769841278 16 110.74 110.159965277778 109.910416666667 0.249548611111105 0.580034722222223 17 110.77 110.367048611111 110.095833333333 0.271215277777774 0.402951388888894 18 110.77 110.958179563492 110.280833333333 0.677346230158727 -0.188179563492085 19 110.78 111.190024801587 110.465833333333 0.724191468253967 -0.410024801587298 20 110.78 111.160024801587 110.650833333333 0.50919146825397 -0.380024801587325 21 110.78 111.108655753968 110.78125 0.327405753968256 -0.328655753968249 22 110.84 110.851096230159 110.899166666667 -0.0480704365079317 -0.0110962301587278 23 110.84 110.617108134921 111.115416666667 -0.49830853174603 0.222891865079376 24 110.84 110.445084325397 111.392916666667 -0.947832341269836 0.394915674603169 25 110.84 110.454548611111 111.676666666667 -1.22211805555555 0.38545138888891 26 110.84 111.713000992063 111.961666666667 -0.248665674603176 -0.873000992063467 27 111.01 112.454012896825 112.247916666667 0.206096230158729 -1.44401289682537 28 112.66 112.782465277778 112.532916666667 0.249548611111105 -0.122465277777778 29 114.04 113.086631944444 112.815416666667 0.271215277777774 0.953368055555572 30 114.16 113.775262896825 113.097916666667 0.677346230158727 0.384737103174601 31 114.2 114.104608134921 113.380416666667 0.724191468253967 0.0953918650793781 32 114.2 114.172108134921 113.662916666667 0.50919146825397 0.0278918650793685 33 114.23 114.338239087302 114.010833333333 0.327405753968256 -0.108239087301570 34 114.23 114.348596230159 114.396666666667 -0.0480704365079317 -0.118596230158715 35 114.23 114.204191468254 114.7025 -0.49830853174603 0.0258085317460512 36 114.23 114.003000992063 114.950833333333 -0.947832341269836 0.226999007936527 37 114.23 113.970381944444 115.1925 -1.22211805555555 0.259618055555563 38 114.23 115.206750992063 115.455416666667 -0.248665674603176 -0.976750992063472 39 115.97 116.008179563492 115.802083333333 0.206096230158729 -0.038179563492065 40 116.96 116.473715277778 116.224166666667 0.249548611111105 0.486284722222237 41 117.08 116.933298611111 116.662083333333 0.271215277777774 0.1467013888889 42 117.08 117.779429563492 117.102083333333 0.677346230158727 -0.699429563492046 43 117.08 118.265858134921 117.541666666667 0.724191468253967 -1.18585813492062 44 117.63 118.489191468254 117.98 0.50919146825397 -0.859191468253954 45 119.12 118.673655753968 118.34625 0.327405753968256 0.446344246031785 46 119.47 118.551096230159 118.599166666667 -0.0480704365079317 0.918903769841293 47 119.5 118.310024801587 118.808333333333 -0.49830853174603 1.18997519841272 48 119.52 118.18341765873 119.13125 -0.947832341269836 1.33658234126987 49 119.49 118.372048611111 119.594166666667 -1.22211805555555 1.11795138888893 50 119.49 119.809250992063 120.057916666667 -0.248665674603176 -0.319250992063473 51 119.5 120.647762896825 120.441666666667 0.206096230158729 -1.14776289682537 52 119.5 121.003298611111 120.75375 0.249548611111105 -1.50329861111109 53 119.56 121.321215277778 121.05 0.271215277777774 -1.76121527777775 54 122.35 122.022346230159 121.345 0.677346230158727 0.327653769841277 55 122.92 122.376691468254 121.6525 0.724191468253967 0.543308531746035 56 122.92 122.685024801587 122.175833333333 0.50919146825397 0.234975198412698 57 123.04 123.286155753968 122.95875 0.327405753968256 -0.246155753968225 58 123.04 123.759012896825 123.807083333333 -0.0480704365079317 -0.71901289682539 59 123.04 124.241691468254 124.74 -0.49830853174603 -1.20169146825396 60 123.06 124.823834325397 125.771666666667 -0.947832341269836 -1.76383432539681 61 123.33 125.622465277778 126.844583333333 -1.22211805555555 -2.29246527777775 62 128.21 127.699250992063 127.947916666667 -0.248665674603176 0.510749007936539 63 129.57 129.267346230159 129.06125 0.206096230158729 0.302653769841271 64 129.79 130.422881944444 130.173333333333 0.249548611111105 -0.632881944444421 65 131.66 131.557881944444 131.286666666667 0.271215277777774 0.102118055555565 66 135.01 133.076512896825 132.399166666667 0.677346230158727 1.9334871031746 67 136.01 134.262941468254 133.53875 0.724191468253967 1.74705853174606 68 136.31 135.213774801587 134.704583333333 0.50919146825397 1.09622519841272 69 136.37 136.206572420635 135.879166666667 0.327405753968256 0.163427579365077 70 136.4 137.019012896825 137.067083333333 -0.0480704365079317 -0.619012896825382 71 136.4 137.681691468254 138.18 -0.49830853174603 -1.28169146825397 72 136.4 138.128000992064 139.075833333333 -0.947832341269836 -1.7280009920635 73 137.34 138.568715277778 139.790833333333 -1.22211805555555 -1.22871527777778 74 142.18 140.203834325397 140.4525 -0.248665674603176 1.97616567460315 75 143.79 141.306096230159 141.1 0.206096230158729 2.48390376984125 76 144.08 141.994965277778 141.745416666667 0.249548611111105 2.08503472222222 77 144.08 142.662465277778 142.39125 0.271215277777774 1.41753472222223 78 144.09 143.714846230159 143.0375 0.677346230158727 0.37515376984129 79 144.09 144.370858134921 143.646666666667 0.724191468253967 -0.280858134920607 80 144.11 144.533358134921 144.024166666667 0.50919146825397 -0.423358134920591 81 144.11 144.467822420635 144.140416666667 0.327405753968256 -0.357822420634932 82 144.15 144.125262896825 144.173333333333 -0.0480704365079317 0.0247371031746013 83 144.15 143.699191468254 144.1975 -0.49830853174603 0.45080853174602 84 144.16 143.283834325397 144.231666666667 -0.947832341269836 0.876165674603158 85 144.2 143.046215277778 144.268333333333 -1.22211805555555 1.15378472222218 86 144.38 144.089250992063 144.337916666667 -0.248665674603176 0.290749007936512 87 144.38 144.756512896825 144.550416666667 0.206096230158729 -0.376512896825375 88 144.28 145.222465277778 144.972916666667 0.249548611111105 -0.942465277777757 89 144.46 145.771215277778 145.5 0.271215277777774 -1.31121527777776 90 144.53 146.713179563492 146.035833333333 0.677346230158727 -2.18317956349202 91 144.53 NA NA 0.724191468253967 NA 92 145.34 NA NA 0.50919146825397 NA 93 147.98 NA NA 0.327405753968256 NA 94 150.42 NA NA -0.0480704365079317 NA 95 150.53 NA NA -0.49830853174603 NA 96 150.64 NA NA -0.947832341269836 NA

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/05/t1244189258qixd28ihy22yzr8/1a8a01244189005.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/05/t1244189258qixd28ihy22yzr8/1a8a01244189005.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/05/t1244189258qixd28ihy22yzr8/2hnbb1244189005.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/05/t1244189258qixd28ihy22yzr8/2hnbb1244189005.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/05/t1244189258qixd28ihy22yzr8/3ip2u1244189005.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/05/t1244189258qixd28ihy22yzr8/3ip2u1244189005.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/05/t1244189258qixd28ihy22yzr8/46s931244189005.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/05/t1244189258qixd28ihy22yzr8/46s931244189005.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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