Paper : Opgave9 (oef2) - Ishia Opgenhaffen

*Unverified author*

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

Title produced by software: Classical Decomposition

Date of computation: Mon, 24 May 2010 10:03:40 +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/May/24/t1274695683rnn98l0uwg9o2kx.htm/, Retrieved Mon, 24 May 2010 12:08:08 +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/May/24/t1274695683rnn98l0uwg9o2kx.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 «

121,67 121,65 121,61 121,5 121,41 121,41 121,4 121,38 121,34 121,19 120,96 120,96 120,96 120,9 120,86 120,73 120,53 120,53 120,53 120,52 120,51 120,43 120,29 120,27 120,27 120,24 120,21 120,06 119,86 119,85 119,85 119,83 119,71 119,57 119,2 119,13 119,13 119,09 118,9 118,54 118,12 118,11 118,1 118,08 117,91 117,63 117,28 117,2 117,17 117,14 116,96 116,34 115,99 115,99 115,97 115,92 115,63 115,31 115,13 115,09 115,07 115,01 114,64 113,86 113,34 113,33 113,32 113,26 113,2 112,61 112,28 112,16

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' @ 72.249.76.132 Classical Decomposition by Moving Averages t Observations Fit Trend Seasonal Random 1 121.67 NA NA 0.105722222222220 NA 2 121.65 NA NA 0.196722222222226 NA 3 121.61 NA NA 0.170222222222224 NA 4 121.5 NA NA -0.0984444444444397 NA 5 121.41 NA NA -0.292611111111109 NA 6 121.41 NA NA -0.152944444444444 NA 7 121.4 121.353805555556 121.34375 0.0100555555555497 0.0461944444444669 8 121.38 121.379305555556 121.282916666667 0.0963888888888898 0.000694444444434339 9 121.34 121.304222222222 121.220416666667 0.0838055555555565 0.0357777777777812 10 121.19 121.168638888889 121.157083333333 0.0115555555555540 0.0213611111111049 11 120.96 120.976805555556 121.088333333333 -0.111527777777781 -0.0168055555555640 12 120.96 120.996055555556 121.015 -0.0189444444444466 -0.0360555555555493 13 120.96 121.047805555556 120.942083333333 0.105722222222220 -0.0878055555555477 14 120.9 121.066722222222 120.87 0.196722222222226 -0.166722222222205 15 120.86 120.969805555556 120.799583333333 0.170222222222224 -0.109805555555567 16 120.73 120.634888888889 120.733333333333 -0.0984444444444397 0.095111111111109 17 120.53 120.381138888889 120.67375 -0.292611111111109 0.148861111111103 18 120.53 120.464138888889 120.617083333333 -0.152944444444444 0.0658611111111043 19 120.53 120.569638888889 120.559583333333 0.0100555555555497 -0.0396388888889163 20 120.52 120.599722222222 120.503333333333 0.0963888888888898 -0.0797222222222302 21 120.51 120.532555555556 120.44875 0.0838055555555565 -0.0225555555555559 22 120.43 120.405305555556 120.39375 0.0115555555555540 0.0246944444444352 23 120.29 120.226388888889 120.337916666667 -0.111527777777781 0.063611111111129 24 120.27 120.262722222222 120.281666666667 -0.0189444444444466 0.00727777777777305 25 120.27 120.330722222222 120.225 0.105722222222220 -0.0607222222222106 26 120.24 120.364638888889 120.167916666667 0.196722222222226 -0.124638888888853 27 120.21 120.276055555556 120.105833333333 0.170222222222224 -0.0660555555555362 28 120.06 119.938222222222 120.036666666667 -0.0984444444444397 0.121777777777794 29 119.86 119.662805555556 119.955416666667 -0.292611111111109 0.197194444444477 30 119.85 119.709555555556 119.8625 -0.152944444444444 0.140444444444455 31 119.85 119.777555555556 119.7675 0.0100555555555497 0.0724444444444572 32 119.83 119.768472222222 119.672083333333 0.0963888888888898 0.0615277777777834 33 119.71 119.653388888889 119.569583333333 0.0838055555555565 0.0566111111111098 34 119.57 119.463222222222 119.451666666667 0.0115555555555540 0.106777777777779 35 119.2 119.204305555556 119.315833333333 -0.111527777777781 -0.0043055555555469 36 119.13 119.151888888889 119.170833333333 -0.0189444444444466 -0.0218888888888955 37 119.13 119.131138888889 119.025416666667 0.105722222222220 -0.0011388888888888 38 119.09 119.076305555556 118.879583333333 0.196722222222226 0.0136944444444538 39 118.9 118.901888888889 118.731666666667 0.170222222222224 -0.00188888888887107 40 118.54 118.477388888889 118.575833333333 -0.0984444444444397 0.0626111111111243 41 118.12 118.122388888889 118.415 -0.292611111111109 -0.00238888888885924 42 118.11 118.101638888889 118.254583333333 -0.152944444444444 0.00836111111111393 43 118.1 118.102555555556 118.0925 0.0100555555555497 -0.00255555555555986 44 118.08 118.025972222222 117.929583333333 0.0963888888888898 0.0540277777777618 45 117.91 117.851305555556 117.7675 0.0838055555555565 0.0586944444444413 46 117.63 117.606555555556 117.595 0.0115555555555540 0.0234444444444364 47 117.28 117.303055555556 117.414583333333 -0.111527777777781 -0.0230555555555583 48 117.2 117.218555555556 117.2375 -0.0189444444444466 -0.018555555555551 49 117.17 117.166138888889 117.060416666667 0.105722222222220 0.00386111111109244 50 117.14 117.078388888889 116.881666666667 0.196722222222226 0.0616111111111053 51 116.96 116.866888888889 116.696666666667 0.170222222222224 0.0931111111111278 52 116.34 116.406555555556 116.505 -0.0984444444444397 -0.0665555555555528 53 115.99 116.026138888889 116.31875 -0.292611111111109 -0.0361388888888996 54 115.99 115.988305555556 116.14125 -0.152944444444444 0.00169444444445332 55 115.97 115.975888888889 115.965833333333 0.0100555555555497 -0.00588888888887595 56 115.92 115.885972222222 115.789583333333 0.0963888888888898 0.0340277777778084 57 115.63 115.687972222222 115.604166666667 0.0838055555555565 -0.0579722222221903 58 115.31 115.415722222222 115.404166666667 0.0115555555555540 -0.105722222222198 59 115.13 115.078888888889 115.190416666667 -0.111527777777781 0.0511111111111262 60 115.09 114.950222222222 114.969166666667 -0.0189444444444466 0.139777777777795 61 115.07 114.853638888889 114.747916666667 0.105722222222220 0.216361111111112 62 115.01 114.723388888889 114.526666666667 0.196722222222226 0.286611111111114 63 114.64 114.484805555556 114.314583333333 0.170222222222224 0.155194444444447 64 113.86 114.002388888889 114.100833333333 -0.0984444444444397 -0.142388888888888 65 113.34 113.576972222222 113.869583333333 -0.292611111111109 -0.236972222222207 66 113.33 113.475805555556 113.62875 -0.152944444444444 -0.145805555555540 67 113.32 NA NA 0.0100555555555497 NA 68 113.26 NA NA 0.0963888888888898 NA 69 113.2 NA NA 0.0838055555555565 NA 70 112.61 NA NA 0.0115555555555540 NA 71 112.28 NA NA -0.111527777777781 NA 72 112.16 NA NA -0.0189444444444466 NA

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/May/24/t1274695683rnn98l0uwg9o2kx/1bqxx1274695415.png (open in new window) http://www.freestatistics.org/blog/date/2010/May/24/t1274695683rnn98l0uwg9o2kx/1bqxx1274695415.ps (open in new window) http://www.freestatistics.org/blog/date/2010/May/24/t1274695683rnn98l0uwg9o2kx/2l0e01274695415.png (open in new window) http://www.freestatistics.org/blog/date/2010/May/24/t1274695683rnn98l0uwg9o2kx/2l0e01274695415.ps (open in new window) http://www.freestatistics.org/blog/date/2010/May/24/t1274695683rnn98l0uwg9o2kx/3l0e01274695415.png (open in new window) http://www.freestatistics.org/blog/date/2010/May/24/t1274695683rnn98l0uwg9o2kx/3l0e01274695415.ps (open in new window) http://www.freestatistics.org/blog/date/2010/May/24/t1274695683rnn98l0uwg9o2kx/4cm5m1274695416.png (open in new window) http://www.freestatistics.org/blog/date/2010/May/24/t1274695683rnn98l0uwg9o2kx/4cm5m1274695416.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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