Opgave 9 - Sofie Loopmans - Eigen reeks

*Unverified author*

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

Title produced by software: Classical Decomposition

Date of computation: Sun, 31 May 2009 01:29:14 -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/May/31/t1243755004k86i00bhwx83uuz.htm/, Retrieved Sun, 31 May 2009 09:30:08 +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/2009/May/31/t1243755004k86i00bhwx83uuz.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 «

30,06 30,46 30,46 30,49 30,49 30,5 30,5 30,5 30,51 30,51 30,61 30,88 30,95 31,09 31,28 31,31 31,32 31,34 31,34 31,34 31,34 31,36 31,36 31,36 31,72 32,07 32,13 32,19 32,26 32,27 32,28 32,28 32,28 32,29 32,61 32,68 32,69 32,74 32,86 32,86 32,9 32,95 32,95 32,96 32,99 33 33,06 33,42 33,48 33,5 33,51 33,52 33,55 33,56 33,56 33,56 33,6 33,61 33,62 33,72 33,83 33,96 34,06 34,11 34,11 34,21 34,19 34,17 34,12 34,15 34,15 34,15

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 1 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Classical Decomposition by Moving Averages t Observations Fit Trend Seasonal Random 1 30.06 NA NA 0.0297361111111099 NA 2 30.46 NA NA 0.106402777777778 NA 3 30.46 NA NA 0.141736111111112 NA 4 30.49 NA NA 0.111319444444446 NA 5 30.49 NA NA 0.0814861111111083 NA 6 30.5 NA NA 0.0627361111111125 NA 7 30.5 30.5193194444444 30.5345833333333 -0.0152638888888861 -0.0193194444444487 8 30.5 30.5240694444444 30.5979166666667 -0.0738472222222223 -0.0240694444444429 9 30.51 30.5413194444444 30.6583333333333 -0.117013888888891 -0.031319444444442 10 30.51 30.5594861111111 30.7266666666667 -0.167180555555555 -0.0494861111111149 11 30.61 30.6659027777778 30.7954166666667 -0.129513888888890 -0.0559027777777814 12 30.88 30.8344027777778 30.865 -0.0305972222222219 0.0455972222222201 13 30.95 30.9647361111111 30.935 0.0297361111111099 -0.0147361111111088 14 31.09 31.1114027777778 31.005 0.106402777777778 -0.0214027777777765 15 31.28 31.2163194444444 31.0745833333333 0.141736111111112 0.0636805555555604 16 31.31 31.2559027777778 31.1445833333333 0.111319444444446 0.0540972222222251 17 31.32 31.2927361111111 31.21125 0.0814861111111083 0.0272638888888928 18 31.34 31.3252361111111 31.2625 0.0627361111111125 0.0147638888888935 19 31.34 31.2993194444444 31.3145833333333 -0.0152638888888861 0.0406805555555607 20 31.34 31.3136527777778 31.3875 -0.0738472222222223 0.0263472222222241 21 31.34 31.3467361111111 31.46375 -0.117013888888891 -0.00673611111110617 22 31.36 31.3686527777778 31.5358333333333 -0.167180555555555 -0.00865277777776896 23 31.36 31.4821527777778 31.6116666666667 -0.129513888888890 -0.122152777777767 24 31.36 31.6589861111111 31.6895833333333 -0.0305972222222219 -0.298986111111102 25 31.72 31.7972361111111 31.7675 0.0297361111111099 -0.0772361111111088 26 32.07 31.9522361111111 31.8458333333333 0.106402777777778 0.117763888888891 27 32.13 32.0659027777778 31.9241666666667 0.141736111111112 0.0640972222222267 28 32.19 32.1134027777778 32.0020833333333 0.111319444444446 0.0765972222222189 29 32.26 32.1744027777778 32.0929166666667 0.0814861111111083 0.0855972222222192 30 32.27 32.2627361111111 32.2 0.0627361111111125 0.00726388888888607 31 32.28 32.2801527777778 32.2954166666667 -0.0152638888888861 -0.000152777777771007 32 32.28 32.2899027777778 32.36375 -0.0738472222222223 -0.00990277777777493 33 32.28 32.3050694444444 32.4220833333333 -0.117013888888891 -0.0250694444444477 34 32.29 32.3132361111111 32.4804166666667 -0.167180555555555 -0.0232361111111175 35 32.61 32.4054861111111 32.535 -0.129513888888890 0.204513888888883 36 32.68 32.5594027777778 32.59 -0.0305972222222219 0.120597222222216 37 32.69 32.6759861111111 32.64625 0.0297361111111099 0.0140138888888828 38 32.74 32.8089027777778 32.7025 0.106402777777778 -0.0689027777777795 39 32.86 32.9021527777778 32.7604166666667 0.141736111111112 -0.0421527777777868 40 32.86 32.9309027777778 32.8195833333333 0.111319444444446 -0.070902777777782 41 32.9 32.9494027777778 32.8679166666667 0.0814861111111083 -0.0494027777777788 42 32.95 32.9802361111111 32.9175 0.0627361111111125 -0.0302361111111082 43 32.95 32.9659861111111 32.98125 -0.0152638888888861 -0.0159861111111042 44 32.96 32.9719861111111 33.0458333333333 -0.0738472222222223 -0.0119861111111135 45 32.99 32.9875694444444 33.1045833333333 -0.117013888888891 0.00243055555556282 46 33 32.9919861111111 33.1591666666667 -0.167180555555555 0.00801388888888965 47 33.06 33.0842361111111 33.21375 -0.129513888888890 -0.024236111111108 48 33.42 33.2356527777778 33.26625 -0.0305972222222219 0.184347222222229 49 33.48 33.3468194444444 33.3170833333333 0.0297361111111099 0.133180555555555 50 33.5 33.4739027777778 33.3675 0.106402777777778 0.0260972222222193 51 33.51 33.5596527777778 33.4179166666667 0.141736111111112 -0.0496527777777871 52 33.52 33.5800694444444 33.46875 0.111319444444446 -0.0600694444444443 53 33.55 33.5989861111111 33.5175 0.0814861111111083 -0.0489861111111125 54 33.56 33.6160694444444 33.5533333333333 0.0627361111111125 -0.0560694444444465 55 33.56 33.5651527777778 33.5804166666667 -0.0152638888888861 -0.00515277777778067 56 33.56 33.5403194444444 33.6141666666667 -0.0738472222222223 0.0196805555555528 57 33.6 33.5392361111111 33.65625 -0.117013888888891 0.0607638888888857 58 33.61 33.5365694444444 33.70375 -0.167180555555555 0.0734305555555537 59 33.62 33.6221527777778 33.7516666666667 -0.129513888888890 -0.00215277777778056 60 33.72 33.7714861111111 33.8020833333333 -0.0305972222222219 -0.0514861111111173 61 33.83 33.8851527777778 33.8554166666667 0.0297361111111099 -0.0551527777777778 62 33.96 34.0134861111111 33.9070833333333 0.106402777777778 -0.0534861111111127 63 34.06 34.0959027777778 33.9541666666667 0.141736111111112 -0.0359027777777712 64 34.11 34.1096527777778 33.9983333333333 0.111319444444446 0.000347222222224275 65 34.11 34.1244027777778 34.0429166666667 0.0814861111111083 -0.0144027777777822 66 34.21 34.1456527777778 34.0829166666667 0.0627361111111125 0.0643472222222172 67 34.19 NA NA -0.0152638888888861 NA 68 34.17 NA NA -0.0738472222222223 NA 69 34.12 NA NA -0.117013888888891 NA 70 34.15 NA NA -0.167180555555555 NA 71 34.15 NA NA -0.129513888888890 NA 72 34.15 NA NA -0.0305972222222219 NA

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/May/31/t1243755004k86i00bhwx83uuz/146cx1243754951.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/May/31/t1243755004k86i00bhwx83uuz/146cx1243754951.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/May/31/t1243755004k86i00bhwx83uuz/2oy5t1243754951.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/May/31/t1243755004k86i00bhwx83uuz/2oy5t1243754951.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/May/31/t1243755004k86i00bhwx83uuz/3tmn61243754951.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/May/31/t1243755004k86i00bhwx83uuz/3tmn61243754951.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/May/31/t1243755004k86i00bhwx83uuz/4h6jd1243754951.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/May/31/t1243755004k86i00bhwx83uuz/4h6jd1243754951.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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