opgave9_deel2

*Unverified author*

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

Title produced by software: Classical Decomposition

Date of computation: Sun, 12 Dec 2010 21:00:37 +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/12/t1292187636d46tc70ef9rfuf0.htm/, Retrieved Sun, 12 Dec 2010 22:00:37 +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/12/t1292187636d46tc70ef9rfuf0.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 «

43,8 43,4 43,3 42,8 41,6 41,5 40,7 40,6 39,9 39,7 39,6 39,4 39,4 39,4 39,3 39,09 38,9 38,8 38,8 38,7 38,6 38,4 38,4 38,4 37,8 37,7 37,7 37,5 37,5 37,49 37,3 37,2 37,1 37,1 36,82 36,74 36,7 36,6 36,6 36,3 36,2 35,9 35,9 35,4 34,7 34,7 34,39 34,3 34,3 34,2 33,4 33,17 33 32,8 32,7 32 31,8 31,8 31,7 31,6 30,98 30,7 30,4 30,3 30,14 29,6 29,5 29,4 29,3 29,2 29,1 29 29 28,9 28,44 28 27,8 24,3 24,1 22,9 22,64 22,3 21,5 21,2

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 'RServer@AstonUniversity' @ vre.aston.ac.uk Classical Decomposition by Moving Averages t Observations Fit Trend Seasonal Random 1 43.8 NA NA 0.071863425925925 NA 2 43.4 NA NA 0.196724537037038 NA 3 43.3 NA NA 0.162835648148149 NA 4 42.8 NA NA 0.156863425925926 NA 5 41.6 NA NA 0.266724537037039 NA 6 41.5 NA NA -0.256192129629629 NA 7 40.7 41.1049189814815 41.175 -0.07008101851852 -0.40491898148148 8 40.6 40.691724537037 40.825 -0.133275462962963 -0.091724537037031 9 39.9 40.2456134259259 40.4916666666667 -0.246053240740741 -0.345613425925926 10 39.7 40.0470023148148 40.1704166666667 -0.123414351851853 -0.347002314814809 11 39.6 39.8301967592593 39.9033333333333 -0.073136574074074 -0.230196759259258 12 39.4 39.725474537037 39.6783333333333 0.0471412037037034 -0.325474537037039 13 39.4 39.5585300925926 39.4866666666667 0.071863425925925 -0.158530092592592 14 39.4 39.5250578703704 39.3283333333333 0.196724537037038 -0.12505787037037 15 39.3 39.3578356481481 39.195 0.162835648148149 -0.057835648148135 16 39.09 39.2435300925926 39.0866666666667 0.156863425925926 -0.153530092592582 17 38.9 39.249224537037 38.9825 0.266724537037039 -0.349224537037038 18 38.8 38.6346412037037 38.8908333333333 -0.256192129629629 0.165358796296296 19 38.8 38.7124189814815 38.7825 -0.07008101851852 0.0875810185185202 20 38.7 38.511724537037 38.645 -0.133275462962963 0.188275462962963 21 38.6 38.2614467592593 38.5075 -0.246053240740741 0.338553240740737 22 38.4 38.2511689814815 38.3745833333333 -0.123414351851853 0.148831018518521 23 38.4 38.1768634259259 38.25 -0.073136574074074 0.223136574074076 24 38.4 38.184224537037 38.1370833333333 0.0471412037037034 0.215775462962966 25 37.8 38.0918634259259 38.02 0.071863425925925 -0.291863425925925 26 37.7 38.091724537037 37.895 0.196724537037038 -0.391724537037028 27 37.7 37.9328356481481 37.77 0.162835648148149 -0.232835648148146 28 37.5 37.8101967592593 37.6533333333333 0.156863425925926 -0.310196759259263 29 37.5 37.8000578703704 37.5333333333333 0.266724537037039 -0.300057870370381 30 37.49 37.1421412037037 37.3983333333333 -0.256192129629629 0.3478587962963 31 37.3 37.2132523148148 37.2833333333333 -0.07008101851852 0.0867476851851805 32 37.2 37.0583912037037 37.1916666666667 -0.133275462962963 0.141608796296296 33 37.1 36.8539467592593 37.1 -0.246053240740741 0.246053240740743 34 37.1 36.8807523148148 37.0041666666667 -0.123414351851853 0.219247685185181 35 36.82 36.8268634259259 36.9 -0.073136574074074 -0.00686342592592837 36 36.74 36.826724537037 36.7795833333333 0.0471412037037034 -0.0867245370370426 37 36.7 36.7268634259259 36.655 0.071863425925925 -0.0268634259259315 38 36.6 36.7183912037037 36.5216666666667 0.196724537037038 -0.118391203703702 39 36.6 36.5095023148148 36.3466666666667 0.162835648148149 0.0904976851851842 40 36.3 36.3035300925926 36.1466666666667 0.156863425925926 -0.00353009259259807 41 36.2 36.2121412037037 35.9454166666667 0.266724537037039 -0.012141203703699 42 35.9 35.4863078703704 35.7425 -0.256192129629629 0.413692129629631 43 35.9 35.4707523148148 35.5408333333333 -0.07008101851852 0.429247685185189 44 35.4 35.2075578703704 35.3408333333333 -0.133275462962963 0.192442129629633 45 34.7 34.8614467592593 35.1075 -0.246053240740741 -0.161446759259249 46 34.7 34.7203356481481 34.84375 -0.123414351851853 -0.0203356481481407 47 34.39 34.5068634259259 34.58 -0.073136574074074 -0.116863425925914 48 34.3 34.3646412037037 34.3175 0.0471412037037034 -0.064641203703701 49 34.3 34.1268634259259 34.055 0.071863425925925 0.173136574074071 50 34.2 33.976724537037 33.78 0.196724537037038 0.223275462962967 51 33.4 33.6803356481482 33.5175 0.162835648148149 -0.280335648148153 52 33.17 33.4326967592593 33.2758333333333 0.156863425925926 -0.262696759259264 53 33 33.3096412037037 33.0429166666667 0.266724537037039 -0.309641203703706 54 32.8 32.5621412037037 32.8183333333333 -0.256192129629629 0.237858796296294 55 32.7 32.4974189814815 32.5675 -0.07008101851852 0.202581018518522 56 32 32.1500578703704 32.2833333333333 -0.133275462962963 -0.150057870370375 57 31.8 31.7664467592593 32.0125 -0.246053240740741 0.0335532407407371 58 31.8 31.6445023148148 31.7679166666667 -0.123414351851853 0.155497685185185 59 31.7 31.4560300925926 31.5291666666667 -0.073136574074074 0.243969907407404 60 31.6 31.3238078703704 31.2766666666667 0.0471412037037034 0.276192129629631 61 30.98 31.0818634259259 31.01 0.071863425925925 -0.101863425925924 62 30.7 30.9650578703704 30.7683333333333 0.196724537037038 -0.265057870370367 63 30.4 30.7186689814815 30.5558333333333 0.162835648148149 -0.31866898148148 64 30.3 30.5001967592593 30.3433333333333 0.156863425925926 -0.200196759259256 65 30.14 30.3933912037037 30.1266666666667 0.266724537037039 -0.253391203703703 66 29.6 29.6538078703704 29.91 -0.256192129629629 -0.0538078703703633 67 29.5 29.6490856481481 29.7191666666667 -0.07008101851852 -0.149085648148144 68 29.4 29.4283912037037 29.5616666666667 -0.133275462962963 -0.028391203703702 69 29.3 29.1589467592593 29.405 -0.246053240740741 0.141053240740742 70 29.2 29.1040856481481 29.2275 -0.123414351851853 0.095914351851853 71 29.1 28.9610300925926 29.0341666666667 -0.073136574074074 0.138969907407411 72 29 28.762974537037 28.7158333333333 0.0471412037037034 0.237025462962968 73 29 28.3418634259259 28.27 0.071863425925925 0.658136574074074 74 28.9 27.9708912037037 27.7741666666667 0.196724537037038 0.92910879629629 75 28.44 27.3886689814815 27.2258333333333 0.162835648148149 1.05133101851852 76 28 26.8176967592593 26.6608333333333 0.156863425925926 1.18230324074074 77 27.8 26.3233912037037 26.0566666666667 0.266724537037039 1.4766087962963 78 24.3 25.1588078703704 25.415 -0.256192129629629 -0.85880787037037 79 24.1 NA NA -0.07008101851852 NA 80 22.9 NA NA -0.133275462962963 NA 81 22.64 NA NA -0.246053240740741 NA 82 22.3 NA NA -0.123414351851853 NA 83 21.5 NA NA -0.073136574074074 NA 84 21.2 NA NA 0.0471412037037034 NA

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/12/t1292187636d46tc70ef9rfuf0/15vjs1292187633.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/12/t1292187636d46tc70ef9rfuf0/15vjs1292187633.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/12/t1292187636d46tc70ef9rfuf0/25vjs1292187633.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/12/t1292187636d46tc70ef9rfuf0/25vjs1292187633.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/12/t1292187636d46tc70ef9rfuf0/3xmiv1292187633.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/12/t1292187636d46tc70ef9rfuf0/3xmiv1292187633.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/12/t1292187636d46tc70ef9rfuf0/4xmiv1292187633.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/12/t1292187636d46tc70ef9rfuf0/4xmiv1292187633.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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