opgave 8 oef 2

*Unverified author*

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

Title produced by software: Classical Decomposition

Date of computation: Sat, 11 Dec 2010 20:42:46 +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/11/t1292100246mk5ogm4lgdzl1gg.htm/, Retrieved Sat, 11 Dec 2010 21:44:07 +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/11/t1292100246mk5ogm4lgdzl1gg.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 «

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

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 24.3 NA NA 0.657507503836322 NA 2 29.4 NA NA 0.842468589465635 NA 3 31.8 NA NA 0.885496296850527 NA 4 36.7 NA NA 1.04309665520377 NA 5 37.1 NA NA 1.09530841428656 NA 6 37.7 NA NA 1.07656062680808 NA 7 39.4 39.2677037977926 34.5166666666667 1.13764472615527 1.00336908424513 8 43.3 42.3717166811437 34.4125 1.23128853414148 1.02190808849785 9 39.6 39.1081565716273 34.3875 1.13727827180305 1.01257649225864 10 34.3 36.333277708013 34.2791666666667 1.05992301567073 0.944038142543783 11 32 32.4774193799343 34.2375 0.94859202278012 0.985299959508811 12 29.6 30.3314181952013 34.2791666666667 0.884835342998458 0.97588578976116 13 22.3 22.5086735479968 34.2333333333333 0.657507503836322 0.99072919390155 14 28.9 28.822956617093 34.2125 0.842468589465635 1.00267298681154 15 31.7 30.2692150806738 34.1833333333333 0.885496296850527 1.04726864953428 16 34.2 35.7086754964757 34.2333333333333 1.04309665520377 0.957750449281587 17 38.6 37.6329716008625 34.3583333333333 1.09530841428656 1.02569630720087 18 37.2 37.0157428850846 34.3833333333333 1.07656062680808 1.00497780405184 19 38.8 39.1131737224899 34.3808333333333 1.13764472615527 0.991993139582284 20 43.4 42.3265694348918 34.3758333333333 1.23128853414148 1.0253606795788 21 38.8 38.9991674039128 34.2916666666667 1.13727827180305 0.99489303446276 22 36.3 36.2860228068976 34.2345833333333 1.05992301567073 1.00038519495996 23 33 32.4118084316921 34.1683333333333 0.94859202278012 1.01814744677229 24 29.2 30.1507643126724 34.075 0.884835342998458 0.96846632799047 25 22.64 22.3947055806651 34.06 0.657507503836322 1.01095323260452 26 28.44 28.6769287282523 34.0391666666667 0.842468589465635 0.991738001984191 27 30.14 30.1301183707102 34.02625 0.885496296850527 1.00032796516655 28 34.39 35.5569918578648 34.0879166666667 1.04309665520377 0.967179679807286 29 36.82 37.3988494273087 34.1445833333333 1.09530841428656 0.984522266428709 30 36.74 36.8461845863624 34.2258333333333 1.07656062680808 0.99711816603118 31 38.9 38.9529554235563 34.24 1.13764472615527 0.99864052873574 32 42.8 42.0526077360453 34.1533333333333 1.23128853414148 1.01777279232351 33 39.09 38.7574957711547 34.0791666666667 1.13727827180305 1.00857909475908 34 37.49 36.1155518552106 34.07375 1.05992301567073 1.03805696089872 35 33.17 32.3888841244749 34.1441666666667 0.94859202278012 1.02411678872675 36 30.98 30.1618247544599 34.0875 0.884835342998458 1.02712618524246 37 21.2 22.343200826198 33.9816666666667 0.657507503836322 0.94883450965282 38 27.8 28.5091370675171 33.84 0.842468589465635 0.975125972215937 39 29 29.740500000096 33.58625 0.885496296850527 0.97510129284667 40 35.4 34.7738001192493 33.3370833333333 1.04309665520377 1.01800780698696 41 37.5 36.2606414334626 33.1054166666667 1.09530841428656 1.03417916830875 42 34.7 35.4269188266871 32.9075 1.07656062680808 0.979481172770254 43 38.4 37.3574086951236 32.8375 1.13764472615527 1.02790855525835 44 39.9 40.4580890843321 32.8583333333333 1.23128853414148 0.986205747800673 45 35.9 37.392761844991 32.8791666666667 1.13727827180305 0.960078855603683 46 34.7 34.8140547188849 32.8458333333333 1.05992301567073 0.996723888676402 47 30.4 31.0782461463337 32.7625 0.94859202278012 0.978176176894278 48 29 29.0115388085619 32.7875 0.884835342998458 0.999602268303034 49 21.5 21.5744649696293 32.8125 0.657507503836322 0.99654846737872 50 28 27.6715828781567 32.8458333333333 0.842468589465635 1.01186838943364 51 29.3 29.2767213146205 33.0625 0.885496296850527 1.00079512610477 52 34.3 34.7525035625389 33.3166666666667 1.04309665520377 0.986979252826357 53 36.6 36.7156508038975 33.5208333333333 1.09530841428656 0.996850095221921 54 36.2 36.2666361155974 33.6875 1.07656062680808 0.998162605558868 55 37.5 38.4713524894839 33.8166666666667 1.13764472615527 0.9747512778567 56 41.6 41.7663331518574 33.9208333333333 1.23128853414148 0.996017530405347 57 39.4 38.6390292845085 33.975 1.13727827180305 1.01969435385885 58 37.3 36.1433748343719 34.1 1.05992301567073 1.03200102842992 59 32.7 32.5011341805038 34.2625 0.94859202278012 1.00611873476143 60 30.7 30.4383357991469 34.4 0.884835342998458 1.00859653440253 61 22.9 22.7305823305415 34.5708333333333 0.657507503836322 1.00745329208882 62 29.1 29.1564337670898 34.6083333333333 0.842468589465635 0.998064448912351 63 29.5 30.6197240315105 34.5791666666667 0.885496296850527 0.963431282713123 64 37.1 36.0520281454803 34.5625 1.04309665520377 1.02906831899417 65 37.7 37.8292143584221 34.5375 1.09530841428656 0.996584270632801 66 38.4 37.2265693411679 34.5791666666667 1.07656062680808 1.03152132145399 67 39.4 39.4383505067159 34.6666666666667 1.13764472615527 0.999027583399834 68 40.6 42.8077980369854 34.7666666666667 1.23128853414148 0.948425330471847 69 39.7 39.7052276643239 34.9125 1.13727827180305 0.999868338135017 70 36.6 37.1767997746509 35.075 1.05992301567073 0.984484953569236 71 32.8 33.3390571339596 35.1458333333333 0.94859202278012 0.983831062414463 72 31.6 31.1019623063958 35.15 0.884835342998458 1.01601306337838 73 24.1 23.1470037496379 35.2041666666667 0.657507503836322 1.04117147345159 74 30.3 29.8163674955046 35.3916666666667 0.842468589465635 1.01622036972037 75 31.8 31.5236681678787 35.6 0.885496296850527 1.00876585271262 76 38.7 37.3298215481049 35.7875 1.04309665520377 1.03670466118166 77 37.8 39.4630494097329 36.0291666666667 1.09530841428656 0.957858061284976 78 38.4 39.0073800446796 36.2333333333333 1.07656062680808 0.984429099211895 79 40.7 NA NA 1.13764472615527 NA 80 43.8 NA NA 1.23128853414148 NA 81 41.5 NA NA 1.13727827180305 NA 82 39.3 NA NA 1.05992301567073 NA 83 35.9 NA NA 0.94859202278012 NA 84 33.4 NA NA 0.884835342998458 NA

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/11/t1292100246mk5ogm4lgdzl1gg/1pii71292100162.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/11/t1292100246mk5ogm4lgdzl1gg/1pii71292100162.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/11/t1292100246mk5ogm4lgdzl1gg/2pii71292100162.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/11/t1292100246mk5ogm4lgdzl1gg/2pii71292100162.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/11/t1292100246mk5ogm4lgdzl1gg/30rha1292100162.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/11/t1292100246mk5ogm4lgdzl1gg/30rha1292100162.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/11/t1292100246mk5ogm4lgdzl1gg/40rha1292100162.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/11/t1292100246mk5ogm4lgdzl1gg/40rha1292100162.ps (open in new window)

Parameters (Session):

par1 = multiplicative ; par2 = 12 ;

Parameters (R input):

par1 = multiplicative ; 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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