opgave 9

*Unverified author*

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

Title produced by software: Classical Decomposition

Date of computation: Sat, 05 Jun 2010 20:08:05 +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/Jun/05/t1275768513ewozxykw8wltn7w.htm/, Retrieved Sat, 05 Jun 2010 22:08:38 +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/Jun/05/t1275768513ewozxykw8wltn7w.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 «

103,6 104,7 105,5 106,6 107,2 107,5 108,3 108,7 108,8 109,8 109,5 109,2 110,6 110,1 109,9 109,7 109,4 109,4 109,4 109,5 109,5 109,9 110 110,8 112,4 112,8 113,7 114,5 114,8 115,6 115,8 115,8 116,3 116,3 116,8 116,7 116,8 117 117,2 117,1 117,3 117,4 117,7 117,9 118,8 119,9 122,4 123,5 125,6 127,4 128,9 129,5 130,8 132,7 134 132,9 133,1 131,7 128,8 125,1 123,9 121,8 119,2 118,9 119,6 120,2 119,6 121 120,4 120,4 121,4 121,7

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 1 103.6 NA NA 1.0007472369393 NA 2 104.7 NA NA 0.99865641622814 NA 3 105.5 NA NA 0.996650073755185 NA 4 106.6 NA NA 0.996381252604108 NA 5 107.2 NA NA 0.998271956179898 NA 6 107.5 NA NA 1.00201337185898 NA 7 108.3 108.262910608017 107.741666666667 1.00483790493944 1.00034258631857 8 108.7 108.435605755290 108.258333333333 1.00163749446808 1.00243826041150 9 108.8 108.905624568176 108.666666666667 1.00219899909364 0.999030127520093 10 109.8 109.247473395573 108.979166666667 1.00246200018877 1.00505756872222 11 109.5 109.273174678851 109.2 1.00067009779168 1.00207576399071 12 109.2 108.875733002351 109.370833333333 0.995473195952773 1.00297832206229 13 110.6 109.577652664699 109.495833333333 1.0007472369393 1.00932988899141 14 110.1 109.427776808199 109.575 0.99865641622814 1.00614307638708 15 109.9 109.270222461334 109.6375 0.996650073755185 1.00576348729306 16 109.7 109.273962290803 109.670833333333 0.996381252604108 1.00389880352342 17 109.4 109.506274126451 109.695833333333 0.998271956179898 0.999029515639186 18 109.4 110.004368007251 109.783333333333 1.00201337185898 0.99450596355218 19 109.4 110.456806700468 109.925 1.00483790493944 0.990432398581522 20 109.5 110.292808609616 110.1125 1.00163749446808 0.992811783292031 21 109.5 110.626066183287 110.383333333333 1.00219899909364 0.989820968763177 22 109.9 111.014312670905 110.741666666667 1.00246200018877 0.989962441381693 23 110 111.241159204508 111.166666666667 1.00067009779168 0.988842626116232 24 110.8 111.144582328127 111.65 0.995473195952773 0.996899692986296 25 112.4 112.258821303666 112.175 1.0007472369393 1.00125761783969 26 112.8 112.552739177312 112.704166666667 0.99865641622814 1.00219684411499 27 113.7 112.870620852775 113.25 0.996650073755185 1.00734805160952 28 114.5 113.388186546347 113.8 0.996381252604108 1.00980537291862 29 114.8 114.152398189171 114.35 0.998271956179898 1.00567313364504 30 115.6 115.110461148016 114.879166666667 1.00201337185898 1.00425277465751 31 115.8 115.866184088725 115.308333333333 1.00483790493944 0.999428788569799 32 115.8 115.856070193475 115.666666666667 1.00163749446808 0.999516035772826 33 116.3 116.242556407374 115.9875 1.00219899909364 1.00049417007335 34 116.3 116.527853671943 116.241666666667 1.00246200018877 0.998044641991047 35 116.8 116.532202346582 116.454166666667 1.00067009779168 1.00229805708659 36 116.7 116.105357087958 116.633333333333 0.995473195952773 1.00512158032115 37 116.8 116.874767934049 116.7875 1.0007472369393 0.999360273090846 38 117 116.797028946282 116.954166666667 0.99865641622814 1.00173781007573 39 117.2 116.753403431779 117.145833333333 0.996650073755185 1.00382512676371 40 117.1 116.975159055722 117.4 0.996381252604108 1.00106724321032 41 117.3 117.579798572056 117.783333333333 0.998271956179898 0.99762035166369 42 117.4 118.538181890917 118.3 1.00201337185898 0.990398183329957 43 117.7 119.525468792546 118.95 1.00483790493944 0.984727365548218 44 117.9 119.946089962553 119.75 1.00163749446808 0.982941586814615 45 118.8 120.936188386462 120.670833333333 1.00219899909364 0.982336235208307 46 119.9 121.974563872969 121.675 1.00246200018877 0.982991832009092 47 122.4 122.836423962670 122.754166666667 1.00067009779168 0.996447112765167 48 123.5 123.393050443329 123.954166666667 0.995473195952773 1.00086673889888 49 125.6 125.364440327417 125.270833333333 1.0007472369393 1.00187899911624 50 127.4 126.404935884077 126.575 0.99865641622814 1.00787203528852 51 128.9 127.367726717272 127.795833333333 0.996650073755185 1.01203031036370 52 129.5 128.416937106459 128.883333333333 0.996381252604108 1.00843395675013 53 130.8 129.417640185756 129.641666666667 0.998271956179898 1.01068138634162 54 132.7 130.236688007370 129.975 1.00201337185898 1.01891411729151 55 134 130.5996198699 129.970833333333 1.00483790493944 1.02603667708595 56 132.9 129.878995116028 129.666666666667 1.00163749446808 1.02326014981309 57 133.1 129.31290168722 129.029166666667 1.00219899909364 1.02928631453913 58 131.7 128.498920724198 128.183333333333 1.00246200018877 1.024911332 59 128.8 127.360286696436 127.275 1.00067009779168 1.01130425614537 60 125.1 125.715821233886 126.2875 0.995473195952773 0.995101481835448 61 123.9 125.260195823569 125.166666666667 1.0007472369393 0.989141037065877 62 121.8 123.904133775106 124.070833333333 0.99865641622814 0.983018050237736 63 119.2 122.633638866935 123.045833333333 0.996650073755185 0.972000840074063 64 118.9 121.604180291779 122.045833333333 0.996381252604108 0.977762439701576 65 119.6 121.057112552749 121.266666666667 0.998271956179898 0.987963428814527 66 120.2 121.059915543429 120.816666666667 1.00201337185898 0.992896777272903 67 119.6 NA NA 1.00483790493944 NA 68 121 NA NA 1.00163749446808 NA 69 120.4 NA NA 1.00219899909364 NA 70 120.4 NA NA 1.00246200018877 NA 71 121.4 NA NA 1.00067009779168 NA 72 121.7 NA NA 0.995473195952773 NA

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Jun/05/t1275768513ewozxykw8wltn7w/15if11275768483.png (open in new window) http://www.freestatistics.org/blog/date/2010/Jun/05/t1275768513ewozxykw8wltn7w/15if11275768483.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Jun/05/t1275768513ewozxykw8wltn7w/25if11275768483.png (open in new window) http://www.freestatistics.org/blog/date/2010/Jun/05/t1275768513ewozxykw8wltn7w/25if11275768483.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Jun/05/t1275768513ewozxykw8wltn7w/3yrem1275768483.png (open in new window) http://www.freestatistics.org/blog/date/2010/Jun/05/t1275768513ewozxykw8wltn7w/3yrem1275768483.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Jun/05/t1275768513ewozxykw8wltn7w/4yrem1275768483.png (open in new window) http://www.freestatistics.org/blog/date/2010/Jun/05/t1275768513ewozxykw8wltn7w/4yrem1275768483.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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