opgave 9 oefening 2

*Unverified author*

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

Title produced by software: Classical Decomposition

Date of computation: Sun, 02 Jan 2011 09:56:53 +0000

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2011/Jan/02/t1293962211i9den5beghwub2n.htm/, Retrieved Sun, 02 Jan 2011 10:56:54 +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/2011/Jan/02/t1293962211i9den5beghwub2n.htm/}, year = {2011}, } @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 = {2011}, 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 «

17,88 18,11 18,16 18,27 18,29 18,35 18,35 18,38 18,41 18,41 18,42 18,43 18,48 18,54 18,65 18,66 18,69 18,72 18,72 18,73 18,84 18,83 18,91 18,91 18,94 18,97 19 19,08 19,18 19,24 19,23 19,25 19,3 19,33 19,35 19,35 19,31 19,47 19,7 19,76 19,9 19,97 20,1 20,26 20,44 20,43 20,57 20,6 20,69 20,93 20,98 21,11 21,14 21,16 21,32 21,32 21,48 21,58 21,74 21,75 21,81 21,89 22,21 22,37 22,47 22,51 22,55 22,61 22,58 22,85 22,93 22,98 23,01 23,11 23,18 23,18 23,21 23,22 23,12 23,15 23,16 23,21 23,21 23,22

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 'Gwilym Jenkins' @ www.wessa.org Classical Decomposition by Moving Averages t Observations Fit Trend Seasonal Random 1 17.88 NA NA 0.996280864644009 NA 2 18.11 NA NA 0.998504763589337 NA 3 18.16 NA NA 1.00182984494166 NA 4 18.27 NA NA 1.00214888932017 NA 5 18.29 NA NA 1.00244279201957 NA 6 18.35 NA NA 1.00113251145279 NA 7 18.35 18.3224336803914 18.3133333333333 1.00049692466644 1.00150451190543 8 18.38 18.3445871735147 18.35625 0.999364640028041 1.00193042373482 9 18.41 18.3975347930674 18.3945833333333 1.00016045265503 1.00067754767543 10 18.41 18.4247196065765 18.43125 0.999645689064847 0.999201094676566 11 18.42 18.4688651866341 18.4641666666667 1.0002544669387 0.997354185753144 12 18.43 18.4544144544664 18.49625 0.997738160679401 0.998677039874301 13 18.48 18.4581786026649 18.5270833333333 0.996280864644009 1.00118220750838 14 18.54 18.5293361066576 18.5570833333333 0.998504763589337 1.00057551405409 15 18.65 18.6235993883634 18.5895833333333 1.00182984494166 1.00141758910756 16 18.66 18.6650230635881 18.625 1.00214888932017 0.999730883612037 17 18.69 18.7085062905619 18.6629166666667 1.00244279201957 0.999010808758622 18 18.72 18.7245150725386 18.7033333333333 1.00113251145279 0.999758868386117 19 18.72 18.7518136105608 18.7425 1.00049692466644 0.998303438204886 20 18.73 18.7676515377933 18.7795833333333 0.999364640028041 0.997993806645576 21 18.84 18.8151017820509 18.8120833333333 1.00016045265503 1.00132331029816 22 18.83 18.8374899723528 18.8441666666667 0.999645689064847 0.999602390108034 23 18.91 18.8868881992755 18.8820833333333 1.0002544669387 1.00122369553315 24 18.91 18.8813632423904 18.9241666666667 0.997738160679401 1.00151666790379 25 18.94 18.8965421831083 18.9670833333333 0.996280864644009 1.00229977614267 26 18.97 18.9815755558333 19.01 0.998504763589337 0.999390168861417 27 19 19.0856934043428 19.0508333333333 1.00182984494166 0.995510071207406 28 19.08 19.1318574211964 19.0908333333333 1.00214888932017 0.997289472733631 29 19.18 19.1767306113344 19.13 1.00244279201957 1.00017048728127 30 19.24 19.1883731361784 19.1666666666667 1.00113251145279 1.00269052844945 31 19.23 19.2099578273143 19.2004166666667 1.00049692466644 1.00104332205546 32 19.25 19.2244444586728 19.2366666666667 0.999364640028041 1.00132932534837 33 19.3 19.2897612635401 19.2866666666667 1.00016045265503 1.00053078606417 34 19.33 19.3373128168852 19.3441666666667 0.999645689064847 0.999621828691789 35 19.35 19.4074372947782 19.4025 1.0002544669387 0.997040449292413 36 19.35 19.4188946764565 19.4629166666667 0.997738160679401 0.99645218342216 37 19.31 19.4569501694706 19.5295833333333 0.996280864644009 0.992447420166541 38 19.47 19.5785981957294 19.6079166666667 0.998504763589337 0.99445321903827 39 19.7 19.7335433707384 19.6975 1.00182984494166 0.99830018511586 40 19.76 19.8333616437205 19.7908333333333 1.00214888932017 0.996301098873787 41 19.9 19.9360810262892 19.8875 1.00244279201957 0.998190164544295 42 19.97 20.0130560424876 19.9904166666667 1.00113251145279 0.997848602312599 43 20.1 20.1099881857955 20.1 1.00049692466644 0.999503322145036 44 20.26 20.2054874136336 20.2183333333333 0.999364640028041 1.00269790999101 45 20.44 20.3357624036085 20.3325 1.00016045265503 1.00512582682285 46 20.43 20.434840479671 20.4420833333333 0.999645689064847 0.99976312613373 47 20.57 20.5552292955904 20.55 1.0002544669387 1.00071858621459 48 20.6 20.6045401907305 20.65125 0.997738160679401 0.999779650956126 49 20.69 20.6744884094709 20.7516666666667 0.996280864644009 1.00075027687369 50 20.93 20.8154959716257 20.8466666666667 0.998504763589337 1.00550090319877 51 20.98 20.9724729456496 20.9341666666667 1.00182984494166 1.00035890161212 52 21.11 21.0705979599937 21.0254166666667 1.00214888932017 1.00187000103562 53 21.14 21.1736801899367 21.1220833333333 1.00244279201957 0.998409336986554 54 21.16 21.2427804773888 21.21875 1.00113251145279 0.99610312418956 55 21.32 21.3239244543908 21.3133333333333 1.00049692466644 0.999815960031225 56 21.32 21.3864032966001 21.4 0.999364640028041 0.996895069466372 57 21.48 21.4946983281225 21.49125 1.00016045265503 0.999316188210781 58 21.58 21.5873486553554 21.595 0.999645689064847 0.999659585089735 59 21.74 21.7084393414318 21.7029166666667 1.0002544669387 1.00145384281531 60 21.75 21.7652422509875 21.8145833333333 0.997738160679401 0.99929969761826 61 21.81 21.8405521381313 21.9220833333333 0.996280864644009 0.998601127941358 62 21.89 21.9941476363126 22.0270833333333 0.998504763589337 0.995264756878294 63 22.21 22.1671550357425 22.1266666666667 1.00182984494166 1.00193281294728 64 22.37 22.2731766271779 22.2254166666667 1.00214888932017 1.00434708413814 65 22.47 22.3824591233136 22.3279166666667 1.00244279201957 1.00391113756554 66 22.51 22.4541508162467 22.42875 1.00113251145279 1.00248725432595 67 22.55 22.5411957127349 22.53 1.00049692466644 1.00039058652333 68 22.61 22.6164546077013 22.6308333333333 0.999364640028041 0.999714605679218 69 22.58 22.725729151932 22.7220833333333 1.00016045265503 0.993587481793971 70 22.85 22.7881730393445 22.79625 0.999645689064847 1.00271311616551 71 22.93 22.8666506596079 22.8608333333333 1.0002544669387 1.00277038125675 72 22.98 22.8694058154727 22.92125 0.997738160679401 1.00483590109072 73 23.01 22.8891377481692 22.9745833333333 0.996280864644009 1.00528033223272 74 23.11 22.9864117451295 23.0208333333333 0.998504763589337 1.00537657883452 75 23.18 23.1097099481918 23.0675 1.00182984494166 1.00304158087513 76 23.18 23.1563203358913 23.1066666666667 1.00214888932017 1.00102260047215 77 23.21 23.189843255386 23.1333333333333 1.00244279201957 1.00086920572908 78 23.22 23.1812233026893 23.155 1.00113251145279 1.00167276320168 79 23.12 NA NA 1.00049692466644 NA 80 23.15 NA NA 0.999364640028041 NA 81 23.16 NA NA 1.00016045265503 NA 82 23.21 NA NA 0.999645689064847 NA 83 23.21 NA NA 1.0002544669387 NA 84 23.22 NA NA 0.997738160679401 NA

Charts produced by software:

http://www.freestatistics.org/blog/date/2011/Jan/02/t1293962211i9den5beghwub2n/1zric1293962211.png (open in new window) http://www.freestatistics.org/blog/date/2011/Jan/02/t1293962211i9den5beghwub2n/1zric1293962211.ps (open in new window) http://www.freestatistics.org/blog/date/2011/Jan/02/t1293962211i9den5beghwub2n/2kc0e1293962211.png (open in new window) http://www.freestatistics.org/blog/date/2011/Jan/02/t1293962211i9den5beghwub2n/2kc0e1293962211.ps (open in new window) http://www.freestatistics.org/blog/date/2011/Jan/02/t1293962211i9den5beghwub2n/3dlxb1293962211.png (open in new window) http://www.freestatistics.org/blog/date/2011/Jan/02/t1293962211i9den5beghwub2n/3dlxb1293962211.ps (open in new window) http://www.freestatistics.org/blog/date/2011/Jan/02/t1293962211i9den5beghwub2n/4oke91293962211.png (open in new window) http://www.freestatistics.org/blog/date/2011/Jan/02/t1293962211i9den5beghwub2n/4oke91293962211.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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