Decompositie consumptieprijs van rundsvlees in Denemarken

*Unverified author*

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

Title produced by software: Classical Decomposition

Date of computation: Wed, 29 Dec 2010 13:58:58 +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/29/t1293631039y8xj4nscgv6tc2j.htm/, Retrieved Wed, 29 Dec 2010 14:57:19 +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/29/t1293631039y8xj4nscgv6tc2j.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 «

98,4 96,5 97,4 99,2 100,8 101,8 102,7 100 100,8 101,7 99 101,7 100,2 101,2 99,5 100,8 100,7 99,5 99,4 101,1 97,2 98,1 97,8 95,5 96,3 93,6 96,7 95,1 97,7 96,5 98,1 97,3 97 93,7 95,6 94,6 95,1 94,5 93,6 92,1 95,9 98,1 98,2 96,2 94,1 95 93,4 95,4 93,5 94,5 94,3 95,7 98,4 99,4 99,2 99 99,4 99,3 98,6 98,7 96 98,7 100,1 100 101,5 101,5 103,8 104,1 101 104,9 104,4 105,6 103,4 101,7 103,5 101,2 105,4 105,4 108,6 110,6 110,2 106,2 108,6 107,5 106,9 108,4 109,9 108,6 106,5 105,7 105,6 104,2 105,1 102,7 108,3 104,2 105,4 104,6 106,4 111 111,7 113,8 115,9 117,3 113,6 113,6 114,6 113,2 112,8 109,6 111,1 109,7 113 111 113,3 111,8 107,2 106,4 110 108,2

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 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 Classical Decomposition by Moving Averages t Observations Fit Trend Seasonal Random 1 98.4 NA NA 0.987932971940836 NA 2 96.5 NA NA 0.984193147235365 NA 3 97.4 NA NA 0.99214821392913 NA 4 99.2 NA NA 0.990511176550081 NA 5 100.8 NA NA 1.00812064400401 NA 6 101.8 NA NA 1.00758844405299 NA 7 102.7 101.923677254773 100.075 1.01847291785933 1.00761670659985 8 100 101.852117805352 100.345833333333 1.01501093191399 0.98181561812105 9 100.8 100.778298555047 100.629166666667 1.00148199466736 1.00021533847330 10 101.7 100.490481619862 100.783333333333 0.997094244615792 1.01203614870425 11 99 100.960524262005 100.845833333333 1.00113728971124 0.980581278907416 12 101.7 100.373882086196 100.745833333333 0.996308023519876 1.01321178264944 13 100.2 99.2996128422032 100.5125 0.987932971940836 1.00906737833135 14 101.2 98.8334960063314 100.420833333333 0.984193147235365 1.02394435175618 15 99.5 99.5290016606572 100.316666666667 0.99214821392913 0.99970861095587 16 100.8 99.0676261746173 100.016666666667 0.990511176550081 1.01748678041734 17 100.7 100.627242282333 99.8166666666666 1.00812064400401 1.00072304195183 18 99.5 100.26344675364 99.5083333333333 1.00758844405299 0.9923855923733 19 99.4 100.917935248386 99.0875 1.01847291785933 0.984958716756834 20 101.1 100.088536311152 98.6083333333333 1.01501093191399 1.01010568968362 21 97.2 98.3204948264678 98.175 1.00148199466736 0.98860364943804 22 98.1 97.5365899201873 97.8208333333333 0.997094244615792 1.00577639714771 23 97.8 97.569171693108 97.4583333333333 1.00113728971124 1.00236579139585 24 95.5 96.8494424529946 97.2083333333333 0.996308023519876 0.986066595544424 25 96.3 95.8583129899426 97.0291666666667 0.987932971940836 1.00460770689866 26 93.6 95.286299871504 96.8166666666666 0.984193147235365 0.982302808758678 27 96.7 95.8911248762504 96.65 0.99214821392913 1.00843534920248 28 95.1 95.5430572380599 96.4583333333333 0.990511176550081 0.995362747949797 29 97.7 96.9644039424521 96.1833333333333 1.00812064400401 1.00758624843385 30 96.5 96.7830683364737 96.0541666666667 1.00758844405299 0.997075228742598 31 98.1 97.7394510172338 95.9666666666667 1.01847291785933 1.00368887873846 32 97.3 97.3945281293634 95.9541666666667 1.01501093191399 0.999029430798845 33 97 96.0045677137995 95.8625 1.00148199466736 1.01036859297328 34 93.7 95.3305189039748 95.6083333333333 0.997094244615792 0.982896149913784 35 95.6 95.5168402492 95.4083333333333 1.00113728971124 1.00087062920615 36 94.6 95.0477854437962 95.4 0.996308023519876 0.99528883874879 37 95.1 94.3187841086682 95.4708333333333 0.987932971940836 1.00828271800484 38 94.5 93.9207318797148 95.4291666666666 0.984193147235365 1.00616762783564 39 93.6 94.5145192294237 95.2625 0.99214821392913 0.990324034477668 40 92.1 94.2925368776654 95.1958333333333 0.990511176550081 0.976747503564253 41 95.9 95.931080282348 95.1583333333333 1.00812064400401 0.999676014465212 42 98.1 95.8216610294398 95.1 1.00758844405299 1.02377686784056 43 98.2 96.8228253911604 95.0666666666667 1.01847291785933 1.01422365649087 44 96.2 96.4260385318288 95 1.01501093191399 0.997655835132601 45 94.1 95.16999938491 95.0291666666667 1.00148199466736 0.988756967617679 46 95 94.9316812061285 95.2083333333333 0.997094244615792 1.00071966274065 47 93.4 95.5710685190594 95.4625 1.00113728971124 0.977283203455799 48 95.4 95.2678034656568 95.6208333333333 0.996308023519876 1.00138763075807 49 93.5 94.5616509642703 95.7166666666667 0.987932971940836 0.988772922707626 50 94.5 94.3595179911906 95.875 0.984193147235365 1.00148879532028 51 94.3 95.4570600326564 96.2125 0.99214821392913 0.987878738018324 52 95.7 95.6957610444447 96.6125 0.990511176550081 1.00004429616849 53 98.4 97.7961034737555 97.0083333333333 1.00812064400401 1.00617505713207 54 99.4 98.1013298841097 97.3625 1.00758844405299 1.01323804802060 55 99.2 99.4072004202284 97.6041666666666 1.01847291785933 0.997915639718727 56 99 99.352653385514 97.8833333333333 1.01501093191399 0.996450488502348 57 99.4 98.4456800758012 98.3 1.00148199466736 1.00969387304211 58 99.3 98.4339747403414 98.7208333333333 0.997094244615792 1.00879803200006 59 98.6 99.1417915190295 99.0291666666667 1.00113728971124 0.994535185306536 60 98.7 98.8794200509164 99.2458333333333 0.996308023519876 0.998185466188779 61 96 98.3240290324116 99.525 0.987932971940836 0.97636356997082 62 98.7 98.349601042274 99.9291666666667 0.984193147235365 1.00356278982337 63 100.1 99.4215189374815 100.208333333333 0.99214821392913 1.00682428783798 64 100 99.5546275030877 100.508333333333 0.990511176550081 1.00447364937304 65 101.5 101.803383033671 100.983333333333 1.00812064400401 0.997019912063521 66 101.5 102.282821926930 101.5125 1.00758844405299 0.992346496584844 67 103.8 103.994572187753 102.108333333333 1.01847291785933 0.998129015931698 68 104.1 104.080912643347 102.541666666667 1.01501093191399 1.00018338959727 69 101 102.960694735093 102.808333333333 1.00148199466736 0.980956861838026 70 104.9 102.700707195427 103 0.997094244615792 1.02141458286542 71 104.4 103.329882514322 103.2125 1.00113728971124 1.01035632151745 72 105.6 103.155241985189 103.5375 0.996308023519876 1.02369979428832 73 103.4 102.646235784653 103.9 0.987932971940836 1.00734332057659 74 101.7 102.721058937911 104.370833333333 0.984193147235365 0.99005988695533 75 103.5 104.200366167907 105.025 0.99214821392913 0.993278659244073 76 101.2 104.461784956913 105.4625 0.990511176550081 0.968775328142647 77 105.4 106.549951065857 105.691666666667 1.00812064400401 0.989207399399497 78 105.4 106.749797362231 105.945833333333 1.00758844405299 0.987355504220294 79 108.6 108.132118416557 106.170833333333 1.01847291785933 1.00432694365277 80 110.6 108.195936129815 106.595833333333 1.01501093191399 1.02221953944093 81 110.2 107.300450045318 107.141666666667 1.00148199466736 1.02702271941503 82 106.2 107.403668382531 107.716666666667 0.997094244615792 0.988793042168318 83 108.6 108.193741180169 108.070833333333 1.00113728971124 1.00375491978926 84 107.5 107.729956326518 108.129166666667 0.996308023519876 0.997865437484993 85 106.9 106.713226519143 108.016666666667 0.987932971940836 1.00175023740683 86 108.4 105.923787471206 107.625 0.984193147235365 1.02337730351142 87 109.9 106.304547171615 107.145833333333 0.99214821392913 1.03382219222082 88 108.6 105.774212265842 106.7875 0.990511176550081 1.02671528034694 89 106.5 107.495064169611 106.629166666667 1.00812064400401 0.990743164095045 90 105.7 107.287177865726 106.479166666667 1.00758844405299 0.985206266980826 91 105.6 108.242452982658 106.279166666667 1.01847291785933 0.975587646899673 92 104.2 107.650367753911 106.058333333333 1.01501093191399 0.967948388603758 93 105.1 105.910893777717 105.754166666667 1.00148199466736 0.992343622560495 94 102.7 105.401170774594 105.708333333333 0.997094244615792 0.974372478457845 95 108.3 106.145581141634 106.025 1.00113728971124 1.02029683040211 96 104.2 106.185678890062 106.579166666667 0.996308023519876 0.98129993695178 97 105.4 106.050488150466 107.345833333333 0.987932971940836 0.993866240864986 98 104.6 106.608621869491 108.320833333333 0.984193147235365 0.981158917222008 99 106.4 108.363254715518 109.220833333333 0.99214821392913 0.981882652743572 100 111 108.985119329825 110.029166666667 0.990511176550081 1.01848766769780 101 111.7 111.645160820761 110.745833333333 1.00812064400401 1.00049119172597 102 113.8 112.228559526769 111.383333333333 1.00758844405299 1.01400214419446 103 115.9 114.136864994769 112.066666666667 1.01847291785933 1.01544755066921 104 117.3 114.273314084650 112.583333333333 1.01501093191399 1.02648637557766 105 113.6 113.154946872478 112.9875 1.00148199466736 1.00393313010012 106 113.6 112.800440981514 113.129166666667 0.997094244615792 1.00708826146005 107 114.6 113.257827303958 113.129166666667 1.00113728971124 1.01185059547752 108 113.2 112.649227192647 113.066666666667 0.996308023519876 1.00488927284349 109 112.8 111.480003108757 112.841666666667 0.987932971940836 1.01184066069639 110 109.6 110.725829868759 112.504166666667 0.984193147235365 0.98983227427518 111 111.1 111.128867861845 112.008333333333 0.99214821392913 0.999740230757312 112 109.7 110.384216366702 111.441666666667 0.990511176550081 0.993801501797785 113 113 111.850985452245 110.95 1.00812064400401 1.01027272619110 114 111 111.388902490059 110.55 1.00758844405299 0.996508606500605 115 113.3 NA NA 1.01847291785933 NA 116 111.8 NA NA 1.01501093191399 NA 117 107.2 NA NA 1.00148199466736 NA 118 106.4 NA NA 0.997094244615792 NA 119 110 NA NA 1.00113728971124 NA 120 108.2 NA NA 0.996308023519876 NA

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/29/t1293631039y8xj4nscgv6tc2j/19d4i1293631134.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/29/t1293631039y8xj4nscgv6tc2j/19d4i1293631134.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/29/t1293631039y8xj4nscgv6tc2j/29d4i1293631134.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/29/t1293631039y8xj4nscgv6tc2j/29d4i1293631134.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/29/t1293631039y8xj4nscgv6tc2j/3jm331293631134.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/29/t1293631039y8xj4nscgv6tc2j/3jm331293631134.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/29/t1293631039y8xj4nscgv6tc2j/4cw261293631134.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/29/t1293631039y8xj4nscgv6tc2j/4cw261293631134.ps (open in new window)

Parameters (Session):

par1 = 4 ;

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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