index huishoudconsumptie elektriciteit en brandstof

*Unverified author*

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

Title produced by software: Classical Decomposition

Date of computation: Thu, 09 Dec 2010 17:53:11 +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/09/t12919171531t6kypikisbnyj9.htm/, Retrieved Thu, 09 Dec 2010 18:52:37 +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/09/t12919171531t6kypikisbnyj9.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 «

132,1 125 127,1 101,5 85,7 79,3 70,9 77,1 83,9 96,2 111,7 127,2 143,6 134,9 135,6 105,3 86,4 74,6 67,6 73,4 78,5 98,2 118,6 136,9 137,9 115,6 119,3 98,5 84,3 73,5 60,7 69,5 77,9 113,9 126,3 135,1 130,5 113,1 110 90,8 85,4 72,5 64,7 67,2 77,9 105,2 107,2 120,7 121,3 107,9 105,6 81,3 71,7 64,8 57,3 61,9 70,1 88,8 106,8 110,7 114,1 108 111,5 86,8 78,4 68 57,3 65,3 73,3 88,6 101,3 122,9 126,6 114,1 124,7

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 4 seconds R Server 'George Udny Yule' @ 72.249.76.132 Classical Decomposition by Moving Averages t Observations Fit Trend Seasonal Random 1 132.1 NA NA 1.36464742142388 NA 2 125 NA NA 1.22502281121452 NA 3 127.1 NA NA 1.23277130163144 NA 4 101.5 NA NA 0.980793667700393 NA 5 85.7 NA NA 0.863786668523817 NA 6 79.3 NA NA 0.753165977400242 NA 7 70.9 66.9441859051867 101.954166666667 0.65661059369998 1.05909122713683 8 77.1 74.2619270409077 102.845833333333 0.722070351651657 1.03821706589339 9 83.9 84.0530591443625 103.6125 0.811225085239353 0.998179017564374 10 96.2 105.124888341529 104.125 1.00960276918635 0.9151020421298 11 111.7 118.517876982603 104.3125 1.13618096568103 0.942473851572589 12 127.2 129.570162726042 104.145833333333 1.24412238664733 0.981707495952958 13 143.6 141.667460436567 103.8125 1.36464742142388 1.01364137930812 14 134.9 126.815382269269 103.520833333333 1.22502281121452 1.06375108118639 15 135.6 127.150086669103 103.141666666667 1.23277130163144 1.06645621369404 16 105.3 101.021747773140 103 0.980793667700393 1.04234981398725 17 86.4 89.2903477475308 103.370833333333 0.863786668523817 0.96762978507259 18 74.6 78.3763345232127 104.0625 0.753165977400242 0.951817923788024 19 67.6 68.4379750058541 104.229166666667 0.65661059369998 0.987755701337124 20 73.4 74.5086344110553 103.1875 0.722070351651657 0.985120725674032 21 78.5 82.504971273364 101.704166666667 0.811225085239353 0.951457818704107 22 98.2 101.709065639115 100.741666666667 1.00960276918635 0.965498988540847 23 118.6 114.039430342877 100.370833333333 1.13618096568103 1.03999116484019 24 136.9 124.707717731562 100.2375 1.24412238664733 1.09776686230986 25 137.9 136.333963431169 99.9041666666667 1.36464742142388 1.01148676770937 26 115.6 121.833622836997 99.4541666666667 1.22502281121452 0.94883495465503 27 119.3 122.373097875281 99.2666666666667 1.23277130163144 0.974887471767586 28 98.5 97.9772007629872 99.8958333333333 0.980793667700393 1.00533592747028 29 84.3 87.1308810762212 100.870833333333 0.863786668523817 0.967510014345605 30 73.5 76.1576330814545 101.116666666667 0.753165977400242 0.965103523127983 31 60.7 66.142573805378 100.733333333333 0.65661059369998 0.917714514385356 32 69.5 72.4386994029873 100.320833333333 0.722070351651657 0.959431913780798 33 77.9 80.9839242385403 99.8291666666667 0.811225085239353 0.961919303521814 34 113.9 100.072667817392 99.1208333333334 1.00960276918635 1.13817291458482 35 126.3 112.306754370213 98.8458333333333 1.13618096568103 1.12459843317757 36 135.1 122.981497920089 98.85 1.24412238664733 1.09853922976109 37 130.5 135.065978535429 98.975 1.36464742142388 0.966194458553223 38 113.1 121.333405189084 99.0458333333333 1.22502281121452 0.932142305111658 39 110 121.982720296431 98.95 1.23277130163144 0.901767067767372 40 90.8 96.6939957144125 98.5875 0.980793667700393 0.939044863428537 41 85.4 84.1580152920518 97.4291666666667 0.863786668523817 1.01475777088657 42 72.5 72.3290393630033 96.0333333333333 0.753165977400242 1.00236365142552 43 64.7 62.4108369311831 95.05 0.65661059369998 1.03667893560442 44 67.2 68.199544713499 94.45 0.722070351651657 0.985343821315846 45 77.9 76.2957192667612 94.05 0.811225085239353 1.02102713951263 46 105.2 94.368412171489 93.4708333333333 1.00960276918635 1.11477980374225 47 107.2 105.101473412853 92.5041666666667 1.13618096568103 1.01996667143670 48 120.7 113.977162146729 91.6125 1.24412238664733 1.05898407827190 49 121.3 124.160171225883 90.9833333333333 1.36464742142388 0.976963858879675 50 107.9 110.808417536066 90.4541666666667 1.22502281121452 0.973752738278031 51 105.6 110.836413110847 89.9083333333333 1.23277130163144 0.952755480226433 52 81.3 87.192557058565 88.9 0.980793667700393 0.932419036012362 53 71.7 76.1859841638007 88.2 0.863786668523817 0.941117986293177 54 64.8 66.1028672831613 87.7666666666667 0.753165977400242 0.980290306053136 55 57.3 57.1579521815832 87.05 0.65661059369998 1.00248518032916 56 61.9 62.6426116322464 86.7541666666667 0.722070351651657 0.988145263856397 57 70.1 70.5799625203456 87.0041666666667 0.811225085239353 0.993199733986721 58 88.8 88.3192089127809 87.4791666666667 1.00960276918635 1.00544378842539 59 106.8 99.96972271786 87.9875 1.13618096568103 1.06832345930794 60 110.7 109.980418979624 88.4 1.24412238664733 1.00654281032071 61 114.1 120.816785043395 88.5333333333333 1.36464742142388 0.94440519965018 62 108 108.628897784447 88.675 1.22502281121452 0.99421058486946 63 111.5 109.655007280117 88.95 1.23277130163144 1.01682543064514 64 86.8 87.3641959504125 89.075 0.980793667700393 0.993542023202128 65 78.4 76.7366481649846 88.8375 0.863786668523817 1.02167610750263 66 68 67.1196413526516 89.1166666666667 0.753165977400242 1.01311625970590 67 57.3 59.1907091445794 90.1458333333333 0.65661059369998 0.968057332444503 68 65.3 65.6512380974617 90.9208333333333 0.722070351651657 0.99464993947349 69 73.3 74.4096209435797 91.725 0.811225085239353 0.98508766837529 70 88.6 NA NA 1.00960276918635 NA 71 101.3 NA NA 1.13618096568103 NA 72 122.9 NA NA 1.24412238664733 NA 73 126.6 NA NA NA NA 74 114.1 NA NA NA NA 75 124.7 NA NA NA NA

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/09/t12919171531t6kypikisbnyj9/1x54c1291917187.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/09/t12919171531t6kypikisbnyj9/1x54c1291917187.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/09/t12919171531t6kypikisbnyj9/2x54c1291917187.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/09/t12919171531t6kypikisbnyj9/2x54c1291917187.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/09/t12919171531t6kypikisbnyj9/37e3x1291917187.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/09/t12919171531t6kypikisbnyj9/37e3x1291917187.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/09/t12919171531t6kypikisbnyj9/47e3x1291917187.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/09/t12919171531t6kypikisbnyj9/47e3x1291917187.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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