decompositie van de gemiddelde consumptieprijs van plat water

*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 18:05:38 +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/t1292090928tsi4xuga8lv2au1.htm/, Retrieved Sat, 11 Dec 2010 19:09:09 +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/t1292090928tsi4xuga8lv2au1.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 - Kristina Henderickx

Dataseries X:

» Textbox « » Textfile « » CSV «

0.65 0.65 0.65 0.65 0.65 0.65 0.66 0.66 0.66 0.65 0.65 0.65 0.65 0.65 0.65 0.65 0.66 0.67 0.66 0.67 0.66 0.66 0.66 0.66 0.71 0.74 0.75 0.75 0.75 0.75 0.7 0.69 0.69 0.68 0.68 0.68 0.67 0.66 0.66 0.67 0.67 0.67 0.67 0.68 0.68 0.67 0.67 0.67 0.67 0.67 0.69 0.69 0.69 0.69 0.69 0.69 0.7 0.69 0.68 0.7 0.7 0.71 0.69 0.7 0.7 0.71 0.71 0.71 0.71 0.7 0.7 0.71 0.71 0.71 0.71 0.7 0.69 0.7 0.7 0.7 0.71 0.7 0.7 0.69

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 'Gwilym Jenkins' @ 72.249.127.135 Classical Decomposition by Moving Averages t Observations Fit Trend Seasonal Random 1 0.65 NA NA 0.998525583822325 NA 2 0.65 NA NA 1.00472503767124 NA 3 0.65 NA NA 1.00628971513117 NA 4 0.65 NA NA 1.00774960154544 NA 5 0.65 NA NA 1.00689773762088 NA 6 0.65 NA NA 1.01324661513453 NA 7 0.66 0.652689694035563 0.6525 1.00029071882845 1.01120027791344 8 0.66 0.65355056995412 0.6525 1.00161006889520 1.00986829534298 9 0.66 0.652635953398177 0.6525 1.00020835769836 1.01128354416192 10 0.65 0.643944631591434 0.6525 0.986888324278059 1.00940355445405 11 0.65 0.642219635713593 0.652916666666667 0.983616544807035 1.01211480287077 12 0.65 0.647593400196113 0.654166666666667 0.989951694567306 1.00371622039872 13 0.65 0.654034257403623 0.655 0.998525583822325 0.993831733799942 14 0.65 0.658513535107024 0.655416666666667 1.00472503767124 0.98707158675844 15 0.65 0.659958338173527 0.655833333333333 1.00628971513117 0.98491065632857 16 0.65 0.661335676014196 0.65625 1.00774960154544 0.982859421583733 17 0.66 0.661615721761722 0.657083333333333 1.00689773762088 0.997557915103015 18 0.67 0.666631835540596 0.657916666666667 1.01324661513453 1.00505251066606 19 0.66 0.6610254500258 0.660833333333333 1.00029071882845 0.998448698116298 20 0.67 0.668157383458842 0.667083333333333 1.00161006889520 1.00275775825692 21 0.66 0.67514064144639 0.675 1.00020835769836 0.977574092689853 22 0.66 0.674373688256674 0.683333333333333 0.986888324278059 0.978685870301626 23 0.66 0.679924936597863 0.69125 0.983616544807035 0.97069538779154 24 0.66 0.691316266706169 0.698333333333333 0.989951694567306 0.954700520999777 25 0.71 0.702296327288369 0.703333333333333 0.998525583822325 1.01096926242143 26 0.74 0.70916842242295 0.705833333333333 1.00472503767124 1.04347567743035 27 0.75 0.712369260836608 0.707916666666667 1.00628971513117 1.05282476551444 28 0.75 0.715502217097264 0.71 1.00774960154544 1.04821478128005 29 0.75 0.716575556606862 0.711666666666667 1.00689773762088 1.04664468817693 30 0.75 0.722782585462634 0.713333333333333 1.01324661513453 1.03765643373926 31 0.7 0.71270713716527 0.7125 1.00029071882845 0.982170604863295 32 0.69 0.708639123743356 0.7075 1.00161006889520 0.973697297935096 33 0.69 0.700562603871223 0.700416666666667 1.00020835769836 0.984922683836027 34 0.68 0.684242571499454 0.693333333333333 0.986888324278059 0.993799608974699 35 0.68 0.67541669410083 0.686666666666667 0.983616544807035 1.00678589371450 36 0.68 0.673167152305768 0.68 0.989951694567306 1.01015029873461 37 0.67 0.674420821406662 0.675416666666667 0.998525583822325 0.993445010494425 38 0.66 0.676933494130997 0.67375 1.00472503767124 0.974984995900173 39 0.66 0.677149120807017 0.672916666666667 1.00628971513117 0.974674528430932 40 0.67 0.677291711371999 0.672083333333333 1.00774960154544 0.98923401652557 41 0.67 0.675880106378018 0.67125 1.00689773762088 0.991300074787629 42 0.67 0.679297418229777 0.670416666666667 1.01324661513453 0.986313184799074 43 0.67 0.670194781615061 0.67 1.00029071882845 0.999709365664425 44 0.68 0.671496083688492 0.670416666666667 1.00161006889520 1.01266413389159 45 0.68 0.67222336706977 0.672083333333333 1.00020835769836 1.01156852515278 46 0.67 0.665327211950791 0.674166666666667 0.986888324278059 1.00702329314851 47 0.67 0.664760848198754 0.675833333333333 0.983616544807035 1.00788125807265 48 0.67 0.67069227306935 0.6775 0.989951694567306 0.998967823102864 49 0.67 0.678165292345996 0.679166666666667 0.998525583822325 0.987959731295376 50 0.67 0.683631661048805 0.680416666666667 1.00472503767124 0.98005993311092 51 0.69 0.685954155814415 0.681666666666667 1.00628971513117 1.00589812621046 52 0.69 0.688628894389385 0.683333333333333 1.00774960154544 1.00199106604702 53 0.69 0.689305409546296 0.684583333333333 1.00689773762088 1.00100766720250 54 0.69 0.695340489636074 0.68625 1.01324661513453 0.992319604976737 55 0.69 0.688950232593094 0.68875 1.00029071882845 1.00152372023006 56 0.69 0.692780297652516 0.691666666666667 1.00161006889520 0.995986754152887 57 0.7 0.69347779467086 0.693333333333333 1.00020835769836 1.00940506729885 58 0.69 0.684653774967903 0.69375 0.986888324278059 1.00780865486698 59 0.68 0.683203658413886 0.694583333333333 0.983616544807035 0.995310829538993 60 0.7 0.68884138746975 0.695833333333333 0.989951694567306 1.01619910291865 61 0.7 0.696471594716072 0.6975 0.998525583822325 1.00506611513046 62 0.71 0.702470255505141 0.699166666666667 1.00472503767124 1.01071895135182 63 0.69 0.704822087973125 0.700416666666667 1.00628971513117 0.978970454777107 64 0.7 0.706684408083741 0.70125 1.00774960154544 0.99054116942828 65 0.7 0.707345660678671 0.7025 1.00689773762088 0.98961517531383 66 0.71 0.713072305400928 0.70375 1.01324661513453 0.995691453198142 67 0.71 0.704788168974544 0.704583333333333 1.00029071882845 1.00739489006043 68 0.71 0.706135098571118 0.705 1.00161006889520 1.00547331726847 69 0.71 0.705980399142089 0.705833333333333 1.00020835769836 1.00569364370852 70 0.7 0.697401082489828 0.706666666666667 0.986888324278059 1.00372657510208 71 0.7 0.694679184769968 0.70625 0.983616544807035 1.00765938485949 72 0.71 0.698328424542687 0.705416666666667 0.989951694567306 1.01671359069331 73 0.71 0.703544484268146 0.704583333333333 0.998525583822325 1.00917570370631 74 0.71 0.707075245261134 0.70375 1.00472503767124 1.00413641229624 75 0.71 0.707757099642257 0.703333333333333 1.00628971513117 1.00316902558643 76 0.7 0.708783886420294 0.703333333333333 1.00774960154544 0.98760710198329 77 0.69 0.708184742126688 0.703333333333333 1.00689773762088 0.974322036264041 78 0.7 0.71180574713201 0.7025 1.01324661513453 0.983414369468668 79 0.7 NA NA 1.00029071882845 NA 80 0.7 NA NA 1.00161006889520 NA 81 0.71 NA NA 1.00020835769836 NA 82 0.7 NA NA 0.986888324278059 NA 83 0.7 NA NA 0.983616544807035 NA 84 0.69 NA NA 0.989951694567306 NA

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/11/t1292090928tsi4xuga8lv2au1/1049v1292090735.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/11/t1292090928tsi4xuga8lv2au1/1049v1292090735.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/11/t1292090928tsi4xuga8lv2au1/2049v1292090735.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/11/t1292090928tsi4xuga8lv2au1/2049v1292090735.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/11/t1292090928tsi4xuga8lv2au1/3tdqg1292090735.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/11/t1292090928tsi4xuga8lv2au1/3tdqg1292090735.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/11/t1292090928tsi4xuga8lv2au1/4tdqg1292090735.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/11/t1292090928tsi4xuga8lv2au1/4tdqg1292090735.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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