Decompositie megaliters bier

*Unverified author*

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

Title produced by software: Classical Decomposition

Date of computation: Sun, 06 Jun 2010 22:24:33 +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/07/t1275863185pfal2orq6pi8srd.htm/, Retrieved Mon, 07 Jun 2010 00:26:25 +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/07/t1275863185pfal2orq6pi8srd.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 «

93.2 96 95.2 77.1 70.9 64.8 70.1 77.3 79.5 100.6 100.7 107.1 95.9 82.8 83.3 80 80.4 67.5 75.7 71.1 89.3 101.1 105.2 114.1 96.3 84.4 91.2 81.9 80.5 70.4 74.8 75.9 86.3 98.7 100.9 113.8 89.8 84.4 87.2 85.6 72 69.2 77.5 78.1 94.3 97.7 100.2 116.4 97.1 93 96 80.5 76.1 69.9 73.6 92.6 94.2 93.5 108.5 109.4 105.1 92.5 97.1 81.4 79.1 72.1 78.7 87.1 91.4 109.9 116.3 113 100 84.8 94.3 87.1 90.3 72.4 84.9 92.7 92.2 114.9 112.5 118.3 106 91.2 96.6 96.3 88.2 70.2 86.5 88.2 102.8 119.1 119.2 125.1

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 93.2 NA NA 1.09102781635458 NA 2 96 NA NA 0.968163112318007 NA 3 95.2 NA NA 1.01657973996773 NA 4 77.1 NA NA 0.930581597816603 NA 5 70.9 NA NA 0.887175287765638 NA 6 64.8 NA NA 0.769454800868965 NA 7 70.1 73.39583340964 86.1541666666667 0.851912754186468 0.955095088419458 8 77.3 78.2651347054293 85.7166666666667 0.913067875233474 0.9876683952687 9 79.5 84.5194044015827 84.6708333333333 0.998211557323943 0.940612402120894 10 100.6 95.9760603443143 84.2958333333333 1.13856232922918 1.04817805230906 11 100.7 100.075616509840 84.8125 1.17996305391115 1.00623911709901 12 107.1 107.103248484466 85.3208333333333 1.25530007502427 0.999969669599082 13 95.9 93.4647162677087 85.6666666666667 1.09102781635458 1.02605564783737 14 82.8 82.9151025441013 85.6416666666667 0.968163112318007 0.998611802427187 15 83.3 87.2140701913984 85.7916666666667 1.01657973996773 0.955121115402496 16 80 80.2355208484124 86.2208333333333 0.930581597816603 0.997064631151864 17 80.4 76.6778208088443 86.4291666666667 0.887175287765638 1.04854310088487 18 67.5 66.8720343188536 86.9083333333334 0.769454800868965 1.00939055746610 19 75.7 74.3009907109631 87.2166666666667 0.851912754186468 1.01882894528929 20 71.1 79.7108255078823 87.3 0.913067875233474 0.891974202336786 21 89.3 87.5389943624876 87.6958333333333 0.998211557323943 1.02011681365930 22 101.1 100.312085214795 88.1041666666667 1.13856232922918 1.00785463469848 23 105.2 104.057991816790 88.1875 1.17996305391115 1.0109747282575 24 114.1 110.858687875580 88.3125 1.25530007502427 1.02923823280371 25 96.3 96.4423130165098 88.3958333333333 1.09102781635458 0.99852437159522 26 84.4 85.7389116216955 88.5583333333333 0.968163112318007 0.984383850968354 27 91.2 90.1028509524733 88.6333333333333 1.01657973996773 1.01217662966187 28 81.9 82.2711680936362 88.4083333333333 0.930581597816603 0.995488479108334 29 80.5 78.1860187980459 88.1291666666667 0.887175287765638 1.02959584382895 30 70.4 67.6639315514146 87.9375 0.769454800868965 1.0404361435384 31 74.8 74.6737025409197 87.6541666666667 0.851912754186468 1.00169132445269 32 75.9 79.786914497485 87.3833333333333 0.913067875233474 0.951283809858224 33 86.3 87.0606846579366 87.2166666666667 0.998211557323943 0.99126259274292 34 98.7 99.2873791184892 87.2041666666667 1.13856232922918 0.994084050523801 35 100.9 102.661702202995 87.0041666666667 1.17996305391115 0.982839733170297 36 113.8 108.708986497101 86.6 1.25530007502427 1.04683157912648 37 89.8 94.5511981348285 86.6625 1.09102781635458 0.9497499954675 38 84.4 84.1011023566909 86.8666666666666 0.968163112318007 1.00355402765164 39 87.2 88.73893980135 87.2916666666667 1.01657973996773 0.982657671989377 40 85.6 81.5034382754375 87.5833333333333 0.930581597816603 1.05026244059445 41 72 77.6389273705904 87.5125 0.887175287765638 0.927369844463791 42 69.2 67.3978284327807 87.5916666666667 0.769454800868965 1.02673931206874 43 77.5 74.971872004885 88.0041666666667 0.851912754186468 1.03372101999734 44 78.1 80.958684937368 88.6666666666667 0.913067875233474 0.96468958284612 45 94.3 89.2317947951162 89.3916666666667 0.998211557323943 1.05679819862999 46 97.7 101.953512572767 89.5458333333333 1.13856232922918 0.958279882022391 47 100.2 105.611609837773 89.5041666666667 1.17996305391115 0.94875932820184 48 116.4 112.605647146656 89.7041666666667 1.25530007502427 1.0336959375439 49 97.1 97.7242707007264 89.5708333333333 1.09102781635458 0.993611917528265 50 93 87.1467821475246 90.0125 0.968163112318007 1.06716504853348 51 96 92.1148316878261 90.6125 1.01657973996773 1.04217744570538 52 80.5 84.1555958292148 90.4333333333333 0.930581597816603 0.956561464591927 53 76.1 80.3817776352658 90.6041666666667 0.887175287765638 0.946731986263174 54 69.9 69.7574898221122 90.6583333333333 0.769454800868965 1.00204293729965 55 73.6 77.2684868047127 90.7 0.851912754186468 0.952522859494009 56 92.6 83.1005899946865 91.0125 0.913067875233474 1.11431218485838 57 94.2 90.8746846498785 91.0375 0.998211557323943 1.03659231790386 58 93.5 103.746748241303 91.1208333333333 1.13856232922918 0.90123306595142 59 108.5 107.710960771190 91.2833333333333 1.17996305391115 1.00732552400574 60 109.4 114.859956864720 91.5 1.25530007502427 0.952464226752663 61 105.1 100.160899490585 91.8041666666667 1.09102781635458 1.04931166287978 62 92.5 88.865271671889 91.7875 0.968163112318007 1.04090156097796 63 97.1 92.957745722216 91.4416666666667 1.01657973996773 1.04456061456312 64 81.4 85.621261845776 92.0083333333333 0.930581597816603 0.950698439210351 65 79.1 82.5220880170004 93.0166666666667 0.887175287765638 0.958531247824274 66 72.1 71.9376117579076 93.4916666666667 0.769454800868965 1.00225734825113 67 78.7 79.5934986963466 93.4291666666666 0.851912754186468 0.98877422514425 68 87.1 84.8202011597096 92.8958333333333 0.913067875233474 1.02687801737227 69 91.4 92.292976904243 92.4583333333333 0.998211557323943 0.990324541105989 70 109.9 105.407151638096 92.5791666666667 1.13856232922918 1.0426237526779 71 116.3 110.070886879012 93.2833333333333 1.17996305391115 1.05659183184228 72 113 117.700073284463 93.7625 1.25530007502427 0.96006737163958 73 100 102.592982331209 94.0333333333333 1.09102781635458 0.974725538996054 74 84.8 91.5156181918596 94.525 0.968163112318007 0.926617791317538 75 94.3 96.363287851108 94.7916666666667 1.01657973996773 0.978588444861948 76 87.1 88.4362711791712 95.0333333333333 0.930581597816603 0.984890009931966 77 90.3 84.355583611716 95.0833333333333 0.887175287765638 1.07046855861546 78 72.4 73.2104182410117 95.1458333333333 0.769454800868965 0.988930288058951 79 84.9 81.4570578461295 95.6166666666667 0.851912754186468 1.04226695936372 80 92.7 87.776258405778 96.1333333333333 0.913067875233474 1.05609422962027 81 92.2 96.3232560669383 96.4958333333333 0.998211557323943 0.957193555997806 82 114.9 110.412081876999 96.975 1.13856232922918 1.04064698397772 83 112.5 114.775989556483 97.2708333333333 1.17996305391115 0.980170159584095 84 118.3 121.879176450898 97.0916666666667 1.25530007502427 0.97063340469535 85 106 105.902433374151 97.0666666666667 1.09102781635458 1.00092128785657 86 91.2 93.8593797262628 96.9458333333333 0.968163112318007 0.971666340284596 87 96.6 98.8115507248636 97.2 1.01657973996773 0.97761849997657 88 96.3 91.0263899597607 97.8166666666666 0.930581597816603 1.05793495757187 89 88.2 87.183454841469 98.2708333333333 0.887175287765638 1.01165984028024 90 70.2 76.047782819216 98.8333333333333 0.769454800868965 0.923103835477786 91 86.5 NA NA 0.851912754186468 NA 92 88.2 NA NA 0.913067875233474 NA 93 102.8 NA NA 0.998211557323943 NA 94 119.1 NA NA 1.13856232922918 NA 95 119.2 NA NA 1.17996305391115 NA 96 125.1 NA NA 1.25530007502427 NA

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Jun/07/t1275863185pfal2orq6pi8srd/1xdiv1275863070.png (open in new window) http://www.freestatistics.org/blog/date/2010/Jun/07/t1275863185pfal2orq6pi8srd/1xdiv1275863070.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Jun/07/t1275863185pfal2orq6pi8srd/2xdiv1275863070.png (open in new window) http://www.freestatistics.org/blog/date/2010/Jun/07/t1275863185pfal2orq6pi8srd/2xdiv1275863070.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Jun/07/t1275863185pfal2orq6pi8srd/3q5zy1275863070.png (open in new window) http://www.freestatistics.org/blog/date/2010/Jun/07/t1275863185pfal2orq6pi8srd/3q5zy1275863070.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Jun/07/t1275863185pfal2orq6pi8srd/4q5zy1275863070.png (open in new window) http://www.freestatistics.org/blog/date/2010/Jun/07/t1275863185pfal2orq6pi8srd/4q5zy1275863070.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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