Additief decompositiemodel - Omzetcijfers Carrefour Burcht - Jeroen Van Eeckhoven

*Unverified author*

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

Title produced by software: Classical Decomposition

Date of computation: Sun, 31 May 2009 06:22:48 -0600

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2009/May/31/t1243772630mwp7isgn776b6g6.htm/, Retrieved Sun, 31 May 2009 14:23:55 +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/2009/May/31/t1243772630mwp7isgn776b6g6.htm/}, year = {2009}, } @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 = {2009}, 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:

Dataseries X:

» Textbox « » Textfile « » CSV «

2291180 1971664 2193270 2197733 2345324 2195121 2170583 2241521 2154945 2912568 2392562 3336621 4080642 3735329 4018383 4171360 3855698 4101316 4199346 3959646 3960841 4784025 4105467 5929558 4048642 3828808 4268127 4171816 4004783 4295447 3968177 3918480 4040260 4530715 4103330 6025506 4632308 4133863 4519182 4151573 4486595 4504699 4180443 4222193 4373727 4734738 4403232 5903985 4414074 4061816 4504697 3994176 4114925 4485120 4171230 4476075 4179369 4823185 4585751 6110454 4279575 3782118 4098678 4065616 4413733 4481214 4345018 4294488 4361269 4535031 4318397 6040168

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 'George Udny Yule' @ 72.249.76.132 Classical Decomposition by Moving Averages t Observations Fit Trend Seasonal Random 1 2291180 NA NA 1.03464187545731 NA 2 1971664 NA NA 0.932091840987793 NA 3 2193270 NA NA 1.00974836055787 NA 4 2197733 NA NA 0.963277787504782 NA 5 2345324 NA NA 0.96726036696828 NA 6 2195121 NA NA 1.00423857820004 NA 7 2170583 2281130.64686061 2441485.25 0.934320879825348 0.95153822205986 8 2241521 2410955.55010919 2589532.20833333 0.931039027956683 0.929723071791383 9 2154945 2503799.35761987 2739064.625 0.914107441922758 0.860670002746749 10 2912568 3084082.74313964 2897345.45833333 1.06445116313942 0.944387113633328 11 2392562 2867947.94344309 3042512.16666667 0.942624971187928 0.834241780946565 12 3336621 4147329.377931 3184869.20833333 1.30219770629179 0.804522789473875 13 4080642 3464835.39766980 3348825.79166667 1.03464187545731 1.17773040610943 14 3735329 3266931.68621428 3504946.125 0.932091840987793 1.14337530097805 15 4018383 3687379.20470081 3651780.33333333 1.00974836055787 1.08976668167929 16 4171360 3665275.23251823 3805003.375 0.963277787504782 1.13807551558251 17 3855698 3824887.76512918 3954351.79166667 0.96726036696828 1.00805519972422 18 4101316 4151283.14796754 4133761.875 1.00423857820004 0.987963444991219 19 4199346 3961957.40333087 4240467.58333333 0.934320879825348 1.05991699872128 20 3959646 3950425.78971848 4243029.20833333 0.931039027956683 1.00233397886008 21 3960841 3891657.18807226 4257330.16666667 0.914107441922758 1.01777746820552 22 4784025 4542816.95115261 4267755.16666667 1.06445116313942 1.05309658113920 23 4105467 4028765.96938365 4273986.04166667 0.942624971187928 1.01903834355215 24 5929558 5584197.11551322 4288286.70833333 1.30219770629179 1.06184611276120 25 4048642 4435244.29133435 4286743.45833333 1.03464187545731 0.912834047926132 26 3828808 3985061.88394049 4275396.16666667 0.932091840987793 0.960790098500056 27 4268127 4318683.68269526 4276990.04166667 1.00974836055787 0.98829349718345 28 4171816 4112950.11544386 4269744.58333333 0.963277787504782 1.01431232640899 29 4004783 4119659.55591245 4259100.95833333 0.96726036696828 0.972115036605977 30 4295447 4281078.85019291 4263009.75 1.00423857820004 1.00335619835791 31 3968177 4009476.41825827 4291327 0.934320879825348 0.989699548282613 32 3918480 4029869.3327228 4328357.04166667 0.931039027956683 0.972359070846712 33 4040260 3977764.39514993 4351528.29166667 0.914107441922758 1.01571123843490 34 4530715 4642226.35574312 4361145.45833333 1.06445116313942 0.97597890598222 35 4103330 4129053.21472975 4380377.5 0.942624971187928 0.993770190551678 36 6025506 5741613.44801302 4409171.83333333 1.30219770629179 1.04944473440392 37 4632308 4580085.48877266 4426735.08333333 1.03464187545731 1.01140208220029 38 4133863 4146162.81239030 4448234.20833333 0.932091840987793 0.997033446840645 39 4519182 4518405.17675786 4474783.375 1.00974836055787 1.00017192421037 40 4151573 4332032.43645010 4497178.79166667 0.963277787504782 0.958343008946172 41 4486595 4370252.25336715 4518175.66666667 0.96726036696828 1.02662151745204 42 4504699 4544790.35262710 4525608.20833333 1.00423857820004 0.991178613419664 43 4180443 4215143.5683496 4511451.75 0.934320879825348 0.991767642599374 44 4222193 4189076.69615705 4499356.70833333 0.931039027956683 1.00790539449262 45 4373727 4109599.63657073 4495751.20833333 0.914107441922758 1.06427082606267 46 4734738 4777884.26977826 4488589.45833333 1.06445116313942 0.99096958667434 47 4403232 4210276.85193458 4466545 0.942624971187928 1.04582956296015 48 5903985 5795096.17278284 4450242.95833333 1.30219770629179 1.01878982228605 49 4414074 4603166.49528075 4449043.29166667 1.03464187545731 0.95892121315303 50 4061816 4156419.20147409 4459237.83333333 0.932091840987793 0.977239254057788 51 4504697 4505212.43577154 4461718 1.00974836055787 0.999885591239284 52 3994176 4293622.93877058 4457305.04166667 0.963277787504782 0.930257746653385 53 4114925 4322295.12165023 4468595.29166667 0.96726036696828 0.952023146080073 54 4485120 4503812.3137571 4484803.125 1.00423857820004 0.99584966857966 55 4171230 4193046.99633185 4487801.875 0.934320879825348 0.99479686339053 56 4476075 4162250.62985124 4470543.66666667 0.931039027956683 1.07539775906286 57 4179369 4060439.77627595 4441972.125 0.914107441922758 1.02928973960380 58 4823185 4713423.10318447 4428031.33333333 1.06445116313942 1.02328708762457 59 4585751 4188514.78343309 4443458.33333333 0.942624971187928 1.09483939704310 60 6110454 5802262.11250232 4455745.91666667 1.30219770629179 1.05311581612861 61 4279575 4617424.93807651 4462824.33333333 1.03464187545731 0.926831525664768 62 3782118 4159459.25785063 4462499.375 0.932091840987793 0.90928117467711 63 4098678 4506014.59669831 4462512.41666667 1.00974836055787 0.909601580741266 64 4065616 4294374.41585455 4458085.16666667 0.963277787504782 0.94673067746259 65 4413733 4289740.72462194 4434939 0.96726036696828 1.02890437519134 66 4481214 4439608.8726996 4420870.66666667 1.00423857820004 1.00937134970520 67 4345018 NA NA 0.934320879825348 NA 68 4294488 NA NA 0.931039027956683 NA 69 4361269 NA NA 0.914107441922758 NA 70 4535031 NA NA 1.06445116313942 NA 71 4318397 NA NA 0.942624971187928 NA 72 6040168 NA NA 1.30219770629179 NA

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/May/31/t1243772630mwp7isgn776b6g6/1k1451243772565.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/May/31/t1243772630mwp7isgn776b6g6/1k1451243772565.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/May/31/t1243772630mwp7isgn776b6g6/2v5fa1243772565.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/May/31/t1243772630mwp7isgn776b6g6/2v5fa1243772565.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/May/31/t1243772630mwp7isgn776b6g6/3jxrk1243772565.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/May/31/t1243772630mwp7isgn776b6g6/3jxrk1243772565.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/May/31/t1243772630mwp7isgn776b6g6/4jfx51243772565.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/May/31/t1243772630mwp7isgn776b6g6/4jfx51243772565.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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