Opgave 9 additief decompositiemodel - gem consumptieprijs tomaten - Bram Op de Beeck 2 MAR 04

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

Title produced by software: Classical Decomposition

Date of computation: Wed, 30 Jul 2008 04:28:49 -0600

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2008/Jul/30/t1217413809t4ynngfc44to3fx.htm/, Retrieved Wed, 30 Jul 2008 10:30:09 +0000

IsPrivate?

No (this computation is public)

User-defined keywords:

Dataseries X:

» Textbox « » Textfile « » CSV «

2.1300 1.8700 2.2300 3.0000 2.1200 1.6000 1.1700 1.0200 1.2200 1.8000 2.1300 2.2100 2.3800 1.9900 1.8200 2.4700 1.9400 1.3900 1.1100 0.9700 1.3800 2.3900 1.8800 2.1100 2.1100 2.1700 2.5400 3.1300 2.2500 1.3900 1.3600 1.3300 1.6000 1.9500 2.2300 2.5300 2.3600 1.9500 2.1600 2.7600 2.0900 1.4900 1.1700 1.3000 1.2600 2.1700 2.0300 2.1800 2.6100 2.5800 3.8600 3.8100 2.4100 1.4700 1.3300 1.3800 1.5700 2.6000 2.1800 2.3600 2.2400 2.4100 2.5100 2.9800 1.8700 1.9000 1.4700 1.4500 2.7100 2.9000 2.1100 2.1800 2.2400 2.0500 2.4200 2.7700 1.9900 1.4700 1.0900 0.9300 1.3200 2.0300 2.0400 2.7800 2.8000 3.0300 3.1100 2.7500 2.7800 1.7600 1.2900 1.2800 1.4300 1.7100 1.8900 1.8400

Text written by user:

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 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 Classical Decomposition by Moving Averages t Observations Fit Trend Seasonal Random 1 2.13 NA NA 0.297731481481481 NA 2 1.87 NA NA 0.167245370370370 NA 3 2.23 NA NA 0.527175925925926 NA 4 3 NA NA 0.95988425925926 NA 5 2.12 NA NA 0.063912037037037 NA 6 1.6 NA NA -0.51275462962963 NA 7 1.17 1.10245370370370 1.88541666666667 -0.782962962962963 0.0675462962962963 8 1.02 1.07939814814815 1.90083333333333 -0.821435185185185 -0.059398148148148 9 1.22 1.46446759259259 1.88875 -0.424282407407407 -0.244467592592593 10 1.8 2.11439814814815 1.84958333333333 0.264814814814815 -0.314398148148148 11 2.13 1.81537037037037 1.82 -0.00462962962962964 0.31462962962963 12 2.21 2.06905092592593 1.80375 0.265300925925926 0.140949074074074 13 2.38 2.09023148148148 1.7925 0.297731481481481 0.289768518518519 14 1.99 1.95516203703704 1.78791666666667 0.167245370370370 0.0348379629629636 15 1.82 2.31967592592593 1.7925 0.527175925925926 -0.499675925925925 16 2.47 2.78363425925926 1.82375 0.95988425925926 -0.313634259259259 17 1.94 1.90182870370370 1.83791666666667 0.063912037037037 0.0381712962962968 18 1.39 1.31057870370370 1.82333333333333 -0.51275462962963 0.0794212962962966 19 1.11 1.02495370370370 1.80791666666667 -0.782962962962963 0.0850462962962966 20 0.97 0.982731481481481 1.80416666666667 -0.821435185185185 -0.0127314814814814 21 1.38 1.41738425925926 1.84166666666667 -0.424282407407407 -0.0373842592592593 22 2.39 2.16398148148148 1.89916666666667 0.264814814814815 0.226018518518519 23 1.88 1.93495370370370 1.93958333333333 -0.00462962962962964 -0.0549537037037038 24 2.11 2.21780092592593 1.9525 0.265300925925926 -0.107800925925926 25 2.11 2.26064814814815 1.96291666666667 0.297731481481481 -0.150648148148148 26 2.17 2.15557870370370 1.98833333333333 0.167245370370370 0.0144212962962962 27 2.54 2.53967592592593 2.0125 0.527175925925926 0.000324074074073977 28 3.13 2.96321759259259 2.00333333333333 0.95988425925926 0.166782407407407 29 2.25 2.06349537037037 1.99958333333333 0.063912037037037 0.186504629629630 30 1.39 1.51891203703704 2.03166666666667 -0.51275462962963 -0.128912037037037 31 1.36 1.27662037037037 2.05958333333333 -0.782962962962963 0.0833796296296296 32 1.33 1.23939814814815 2.06083333333333 -0.821435185185185 0.0906018518518521 33 1.6 1.61155092592593 2.03583333333333 -0.424282407407407 -0.0115509259259259 34 1.95 2.26939814814815 2.00458333333333 0.264814814814815 -0.319398148148148 35 2.23 1.97787037037037 1.9825 -0.00462962962962964 0.252129629629630 36 2.53 2.24530092592593 1.98 0.265300925925926 0.284699074074074 37 2.36 2.27398148148148 1.97625 0.297731481481481 0.0860185185185185 38 1.95 2.13432870370370 1.96708333333333 0.167245370370370 -0.184328703703704 39 2.16 2.47884259259259 1.95166666666667 0.527175925925926 -0.318842592592592 40 2.76 2.90655092592593 1.94666666666667 0.95988425925926 -0.146550925925926 41 2.09 2.01141203703704 1.9475 0.063912037037037 0.0785879629629627 42 1.49 1.41182870370370 1.92458333333333 -0.51275462962963 0.0781712962962966 43 1.17 1.13745370370370 1.92041666666667 -0.782962962962963 0.0325462962962964 44 1.3 1.13564814814815 1.95708333333333 -0.821435185185185 0.164351851851852 45 1.26 1.62988425925926 2.05416666666667 -0.424282407407407 -0.369884259259259 46 2.17 2.43356481481481 2.16875 0.264814814814815 -0.263564814814815 47 2.03 2.22120370370370 2.22583333333333 -0.00462962962962964 -0.191203703703704 48 2.18 2.50363425925926 2.23833333333333 0.265300925925926 -0.323634259259259 49 2.61 2.54189814814815 2.24416666666667 0.297731481481481 0.0681018518518512 50 2.58 2.42141203703704 2.25416666666667 0.167245370370370 0.158587962962963 51 3.86 2.79759259259259 2.27041666666667 0.527175925925926 1.06240740740741 52 3.81 3.26113425925926 2.30125 0.95988425925926 0.548865740740741 53 2.41 2.38932870370370 2.32541666666667 0.063912037037037 0.0206712962962965 54 1.47 1.82641203703704 2.33916666666667 -0.51275462962963 -0.356412037037037 55 1.33 1.54828703703704 2.33125 -0.782962962962963 -0.218287037037037 56 1.38 1.48731481481482 2.30875 -0.821435185185185 -0.107314814814815 57 1.57 1.82113425925926 2.24541666666667 -0.424282407407407 -0.251134259259259 58 2.6 2.41939814814815 2.15458333333333 0.264814814814815 0.180601851851852 59 2.18 2.09287037037037 2.0975 -0.00462962962962964 0.0871296296296298 60 2.36 2.35821759259259 2.09291666666667 0.265300925925926 0.00178240740740776 61 2.24 2.41439814814815 2.11666666666667 0.297731481481481 -0.174398148148148 62 2.41 2.29266203703704 2.12541666666667 0.167245370370370 0.117337962962963 63 2.51 2.70300925925926 2.17583333333333 0.527175925925926 -0.19300925925926 64 2.98 3.19571759259259 2.23583333333333 0.95988425925926 -0.215717592592593 65 1.87 2.30932870370370 2.24541666666667 0.063912037037037 -0.439328703703704 66 1.9 1.72224537037037 2.235 -0.51275462962963 0.177754629629630 67 1.47 1.44453703703704 2.2275 -0.782962962962963 0.0254629629629632 68 1.45 1.39106481481481 2.2125 -0.821435185185185 0.0589351851851849 69 2.71 1.76946759259259 2.19375 -0.424282407407407 0.940532407407407 70 2.9 2.44606481481481 2.18125 0.264814814814815 0.453935185185185 71 2.11 2.17287037037037 2.1775 -0.00462962962962964 -0.0628703703703706 72 2.18 2.42988425925926 2.16458333333333 0.265300925925926 -0.249884259259259 73 2.24 2.42856481481481 2.13083333333333 0.297731481481481 -0.188564814814814 74 2.05 2.26057870370370 2.09333333333333 0.167245370370370 -0.210578703703704 75 2.42 2.54092592592593 2.01375 0.527175925925926 -0.120925925925926 76 2.77 2.87946759259259 1.91958333333333 0.95988425925926 -0.109467592592593 77 1.99 1.94432870370370 1.88041666666667 0.063912037037037 0.0456712962962962 78 1.47 1.38974537037037 1.9025 -0.51275462962963 0.0802546296296296 79 1.09 1.16787037037037 1.95083333333333 -0.782962962962963 -0.0778703703703703 80 0.93 1.19356481481481 2.015 -0.821435185185185 -0.263564814814814 81 1.32 1.66030092592593 2.08458333333333 -0.424282407407407 -0.340300925925925 82 2.03 2.37731481481481 2.1125 0.264814814814815 -0.347314814814815 83 2.04 2.13995370370370 2.14458333333333 -0.00462962962962964 -0.0999537037037035 84 2.78 2.45488425925926 2.18958333333333 0.265300925925926 0.325115740740741 85 2.8 2.50773148148148 2.21 0.297731481481481 0.292268518518518 86 3.03 2.40016203703704 2.23291666666667 0.167245370370370 0.629837962962963 87 3.11 2.77925925925926 2.25208333333333 0.527175925925926 0.330740740740741 88 2.75 3.20321759259259 2.24333333333333 0.95988425925926 -0.453217592592593 89 2.78 2.28766203703704 2.22375 0.063912037037037 0.492337962962963 90 1.76 1.66557870370370 2.17833333333333 -0.51275462962963 0.0944212962962965 91 1.29 NA NA -0.782962962962963 NA 92 1.28 NA NA -0.821435185185185 NA 93 1.43 NA NA -0.424282407407407 NA 94 1.71 NA NA 0.264814814814815 NA 95 1.89 NA NA -0.00462962962962964 NA 96 1.84 NA NA 0.265300925925926 NA

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Jul/30/t1217413809t4ynngfc44to3fx/1x77e1217413721.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Jul/30/t1217413809t4ynngfc44to3fx/1x77e1217413721.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Jul/30/t1217413809t4ynngfc44to3fx/2sgwx1217413721.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Jul/30/t1217413809t4ynngfc44to3fx/2sgwx1217413721.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Jul/30/t1217413809t4ynngfc44to3fx/3gtry1217413721.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Jul/30/t1217413809t4ynngfc44to3fx/3gtry1217413721.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Jul/30/t1217413809t4ynngfc44to3fx/4a5u51217413721.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Jul/30/t1217413809t4ynngfc44to3fx/4a5u51217413721.ps (open in new window)

Parameters (Session):

par1 = additive ; par2 = 12 ;

Parameters (R input):

par1 = additive ; 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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