Gemiddelde prijs van een kiwi

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

Title produced by software: Classical Decomposition

Date of computation: Thu, 15 May 2008 07:34:55 -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/May/15/t1210858729zraewxnx3r2dru4.htm/, Retrieved Thu, 15 May 2008 15:38:54 +0200

User-defined keywords:

Dataseries X:

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0,33 0,35 0,35 0,34 0,37 0,38 0,39 0,37 0,37 0,37 0,37 0,38 0,36 0,36 0,36 0,36 0,38 0,37 0,39 0,39 0,4 0,42 0,42 0,4 0,36 0,36 0,36 0,36 0,35 0,38 0,4 0,39 0,39 0,39 0,36 0,35 0,35 0,33 0,33 0,32 0,36 0,37 0,38 0,38 0,38 0,38 0,39 0,4 0,38 0,41 0,41 0,43 0,42 0,41 0,41 0,43 0,44 0,46 0,44 0,43 0,43 0,42 0,42 0,42 0,43 0,44 0,45 0,44 0,47 0,48 0,48 0,45 0,44 0,44 0,45 0,46 0,45 0,46 0,47 0,48 0,48 0,46 0,47 0,47 0,43 0,41 0,39 0,41 0,44 0,45 0,45 0,46 0,45 0,45 0,46 0,46

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 3 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Classical Decomposition by Moving Averages t Observations Fit Trend Seasonal Random 1 0.33 NA NA -0.0145023148148148 NA 2 0.35 NA NA -0.0158217592592592 NA 3 0.35 NA NA -0.0156828703703704 NA 4 0.34 NA NA -0.0137384259259259 NA 5 0.37 NA NA -0.00839120370370371 NA 6 0.38 NA NA -0.00304398148148150 NA 7 0.39 0.372928240740741 0.365416666666667 0.00751157407407407 0.0170717592592594 8 0.37 0.375428240740741 0.367083333333333 0.0083449074074074 -0.00542824074074066 9 0.37 0.384039351851852 0.367916666666667 0.0161226851851852 -0.0140393518518518 10 0.37 0.389733796296296 0.369166666666667 0.0205671296296296 -0.0197337962962963 11 0.37 0.385219907407407 0.370416666666667 0.0148032407407407 -0.0152199074074074 12 0.38 0.374247685185185 0.370416666666667 0.00383101851851851 0.00575231481481492 13 0.36 0.355497685185185 0.37 -0.0145023148148148 0.00450231481481489 14 0.36 0.355011574074074 0.370833333333333 -0.0158217592592592 0.00498842592592591 15 0.36 0.357233796296296 0.372916666666667 -0.0156828703703704 0.00276620370370367 16 0.36 0.362511574074074 0.37625 -0.0137384259259259 -0.00251157407407410 17 0.38 0.372025462962963 0.380416666666667 -0.00839120370370371 0.00797453703703699 18 0.37 0.380289351851852 0.383333333333333 -0.00304398148148150 -0.0102893518518519 19 0.39 0.391678240740741 0.384166666666667 0.00751157407407407 -0.00167824074074069 20 0.39 0.392511574074074 0.384166666666667 0.0083449074074074 -0.00251157407407415 21 0.4 0.400289351851852 0.384166666666667 0.0161226851851852 -0.000289351851851805 22 0.42 0.404733796296296 0.384166666666667 0.0205671296296296 0.0152662037037037 23 0.42 0.397719907407407 0.382916666666667 0.0148032407407407 0.0222800925925927 24 0.4 0.385914351851852 0.382083333333333 0.00383101851851851 0.0140856481481482 25 0.36 0.368414351851852 0.382916666666667 -0.0145023148148148 -0.00841435185185174 26 0.36 0.367511574074074 0.383333333333333 -0.0158217592592592 -0.00751157407407405 27 0.36 0.367233796296296 0.382916666666667 -0.0156828703703704 -0.00723379629629628 28 0.36 0.367511574074074 0.38125 -0.0137384259259259 -0.00751157407407405 29 0.35 0.369108796296296 0.3775 -0.00839120370370371 -0.0191087962962963 30 0.38 0.369872685185185 0.372916666666667 -0.00304398148148150 0.0101273148148148 31 0.4 0.377928240740741 0.370416666666667 0.00751157407407407 0.0220717592592592 32 0.39 0.377094907407407 0.36875 0.0083449074074074 0.0129050925925926 33 0.39 0.382372685185185 0.36625 0.0161226851851852 0.00762731481481477 34 0.39 0.383900462962963 0.363333333333333 0.0205671296296296 0.00609953703703703 35 0.36 0.376886574074074 0.362083333333333 0.0148032407407407 -0.0168865740740741 36 0.35 0.365914351851852 0.362083333333333 0.00383101851851851 -0.0159143518518518 37 0.35 0.346331018518518 0.360833333333333 -0.0145023148148148 0.00366898148148154 38 0.33 0.343761574074074 0.359583333333333 -0.0158217592592592 -0.0137615740740740 39 0.33 0.343067129629630 0.35875 -0.0156828703703704 -0.0130671296296295 40 0.32 0.344178240740741 0.357916666666667 -0.0137384259259259 -0.0241782407407407 41 0.36 0.350358796296296 0.35875 -0.00839120370370371 0.0096412037037037 42 0.37 0.359039351851852 0.362083333333333 -0.00304398148148150 0.0109606481481481 43 0.38 0.372928240740741 0.365416666666667 0.00751157407407407 0.00707175925925924 44 0.38 0.378344907407407 0.37 0.0083449074074074 0.00165509259259256 45 0.38 0.392789351851852 0.376666666666667 0.0161226851851852 -0.0127893518518519 46 0.38 0.405150462962963 0.384583333333333 0.0205671296296296 -0.0251504629629629 47 0.39 0.406469907407407 0.391666666666667 0.0148032407407407 -0.0164699074074074 48 0.4 0.399664351851852 0.395833333333333 0.00383101851851851 0.000335648148148071 49 0.38 0.384247685185185 0.39875 -0.0145023148148148 -0.0042476851851852 50 0.41 0.386261574074074 0.402083333333333 -0.0158217592592592 0.0237384259259259 51 0.41 0.390983796296296 0.406666666666667 -0.0156828703703704 0.0190162037037037 52 0.43 0.398761574074074 0.4125 -0.0137384259259259 0.031238425925926 53 0.42 0.409525462962963 0.417916666666667 -0.00839120370370371 0.0104745370370370 54 0.41 0.418206018518519 0.42125 -0.00304398148148150 -0.00820601851851854 55 0.41 0.432094907407407 0.424583333333333 0.00751157407407407 -0.0220949074074074 56 0.43 0.435428240740741 0.427083333333333 0.0083449074074074 -0.00542824074074072 57 0.44 0.444039351851852 0.427916666666667 0.0161226851851852 -0.00403935185185184 58 0.46 0.448483796296296 0.427916666666667 0.0205671296296296 0.0115162037037038 59 0.44 0.442719907407407 0.427916666666667 0.0148032407407407 -0.00271990740740741 60 0.43 0.433414351851852 0.429583333333333 0.00383101851851851 -0.00341435185185185 61 0.43 0.417997685185185 0.4325 -0.0145023148148148 0.0120023148148148 62 0.42 0.418761574074074 0.434583333333333 -0.0158217592592592 0.00123842592592588 63 0.42 0.42056712962963 0.43625 -0.0156828703703704 -0.000567129629629626 64 0.42 0.424594907407407 0.438333333333333 -0.0137384259259259 -0.00459490740740737 65 0.43 0.432442129629629 0.440833333333333 -0.00839120370370371 -0.00244212962962947 66 0.44 0.440289351851852 0.443333333333333 -0.00304398148148150 -0.000289351851851749 67 0.45 0.452094907407407 0.444583333333333 0.00751157407407407 -0.00209490740740725 68 0.44 0.454178240740741 0.445833333333333 0.0083449074074074 -0.0141782407407406 69 0.47 0.464039351851852 0.447916666666667 0.0161226851851852 0.00596064814814823 70 0.48 0.471400462962963 0.450833333333333 0.0205671296296296 0.00859953703703698 71 0.48 0.468136574074074 0.453333333333333 0.0148032407407407 0.0118634259259259 72 0.45 0.458831018518519 0.455 0.00383101851851851 -0.00883101851851853 73 0.44 0.442164351851852 0.456666666666667 -0.0145023148148148 -0.00216435185185176 74 0.44 0.443344907407407 0.459166666666667 -0.0158217592592592 -0.00334490740740739 75 0.45 0.445567129629630 0.46125 -0.0156828703703704 0.00443287037037043 76 0.46 0.447094907407407 0.460833333333333 -0.0137384259259259 0.0129050925925927 77 0.45 0.45119212962963 0.459583333333333 -0.00839120370370371 -0.00119212962962956 78 0.46 0.456956018518518 0.46 -0.00304398148148150 0.00304398148148161 79 0.47 0.467928240740741 0.460416666666667 0.00751157407407407 0.00207175925925934 80 0.48 0.467094907407407 0.45875 0.0083449074074074 0.0129050925925927 81 0.48 0.471122685185185 0.455 0.0161226851851852 0.0088773148148149 82 0.46 0.470983796296296 0.450416666666667 0.0205671296296296 -0.0109837962962962 83 0.47 0.462719907407407 0.447916666666667 0.0148032407407407 0.00728009259259266 84 0.47 0.450914351851852 0.447083333333333 0.00383101851851851 0.0190856481481482 85 0.43 0.431331018518518 0.445833333333333 -0.0145023148148148 -0.00133101851851847 86 0.41 0.428344907407407 0.444166666666667 -0.0158217592592592 -0.0183449074074075 87 0.39 0.426400462962963 0.442083333333333 -0.0156828703703704 -0.036400462962963 88 0.41 0.426678240740741 0.440416666666667 -0.0137384259259259 -0.0166782407407408 89 0.44 0.43119212962963 0.439583333333333 -0.00839120370370371 0.00880787037037029 90 0.45 0.435706018518519 0.43875 -0.00304398148148150 0.0142939814814814 91 0.45 NA NA 0.00751157407407407 NA 92 0.46 NA NA 0.0083449074074074 NA 93 0.45 NA NA 0.0161226851851852 NA 94 0.45 NA NA 0.0205671296296296 NA 95 0.46 NA NA 0.0148032407407407 NA 96 0.46 NA NA 0.00383101851851851 NA

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/15/t1210858729zraewxnx3r2dru4/1sanr1210858491.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/15/t1210858729zraewxnx3r2dru4/1sanr1210858491.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/15/t1210858729zraewxnx3r2dru4/272d71210858491.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/15/t1210858729zraewxnx3r2dru4/272d71210858491.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/15/t1210858729zraewxnx3r2dru4/39po91210858491.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/15/t1210858729zraewxnx3r2dru4/39po91210858491.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/15/t1210858729zraewxnx3r2dru4/4s0yx1210858491.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/15/t1210858729zraewxnx3r2dru4/4s0yx1210858491.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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