multiplicatief model eigen reeks - Opgave 9 verbetering - Vanovermeire bart

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

Title produced by software: Classical Decomposition

Date of computation: Sun, 01 Jun 2008 05:55:52 -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/Jun/01/t1212321399q990jlvgfnih2np.htm/, Retrieved Sun, 01 Jun 2008 11:56:39 +0000

User-defined keywords:

Dataseries X:

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42.3 50.8 54.1 38.2 48.4 61.1 54.1 61.4 64.3 57.4 71.7 55.3 55.1 66.8 59.4 64.9 59.2 77.4 75.8 38.3 54 61.8 61.3 104.3 39.7 62.6 50.2 90.9 56.2 50.2 52.8 45.6 69 81.9 73.9 54.9 55.4 64.6 49.6 55.8 44.6 61.5 40.5 48.3 50.9 65.3 56.5 53.2 56.9 79.5 94 68.4 65.9 85.5 77.5 114.8 87.4 107.5 151.7 94.4 67.5 95.2 96.2 70.6 80.1 83.4 115.4 61.5 80.6 94.3 82.6 107.7 79.1 102.8 125.2 106.4 62.3 107.4 67.9 88 76.5 130.5 100.9 85.6

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 5 seconds R Server 'Herman Ole Andreas Wold' @ 193.190.124.10:1001 Classical Decomposition by Moving Averages t Observations Fit Trend Seasonal Random 1 42.3 NA NA 0.813113645709838 NA 2 50.8 NA NA 1.07520643343634 NA 3 54.1 NA NA 0.996069509131121 NA 4 38.2 NA NA 1.03585288583246 NA 5 48.4 NA NA 0.876694222326524 NA 6 61.1 NA NA 1.02735363911978 NA 7 54.1 55.4789709408933 55.4583333333333 1.00037212816036 0.975144258851477 8 61.4 47.5585081539472 56.6583333333333 0.839391230838897 1.29104133799252 9 64.3 54.4900816620239 57.5458333333333 0.946898819700655 1.18003126511761 10 57.4 66.3730547011369 58.8791666666667 1.12727571497225 0.864808773054961 11 71.7 68.6920543881056 60.4416666666667 1.13650165815148 1.04378884339227 12 55.3 69.2838185591256 61.5708333333333 1.12527011262030 0.798166168523288 13 55.1 51.3515147001001 63.1541666666667 0.813113645709838 1.07299658679577 14 66.8 67.841045923027 63.0958333333333 1.07520643343634 0.9846546304105 15 59.4 61.4616390030116 61.7041666666667 0.996069509131121 0.966456491618934 16 64.9 63.6617919417868 61.4583333333333 1.03585288583246 1.01944978330716 17 59.2 53.6609921915693 61.2083333333333 0.876694222326524 1.10322223988435 18 77.4 64.534931097374 62.8166666666667 1.02735363911978 1.19935047088242 19 75.8 64.2405634966977 64.2166666666667 1.00037212816036 1.17993983667183 20 38.3 53.2174040351861 63.4 0.839391230838897 0.719689370317217 21 54 59.5046999946887 62.8416666666667 0.946898819700655 0.907491341101123 22 61.8 71.628977722195 63.5416666666667 1.12727571497225 0.862779310346777 23 61.3 73.3043569507704 64.5 1.13650165815148 0.836239516310985 24 104.3 71.1639573722954 63.2416666666667 1.12527011262030 1.46562956658457 25 39.7 49.7218994351566 61.15 0.813113645709838 0.798440937514337 26 62.6 65.0455091960926 60.4958333333333 1.07520643343634 0.962403104744402 27 50.2 61.1835695983791 61.425 0.996069509131121 0.82048171313839 28 90.9 65.142198357789 62.8875 1.03585288583246 1.39540884851227 29 56.2 56.3276037844792 64.25 0.876694222326524 0.997734613654658 30 50.2 64.4321957334621 62.7166666666667 1.02735363911978 0.779113600406593 31 52.8 61.335316107832 61.3125 1.00037212816036 0.860841736059103 32 45.6 52.0842258735536 62.05 0.839391230838897 0.875504996670287 33 69 58.8103075269082 62.1083333333333 0.946898819700655 1.17326371688211 34 81.9 68.3363932380469 60.6208333333333 1.12727571497225 1.19848291838735 35 73.9 66.684234792038 58.675 1.13650165815148 1.10820796295354 36 54.9 66.0111579815883 58.6625 1.12527011262030 0.831677578134785 37 55.4 47.6653995062155 58.6208333333333 0.813113645709838 1.16226865973873 38 64.6 62.5994145600249 58.2208333333333 1.07520643343634 1.03195853274405 39 49.6 57.3528522778457 57.5791666666667 0.996069509131121 0.864821853318 40 55.8 58.1458753247289 56.1333333333333 1.03585288583246 0.959655344224714 41 44.6 47.969785531633 54.7166666666667 0.876694222326524 0.929751915830209 42 61.5 55.395764349371 53.9208333333333 1.02735363911978 1.11019318394328 43 40.5 53.9325623594454 53.9125 1.00037212816036 0.750937805069948 44 48.3 45.8272637403419 54.5958333333333 0.839391230838897 1.05395775479131 45 50.9 54.0363593109174 57.0666666666667 0.946898819700655 0.94195835265527 46 65.3 67.0071472908087 59.4416666666667 1.12727571497225 0.97452290748329 47 56.5 69.1608613220931 60.8541666666667 1.13650165815148 0.816936037520854 48 53.2 70.6013223159852 62.7416666666667 1.12527011262030 0.7535269631622 49 56.9 53.0827691707573 65.2833333333333 0.813113645709838 1.07191092116848 50 79.5 74.8298877403632 69.5958333333333 1.07520643343634 1.06240971890591 51 94 73.5970858559257 73.8875 0.996069509131121 1.27722448391524 52 68.4 79.9333143567384 77.1666666666667 1.03585288583246 0.855713297396055 53 65.9 72.6706452456828 82.8916666666667 0.876694222326524 0.90683108395704 54 85.5 90.9978485850343 88.575 1.02735363911978 0.939582653100894 55 77.5 90.76709776175 90.7333333333333 1.00037212816036 0.853833623758974 56 114.8 77.0805972352436 91.8291666666667 0.839391230838897 1.48935016226768 57 87.4 87.6591582337881 92.575 0.946898819700655 0.997043569217298 58 107.5 104.564216527967 92.7583333333333 1.12727571497225 1.02807636847016 59 151.7 106.196609107104 93.4416666666666 1.13650165815148 1.42848252195137 60 94.4 105.714438455208 93.9458333333333 1.12527011262030 0.892971682765909 61 67.5 77.6015335624326 95.4375 0.813113645709838 0.869828170930337 62 95.2 101.925089862959 94.7958333333333 1.07520643343634 0.93401928934278 63 96.2 91.9289151135597 92.2916666666667 0.996069509131121 1.04646073415708 64 70.6 94.7373785167607 91.4583333333333 1.03585288583246 0.745218002707449 65 80.1 77.1746618128853 88.0291666666667 0.876694222326524 1.03790542282138 66 83.4 88.048487512728 85.7041666666667 1.02735363911978 0.947205367814456 67 115.4 86.7739456835098 86.7416666666667 1.00037212816036 1.32989227458779 68 61.5 73.4817073330218 87.5416666666667 0.839391230838897 0.836942992101146 69 80.6 84.3371215413383 89.0666666666667 0.946898819700655 0.955688296291846 70 94.3 103.446334777287 91.7666666666667 1.12727571497225 0.91158377146007 71 82.6 105.145345073314 92.5166666666667 1.13650165815148 0.785579237410898 72 107.7 104.396934698348 92.775 1.12527011262030 1.03163948550018 73 79.1 74.6404447026393 91.7958333333333 0.813113645709838 1.05974716944851 74 102.8 97.7586649333932 90.9208333333333 1.07520643343634 1.05156918898229 75 125.2 91.4931347033149 91.8541666666667 0.996069509131121 1.36840868340544 76 106.4 96.5328568522036 93.1916666666666 1.03585288583246 1.10221538520199 77 62.3 83.6914221988458 95.4625 0.876694222326524 0.74440125837483 78 107.4 97.9110824482778 95.3041666666667 1.02735363911978 1.09691362115963 79 67.9 NA NA 1.00037212816036 NA 80 88 NA NA 0.839391230838897 NA 81 76.5 NA NA 0.946898819700655 NA 82 130.5 NA NA 1.12727571497225 NA 83 100.9 NA NA 1.13650165815148 NA 84 85.6 NA NA 1.12527011262030 NA

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Jun/01/t1212321399q990jlvgfnih2np/1actu1212321345.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Jun/01/t1212321399q990jlvgfnih2np/1actu1212321345.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Jun/01/t1212321399q990jlvgfnih2np/2kjwa1212321345.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Jun/01/t1212321399q990jlvgfnih2np/2kjwa1212321345.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Jun/01/t1212321399q990jlvgfnih2np/3e2x21212321345.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Jun/01/t1212321399q990jlvgfnih2np/3e2x21212321345.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Jun/01/t1212321399q990jlvgfnih2np/4hork1212321345.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Jun/01/t1212321399q990jlvgfnih2np/4hork1212321345.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')

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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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