additief compositiemodel - Invoer klasse 3-5, oliën, vetten,... - Vanovermeire Bart

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

Title produced by software: Classical Decomposition

Date of computation: Sun, 18 May 2008 14:23:47 -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/18/t12111424380399g1uyn5mhdlj.htm/, Retrieved Sun, 18 May 2008 22:27:22 +0200

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 2 seconds R Server 'George Udny Yule' @ 72.249.76.132 Classical Decomposition by Moving Averages t Observations Fit Trend Seasonal Random 1 42.3 NA NA -13.6567361111111 NA 2 50.8 NA NA 4.65159722222222 NA 3 54.1 NA NA 0.654930555555557 NA 4 38.2 NA NA 0.451597222222223 NA 5 48.4 NA NA -8.8667361111111 NA 6 61.1 NA NA 1.00576388888889 NA 7 54.1 56.6274305555556 55.4583333333333 1.16909722222222 -2.52743055555555 8 61.4 46.6274305555555 56.6583333333333 -10.0309027777778 14.7725694444445 9 64.3 53.3465972222222 57.5458333333333 -4.19923611111111 10.9534027777778 10 57.4 67.5657638888889 58.8791666666667 8.68659722222222 -10.1657638888889 11 71.7 71.7965972222222 60.4416666666667 11.3549305555556 -0.096597222222222 12 55.3 70.3499305555556 61.5708333333333 8.77909722222222 -15.0499305555556 13 55.1 49.4974305555556 63.1541666666667 -13.6567361111111 5.60256944444444 14 66.8 67.7474305555556 63.0958333333333 4.65159722222222 -0.947430555555556 15 59.4 62.3590972222222 61.7041666666667 0.654930555555557 -2.95909722222223 16 64.9 61.9099305555556 61.4583333333333 0.451597222222223 2.99006944444445 17 59.2 52.3415972222222 61.2083333333333 -8.8667361111111 6.85840277777778 18 77.4 63.8224305555556 62.8166666666667 1.00576388888889 13.5775694444444 19 75.8 65.3857638888889 64.2166666666667 1.16909722222222 10.4142361111111 20 38.3 53.3690972222222 63.4 -10.0309027777778 -15.0690972222222 21 54 58.6424305555556 62.8416666666667 -4.19923611111111 -4.64243055555556 22 61.8 72.2282638888889 63.5416666666667 8.68659722222222 -10.4282638888889 23 61.3 75.8549305555556 64.5 11.3549305555556 -14.5549305555556 24 104.3 72.0207638888889 63.2416666666667 8.77909722222222 32.2792361111111 25 39.7 47.4932638888889 61.15 -13.6567361111111 -7.79326388888888 26 62.6 65.1474305555556 60.4958333333333 4.65159722222222 -2.54743055555555 27 50.2 62.0799305555555 61.425 0.654930555555557 -11.8799305555555 28 90.9 63.3390972222222 62.8875 0.451597222222223 27.5609027777778 29 56.2 55.3832638888889 64.25 -8.8667361111111 0.816736111111112 30 50.2 63.7224305555556 62.7166666666667 1.00576388888889 -13.5224305555556 31 52.8 62.4815972222222 61.3125 1.16909722222222 -9.68159722222224 32 45.6 52.0190972222222 62.05 -10.0309027777778 -6.41909722222223 33 69 57.9090972222222 62.1083333333333 -4.19923611111111 11.0909027777778 34 81.9 69.3074305555556 60.6208333333333 8.68659722222222 12.5925694444445 35 73.9 70.0299305555556 58.675 11.3549305555556 3.87006944444445 36 54.9 67.4415972222222 58.6625 8.77909722222222 -12.5415972222222 37 55.4 44.9640972222222 58.6208333333333 -13.6567361111111 10.4359027777778 38 64.6 62.8724305555555 58.2208333333333 4.65159722222222 1.72756944444445 39 49.6 58.2340972222222 57.5791666666667 0.654930555555557 -8.63409722222222 40 55.8 56.5849305555556 56.1333333333333 0.451597222222223 -0.784930555555562 41 44.6 45.8499305555555 54.7166666666667 -8.8667361111111 -1.24993055555555 42 61.5 54.9265972222222 53.9208333333333 1.00576388888889 6.57340277777778 43 40.5 55.0815972222222 53.9125 1.16909722222222 -14.5815972222222 44 48.3 44.5649305555555 54.5958333333333 -10.0309027777778 3.73506944444445 45 50.9 52.8674305555556 57.0666666666667 -4.19923611111111 -1.96743055555556 46 65.3 68.1282638888889 59.4416666666667 8.68659722222222 -2.82826388888889 47 56.5 72.2090972222222 60.8541666666667 11.3549305555556 -15.7090972222222 48 53.2 71.5207638888889 62.7416666666667 8.77909722222222 -18.3207638888889 49 56.9 51.6265972222222 65.2833333333333 -13.6567361111111 5.27340277777778 50 79.5 74.2474305555556 69.5958333333333 4.65159722222222 5.25256944444445 51 94 74.5424305555556 73.8875 0.654930555555557 19.4575694444444 52 68.4 77.6182638888889 77.1666666666667 0.451597222222223 -9.21826388888887 53 65.9 74.0249305555556 82.8916666666667 -8.8667361111111 -8.12493055555555 54 85.5 89.5807638888889 88.575 1.00576388888889 -4.08076388888888 55 77.5 91.9024305555556 90.7333333333333 1.16909722222222 -14.4024305555556 56 114.8 81.7982638888889 91.8291666666667 -10.0309027777778 33.0017361111111 57 87.4 88.3757638888889 92.575 -4.19923611111111 -0.975763888888878 58 107.5 101.444930555556 92.7583333333333 8.68659722222222 6.05506944444446 59 151.7 104.796597222222 93.4416666666666 11.3549305555556 46.9034027777778 60 94.4 102.724930555556 93.9458333333333 8.77909722222222 -8.32493055555554 61 67.5 81.7807638888889 95.4375 -13.6567361111111 -14.2807638888889 62 95.2 99.4474305555556 94.7958333333333 4.65159722222222 -4.24743055555555 63 96.2 92.9465972222222 92.2916666666667 0.654930555555557 3.25340277777778 64 70.6 91.9099305555556 91.4583333333333 0.451597222222223 -21.3099305555556 65 80.1 79.1624305555556 88.0291666666667 -8.8667361111111 0.937569444444435 66 83.4 86.7099305555556 85.7041666666667 1.00576388888889 -3.30993055555557 67 115.4 87.9107638888889 86.7416666666667 1.16909722222222 27.4892361111111 68 61.5 77.5107638888889 87.5416666666667 -10.0309027777778 -16.0107638888889 69 80.6 84.8674305555556 89.0666666666667 -4.19923611111111 -4.26743055555556 70 94.3 100.453263888889 91.7666666666667 8.68659722222222 -6.1532638888889 71 82.6 103.871597222222 92.5166666666667 11.3549305555556 -21.2715972222222 72 107.7 101.554097222222 92.775 8.77909722222222 6.14590277777778 73 79.1 78.1390972222222 91.7958333333333 -13.6567361111111 0.960902777777775 74 102.8 95.5724305555556 90.9208333333333 4.65159722222222 7.22756944444444 75 125.2 92.5090972222222 91.8541666666667 0.654930555555557 32.6909027777778 76 106.4 93.6432638888889 93.1916666666666 0.451597222222223 12.7567361111111 77 62.3 86.5957638888889 95.4625 -8.8667361111111 -24.2957638888889 78 107.4 96.3099305555556 95.3041666666667 1.00576388888889 11.0900694444445 79 67.9 NA NA 1.16909722222222 NA 80 88 NA NA -10.0309027777778 NA 81 76.5 NA NA -4.19923611111111 NA 82 130.5 NA NA 8.68659722222222 NA 83 100.9 NA NA 11.3549305555556 NA 84 85.6 NA NA 8.77909722222222 NA

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/18/t12111424380399g1uyn5mhdlj/1oixi1211142225.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/18/t12111424380399g1uyn5mhdlj/1oixi1211142225.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/18/t12111424380399g1uyn5mhdlj/2k5h11211142225.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/18/t12111424380399g1uyn5mhdlj/2k5h11211142225.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/18/t12111424380399g1uyn5mhdlj/3ct3u1211142225.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/18/t12111424380399g1uyn5mhdlj/3ct3u1211142225.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/18/t12111424380399g1uyn5mhdlj/4mot01211142225.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/18/t12111424380399g1uyn5mhdlj/4mot01211142225.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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