Classical decomposition prijs kleurentv - Tjitse Voortman

*Unverified author*

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

Title produced by software: Classical Decomposition

Date of computation: Fri, 29 May 2009 08:20:55 -0600

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2009/May/29/t1243607020w4so8ett80s312h.htm/, Retrieved Fri, 29 May 2009 16:23:45 +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/29/t1243607020w4so8ett80s312h.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 «

666,27 664,45 660,76 660,40 660,69 660,69 662,23 661,41 659,02 655,43 652,59 652,59 648,20 645,84 644,67 642,71 640,14 640,14 639,64 630,28 614,57 614,70 615,08 615,08 614,43 604,55 598,98 594,05 593,05 593,05 593,34 584,72 580,70 577,08 569,92 569,92 568,86 559,38 548,22 545,61 545,33 530,30 527,76 521,41 1601,93 1577,49 1551,43 1551,43 1516,88 1485,95 1438,22 1385,06 1329,49 1329,49 1276,16 1242,34 1181,59 1160,21 1135,18 1135,18 1084,96 1077,35 1061,13 1029,98 1013,08 1013,08 996,04 975,02 951,89 944,40 932,47 932,47 920,44 900,18 886,90

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 'Gwilym Jenkins' @ 72.249.127.135 Classical Decomposition by Moving Averages t Observations Fit Trend Seasonal Random 1 666.27 NA NA 30.4588402777778 NA 2 664.45 NA NA 13.0116736111111 NA 3 660.76 NA NA -8.41232638888888 NA 4 660.4 NA NA -32.0229930555556 NA 5 660.69 NA NA -52.0274097222223 NA 6 660.69 NA NA -59.6980763888889 NA 7 662.23 580.034840277778 658.957916666667 -78.923076388889 82.1951597222222 8 661.41 558.284340277778 657.429583333333 -99.1452430555556 103.125659722222 9 659.02 748.082423611111 655.98375 92.0986736111112 -89.0624236111112 10 655.43 733.442673611111 654.57625 78.8664236111111 -78.0126736111113 11 652.59 714.081923611111 652.982916666667 61.0990069444445 -61.491923611111 12 652.59 705.964923611111 651.270416666667 54.6945069444445 -53.3749236111109 13 648.2 679.931756944444 649.472916666667 30.4588402777778 -31.7317569444443 14 645.84 660.246256944444 647.234583333333 13.0116736111111 -14.4062569444443 15 644.67 635.673090277778 644.085416666667 -8.41232638888888 8.99690972222231 16 642.71 608.513256944444 640.53625 -32.0229930555556 34.1967430555557 17 640.14 585.248840277778 637.27625 -52.0274097222223 54.8911597222223 18 640.14 574.452340277778 634.150416666667 -59.6980763888889 65.6876597222222 19 639.64 552.257340277778 631.180416666667 -78.923076388889 87.3826597222222 20 630.28 528.907673611111 628.052916666667 -99.1452430555556 101.372326388889 21 614.57 716.527423611111 624.42875 92.0986736111112 -101.957423611111 22 614.7 699.363923611111 620.4975 78.8664236111111 -84.663923611111 23 615.08 677.606923611111 616.507916666667 61.0990069444445 -62.5269236111111 24 615.08 667.278256944444 612.58375 54.6945069444445 -52.1982569444443 25 614.43 639.151340277778 608.6925 30.4588402777778 -24.7213402777777 26 604.55 617.876673611111 604.865 13.0116736111111 -13.326673611111 27 598.98 593.143090277778 601.555416666667 -8.41232638888888 5.83690972222223 28 594.05 566.553673611111 598.576666666667 -32.0229930555556 27.4963263888890 29 593.05 543.100090277778 595.1275 -52.0274097222223 49.9499097222222 30 593.05 531.666090277778 591.364166666667 -59.6980763888889 61.3839097222223 31 593.34 508.660673611111 587.58375 -78.923076388889 84.6793263888889 32 584.72 484.657673611111 583.802916666667 -99.1452430555556 100.062326388889 33 580.7 671.904506944445 579.805833333333 92.0986736111112 -91.2045069444445 34 577.08 654.538923611111 575.6725 78.8664236111111 -77.458923611111 35 569.92 632.764840277778 571.665833333333 61.0990069444445 -62.8448402777778 36 569.92 621.757423611111 567.062916666667 54.6945069444445 -51.8374236111113 37 568.86 592.174673611111 561.715833333333 30.4588402777778 -23.3146736111111 38 559.38 569.357090277778 556.345416666667 13.0116736111111 -9.97709027777773 39 548.22 587.846423611111 596.25875 -8.41232638888888 -39.6264236111111 40 545.61 648.470756944444 680.49375 -32.0229930555556 -102.860756944444 41 545.33 711.046340277778 763.07375 -52.0274097222223 -165.716340277778 42 530.3 785.16817361111 844.86625 -59.6980763888889 -254.868173611111 43 527.76 846.340256944444 925.263333333333 -78.923076388889 -318.580256944444 44 521.41 904.226006944444 1003.37125 -99.1452430555556 -382.816006944444 45 1601.93 1171.16034027778 1079.06166666667 92.0986736111112 430.769659722223 46 1577.49 1229.98850694444 1151.12208333333 78.8664236111111 347.501493055556 47 1551.43 1279.87150694444 1218.7725 61.0990069444445 271.558493055556 48 1551.43 1339.43992361111 1284.74541666667 54.6945069444445 211.990076388889 49 1516.88 1379.68717361111 1349.22833333333 30.4588402777778 137.192826388889 50 1485.95 1423.46209027778 1410.45041666667 13.0116736111111 62.4879097222224 51 1438.22 1414.56267361111 1422.975 -8.41232638888888 23.6573263888890 52 1385.06 1356.05117361111 1388.07416666667 -32.0229930555556 29.0088263888888 53 1329.49 1301.31634027778 1353.34375 -52.0274097222223 28.1736597222221 54 1329.49 1258.95817361111 1318.65625 -59.6980763888889 70.531826388889 55 1276.16 1204.39275694444 1283.31583333333 -78.923076388889 71.7672430555556 56 1242.34 1149.14892361111 1248.29416666667 -99.1452430555556 93.1910763888886 57 1181.59 1307.65575694444 1215.55708333333 92.0986736111112 -126.065756944444 58 1160.21 1263.91642361111 1185.05 78.8664236111111 -103.706423611111 59 1135.18 1218.17025694444 1157.07125 61.0990069444445 -82.9902569444444 60 1135.18 1185.39825694444 1130.70375 54.6945069444445 -50.2182569444444 61 1084.96 1136.30717361111 1105.84833333333 30.4588402777778 -51.3471736111112 62 1077.35 1096.05000694444 1083.03833333333 13.0116736111111 -18.7000069444446 63 1061.13 1053.91684027778 1062.32916666667 -8.41232638888888 7.21315972222237 64 1029.98 1011.74325694444 1043.76625 -32.0229930555556 18.2367430555555 65 1013.08 974.300506944444 1026.32791666667 -52.0274097222223 38.7794930555558 66 1013.08 949.737340277778 1009.43541666667 -59.6980763888889 63.3426597222223 67 996.04 915.211090277778 994.134166666667 -78.923076388889 80.8289097222223 68 975.02 880.751840277778 979.897083333333 -99.1452430555556 94.2681597222222 69 951.89 1057.35409027778 965.255416666667 92.0986736111112 -105.464090277778 70 944.4 NA NA 78.8664236111111 NA 71 932.47 NA NA 61.0990069444445 NA 72 932.47 NA NA 54.6945069444445 NA 73 920.44 NA NA NA NA 74 900.18 NA NA NA NA 75 886.9 NA NA NA NA

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/May/29/t1243607020w4so8ett80s312h/1iprq1243606853.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/May/29/t1243607020w4so8ett80s312h/1iprq1243606853.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/May/29/t1243607020w4so8ett80s312h/2em4y1243606853.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/May/29/t1243607020w4so8ett80s312h/2em4y1243606853.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/May/29/t1243607020w4so8ett80s312h/38qhc1243606853.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/May/29/t1243607020w4so8ett80s312h/38qhc1243606853.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/May/29/t1243607020w4so8ett80s312h/4xytk1243606853.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/May/29/t1243607020w4so8ett80s312h/4xytk1243606853.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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