JUISTE REEKS Classical decomposition - Nieuwe geregistreerde domeinnamen - Dorien Dhanis

*Unverified author*

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

Title produced by software: Classical Decomposition

Date of computation: Tue, 02 Jun 2009 08:44:09 -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/Jun/02/t1243953882snhisj2lzcgf3mc.htm/, Retrieved Tue, 02 Jun 2009 16:44:46 +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/Jun/02/t1243953882snhisj2lzcgf3mc.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 «

8166 7102 6047 5854 5764 5209 5616 5597 6251 7024 7237 9230 9016 8201 7630 7107 6820 6082 6019 6576 8086 8323 7842 9077 10737 10176 10416 9807 9565 10439 9115 9535 10790 11340 11196 12132 12013 12692 13330 11926 11356 11221 9999 11772 12543 14176 2924 2322 15557 13381 13145 12448 12178 11836 9815 12382 12662 12767 13136 13533 17808 15892 16830 14444 15550 15092 16364 14314 15874 17846 18504 15130 19845 18137 18898 19573 17368 18938 16713 16379 19139 21461 19796 16668

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 8166 NA NA 1.21205858015325 NA 2 7102 NA NA 1.10704246641014 NA 3 6047 NA NA 1.10593605962239 NA 4 5854 NA NA 1.01742356109086 NA 5 5764 NA NA 0.97143515335351 NA 6 5209 NA NA 0.961727678604161 NA 7 5616 5761.48182305108 6626.83333333333 0.869417040273296 0.974749235089309 8 5597 6130.6611271079 6708.04166666667 0.913927108946287 0.912952108093495 9 6251 6804.7999408096 6819.79166666667 0.997801732576328 0.91861627885805 10 7024 7346.96093802763 6937.95833333333 1.05895143571694 0.956041560483057 11 7237 6160.39955941841 7034.16666666667 0.875782427591765 1.17476146314822 12 9230 6463.53802218217 7114.54166666667 0.90849675566107 1.42801047480863 13 9016 8687.68238545263 7167.70833333333 1.21205858015325 1.03779116224335 14 8201 7998.70470719927 7225.29166666667 1.10704246641014 1.02529100650742 15 7630 8120.38159844655 7342.54166666667 1.10593605962239 0.93961101550445 16 7107 7603.33344997716 7473.125 1.01742356109086 0.93472159898779 17 6820 7336.72351923767 7552.45833333333 0.97143515335351 0.92957026145489 18 6082 7281.52075861836 7571.29166666667 0.961727678604161 0.835265077394908 19 6019 6639.41190517706 7636.625 0.869417040273296 0.906556195934569 20 6576 7120.06338313467 7790.625 0.913927108946287 0.923587283727924 21 8086 7971.43804155228 7989 0.997801732576328 1.01437155477475 22 8323 8702.02166895692 8217.58333333333 1.05895143571694 0.956444412186536 23 7842 7395.50821886417 8444.45833333333 0.875782427591765 1.06037337366443 24 9077 7940.60233076113 8740.375 0.90849675566107 1.14311227560617 25 10737 10970.2412040854 9050.91666666667 1.21205858015325 0.978738735115638 26 10176 10299.0466988607 9303.20833333333 1.10704246641014 0.98805261278461 27 10416 10549.7083954146 9539.16666666667 1.10593605962239 0.987325868128005 28 9807 9947.9012612143 9777.54166666667 1.01742356109086 0.985836081650342 29 9565 9756.12324512931 10043 0.97143515335351 0.980409918947597 30 10439 9915.4524383955 10310.0416666667 0.961727678604161 1.05280117723899 31 9115 9120.619460987 10490.5 0.869417040273296 0.999383872881547 32 9535 9731.95281961454 10648.5 0.913927108946287 0.97976225087964 33 10790 10850.8443913344 10874.75 0.997801732576328 0.994392658383064 34 11340 11737.9030662279 11084.4583333333 1.05895143571694 0.966101009355516 35 11196 9850.25338153493 11247.375 0.875782427591765 1.13662050775138 36 12132 10315.6021202166 11354.5833333333 0.90849675566107 1.17608258428498 37 12013 13846.5572196707 11424 1.21205858015325 0.867580280745458 38 12692 12790.8147836721 11554.0416666667 1.10704246641014 0.992274551282043 39 13330 12961.8931834585 11720.2916666667 1.10593605962239 1.02839915522613 40 11926 12119.0407479338 11911.5 1.01742356109086 0.984071284852579 41 11356 11351.2197669358 11685 0.97143515335351 1.00042112065156 42 11221 10513.2062626346 10931.5833333333 0.961727678604161 1.06732425101189 43 9999 9277.1145282362 10670.5 0.869417040273296 1.07781357765571 44 11772 9913.25310985176 10846.875 0.913927108946287 1.18750120364636 45 12543 10843.9845044230 10867.875 0.997801732576328 1.15667815597524 46 14176 11523.4212775187 10881.9166666667 1.05895143571694 1.23019020641519 47 2924 9579.23521112976 10937.9166666667 0.875782427591765 0.305243574831811 48 2322 9991.45804860303 10997.7916666667 0.90849675566107 0.232398513680859 49 15557 13351.7343043232 11015.75 1.21205858015325 1.16516698470870 50 13381 12214.5530531362 11033.5 1.10704246641014 1.09549649027594 51 13145 12235.9383216547 11063.875 1.10593605962239 1.07429439855352 52 12448 11201.9605855555 11010.125 1.01742356109086 1.11123404737303 53 12178 11051.9367867735 11376.9166666667 0.97143515335351 1.10188831468654 54 11836 11799.9578246204 12269.5416666667 0.961727678604161 1.00305443255945 55 9815 11155.0191095165 12830.4583333333 0.869417040273296 0.879872988440394 56 12382 11907.4420615726 13028.875 0.913927108946287 1.03985389439424 57 12662 13257.8331958139 13287.0416666667 0.997801732576328 0.955058025922215 58 12767 14320.9944787769 13523.75 1.05895143571694 0.891488368277783 59 13136 12039.7459414488 13747.4166666667 0.875782427591765 1.09105292286751 60 13533 12740.379961076 14023.5833333333 0.90849675566107 1.06221321823569 61 17808 17492.5809360942 14432.125 1.21205858015325 1.01803159093893 62 15892 16368.1763871071 14785.5 1.10704246641014 0.97090840324264 63 16830 16588.8565716592 14999.8333333333 1.10593605962239 1.01453647075066 64 14444 15612.6612934780 15345.2916666667 1.01742356109086 0.925146567166857 65 15550 15329.8133904245 15780.5833333333 0.97143515335351 1.01436329353578 66 15092 15455.7251629144 16070.7916666667 0.961727678604161 0.976466638796918 67 16364 14103.8643558635 16222.2083333333 0.869417040273296 1.16024938889865 68 14314 14988.9757911622 16400.625 0.913927108946287 0.954968518158514 69 15874 16543.8853266930 16580.3333333333 0.997801732576328 0.959508584986853 70 17846 17875.3208497844 16880.2083333333 1.05895143571694 0.99835970218209 71 18504 15036.8923542747 17169.6666666667 0.875782427591765 1.23057341663682 72 15130 15812.9916967847 17405.6666666667 0.90849675566107 0.956808192283843 73 19845 21308.5453659434 17580.4583333333 1.21205858015325 0.931316505148093 74 18137 19573.6639753671 17681.0416666667 1.10704246641014 0.92660219480752 75 18898 19799.7115174271 17903.125 1.10593605962239 0.954458350737412 76 19573 18506.7226130009 18189.7916666667 1.01742356109086 1.05761567886958 77 17368 17868.8210695728 18394.25 0.97143515335351 0.971972349623802 78 18938 17803.6630742667 18512.1666666667 0.961727678604161 1.06371368189802 79 16713 NA NA 0.869417040273296 NA 80 16379 NA NA 0.913927108946287 NA 81 19139 NA NA 0.997801732576328 NA 82 21461 NA NA 1.05895143571694 NA 83 19796 NA NA 0.875782427591765 NA 84 16668 NA NA 0.90849675566107 NA

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/02/t1243953882snhisj2lzcgf3mc/1z3yw1243953846.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/02/t1243953882snhisj2lzcgf3mc/1z3yw1243953846.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/02/t1243953882snhisj2lzcgf3mc/2evrk1243953846.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/02/t1243953882snhisj2lzcgf3mc/2evrk1243953846.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/02/t1243953882snhisj2lzcgf3mc/3wcde1243953846.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/02/t1243953882snhisj2lzcgf3mc/3wcde1243953846.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/02/t1243953882snhisj2lzcgf3mc/4al3x1243953846.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/02/t1243953882snhisj2lzcgf3mc/4al3x1243953846.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')

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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