Klassieke ontbinding

*The author of this computation has been verified*

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

Title produced by software: Classical Decomposition

Date of computation: Sun, 12 Dec 2010 17:12:29 +0000

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2010/Dec/12/t1292173863pn84u7q1wnrcm99.htm/, Retrieved Sun, 12 Dec 2010 18:11:07 +0100

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/2010/Dec/12/t1292173863pn84u7q1wnrcm99.htm/}, year = {2010}, } @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 = {2010}, 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 «

63.152 60.106 72.616 73.159 68.848 77.056 62.246 60.777 64.513 58.353 56.511 44.554 71.414 65.719 80.997 69.826 65.386 75.589 65.520 59.003 63.961 59.716 57.520 42.886 69.805 64.656 80.353 71.321 76.577 81.580 71.127 63.478 48.152 69.236 57.038 43.621 69.551 72.009 72.140 81.519 73.310 80.406 70.697 59.328 68.281 70.041 51.244 46.538 61.443 62.256 73.117 74.155 65.191 77.889 68.688 59.983 65.470 65.089 54.795 47.123

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 'George Udny Yule' @ 72.249.76.132 Classical Decomposition by Moving Averages t Observations Fit Trend Seasonal Random 1 63.152 NA NA 2.44608333333333 NA 2 60.106 NA NA 0.493999999999997 NA 3 72.616 NA NA 10.9840520833333 NA 4 73.159 NA NA 8.45741666666667 NA 5 68.848 NA NA 4.31587500000001 NA 6 77.056 NA NA 13.0569895833333 NA 7 62.246 65.6919479166667 63.8351666666667 1.85678125 -3.44594791666666 8 60.777 59.5144791666667 64.4132916666667 -4.8988125 1.26252083333333 9 64.513 60.6501979166667 64.996375 -4.34617708333333 3.86280208333334 10 58.353 63.9546875 65.2067083333333 -1.25202083333334 -5.6016875 11 56.511 54.94103125 64.9235833333333 -9.98255208333334 1.56996875 12 44.554 43.5865729166667 64.7182083333333 -21.1316354166667 0.967427083333334 13 71.414 67.2395833333333 64.7935 2.44608333333333 4.17441666666669 14 65.719 65.35 64.856 0.493999999999997 0.369 15 80.997 75.7431354166667 64.7590833333333 10.9840520833333 5.25386458333334 16 69.826 73.2502916666667 64.792875 8.45741666666667 -3.42429166666667 17 65.386 69.2075833333333 64.8917083333333 4.31587500000001 -3.82158333333334 18 75.589 77.9212395833333 64.86425 13.0569895833333 -2.33223958333332 19 65.52 66.5844895833333 64.7277083333333 1.85678125 -1.06448958333333 20 59.003 59.7175625 64.616375 -4.8988125 -0.714562499999992 21 63.961 60.1990729166667 64.54525 -4.34617708333333 3.76192708333333 22 59.716 63.3286875 64.5807083333333 -1.25202083333334 -3.61268749999999 23 57.52 55.1267395833333 65.1092916666667 -9.98255208333334 2.39326041666668 24 42.886 44.6935729166667 65.8252083333333 -21.1316354166667 -1.80757291666666 25 69.805 68.7545416666667 66.3084583333333 2.44608333333333 1.05045833333334 26 64.656 67.2225416666667 66.7285416666667 0.493999999999997 -2.56654166666667 27 80.353 77.24034375 66.2562916666667 10.9840520833333 3.11265625 28 71.321 74.4516666666667 65.99425 8.45741666666667 -3.13066666666666 29 76.577 70.6867083333333 66.3708333333333 4.31587500000001 5.89029166666667 30 81.58 79.4383645833333 66.381375 13.0569895833333 2.14163541666667 31 71.127 68.2581979166667 66.4014166666667 1.85678125 2.86880208333334 32 63.478 61.7983958333333 66.6972083333333 -4.8988125 1.67960416666668 33 48.152 62.3151979166667 66.661375 -4.34617708333333 -14.1631979166667 34 69.236 65.4920625 66.7440833333333 -1.25202083333334 3.7439375 35 57.038 57.0503229166667 67.032875 -9.98255208333334 -0.0123229166666761 36 43.621 45.7161979166667 66.8478333333333 -21.1316354166667 -2.09519791666666 37 69.551 69.2270833333333 66.781 2.44608333333333 0.323916666666662 38 72.009 67.0841666666667 66.5901666666667 0.493999999999997 4.92483333333334 39 72.14 78.2400104166667 67.2559583333333 10.9840520833333 -6.10001041666666 40 81.519 76.585625 68.1282083333333 8.45741666666667 4.93337500000001 41 73.31 72.2362083333333 67.9203333333333 4.31587500000001 1.07379166666668 42 80.406 80.8574479166667 67.8004583333333 13.0569895833333 -0.451447916666666 43 70.697 69.4409479166667 67.5841666666667 1.85678125 1.25605208333334 44 59.328 61.9411458333333 66.8399583333333 -4.8988125 -2.61314583333332 45 68.281 62.1281145833333 66.4742916666667 -4.34617708333333 6.15288541666668 46 70.041 64.9561458333333 66.2081666666667 -1.25202083333334 5.08485416666666 47 51.244 55.5804895833333 65.5630416666667 -9.98255208333334 -4.33648958333333 48 46.538 43.9882395833333 65.119875 -21.1316354166667 2.54976041666667 49 61.443 67.377375 64.9312916666667 2.44608333333333 -5.934375 50 62.256 65.368875 64.874875 0.493999999999997 -3.112875 51 73.117 75.76909375 64.7850416666667 10.9840520833333 -2.65209375000001 52 74.155 72.919 64.4615833333333 8.45741666666667 1.23600000000000 53 65.191 68.7190833333333 64.4032083333333 4.31587500000001 -3.52808333333333 54 77.889 77.63253125 64.5755416666667 13.0569895833333 0.256468749999996 55 68.688 NA NA 1.85678125 NA 56 59.983 NA NA -4.8988125 NA 57 65.47 NA NA -4.34617708333333 NA 58 65.089 NA NA -1.25202083333334 NA 59 54.795 NA NA -9.98255208333334 NA 60 47.123 NA NA -21.1316354166667 NA

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/12/t1292173863pn84u7q1wnrcm99/1vd3q1292173946.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/12/t1292173863pn84u7q1wnrcm99/1vd3q1292173946.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/12/t1292173863pn84u7q1wnrcm99/2vd3q1292173946.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/12/t1292173863pn84u7q1wnrcm99/2vd3q1292173946.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/12/t1292173863pn84u7q1wnrcm99/3onkt1292173946.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/12/t1292173863pn84u7q1wnrcm99/3onkt1292173946.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/12/t1292173863pn84u7q1wnrcm99/4onkt1292173946.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/12/t1292173863pn84u7q1wnrcm99/4onkt1292173946.ps (open in new window)

Parameters (Session):

par1 = aantal aanvragen nieuwe kentekenplaten ; par4 = 12 ;

Parameters (R input):

par1 = additive ; par2 = 12 ; par4 = 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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