Decompositie consumptieprijs biefstuk - Mattias Dierckx

*Unverified author*

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

Title produced by software: Classical Decomposition

Date of computation: Mon, 01 Jun 2009 14:38:22 -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/01/t1243889355tpmxpmvf7d2h3lg.htm/, Retrieved Mon, 01 Jun 2009 22:49:15 +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/01/t1243889355tpmxpmvf7d2h3lg.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 «

9,370 9,330 9,310 9,260 9,350 9,380 9,430 9,270 9,290 9,270 9,290 9,310 9,330 9,350 9,340 9,470 9,630 9,620 9,630 9,500 9,550 9,580 9,610 9,570 9,610 9,650 9,620 9,650 9,960 10,030 10,030 9,720 9,750 9,770 9,780 9,820 9,840 9,900 9,940 10,120 10,520 10,570 10,570 10,120 10,050 10,140 10,170 10,200 10,200 10,350 10,430 10,570 10,820 10,900 10,830 10,650 10,570 10,610 10,630 10,710 10,720 10,770 10,790 10,920 10,900 11,000 10,990 10,910 10,880 10,870 11,000 10,990 11,030 11,040 10,990

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 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 R Framework error message Warning: there are blank lines in the 'Data' field. Please, use NA for missing data - blank lines are simply deleted and are NOT treated as missing values. Classical Decomposition by Moving Averages t Observations Fit Trend Seasonal Random 1 9.37 NA NA -0.107270833333333 NA 2 9.33 NA NA -0.0699374999999999 NA 3 9.31 NA NA -0.0768541666666677 NA 4 9.26 NA NA 0.0185625000000006 NA 5 9.35 NA NA 0.210979166666667 NA 6 9.38 NA NA 0.240729166666667 NA 7 9.43 9.5185625 9.32 0.1985625 -0.0885625000000001 8 9.27 9.25947916666667 9.31916666666667 -0.0596875000000003 0.0105208333333344 9 9.29 9.21372916666667 9.32125 -0.107520833333333 0.0762708333333304 10 9.27 9.27189583333333 9.33125 -0.0593541666666673 -0.00189583333333410 11 9.29 9.2558125 9.35166666666666 -0.095854166666666 0.0341874999999998 12 9.31 9.28097916666667 9.37333333333333 -0.0923541666666671 0.0290208333333339 13 9.33 9.28439583333333 9.39166666666666 -0.107270833333333 0.0456041666666689 14 9.35 9.33964583333333 9.40958333333333 -0.0699374999999999 0.0103541666666676 15 9.34 9.35314583333333 9.43 -0.0768541666666677 -0.0131458333333327 16 9.47 9.4723125 9.45375 0.0185625000000006 -0.00231249999999861 17 9.63 9.69097916666667 9.48 0.210979166666667 -0.0609791666666659 18 9.62 9.74489583333333 9.50416666666667 0.240729166666667 -0.124895833333333 19 9.63 9.72522916666667 9.52666666666667 0.1985625 -0.095229166666666 20 9.5 9.49114583333333 9.55083333333334 -0.0596875000000003 0.00885416666666572 21 9.55 9.46747916666667 9.575 -0.107520833333333 0.0825208333333336 22 9.58 9.5348125 9.59416666666667 -0.0593541666666673 0.0451875000000008 23 9.61 9.5195625 9.61541666666666 -0.095854166666666 0.0904375000000002 24 9.57 9.55389583333333 9.64625 -0.0923541666666671 0.0161041666666666 25 9.61 9.57272916666667 9.68 -0.107270833333333 0.0372708333333325 26 9.65 9.63589583333333 9.70583333333333 -0.0699374999999999 0.0141041666666677 27 9.62 9.64647916666667 9.72333333333333 -0.0768541666666677 -0.0264791666666682 28 9.65 9.75814583333333 9.73958333333333 0.0185625000000006 -0.108145833333332 29 9.96 9.9655625 9.75458333333333 0.210979166666667 -0.00556249999999814 30 10.03 10.0128125 9.77208333333333 0.240729166666667 0.0171875000000004 31 10.03 9.99064583333333 9.79208333333333 0.1985625 0.0393541666666675 32 9.72 9.75239583333333 9.81208333333333 -0.0596875000000003 -0.0323958333333323 33 9.75 9.7283125 9.83583333333333 -0.107520833333333 0.0216874999999987 34 9.77 9.80939583333333 9.86875 -0.0593541666666673 -0.0393958333333337 35 9.78 9.8158125 9.91166666666667 -0.095854166666666 -0.0358125000000005 36 9.82 9.86514583333333 9.9575 -0.0923541666666671 -0.0451458333333346 37 9.84 9.89522916666667 10.0025 -0.107270833333333 -0.0552291666666687 38 9.9 9.97172916666667 10.0416666666667 -0.0699374999999999 -0.0717291666666657 39 9.94 9.99397916666667 10.0708333333333 -0.0768541666666677 -0.0539791666666662 40 10.12 10.1173125 10.09875 0.0185625000000006 0.0026875000000004 41 10.52 10.3413958333333 10.1304166666667 0.210979166666667 0.178604166666666 42 10.57 10.4032291666667 10.1625 0.240729166666667 0.166770833333336 43 10.57 10.3918958333333 10.1933333333333 0.1985625 0.178104166666669 44 10.12 10.1673958333333 10.2270833333333 -0.0596875000000003 -0.0473958333333329 45 10.05 10.1587291666667 10.26625 -0.107520833333333 -0.108729166666667 46 10.14 10.2460625 10.3054166666667 -0.0593541666666673 -0.106062499999998 47 10.17 10.2408125 10.3366666666667 -0.095854166666666 -0.0708125000000006 48 10.2 10.2705625 10.3629166666667 -0.0923541666666671 -0.0705625000000012 49 10.2 10.2802291666667 10.3875 -0.107270833333333 -0.0802291666666672 50 10.35 10.3504791666667 10.4204166666667 -0.0699374999999999 -0.000479166666666586 51 10.43 10.3873125 10.4641666666667 -0.0768541666666677 0.0426875000000013 52 10.57 10.5239791666667 10.5054166666667 0.0185625000000006 0.0460208333333352 53 10.82 10.7551458333333 10.5441666666667 0.210979166666667 0.0648541666666684 54 10.9 10.8253125 10.5845833333333 0.240729166666667 0.0746875000000014 55 10.83 10.8260625 10.6275 0.1985625 0.00393749999999926 56 10.65 10.6069791666667 10.6666666666667 -0.0596875000000003 0.0430208333333351 57 10.57 10.5916458333333 10.6991666666667 -0.107520833333333 -0.0216458333333343 58 10.61 10.6693958333333 10.72875 -0.0593541666666673 -0.0593958333333351 59 10.63 10.6508125 10.7466666666667 -0.095854166666666 -0.0208124999999999 60 10.71 10.6618125 10.7541666666667 -0.0923541666666671 0.0481875000000027 61 10.72 10.6577291666667 10.765 -0.107270833333333 0.0622708333333346 62 10.77 10.7125625 10.7825 -0.0699374999999999 0.0574374999999989 63 10.79 10.7293958333333 10.80625 -0.0768541666666677 0.0606041666666641 64 10.92 10.8485625 10.83 0.0185625000000006 0.0714375 65 10.9 11.0672291666667 10.85625 0.210979166666667 -0.167229166666667 66 11 11.1240625 10.8833333333333 0.240729166666667 -0.124062499999999 67 10.99 11.1064791666667 10.9079166666667 0.1985625 -0.116479166666666 68 10.91 10.8723958333333 10.9320833333333 -0.0596875000000003 0.037604166666668 69 10.88 10.8441458333333 10.9516666666667 -0.107520833333333 0.0358541666666667 70 10.87 NA NA -0.0593541666666673 NA 71 11 NA NA -0.095854166666666 NA 72 10.99 NA NA -0.0923541666666671 NA 73 11.03 NA NA NA NA 74 11.04 NA NA NA NA 75 10.99 NA NA NA NA

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/01/t1243889355tpmxpmvf7d2h3lg/1ph1y1243888697.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/01/t1243889355tpmxpmvf7d2h3lg/1ph1y1243888697.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/01/t1243889355tpmxpmvf7d2h3lg/2m1341243888697.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/01/t1243889355tpmxpmvf7d2h3lg/2m1341243888697.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/01/t1243889355tpmxpmvf7d2h3lg/36hzd1243888697.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/01/t1243889355tpmxpmvf7d2h3lg/36hzd1243888697.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/01/t1243889355tpmxpmvf7d2h3lg/4tspg1243888697.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/01/t1243889355tpmxpmvf7d2h3lg/4tspg1243888697.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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