Classical Decomposition - Verkoopcijfers autos - Alexander De Houwer

*Unverified author*

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

Title produced by software: Classical Decomposition

Date of computation: Mon, 11 Jan 2010 15:51:40 -0700

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2010/Jan/11/t1263250352x8k30qzb9ny12lm.htm/, Retrieved Mon, 11 Jan 2010 23:52:38 +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/Jan/11/t1263250352x8k30qzb9ny12lm.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:

KDGP2W52

Dataseries X:

» Textbox « » Textfile « » CSV «

68897 38683 44720 39525 45315 50380 40600 36279 42438 38064 31879 11379 70249 39253 47060 41697 38708 49267 39018 32228 40870 39383 34571 12066 70938 34077 45409 40809 37013 44953 37848 32745 43412 34931 33008 8620 68906 39556 50669 36432 40891 48428 36222 33425 39401 37967 34801 12657 69116 41519 51321 38529 41547 52073 38401 40898 40439 41888 37898 8771 68184 50530 47221 41756 45633 48138 39486 39341 41117 41629 29722 7054 56231 34418 34568 29789 30630 35502 33091 27630 33520

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 68897 NA NA 28717.1430555556 NA 2 38683 NA NA 209.309722222222 NA 3 44720 NA NA 7543.80138888889 NA 4 39525 NA NA -966.29861111111 NA 5 45315 NA NA -64.2319444444433 NA 6 50380 NA NA 7803.18472222222 NA 7 40600 38315.4930555556 40736.25 -2420.75694444444 2284.50694444445 8 36279 36085.0847222222 40816.3333333333 -4731.24861111111 193.915277777778 9 42438 41671.1263888889 40937.5833333333 733.543055555554 766.873611111114 10 38064 40174.2597222222 41125.5833333333 -951.323611111115 -2110.25972222221 11 31879 34996.4180555556 40940.7916666667 -5944.37361111111 -3117.41805555555 12 11379 10690.3763888889 40619.125 -29928.7486111111 688.623611111107 13 70249 69223.9763888889 40506.8333333333 28717.1430555556 1025.02361111112 14 39253 40481.4347222222 40272.125 209.309722222222 -1228.43472222222 15 47060 47581.8013888889 40038 7543.80138888889 -521.801388888889 16 41697 39061.3263888889 40027.625 -966.29861111111 2635.67361111111 17 38708 40130.5180555556 40194.75 -64.2319444444433 -1422.51805555556 18 49267 48138.7263888889 40335.5416666667 7803.18472222222 1128.27361111111 19 39018 37972.1180555555 40392.875 -2420.75694444444 1045.88194444445 20 32228 35474.6680555556 40205.9166666667 -4731.24861111111 -3246.66805555555 21 40870 40655.0013888889 39921.4583333333 733.543055555554 214.998611111114 22 39383 38864.3430555555 39815.6666666667 -951.323611111115 518.656944444454 23 34571 33763.6680555556 39708.0416666667 -5944.37361111111 807.331944444442 24 12066 9528.91805555555 39457.6666666667 -29928.7486111111 2537.08194444444 25 70938 67946.3097222222 39229.1666666667 28717.1430555556 2991.69027777778 26 34077 39411.2680555556 39201.9583333333 209.309722222222 -5334.26805555556 27 45409 46873.2180555555 39329.4166666667 7543.80138888889 -1464.21805555555 28 40809 38283.5347222222 39249.8333333333 -966.29861111111 2525.46527777778 29 37013 38934.9763888889 38999.2083333333 -64.2319444444433 -1921.97638888889 30 44953 46593.6847222222 38790.5 7803.18472222222 -1640.68472222222 31 37848 36141.4930555556 38562.25 -2420.75694444444 1706.50694444445 32 32745 33974.6263888889 38705.875 -4731.24861111111 -1229.62638888889 33 43412 39886.8763888889 39153.3333333333 733.543055555554 3525.12361111112 34 34931 38238.8013888889 39190.125 -951.323611111115 -3307.80138888888 35 33008 33224.9597222222 39169.3333333333 -5944.37361111111 -216.959722222222 36 8620 9546.95972222222 39475.7083333333 -29928.7486111111 -926.959722222222 37 68906 68269.8930555556 39552.75 28717.1430555556 636.106944444444 38 39556 39722.6430555555 39513.3333333333 209.309722222222 -166.643055555549 39 50669 46918.3430555556 39374.5416666667 7543.80138888889 3750.65694444445 40 36432 38367.6180555556 39333.9166666667 -966.29861111111 -1935.61805555555 41 40891 39470.8930555556 39535.125 -64.2319444444433 1420.10694444444 42 48428 47581.2263888889 39778.0416666667 7803.18472222222 846.773611111115 43 36222 37534.2430555556 39955 -2420.75694444444 -1312.24305555555 44 33425 35314.2930555556 40045.5416666667 -4731.24861111111 -1889.29305555555 45 39401 40888.0430555556 40154.5 733.543055555554 -1487.04305555556 46 37967 39317.7180555556 40269.0416666667 -951.323611111115 -1350.71805555555 47 34801 34439.3763888889 40383.75 -5944.37361111111 361.623611111114 48 12657 10634.2097222222 40562.9583333333 -29928.7486111111 2022.79027777777 49 69116 69522.7680555556 40805.625 28717.1430555556 -406.768055555549 50 41519 41417.1013888889 41207.7916666667 209.309722222222 101.898611111115 51 51321 49106.2180555556 41562.4166666667 7543.80138888889 2214.78194444444 52 38529 40802.7430555556 41769.0416666667 -966.29861111111 -2273.74305555556 53 41547 41997.2263888889 42061.4583333333 -64.2319444444433 -450.226388888877 54 52073 49831.7680555555 42028.5833333333 7803.18472222222 2241.23194444445 55 38401 39407.0763888889 41827.8333333333 -2420.75694444444 -1006.07638888888 56 40898 37433.2097222222 42164.4583333333 -4731.24861111111 3464.79027777777 57 40439 43102.6263888889 42369.0833333333 733.543055555554 -2663.62638888889 58 41888 41381.3847222222 42332.7083333333 -951.323611111115 506.615277777782 59 37898 36693.0430555556 42637.4166666667 -5944.37361111111 1204.95694444444 60 8771 12714.9597222222 42643.7083333333 -29928.7486111111 -3943.95972222223 61 68184 71242.1013888889 42524.9583333333 28717.1430555556 -3058.10138888888 62 50530 42714.6013888889 42505.2916666667 209.309722222222 7815.39861111112 63 47221 50012.4680555555 42468.6666666667 7543.80138888889 -2791.46805555555 64 41756 41519.8263888889 42486.125 -966.29861111111 236.173611111117 65 45633 42070.4347222222 42134.6666666667 -64.2319444444433 3562.56527777778 66 48138 49525.6430555556 41722.4583333333 7803.18472222222 -1387.64305555556 67 39486 38732.1180555556 41152.875 -2420.75694444444 753.881944444445 68 39341 35252.2513888889 39983.5 -4731.24861111111 4088.74861111111 69 41117 39518.5013888889 38784.9583333333 733.543055555554 1598.49861111112 70 41629 36807.8013888889 37759.125 -951.323611111115 4821.19861111111 71 29722 30691.0013888889 36635.375 -5944.37361111111 -969.001388888886 72 7054 5555.00138888889 35483.75 -29928.7486111111 1498.99861111111 73 56231 NA 34690.7916666667 NA NA 74 34418 NA 33936.375 NA NA 75 34568 NA 33131.875 NA NA 76 29789 NA NA NA NA 77 30630 NA NA NA NA 78 35502 NA NA NA NA 79 33091 NA NA NA NA 80 27630 NA NA NA NA 81 33520 NA NA NA NA

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Jan/11/t1263250352x8k30qzb9ny12lm/1uhqd1263250297.png (open in new window) http://www.freestatistics.org/blog/date/2010/Jan/11/t1263250352x8k30qzb9ny12lm/1uhqd1263250297.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Jan/11/t1263250352x8k30qzb9ny12lm/2vdwd1263250297.png (open in new window) http://www.freestatistics.org/blog/date/2010/Jan/11/t1263250352x8k30qzb9ny12lm/2vdwd1263250297.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Jan/11/t1263250352x8k30qzb9ny12lm/3lkmj1263250297.png (open in new window) http://www.freestatistics.org/blog/date/2010/Jan/11/t1263250352x8k30qzb9ny12lm/3lkmj1263250297.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Jan/11/t1263250352x8k30qzb9ny12lm/4gt2u1263250297.png (open in new window) http://www.freestatistics.org/blog/date/2010/Jan/11/t1263250352x8k30qzb9ny12lm/4gt2u1263250297.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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