multiplicatief model inschrijvingen nieuwe wagens - Sam Roggeman

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

Title produced by software: Classical Decomposition

Date of computation: Sun, 25 May 2008 07:04:06 -0600

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2008/May/25/t121172093417aps8agsfgjj2p.htm/, Retrieved Sun, 25 May 2008 15:08:54 +0200

User-defined keywords:

Dataseries X:

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41086 39690 43129 37863 35953 29133 24693 22205 21725 27192 21790 13253 37702 30364 32609 30212 29965 28352 25814 22414 20506 28806 22228 13971 36845 35338 35022 34777 26887 23970 22780 17351 21382 24561 17409 11514 31514 27071 29462 26105 22397 23843 21705 18089 20764 25316 17704 15548 28029 29383 36438 32034 22679 24319 18004 17537 20366 22782 19169 13807 29743 25591 29096 26482 22405 27044 17970 18730 19684 19785 18479 10698

Text written by user:

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 6 seconds R Server 'Herman Ole Andreas Wold' @ 193.190.124.10:1001 Classical Decomposition by Moving Averages t Observations Fit Trend Seasonal Random 1 41086 NA NA 1.32421588517829 NA 2 39690 NA NA 1.21329933823845 NA 3 43129 NA NA 1.33387275027617 NA 4 37863 NA NA 1.22993991735704 NA 5 35953 NA NA 1.01759239347436 NA 6 29133 NA NA 1.00791263025947 NA 7 24693 26290.2052461748 29668.3333333333 0.886136910718773 0.939247136672423 8 22205 22176.1510359608 29138.75 0.761053615407689 1.0013009004129 9 21725 23955.9690249967 28311.8333333333 0.846146865268233 0.90687210262007 10 27192 28441.1365133816 27554.7083333333 1.03216975368873 0.95607993679177 11 21790 21127.2535358898 26986.4166666667 0.782884730375707 1.03136926732972 12 13253 15081.9689920566 26704.375 0.564775209757075 0.878731418091373 13 37702 35381.117303798 26718.5416666667 1.32421588517829 1.06559664795981 14 30364 32484.8259278572 26773.9583333333 1.21329933823845 0.934713335618076 15 32609 35656.9196262889 26731.875 1.33387275027617 0.914520949699712 16 30212 32898.8428894386 26748.3333333333 1.22993991735704 0.918330170502708 17 29965 27305.9046877587 26833.8333333333 1.01759239347436 1.09738169610741 18 28352 27094.7073266351 26882 1.00791263025947 1.04640362629527 19 25814 23816.0002243342 26876.2083333333 0.886136910718773 1.08389317084505 20 22414 20584.7879261433 27047.75 0.761053615407689 1.08886232301347 21 20506 23146.8058289645 27355.5416666667 0.846146865268233 0.885910572349469 22 28806 28535.6660599903 27646.2916666667 1.03216975368873 1.00947354582302 23 22228 21692.3658304327 27708.25 0.782884730375707 1.02469228915621 24 13971 15473.3817447186 27397.4166666667 0.564775209757075 0.902905404293316 25 36845 35870.9116543283 27088.4166666667 1.32421588517829 1.02715538303176 26 35338 32457.0211513559 26751.0416666667 1.21329933823845 1.08876288539263 27 35022 35449.7803037773 26576.5833333333 1.33387275027617 0.98793277983357 28 34777 32514.9478927336 26436.2083333333 1.22993991735704 1.06956960579266 29 26887 26516.9737850347 26058.5416666667 1.01759239347436 1.01395431537418 30 23970 25959.167759569 25755.375 1.00791263025947 0.923373207569962 31 22780 22535.2370093753 25430.875 0.886136910718773 1.01086134530215 32 17351 18923.0590674679 24864.2916666667 0.761053615407689 0.916923629426778 33 21382 20551.3560881124 24288.1666666667 0.846146865268233 1.04041796114701 34 24561 24457.4343419469 23695.1666666667 1.03216975368873 1.00423452667214 35 17409 18121.2371328239 23146.75 0.782884730375707 0.960695998424204 36 11514 12964.0619554676 22954.375 0.564775209757075 0.88814756050622 37 31514 30330.2268637567 22904.2916666667 1.32421588517829 1.03902948505993 38 27071 27772.7251771127 22890.25 1.21329933823845 0.974733297771908 39 29462 30539.3500857606 22895.25 1.33387275027617 0.964722560148295 40 26105 28166.8027998971 22900.9583333333 1.22993991735704 0.926800254379435 41 22397 23348.3606704878 22944.7083333333 1.01759239347436 0.959253641661863 42 23843 23308.0635674694 23125.0833333333 1.00791263025947 1.02295070248895 43 21705 20512.2602869469 23147.9583333333 0.886136910718773 1.05814764908244 44 18089 17579.6408834368 23099.0833333333 0.761053615407689 1.02897437552567 45 20764 19872.6757899285 23486.0833333333 0.846146865268233 1.04485174616109 46 25316 24796.6311272528 24023.7916666667 1.03216975368873 1.02094513847796 47 17704 19010.4637057423 24282.5833333333 0.782884730375707 0.931276599773436 48 15548 13732.0385792351 24314.1666666667 0.564775209757075 1.13224266814331 49 28029 32019.2642253016 24179.7916666667 1.32421588517829 0.875379265518897 50 29383 29122.3184743467 24002.5833333333 1.21329933823845 1.00895126278778 51 36438 31963.5927148680 23963 1.33387275027617 1.13998449188882 52 32034 29322.7925797230 23840.8333333333 1.22993991735704 1.09246075089559 53 22679 24214.9253928973 23796.2916666667 1.01759239347436 0.93657112842694 54 24319 23972.9919289235 23784.7916666667 1.00791263025947 1.01443324521621 55 18004 21075.5849055651 23783.6666666667 0.886136910718773 0.85425861634075 56 17537 18034.7509454506 23697.0833333333 0.761053615407689 0.97240045360448 57 20366 19658.6711452544 23233.1666666667 0.846146865268233 1.03598050191283 58 22782 23426.0387155734 22695.9166666667 1.03216975368873 0.972507570597276 59 19169 17578.2413319141 22453.1666666667 0.782884730375707 1.09049589421655 60 13807 12738.6695821737 22555.2916666667 0.564775209757075 1.08386514862756 61 29743 30016.5532259551 22667.4166666667 1.32421588517829 0.990886587680608 62 25591 27560.9538884510 22715.7083333333 1.21329933823845 0.928523740635968 63 29096 30328.2647230294 22737 1.33387275027617 0.95936909894836 64 26482 27776.6043611156 22583.7083333333 1.22993991735704 0.953392274149684 65 22405 22824.682184996 22430.0833333333 1.01759239347436 0.98161279173158 66 27044 22448.0201193410 22271.7916666667 1.00791263025947 1.20473876342882 67 17970 NA NA 0.886136910718773 NA 68 18730 NA NA 0.761053615407689 NA 69 19684 NA NA 0.846146865268233 NA 70 19785 NA NA 1.03216975368873 NA 71 18479 NA NA 0.782884730375707 NA 72 10698 NA NA 0.564775209757075 NA

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/25/t121172093417aps8agsfgjj2p/11p1b1211720639.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/25/t121172093417aps8agsfgjj2p/11p1b1211720639.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/25/t121172093417aps8agsfgjj2p/29g561211720639.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/25/t121172093417aps8agsfgjj2p/29g561211720639.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/25/t121172093417aps8agsfgjj2p/33xwh1211720639.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/25/t121172093417aps8agsfgjj2p/33xwh1211720639.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/25/t121172093417aps8agsfgjj2p/4qvh91211720639.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/25/t121172093417aps8agsfgjj2p/4qvh91211720639.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')

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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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