Home » date » 2008 » Jun » 01 »

Multiplicatief decompositiemodel Inschrijvingen nieuwe personenwagens - Stephanie Van Mechelen (verbetering)

R Software Module: rwasp_decompose.wasp (opens new window with default values)
Title produced by software: Classical Decomposition
Date of computation: Sun, 01 Jun 2008 15:23:35 -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/Jun/01/t1212355643cfwzwz6ay8y4kip.htm/, Retrieved Sun, 01 Jun 2008 21:27:24 +0000
 
IsPrivate?
No (this computation is public)
 
User-defined keywords:
 
Dataseries X:
» Textbox « » Textfile « » CSV «
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 Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time8 seconds
R Server'Herman Ole Andreas Wold' @ 193.190.124.10:1001


Classical Decomposition by Moving Averages
tObservationsFitTrendSeasonalRandom
141086NANA1.32421588517829NA
239690NANA1.21329933823845NA
343129NANA1.33387275027617NA
437863NANA1.22993991735704NA
535953NANA1.01759239347436NA
629133NANA1.00791263025947NA
72469326290.205246174829668.33333333330.8861369107187730.939247136672423
82220522176.151035960829138.750.7610536154076891.0013009004129
92172523955.969024996728311.83333333330.8461468652682330.90687210262007
102719228441.136513381627554.70833333331.032169753688730.95607993679177
112179021127.253535889826986.41666666670.7828847303757071.03136926732972
121325315081.968992056626704.3750.5647752097570750.878731418091373
133770235381.11730379826718.54166666671.324215885178291.06559664795981
143036432484.825927857226773.95833333331.213299338238450.934713335618076
153260935656.919626288926731.8751.333872750276170.914520949699712
163021232898.842889438626748.33333333331.229939917357040.918330170502708
172996527305.904687758726833.83333333331.017592393474361.09738169610741
182835227094.7073266351268821.007912630259471.04640362629527
192581423816.000224334226876.20833333330.8861369107187731.08389317084505
202241420584.787926143327047.750.7610536154076891.08886232301347
212050623146.805828964527355.54166666670.8461468652682330.885910572349469
222880628535.666059990327646.29166666671.032169753688731.00947354582302
232222821692.365830432727708.250.7828847303757071.02469228915621
241397115473.381744718627397.41666666670.5647752097570750.902905404293316
253684535870.911654328327088.41666666671.324215885178291.02715538303176
263533832457.021151355926751.04166666671.213299338238451.08876288539263
273502235449.780303777326576.58333333331.333872750276170.98793277983357
283477732514.947892733626436.20833333331.229939917357041.06956960579266
292688726516.973785034726058.54166666671.017592393474361.01395431537418
302397025959.16775956925755.3751.007912630259470.923373207569962
312278022535.237009375325430.8750.8861369107187731.01086134530215
321735118923.059067467924864.29166666670.7610536154076890.916923629426778
332138220551.356088112424288.16666666670.8461468652682331.04041796114701
342456124457.434341946923695.16666666671.032169753688731.00423452667214
351740918121.237132823923146.750.7828847303757070.960695998424204
361151412964.061955467622954.3750.5647752097570750.88814756050622
373151430330.226863756722904.29166666671.324215885178291.03902948505993
382707127772.725177112722890.251.213299338238450.974733297771908
392946230539.350085760622895.251.333872750276170.964722560148295
402610528166.802799897122900.95833333331.229939917357040.926800254379435
412239723348.360670487822944.70833333331.017592393474360.959253641661863
422384323308.063567469423125.08333333331.007912630259471.02295070248895
432170520512.260286946923147.95833333330.8861369107187731.05814764908244
441808917579.640883436823099.08333333330.7610536154076891.02897437552567
452076419872.675789928523486.08333333330.8461468652682331.04485174616109
462531624796.631127252824023.79166666671.032169753688731.02094513847796
471770419010.463705742324282.58333333330.7828847303757070.931276599773436
481554813732.038579235124314.16666666670.5647752097570751.13224266814331
492802932019.264225301624179.79166666671.324215885178290.875379265518897
502938329122.318474346724002.58333333331.213299338238451.00895126278778
513643831963.5927148680239631.333872750276171.13998449188882
523203429322.792579723023840.83333333331.229939917357041.09246075089559
532267924214.925392897323796.29166666671.017592393474360.93657112842694
542431923972.991928923523784.79166666671.007912630259471.01443324521621
551800421075.584905565123783.66666666670.8861369107187730.85425861634075
561753718034.750945450623697.08333333330.7610536154076890.97240045360448
572036619658.671145254423233.16666666670.8461468652682331.03598050191283
582278223426.038715573422695.91666666671.032169753688730.972507570597276
591916917578.241331914122453.16666666670.7828847303757071.09049589421655
601380712738.669582173722555.29166666670.5647752097570751.08386514862756
612974330016.553225955122667.41666666671.324215885178290.990886587680608
622559127560.953888451022715.70833333331.213299338238450.928523740635968
632909630328.2647230294227371.333872750276170.95936909894836
642648227776.604361115622583.70833333331.229939917357040.953392274149684
652240522824.68218499622430.08333333331.017592393474360.98161279173158
662704422448.020119341022271.79166666671.007912630259471.20473876342882
6717970NANA0.886136910718773NA
6818730NANA0.761053615407689NA
6919684NANA0.846146865268233NA
7019785NANA1.03216975368873NA
7118479NANA0.782884730375707NA
7210698NANA0.564775209757075NA
 
Charts produced by software:
http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Jun/01/t1212355643cfwzwz6ay8y4kip/10i3f1212355405.png (open in new window)
http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Jun/01/t1212355643cfwzwz6ay8y4kip/10i3f1212355405.ps (open in new window)


http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Jun/01/t1212355643cfwzwz6ay8y4kip/2x7nm1212355405.png (open in new window)
http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Jun/01/t1212355643cfwzwz6ay8y4kip/2x7nm1212355405.ps (open in new window)


http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Jun/01/t1212355643cfwzwz6ay8y4kip/3lfid1212355405.png (open in new window)
http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Jun/01/t1212355643cfwzwz6ay8y4kip/3lfid1212355405.ps (open in new window)


http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Jun/01/t1212355643cfwzwz6ay8y4kip/48a291212355405.png (open in new window)
http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Jun/01/t1212355643cfwzwz6ay8y4kip/48a291212355405.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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Creative Commons License

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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