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Klassieke decompositie - personenwagens - Nhu Truong

R Software Module: rwasp_decompose.wasp (opens new window with default values)
Title produced by software: Classical Decomposition
Date of computation: Fri, 23 May 2008 04:45:33 -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/23/t1211539708xp21qz2p1lgtlwj.htm/, Retrieved Fri, 23 May 2008 12:48:33 +0200
 
User-defined keywords:
 
Dataseries X:
» Textbox « » Textfile « » CSV «
41086 39690 43129 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 time6 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135


Classical Decomposition by Moving Averages
tObservationsFitTrendSeasonalRandom
141086NANA1.03178125384286NA
239690NANA0.774216708743183NA
343129NANA0.53786668363701NA
441086NANA1.32916647189612NA
539690NANA1.21783526297389NA
643129NANA1.33885943922524NA
73786342430.558489664434369.58333333331.234538052968280.892352147785729
83595333751.882627066533044.83333333331.021396666964571.06521465475731
92913331416.901565438131054.16666666671.011680715913740.927303411487574
102469326388.491290284129668.33333333330.8894497373277050.935748835671088
112220522259.056670910629138.750.7638988175851950.99757147521075
122172524045.52851706728311.83333333330.8493101889221940.903494385019654
132719228430.431513440927554.70833333331.031781253842860.956439932582261
142179020893.334692438926986.41666666670.7742167087431831.04291633292438
151325314363.393619849126704.3750.537866683637010.922692808591238
163770235513.389761292926718.54166666671.329166471896121.06162774810904
173036432606.27058772726773.95833333331.217835262973890.931231921120994
183260935790.223171939326731.8751.338859439225240.911114743357246
193021233021.835353479926748.33333333331.234538052968280.914909776413025
202996527407.987928549626833.83333333331.021396666964571.09329441030536
212835227196.0010051933268821.011680715913741.04250621238711
222581423905.03644244826876.20833333330.8894497373277051.079856124133
232241420661.744243340027047.750.7638988175851951.08480676829716
242050623233.340260985627355.54166666670.8493101889221940.88261092764326
252880628524.925479938727646.29166666671.031781253842861.00985364607732
262222821452.190120033327708.250.7742167087431831.03616460024015
271397114736.157642721327397.41666666670.537866683637010.948076176892742
283684536005.015210085527088.41666666671.329166471896121.02332966074346
293533832578.361862950526751.04166666671.217835262973891.08470770103968
303502235582.309458189626576.58333333331.338859439225240.98425314526456
313477732636.505163697226436.20833333331.234538052968281.06558590834302
322688726616.107604290826058.54166666671.021396666964571.01017776151707
332397026056.216218626925755.3751.011680715913740.919934030285811
342278022619.485088763725430.8750.8894497373277051.00709631145919
351735118993.803004260124864.29166666670.7638988175851950.913508474111707
362138220628.187420240424288.16666666670.8493101889221941.03654284132691
372456124448.228773348823695.16666666671.031781253842861.00461265426206
381740917920.600603101323146.750.7742167087431830.971451816017107
391151412346.393556210322954.3750.537866683637010.932580024083909
403151430443.616545863122904.29166666671.329166471896121.03515953672995
412707127876.553628288122890.251.217835262973890.971102825728404
422946230653.521575921722895.251.338859439225240.961129373896875
432610528272.104511941022900.95833333331.234538052968280.923348312785639
442239723435.648616141022944.70833333331.021396666964570.955680824834283
452384323395.200862231723125.08333333331.011680715913741.01914064086927
462170520588.94545925623147.95833333330.8894497373277051.05420649362312
471808917645.362445635223099.08333333330.7638988175851951.02514187825450
482076419946.969872875723486.08333333330.8493101889221941.04096011235447
492531624787.297887892924023.79166666671.031781253842861.02132955816718
501770418799.981748115424282.58333333330.7742167087431830.941703041907194
511554813077.780190397524314.16666666670.537866683637011.18888678152094
522802932138.968380766624179.79166666671.329166471896120.872118845506372
532938329231.192385802824002.58333333331.217835262973891.00519334319974
543643832083.0887421545239631.338859439225241.13573852857015
553203429432.415964474623840.83333333331.234538052968281.08839179354714
562267924305.452994450223796.29166666671.021396666964570.933082794432114
572431924062.615061192623784.79166666671.011680715913741.01065490754664
581800421154.376069356323783.66666666670.8894497373277050.851076861873516
591753718102.173938551223697.08333333330.7638988175851950.96877867042546
602036619732.165170927523233.16666666670.8493101889221941.03212190976418
612278223417.221355446322695.91666666671.031781253842860.972873751936474
621916917383.616797528822453.16666666670.7742167087431831.10270493322914
631380712131.739927215522555.29166666670.537866683637011.13808901961592
642974330128.770237832722667.41666666671.329166471896120.987195951418279
652559127663.990631763222715.70833333331.217835262973890.925065379779913
662909630441.6470696643227371.338859439225240.95579585209089
672648227880.447314636922583.70833333331.234538052968280.949841288453694
682240522910.012356404322430.08333333331.021396666964570.97795669646319
692704422531.942138015122271.79166666671.011680715913741.20025161765227
7017970NANA0.889449737327705NA
7118730NANA0.763898817585195NA
7219684NANA0.849310188922194NA
7319785NANANANA
7418479NANANANA
7510698NANANANA
 
Charts produced by software:
http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/23/t1211539708xp21qz2p1lgtlwj/1b4fe1211539527.png (open in new window)
http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/23/t1211539708xp21qz2p1lgtlwj/1b4fe1211539527.ps (open in new window)


http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/23/t1211539708xp21qz2p1lgtlwj/2xhpb1211539527.png (open in new window)
http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/23/t1211539708xp21qz2p1lgtlwj/2xhpb1211539527.ps (open in new window)


http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/23/t1211539708xp21qz2p1lgtlwj/3einy1211539527.png (open in new window)
http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/23/t1211539708xp21qz2p1lgtlwj/3einy1211539527.ps (open in new window)


http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/23/t1211539708xp21qz2p1lgtlwj/4ucy61211539527.png (open in new window)
http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/23/t1211539708xp21qz2p1lgtlwj/4ucy61211539527.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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