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Classical Decomposition: uitvoer

*The author of this computation has been verified*
R Software Module: /rwasp_decompose.wasp (opens new window with default values)
Title produced by software: Classical Decomposition
Date of computation: Sun, 05 Dec 2010 20:08:11 +0000
 
Cite this page as follows:
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2010/Dec/05/t12915799638dm7vka0fd5fbgi.htm/, Retrieved Sun, 05 Dec 2010 21:12:48 +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/Dec/05/t12915799638dm7vka0fd5fbgi.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:
 
Dataseries X:
» Textbox « » Textfile « » CSV «
16198.9 16554.2 19554.2 15903.8 18003.8 18329.6 16260.7 14851.9 18174.1 18406.6 18466.5 16016.5 17428.5 17167.2 19630.00 17183.6 18344.7 19301.4 18147.5 16192.9 18374.4 20515.2 18957.2 16471.5 18746.8 19009.5 19211.2 20547.7 19325.8 20605.5 20056.9 16141.4 20359.8 19711.6 15638.6 14384.5 13855.6 14308.3 15290.6 14423.8 13779.7 15686.3 14733.8 12522.5 16189.4 16059.1 16007.1 15806.8 15160.00 15692.1 18908.9 16969.9 16997.5 19858.9 17681.2
 
Output produced by software:


Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time3 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135


Classical Decomposition by Moving Averages
tObservationsFitTrendSeasonalRandom
116198.9NANA-815.591782407408NA
216554.2NANA-610.665393518518NA
319554.2NANA664.852662037038NA
415903.8NANA66.1221064814794NA
518003.8NANA-102.082060185186NA
618329.6NANA1315.98877314815NA
716260.717743.374884259317277.9666666667465.408217592592-1482.67488425926
814851.915178.344328703717354.7416666667-2176.39733796296-326.444328703703
918174.118593.147106481517383.44166666671209.70543981481-419.047106481481
1018406.619116.713773148117439.9251676.78877314815-710.113773148147
1118466.517311.588773148117507.4541666667-195.8653935185161154.91122685185
1216016.516063.885995370417562.15-1498.26400462963-47.3859953703686
1317428.516865.666550925917681.2583333333-815.591782407408562.833449074074
1417167.217205.084606481517815.75-610.665393518518-37.8846064814825
151963018544.823495370417879.9708333333664.8526620370381085.17650462963
1617183.618042.297106481517976.17566.1221064814794-858.697106481482
1718344.717982.397106481518084.4791666667-102.082060185186362.302893518518
1819301.419439.872106481518123.88333333331315.98877314815-138.472106481480
1918147.518663.179050925918197.7708333333465.408217592592-515.679050925926
2016192.916153.065162037018329.4625-2176.3973379629639.8348379629606
2118374.419598.480439814818388.7751209.70543981481-1224.08043981481
2220515.220188.284606481518511.49583333331676.78877314815326.915393518517
2318957.218496.680439814818692.5458333333-195.865393518516460.519560185185
2416471.517289.498495370418787.7625-1498.26400462963-817.998495370372
2518746.818106.066550925918921.6583333333-815.591782407408640.733449074076
2619009.518388.405439814818999.0708333333-610.665393518518621.094560185185
2719211.219744.502662037019079.65664.852662037038-533.302662037036
2820547.719195.013773148119128.891666666766.12210648147941352.68622685186
2919325.818855.051273148218957.1333333333-102.082060185186470.748726851849
3020605.520047.888773148118731.91315.98877314815557.611226851852
3120056.918906.549884259318441.1416666667465.4082175925921150.35011574074
3216141.415865.060995370418041.4583333333-2176.39733796296276.33900462963
3320359.818891.922106481517682.21666666671209.705439814811467.87789351852
3419711.618940.484606481517263.69583333331676.78877314815771.115393518518
3515638.616581.580439814816777.4458333333-195.865393518516-942.980439814815
3614384.514843.127662037016341.3916666667-1498.26400462963-458.627662037035
3713855.615099.037384259315914.6291666667-815.591782407408-1243.43738425926
3814308.314931.380439814815542.0458333333-610.665393518518-623.080439814816
3915290.615882.344328703715217.4916666667664.852662037038-591.744328703706
4014423.814957.659606481514891.537566.1221064814794-533.859606481483
4113779.714652.622106481514754.7041666667-102.082060185186-872.92210648148
4215686.316145.309606481514829.32083333331315.98877314815-459.009606481481
4314733.815408.341550925914942.9333333333465.408217592592-674.541550925927
4412522.512878.544328703715054.9416666667-2176.39733796296-356.044328703703
4516189.416473.067939814815263.36251209.70543981481-283.667939814814
4616059.117197.001273148115520.21251676.78877314815-1137.90127314815
4716007.115564.509606481515760.375-195.865393518516442.59039351852
4815806.814570.044328703716068.3083333333-1498.264004629631236.75567129630
4915160NA16364.975NANA
5015692.1NANANANA
5118908.9NANANANA
5216969.9NANANANA
5316997.5NANANANA
5419858.9NANANANA
5517681.2NANANANA
 
Charts produced by software:
http://www.freestatistics.org/blog/date/2010/Dec/05/t12915799638dm7vka0fd5fbgi/1da5h1291579688.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/05/t12915799638dm7vka0fd5fbgi/1da5h1291579688.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/05/t12915799638dm7vka0fd5fbgi/2da5h1291579688.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/05/t12915799638dm7vka0fd5fbgi/2da5h1291579688.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/05/t12915799638dm7vka0fd5fbgi/36jm21291579688.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/05/t12915799638dm7vka0fd5fbgi/36jm21291579688.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/05/t12915799638dm7vka0fd5fbgi/46jm21291579688.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/05/t12915799638dm7vka0fd5fbgi/46jm21291579688.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$trend)) {
a<-table.row.start(a)
a<-table.element(a,i,header=TRUE)
a<-table.element(a,x[i])
a<-table.element(a,m$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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