Home » date » 2009 » Aug » 18 »

Multiplicatief decompositiemodel index benzine

*Unverified author*
R Software Module: rwasp_decompose.wasp (opens new window with default values)
Title produced by software: Classical Decomposition
Date of computation: Tue, 18 Aug 2009 14:42:53 -0600
 
Cite this page as follows:
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2009/Aug/18/t125062826614kyeoj0r1mrtgt.htm/, Retrieved Tue, 18 Aug 2009 22:44:26 +0200
 
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/2009/Aug/18/t125062826614kyeoj0r1mrtgt.htm/},
    year = {2009},
}
@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 = {2009},
    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 «
105,46 104,66 103,52 103,71 103,78 103,67 103,66 102,76 102 101,5 101,5 99,22 98,97 98,9 99,78 104,4 106,21 105,46 108,33 111,72 111,88 112,86 113,09 116,9 114,62 118,86 124,71 122,53 127,89 136,16 134,12 130,26 135,35 131,43 129,61 123,96 121,1 125,38 123,1 129,92 136,68 131,17 124,82 122,47 126,15 118,74 116,8 116,64 116,53 117,68 119,46 126,19 124,39 121,9 122,53 122,93 124,66 124,41 120,93 120,18 123,44 126,1 125,82 122,18 117,27 117,86 119,09 123,08 125,42 121,81 121,66 121,27 120,92 122,16 124,17 127,26 134,16 134,09 135,57 136,13 136,23 140,6 136,5 130,59 129,5 135,25 138,06 146,28 145,04 147,96 156,71 160,97 168,17 163,91 153,05 151,76 119,55 119,44 120,25 124,92 126,34 125,88 127,34 127,48 119,41 114,82 115,28 116,37 111,99 113,57 117,69 120,74 122,37 123,57 124,86 122,08 123,56 126,92 134,88 130,64 131,65 130,97 136,77 138,17 146,4 152,07 153,05 142,89 141,11 131,9 118,42 etc...
 
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'Sir Ronald Aylmer Fisher' @ 193.190.124.24


Classical Decomposition by Moving Averages
tObservationsFitTrendSeasonalRandom
1105.46NANA0.951859805251062NA
2104.66NANA0.96449870167156NA
3103.52NANA0.978632752962902NA
4103.71NANA1.00202067449952NA
5103.78NANA1.01909469962581NA
6103.67NANA1.02477747651069NA
7103.66104.652845862866102.6829166666671.019184585519660.990512958776419
8102.76104.154365447800102.17251.019397249238300.986612510749742
9102104.464629230269101.7766666666671.026410400847630.976407045634206
10101.5102.802664660938101.6495833333331.011343689662000.987328493232794
11101.5101.669134035182101.7795833333330.9989148187236150.998336426912782
1299.22100.310380851964101.9554166666670.9838651454872570.989129930095939
1398.9797.3034719835376102.2245833333330.9518598052510621.01712711769159
1498.999.1432327915738102.79250.964498701671560.9975466526083
1599.78101.364333970015103.57750.9786327529629020.984369906968623
16104.4104.673584709906104.46251.002020674499520.997386306099438
17106.21107.431689366178105.418751.019094699625810.988628221585403
18105.46109.280562132639106.6383333333331.024777476510690.965038959737395
19108.33110.099538151981108.0270833333331.019184585519660.983927833107364
20111.72111.635042261794109.5108333333331.019397249238301.00076103109279
21111.88114.322873459411111.381251.026410400847630.978631804944285
22112.86114.459243470700113.1754166666671.011343689662000.98602783469288
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26118.86116.914121369872121.21750.964498701671561.01664365781762
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37121.1122.744701536638128.95250.9518598052510620.986600631098144
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39123.1124.807481567241127.53250.9786327529629020.98631907682296
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46118.74124.327429522577122.9329166666671.011343689662000.955058754580278
47116.8122.132736525751122.2654166666670.9989148187236150.956336550891691
48116.64119.408843101114121.3670833333330.9838651454872570.976812076650225
49116.53115.065969166027120.8854166666670.9518598052510621.01272340418791
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53124.39123.665867931217121.348751.019094699625811.00585555320071
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60120.18120.550536613690122.52750.9838651454872570.996926296438835
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62126.1117.744795876687122.078750.964498701671561.07096028373146
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68123.08124.069989707085121.7091666666671.019397249238300.992020715811917
69125.42124.684486455967121.476251.026410400847631.00589899806254
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71121.66122.401611097790122.5345833333330.9989148187236150.99394116555216
72121.27121.915239559243123.9145833333330.9838651454872570.994707474130589
73120.92119.24661675234125.27750.9518598052510621.01403296205154
74122.16122.016721376174126.5079166666670.964498701671561.0011742540056
75124.17124.777714821005127.5020833333330.9786327529629020.995129620526573
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77134.16132.621587226970130.1366666666671.019094699625811.01160001780402
78134.09134.392734194534131.1433333333331.024777476510690.99774739165515
79135.57134.419405663700131.8891666666671.019184585519661.00855973384661
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82140.6136.822159415147135.28751.011343689662001.02761132115588
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85129.5132.330722992020139.0233333333330.9518598052510620.978608724202384
86135.25135.935643264672140.9391666666670.964498701671560.9949561185852
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115124.86125.928748745833123.5583333333331.019184585519660.991513067854028
116122.08127.529144372834125.10251.019397249238300.957271379811791
117123.56129.966650981330126.62251.026410400847630.950705423791755
118126.92129.597372932124128.143751.011343689662000.979340839466501
119134.88129.730316151159129.871250.9989148187236151.03969530023222
120130.64129.929231113047132.060.9838651454872571.00547043094817
121131.65127.950978063109134.4220833333330.9518598052510621.02890968082375
122130.97131.619109700232136.463750.964498701671560.995068271608046
123136.77135.112076692294138.0620833333330.9786327529629021.01227072626144
124138.17139.281708772662139.0008333333331.002020674499520.992018271584557
125146.4141.167545528916138.52251.019094699625811.03706556242428
126152.07140.296733431081136.9045833333331.024777476510691.08391689728615
127153.05NANA1.01918458551966NA
128142.89NANA1.01939724923830NA
129141.11NANA1.02641040084763NA
130131.9NANA1.01134368966200NA
131118.42NANA0.998914818723615NA
132108.27NANA0.983865145487257NA
 
Charts produced by software:
http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Aug/18/t125062826614kyeoj0r1mrtgt/1746x1250628169.png (open in new window)
http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Aug/18/t125062826614kyeoj0r1mrtgt/1746x1250628169.ps (open in new window)


http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Aug/18/t125062826614kyeoj0r1mrtgt/2s75s1250628169.png (open in new window)
http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Aug/18/t125062826614kyeoj0r1mrtgt/2s75s1250628169.ps (open in new window)


http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Aug/18/t125062826614kyeoj0r1mrtgt/3x5al1250628169.png (open in new window)
http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Aug/18/t125062826614kyeoj0r1mrtgt/3x5al1250628169.ps (open in new window)


http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Aug/18/t125062826614kyeoj0r1mrtgt/4f1b71250628169.png (open in new window)
http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Aug/18/t125062826614kyeoj0r1mrtgt/4f1b71250628169.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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