Home » date » 2009 » Jun » 01 »

Opgave 9 - Aantal maandelijkse passagiers - William Baeyaert 202

*Unverified author*
R Software Module: rwasp_decompose.wasp (opens new window with default values)
Title produced by software: Classical Decomposition
Date of computation: Mon, 01 Jun 2009 02:57:22 -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/Jun/01/t1243846678g0gtzc2iodeyg31.htm/, Retrieved Mon, 01 Jun 2009 10:57:58 +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/Jun/01/t1243846678g0gtzc2iodeyg31.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 «
29133 112 118 132 129 121 135 148 148 136 119 104 118 115 126 141 135 125 149 170 170 158 133 114 140 145 150 178 163 172 178 199 199 184 162 146 166 171 180 193 181 183 218 230 242 209 191 172 194 196 196 236 235 229 243 264 272 237 211 180 201 204 188 235 227 234 264 302 293 259 229 203 229 242 233 267 269 270 315 364 347 312 274 237 278 284 277 317 313 318 374 413 405 355 306 271 306 315 301 356 348 355 422 465 467 404 347 305 336 340 318 362 348 363 435 491 505 404 359 310 337 360 342 406 396 420 472 548 559 463 407 362 405 417 391 419 461 472 535 622 606 508 461 390 432
 
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
129133NANA0.903660146224586NA
2112NANA0.915127488798271NA
3118NANA0.888379304571098NA
4132NANA1.01278600903645NA
5129NANA0.981156474638859NA
6121NANA0.986657929746024NA
71351494.247379010221335.6251.118762660934180.0903464860613798
8148143.976272307550126.7916666666671.135534188426291.02794715843076
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10136137.602999026133127.9583333333331.075373486365090.988350551677815
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15126118.228479116670133.0833333333330.8883793045710981.06573306991170
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19149155.228319204618138.751.118762660934180.959876398607344
20170160.015692719072140.9166666666671.135534188426291.06239580075722
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22158156.690878409113145.7083333333331.075373486365091.00835480408419
23133139.406958379020148.4166666666670.9392944977811580.95404133012069
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25140139.803755122162154.7083333333330.9036601462245861.00140371678620
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28178163.902535795731161.8333333333331.012786009036451.08601126355872
29163161.032306400103164.1250.9811564746388591.01221924745342
30172164.442988291004166.6666666666670.9866579297460241.04595520786586
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32199194.460229768003171.251.135534188426291.02334549453846
33199212.962300068936173.5833333333331.226859145860410.934437691251379
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106404400.218165842206372.1666666666671.075373486365091.00944943153651
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126420414.889659458203420.50.9866579297460241.01231734854147
127472476.033512227495425.51.118762660934180.99152683135979
128548489.084037740108430.7083333333331.135534188426291.12046183828064
129559533.837085842511435.1251.226859145860411.04713594245289
130463470.699936427718437.7083333333331.075373486365090.983641518020684
131407414.18973625075440.9583333333330.9392944977811580.982641442745947
132362363.982197646173445.8333333333330.8164086676175850.994554135726989
133405407.211853392454450.6250.9036601462245860.994568298113066
134417417.603177388278456.3333333333330.9151274887982710.998555620692232
135391409.87600164649461.3750.8883793045710980.953947043567653
136419471.156491287163465.2083333333331.012786009036450.889301129769695
137461460.489438763838469.3333333333330.9811564746388591.00110873603862
138472466.442536287433472.750.9866579297460241.01191457313649
139535531.45887905461475.0416666666671.118762660934181.00666301963322
140622NANA1.13553418842629NA
141606NANA1.22685914586041NA
142508NANA1.07537348636509NA
143461NANA0.939294497781158NA
144390NANA0.816408667617585NA
145432NANANANA
 
Charts produced by software:
http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/01/t1243846678g0gtzc2iodeyg31/1mf3p1243846637.png (open in new window)
http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/01/t1243846678g0gtzc2iodeyg31/1mf3p1243846637.ps (open in new window)


http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/01/t1243846678g0gtzc2iodeyg31/2g9v01243846637.png (open in new window)
http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/01/t1243846678g0gtzc2iodeyg31/2g9v01243846637.ps (open in new window)


http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/01/t1243846678g0gtzc2iodeyg31/3iaji1243846637.png (open in new window)
http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/01/t1243846678g0gtzc2iodeyg31/3iaji1243846637.ps (open in new window)


http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/01/t1243846678g0gtzc2iodeyg31/4zfu01243846637.png (open in new window)
http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/01/t1243846678g0gtzc2iodeyg31/4zfu01243846637.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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