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opgave 9 deel 2

*Unverified author*
R Software Module: /rwasp_decompose.wasp (opens new window with default values)
Title produced by software: Classical Decomposition
Date of computation: Sat, 11 Dec 2010 12:43:41 +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/11/t1292071339iibwrgo1gwx97qe.htm/, Retrieved Sat, 11 Dec 2010 13:42:20 +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/11/t1292071339iibwrgo1gwx97qe.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:
KDGP2W92
 
Dataseries X:
» Textbox « » Textfile « » CSV «
68897 38683 44720 39525 45315 50380 40600 36279 42438 38064 31879 11379 70249 39253 47060 41697 38708 49267 39018 32228 40870 39383 34571 12066 70938 34077 45409 40809 37013 44953 37848 32745 43412 34931 33008 8620 68906 39556 50669 36432 40891 48428 36222 33425 39401 37967 34801 12657 69116 41519 51321 38529 41547 52073 38401 40898 40439 41888 37898 8771 68184 50530 47221 41756 45633 48138 39486 39341 41117 41629 29722 7054 56676 34870 35117 30169 30936 35699 33228 27733 33666 35429 27438 8170
 
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'RServer@AstonUniversity' @ vre.aston.ac.uk


Classical Decomposition by Moving Averages
tObservationsFitTrendSeasonalRandom
168897NANA1.70570514434734NA
238683NANA1.01320162356912NA
344720NANA1.1726110134811NA
439525NANA0.974858322276653NA
545315NANA0.99791440273531NA
650380NANA1.18540381840308NA
74060038994.833813185140736.250.9572514360842021.04116356013991
83627936314.278706408340816.33333333330.8896996800237230.999028516945263
94243842160.425366501140937.58333333331.029870889622641.00658377212957
103806440117.089636225741125.58333333330.9754777047432130.948822567767434
113187934554.692578948440940.79166666670.8440162286134370.922566448165176
121137910316.8408393740619.1250.2539897361001751.10295391556073
137024969092.71399788740506.83333333331.705705144347341.01673528126494
143925340803.782434578740272.1251.013201623569120.961994150001533
154706046948.9997577564400381.17261101348111.00236427278145
164169739021.26335221940027.6250.9748583222766531.06857124598014
173870840110.919939345140194.750.997914402735310.965023989939235
184926747813.905109023140335.54166666671.185403818403081.0303906340146
193901838666.137601319640392.8750.9572514360842021.00910001413403
203222835771.191193393840205.91666666670.8896996800237230.900948470677484
214087041113.947808783339921.45833333331.029870889622640.9940665437939
223938338839.295132820939815.66666666670.9754777047432131.01399883456483
233457133514.231573125239708.04166666670.8440162286134371.03153193068351
241206610021.842343795339457.66666666670.2539897361001751.20397024679502
257093866913.391391792539229.16666666671.705705144347341.06014653456499
263407739719.487830422439201.95833333331.013201623569120.857941576323633
274540946118.107137120639329.41666666671.17261101348110.98462410577667
284080938263.026672971639249.83333333330.9748583222766531.0665387332996
293701338917.871691108238999.20833333330.997914402735310.951054063124848
304495345982.406817764538790.51.185403818403080.97761302878633
313784836913.76919113838562.250.9572514360842021.02530846427588
323274534436.604602538238705.8750.8896996800237230.95087771799623
334341240322.878231691939153.33333333331.029870889622641.07660965446361
343493138229.093183599639190.1250.9754777047432130.913728186861243
353300833059.552997302639169.33333333330.8440162286134370.998440602106544
36862010026.424741950839475.70833333330.2539897361001750.859728190441974
376890667465.329148084339552.751.705705144347341.02135424032029
383955640034.973485961339513.33333333331.013201623569120.988036123312802
395066946171.021209103939374.54166666671.17261101348111.09741995461883
403643238344.996010236439333.91666666670.9748583222766530.950110934690783
414089139452.670651440839535.1250.997914402735311.03645708452202
424842847153.042480263339778.04166666671.185403818403081.02703871166469
433622238246.9811287443399550.9572514360842020.947055138236194
443342535628.5056072140045.54166666670.8896996800237230.938153296927389
453940141353.950637352440154.51.029870889622640.952774750483247
463796739281.552337208840269.04166666670.9754777047432130.966535122494036
473480134084.540372267940383.750.8440162286134371.02102007596133
481265710302.575082525740562.95833333330.2539897361001751.22852780966068
496911669602.364480808440805.6251.705705144347340.993012241977174
504151941751.801420364841207.79166666671.013201623569120.994424158660342
515132148736.547530223941562.41666666671.17261101348111.05302904289996
523852940718.897882270341769.04166666670.9748583222766530.946219126838798
534154741973.735070884442061.45833333330.997914402735310.989833283357705
545207349820.843165405242028.58333333331.185403818403081.0452051127902
553840140039.75352662441827.83333333330.9572514360842020.95907183780404
564089837513.705087540242164.45833333330.8896996800237231.09021489358522
574043943634.685544995942369.08333333331.029870889622640.926762723162046
584188841294.613160563942332.70833333330.9754777047432131.01436959433737
593789835986.671612819742637.41666666670.8440162286134371.05311211905742
60877110831.064225916242643.70833333330.2539897361001750.809800386836696
616818472535.04019232342524.95833333331.705705144347340.940014644221794
625053043066.430526945842505.29166666671.013201623569121.17330364698752
634722149799.226261191142468.66666666671.17261101348110.948227583945408
644175641417.952537536242486.1250.9748583222766531.00816185836703
654563342046.79072111842134.66666666670.997914402735311.08529091560562
664813849457.961421496641722.45833333331.185403818403080.973311447064155
673948639411.397729787741171.41666666670.9572514360842021.00189291104882
683934135623.056196669840039.41666666670.8896996800237231.10436902950729
694111740044.040688326638882.58333333331.029870889622641.0267944816065
704162936966.174715192137895.45833333330.9754777047432131.12613761961396
712972231060.043384374536800.29166666670.8440162286134370.956920749664902
7270549059.718640542235669.6250.2539897361001750.778611376343785
735667659513.047480946234890.58333333331.705705144347340.95232898328968
743487034596.951505328534146.16666666671.013201623569121.00789227035305
753511739108.97138041433352.04166666671.17261101348110.89792696561656
763016931959.024093776132783.250.9748583222766530.94399002646252
773093632362.114602105432429.750.997914402735310.955932589089447
783569938384.659827361632381.08333333331.185403818403080.930032991318914
7933228NANA0.957251436084202NA
8027733NANA0.889699680023723NA
8133666NANA1.02987088962264NA
8235429NANA0.975477704743213NA
8327438NANA0.844016228613437NA
848170NANA0.253989736100175NA
 
Charts produced by software:
http://www.freestatistics.org/blog/date/2010/Dec/11/t1292071339iibwrgo1gwx97qe/1j2hg1292071416.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/11/t1292071339iibwrgo1gwx97qe/1j2hg1292071416.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/11/t1292071339iibwrgo1gwx97qe/2j2hg1292071416.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/11/t1292071339iibwrgo1gwx97qe/2j2hg1292071416.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/11/t1292071339iibwrgo1gwx97qe/3bbg11292071416.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/11/t1292071339iibwrgo1gwx97qe/3bbg11292071416.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/11/t1292071339iibwrgo1gwx97qe/4bbg11292071416.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/11/t1292071339iibwrgo1gwx97qe/4bbg11292071416.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')
 





Copyright

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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