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opgave 9/2 Jasper Ledeganck

R Software Module: rwasp_decompose.wasp (opens new window with default values)
Title produced by software: Classical Decomposition
Date of computation: Mon, 19 May 2008 11:36:49 -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/19/t1211218632tnr8xkmsj4bsd3f.htm/, Retrieved Mon, 19 May 2008 19:37:16 +0200
 
User-defined keywords:
 
Dataseries X:
» Textbox « » Textfile « » CSV «
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 31956 29506 34506 27165 26736 23691 18157 17328 18205 20995 17382 9367 31124 26551 30651 25859 25100 25778 20418 18688 20424 24776 19814 12738 42553
 
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 time2 seconds
R Server'George Udny Yule' @ 72.249.76.132


Classical Decomposition by Moving Averages
tObservationsFitTrendSeasonalRandom
136845NANA1.31014144464039NA
235338NANA1.20564886893352NA
335022NANA1.40059267535936NA
434777NANA1.21281435450242NA
526887NANA1.02539939795535NA
623970NANA1.07865775611062NA
72278021033.218500103925430.8750.8270741175875361.08304870221775
81735119455.059761567824864.29166666670.7824497887325480.89185025451712
92138220916.659686406424288.16666666670.861187259354271.02224735309415
102456122933.875604643423695.16666666670.9678714620271381.07094851404126
111740918357.487232179723146.750.793091351147770.948332404093028
121151412282.232405662122954.3750.535071523649070.937451728619956
133151430007.861772631522904.29166666671.310141444640391.05019145445218
142707127597.604022105522890.251.205648868933520.980918487645387
152946232066.919450521422895.251.400592675359360.918766146073346
162610527774.610998528622900.95833333331.212814354502420.939887150944544
172239723527.49011126122944.70833333331.025399397955350.95195024603496
182384324944.050498204523125.08333333331.078657756110620.95585919382725
192170519145.077212494723147.95833333330.8270741175875361.13371180273092
201808918073.872874082223099.08333333330.7824497887325481.00083696095592
212076420225.915738799323486.08333333330.861187259354271.02660370329579
222531623251.942363852024023.79166666670.9678714620271381.08876925651410
231770419258.306825191724282.58333333330.793091351147770.919291615856982
241554813009.818204590824314.16666666670.535071523649071.19509740685797
252802931678.947185270424179.79166666671.310141444640390.884783191691186
262938328938.687447315924002.58333333331.205648868933521.01535358345098
273643833562.4022796364239631.400592675359361.08567913871017
283203428914.504889966523840.83333333331.212814354502421.10788685892789
292267924400.703148569823796.29166666671.025399397955350.929440428905397
302431925655.650008725423784.79166666671.078657756110620.94790036470443
311800419670.855121329423783.66666666670.8270741175875360.915262701542546
321753718541.777847744223697.08333333330.7824497887325480.945810058992456
332036620008.107127787623233.16666666670.861187259354271.01788739284164
342278221966.730046212722695.91666666670.9678714620271381.03711385135940
351916917807.412289212722453.16666666670.793091351147771.07646185131638
361380712068.694278432522555.29166666670.535071523649071.14403428253825
372974329697.522017932322667.41666666671.310141444640391.00153137295564
382559127387.168059107122715.70833333331.205648868933520.934415706829177
392909631845.2756596458227371.400592675359360.913667707291048
402648227389.845644562722583.70833333331.212814354502420.966854663719404
412240522999.793946088222430.08333333331.025399397955350.974139161964562
422704424023.64082373022271.79166666671.078657756110621.12572445610686
431797018389.545006078522234.45833333330.8270741175875360.977185677734831
441873017597.132738222322489.79166666670.7824497887325481.06437794603418
451968419702.529181926822878.33333333330.861187259354270.999059553128653
461978522389.004299499723132.20833333330.9678714620271380.883692715197796
471847918511.64436355923341.1250.793091351147770.998236549767385
481069812510.975482022123381.8750.535071523649070.855089198709781
493195630460.733998662223249.95833333331.310141444640391.04908831157527
502950627970.249993345123199.33333333331.205648868933521.05490655274874
513450632324.686860815723079.29166666671.400592675359361.06748133859971
522716527977.302597524823068.08333333331.212814354502420.970965657082444
532673623658.826684149123072.79166666671.025399397955351.13006449376936
542369124778.521476714722971.6251.078657756110620.956110316035736
551815718924.696421579222881.50.8270741175875360.959434148665982
561732817780.160784636722723.70833333330.7824497887325480.974569364691719
571820519325.006217107322439.95833333330.861187259354270.942043681408193
582099521510.862587598022224.91666666670.9678714620271380.976018507602973
591738217529.169406851722102.33333333330.793091351147770.99160431373353
60936711836.384058581522121.1250.535071523649070.791373442568281
613112429219.15662295822302.29166666671.310141444640391.06519159336534
622655127070.634995642522453.16666666671.205648868933520.980804477038454
633065131656.604154669322602.29166666671.400592675359360.968233985245036
642585927715.587366609422852.29166666671.212814354502420.933012880367595
652510023698.176386045723111.16666666671.025399397955351.05915322728291
662577825189.849634378323352.95833333331.078657756110621.02334870490132
672041819824.656445779123969.6250.8270741175875361.02992957561931
6818688NANA0.782449788732548NA
6920424NANA0.86118725935427NA
7024776NANA0.967871462027138NA
7119814NANA0.79309135114777NA
7212738NANA0.53507152364907NA
7342553NANANANA
 
Charts produced by software:
http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/19/t1211218632tnr8xkmsj4bsd3f/1u26s1211218607.png (open in new window)
http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/19/t1211218632tnr8xkmsj4bsd3f/1u26s1211218607.ps (open in new window)


http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/19/t1211218632tnr8xkmsj4bsd3f/2itll1211218607.png (open in new window)
http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/19/t1211218632tnr8xkmsj4bsd3f/2itll1211218607.ps (open in new window)


http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/19/t1211218632tnr8xkmsj4bsd3f/3mv4l1211218607.png (open in new window)
http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/19/t1211218632tnr8xkmsj4bsd3f/3mv4l1211218607.ps (open in new window)


http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/19/t1211218632tnr8xkmsj4bsd3f/4y8w61211218607.png (open in new window)
http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/19/t1211218632tnr8xkmsj4bsd3f/4y8w61211218607.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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