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opgave 9 verbetering oef 2

*Unverified author*
R Software Module: /rwasp_decompose.wasp (opens new window with default values)
Title produced by software: Classical Decomposition
Date of computation: Fri, 20 May 2011 07:35:21 +0000
 
Cite this page as follows:
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2011/May/20/t1305876852z28bygc86vvcp9l.htm/, Retrieved Fri, 20 May 2011 09:34:12 +0200
 
Original text written by user:
 
IsPrivate?
No (this computation is public)
 
User-defined keywords:
KDGP2W92
 
Dataseries X:
» Textbox « » Textfile « » CSV «
10407 10463 10556 10646 10702 11353 11346 11451 11964 12574 13031 13812 14544 14931 14886 16005 17064 15168 16050 15839 15137 14954 15648 15305 15579 16348 15928 16171 15937 15713 15594 15683 16438 17032 17696 17745 19394 20148 20108 18584 18441 18391 19178 18079 18483 19644 19195 19650 20830 23595 22937 21814 21928 21777 21383 21467 22052 22680 24320 24977 25204 25739 26434 27525 30695 32436 30160 30236 31293 31077 32226 33865 32810
 
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'Sir Ronald Aylmer Fisher' @ 193.190.124.24


Classical Decomposition by Moving Averages
tObservationsFitTrendSeasonalRandom
110407NANA1.0174553195542NA
210463NANA1.05572692071106NA
310556NANA1.03255588225104NA
410646NANA1.01654156194664NA
510702NANA1.03447011680431NA
611353NANA1.00174494218443NA
71134611544.615421740311697.79166666670.9869055417217930.982795839057027
81145111557.844302702912056.33333333330.9586533470128760.990755689391151
91196411855.563092525912422.91666666670.9543300829133731.00914649997033
101257412433.844627496212826.6250.9693777301118721.01127208652695
111303113168.5420870344133150.989000532259440.989555253259975
121381213508.748159776413739.04166666670.9832380225289641.02244855234822
131454414340.0152737969140941.01745531955421.0142248611531
141493115279.359768964514472.83333333331.055726920711060.977200630508613
151488615269.307317243114787.8751.032555882251040.974896875851712
161600515267.691854267115019.251.016541561946641.04829205047958
171706415752.35060071615227.45833333331.034470116804311.08326689981268
181516815425.578189089915398.70833333331.001744942184430.98330187783353
191605015301.024639918915504.04166666670.9869055417217931.04894935977863
201583914960.943852930215606.20833333330.9586533470128761.05868988986933
211513714991.253162458515708.66666666670.9543300829133731.00972212502597
221495415276.423648833157590.9693777301118720.978894035918046
231564815546.058158230615718.95833333330.989000532259441.00655740771917
241530515431.633985835515694.70833333330.9832380225289640.991793870567968
251557915972.437546078315698.41666666671.01745531955420.975367720490797
261634816546.320051061115672.91666666671.055726920711060.988014250271413
271592816232.423816412815720.6251.032555882251040.981245942081368
281617116123.789273019915861.41666666671.016541561946641.00292801686879
291593716586.004206095716033.33333333331.034470116804310.960870370100522
301571316248.636877212216220.33333333331.001744942184430.967034965378336
311559416265.149112052616480.95833333330.9869055417217930.958736983754098
321568316103.69858645916798.250.9586533470128760.973875654452898
331643816348.390067868317130.750.9543300829133731.00548126951704
341703216872.463690723417405.45833333330.9693777301118721.00945542466121
351769617416.629039932817610.33333333330.989000532259441.01604047255222
361774517527.446799106917826.250.9832380225289641.01241214441479
371939418402.883940663418087.16666666671.01745531955421.0538565619678
382014819358.160727131618336.33333333331.055726920711061.04080135938542
392010819124.354703627418521.3751.032555882251041.05143416923689
401858419024.998890815618715.41666666671.016541561946640.976820030668781
411844119537.735375632218886.70833333331.034470116804310.943865788201837
421839119061.74537172919028.54166666671.001744942184430.964811964557884
431917818916.758697337919167.750.9869055417217931.01381004572939
441807918570.273704433719371.20833333330.9586533470128760.973545155432124
451848318736.084171564119632.70833333330.9543300829133730.986492152295719
461964419276.237726229619885.16666666670.9693777301118721.01907852968995
471919519943.236941367120165.04166666670.989000532259440.962481670173858
481965020108.610481249220451.41666666670.9832380225289640.977193328117979
492083021045.427375403920684.3751.01745531955420.98976369680876
502359522083.079886730320917.41666666671.055726920711061.06846509277803
512293721897.713757030221207.29166666671.032555882251041.04746094749897
522181421837.854104518821482.51.016541561946640.998907671770102
532192822574.767226883521822.54166666671.034470116804310.971349993539987
542177722296.880662513622258.04166666671.001744942184430.976683704309023
552138322365.500112884722662.250.9869055417217930.956070729117356
562146721985.596084835422933.83333333330.9586533470128760.976412007077982
572205222110.754399759623168.8750.9543300829133730.997342722971035
582268022831.309379198623552.54166666670.9693777301118720.99337272441604
592432023890.090815481524155.79166666670.989000532259441.01799529302082
602497724546.742073690324965.20833333330.9832380225289641.01752810719313
612520426224.953255481125775.04166666671.01745531955420.961069396557733
622573927983.229726232626506.1251.055726920711060.919800904034721
632643428143.902427737327256.54166666671.032555882251040.93924430230926
642752528454.480775331127991.45833333331.016541561946640.96733446719095
653069529659.034101367128670.751.034470116804311.03492918532317
663243629421.749824427729370.51.001744942184431.10244972489942
673016029664.160046688230057.750.9869055417217931.01671511859872
6830236NANA0.958653347012876NA
6931293NANA0.954330082913373NA
7031077NANA0.969377730111872NA
7132226NANA0.98900053225944NA
7233865NANA0.983238022528964NA
7332810NANANANA
 
Charts produced by software:
http://www.freestatistics.org/blog/date/2011/May/20/t1305876852z28bygc86vvcp9l/18wxs1305876917.png (open in new window)
http://www.freestatistics.org/blog/date/2011/May/20/t1305876852z28bygc86vvcp9l/18wxs1305876917.ps (open in new window)


http://www.freestatistics.org/blog/date/2011/May/20/t1305876852z28bygc86vvcp9l/2iv4u1305876917.png (open in new window)
http://www.freestatistics.org/blog/date/2011/May/20/t1305876852z28bygc86vvcp9l/2iv4u1305876917.ps (open in new window)


http://www.freestatistics.org/blog/date/2011/May/20/t1305876852z28bygc86vvcp9l/3bsk41305876917.png (open in new window)
http://www.freestatistics.org/blog/date/2011/May/20/t1305876852z28bygc86vvcp9l/3bsk41305876917.ps (open in new window)


http://www.freestatistics.org/blog/date/2011/May/20/t1305876852z28bygc86vvcp9l/4t1jw1305876917.png (open in new window)
http://www.freestatistics.org/blog/date/2011/May/20/t1305876852z28bygc86vvcp9l/4t1jw1305876917.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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