Home » date » 2008 » Jun » 01 »

Correctie decompositiemodel Nathalie Wouters

R Software Module: rwasp_decompose.wasp (opens new window with default values)
Title produced by software: Classical Decomposition
Date of computation: Sun, 01 Jun 2008 10:35:20 -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/Jun/01/t1212338167kj387tkzj9003b0.htm/, Retrieved Sun, 01 Jun 2008 16:36:07 +0000
 
IsPrivate?
No (this computation is public)
 
User-defined keywords:
 
Dataseries X:
» Textbox « » Textfile « » CSV «
7840 8292 8534 8441 8602 8468 8832 9006 8749 8714 8193 8251 8192 9056 9407 9068 9431 9907 10044 10838 10871 11127 11303 11349 11493 12694 13227 12253 12234 12491 13248 14042 14392 14834 15542 15518 16197 17325 24016 23671 24998 25329 25904 26548 26752 26967 27034 27056 27476 28497 29085 28720 29067 29249 29672 29761 30066 30315 30571 30757 30742 31310 31381 31470 31226 31081 31061 31114 30828 30418 30195 29877 29192 29876 29409 28458 28340 28164 28438 28053
 
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 time3 seconds
R Server'Sir Ronald Aylmer Fisher' @ 193.190.124.24


Classical Decomposition by Moving Averages
tObservationsFitTrendSeasonalRandom
17840NANA-997.262152777776NA
28292NANA-377.043402777779NA
38534NANA1225.45659722222NA
48441NANA272.644097222221NA
58602NANA319.029513888887NA
68468NANA162.987847222224NA
788328674.821180555568508.16666666667166.65451388889157.178819444443
890068834.862847222228554.66666666667280.196180555555171.137152777779
987498665.362847222228622.87542.487847222221883.6371527777774
1087148556.112847222228685.375-129.262152777778157.887152777777
1181938458.144097222228746.04166666667-287.897569444443-265.144097222223
1282518162.550347222228840.54166666667-677.99131944444288.4496527777756
1381927953.737847222228951-997.262152777776238.262152777776
1490568700.789930555559077.83333333333-377.043402777779355.210069444445
15940710468.03993055569242.583333333331225.45659722222-1061.03993055555
1690689704.185763888899431.54166666667272.644097222221-636.185763888889
1794319980.696180555559661.66666666667319.029513888887-549.696180555555
18990710083.32118055569920.33333333333162.987847222224-176.321180555557
191004410353.612847222210186.9583333333166.65451388889-309.612847222223
201083810756.279513888910476.0833333333280.19618055555581.7204861111131
211087110829.321180555610786.833333333342.487847222221841.6788194444434
221112710949.446180555611078.7083333333-129.262152777778177.553819444443
231130311040.310763888911328.2083333333-287.897569444443262.689236111109
241134910874.675347222211552.6666666667-677.991319444442474.324652777776
251149310796.571180555611793.8333333333-997.262152777776696.428819444443
261269411683.789930555612060.8333333333-377.0434027777791010.21006944445
271322713566.498263888912341.04166666671225.45659722222-339.498263888885
281225312914.852430555612642.2083333333272.644097222221-661.852430555553
291223413292.321180555612973.2916666667319.029513888887-1058.32118055555
301249113486.612847222213323.625162.987847222224-995.612847222224
311324813859.987847222213693.3333333333166.65451388889-611.987847222224
321404214362.487847222214082.2916666667280.196180555555-320.487847222221
331439214767.279513888914724.791666666742.4878472222218-375.279513888889
341483415520.821180555615650.0833333333-129.262152777778-686.821180555553
351554216369.769097222216657.6666666667-287.897569444443-827.76909722222
361551817046.425347222217724.4166666667-677.991319444442-1528.42534722222
371619717789.404513888918786.6666666667-997.262152777776-1592.40451388889
381732519458.039930555619835.0833333333-377.043402777779-2133.03993055555
392401622096.623263888920871.16666666671225.456597222221919.37673611111
402367122164.352430555621891.7083333333272.6440972222211506.64756944445
412499823195.112847222222876.0833333333319.0295138888871802.88715277778
422532923998.654513888923835.6666666667162.9878472222241330.34548611112
432590424953.029513888924786.375166.65451388889950.970486111113
442654826002.029513888925721.8333333333280.196180555555545.970486111113
452675226441.029513888926398.541666666742.4878472222218310.970486111113
462696726690.862847222226820.125-129.262152777778276.137152777777
472703426912.144097222227200.0416666667-287.897569444443121.855902777781
482705626854.925347222227532.9166666667-677.991319444442201.074652777777
492747626855.987847222227853.25-997.262152777776620.012152777785
502849727767.081597222228144.125-377.043402777779729.918402777777
512908529641.539930555628416.08333333331225.45659722222-556.539930555555
522872028966.310763888928693.6666666667272.644097222221-246.310763888891
532906729299.571180555628980.5416666667319.029513888887-232.571180555555
542924929445.112847222229282.125162.987847222224-196.112847222219
552967229739.071180555629572.4166666667166.65451388889-67.0711805555511
562976130105.904513888929825.7083333333280.196180555555-344.904513888887
573006630081.071180555630038.583333333342.4878472222218-15.0711805555547
583031530119.571180555630248.8333333333-129.262152777778195.428819444445
593057130165.477430555630453.375-287.897569444443405.522569444445
603075729941.675347222230619.6666666667-677.991319444442815.324652777781
613074229756.612847222230753.875-997.262152777776985.387152777785
623131030491.081597222230868.125-377.043402777779818.918402777781
633138132181.706597222230956.251225.45659722222-800.706597222215
643147031264.935763888930992.2916666667272.644097222221205.064236111117
653122631299.946180555630980.9166666667319.029513888887-73.9461805555511
663108131091.571180555630928.5833333333162.987847222224-10.5711805555547
673106130993.987847222230827.3333333333166.6545138888967.0121527777737
683111430983.196180555630703280.196180555555130.803819444449
693082830603.571180555630561.083333333342.4878472222218224.428819444445
703041830224.154513888930353.4166666667-129.262152777778193.845486111113
713019529819.769097222230107.6666666667-287.897569444443375.230902777777
722987729187.883680555629865.875-677.991319444442689.116319444442
7329192NA29635.0416666667NANA
7429876NA29398.2083333333NANA
7529409NANANANA
7628458NANANANA
7728340NANANANA
7828164NANANANA
7928438NANANANA
8028053NANANANA
 
Charts produced by software:
http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Jun/01/t1212338167kj387tkzj9003b0/1ymv21212338115.png (open in new window)
http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Jun/01/t1212338167kj387tkzj9003b0/1ymv21212338115.ps (open in new window)


http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Jun/01/t1212338167kj387tkzj9003b0/2erlq1212338115.png (open in new window)
http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Jun/01/t1212338167kj387tkzj9003b0/2erlq1212338115.ps (open in new window)


http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Jun/01/t1212338167kj387tkzj9003b0/34kek1212338115.png (open in new window)
http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Jun/01/t1212338167kj387tkzj9003b0/34kek1212338115.ps (open in new window)


http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Jun/01/t1212338167kj387tkzj9003b0/4t4p81212338115.png (open in new window)
http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Jun/01/t1212338167kj387tkzj9003b0/4t4p81212338115.ps (open in new window)


 
Parameters (Session):
par1 = additive ; par2 = 12 ;
 
Parameters (R input):
par1 = additive ; 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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