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Additief model

*Unverified author*
R Software Module: /rwasp_decompose.wasp (opens new window with default values)
Title produced by software: Classical Decomposition
Date of computation: Mon, 16 May 2011 17:45:12 +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/16/t1305567754mf43v9uqojq6vrq.htm/, Retrieved Mon, 16 May 2011 19:42:37 +0200
 
Original text written by user:
 
IsPrivate?
No (this computation is public)
 
User-defined keywords:
KDGP2W92
 
Dataseries X:
» Textbox « » Textfile « » CSV «
70938 34077 45409 40809 37013 44953 19848 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 63410 38040 45389 37353 37024 50957 37994 36454 46080 43373 37395 10963 75001
 
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'Herman Ole Andreas Wold' @ www.yougetit.org


Classical Decomposition by Moving Averages
tObservationsFitTrendSeasonalRandom
170938NANA26257.7465277778NA
234077NANA1720.22152777778NA
345409NANA6707.47986111111NA
440809NANA-2480.70347222222NA
537013NANA-229.211805555553NA
644953NANA7567.50486111111NA
71984834566.638194444437062.25-2495.61180555556-14718.6381944444
83274530709.504861111137205.875-6496.370138888892035.49513888889
94341236409.471527777837653.3333333333-1243.861805555567002.52847222222
103493138984.496527777837690.1251294.37152777778-4053.49652777777
113300831873.896527777837669.3333333333-5795.436805555561134.10347222222
12862013169.579861111137975.7083333333-24806.1284722222-4549.5798611111
136890665060.496527777838802.7526257.74652777783845.50347222222
143955641233.554861111139513.33333333331720.22152777778-1677.55486111111
155066946082.021527777839374.54166666676707.479861111114586.97847222222
163643236853.213194444439333.9166666667-2480.70347222222-421.213194444441
174089139305.913194444439535.125-229.2118055555531585.08680555555
184842847345.546527777839778.04166666677567.504861111111082.45347222222
193622237459.388194444439955-2495.61180555556-1237.38819444444
203342533549.171527777840045.5416666667-6496.37013888889-124.171527777769
213940138910.638194444540154.5-1243.86180555556490.361805555549
223796741563.413194444440269.04166666671294.37152777778-3596.41319444445
233480134588.313194444440383.75-5795.43680555556212.686805555561
241265715756.829861111140562.9583333333-24806.1284722222-3099.82986111111
256911667063.371527777840805.62526257.74652777782052.62847222223
264151942928.013194444441207.79166666671720.22152777778-1409.01319444444
275132148269.896527777841562.41666666676707.479861111113051.10347222222
283852939288.338194444441769.0416666667-2480.70347222222-759.338194444448
294154741832.246527777842061.4583333333-229.211805555553-285.246527777766
305207349596.088194444442028.58333333337567.504861111112476.91180555556
313840139332.221527777841827.8333333333-2495.61180555556-931.221527777772
324089835668.088194444442164.4583333333-6496.370138888895229.91180555555
334043941125.221527777842369.0833333333-1243.86180555556-686.22152777778
344188843627.079861111142332.70833333331294.37152777778-1739.07986111111
353789836841.979861111142637.4166666667-5795.436805555561056.02013888889
36877117837.579861111142643.7083333333-24806.1284722222-9066.57986111111
376818468782.704861111142524.958333333326257.7465277778-598.70486111111
385053044225.513194444442505.29166666671720.221527777786304.48680555556
394722149176.146527777842468.66666666676707.47986111111-1955.14652777777
404175640005.421527777842486.125-2480.703472222221750.57847222223
414563341905.454861111142134.6666666667-229.2118055555533727.54513888889
424813849289.963194444441722.45833333337567.50486111111-1151.96319444445
433948638675.804861111141171.4166666667-2495.61180555556810.195138888885
443934133543.046527777840039.4166666667-6496.370138888895797.95347222222
454111737638.721527777838882.5833333333-1243.861805555563478.27847222223
464162939189.829861111137895.45833333331294.371527777782439.17013888889
472972231004.854861111136800.2916666667-5795.43680555556-1282.85486111111
48705410863.496527777835669.625-24806.1284722222-3809.49652777777
495667661148.329861111134890.583333333326257.7465277778-4472.32986111111
503487035866.388194444434146.16666666671720.22152777778-996.388194444444
513511740059.521527777833352.04166666676707.47986111111-4942.52152777778
523016930302.546527777832783.25-2480.70347222222-133.546527777773
533093632200.538194444432429.75-229.211805555553-1264.53819444444
543569939948.588194444432381.08333333337567.50486111111-4249.58819444444
553322830212.554861111132708.1666666667-2495.611805555563015.44513888889
562773326624.463194444433120.8333333333-6496.370138888891108.53680555555
573366632437.054861111133680.9166666667-1243.861805555561228.94513888889
583542935702.621527777834408.251294.37152777778-273.621527777766
592743829165.813194444434961.25-5795.43680555556-1727.81319444444
60817011044.538194444435850.6666666667-24806.1284722222-2874.53819444444
616341062942.74652777783668526257.7465277778467.253472222219
623804038967.179861111137246.95833333331720.22152777778-927.179861111108
634538944835.063194444438127.58333333336707.47986111111553.936805555553
643735336495.129861111138975.8333333333-2480.70347222222857.870138888888
653702439492.496527777839721.7083333333-229.211805555553-2468.49652777778
665095747820.463194444440252.95833333337567.504861111113136.53680555556
673799438356.679861111140852.2916666667-2495.61180555556-362.679861111115
6836454NANA-6496.37013888889NA
6946080NANA-1243.86180555556NA
7043373NANA1294.37152777778NA
7137395NANA-5795.43680555556NA
7210963NANA-24806.1284722222NA
7375001NANANANA
 
Charts produced by software:
http://www.freestatistics.org/blog/date/2011/May/16/t1305567754mf43v9uqojq6vrq/1pu9u1305567910.png (open in new window)
http://www.freestatistics.org/blog/date/2011/May/16/t1305567754mf43v9uqojq6vrq/1pu9u1305567910.ps (open in new window)


http://www.freestatistics.org/blog/date/2011/May/16/t1305567754mf43v9uqojq6vrq/2ig221305567910.png (open in new window)
http://www.freestatistics.org/blog/date/2011/May/16/t1305567754mf43v9uqojq6vrq/2ig221305567910.ps (open in new window)


http://www.freestatistics.org/blog/date/2011/May/16/t1305567754mf43v9uqojq6vrq/3lpjk1305567910.png (open in new window)
http://www.freestatistics.org/blog/date/2011/May/16/t1305567754mf43v9uqojq6vrq/3lpjk1305567910.ps (open in new window)


http://www.freestatistics.org/blog/date/2011/May/16/t1305567754mf43v9uqojq6vrq/466zx1305567910.png (open in new window)
http://www.freestatistics.org/blog/date/2011/May/16/t1305567754mf43v9uqojq6vrq/466zx1305567910.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')
 





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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