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classical decomposition (bevolkingsaantal) - jonas poels

*Unverified author*
R Software Module: /rwasp_decompose.wasp (opens new window with default values)
Title produced by software: Classical Decomposition
Date of computation: Mon, 20 Dec 2010 13:24:37 +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/20/t1292851357gtlkowoarp4fy6w.htm/, Retrieved Mon, 20 Dec 2010 14:22:42 +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/20/t1292851357gtlkowoarp4fy6w.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:
 
Dataseries X:
» Textbox « » Textfile « » CSV «
16306977 16307888 16307482 16308869 16311019 16312596 16315238 16319511 16327575 16330818 16331930 16334210 16334715 16335459 16334090 16333559 16334600 16336676 16337253 16342333 16348917 16352678 16352972 16357992 16359133 16362938 16365065 16367596 16371278 16374541 16377339 16383275 16393843 16399139 16401009 16405399 16409106 16414307 16418055 16423337 16428686 16434935 16440452 16449092 16464859 16473709 16479291 16485787 16489042 16495231 16501683 16506782 16513615 16520661 16528400 16538542 16554596 16562317 16568499 16574989
 
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'Gwilym Jenkins' @ 72.249.127.135


Classical Decomposition by Moving Averages
tObservationsFitTrendSeasonalRandom
116306977NANA1271.23263888802NA
216307888NANA753.972222221976NA
316307482NANA-1152.90277777709NA
416308869NANA-2833.90277777756NA
516311019NANA-3483.36111111198NA
616312596NANA-3797.23611111058NA
71631523816314585.638888916318998.5-4412.86111111012652.361111110076
81631951116319024.430555616321303.0416667-2278.61111111105486.569444445893
91632757516327553.222222216323560.53992.7222222224421.7777777798474
101633081816330893.618055616325697.91666675195.70138888849-75.6180555541068
111633193016330947.440972216327709.20833333238.23263888849982.55902778171
121633421016333202.097222216329695.08333333507.013888888951007.90277778171
131633471516332886.940972216331615.70833331271.232638888021828.05902777985
141633545916334237.888888916333483.9166667753.9722222219761221.11111111380
151633409016334171.180555616335324.0833333-1152.90277777709-81.1805555522442
161633355916334290.263888916337124.1666667-2833.90277777756-731.263888884336
171633460016335428.388888916338911.75-3483.36111111198-828.388888886198
181633667616336982.180555616340779.4166667-3797.23611111058-306.180555554107
191633725316338374.888888916342787.75-4412.86111111012-1121.88888888620
201634233316342671.513888916344950.125-2278.61111111105-338.513888886198
211634891716351378.430555616347385.70833333992.72222222244-2461.43055555411
221635267816355290.243055616350094.54166675195.70138888849-2612.24305555411
231635297216356279.2326389163530413238.23263888849-3307.23263888806
241635799216359653.972222216356146.95833333507.01388888895-1661.97222222015
251635913316360666.149305616359394.91666671271.23263888802-1533.14930555411
261636293816363525.055555616362771.0833333753.972222221976-587.055555552244
271636506516365196.013888916366348.9166667-1152.90277777709-131.013888886198
281636759616367322.805555616370156.7083333-2833.90277777756273.194444445893
291637127816370610.763888916374094.125-3483.36111111198667.236111111939
301637454116374273.722222216378070.9583333-3797.23611111058267.277777777985
311637733916377715.597222216382128.4583333-4412.86111111012-376.597222222015
321638327516384072.430555616386351.0416667-2278.61111111105-797.430555554107
331639384316394692.055555616390699.33333333992.72222222244-849.05555555597
341639913916400425.493055616395229.79166675195.70138888849-1286.49305555411
351640100916403182.565972216399944.33333333238.23263888849-2173.56597222202
361640539916408359.763888916404852.753507.01388888895-2960.7638888862
371640910616411270.107638916409998.8751271.23263888802-2164.1076388862
381641430716416124.930555616415370.9583333753.972222221976-1817.93055555411
391641805516419919.430555616421072.3333333-1152.90277777709-1864.43055555224
401642333716424304.513888916427138.4166667-2833.90277777756-967.513888888061
411642868616430023.888888916433507.25-3483.36111111198-1337.88888888620
421643493516436321.263888916440118.5-3797.23611111058-1386.26388888620
431644045216442385.805555616446798.6666667-4412.86111111012-1933.80555555224
441644909216451222.555555616453501.1666667-2278.61111111105-2130.55555555224
451646485916464350.222222216460357.53992.72222222244508.77777778171
461647370916472514.576388916467318.8755195.701388888491194.42361111380
471647929116477572.690972216474334.45833333238.232638888491718.30902777985
481648578716484952.097222216481445.08333333507.01388888895834.902777777985
491648904216489952.732638916488681.51271.23263888802-910.732638889924
501649523116496827.055555616496073.0833333753.972222221976-1596.05555555783
511650168316502386.305555616503539.2083333-1152.90277777709-703.30555555597
521650678216508136.347222216510970.25-2833.90277777756-1354.34722222202
531651361516514895.888888916518379.25-3483.36111111198-1280.88888888992
541652066116522015.763888916525813-3797.23611111058-1354.76388888992
5516528400NANA-4412.86111111012NA
5616538542NANA-2278.61111111105NA
5716554596NANA3992.72222222244NA
5816562317NANA5195.70138888849NA
5916568499NANA3238.23263888849NA
6016574989NANA3507.01388888895NA
 
Charts produced by software:
http://www.freestatistics.org/blog/date/2010/Dec/20/t1292851357gtlkowoarp4fy6w/1lkfo1292851474.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/20/t1292851357gtlkowoarp4fy6w/1lkfo1292851474.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/20/t1292851357gtlkowoarp4fy6w/2dtwr1292851474.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/20/t1292851357gtlkowoarp4fy6w/2dtwr1292851474.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/20/t1292851357gtlkowoarp4fy6w/3dtwr1292851474.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/20/t1292851357gtlkowoarp4fy6w/3dtwr1292851474.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/20/t1292851357gtlkowoarp4fy6w/462wc1292851474.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/20/t1292851357gtlkowoarp4fy6w/462wc1292851474.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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