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Klassieke Decompositie (additief) bel20

*Unverified author*
R Software Module: /rwasp_decompose.wasp (opens new window with default values)
Title produced by software: Classical Decomposition
Date of computation: Sat, 11 Dec 2010 15:26:34 +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/11/t1292081290jq19xc0966yahw0.htm/, Retrieved Sat, 11 Dec 2010 16:28:10 +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/11/t1292081290jq19xc0966yahw0.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:
KDGP2W92
 
Dataseries X:
» Textbox « » Textfile « » CSV «
2981,85 3080,58 3106,22 3119,31 3061,26 3097,31 3161,69 3257,16 3277,01 3295,32 3363,99 3494,17 3667,03 3813,06 3917,96 3895,51 3801,06 3570,12 3701,61 3862,27 3970,1 4138,52 4199,75 4290,89 4443,91 4502,64 4356,98 4591,27 4696,96 4621,4 4562,84 4202,52 4296,49 4435,23 4105,18 4116,68 3844,49 3720,98 3674,4 3857,62 3801,06 3504,37 3032,6 3047,03 2962,34 2197,82 2014,45 1862,83 1905,41 1810,99 1670,07 1864,44 2052,02 2029,6 2070,83 2293,41 2443,27 2513,17 2466,92 2502,66
 
Output produced by software:


Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time4 seconds
R Server'Sir Ronald Aylmer Fisher' @ 193.190.124.24


Classical Decomposition by Moving Averages
tObservationsFitTrendSeasonalRandom
12981.85NANA-26.4728819444445NA
23080.58NANA-8.3631944444443NA
33106.22NANA-46.7043402777780NA
43119.31NANA117.485347222222NA
53061.26NANA170.542222222222NA
63097.31NANA33.8124305555554NA
73161.693194.515243055563219.87166666667-25.3564236111111-32.8252430555558
83257.163255.582222222223278.94083333333-23.35861111111121.57777777777801
93277.013382.349513888893343.2833333333339.0661805555555-105.339513888889
103295.323366.782638888893409.4475-42.6648611111113-71.4626388888883
113363.993357.653784722223472.61416666667-114.9603819444446.33621527777814
123494.173450.114097222223523.13958333333-73.02548611111144.0559027777776
133667.033538.863784722223565.33666666667-26.4728819444445128.166215277778
143813.063604.683055555563613.04625-8.3631944444443208.376944444444
153917.963620.433576388893667.13791666667-46.7043402777780297.526423611112
163895.513848.635347222223731.15117.48534722222246.8746527777776
173801.063971.648888888893801.10666666667170.542222222222-170.588888888889
183570.123902.939097222223869.1266666666733.8124305555554-332.819097222222
193701.613909.336909722223934.69333333333-25.3564236111111-207.726909722223
203862.273972.437222222223995.79583333333-23.3586111111112-110.167222222223
213970.14081.887013888894042.8208333333339.0661805555555-111.787013888889
224138.524047.438472222224090.10333333333-42.664861111111391.0815277777783
234199.754041.462118055564156.4225-114.960381944444158.287881944445
244290.894164.529513888894237.555-73.025486111111126.360486111112
254443.914290.770034722224317.24291666667-26.4728819444445153.139965277778
264502.644358.941388888894367.30458333333-8.3631944444443143.698611111112
274356.984348.376909722224395.08125-46.70434027777808.60309027777748
284591.274538.529097222224421.04375117.48534722222252.7409027777785
294696.964600.008472222224429.46625170.54222222222296.9515277777782
304621.44452.079513888894418.2670833333333.8124305555554169.32048611111
314562.844360.676076388894386.0325-25.3564236111111202.163923611112
324202.524305.128888888894328.4875-23.3586111111112-102.608888888889
334296.494306.543680555564267.477539.0661805555555-10.0536805555557
344435.234165.803055555564208.46791666667-42.6648611111113269.426944444444
354105.184025.609618055564140.57-114.96038194444479.5703819444443
364116.683983.672430555564056.69791666667-73.025486111111133.007569444444
373844.493919.922118055563946.395-26.4728819444445-75.4321180555562
383720.983826.126388888893834.48958333333-8.3631944444443-105.146388888889
393674.43684.050243055563730.75458333333-46.7043402777780-9.65024305555517
403857.623699.424930555563581.93958333333117.485347222222158.195069444444
413801.063572.142638888893401.60041666667170.542222222222228.917361111111
423504.373254.388680555553220.5762533.8124305555554249.981319444445
433032.63020.514409722223045.87083333333-25.356423611111112.085590277778
443047.032862.134305555562885.49291666667-23.3586111111112184.895694444445
452962.342761.462430555562722.3962539.0661805555555200.877569444445
462197.822513.168472222222555.83333333333-42.6648611111113-315.348472222222
472014.452284.947118055562399.9075-114.960381944444-270.497118055555
481862.832192.556597222222265.58208333333-73.025486111111-329.726597222222
491905.412137.586701388892164.05958333333-26.4728819444445-232.176701388889
501810.992084.221805555562092.585-8.3631944444443-273.231805555556
511670.071992.851909722222039.55625-46.7043402777780-322.781909722222
521864.442148.553263888892031.06791666667117.485347222222-284.113263888889
532052.022233.602638888892063.06041666667170.542222222222-181.582638888889
542029.62142.385347222222108.5729166666733.8124305555554-112.785347222222
552070.83NANA-25.3564236111111NA
562293.41NANA-23.3586111111112NA
572443.27NANA39.0661805555555NA
582513.17NANA-42.6648611111113NA
592466.92NANA-114.960381944444NA
602502.66NANA-73.025486111111NA
 
Charts produced by software:
http://www.freestatistics.org/blog/date/2010/Dec/11/t1292081290jq19xc0966yahw0/1gw0w1292081189.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/11/t1292081290jq19xc0966yahw0/1gw0w1292081189.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/11/t1292081290jq19xc0966yahw0/2gw0w1292081189.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/11/t1292081290jq19xc0966yahw0/2gw0w1292081189.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/11/t1292081290jq19xc0966yahw0/386zz1292081189.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/11/t1292081290jq19xc0966yahw0/386zz1292081189.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/11/t1292081290jq19xc0966yahw0/486zz1292081189.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/11/t1292081290jq19xc0966yahw0/486zz1292081189.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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