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Decompositie prijs per liter diesel multiplicatief model Inez Van Dijck

R Software Module: rwasp_decompose.wasp (opens new window with default values)
Title produced by software: Classical Decomposition
Date of computation: Thu, 15 May 2008 06:47:00 -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/May/15/t121085565816af5ufa408uwhu.htm/, Retrieved Thu, 15 May 2008 14:47:43 +0200
 
User-defined keywords:
 
Dataseries X:
» Textbox « » Textfile « » CSV «
0.73 0.74 0.75 0.74 0.76 0.76 0.78 0.79 0.89 0.88 0.88 0.84 0.76 0.77 0.76 0.77 0.78 0.79 0.78 0.76 0.78 0.76 0.74 0.73 0.72 0.71 0.73 0.75 0.75 0.72 0.72 0.72 0.74 0.78 0.74 0.74 0.75 0.78 0.81 0.75 0.7 0.71 0.71 0.73 0.74 0.74 0.75 0.74 0.74 0.73 0.76 0.8 0.83 0.81 0.83 0.88 0.89 0.93 0.91 0.9 0.86 0.88 0.93 0.98 0.97 1.03 1.06 1.06 1.08 1.09 1.04 1
 
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 time2 seconds
R Server'George Udny Yule' @ 72.249.76.132


Classical Decomposition by Moving Averages
tObservationsFitTrendSeasonalRandom
10.73NANA0.976833657116767NA
20.74NANA0.981603705572407NA
30.75NANA1.00392995926241NA
40.74NANA1.00572651354412NA
50.76NANA1.00031121268256NA
60.76NANA0.989629469084281NA
70.780.7887615497151570.796250.990595352860480.988892017215696
80.790.801214164681930.798751.003085026205860.986003536661909
90.890.815924928437010.8004166666666671.019375235944211.09078662629525
100.880.8284312312578740.8020833333333331.032849327282541.06224870187936
110.880.8085015545454710.8041666666666671.005390534149811.08843328136175
120.840.7987276925749860.806250.9906700062945561.05167256351405
130.760.788793178121790.80750.9768336571167670.963497176547153
140.770.7914179876177530.806250.9816037055724070.972937198859703
150.760.803562271559620.8004166666666671.003929959262410.945788555409563
160.770.7953620511278110.7908333333333331.005726513544120.96811257075712
170.780.7802427458923930.781.000311212682560.999688884140646
180.790.7616023455827780.7695833333333330.9896294690842811.03728672132107
190.780.7561544526834990.7633333333333330.990595352860481.03153528651703
200.760.7615087157279480.7591666666666671.003085026205860.998018780748286
210.780.770053042819520.7554166666666671.019375235944211.01291723638161
220.760.778079826552850.7533333333333331.032849327282540.976763532563298
230.740.7552996387800440.751251.005390534149810.979743616977289
240.730.7401130505358910.7470833333333330.9906700062945560.986335802985005
250.720.7244849623616020.7416666666666670.9768336571167670.993809447269985
260.710.723932732859650.73750.9816037055724070.980754105696238
270.730.7370519117584850.7341666666666671.003929959262410.99043227261746
280.750.7375327765990240.7333333333333331.005726513544121.01690395843622
290.750.7343951486444430.7341666666666671.000311212682561.02124857630713
300.720.7269653141648280.7345833333333330.9896294690842810.990418643050624
310.720.7293258285435280.736250.990595352860480.98721308340039
320.720.7427008714865880.7404166666666671.003085026205860.969434704659562
330.740.7611335095050090.7466666666666671.019375235944210.972234162284153
340.780.7746369954619080.751.032849327282541.00692324865648
350.740.7519483369995450.7479166666666671.005390534149810.984110162345432
360.740.7384619338587330.7454166666666670.9906700062945561.00208279678443
370.750.7273340605281930.7445833333333330.9768336571167671.03116303869414
380.780.7308857591074550.7445833333333330.9816037055724071.06719824580044
390.810.7479278196504950.7451.003929959262411.08299220689305
400.750.7475900417344650.7433333333333331.005726513544121.00322363612541
410.70.742314279078180.7420833333333331.000311212682560.94299681378792
420.710.7347998807950790.74250.9896294690842810.966249476295172
430.710.7351043014352140.7420833333333330.990595352860480.965849334052052
440.730.7418649672980830.7395833333333331.003085026205860.984006567473733
450.740.7496655381006370.7354166666666671.019375235944210.987106866183118
460.740.7595746094390380.7354166666666671.032849327282540.97422951057633
470.750.7469213843287960.7429166666666671.005390534149811.004121739899
480.740.7454791797366530.75250.9906700062945560.992650123725
490.740.7440216355039380.7616666666666660.9768336571167670.994594733120612
500.730.7586978640986730.7729166666666670.9816037055724070.96217484527551
510.760.7885033221706840.7854166666666671.003929959262410.963851360711815
520.80.8041621581213220.7995833333333331.005726513544120.994824230313143
530.830.8144200456590470.8141666666666671.000311212682561.01913012139620
540.810.8189183856672430.82750.9896294690842810.98910955496502
550.830.8312746002754190.8391666666666670.990595352860480.998466691662422
560.880.8530402243692330.8504166666666671.003085026205861.03160434275031
570.890.880485360046810.863751.019375235944211.01080613078301
580.930.9071859924631680.8783333333333341.032849327282541.02514810383578
590.910.896473226283580.8916666666666671.005390534149811.01508887641017
600.90.898207472373730.9066666666666670.9906700062945561.00199567213745
610.860.9039781468568080.9254166666666670.9768336571167670.951350431412835
620.880.9251614925019940.94250.9816037055724070.951185287252002
630.930.961681240143450.9579166666666671.003929959262410.967056402037411
640.980.978069034421660.97251.005726513544121.00197426307386
650.970.98488974815370.9845833333333331.000311212682560.984881812221508
661.030.9838566305146230.9941666666666670.9896294690842811.04690050161195
671.06NANA0.99059535286048NA
681.06NANA1.00308502620586NA
691.08NANA1.01937523594421NA
701.09NANA1.03284932728254NA
711.04NANA1.00539053414981NA
721NANA0.990670006294556NA
 
Charts produced by software:
http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/15/t121085565816af5ufa408uwhu/1edby1210855618.png (open in new window)
http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/15/t121085565816af5ufa408uwhu/1edby1210855618.ps (open in new window)


http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/15/t121085565816af5ufa408uwhu/2686a1210855618.png (open in new window)
http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/15/t121085565816af5ufa408uwhu/2686a1210855618.ps (open in new window)


http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/15/t121085565816af5ufa408uwhu/3k6hm1210855618.png (open in new window)
http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/15/t121085565816af5ufa408uwhu/3k6hm1210855618.ps (open in new window)


http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/15/t121085565816af5ufa408uwhu/4at8x1210855618.png (open in new window)
http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/15/t121085565816af5ufa408uwhu/4at8x1210855618.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')
 





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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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