Home » date » 2009 » Jun » 05 »

Ken Soltvedt Sigaretten O9

*Unverified author*
R Software Module: rwasp_decompose.wasp (opens new window with default values)
Title produced by software: Classical Decomposition
Date of computation: Fri, 05 Jun 2009 15:58:09 -0600
 
Cite this page as follows:
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2009/Jun/05/t1244239131ysrv72l8voirich.htm/, Retrieved Fri, 05 Jun 2009 23:58:56 +0200
 
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/2009/Jun/05/t1244239131ysrv72l8voirich.htm/},
    year = {2009},
}
@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 = {2009},
    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 «
3.42 3.42 3.43 3.47 3.51 3.52 3.52 3.52 3.52 3.52 3.52 3.52 3.52 3.52 3.58 3.6 3.61 3.61 3.61 3.63 3.68 3.69 3.69 3.69 3.69 3.69 3.69 3.69 3.69 3.78 3.79 3.79 3.8 3.8 3.8 3.8 3.81 3.95 3.99 4 4.06 4.16 4.19 4.2 4.2 4.2 4.2 4.2 4.23 4.38 4.43 4.44 4.44 4.44 4.44 4.44 4.45 4.45 4.45 4.45 4.45 4.45 4.45 4.45 4.46 4.46 4.46 4.48 4.58 4.67 4.68 4.68
 
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
13.42NANA0.989694259333962NA
23.42NANA0.9997938720852NA
33.43NANA1.00325296587264NA
43.47NANA1.00075703241194NA
53.51NANA1.00000958178297NA
63.52NANA1.00500957163150NA
73.523.519105675922853.4951.006897189105251.00025413390773
83.523.517668266389363.503333333333331.004091798208191.00066286341805
93.523.525754509601293.513751.003416438164720.998367864357652
103.523.524388691904133.525416666666670.9997084104207440.998754765070545
113.523.519653393616353.5350.9956586686326311.00009847741947
123.523.513546639839273.542916666666670.9917102123502581.00183670826724
133.523.513826993243623.550416666666670.9896942593339621.00175677595062
143.523.55801644228323.558750.99979387208520.989315270769573
153.583.581613088165313.571.003252965872640.999549619647459
163.63.586463014906283.583751.000757032411941.00377446666464
173.613.597951141123323.597916666666671.000009581782971.00334881114392
183.613.63017832353063.612083333333331.005009571631500.994441506247831
193.613.651260931992913.626251.006897189105250.98869953893698
203.633.65531251706043.640416666666671.004091798208190.993075142838742
213.683.664560450214083.652083333333331.003416438164721.00421320646655
223.693.659349327310933.660416666666670.9997084104207441.00837598981336
233.693.651578167210183.66750.9956586686326311.01052198009475
243.693.647427518506553.677916666666670.9917102123502581.01167191980579
253.693.654446052590663.69250.9896942593339621.00972895670033
263.693.705902619195803.706666666666670.99979387208520.995708840509345
273.693.730428944769753.718333333333331.003252965872640.989162387122683
283.693.730738820412343.727916666666671.000757032411940.98908022716856
293.693.737119141254793.737083333333331.000009581782970.987391587082512
303.783.76501710772453.746251.005009571631501.00397950177830
313.793.781738026081133.755833333333331.006897189105251.00218470286992
323.793.787099565575233.771666666666671.004091798208191.00076587223931
333.83.807965382835123.7951.003416438164720.997908231290383
343.83.819302672978253.820416666666670.9997084104207440.99494602166128
353.83.832041300899843.848750.9956586686326310.99163858153295
363.83.8478356239193.880.9917102123502580.987568173748992
373.813.872178789644123.91250.9896942593339620.983942169764883
383.953.945436567716213.946250.99979387208521.00115663557263
393.993.992946804173093.981.003252965872640.999261997637932
4044.016371556746574.013333333333331.000757032411940.995923794271704
414.064.046705440948434.046666666666671.000009581782971.00328527965417
424.164.100439052256514.081.005009571631501.01452550494824
434.194.142542852177184.114166666666671.006897189105251.01145604270524
444.24.166562590981414.149583333333331.004091798208191.00802517861869
454.24.20013397408454.185833333333331.003416438164720.999968102425941
464.24.221268763001594.22250.9997084104207440.994961523609203
474.24.238187066146234.256666666666670.9956586686326310.99098976388955
484.24.24865183474394.284166666666670.9917102123502580.988548876999983
494.234.261870904256874.306250.9896942593339620.992521851324722
504.384.325774819888634.326666666666670.99979387208521.01253536819856
514.434.361224247062174.347083333333331.003252965872641.01576982724155
524.444.371223321155974.367916666666671.000757032411941.01573396593836
534.444.388792052050034.388751.000009581782971.01166789115152
544.444.431673456906724.409583333333331.005009571631501.00187887107980
554.444.459715466745334.429166666666671.006897189105250.995579209729333
564.444.459422698792134.441251.004091798208190.99564457103441
574.454.460186067642194.4451.003416438164720.997716223608678
584.454.444953519833244.446250.9997084104207441.00113532799483
594.454.428191928743634.44750.9956586686326311.00492482521248
604.454.412284019781694.449166666666670.9917102123502581.0085479493272
614.454.404964199252244.450833333333330.9896942593339621.01022387440865
624.454.452415377019424.453333333333330.99979387208520.999457513099095
634.454.474926249861074.460416666666671.003252965872640.994429796499585
644.454.478387720043424.4751.000757032411940.993661174105947
654.464.493793058137244.493751.000009581782970.992480059117086
664.464.535524445975314.512916666666671.005009571631500.98334824409505
674.46NANA1.00689718910525NA
684.48NANA1.00409179820819NA
694.58NANA1.00341643816472NA
704.67NANA0.999708410420744NA
714.68NANA0.995658668632631NA
724.68NANA0.991710212350258NA
 
Charts produced by software:
http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/05/t1244239131ysrv72l8voirich/130oq1244239087.png (open in new window)
http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/05/t1244239131ysrv72l8voirich/130oq1244239087.ps (open in new window)


http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/05/t1244239131ysrv72l8voirich/2llt21244239087.png (open in new window)
http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/05/t1244239131ysrv72l8voirich/2llt21244239087.ps (open in new window)


http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/05/t1244239131ysrv72l8voirich/3jbc71244239087.png (open in new window)
http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/05/t1244239131ysrv72l8voirich/3jbc71244239087.ps (open in new window)


http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/05/t1244239131ysrv72l8voirich/4u6db1244239087.png (open in new window)
http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/05/t1244239131ysrv72l8voirich/4u6db1244239087.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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