Home » date » 2010 » Dec » 09 »

Prijsevolutie korte sigaretten (Opgave 9, deel 2)

*Unverified author*
R Software Module: /rwasp_decompose.wasp (opens new window with default values)
Title produced by software: Classical Decomposition
Date of computation: Thu, 09 Dec 2010 17:53:37 +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/09/t12919171872moxu5uq8wpe4gr.htm/, Retrieved Thu, 09 Dec 2010 18:53:07 +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/09/t12919171872moxu5uq8wpe4gr.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 «
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 4,69 4,69 4,69 4,69 4,69 4,69 4,69 4,73 4,78 4,79 4,79 4,8 4,8 4,81 5,16 5,26 5,29 5,29 5,29 5,3 5,3 5,3 5,3 5,3 5,3 5,3 5,3 5,35 5,44 5,47 5,47 5,48 5,48 5,48 5,48 5,48 5,48 5,48 5,5 5,55 5,57 5,58 5,58 5,58 5,59 5,59 5,59 5,55
 
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'Sir Ronald Aylmer Fisher' @ 193.190.124.24


Classical Decomposition by Moving Averages
tObservationsFitTrendSeasonalRandom
14.23NANA-0.0360694444444444NA
24.38NANA-0.0530694444444444NA
34.43NANA0.00193055555555528NA
44.44NANA0.0229305555555552NA
54.44NANA0.0339305555555555NA
64.44NANA0.0232638888888888NA
74.444.431513888888894.429166666666670.002347222222222460.00848611111111097
84.444.440013888888894.44125-0.00123611111111064-1.3888888889646e-05
94.454.457680555555564.4450.0126805555555558-0.00768055555555591
104.454.460763888888894.446250.0145138888888891-0.0107638888888886
114.454.445347222222224.4475-0.002152777777777890.00465277777777739
124.454.430097222222224.44916666666667-0.01906944444444460.0199027777777774
134.454.414763888888894.45083333333333-0.03606944444444440.0352361111111099
144.454.400263888888894.45333333333333-0.05306944444444440.0497361111111108
154.454.462347222222224.460416666666670.00193055555555528-0.0123472222222230
164.454.497930555555564.4750.0229305555555552-0.0479305555555563
174.464.527680555555564.493750.0339305555555555-0.0676805555555564
184.464.536180555555564.512916666666670.0232638888888888-0.0761805555555561
194.464.534847222222224.53250.00234722222222246-0.074847222222223
204.484.551263888888894.5525-0.00123611111111064-0.0712638888888888
214.584.585180555555564.57250.0126805555555558-0.00518055555555641
224.674.607013888888894.59250.01451388888888910.0629861111111101
234.684.609930555555564.61208333333333-0.002152777777777890.0700694444444432
244.684.612180555555564.63125-0.01906944444444460.067819444444444
254.694.614347222222224.65041666666667-0.03606944444444440.075652777777778
264.694.617347222222224.67041666666667-0.05306944444444440.0726527777777788
274.694.691097222222224.689166666666670.00193055555555528-0.00109722222222075
284.694.725430555555554.70250.0229305555555552-0.0354305555555534
294.694.746013888888894.712083333333330.0339305555555555-0.056013888888887
304.694.744930555555554.721666666666670.0232638888888888-0.0549305555555542
314.694.733597222222224.731250.00234722222222246-0.0435972222222212
324.734.739597222222224.74083333333333-0.00123611111111064-0.00959722222222048
334.784.778097222222224.765416666666670.01268055555555580.00190277777777847
344.794.823263888888894.808750.0145138888888891-0.0332638888888885
354.794.855347222222224.8575-0.00215277777777789-0.065347222222222
364.84.888430555555554.9075-0.0190694444444446-0.0884305555555551
374.84.921430555555564.9575-0.0360694444444444-0.121430555555556
384.814.953180555555565.00625-0.0530694444444444-0.143180555555556
395.165.053597222222225.051666666666670.001930555555555280.106402777777777
405.265.117513888888895.094583333333330.02293055555555520.142486111111110
415.295.171013888888895.137083333333330.03393055555555550.118986111111111
425.295.202430555555565.179166666666670.02326388888888880.0875694444444441
435.295.223180555555555.220833333333330.002347222222222460.0668194444444454
445.35.260847222222225.26208333333333-0.001236111111110640.0391527777777787
455.35.301013888888895.288333333333330.0126805555555558-0.00101388888888820
465.35.312430555555555.297916666666660.0145138888888891-0.0124305555555537
475.35.305763888888895.30791666666667-0.00215277777777789-0.00576388888888779
485.35.302597222222225.32166666666667-0.0190694444444446-0.00259722222222081
495.35.300597222222225.33666666666667-0.0360694444444444-0.000597222222221028
505.35.298597222222225.35166666666666-0.05306944444444440.00140277777777964
515.35.368597222222225.366666666666670.00193055555555528-0.0685972222222206
525.355.404597222222225.381666666666670.0229305555555552-0.0545972222222213
535.445.430597222222225.396666666666670.03393055555555550.00940277777777965
545.475.434930555555555.411666666666670.02326388888888880.0350694444444448
555.475.429013888888895.426666666666670.002347222222222460.0409861111111116
565.485.440430555555565.44166666666667-0.001236111111110640.039569444444445
575.485.470180555555565.45750.01268055555555580.00981944444444505
585.485.488680555555555.474166666666670.0145138888888891-0.00868055555555447
595.485.485763888888895.48791666666667-0.00215277777777789-0.00576388888888779
605.485.478847222222225.49791666666667-0.01906944444444460.00115277777777845
615.485.471013888888895.50708333333333-0.03606944444444440.00898611111111158
625.485.462763888888895.51583333333333-0.05306944444444440.017236111111111
635.55.526513888888895.524583333333330.00193055555555528-0.0265138888888892
645.555.556680555555565.533750.0229305555555552-0.00668055555555558
655.575.576847222222225.542916666666670.0339305555555555-0.00684722222222245
665.585.573680555555565.550416666666670.02326388888888880.00631944444444432
675.58NANA0.00234722222222246NA
685.58NANA-0.00123611111111064NA
695.59NANA0.0126805555555558NA
705.59NANA0.0145138888888891NA
715.59NANA-0.00215277777777789NA
725.55NANA-0.0190694444444446NA
 
Charts produced by software:
http://www.freestatistics.org/blog/date/2010/Dec/09/t12919171872moxu5uq8wpe4gr/1vsph1291917213.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/09/t12919171872moxu5uq8wpe4gr/1vsph1291917213.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/09/t12919171872moxu5uq8wpe4gr/2vsph1291917213.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/09/t12919171872moxu5uq8wpe4gr/2vsph1291917213.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/09/t12919171872moxu5uq8wpe4gr/3njok1291917213.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/09/t12919171872moxu5uq8wpe4gr/3njok1291917213.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/09/t12919171872moxu5uq8wpe4gr/4ga551291917213.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/09/t12919171872moxu5uq8wpe4gr/4ga551291917213.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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