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TIJDREEKS - STAP 24

*Unverified author*
R Software Module: /rwasp_decompose.wasp (opens new window with default values)
Title produced by software: Classical Decomposition
Date of computation: Thu, 19 Aug 2010 23:07:34 +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/Aug/20/t1282259256ctub48k8lve9o5z.htm/, Retrieved Fri, 20 Aug 2010 01:07:41 +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/2010/Aug/20/t1282259256ctub48k8lve9o5z.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:
Mertens Jeroen
 
Dataseries X:
» Textbox « » Textfile « » CSV «
76 75 74 72 70 69 70 72 73 73 74 76 74 67 66 58 55 58 64 68 66 76 75 88 85 83 77 66 65 65 63 62 57 68 69 79 74 76 82 75 75 76 78 77 67 74 68 87 76 88 95 96 96 105 108 113 101 107 102 116 105 121 134 140 131 141 131 128 123 129 125 144 135 141 156 159 146 154 145 133 126 127 122 148
 
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
176NANA-1.72453703703705NA
275NANA1.83101851851852NA
374NANA6.70601851851852NA
472NANA3.29629629629631NA
570NANA-1.74537037037037NA
669NANA2.58796296296296NA
77071.622685185185272.75-1.12731481481482-1.62268518518520
87271.33796296296372.3333333333333-0.9953703703703710.662037037037038
97364.143518518518571.6666666666667-7.523148148148158.85648148148148
107368.719907407407470.75-2.030092592592594.2800925925926
117464.046296296296369.5416666666667-5.495370370370379.9537037037037
127674.678240740740768.45833333333336.21990740740741.32175925925927
137466.02546296296367.75-1.724537037037057.97453703703702
146769.164351851851867.33333333333331.83101851851852-2.16435185185185
156673.581018518518566.8756.70601851851852-7.58101851851852
165870.004629629629666.70833333333333.29629629629631-12.0046296296296
175565.129629629629666.875-1.74537037037037-10.1296296296296
185870.004629629629667.41666666666672.58796296296296-12.0046296296296
196467.247685185185268.375-1.12731481481482-3.24768518518519
206868.504629629629669.5-0.995370370370371-0.504629629629648
216663.101851851851970.625-7.523148148148152.89814814814815
227669.386574074074171.4166666666667-2.030092592592596.61342592592592
237566.671296296296372.1666666666667-5.495370370370378.32870370370371
248879.094907407407472.8756.21990740740748.9050925925926
258571.40046296296373.125-1.7245370370370513.5995370370370
268374.664351851851872.83333333333331.831018518518528.33564814814815
277778.914351851851872.20833333333336.70601851851852-1.91435185185185
286674.796296296296371.53.29629629629631-8.7962962962963
296569.171296296296370.9166666666667-1.74537037037037-4.17129629629629
306572.879629629629670.29166666666672.58796296296296-7.87962962962962
316368.331018518518569.4583333333333-1.12731481481482-5.33101851851852
326267.71296296296368.7083333333333-0.995370370370371-5.71296296296294
335761.101851851851868.625-7.52314814814815-4.10185185185183
346867.178240740740769.2083333333333-2.030092592592590.82175925925928
356964.504629629629670-5.495370370370374.49537037037038
367977.094907407407470.8756.21990740740741.90509259259261
377470.233796296296371.9583333333333-1.724537037037053.76620370370371
387675.039351851851873.20833333333331.831018518518520.960648148148152
398280.956018518518574.256.706018518518521.04398148148147
407578.21296296296374.91666666666673.29629629629631-3.21296296296296
417573.379629629629675.125-1.745370370370371.62037037037037
427678.004629629629675.41666666666672.58796296296296-2.00462962962963
437874.706018518518575.8333333333333-1.127314814814823.29398148148148
447775.421296296296376.4166666666667-0.9953703703703711.57870370370371
456769.935185185185277.4583333333333-7.52314814814815-2.93518518518518
467476.844907407407478.875-2.03009259259259-2.84490740740739
476875.129629629629680.625-5.49537037037037-7.12962962962962
488788.928240740740782.70833333333336.2199074074074-1.92824074074075
497683.442129629629685.1666666666667-1.72453703703705-7.44212962962963
508889.747685185185287.91666666666671.83101851851852-1.74768518518518
519597.539351851851990.83333333333336.70601851851852-2.53935185185186
529696.921296296296393.6253.29629629629631-0.92129629629629
539694.671296296296396.4166666666667-1.745370370370371.32870370370371
54105101.62962962963099.04166666666672.587962962962963.37037037037038
55108100.331018518519101.458333333333-1.127314814814827.66898148148148
56113103.046296296296104.041666666667-0.9953703703703719.9537037037037
5710199.5185185185185107.041666666667-7.523148148148151.48148148148150
58107108.469907407407110.5-2.03009259259259-1.46990740740740
59102108.296296296296113.791666666667-5.49537037037037-6.2962962962963
60116122.969907407407116.756.2199074074074-6.96990740740742
61105117.483796296296119.208333333333-1.72453703703705-12.4837962962963
62121122.622685185185120.7916666666671.83101851851852-1.62268518518519
63134129.039351851852122.3333333333336.706018518518524.96064814814815
64140127.462962962963124.1666666666673.2962962962963112.5370370370370
65131124.296296296296126.041666666667-1.745370370370376.70370370370371
66141130.754629629630128.1666666666672.5879629629629610.2453703703704
67131129.456018518519130.583333333333-1.127314814814821.54398148148147
68128131.671296296296132.666666666667-0.995370370370371-3.67129629629630
69123126.893518518519134.416666666667-7.52314814814815-3.89351851851853
70129134.094907407407136.125-2.03009259259259-5.09490740740745
71125132.046296296296137.541666666667-5.49537037037037-7.0462962962963
72144144.928240740741138.7083333333336.2199074074074-0.928240740740733
73135138.108796296296139.833333333333-1.72453703703705-3.10879629629630
74141142.456018518519140.6251.83101851851852-1.45601851851853
75156147.664351851852140.9583333333336.706018518518528.33564814814815
76159144.2962962962961413.2962962962963114.7037037037037
77146139.046296296296140.791666666667-1.745370370370376.95370370370372
78154143.421296296296140.8333333333332.5879629629629610.5787037037037
79145NANA-1.12731481481482NA
80133NANA-0.995370370370371NA
81126NANA-7.52314814814815NA
82127NANA-2.03009259259259NA
83122NANA-5.49537037037037NA
84148NANA6.2199074074074NA
 
Charts produced by software:
http://www.freestatistics.org/blog/date/2010/Aug/20/t1282259256ctub48k8lve9o5z/1haxy1282259252.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Aug/20/t1282259256ctub48k8lve9o5z/1haxy1282259252.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Aug/20/t1282259256ctub48k8lve9o5z/2haxy1282259252.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Aug/20/t1282259256ctub48k8lve9o5z/2haxy1282259252.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Aug/20/t1282259256ctub48k8lve9o5z/3a1w11282259252.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Aug/20/t1282259256ctub48k8lve9o5z/3a1w11282259252.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Aug/20/t1282259256ctub48k8lve9o5z/4a1w11282259252.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Aug/20/t1282259256ctub48k8lve9o5z/4a1w11282259252.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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