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Tijdreeks A - 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 20:43:05 +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/19/t1282250574d19w8uj995s7u3g.htm/, Retrieved Thu, 19 Aug 2010 22:42:59 +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/19/t1282250574d19w8uj995s7u3g.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:
Gregory Goris
 
Dataseries X:
» Textbox « » Textfile « » CSV «
225 224 223 221 219 218 219 221 222 222 223 225 226 229 235 229 231 229 226 232 234 230 232 231 237 241 256 255 264 259 253 258 265 258 257 255 255 255 275 276 278 268 253 257 255 253 245 248 246 243 260 262 262 251 236 238 239 243 233 238 232 224 238 236 231 209 179 179 165 174 163 166 164 152 163 167 157 138 111 110 91 100 88 89
 
Output produced by software:


Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time1 seconds
R Server'George Udny Yule' @ 72.249.76.132


Classical Decomposition by Moving Averages
tObservationsFitTrendSeasonalRandom
1225NANA-3.66087962962962NA
2224NANA-4.80671296296296NA
3223NANA10.7071759259259NA
4221NANA12.1307870370370NA
5219NANA13.5821759259259NA
6218NANA3.96412037037035NA
7219213.484953703704221.875-8.390046296296295.51504629629633
8221217.825231481481222.125-4.299768518518523.17476851851853
9222218.616898148148222.833333333333-4.216435185185183.38310185185190
10222220.241898148148223.666666666667-3.424768518518531.75810185185188
11223217.380787037037224.5-7.119212962962965.61921296296299
12225220.991898148148225.458333333333-4.466435185185174.00810185185185
13226222.547453703704226.208333333333-3.660879629629623.45254629629630
14229222.151620370370226.958333333333-4.806712962962966.84837962962962
15235238.623842592593227.91666666666710.7071759259259-3.62384259259261
16229240.880787037037228.7512.1307870370370-11.8807870370371
17231243.040509259259229.45833333333313.5821759259259-12.0405092592593
18229234.047453703704230.0833333333333.96412037037035-5.04745370370372
19226222.401620370370230.791666666667-8.390046296296293.59837962962962
20232227.450231481481231.75-4.299768518518524.54976851851853
21234228.908564814815233.125-4.216435185185185.09143518518516
22230231.658564814815235.083333333333-3.42476851851853-1.65856481481481
23232230.422453703704237.541666666667-7.119212962962961.57754629629628
24231235.700231481481240.166666666667-4.46643518518517-4.7002314814815
25237238.880787037037242.541666666667-3.66087962962962-1.88078703703704
26241239.943287037037244.75-4.806712962962961.05671296296299
27256257.832175925926247.12510.7071759259259-1.83217592592592
28255261.714120370370249.58333333333312.1307870370370-6.71412037037041
29264265.373842592593251.79166666666713.5821759259259-1.37384259259258
30259257.797453703704253.8333333333333.964120370370351.20254629629630
31253247.193287037037255.583333333333-8.390046296296295.80671296296296
32258252.616898148148256.916666666667-4.299768518518525.38310185185185
33265254.075231481481258.291666666667-4.2164351851851810.9247685185185
34258256.533564814815259.958333333333-3.424768518518531.46643518518516
35257254.297453703704261.416666666667-7.119212962962962.70254629629625
36255257.908564814815262.375-4.46643518518517-2.90856481481484
37255259.089120370370262.75-3.66087962962962-4.08912037037038
38255257.901620370370262.708333333333-4.80671296296296-2.90162037037038
39275272.957175925926262.2510.70717592592592.04282407407408
40276273.755787037037261.62512.13078703703702.24421296296299
41278274.498842592593260.91666666666713.58217592592593.50115740740745
42268264.089120370370260.1253.964120370370353.91087962962962
43253251.068287037037259.458333333333-8.390046296296291.93171296296299
44257254.283564814815258.583333333333-4.299768518518522.71643518518522
45255253.241898148148257.458333333333-4.216435185185181.75810185185185
46253252.825231481481256.25-3.424768518518530.174768518518533
47245247.880787037037255-7.11921296296296-2.88078703703704
48248249.158564814815253.625-4.46643518518517-1.15856481481481
49246248.547453703704252.208333333333-3.66087962962962-2.54745370370367
50243245.901620370370250.708333333333-4.80671296296296-2.90162037037035
51260259.957175925926249.2510.70717592592590.0428240740741046
52262260.297453703704248.16666666666712.13078703703701.70254629629636
53262260.832175925926247.2513.58217592592591.16782407407408
54251250.297453703704246.3333333333333.964120370370350.702546296296305
55236236.943287037037245.333333333333-8.39004629629629-0.94328703703701
56238239.658564814815243.958333333333-4.29976851851852-1.65856481481478
57239238.033564814815242.25-4.216435185185180.96643518518519
58243236.825231481481240.25-3.424768518518536.17476851851856
59233230.755787037037237.875-7.119212962962962.24421296296302
60238230.366898148148234.833333333333-4.466435185185177.63310185185185
61232227.047453703704230.708333333333-3.660879629629624.9525462962963
62224221.068287037037225.875-4.806712962962962.93171296296299
63238231.040509259259220.33333333333310.70717592592596.95949074074073
64236226.505787037037214.37512.13078703703709.49421296296296
65231222.165509259259208.58333333333313.58217592592598.83449074074073
66209206.630787037037202.6666666666673.964120370370352.36921296296299
67179188.443287037037196.833333333333-8.39004629629629-9.4432870370370
68179186.700231481481191-4.29976851851852-7.70023148148147
69165180.658564814815184.875-4.21643518518518-15.6585648148148
70174175.450231481481178.875-3.42476851851853-1.45023148148147
71163165.797453703704172.916666666667-7.11921296296296-2.79745370370367
72166162.408564814815166.875-4.466435185185173.59143518518519
73164157.422453703704161.083333333333-3.660879629629626.5775462962963
74152150.568287037037155.375-4.806712962962961.43171296296296
75163160.123842592593149.41666666666710.70717592592592.87615740740742
76167155.380787037037143.2512.130787037037011.6192129629630
77157150.623842592593137.04166666666713.58217592592596.37615740740742
78138134.672453703704130.7083333333333.964120370370353.32754629629630
79111NANA-8.39004629629629NA
80110NANA-4.29976851851852NA
8191NANA-4.21643518518518NA
82100NANA-3.42476851851853NA
8388NANA-7.11921296296296NA
8489NANA-4.46643518518517NA
 
Charts produced by software:
http://www.freestatistics.org/blog/date/2010/Aug/19/t1282250574d19w8uj995s7u3g/1v44q1282250583.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Aug/19/t1282250574d19w8uj995s7u3g/1v44q1282250583.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Aug/19/t1282250574d19w8uj995s7u3g/26dlb1282250583.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Aug/19/t1282250574d19w8uj995s7u3g/26dlb1282250583.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Aug/19/t1282250574d19w8uj995s7u3g/36dlb1282250583.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Aug/19/t1282250574d19w8uj995s7u3g/36dlb1282250583.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Aug/19/t1282250574d19w8uj995s7u3g/4gmkw1282250583.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Aug/19/t1282250574d19w8uj995s7u3g/4gmkw1282250583.ps (open in new window)


 
Parameters (Session):
par1 = 0.01 ; par2 = 0.99 ; par3 = 0.01 ;
 
Parameters (R input):
par1 = additive ; par2 = 12 ; par3 = 0.01 ;
 
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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