Home » date » 2011 » May » 17 »

IKO opdracht 9 werknemers in Belgiƫ Marin Peeters MAR 201B

*Unverified author*
R Software Module: /rwasp_decompose.wasp (opens new window with default values)
Title produced by software: Classical Decomposition
Date of computation: Tue, 17 May 2011 10:05:04 +0000
 
Cite this page as follows:
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2011/May/17/t1305626603a2nylqato2z9j62.htm/, Retrieved Tue, 17 May 2011 12:03:27 +0200
 
Original text written by user:
 
IsPrivate?
No (this computation is public)
 
User-defined keywords:
KDGP2W92
 
Dataseries X:
» Textbox « » Textfile « » CSV «
3893,9 3799,2 3769,6 3768,6 3854,9 3778,5 3779,7 3803,2 3900,3 3792,6 3767,4 3752,6 3829,6 3722,6 3692,9 3681 3762,9 3661,7 3633,1 3621,5 3710 3619,4 3595,2 3573,2 3650,1 3554,2 3537 3528,6 3597,1 3521,9 3516,5 3515,7 3600,2 3517,1 3513,7 3528,2 3608,3 3502,5 3502,5 3495,3 3543,8 3425,3 3418,4 3406,4 3446,1 3341,1 3347 3354,9 3399 3288,9 3279 3275,2 3314 3227,1 3225,3 3228,6 3287,1 3210,1 3213,1 3228 3287 3211 3199,8 3166,3 3164 3156,7 3156 3165,5 3179,2 3182,5 3179,5 3193,5 3219,6 3221,9 3210,1
 
Output produced by software:


Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time3 seconds
R Server'Gwilym Jenkins' @ www.wessa.org


Classical Decomposition by Moving Averages
tObservationsFitTrendSeasonalRandom
13893.9NANA73.011736111111NA
23799.2NANA-15.4365972222225NA
33769.6NANA-17.7132638888885NA
43768.6NANA-19.5799305555555NA
53854.9NANA37.4834027777778NA
63778.5NANA-30.7782638888888NA
73779.73772.646736111113802.3625-29.71576388888887.05326388888898
83803.23773.711736111113796.49166666667-22.779930555555429.4882638888885
93900.33832.500069444443790.1041666666742.395902777777767.7999305555554
103792.63759.034236111113783.25833333333-24.224097222222133.5657638888883
113767.43762.965069444443775.775-12.80993055555574.43493055555564
123752.63787.221736111113767.07520.1467361111109-34.6217361111107
133829.63829.111736111113756.173.0117361111110.488263888888923
143722.63726.984236111113742.42083333333-15.4365972222225-4.38423611111102
153692.93709.207569444443726.92083333333-17.7132638888885-16.3075694444442
1636813692.195069444443711.775-19.5799305555555-11.1950694444445
173762.93734.866736111113697.3833333333337.483402777777828.033263888889
183661.73651.955069444443682.73333333333-30.77826388888889.74493055555604
193633.13638.063402777783667.77916666667-29.7157638888888-4.96340277777699
203621.53630.503402777783653.28333333333-22.7799305555554-9.00340277777696
2137103682.166736111113639.7708333333342.395902777777727.8332638888892
223619.43602.700902777783626.925-24.224097222222116.6990972222229
233595.23600.856736111113613.66666666667-12.8099305555557-5.65673611111015
243573.23621.080069444443600.9333333333320.1467361111109-47.8800694444435
253650.13663.261736111113590.2573.011736111111-13.1617361111107
263554.23565.546736111113580.98333333333-15.4365972222225-11.3467361111107
2735373554.286736111113572-17.7132638888885-17.2867361111107
283528.63543.582569444443563.1625-19.5799305555555-14.9825694444444
293597.13592.987569444443555.5041666666737.48340277777784.11243055555542
303521.93519.455069444443550.23333333333-30.77826388888882.4449305555554
313516.53516.900902777783546.61666666667-29.7157638888888-0.400902777777446
323515.73519.940902777783542.72083333333-22.7799305555554-4.24090277777805
333600.23581.525069444443539.1291666666742.395902777777718.6749305555554
343517.13512.080069444443536.30416666667-24.22409722222215.01993055555567
353513.73519.885902777783532.69583333333-12.8099305555557-6.18590277777776
363528.23546.596736111113526.4520.1467361111109-18.3967361111108
373608.33591.349236111113518.337573.01173611111116.9507638888886
383502.53494.259236111113509.69583333333-15.43659722222258.24076388888852
393502.53481.007569444443498.72083333333-17.713263888888521.4924305555551
403495.33465.386736111113484.96666666667-19.579930555555529.9132638888891
413543.83508.170902777783470.687537.483402777777835.6290972222228
423425.33425.742569444443456.52083333333-30.7782638888888-0.442569444443961
433418.43410.863402777783440.57916666667-29.71576388888887.53659722222255
443406.43400.178402777783422.95833333333-22.77993055555546.2215972222225
453446.13447.141736111113404.7458333333342.3959027777777-1.04173611111128
463341.13362.038402777783386.2625-24.2240972222221-20.9384027777778
4733473354.706736111113367.51666666667-12.8099305555557-7.70673611111124
483354.93369.830069444443349.6833333333320.1467361111109-14.9300694444446
4933993406.390902777783333.3791666666773.011736111111-7.39090277777768
503288.93302.488402777783317.925-15.4365972222225-13.5884027777765
5132793286.178402777783303.89166666667-17.7132638888885-7.17840277777759
523275.23272.228402777783291.80833333333-19.57993055555552.97159722222295
5333143318.254236111113280.7708333333337.4834027777778-4.25423611111091
543227.13239.125902777783269.90416666667-30.7782638888888-12.0259027777774
553225.33230.234236111113259.95-29.7157638888888-4.93423611111075
563228.63229.257569444443252.0375-22.7799305555554-0.657569444444107
573287.13287.887569444443245.4916666666742.3959027777777-0.787569444444216
583210.13213.430069444443237.65416666667-24.2240972222221-3.33006944444423
593213.13214.056736111113226.86666666667-12.8099305555557-0.956736111110786
6032283237.830069444443217.6833333333320.1467361111109-9.83006944444423
6132873284.874236111113211.862573.0117361111112.12576388888965
6232113190.909236111113206.34583333333-15.436597222222520.0907638888893
633199.83181.507569444443199.22083333333-17.713263888888518.2924305555557
643166.33173.995069444443193.575-19.5799305555555-7.69506944444356
6531643228.508402777783191.02537.4834027777778-64.5084027777775
663156.73157.409236111113188.1875-30.7782638888888-0.70923611111084
6731563154.225902777783183.94166666667-29.71576388888881.77409722222183
683165.53158.807569444443181.5875-22.77993055555546.6924305555558
693179.23224.866736111113182.4708333333342.3959027777777-45.6667361111108
703182.5NANA-24.2240972222221NA
713179.5NANA-12.8099305555557NA
723193.5NANA20.1467361111109NA
733219.6NANANANA
743221.9NANANANA
753210.1NANANANA
 
Charts produced by software:
http://www.freestatistics.org/blog/date/2011/May/17/t1305626603a2nylqato2z9j62/1gmwr1305626700.png (open in new window)
http://www.freestatistics.org/blog/date/2011/May/17/t1305626603a2nylqato2z9j62/1gmwr1305626700.ps (open in new window)


http://www.freestatistics.org/blog/date/2011/May/17/t1305626603a2nylqato2z9j62/2zv7w1305626700.png (open in new window)
http://www.freestatistics.org/blog/date/2011/May/17/t1305626603a2nylqato2z9j62/2zv7w1305626700.ps (open in new window)


http://www.freestatistics.org/blog/date/2011/May/17/t1305626603a2nylqato2z9j62/3uqb41305626700.png (open in new window)
http://www.freestatistics.org/blog/date/2011/May/17/t1305626603a2nylqato2z9j62/3uqb41305626700.ps (open in new window)


http://www.freestatistics.org/blog/date/2011/May/17/t1305626603a2nylqato2z9j62/49jhv1305626700.png (open in new window)
http://www.freestatistics.org/blog/date/2011/May/17/t1305626603a2nylqato2z9j62/49jhv1305626700.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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Software written by Ed van Stee & Patrick Wessa


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