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Aantal personenwagens additief model

R Software Module: rwasp_decompose.wasp (opens new window with default values)
Title produced by software: Classical Decomposition
Date of computation: Mon, 28 Apr 2008 12:34:59 -0600
 
Cite this page as follows:
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2008/Apr/28/t1209407907g8z0zqos610jcwo.htm/, Retrieved Mon, 28 Apr 2008 20:38:32 +0200
 
User-defined keywords:
 
Dataseries X:
» Textbox « » Textfile « » CSV «
56421 53152 53536 52408 41454 38271 35306 26414 31917 38030 27534 18387 50556 43901 48572 43899 37532 40357 35489 29027 34485 42598 30306 26451 47460 50104 61465 53726 39477 43895 31481 29896 33842 39120 33702 25094 51442 45594 52518 48564 41745 49585 32747 33379 35645 37034 35681 20972 58552 54955 65540 51570 51145 46641 35704 33253 35193 41668 34865 21210 56126 49231 59723 48103 47472 50497 40059 34149 36860 46356 36577
 
Text written by user:
 
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'Gwilym Jenkins' @ 72.249.127.135


Classical Decomposition by Moving Averages
tObservationsFitTrendSeasonalRandom
156421NANA3176.13756613757NA
25315252121.851851851954369.6666666667-2247.814814814811030.14814814815
35353652103.677248677253032-928.3227513227521432.32275132275
45240852308.804232804249132.66666666673176.1375661375799.1957671957716
54145441796.518518518544044.3333333333-2247.81481481481-342.518518518511
63827137415.343915343938343.6666666667-928.322751322752855.65608465609
73530636506.470899470933330.33333333333176.13756613757-1200.47089947089
82641428964.518518518531212.3333333333-2247.81481481481-2550.51851851851
93191731192.010582010632120.3333333333-928.322751322752724.989417989425
103803035669.804232804232493.66666666673176.137566137572360.19576719577
112753425735.851851851927983.6666666667-2247.814814814811798.14814814815
121838731230.677248677232159-928.322751322752-12843.6772486772
135055640790.804232804237614.66666666673176.137566137579765.19576719577
144390145428.518518518547676.3333333333-2247.81481481481-1527.51851851851
154857244529.010582010645457.3333333333-928.3227513227524042.98941798943
164389946510.470899470943334.33333333333176.13756613757-2611.47089947089
173753238348.185185185240596-2247.81481481481-816.185185185182
184035736864.343915343937792.6666666667-928.3227513227523492.65608465609
193548938133.804232804234957.66666666673176.13756613757-2644.80423280423
202902730752.518518518533000.3333333333-2247.81481481481-1725.51851851851
213448534441.677248677235370-928.32275132275243.3227513227539
224259838972.470899470935796.33333333333176.137566137573625.52910052911
233030630870.518518518533118.3333333333-2247.81481481481-564.518518518515
242645133810.677248677234739-928.322751322752-7359.67724867725
254746044514.470899470941338.33333333333176.137566137572945.52910052911
265010450761.851851851953009.6666666667-2247.81481481481-657.851851851847
276146554170.010582010655098.3333333333-928.3227513227527294.98941798943
285372654732.1375661376515563176.13756613757-1006.13756613756
293947743451.518518518545699.3333333333-2247.81481481481-3974.51851851851
304389537356.010582010638284.3333333333-928.3227513227526538.98941798943
313148138266.804232804235090.66666666673176.13756613757-6785.80423280423
322989629491.851851851931739.6666666667-2247.81481481481404.14814814815
333384233357.677248677234286-928.322751322752484.322751322754
343912038730.804232804235554.66666666673176.13756613757389.195767195772
353370230390.851851851932638.6666666667-2247.814814814813311.14814814815
362509435817.677248677236746-928.322751322752-10723.6772486772
375144243886.1375661376407103176.137566137577555.86243386244
384559447603.518518518549851.3333333333-2247.81481481481-2009.51851851851
395251847963.677248677248892-928.3227513227524554.32275132275
404856450785.1375661376476093176.13756613757-2221.13756613756
414174544383.518518518546631.3333333333-2247.81481481481-2638.51851851851
424958540430.677248677241359-928.3227513227529154.32275132275
433274741746.470899470938570.33333333333176.13756613757-8999.4708994709
443337931675.851851851933923.6666666667-2247.814814814811703.14814814815
453564534424.343915343935352.6666666667-928.3227513227521220.65608465609
463703439296.1375661376361203176.13756613757-2262.13756613756
473568128981.185185185231229-2247.814814814816699.81481481482
482097237473.343915343938401.6666666667-928.322751322752-16501.3439153439
495855248002.470899470944826.33333333333176.1375661375710549.5291005291
505495557434.518518518559682.3333333333-2247.81481481481-2479.51851851851
516554056426.677248677257355-928.3227513227529113.32275132275
525157059261.1375661376560853176.13756613757-7691.13756613756
535114547537.518518518549785.3333333333-2247.814814814813607.48148148149
544664143568.343915343944496.6666666667-928.3227513227523072.65608465609
553570441708.804232804238532.66666666673176.13756613757-6004.80423280423
563325332468.851851851934716.6666666667-2247.81481481481784.148148148153
573519335776.343915343936704.6666666667-928.322751322752-583.34391534391
584166840418.1375661376372423176.137566137571249.86243386244
593486530333.185185185232581-2247.814814814814531.81481481482
602121036472.010582010637400.3333333333-928.322751322752-15262.0105820106
615612645365.1375661376421893176.1375661375710760.8624338624
624923152778.851851851955026.6666666667-2247.81481481481-3547.85185185185
635972351424.010582010652352.3333333333-928.3227513227528298.98941798943
644810354942.1375661376517663176.13756613757-6839.13756613756
654747246442.851851851948690.6666666667-2247.814814814811029.14814814815
665049745081.010582010646009.3333333333-928.3227513227525415.98941798943
674005944744.470899470941568.33333333333176.13756613757-4685.47089947089
683414934774.851851851937022.6666666667-2247.81481481481-625.851851851847
693686038193.343915343939121.6666666667-928.322751322752-1333.34391534391
7046356NA39931NANA
7136577NANANANA
 
Charts produced by software:
http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Apr/28/t1209407907g8z0zqos610jcwo/1ed3n1209407696.png (open in new window)
http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Apr/28/t1209407907g8z0zqos610jcwo/1ed3n1209407696.ps (open in new window)


http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Apr/28/t1209407907g8z0zqos610jcwo/2k9jr1209407696.png (open in new window)
http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Apr/28/t1209407907g8z0zqos610jcwo/2k9jr1209407696.ps (open in new window)


http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Apr/28/t1209407907g8z0zqos610jcwo/3npr51209407696.png (open in new window)
http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Apr/28/t1209407907g8z0zqos610jcwo/3npr51209407696.ps (open in new window)


http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Apr/28/t1209407907g8z0zqos610jcwo/4bff21209407696.png (open in new window)
http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Apr/28/t1209407907g8z0zqos610jcwo/4bff21209407696.ps (open in new window)


 
Parameters (Session):
par1 = additive ; par2 = 3 ;
 
Parameters (R input):
par1 = additive ; par2 = 3 ;
 
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$trend)) {
a<-table.row.start(a)
a<-table.element(a,i,header=TRUE)
a<-table.element(a,x[i])
a<-table.element(a,m$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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