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Opgave 9: Goudkoers Multiplicatieve decompositie

*Unverified author*
R Software Module: /rwasp_decompose.wasp (opens new window with default values)
Title produced by software: Classical Decomposition
Date of computation: Fri, 04 Jun 2010 14:55:19 +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/Jun/04/t1275663500bl40s99v83h50mw.htm/, Retrieved Fri, 04 Jun 2010 16:58:20 +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/Jun/04/t1275663500bl40s99v83h50mw.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:
KDGP2W52
 
Dataseries X:
» Textbox « » Textfile « » CSV «
23100 22650 22440 22910 22980 22535 22300 22780 22780 23300 23800 24510 24660 24730 25070 24690 24880 23920 23880 23990 24590 23610 23580 23360 23910 23940 23060 22800 23020 22890 22780 22530 22290 22820 22480 22110 22000 22230 22260 22590 22820 22420 22230 21600 21000 21360 21640 21450 21710 21620 21800 21490 21670 22130 22050 22050 22140 22390 22220 21790 21510 21670 21745 21850 22105 22050 21670 21680 21800 21920 21980 22270 21740 21950 22010 21890 21920 22110 22340 22210 22240 21960 22220 22060 22090 21960 21940 21790 21710 21690 21710 21670 21640 21500 21290 21250 21580 21670 21620 21510 21360 21420 21470 21370 21370 21340 21130 21130 20990 21240 21320 21430 21390 21530 21510 21630 21560 21610 21560 21310 21340 21410 21550 21380 21600 21530 21560 21670 21540 21540 21550 21590 21420 21420 21370 21380 21210 21505 21365 21385 21350 21360 21530 21380 21630 22145 22 etc...
 
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'Sir Ronald Aylmer Fisher' @ 193.190.124.24


Classical Decomposition by Moving Averages
tObservationsFitTrendSeasonalRandom
123100NANA1.00101450854227NA
222650NANA0.998537610991772NA
32244022795.8871819913228160.9991184774715670.98438809689002
42291022700.6231527430227030.9998953069084711.00922339646133
52298022665.4578967126226331.001434096085921.01387759756369
62253522724.0303584181227011.001014508542270.991681477473998
72230022641.8403292384226750.9985376109917720.984902272771661
82278022718.9550592260227390.9991184774715671.00268696076095
92278022989.5928964396229920.9998953069084710.990883140150253
102330023467.6066076774234341.001434096085920.992857959037774
112380023834.1554483914238101.001014508542270.99856695369528
122451024164.6101860009242000.9985376109917721.01429320859474
132466024532.3550958369245540.9991184774715671.00520312475767
142473024729.4107304603247320.9998953069084711.00002382869313
152507024841.5741875073248061.001434096085921.00919530343643
162469024683.0157516353246581.001014508542271.00028295765943
172488024452.1890179665244880.9985376109917721.01749581527115
182392024250.6036851899242720.9991184774715670.986367197720864
192388024249.4609831442242520.9998953069084710.984764156885753
202399024032.4154378699239981.001434096085920.998235073874303
212459023954.2771894165239301.001014508542271.02653901036364
222361023791.1571194900238260.9985376109917720.992385527169607
232358023789.010948598238100.9991184774715670.991213970641754
242336023677.5208675926236800.9998953069084710.986589775620167
252391023603.8016447451235701.001434096085921.01297241689552
262394023437.7537030087234141.001014508542271.02142894337723
272306023311.8590662139233460.9985376109917720.989196096909365
282280023121.599805647231420.9991184774715670.986090936252237
292302022907.6014812731229100.9998953069084711.00490660354899
302289022836.7031271433228041.001434096085921.00233382518308
312278022725.0313729266227021.001014508542271.00241885813803
322253022628.8593402955226620.9985376109917720.995631271607248
332229022560.095221308225800.9991184774715670.988027744623485
342282022443.6500588675224460.9998953069084711.01676866018430
352248022372.0377065594223401.001434096085921.00482576933119
362211022350.6519467318223281.001014508542270.98923288916559
372200022183.5115657932222160.9985376109917720.991727569133997
382223022218.3967020127222380.9991184774715671.00052223831192
392226022377.6569686116223800.9998953069084710.994742212342578
402259022496.2155344741224641.001434096085921.00416889967035
412282022486.7899198935224641.001014508542271.01481803678042
422242022299.3419286683223320.9985376109917721.00541083551782
432223021994.5941630591220140.9991184774715671.01070289522942
442160021719.7258566658217220.9998953069084710.994487690247293
452100021596.9277161889215661.001434096085920.972360526273304
462136021431.72062789214101.001014508542270.996653529171305
472164021400.6580787757214320.9985376109917721.01118385800770
482145021536.9979003771215560.9991184774715670.99596053726803
492171021641.7340227270216440.9998953069084711.00315436726102
502162021644.9965528011216141.001434096085920.998845157921828
512180021679.9722260085216581.001014508542271.00553634353127
522149021710.2047381831217420.9985376109917720.989857086064425
532167021808.7581262494218280.9991184774715670.993637504462835
542213021875.7095245435218780.9998953069084711.01162433040954
552205022039.5615866589220081.001434096085921.00047362164170
562205022174.4733932283221521.001014508542270.994386635884379
572214022137.5788356876221700.9985376109917721.00010936897528
582239022098.5024847161221180.9991184774715671.01319082664020
592222022007.6957050555220100.9998953069084711.00964682072080
602179021947.429649819219161.001434096085920.992826966422452
612151021809.1030976104217871.001014508542270.986285401271582
622167021681.2471474644217130.9985376109917720.999481249976634
632174521756.8039654208217760.9991184774715670.9994574586672
642185021881.708896385218840.9998953069084710.998550894880508
652210521915.3837587443218841.001434096085921.00865219807890
662205021893.1883163280218711.001014508542271.00716257867088
672167021829.0307138911218610.9985376109917720.992714714822865
682168021804.7616523395218240.9991184774715670.994278238197293
692180021807.7166436738218100.9998953069084710.999646150773149
702192021961.4497271642219301.001434096085920.998112614254561
712198021964.2603464345219421.001014508542271.0007166029412
722227021939.8683887112219720.9985376109917721.01504710992062
732174021970.6153195998219900.9991184774715670.989503465595066
742195021969.6996833929219720.9998953069084710.999103324866665
752201021933.4095724738219021.001434096085921.00349195264298
762189021998.2948397249219761.001014508542270.995077125726611
772192022021.7484728125220540.9985376109917720.995379636955796
782211022074.5236412568220940.9991184774715671.00160711774894
792234022161.6795823194221640.9998953069084711.00804634039664
802221022203.796778417221721.001434096085921.00027937661495
812224022216.5160025871221941.001014508542271.00105705131309
822196022105.6256321359221380.9985376109917720.99341228180739
832222022094.5060108062221140.9991184774715671.00567987304773
842206022055.6906797871220580.9998953069084711.00019538359852
852209022085.6275550789220541.001434096085921.00019797693818
862196021990.2867236566219681.001014508542270.998622722657636
872194021865.9766054978218980.9985376109917721.00338532304492
882179021798.7669414747218180.9991184774715670.999597823973338
892171021765.7210407836217680.9998953069084710.99743996347839
902169021745.1399624097217141.001434096085920.997464262703989
912171021705.9986032306216841.001014508542271.00018434520533
922167021610.3509770839216420.9985376109917721.00276020611508
932164021542.9926112419215620.9991184774715671.00450296718328
942150021467.7522393249214700.9998953069084711.00150214891227
952129021482.7642292352214521.001434096085920.991027028590072
962125021479.7693243214581.001014508542270.989302989206682
972158021450.5849593253214820.9985376109917721.00603317070001
982167021507.0243460530215260.9991184774715671.00757778720686
992162021545.7440732637215480.9998953069084711.00344643129909
1002151021546.8560113847215161.001434096085920.998289494700982
1012136021497.7875854538214761.001014508542270.993590615550272
1022142021394.6668531097214260.9985376109917721.00118408700001
1032147021379.1371809366213980.9991184774715671.00425006950909
1042137021391.7601959998213940.9998953069084710.998982776742051
1052137021366.5978740892213361.001434096085921.00015922637431
1062134021289.576567677212681.001014508542271.00236845632710
1072113021161.0090521376211920.9985376109917720.998534613729372
1082113021147.3416941632211660.9991184774715670.999179958672159
1092099021159.7844847971211620.9998953069084710.99197607683958
1102124021252.4343871354212221.001434096085920.99941491939658
1112132021295.5826547282212741.001014508542271.00114659202651
1122143021350.7311982261213820.9985376109917721.00371269728601
1132139021417.1036830805214360.9991184774715670.998734484200965
1142153021495.7493079183214980.9998953069084711.00159337046553
1152151021554.8674841533215241.001434096085920.997918452331645
1162163021589.8809202397215681.001014508542271.00185823534222
1172156021542.4504195365215740.9985376109917721.0008146510783
1182161021515.0172938727215340.9991184774715671.00441471669903
1192156021473.7516111663214760.9998953069084711.00401645648117
1202131021476.7556246586214461.001434096085920.99223553000402
1212134021455.744976095214341.001014508542270.994605408657497
1222141021366.7078000019213980.9985376109917721.00202615210557
1232155021437.0860526299214560.9991184774715671.00526722461686
1242138021491.7497266907214940.9998953069084710.994800343010141
1252160021554.8674841533215241.001434096085921.00209384334559
1262153021569.8606300688215481.001014508542270.998152021899796
1272156021548.4416452024215800.9985376109917721.00053638935882
1282167021548.9873221068215680.9991184774715671.00561570138236
1292154021569.7415606295215720.9998953069084710.998621144321737
1302154021608.9449253420215781.001434096085920.99680942657866
1312155021549.8403398980215281.001014508542271.00000740887633
1322159021472.5527867671215040.9985376109917721.00546964370745
1332142021451.0737113145214700.9991184774715670.99855141464093
1342142021433.75579889214360.9998953069084710.999358217989462
1352137021390.6322923952213601.001434096085920.999035451962653
1362138021398.6871491081213771.001014508542270.999126715158838
1372121021334.7545964502213660.9985376109917720.994152517860648
1382150521350.1627450899213690.9991184774715671.00725227515868
1392136521360.7634414857213630.9998953069084711.00019833366565
1402138521423.6796175661213931.001434096085920.998194539021468
1412135021419.7084537875213981.001014508542270.99674559278256
1422136021369.7034128349214010.9985376109917720.99954592664917
1432153021431.0913417651214500.9991184774715671.0046151946561
1442138021606.7376869852216090.9998953069084710.989506158205376
1452163021831.2632946731218001.001434096085920.990780959765981
1462214521984.2806366053219621.001014508542271.00731064918845
1472231522141.5729861316221740.9985376109917721.00783264197070
1482234022255.3640856792222750.9991184774715671.00380294449442
1492244022234.6719397237222370.9998953069084711.00923458915126
1502213522217.8168557622221861.001434096085920.996272502546048
1512195522150.4490450233221281.001014508542270.991176294231053
1522206022014.7587095356220470.9985376109917721.00205504366690
1532205022056.5395086623220760.9991184774715670.99970351157489
1542203522145.6812574088221480.9998953069084710.99500212903264
1552228022208.8039488974221771.001434096085921.00320575800779
1562231522183.4825238052221611.001014508542271.00592862171454
1572220522136.5802980766221690.9985376109917721.00309079817217
1582197022116.4866173106221360.9991184774715670.993376587346566
1592207522091.6869108358220940.9998953069084710.999244652031187
1602211522061.5931367728220301.001434096085921.00242080718723
1612210522019.3161444043219971.001014508542271.00389130411834
1622188521931.8800878233219640.9985376109917720.997862468350385
1632180521920.6593957262219400.9991184774715670.99472372643367
1642191021965.7001021653219680.9998953069084710.997464223680273
1652199522042.5658889472220111.001434096085920.997842089292743
1662224522098.3962905791220761.001014508542271.00663413342277
1672210022121.6022339117221540.9985376109917720.999023477879979
1682213022318.3085497599223380.9991184774715670.991562597616212
1692230022494.6447195199224970.9998953069084710.991347064070277
1702291522685.4865786343226531.001434096085921.01011719191343
1712304022850.1581864944228271.001014508542271.00830811813013
1722288022965.3665151998229990.9985376109917720.996282815031788
1732300022999.7073513955230200.9991184774715671.00001272401427
1742316022963.5956184600229660.9998953069084711.00855285839393
1752302022954.8723504815229221.001434096085921.00283720373279
1762277022893.2018103617228701.001014508542270.994618410680068
1772266022785.6297452213228190.9985376109917720.994486448405158
1782274022738.9374287754227590.9991184774715671.00004672915029
1792290522743.6186509401227460.9998953069084711.00709567600199
1802272022793.6414610116227611.001434096085920.996769210345895
1812270522756.0628226914227331.001014508542270.997756078321225
1822273522620.8710394076226540.9985376109917721.00504529469239
1832260022602.0581973618226220.9991184774715670.999908937613388
1842251022593.6343549038225960.9998953069084710.996298322191548
1852256022618.3904941966225861.001434096085920.99741845052098
1862257522684.9907925849226621.001014508542270.995151384737575
1872268522781.6355947773228150.9985376109917720.995758180119454
1882298023051.661512224230720.9991184774715670.996891264771262
1892327523282.5622213638232850.9998953069084710.999675198060598
1902384523509.6668397131234761.001434096085921.01426362876910
1912364023670.9900834990236471.001014508542270.998690799016445
1922364023682.3165198919237170.9985376109917720.998213159601329
1932383523738.0559062470237590.9991184774715671.00408391041524
1942362523829.5049542427238320.9998953069084710.99141799401056
1952405524003.3738490834239691.001434096085921.00215078727021
1962400524115.4405252918240911.001014508542270.995420339712394
1972432524264.4639471001243000.9985376109917721.00249484402507
1982444524390.4802720359244120.9991184774715671.00223528718402
1992467024548.4296799099245510.9998953069084711.00495226463262
2002461524734.4207392261246991.001434096085920.995171880494588
2012470024872.2074937498248471.001014508542270.993076308413998
2022506524920.5031575217249570.9985376109917721.00579831159768
2032518525058.8905334644250810.9991184774715671.00503252394064
2042522025133.3684344513251360.9998953069084711.00344687445197
2052523525170.0445710235251341.001434096085921.00258066404266
2062497525226.5666297737252011.001014508542270.990027710331504
2072505525295.9532992546253330.9985376109917720.990474630609724
2082552025455.5405690206254780.9991184774715671.0025322357938
2092588025628.3166113710256310.9998953069084711.00982051971831
2102596025649.7315030487256131.001434096085921.01209636431923
2112574025581.9267803062255561.001014508542271.00617909749532
2122496525321.9152771404253590.9985376109917720.98590488621283
2132523525071.8790736715250940.9991184774715671.00650613086674
2142489524910.3917810107249130.9998953069084710.9993821140532
2152463524882.6329854468248471.001434096085920.990047958928153
2162483524764.0979268272247391.001014508542271.00286309937000
2172463524741.7649251541247780.9985376109917720.995684829862497
2182469524873.0544966547248950.9991184774715670.992841470408123
2192509024873.3956546551248760.9998953069084711.00870827402709
2202522024985.7806973437249501.001434096085921.00937410383503
2212474024966.3028575527249411.001014508542270.990935668014446
2222500524778.7108167608248150.9985376109917721.00913240341326
2232465024685.22022289247070.9991184774715670.998573226304161
2242446024724.4112539258247270.9998953069084710.989305660255761
2252468024687.3533367101246521.001434096085920.99970214155362
22624840NA24620NANA
22724630NA24667NANA
22824490NANANANA
22924695NANANANA
 
Charts produced by software:
http://www.freestatistics.org/blog/date/2010/Jun/04/t1275663500bl40s99v83h50mw/17ifd1275663314.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Jun/04/t1275663500bl40s99v83h50mw/17ifd1275663314.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Jun/04/t1275663500bl40s99v83h50mw/27ifd1275663314.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Jun/04/t1275663500bl40s99v83h50mw/27ifd1275663314.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Jun/04/t1275663500bl40s99v83h50mw/309wg1275663314.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Jun/04/t1275663500bl40s99v83h50mw/309wg1275663314.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Jun/04/t1275663500bl40s99v83h50mw/409wg1275663314.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Jun/04/t1275663500bl40s99v83h50mw/409wg1275663314.ps (open in new window)


 
Parameters (Session):
par1 = multiplicative ; par2 = 5 ;
 
Parameters (R input):
par1 = multiplicative ; par2 = 5 ;
 
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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