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Opgave9:Classical Decomposition Eigen Reeks

*Unverified author*
R Software Module: /rwasp_decompose.wasp (opens new window with default values)
Title produced by software: Classical Decomposition
Date of computation: Sun, 30 May 2010 13:27:56 +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/May/30/t1275226119rgxqwm2gqe3ts0x.htm/, Retrieved Sun, 30 May 2010 15:28:41 +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/May/30/t1275226119rgxqwm2gqe3ts0x.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 time4 seconds
R Server'RServer@AstonUniversity' @ vre.aston.ac.uk


Classical Decomposition by Moving Averages
tObservationsFitTrendSeasonalRandom
123100NANA23.15NA
222650NANA-34.1227272727273NA
32244022794.718181818222816-21.2818181818182-354.718181818182
42291022700.218181818222703-2.78181818181818209.781818181818
52298022668.03636363642263335.0363636363636311.963636363635
62253522724.152270123.15-189.150000000001
72230022640.877272727322675-34.1227272727273-340.877272727274
82278022717.718181818222739-21.281818181818262.2818181818184
92278022989.218181818222992-2.78181818181818-209.218181818182
102330023469.03636363642343435.0363636363636-169.036363636365
112380023833.152381023.15-33.1500000000015
122451024165.877272727324200-34.1227272727273344.122727272726
132466024532.718181818224554-21.2818181818182127.281818181818
142473024729.218181818224732-2.781818181818180.78181818181838
152507024841.03636363642480635.0363636363636228.963636363635
162469024681.152465823.158.84999999999854
172488024453.877272727324488-34.1227272727273426.122727272726
182392024250.718181818224272-21.2818181818182-330.718181818182
192388024249.218181818224252-2.78181818181818-369.218181818182
202399024033.03636363642399835.0363636363636-43.0363636363654
212459023953.152393023.15636.849999999999
222361023791.877272727323826-34.1227272727273-181.877272727274
232358023788.718181818223810-21.2818181818182-208.718181818182
242336023677.218181818223680-2.78181818181818-317.218181818182
252391023605.03636363642357035.0363636363636304.963636363635
262394023437.152341423.15502.849999999999
272306023311.877272727323346-34.1227272727273-251.877272727274
282280023120.718181818223142-21.2818181818182-320.718181818182
292302022907.218181818222910-2.78181818181818112.781818181818
302289022839.03636363642280435.036363636363650.9636363636346
312278022725.152270223.1554.8499999999985
322253022627.877272727322662-34.1227272727273-97.8772727272735
332229022558.718181818222580-21.2818181818182-268.718181818182
342282022443.218181818222446-2.78181818181818376.781818181818
352248022375.03636363642234035.0363636363636104.963636363635
362211022351.152232823.15-241.150000000001
372200022181.877272727322216-34.1227272727273-181.877272727274
382223022216.718181818222238-21.281818181818213.2818181818184
392226022377.218181818222380-2.78181818181818-117.218181818182
402259022499.03636363642246435.036363636363690.9636363636346
412282022487.152246423.15332.849999999999
422242022297.877272727322332-34.1227272727273122.122727272726
432223021992.718181818222014-21.2818181818182237.281818181818
442160021719.218181818221722-2.78181818181818-119.218181818182
452100021601.03636363642156635.0363636363636-601.036363636365
462136021433.152141023.15-73.1500000000015
472164021397.877272727321432-34.1227272727273242.122727272726
482145021534.718181818221556-21.2818181818182-84.7181818181816
492171021641.218181818221644-2.7818181818181868.7818181818184
502162021649.03636363642161435.0363636363636-29.0363636363654
512180021681.152165823.15118.849999999999
522149021707.877272727321742-34.1227272727273-217.877272727274
532167021806.718181818221828-21.2818181818182-136.718181818182
542213021875.218181818221878-2.78181818181818254.781818181818
552205022043.03636363642200835.03636363636366.96363636363458
562205022175.152215223.15-125.150000000001
572214022135.877272727322170-34.12272727272734.12272727272648
582239022096.718181818222118-21.2818181818182293.281818181818
592222022007.218181818222010-2.78181818181818212.781818181818
602179021951.03636363642191635.0363636363636-161.036363636365
612151021810.152178723.15-300.150000000001
622167021678.877272727321713-34.1227272727273-8.87727272727352
632174521754.718181818221776-21.2818181818182-9.71818181818162
642185021881.218181818221884-2.78181818181818-31.2181818181816
652210521919.03636363642188435.0363636363636185.963636363635
662205021894.152187123.15155.849999999999
672167021826.877272727321861-34.1227272727273-156.877272727274
682168021802.718181818221824-21.2818181818182-122.718181818182
692180021807.218181818221810-2.78181818181818-7.21818181818162
702192021965.03636363642193035.0363636363636-45.0363636363654
712198021965.152194223.1514.8499999999985
722227021937.877272727321972-34.1227272727273332.122727272726
732174021968.718181818221990-21.2818181818182-228.718181818182
742195021969.218181818221972-2.78181818181818-19.2181818181816
752201021937.03636363642190235.036363636363672.9636363636346
762189021999.152197623.15-109.150000000001
772192022019.877272727322054-34.1227272727273-99.8772727272735
782211022072.718181818222094-21.281818181818237.2818181818184
792234022161.218181818222164-2.78181818181818178.781818181818
802221022207.03636363642217235.03636363636362.96363636363458
812224022217.152219423.1522.8499999999985
822196022103.877272727322138-34.1227272727273-143.877272727274
832222022092.718181818222114-21.2818181818182127.281818181818
842206022055.218181818222058-2.781818181818184.78181818181838
852209022089.03636363642205435.03636363636360.963636363634578
862196021991.152196823.15-31.1500000000015
872194021863.877272727321898-34.122727272727376.1227272727265
882179021796.718181818221818-21.2818181818182-6.71818181818162
892171021765.218181818221768-2.78181818181818-55.2181818181816
902169021749.03636363642171435.0363636363636-59.0363636363654
912171021707.152168423.152.84999999999854
922167021607.877272727321642-34.122727272727362.1227272727265
932164021540.718181818221562-21.281818181818299.2818181818184
942150021467.218181818221470-2.7818181818181832.7818181818184
952129021487.03636363642145235.0363636363636-197.036363636365
962125021481.152145823.15-231.150000000001
972158021447.877272727321482-34.1227272727273132.122727272726
982167021504.718181818221526-21.2818181818182165.281818181818
992162021545.218181818221548-2.7818181818181874.7818181818184
1002151021551.03636363642151635.0363636363636-41.0363636363654
1012136021499.152147623.15-139.150000000001
1022142021391.877272727321426-34.122727272727328.1227272727265
1032147021376.718181818221398-21.281818181818293.2818181818184
1042137021391.218181818221394-2.78181818181818-21.2181818181816
1052137021371.03636363642133635.0363636363636-1.03636363636542
1062134021291.152126823.1548.8499999999985
1072113021157.877272727321192-34.1227272727273-27.8772727272735
1082113021144.718181818221166-21.2818181818182-14.7181818181816
1092099021159.218181818221162-2.78181818181818-169.218181818182
1102124021257.03636363642122235.0363636363636-17.0363636363654
1112132021297.152127423.1522.8499999999985
1122143021347.877272727321382-34.122727272727382.1227272727265
1132139021414.718181818221436-21.2818181818182-24.7181818181816
1142153021495.218181818221498-2.7818181818181834.7818181818184
1152151021559.03636363642152435.0363636363636-49.0363636363654
1162163021591.152156823.1538.8499999999985
1172156021539.877272727321574-34.122727272727320.1227272727265
1182161021512.718181818221534-21.281818181818297.2818181818184
1192156021473.218181818221476-2.7818181818181886.7818181818184
1202131021481.03636363642144635.0363636363636-171.036363636365
1212134021457.152143423.15-117.150000000001
1222141021363.877272727321398-34.122727272727346.1227272727265
1232155021434.718181818221456-21.2818181818182115.281818181818
1242138021491.218181818221494-2.78181818181818-111.218181818182
1252160021559.03636363642152435.036363636363640.9636363636346
1262153021571.152154823.15-41.1500000000015
1272156021545.877272727321580-34.122727272727314.1227272727265
1282167021546.718181818221568-21.2818181818182123.281818181818
1292154021569.218181818221572-2.78181818181818-29.2181818181816
1302154021613.03636363642157835.0363636363636-73.0363636363654
1312155021551.152152823.15-1.15000000000146
1322159021469.877272727321504-34.1227272727273120.122727272726
1332142021448.718181818221470-21.2818181818182-28.7181818181816
1342142021433.218181818221436-2.78181818181818-13.2181818181816
1352137021395.03636363642136035.0363636363636-25.0363636363654
1362138021400.152137723.15-20.1500000000015
1372121021331.877272727321366-34.1227272727273-121.877272727274
1382150521347.718181818221369-21.2818181818182157.281818181818
1392136521360.218181818221363-2.781818181818184.78181818181838
1402138521428.03636363642139335.0363636363636-43.0363636363654
1412135021421.152139823.15-71.1500000000015
1422136021366.877272727321401-34.1227272727273-6.87727272727352
1432153021428.718181818221450-21.2818181818182101.281818181818
1442138021606.218181818221609-2.78181818181818-226.218181818182
1452163021835.03636363642180035.0363636363636-205.036363636365
1462214521985.152196223.15159.849999999999
1472231522139.877272727322174-34.1227272727273175.122727272726
1482234022253.718181818222275-21.281818181818286.2818181818184
1492244022234.218181818222237-2.78181818181818205.781818181818
1502213522221.03636363642218635.0363636363636-86.0363636363654
1512195522151.152212823.15-196.150000000001
1522206022012.877272727322047-34.122727272727347.1227272727265
1532205022054.718181818222076-21.2818181818182-4.71818181818162
1542203522145.218181818222148-2.78181818181818-110.218181818182
1552228022212.03636363642217735.036363636363667.9636363636346
1562231522184.152216123.15130.849999999999
1572220522134.877272727322169-34.122727272727370.1227272727265
1582197022114.718181818222136-21.2818181818182-144.718181818182
1592207522091.218181818222094-2.78181818181818-16.2181818181816
1602211522065.03636363642203035.036363636363649.9636363636346
1612210522020.152199723.1584.8499999999985
1622188521929.877272727321964-34.1227272727273-44.8772727272735
1632180521918.718181818221940-21.2818181818182-113.718181818182
1642191021965.218181818221968-2.78181818181818-55.2181818181816
1652199522046.03636363642201135.0363636363636-51.0363636363654
1662224522099.152207623.15145.849999999999
1672210022119.877272727322154-34.1227272727273-19.8772727272735
1682213022316.718181818222338-21.2818181818182-186.718181818182
1692230022494.218181818222497-2.78181818181818-194.218181818182
1702291522688.03636363642265335.0363636363636226.963636363635
1712304022850.152282723.15189.849999999999
1722288022964.877272727322999-34.1227272727273-84.8772727272735
1732300022998.718181818223020-21.28181818181821.28181818181838
1742316022963.218181818222966-2.78181818181818196.781818181818
1752302022957.03636363642292235.036363636363662.9636363636346
1762277022893.152287023.15-123.150000000001
1772266022784.877272727322819-34.1227272727273-124.877272727274
1782274022737.718181818222759-21.28181818181822.28181818181838
1792290522743.218181818222746-2.78181818181818161.781818181818
1802272022796.03636363642276135.0363636363636-76.0363636363654
1812270522756.152273323.15-51.1500000000015
1822273522619.877272727322654-34.1227272727273115.122727272726
1832260022600.718181818222622-21.2818181818182-0.71818181818162
1842251022593.218181818222596-2.78181818181818-83.2181818181816
1852256022621.03636363642258635.0363636363636-61.0363636363654
1862257522685.152266223.15-110.150000000001
1872268522780.877272727322815-34.1227272727273-95.8772727272735
1882298023050.718181818223072-21.2818181818182-70.7181818181816
1892327523282.218181818223285-2.78181818181818-7.21818181818162
1902384523511.03636363642347635.0363636363636333.963636363635
1912364023670.152364723.15-30.1500000000015
1922364023682.877272727323717-34.1227272727273-42.8772727272735
1932383523737.718181818223759-21.281818181818297.2818181818184
1942362523829.218181818223832-2.78181818181818-204.218181818182
1952405524004.03636363642396935.036363636363650.9636363636346
1962400524114.152409123.15-109.150000000001
1972432524265.877272727324300-34.122727272727359.1227272727265
1982444524390.718181818224412-21.281818181818254.2818181818184
1992467024548.218181818224551-2.78181818181818121.781818181818
2002461524734.03636363642469935.0363636363636-119.036363636365
2012470024870.152484723.15-170.150000000001
2022506524922.877272727324957-34.1227272727273142.122727272726
2032518525059.718181818225081-21.2818181818182125.281818181818
2042522025133.218181818225136-2.7818181818181886.7818181818184
2052523525169.03636363642513435.036363636363665.9636363636346
2062497525224.152520123.15-249.150000000001
2072505525298.877272727325333-34.1227272727273-243.877272727274
2082552025456.718181818225478-21.281818181818263.2818181818184
2092588025628.218181818225631-2.78181818181818251.781818181818
2102596025648.03636363642561335.0363636363636311.963636363635
2112574025579.152555623.15160.849999999999
2122496525324.877272727325359-34.1227272727273-359.877272727274
2132523525072.718181818225094-21.2818181818182162.281818181818
2142489524910.218181818224913-2.78181818181818-15.2181818181816
2152463524882.03636363642484735.0363636363636-247.036363636365
2162483524762.152473923.1572.8499999999985
2172463524743.877272727324778-34.1227272727273-108.877272727274
2182469524873.718181818224895-21.2818181818182-178.718181818182
2192509024873.218181818224876-2.78181818181818216.781818181818
2202522024985.03636363642495035.0363636363636234.963636363635
2212474024964.152494123.15-224.150000000001
2222500524780.877272727324815-34.1227272727273224.122727272726
2232465024685.718181818224707-21.2818181818182-35.7181818181816
2242446024724.218181818224727-2.78181818181818-264.218181818182
2252468024687.03636363642465235.0363636363636-7.03636363636542
22624840NA24620NANA
22724630NA24667NANA
22824490NANANANA
22924695NANANANA
 
Charts produced by software:
http://www.freestatistics.org/blog/date/2010/May/30/t1275226119rgxqwm2gqe3ts0x/171371275226070.png (open in new window)
http://www.freestatistics.org/blog/date/2010/May/30/t1275226119rgxqwm2gqe3ts0x/171371275226070.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/May/30/t1275226119rgxqwm2gqe3ts0x/271371275226070.png (open in new window)
http://www.freestatistics.org/blog/date/2010/May/30/t1275226119rgxqwm2gqe3ts0x/271371275226070.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/May/30/t1275226119rgxqwm2gqe3ts0x/30skr1275226070.png (open in new window)
http://www.freestatistics.org/blog/date/2010/May/30/t1275226119rgxqwm2gqe3ts0x/30skr1275226070.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/May/30/t1275226119rgxqwm2gqe3ts0x/4bjjc1275226070.png (open in new window)
http://www.freestatistics.org/blog/date/2010/May/30/t1275226119rgxqwm2gqe3ts0x/4bjjc1275226070.ps (open in new window)


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