eigen reeks oef 9

*Unverified author*

R Software Module: /rwasp_decompose.wasp (opens new window with default values)

Title produced by software: Classical Decomposition

Date of computation: Mon, 24 May 2010 18:17:11 +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/24/t1274725260a9eutgr3avgvyfe.htm/, Retrieved Mon, 24 May 2010 20:21:05 +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/24/t1274725260a9eutgr3avgvyfe.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 «

18288.3 16049 16764.5 17880.2 16555.9 16087.1 16373.5 17842.2 22321.5 22786.7 18274.1 22392.9 23899.3 21343.5 22952.3 21374.4 21164.1 20906.5 17877.4 20664.3 22160 19813.6 17735.4 19640.2 20844.4 19823.1 18594.6 21350.6 18574.1 18924.2 17343.4 19961.2 19932.1 19464.6 16165.4 17574.9 19795.4 19439.5 17170 21072.4 17751.8 17515.5 18040.3 19090.1 17746.5 19202.1 15141.6 16258.1 18586.5 17209.4 17838.7 19123.5 16583.6 15991.2 16704.5 17422 17872 17823.2 13866.5 15912.8 17870.5 15420.3 16379.4 17903.9 15305.8 14583.3 14861 14968.9 16726.5 16283.6 11703.7 15101.8 15469.7 14956.9 15370.6 15998.1 14725.1 14768.9 13659.6 15070.3 16942.6 15761.3 12083 15023.6 15106.5

Output produced by software:

Summary of computational transaction Raw Input view raw input (R code) Raw Output view raw output of R engine Computing time 2 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Classical Decomposition by Moving Averages t Observations Fit Trend Seasonal Random 1 18288.3 NA NA 1364.43969907407 NA 2 16049 NA NA 23.6855324074068 NA 3 16764.5 NA NA 99.1049768518524 NA 4 17880.2 NA NA 1604.79594907407 NA 5 16555.9 NA NA -423.15613425926 NA 6 16087.1 NA NA -564.80335648148 NA 7 16373.5 17548.6501157407 18701.7833333333 -1153.13321759259 -1175.15011574074 8 17842.2 19149.7181712963 19156.1791666667 -6.46099537037110 -1307.51817129630 9 22321.5 20282.6334490741 19634.6083333333 648.025115740739 2038.86655092593 10 22786.7 21134.5146990741 20038.025 1096.48969907407 1652.18530092592 11 18274.1 18568.7126157407 20375.625 -1806.91238425926 -294.612615740742 12 22392.9 19886.3667824074 20768.4416666667 -882.07488425926 2506.53321759259 13 23899.3 22396.3521990741 21031.9125 1364.43969907407 1502.94780092592 14 21343.5 21235.8480324074 21212.1625 23.6855324074068 107.651967592596 15 22952.3 21422.1258101852 21323.0208333333 99.1049768518524 1530.17418981481 16 21374.4 22797.2084490741 21192.4125 1604.79594907407 -1422.80844907408 17 21164.1 20622.9313657407 21046.0875 -423.15613425926 541.168634259258 18 20906.5 20344.1424768519 20908.9458333333 -564.80335648148 562.357523148148 19 17877.4 19513.8292824074 20666.9625 -1153.13321759259 -1636.42928240740 20 20664.3 20469.8640046296 20476.325 -6.46099537037110 194.435995370372 21 22160 20879.4292824074 20231.4041666667 648.025115740739 1280.57071759259 22 19813.6 21145.3313657407 20048.8416666667 1096.48969907407 -1331.73136574074 23 17735.4 18133.0209490741 19939.9333333333 -1806.91238425926 -397.620949074073 24 19640.2 18867.3459490741 19749.4208333333 -882.07488425926 772.854050925926 25 20844.4 21009.0146990741 19644.575 1364.43969907407 -164.614699074074 26 19823.1 19616.7146990741 19593.0291666667 23.6855324074068 206.385300925926 27 18594.6 19570.0091435185 19470.9041666667 99.1049768518524 -975.409143518518 28 21350.6 20968.3292824074 19363.5333333333 1604.79594907407 382.27071759259 29 18574.1 18860.4188657407 19283.575 -423.15613425926 -286.318865740737 30 18924.2 18567.3008101852 19132.1041666667 -564.80335648148 356.899189814812 31 17343.4 17849.2084490741 19002.3416666667 -1153.13321759259 -505.808449074069 32 19961.2 18936.1890046296 18942.65 -6.46099537037110 1025.01099537037 33 19932.1 19515.3334490741 18867.3083333333 648.025115740739 416.766550925924 34 19464.6 19892.8480324074 18796.3583333333 1096.48969907407 -428.24803240741 35 16165.4 16943.5917824074 18750.5041666667 -1806.91238425926 -778.191782407408 36 17574.9 17775.4709490741 18657.5458333333 -882.07488425926 -200.570949074074 37 19795.4 19992.3271990741 18627.8875 1364.43969907407 -196.927199074074 38 19439.5 18644.3146990741 18620.6291666667 23.6855324074068 795.185300925928 39 17170 18592.3716435185 18493.2666666667 99.1049768518524 -1422.37164351852 40 21072.4 19996.0584490741 18391.2625 1604.79594907407 1076.34155092592 41 17751.8 17914.5105324074 18337.6666666667 -423.15613425926 -162.710532407407 42 17515.5 17675.3383101852 18240.1416666667 -564.80335648148 -159.838310185183 43 18040.3 16981.7709490741 18134.9041666667 -1153.13321759259 1058.52905092592 44 19090.1 17985.1515046296 17991.6125 -6.46099537037110 1104.94849537037 45 17746.5 18574.5792824074 17926.5541666667 648.025115740739 -828.079282407409 46 19202.1 18969.7021990741 17873.2125 1096.48969907407 232.397800925923 47 15141.6 15936.4209490741 17743.3333333333 -1806.91238425926 -794.820949074074 48 16258.1 16749.0709490741 17631.1458333333 -882.07488425926 -490.970949074072 49 18586.5 18876.4146990741 17511.975 1364.43969907407 -289.914699074074 50 17209.4 17410.4980324074 17386.8125 23.6855324074068 -201.098032407404 51 17838.7 17421.6424768519 17322.5375 99.1049768518524 417.057523148149 52 19123.5 18875.1084490741 17270.3125 1604.79594907407 248.391550925920 53 16583.6 16736.5730324074 17159.7291666667 -423.15613425926 -152.973032407408 54 15991.2 16527.4091435185 17092.2125 -564.80335648148 -536.209143518518 55 16704.5 15894.8584490741 17047.9916666667 -1153.13321759259 809.641550925924 56 17422 16937.1515046296 16943.6125 -6.46099537037110 484.84849537037 57 17872 17456.2876157407 16808.2625 648.025115740739 415.712384259259 58 17823.2 17793.1313657407 16696.6416666667 1096.48969907407 30.0686342592599 59 13866.5 14785.6709490741 16592.5833333333 -1806.91238425926 -919.170949074074 60 15912.8 15598.6042824074 16480.6791666667 -882.07488425926 314.195717592593 61 17870.5 17709.6438657407 16345.2041666667 1364.43969907407 160.856134259258 62 15420.3 16189.8646990741 16166.1791666667 23.6855324074068 -769.564699074075 63 16379.4 16115.3424768518 16016.2375 99.1049768518524 264.057523148151 64 17903.9 17509.1542824074 15904.3583333333 1604.79594907407 394.745717592597 65 15305.8 15326.9355324074 15750.0916666667 -423.15613425926 -21.1355324074066 66 14583.3 15061.3799768519 15626.1833333333 -564.80335648148 -478.079976851852 67 14861 14339.2251157407 15492.3583333333 -1153.13321759259 521.774884259261 68 14968.9 15366.5556712963 15373.0166666667 -6.46099537037110 -397.655671296296 69 16726.5 15959.7001157407 15311.675 648.025115740739 766.799884259261 70 16283.6 16286.7230324074 15190.2333333333 1096.48969907407 -3.12303240740403 71 11703.7 13279.7167824074 15086.6291666667 -1806.91238425926 -1576.01678240741 72 15101.8 14188.0917824074 15070.1666666667 -882.07488425926 913.708217592592 73 15469.7 16392.2813657407 15027.8416666667 1364.43969907407 -922.581365740738 74 14956.9 15005.6938657407 14982.0083333333 23.6855324074068 -48.7938657407431 75 15370.6 15094.3424768519 14995.2375 99.1049768518524 276.257523148148 76 15998.1 16587.2751157407 14982.4791666667 1604.79594907407 -589.175115740738 77 14725.1 14553.3646990741 14976.5208333333 -423.15613425926 171.735300925928 78 14768.9 14424.2633101852 14989.0666666667 -564.80335648148 344.636689814814 79 13659.6 13817.5417824074 14970.675 -1153.13321759259 -157.941782407406 80 15070.3 NA NA -6.46099537037110 NA 81 16942.6 NA NA 648.025115740739 NA 82 15761.3 NA NA 1096.48969907407 NA 83 12083 NA NA -1806.91238425926 NA 84 15023.6 NA NA -882.07488425926 NA 85 15106.5 NA NA NA NA

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/May/24/t1274725260a9eutgr3avgvyfe/1ex9r1274725028.png (open in new window) http://www.freestatistics.org/blog/date/2010/May/24/t1274725260a9eutgr3avgvyfe/1ex9r1274725028.ps (open in new window) http://www.freestatistics.org/blog/date/2010/May/24/t1274725260a9eutgr3avgvyfe/2ex9r1274725028.png (open in new window) http://www.freestatistics.org/blog/date/2010/May/24/t1274725260a9eutgr3avgvyfe/2ex9r1274725028.ps (open in new window) http://www.freestatistics.org/blog/date/2010/May/24/t1274725260a9eutgr3avgvyfe/3oorc1274725028.png (open in new window) http://www.freestatistics.org/blog/date/2010/May/24/t1274725260a9eutgr3avgvyfe/3oorc1274725028.ps (open in new window) http://www.freestatistics.org/blog/date/2010/May/24/t1274725260a9eutgr3avgvyfe/4hgqf1274725028.png (open in new window) http://www.freestatistics.org/blog/date/2010/May/24/t1274725260a9eutgr3avgvyfe/4hgqf1274725028.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')

Copyright

This work is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 3.0 License.

Software written by Ed van Stee & Patrick Wessa

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