IKO opgave 9 oplossing 2

*Unverified author*

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

Title produced by software: Classical Decomposition

Date of computation: Mon, 16 May 2011 17:03:54 +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/16/t1305565633mujnbc65n67x6z8.htm/, Retrieved Mon, 16 May 2011 19:07:14 +0200

Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords:

KDGP2W92

Dataseries X:

» Textbox « » Textfile « » CSV «

5939520,00 89948768,00 80953652,00 85942882,00 8944937,00 82975432,00 24940816,00 21973899,00 37950221,00 45949881,00 85950373,00 48960313,00 81954506,00 24960419,00 65973338,00 22950513,00 54963528,00 90995659,00 91967517,00 28999053,00 96990529,00 38979852,00 81496957,00 74982424,00 70976192,00 90990000,00 12998850,00 92986156,00 67994976,00 91022206,00 87992489,00 421022698,00 11018942,00 79100042,00 65996442,00 51000620,00 12996871,00 44994249,00 99996135,00 91977037,00 63974211,00 15998036,00 65974265,00 33984410,00 45939098,00 67935827,00 66921032,00 89911836,00 71890975,00 72880342,00 28871286,00 41844334,00 82847667,00 24871401,00 3867451,00 99896846,00 41890361,00 45884264,00 69884586,00 95896400,00 39904491,00 81900399,00 27909863,00 88900470,00 89917101,00 2945005,00 4934411,00 61957264,00 31946515,00 3938309,00 52933321,00 21947613,00

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 3 seconds R Server 'RServer@AstonUniversity' @ vre.aston.ac.uk Classical Decomposition by Moving Averages t Observations Fit Trend Seasonal Random 1 5939520 NA NA -8796716.34861111 NA 2 89948768 NA NA -1362716.21527778 NA 3 80953652 NA NA -17641067.4402778 NA 4 85942882 NA NA 3340867.47638889 NA 5 8944937 NA NA 8173900.60972222 NA 6 82975432 NA NA -18098886.6569444 NA 7 24940816 44640140.6680555 54869848.9166667 -10229708.2486111 -19699324.6680555 8 21973899 111110485.793056 55329292.125 55781193.6680556 -89136586.7930556 9 37950221 33870008.4847222 51997264.5 -18127256.0152778 4080212.51527777 10 45949881 39850674.7013889 48748402.7083333 -8897728.00694444 6099206.29861111 11 85950373 56923924.1513889 48041161.9583333 8882762.19305556 29026448.8486111 12 48960313 57268134.3597222 50292779.375 6975354.98472222 -8307821.35972222 13 81954506 44623018.3597222 53419734.7083333 -8796716.34861111 37331487.6402778 14 24960419 55142512.4513889 56505228.6666667 -1362716.21527778 -30182093.4513889 15 65973338 41616888.8097222 59257956.25 -17641067.4402778 24356449.1902778 16 22950513 64768418.6847222 61427551.2083333 3340867.47638889 -41817905.6847222 17 54963528 69125474.9430555 60951574.3333333 8173900.60972222 -14161946.9430555 18 90995659 43751383.3013889 61850269.9583333 -18098886.6569444 47244275.6986111 19 91967517 52247386.5847222 62477094.8333333 -10229708.2486111 39720130.4152778 20 28999053 120552091.293056 64770897.625 55781193.6680556 -91553038.2930556 21 96990529 47187603.8180556 65314859.8333333 -18127256.0152778 49802925.1819444 22 38979852 57128013.2847222 66025741.2916667 -8897728.00694444 -18148161.2847222 23 81496957 78369632.2763889 69486870.0833333 8882762.19305556 3127324.72361112 24 74982424 77006308.1930556 70030953.2083333 6975354.98472222 -2023884.19305557 25 70976192 61069716.8180556 69866433.1666667 -8796716.34861111 9906475.18194444 26 90990000 84672409.3263889 86035125.5416667 -1362716.21527778 6317590.67361112 27 12998850 81146227.1847222 98787294.625 -17641067.4402778 -68147377.1847222 28 92986156 100217687.226389 96876819.75 3340867.47638889 -7231531.22638887 29 67994976 106076540.151389 97902639.5416667 8173900.60972222 -38081564.1513889 30 91022206 78158656.2597222 96257542.9166667 -18098886.6569444 12863549.7402778 31 87992489 82612787.7930555 92842496.0416667 -10229708.2486111 5379701.20694445 32 421022698 144291395.043056 88510201.375 55781193.6680556 276731302.956944 33 11018942 72091342.6097222 90218598.625 -18127256.0152778 -61072400.6097222 34 79100042 84903710.8680555 93801438.875 -8897728.00694444 -5803668.86805554 35 65996442 102474622.568056 93591860.375 8882762.19305556 -36478180.5680555 36 51000620 97273676.4013889 90298321.4166667 6975354.98472222 -46273056.4013889 37 12996871 77458171.9847222 86254888.3333333 -8796716.34861111 -64461300.9847222 38 44994249 67848150.7847222 69210867 -1362716.21527778 -22853901.7847222 39 99996135 36898210.7263889 54539278.1666667 -17641067.4402778 63097924.2736111 40 91977037 58869976.5180556 55529109.0416667 3340867.47638889 33107060.4819444 41 63974211 63276358.6097222 55102458 8173900.60972222 697852.39027778 42 15998036 38663396.5930556 56762283.25 -18098886.6569444 -22665360.5930556 43 65974265 50607796.6680555 60837504.9166667 -10229708.2486111 15366468.3319444 44 33984410 120234540.126389 64453346.4583333 55781193.6680556 -86250130.1263889 45 45939098 44524475.6097222 62651731.625 -18127256.0152778 1414622.39027779 46 67935827 48701605.6180555 57599333.625 -8897728.00694444 19234221.3819445 47 66921032 65179627.1930555 56296865 8882762.19305556 1741404.80694446 48 89911836 64428337.5263889 57452982.5416667 6975354.98472222 25483498.4736111 49 71890975 46438205.8180555 55234922.1666667 -8796716.34861111 25452769.1819445 50 72880342 54030773.5347222 55393489.75 -1362716.21527778 18849568.4652778 51 28871286 40330076.4347222 57971143.875 -17641067.4402778 -11458790.4347222 52 41844334 60224498.8513889 56883631.375 3340867.47638889 -18380164.8513889 53 82847667 64262198.2763889 56088297.6666667 8173900.60972222 18585468.7236111 54 24871401 38362249.2597222 56461135.9166667 -18098886.6569444 -13490848.2597222 55 3867451 45148014.3347222 55377722.5833333 -10229708.2486111 -41280563.3347222 56 99896846 110201981.793056 54420788.125 55781193.6680556 -10305135.7930556 57 41890361 36629308.5263889 54756564.5416667 -18127256.0152778 5261052.4736111 58 45884264 47779449.5763889 56677177.5833333 -8897728.00694444 -1895185.57638889 59 69884586 67815171.8597222 58932409.6666667 8882762.19305556 2069414.14027779 60 95896400 65288724.5680556 58313369.5833333 6975354.98472222 30607675.4319444 61 39904491 48647510.0680555 57444226.4166667 -8796716.34861111 -8743019.06805555 62 81900399 54545150.9513889 55907867.1666667 -1362716.21527778 27355248.0486111 63 27909863 36271656.8930556 53912724.3333333 -17641067.4402778 -8361793.89305555 64 88900470 55091516.7680556 51750649.2916667 3340867.47638889 33808953.2319444 65 89917101 57470499.0680556 49296598.4583333 8173900.60972222 32446601.9319444 66 2945005 27410209.6347222 45509096.2916667 -18098886.6569444 -24465204.6347222 67 4934411 NA NA -10229708.2486111 NA 68 61957264 NA NA 55781193.6680556 NA 69 31946515 NA NA -18127256.0152778 NA 70 3938309 NA NA -8897728.00694444 NA 71 52933321 NA NA 8882762.19305556 NA 72 21947613 NA NA 6975354.98472222 NA

Charts produced by software:

http://www.freestatistics.org/blog/date/2011/May/16/t1305565633mujnbc65n67x6z8/11sdj1305565430.png (open in new window) http://www.freestatistics.org/blog/date/2011/May/16/t1305565633mujnbc65n67x6z8/11sdj1305565430.ps (open in new window) http://www.freestatistics.org/blog/date/2011/May/16/t1305565633mujnbc65n67x6z8/22r6l1305565430.png (open in new window) http://www.freestatistics.org/blog/date/2011/May/16/t1305565633mujnbc65n67x6z8/22r6l1305565430.ps (open in new window) http://www.freestatistics.org/blog/date/2011/May/16/t1305565633mujnbc65n67x6z8/3r35h1305565430.png (open in new window) http://www.freestatistics.org/blog/date/2011/May/16/t1305565633mujnbc65n67x6z8/3r35h1305565430.ps (open in new window) http://www.freestatistics.org/blog/date/2011/May/16/t1305565633mujnbc65n67x6z8/41pam1305565430.png (open in new window) http://www.freestatistics.org/blog/date/2011/May/16/t1305565633mujnbc65n67x6z8/41pam1305565430.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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