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: Thu, 19 May 2011 14:12:00 +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/19/t1305814168wdnn9oxo3e3ylk0.htm/, Retrieved Thu, 19 May 2011 16:09:32 +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 'Gwilym Jenkins' @ www.wessa.org Classical Decomposition by Moving Averages t Observations Fit Trend Seasonal Random 1 5939520 NA NA 0.931926222047411 NA 2 89948768 NA NA 0.978174594733034 NA 3 80953652 NA NA 0.812314674550418 NA 4 85942882 NA NA 1.08001423064022 NA 5 8944937 NA NA 1.20205026478045 NA 6 82975432 NA NA 0.635689388963795 NA 7 24940816 43858541.3228133 54869848.9166667 0.799319520442333 0.568664968048696 8 21973899 87434453.0044588 55329292.125 1.58025612919332 0.25131853914474 9 37950221 39567482.4898864 51997264.5 0.760953155331592 0.959126500143147 10 45949881 42221872.8678602 48748402.7083333 0.866118078175361 1.0882956600198 11 85950373 57588475.5401827 48041161.9583333 1.19873194553724 1.4924925897722 12 48960313 58060589.4554261 50292779.375 1.15445179560483 0.843262417058089 13 81954506 49783251.5495121 53419734.7083333 0.931926222047411 1.64622646069014 14 24960419 55271979.1513141 56505228.6666667 0.978174594733034 0.451592640308169 15 65973338 48136107.4457417 59257956.25 0.812314674550418 1.37055822543117 16 22950513 66342629.458381 61427551.2083333 1.08001423064022 0.345939152357499 17 54963528 73266856.0661685 60951574.3333333 1.20205026478045 0.750182701307144 18 90995659 39317560.3170587 61850269.9583333 0.635689388963795 2.31437704339249 19 91967517 49939161.4808102 62477094.8333333 0.799319520442333 1.84159113355037 20 28999053 102354607.965259 64770897.625 1.58025612919332 0.283319467256841 21 96990529 49701548.6802157 65314859.8333333 0.760953155331592 1.95145888962225 22 38979852 57186088.1576419 66025741.2916667 0.866118078175361 0.681631726453229 23 81496957 83296130.9642877 69486870.0833333 1.19873194553724 0.97840026969489 24 74982424 80847359.679278 70030953.2083333 1.15445179560483 0.927456682536767 25 70976192 65110361.1089396 69866433.1666667 0.931926222047411 1.09009059067029 26 90990000 84157374.0595254 86035125.5416667 0.978174594733034 1.08118867795996 27 12998850 80246369.0830231 98787294.625 0.812314674550418 0.161986768355231 28 92986156 104628343.949168 96876819.75 1.08001423064022 0.888728163805938 29 67994976 117683893.783765 97902639.5416667 1.20205026478045 0.577776395850187 30 91022206 61189898.6398521 96257542.9166667 0.635689388963795 1.48753647290271 31 87992489 74210819.4126942 92842496.0416667 0.799319520442333 1.18570970778081 32 421022698 139868788.218978 88510201.375 1.58025612919332 3.01012615724423 33 11018942 68652127.2932882 90218598.625 0.760953155331592 0.160504013996916 34 79100042 81243121.9684986 93801438.875 0.866118078175361 0.973621398137192 35 65996442 112191552.873774 93591860.375 1.19873194553724 0.588247869910958 36 51000620 104245059.299573 90298321.4166667 1.15445179560483 0.489237766688182 37 12996871 80383192.2176047 86254888.3333333 0.931926222047411 0.16168642525189 38 44994249 67700311.7788469 69210867 0.978174594733034 0.66460918447437 39 99996135 44303055.9941706 54539278.1666667 0.812314674550418 2.25709339358345 40 91977037 59972227.9797727 55529109.0416667 1.08001423064022 1.53366049750598 41 63974211 66235924.2289535 55102458 1.20205026478045 0.965853677512892 42 15998036 36083181.1553824 56762283.25 0.635689388963795 0.443365454146319 43 65974265 48628605.2548981 60837504.9166667 0.799319520442333 1.35669663265439 44 33984410 101852795.787802 64453346.4583333 1.58025612919332 0.333662024072491 45 45939098 47675032.8670318 62651731.625 0.760953155331592 0.963588176816292 46 67935827 49887824.1434664 57599333.625 0.866118078175361 1.36177169813282 47 66921032 67484850.5090975 56296865 1.19873194553724 0.991645258086162 48 89911836 66326698.8580798 57452982.5416667 1.15445179560483 1.35559039644632 49 71890975 51474872.3398645 55234922.1666667 0.931926222047411 1.39662269631943 50 72880342 54184504.3870547 55393489.75 0.978174594733034 1.34504029933347 51 28871286 47090810.8701361 57971143.875 0.812314674550418 0.613098085730979 52 41844334 61435131.3754927 56883631.375 1.08001423064022 0.68111409649711 53 82847667 67420953.0613012 56088297.6666667 1.20205026478045 1.22881186394195 54 24871401 35891744.9910676 56461135.9166667 0.635689388963795 0.692956026690532 55 3867451 44264494.6584985 55377722.5833333 0.799319520442333 0.087371402968394 56 99896846 85998783.9900621 54420788.125 1.58025612919332 1.16160765728436 57 41890361 41667180.5630992 54756564.5416667 0.760953155331592 1.00535626442405 58 45884264 49089128.1248803 56677177.5833333 0.866118078175361 0.934713362259617 59 69884586 70644162.0949211 58932409.6666667 1.19873194553724 0.989247857538455 60 95896400 67319974.223247 58313369.5833333 1.15445179560483 1.4244865822733 61 39904491 53533780.9029203 57444226.4166667 0.931926222047411 0.745407672070911 62 81900399 54687655.3081424 55907867.1666667 0.978174594733034 1.49760304292669 63 27909863 43794097.1209581 53912724.3333333 0.812314674550418 0.637297371901828 64 88900470 55891437.6798714 51750649.2916667 1.08001423064022 1.59059193483614 65 89917101 59256989.229615 49296598.4583333 1.20205026478045 1.51740920639049 66 2945005 28929649.6139441 45509096.2916667 0.635689388963795 0.101798847870612 67 4934411 NA NA 0.799319520442333 NA 68 61957264 NA NA 1.58025612919332 NA 69 31946515 NA NA 0.760953155331592 NA 70 3938309 NA NA 0.866118078175361 NA 71 52933321 NA NA 1.19873194553724 NA 72 21947613 NA NA 1.15445179560483 NA

Charts produced by software:

http://www.freestatistics.org/blog/date/2011/May/19/t1305814168wdnn9oxo3e3ylk0/19ufc1305814317.png (open in new window) http://www.freestatistics.org/blog/date/2011/May/19/t1305814168wdnn9oxo3e3ylk0/19ufc1305814317.ps (open in new window) http://www.freestatistics.org/blog/date/2011/May/19/t1305814168wdnn9oxo3e3ylk0/25h421305814317.png (open in new window) http://www.freestatistics.org/blog/date/2011/May/19/t1305814168wdnn9oxo3e3ylk0/25h421305814317.ps (open in new window) http://www.freestatistics.org/blog/date/2011/May/19/t1305814168wdnn9oxo3e3ylk0/3xyne1305814317.png (open in new window) http://www.freestatistics.org/blog/date/2011/May/19/t1305814168wdnn9oxo3e3ylk0/3xyne1305814317.ps (open in new window) http://www.freestatistics.org/blog/date/2011/May/19/t1305814168wdnn9oxo3e3ylk0/4fy191305814317.png (open in new window) http://www.freestatistics.org/blog/date/2011/May/19/t1305814168wdnn9oxo3e3ylk0/4fy191305814317.ps (open in new window)

Parameters (Session):

par1 = multiplicative ; par2 = 12 ;

Parameters (R input):

par1 = multiplicative ; 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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