Opgave 9 oefening 2 stap 1 verbetering Magali Leys

*Unverified author*

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

Title produced by software: Classical Decomposition

Date of computation: Sat, 24 Jan 2009 10:43:24 -0700

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2009/Jan/24/t1232819054abpm3y806cctuqx.htm/, Retrieved Sat, 24 Jan 2009 18:44:19 +0100

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/2009/Jan/24/t1232819054abpm3y806cctuqx.htm/}, year = {2009}, } @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 = {2009}, 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:

Dataseries X:

» Textbox « » Textfile « » CSV «

0.89 0.88 0.87 0.87 0.87 0.87 0.88 0.87 0.86 0.86 0.86 0.84 0.84 0.83 0.84 0.88 0.9 0.89 0.91 0.94 0.94 0.95 0.95 0.98 0.96 1 1.05 1.03 1.07 1.12 1.1 1.06 1.11 1.08 1.07 1.02 1 1.04 1.02 1.07 1.12 1.08 1.02 1.01 1.04 0.98 0.95 0.94 0.94 0.96 0.97 1.03 1.01 0.99 1 1 1.02 1.01 0.99 0.98 1.01 1.03 1.03 1 0.96 0.97 0.98 1.02 1.04 1.01 1.01 1 1.01 1.02 1.03 1.06 1.12 1.12 1.13 1.13 1.13 1.17 1.14 1.08 1.07 1.12 1.14 1.21 1.2 1.23 1.29 1.31 1.37 1.35 1.26 1.26

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 0.89 NA NA 0.964797642150465 NA 2 0.88 NA NA 0.982901728577316 NA 3 0.87 NA NA 0.988859632010012 NA 4 0.87 NA NA 1.01078232211031 NA 5 0.87 NA NA 1.01958615719100 NA 6 0.87 NA NA 1.01667345176799 NA 7 0.88 0.876543143850365 0.86625 1.01188241714328 1.00394373759454 8 0.87 0.871809283413939 0.862083333333333 1.01128191406160 0.997924679802842 9 0.86 0.878721770474682 0.85875 1.02325679240138 0.97869431359989 10 0.86 0.864773943630717 0.857916666666667 1.00799294060890 0.994479547324618 11 0.86 0.85247875855348 0.859583333333333 0.99173486210778 1.00882279044616 12 0.84 0.83603220385462 0.861666666666667 0.970250139869965 1.00474598481624 13 0.84 0.833343963407464 0.86375 0.964797642150465 1.00798714202635 14 0.83 0.853076791927728 0.867916666666667 0.982901728577316 0.972948751922343 15 0.84 0.864428128315419 0.874166666666667 0.988859632010012 0.971740706352275 16 0.88 0.890751921359708 0.88125 1.01078232211031 0.987929387406434 17 0.9 0.906157197203506 0.88875 1.01958615719100 0.99320515554861 18 0.89 0.913311650838249 0.898333333333333 1.01667345176799 0.974475688756567 19 0.91 0.919969764252764 0.909166666666667 1.01188241714328 0.989162943566019 20 0.94 0.93164346332925 0.92125 1.01128191406160 1.00896967241190 21 0.94 0.958876885879457 0.937083333333333 1.02325679240138 0.980313545818613 22 0.95 0.959693278871388 0.952083333333333 1.00799294060890 0.989899607421668 23 0.95 0.95743736479322 0.965416666666666 0.99173486210778 0.992232009041317 24 0.98 0.952866491530628 0.982083333333333 0.970250139869965 1.02847566653938 25 0.96 0.964395643132902 0.999583333333333 0.964797642150465 0.99544207487435 26 1 0.995188000184532 1.0125 0.982901728577316 1.00483526711996 27 1.05 1.01316909796359 1.02458333333333 0.988859632010012 1.03635217666077 28 1.03 1.04826549988856 1.03708333333333 1.01078232211031 0.98257550220769 29 1.07 1.06801649965758 1.0475 1.01958615719100 1.00185718136663 30 1.12 1.07174326373876 1.05416666666667 1.01667345176799 1.04502639568071 31 1.1 1.07006565612902 1.0575 1.01188241714328 1.02797430578164 32 1.06 1.07280156383368 1.06083333333333 1.01128191406160 0.988067165200677 33 1.11 1.08593127093596 1.06125 1.02325679240138 1.02216413663389 34 1.08 1.07015250527978 1.06166666666667 1.00799294060890 1.00920195455474 35 1.07 1.05661085100400 1.06541666666667 0.99173486210778 1.01267178827785 36 1.02 1.03412494074474 1.06583333333333 0.970250139869965 0.986341166151 37 1 1.02348949871462 1.06083333333333 0.964797642150465 0.977049594798854 38 1.04 1.03737086603598 1.05541666666667 0.982901728577316 1.00253442047594 39 1.02 1.03871463845718 1.05041666666667 0.988859632010012 0.981982887537831 40 1.07 1.05458288940175 1.04333333333333 1.01078232211031 1.01461915488406 41 1.12 1.05442201756170 1.03416666666667 1.01958615719100 1.06219329769872 42 1.08 1.04293751593867 1.02583333333333 1.01667345176799 1.03553662946718 43 1.02 1.03212006548614 1.02 1.01188241714328 0.988257116694621 44 1.01 1.02560840784414 1.01416666666667 1.01128191406160 0.984781318362093 45 1.04 1.03221028933489 1.00875 1.02325679240138 1.00754663148159 46 0.98 1.01303290531194 1.005 1.00799294060890 0.96739207074249 47 0.95 0.990495193530145 0.99875 0.99173486210778 0.959116213996133 48 0.94 0.960951909362878 0.990416666666667 0.970250139869965 0.978196713947143 49 0.94 0.951129675553333 0.985833333333333 0.964797642150465 0.988298466718685 50 0.96 0.967748660261749 0.984583333333333 0.982901728577316 0.991993106702258 51 0.97 0.972378638143179 0.983333333333333 0.988859632010012 0.997553794324687 52 1.03 0.994357109376015 0.98375 1.01078232211031 1.03584516094661 53 1.01 1.00599167509512 0.986666666666667 1.01958615719100 1.00398445136685 54 0.99 1.00650671725031 0.99 1.01667345176799 0.983599992958408 55 1 1.00640138738375 0.994583333333333 1.01188241714328 0.993639329730662 56 1 1.01170328152579 1.00041666666667 1.01128191406160 0.988432100854567 57 1.02 1.02922579035705 1.00583333333333 1.02325679240138 0.99103618424306 58 1.01 1.01513289060488 1.00708333333333 1.00799294060890 0.994943626935564 59 0.99 0.995453867840684 1.00375 0.99173486210778 0.994521224923748 60 0.98 0.97105868165319 1.00083333333333 0.970250139869965 1.00920780434359 61 1.01 0.96399364411534 0.999166666666667 0.964797642150465 1.04772475022580 62 1.03 0.982082643803501 0.999166666666666 0.982901728577316 1.04879157217454 63 1.03 0.989683681703354 1.00083333333333 0.988859632010012 1.04073656971615 64 1 1.01246695931382 1.00166666666667 1.01078232211031 0.987686551942126 65 0.96 1.02213512258398 1.0025 1.01958615719100 0.939210461306815 66 0.97 1.02090959115036 1.00416666666667 1.01667345176799 0.950133105231192 67 0.98 1.01694182922899 1.005 1.01188241714328 0.963673606329083 68 1.02 1.01591695616772 1.00458333333333 1.01128191406160 1.00401907243254 69 1.04 1.02752036236972 1.00416666666667 1.02325679240138 1.01214539204022 70 1.01 1.01471289354629 1.00666666666667 1.00799294060890 0.995355441350687 71 1.01 1.00743733075782 1.01583333333333 0.99173486210778 1.00254375052814 72 1 0.998144831391227 1.02875 0.970250139869965 1.00185861665605 73 1.01 1.00459554488917 1.04125 0.964797642150465 1.00537973230951 74 1.02 1.03409452694072 1.05208333333333 0.982901728577316 0.986370175478624 75 1.03 1.04860323477728 1.06041666666667 0.988859632010012 0.982259033578859 76 1.06 1.08237940325979 1.07083333333333 1.01078232211031 0.97932388292646 77 1.12 1.10412684272476 1.08291666666667 1.01958615719100 1.01437620811398 78 1.12 1.10986851818006 1.09166666666667 1.01667345176799 1.00912854239397 79 1.13 1.11054095281475 1.0975 1.01188241714328 1.01752213381770 80 1.13 1.11662378010969 1.10416666666667 1.01128191406160 1.01197916444964 81 1.13 1.13879953854337 1.11291666666667 1.02325679240138 0.992272969697001 82 1.17 1.13273206700925 1.12375 1.00799294060890 1.03290092518450 83 1.14 1.12396617705548 1.13333333333333 0.99173486210778 1.01426539630091 84 1.08 1.10729797212660 1.14125 0.970250139869965 0.975347221060857 85 1.07 1.11192928257841 1.1525 0.964797642150465 0.962291412560714 86 1.12 1.14671868334020 1.16666666666667 0.982901728577316 0.976699879640598 87 1.14 1.17097461423852 1.18416666666667 0.988859632010012 0.97354800534368 88 1.21 1.21462342373589 1.20166666666667 1.01078232211031 0.996193533200879 89 1.2 1.23794752585608 1.21416666666667 1.01958615719100 0.969346418112644 90 1.23 1.24711943416874 1.22666666666667 1.01667345176799 0.98627281902623 91 1.29 NA NA 1.01188241714328 NA 92 1.31 NA NA 1.01128191406160 NA 93 1.37 NA NA 1.02325679240138 NA 94 1.35 NA NA 1.00799294060890 NA 95 1.26 NA NA 0.99173486210778 NA 96 1.26 NA NA 0.970250139869965 NA

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jan/24/t1232819054abpm3y806cctuqx/1jnzs1232819002.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jan/24/t1232819054abpm3y806cctuqx/1jnzs1232819002.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jan/24/t1232819054abpm3y806cctuqx/2myku1232819002.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jan/24/t1232819054abpm3y806cctuqx/2myku1232819002.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jan/24/t1232819054abpm3y806cctuqx/313v01232819002.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jan/24/t1232819054abpm3y806cctuqx/313v01232819002.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jan/24/t1232819054abpm3y806cctuqx/4zmkx1232819002.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jan/24/t1232819054abpm3y806cctuqx/4zmkx1232819002.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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