opgave 9 2

*Unverified author*

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

Title produced by software: Classical Decomposition

Date of computation: Tue, 12 Jan 2010 11:42:00 -0700

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2010/Jan/12/t1263321818emgb7mwn4juigwr.htm/, Retrieved Tue, 12 Jan 2010 19:43:43 +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/2010/Jan/12/t1263321818emgb7mwn4juigwr.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 «

100.09 100.09 100.06 100.11 100.08 100.08 100.08 100.08 99.94 99.79 99.98 99.98 99.98 99.98 99.93 99.93 99.93 99.93 99.93 99.93 99.76 99.48 99.55 99.56 99.57 99.56 99.65 99.5 99.5 99.49 99.49 99.46 99.52 99.49 99.55 99.57 99.57 99.57 99.57 99.57 99.57 99.57 99.57 99.53 100.38 100.32 100.46 100.47 100.47 100.47 100.51 100.5 100.51 100.51 100.51 100.51 101.65 102.13 102.2 102.13 102.13 102.12 102.13 102.05 102 102.01 102.01 102.02 102.78 103.39 103.41 103.5 103.5 103.49 103.38 103.24 103.25 103.25 103.25 103.25 103.83 104.33 104.36 104.48 104.5 104.48 104.35 104.48 104.48 104.47 104.47 104.86 105.22 105.96 106.03 106.03

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 1 100.09 NA NA 1.00153946186072 NA 2 100.09 NA NA 1.00093832911167 NA 3 100.06 NA NA 1.00015335259084 NA 4 100.11 NA NA 0.99913055673448 NA 5 100.08 NA NA 0.998379748611748 NA 6 100.08 NA NA 0.997665615581926 NA 7 100.08 99.803056260336 100.025416666667 0.997776960959116 1.00277490239319 8 100.08 99.7342472034749 100.01625 0.997180430214839 1.00346674092621 9 99.94 100.105940810497 100.00625 1.00099684580210 0.99834234802497 10 99.79 100.189187081573 99.9933333333333 1.00195866806027 0.996015667027542 11 99.98 100.206722026000 99.9795833333333 1.00227185076286 0.997737456914907 12 99.98 100.167835201695 99.9670833333333 1.00200817970943 0.998124795236751 13 99.98 100.108459602179 99.9545833333333 1.00153946186072 0.998716795736452 14 99.98 100.035861899606 99.9420833333333 1.00093832911167 0.99944158126351 15 99.93 99.9436576021487 99.9283333333333 1.00015335259084 0.999863346984928 16 99.93 99.8210524013487 99.9079166666667 0.99913055673448 1.00109142907263 17 99.93 99.715257350408 99.8770833333333 0.998379748611748 1.00215355859573 18 99.93 99.6085978357254 99.8416666666667 0.997665615581926 1.00322665082390 19 99.93 99.5852082905266 99.8070833333333 0.997776960959116 1.00346227833824 20 99.93 99.49118447361 99.7725 0.997180430214839 1.00441059706658 21 99.76 99.8427620564546 99.7433333333333 1.00099684580210 0.999171076052486 22 99.48 99.9090561372943 99.71375 1.00195866806027 0.99570553307295 23 99.55 99.9043700176863 99.6779166666667 1.00227185076286 0.996452907739436 24 99.56 99.8417650398807 99.6416666666667 1.00200817970943 0.997177884027108 25 99.57 99.7583380986366 99.605 1.00153946186072 0.998112056573653 26 99.56 99.6605100261888 99.5670833333333 1.00093832911167 0.99899147589991 27 99.65 99.5527643335111 99.5375 1.00015335259084 1.00097672492713 28 99.5 99.4413827897896 99.5279166666667 0.99913055673448 1.00058946495479 29 99.5 99.3670724130796 99.5283333333333 0.998379748611748 1.00133774281251 30 99.49 99.2964116368496 99.52875 0.997665615581926 1.00194960079583 31 99.49 99.30790944346 99.5291666666667 0.997776960959116 1.00183359570814 32 99.46 99.248952727437 99.5295833333334 0.997180430214839 1.00212644332019 33 99.52 99.6258794065308 99.5266666666667 1.00099684580210 0.99893722989286 34 99.49 99.721188887033 99.52625 1.00195866806027 0.997681647304718 35 99.55 99.7582053727834 99.5320833333333 1.00227185076286 0.99791289977596 36 99.57 99.738224194644 99.5383333333333 1.00200817970943 0.998313342793072 37 99.57 99.698245730925 99.545 1.00153946186072 0.99871366110823 38 99.57 99.6446618359778 99.55125 1.00093832911167 0.99925071915944 39 99.57 99.6052723845222 99.59 1.00015335259084 0.99964587833879 40 99.57 99.573767588557 99.6604166666667 0.99913055673448 0.999962162840192 41 99.57 99.571324269983 99.7329166666667 0.998379748611748 0.99998670028753 42 99.57 99.575342315206 99.8083333333333 0.997665615581926 0.999946349014909 43 99.57 99.6612887837997 99.8833333333333 0.997776960959116 0.999084009599778 44 99.53 99.6764938368916 99.9583333333333 0.997180430214839 0.998530307083922 45 100.38 100.134719469814 100.035 1.00099684580210 1.00244950534126 46 100.32 100.309004638962 100.112916666667 1.00195866806027 1.00010961489527 47 100.46 100.418451954473 100.190833333333 1.00227185076286 1.00041374911401 48 100.47 100.470525172648 100.269166666667 1.00200817970943 0.999994772868485 49 100.47 100.501981149068 100.3475 1.00153946186072 0.999681785884193 50 100.47 100.521734046862 100.4275 1.00093832911167 0.999485344663495 51 100.51 100.536665194122 100.52125 1.00015335259084 0.999734771448099 52 100.5 100.562074230927 100.649583333333 0.99913055673448 0.999382727221952 53 100.51 100.634182710693 100.7975 0.998379748611748 0.998765998715867 54 100.51 100.703535848827 100.939166666667 0.997665615581926 0.998078162328708 55 100.51 100.852800771345 101.0775 0.997776960959116 0.99660097916247 56 100.51 100.930032736041 101.215416666667 0.997180430214839 0.99583837709496 57 101.65 101.452698650120 101.351666666667 1.00099684580210 1.00194476196795 58 102.13 101.682522979761 101.48375 1.00195866806027 1.00440072696001 59 102.2 101.841260369286 101.610416666667 1.00227185076286 1.00352253722522 60 102.13 101.939302162739 101.735 1.00200817970943 1.00187069984996 61 102.13 102.016809585133 101.86 1.00153946186072 1.00110952709978 62 102.12 102.081112552090 101.985416666667 1.00093832911167 1.00038094655257 63 102.13 102.111073263326 102.095416666667 1.00015335259084 1.00018535439957 64 102.05 102.106147245480 102.195 0.99913055673448 0.999450109058123 65 102 102.132168325172 102.297916666667 0.998379748611748 0.998705908947792 66 102.01 102.166363057674 102.405416666667 0.997665615581926 0.998469525066823 67 102.01 102.291678297128 102.519583333333 0.997776960959116 0.997246322459292 68 102.02 102.344366979562 102.63375 0.997180430214839 0.996830631825326 69 102.78 102.845335511842 102.742916666667 1.00099684580210 0.999364720708853 70 103.39 103.046021733880 102.844583333333 1.00195866806027 1.00333810330891 71 103.41 103.180128516596 102.94625 1.00227185076286 1.00222786583724 72 103.5 103.256942919057 103.05 1.00200817970943 1.00235390545247 73 103.5 103.312133955806 103.153333333333 1.00153946186072 1.00181843155301 74 103.49 103.353138345337 103.25625 1.00093832911167 1.00132421382509 75 103.38 103.367099181954 103.35125 1.00015335259084 1.00012480584391 76 103.24 103.344236527034 103.434166666667 0.99913055673448 0.998991365841612 77 103.25 103.345199719735 103.512916666667 0.998379748611748 0.99907881817449 78 103.25 103.351506670184 103.593333333333 0.997665615581926 0.999017850117004 79 103.25 103.445357908237 103.675833333333 0.997776960959116 0.9981114869513 80 103.25 103.466194963554 103.75875 0.997180430214839 0.997910477295216 81 103.83 103.943929550110 103.840416666667 1.00099684580210 0.998903932624033 82 104.33 104.136069268174 103.9325 1.00195866806027 1.00186228204300 83 104.36 104.271769607385 104.035416666667 1.00227185076286 1.00084615800563 84 104.48 104.346626814491 104.1375 1.00200817970943 1.00127817438455 85 104.5 104.399638888143 104.239166666667 1.00153946186072 1.00096131665709 86 104.48 104.455004622634 104.357083333333 1.00093832911167 1.00023929324839 87 104.35 104.498105931509 104.482083333333 1.00015335259084 0.998582692669986 88 104.48 104.516966018001 104.607916666667 0.99913055673448 0.999646315623107 89 104.48 104.575702759899 104.745416666667 0.998379748611748 0.999084847078492 90 104.47 104.634754068226 104.879583333333 0.997665615581926 0.998425436465226 91 104.47 NA NA 0.997776960959116 NA 92 104.86 NA NA 0.997180430214839 NA 93 105.22 NA NA 1.00099684580210 NA 94 105.96 NA NA 1.00195866806027 NA 95 106.03 NA NA 1.00227185076286 NA 96 106.03 NA NA 1.00200817970943 NA

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Jan/12/t1263321818emgb7mwn4juigwr/10w4o1263321717.png (open in new window) http://www.freestatistics.org/blog/date/2010/Jan/12/t1263321818emgb7mwn4juigwr/10w4o1263321717.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Jan/12/t1263321818emgb7mwn4juigwr/2i85m1263321717.png (open in new window) http://www.freestatistics.org/blog/date/2010/Jan/12/t1263321818emgb7mwn4juigwr/2i85m1263321717.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Jan/12/t1263321818emgb7mwn4juigwr/38c841263321717.png (open in new window) http://www.freestatistics.org/blog/date/2010/Jan/12/t1263321818emgb7mwn4juigwr/38c841263321717.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Jan/12/t1263321818emgb7mwn4juigwr/46eu01263321717.png (open in new window) http://www.freestatistics.org/blog/date/2010/Jan/12/t1263321818emgb7mwn4juigwr/46eu01263321717.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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