opgave 9 - oef 2/blog - Van Mechelen Wout

*Unverified author*

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

Title produced by software: Classical Decomposition

Date of computation: Mon, 01 Jun 2009 10:29:12 -0600

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2009/Jun/01/t1243873782wi8lx4abng035bj.htm/, Retrieved Mon, 01 Jun 2009 18:29:46 +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/2009/Jun/01/t1243873782wi8lx4abng035bj.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 «

266433 267722 266003 262971 265521 264676 270223 269508 268457 265814 266680 263018 269285 269829 270911 266844 271244 269907 271296 270157 271322 267179 264101 265518 269419 268714 272482 268351 268175 270674 272764 272599 270333 270846 270491 269160 274027 273784 276663 274525 271344 271115 270798 273911 273985 271917 273338 270601 273547 275363 281229 277793 279913 282500 280041 282166 290304 283519 287816 285226 287595 289741 289148 288301 290155 289648 288225 289351 294735 305333 309030 310215 321935

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 1 seconds R Server 'George Udny Yule' @ 72.249.76.132 Classical Decomposition by Moving Averages t Observations Fit Trend Seasonal Random 1 266433 NA NA 1.00098850530469 NA 2 267722 NA NA 1.00237258862511 NA 3 266003 NA NA 1.01052881148383 NA 4 262971 NA NA 0.997848682072232 NA 5 265521 NA NA 0.99905471515886 NA 6 264676 NA NA 0.998631083401062 NA 7 270223 265312.329736854 266537.666666667 0.99540276259961 1.01850901640349 8 269508 268056.286033123 266744.291666667 1.00491854711589 1.00541570573987 9 268457 269189.43357316 267036.583333333 1.00806200488695 0.997279114698382 10 265814 266567.076619588 267402.458333333 0.996875938542406 0.997174907609978 11 266680 266441.979294315 267802.291666667 0.994920460299701 1.0008933303465 12 263018 265682.325009351 268258.708333333 0.990395900509665 0.989971764176418 13 269285 268786.809803611 268521.375 1.00098850530469 1.00185347709864 14 269829 269230.385993158 268593.125 1.00237258862511 1.00222342661893 15 270911 271569.049639125 268739.541666667 1.01052881148383 0.9975768606916 16 266844 268337.268302994 268915.791666667 0.997848682072232 0.994435106564072 17 271244 268611.054127586 268865.208333333 0.99905471515886 1.00980207564788 18 269907 268493.867126119 268861.916666667 0.998631083401062 1.00526318492488 19 271296 267735.140061021 268971.666666667 0.99540276259961 1.01329993492138 20 270157 270253.540436391 268930.791666667 1.00491854711589 0.99964277827319 21 271322 271118.066201426 268949.791666667 1.00806200488695 1.00075219553396 22 267179 268237.425327611 269078.041666667 0.996875938542406 0.996054147454188 23 264101 267646.496331584 269012.958333333 0.994920460299701 0.98675306278924 24 265518 266334.335643853 268917.041666667 0.990395900509665 0.996934921508036 25 269419 269276.084643433 269010.166666667 1.00098850530469 1.00053073913621 26 268714 269811.720329036 269173.083333333 1.00237258862511 0.995931532078379 27 272482 272068.335082732 269233.625 1.01052881148383 1.00152044491742 28 268351 268765.761157887 269345.208333333 0.997848682072232 0.998456793171494 29 268175 269509.245943793 269764.25 0.99905471515886 0.99504935001721 30 270674 269812.393033237 270182.25 0.998631083401062 1.00319335578725 31 272764 269282.327755022 270526 0.99540276259961 1.01292944945182 32 272599 272261.828281196 270929.25 1.00491854711589 1.00123840980916 33 270333 273502.048837817 271314.708333333 1.00806200488695 0.988413070939384 34 270846 270897.214941135 271746.166666667 0.996875938542406 0.999810943271803 35 270491 270753.13546887 272135.458333333 0.994920460299701 0.999031828501575 36 269160 269670.814366687 272285.875 0.990395900509665 0.99810578550042 37 274027 272491.426553890 272222.333333333 1.00098850530469 1.00563530921149 38 273784 272840.890291861 272195.083333333 1.00237258862511 1.00345662890606 39 276663 275269.985095083 272401.916666667 1.01052881148383 1.00506054048877 40 274525 272012.261845010 272598.708333333 0.997848682072232 1.00923759148925 41 271344 272504.120588881 272761.958333333 0.99905471515886 0.99574274111388 42 271115 272566.992047913 272940.625 0.998631083401062 0.994672898442311 43 270798 271725.709736283 272980.666666667 0.99540276259961 0.996585859552328 44 273911 274369.351832529 273026.458333333 1.00491854711589 0.99832943501354 45 273985 275485.704850517 273282.5 1.00806200488695 0.994552512801595 46 271917 272754.145595654 273608.916666667 0.996875938542406 0.996930768572458 47 273338 272709.812374126 274102.125 0.994920460299701 1.00230350210139 48 270601 272293.05257927 274933.541666667 0.990395900509665 0.993785913510308 49 273547 276065.664551352 275793.041666667 1.00098850530469 0.990876574399627 50 275363 277178.198248366 276522.125 1.00237258862511 0.993451150704357 51 281229 280468.271617458 277546.041666667 1.01052881148383 1.00271235094849 52 277793 278109.824101954 278709.416666667 0.997848682072232 0.998860795000763 53 279913 279531.596337148 279796.083333333 0.99905471515886 1.00136443846724 54 282500 280624.030848050 281008.708333333 0.998631083401062 1.00668499111171 55 280041 280906.060565049 282203.416666667 0.99540276259961 0.996920463149465 56 282166 284781.689743652 283387.833333333 1.00491854711589 0.990815105612982 57 290304 286609.039035691 284316.875 1.00806200488695 1.01289199034595 58 283519 284194.044647382 285084.666666667 0.996875938542406 0.997624705161503 59 287816 284496.759432354 285949.25 0.994920460299701 1.01166705931649 60 285226 283920.589316724 286673.833333333 0.990395900509665 1.00459780210522 61 287595 287596.676761772 287312.666666667 1.00098850530469 0.999994169745661 62 289741 288636.235777891 287953.041666667 1.00237258862511 1.00382753128391 63 289148 291473.940903328 288437.041666667 1.01052881148383 0.992020072545356 64 288301 288907.710998771 289530.583333333 0.997848682072232 0.99789998336606 65 290155 291048.033057022 291323.416666667 0.99905471515886 0.996931664345426 66 289648 292847.108870365 293248.541666667 0.998631083401062 0.989075839325493 67 288225 294361.085607568 295720.583333333 0.99540276259961 0.979154562516635 68 289351 NA NA 1.00491854711589 NA 69 294735 NA NA 1.00806200488695 NA 70 305333 NA NA 0.996875938542406 NA 71 309030 NA NA 0.994920460299701 NA 72 310215 NA NA 0.990395900509665 NA 73 321935 NA NA NA NA

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/01/t1243873782wi8lx4abng035bj/1zh5c1243873749.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/01/t1243873782wi8lx4abng035bj/1zh5c1243873749.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/01/t1243873782wi8lx4abng035bj/260r21243873749.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/01/t1243873782wi8lx4abng035bj/260r21243873749.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/01/t1243873782wi8lx4abng035bj/3i79n1243873749.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/01/t1243873782wi8lx4abng035bj/3i79n1243873749.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/01/t1243873782wi8lx4abng035bj/4bk9m1243873749.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/01/t1243873782wi8lx4abng035bj/4bk9m1243873749.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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