WS9

*The author of this computation has been verified*

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

Title produced by software: Classical Decomposition

Date of computation: Sun, 06 Dec 2009 12:38:59 -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/Dec/06/t1260128386ih75oybn8r2ixds.htm/, Retrieved Sun, 06 Dec 2009 20:39:52 +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/Dec/06/t1260128386ih75oybn8r2ixds.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 «

286602 283042 276687 277915 277128 277103 275037 270150 267140 264993 287259 291186 292300 288186 281477 282656 280190 280408 276836 275216 274352 271311 289802 290726 292300 278506 269826 265861 269034 264176 255198 253353 246057 235372 258556 260993 254663 250643 243422 247105 248541 245039 237080 237085 225554 226839 247934 248333 246969 245098 246263 255765 264319 268347 273046 273963 267430 271993 292710 295881 293299

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 'Gwilym Jenkins' @ 72.249.127.135 Classical Decomposition by Moving Averages t Observations Fit Trend Seasonal Random 1 286602 NA NA 1.03453131571099 NA 2 283042 NA NA 1.01221054805438 NA 3 276687 NA NA 0.99189929062336 NA 4 277915 NA NA 1.00205469182651 NA 5 277128 NA NA 1.01206860509960 NA 6 277103 NA NA 1.00753910576043 NA 7 275037 275806.405915469 278090.916666667 0.991785022040343 0.99721034066299 8 270150 274446.233453016 278542.666666667 0.985293336698925 0.984345810110191 9 267140 268821.413512387 278956.583333333 0.963667572566892 0.99374523967262 10 264993 265669.392744893 279353.708333333 0.951014376468874 0.997454005755408 11 287259 282964.383047754 279678.833333333 1.01174758087790 1.01517723504983 12 291186 290074.898160633 279944.125 1.03618855427180 1.00383039637836 13 292300 289830.974288287 280156.791666667 1.03453131571099 1.00851884695132 14 288186 283867.194026256 280442.833333333 1.01221054805438 1.01521417784313 15 281477 278678.486589166 280954.416666667 0.99189929062336 1.01004208629480 16 282656 282096.599742731 281518.166666667 1.00205469182651 1.00198300957112 17 280190 285289.362411437 281887.375 1.01206860509960 0.982125648259947 18 280408 284099.999730875 281974.166666667 1.00753910576043 0.987004576788552 19 276836 279638.745889385 281955 0.991785022040343 0.98997726198324 20 275216 277410.981103143 281551.666666667 0.985293336698925 0.992087619983842 21 274352 270465.711460895 280662.875 0.963667572566892 1.01436887699411 22 271311 265787.239276377 279477.625 0.951014376468874 1.02078264080195 23 289802 281582.504476872 278313 1.01174758087790 1.02919036301065 24 290726 287202.28126653 277171.833333333 1.03618855427180 1.01226911819060 25 292300 285110.537211113 275593.916666667 1.03453131571099 1.02521640504491 26 278506 277124.395635831 273781.375 1.01221054805438 1.00498550248887 27 269826 269490.564789259 271691.458333333 0.99189929062336 1.00124470113083 28 265861 269567.784673987 269015.041666667 1.00205469182651 0.986249155556661 29 269034 269428.518418993 266215.666666667 1.01206860509960 0.998535721380543 30 264176 265662.747768992 263674.875 1.00753910576043 0.994403627224826 31 255198 258724.768507741 260867.791666667 0.991785022040343 0.986368647547422 32 253353 254342.267157122 258138.625 0.985293336698925 0.996110488562598 33 246057 246580.849299485 255877.5 0.963667572566892 0.997875547509171 34 235372 241553.689063192 253995.833333333 0.951014376468874 0.974408633181442 35 258556 255325.083227989 252360.458333333 1.01174758087790 1.01265412990829 36 260993 259782.012125543 250709.208333333 1.03618855427180 1.00466155398732 37 254663 257760.632817661 249156.916666667 1.03453131571099 0.987982521676022 38 250643 250749.014507980 247724.166666667 1.01221054805438 0.999577208675422 39 243422 244197.711486283 246192.041666667 0.99189929062336 0.996823428517975 40 247105 245485.571274436 244982.208333333 1.00205469182651 1.00659683873540 41 248541 247131.044606691 244184.083333333 1.01206860509960 1.00570529451512 42 245039 245047.616068416 243214 1.00753910576043 0.999964839207356 43 237080 240374.886003078 242365.916666667 0.991785022040343 0.986292719435608 44 237085 238258.010297737 241814.291666667 0.985293336698925 0.995076722514928 45 225554 232920.018249223 241701.625 0.963667572566892 0.968375331993398 46 226839 230317.454205212 242180.833333333 0.951014376468874 0.984897131582077 47 247934 246056.084234224 243199.083333333 1.01174758087790 1.00763206393218 48 248333 253687.625969071 244827.666666667 1.03618855427180 0.978892837407357 49 246969 255836.921836097 247297.416666667 1.03453131571099 0.965337599544065 50 245098 253389.281371701 250332.583333333 1.01221054805438 0.967278484208894 51 246263 251559.546692153 253614 0.99189929062336 0.978945157272706 52 255765 257768.799439124 257240.25 1.00205469182651 0.99222636935314 53 264319 264137.086395331 260987.333333333 1.01206860509960 1.00068870906071 54 268347 266830.779458141 264834.166666667 1.00753910576043 1.00568232999558 55 273046 266538.009586999 268745.75 0.991785022040343 1.02441674425004 56 273963 NA NA 0.985293336698925 NA 57 267430 NA NA 0.963667572566892 NA 58 271993 NA NA 0.951014376468874 NA 59 292710 NA NA 1.01174758087790 NA 60 295881 NA NA 1.03618855427180 NA 61 293299 NA NA NA NA

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Dec/06/t1260128386ih75oybn8r2ixds/1ay391260128337.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/06/t1260128386ih75oybn8r2ixds/1ay391260128337.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/06/t1260128386ih75oybn8r2ixds/22y3d1260128337.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/06/t1260128386ih75oybn8r2ixds/22y3d1260128337.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/06/t1260128386ih75oybn8r2ixds/3sjdi1260128337.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/06/t1260128386ih75oybn8r2ixds/3sjdi1260128337.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/06/t1260128386ih75oybn8r2ixds/46hpw1260128337.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/06/t1260128386ih75oybn8r2ixds/46hpw1260128337.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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