Ad hoc 1

*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: Mon, 07 Dec 2009 13:37:22 -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/07/t1260218288xffa1a4knb1gvev.htm/, Retrieved Mon, 07 Dec 2009 21:38:13 +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/07/t1260218288xffa1a4knb1gvev.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 «

97.4 97 105.4 102.7 98.1 104.5 87.4 89.9 109.8 111.7 98.6 96.9 95.1 97 112.7 102.9 97.4 111.4 87.4 96.8 114.1 110.3 103.9 101.6 94.6 95.9 104.7 102.8 98.1 113.9 80.9 95.7 113.2 105.9 108.8 102.3 99 100.7 115.5 100.7 109.9 114.6 85.4 100.5 114.8 116.5 112.9 102 106 105.3 118.8 106.1 109.3 117.2 92.5 104.2 112.5 122.4 113.3

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 97.4 NA NA 0.941348969591916 NA 2 97 NA NA 0.95620732762372 NA 3 105.4 NA NA 1.08173362797653 NA 4 102.7 NA NA 0.99502820213238 NA 5 98.1 NA NA 0.988058884194956 NA 6 104.5 NA NA 1.09785548270142 NA 7 87.4 81.6380657545254 99.8541666666667 0.81757295143276 1.0705790147307 8 89.9 93.9394242606819 99.7583333333333 0.94166994497384 0.956999691104424 9 109.8 109.845183044451 100.0625 1.09776572686522 0.999588666128105 10 111.7 106.968163267660 100.375 1.06568531275377 1.04423593513988 11 98.6 104.451133531461 100.354166666667 1.04082507982357 0.943982096377164 12 96.9 98.222801193074 100.6125 0.97624848992992 0.98653264642215 13 95.1 94.9821110318244 100.9 0.941348969591916 1.00124117022558 14 97 96.7562289639252 101.1875 0.95620732762372 1.00251943506568 15 112.7 109.962730507264 101.654166666667 1.08173362797653 1.02489270210106 16 102.9 101.268995272023 101.775 0.99502820213238 1.01610566712542 17 97.4 100.720252507623 101.9375 0.988058884194956 0.967034906833936 18 111.4 112.370083052335 102.354166666667 1.09785548270142 0.991367070077868 19 87.4 83.8250733996081 102.529166666667 0.81757295143276 1.04264746161748 20 96.8 96.485856736882 102.4625 0.94166994497384 1.00325584778684 21 114.1 112.063584617491 102.083333333333 1.09776572686522 1.01817196361744 22 110.3 108.429040217226 101.745833333333 1.06568531275377 1.01725515396083 23 103.9 105.925635727878 101.770833333333 1.04082507982357 0.980876813115553 24 101.6 99.4837888259002 101.904166666667 0.97624848992992 1.02127191976779 25 94.6 95.7704907938576 101.7375 0.941348969591916 0.987778168576195 26 95.9 96.9793440070373 101.420833333333 0.95620732762372 0.98887037215926 27 104.7 109.620181525071 101.3375 1.08173362797653 0.95511609763257 28 102.8 100.613935038953 101.116666666667 0.99502820213238 1.02172725835841 29 98.1 99.9298054002673 101.1375 0.988058884194956 0.98168909272926 30 113.9 111.290525161012 101.370833333333 1.09785548270142 1.02344741239394 31 80.9 83.0517856497112 101.583333333333 0.81757295143276 0.974091036900918 32 95.7 96.0189453891658 101.966666666667 0.94166994497384 0.996678307724865 33 113.2 112.649059671819 102.616666666667 1.09776572686522 1.00489076721800 34 105.9 109.743385436289 102.979166666667 1.06568531275377 0.964978431993783 35 108.8 107.603966169094 103.383333333333 1.04082507982357 1.01111514634160 36 102.3 101.43628580576 103.904166666667 0.97624848992992 1.00851484444032 37 99 98.014039171385 104.120833333333 0.941348969591916 1.01005938370615 38 100.7 99.9316341310756 104.508333333333 0.95620732762372 1.00768891528299 39 115.5 113.338640871241 104.775 1.08173362797653 1.01906992277431 40 100.7 104.759885881171 105.283333333333 0.99502820213238 0.961245797024102 41 109.9 104.631318924228 105.895833333333 0.988058884194956 1.05035472294473 42 114.6 116.432148338330 106.054166666667 1.09785548270142 0.984264240036125 43 85.4 86.9352571690169 106.333333333333 0.81757295143276 0.982340223989537 44 100.5 100.586044622289 106.816666666667 0.94166994497384 0.999144566996227 45 114.8 117.621023609746 107.145833333333 1.09776572686522 0.976015991672493 46 116.5 114.57005183197 107.508333333333 1.06568531275377 1.01684513655332 47 112.9 112.105534639331 107.708333333333 1.04082507982357 1.00708676305078 48 102 105.231451810363 107.791666666667 0.97624848992992 0.969291958299825 49 106 NA 108.195833333333 NA NA 50 105.3 NA 108.645833333333 NA NA 51 118.8 NA 108.704166666667 NA NA 52 106.1 NA 108.854166666667 NA NA 53 109.3 NA 109.116666666667 NA NA 54 117.2 NA NA NA NA 55 92.5 NA NA NA NA 56 104.2 NA NA NA NA 57 112.5 NA NA NA NA 58 122.4 NA NA NA NA 59 113.3 NA NA NA NA

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Dec/07/t1260218288xffa1a4knb1gvev/1ncps1260218240.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/07/t1260218288xffa1a4knb1gvev/1ncps1260218240.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/07/t1260218288xffa1a4knb1gvev/2czce1260218240.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/07/t1260218288xffa1a4knb1gvev/2czce1260218240.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/07/t1260218288xffa1a4knb1gvev/3946x1260218240.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/07/t1260218288xffa1a4knb1gvev/3946x1260218240.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/07/t1260218288xffa1a4knb1gvev/4gdjh1260218240.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/07/t1260218288xffa1a4knb1gvev/4gdjh1260218240.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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