workshop 9

*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: Fri, 04 Dec 2009 11:58:48 -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/04/t1259953160rlslsg6ot8zf2b5.htm/, Retrieved Fri, 04 Dec 2009 19:59:25 +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/04/t1259953160rlslsg6ot8zf2b5.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 «

0.7461 0.7775 0.7790 0.7744 0.7905 0.7719 0.7811 0.7557 0.7637 0.7595 0.7471 0.7615 0.7487 0.7389 0.7337 0.7510 0.7382 0.7159 0.7542 0.7636 0.7433 0.7658 0.7627 0.7480 0.7692 0.7850 0.7913 0.7720 0.7880 0.8070 0.8268 0.8244 0.8487 0.8572 0.8214 0.8827 0.9216 0.8865 0.8816 0.8884 0.9466 0.9180 0.9337 0.9559 0.9626 0.9434 0.8639 0.7996 0.6680 0.6572 0.6928 0.6438 0.6454 0.6873 0.7265 0.7912 0.8114 0.8281 0.8393

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 0.7461 NA NA 1.00447820979037 NA 2 0.7775 NA NA 0.987963294714205 NA 3 0.779 NA NA 0.980072838319878 NA 4 0.7744 NA NA 0.976032293476133 NA 5 0.7905 NA NA 0.993203799768895 NA 6 0.7719 NA NA 0.978112983912604 NA 7 0.7811 0.774374085534364 0.767441666666667 1.00903315413901 1.00868561408663 8 0.7557 0.783538502032904 0.765941666666667 1.02297411948199 0.964470792487317 9 0.7637 0.784150485167401 0.762445833333333 1.02846713941524 0.97392020338668 10 0.7595 0.787820559974768 0.759583333333333 1.03717462640672 0.964052017155181 11 0.7471 0.752316113535205 0.756429166666667 0.994562540271171 0.993066593362338 12 0.7615 0.74283727314509 0.751916666666667 0.987925000303789 1.02512357353299 13 0.7487 0.751814272095224 0.7484625 1.00447820979037 0.995857657654537 14 0.7389 0.738671339861715 0.747670833333333 0.987963294714205 1.00030955599053 15 0.7337 0.732261421150697 0.74715 0.980072838319878 1.00196457003981 16 0.751 0.728669109098275 0.7465625 0.976032293476133 1.03064613364681 17 0.7382 0.742395010232255 0.747475 0.993203799768895 0.994349355566193 18 0.7159 0.731200587536166 0.7475625 0.978112983912604 0.979074705632114 19 0.7542 0.754609648627666 0.747854166666666 1.00903315413901 0.999457138364967 20 0.7636 0.767874210828336 0.750629166666667 1.02297411948199 0.994433709625792 21 0.7433 0.776441266901534 0.75495 1.02846713941524 0.957316453524182 22 0.7658 0.786411731107233 0.758225 1.03717462640672 0.973790153056069 23 0.7627 0.757036141590909 0.761175 0.994562540271171 1.00748162220788 24 0.748 0.757783755128853 0.767045833333333 0.987925000303789 0.987088988035657 25 0.7692 0.777332203949773 0.773866666666667 1.00447820979037 0.9895383159112 26 0.785 0.77004329098262 0.779425 0.987963294714205 1.01942320541264 27 0.7913 0.770680276412836 0.78635 0.980072838319878 1.02675522420677 28 0.772 0.775506458781461 0.79455 0.976032293476133 0.995478491840067 29 0.788 0.795361741204097 0.800804166666667 0.993203799768895 0.99074415976691 30 0.807 0.791158913450008 0.8088625 0.978112983912604 1.02002263550431 31 0.8268 0.828239638746151 0.820825 1.00903315413901 0.998261808927268 32 0.8244 0.850504945329494 0.831404166666667 1.02297411948199 0.969306533168504 33 0.8487 0.863291031545404 0.839395833333333 1.02846713941524 0.983098363110197 34 0.8572 0.879532726314782 0.848008333333333 1.03717462640672 0.974608419167805 35 0.8214 0.854793351278396 0.859466666666667 0.994562540271171 0.960934006764964 36 0.8827 0.860186297764509 0.8707 0.987925000303789 1.02617305378382 37 0.9216 0.883719002344196 0.879779166666667 1.00447820979037 1.04286543296604 38 0.8865 0.879003292848412 0.8897125 0.987963294714205 1.00852864512861 39 0.8816 0.882004299935496 0.8999375 0.980072838319878 0.999541612285195 40 0.8884 0.886505731357034 0.908275 0.976032293476133 1.00213678104491 41 0.9466 0.907428236611354 0.9136375 0.993203799768895 1.04316789119868 42 0.918 0.891986060208333 0.911945833333333 0.978112983912604 1.02916406539536 43 0.9337 0.90602768632065 0.897916666666667 1.00903315413901 1.03054245923955 44 0.9559 0.89796241968913 0.877795833333333 1.02297411948199 1.06452116373748 45 0.9626 0.884867415074386 0.860375 1.02846713941524 1.08784658989740 46 0.9434 0.873629474066151 0.842316666666667 1.03717462640672 1.07986283430791 47 0.8639 0.815118593942745 0.819575 0.994562540271171 1.05984577755894 48 0.7996 0.787783744304745 0.7974125 0.987925000303789 1.01499936471231 49 0.668 NA 0.779166666666667 NA NA 50 0.6572 NA 0.763670833333333 NA NA 51 0.6928 NA 0.750508333333333 NA NA 52 0.6438 NA 0.739404166666667 NA NA 53 0.6454 NA 0.733575 NA NA 54 0.6873 NA NA NA NA 55 0.7265 NA NA NA NA 56 0.7912 NA NA NA NA 57 0.8114 NA NA NA NA 58 0.8281 NA NA NA NA 59 0.8393 NA NA NA NA

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Dec/04/t1259953160rlslsg6ot8zf2b5/1h7v31259953126.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/04/t1259953160rlslsg6ot8zf2b5/1h7v31259953126.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/04/t1259953160rlslsg6ot8zf2b5/2ikgp1259953126.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/04/t1259953160rlslsg6ot8zf2b5/2ikgp1259953126.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/04/t1259953160rlslsg6ot8zf2b5/3r6nz1259953126.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/04/t1259953160rlslsg6ot8zf2b5/3r6nz1259953126.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/04/t1259953160rlslsg6ot8zf2b5/4s3qi1259953126.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/04/t1259953160rlslsg6ot8zf2b5/4s3qi1259953126.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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