workshop 9 - ad hoc link 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: Fri, 04 Dec 2009 03:22:32 -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/t1259922202jlj8l2laiwlbf5l.htm/, Retrieved Fri, 04 Dec 2009 11:23:27 +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/t1259922202jlj8l2laiwlbf5l.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 «

8,6 8,5 8,3 7,8 7,8 8 8,6 8,9 8,9 8,6 8,3 8,3 8,3 8,4 8,5 8,4 8,6 8,5 8,5 8,4 8,5 8,5 8,5 8,5 8,5 8,5 8,5 8,5 8,6 8,4 8,1 8 8 8 8 7,9 7,8 7,8 7,9 8,1 8 7,6 7,3 7 6,8 7 7,1 7,2 7,1 6,9 6,7 6,7 6,6 6,9 7,3 7,5 7,3 7,1 6,9 7,1

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 8.6 NA NA 0.996702710921287 NA 2 8.5 NA NA 0.995771473511747 NA 3 8.3 NA NA 0.998520165033688 NA 4 7.8 NA NA 1.00619273018036 NA 5 7.8 NA NA 1.01232015874663 NA 6 8 NA NA 1.00437744260445 NA 7 8.6 8.3640673297507 8.37083333333333 0.999191716844284 1.02820788749633 8 8.9 8.32165964665616 8.35416666666667 0.996108885385276 1.06949819842443 9 8.9 8.32843497316147 8.35833333333333 0.99642292799539 1.06862814306414 10 8.6 8.37548837152225 8.39166666666667 0.998072099883485 1.02680579549739 11 8.3 8.41760187683547 8.45 0.99616590258408 0.986029052150934 12 8.3 8.5054744910722 8.50416666666667 1.00015378630932 0.975842089552103 13 8.3 8.4927376826418 8.52083333333333 0.996702710921287 0.9773055886283 14 8.4 8.45990847704355 8.49583333333333 0.995771473511747 0.992918543125364 15 8.5 8.44581639590994 8.45833333333333 0.998520165033688 1.00641543712888 16 8.4 8.48975116089679 8.4375 1.00619273018036 0.98942829310355 17 8.6 8.54566934008615 8.44166666666667 1.01232015874663 1.00635768337759 18 8.5 8.49535920202928 8.45833333333333 1.00437744260445 1.00054627448473 19 8.5 8.4681498002553 8.475 0.999191716844284 1.00376117575810 20 8.4 8.45447416470753 8.4875 0.996108885385276 0.993556764897937 21 8.5 8.46129136356085 8.49166666666667 0.99642292799539 1.00457479062899 22 8.5 8.4794542152601 8.49583333333333 0.998072099883485 1.00242300792225 23 8.5 8.46741017196467 8.5 0.99616590258408 1.00384885429824 24 8.5 8.49713987618628 8.49583333333333 1.00015378630932 1.00033659841492 25 8.5 8.4470554750579 8.475 0.996702710921287 1.00626780836215 26 8.5 8.40597085556167 8.44166666666666 0.995771473511747 1.01118599458100 27 8.5 8.39172988697062 8.40416666666667 0.998520165033688 1.01290200167161 28 8.5 8.41428670613326 8.3625 1.00619273018036 1.01018663813823 29 8.6 8.42334732090427 8.32083333333333 1.01232015874663 1.02097179094792 30 8.4 8.3112233375518 8.275 1.00437744260445 1.01068153974964 31 8.1 8.21418857222405 8.22083333333333 0.999191716844284 0.98609861811425 32 8 8.13073877695731 8.1625 0.996108885385276 0.983920430782031 33 8 8.07932924116262 8.10833333333333 0.99642292799539 0.990181209504564 34 8 8.05111493906011 8.06666666666667 0.998072099883485 0.99365119744942 35 8 7.99423136823724 8.025 0.99616590258408 1.00072159930043 36 7.9 7.96789183093094 7.96666666666667 1.00015378630932 0.991479323217293 37 7.8 7.87395141627817 7.9 0.996702710921287 0.99060809339955 38 7.8 7.79191178022942 7.825 0.995771473511747 1.00103802763670 39 7.9 7.72188927626052 7.73333333333333 0.998520165033688 1.02306569252256 40 8.1 7.68898944646159 7.64166666666667 1.00619273018036 1.05345443070254 41 8 7.6556712005214 7.5625 1.01232015874663 1.04497695766442 42 7.6 7.5286459135225 7.49583333333333 1.00437744260445 1.00947767862868 43 7.3 7.43148839402936 7.4375 0.999191716844284 0.982306586909965 44 7 7.3421525760273 7.37083333333333 0.996108885385276 0.95339887417425 45 6.8 7.25728032556642 7.28333333333333 0.99642292799539 0.93699012508095 46 7 7.161167316664 7.175 0.998072099883485 0.977494267409592 47 7.1 7.0312709957393 7.05833333333333 0.99616590258408 1.00977476252904 48 7.2 6.97190535206457 6.97083333333333 1.00015378630932 1.03271625709432 49 7.1 6.9187779849786 6.94166666666667 0.996702710921287 1.02619277788865 50 6.9 6.93305888432554 6.9625 0.995771473511747 0.995231702935586 51 6.7 6.99380165592345 7.00416666666667 0.998520165033688 0.95799113695559 52 6.7 7.07269639922612 7.02916666666667 1.00619273018036 0.947304906334323 53 6.6 7.11154911519509 7.025 1.01232015874663 0.92806783628871 54 6.9 7.04319681626368 7.0125 1.00437744260445 0.979668775415587 55 7.3 NA NA 0.999191716844284 NA 56 7.5 NA NA 0.996108885385276 NA 57 7.3 NA NA 0.99642292799539 NA 58 7.1 NA NA 0.998072099883485 NA 59 6.9 NA NA 0.99616590258408 NA 60 7.1 NA NA 1.00015378630932 NA

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Dec/04/t1259922202jlj8l2laiwlbf5l/1xhh01259922148.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/04/t1259922202jlj8l2laiwlbf5l/1xhh01259922148.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/04/t1259922202jlj8l2laiwlbf5l/2rv9e1259922148.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/04/t1259922202jlj8l2laiwlbf5l/2rv9e1259922148.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/04/t1259922202jlj8l2laiwlbf5l/3fqro1259922149.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/04/t1259922202jlj8l2laiwlbf5l/3fqro1259922149.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/04/t1259922202jlj8l2laiwlbf5l/4sl281259922149.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/04/t1259922202jlj8l2laiwlbf5l/4sl281259922149.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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