*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 10:35:06 -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/t1259948172hq20z6zc008zfdt.htm/, Retrieved Fri, 04 Dec 2009 18:36:18 +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/t1259948172hq20z6zc008zfdt.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 «

92.9 107.7 103.5 91.1 79.8 71.9 82.9 90.1 100.7 90.7 108.8 44.1 93.6 107.4 96.5 93.6 76.5 76.7 84 103.3 88.5 99 105.9 44.7 94 107.1 104.8 102.5 77.7 85.2 91.3 106.5 92.4 97.5 107 51.1 98.6 102.2 114.3 99.4 72.5 92.3 99.4 85.9 109.4 97.6 104.7 56.9 86.7 108.5 103.4 86.2 71 75.9 87.1 102 88.5 87.8 100.8 50.6 85.9

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 92.9 NA NA 1.01439710936772 NA 2 107.7 NA NA 1.15553139141945 NA 3 103.5 NA NA 1.13714573088061 NA 4 91.1 NA NA 1.03762251436317 NA 5 79.8 NA NA 0.811294061553772 NA 6 71.9 NA NA 0.89800314598119 NA 7 82.9 87.3954464718251 88.7125 0.985153687155983 0.948562005764518 8 90.1 91.2660591205657 88.7291666666667 1.02859141530574 0.987223518449226 9 100.7 91.1999310265164 88.425 1.03138174754330 1.10416750173552 10 90.7 94.693438112965 88.2375 1.07316546947687 0.95782772077405 11 108.8 97.9357046767527 88.2041666666667 1.11032968597575 1.1109329366558 12 44.1 63.321098016854 88.2666666666667 0.717384040976443 0.696450336162238 13 93.6 89.7868241429105 88.5125 1.01439710936772 1.04246921409115 14 107.4 102.967476403735 89.1083333333333 1.15553139141945 1.04304780257880 15 96.5 101.376541908006 89.15 1.13714573088061 0.951896742419648 16 93.6 92.335433496893 88.9875 1.03762251436317 1.01369535459158 17 76.5 72.3775714663659 89.2125 0.811294061553772 1.05695726521509 18 76.7 80.0270470260237 89.1166666666667 0.89800314598119 0.958425967848822 19 84 87.8346608240155 89.1583333333333 0.985153687155983 0.956342282328629 20 103.3 91.7117820671982 89.1625 1.02859141530574 1.12635473514527 21 88.5 92.3043689811774 89.4958333333333 1.03138174754330 0.958784518835147 22 99 96.812939915182 90.2125 1.07316546947687 1.02259057608140 23 105.9 100.632880538935 90.6333333333333 1.11032968597575 1.05233994528286 24 44.7 65.3088496303929 91.0375 0.717384040976443 0.68444016780228 25 94 93.0159882743977 91.6958333333333 1.01439710936772 1.01057895254200 26 107.1 106.462958862778 92.1333333333333 1.15553139141945 1.00598368807354 27 104.8 105.105432283852 92.4291666666667 1.13714573088061 0.997094039030948 28 102.5 96.0103465685958 92.5291666666667 1.03762251436317 1.06759327159357 29 77.7 75.0548418694934 92.5125 0.811294061553772 1.03524300451004 30 85.2 83.357142025704 92.825 0.89800314598119 1.02210797934660 31 91.3 91.898419783534 93.2833333333333 0.985153687155983 0.993488247295835 32 106.5 95.9375784650793 93.2708333333333 1.02859141530574 1.11009681194700 33 92.4 96.3955165797658 93.4625 1.03138174754330 0.958550804834792 34 97.5 100.586905149509 93.7291666666667 1.07316546947687 0.96931106345383 35 107 103.686287175369 93.3833333333333 1.11032968597575 1.03195902674214 36 51.1 67.0485059297608 93.4625 0.717384040976443 0.76213480511455 37 98.6 95.4505413368803 94.0958333333333 1.01439710936772 1.03299571295258 38 102.2 108.128849952075 93.575 1.15553139141945 0.945168657997355 39 114.3 106.237839907521 93.425 1.13714573088061 1.07588783901760 40 99.4 97.6791894458633 94.1375 1.03762251436317 1.01761696185133 41 72.5 76.2988260972092 94.0458333333333 0.811294061553772 0.95021121173779 42 92.3 84.5844129918782 94.1916666666666 0.89800314598119 1.09121759831640 43 99.4 92.5428744872151 93.9375 0.985153687155983 1.07409674219415 44 85.9 96.3833014117117 93.7041666666667 1.02859141530574 0.891233219259307 45 109.4 96.447085667143 93.5125 1.03138174754330 1.13430073333226 46 97.6 99.2767489721893 92.5083333333333 1.07316546947687 0.983110355752494 47 104.7 102.034671767480 91.8958333333333 1.11032968597575 1.02612178964611 48 56.9 65.3895553350028 91.15 0.717384040976443 0.870169550909022 49 86.7 91.249246642249 89.9541666666667 1.01439710936772 0.950144830673675 50 108.5 104.127822509285 90.1125 1.15553139141945 1.0419885616097 51 103.4 102.243615527803 89.9125 1.13714573088061 1.01131008979121 52 86.2 91.9679421897226 88.6333333333333 1.03762251436317 0.937283122222918 53 71 71.4445832955791 88.0625 0.811294061553772 0.993777228796482 54 75.9 78.6987507059265 87.6375 0.89800314598119 0.964437164747575 55 87.1 86.0449649590155 87.3416666666667 0.985153687155983 1.01226143844079 56 102 NA NA 1.02859141530574 NA 57 88.5 NA NA 1.03138174754330 NA 58 87.8 NA NA 1.07316546947687 NA 59 100.8 NA NA 1.11032968597575 NA 60 50.6 NA NA 0.717384040976443 NA 61 85.9 NA NA NA NA

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Dec/04/t1259948172hq20z6zc008zfdt/1hgnu1259948104.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/04/t1259948172hq20z6zc008zfdt/1hgnu1259948104.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/04/t1259948172hq20z6zc008zfdt/26u481259948104.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/04/t1259948172hq20z6zc008zfdt/26u481259948104.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/04/t1259948172hq20z6zc008zfdt/3n88u1259948104.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/04/t1259948172hq20z6zc008zfdt/3n88u1259948104.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/04/t1259948172hq20z6zc008zfdt/42w0a1259948104.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/04/t1259948172hq20z6zc008zfdt/42w0a1259948104.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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