*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 15:40:18 -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/t12599664543vvmqek8ce5dwai.htm/, Retrieved Fri, 04 Dec 2009 23:40:59 +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/t12599664543vvmqek8ce5dwai.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 «

102.80 118.72 119.01 118.61 120.43 111.83 116.79 131.71 120.57 117.83 130.80 107.46 112.09 129.47 119.72 134.81 135.80 129.27 126.94 153.45 121.86 133.47 135.34 117.10 120.65 132.49 137.60 138.69 125.53 133.09 129.08 145.94 129.07 139.69 142.09 137.29 127.03 137.25 156.87 150.89 139.14 158.30 149.00 158.36 168.06 153.38 173.86 162.47 145.17 168.89 166.64 140.07 128.84 123.40 120.30 129.66 118.12 113.91 131.09 119.14 115.33

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 1 102.8 NA NA 0.91305423037476 NA 2 118.72 NA NA 1.02683812043166 NA 3 119.01 NA NA 1.04797023984225 NA 4 118.61 NA NA 1.02408310415181 NA 5 120.43 NA NA 0.961623699822525 NA 6 111.83 NA NA 0.983786916052064 NA 7 116.79 112.473451041067 118.43375 0.94967398263643 1.03837838102217 8 131.71 126.544503019225 119.26875 1.06100301226620 1.04081960778644 9 120.57 120.715449862084 119.74625 1.00809378049070 0.998795101519728 10 117.83 120.215197698611 120.450833333333 0.998043719348377 0.980158933776482 11 130.8 125.569087440401 121.76625 1.03123063607856 1.04165764573293 12 107.46 122.468235837208 123.133333333333 0.994598558504665 0.877452012478095 13 112.09 113.477042825814 124.282916666667 0.91305423037476 0.987776886044319 14 129.47 128.982847704288 125.611666666667 1.02683812043166 1.00377687657221 15 119.72 132.642903219633 126.57125 1.04797023984225 0.90257373062594 16 134.81 130.341883886095 127.276666666667 1.02408310415181 1.03427997187619 17 135.8 123.200824362012 128.1175 0.961623699822525 1.10226535173958 18 129.27 126.621574320201 128.708333333333 0.983786916052064 1.02091606974576 19 126.94 122.951124951996 129.466666666667 0.94967398263643 1.03244276983688 20 153.45 137.876457274816 129.949166666667 1.06100301226620 1.11295287848993 21 121.86 131.878828363793 130.82 1.00809378049070 0.924030047217619 22 133.47 131.468972337364 131.726666666667 0.998043719348377 1.01522053171224 23 135.34 135.566009098319 131.460416666667 1.03123063607856 0.998332848331064 24 117.1 130.483042554491 131.191666666667 0.994598558504665 0.89743462221229 25 120.65 120.011848040458 131.44 0.91305423037476 1.00531740798897 26 132.49 134.737847520091 131.21625 1.02683812043166 0.983316881177312 27 137.6 137.497625355703 131.20375 1.04797023984225 1.0007445557262 28 138.69 134.936603413389 131.763333333333 1.02408310415181 1.02781600019316 29 125.53 127.226421575394 132.30375 0.961623699822525 0.986666122064991 30 133.09 131.262999007892 133.42625 0.983786916052064 1.01391862905706 31 129.08 127.762806464021 134.533333333333 0.94967398263643 1.01030967910328 32 145.94 143.232754148407 134.9975 1.06100301226620 1.01890102489259 33 129.07 137.099494029509 135.99875 1.00809378049070 0.941433087799864 34 139.69 137.041383103726 137.31 0.998043719348377 1.01932713196765 35 142.09 142.707281253163 138.385416666667 1.03123063607856 0.995674493636606 36 137.29 139.246699103115 140.002916666667 0.994598558504665 0.985947967774328 37 127.03 129.547177719672 141.883333333333 0.91305423037476 0.980569412904394 38 137.25 147.074879687860 143.230833333333 1.02683812043166 0.933198111678133 39 156.87 152.346490345734 145.372916666667 1.04797023984225 1.02969224721883 40 150.89 151.121810173215 147.567916666667 1.02408310415181 0.998466070695225 41 139.14 143.726281558182 149.462083333333 0.961623699822525 0.968090167584793 42 158.3 149.373286398765 151.835 0.983786916052064 1.05976111135029 43 149 145.907910692261 153.64 0.94967398263643 1.02119206075304 44 158.36 165.213199885855 155.714166666667 1.06100301226620 0.958519053619265 45 168.06 158.713864761380 157.439583333333 1.00809378049070 1.05888669684070 46 153.38 157.087922909937 157.395833333333 0.998043719348377 0.97639587537189 47 173.86 161.403922364699 156.515833333333 1.03123063607856 1.07717332672471 48 162.47 153.797261597973 154.6325 0.994598558504665 1.05639072056236 49 145.17 138.768264567932 151.9825 0.91305423037476 1.04613256101458 50 168.89 153.605570133806 149.590833333333 1.02683812043166 1.09950439852461 51 166.64 153.332892333986 146.314166666667 1.04797023984225 1.08678573438130 52 140.07 146.022729717126 142.58875 1.02408310415181 0.959234225187698 53 128.84 133.821557450010 139.162083333333 0.961623699822525 0.962774626562904 54 123.4 133.376501232544 135.574583333333 0.983786916052064 0.925200457799163 55 120.3 125.856335943878 132.525833333333 0.94967398263643 0.955851758259067 56 129.66 NA NA 1.06100301226620 NA 57 118.12 NA NA 1.00809378049070 NA 58 113.91 NA NA 0.998043719348377 NA 59 131.09 NA NA 1.03123063607856 NA 60 119.14 NA NA 0.994598558504665 NA 61 115.33 NA NA NA NA

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Dec/04/t12599664543vvmqek8ce5dwai/13o6z1259966415.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/04/t12599664543vvmqek8ce5dwai/13o6z1259966415.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/04/t12599664543vvmqek8ce5dwai/2blmu1259966415.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/04/t12599664543vvmqek8ce5dwai/2blmu1259966415.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/04/t12599664543vvmqek8ce5dwai/3az6m1259966415.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/04/t12599664543vvmqek8ce5dwai/3az6m1259966415.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/04/t12599664543vvmqek8ce5dwai/41qds1259966415.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/04/t12599664543vvmqek8ce5dwai/41qds1259966415.ps (open in new window)

Parameters (Session):

par1 = 36 ; par2 = 1 ; par3 = 1 ; par4 = 1 ; par5 = 12 ; par6 = MA ; par7 = 0.95 ;

Parameters (R input):

par1 = multiplicative ; par2 = 12 ; par3 = 1 ; par4 = 1 ; par5 = 12 ; par6 = MA ; par7 = 0.95 ;

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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