*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: Wed, 09 Dec 2009 09:07:46 -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/09/t1260374939iq4kxnwfk5otmtw.htm/, Retrieved Wed, 09 Dec 2009 17:09:04 +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/09/t1260374939iq4kxnwfk5otmtw.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 «

153.4 145 137.7 148.3 152.2 169.4 168.6 161.1 174.1 179 190.6 190 181.6 174.8 180.5 196.8 193.8 197 216.3 221.4 217.9 229.7 227.4 204.2 196.6 198.8 207.5 190.7 201.6 210.5 223.5 223.8 231.2 244 234.7 250.2 265.7 287.6 283.3 295.4 312.3 333.8 347.7 383.2 407.1 413.6 362.7 321.9 239.4 191 159.7 163.4 157.6 166.2 176.7 198.3 226.2 216.2 235.9 226.9

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 1 153.4 NA NA 0.95714564467741 NA 2 145 NA NA 0.91854708876129 NA 3 137.7 NA NA 0.893643458283059 NA 4 148.3 NA NA 0.903290503909825 NA 5 152.2 NA NA 0.915789291918695 NA 6 169.4 NA NA 0.95501459237464 NA 7 168.6 172.597249064462 165.291666666667 1.04419812895061 0.97684059806209 8 161.1 177.381286982606 167.708333333333 1.05767723915094 0.908213051897618 9 174.1 186.722617678812 170.733333333333 1.09365063068418 0.93239909639375 10 179 197.288756282014 174.5375 1.13035167962194 0.90729955104045 11 190.6 194.381536205865 178.291666666667 1.09024465270875 0.980545805534431 12 190 188.503001342085 181.175 1.04044708895866 1.00794151099588 13 181.6 176.413906634605 184.3125 0.95714564467741 1.02939730469286 14 174.8 173.433172196741 188.8125 0.91854708876129 1.00788100561125 15 180.5 172.607233967373 193.15 0.893643458283059 1.04572673955322 16 196.8 178.027267189328 197.0875 0.903290503909825 1.10544863776799 17 193.8 183.829437197813 200.733333333333 0.915789291918695 1.05423811851993 18 197 193.732668518132 202.858333333333 0.95501459237464 1.01686515499353 19 216.3 213.094733165596 204.075 1.04419812895061 1.01504151128838 20 221.4 217.564208093349 205.7 1.05767723915094 1.01763062012942 21 217.9 227.287942321939 207.825 1.09365063068418 0.958695818942116 22 229.7 235.899685738433 208.695833333333 1.13035167962194 0.973718974151973 23 227.4 227.606741997164 208.766666666667 1.09024465270875 0.99909167015287 24 204.2 218.134067396386 209.654166666667 1.04044708895866 0.936121544137047 25 196.6 201.495110632006 210.516666666667 0.95714564467741 0.975706057498606 26 198.8 193.736890137902 210.916666666667 0.91854708876129 1.02613394825577 27 207.5 189.068891171829 211.570833333333 0.893643458283059 1.09748356122436 28 190.7 192.148708733784 212.720833333333 0.903290503909825 0.992460481554463 29 201.6 195.631671697415 213.620833333333 0.915789291918695 1.03050798600656 30 210.5 206.131941309130 215.841666666667 0.95501459237464 1.02119059599948 31 223.5 230.389264676341 220.6375 1.04419812895061 0.970097284324342 32 223.8 240.32189668908 227.216666666667 1.05767723915094 0.931250972480233 33 231.2 255.996271377399 234.075 1.09365063068418 0.903138154145834 34 244 273.088255997995 241.595833333333 1.13035167962194 0.89348404642414 35 234.7 273.183511166443 250.570833333333 1.09024465270875 0.859129451107333 36 250.2 270.850053236958 260.320833333333 1.04044708895866 0.923758356366678 37 265.7 259.035516304530 270.633333333333 0.95714564467741 1.02572806922598 38 287.6 259.443625220626 282.45 0.91854708876129 1.10852598423041 39 283.3 264.894538607146 296.420833333333 0.893643458283059 1.06948222296176 40 295.4 280.757743456905 310.816666666667 0.903290503909825 1.05215263651434 41 312.3 295.998362302988 323.216666666667 0.915789291918695 1.05507340503569 42 333.8 316.623150419407 331.5375 0.95501459237464 1.05425013792529 43 347.7 348.166111970895 333.429166666667 1.04419812895061 0.998661236820963 44 383.2 347.244251590248 328.308333333333 1.05767723915094 1.10354598598850 45 407.1 349.020371272344 319.133333333333 1.09365063068418 1.16640756101407 46 413.6 348.694653968707 308.483333333333 1.13035167962194 1.18613805887921 47 362.7 323.298423702622 296.5375 1.09024465270875 1.12187370370114 48 321.9 294.559241276604 283.108333333333 1.04044708895866 1.09281921899616 49 239.4 257.472178418223 269 0.95714564467741 0.929809199078326 50 191 233.467879006364 254.170833333333 0.91854708876129 0.818099692398342 51 159.7 213.517486784689 238.929166666667 0.893643458283059 0.747948106756432 52 163.4 201.584330789209 223.166666666667 0.903290503909825 0.810578874658976 53 157.6 192.002856628187 209.658333333333 0.915789291918695 0.82082112093359 54 166.2 191.400841221751 200.416666666667 0.95501459237464 0.868334741577473 55 176.7 NA NA 1.04419812895061 NA 56 198.3 NA NA 1.05767723915094 NA 57 226.2 NA NA 1.09365063068418 NA 58 216.2 NA NA 1.13035167962194 NA 59 235.9 NA NA 1.09024465270875 NA 60 226.9 NA NA 1.04044708895866 NA

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Dec/09/t1260374939iq4kxnwfk5otmtw/1hut81260374863.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/09/t1260374939iq4kxnwfk5otmtw/1hut81260374863.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/09/t1260374939iq4kxnwfk5otmtw/2rmea1260374863.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/09/t1260374939iq4kxnwfk5otmtw/2rmea1260374863.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/09/t1260374939iq4kxnwfk5otmtw/3w3vs1260374863.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/09/t1260374939iq4kxnwfk5otmtw/3w3vs1260374863.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/09/t1260374939iq4kxnwfk5otmtw/4fqc91260374863.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/09/t1260374939iq4kxnwfk5otmtw/4fqc91260374863.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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