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 05:04:40 -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/t12599284393tzkzy5d1g8athx.htm/, Retrieved Fri, 04 Dec 2009 13:07:25 +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/t12599284393tzkzy5d1g8athx.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 «

283.042 276.687 277.915 277.128 277.103 275.037 270.150 267.140 264.993 287.259 291.186 292.300 288.186 281.477 282.656 280.190 280.408 276.836 275.216 274.352 271.311 289.802 290.726 292.300 278.506 269.826 265.861 269.034 264.176 255.198 253.353 246.057 235.372 258.556 260.993 254.663 250.643 243.422 247.105 248.541 245.039 237.080 237.085 225.554 226.839 247.934 248.333 246.969 245.098 246.263 255.765 264.319 268.347 273.046 273.963 267.430 271.993 292.710 295.881 293.299

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 1 283.042 NA NA 1.01112979103349 NA 2 276.687 NA NA 0.99084022032972 NA 3 277.915 NA NA 1.00098477841217 NA 4 277.128 NA NA 1.01098799963399 NA 5 277.103 NA NA 1.00646333652994 NA 6 275.037 NA NA 0.990726073753508 NA 7 270.15 272.816853699805 278.542666666667 0.979443677209733 0.990224747248425 8 267.14 267.679670971462 278.956583333333 0.959574668476649 0.997983892577634 9 264.993 264.752532107580 279.353708333333 0.9477322985513 1.00090827419291 10 287.259 288.163595661712 279.678833333333 1.03033752045964 0.99686082601921 11 291.186 290.680796618511 279.944125 1.03835290924041 1.00173800053999 12 292.3 289.521516082251 280.156791666667 1.03342672636945 1.00959681323636 13 288.186 283.564103465173 280.442833333333 1.01112979103349 1.01629930050506 14 281.477 278.380936112608 280.954416666667 0.99084022032972 1.01112168071071 15 282.656 281.795399679833 281.518166666667 1.00098477841217 1.00305398995564 16 280.19 284.984753373325 281.887375 1.01098799963399 0.983175403888909 17 280.408 283.796660598584 281.974166666667 1.00646333652994 0.988059547313078 18 276.836 279.34017012517 281.955 0.990726073753508 0.99103540989451 19 275.216 275.763999724529 281.551666666667 0.979443677209733 0.998012794545059 20 274.352 269.316985231828 280.662875 0.959574668476649 1.01869549654968 21 271.311 264.869971934908 279.477625 0.9477322985513 1.02431769829565 22 289.802 286.756326331685 278.313 1.03033752045964 1.01062112110054 23 290.726 287.802179501165 277.171833333333 1.03835290924041 1.01015913258163 24 292.3 284.806119108168 275.593916666667 1.03342672636945 1.02631221869565 25 278.506 276.828504492612 273.781375 1.01112979103349 1.00605969212044 26 269.826 269.202824436703 271.691458333333 0.99084022032972 1.00231489236638 27 265.861 269.279961872248 269.015041666667 1.00098477841217 0.987303318641027 28 269.034 269.140844314561 266.215666666667 1.01098799963399 0.99960301709377 29 264.176 265.379094451616 263.674875 1.00646333652994 0.995466506304492 30 255.198 258.448523006665 260.867791666667 0.990726073753508 0.987422938352096 31 253.353 252.832244099864 258.138625 0.979443677209733 1.00205968942763 32 246.057 245.533567233134 255.8775 0.959574668476649 1.00213181754643 33 235.372 240.720054947453 253.995833333333 0.9477322985513 0.977783093524881 34 258.556 260.016448901226 252.360458333333 1.03033752045964 0.994383244185523 35 260.993 260.324635846277 250.709208333333 1.03835290924041 1.00256742567429 36 254.663 257.485416743139 249.156916666667 1.03342672636945 0.989038537487525 37 250.643 250.481284875612 247.724166666667 1.01112979103349 1.00064561759362 38 243.422 243.936976808424 246.192041666667 0.99084022032972 0.997888894028444 39 247.105 245.223461523465 244.982208333333 1.00098477841217 1.00767275066116 40 248.541 246.867177951625 244.184083333333 1.01098799963399 1.00678025350419 41 245.039 244.785973930794 243.214 1.00646333652994 1.00103366244864 42 237.08 240.118233030836 242.365916666667 0.990726073753508 0.987346929083697 43 237.085 236.843479031867 241.814291666667 0.979443677209733 1.00101974928387 44 225.554 231.930756679642 241.701625 0.959574668476649 0.972505773831237 45 226.839 229.522597840069 242.180833333333 0.9477322985513 0.988307914491542 46 247.934 250.577140499725 243.199083333333 1.03033752045964 0.989451789199711 47 248.333 254.217519945875 244.827666666667 1.03835290924041 0.976852421709064 48 246.969 255.563759745455 247.297416666667 1.03342672636945 0.966369411085455 49 245.098 253.118732674707 250.332583333333 1.01112979103349 0.968312370285865 50 246.263 251.290951638702 253.614 0.99084022032972 0.979991513399453 51 255.765 257.493574644940 257.24025 1.00098477841217 0.993286921247165 52 264.319 263.855062056475 260.987333333333 1.01098799963399 1.00175830601812 53 268.347 266.545879010461 264.834166666667 1.00646333652994 1.00675726443877 54 273.046 266.253421735442 268.74575 0.990726073753508 1.02551170317468 55 273.963 NA NA 0.979443677209733 NA 56 267.43 NA NA 0.959574668476649 NA 57 271.993 NA NA 0.9477322985513 NA 58 292.71 NA NA 1.03033752045964 NA 59 295.881 NA NA 1.03835290924041 NA 60 293.299 NA NA 1.03342672636945 NA

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Dec/04/t12599284393tzkzy5d1g8athx/147971259928278.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/04/t12599284393tzkzy5d1g8athx/147971259928278.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/04/t12599284393tzkzy5d1g8athx/2i7hi1259928278.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/04/t12599284393tzkzy5d1g8athx/2i7hi1259928278.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/04/t12599284393tzkzy5d1g8athx/3lc8f1259928278.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/04/t12599284393tzkzy5d1g8athx/3lc8f1259928278.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/04/t12599284393tzkzy5d1g8athx/49v041259928278.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/04/t12599284393tzkzy5d1g8athx/49v041259928278.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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