*Unverified author*

R Software Module: /rwasp_decompose.wasp (opens new window with default values)

Title produced by software: Classical Decomposition

Date of computation: Tue, 14 Dec 2010 14:18:13 +0000

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2010/Dec/14/t129233618472jvbkmh92k3uxo.htm/, Retrieved Tue, 14 Dec 2010 15:16:29 +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/2010/Dec/14/t129233618472jvbkmh92k3uxo.htm/}, year = {2010}, } @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 = {2010}, 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:

KDGP2W92

Dataseries X:

» Textbox « » Textfile « » CSV «

47 19 52 136 80 42 54 66 81 63 137 72 107 58 36 52 79 77 54 84 48 96 83 66 61 53 30 74 69 59 42 65 70 100 63 105 82 81 75 102 121 98 76 77 63 37 35 23 40 29 37 51 20 28 13 22 25 13 16 13 16 17 9 17 25 14 8 7 10 7 10 3

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 3 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Classical Decomposition by Moving Averages t Observations Fit Trend Seasonal Random 1 47 NA NA 1.06203925271391 NA 2 19 NA NA 0.88835534534093 NA 3 52 NA NA 0.72148677507893 NA 4 136 NA NA 1.21255999585695 NA 5 80 NA NA 1.26161693313592 NA 6 42 NA NA 1.09886963250546 NA 7 54 54.8924658341364 73.25 0.749385199100838 0.983741560511545 8 66 79.7739776586576 77.375 1.03100455778556 0.82733745937059 9 81 76.5609779434321 78.3333333333333 0.9773741865119 1.05798021623819 10 63 73.1058890487712 74.1666666666667 0.98569738043287 0.86176368032363 11 137 78.182604907808 70.625 1.10701033497781 1.75230794831598 12 72 65.1689209558494 72.0416666666667 0.904600406558927 1.10482111632289 13 107 78.0598850744723 73.5 1.06203925271391 1.37074247416477 14 58 65.960384391564 74.25 0.88835534534093 0.879315676144208 15 36 53.1194638151862 73.625 0.72148677507893 0.677717684147783 16 52 89.2747296949681 73.625 1.21255999585695 0.582471659983429 17 79 91.7826318856379 72.75 1.26161693313592 0.860729294605921 18 77 77.1955916835083 70.25 1.09886963250546 0.997466284288484 19 54 51.0206423054487 68.0833333333333 0.749385199100838 1.05839514282699 20 84 68.0033422906058 65.9583333333333 1.03100455778556 1.23523340427937 21 48 64.0180092165294 65.5 0.9773741865119 0.74978901386403 22 96 65.2203100053082 66.1666666666667 0.98569738043287 1.47193412592161 23 83 73.8006889985209 66.6666666666667 1.10701033497781 1.12465074684687 24 66 59.2513266296097 65.5 0.904600406558927 1.11389911001618 25 61 68.2360219868686 64.25 1.06203925271391 0.893955981369179 26 53 55.9293719504227 62.9583333333333 0.88835534534093 0.94762372885182 27 30 45.5137907278958 63.0833333333333 0.72148677507893 0.659140878406612 28 74 77.8059330674877 64.1666666666667 1.21255999585695 0.951084282169247 29 69 80.1126752541306 63.5 1.26161693313592 0.86128692845571 30 59 70.6481601231633 64.2916666666667 1.09886963250546 0.835124366963603 31 42 50.0526864232768 66.7916666666667 0.749385199100838 0.839115799795874 32 65 70.9674803942392 68.8333333333333 1.03100455778556 0.915912466370672 33 70 70.2487696555428 71.875 0.9773741865119 0.99645873291785 34 100 73.8451620840958 74.9166666666667 0.98569738043287 1.35418485351984 35 63 86.6235587120139 78.25 1.10701033497781 0.72728482801599 36 105 74.2149250214386 82.0416666666667 0.904600406558927 1.41480975652362 37 82 90.3618397517417 85.0833333333333 1.06203925271391 0.907462710202505 38 81 77.2869150446609 87 0.88835534534093 1.04804286667664 39 75 62.919659176675 87.2083333333333 0.72148677507893 1.19199628512615 40 102 102.208702984109 84.2916666666667 1.21255999585695 0.997958070320672 41 121 101.560163117441 80.5 1.26161693313592 1.19141202894760 42 98 83.4225196010392 75.9166666666667 1.09886963250546 1.17474274894449 43 76 53.0190028363843 70.75 0.749385199100838 1.43344830974160 44 77 68.9054712786681 66.8333333333333 1.03100455778556 1.11747294621346 45 63 61.6560215991257 63.0833333333333 0.9773741865119 1.02179800716972 46 37 58.5257819632017 59.375 0.98569738043287 0.632200010984286 47 35 58.7176731844482 53.0416666666667 1.10701033497781 0.596072666061809 48 23 41.5362353344974 45.9166666666667 0.904600406558927 0.553733380379267 49 40 42.8798348283241 40.375 1.06203925271391 0.932839414147608 50 29 31.4995999535471 35.4583333333333 0.88835534534093 0.920646612743231 51 37 22.7869573129095 31.5833333333333 0.72148677507893 1.62373587188134 52 51 35.1642398798516 29 1.21255999585695 1.45033705191 53 20 34.3264940557397 27.2083333333333 1.26161693313592 0.582640335116188 54 28 28.5706104451419 26 1.09886963250546 0.98002806253519 55 13 18.4223861445623 24.5833333333333 0.749385199100838 0.705663202257716 56 22 23.7990218755500 23.0833333333333 1.03100455778556 0.924407738899631 57 25 20.9320971611299 21.4166666666667 0.9773741865119 1.19433804494392 58 13 18.5639673314857 18.8333333333333 0.98569738043287 0.700281344384351 59 16 19.5110571539840 17.625 1.10701033497781 0.82004782589307 60 13 15.6043570131415 17.25 0.904600406558927 0.833100651891764 61 16 17.4793960342497 16.4583333333333 1.06203925271391 0.915363435249652 62 17 13.8805522709520 15.625 0.88835534534093 1.22473513071782 63 9 10.3713723917596 14.375 0.72148677507893 0.867773295571836 64 17 16.3695599440688 13.5 1.21255999585695 1.03851295075037 65 25 16.4010201307669 13 1.26161693313592 1.52429542800830 66 14 13.5527254675673 12.3333333333333 1.09886963250546 1.03300255240196 67 8 NA NA 0.749385199100838 NA 68 7 NA NA 1.03100455778556 NA 69 10 NA NA 0.9773741865119 NA 70 7 NA NA 0.98569738043287 NA 71 10 NA NA 1.10701033497781 NA 72 3 NA NA 0.904600406558927 NA

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/14/t129233618472jvbkmh92k3uxo/11fsl1292336290.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t129233618472jvbkmh92k3uxo/11fsl1292336290.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t129233618472jvbkmh92k3uxo/21fsl1292336290.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t129233618472jvbkmh92k3uxo/21fsl1292336290.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t129233618472jvbkmh92k3uxo/3cor61292336290.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t129233618472jvbkmh92k3uxo/3cor61292336290.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t129233618472jvbkmh92k3uxo/4cor61292336290.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t129233618472jvbkmh92k3uxo/4cor61292336290.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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