*Unverified author*

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

Title produced by software: Classical Decomposition

Date of computation: Wed, 02 Jun 2010 12:46:32 +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/Jun/02/t1275482823com4fe58eetshgm.htm/, Retrieved Wed, 02 Jun 2010 14:47:08 +0200

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/Jun/02/t1275482823com4fe58eetshgm.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:

KDGP2W52

Dataseries X:

» Textbox « » Textfile « » CSV «

2 2.4 1.5 1.2 1.5 0.6 2.7 3.7 4.9 6.6 7.4 7.2 5.3 4.7 6.1 6.6 7 7.5 6.6 7.8 4.7 5.4 4.3 4.5 5.8 4.6 5.2 3.6 4.8 6.7 6.3 4.8 8.7 6.8 7.4 9 7.9 9.1 8.7 9.8 6.4 6.1 4.7 4.8 4.2 2.8 6.1 5.8 4.9 4.6 4.1 3.6 5.9 4.5 4.8 5.7 5 7 4.6 2.6 5 4.1 3.2 0 2.3 3.8 4.5 5.9 5 4.2 4.5 6

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 2 NA NA 1.07148892459575 NA 2 2.4 NA NA 0.976721871038515 NA 3 1.5 NA NA 0.963823462607119 NA 4 1.2 NA NA 0.77012676286413 NA 5 1.5 NA NA 0.944886905217081 NA 6 0.6 NA NA 1.04704606135604 NA 7 2.7 3.36703644771791 3.6125 0.932051611825026 0.801892121432185 8 3.7 3.88619676440914 3.84583333333333 1.01049536669360 0.952087664187675 9 4.9 4.22024189020787 4.13333333333333 1.02102626375997 1.16107088822784 10 6.6 4.97161942977982 4.55 1.09266361094062 1.32753524142782 11 7.4 5.63048280264949 5.00416666666667 1.12515892808982 1.31427450529071 12 7.2 5.76656690038057 5.52083333333333 1.04451023101233 1.24857651430782 13 5.3 6.39768178727379 5.97083333333333 1.07148892459575 0.828425072741616 14 4.7 6.15741746200530 6.30416666666667 0.976721871038515 0.763307024251907 15 6.1 6.2327250581927 6.46666666666667 0.963823462607119 0.978705131871935 16 6.6 4.9352290053543 6.40833333333333 0.77012676286413 1.33732396061856 17 7 5.88585801374807 6.22916666666667 0.944886905217081 1.18929134607895 18 7.5 6.26918829236927 5.9875 1.04704606135604 1.19632712405988 19 6.6 5.49522096138505 5.89583333333333 0.932051611825026 1.20104360614036 20 7.8 5.97455385557592 5.9125 1.01049536669360 1.30553681304930 21 4.7 5.99427502349081 5.87083333333333 1.02102626375997 0.78408147467063 22 5.4 6.2372881124527 5.70833333333333 1.09266361094062 0.865760872777215 23 4.3 6.17899778009328 5.49166666666667 1.12515892808982 0.695905736340155 24 4.5 5.60553823976616 5.36666666666667 1.04451023101233 0.802777504589411 25 5.8 5.70121398628655 5.32083333333333 1.07148892459575 1.01732718925321 26 4.6 5.06267503154964 5.18333333333333 0.976721871038515 0.908610560886027 27 5.2 5.0359775921222 5.225 0.963823462607119 1.03257012265789 28 3.6 4.19719085760951 5.45 0.77012676286413 0.857716535209067 29 4.8 5.32679992816129 5.6375 0.944886905217081 0.901103864371506 30 6.7 6.23428675699074 5.95416666666667 1.04704606135604 1.07470192841018 31 6.3 5.80590483199339 6.22916666666667 0.932051611825026 1.08510218171057 32 4.8 6.57243028086963 6.50416666666667 1.01049536669360 0.730323456449794 33 8.7 6.98126707845879 6.8375 1.02102626375997 1.24619211702192 34 6.8 7.91270564922832 7.24166666666667 1.09266361094062 0.859377348462742 35 7.4 8.51370255587967 7.56666666666667 1.12515892808982 0.869187048928491 36 9 7.9469820076188 7.60833333333333 1.04451023101233 1.13250539530248 37 7.9 8.05402508321138 7.51666666666667 1.07148892459575 0.980876011482451 38 9.1 7.27657793923694 7.45 0.976721871038515 1.25058785544380 39 8.7 6.9997678971842 7.2625 0.963823462607119 1.24289835431540 40 9.8 5.32029238678636 6.90833333333333 0.77012676286413 1.84200402675980 41 6.4 6.31893117863923 6.6875 0.944886905217081 1.01282951484498 42 6.1 6.80579939881424 6.5 1.04704606135604 0.896294416356554 43 4.7 5.81755547714121 6.24166666666667 0.932051611825026 0.80789947228997 44 4.8 5.99139544502081 5.92916666666667 1.01049536669360 0.801148921657153 45 4.2 5.66669576386783 5.55 1.02102626375997 0.741172664814684 46 2.8 5.57258441579716 5.1 1.09266361094062 0.502459862620037 47 6.1 5.42420366583302 4.82083333333333 1.12515892808982 1.12458904123085 48 5.8 4.94401509345836 4.73333333333333 1.04451023101233 1.17313557712925 49 4.9 5.00474618529931 4.67083333333333 1.07148892459575 0.9790706298739 50 4.6 4.602801817269 4.7125 0.976721871038515 0.999391280055011 51 4.1 4.61028889613739 4.78333333333333 0.963823462607119 0.889315201794638 52 3.6 3.84421609129678 4.99166666666667 0.77012676286413 0.936471809727429 53 5.9 4.82286024537885 5.10416666666667 0.944886905217081 1.22334044525823 54 4.5 5.13925108448922 4.90833333333333 1.04704606135604 0.875613961260126 55 4.8 4.45442999484710 4.77916666666667 0.932051611825026 1.07757895074177 56 5.7 4.81248418387828 4.7625 1.01049536669360 1.18441947697093 57 5 4.80307771577085 4.70416666666667 1.02102626375997 1.04099918757978 58 7 4.93519730941513 4.51666666666667 1.09266361094062 1.41838300702704 59 4.6 4.74442014677876 4.21666666666667 1.12515892808982 0.969560000524656 60 2.6 4.21721005771228 4.0375 1.04451023101233 0.616521340985901 61 5 4.28149116119718 3.99583333333333 1.07148892459575 1.16781742896368 62 4.1 3.89874813522874 3.99166666666667 0.976721871038515 1.05161961167811 63 3.2 3.85529385042848 4 0.963823462607119 0.830027521674996 64 0 2.99065892912237 3.88333333333333 0.77012676286413 0 65 2.3 3.55513698087927 3.7625 0.944886905217081 0.646951161761187 66 3.8 4.08347963928855 3.9 1.04704606135604 0.930578902228116 67 4.5 NA NA 0.932051611825026 NA 68 5.9 NA NA 1.01049536669360 NA 69 5 NA NA 1.02102626375997 NA 70 4.2 NA NA 1.09266361094062 NA 71 4.5 NA NA 1.12515892808982 NA 72 6 NA NA 1.04451023101233 NA

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Jun/02/t1275482823com4fe58eetshgm/12pcl1275482790.png (open in new window) http://www.freestatistics.org/blog/date/2010/Jun/02/t1275482823com4fe58eetshgm/12pcl1275482790.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Jun/02/t1275482823com4fe58eetshgm/22pcl1275482790.png (open in new window) http://www.freestatistics.org/blog/date/2010/Jun/02/t1275482823com4fe58eetshgm/22pcl1275482790.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Jun/02/t1275482823com4fe58eetshgm/3dhto1275482790.png (open in new window) http://www.freestatistics.org/blog/date/2010/Jun/02/t1275482823com4fe58eetshgm/3dhto1275482790.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Jun/02/t1275482823com4fe58eetshgm/4dhto1275482790.png (open in new window) http://www.freestatistics.org/blog/date/2010/Jun/02/t1275482823com4fe58eetshgm/4dhto1275482790.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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