*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 07:27:44 -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/t1259937000hmvjbkzw8k0zzwp.htm/, Retrieved Fri, 04 Dec 2009 15:30:06 +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/t1259937000hmvjbkzw8k0zzwp.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:

WS9,DT

Dataseries X:

» Textbox « » Textfile « » CSV «

1.3 1.2 1.1 1.4 1.2 1.5 1.1 1.3 1.5 1.1 1.4 1.3 1.5 1.6 1.7 1.1 1.6 1.3 1.7 1.6 1.7 1.9 1.8 1.9 1.6 1.5 1.6 1.6 1.7 2 2 1.9 1.7 1.8 1.9 1.7 2 2.1 2.4 2.5 2.5 2.6 2.2 2.5 2.8 2.8 2.9 3 3.1 2.9 2.7 2.2 2.5 2.3 2.6 2.3 2.2 1.8 1.8

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 1.3 NA NA 0.977823099788985 NA 2 1.2 NA NA 0.978955275625813 NA 3 1.1 NA NA 1.04782014325768 NA 4 1.4 NA NA 0.90585122934769 NA 5 1.2 NA NA 1.01108505242803 NA 6 1.5 NA NA 0.996841862713922 NA 7 1.1 1.30052167440548 1.29166666666667 1.00685548986231 0.845814430968907 8 1.3 1.30149085279753 1.31666666666667 0.988474065415844 0.998854503822041 9 1.5 1.35275837367393 1.35833333333333 0.995895735220075 1.10884547395273 10 1.1 1.42442878886823 1.37083333333333 1.03909698884005 0.77223937665146 11 1.4 1.42011996515285 1.375 1.03281452011116 0.98583220738631 12 1.3 1.40890637672068 1.38333333333333 1.01848653738844 0.922701480722823 13 1.5 1.36895233970458 1.4 0.977823099788985 1.09572843151260 14 1.6 1.40724820871211 1.4375 0.978955275625813 1.13697071354903 15 1.7 1.52807104225079 1.45833333333333 1.04782014325768 1.11251372023644 16 1.1 1.35877684402153 1.5 0.90585122934769 0.809551623461849 17 1.6 1.56718183126344 1.55 1.01108505242803 1.02094088132077 18 1.3 1.58663996481966 1.59166666666667 0.996841862713922 0.819341519704983 19 1.7 1.63194493981849 1.62083333333333 1.00685548986231 1.04170181145271 20 1.6 1.60215171436151 1.62083333333333 0.988474065415844 0.998656984639952 21 1.7 1.60588187304237 1.6125 0.995895735220075 1.05860837496056 22 1.9 1.69286217765191 1.62916666666667 1.03909698884005 1.12235953114352 23 1.8 1.70844735201721 1.65416666666667 1.03281452011116 1.05358821732182 24 1.9 1.71869603184300 1.6875 1.01848653738844 1.10548925743582 25 1.6 1.69081911005179 1.72916666666667 0.977823099788985 0.946286915311122 26 1.5 1.71725071266028 1.75416666666667 0.978955275625813 0.8734892284169 27 1.6 1.85114891975524 1.76666666666667 1.04782014325768 0.864328084534416 28 1.6 1.59656279172530 1.7625 0.90585122934769 1.00215288010751 29 1.7 1.7820374049044 1.7625 1.01108505242803 0.95396426321994 30 2 1.75278027527198 1.75833333333333 0.996841862713922 1.14104433294679 31 2 1.77877803209007 1.76666666666667 1.00685548986231 1.12436738250584 32 1.9 1.78749060162699 1.80833333333333 0.988474065415844 1.06294265170995 33 1.7 1.85900537241081 1.86666666666667 0.995895735220075 0.914467502477089 34 1.8 2.01325041587760 1.9375 1.03909698884005 0.89407655689735 35 1.9 2.07423582788991 2.00833333333333 1.03281452011116 0.915999991154737 36 1.7 2.10487217726945 2.06666666666667 1.01848653738844 0.807649993362223 37 2 2.05342850955687 2.1 0.977823099788985 0.97398082801120 38 2.1 2.08843792133507 2.13333333333333 0.978955275625813 1.00553623286899 39 2.4 2.30957023243048 2.20416666666667 1.04782014325768 1.03915437006406 40 2.5 2.07590906725512 2.29166666666667 0.90585122934769 1.20429167126556 41 2.5 2.40132699951657 2.375 1.01108505242803 1.04109103029421 42 2.6 2.46303010245565 2.47083333333333 0.996841862713922 1.05561032218315 43 2.2 2.58845765518768 2.57083333333333 1.00685548986231 0.849926980876374 44 2.5 2.61945627335199 2.65 0.988474065415844 0.954396538485017 45 2.8 2.68476891953078 2.69583333333333 0.995895735220075 1.04292029739727 46 2.8 2.8012322990813 2.69583333333333 1.03909698884005 0.99956008679405 47 2.9 2.77138562896495 2.68333333333333 1.03281452011116 1.04640796635836 48 3 2.72020779360830 2.67083333333333 1.01848653738844 1.10285692403688 49 3.1 NA 2.675 NA NA 50 2.9 NA 2.68333333333333 NA NA 51 2.7 NA 2.65 NA NA 52 2.2 NA 2.58333333333333 NA NA 53 2.5 NA 2.49583333333333 NA NA 54 2.3 NA NA NA NA 55 2.6 NA NA NA NA 56 2.3 NA NA NA NA 57 2.2 NA NA NA NA 58 1.8 NA NA NA NA 59 1.8 NA NA NA NA

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Dec/04/t1259937000hmvjbkzw8k0zzwp/1i3vb1259936861.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/04/t1259937000hmvjbkzw8k0zzwp/1i3vb1259936861.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/04/t1259937000hmvjbkzw8k0zzwp/2b6xn1259936861.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/04/t1259937000hmvjbkzw8k0zzwp/2b6xn1259936861.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/04/t1259937000hmvjbkzw8k0zzwp/3jd231259936861.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/04/t1259937000hmvjbkzw8k0zzwp/3jd231259936861.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/04/t1259937000hmvjbkzw8k0zzwp/4mknw1259936861.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/04/t1259937000hmvjbkzw8k0zzwp/4mknw1259936861.ps (open in new window)

Parameters (Session):

par1 = 36 ; par2 = 0.0 ; par3 = 1 ; par4 = 0 ; par5 = 12 ; par6 = MA ; par7 = 0.95 ;

Parameters (R input):

par1 = multiplicative ; par2 = 12 ; par3 = 1 ; par4 = 0 ; 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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