ad hoc 1

*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: Tue, 01 Dec 2009 13:00:13 -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/01/t1259697680tjy2qh4slby6i20.htm/, Retrieved Tue, 01 Dec 2009 21:01:25 +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/01/t1259697680tjy2qh4slby6i20.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 «

105.7 105.7 111.1 82.4 60 107.3 99.3 113.5 108.9 100.2 103.9 138.7 120.2 100.2 143.2 70.9 85.2 133 136.6 117.9 106.3 122.3 125.5 148.4 126.3 99.6 140.4 80.3 92.6 138.5 110.9 119.6 105 109 129.4 148.6 101.4 134.8 143.7 81.6 90.3 141.5 140.7 140.2 100.2 125.7 119.6 134.7 109 116.3 146.9 97.4 89.4 132.1 139.8 129 112.5 121.9 121.7 123.1

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 1 105.7 NA NA 0.97318121800085 NA 2 105.7 NA NA 0.95152026739898 NA 3 111.1 NA NA 1.21299453867965 NA 4 82.4 NA NA 0.694503848470929 NA 5 60 NA NA 0.75073415971995 NA 6 107.3 NA NA 1.144233413222 NA 7 99.3 109.628344220261 103.6625 1.05755064965886 0.905787647403394 8 113.5 111.164689304183 104.0375 1.06850596471641 1.02100766628715 9 108.9 96.1580898841023 105.145833333333 0.914521163946287 1.13251001690295 10 100.2 104.364036104902 106.004166666667 0.984527678360778 0.960100852167917 11 103.9 109.412994093463 106.575 1.02662907899097 0.949612985741408 12 138.7 132.728266738881 108.695833333333 1.22109801883434 1.04499217391927 13 120.2 108.335344172203 111.320833333333 0.97318121800085 1.10951786712320 14 100.2 107.577295565016 113.058333333333 0.95152026739898 0.931423303344173 15 143.2 137.230115475958 113.133333333333 1.21299453867965 1.04350272899893 16 70.9 79.135819767227 113.945833333333 0.694503848470929 0.895928041290882 17 85.2 86.9099912235796 115.766666666667 0.75073415971995 0.980324572589352 18 133 133.956359213744 117.070833333333 1.144233413222 0.992860665821635 19 136.6 124.504556692129 117.729166666667 1.05755064965886 1.0971485994507 20 117.9 126.039182754673 117.958333333333 1.06850596471641 0.935423393132313 21 106.3 107.745835132272 117.816666666667 0.914521163946287 0.986581057815397 22 122.3 116.264514417088 118.091666666667 0.984527678360778 1.05191167410944 23 125.5 121.954979341802 118.791666666667 1.02662907899097 1.02906827320484 24 148.4 145.712609005819 119.329166666667 1.22109801883434 1.01844309159322 25 126.3 115.309809567876 118.4875 0.97318121800085 1.09531010824934 26 99.6 111.791737416038 117.4875 0.95152026739898 0.890942410433558 27 140.4 142.53191243877 117.504166666667 1.21299453867965 0.98504256062876 28 80.3 81.1846061202163 116.895833333333 0.694503848470929 0.989103770252868 29 92.6 87.463657666373 116.504166666667 0.75073415971995 1.05872544632445 30 138.5 133.503433487677 116.675 1.144233413222 1.03742650193925 31 110.9 122.301326172007 115.645833333333 1.05755064965886 0.906776757629172 32 119.6 124.026829854457 116.075 1.06850596471641 0.964307482021011 33 105 107.620088472229 117.679166666667 0.914521163946287 0.975654280632698 34 109 116.047097888117 117.870833333333 0.984527678360778 0.9392738119577 35 129.4 120.966848853273 117.829166666667 1.02662907899097 1.06971456416919 36 148.6 143.916577336450 117.858333333333 1.22109801883434 1.03254262122007 37 101.4 116.027530716151 119.225 0.97318121800085 0.873930517818776 38 134.8 115.443196442181 121.325 0.95152026739898 1.16767383574236 39 143.7 147.965117143272 121.983333333333 1.21299453867965 0.971174846980031 40 81.6 85.0622526075123 122.479166666667 0.694503848470929 0.959297426280402 41 90.3 92.1651303416192 122.766666666667 0.75073415971995 0.979763167103373 42 141.5 139.343791534331 121.779166666667 1.144233413222 1.01547401891341 43 140.7 128.510029777712 121.516666666667 1.05755064965886 1.09485617771137 44 140.2 129.356003353480 121.0625 1.06850596471641 1.08383064075416 45 100.2 110.131211168232 120.425 0.914521163946287 0.90982382684359 46 125.7 119.341163411966 121.216666666667 0.984527678360778 1.05328284395958 47 119.6 125.081920411562 121.8375 1.02662907899097 0.956173359079195 48 134.7 148.251475303312 121.408333333333 1.22109801883434 0.908591295462072 49 109 117.734652769394 120.979166666667 0.97318121800085 0.925810689003323 50 116.3 114.634404214892 120.475 0.95152026739898 1.01452963267455 51 146.9 146.191112630453 120.520833333333 1.21299453867965 1.00484904558691 52 97.4 83.9481526839235 120.875 0.694503848470929 1.16023994437048 53 89.4 90.6918145531688 120.804166666667 0.75073415971995 0.985755996177456 54 132.1 137.775238230372 120.408333333333 1.144233413222 0.958807995520335 55 139.8 NA NA 1.05755064965886 NA 56 129 NA NA 1.06850596471641 NA 57 112.5 NA NA 0.914521163946287 NA 58 121.9 NA NA 0.984527678360778 NA 59 121.7 NA NA 1.02662907899097 NA 60 123.1 NA NA 1.22109801883434 NA

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Dec/01/t1259697680tjy2qh4slby6i20/1uxnu1259697610.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/01/t1259697680tjy2qh4slby6i20/1uxnu1259697610.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/01/t1259697680tjy2qh4slby6i20/2hay61259697610.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/01/t1259697680tjy2qh4slby6i20/2hay61259697610.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/01/t1259697680tjy2qh4slby6i20/34kkb1259697610.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/01/t1259697680tjy2qh4slby6i20/34kkb1259697610.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/01/t1259697680tjy2qh4slby6i20/4qg4j1259697610.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/01/t1259697680tjy2qh4slby6i20/4qg4j1259697610.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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