WS 9, Populair model 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: Fri, 04 Dec 2009 08:53:21 -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/t1259942251nfyk2dd52rld7w4.htm/, Retrieved Fri, 04 Dec 2009 16:57:36 +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/t1259942251nfyk2dd52rld7w4.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 «

95.1 97 112.7 102.9 97.4 111.4 87.4 96.8 114.1 110.3 103.9 101.6 94.6 95.9 104.7 102.8 98.1 113.9 80.9 95.7 113.2 105.9 108.8 102.3 99 100.7 115.5 100.7 109.9 114.6 85.4 100.5 114.8 116.5 112.9 102 106 105.3 118.8 106.1 109.3 117.2 92.5 104.2 112.5 122.4 113.3 100 110.7 112.8 109.8 117.3 109.1 115.9 96 99.8 116.8 115.7 99.4 94.3

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 2 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Classical Decomposition by Moving Averages t Observations Fit Trend Seasonal Random 1 95.1 NA NA 0.965174011311693 NA 2 97 NA NA 0.974505675453896 NA 3 112.7 NA NA 1.05513816877183 NA 4 102.9 NA NA 1.00231085470890 NA 5 97.4 NA NA 1.00104366051728 NA 6 111.4 NA NA 1.08588163191988 NA 7 87.4 84.4747140159778 102.529166666667 0.823909105694911 1.03462913154662 8 96.8 96.5718598066724 102.4625 0.942509306396705 1.00236238790248 9 114.1 110.092737041569 102.083333333333 1.07845946489700 1.03639897659114 10 110.3 109.555392550455 101.745833333333 1.07675556788195 1.00679662983455 11 103.9 105.470068464427 101.770833333333 1.03634867682549 0.985113611024565 12 101.6 97.6205104418725 101.904166666667 0.957963875620452 1.04076489192809 13 94.6 98.1943909758234 101.7375 0.965174011311693 0.963395149762593 14 95.9 98.835177692597 101.420833333333 0.974505675453896 0.970302297611826 15 104.7 106.925064177916 101.3375 1.05513816877183 0.979190434020097 16 102.8 101.350332591982 101.116666666667 1.00231085470890 1.01430352886807 17 98.1 101.243053215567 101.1375 1.00104366051728 0.968955369126665 18 113.9 110.076725929078 101.370833333333 1.08588163191988 1.03473281057964 19 80.9 83.6954333201747 101.583333333333 0.823909105694911 0.966599930136202 20 95.7 96.104532275584 101.966666666667 0.942509306396705 0.995790705536925 21 113.2 110.667915422848 102.616666666667 1.07845946489700 1.02288002414681 22 105.9 110.883391084176 102.979166666667 1.07675556788195 0.95505737121267 23 108.8 107.141180705808 103.383333333333 1.03634867682549 1.01548255566407 24 102.3 99.5364381931134 103.904166666667 0.957963875620452 1.02776432286561 25 99 100.494722369450 104.120833333333 0.965174011311693 0.98512635953205 26 100.7 101.843963965561 104.508333333333 0.974505675453896 0.988767483893814 27 115.5 110.552101633069 104.775 1.05513816877183 1.04475625785346 28 100.7 105.526627819935 105.283333333333 1.00231085470890 0.954261517498964 29 109.9 106.006352633528 105.895833333333 1.00104366051728 1.03673032105852 30 114.6 115.162271571903 106.054166666667 1.08588163191988 0.995117571369265 31 85.4 87.6090015722255 106.333333333333 0.823909105694911 0.974785678040123 32 100.5 100.675702411608 106.816666666667 0.942509306396705 0.998254768455553 33 114.8 115.552438082610 107.145833333333 1.07845946489700 0.993488340920402 34 116.5 115.760196510375 107.508333333333 1.07675556788195 1.00639082786594 35 112.9 111.623388733079 107.708333333333 1.03634867682549 1.01143677218019 36 102 103.260522759588 107.791666666667 0.957963875620452 0.987792791224555 37 106 104.427806465545 108.195833333333 0.965174011311693 1.01505531512791 38 105.3 105.875981197751 108.645833333333 0.974505675453896 0.99455985020176 39 118.8 114.697915354535 108.704166666667 1.05513816877183 1.03576424761327 40 106.1 109.105712830292 108.854166666667 1.00231085470890 0.972451370763994 41 109.3 109.230547423444 109.116666666667 1.00104366051728 1.00063583473849 42 117.2 118.415391960863 109.05 1.08588163191988 0.989736199486087 43 92.5 89.9399777504207 109.1625 0.823909105694911 1.0284636744817 44 104.2 103.365781056949 109.670833333333 0.942509306396705 1.00807055230968 45 112.5 118.208144514919 109.608333333333 1.07845946489700 0.951711072546285 46 122.4 118.120085796650 109.7 1.07675556788195 1.03623358529148 47 113.3 114.162442991301 110.158333333333 1.03634867682549 0.992445475335818 48 100 105.467831189663 110.095833333333 0.957963875620452 0.948156408186392 49 110.7 106.350111371407 110.1875 0.965174011311693 1.04090158978209 50 112.8 107.341800151247 110.15 0.974505675453896 1.05084878249724 51 109.8 116.219072881181 110.145833333333 1.05513816877183 0.944767474717824 52 117.3 110.300133265487 110.045833333333 1.00231085470890 1.06346199707361 53 109.1 109.301454682731 109.1875 1.00104366051728 0.998156889280974 54 115.9 117.677897352518 108.370833333333 1.08588163191988 0.984891832769652 55 96 NA NA 0.823909105694911 NA 56 99.8 NA NA 0.942509306396705 NA 57 116.8 NA NA 1.07845946489700 NA 58 115.7 NA NA 1.07675556788195 NA 59 99.4 NA NA 1.03634867682549 NA 60 94.3 NA NA 0.957963875620452 NA

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Dec/04/t1259942251nfyk2dd52rld7w4/1aq9e1259941999.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/04/t1259942251nfyk2dd52rld7w4/1aq9e1259941999.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/04/t1259942251nfyk2dd52rld7w4/22f3a1259941999.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/04/t1259942251nfyk2dd52rld7w4/22f3a1259941999.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/04/t1259942251nfyk2dd52rld7w4/3duwp1259941999.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/04/t1259942251nfyk2dd52rld7w4/3duwp1259941999.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/04/t1259942251nfyk2dd52rld7w4/4812u1259941999.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/04/t1259942251nfyk2dd52rld7w4/4812u1259941999.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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