Review WS 9: classical decomposition

*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: Sun, 06 Dec 2009 13:33:04 -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/06/t12601316376vt410bp1e0ibts.htm/, Retrieved Sun, 06 Dec 2009 21:34:03 +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/06/t12601316376vt410bp1e0ibts.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 «

2360 2214 2825 2355 2333 3016 2155 2172 2150 2533 2058 2160 2260 2498 2695 2799 2947 2930 2318 2540 2570 2669 2450 2842 3440 2678 2981 2260 2844 2546 2456 2295 2379 2479 2057 2280 2351 2276 2548 2311 2201 2725 2408 2139 1898 2537 2069 2063 2524 2437 2189 2793 2074 2622 2278 2144 2427 2139 1828 2072 1800 1758 2246 1987 1868 2514 2121

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 2360 NA NA 1.06659487570272 NA 2 2214 NA NA 1.00250389158119 NA 3 2825 NA NA 1.05149152073521 NA 4 2355 NA NA 1.03364226542565 NA 5 2333 NA NA 1.01674352949734 NA 6 3016 NA NA 1.1033769432302 NA 7 2155 2281.83126877529 2356.75 0.96821099767701 0.944416894224013 8 2172 2210.4009100658 2364.41666666667 0.934860991815796 0.982627174151564 9 2150 2271.09554229259 2370.83333333333 0.957931335940636 0.946679679459742 10 2533 2428.51726492885 2383.91666666667 1.01870895861664 1.04302326221025 11 2058 2127.19226676746 2428 0.876108841337504 0.967472490452117 12 2160 2376.07332867825 2450 0.969825848440101 0.90906285337648 13 2260 2616.57943736455 2453.20833333333 1.06659487570272 0.863723060621578 14 2498 2481.53129962731 2475.33333333333 1.00250389158119 1.00663650721438 15 2695 2637.31598259069 2508.16666666667 1.05149152073521 1.02187224351958 16 2799 2616.49312121413 2531.33333333333 1.03364226542565 1.06975247796607 17 2947 2596.08514531654 2553.33333333333 1.01674352949734 1.13517078024830 18 2930 2866.66524659066 2598.08333333333 1.1033769432302 1.02209352957575 19 2318 2590.60989278445 2675.66666666667 0.96821099767701 0.894769994685907 20 2540 2554.35184997136 2732.33333333333 0.934860991815796 0.994381412266473 21 2570 2635.98755367465 2751.75 0.957931335940636 0.974966667204988 22 2669 2792.49348660126 2741.20833333333 1.01870895861664 0.955776625014958 23 2450 2378.1609452756 2714.45833333333 0.876108841337504 1.03020781872106 24 2842 2612.87247333904 2694.16666666667 0.969825848440101 1.08769181389406 25 3440 2862.65176347980 2683.91666666667 1.06659487570272 1.20168301428965 26 2678 2686.16740649632 2679.45833333333 1.00250389158119 0.99695945737538 27 2981 2798.32562170327 2661.29166666667 1.05149152073521 1.06527988625768 28 2260 2734.41447632811 2645.41666666667 1.03364226542565 0.826502353452586 29 2844 2665.01188375371 2621.125 1.01674352949734 1.06716222067805 30 2546 2848.18368279156 2581.33333333333 1.1033769432302 0.893903021558153 31 2456 2432.67047378839 2512.54166666667 0.96821099767701 1.00959008894258 32 2295 2290.79895536196 2450.41666666667 0.934860991815796 1.00183387748986 33 2379 2314.0028833816 2415.625 0.957931335940636 1.02808860658091 34 2479 2444.60437723367 2399.70833333333 1.01870895861664 1.01407001602658 35 2057 2080.79500271163 2375.04166666667 0.876108841337504 0.98856446565826 36 2280 2284.62683305242 2355.70833333333 0.969825848440101 0.99797479702791 37 2351 2518.40826734675 2361.16666666667 1.06659487570272 0.933526160346066 38 2276 2358.55748892668 2352.66666666667 1.00250389158119 0.964996617926726 39 2548 2445.90071367019 2326.125 1.05149152073521 1.04174302160312 40 2311 2386.16316973511 2308.5 1.03364226542565 0.968500406557085 41 2201 2350.11793980564 2311.41666666667 1.01674352949734 0.936548741967405 42 2725 2540.93917814125 2302.875 1.1033769432302 1.07243810613106 43 2408 2227.8938477797 2301.04166666667 0.96821099767701 1.08084144242321 44 2139 2164.16424351224 2314.95833333333 0.934860991815796 0.988372304187318 45 1898 2209.6681953754 2306.70833333333 0.957931335940636 0.85895249068268 46 2537 2355.08532749524 2311.83333333333 1.01870895861664 1.07724334671909 47 2069 2038.37673297687 2326.625 0.876108841337504 1.01502335977825 48 2063 2247.12690024607 2317.04166666667 0.969825848440101 0.918061191726242 49 2524 2460.98990987142 2307.33333333333 1.06659487570272 1.02560355484427 50 2437 2307.88927140635 2302.125 1.00250389158119 1.05594320758507 51 2189 2444.0606035089 2324.375 1.05149152073521 0.895640638721187 52 2793 2408.21420473086 2329.83333333333 1.03364226542565 1.15978055212582 53 2074 2341.77217000101 2303.20833333333 1.01674352949734 0.885654047207804 54 2622 2530.64099333777 2293.54166666667 1.1033769432302 1.0361011328366 55 2278 2191.78764599133 2263.75 0.96821099767701 1.03933426404987 56 2144 2061.64115474311 2205.29166666667 0.934860991815796 1.03994819615791 57 2427 2087.69160526562 2179.375 0.957931335940636 1.16252802563298 58 2139 2188.35662793498 2148.16666666667 1.01870895861664 0.977445802340931 59 1828 1845.08521985678 2106 0.876108841337504 0.990740145943985 60 2072 2029.76468196443 2092.91666666667 0.969825848440101 1.02080798745335 61 1800 NA 2081.875 NA NA 62 1758 NA NA NA NA 63 2246 NA NA NA NA 64 1987 NA NA NA NA 65 1868 NA NA NA NA 66 2514 NA NA NA NA 67 2121 NA NA NA NA

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Dec/06/t12601316376vt410bp1e0ibts/1fe171260131582.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/06/t12601316376vt410bp1e0ibts/1fe171260131582.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/06/t12601316376vt410bp1e0ibts/2pvyy1260131582.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/06/t12601316376vt410bp1e0ibts/2pvyy1260131582.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/06/t12601316376vt410bp1e0ibts/3n0gs1260131582.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/06/t12601316376vt410bp1e0ibts/3n0gs1260131582.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/06/t12601316376vt410bp1e0ibts/4m0001260131582.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/06/t12601316376vt410bp1e0ibts/4m0001260131582.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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