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: Tue, 01 Dec 2009 07:47:36 -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/t1259679081l5m9z3sr8z4eh3p.htm/, Retrieved Tue, 01 Dec 2009 15:51:26 +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/t1259679081l5m9z3sr8z4eh3p.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 «

1901 1395 1639 1643 1751 1797 1373 1558 1555 2061 2010 2119 1985 1963 2017 1975 1589 1679 1392 1511 1449 1767 1899 2179 2217 2049 2343 2175 1607 1702 1764 1766 1615 1953 2091 2411 2550 2351 2786 2525 2474 2332 1978 1789 1904 1997 2207 2453 1948 1384 1989 2140 2100 2045 2083 2022 1950 1422 1859 2147

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 1901 NA NA 1.09947283558690 NA 2 1395 NA NA 0.975000431538964 NA 3 1639 NA NA 1.14004119014643 NA 4 1643 NA NA 1.10348928263025 NA 5 1751 NA NA 0.970269494375522 NA 6 1797 NA NA 0.973688591268436 NA 7 1373 1448.88983312073 1737 0.834133467542159 0.94762208182711 8 1558 1507.79878892572 1764.16666666667 0.85468046608921 1.03329437020576 9 1555 1511.16256021366 1803.58333333333 0.837866780139716 1.02900908276880 10 2061 1830.39025879509 1833.16666666667 0.99848545802078 1.12598938401077 11 2010 1927.37845382789 1840.25 1.04734598767988 1.04286731856321 12 2119 2131.26144556206 1828.58333333333 1.16552601498176 0.994246859958173 13 1985 2005.94237716014 1824.45833333333 1.09947283558690 0.98955983113045 14 1963 1777.71016182140 1823.29166666667 0.975000431538964 1.10422949823764 15 2017 2071.35983906356 1816.91666666667 1.14004119014643 0.973756448281757 16 1975 1986.5565810551 1800.25 1.10348928263025 0.994182606644427 17 1589 1730.35435953195 1783.375 0.970269494375522 0.918309010664046 18 1679 1734.38280319690 1781.25 0.973688591268436 0.968067716599347 19 1392 1495.94886291457 1793.41666666667 0.834133467542159 0.930513090726883 20 1511 1544.12270873451 1806.66666666667 0.85468046608921 0.978549173231411 21 1449 1528.12936251149 1823.83333333333 0.837866780139716 0.94821815190997 22 1767 1842.95453414186 1845.75 0.99848545802078 0.95878653936668 23 1899 1942.65224948156 1854.83333333333 1.04734598767988 0.97752956068529 24 2179 2163.84761039760 1856.54166666667 1.16552601498176 1.00700252158682 25 2217 2059.31262105426 1873 1.09947283558690 1.07657282208323 26 2049 1851.64769454643 1899.125 0.975000431538964 1.10658199507110 27 2343 2185.07894778066 1916.66666666667 1.14004119014643 1.07227246977952 28 2175 2131.20563451988 1931.33333333333 1.10348928263025 1.02054910364855 29 1607 1889.19556134034 1947.08333333333 0.970269494375522 0.850626601546677 30 1702 1913.05465969466 1964.75 0.973688591268436 0.889676618163986 31 1764 1658.50062240184 1988.29166666667 0.834133467542159 1.06361129816483 32 1766 1721.96746905324 2014.75 0.85468046608921 1.02557105853514 33 1615 1714.10087658666 2045.79166666667 0.837866780139716 0.942184921587576 34 1953 2075.6848529822 2078.83333333333 0.99848545802078 0.940894277468984 35 2091 2230.36692018045 2129.54166666667 1.04734598767988 0.937513904586974 36 2411 2554.73589767210 2191.91666666667 1.16552601498176 0.943737472901572 37 2550 2448.61762758831 2227.08333333333 1.09947283558690 1.04140392165335 38 2351 2181.03534033468 2236.95833333333 0.975000431538964 1.07792842991679 39 2786 2565.04517611322 2249.95833333333 1.14004119014643 1.08614071437977 40 2525 2498.11582099444 2263.83333333333 1.10348928263025 1.01076178245205 41 2474 2202.99688697962 2270.5 0.970269494375522 1.12301565863397 42 2332 2217.17006303417 2277.08333333333 0.973688591268436 1.05179121749853 43 1978 1879.92830247314 2253.75 0.834133467542159 1.05216778607878 44 1789 1870.36136497798 2188.375 0.85468046608921 0.956499654825294 45 1904 1771.98350664798 2114.875 0.837866780139716 1.07450210053126 46 1997 2062.49652422418 2065.625 0.99848545802078 0.968244055951167 47 2207 2130.30173894087 2034 1.04734598767988 1.03600347296213 48 2453 2338.57938547695 2006.45833333333 1.16552601498176 1.04892740235103 49 1948 2197.70876423376 1998.875 1.09947283558690 0.886377681930564 50 1384 1962.63524366995 2012.95833333333 0.975000431538964 0.705174333572061 51 1989 2308.10839288397 2024.58333333333 1.14004119014643 0.861744624356553 52 2140 2209.78326718718 2002.54166666667 1.10348928263025 0.968420764052575 53 2100 1905.69014274472 1964.08333333333 0.970269494375522 1.10196298595291 54 2045 1885.87251985508 1936.83333333333 0.973688591268436 1.08437870453574 55 2083 NA NA 0.834133467542159 NA 56 2022 NA NA 0.85468046608921 NA 57 1950 NA NA 0.837866780139716 NA 58 1422 NA NA 0.99848545802078 NA 59 1859 NA NA 1.04734598767988 NA 60 2147 NA NA 1.16552601498176 NA

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Dec/01/t1259679081l5m9z3sr8z4eh3p/1f2n21259678854.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/01/t1259679081l5m9z3sr8z4eh3p/1f2n21259678854.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/01/t1259679081l5m9z3sr8z4eh3p/2q8l51259678854.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/01/t1259679081l5m9z3sr8z4eh3p/2q8l51259678854.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/01/t1259679081l5m9z3sr8z4eh3p/3pdts1259678854.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/01/t1259679081l5m9z3sr8z4eh3p/3pdts1259678854.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/01/t1259679081l5m9z3sr8z4eh3p/4mlwp1259678854.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/01/t1259679081l5m9z3sr8z4eh3p/4mlwp1259678854.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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