*Unverified author*

R Software Module: /rwasp_decompose.wasp (opens new window with default values)

Title produced by software: Classical Decomposition

Date of computation: Tue, 14 Dec 2010 20:24:27 +0000

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2010/Dec/14/t12923581627dbwxk3vuxxio9f.htm/, Retrieved Tue, 14 Dec 2010 21:22:46 +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/2010/Dec/14/t12923581627dbwxk3vuxxio9f.htm/}, year = {2010}, } @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 = {2010}, 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:

KDGP2W92

Dataseries X:

» Textbox « » Textfile « » CSV «

84.9 81.9 95.9 81 89.2 102.5 89.8 88.8 83.2 90.2 100.4 187.1 87.6 85.4 86.1 86.7 89.1 103.7 86.9 85.2 80.8 91.2 102.8 182.5 80.9 83.1 88.3 86.6 93 105.3 93.8 86.4 87 96.7 100.5 196.7 86.8 88.2 93.8 85 90.4 115.9 94.9 87.7 91.7 95.9 106.8 204.5 90.2 90.5 93.2 97.8 99.4 120 108.2 98.5 104.3 102.9 111.1 188.1 93.8 94.5 112.4 102.5 115.8 136.5 122.1 110.6 116.4 112.6 121.5 199.3

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 3 seconds R Server 'George Udny Yule' @ 72.249.76.132 Classical Decomposition by Moving Averages t Observations Fit Trend Seasonal Random 1 84.9 NA NA -15.8064583333333 NA 2 81.9 NA NA -15.7772916666667 NA 3 95.9 NA NA -9.815625 NA 4 81 NA NA -13.3189583333333 NA 5 89.2 NA NA -7.86145833333333 NA 6 102.5 NA NA 10.6010416666667 NA 7 89.8 91.2710416666667 98.0208333333333 -6.74979166666666 -1.47104166666666 8 88.8 85.9502083333333 98.2791666666667 -12.3289583333333 2.84979166666666 9 83.2 85.5252083333333 98.0166666666667 -12.4914583333333 -2.32520833333334 10 90.2 91.0177083333333 97.8458333333333 -6.828125 -0.817708333333343 11 100.4 99.7902083333334 98.0791666666667 1.71104166666667 0.609791666666652 12 187.1 186.791041666667 98.125 88.6660416666667 0.308958333333351 13 87.6 82.2477083333333 98.0541666666667 -15.8064583333333 5.35229166666664 14 85.4 82.0060416666667 97.7833333333333 -15.7772916666667 3.39395833333334 15 86.1 87.7177083333333 97.5333333333333 -9.815625 -1.61770833333333 16 86.7 84.1560416666667 97.475 -13.3189583333333 2.54395833333334 17 89.1 89.7552083333333 97.6166666666667 -7.86145833333333 -0.655208333333334 18 103.7 108.126041666667 97.525 10.6010416666667 -4.42604166666666 19 86.9 90.304375 97.0541666666667 -6.74979166666666 -3.404375 20 85.2 84.3502083333333 96.6791666666667 -12.3289583333333 0.849791666666675 21 80.8 84.1835416666667 96.675 -12.4914583333333 -3.38354166666666 22 91.2 89.934375 96.7625 -6.828125 1.265625 23 102.8 98.631875 96.9208333333333 1.71104166666667 4.16812500000002 24 182.5 185.816041666667 97.15 88.6660416666667 -3.31604166666664 25 80.9 81.6977083333333 97.5041666666666 -15.8064583333333 -0.797708333333318 26 83.1 82.064375 97.8416666666667 -15.7772916666667 1.03562500000001 27 88.3 88.334375 98.15 -9.815625 -0.0343749999999829 28 86.6 85.3185416666666 98.6375 -13.3189583333333 1.28145833333335 29 93 90.909375 98.7708333333333 -7.86145833333333 2.09062500000000 30 105.3 109.867708333333 99.2666666666667 10.6010416666667 -4.56770833333333 31 93.8 93.354375 100.104166666667 -6.74979166666666 0.445625000000007 32 86.4 88.2335416666667 100.5625 -12.3289583333333 -1.83354166666668 33 87 88.5127083333333 101.004166666667 -12.4914583333333 -1.51270833333332 34 96.7 94.3385416666667 101.166666666667 -6.828125 2.36145833333333 35 100.5 102.702708333333 100.991666666667 1.71104166666667 -2.20270833333335 36 196.7 189.991041666667 101.325 88.6660416666667 6.70895833333333 37 86.8 86.0060416666667 101.8125 -15.8064583333333 0.793958333333322 38 88.2 86.1352083333333 101.9125 -15.7772916666667 2.06479166666668 39 93.8 92.346875 102.1625 -9.815625 1.453125 40 85 89.0060416666666 102.325 -13.3189583333333 -4.00604166666665 41 90.4 94.6927083333333 102.554166666667 -7.86145833333333 -4.29270833333332 42 115.9 113.742708333333 103.141666666667 10.6010416666667 2.15729166666668 43 94.9 96.8585416666666 103.608333333333 -6.74979166666666 -1.95854166666663 44 87.7 91.516875 103.845833333333 -12.3289583333333 -3.81687499999998 45 91.7 91.4252083333333 103.916666666667 -12.4914583333333 0.274791666666673 46 95.9 97.596875 104.425 -6.828125 -1.69687499999999 47 106.8 107.044375 105.333333333333 1.71104166666667 -0.244374999999991 48 204.5 194.545208333333 105.879166666667 88.6660416666667 9.95479166666668 49 90.2 90.7977083333333 106.604166666667 -15.8064583333333 -0.597708333333316 50 90.5 91.8310416666667 107.608333333333 -15.7772916666667 -1.33104166666666 51 93.2 98.7677083333334 108.583333333333 -9.815625 -5.56770833333336 52 97.8 96.0810416666667 109.4 -13.3189583333333 1.71895833333333 53 99.4 102.009375 109.870833333333 -7.86145833333333 -2.60937499999999 54 120 119.967708333333 109.366666666667 10.6010416666667 0.0322916666666799 55 108.2 102.083541666667 108.833333333333 -6.74979166666666 6.11645833333334 56 98.5 96.8210416666666 109.15 -12.3289583333333 1.67895833333336 57 104.3 97.6252083333333 110.116666666667 -12.4914583333333 6.67479166666666 58 102.9 104.284375 111.1125 -6.828125 -1.38437499999998 59 111.1 113.702708333333 111.991666666667 1.71104166666667 -2.60270833333333 60 188.1 202.028541666667 113.3625 88.6660416666667 -13.9285416666667 61 93.8 98.8227083333333 114.629166666667 -15.8064583333333 -5.0227083333333 62 94.5 99.9352083333333 115.7125 -15.7772916666667 -5.43520833333334 63 112.4 106.905208333333 116.720833333333 -9.815625 5.49479166666669 64 102.5 104.310208333333 117.629166666667 -13.3189583333333 -1.81020833333332 65 115.8 110.605208333333 118.466666666667 -7.86145833333333 5.19479166666667 66 136.5 129.967708333333 119.366666666667 10.6010416666667 6.53229166666665 67 122.1 NA NA -6.74979166666666 NA 68 110.6 NA NA -12.3289583333333 NA 69 116.4 NA NA -12.4914583333333 NA 70 112.6 NA NA -6.828125 NA 71 121.5 NA NA 1.71104166666667 NA 72 199.3 NA NA 88.6660416666667 NA

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/14/t12923581627dbwxk3vuxxio9f/1hwwn1292358264.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t12923581627dbwxk3vuxxio9f/1hwwn1292358264.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t12923581627dbwxk3vuxxio9f/2hwwn1292358264.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t12923581627dbwxk3vuxxio9f/2hwwn1292358264.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t12923581627dbwxk3vuxxio9f/3snv81292358264.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t12923581627dbwxk3vuxxio9f/3snv81292358264.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t12923581627dbwxk3vuxxio9f/4snv81292358264.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t12923581627dbwxk3vuxxio9f/4snv81292358264.ps (open in new window)

Parameters (Session):

par1 = additive ; par2 = 12 ;

Parameters (R input):

par1 = additive ; 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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