*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 14:19:28 +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/t1292336279lt09zbu0vo9prjw.htm/, Retrieved Tue, 14 Dec 2010 15:18:04 +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/t1292336279lt09zbu0vo9prjw.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 «

47 19 52 136 80 42 54 66 81 63 137 72 107 58 36 52 79 77 54 84 48 96 83 66 61 53 30 74 69 59 42 65 70 100 63 105 82 81 75 102 121 98 76 77 63 37 35 23 40 29 37 51 20 28 13 22 25 13 16 13 16 17 9 17 25 14 8 7 10 7 10 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 'Gwilym Jenkins' @ 72.249.127.135 Classical Decomposition by Moving Averages t Observations Fit Trend Seasonal Random 1 47 NA NA 5.00555555555555 NA 2 19 NA NA -7.71944444444444 NA 3 52 NA NA -16.8361111111111 NA 4 136 NA NA 6.02222222222222 NA 5 80 NA NA 11.1472222222222 NA 6 42 NA NA 5.18055555555556 NA 7 54 60.0972222222222 73.25 -13.1527777777778 -6.09722222222221 8 66 79.4972222222222 77.375 2.12222222222222 -13.4972222222222 9 81 75.4305555555555 78.3333333333333 -2.90277777777778 5.56944444444446 10 63 77.0138888888889 74.1666666666667 2.84722222222222 -14.0138888888889 11 137 79.9222222222222 70.625 9.29722222222223 57.0777777777778 12 72 71.0305555555556 72.0416666666667 -1.01111111111111 0.969444444444449 13 107 78.5055555555556 73.5 5.00555555555555 28.4944444444444 14 58 66.5305555555556 74.25 -7.71944444444444 -8.53055555555555 15 36 56.7888888888889 73.625 -16.8361111111111 -20.7888888888889 16 52 79.6472222222222 73.625 6.02222222222222 -27.6472222222222 17 79 83.8972222222222 72.75 11.1472222222222 -4.89722222222223 18 77 75.4305555555556 70.25 5.18055555555556 1.56944444444444 19 54 54.9305555555556 68.0833333333333 -13.1527777777778 -0.930555555555557 20 84 68.0805555555555 65.9583333333333 2.12222222222222 15.9194444444445 21 48 62.5972222222222 65.5 -2.90277777777778 -14.5972222222222 22 96 69.0138888888889 66.1666666666667 2.84722222222222 26.9861111111111 23 83 75.9638888888889 66.6666666666667 9.29722222222223 7.03611111111111 24 66 64.4888888888889 65.5 -1.01111111111111 1.51111111111111 25 61 69.2555555555556 64.25 5.00555555555555 -8.25555555555555 26 53 55.2388888888889 62.9583333333333 -7.71944444444444 -2.23888888888888 27 30 46.2472222222222 63.0833333333333 -16.8361111111111 -16.2472222222222 28 74 70.1888888888889 64.1666666666667 6.02222222222222 3.81111111111112 29 69 74.6472222222222 63.5 11.1472222222222 -5.64722222222222 30 59 69.4722222222222 64.2916666666667 5.18055555555556 -10.4722222222222 31 42 53.6388888888889 66.7916666666667 -13.1527777777778 -11.6388888888889 32 65 70.9555555555555 68.8333333333333 2.12222222222222 -5.95555555555555 33 70 68.9722222222222 71.875 -2.90277777777778 1.02777777777779 34 100 77.7638888888889 74.9166666666667 2.84722222222222 22.2361111111111 35 63 87.5472222222222 78.25 9.29722222222223 -24.5472222222222 36 105 81.0305555555556 82.0416666666667 -1.01111111111111 23.9694444444444 37 82 90.0888888888889 85.0833333333333 5.00555555555555 -8.08888888888889 38 81 79.2805555555555 87 -7.71944444444444 1.71944444444446 39 75 70.3722222222222 87.2083333333333 -16.8361111111111 4.62777777777777 40 102 90.3138888888889 84.2916666666667 6.02222222222222 11.6861111111111 41 121 91.6472222222222 80.5 11.1472222222222 29.3527777777778 42 98 81.0972222222222 75.9166666666667 5.18055555555556 16.9027777777778 43 76 57.5972222222222 70.75 -13.1527777777778 18.4027777777778 44 77 68.9555555555555 66.8333333333333 2.12222222222222 8.04444444444445 45 63 60.1805555555556 63.0833333333333 -2.90277777777778 2.81944444444446 46 37 62.2222222222222 59.375 2.84722222222222 -25.2222222222222 47 35 62.3388888888889 53.0416666666667 9.29722222222223 -27.3388888888889 48 23 44.9055555555556 45.9166666666667 -1.01111111111111 -21.9055555555556 49 40 45.3805555555556 40.375 5.00555555555555 -5.38055555555555 50 29 27.7388888888889 35.4583333333333 -7.71944444444444 1.26111111111111 51 37 14.7472222222222 31.5833333333333 -16.8361111111111 22.2527777777778 52 51 35.0222222222222 29 6.02222222222222 15.9777777777778 53 20 38.3555555555556 27.2083333333333 11.1472222222222 -18.3555555555556 54 28 31.1805555555556 26 5.18055555555556 -3.18055555555555 55 13 11.4305555555556 24.5833333333333 -13.1527777777778 1.56944444444444 56 22 25.2055555555556 23.0833333333333 2.12222222222222 -3.20555555555555 57 25 18.5138888888889 21.4166666666667 -2.90277777777778 6.48611111111111 58 13 21.6805555555556 18.8333333333333 2.84722222222222 -8.68055555555555 59 16 26.9222222222222 17.625 9.29722222222223 -10.9222222222222 60 13 16.2388888888889 17.25 -1.01111111111111 -3.23888888888889 61 16 21.4638888888889 16.4583333333333 5.00555555555555 -5.46388888888889 62 17 7.90555555555556 15.625 -7.71944444444444 9.09444444444444 63 9 -2.46111111111111 14.375 -16.8361111111111 11.4611111111111 64 17 19.5222222222222 13.5 6.02222222222222 -2.52222222222222 65 25 24.1472222222222 13 11.1472222222222 0.852777777777778 66 14 17.5138888888889 12.3333333333333 5.18055555555556 -3.51388888888889 67 8 NA NA -13.1527777777778 NA 68 7 NA NA 2.12222222222222 NA 69 10 NA NA -2.90277777777778 NA 70 7 NA NA 2.84722222222222 NA 71 10 NA NA 9.29722222222223 NA 72 3 NA NA -1.01111111111111 NA

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/14/t1292336279lt09zbu0vo9prjw/1jyil1292336365.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292336279lt09zbu0vo9prjw/1jyil1292336365.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292336279lt09zbu0vo9prjw/2jyil1292336365.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292336279lt09zbu0vo9prjw/2jyil1292336365.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292336279lt09zbu0vo9prjw/3c7zo1292336365.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292336279lt09zbu0vo9prjw/3c7zo1292336365.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292336279lt09zbu0vo9prjw/4c7zo1292336365.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292336279lt09zbu0vo9prjw/4c7zo1292336365.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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