classical decomposition (faillissementen) - jonas poels

*Unverified author*

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

Title produced by software: Classical Decomposition

Date of computation: Mon, 20 Dec 2010 13:22:19 +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/20/t12928512397j8vcv44a09ap6v.htm/, Retrieved Mon, 20 Dec 2010 14:20:44 +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/20/t12928512397j8vcv44a09ap6v.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:

Dataseries X:

» Textbox « » Textfile « » CSV «

48 49 59 56 47 56 50 54 79 50 54 56 50 46 47 43 52 48 36 41 34 37 37 34 55 37 27 38 43 26 32 29 41 55 50 30 35 29 22 39 24 38 30 31 39 33 57 49 74 74 115 67 51 114 70 73 77 67 60 73

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 R Framework error message Warning: there are blank lines in the 'Data' field. Please, use NA for missing data - blank lines are simply deleted and are NOT treated as missing values. Classical Decomposition by Moving Averages t Observations Fit Trend Seasonal Random 1 48 NA NA 6.24045138888889 NA 2 49 NA NA -1.16579861111111 NA 3 59 NA NA 4.90711805555555 NA 4 56 NA NA -1.24913194444444 NA 5 47 NA NA -5.73871527777778 NA 6 56 NA NA 8.02170138888888 NA 7 50 48.3446180555555 54.9166666666667 -6.57204861111111 1.65538194444446 8 54 49.5217013888889 54.875 -5.35329861111111 4.47829861111112 9 79 57.5529513888889 54.25 3.30295138888889 21.4470486111111 10 50 51.3133680555556 53.2083333333333 -1.89496527777778 -1.31336805555555 11 54 56.5737847222222 52.875 3.69878472222222 -2.57378472222221 12 56 48.5529513888889 52.75 -4.19704861111111 7.44704861111112 13 50 58.0737847222222 51.8333333333333 6.24045138888889 -8.07378472222222 14 46 49.5425347222222 50.7083333333333 -1.16579861111111 -3.54253472222221 15 47 53.1987847222222 48.2916666666667 4.90711805555555 -6.19878472222221 16 43 44.6258680555556 45.875 -1.24913194444444 -1.62586805555556 17 52 38.8862847222222 44.625 -5.73871527777778 13.1137152777778 18 48 51.0217013888889 43 8.02170138888888 -3.02170138888889 19 36 35.7196180555556 42.2916666666667 -6.57204861111111 0.28038194444445 20 41 36.7717013888889 42.125 -5.35329861111111 4.22829861111111 21 34 44.2196180555556 40.9166666666667 3.30295138888889 -10.2196180555556 22 37 37.9800347222222 39.875 -1.89496527777778 -0.980034722222221 23 37 42.9904513888889 39.2916666666667 3.69878472222222 -5.99045138888889 24 34 33.8029513888889 38 -4.19704861111111 0.197048611111121 25 55 43.1571180555556 36.9166666666667 6.24045138888889 11.8428819444444 26 37 35.0842013888889 36.25 -1.16579861111111 1.91579861111111 27 27 40.9487847222222 36.0416666666667 4.90711805555555 -13.9487847222222 28 38 35.8342013888889 37.0833333333333 -1.24913194444444 2.16579861111111 29 43 32.6362847222222 38.375 -5.73871527777778 10.3637152777778 30 26 46.7717013888889 38.75 8.02170138888888 -20.7717013888889 31 32 31.1779513888889 37.75 -6.57204861111111 0.822048611111114 32 29 31.2300347222222 36.5833333333333 -5.35329861111111 -2.23003472222223 33 41 39.3446180555556 36.0416666666667 3.30295138888889 1.65538194444444 34 55 33.9800347222222 35.875 -1.89496527777778 21.0199652777778 35 50 38.8237847222222 35.125 3.69878472222222 11.1762152777778 36 30 30.6362847222222 34.8333333333333 -4.19704861111111 -0.636284722222221 37 35 41.4904513888889 35.25 6.24045138888889 -6.49045138888889 38 29 34.0842013888889 35.25 -1.16579861111111 -5.08420138888889 39 22 40.1571180555556 35.25 4.90711805555555 -18.1571180555556 40 39 33.0008680555556 34.25 -1.24913194444444 5.99913194444444 41 24 27.8862847222222 33.625 -5.73871527777778 -3.88628472222222 42 38 42.7300347222222 34.7083333333333 8.02170138888888 -4.73003472222222 43 30 30.5529513888889 37.125 -6.57204861111111 -0.552951388888886 44 31 35.2717013888889 40.625 -5.35329861111111 -4.27170138888889 45 39 49.6779513888889 46.375 3.30295138888889 -10.6779513888889 46 33 49.5217013888889 51.4166666666667 -1.89496527777778 -16.5217013888889 47 57 57.4071180555556 53.7083333333333 3.69878472222222 -0.40711805555555 48 49 53.8029513888889 58 -4.19704861111111 -4.80295138888889 49 74 69.0737847222222 62.8333333333333 6.24045138888889 4.92621527777779 50 74 65.0842013888889 66.25 -1.16579861111111 8.91579861111111 51 115 74.4904513888889 69.5833333333333 4.90711805555555 40.5095486111111 52 67 71.3342013888889 72.5833333333333 -1.24913194444444 -4.33420138888889 53 51 68.3862847222222 74.125 -5.73871527777778 -17.3862847222222 54 114 83.2717013888889 75.25 8.02170138888888 30.7282986111111 55 70 NA NA -6.57204861111111 NA 56 73 NA NA -5.35329861111111 NA 57 77 NA NA 3.30295138888889 NA 58 67 NA NA -1.89496527777778 NA 59 60 NA NA 3.69878472222222 NA 60 73 NA NA -4.19704861111111 NA

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/20/t12928512397j8vcv44a09ap6v/1kvk31292851336.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/20/t12928512397j8vcv44a09ap6v/1kvk31292851336.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/20/t12928512397j8vcv44a09ap6v/2kvk31292851336.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/20/t12928512397j8vcv44a09ap6v/2kvk31292851336.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/20/t12928512397j8vcv44a09ap6v/3kvk31292851336.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/20/t12928512397j8vcv44a09ap6v/3kvk31292851336.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/20/t12928512397j8vcv44a09ap6v/4u4j61292851336.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/20/t12928512397j8vcv44a09ap6v/4u4j61292851336.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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