opgave 9(2) dennis gys

*Unverified author*

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

Title produced by software: Classical Decomposition

Date of computation: Sat, 06 Jun 2009 05:16:53 -0600

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2009/Jun/06/t1244287061ph5zxi1rrvdhd3g.htm/, Retrieved Sat, 06 Jun 2009 13:17:41 +0200

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/Jun/06/t1244287061ph5zxi1rrvdhd3g.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 «

10.738 10.171 9.721 9.897 9.828 9.924 10.371 10.846 10.413 10.709 10.662 10.570 10.297 10.635 10.872 10.296 10.383 10.431 10.574 10.653 10.805 10.872 10.625 10.407 10.463 10.556 10.646 10.702 11.353 11.346 11.451 11.964 12.574 13.031 13.812 14.544 14.931 14.886 16.005 17.064 15.168 16.050 15.839 15.137 14.954 15.648 15.305 15.579 16.348 15.928 16.171 15.937 15.713 15.594 15.683 16.438 17.032 17.696 17.745 19.394 20.148 20.108 18.584 18.441 18.391 19.178 18.079 18.483 19.644 19.195 19.650 20.830

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 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 Classical Decomposition by Moving Averages t Observations Fit Trend Seasonal Random 1 10.738 NA NA 0.456913194444444 NA 2 10.171 NA NA 0.314238194444444 NA 3 9.721 NA NA 0.206671527777778 NA 4 9.897 NA NA 0.091429861111111 NA 5 9.828 NA NA -0.340586805555557 NA 6 9.924 NA NA -0.182786805555556 NA 7 10.371 10.0009048611111 10.3024583333333 -0.301553472222222 0.370095138888889 8 10.846 10.0646381944444 10.3034166666667 -0.238778472222223 0.781361805555555 9 10.413 10.1232631944444 10.3707083333333 -0.247445138888888 0.289736805555554 10 10.709 10.4783881944444 10.4352916666667 0.0430965277777783 0.230611805555553 11 10.662 10.4141798611111 10.4750416666667 -0.0608618055555543 0.247820138888889 12 10.57 10.7789548611111 10.5192916666667 0.259663194444445 -0.208954861111112 13 10.297 11.0057881944444 10.548875 0.456913194444444 -0.708788194444445 14 10.635 10.8635298611111 10.5492916666667 0.314238194444444 -0.228529861111111 15 10.872 10.7642548611111 10.5575833333333 0.206671527777778 0.107745138888889 16 10.296 10.6721381944444 10.5807083333333 0.091429861111111 -0.376138194444444 17 10.383 10.2453715277778 10.5859583333333 -0.340586805555557 0.137628472222223 18 10.431 10.3948381944444 10.577625 -0.182786805555556 0.0361618055555546 19 10.574 10.2761965277778 10.57775 -0.301553472222222 0.297803472222222 20 10.653 10.3425965277778 10.581375 -0.238778472222223 0.310403472222221 21 10.805 10.3212215277778 10.5686666666667 -0.247445138888888 0.483778472222221 22 10.872 10.6192631944444 10.5761666666667 0.0430965277777783 0.252736805555555 23 10.625 10.5726381944444 10.6335 -0.0608618055555543 0.0523618055555541 24 10.407 10.9717048611111 10.7120416666667 0.259663194444445 -0.56470486111111 25 10.463 11.2436215277778 10.7867083333333 0.456913194444444 -0.780621527777779 26 10.556 11.1921131944444 10.877875 0.314238194444444 -0.636113194444446 27 10.646 11.2128798611111 11.0062083333333 0.206671527777778 -0.56687986111111 28 10.702 11.2613048611111 11.169875 0.091429861111111 -0.55930486111111 29 11.353 11.0520381944444 11.392625 -0.340586805555557 0.300961805555554 30 11.346 11.5150048611111 11.6977916666667 -0.182786805555556 -0.169004861111111 31 11.451 11.7547798611111 12.0563333333333 -0.301553472222222 -0.303779861111112 32 11.964 12.1841381944444 12.4229166666667 -0.238778472222223 -0.220138194444443 33 12.574 12.5791798611111 12.826625 -0.247445138888888 -0.00517986111111135 34 13.031 13.3580965277778 13.315 0.0430965277777783 -0.327096527777778 35 13.812 13.6781798611111 13.7390416666667 -0.0608618055555543 0.133820138888888 36 14.544 14.3536631944444 14.094 0.259663194444445 0.190336805555559 37 14.931 14.9297465277778 14.4728333333333 0.456913194444444 0.00125347222222416 38 14.886 15.1021131944444 14.787875 0.314238194444444 -0.216113194444443 39 16.005 15.2259215277778 15.01925 0.206671527777778 0.779078472222226 40 17.064 15.3188881944444 15.2274583333333 0.091429861111111 1.74511180555556 41 15.168 15.0581215277778 15.3987083333333 -0.340586805555557 0.109878472222224 42 16.05 15.3212548611111 15.5040416666667 -0.182786805555556 0.72874513888889 43 15.839 15.3046548611111 15.6062083333333 -0.301553472222222 0.534345138888892 44 15.137 15.4698881944444 15.7086666666667 -0.238778472222223 -0.332888194444443 45 14.954 15.5115548611111 15.759 -0.247445138888888 -0.557554861111113 46 15.648 15.7620548611111 15.7189583333333 0.0430965277777783 -0.114054861111113 47 15.305 15.6338465277778 15.6947083333333 -0.0608618055555543 -0.328846527777779 48 15.579 15.9580798611111 15.6984166666667 0.259663194444445 -0.37907986111111 49 16.348 16.1298298611111 15.6729166666667 0.456913194444444 0.218170138888887 50 15.928 16.0348631944444 15.720625 0.314238194444444 -0.106863194444443 51 16.171 16.0680881944444 15.8614166666667 0.206671527777778 0.102911805555555 52 15.937 16.1247631944444 16.0333333333333 0.091429861111111 -0.187763194444443 53 15.713 15.8797465277778 16.2203333333333 -0.340586805555557 -0.166746527777775 54 15.594 16.2981715277778 16.4809583333333 -0.182786805555556 -0.704171527777778 55 15.683 16.4966965277778 16.79825 -0.301553472222222 -0.813696527777777 56 16.438 16.8919715277778 17.13075 -0.238778472222223 -0.453971527777778 57 17.032 17.1580131944444 17.4054583333333 -0.247445138888888 -0.126013194444440 58 17.696 17.6534298611111 17.6103333333333 0.0430965277777783 0.0425701388888946 59 17.745 17.7653881944444 17.82625 -0.0608618055555543 -0.0203881944444397 60 19.394 18.3468298611111 18.0871666666667 0.259663194444445 1.04717013888889 61 20.148 18.7932465277778 18.3363333333333 0.456913194444444 1.35475347222222 62 20.108 18.8356131944444 18.521375 0.314238194444444 1.27238680555556 63 18.584 18.9220881944444 18.7154166666667 0.206671527777778 -0.338088194444445 64 18.441 18.9781381944444 18.8867083333333 0.091429861111111 -0.537138194444445 65 18.391 18.6879548611111 19.0285416666667 -0.340586805555557 -0.296954861111111 66 19.178 18.9849631944444 19.16775 -0.182786805555556 0.193036805555558 67 18.079 NA NA -0.301553472222222 NA 68 18.483 NA NA -0.238778472222223 NA 69 19.644 NA NA -0.247445138888888 NA 70 19.195 NA NA 0.0430965277777783 NA 71 19.65 NA NA -0.0608618055555543 NA 72 20.83 NA NA 0.259663194444445 NA

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/06/t1244287061ph5zxi1rrvdhd3g/16l741244287009.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/06/t1244287061ph5zxi1rrvdhd3g/16l741244287009.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/06/t1244287061ph5zxi1rrvdhd3g/2mx211244287009.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/06/t1244287061ph5zxi1rrvdhd3g/2mx211244287009.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/06/t1244287061ph5zxi1rrvdhd3g/3vn7c1244287009.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/06/t1244287061ph5zxi1rrvdhd3g/3vn7c1244287009.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/06/t1244287061ph5zxi1rrvdhd3g/4z0n11244287009.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/06/t1244287061ph5zxi1rrvdhd3g/4z0n11244287009.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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