Jurgen Leemans - opgave 9 - oef 2

*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 06:33:44 -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/t12442916589eeq5noh9s0unrd.htm/, Retrieved Sat, 06 Jun 2009 14:34:18 +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/t12442916589eeq5noh9s0unrd.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 «

163.40 162.89 162.29 161.26 161.43 161.44 161.44 161.44 161.92 162.23 161.89 161.40 161.40 159.55 158.93 158.59 158.29 158.03 158.03 163.94 164.36 164.39 163.22 163.22 163.56 162.82 162.80 162.44 161.98 161.53 161.53 161.52 162.07 161.84 161.54 161.47 161.47 161.54 161.57 160.75 160.31 160.57 160.57 159.65 158.76 158.95 159.25 158.72 158.72 158.72 158.53 157.92 157.89 157.81 157.81 157.88 157.52 156.11 155.61 155.31 155.31 155.31 153.09 151.94 151.73 151.65 151.65 151.09 149.94 149.47 149.15 149.22

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 1 163.4 NA NA 0.491638888888901 NA 2 162.89 NA NA 0.155472222222233 NA 3 162.29 NA NA -0.262444444444434 NA 4 161.26 NA NA -0.712277777777785 NA 5 161.43 NA NA -0.787777777777778 NA 6 161.44 NA NA -0.702111111111116 NA 7 161.44 161.202638888889 161.835833333333 -0.633194444444449 0.237361111111142 8 161.44 162.120722222222 161.613333333333 0.507388888888876 -0.680722222222187 9 161.92 162.021388888889 161.334166666667 0.68722222222221 -0.101388888888863 10 162.23 161.702472222222 161.082916666667 0.619555555555548 0.527527777777806 11 161.89 161.216888888889 160.840833333333 0.376055555555559 0.673111111111126 12 161.4 160.828388888889 160.567916666667 0.260472222222234 0.571611111111167 13 161.4 160.775388888889 160.28375 0.491638888888901 0.624611111111136 14 159.55 160.401305555556 160.245833333333 0.155472222222233 -0.85130555555557 15 158.93 160.189222222222 160.451666666667 -0.262444444444434 -1.25922222222223 16 158.59 159.931055555556 160.643333333333 -0.712277777777785 -1.34105555555556 17 158.29 160.000972222222 160.78875 -0.787777777777778 -1.71097222222221 18 158.03 160.217888888889 160.92 -0.702111111111116 -2.18788888888886 19 158.03 160.452638888889 161.085833333333 -0.633194444444449 -2.42263888888888 20 163.94 161.819472222222 161.312083333333 0.507388888888876 2.12052777777777 21 164.36 162.296805555556 161.609583333333 0.68722222222221 2.06319444444446 22 164.39 162.550805555556 161.93125 0.619555555555548 1.83919444444447 23 163.22 162.621472222222 162.245416666667 0.376055555555559 0.598527777777832 24 163.22 162.805472222222 162.545 0.260472222222234 0.414527777777778 25 163.56 163.328305555556 162.836666666667 0.491638888888901 0.231694444444486 26 162.82 163.037138888889 162.881666666667 0.155472222222233 -0.217138888888883 27 162.8 162.422972222222 162.685416666667 -0.262444444444434 0.37702777777784 28 162.44 161.771472222222 162.48375 -0.712277777777785 0.668527777777825 29 161.98 161.519722222222 162.3075 -0.787777777777778 0.460277777777804 30 161.53 161.462472222222 162.164583333333 -0.702111111111116 0.0675277777777978 31 161.53 161.371388888889 162.004583333333 -0.633194444444449 0.158611111111128 32 161.52 162.371555555556 161.864166666667 0.507388888888876 -0.851555555555535 33 162.07 162.446805555556 161.759583333333 0.68722222222221 -0.376805555555535 34 161.84 162.257472222222 161.637916666667 0.619555555555548 -0.417472222222216 35 161.54 161.873972222222 161.497916666667 0.376055555555559 -0.333972222222229 36 161.47 161.648805555556 161.388333333333 0.260472222222234 -0.178805555555527 37 161.47 161.799972222222 161.308333333333 0.491638888888901 -0.329972222222239 38 161.54 161.345888888889 161.190416666667 0.155472222222233 0.194111111111084 39 161.57 160.712138888889 160.974583333333 -0.262444444444434 0.857861111111106 40 160.75 160.003972222222 160.71625 -0.712277777777785 0.746027777777783 41 160.31 159.712638888889 160.500416666667 -0.787777777777778 0.597361111111127 42 160.57 159.588305555556 160.290416666667 -0.702111111111116 0.981694444444457 43 160.57 159.428055555556 160.06125 -0.633194444444449 1.14194444444448 44 159.65 160.336555555555 159.829166666667 0.507388888888876 -0.686555555555486 45 158.76 160.272222222222 159.585 0.68722222222221 -1.51222222222222 46 158.95 159.959972222222 159.340416666667 0.619555555555548 -1.00997222222219 47 159.25 159.497722222222 159.121666666667 0.376055555555559 -0.247722222222222 48 158.72 159.166305555556 158.905833333333 0.260472222222234 -0.446305555555568 49 158.72 159.167472222222 158.675833333333 0.491638888888901 -0.447472222222217 50 158.72 158.642555555556 158.487083333333 0.155472222222233 0.0774444444444669 51 158.53 158.099222222222 158.361666666667 -0.262444444444434 0.430777777777791 52 157.92 157.479388888889 158.191666666667 -0.712277777777785 0.44061111111111 53 157.89 157.133888888889 157.921666666667 -0.787777777777778 0.756111111111153 54 157.81 156.925805555556 157.627916666667 -0.702111111111116 0.88419444444446 55 157.81 156.710555555556 157.34375 -0.633194444444449 1.09944444444446 56 157.88 157.566972222222 157.059583333333 0.507388888888876 0.313027777777762 57 157.52 157.378055555556 156.690833333333 0.68722222222221 0.141944444444476 58 156.11 156.834555555556 156.215 0.619555555555548 -0.724555555555526 59 155.61 156.085222222222 155.709166666667 0.376055555555559 -0.475222222222186 60 155.31 155.456305555556 155.195833333333 0.260472222222234 -0.146305555555557 61 155.31 155.174138888889 154.6825 0.491638888888901 0.135861111111126 62 155.31 154.298388888889 154.142916666667 0.155472222222233 1.01161111111114 63 153.09 153.281722222222 153.544166666667 -0.262444444444434 -0.191722222222182 64 151.94 152.239388888889 152.951666666667 -0.712277777777785 -0.299388888888870 65 151.73 151.618055555556 152.405833333333 -0.787777777777778 0.111944444444475 66 151.65 151.180805555556 151.882916666667 -0.702111111111116 0.469194444444469 67 151.65 NA NA -0.633194444444449 NA 68 151.09 NA NA 0.507388888888876 NA 69 149.94 NA NA 0.68722222222221 NA 70 149.47 NA NA 0.619555555555548 NA 71 149.15 NA NA 0.376055555555559 NA 72 149.22 NA NA 0.260472222222234 NA

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/06/t12442916589eeq5noh9s0unrd/1aeah1244291620.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/06/t12442916589eeq5noh9s0unrd/1aeah1244291620.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/06/t12442916589eeq5noh9s0unrd/24lgk1244291620.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/06/t12442916589eeq5noh9s0unrd/24lgk1244291620.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/06/t12442916589eeq5noh9s0unrd/3y3th1244291620.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/06/t12442916589eeq5noh9s0unrd/3y3th1244291620.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/06/t12442916589eeq5noh9s0unrd/4iyau1244291620.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/06/t12442916589eeq5noh9s0unrd/4iyau1244291620.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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