Mauro De Colfmaker Opgave 9 Oefening 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 12:11:25 -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/t1244311916udqrz80ipusux8v.htm/, Retrieved Sat, 06 Jun 2009 20:11:56 +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/t1244311916udqrz80ipusux8v.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 «

0.22 0.22 0.2 0.21 0.21 0.19 0.19 0.18 0.18 0.19 0.18 0.17 0.17 0.17 0.18 0.2 0.2 0.2 0.22 0.23 0.25 0.25 0.27 0.29 0.3 0.31 0.33 0.31 0.33 0.33 0.35 0.37 0.47 0.45 0.45 0.4 0.34 0.35 0.34 0.35 0.36 0.37 0.36 0.35 0.36 0.33 0.3 0.28 0.28 0.28 0.3 0.32 0.32 0.3 0.3 0.31 0.33 0.34 0.31 0.33 0.35 0.38 0.4 0.32 0.29 0.3 0.3 0.32 0.32 0.32 0.32 0.32 0.33 0.31 0.33 0.35 0.37 0.37 0.38 0.42 0.42 0.49 0.45 0.41 0.4 0.42 0.47 0.49 0.47 0.52 0.56 0.57 0.61 0.52 0.5 0.5

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 2 seconds R Server 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 Classical Decomposition by Moving Averages t Observations Fit Trend Seasonal Random 1 0.22 NA NA -0.0151240079365079 NA 2 0.22 NA NA -0.0125049603174603 NA 3 0.2 NA NA 0.00118551587301588 NA 4 0.21 NA NA -0.00476686507936508 NA 5 0.21 NA NA -0.0086359126984127 NA 6 0.19 NA NA -0.00536210317460317 NA 7 0.19 0.187018849206349 0.192916666666667 -0.00589781746031747 0.00298115079365086 8 0.18 0.192018849206349 0.18875 0.00326884920634919 -0.0120188492063492 9 0.18 0.207733134920635 0.185833333333333 0.0218998015873016 -0.0277331349206349 10 0.19 0.208923611111111 0.184583333333333 0.0243402777777778 -0.0189236111111111 11 0.18 0.192018849206349 0.18375 0.0082688492063492 -0.0120188492063492 12 0.17 0.177078373015873 0.18375 -0.00667162698412698 -0.00707837301587297 13 0.17 0.170292658730159 0.185416666666667 -0.0151240079365079 -0.000292658730158712 14 0.17 0.176245039682540 0.18875 -0.0125049603174603 -0.00624503968253964 15 0.18 0.194935515873016 0.19375 0.00118551587301588 -0.0149355158730159 16 0.2 0.194399801587302 0.199166666666667 -0.00476686507936508 0.00560019841269846 17 0.2 0.196780753968254 0.205416666666667 -0.0086359126984127 0.00321924603174609 18 0.2 0.208804563492063 0.214166666666667 -0.00536210317460317 -0.00880456349206343 19 0.22 0.218685515873016 0.224583333333333 -0.00589781746031747 0.00131448412698418 20 0.23 0.239102182539683 0.235833333333333 0.00326884920634919 -0.0091021825396825 21 0.25 0.269816468253968 0.247916666666667 0.0218998015873016 -0.0198164682539682 22 0.25 0.283090277777778 0.25875 0.0243402777777778 -0.0330902777777778 23 0.27 0.277018849206349 0.26875 0.0082688492063492 -0.00701884920634921 24 0.29 0.272911706349206 0.279583333333333 -0.00667162698412698 0.0170882936507936 25 0.3 0.275292658730159 0.290416666666667 -0.0151240079365079 0.0247073412698413 26 0.31 0.289161706349206 0.301666666666667 -0.0125049603174603 0.0208382936507936 27 0.33 0.317852182539683 0.316666666666667 0.00118551587301588 0.0121478174603175 28 0.31 0.329399801587302 0.334166666666667 -0.00476686507936508 -0.0193998015873016 29 0.33 0.341364087301587 0.35 -0.0086359126984127 -0.0113640873015873 30 0.33 0.35672123015873 0.362083333333333 -0.00536210317460317 -0.0267212301587302 31 0.35 0.362435515873016 0.368333333333333 -0.00589781746031747 -0.0124355158730159 32 0.37 0.374935515873016 0.371666666666667 0.00326884920634919 -0.00493551587301588 33 0.47 0.395649801587302 0.37375 0.0218998015873016 0.0743501984126984 34 0.45 0.400173611111111 0.375833333333333 0.0243402777777778 0.0498263888888889 35 0.45 0.387018849206349 0.37875 0.0082688492063492 0.0629811507936508 36 0.4 0.37499503968254 0.381666666666667 -0.00667162698412698 0.0250049603174604 37 0.34 0.368625992063492 0.38375 -0.0151240079365079 -0.0286259920634920 38 0.35 0.370828373015873 0.383333333333333 -0.0125049603174603 -0.0208283730158730 39 0.34 0.379102182539683 0.377916666666667 0.00118551587301588 -0.0391021825396825 40 0.35 0.363566468253968 0.368333333333333 -0.00476686507936508 -0.0135664682539682 41 0.36 0.348447420634921 0.357083333333333 -0.0086359126984127 0.0115525793650794 42 0.37 0.34047123015873 0.345833333333333 -0.00536210317460317 0.0295287698412698 43 0.36 0.332435515873016 0.338333333333333 -0.00589781746031747 0.0275644841269841 44 0.35 0.336185515873016 0.332916666666667 0.00326884920634919 0.0138144841269842 45 0.36 0.350233134920635 0.328333333333333 0.0218998015873016 0.00976686507936503 46 0.33 0.349756944444444 0.325416666666667 0.0243402777777778 -0.0197569444444444 47 0.3 0.330768849206349 0.3225 0.0082688492063492 -0.0307688492063491 48 0.28 0.31124503968254 0.317916666666667 -0.00667162698412698 -0.0312450396825397 49 0.28 0.297375992063492 0.3125 -0.0151240079365079 -0.0173759920634921 50 0.28 0.295828373015873 0.308333333333333 -0.0125049603174603 -0.0158283730158731 51 0.3 0.306602182539683 0.305416666666667 0.00118551587301588 -0.00660218253968264 52 0.32 0.299816468253968 0.304583333333333 -0.00476686507936508 0.0201835317460317 53 0.32 0.296780753968254 0.305416666666667 -0.0086359126984127 0.023219246031746 54 0.3 0.302554563492063 0.307916666666667 -0.00536210317460317 -0.00255456349206346 55 0.3 0.307018849206349 0.312916666666667 -0.00589781746031747 -0.00701884920634921 56 0.31 0.323268849206349 0.32 0.00326884920634919 -0.0132688492063492 57 0.33 0.350233134920635 0.328333333333333 0.0218998015873016 -0.0202331349206349 58 0.34 0.356840277777778 0.3325 0.0243402777777778 -0.0168402777777777 59 0.31 0.339518849206349 0.33125 0.0082688492063492 -0.0295188492063492 60 0.33 0.323328373015873 0.33 -0.00667162698412698 0.00667162698412699 61 0.35 0.314875992063492 0.33 -0.0151240079365079 0.0351240079365079 62 0.38 0.317911706349206 0.330416666666667 -0.0125049603174603 0.0620882936507936 63 0.4 0.331602182539683 0.330416666666667 0.00118551587301588 0.0683978174603175 64 0.32 0.324399801587302 0.329166666666667 -0.00476686507936508 -0.00439980158730158 65 0.29 0.320114087301587 0.32875 -0.0086359126984127 -0.0301140873015874 66 0.3 0.323387896825397 0.32875 -0.00536210317460317 -0.0233878968253968 67 0.3 0.321602182539683 0.3275 -0.00589781746031747 -0.0216021825396825 68 0.32 0.327018849206349 0.32375 0.00326884920634919 -0.00701884920634915 69 0.32 0.339816468253968 0.317916666666667 0.0218998015873016 -0.0198164682539683 70 0.32 0.340590277777778 0.31625 0.0243402777777778 -0.0205902777777778 71 0.32 0.329102182539683 0.320833333333333 0.0082688492063492 -0.00910218253968259 72 0.32 0.320411706349206 0.327083333333333 -0.00667162698412698 -0.000411706349206342 73 0.33 0.318209325396825 0.333333333333333 -0.0151240079365079 0.0117906746031746 74 0.31 0.328328373015873 0.340833333333333 -0.0125049603174603 -0.0183283730158730 75 0.33 0.350352182539683 0.349166666666667 0.00118551587301588 -0.0203521825396826 76 0.35 0.355649801587302 0.360416666666667 -0.00476686507936508 -0.00564980158730161 77 0.37 0.364280753968254 0.372916666666667 -0.0086359126984127 0.00571924603174601 78 0.37 0.37672123015873 0.382083333333333 -0.00536210317460317 -0.00672123015873016 79 0.38 0.382852182539683 0.38875 -0.00589781746031747 -0.0028521825396825 80 0.42 0.399518849206349 0.39625 0.00326884920634919 0.0204811507936508 81 0.42 0.428566468253968 0.406666666666667 0.0218998015873016 -0.00856646825396823 82 0.49 0.442673611111111 0.418333333333333 0.0243402777777778 0.047326388888889 83 0.45 0.436602182539682 0.428333333333333 0.0082688492063492 0.0133978174603175 84 0.41 0.432078373015873 0.43875 -0.00667162698412698 -0.0220783730158730 85 0.4 0.437375992063492 0.4525 -0.0151240079365079 -0.0373759920634920 86 0.42 0.45374503968254 0.46625 -0.0125049603174603 -0.0337450396825397 87 0.47 0.481602182539683 0.480416666666667 0.00118551587301588 -0.0116021825396825 88 0.49 0.484816468253968 0.489583333333333 -0.00476686507936508 0.00518353174603181 89 0.47 0.484280753968254 0.492916666666667 -0.0086359126984127 -0.0142807539682540 90 0.52 0.493387896825397 0.49875 -0.00536210317460317 0.0266121031746032 91 0.56 NA NA -0.00589781746031747 NA 92 0.57 NA NA 0.00326884920634919 NA 93 0.61 NA NA 0.0218998015873016 NA 94 0.52 NA NA 0.0243402777777778 NA 95 0.5 NA NA 0.0082688492063492 NA 96 0.5 NA NA -0.00667162698412698 NA

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/06/t1244311916udqrz80ipusux8v/1pg2w1244311881.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/06/t1244311916udqrz80ipusux8v/1pg2w1244311881.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/06/t1244311916udqrz80ipusux8v/2c3df1244311881.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/06/t1244311916udqrz80ipusux8v/2c3df1244311881.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/06/t1244311916udqrz80ipusux8v/3aa0v1244311881.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/06/t1244311916udqrz80ipusux8v/3aa0v1244311881.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/06/t1244311916udqrz80ipusux8v/4oudx1244311881.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/06/t1244311916udqrz80ipusux8v/4oudx1244311881.ps (open in new window)

Parameters (Session):

par1 = 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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