ben eysackers, 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: Mon, 01 Jun 2009 06:50:58 -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/01/t12438609077edjx47xt869pcz.htm/, Retrieved Mon, 01 Jun 2009 14:55:11 +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/01/t12438609077edjx47xt869pcz.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 «

-46 -33 -34 -25 -33 -18 -26 -21 -23 -24 -26 -32 -47 -45 -47 -43 -48 -48 -43 -44 -46 -36 -32 -18 -31 -37 -32 -29 -29 -40 -26 -29 -19 -30 -12 -24 -40 -43 -49 -49 -48 -33 -46 -46 -43 -44 -38 -38 -39 -47 -41 -36 -38 -11 -24 -30 -18 -21 -22 -15 -15 -16 -26 -39 -28 -25 -25 -13 -20 -13 -10 -19 -31 -36 -26 -33 -42 -44 -45 -42 -60 -42 -63 -71

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 -46 NA NA 1.05149101308296 NA 2 -33 NA NA 1.14744145995573 NA 3 -34 NA NA 1.13701088537295 NA 4 -25 NA NA 1.20863595304480 NA 5 -33 NA NA 1.18822773919206 NA 6 -18 NA NA 1.01579941181016 NA 7 -26 -28.9615379965479 -28.4583333333333 1.01768215507635 0.897742378291482 8 -21 -27.8361109027388 -29 0.95986589319789 0.754415732620673 9 -23 -26.5320376584586 -30.0416666666667 0.883174623859925 0.866876502139573 10 -24 -27.6368324934234 -31.3333333333333 0.882026568939043 0.868406319925093 11 -26 -24.2321360008155 -32.7083333333333 0.74085511340073 1.07295535148552 12 -32 -26.5527092477475 -34.5833333333333 0.767789183067398 1.20515009227221 13 -47 -38.4232341030733 -36.5416666666667 1.05149101308296 1.22321821931800 14 -45 -43.8418257824751 -38.2083333333333 1.14744145995573 1.02641710733652 15 -47 -45.6225617755896 -40.125 1.13701088537295 1.03019204031518 16 -43 -50.2591117141129 -41.5833333333333 1.20863595304480 0.855566255221449 17 -48 -50.3016409591304 -42.3333333333333 1.18822773919206 0.954243223178335 18 -48 -42.6635752960269 -42 1.01579941181016 1.12508151665550 19 -43 -41.4705478193614 -40.75 1.01768215507635 1.03688044313523 20 -44 -38.1546692546162 -39.75 0.95986589319789 1.15320092821082 21 -46 -34.2598156172329 -38.7916666666667 0.883174623859925 1.34268089805077 22 -36 -33.1494985492924 -37.5833333333333 0.882026568939043 1.08598927813249 23 -32 -26.8251288977181 -36.2083333333333 0.74085511340073 1.19291132288733 24 -18 -26.9366038392812 -35.0833333333333 0.767789183067398 0.668235688039889 25 -31 -35.7945065703658 -34.0416666666667 1.05149101308296 0.86605468185626 26 -37 -37.5308977527186 -32.7083333333333 1.14744145995573 0.985854381735908 27 -32 -35.1999619930043 -30.9583333333333 1.13701088537295 0.909091890677603 28 -29 -35.7554802775753 -29.5833333333333 1.20863595304480 0.811064479483104 29 -29 -33.8644905669737 -28.5 1.18822773919206 0.856354237564768 30 -40 -28.3577335797004 -27.9166666666667 1.01579941181016 1.41054996118003 31 -26 -29.0463448428042 -28.5416666666667 1.01768215507635 0.895121232661434 32 -29 -27.9960885516052 -29.1666666666667 0.95986589319789 1.03585898960651 33 -19 -26.6056355437802 -30.125 0.883174623859925 0.714134415948645 34 -30 -27.9308413497364 -31.6666666666667 0.882026568939043 1.07408150095998 35 -12 -24.6643014836326 -33.2916666666667 0.74085511340073 0.486533138105017 36 -24 -25.9448761444858 -33.7916666666667 0.767789183067398 0.925038141109061 37 -40 -36.1011914491817 -34.3333333333333 1.05149101308296 1.10799667252829 38 -43 -41.1644623759118 -35.875 1.14744145995573 1.04459034609334 39 -49 -42.7326591086001 -37.5833333333333 1.13701088537295 1.14666395731359 40 -49 -47.3382414942546 -39.1666666666667 1.20863595304480 1.03510393401384 41 -48 -48.5192993503424 -40.8333333333333 1.18822773919206 0.98929705586652 42 -33 -43.171475001932 -42.5 1.01579941181016 0.764393618668883 43 -46 -43.8027360914113 -43.0416666666667 1.01768215507635 1.05016270910573 44 -46 -41.4342110563756 -43.1666666666667 0.95986589319789 1.11019369808712 45 -43 -37.9765088259768 -43 0.883174623859925 1.13227890949752 46 -44 -37.1553692165572 -42.125 0.882026568939043 1.18421646528526 47 -38 -30.4985355016634 -41.1666666666667 0.74085511340073 1.24596146585227 48 -38 -30.5836024588514 -39.8333333333333 0.767789183067398 1.24249587834288 49 -39 -39.9566584971526 -38 1.05149101308296 0.976057595075906 50 -47 -41.7859931667211 -36.4166666666667 1.14744145995573 1.12477881792771 51 -41 -39.4637528131528 -34.7083333333333 1.13701088537295 1.03892805618667 52 -36 -39.5324676308403 -32.7083333333333 1.20863595304480 0.910643887352872 53 -38 -36.9340788932198 -31.0833333333333 1.18822773919206 1.02886009719809 54 -11 -29.9237576729077 -29.4583333333333 1.01579941181016 0.367600891580509 55 -24 -27.9862592645997 -27.5 1.01768215507635 0.857563698423891 56 -30 -24.1966193910302 -25.2083333333333 0.95986589319789 1.23984262078864 57 -18 -20.5706089474041 -23.2916666666667 0.883174623859925 0.875034863869284 58 -21 -20.1028555504024 -22.7916666666667 0.882026568939043 1.04462771208539 59 -22 -16.6692400515164 -22.5 0.74085511340073 1.31979621938426 60 -15 -17.4032214828610 -22.6666666666667 0.767789183067398 0.861909389291646 61 -15 -24.490978179724 -23.2916666666667 1.05149101308296 0.612470432578248 62 -16 -25.9608630314983 -22.625 1.14744145995573 0.616312330625803 63 -26 -25.0142394782049 -22 1.13701088537295 1.03940797491181 64 -39 -26.2878319787244 -21.75 1.20863595304480 1.48357612874139 65 -28 -24.8537635447672 -20.9166666666667 1.18822773919206 1.12658994077761 66 -25 -20.9085378930925 -20.5833333333333 1.01579941181016 1.19568379806505 67 -25 -21.7953594878852 -21.4166666666667 1.01768215507635 1.14703315693857 68 -13 -21.9969267191183 -22.9166666666667 0.95986589319789 0.590991649242584 69 -20 -20.9753973166732 -23.75 0.883174623859925 0.953498029050545 70 -13 -20.7276243700675 -23.5 0.882026568939043 0.627182342168123 71 -10 -17.6570468693840 -23.8333333333333 0.74085511340073 0.566346120841941 72 -19 -19.3546856564907 -25.2083333333333 0.767789183067398 0.981674429500656 73 -31 -28.2150088510595 -26.8333333333333 1.05149101308296 1.09870601719963 74 -36 -33.1323721562216 -28.875 1.14744145995573 1.08655063483705 75 -26 -36.1000956105912 -31.75 1.13701088537295 0.72021969915149 76 -33 -41.8490198741762 -34.625 1.20863595304480 0.788548933743688 77 -42 -45.2021635784312 -38.0416666666667 1.18822773919206 0.929159063971018 78 -44 -43.0868250509478 -42.4166666666667 1.01579941181016 1.02119383240637 79 -45 NA NA 1.01768215507635 NA 80 -42 NA NA 0.95986589319789 NA 81 -60 NA NA 0.883174623859925 NA 82 -42 NA NA 0.882026568939043 NA 83 -63 NA NA 0.74085511340073 NA 84 -71 NA NA 0.767789183067398 NA

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/01/t12438609077edjx47xt869pcz/1jdtx1243860655.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/01/t12438609077edjx47xt869pcz/1jdtx1243860655.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/01/t12438609077edjx47xt869pcz/2f12a1243860655.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/01/t12438609077edjx47xt869pcz/2f12a1243860655.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/01/t12438609077edjx47xt869pcz/32pip1243860655.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/01/t12438609077edjx47xt869pcz/32pip1243860655.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/01/t12438609077edjx47xt869pcz/4bfbl1243860655.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/01/t12438609077edjx47xt869pcz/4bfbl1243860655.ps (open in new window)

Parameters (Session):

par1 = multiplicative ; par2 = 12 ;

Parameters (R input):

par1 = multiplicative ; 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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