Classical Decomposition inschrijving eigen reeks

*Unverified author*

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

Title produced by software: Classical Decomposition

Date of computation: Tue, 12 Jan 2010 12:43:41 -0700

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2010/Jan/12/t1263325489wwm92qz30m4hrid.htm/, Retrieved Tue, 12 Jan 2010 20:44:55 +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/Jan/12/t1263325489wwm92qz30m4hrid.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:

KDGP2W52

Dataseries X:

» Textbox « » Textfile « » CSV «

76,3 71 85,9 109 83,4 101,3 81,6 84,8 88 88,4 85,2 139,5 85,3 79,4 131 91,4 88,2 113,2 81,9 89 95,7 93,2 98 140,5 93 82,1 86,3 84,8 85,7 110,8 87,4 100,4 100,4 99,4 108 161,7 109,3 99,9 110,3 97,5 102,4 122,3 104,4 122,7 127,3 128,4 125,3 187,1 131,4 125,6 142,9 116,6 129,7 155,4 138,7 167,7 155,8 157,1 159,9 244,6 154,3 143,2 147,3 155,1 152,2 166,5 161,2 180,1 169,2 164,5 166,2 267,3

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 'Gwilym Jenkins' @ 72.249.127.135 Classical Decomposition by Moving Averages t Observations Fit Trend Seasonal Random 1 76.3 NA NA 0.962459102149803 NA 2 71 NA NA 0.877076078060531 NA 3 85.9 NA NA 1.03044273310302 NA 4 109 NA NA 0.885313990310911 NA 5 83.4 NA NA 0.894567966560115 NA 6 101.3 NA NA 1.06969376643926 NA 7 81.6 80.1677347766401 91.575 0.87543253919345 1.01786585622446 8 84.8 90.50832646733 92.3 0.980588585778223 0.936930372153213 9 88 92.9719141309046 94.5291666666667 0.983526221687182 0.94652240757457 10 88.4 93.2053810556108 95.675 0.974187416311584 0.948443094152004 11 85.2 93.797137846799 95.1416666666667 0.985868138882008 0.908343281637859 12 139.5 141.920335243798 95.8375 1.48084346152391 0.982945817879868 13 85.3 92.7289242458746 96.3458333333333 0.962459102149803 0.919885577167093 14 79.4 84.66707740211 96.5333333333333 0.877076078060531 0.937790726174532 15 131 99.9829996907086 97.0291666666667 1.03044273310302 1.31022274191853 16 91.4 86.3623797548294 97.55 0.885313990310911 1.05833118841180 17 88.2 87.92112164675 98.2833333333333 0.894567966560115 1.00317191532622 18 113.2 105.748142927241 98.8583333333333 1.06969376643926 1.07046797103459 19 81.9 86.86114606589 99.2208333333333 0.87543253919345 0.942884174448646 20 89 97.719738358574 99.6541666666667 0.980588585778223 0.910767890857651 21 95.7 96.2913151290988 97.9041666666667 0.983526221687182 0.993859102159878 22 93.2 93.2946815687726 95.7666666666667 0.974187416311584 0.998985134337986 23 98 94.0394970976075 95.3875 0.985868138882008 1.04211531350792 24 140.5 140.951616812718 95.1833333333333 1.48084346152391 0.996795944431643 25 93 91.7343831736531 95.3125 0.962459102149803 1.01379653716046 26 82.1 84.2139214284453 96.0166666666667 0.877076078060531 0.974898195065747 27 86.3 99.6309317568984 96.6875 1.03044273310302 0.866196857523864 28 84.8 86.000876542119 97.1416666666667 0.885313990310911 0.986036461598959 29 85.7 87.5036565956885 97.8166666666666 0.894567966560115 0.979387643147048 30 110.8 106.024480483571 99.1166666666667 1.06969376643926 1.0450416686283 31 87.4 88.1378185188805 100.679166666667 0.87543253919345 0.991628808934925 32 100.4 100.118094607957 102.1 0.980588585778223 1.00281572869667 33 100.4 102.131002070366 103.841666666667 0.983526221687182 0.983051159439581 34 99.4 102.650939879598 105.370833333333 0.974187416311584 0.96833014989038 35 108 105.08943582091 106.595833333333 0.985868138882008 1.02769606817616 36 161.7 159.59173388465 107.770833333333 1.48084346152391 1.01321037164039 37 109.3 104.867939671739 108.958333333333 0.962459102149803 1.04226325359432 38 99.9 97.0009597498362 110.595833333333 0.877076078060531 1.02988671717930 39 110.3 116.075080372668 112.645833333333 1.03044273310302 0.950247026716448 40 97.5 101.788976035997 114.975 0.885313990310911 0.957864041834155 41 102.4 104.578722657405 116.904166666667 0.894567966560115 0.9791666736594 42 122.3 126.954821846899 118.683333333333 1.06969376643926 0.963334816439561 43 104.4 105.631878760430 120.6625 0.87543253919345 0.988338001984955 44 122.7 120.273275831473 122.654166666667 0.980588585778223 1.02017675291332 45 127.3 123.022738229372 125.083333333333 0.983526221687182 1.03476805858973 46 128.4 123.953171382945 127.2375 0.974187416311584 1.03587506933015 47 125.3 127.345409056171 129.170833333333 0.985868138882008 0.983938101331403 48 187.1 195.00857333943 131.6875 1.48084346152391 0.959444996678866 49 131.4 129.446738992890 134.495833333333 0.962459102149803 1.01508930253714 50 125.6 120.861083556741 137.8 0.877076078060531 1.03920961407759 51 142.9 145.150739491724 140.8625 1.03044273310302 0.984493778677216 52 116.6 126.817540303745 143.245833333333 0.885313990310911 0.9194311742739 53 129.7 130.502556855011 145.883333333333 0.894567966560115 0.99385025953244 54 155.4 160.155442122758 149.720833333333 1.06969376643926 0.970307333552157 55 138.7 134.003188301457 153.070833333333 0.87543253919345 1.03504999961625 56 167.7 151.754255220728 154.758333333333 0.980588585778223 1.10507609658839 57 155.8 153.110444561152 155.675 0.983526221687182 1.01756611344547 58 157.1 153.397986040963 157.4625 0.974187416311584 1.02413339349872 59 159.9 157.743010005033 160.004166666667 0.985868138882008 1.0136740765559 60 244.6 239.014304871049 161.404166666667 1.48084346152391 1.02336971057847 61 154.3 156.692352076247 162.804166666667 0.962459102149803 0.984732170750218 62 143.2 144.067054788759 164.258333333333 0.877076078060531 0.99398158871207 63 147.3 170.366531873033 165.333333333333 1.03044273310302 0.864606436373175 64 155.1 147.139185189673 166.2 0.885313990310911 1.05410397509042 65 152.2 149.187845256536 166.770833333333 0.894567966560115 1.02019034954412 66 166.5 179.686267474995 167.979166666667 1.06969376643926 0.926615051554623 67 161.2 NA NA 0.87543253919345 NA 68 180.1 NA NA 0.980588585778223 NA 69 169.2 NA NA 0.983526221687182 NA 70 164.5 NA NA 0.974187416311584 NA 71 166.2 NA NA 0.985868138882008 NA 72 267.3 NA NA 1.48084346152391 NA

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Jan/12/t1263325489wwm92qz30m4hrid/1dot81263325419.png (open in new window) http://www.freestatistics.org/blog/date/2010/Jan/12/t1263325489wwm92qz30m4hrid/1dot81263325419.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Jan/12/t1263325489wwm92qz30m4hrid/2t57i1263325419.png (open in new window) http://www.freestatistics.org/blog/date/2010/Jan/12/t1263325489wwm92qz30m4hrid/2t57i1263325419.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Jan/12/t1263325489wwm92qz30m4hrid/3j30k1263325419.png (open in new window) http://www.freestatistics.org/blog/date/2010/Jan/12/t1263325489wwm92qz30m4hrid/3j30k1263325419.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Jan/12/t1263325489wwm92qz30m4hrid/4miv21263325419.png (open in new window) http://www.freestatistics.org/blog/date/2010/Jan/12/t1263325489wwm92qz30m4hrid/4miv21263325419.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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