*Unverified author*

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

Title produced by software: Classical Decomposition

Date of computation: Mon, 31 May 2010 18:37:21 +0000

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2010/May/31/t1275331075cg0eslmxcbfvqul.htm/, Retrieved Mon, 31 May 2010 20:38:00 +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/2010/May/31/t1275331075cg0eslmxcbfvqul.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 «

127.87 127.94 122.44 120.25 118.13 114.93 112.57 110.81 109.02 106.39 103.75 102.60 101.63 100.00 97.98 96.56 94.32 91.79 89.61 86.83 83.94 81.41 80.47 79.24 78.23 74.60 70.14 65.15 59.92 55.67 52.20 49.97 47.83 44.66 40.91 36.28 32.20 30.10 28.55 27.36 26.33 25.38 24.69 24.01 23.05 22.15 21.26 20.81 20.52 20.32 20.26 20.02 19.76 19.15 18.63 18.73 18.48 18.53 18.37 16.80 16.94 17.21 15.26 14.99 15.80 4.71 4.65 4.50

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 1 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Classical Decomposition by Moving Averages t Observations Fit Trend Seasonal Random 1 127.87 NA NA 0.993528169696577 NA 2 127.94 NA NA 0.995624810269576 NA 3 122.44 NA NA 0.998193115366954 NA 4 120.25 NA NA 0.999299974571145 NA 5 118.13 NA NA 0.996161766663678 NA 6 114.93 NA NA 0.990811501964716 NA 7 112.57 112.717790793206 113.631666666667 0.991957559892685 0.998688842354294 8 110.81 111.810171147522 111.374166666667 1.00391477210474 0.99105473914173 9 109.02 110.102972624731 109.190833333333 1.00835362514922 0.990164001943684 10 106.39 108.571857759772 107.184583333333 1.01294285412413 0.979904021126727 11 103.75 106.642183284439 105.205416666667 1.01365677417851 0.9728795567067 12 102.6 102.790231969635 103.249166666667 0.99555507601806 0.998149318607523 13 101.63 100.672553555071 101.328333333333 0.993528169696577 1.00951050123513 14 100 98.9377264585135 99.3725 0.995624810269576 1.01073678948881 15 97.98 97.1524722634733 97.3283333333333 0.998193115366954 1.00851782478867 16 96.56 95.1758278280923 95.2425 0.999299974571145 1.01454331633876 17 94.32 92.8738217756658 93.2316666666667 0.996161766663678 1.01557143010468 18 91.79 90.4495306618557 91.2883333333333 0.990811501964716 1.01482008063873 19 89.61 88.6214884008125 89.34 0.991957559892685 1.01115431050669 20 86.83 87.6484523698915 87.3066666666667 1.00391477210474 0.99066210129487 21 83.94 85.7991293745722 85.0883333333333 1.00835362514922 0.978331605598749 22 81.41 83.6889165482131 82.6195833333333 1.01294285412413 0.972769195226703 23 80.47 80.968368979444 79.8775 1.01365677417851 0.993844892941212 24 79.24 76.5971779195995 76.9391666666667 0.99555507601806 1.03450286488589 25 78.23 73.3973075064053 73.8754166666667 0.993528169696577 1.06584291246887 26 74.6 70.4711537582225 70.7808333333333 0.995624810269576 1.05858916764642 27 70.14 67.6180175487555 67.7404166666667 0.998193115366954 1.03729749174362 28 65.15 64.6592884796366 64.7045833333333 0.999299974571145 1.00758918837342 29 59.92 61.2888526939828 61.525 0.996161766663678 0.97766555199169 30 55.67 57.5529374441238 58.0866666666667 0.990811501964716 0.967283382434617 31 52.2 53.9414121600144 54.37875 0.991957559892685 0.967716600469255 32 49.97 50.8047802336474 50.6066666666667 1.00391477210474 0.983568864390156 33 47.83 47.4123673071727 47.0195833333333 1.00835362514922 1.00880851804175 34 44.66 44.2778424513785 43.7120833333333 1.01294285412413 1.00863089815275 35 40.91 41.2942651950863 40.7379166666667 1.01365677417851 0.990694465847233 36 36.28 37.9070039632326 38.07625 0.99555507601806 0.957079067372068 37 32.2 35.4370799627233 35.6679166666667 0.993528169696577 0.908652745482177 38 30.1 33.2936936554146 33.44 0.995624810269576 0.904075117394034 39 28.55 31.269231166466 31.3258333333333 0.998193115366954 0.913038118782334 40 27.36 29.3348671285254 29.3554166666667 0.999299974571145 0.932678504392985 41 26.33 27.4928195577092 27.59875 0.996161766663678 0.957704608824557 42 25.38 25.8952714419737 26.1354166666667 0.990811501964716 0.980101716905022 43 24.69 24.8030721538167 25.0041666666667 0.991957559892685 0.99544120368979 44 24.01 24.2043851554454 24.11 1.00391477210474 0.99196901081366 45 23.05 23.5521996520792 23.3570833333333 1.00835362514922 0.978677165636422 46 22.15 22.9997116219335 22.7058333333333 1.01294285412413 0.963055553221669 47 21.26 22.4284231996673 22.12625 1.01365677417851 0.947904353807423 48 20.81 21.4969377935350 21.5929166666667 0.99555507601806 0.96804485363764 49 20.52 20.9444017573452 21.0808333333333 0.993528169696577 0.979736744822687 50 20.32 20.5181679649722 20.6083333333333 0.995624810269576 0.990341829479586 51 20.26 20.1614213614221 20.1979166666667 0.998193115366954 1.00488946869423 52 20.02 19.8427664950677 19.8566666666667 0.999299974571145 1.00893189490368 53 19.76 19.5102432675109 19.5854166666667 0.996161766663678 1.01280131308793 54 19.15 19.1205977972899 19.2979166666667 0.990811501964716 1.00153772403048 55 18.63 18.829007749363 18.9816666666667 0.991957559892685 0.989430789343123 56 18.73 18.7761343231107 18.7029166666667 1.00391477210474 0.99754292751017 57 18.48 18.5184143258655 18.365 1.00835362514922 0.997925614731935 58 18.53 18.1793698148703 17.9470833333333 1.01294285412413 1.01928725740773 59 18.37 17.8124836642519 17.5725 1.01365677417851 1.03129919141299 60 16.8 16.7311326817135 16.8058333333333 0.99555507601806 1.00411611811326 61 16.94 NA 15.6216666666667 NA NA 62 17.21 NA 14.44625 NA NA 63 15.26 NA NA NA NA 64 14.99 NA NA NA NA 65 15.8 NA NA NA NA 66 4.71 NA NA NA NA 67 4.65 NA NA NA NA 68 4.5 NA NA NA NA

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/May/31/t1275331075cg0eslmxcbfvqul/1u3ov1275331039.png (open in new window) http://www.freestatistics.org/blog/date/2010/May/31/t1275331075cg0eslmxcbfvqul/1u3ov1275331039.ps (open in new window) http://www.freestatistics.org/blog/date/2010/May/31/t1275331075cg0eslmxcbfvqul/2u3ov1275331039.png (open in new window) http://www.freestatistics.org/blog/date/2010/May/31/t1275331075cg0eslmxcbfvqul/2u3ov1275331039.ps (open in new window) http://www.freestatistics.org/blog/date/2010/May/31/t1275331075cg0eslmxcbfvqul/3nuny1275331039.png (open in new window) http://www.freestatistics.org/blog/date/2010/May/31/t1275331075cg0eslmxcbfvqul/3nuny1275331039.ps (open in new window) http://www.freestatistics.org/blog/date/2010/May/31/t1275331075cg0eslmxcbfvqul/4nuny1275331039.png (open in new window) http://www.freestatistics.org/blog/date/2010/May/31/t1275331075cg0eslmxcbfvqul/4nuny1275331039.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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