dennis volkaerts - opg9 - multiplicatief

*Unverified author*

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

Title produced by software: Classical Decomposition

Date of computation: Wed, 19 Aug 2009 06:18:29 -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/Aug/19/t12506843528dhshfw3u2euxeh.htm/, Retrieved Wed, 19 Aug 2009 14:19:18 +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/Aug/19/t12506843528dhshfw3u2euxeh.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 «

17.23 17.36 17.39 17.29 17.28 17.4 17.51 17.54 17.64 17.65 17.5 17.37 17.56 17.49 17.61 17.79 17.83 17.56 17.95 18.09 18.38 18.38 18.44 18.84 19.01 19.06 19.06 18.97 18.98 19.41 19.55 19.64 19.71 19.48 19.48 19.41 19.25 19.14 19.21 19.3 19.53 19.14 19.16 19.24 19.38 19.27 19.27 19.07 19.15 19.24 19.36 19.57 19.59 19.36 19.46 19.65 19.46 19.51 19.64 19.64 19.69 19.28 19.67 19.65 19.6 19.53 19.64 19.67 19.81 19.73 19.87 19.97 20.12 19.94 20.31 20.13 20.22 20.38 20.44 20.34 20.14 19.97 19.82 19.98 20.12

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 5 seconds R Server 'George Udny Yule' @ 72.249.76.132 Classical Decomposition by Moving Averages t Observations Fit Trend Seasonal Random 1 17.23 NA NA 1.00026155100263 NA 2 17.36 NA NA 0.992776449509761 NA 3 17.39 NA NA 0.999972777562938 NA 4 17.29 NA NA 1.00002413830366 NA 5 17.28 NA NA 1.00125179913789 NA 6 17.4 NA NA 0.996017632983819 NA 7 17.51 17.4678135155530 17.44375 1.00137949211339 1.00241509816952 8 17.54 17.5045227974936 17.4629166666667 1.00238254190987 1.00202674491141 9 17.64 17.5579471138210 17.4775 1.00460289594170 1.00467326195067 10 17.65 17.5143076812473 17.5075 1.00038884370968 1.00774751256072 11 17.5 17.5739808529061 17.55125 1.00129511304928 0.995790319021893 12 17.37 17.5746231637220 17.5808333333333 0.999646764775387 0.988356896087289 13 17.56 17.6104381566938 17.6058333333333 1.00026155100263 0.997135894277868 14 17.49 17.5196087358696 17.6470833333333 0.992776449509761 0.99830996591785 15 17.61 17.7003514735120 17.7008333333333 0.999972777562938 0.994895498338145 16 17.79 17.7625120798944 17.7620833333333 1.00002413830366 1.00154752435815 17 17.83 17.8539883316271 17.8316666666667 1.00125179913789 0.998656416080178 18 17.56 17.8606711961353 17.9320833333333 0.996017632983819 0.983165739247229 19 17.95 18.0786550057422 18.05375 1.00137949211339 0.992883596390255 20 18.09 18.2228969525290 18.1795833333333 1.00238254190987 0.99270714459533 21 18.38 18.3896745947528 18.3054166666667 1.00460289594170 0.99947391158539 22 18.38 18.4221605569138 18.415 1.00038884370968 0.99771142169869 23 18.44 18.5360585740276 18.5120833333333 1.00129511304928 0.99481774544227 24 18.84 18.6305000590160 18.6370833333333 0.999646764775387 1.01124499827275 25 19.01 18.7857454791219 18.7808333333333 1.00026155100263 1.01193748319050 26 19.06 18.7754709444994 18.9120833333333 0.992776449509761 1.01515429660016 27 19.06 19.0315652336426 19.0320833333333 0.999972777562938 1.00149408448587 28 18.97 19.1337951795433 19.1333333333333 1.00002413830366 0.99143948296685 29 18.98 19.246562708928 19.2225 1.00125179913789 0.986150113505496 30 19.41 19.2127651329108 19.2895833333333 0.996017632983819 1.01026582408752 31 19.55 19.3499897192712 19.3233333333333 1.00137949211339 1.01033645410827 32 19.64 19.3827370853972 19.3366666666667 1.00238254190987 1.01327278564783 33 19.71 19.4352987756121 19.34625 1.00460289594170 1.01413413951385 34 19.48 19.3737804444926 19.36625 1.00038884370968 1.00548264474307 35 19.48 19.4280456372357 19.4029166666667 1.00129511304928 1.00267419398402 36 19.41 19.4077254186288 19.4145833333333 0.999646764775387 1.00011719979143 37 19.25 19.3921540444173 19.3870833333333 1.00026155100263 0.992669507260943 38 19.14 19.2143608665535 19.3541666666667 0.992776449509761 0.996129932862719 39 19.21 19.3232239604318 19.32375 0.999972777562938 0.994140524341918 40 19.3 19.3017158994335 19.30125 1.00002413830366 0.999911101197303 41 19.53 19.3078893816252 19.28375 1.00125179913789 1.01150361978903 42 19.14 19.1841296259625 19.2608333333333 0.996017632983819 0.997699680578535 43 19.16 19.269044876992 19.2425 1.00137949211339 0.994340929833933 44 19.24 19.2883460627007 19.2425 1.00238254190987 0.997493509161256 45 19.38 19.3415358386575 19.2529166666667 1.00460289594170 1.00198868185357 46 19.27 19.2779098469705 19.2704166666667 1.00038884370968 0.999589693746197 47 19.27 19.3091418425611 19.2841666666667 1.00129511304928 0.99797288544047 48 19.07 19.2890173653117 19.2958333333333 0.999646764775387 0.988645488717035 49 19.15 19.3225525114933 19.3175 1.00026155100263 0.99106989040963 50 19.24 19.2073287000361 19.3470833333333 0.992776449509761 1.00170098093671 51 19.36 19.3669727694502 19.3675 0.999972777562938 0.999639965959925 52 19.57 19.3813011537735 19.3808333333333 1.00002413830366 1.00973612889709 53 19.59 19.4305427270196 19.40625 1.00125179913789 1.00820652697254 54 19.36 19.3679778807174 19.4454166666667 0.996017632983819 0.999588089124917 55 19.46 19.5185552671102 19.4916666666667 1.00137949211339 0.99700002042626 56 19.65 19.5623306241560 19.5158333333333 1.00238254190987 1.00448154044261 57 19.46 19.6203131422813 19.5304166666667 1.00460289594170 0.991829226112816 58 19.51 19.5542672650453 19.5466666666667 1.00038884370968 0.997736183900668 59 19.64 19.5757366664104 19.5504166666667 1.00129511304928 1.00328280537712 60 19.64 19.5510081215800 19.5579166666667 0.999646764775387 1.00455177952291 61 19.69 19.577619206999 19.5725 1.00026155100263 1.00574026861044 62 19.28 19.4393901951091 19.5808333333333 0.992776449509761 0.991800658687886 63 19.67 19.5957165423177 19.59625 0.999972777562938 1.00379080078658 64 19.65 19.6204735935177 19.62 1.00002413830366 1.00150487735892 65 19.6 19.6633337703191 19.63875 1.00125179913789 0.99677909295245 66 19.53 19.5837817011973 19.6620833333333 0.996017632983819 0.99725376324053 67 19.64 19.7209173728082 19.69375 1.00137949211339 0.995896875825881 68 19.67 19.7861960585159 19.7391666666667 1.00238254190987 0.994127418015455 69 19.81 19.884439987006 19.7933333333333 1.00460289594170 0.996256369952855 70 19.73 19.8477146592001 19.84 1.00038884370968 0.994069107641794 71 19.87 19.9115877355791 19.8858333333333 1.00129511304928 0.997911380240927 72 19.97 19.9400373208717 19.9470833333333 0.999646764775387 1.00150263906963 73 20.12 20.0210684946102 20.0158333333333 1.00026155100263 1.00494136990823 74 19.94 19.9320555081783 20.0770833333333 0.992776449509761 1.00039857865229 75 20.31 20.1182023185943 20.11875 0.999972777562938 1.00953353974517 76 20.13 20.1429862057814 20.1425 1.00002413830366 0.999355298879285 77 20.22 20.1756409408780 20.1504166666667 1.00125179913789 1.00219864435791 78 20.38 20.0685102825827 20.14875 0.996017632983819 1.01552131737888 79 20.44 20.1769622831748 20.1491666666667 1.00137949211339 1.01303653707300 80 20.34 NA NA 1.00238254190987 NA 81 20.14 NA NA 1.00460289594170 NA 82 19.97 NA NA 1.00038884370968 NA 83 19.82 NA NA 1.00129511304928 NA 84 19.98 NA NA 0.999646764775387 NA 85 20.12 NA NA NA NA

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Aug/19/t12506843528dhshfw3u2euxeh/1b2yd1250684303.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Aug/19/t12506843528dhshfw3u2euxeh/1b2yd1250684303.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Aug/19/t12506843528dhshfw3u2euxeh/2ggta1250684303.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Aug/19/t12506843528dhshfw3u2euxeh/2ggta1250684303.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Aug/19/t12506843528dhshfw3u2euxeh/3hmrg1250684303.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Aug/19/t12506843528dhshfw3u2euxeh/3hmrg1250684303.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Aug/19/t12506843528dhshfw3u2euxeh/48wil1250684303.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Aug/19/t12506843528dhshfw3u2euxeh/48wil1250684303.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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