Multiplicatief decompositiemodel eigen reeks maarten verhaegen 2MAR03

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

Title produced by software: Classical Decomposition

Date of computation: Sun, 01 Jun 2008 06:39:54 -0600

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2008/Jun/01/t1212324338eybwctri78whnwt.htm/, Retrieved Sun, 01 Jun 2008 12:45:38 +0000

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Dataseries X:

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0,63 0,64 0,65 0,65 0,65 0,65 0,65 0,66 0,65 0,66 0,66 0,66 0,66 0,68 0,69 0,7 0,71 0,71 0,7 0,7 0,7 0,7 0,71 0,7 0,7 0,7 0,69 0,7 0,69 0,69 0,69 0,7 0,7 0,71 0,71 0,71 0,72 0,73 0,74 0,74 0,74 0,74 0,75 0,75 0,76 0,76 0,76 0,76 0,76 0,77 0,77 0,78 0,78 0,78 0,78 0,78 0,78 0,78 0,8 0,8 0,8 0,81 0,81 0,81 0,8 0,81 0,81 0,81 0,8 0,82 0,83 0,83

Text written by user:

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 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 Classical Decomposition by Moving Averages t Observations Fit Trend Seasonal Random 1 0.63 NA NA 0.991737646454424 NA 2 0.64 NA NA 1.00220466932951 NA 3 0.65 NA NA 1.00207372041151 NA 4 0.65 NA NA 1.00888561980387 NA 5 0.65 NA NA 1.00516173126243 NA 6 0.65 NA NA 1.00115155945410 NA 7 0.65 0.650088984225145 0.652083333333333 0.996941573252618 0.999863119930803 8 0.66 0.652841403387668 0.655 0.996704432652928 1.01096529199157 9 0.65 0.656049048488156 0.658333333333333 0.996530200235173 0.990779578901767 10 0.66 0.660004438270228 0.662083333333333 0.996860070389268 0.99999327539336 11 0.66 0.66930567546143 0.666666666666667 1.00395851319214 0.986096523901408 12 0.66 0.670182460359161 0.671666666666667 0.997790263562026 0.98480643561799 13 0.66 0.670662583414805 0.67625 0.991737646454424 0.984101418987005 14 0.68 0.681499175144066 0.68 1.00220466932951 0.997800180544974 15 0.69 0.685167906331371 0.68375 1.00207372041151 1.00705242266016 16 0.7 0.69360886361516 0.6875 1.00888561980387 1.00921432340344 17 0.71 0.694818046735155 0.69125 1.00516173126243 1.02185025754035 18 0.71 0.695800333820598 0.695 1.00115155945410 1.02040767370926 19 0.7 0.696197531988078 0.698333333333333 0.996941573252618 1.00546176600348 20 0.7 0.69852368988426 0.700833333333333 0.996704432652928 1.00211347179361 21 0.7 0.69923202383168 0.701666666666667 0.996530200235173 1.00109831378161 22 0.7 0.699463482723136 0.701666666666667 0.996860070389268 1.00076704115385 23 0.71 0.703607591328828 0.700833333333333 1.00395851319214 1.00908519002630 24 0.7 0.697621692607117 0.699166666666667 0.997790263562026 1.00340916490712 25 0.7 0.692150232421317 0.697916666666667 0.991737646454424 1.01134113261975 26 0.7 0.699037756857333 0.6975 1.00220466932951 1.00137652527811 27 0.69 0.69894641998703 0.6975 1.00207372041151 0.987200134758262 28 0.7 0.70411808882145 0.697916666666667 1.00888561980387 0.994151423054131 29 0.69 0.701937942331597 0.698333333333333 1.00516173126243 0.98299288069264 30 0.69 0.699554652168551 0.69875 1.00115155945410 0.986341807407138 31 0.69 0.697859101276833 0.7 0.996941573252618 0.988738269283222 32 0.7 0.699769570425077 0.702083333333333 0.996704432652928 1.00032929350555 33 0.7 0.702969012082562 0.705416666666667 0.996530200235173 0.995776468049757 34 0.71 0.706939933251056 0.709166666666667 0.996860070389268 1.00432860927076 35 0.71 0.715738756696567 0.712916666666667 1.00395851319214 0.991982051212298 36 0.71 0.715498768162603 0.717083333333333 0.997790263562026 0.992314776199093 37 0.72 0.71570400152461 0.721666666666667 0.991737646454424 1.00600247932978 38 0.73 0.727851141100556 0.72625 1.00220466932951 1.00295233294021 39 0.74 0.73234887733408 0.730833333333333 1.00207372041151 1.01044737406272 40 0.74 0.741951299564095 0.735416666666667 1.00888561980387 0.99737004360631 41 0.74 0.743400863746172 0.739583333333333 1.00516173126243 0.995425262584396 42 0.74 0.744606472343985 0.74375 1.00115155945410 0.99381354780669 43 0.75 0.745213826006332 0.7475 0.996941573252618 1.00642255125528 44 0.75 0.748358911516907 0.750833333333333 0.996704432652928 1.00219291633712 45 0.76 0.751134638427262 0.75375 0.996530200235173 1.01180262647892 46 0.76 0.754290786594546 0.756666666666667 0.996860070389268 1.00756898202513 47 0.76 0.76300847002603 0.76 1.00395851319214 0.996057094849907 48 0.76 0.761646567852347 0.763333333333333 0.997790263562026 0.997838147085741 49 0.76 0.759918971595703 0.76625 0.991737646454424 1.00010662768970 50 0.77 0.77044483954706 0.76875 1.00220466932951 0.999422619862933 51 0.77 0.77243182615054 0.770833333333333 1.00207372041151 0.996851727145088 52 0.78 0.779364141298489 0.7725 1.00888561980387 1.00081586856235 53 0.78 0.779000341728383 0.775 1.00516173126243 1.00128325780885 54 0.78 0.779229630441773 0.778333333333333 1.00115155945410 1.00098862970315 55 0.78 0.779275996425797 0.781666666666667 0.996941573252618 1.00092907208425 56 0.78 0.782412979632549 0.785 0.996704432652928 0.99691597698995 57 0.78 0.785597974518728 0.788333333333333 0.996530200235173 0.992874250315936 58 0.78 0.788765530695508 0.79125 0.996860070389268 0.988887026176488 59 0.8 0.796473753799101 0.793333333333333 1.00395851319214 1.00442732253772 60 0.8 0.793659005474961 0.795416666666666 0.997790263562026 1.00798957043427 61 0.8 0.79132399706676 0.797916666666667 0.991737646454424 1.01096390728122 62 0.81 0.802181320742495 0.800416666666667 1.00220466932951 1.00974677302417 63 0.81 0.804164160630238 0.8025 1.00207372041151 1.00725702494027 64 0.81 0.812152923942114 0.805 1.00888561980387 0.997349115075934 65 0.8 0.812086915382438 0.807916666666667 1.00516173126243 0.985116229367215 66 0.81 0.811349909640925 0.810416666666667 1.00115155945410 0.99833621767269 67 0.81 NA NA 0.996941573252618 NA 68 0.81 NA NA 0.996704432652928 NA 69 0.8 NA NA 0.996530200235173 NA 70 0.82 NA NA 0.996860070389268 NA 71 0.83 NA NA 1.00395851319214 NA 72 0.83 NA NA 0.997790263562026 NA

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Jun/01/t1212324338eybwctri78whnwt/1bdgb1212323989.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Jun/01/t1212324338eybwctri78whnwt/1bdgb1212323989.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Jun/01/t1212324338eybwctri78whnwt/2whop1212323989.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Jun/01/t1212324338eybwctri78whnwt/2whop1212323989.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Jun/01/t1212324338eybwctri78whnwt/3aj0u1212323989.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Jun/01/t1212324338eybwctri78whnwt/3aj0u1212323989.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Jun/01/t1212324338eybwctri78whnwt/4kuqv1212323989.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Jun/01/t1212324338eybwctri78whnwt/4kuqv1212323989.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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