classical decomposition

*The author of this computation has been verified*

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

Title produced by software: Classical Decomposition

Date of computation: Tue, 30 Nov 2010 18:43:48 +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/Nov/30/t1291142526aps9qthlfqpoghp.htm/, Retrieved Tue, 30 Nov 2010 19:42:06 +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/Nov/30/t1291142526aps9qthlfqpoghp.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:

Dataseries X:

» Textbox « » Textfile « » CSV «

0,96 0,95 0,95 0,96 0,96 0,96 0,95 0,96 0,96 0,96 0,95 0,95 0,96 0,96 0,96 0,96 0,96 0,95 0,95 0,95 0,95 0,95 0,95 0,95 0,95 0,95 0,95 0,96 0,96 0,96 0,97 0,97 0,97 0,96 0,95 0,95 0,95 0,95 0,95 0,95 0,95 0,95 0,95 0,95 0,95 0,95 0,94 0,94 0,94 0,93 0,93 0,93 0,93 0,92 0,93

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 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 Classical Decomposition by Moving Averages t Observations Fit Trend Seasonal Random 1 0.96 NA NA -0.00238425925925918 NA 2 0.95 NA NA -0.00224537037037038 NA 3 0.95 NA NA -0.00196759259259259 NA 4 0.96 NA NA 0.00164351851851853 NA 5 0.96 NA NA 0.00192129629629635 NA 6 0.96 NA NA -0.00113425925925928 NA 7 0.95 0.958449074074074 0.955833333333333 0.00261574074074075 -0.00844907407407391 8 0.96 0.959560185185185 0.95625 0.00331018518518519 0.000439814814814921 9 0.96 0.961226851851851 0.957083333333333 0.00414351851851851 -0.00122685185185145 10 0.96 0.959143518518518 0.9575 0.00164351851851856 0.000856481481481652 11 0.95 0.953310185185185 0.9575 -0.00418981481481491 -0.00331018518518478 12 0.95 0.953726851851851 0.957083333333333 -0.00335648148148155 -0.00372685185185151 13 0.96 0.954282407407407 0.956666666666666 -0.00238425925925918 0.00571759259259297 14 0.96 0.95400462962963 0.95625 -0.00224537037037038 0.00599537037037057 15 0.96 0.953449074074074 0.955416666666666 -0.00196759259259259 0.0065509259259261 16 0.96 0.956226851851852 0.954583333333333 0.00164351851851853 0.00377314814814833 17 0.96 0.956087962962963 0.954166666666666 0.00192129629629635 0.00391203703703724 18 0.95 0.953032407407407 0.954166666666666 -0.00113425925925928 -0.00303240740740729 19 0.95 0.95636574074074 0.95375 0.00261574074074075 -0.0063657407407407 20 0.95 0.956226851851852 0.952916666666667 0.00331018518518519 -0.00622685185185179 21 0.95 0.956226851851852 0.952083333333333 0.00414351851851851 -0.00622685185185168 22 0.95 0.953310185185185 0.951666666666666 0.00164351851851856 -0.00331018518518489 23 0.95 0.947476851851851 0.951666666666666 -0.00418981481481491 0.00252314814814847 24 0.95 0.948726851851851 0.952083333333333 -0.00335648148148155 0.00127314814814861 25 0.95 0.950949074074074 0.953333333333333 -0.00238425925925918 -0.000949074074073741 26 0.95 0.95275462962963 0.955 -0.00224537037037038 -0.00275462962962925 27 0.95 0.954699074074074 0.956666666666666 -0.00196759259259259 -0.00469907407407377 28 0.96 0.959560185185185 0.957916666666666 0.00164351851851853 0.000439814814815143 29 0.96 0.96025462962963 0.958333333333333 0.00192129629629635 -0.000254629629629410 30 0.96 0.957199074074074 0.958333333333333 -0.00113425925925928 0.00280092592592618 31 0.97 0.960949074074074 0.958333333333333 0.00261574074074075 0.00905092592592616 32 0.97 0.961643518518518 0.958333333333333 0.00331018518518519 0.00835648148148171 33 0.97 0.962476851851852 0.958333333333333 0.00414351851851851 0.00752314814814836 34 0.96 0.959560185185185 0.957916666666667 0.00164351851851856 0.000439814814814921 35 0.95 0.952893518518518 0.957083333333333 -0.00418981481481491 -0.00289351851851838 36 0.95 0.952893518518518 0.95625 -0.00335648148148155 -0.00289351851851838 37 0.95 0.95261574074074 0.955 -0.00238425925925918 -0.00261574074074067 38 0.95 0.951087962962963 0.953333333333333 -0.00224537037037038 -0.00108796296296276 39 0.95 0.949699074074074 0.951666666666666 -0.00196759259259259 0.000300925925926121 40 0.95 0.952060185185185 0.950416666666666 0.00164351851851853 -0.00206018518518503 41 0.95 0.95150462962963 0.949583333333333 0.00192129629629635 -0.00150462962962938 42 0.95 0.94761574074074 0.94875 -0.00113425925925928 0.00238425925925945 43 0.95 0.950532407407407 0.947916666666666 0.00261574074074075 -0.000532407407407121 44 0.95 0.949976851851852 0.946666666666666 0.00331018518518519 2.31481481484108e-05 45 0.95 0.949143518518518 0.945 0.00414351851851851 0.000856481481481874 46 0.95 0.944976851851851 0.943333333333333 0.00164351851851856 0.00502314814814853 47 0.94 0.937476851851851 0.941666666666666 -0.00418981481481491 0.00252314814814847 48 0.94 0.936226851851852 0.939583333333333 -0.00335648148148155 0.00377314814814833 49 0.94 NA 0.9375 NA NA 50 0.93 NA NA NA NA 51 0.93 NA NA NA NA 52 0.93 NA NA NA NA 53 0.93 NA NA NA NA 54 0.92 NA NA NA NA 55 0.93 NA NA NA NA

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Nov/30/t1291142526aps9qthlfqpoghp/10g9l1291142625.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t1291142526aps9qthlfqpoghp/10g9l1291142625.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t1291142526aps9qthlfqpoghp/20g9l1291142625.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t1291142526aps9qthlfqpoghp/20g9l1291142625.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t1291142526aps9qthlfqpoghp/30g9l1291142625.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t1291142526aps9qthlfqpoghp/30g9l1291142625.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t1291142526aps9qthlfqpoghp/4spqn1291142625.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t1291142526aps9qthlfqpoghp/4spqn1291142625.ps (open in new window)

Parameters (Session):

par1 = additive ; par2 = 12 ;

Parameters (R input):

par1 = additive ; 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$trend)) { a<-table.row.start(a) a<-table.element(a,i,header=TRUE) a<-table.element(a,x[i]) a<-table.element(a,m$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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