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: Fri, 04 Dec 2009 05:18:20 -0700

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2009/Dec/04/t1259929150djjpc2d9710nw1i.htm/, Retrieved Fri, 04 Dec 2009 13:19:15 +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/2009/Dec/04/t1259929150djjpc2d9710nw1i.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 «

19 18 19 19 22 23 20 14 14 14 15 11 17 16 20 24 23 20 21 19 23 23 23 23 27 26 17 24 26 24 27 27 26 24 23 23 24 17 21 19 22 22 18 16 14 12 14 16 8 3 0 5 1 1 3 6 7 8 14 14 13

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 19 NA NA 1.10832915792510 NA 2 18 NA NA 0.850215644153432 NA 3 19 NA NA 0.748795488731985 NA 4 19 NA NA 1.07113401149497 NA 5 22 NA NA 0.949344012552167 NA 6 23 NA NA 0.887929621038847 NA 7 20 16.6764178213541 17.25 0.966748859208934 1.19929832738959 8 14 18.8639329716286 17.0833333333333 1.10423022272948 0.742157005172572 9 14 17.7142707857669 17.0416666666667 1.03946821236774 0.790323246681355 10 14 17.4185903332531 17.2916666666667 1.00734016385078 0.80373897842199 11 15 18.8057823912857 17.5416666666667 1.07206360425381 0.797627011091588 12 11 20.8522508212193 17.4583333333333 1.19440100169275 0.527520990146847 13 17 19.2572191189486 17.375 1.10832915792510 0.882785821514198 14 16 14.9850507282042 17.625 0.850215644153432 1.06773078651549 15 20 13.6343178573282 18.2083333333333 0.748795488731985 1.46688673458279 16 24 20.3069156345920 18.9583333333333 1.07113401149497 1.18186338249798 17 23 18.6704322468593 19.6666666666667 0.949344012552167 1.23189435016262 18 20 18.2025572312964 20.5 0.887929621038847 1.09874671706090 19 21 20.704538068058 21.4166666666667 0.966748859208934 1.01427039477871 20 19 24.5691224557309 22.25 1.10423022272948 0.773328393565321 21 23 23.4313459537895 22.5416666666667 1.03946821236774 0.98159107229093 22 23 22.5812086729884 22.4166666666667 1.00734016385078 1.01854601022808 23 23 24.1661004125547 22.5416666666667 1.07206360425381 0.951746438496593 24 23 27.2721562053178 22.8333333333333 1.19440100169275 0.843350992376437 25 27 25.7686529217586 23.25 1.10832915792510 1.04778468948222 26 26 20.2634728523235 23.8333333333333 0.850215644153432 1.28309693947742 27 17 18.1894904137811 24.2916666666667 0.748795488731985 0.934605621888124 28 24 26.1981526978144 24.4583333333333 1.07113401149497 0.916095126127052 29 26 23.2589283075281 24.5 0.949344012552167 1.11785030059122 30 24 21.7542757154518 24.5 0.887929621038847 1.10323139753870 31 27 23.5645034432178 24.375 0.966748859208934 1.14579117124452 32 27 26.3634965676663 23.875 1.10423022272948 1.02414336166297 33 26 24.6007476927032 23.6666666666667 1.03946821236774 1.05687844632917 34 24 23.7984113709747 23.625 1.00734016385078 1.00847067587340 35 23 24.9254787989012 23.25 1.07206360425381 0.922750579259241 36 23 27.4712230389333 23 1.19440100169275 0.837239753301245 37 24 24.983586434895 22.5416666666667 1.10832915792510 0.960630694978155 38 17 18.4567646084974 21.7083333333333 0.850215644153432 0.921071507417571 39 21 15.5375063911887 20.75 0.748795488731985 1.3515682292436 40 19 21.1548967270256 19.75 1.07113401149497 0.898137213580785 41 22 17.9188682369221 18.875 0.949344012552167 1.2277561121114 42 22 16.1677185164157 18.2083333333333 0.887929621038847 1.36073620886352 43 18 16.6764178213541 17.25 0.966748859208934 1.07936849465063 44 16 17.6676835636716 16 1.10423022272948 0.905608250359389 45 14 15.1156002548476 14.5416666666667 1.03946821236774 0.926195438087892 46 12 13.1793671437144 13.0833333333333 1.00734016385078 0.910514129331554 47 14 12.4627393994506 11.625 1.07206360425381 1.12334853127212 48 16 11.7947098917159 9.875 1.19440100169275 1.35654035977923 49 8 9.28225669762273 8.375 1.10832915792510 0.861859379739937 50 3 6.23491472379183 7.33333333333333 0.850215644153432 0.481161352304032 51 0 4.9607701128494 6.625 0.748795488731985 0 52 5 6.60532640421895 6.16666666666667 1.07113401149497 0.756964863508698 53 1 5.696064075313 6 0.949344012552167 0.175559822849263 54 1 5.25358359114651 5.91666666666667 0.887929621038847 0.190346262251395 55 3 5.84077435772064 6.04166666666667 0.966748859208934 0.513630525040647 56 6 NA NA 1.10423022272948 NA 57 7 NA NA 1.03946821236774 NA 58 8 NA NA 1.00734016385078 NA 59 14 NA NA 1.07206360425381 NA 60 14 NA NA 1.19440100169275 NA 61 13 NA NA NA NA

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Dec/04/t1259929150djjpc2d9710nw1i/17qx21259929098.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/04/t1259929150djjpc2d9710nw1i/17qx21259929098.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/04/t1259929150djjpc2d9710nw1i/2zrly1259929098.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/04/t1259929150djjpc2d9710nw1i/2zrly1259929098.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/04/t1259929150djjpc2d9710nw1i/33zz61259929098.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/04/t1259929150djjpc2d9710nw1i/33zz61259929098.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/04/t1259929150djjpc2d9710nw1i/403e31259929098.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/04/t1259929150djjpc2d9710nw1i/403e31259929098.ps (open in new window)

Parameters (Session):

par1 = FALSE ; par2 = 1 ; par3 = 1 ; par4 = 1 ; par5 = 12 ; par6 = 3 ; par7 = 1 ; par8 = 2 ; par9 = 1 ;

Parameters (R input):

par1 = multiplicative ; par2 = 12 ; par3 = 1 ; par4 = 1 ; par5 = 12 ; par6 = 3 ; par7 = 1 ; par8 = 2 ; par9 = 1 ;

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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