WS9

*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, 11 Dec 2009 05:41:48 -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/11/t1260535356p92grqkago3tz48.htm/, Retrieved Fri, 11 Dec 2009 13:42:41 +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/11/t1260535356p92grqkago3tz48.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 «

7.55 7.55 7.59 7.59 7.59 7.57 7.57 7.59 7.6 7.64 7.64 7.76 7.76 7.76 7.77 7.83 7.94 7.94 7.94 8.09 8.18 8.26 8.28 8.28 8.28 8.29 8.3 8.3 8.31 8.33 8.33 8.34 8.48 8.59 8.67 8.67 8.67 8.71 8.72 8.72 8.72 8.74 8.74 8.74 8.74 8.79 8.85 8.86 8.87 8.92 8.96 8.97 8.99 8.98 8.98 9.01 9.01 9.03 9.05 9.05

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 'Gwilym Jenkins' @ 72.249.127.135 Classical Decomposition by Moving Averages t Observations Fit Trend Seasonal Random 1 7.55 NA NA 1.00224969286558 NA 2 7.55 NA NA 1.00156200307055 NA 3 7.59 NA NA 1.00004047069589 NA 4 7.59 NA NA 0.998713051372467 NA 5 7.59 NA NA 0.999531601637073 NA 6 7.57 NA NA 0.997047943831932 NA 7 7.57 7.5574257970567 7.61208333333333 0.992819635061365 1.00166382089364 8 7.59 7.59170241165922 7.62958333333333 0.995034994701661 0.999775753636417 9 7.6 7.63678881846239 7.64583333333333 0.998817066174917 0.995182684851328 10 7.64 7.69216794636486 7.66333333333333 1.00376267242691 0.993218043764956 11 7.64 7.72557919995152 7.68791666666667 1.00489892579717 0.988922616966756 12 7.76 7.76053455767394 7.71791666666667 1.00552194236449 0.999931118446807 13 7.76 7.76618230759216 7.74875 1.00224969286558 0.99920394508559 14 7.76 7.79716019390422 7.785 1.00156200307055 0.995234137432078 15 7.77 7.83031688554878 7.83 1.00004047069589 0.992297005800608 16 7.83 7.86985884481504 7.88 0.998713051372467 0.99493525289322 17 7.94 7.92878442998608 7.9325 0.999531601637073 1.00141453839652 18 7.94 7.95727346506534 7.98083333333333 0.997047943831932 0.997829223145192 19 7.94 7.96655022167157 8.02416666666667 0.992819635061365 0.996667287479173 20 8.09 8.02785941767011 8.06791666666667 0.995034994701661 1.00774061665718 21 8.18 8.10248727556644 8.11208333333333 0.998817066174917 1.00956653454641 22 8.26 8.18442989030088 8.15375 1.00376267242691 1.00923339935854 23 8.28 8.22886607862161 8.18875 1.00489892579717 1.00621396932334 24 8.28 8.2658093337121 8.22041666666667 1.00552194236449 1.00171679090516 25 8.28 8.2714831944119 8.25291666666666 1.00224969286558 1.00102965881547 26 8.29 8.29251606792286 8.27958333333333 1.00156200307055 0.999696585704236 27 8.3 8.30283600795259 8.3025 1.00004047069589 0.999658429005478 28 8.3 8.31803132661843 8.32875 0.998713051372467 0.99783226031372 29 8.31 8.35483477518389 8.35875 0.999531601637073 0.99463367303001 30 8.33 8.3664785586797 8.39125 0.997047943831932 0.9956399148789 31 8.33 8.36326440084817 8.42375 0.992819635061365 0.996022557789181 32 8.34 8.4155084676893 8.4575 0.995034994701661 0.991027462217024 33 8.48 8.48245393449048 8.4925 0.998817066174917 0.999710704648745 34 8.59 8.55958618912044 8.5275 1.00376267242691 1.00355318705923 35 8.67 8.60402834425255 8.56208333333333 1.00489892579717 1.00766753119677 36 8.67 8.64371799705076 8.59625 1.00552194236449 1.00304059005144 37 8.67 8.64983245346865 8.63041666666667 1.00224969286558 1.00233155343064 38 8.71 8.67770012160374 8.66416666666667 1.00156200307055 1.00372217038428 39 8.72 8.69201842446507 8.69166666666667 1.00004047069589 1.00321922644068 40 8.72 8.69962293833033 8.71083333333333 0.998713051372467 1.00234229251246 41 8.72 8.7225791102862 8.72666666666667 0.999531601637073 0.999704317925515 42 8.74 8.7162762123074 8.74208333333333 0.997047943831932 1.00272178016331 43 8.74 8.69544530374578 8.75833333333333 0.992819635061365 1.00512391196746 44 8.74 8.73184667642154 8.77541666666667 0.995034994701661 1.00093374561884 45 8.74 8.78376374945325 8.79416666666667 0.998817066174917 0.99501765408297 46 8.79 8.84774972299633 8.81458333333333 1.00376267242691 0.993472947946727 47 8.85 8.87953813307528 8.83625 1.00489892579717 0.996673460642592 48 8.86 8.90641060449347 8.8575 1.00552194236449 0.994789078726051 49 8.87 8.89747164841419 8.8775 1.00224969286558 0.996912420797757 50 8.92 8.91264987482404 8.89875 1.00156200307055 1.00082468460886 51 8.96 8.92161104919567 8.92125 1.00004047069589 1.00430291688269 52 8.97 8.93099146189829 8.9425 0.998713051372467 1.00436777241005 53 8.99 8.95663609366954 8.96083333333333 0.999531601637073 1.00372504877741 54 8.98 8.9505824791079 8.97708333333333 0.997047943831932 1.00328665994205 55 8.98 NA NA 0.992819635061365 NA 56 9.01 NA NA 0.995034994701661 NA 57 9.01 NA NA 0.998817066174917 NA 58 9.03 NA NA 1.00376267242691 NA 59 9.05 NA NA 1.00489892579717 NA 60 9.05 NA NA 1.00552194236449 NA

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Dec/11/t1260535356p92grqkago3tz48/1yyrd1260535305.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/11/t1260535356p92grqkago3tz48/1yyrd1260535305.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/11/t1260535356p92grqkago3tz48/2ed3b1260535305.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/11/t1260535356p92grqkago3tz48/2ed3b1260535305.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/11/t1260535356p92grqkago3tz48/3deo51260535305.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/11/t1260535356p92grqkago3tz48/3deo51260535305.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/11/t1260535356p92grqkago3tz48/4ceaz1260535305.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/11/t1260535356p92grqkago3tz48/4ceaz1260535305.ps (open in new window)

Parameters (Session):

par1 = 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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