review workshop 9

*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: Sun, 06 Dec 2009 03:41:04 -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/06/t1260096099w3rlpqnk7prrvt6.htm/, Retrieved Sun, 06 Dec 2009 11:41:44 +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/06/t1260096099w3rlpqnk7prrvt6.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 «

102.86 102.55 102.28 102.26 102.57 103.08 102.76 102.51 102.87 103.14 103.12 103.16 102.48 102.57 102.88 102.63 102.38 101.69 101.96 102.19 101.87 101.6 101.63 101.22 101.21 101.49 101.64 101.66 101.77 101.82 101.78 101.28 101.29 101.37 101.12 101.51 102.24 102.94 103.09 103.46 103.64 104.39 104.15 105.21 105.8 105.91 105.39 105.46 104.72 103.14 102.63 102.32 101.93 100.62 100.6 99.63 98.9 98.32 99.22 98.81

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 1 102.86 NA NA 0.999475561748494 NA 2 102.55 NA NA 0.998783384067919 NA 3 102.28 NA NA 0.999724161541092 NA 4 102.26 NA NA 1.00020201637734 NA 5 102.57 NA NA 1.00024741222967 NA 6 103.08 NA NA 0.998121731017254 NA 7 102.76 102.652125682148 102.7475 0.999071760209716 1.00105087271340 8 102.51 102.741488000615 102.7325 1.00008748935940 0.997746888767921 9 102.87 102.914331482291 102.758333333333 1.00151810703714 0.999569238981074 10 103.14 102.997087229921 102.79875 1.00192937394590 1.00138754186087 11 103.12 102.822335652294 102.80625 1.00015646570412 1.00289493859304 12 103.16 102.810540777981 102.740416666667 1.00068253676196 1.00339906024591 13 102.48 102.595333517181 102.649166666667 0.999475561748494 0.99887584051606 14 102.57 102.477672163829 102.6025 0.998783384067919 1.00090095563474 15 102.88 102.519213455635 102.5475 0.999724161541092 1.00351920905559 16 102.63 102.462361561055 102.441666666667 1.00020201637734 1.00163609774741 17 102.38 102.340730752034 102.315416666667 1.00024741222967 1.00038371084199 18 101.69 101.980592562360 102.1725 0.998121731017254 0.997150511140806 19 101.96 101.944033572099 102.03875 0.999071760209716 1.00015661954252 20 102.19 101.949752071538 101.940833333333 1.00008748935940 1.00235653273873 21 101.87 101.998777012775 101.844166666667 1.00151810703714 0.99873746512903 22 101.6 101.948401151857 101.752083333333 1.00192937394590 0.996582573655682 23 101.63 101.702160410705 101.68625 1.00015646570412 0.99929047317762 24 101.22 101.735640953076 101.66625 1.00068253676196 0.99493156038292 25 101.21 101.610850088859 101.664166666667 0.999475561748494 0.99605504639998 26 101.49 101.495119009752 101.61875 0.998783384067919 0.99994956398099 27 101.64 101.528653432241 101.556666666667 0.999724161541092 1.00109670092131 28 101.66 101.543425958509 101.522916666667 1.00020201637734 1.00114802155227 29 101.77 101.517193715965 101.492083333333 1.00024741222967 1.00249028046168 30 101.82 101.292304452013 101.482916666667 0.998121731017254 1.00520963118414 31 101.78 101.443665132194 101.537916666667 0.999071760209716 1.00331548418886 32 101.28 101.650142527851 101.64125 1.00008748935940 0.996358661988597 33 101.29 101.916569068156 101.762083333333 1.00151810703714 0.993852137352302 34 101.37 102.094098381652 101.8975 1.00192937394590 0.99290753928846 35 101.12 102.066384056966 102.050416666667 1.00015646570412 0.990727759529154 36 101.51 102.305196096916 102.235416666667 1.00068253676196 0.992227216922954 37 102.24 102.387525889968 102.44125 0.999475561748494 0.998559141959086 38 102.94 102.578798981466 102.70375 0.998783384067919 1.00352120537695 39 103.09 103.026990019351 103.055416666667 0.999724161541092 1.00061158712525 40 103.46 103.453395058949 103.4325 1.00020201637734 1.00006384460411 41 103.64 103.825264619685 103.799583333333 1.00024741222967 0.998215611389354 42 104.39 103.946476488410 104.142083333333 0.998121731017254 1.00426684507810 43 104.15 104.313082483496 104.41 0.999071760209716 0.998436605652774 44 105.21 104.530811200327 104.521666666667 1.00008748935940 1.00649749860232 45 105.8 104.669491964874 104.510833333333 1.00151810703714 1.01080074063515 46 105.91 104.645678520634 104.444166666667 1.00192937394590 1.01208192729255 47 105.39 104.341740016443 104.325416666667 1.00015646570412 1.01004641079775 48 105.46 104.168133419521 104.097083333333 1.00068253676196 1.01240174454577 49 104.72 103.737650794630 103.792083333333 0.999475561748494 1.00946955322244 50 103.14 103.285854385437 103.411666666667 0.998783384067919 0.998587857104879 51 102.63 102.863285187899 102.891666666667 0.999724161541092 0.997732084995413 52 102.32 102.308580501037 102.287916666667 1.00020201637734 1.00011161819377 53 101.93 101.739748765186 101.714583333333 1.00024741222967 1.00186997940454 54 100.62 100.990372628380 101.180416666667 0.998121731017254 0.996332594694514 55 100.6 NA NA 0.999071760209716 NA 56 99.63 NA NA 1.00008748935940 NA 57 98.9 NA NA 1.00151810703714 NA 58 98.32 NA NA 1.00192937394590 NA 59 99.22 NA NA 1.00015646570412 NA 60 98.81 NA NA 1.00068253676196 NA

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Dec/06/t1260096099w3rlpqnk7prrvt6/19dcu1260096062.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/06/t1260096099w3rlpqnk7prrvt6/19dcu1260096062.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/06/t1260096099w3rlpqnk7prrvt6/2z1281260096062.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/06/t1260096099w3rlpqnk7prrvt6/2z1281260096062.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/06/t1260096099w3rlpqnk7prrvt6/3hir91260096062.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/06/t1260096099w3rlpqnk7prrvt6/3hir91260096062.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/06/t1260096099w3rlpqnk7prrvt6/4zn1j1260096062.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/06/t1260096099w3rlpqnk7prrvt6/4zn1j1260096062.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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