*Unverified author*

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

Title produced by software: Classical Decomposition

Date of computation: Tue, 12 Jan 2010 02:35:07 -0700

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2010/Jan/12/t1263289052ftgjyu1opa5ci7n.htm/, Retrieved Tue, 12 Jan 2010 10:37:37 +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/Jan/12/t1263289052ftgjyu1opa5ci7n.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:

KDGP2W52

Dataseries X:

» Textbox « » Textfile « » CSV «

2.25 2.06 1.99 2.25 2.26 2.36 2.3 2.19 2.31 2.21 2.21 2.26 2.18 2.21 2.33 2.12 2.08 1.97 2.09 2.11 2.24 2.45 2.68 2.73 2.76 2.83 3.16 3.22 3.22 3.34 3.35 3.42 3.58 3.71 3.68 3.83 3.94 3.88 4.03 4.15 4.32 4.4 4.37 4.14 4.11 4.16 3.98 4.13 3.76 3.66 3.85 4.03 4.31 4.58 4.46 4.41 3.84 2.84 2.66 2.17 1.43 1.47 1.29 1.23 1.09 0.94 0.76 0.67

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 2.25 NA NA -0.130086805555555 NA 2 2.06 NA NA -0.190711805555556 NA 3 1.99 NA NA -0.0322743055555553 NA 4 2.25 NA NA -0.0172743055555555 NA 5 2.26 NA NA 0.0739756944444445 NA 6 2.36 NA NA 0.160225694444444 NA 7 2.3 2.38189236111111 2.21791666666667 0.163975694444444 -0.081892361111111 8 2.19 2.35324652777778 2.22125 0.131996527777778 -0.163246527777777 9 2.31 2.31470486111111 2.24166666666667 0.0730381944444445 -0.00470486111111068 10 2.21 2.19105902777778 2.25041666666667 -0.059357638888889 0.0189409722222225 11 2.21 2.15772569444444 2.2375 -0.0797743055555557 0.0522743055555557 12 2.26 2.12001736111111 2.21375 -0.093732638888889 0.139982638888889 13 2.18 2.05866319444444 2.18875 -0.130086805555555 0.121336805555556 14 2.21 1.98595486111111 2.17666666666667 -0.190711805555556 0.224045138888889 15 2.33 2.13814236111111 2.17041666666667 -0.0322743055555553 0.191857638888889 16 2.12 2.16022569444444 2.1775 -0.0172743055555555 -0.0402256944444441 17 2.08 2.28105902777778 2.20708333333333 0.0739756944444445 -0.201059027777778 18 1.97 2.40647569444444 2.24625 0.160225694444444 -0.436475694444444 19 2.09 2.45397569444444 2.29 0.163975694444444 -0.363975694444445 20 2.11 2.47199652777778 2.34 0.131996527777778 -0.361996527777778 21 2.24 2.47345486111111 2.40041666666667 0.0730381944444445 -0.233454861111111 22 2.45 2.42147569444444 2.48083333333333 -0.059357638888889 0.0285243055555560 23 2.68 2.49439236111111 2.57416666666667 -0.0797743055555557 0.185607638888889 24 2.73 2.58501736111111 2.67875 -0.093732638888889 0.144982638888889 25 2.76 2.65824652777778 2.78833333333333 -0.130086805555555 0.101753472222223 26 2.83 2.70470486111111 2.89541666666667 -0.190711805555556 0.125295138888889 27 3.16 2.97355902777778 3.00583333333333 -0.0322743055555553 0.186440972222222 28 3.22 3.09689236111111 3.11416666666667 -0.0172743055555555 0.123107638888889 29 3.22 3.28230902777778 3.20833333333333 0.0739756944444445 -0.0623090277777774 30 3.34 3.45605902777778 3.29583333333333 0.160225694444444 -0.116059027777778 31 3.35 3.55480902777778 3.39083333333333 0.163975694444444 -0.204809027777777 32 3.42 3.61574652777778 3.48375 0.131996527777778 -0.195746527777777 33 3.58 3.63678819444445 3.56375 0.0730381944444445 -0.0567881944444451 34 3.71 3.57939236111111 3.63875 -0.059357638888889 0.130607638888889 35 3.68 3.64355902777778 3.72333333333333 -0.0797743055555557 0.0364409722222221 36 3.83 3.71960069444444 3.81333333333333 -0.093732638888889 0.110399305555556 37 3.94 3.76991319444444 3.9 -0.130086805555555 0.170086805555556 38 3.88 3.78178819444444 3.9725 -0.190711805555556 0.098211805555556 39 4.03 3.99230902777778 4.02458333333333 -0.0322743055555553 0.0376909722222223 40 4.15 4.04814236111111 4.06541666666667 -0.0172743055555555 0.101857638888889 41 4.32 4.17064236111111 4.09666666666667 0.0739756944444445 0.149357638888890 42 4.4 4.28189236111111 4.12166666666667 0.160225694444444 0.118107638888890 43 4.37 4.29064236111111 4.12666666666667 0.163975694444444 0.0793576388888892 44 4.14 4.24199652777778 4.11 0.131996527777778 -0.101996527777779 45 4.11 4.16637152777778 4.09333333333333 0.0730381944444445 -0.0563715277777765 46 4.16 4.02147569444444 4.08083333333333 -0.059357638888889 0.138524305555556 47 3.98 3.99564236111111 4.07541666666667 -0.0797743055555557 -0.0156423611111105 48 4.13 3.98876736111111 4.0825 -0.093732638888889 0.141232638888889 49 3.76 3.96366319444444 4.09375 -0.130086805555555 -0.203663194444444 50 3.66 3.91803819444445 4.10875 -0.190711805555556 -0.258038194444445 51 3.85 4.07647569444444 4.10875 -0.0322743055555553 -0.226475694444444 52 4.03 4.02522569444444 4.0425 -0.0172743055555555 0.00477430555555536 53 4.31 4.00647569444445 3.9325 0.0739756944444445 0.303524305555555 54 4.58 3.95605902777778 3.79583333333333 0.160225694444444 0.623940972222222 55 4.46 3.78105902777778 3.61708333333333 0.163975694444444 0.678940972222223 56 4.41 3.56074652777778 3.42875 0.131996527777778 0.849253472222223 57 3.84 3.30387152777778 3.23083333333333 0.0730381944444445 0.536128472222222 58 2.84 2.94814236111111 3.0075 -0.059357638888889 -0.108142361111111 59 2.66 2.67689236111111 2.75666666666667 -0.0797743055555557 -0.0168923611111111 60 2.17 2.37710069444444 2.47083333333333 -0.093732638888889 -0.207100694444444 61 1.43 NA 2.165 NA NA 62 1.47 NA 1.855 NA NA 63 1.29 NA NA NA NA 64 1.23 NA NA NA NA 65 1.09 NA NA NA NA 66 0.94 NA NA NA NA 67 0.76 NA NA NA NA 68 0.67 NA NA NA NA

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Jan/12/t1263289052ftgjyu1opa5ci7n/1y1yv1263288905.png (open in new window) http://www.freestatistics.org/blog/date/2010/Jan/12/t1263289052ftgjyu1opa5ci7n/1y1yv1263288905.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Jan/12/t1263289052ftgjyu1opa5ci7n/22eie1263288905.png (open in new window) http://www.freestatistics.org/blog/date/2010/Jan/12/t1263289052ftgjyu1opa5ci7n/22eie1263288905.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Jan/12/t1263289052ftgjyu1opa5ci7n/35z3t1263288905.png (open in new window) http://www.freestatistics.org/blog/date/2010/Jan/12/t1263289052ftgjyu1opa5ci7n/35z3t1263288905.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Jan/12/t1263289052ftgjyu1opa5ci7n/4u4wp1263288905.png (open in new window) http://www.freestatistics.org/blog/date/2010/Jan/12/t1263289052ftgjyu1opa5ci7n/4u4wp1263288905.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$trend)) { a<-table.row.start(a) a<-table.element(a,i,header=TRUE) a<-table.element(a,x[i]) if (par1 == 'additive') a<-table.element(a,m$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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