CD

*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, 24 Dec 2010 14:41:18 +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/Dec/24/t1293201580y9akb4m5lui0r6x.htm/, Retrieved Fri, 24 Dec 2010 15:39:40 +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/Dec/24/t1293201580y9akb4m5lui0r6x.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 «

1,8 1,7 1,4 1,2 1 1,7 2,4 2 2,1 2 1,8 2,7 2,3 1,9 2 2,3 2,8 2,4 2,3 2,7 2,7 2,9 3 2,2 2,3 2,8 2,8 2,8 2,2 2,6 2,8 2,5 2,4 2,3 1,9 1,7 2 2,1 1,7 1,8 1,8 1,8 1,3 1,3 1,3 1,2 1,4 2,2 2,9 3,1 3,5 3,6 4,4 4,1 5,1 5,8 5,9 5,4 5,5 4,8 3,2

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 1.8 NA NA 0.0160590277777778 NA 2 1.7 NA NA 0.0483506944444445 NA 3 1.4 NA NA -0.00581597222222216 NA 4 1.2 NA NA 0.0441840277777779 NA 5 1 NA NA 0.145225694444444 NA 6 1.7 NA NA 0.00980902777777784 NA 7 2.4 1.96605902777778 1.8375 0.128559027777778 0.433940972222222 8 2 1.99730902777778 1.86666666666667 0.130642361111111 0.00269097222222192 9 2.1 1.87022569444444 1.9 -0.0297743055555556 0.229774305555556 10 2 1.92855902777778 1.97083333333333 -0.0422743055555558 0.0714409722222227 11 1.8 1.95147569444444 2.09166666666667 -0.140190972222222 -0.151475694444444 12 2.7 1.89105902777778 2.19583333333333 -0.304774305555555 0.808940972222222 13 2.3 2.23689236111111 2.22083333333333 0.0160590277777778 0.0631076388888889 14 1.9 2.29418402777778 2.24583333333333 0.0483506944444445 -0.394184027777778 15 2 2.29418402777778 2.3 -0.00581597222222216 -0.294184027777778 16 2.3 2.40668402777778 2.3625 0.0441840277777779 -0.106684027777778 17 2.8 2.59522569444444 2.45 0.145225694444444 0.204774305555555 18 2.4 2.48897569444444 2.47916666666667 0.00980902777777784 -0.0889756944444442 19 2.3 2.58689236111111 2.45833333333333 0.128559027777778 -0.28689236111111 20 2.7 2.62647569444444 2.49583333333333 0.130642361111111 0.0735243055555559 21 2.7 2.53689236111111 2.56666666666667 -0.0297743055555556 0.163107638888889 22 2.9 2.57855902777778 2.62083333333333 -0.0422743055555558 0.321440972222222 23 3 2.47647569444444 2.61666666666667 -0.140190972222222 0.523524305555556 24 2.2 2.29522569444444 2.6 -0.304774305555555 -0.0952256944444443 25 2.3 2.64522569444444 2.62916666666667 0.0160590277777778 -0.345225694444444 26 2.8 2.69001736111111 2.64166666666667 0.0483506944444445 0.109982638888889 27 2.8 2.61501736111111 2.62083333333333 -0.00581597222222216 0.184982638888889 28 2.8 2.62751736111111 2.58333333333333 0.0441840277777779 0.172482638888888 29 2.2 2.65772569444444 2.5125 0.145225694444444 -0.457725694444444 30 2.6 2.45564236111111 2.44583333333333 0.00980902777777784 0.144357638888888 31 2.8 2.54105902777778 2.4125 0.128559027777778 0.258940972222223 32 2.5 2.50147569444444 2.37083333333333 0.130642361111111 -0.00147569444444429 33 2.4 2.26605902777778 2.29583333333333 -0.0297743055555556 0.133940972222222 34 2.3 2.16605902777778 2.20833333333333 -0.0422743055555558 0.133940972222222 35 1.9 2.00980902777778 2.15 -0.140190972222222 -0.109809027777778 36 1.7 1.79522569444444 2.1 -0.304774305555555 -0.0952256944444443 37 2 2.02022569444444 2.00416666666667 0.0160590277777778 -0.0202256944444443 38 2.1 1.94001736111111 1.89166666666667 0.0483506944444445 0.159982638888889 39 1.7 1.79001736111111 1.79583333333333 -0.00581597222222216 -0.0900173611111115 40 1.8 1.74835069444444 1.70416666666667 0.0441840277777779 0.0516493055555556 41 1.8 1.78272569444444 1.6375 0.145225694444444 0.0172743055555558 42 1.8 1.64730902777778 1.6375 0.00980902777777784 0.152690972222222 43 1.3 1.82439236111111 1.69583333333333 0.128559027777778 -0.524392361111111 44 1.3 1.90564236111111 1.775 0.130642361111111 -0.605642361111111 45 1.3 1.86189236111111 1.89166666666667 -0.0297743055555556 -0.561892361111111 46 1.2 1.99939236111111 2.04166666666667 -0.0422743055555558 -0.799392361111111 47 1.4 2.08480902777778 2.225 -0.140190972222222 -0.684809027777778 48 2.2 2.12439236111111 2.42916666666667 -0.304774305555555 0.0756076388888891 49 2.9 2.69939236111111 2.68333333333333 0.0160590277777778 0.200607638888889 50 3.1 3.07751736111111 3.02916666666667 0.0483506944444445 0.0224826388888895 51 3.5 3.40251736111111 3.40833333333333 -0.00581597222222216 0.0974826388888892 52 3.6 3.81918402777778 3.775 0.0441840277777779 -0.219184027777777 53 4.4 4.26605902777778 4.12083333333333 0.145225694444444 0.133940972222223 54 4.1 4.40980902777778 4.4 0.00980902777777784 -0.309809027777778 55 5.1 4.64939236111111 4.52083333333333 0.128559027777778 0.450607638888888 56 5.8 NA NA 0.130642361111111 NA 57 5.9 NA NA -0.0297743055555556 NA 58 5.4 NA NA -0.0422743055555558 NA 59 5.5 NA NA -0.140190972222222 NA 60 4.8 NA NA -0.304774305555555 NA 61 3.2 NA NA NA NA

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/24/t1293201580y9akb4m5lui0r6x/1o0al1293201675.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/24/t1293201580y9akb4m5lui0r6x/1o0al1293201675.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/24/t1293201580y9akb4m5lui0r6x/2o0al1293201675.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/24/t1293201580y9akb4m5lui0r6x/2o0al1293201675.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/24/t1293201580y9akb4m5lui0r6x/3o0al1293201675.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/24/t1293201580y9akb4m5lui0r6x/3o0al1293201675.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/24/t1293201580y9akb4m5lui0r6x/4zsro1293201675.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/24/t1293201580y9akb4m5lui0r6x/4zsro1293201675.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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