*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: Thu, 03 Dec 2009 10:45:37 -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/03/t12598623685nwny0iqri97lj9.htm/, Retrieved Thu, 03 Dec 2009 18:46:14 +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/03/t12598623685nwny0iqri97lj9.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 «

115.47 103.34 102.60 100.69 105.67 123.61 113.08 106.46 123.38 109.87 95.74 123.06 123.39 120.28 115.33 110.4 114.49 132.03 123.16 118.82 128.32 112.24 104.53 132.57 122.52 131.8 124.55 120.96 122.6 145.52 118.57 134.25 136.7 121.37 111.63 134.42 137.65 137.86 119.77 130.69 128.28 147.45 128.42 136.9 143.95 135.64 122.48 136.83 153.04 142.71 123.46 144.37 146.15 147.61 158.51 147.4 165.05 154.64 126.2 157.36 154.15 123.21 113.07 110.45 113.57 122.44 114.93 111.85 126.04 121.34

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 1 115.47 NA NA 5.81928819444444 NA 2 103.34 NA NA 3.93210069444445 NA 3 102.6 NA NA -9.31342013888889 NA 4 100.69 NA NA -4.38633680555555 NA 5 105.67 NA NA -3.89498263888889 NA 6 123.61 NA NA 10.7029340277778 NA 7 113.08 109.615225694444 110.5775 -0.962274305555566 3.46477430555557 8 106.46 112.477621527778 111.613333333333 0.864288194444442 -6.01762152777776 9 123.38 122.869392361111 112.849583333333 10.0198090277778 0.510607638888914 10 109.87 111.294913194444 113.784583333333 -2.48967013888889 -1.42491319444444 11 95.74 97.3135590277778 114.556666666667 -17.2431076388889 -1.57355902777779 12 123.06 122.226371527778 115.275 6.95137152777777 0.833628472222216 13 123.39 121.865121527778 116.045833333333 5.81928819444444 1.52487847222224 14 120.28 120.912934027778 116.980833333333 3.93210069444445 -0.632934027777793 15 115.33 108.388246527778 117.701666666667 -9.31342013888889 6.94175347222223 16 110.4 113.619913194444 118.00625 -4.38633680555555 -3.21991319444444 17 114.49 114.576267361111 118.47125 -3.89498263888889 -0.0862673611110978 18 132.03 129.936684027778 119.23375 10.7029340277778 2.09331597222223 19 123.16 118.631475694444 119.59375 -0.962274305555566 4.52852430555558 20 118.82 120.901788194444 120.0375 0.864288194444442 -2.08178819444446 21 128.32 130.921475694444 120.901666666667 10.0198090277778 -2.60147569444445 22 112.24 119.236163194444 121.725833333333 -2.48967013888889 -6.99616319444445 23 104.53 105.260642361111 122.50375 -17.2431076388889 -0.730642361111109 24 132.57 130.355121527778 123.40375 6.95137152777777 2.21487847222222 25 122.52 129.593871527778 123.774583333333 5.81928819444444 -7.07387152777775 26 131.8 128.158350694444 124.22625 3.93210069444445 3.64164930555557 27 124.55 115.904913194444 125.218333333333 -9.31342013888889 8.6450868055556 28 120.96 121.561579861111 125.947916666667 -4.38633680555555 -0.601579861111105 29 122.6 122.729184027778 126.624166666667 -3.89498263888889 -0.129184027777768 30 145.52 137.700017361111 126.997083333333 10.7029340277778 7.81998263888892 31 118.57 126.742309027778 127.704583333333 -0.962274305555566 -8.17230902777776 32 134.25 129.451788194444 128.5875 0.864288194444442 4.79821180555558 33 136.7 138.660642361111 128.640833333333 10.0198090277778 -1.9606423611111 34 121.37 126.357413194444 128.847083333333 -2.48967013888889 -4.98741319444441 35 111.63 112.246059027778 129.489166666667 -17.2431076388889 -0.616059027777766 36 134.42 136.757621527778 129.80625 6.95137152777777 -2.33762152777777 37 137.65 136.116371527778 130.297083333333 5.81928819444444 1.53362847222223 38 137.86 134.750017361111 130.817916666667 3.93210069444445 3.10998263888894 39 119.77 121.916996527778 131.230416666667 -9.31342013888889 -2.14699652777776 40 130.69 127.740746527778 132.127083333333 -4.38633680555555 2.94925347222224 41 128.28 129.278767361111 133.17375 -3.89498263888889 -0.998767361111106 42 147.45 144.429184027778 133.72625 10.7029340277778 3.02081597222221 43 128.42 133.505642361111 134.467916666667 -0.962274305555566 -5.08564236111113 44 136.9 136.175538194444 135.31125 0.864288194444442 0.724461805555563 45 143.95 145.686892361111 135.667083333333 10.0198090277778 -1.73689236111110 46 135.64 133.901163194444 136.390833333333 -2.48967013888889 1.73883680555556 47 122.48 120.462309027778 137.705416666667 -17.2431076388889 2.01769097222225 48 136.83 145.408038194444 138.456666666667 6.95137152777777 -8.57803819444445 49 153.04 145.536371527778 139.717083333333 5.81928819444444 7.5036284722222 50 142.71 145.340434027778 141.408333333333 3.93210069444445 -2.63043402777777 51 123.46 133.411579861111 142.725 -9.31342013888889 -9.9515798611111 52 144.37 140.009496527778 144.395833333333 -4.38633680555555 4.36050347222223 53 146.15 141.447517361111 145.3425 -3.89498263888889 4.70248263888888 54 147.61 157.055850694444 146.352916666667 10.7029340277778 -9.4458506944444 55 158.51 146.292309027778 147.254583333333 -0.962274305555566 12.2176909722222 56 147.4 147.352621527778 146.488333333333 0.864288194444442 0.047378472222249 57 165.05 155.262725694444 145.242916666667 10.0198090277778 9.78727430555557 58 154.64 140.906996527778 143.396666666667 -2.48967013888889 13.7330034722222 59 126.2 123.382725694444 140.625833333333 -17.2431076388889 2.81727430555557 60 157.36 145.170954861111 138.219583333333 6.95137152777777 12.1890451388889 61 154.15 NA 135.355 NA NA 62 123.21 NA 132.057916666667 NA NA 63 113.07 NA 128.95125 NA NA 64 110.45 NA 125.938333333333 NA NA 65 113.57 NA NA NA NA 66 122.44 NA NA NA NA 67 114.93 NA NA NA NA 68 111.85 NA NA NA NA 69 126.04 NA NA NA NA 70 121.34 NA NA NA NA

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Dec/03/t12598623685nwny0iqri97lj9/1and01259862335.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/03/t12598623685nwny0iqri97lj9/1and01259862335.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/03/t12598623685nwny0iqri97lj9/2gxvv1259862335.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/03/t12598623685nwny0iqri97lj9/2gxvv1259862335.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/03/t12598623685nwny0iqri97lj9/30mml1259862335.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/03/t12598623685nwny0iqri97lj9/30mml1259862335.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/03/t12598623685nwny0iqri97lj9/4ezm71259862335.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/03/t12598623685nwny0iqri97lj9/4ezm71259862335.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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