1

*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, 27 Dec 2009 03:48:31 -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/27/t126191093145tx6jjq3j4605b.htm/, Retrieved Sun, 27 Dec 2009 11:48:56 +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/27/t126191093145tx6jjq3j4605b.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 «

2756.76 2849.27 2921.44 2981.85 3080.58 3106.22 3119.31 3061.26 3097.31 3161.69 3257.16 3277.01 3295.32 3363.99 3494.17 3667.03 3813.06 3917.96 3895.51 3801.06 3570.12 3701.61 3862.27 3970.10 4138.52 4199.75 4290.89 4443.91 4502.64 4356.98 4591.27 4696.96 4621.40 4562.84 4202.52 4296.49 4435.23 4105.18 4116.68 3844.49 3720.98 3674.40 3857.62 3801.06 3504.37 3032.60 3047.03 2962.34 2197.82 2014.45 1862.83 1905.41 1810.99 1670.07 1864.44 2052.02 2029.60 2070.83 2293.41 2443.27

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 2756.76 NA NA -46.1367881944446 NA 2 2849.27 NA NA -118.432309027778 NA 3 2921.44 NA NA -76.4974131944443 NA 4 2981.85 NA NA -29.9448090277778 NA 5 3080.58 NA NA -11.8351215277776 NA 6 3106.22 NA NA -50.1762673611113 NA 7 3119.31 3244.19414930556 3078.26166666667 165.932482638889 -124.884149305556 8 3061.26 3276.75664930556 3122.14833333333 154.608315972222 -215.496649305556 9 3097.31 3200.00529513889 3167.45875 32.5465451388886 -102.695295138888 10 3161.69 3191.04331597222 3219.87166666667 -28.8283506944444 -29.3533159722224 11 3257.16 3252.11029513889 3278.94083333333 -26.8305381944445 5.04970486111142 12 3277.01 3378.87758680556 3343.28333333333 35.5942534722221 -101.867586805555 13 3295.32 3363.31071180556 3409.4475 -46.1367881944446 -67.9907118055548 14 3363.99 3354.18185763889 3472.61416666667 -118.432309027778 9.8081423611111 15 3494.17 3446.64217013889 3523.13958333333 -76.4974131944443 47.5278298611106 16 3667.03 3535.39185763889 3565.33666666667 -29.9448090277778 131.638142361111 17 3813.06 3601.21112847222 3613.04625 -11.8351215277776 211.848871527778 18 3917.96 3616.96164930556 3667.13791666667 -50.1762673611113 300.998350694445 19 3895.51 3897.08248263889 3731.15 165.932482638889 -1.57248263888914 20 3801.06 3955.71498263889 3801.10666666667 154.608315972222 -154.654982638889 21 3570.12 3901.67321180556 3869.12666666667 32.5465451388886 -331.553211805556 22 3701.61 3905.86498263889 3934.69333333333 -28.8283506944444 -204.254982638889 23 3862.27 3968.96529513889 3995.79583333333 -26.8305381944445 -106.695295138889 24 3970.1 4078.41508680556 4042.82083333333 35.5942534722221 -108.315086805556 25 4138.52 4043.96654513889 4090.10333333333 -46.1367881944446 94.5534548611113 26 4199.75 4037.99019097222 4156.4225 -118.432309027778 161.759809027778 27 4290.89 4161.05758680555 4237.555 -76.4974131944443 129.832413194446 28 4443.91 4287.29810763889 4317.24291666667 -29.9448090277778 156.611892361111 29 4502.64 4355.46946180555 4367.30458333333 -11.8351215277776 147.170538194446 30 4356.98 4344.90498263889 4395.08125 -50.1762673611113 12.0750173611104 31 4591.27 4586.97623263889 4421.04375 165.932482638889 4.29376736111135 32 4696.96 4584.07456597222 4429.46625 154.608315972222 112.885434027779 33 4621.4 4450.81362847222 4418.26708333333 32.5465451388886 170.586371527776 34 4562.84 4357.20414930555 4386.0325 -28.8283506944444 205.635850694445 35 4202.52 4301.65696180556 4328.4875 -26.8305381944445 -99.136961805555 36 4296.49 4303.07175347222 4267.4775 35.5942534722221 -6.58175347222277 37 4435.23 4162.33112847222 4208.46791666667 -46.1367881944446 272.898871527776 38 4105.18 4022.13769097222 4140.57 -118.432309027778 83.0423090277773 39 4116.68 3980.20050347222 4056.69791666667 -76.4974131944443 136.479496527778 40 3844.49 3916.45019097222 3946.395 -29.9448090277778 -71.9601909722228 41 3720.98 3822.65446180556 3834.48958333333 -11.8351215277776 -101.674461805556 42 3674.4 3680.57831597222 3730.75458333333 -50.1762673611113 -6.17831597222221 43 3857.62 3747.87206597222 3581.93958333333 165.932482638889 109.747934027778 44 3801.06 3556.20873263889 3401.60041666667 154.608315972222 244.851267361111 45 3504.37 3253.12279513889 3220.57625 32.5465451388886 251.247204861112 46 3032.6 3017.04248263889 3045.87083333333 -28.8283506944444 15.5575173611114 47 3047.03 2858.66237847222 2885.49291666667 -26.8305381944445 188.367621527778 48 2962.34 2757.99050347222 2722.39625 35.5942534722221 204.349496527778 49 2197.82 2509.69654513889 2555.83333333333 -46.1367881944446 -311.876545138889 50 2014.45 2281.47519097222 2399.9075 -118.432309027778 -267.025190972222 51 1862.83 2189.08467013889 2265.58208333333 -76.4974131944443 -326.254670138889 52 1905.41 2134.11477430556 2164.05958333333 -29.9448090277778 -228.704774305555 53 1810.99 2080.74987847222 2092.585 -11.8351215277776 -269.759878472222 54 1670.07 1989.37998263889 2039.55625 -50.1762673611113 -319.309982638889 55 1864.44 NA NA 165.932482638889 NA 56 2052.02 NA NA 154.608315972222 NA 57 2029.6 NA NA 32.5465451388886 NA 58 2070.83 NA NA -28.8283506944444 NA 59 2293.41 NA NA -26.8305381944445 NA 60 2443.27 NA NA 35.5942534722221 NA

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Dec/27/t126191093145tx6jjq3j4605b/1o7x61261910908.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/27/t126191093145tx6jjq3j4605b/1o7x61261910908.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/27/t126191093145tx6jjq3j4605b/2vovm1261910908.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/27/t126191093145tx6jjq3j4605b/2vovm1261910908.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/27/t126191093145tx6jjq3j4605b/3okff1261910908.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/27/t126191093145tx6jjq3j4605b/3okff1261910908.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/27/t126191093145tx6jjq3j4605b/4uhcc1261910908.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/27/t126191093145tx6jjq3j4605b/4uhcc1261910908.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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