*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, 04 Dec 2009 12:47:16 -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/04/t1259956150rrcigbo6sm58ai9.htm/, Retrieved Fri, 04 Dec 2009 20:49:15 +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/04/t1259956150rrcigbo6sm58ai9.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 «

6802.96 7132.68 7073.29 7264.5 7105.33 7218.71 7225.72 7354.25 7745.46 8070.26 8366.33 8667.51 8854.34 9218.1 9332.9 9358.31 9248.66 9401.2 9652.04 9957.38 10110.63 10169.26 10343.78 10750.21 11337.5 11786.96 12083.04 12007.74 11745.93 11051.51 11445.9 11924.88 12247.63 12690.91 12910.7 13202.12 13654.67 13862.82 13523.93 14211.17 14510.35 14289.23 14111.82 13086.59 13351.54 13747.69 12855.61 12926.93 12121.95 11731.65 11639.51 12163.78 12029.53 11234.18 9852.13 9709.04 9332.75 7108.6 6691.49 6143.05

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 6802.96 NA NA 1.00587694394746 NA 2 7132.68 NA NA 1.01565698086674 NA 3 7073.29 NA NA 1.01184043881666 NA 4 7264.5 NA NA 1.03324830456141 NA 5 7105.33 NA NA 1.03034158057347 NA 6 7218.71 NA NA 1.00276944965960 NA 7 7225.72 7372.14374844152 7587.72416666667 0.97158826368884 0.980138240186584 8 7354.25 7473.53048325091 7760.09083333333 0.963072552082573 0.98403960704807 9 7745.46 7781.40073593963 7941.13375 0.979885364094217 0.995381199714644 10 8070.26 8097.31793554215 8122.52625 0.996896493322154 0.996658407665657 11 8366.33 8188.07004903346 8299.07375 0.986624567474588 1.02177069198224 12 8667.51 8497.96278292833 8479.31625 1.00219906091229 1.01995151325118 13 8854.34 8722.31103789877 8671.35 1.00587694394746 1.01513692432287 14 9218.1 9019.95866113965 8880.91041666667 1.01565698086674 1.02196698968411 15 9332.9 9195.52791193196 9087.92291666667 1.01184043881666 1.01493901050420 16 9358.31 9582.27244912118 9273.93 1.03324830456141 0.976627417941794 17 9248.66 9730.3213583331 9443.78208333333 1.03034158057347 0.950498925924928 18 9401.2 9639.57759495251 9612.955 1.00276944965960 0.975270950142325 19 9652.04 9524.67325693755 9803.19916666667 0.97158826368884 1.01337229526164 20 9957.38 9643.91961478926 10013.7 0.963072552082573 1.03250342160982 21 10110.63 10029.4451642476 10235.325 0.979885364094217 1.00809464874904 22 10169.26 10427.8434504479 10460.3070833333 0.996896493322154 0.975202595658762 23 10343.78 10531.9734793043 10674.7529166667 0.986624567474588 0.982131223585574 24 10750.21 10871.4232144315 10847.56875 1.00219906091229 0.988850290156066 25 11337.5 11055.6697699281 10991.0758333333 1.00587694394746 1.02549191825886 26 11786.96 11322.3400449254 11147.7991666667 1.01565698086674 1.04103568283862 27 12083.04 11452.8397956868 11318.82 1.01184043881666 1.05502567184696 28 12007.74 11895.7158335543 11512.9304166667 1.03324830456141 1.00941718581825 29 11745.93 12080.7078082349 11724.9541666667 1.03034158057347 0.97228822900537 30 11051.51 11967.1228952022 11934.0720833333 1.00276944965960 0.92348930455379 31 11445.9 11788.0702973747 12132.78375 0.97158826368884 0.970973171287341 32 11924.88 11861.0346188733 12315.8266666667 0.963072552082573 1.00538278347364 33 12247.63 12211.6821246454 12462.3579166667 0.979885364094217 1.00294372838956 34 12690.91 12575.0563151732 12614.2045833333 0.996896493322154 1.00921297542715 35 12910.7 12649.7092601309 12821.1983333333 0.986624567474588 1.02063215323784 36 13202.12 13100.0320574146 13071.2875 1.00219906091229 1.00779295364607 37 13654.67 13395.5373640155 13317.2725 1.00587694394746 1.01934469883087 38 13862.82 13687.7624111328 13476.7570833333 1.01565698086674 1.01278935034150 39 13523.93 13731.8463816502 13571.1579166667 1.01184043881666 0.984858818262926 40 14211.17 14115.3979616302 13661.1866666667 1.03324830456141 1.00678493363277 41 14510.35 14118.6921150528 13702.92375 1.03034158057347 1.02774038004056 42 14289.23 13727.0735286052 13689.1620833333 1.00276944965960 1.04095239019615 43 14111.82 13227.0398808257 13613.8325 0.97158826368884 1.06689177073223 44 13086.59 12964.0837471976 13461.1704166667 0.963072552082573 1.00944966533627 45 13351.54 13026.4531303196 13293.8541666667 0.979885364094217 1.02495590061455 46 13747.69 13089.2796180941 13130.02875 0.996896493322154 1.05030149871623 47 12855.61 12768.2571350358 12941.3533333333 0.986624567474588 1.00684140866215 48 12926.93 12738.6436694620 12710.6920833333 1.00219906091229 1.01478072041448 49 12121.95 12478.8200950334 12405.91125 1.00587694394746 0.971401936055205 50 11731.65 12276.9496933860 12087.6929166667 1.01565698086674 0.955583454603571 51 11639.51 11918.9866754461 11779.5120833333 1.01184043881666 0.976551976853716 52 12163.78 11712.3177036557 11335.43375 1.03324830456141 1.03854594007499 53 12029.53 11129.7154086353 10801.9666666667 1.03034158057347 1.08084794249694 54 11234.18 10290.8880514833 10262.4666666667 1.00276944965960 1.09166283257553 55 9852.13 NA NA 0.97158826368884 NA 56 9709.04 NA NA 0.963072552082573 NA 57 9332.75 NA NA 0.979885364094217 NA 58 7108.6 NA NA 0.996896493322154 NA 59 6691.49 NA NA 0.986624567474588 NA 60 6143.05 NA NA 1.00219906091229 NA

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Dec/04/t1259956150rrcigbo6sm58ai9/1uu4p1259956034.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/04/t1259956150rrcigbo6sm58ai9/1uu4p1259956034.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/04/t1259956150rrcigbo6sm58ai9/2ckcp1259956034.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/04/t1259956150rrcigbo6sm58ai9/2ckcp1259956034.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/04/t1259956150rrcigbo6sm58ai9/3tmjj1259956034.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/04/t1259956150rrcigbo6sm58ai9/3tmjj1259956034.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/04/t1259956150rrcigbo6sm58ai9/4a9r01259956034.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/04/t1259956150rrcigbo6sm58ai9/4a9r01259956034.ps (open in new window)

Parameters (Session):

par1 = Aandelenkoers ; par2 = belgostat ; par3 = euronext brussel ;

Parameters (R input):

par1 = multiplicative ; par2 = 12 ; par3 = euronext brussel ;

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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