blog 4

*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: Mon, 07 Dec 2009 13:38:59 -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/07/t1260218370n9m0ck1raprrqyx.htm/, Retrieved Mon, 07 Dec 2009 21:39:34 +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/07/t1260218370n9m0ck1raprrqyx.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 «

8,00 8,10 7,70 7,50 7,60 7,80 7,80 7,80 7,50 7,50 7,10 7,50 7,50 7,60 7,70 7,70 7,90 8,10 8,20 8,20 8,20 7,90 7,30 6,90 6,60 6,70 6,90 7,00 7,10 7,20 7,10 6,90 7,00 6,80 6,40 6,70 6,60 6,40 6,30 6,20 6,50 6,80 6,80 6,40 6,10 5,80 6,10 7,20 7,30 6,90 6,10 5,80 6,20 7,10 7,70 7,90 7,70 7,40 7,50 8,00

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 8 NA NA 1.00552962104264 NA 2 8.1 NA NA 0.98856941755259 NA 3 7.7 NA NA 0.964370654622362 NA 4 7.5 NA NA 0.953544306799368 NA 5 7.6 NA NA 0.990249195263794 NA 6 7.8 NA NA 1.04307103653197 NA 7 7.8 8.00703233980875 7.6375 1.04838393974583 0.974143686321904 8 7.8 7.80899839456916 7.59583333333333 1.02806342002008 0.998847689023035 9 7.5 7.6838920928996 7.575 1.01437519378213 0.976067845477746 10 7.5 7.51062272967926 7.58333333333333 0.990411788529133 0.998585639292294 11 7.1 7.287384258583 7.60416666666667 0.958340943594477 0.974286485804244 12 7.5 7.74429447285886 7.62916666666667 1.01509048251563 0.968454909131487 13 7.5 7.7006810144849 7.65833333333333 1.00552962104264 0.973939835436967 14 7.6 7.60374643667533 7.69166666666667 0.98856941755259 0.999507290688014 15 7.7 7.46181794014053 7.7375 0.964370654622362 1.03192011139513 16 7.7 7.42175318792175 7.78333333333333 0.953544306799368 1.03749071210440 17 7.9 7.73219579968479 7.80833333333333 0.990249195263794 1.02170201126077 18 8.1 8.12726182631162 7.79166666666667 1.04307103653197 0.99664563208443 19 8.2 8.10313420095212 7.72916666666667 1.04838393974583 1.01195411511715 20 8.2 7.868968760737 7.65416666666667 1.02806342002008 1.04206793155854 21 8.2 7.69234521951445 7.58333333333333 1.01437519378213 1.06599479950506 22 7.9 7.44872199289619 7.52083333333333 0.990411788529133 1.06058462210486 23 7.3 7.14762620430881 7.45833333333333 0.958340943594477 1.02131809797207 24 6.9 7.49898093958425 7.3875 1.01509048251563 0.920125021731625 25 6.6 7.34455594036563 7.30416666666667 1.00552962104264 0.89862478461447 26 6.7 7.12181884561845 7.20416666666667 0.98856941755259 0.940770910526886 27 6.9 6.84703164781877 7.1 0.964370654622362 1.00773595842778 28 7 6.6787832488739 7.00416666666667 0.953544306799368 1.04809510043319 29 7.1 6.85334963888817 6.92083333333333 0.990249195263794 1.03598975305626 30 7.2 7.17111337615731 6.875 1.04307103653197 1.00402819232209 31 7.1 7.19890305292134 6.86666666666667 1.04838393974583 0.98626137174035 32 6.9 7.04651802472094 6.85416666666667 1.02806342002008 0.979207031869228 33 7 6.91465757094816 6.81666666666667 1.01437519378213 1.01234224951506 34 6.8 6.69353300414272 6.75833333333333 0.990411788529133 1.01590594918877 35 6.4 6.420884322083 6.7 0.958340943594477 0.99674743835344 36 6.7 6.75881079608326 6.65833333333333 1.01509048251563 0.99129864737191 37 6.6 6.66582344616185 6.62916666666667 1.00552962104264 0.990125234084958 38 6.4 6.52043911660729 6.59583333333333 0.98856941755259 0.981528986859101 39 6.3 6.30457315459369 6.5375 0.964370654622362 0.999274628990488 40 6.2 6.15830698141259 6.45833333333333 0.953544306799368 1.00677020790182 41 6.5 6.34172088800188 6.40416666666667 0.990249195263794 1.02495838508087 42 6.8 6.68869302176127 6.4125 1.04307103653197 1.01664106543335 43 6.8 6.77518121060741 6.4625 1.04838393974583 1.00366319196802 44 6.4 6.69526302288074 6.5125 1.02806342002008 0.955899712696619 45 6.1 6.61879813942837 6.525 1.01437519378213 0.921617470649562 46 5.8 6.43767662543936 6.5 0.990411788529133 0.900946154561492 47 6.1 6.20126452250926 6.47083333333333 0.958340943594477 0.983670343017671 48 7.2 6.56848133061158 6.47083333333333 1.01509048251563 1.09614378691240 49 7.3 6.55689107054889 6.52083333333333 1.00552962104264 1.11333251101103 50 6.9 6.5451533520461 6.62083333333333 0.98856941755259 1.05421517707342 51 6.1 6.50950191870094 6.75 0.964370654622362 0.937091666334025 52 5.8 6.56356331180232 6.88333333333333 0.953544306799368 0.883666344708017 53 6.2 6.93999644347376 7.00833333333333 0.990249195263794 0.893372215749529 54 7.1 7.40580435937701 7.1 1.04307103653197 0.958707475307553 55 7.7 NA NA 1.04838393974583 NA 56 7.9 NA NA 1.02806342002008 NA 57 7.7 NA NA 1.01437519378213 NA 58 7.4 NA NA 0.990411788529133 NA 59 7.5 NA NA 0.958340943594477 NA 60 8 NA NA 1.01509048251563 NA

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Dec/07/t1260218370n9m0ck1raprrqyx/188d71260218337.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/07/t1260218370n9m0ck1raprrqyx/188d71260218337.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/07/t1260218370n9m0ck1raprrqyx/2nfvy1260218337.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/07/t1260218370n9m0ck1raprrqyx/2nfvy1260218337.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/07/t1260218370n9m0ck1raprrqyx/3dhvb1260218337.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/07/t1260218370n9m0ck1raprrqyx/3dhvb1260218337.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/07/t1260218370n9m0ck1raprrqyx/4l8cn1260218337.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/07/t1260218370n9m0ck1raprrqyx/4l8cn1260218337.ps (open in new window)

Parameters (Session):

par1 = multiplicative ; par2 = 12 ;

Parameters (R input):

par1 = multiplicative ; 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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