workshop 9 - review link 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: Fri, 11 Dec 2009 05:05:10 -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/11/t1260533182nydu6l0zifvp65f.htm/, Retrieved Fri, 11 Dec 2009 13:06:28 +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/11/t1260533182nydu6l0zifvp65f.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 «

5.4 5.4 5.6 5.7 5.8 5.8 5.8 5.9 6.1 6.4 6.4 6.3 6.2 6.2 6.3 6.4 6.5 6.6 6.6 6.6 6.8 7 7.2 7.3 7.5 7.6 7.6 7.7 7.7 7.7 7.7 7.6 7.7 7.9 7.9 7.9 7.8 7.6 7.4 7 7 7.2 7.5 7.8 7.8 7.7 7.6 7.6 7.5 7.5 7.6 7.6 7.9 7.6 7.5 7.5 7.6 7.7 7.8 7.9 7.9

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 1 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Classical Decomposition by Moving Averages t Observations Fit Trend Seasonal Random 1 5.4 NA NA 1.00516082012022 NA 2 5.4 NA NA 0.996874593128301 NA 3 5.6 NA NA 0.992777735408245 NA 4 5.7 NA NA 0.9825635645486 NA 5 5.8 NA NA 0.992173282025906 NA 6 5.8 NA NA 0.988244689581904 NA 7 5.8 5.85780640152322 5.91666666666667 0.990051786172938 0.990131732331033 8 5.9 5.94251490781407 5.98333333333333 0.99317797902185 0.99284563716312 9 6.1 6.05344409285955 6.04583333333333 1.00125884375348 1.00769081310181 10 6.4 6.18674523925211 6.10416666666667 1.01352823032117 1.03446962053567 11 6.4 6.306176141408 6.1625 1.02331458684105 1.01487809038126 12 6.3 6.35493995950023 6.225 1.02087388907634 0.991354763404476 13 6.2 6.32413682658971 6.29166666666667 1.00516082012022 0.980370945475472 14 6.2 6.33430731050275 6.35416666666667 0.996874593128301 0.978796843298074 15 6.3 6.36618722830537 6.4125 0.992777735408245 0.989603317349655 16 6.4 6.35391105074761 6.46666666666667 0.9825635645486 1.00725363463295 17 6.5 6.47393066521903 6.525 0.992173282025906 1.00402681711144 18 6.6 6.52241495124056 6.6 0.988244689581904 1.01189514149888 19 6.6 6.62922175158296 6.69583333333333 0.990051786172938 0.995591978564303 20 6.6 6.76188674050709 6.80833333333333 0.99317797902185 0.976058939358255 21 6.8 6.92954558114384 6.92083333333333 1.00125884375348 0.981305328087262 22 7 7.12425885229919 7.02916666666667 1.01352823032117 0.982558346787317 23 7.2 7.29964405279952 7.13333333333333 1.02331458684105 0.986349464154857 24 7.3 7.38006748978106 7.22916666666667 1.02087388907634 0.98915084585718 25 7.5 7.35861483729676 7.32083333333333 1.00516082012022 1.01921355660397 26 7.6 7.3851792774255 7.40833333333333 0.996874593128301 1.02908808500169 27 7.6 7.43342329386923 7.4875 0.992777735408245 1.02240915114684 28 7.7 7.43063695689878 7.5625 0.9825635645486 1.03625033017541 29 7.7 7.5694553307893 7.62916666666667 0.992173282025906 1.01724624342252 30 7.7 7.59301336495429 7.68333333333333 0.988244689581904 1.01409014180582 31 7.7 7.64402483241023 7.72083333333333 0.990051786172938 1.00732273492263 32 7.6 7.6805763711023 7.73333333333333 0.99317797902185 0.989509072339224 33 7.7 7.7347245679956 7.725 1.00125884375348 0.995510561793075 34 7.9 7.79149827059396 7.6875 1.01352823032117 1.01392565661158 35 7.9 7.80703753544155 7.62916666666667 1.02331458684105 1.01190752114825 36 7.9 7.73737335095778 7.57916666666667 1.02087388907634 1.02101832775358 37 7.8 7.58896419190765 7.55 1.00516082012022 1.0278082492888 38 7.6 7.52640317811867 7.55 0.996874593128301 1.00977848517274 39 7.4 7.50788162402485 7.5625 0.992777735408245 0.985630883726292 40 7 7.4265429420465 7.55833333333333 0.9825635645486 0.94256507430509 41 7 7.47850611327026 7.5375 0.992173282025906 0.936015815722718 42 7.2 7.42418823048405 7.5125 0.988244689581904 0.969802997509745 43 7.5 7.41301274896987 7.4875 0.990051786172938 1.01173439922145 44 7.8 7.41986715160907 7.47083333333333 0.99317797902185 1.05123175935953 45 7.8 7.48440985705723 7.475 1.00125884375348 1.04216633628705 46 7.7 7.60990779599475 7.50833333333333 1.01352823032117 1.01183880362554 47 7.6 7.74734418454249 7.57083333333333 1.02331458684105 0.980981329726325 48 7.6 7.78416340420711 7.625 1.02087388907634 0.976341272061738 49 7.5 7.681103933752 7.64166666666667 1.00516082012022 0.97642214773892 50 7.5 7.60532241674133 7.62916666666667 0.996874593128301 0.986151485634654 51 7.6 7.55338393689773 7.60833333333333 0.992777735408245 1.00617154688438 52 7.6 7.46748309056936 7.6 0.9825635645486 1.01774585999371 53 7.9 7.54878505408043 7.60833333333333 0.992173282025906 1.04652602285049 54 7.6 7.5394834442686 7.62916666666666 0.988244689581904 1.00802661829272 55 7.5 7.58214659577442 7.65833333333333 0.990051786172938 0.989165786398776 56 7.5 NA NA 0.99317797902185 NA 57 7.6 NA NA 1.00125884375348 NA 58 7.7 NA NA 1.01352823032117 NA 59 7.8 NA NA 1.02331458684105 NA 60 7.9 NA NA 1.02087388907634 NA 61 7.9 NA NA NA NA

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Dec/11/t1260533182nydu6l0zifvp65f/1qcok1260533108.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/11/t1260533182nydu6l0zifvp65f/1qcok1260533108.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/11/t1260533182nydu6l0zifvp65f/2n3yd1260533108.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/11/t1260533182nydu6l0zifvp65f/2n3yd1260533108.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/11/t1260533182nydu6l0zifvp65f/3ec4f1260533108.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/11/t1260533182nydu6l0zifvp65f/3ec4f1260533108.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/11/t1260533182nydu6l0zifvp65f/4wmtm1260533108.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/11/t1260533182nydu6l0zifvp65f/4wmtm1260533108.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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