workshop 9

*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 09:14:37 -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/04/t1259943417rydtkyokoul3v3g.htm/, Retrieved Fri, 04 Dec 2009 17:17:03 +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/t1259943417rydtkyokoul3v3g.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 «

102.86 102.55 102.28 102.26 102.57 103.08 102.76 102.51 102.87 103.14 103.12 103.16 102.48 102.57 102.88 102.63 102.38 101.69 101.96 102.19 101.87 101.6 101.63 101.22 101.21 101.49 101.64 101.66 101.77 101.82 101.78 101.28 101.29 101.37 101.12 101.51 102.24 102.94 103.09 103.46 103.64 104.39 104.15 105.21 105.8 105.91 105.39 105.46 104.72 103.14 102.63 102.32 101.93 100.62 100.6 99.63 98.9 98.32 99.22 98.81

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 1 102.86 NA NA -0.0528125000000005 NA 2 102.55 NA NA -0.127812500000003 NA 3 102.28 NA NA -0.0314583333333284 NA 4 102.26 NA NA 0.0176041666666732 NA 5 102.57 NA NA 0.0209375000000035 NA 6 103.08 NA NA -0.193125000000004 NA 7 102.76 102.6478125 102.7475 -0.099687499999997 0.112187500000019 8 102.51 102.742291666667 102.7325 0.00979166666666605 -0.23229166666664 9 102.87 102.918333333333 102.758333333333 0.160000000000001 -0.0483333333333178 10 103.14 103.001979166667 102.79875 0.203229166666661 0.138020833333329 11 103.12 102.825520833333 102.80625 0.0192708333333383 0.294479166666662 12 103.16 102.814479166667 102.740416666667 0.0740624999999907 0.345520833333339 13 102.48 102.596354166667 102.649166666667 -0.0528125000000005 -0.116354166666667 14 102.57 102.4746875 102.6025 -0.127812500000003 0.0953125000000057 15 102.88 102.516041666667 102.5475 -0.0314583333333284 0.363958333333343 16 102.63 102.459270833333 102.441666666667 0.0176041666666732 0.170729166666675 17 102.38 102.336354166667 102.315416666667 0.0209375000000035 0.0436458333333434 18 101.69 101.979375 102.1725 -0.193125000000004 -0.289374999999993 19 101.96 101.9390625 102.03875 -0.099687499999997 0.0209375000000165 20 102.19 101.950625 101.940833333333 0.00979166666666605 0.239374999999995 21 101.87 102.004166666667 101.844166666667 0.160000000000001 -0.134166666666644 22 101.6 101.9553125 101.752083333333 0.203229166666661 -0.355312499999997 23 101.63 101.705520833333 101.68625 0.0192708333333383 -0.0755208333333286 24 101.22 101.7403125 101.66625 0.0740624999999907 -0.520312500000003 25 101.21 101.611354166667 101.664166666667 -0.0528125000000005 -0.401354166666664 26 101.49 101.4909375 101.61875 -0.127812500000003 -0.000937500000006253 27 101.64 101.525208333333 101.556666666667 -0.0314583333333284 0.114791666666662 28 101.66 101.540520833333 101.522916666667 0.0176041666666732 0.119479166666665 29 101.77 101.513020833333 101.492083333333 0.0209375000000035 0.256979166666667 30 101.82 101.289791666667 101.482916666667 -0.193125000000004 0.530208333333334 31 101.78 101.438229166667 101.537916666667 -0.099687499999997 0.341770833333342 32 101.28 101.651041666667 101.64125 0.00979166666666605 -0.37104166666667 33 101.29 101.922083333333 101.762083333333 0.160000000000001 -0.632083333333327 34 101.37 102.100729166667 101.8975 0.203229166666661 -0.730729166666649 35 101.12 102.0696875 102.050416666667 0.0192708333333383 -0.949687499999996 36 101.51 102.309479166667 102.235416666667 0.0740624999999907 -0.799479166666671 37 102.24 102.3884375 102.44125 -0.0528125000000005 -0.148437499999986 38 102.94 102.5759375 102.70375 -0.127812500000003 0.364062500000017 39 103.09 103.023958333333 103.055416666667 -0.0314583333333284 0.0660416666666777 40 103.46 103.450104166667 103.4325 0.0176041666666732 0.00989583333333144 41 103.64 103.820520833333 103.799583333333 0.0209375000000035 -0.180520833333333 42 104.39 103.948958333333 104.142083333333 -0.193125000000004 0.441041666666678 43 104.15 104.3103125 104.41 -0.099687499999997 -0.160312500000003 44 105.21 104.531458333333 104.521666666667 0.00979166666666605 0.678541666666646 45 105.8 104.670833333333 104.510833333333 0.160000000000001 1.12916666666666 46 105.91 104.647395833333 104.444166666667 0.203229166666661 1.26260416666668 47 105.39 104.3446875 104.325416666667 0.0192708333333383 1.04531250000002 48 105.46 104.171145833333 104.097083333333 0.0740624999999907 1.28885416666667 49 104.72 103.739270833333 103.792083333333 -0.0528125000000005 0.980729166666677 50 103.14 103.283854166667 103.411666666667 -0.127812500000003 -0.143854166666657 51 102.63 102.860208333333 102.891666666667 -0.0314583333333284 -0.230208333333309 52 102.32 102.305520833333 102.287916666667 0.0176041666666732 0.0144791666666890 53 101.93 101.735520833333 101.714583333333 0.0209375000000035 0.194479166666682 54 100.62 100.987291666667 101.180416666667 -0.193125000000004 -0.367291666666645 55 100.6 NA NA -0.099687499999997 NA 56 99.63 NA NA 0.00979166666666605 NA 57 98.9 NA NA 0.160000000000001 NA 58 98.32 NA NA 0.203229166666661 NA 59 99.22 NA NA 0.0192708333333383 NA 60 98.81 NA NA 0.0740624999999907 NA

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Dec/04/t1259943417rydtkyokoul3v3g/1tqck1259943275.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/04/t1259943417rydtkyokoul3v3g/1tqck1259943275.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/04/t1259943417rydtkyokoul3v3g/2sd6u1259943275.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/04/t1259943417rydtkyokoul3v3g/2sd6u1259943275.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/04/t1259943417rydtkyokoul3v3g/3d9gc1259943275.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/04/t1259943417rydtkyokoul3v3g/3d9gc1259943275.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/04/t1259943417rydtkyokoul3v3g/4kae31259943275.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/04/t1259943417rydtkyokoul3v3g/4kae31259943275.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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