*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: Thu, 03 Dec 2009 13:05:50 -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/03/t1259870825esjrrmonragnxbj.htm/, Retrieved Thu, 03 Dec 2009 21:07:10 +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/03/t1259870825esjrrmonragnxbj.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 «

1915 1843 1761 2858 3968 5061 4661 4269 3857 3568 3274 2987 1683 1381 1071 2772 4485 6181 5479 4782 4067 3489 2903 2330 1736 1483 1242 2334 3423 4523 3986 3462 2908 2575 2237 1904 1610 1251 941 2450 3946 5409 4741 4069 3539 3189 2960 2704 1697 1598 1456 2316 3083 4158 3469 2892 2578 2233 1947 2049

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 3 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Classical Decomposition by Moving Averages t Observations Fit Trend Seasonal Random 1 1915 NA NA 0.55395025528304 NA 2 1843 NA NA 0.476287260569315 NA 3 1761 NA NA 0.399508434052524 NA 4 2858 NA NA 0.834526828269122 NA 5 3968 NA NA 1.26664061052328 NA 6 5061 NA NA 1.73103390556832 NA 7 4661 5017.81183560472 3325.5 1.50888944086746 0.928890949422833 8 4269 4374.04448270278 3296.58333333333 1.32684177538445 0.975984587464033 9 3857 3738.34270978516 3248.58333333333 1.15076090904810 1.03174061326808 10 3568 3312.39216062001 3216.25 1.02989262669880 1.07716714295452 11 3274 2966.96491439703 3234.20833333333 0.917369757482236 1.10348456906690 12 2987 2656.12776827707 3302.41666666667 0.804298196253372 1.12456939597358 13 1683 1874.10603866507 3383.16666666667 0.55395025528304 0.89802816130874 14 1381 1637.77328137516 3438.625 0.476287260569315 0.843218054479702 15 1071 1385.79488061969 3468.75 0.399508434052524 0.772841648484857 16 2772 2899.32006116282 3474.20833333333 0.834526828269122 0.956086234538813 17 4485 4376.82385297108 3455.45833333333 1.26664061052328 1.02471567297722 18 6181 5907.36958199008 3412.625 1.73103390556832 1.04632017926289 19 5479 5111.30011054514 3387.45833333333 1.50888944086746 1.07193862256224 20 4782 4503.19041550687 3393.91666666667 1.32684177538445 1.06191378972851 21 4067 3918.67653390725 3405.29166666667 1.15076090904810 1.03785039791097 22 3489 3495.62722378684 3394.16666666667 1.02989262669880 0.998104138867628 23 2903 3056.37024201165 3331.66666666667 0.917369757482236 0.949819481977843 24 2330 2588.4996949421 3218.33333333333 0.804298196253372 0.900135319526131 25 1736 1710.06751931938 3087.04166666667 0.55395025528304 1.01516459460673 26 1483 1414.49378268077 2969.83333333333 0.476287260569315 1.04843161430473 27 1242 1145.20757239631 2866.54166666667 0.399508434052524 1.08451954906406 28 2334 2320.12367039287 2780.16666666667 0.834526828269122 1.00598085773798 29 3423 3438.08483049701 2714.33333333333 1.26664061052328 0.995612432141521 30 4523 4619.84098831091 2668.83333333333 1.73103390556832 0.979038025647217 31 3986 3992.26997896181 2645.83333333333 1.50888944086746 0.998429470202454 32 3462 3490.81014088853 2630.91666666667 1.32684177538445 0.991746861122273 33 2908 3001.99957310801 2608.70833333333 1.15076090904810 0.968687679388743 34 2575 2678.75072204357 2601 1.02989262669880 0.961268989611537 35 2237 2410.50370900426 2627.625 0.917369757482236 0.928021803759874 36 1904 2160.61305453531 2686.33333333333 0.804298196253372 0.881231369033592 37 1610 1525.97138448032 2754.70833333333 0.55395025528304 1.05506565612847 38 1251 1339.06178778810 2811.45833333333 0.476287260569315 0.934236202846497 39 941 1143.80929287713 2863.04166666667 0.399508434052524 0.82268959157782 40 2450 2432.57616050213 2914.91666666667 0.834526828269122 1.00716271078406 41 3946 3762.71426363571 2970.625 1.26664061052328 1.04871104301903 42 5409 5252.10112231974 3034.08333333333 1.73103390556832 1.02987354470642 43 4741 4633.86234329733 3071.04166666667 1.50888944086746 1.02312059546992 44 4069 4098.78009938448 3089.125 1.32684177538445 0.992734399342636 45 3539 3596.17578914652 3125.04166666667 1.15076090904810 0.984100947089662 46 3189 3234.80691607537 3140.91666666667 1.02989262669880 0.985839366223768 47 2960 2843.27289209651 3099.375 0.917369757482236 1.04105378285284 48 2704 2421.97645589281 3011.29166666667 0.804298196253372 1.11644355312415 49 1697 1609.87176689506 2906.16666666667 0.55395025528304 1.05412122561351 50 1598 1335.56901454393 2804.125 0.476287260569315 1.19649376602652 51 1456 1084.68204463736 2715.04166666667 0.399508434052524 1.34232884853071 52 2316 2199.11728029385 2635.16666666667 0.834526828269122 1.05314983459660 53 3083 3233.89180874224 2553.125 1.26664061052328 0.953340489519677 54 4158 4299.23908371711 2483.625 1.73103390556832 0.967147888040924 55 3469 NA NA 1.50888944086746 NA 56 2892 NA NA 1.32684177538445 NA 57 2578 NA NA 1.15076090904810 NA 58 2233 NA NA 1.02989262669880 NA 59 1947 NA NA 0.917369757482236 NA 60 2049 NA NA 0.804298196253372 NA

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Dec/03/t1259870825esjrrmonragnxbj/11b2j1259870747.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/03/t1259870825esjrrmonragnxbj/11b2j1259870747.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/03/t1259870825esjrrmonragnxbj/2741g1259870747.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/03/t1259870825esjrrmonragnxbj/2741g1259870747.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/03/t1259870825esjrrmonragnxbj/3nq111259870747.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/03/t1259870825esjrrmonragnxbj/3nq111259870747.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/03/t1259870825esjrrmonragnxbj/4qbmt1259870747.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/03/t1259870825esjrrmonragnxbj/4qbmt1259870747.ps (open in new window)

Parameters (Session):

par1 = FALSE ; par2 = 0.5 ; par3 = 1 ; par4 = 1 ; par5 = 12 ; par6 = 1 ; par7 = 1 ; par8 = 2 ; par9 = 1 ;

Parameters (R input):

par1 = multiplicative ; par2 = 12 ; par3 = 1 ; par4 = 1 ; par5 = 12 ; par6 = 1 ; par7 = 1 ; par8 = 2 ; par9 = 1 ;

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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