ws9

*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: Tue, 01 Dec 2009 10:40:35 -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/01/t125968931123p3ahpv3575nez.htm/, Retrieved Tue, 01 Dec 2009 18:41:56 +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/01/t125968931123p3ahpv3575nez.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 «

325412 326011 328282 317480 317539 313737 312276 309391 302950 300316 304035 333476 337698 335932 323931 313927 314485 313218 309664 302963 298989 298423 310631 329765 335083 327616 309119 295916 291413 291542 284678 276475 272566 264981 263290 296806 303598 286994 276427 266424 267153 268381 262522 255542 253158 243803 250741 280445 285257 270976 261076 255603 260376 263903 264291 263276 262572 256167 264221 293860 300713 287224

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 325412 NA NA 1.08837280424321 NA 2 326011 NA NA 1.05568185749621 NA 3 328282 NA NA 1.01496970182463 NA 4 317480 NA NA 0.984632916839484 NA 5 317539 NA NA 0.989403446223187 NA 6 313737 NA NA 0.995753404241022 NA 7 312276 311557.78688835 316420.666666667 0.984631598720954 1.00230523242196 8 309391 307083.893271311 317345.958333333 0.967662846201296 1.00751295258150 9 302950 306506.898029094 317578.041666667 0.96513882515469 0.988395373637705 10 300316 301478.207485489 317248.708333333 0.950289787054787 0.996144970161582 11 304035 305501.475564377 316973.416666667 0.96380787631048 0.995199775838503 12 333476 329388.198491489 316824.541666667 1.03965493569005 1.01241028527200 13 337698 344681.227564733 316694.083333333 1.08837280424321 0.979740040923982 14 335932 333930.557985069 316317.416666667 1.05568185749621 1.00599358749019 15 323931 320613.239066425 315884.541666667 1.01496970182463 1.01034817196955 16 313927 310790.149266788 315640.625 0.984632916839484 1.01009314722687 17 314485 312489.803993357 315836.583333333 0.989403446223187 1.0063848355407 18 313218 314615.050895155 315956.791666667 0.995753404241022 0.995559491222115 19 309664 310841.508426597 315693.208333333 0.984631598720954 0.996211868766956 20 302963 305043.858395093 315237.75 0.967662846201296 0.993178494377692 21 298989 303318.1195649 314274.083333333 0.96513882515469 0.985727461415394 22 298423 297351.811657651 312906.458333333 0.950289787054787 1.00360242749616 23 310631 299931.870799148 311194.666666667 0.96380787631048 1.03567186498836 24 329765 321596.634532827 309330.166666667 1.03965493569005 1.02539941215193 25 335083 334550.47210737 307385.916666667 1.08837280424321 1.00159177145761 26 327616 322237.561810976 305241.166666667 1.05568185749621 1.01669091014343 27 309119 307572.908337383 303036.541666667 1.01496970182463 1.00502674852273 28 295916 295923.710198258 300542.166666667 0.984632916839484 0.999973945317686 29 291413 294027.16466054 297176.208333333 0.989403446223187 0.991109104957843 30 291542 292582.596175666 293830.375 0.995753404241022 0.996443410547081 31 284678 286670.771941195 291145.208333333 0.984631598720954 0.9930485695221 32 276475 278823.098251576 288140.75 0.967662846201296 0.991578537551945 33 272566 275147.56710805 285086 0.96513882515469 0.990617517955244 34 264981 268452.113394042 282495 0.950289787054787 0.987069897308102 35 263290 270112.297644686 280255.333333333 0.96380787631048 0.974742735876246 36 296806 289314.612357405 278279.458333333 1.03965493569005 1.02589356818708 37 303598 300816.719830786 276391.25 1.08837280424321 1.00924576323676 38 286994 289885.883380797 274595.875 1.05568185749621 0.990024062755074 39 276427 277000.456173468 272915 1.01496970182463 0.997929764515952 40 266424 267055.996184129 271223.916666667 0.984632916839484 0.997633469410312 41 267153 266959.477430202 269818.625 0.989403446223187 1.00072491365229 42 268381 267473.346416523 268614.041666667 0.995753404241022 1.00339343562877 43 262522 263062.178046030 267168.125 0.984631598720954 0.997946576546876 44 255542 257143.337929571 265736.5 0.967662846201296 0.993772586361894 45 253158 255211.136752124 264429.458333333 0.96513882515469 0.991955144363005 46 243803 250248.322637813 263338.958333333 0.950289787054787 0.974244292349799 47 250741 253101.450055759 262605.708333333 0.96380787631048 0.990673897540929 48 280445 272531.76596325 262136.75 1.03965493569005 1.02903600616530 49 285257 285179.659612423 262023.875 1.08837280424321 1.00027119882141 50 270976 277031.857097179 262419.833333333 1.05568185749621 0.97814021405107 51 261076 267073.375843155 263134.333333333 1.01496970182463 0.977544089431523 52 255603 259984.198469902 264041.75 0.984632916839484 0.983148212484886 53 260376 262309.240007809 265118.583333333 0.989403446223187 0.992629920288924 54 263903 265108.598040351 266239.208333333 0.995753404241022 0.99545243704179 55 264291 263332.008130396 267442.166666667 0.984631598720954 1.00364175960383 56 263276 260072.13081074 268763.166666667 0.967662846201296 1.01231915614823 57 262572 NA NA 0.96513882515469 NA 58 256167 NA NA 0.950289787054787 NA 59 264221 NA NA 0.96380787631048 NA 60 293860 NA NA 1.03965493569005 NA 61 300713 NA NA NA NA 62 287224 NA NA NA NA

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Dec/01/t125968931123p3ahpv3575nez/16ic51259689233.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/01/t125968931123p3ahpv3575nez/16ic51259689233.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/01/t125968931123p3ahpv3575nez/20y0q1259689233.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/01/t125968931123p3ahpv3575nez/20y0q1259689233.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/01/t125968931123p3ahpv3575nez/3x5hg1259689233.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/01/t125968931123p3ahpv3575nez/3x5hg1259689233.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/01/t125968931123p3ahpv3575nez/4umld1259689233.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/01/t125968931123p3ahpv3575nez/4umld1259689233.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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