opgave 9-oefening 2- elke torfs mar 201

*Unverified author*

R Software Module: rwasp_decompose.wasp (opens new window with default values)

Title produced by software: Classical Decomposition

Date of computation: Mon, 01 Jun 2009 05:19:22 -0600

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2009/Jun/01/t1243855542neo2ed2tljcgbgo.htm/, Retrieved Mon, 01 Jun 2009 13:25:42 +0200

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/Jun/01/t1243855542neo2ed2tljcgbgo.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 «

356445 291705 310900 332340 257166 334551 317365 270863 317904 423141 317684 411063 371161 299023 326964 327146 303447 351994 320317 257151 320274 476982 301723 363567 338831 265802 307691 334207 303127 318863 292123 245155 284794 391604 304982 369552 356021 247577 277885 294032 310845 311023 298462 234188 297478 371017 291128 316374 326001 222302 227424 255428 278250 280335 241894 255075 255115 319482 270694 300209 283531 218924 236466 267980 219994 256052 230444 200778 240960 277837 209776 232065

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 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 Classical Decomposition by Moving Averages t Observations Fit Trend Seasonal Random 1 356445 NA NA 1.10865727819952 NA 2 291705 NA NA 0.831157667844844 NA 3 310900 NA NA 0.914563040870463 NA 4 332340 NA NA 0.993147513943636 NA 5 257166 NA NA 0.956503869185266 NA 6 334551 NA NA 1.03474533760250 NA 7 317365 312312.910955527 329040.416666667 0.949162762797964 1.01617636949115 8 270863 271788.758062098 329958.5 0.823705884413035 0.996593832398738 9 317904 318304.761946399 330932.75 0.961841225887733 0.998740948944816 10 423141 428119.735473836 331385.666666667 1.29190782383618 0.988370693847305 11 317684 325672.322667461 333097.625 0.977708330005237 0.975471287820741 12 411063 388432.158039739 335752.791666667 1.15689926541362 1.05826201948487 13 371161 373176.903873262 336602.583333333 1.10865727819952 0.994597994001402 14 299023 279397.182466133 336154.25 0.831157667844844 1.07024343395534 15 326964 307002.045831132 335681.666666667 0.914563040870463 1.06502221871136 16 327146 335707.488347552 338023.791666667 0.993147513943636 0.974497177916126 17 303447 324830.746546038 339602.125 0.956503869185266 0.934169573621297 18 351994 348665.80569664 336958.083333333 1.03474533760250 1.00954551392475 19 320317 316671.070877810 333632 0.949162762797964 1.01151330025848 20 257151 272564.860610608 330900.708333333 0.823705884413035 0.943448834247829 21 320274 316170.155729130 328713.458333333 0.961841225887733 1.01297985972587 22 476982 424010.12285672 328204.625 1.29190782383618 1.12493068982973 23 301723 321163.009635935 328485.5 0.977708330005237 0.939469960572445 24 363567 378412.157093719 327091.708333333 1.15689926541362 0.96076987270247 25 338831 359799.752766400 324536.5 1.10865727819952 0.941721047318191 26 265802 268349.157692583 322861.916666667 0.831157667844844 0.990508046626697 27 307691 293468.418165917 320883.75 0.914563040870463 1.04846375607627 28 334207 313683.655984070 315848 0.993147513943636 1.06542688350002 29 303127 298837.036523027 312426.375 0.956503869185266 1.01435552810618 30 318863 323680.284287832 312811.541666667 1.03474533760250 0.985117152567907 31 292123 297825.602416251 313777.166666667 0.949162762797964 0.980852544677202 32 245155 258424.576261518 313734.041666667 0.823705884413035 0.948652034363446 33 284794 299837.410409354 311732.75 0.961841225887733 0.949828107210451 34 391604 398962.936945140 308816.875 1.29190782383618 0.981554835640905 35 304982 300610.602830895 307464.5 0.977708330005237 1.01454172649913 36 369552 355699.573286167 307459.416666667 1.15689926541362 1.03894417579941 37 356021 340797.782764539 307396.875 1.10865727819952 1.04466935527564 38 247577 255334.994824177 307204.041666667 0.831157667844844 0.969616405970834 39 277885 281022.891878579 307275.583333333 0.914563040870463 0.98883403463112 40 294032 304842.946482968 306946.291666667 0.993147513943636 0.964536012370645 41 310845 292222.692704627 305511.25 0.956503869185266 1.06372642426574 42 311023 313236.297794687 302718.25 1.03474533760250 0.9929340954089 43 298462 284038.538705229 299251.666666667 0.949162762797964 1.05077994472342 44 234188 244597.574717132 296947.708333333 0.823705884413035 0.95744203625416 45 297478 282581.297512727 293792.041666667 0.961841225887733 1.05271651952339 46 371017 374757.913446224 290081 1.29190782383618 0.99001778665106 47 291128 280714.116101747 287114.375 0.977708330005237 1.03709782765067 48 316374 329111.907184975 284477.583333333 1.15689926541362 0.961296121754064 49 326001 311357.434936004 280841.916666667 1.10865727819952 1.04703136466616 50 222302 232188.223458644 279355.208333333 0.831157667844844 0.957421512119004 51 227424 254669.567321929 278460.375 0.914563040870463 0.893016006551391 52 255428 272666.622277051 274547.958333333 0.993147513943636 0.936777658618094 53 278250 259737.908299357 271549.25 0.956503869185266 1.07127219827807 54 280335 279406.3768415 270024.291666667 1.03474533760250 1.00332355749714 55 241894 253978.079426036 267581.166666667 0.949162762797964 0.952420777992555 56 255075 218834.628733581 265670.833333333 0.823705884413035 1.16560619987863 57 255115 255760.154545258 265906.833333333 0.961841225887733 0.997477501738277 58 319482 344689.512459334 266806.583333333 1.29190782383618 0.926868931173797 59 270694 258997.13646213 264902.25 0.977708330005237 1.04516213459982 60 300209 302486.497245250 261463.125 1.15689926541362 0.99247074740198 61 283531 288222.344406962 259974.25 1.10865727819952 0.9837231758814 62 218924 213802.669530221 257234.791666667 0.831157667844844 1.02395353847093 63 236466 232648.947064611 254382.625 0.914563040870463 1.01640692117265 64 267980 250330.403639287 252057.625 0.993147513943636 1.07050520473791 65 219994 237006.514139513 247784.166666667 0.956503869185266 0.928219212871512 66 256052 250829.081908318 242406.583333333 1.03474533760250 1.02082261774411 67 230444 NA NA 0.949162762797964 NA 68 200778 NA NA 0.823705884413035 NA 69 240960 NA NA 0.961841225887733 NA 70 277837 NA NA 1.29190782383618 NA 71 209776 NA NA 0.977708330005237 NA 72 232065 NA NA 1.15689926541362 NA

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/01/t1243855542neo2ed2tljcgbgo/1qz6s1243855158.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/01/t1243855542neo2ed2tljcgbgo/1qz6s1243855158.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/01/t1243855542neo2ed2tljcgbgo/20i9f1243855158.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/01/t1243855542neo2ed2tljcgbgo/20i9f1243855158.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/01/t1243855542neo2ed2tljcgbgo/3xx361243855158.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/01/t1243855542neo2ed2tljcgbgo/3xx361243855158.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/01/t1243855542neo2ed2tljcgbgo/4twni1243855158.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/01/t1243855542neo2ed2tljcgbgo/4twni1243855158.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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