Classical_Decomposition_Reeks_B_Jeroen_Kinne

*Unverified author*

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

Title produced by software: Classical Decomposition

Date of computation: Mon, 16 Aug 2010 15:14:53 +0000

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2010/Aug/16/t12819719308avxechb3di7h4s.htm/, Retrieved Mon, 16 Aug 2010 17:18:51 +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/2010/Aug/16/t12819719308avxechb3di7h4s.htm/}, year = {2010}, } @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 = {2010}, 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:

Jeroen_Kinne

Dataseries X:

» Textbox « » Textfile « » CSV «

430 429 428 426 424 423 424 426 427 427 428 430 432 435 426 411 405 403 402 399 392 387 380 379 386 385 365 356 338 338 343 338 320 316 317 315 317 321 303 303 290 285 300 291 278 273 277 269 275 278 255 254 245 240 261 247 229 213 218 206 217 219 196 193 188 171 190 180 149 135 151 134 145 151 137 124 125 109 131 133 103 85 104 82

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 'RServer@AstonUniversity' @ vre.aston.ac.uk Classical Decomposition by Moving Averages t Observations Fit Trend Seasonal Random 1 430 NA NA 1.02692740276108 NA 2 429 NA NA 1.05845208192879 NA 3 428 NA NA 1.00352682637368 NA 4 426 NA NA 0.99314617753391 NA 5 424 NA NA 0.985689219406246 NA 6 423 NA NA 0.961430830072772 NA 7 424 442.197401121241 426.916666666667 1.03579324877121 0.95884778817085 8 426 437.626910989946 427.25 1.02428767932111 0.973431910383105 9 427 417.392634992478 427.416666666667 0.976547401035239 1.02301757195044 10 427 409.065762426125 426.708333333333 0.958654262105946 1.04384194235056 11 428 423.659264815661 425.291666666667 0.996161688603494 1.01024581673253 12 430 414.932008143991 423.666666666667 0.979383182086525 1.03631436370361 13 432 433.277786681611 421.916666666667 1.02692740276108 0.99705088347271 14 435 444.417567899851 419.875 1.05845208192879 0.978809190769945 15 426 418.763381922185 417.291666666667 1.00352682637368 1.0172809237632 16 411 411.328041861961 414.166666666667 0.99314617753391 0.999202481162053 17 405 404.625424566264 410.5 0.985689219406246 1.00092573380464 18 403 390.701453570823 406.375 0.961430830072772 1.03147811792553 19 402 416.734150422282 402.333333333333 1.03579324877121 0.964643765318125 20 399 408.007925596244 398.333333333333 1.02428767932111 0.977922179861874 21 392 384.474849682582 393.708333333333 0.976547401035239 1.01957254245273 22 387 372.79667617645 388.875 0.958654262105946 1.03809938427892 23 380 382.318554738616 383.791666666667 0.996161688603494 0.993935542207202 24 379 370.492496256815 378.291666666667 0.979383182086525 1.02296268839218 25 386 383.172287155227 373.125 1.02692740276108 1.00737974258464 26 385 389.642672660036 368.125 1.05845208192879 0.988084794131143 27 365 363.862101795991 362.583333333333 1.00352682637368 1.00312727870914 28 356 354.18075556303 356.625 0.99314617753391 1.00513648584344 29 338 346.017986395734 351.041666666667 0.985689219406246 0.976827833491394 30 338 332.414709497661 345.75 0.961430830072772 1.01680217614551 31 343 352.385494842371 340.208333333333 1.03579324877121 0.973365830944407 32 338 342.794943346133 334.666666666667 1.02428767932111 0.98601221097567 33 320 321.690989691025 329.416666666667 0.976547401035239 0.994743434708417 34 316 311.203139836143 324.625 0.958654262105946 1.01541391955873 35 317 319.186807723369 320.416666666667 0.996161688603494 0.993148815457108 36 315 309.689123702277 316.208333333333 0.979383182086525 1.01714905655786 37 317 320.615292870364 312.208333333333 1.02692740276108 0.9887238913715 38 321 326.488365104951 308.458333333333 1.05845208192879 0.983189706918998 39 303 305.82480033738 304.75 1.00352682637368 0.990763337916795 40 303 299.14390489136 301.208333333333 0.99314617753391 1.01289043515709 41 290 293.48896507821 297.75 0.985689219406246 0.988112108142532 42 285 282.820902513074 294.166666666667 0.961430830072772 1.00770486717058 43 300 300.897938768035 290.5 1.03579324877121 0.997015802860891 44 291 293.927885311855 286.958333333333 1.02428767932111 0.990038763049827 45 278 276.525672393145 283.166666666667 0.976547401035239 1.00533161204924 46 273 267.584370910322 279.125 0.958654262105946 1.02023895891697 47 277 274.151998051087 275.208333333333 0.996161688603494 1.01038840485993 48 269 265.861726303905 271.458333333333 0.979383182086525 1.011804157521 49 275 275.173755298187 267.958333333333 1.02692740276108 0.999368561518527 50 278 279.960575670165 264.5 1.05845208192879 0.992996957998562 51 255 261.544179123641 260.625 1.00352682637368 0.97497868564474 52 254 254.328183630142 256.083333333333 0.99314617753391 0.998709605732808 53 245 247.531205223394 251.125 0.985689219406246 0.989774197474984 54 240 236.552043815822 246.041666666667 0.961430830072772 1.01457588836925 55 261 249.626172953861 241 1.03579324877121 1.04556343956866 56 247 241.859928279698 236.125 1.02428767932111 1.02125226678459 57 229 225.785897014356 231.208333333333 0.976547401035239 1.01423518044371 58 213 216.855582873883 226.208333333333 0.958654262105946 0.982220504435319 59 218 220.442280340548 221.291666666667 0.996161688603494 0.988920998563546 60 206 211.587574963276 216.041666666667 0.979383182086525 0.973592140444702 61 217 215.868697788735 210.208333333333 1.02692740276108 1.00524069595478 62 219 216.409348584357 204.458333333333 1.05845208192879 1.01197106979245 63 196 199.032820564114 198.333333333333 1.00352682637368 0.984762208787888 64 193 190.435779542127 191.75 0.99314617753391 1.01346501410627 65 188 183.050702120568 185.708333333333 0.985689219406246 1.02703785247528 66 171 172.97743017726 179.916666666667 0.961430830072772 0.9885682763628 67 190 180.141709182126 173.916666666667 1.03579324877121 1.05472519863741 68 180 172.165687432557 168.083333333333 1.02428767932111 1.04550449444528 69 149 158.973778993528 162.791666666667 0.976547401035239 0.937261483895818 70 135 150.948102354099 157.458333333333 0.958654262105946 0.894347115959847 71 151 151.375069930706 151.958333333333 0.996161688603494 0.997522247680033 72 134 143.724481971198 146.75 0.979383182086525 0.932339418881007 73 145 145.524170699601 141.708333333333 1.02692740276108 0.996398050598187 74 151 145.316650414807 137.291666666667 1.05845208192879 1.03911010588924 75 137 133.887204085356 133.416666666667 1.00352682637368 1.02324939067859 76 124 128.52966780918 129.416666666667 0.99314617753391 0.964757803498684 77 125 123.580785883058 125.375 0.985689219406246 1.01148410011152 78 109 116.573488146324 121.25 0.961430830072772 0.935032499526674 79 131 NA NA 1.03579324877121 NA 80 133 NA NA 1.02428767932111 NA 81 103 NA NA 0.976547401035239 NA 82 85 NA NA 0.958654262105946 NA 83 104 NA NA 0.996161688603494 NA 84 82 NA NA 0.979383182086525 NA

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Aug/16/t12819719308avxechb3di7h4s/11vo71281971689.png (open in new window) http://www.freestatistics.org/blog/date/2010/Aug/16/t12819719308avxechb3di7h4s/11vo71281971689.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Aug/16/t12819719308avxechb3di7h4s/21vo71281971689.png (open in new window) http://www.freestatistics.org/blog/date/2010/Aug/16/t12819719308avxechb3di7h4s/21vo71281971689.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Aug/16/t12819719308avxechb3di7h4s/3u45r1281971689.png (open in new window) http://www.freestatistics.org/blog/date/2010/Aug/16/t12819719308avxechb3di7h4s/3u45r1281971689.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Aug/16/t12819719308avxechb3di7h4s/4u45r1281971689.png (open in new window) http://www.freestatistics.org/blog/date/2010/Aug/16/t12819719308avxechb3di7h4s/4u45r1281971689.ps (open in new window)

Parameters (Session):

par1 = 4 ;

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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