oef 9nr 2decompositie model (eigen reeks)wauters kathleen

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

Title produced by software: Classical Decomposition

Date of computation: Sat, 24 May 2008 16:07:15 -0600

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2008/May/25/t1211667041llifto92cao2q2b.htm/, Retrieved Sun, 25 May 2008 00:10:41 +0200

User-defined keywords:

Dataseries X:

» Textbox « » Textfile « » CSV «

209 214 265 290 287 270 263 265 252 281 259 312 275 250 312 331 256 247 291 318 296 291 313 311 273 258 361 391 446 433 449 479 460 466 410 415 382 409 496 471 488 584 610 684 626 580 444 552 473 431 513 467 470 455 406 424 406 373 332 310 301 296 333 374 422 424 341 216 319 383 360 400

Text written by user:

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 1 209 NA NA 0.885786685146502 NA 2 214 NA NA 0.83739548142601 NA 3 265 NA NA 1.04207724786566 NA 4 290 NA NA 1.03442208928219 NA 5 287 NA NA 1.00921200424603 NA 6 270 NA NA 1.02878817995583 NA 7 263 282.481145244178 266.666666666667 1.05930429466567 0.931035590968955 8 265 309.992409869751 270.916666666667 1.14423528712304 0.854859640309723 9 252 294.5675344897 274.375 1.07359465873239 0.855491425545443 10 281 285.764402960724 278.041666666667 1.02777546396784 0.983327514164252 11 259 255.094983591689 278.458333333333 0.916097502050058 1.01530808780843 12 312 259.997971609022 276.208333333333 0.941311105538774 1.20000936187755 13 275 244.846202885912 276.416666666667 0.885786685146502 1.12315403203593 14 250 234.296277407319 279.791666666667 0.83739548142601 1.06702506231194 15 312 295.776258852536 283.833333333333 1.04207724786566 1.05485139750703 16 331 295.930919375481 286.083333333333 1.03442208928219 1.11850428031828 17 256 291.409966226042 288.75 1.00921200424603 0.87848745640163 18 247 299.334494192983 290.958333333333 1.02878817995583 0.82516383775255 19 291 308.080999031931 290.833333333333 1.05930429466567 0.944556791604792 20 318 333.067821493400 291.083333333333 1.14423528712304 0.954760500651672 21 296 315.055299227177 293.458333333333 1.07359465873239 0.939517604452555 22 291 306.277088262415 298 1.02777546396784 0.950120042119096 23 313 282.539737923939 308.416666666667 0.916097502050058 1.10780877160812 24 311 305.063240786691 324.083333333333 0.941311105538774 1.01946074918105 25 273 299.764977364995 338.416666666667 0.885786685146502 0.910713460924402 26 258 294.518969113206 351.708333333333 0.83739548142601 0.876004695985577 27 361 380.618714782932 365.25 1.04207724786566 0.948455727422336 28 391 392.433880121432 379.375 1.03442208928219 0.996346186723256 29 446 394.30754015896 390.708333333333 1.00921200424603 1.13109680788808 30 433 410.572216150707 399.083333333333 1.02878817995583 1.05462567355279 31 449 432.152014544647 407.958333333333 1.05930429466567 1.03898624763581 32 479 479.196202953072 418.791666666667 1.14423528712304 0.999590558205882 33 460 462.406166138198 430.708333333333 1.07359465873239 0.994796422897444 34 466 451.878612324525 439.666666666667 1.02777546396784 1.03125040063931 35 410 407.434364036763 444.75 0.916097502050058 1.00629705343903 36 415 426.217824328744 452.791666666667 0.941311105538774 0.97368053683252 37 382 412.592056385531 465.791666666667 0.885786685146502 0.92585398600853 38 409 402.822118044304 481.041666666667 0.83739548142601 1.01533650134628 39 496 517.391353565299 496.5 1.04207724786566 0.958655370991623 40 471 525.658825036901 508.166666666667 1.03442208928219 0.896018439273679 41 488 519.071374183875 514.333333333333 1.00921200424603 0.940140459040477 42 584 536.470169672803 521.458333333333 1.02878817995583 1.08859734056823 43 610 562.446442788524 530.958333333333 1.05930429466567 1.08454770729052 44 684 612.928702135578 535.666666666667 1.14423528712304 1.11595361355537 45 626 576.833463514758 537.291666666667 1.07359465873239 1.08523523615579 46 580 552.771903704034 537.833333333333 1.02777546396784 1.04925738105268 47 444 491.868017142377 536.916666666667 0.916097502050058 0.902681175693273 48 552 499.640090560768 530.791666666667 0.941311105538774 1.10479525247957 49 473 457.877900663646 516.916666666667 0.885786685146502 1.03302648875265 50 431 416.674034966226 497.583333333333 0.83739548142601 1.03438170807772 51 513 497.678725626507 477.583333333333 1.04207724786566 1.03078547180052 52 467 475.618656467875 459.791666666667 1.03442208928219 0.981879061406295 53 470 450.613159895853 446.5 1.00921200424603 1.04302324439133 54 455 444.179296695931 431.75 1.02878817995583 1.02436111584795 55 406 439.081630138919 414.5 1.05930429466567 0.924657221190392 56 424 459.648850131386 401.708333333333 1.14423528712304 0.92244329530859 57 406 417.180991139096 388.583333333333 1.07359465873239 0.973198704215726 58 373 387.685469804201 377.208333333333 1.02777546396784 0.96212014391043 59 332 340.177539094588 371.333333333333 0.916097502050058 0.975960966981085 60 310 346.441708134333 368.041666666667 0.941311105538774 0.89481142922837 61 301 322.463261171874 364.041666666667 0.885786685146502 0.933439669704158 62 296 295.321473116240 352.666666666667 0.83739548142601 1.00229758735997 63 333 354.697043242273 340.375 1.04207724786566 0.938829365353764 64 374 348.772647769646 337.166666666667 1.03442208928219 1.07233179663508 65 422 341.870566438343 338.75 1.00921200424603 1.23438529498593 66 424 353.560204511488 343.666666666667 1.02878817995583 1.19922998852724 67 341 NA NA 1.05930429466567 NA 68 216 NA NA 1.14423528712304 NA 69 319 NA NA 1.07359465873239 NA 70 383 NA NA 1.02777546396784 NA 71 360 NA NA 0.916097502050058 NA 72 400 NA NA 0.941311105538774 NA

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/25/t1211667041llifto92cao2q2b/10z7g1211666830.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/25/t1211667041llifto92cao2q2b/10z7g1211666830.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/25/t1211667041llifto92cao2q2b/2oga91211666830.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/25/t1211667041llifto92cao2q2b/2oga91211666830.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/25/t1211667041llifto92cao2q2b/3auis1211666830.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/25/t1211667041llifto92cao2q2b/3auis1211666830.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/25/t1211667041llifto92cao2q2b/4hxyj1211666831.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/25/t1211667041llifto92cao2q2b/4hxyj1211666831.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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