multiplicatief model prijs eenpersoonskamers - Sam Roggeman

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

Title produced by software: Classical Decomposition

Date of computation: Sun, 25 May 2008 07:21:36 -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/t12117217529npgrkg73tkeo8z.htm/, Retrieved Sun, 25 May 2008 15:22:32 +0200

User-defined keywords:

Dataseries X:

» Textbox « » Textfile « » CSV «

430,00 433,87 434,55 434,55 434,55 434,55 434,71 434,71 434,71 434,71 434,73 436,34 437,55 439,58 439,65 439,76 439,76 439,76 440,06 440,13 441,18 441,14 441,14 441,19 449,06 456,46 456,79 456,87 457,25 455,93 456,00 456,22 456,22 456,58 457,61 457,61 460,43 460,43 462,18 462,37 462,59 463,19 463,48 464,30 461,41 463,35 463,35 463,35 464,27 472,28 472,36 472,56 472,56 472,56 474,15 474,59 474,97 474,99 474,99 474,99 478,34 485,70 485,75 485,85 485,84 485,85 485,84 486,00 488,79 489,71 489,71 489,71

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 6 seconds R Server 'Herman Ole Andreas Wold' @ 193.190.124.10:1001 Classical Decomposition by Moving Averages t Observations Fit Trend Seasonal Random 1 430 NA NA 0.999243518318136 NA 2 433.87 NA NA 1.00697616543581 NA 3 434.55 NA NA 1.00634489447381 NA 4 434.55 NA NA 1.00481138613321 NA 5 434.55 NA NA 1.00329589799725 NA 6 434.55 NA NA 1.00109680421736 NA 7 434.71 434.862675966241 434.64625 1.00049793588750 0.99964890993254 8 434.71 434.918671830980 435.19875 0.999356436182275 0.999520204938312 9 434.71 434.124297626871 435.649166666667 0.99649977744371 1.00134915823954 10 434.71 434.179950234911 436.07875 0.995645741130268 1.00122080663744 11 434.73 433.95230093831 436.512916666667 0.99413392907612 1.00179213028715 12 436.34 433.494114995458 436.947083333333 0.992097513704557 1.00656499109468 13 437.55 437.056208016908 437.387083333333 0.999243518318136 1.00112981345199 14 439.58 440.890248540393 437.835833333333 1.00697616543581 0.997028175277792 15 439.65 441.112415525822 438.33125 1.00634489447381 0.996684710122977 16 439.76 440.980317018047 438.86875 1.00481138613321 0.997232717717882 17 439.76 440.851979939609 439.40375 1.00329589799725 0.997523023624033 18 439.76 440.355371136771 439.872916666667 1.00109680421736 0.998647975758229 19 440.06 440.773951270776 440.554583333333 1.00049793588750 0.998380232614206 20 440.13 441.453213728067 441.7375 0.999356436182275 0.997002595774775 21 441.18 441.603858873068 443.155 0.99649977744371 0.999040183040634 22 441.14 442.646257853655 444.582083333333 0.995645741130268 0.996597152179804 23 441.14 443.407343048765 446.02375 0.99413392907612 0.994886546007166 24 441.19 443.890470191154 447.42625 0.992097513704557 0.993916359164028 25 449.06 448.424684795106 448.764166666667 0.999243518318136 1.00141677125822 26 456.46 453.238713342452 450.09875 1.00697616543581 1.00710726282358 27 456.79 454.25989226175 451.395833333333 1.00634489447381 1.00556973613861 28 456.87 454.843783446809 452.665833333333 1.00481138613321 1.00445475265779 29 457.25 455.491739251219 453.995416666667 1.00329589799725 1.00386013751132 30 455.93 455.865280499777 455.365833333333 1.00109680421736 1.00014197067202 31 456 456.751069558619 456.52375 1.00049793588750 0.998355626053936 32 456.22 456.868703154694 457.162916666667 0.999356436182275 0.998580110324444 33 456.22 455.951379627054 457.552916666667 0.99649977744371 1.00058914258175 34 456.58 456.012387075937 458.006666666667 0.995645741130268 1.00124473137167 35 457.61 455.768984234356 458.458333333333 0.99413392907612 1.00403936167078 36 457.61 455.35622383183 458.983333333333 0.992097513704557 1.00494947922135 37 460.43 459.24982291022 459.5975 0.999243518318136 1.00256979323868 38 460.43 463.45658440781 460.245833333333 1.00697616543581 0.993469540600708 39 462.18 463.722469442412 460.79875 1.00634489447381 0.996673722875092 40 462.37 463.516561723371 461.297083333333 1.00481138613321 0.99752638456087 41 462.59 463.34043945326 461.818333333333 1.00329589799725 0.998380371343919 42 463.19 462.80371560034 462.296666666667 1.00109680421736 1.00083466140534 43 463.48 462.926226193745 462.695833333333 1.00049793588750 1.00119624634536 44 464.3 463.051388306542 463.349583333333 0.999356436182275 1.00269648623239 45 461.41 462.642460424348 464.2675 0.99649977744371 0.997336041263447 46 463.35 463.091013442981 465.11625 0.995645741130268 1.00055925627901 47 463.35 463.222917590074 465.95625 0.99413392907612 1.00027434396076 48 463.35 463.073502366559 466.762083333333 0.992097513704557 1.00059709232342 49 464.27 467.243354705299 467.597083333333 0.999243518318136 0.993636389527307 50 472.28 471.738543795117 468.470416666667 1.00697616543581 1.00114778877411 51 472.36 472.442867263401 469.464166666667 1.00634489447381 0.999824598339519 52 472.56 472.777992003643 470.514166666667 1.00481138613321 0.999538912539648 53 472.56 473.038130387318 471.484166666667 1.00329589799725 0.99898923499692 54 472.56 472.972356389178 472.454166666667 1.00109680421736 0.999128159640605 55 474.15 473.761201965267 473.525416666667 1.00049793588750 1.00082066246269 56 474.59 474.36535235967 474.670833333333 0.999356436182275 1.00047357514458 57 474.97 474.12255306874 475.787916666667 0.99649977744371 1.00178740059036 58 474.99 474.823039092632 476.899583333333 0.995645741130268 1.00035162764572 59 474.99 475.202645657912 478.006666666667 0.99413392907612 0.999552515837496 60 474.99 475.327560156667 479.11375 0.992097513704557 0.999289836767396 61 478.34 479.791355186579 480.154583333333 0.999243518318136 0.996975028476671 62 485.7 484.473435700662 481.117083333333 1.00697616543581 1.00253174727230 63 485.75 485.227640526945 482.168333333333 1.00634489447381 1.00107652456173 64 485.85 485.683119572881 483.3575 1.00481138613321 1.00034359939721 65 485.84 486.181306651082 484.584166666667 1.00329589799725 0.999297984833203 66 485.85 486.343672704175 485.810833333333 1.00109680421736 0.99898493034477 67 485.84 NA NA 1.00049793588750 NA 68 486 NA NA 0.999356436182275 NA 69 488.79 NA NA 0.99649977744371 NA 70 489.71 NA NA 0.995645741130268 NA 71 489.71 NA NA 0.99413392907612 NA 72 489.71 NA NA 0.992097513704557 NA

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/25/t12117217529npgrkg73tkeo8z/1k81j1211721688.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/25/t12117217529npgrkg73tkeo8z/1k81j1211721688.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/25/t12117217529npgrkg73tkeo8z/250el1211721688.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/25/t12117217529npgrkg73tkeo8z/250el1211721688.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/25/t12117217529npgrkg73tkeo8z/3j5ke1211721688.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/25/t12117217529npgrkg73tkeo8z/3j5ke1211721688.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/25/t12117217529npgrkg73tkeo8z/4nuy41211721688.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/25/t12117217529npgrkg73tkeo8z/4nuy41211721688.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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