Michaël Mertens opdracht 9

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

Title produced by software: Classical Decomposition

Date of computation: Mon, 11 Aug 2008 12:38:25 -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/Aug/11/t1218479932npcmyf853pw25q0.htm/, Retrieved Mon, 11 Aug 2008 18:38:57 +0000

IsPrivate?

No (this computation is public)

User-defined keywords:

Dataseries X:

» Textbox « » Textfile « » CSV «

15,14 15,09 15,17 15,18 15,21 15,27 15,28 15,3 15,34 15,33 15,27 15,35 15,38 15,38 15,39 15,4 15,39 15,43 15,43 15,47 15,48 15,47 15,58 15,56 15,61 15,56 15,62 15,63 15,58 15,65 15,79 15,76 15,77 15,79 15,87 15,79 15,9 15,96 16,05 16,18 16,29 16,43 16,38 16,39 16,35 16,48 16,52 16,44 16,46 16,52 16,47 16,59 16,59 16,59 16,54 16,48 16,47 16,56 16,61 16,57 16,72 16,69 16,72 16,81 16,75 16,85 16,84 16,92 17,02 17,11 17,2 17,3 17,37 17,42 17,51 17,56 17,62 17,59 17,78 17,73 17,79 17,85 17,86 17,79 17,97 17,96 18,03 18,02 18,03 18,14 18,16 18,24 18,28 18,18 18,19 18,32

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 5 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Classical Decomposition by Moving Averages t Observations Fit Trend Seasonal Random 1 15.14 NA NA 1.00061085861474 NA 2 15.09 NA NA 0.999410858151133 NA 3 15.17 NA NA 0.99969937898369 NA 4 15.18 NA NA 1.00175638135208 NA 5 15.21 NA NA 1.00007670526470 NA 6 15.27 NA NA 1.00128367876243 NA 7 15.28 15.2746491773683 15.2541666666667 1.00134274858465 1.00035030739951 8 15.3 15.2626712689368 15.27625 0.999111121442556 1.00244575345989 9 15.34 15.2697470420702 15.2975 0.998185784740654 1.00460079382692 10 15.33 15.3121953283223 15.3158333333333 0.999762467706993 1.00116277720444 11 15.27 15.3549818233666 15.3325 1.00146628556116 0.994465521070352 12 15.35 15.3051344558843 15.3466666666667 0.997293730835206 1.00293140476779 13 15.38 15.3689658671313 15.3595833333333 1.00061085861474 1.00071794894751 14 15.38 15.3638598381192 15.3729166666667 0.999410858151133 1.00105052780036 15 15.39 15.3812080284799 15.3858333333333 0.99969937898369 1.00057160474677 16 15.4 15.4245438818686 15.3975 1.00175638135208 0.998408777461648 17 15.39 15.4174325075370 15.41625 1.00007670526470 0.99822068249538 18 15.43 15.4577339924279 15.4379166666667 1.00128367876243 0.998205817719371 19 15.43 15.4770038578115 15.45625 1.00134274858465 0.996962987265281 20 15.47 15.4595794191211 15.4733333333333 0.999111121442556 1.00067405332295 21 15.48 15.4623137163764 15.4904166666667 0.998185784740654 1.00114383163788 22 15.47 15.5058993064406 15.5095833333333 0.999762467706993 0.99768479688078 23 15.58 15.549850471432 15.5270833333333 1.00146628556116 1.00193889507963 24 15.56 15.5020999677242 15.5441666666667 0.997293730835206 1.00373497993151 25 15.61 15.5778433838672 15.5683333333333 1.00061085861474 1.00206425339763 26 15.56 15.5862287540578 15.5954166666667 0.999410858151133 0.998317184068597 27 15.62 15.6148877583173 15.6195833333333 0.99969937898369 1.00032739535255 28 15.63 15.6724785862533 15.645 1.00175638135208 0.99728960636191 29 15.58 15.6716186701251 15.6704166666667 1.00007670526470 0.994153847662224 30 15.65 15.7122269274467 15.6920833333333 1.00128367876243 0.996039585748474 31 15.79 15.734849615572 15.71375 1.00134274858465 1.00350498325535 32 15.76 15.7285068293094 15.7425 0.999111121442556 1.00200229882165 33 15.77 15.7484603080020 15.7770833333333 0.998185784740654 1.0013677331991 34 15.79 15.8141594006502 15.8179166666667 0.999762467706993 0.998472293086331 35 15.87 15.8936872294747 15.8704166666667 1.00146628556116 0.998509645425088 36 15.79 15.8893823665319 15.9325 0.997293730835206 0.993745359999565 37 15.9 15.9993507080586 15.9895833333333 1.00061085861474 0.993790328753242 38 15.96 16.0309665859351 16.0404166666667 0.999410858151133 0.9955731561441 39 16.05 16.0859960906634 16.0908333333333 0.99969937898369 0.997762271577059 40 16.18 16.1721045814526 16.14375 1.00175638135208 1.00048821218708 41 16.29 16.200825926661 16.1995833333333 1.00007670526470 1.00550429180232 42 16.43 16.2746145936849 16.25375 1.00128367876243 1.00954771650171 43 16.38 16.3260590633822 16.3041666666667 1.00134274858465 1.00330397779454 44 16.39 16.336299428187 16.3508333333333 0.999111121442556 1.00328719316447 45 16.35 16.3619286548739 16.3916666666667 0.998185784740654 0.999270950563012 46 16.48 16.422348235172 16.42625 0.999762467706993 1.00351056767293 47 16.52 16.4799622841469 16.4558333333333 1.00146628556116 1.00242947860940 48 16.44 16.43041421551 16.475 0.997293730835206 1.00058341709249 49 16.46 16.4984053737927 16.4883333333333 1.00061085861474 0.997672176618127 50 16.52 16.489029895921 16.49875 0.999410858151133 1.00187822475152 51 16.47 16.5025374985733 16.5075 0.99969937898369 0.99802833360772 52 16.59 16.5448414350140 16.5158333333333 1.00175638135208 1.00272946496123 53 16.59 16.5241840613632 16.5229166666667 1.00007670526470 1.00398300686995 54 16.59 16.5533052176071 16.5320833333333 1.00128367876243 1.00221676468297 55 16.54 16.5705535844949 16.5483333333333 1.00134274858465 0.998156151854604 56 16.48 16.5515246155977 16.56625 0.999111121442556 0.995678669049597 57 16.47 16.5536635076928 16.58375 0.998185784740654 0.994945921931182 58 16.56 16.5993895054951 16.6033333333333 0.999762467706993 0.997627050953768 59 16.61 16.6435351107886 16.6191666666667 1.00146628556116 0.99798509688204 60 16.57 16.5916433686617 16.6366666666667 0.997293730835206 0.998695525923455 61 16.72 16.6701769045216 16.66 1.00061085861474 1.00298875625398 62 16.69 16.6810000649242 16.6908333333333 0.999410858151133 1.00053953210484 63 16.72 16.7270533174367 16.7320833333333 0.99969937898369 0.99957832875266 64 16.81 16.8073850866267 16.7779166666667 1.00175638135208 1.00015558121384 65 16.75 16.8267072647058 16.8254166666667 1.00007670526470 0.995441338373627 66 16.85 16.9020856990427 16.8804166666667 1.00128367876243 0.996918386288525 67 16.84 16.9606600302977 16.9379166666667 1.00134274858465 0.992885888280163 68 16.92 16.9803098052168 16.9954166666667 0.999111121442556 0.9964482505968 69 17.02 17.0278017554446 17.05875 0.998185784740654 0.999541822511403 70 17.11 17.1188494210079 17.1229166666667 0.999762467706993 0.999483059825446 71 17.2 17.2156227264154 17.1904166666667 1.00146628556116 0.999092526209265 72 17.3 17.2107965598886 17.2575 0.997293730835206 1.0051829931172 73 17.37 17.3380846526469 17.3275 1.00061085861474 1.00184076545896 74 17.42 17.3901653530206 17.4004166666667 0.999410858151133 1.00171560456003 75 17.51 17.4609992781739 17.46625 0.99969937898369 1.00280629539269 76 17.56 17.5599545681175 17.5291666666667 1.00175638135208 1.00000258724374 77 17.62 17.5888490538430 17.5875 1.00007670526470 1.00177106222594 78 17.59 17.6580548765083 17.6354166666667 1.00128367876243 0.99614595848839 79 17.78 17.7045742472671 17.6808333333333 1.00134274858465 1.00426024097951 80 17.73 17.7125749979741 17.7283333333333 0.999111121442556 1.00098376447399 81 17.79 17.7402568593033 17.7725 0.998185784740654 1.00280396958687 82 17.85 17.8091020914206 17.8133333333333 0.999762467706993 1.00229646101019 83 17.86 17.8757559196478 17.8495833333333 1.00146628556116 0.999118587224024 84 17.79 17.8411693055873 17.8895833333333 0.997293730835206 0.997131953365226 85 17.97 17.9392850101980 17.9283333333333 1.00061085861474 1.00171216354412 86 17.96 17.954832487876 17.9654166666667 0.999410858151133 1.00028780620078 87 18.03 18.0016700256409 18.0070833333333 0.99969937898369 1.00157374145392 88 18.02 18.0729373150682 18.04125 1.00175638135208 0.9970709069508 89 18.03 18.0701359682516 18.06875 1.00007670526470 0.997778878458794 90 18.14 18.1278238024610 18.1045833333333 1.00128367876243 1.00067168556312 91 18.16 NA NA 1.00134274858465 NA 92 18.24 NA NA 0.999111121442556 NA 93 18.28 NA NA 0.998185784740654 NA 94 18.18 NA NA 0.999762467706993 NA 95 18.19 NA NA 1.00146628556116 NA 96 18.32 NA NA 0.997293730835206 NA

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Aug/11/t1218479932npcmyf853pw25q0/1xlju1218479900.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Aug/11/t1218479932npcmyf853pw25q0/1xlju1218479900.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Aug/11/t1218479932npcmyf853pw25q0/23ifp1218479900.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Aug/11/t1218479932npcmyf853pw25q0/23ifp1218479900.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Aug/11/t1218479932npcmyf853pw25q0/3r6nj1218479900.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Aug/11/t1218479932npcmyf853pw25q0/3r6nj1218479900.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Aug/11/t1218479932npcmyf853pw25q0/48pej1218479900.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Aug/11/t1218479932npcmyf853pw25q0/48pej1218479900.ps (open in new window)

Parameters (Session):

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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