vraag 1: klassieke decompositie

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

Title produced by software: Classical Decomposition

Date of computation: Mon, 05 May 2008 08:59:04 -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/05/t12099996060sx4tmozik95kj5.htm/, Retrieved Mon, 05 May 2008 17:00:06 +0200

User-defined keywords:

vraag 1

Dataseries X:

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56421 53152 53536 52408 41454 38271 35306 26414 31917 38030 27534 18387 50556 43901 48572 43899 37532 40357 35489 29027 34485 42598 30306 26451 47460 50104 61465 53726 39477 43895 31481 29896 33842 39120 33702 25094 51442 45594 52518 48564 41745 49585 32747 33379 35645 37034 35681 20972 58552 54955 65540 51570 51145 46641 35704 33253 35193 41668 34865 21210 56126 49231 59723 48103 47472 50497 40059 34149 36860 46356 36577

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 3 seconds R Server 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 Classical Decomposition by Moving Averages t Observations Fit Trend Seasonal Random 1 56421 NA NA 1.27290607861445 NA 2 53152 NA NA 1.18369742267573 NA 3 53536 NA NA 1.37107436141492 NA 4 52408 NA NA 1.23211188309341 NA 5 41454 NA NA 0.999602689108356 NA 6 38271 NA NA 1.12129732166393 NA 7 35306 32697.8945923115 39158.125 0.835021967786033 1.07976371078956 8 26414 29505.1072587987 38528.2916666667 0.76580367263793 0.895234840812961 9 31917 32440.8642508675 37936 0.855147201889169 0.983851717179406 10 38030 36273.3889883059 37374.625 0.970535195692423 1.04842698905968 11 27534 29744.6706946587 36856.6666666667 0.80703637590645 0.92567842766349 12 18387 21544.5648372811 36780.1666666667 0.585765829517206 0.853440305658104 13 50556 46938.0403846349 36874.7083333333 1.27290607861445 1.07707947723675 14 43901 43786.3979658278 36991.2083333333 1.18369742267573 1.00261729759688 15 48572 51013.6780213616 37207.0833333333 1.37107436141492 0.952136797108823 16 43899 46209.6374434864 37504.4166666667 1.23211188309341 0.949996633357874 17 37532 37795.2275758592 37810.25 0.999602689108356 0.993035428207678 18 40357 42902.7977971747 38261.75 1.12129732166393 0.940661263882834 19 35489 32122.251323269 38468.75 0.835021967786033 1.10481048301531 20 29027 29558.6496989106 38598.2083333333 0.76580367263793 0.982013735257662 21 34485 33687.5619778217 39393.875 0.855147201889169 1.02367158605017 22 42598 39151.9155007967 40340.5416666667 0.970535195692423 1.08801828608190 23 30306 32952.1358911519 40831.0416666667 0.80703637590645 0.919697591078992 24 26451 24051.2520770617 41059.5 0.585765829517206 1.09977642391545 25 47460 52239.9593908305 41039.9166666667 1.27290607861445 0.908499940532697 26 50104 48424.0258264194 40909.125 1.18369742267573 1.03469298855082 27 61465 56102.3633856547 40918.5416666667 1.37107436141492 1.09558664360505 28 53726 50204.6575484265 40746.8333333333 1.23211188309341 1.07013975641955 29 39477 40727.2288634622 40743.4166666667 0.999602689108356 0.96930238323718 30 43895 45780.7475353904 40828.375 1.12129732166393 0.958809158065132 31 31481 34183.9205617327 40937.75 0.835021967786033 0.920930059591863 32 29896 31333.4316187354 40915.75 0.76580367263793 0.954124666706601 33 33842 34509.5009633708 40355.0416666667 0.855147201889169 0.980657472732529 34 39120 38595.4348829666 39767.1666666667 0.970535195692423 1.01359137728657 35 33702 31996.2349304064 39646.5833333333 0.80703637590645 1.05331143096379 36 25094 23417.8439600771 39978.1666666667 0.585765829517206 1.07157601881627 37 51442 51257.3819736467 40268 1.27290607861445 1.00360178415761 38 45594 47899.3519438184 40465.875 1.18369742267573 0.951870915779354 39 52518 55783.7028528225 40686.125 1.37107436141492 0.941457761213188 40 48564 50115.3294369022 40674.3333333333 1.23211188309341 0.969044812149636 41 41745 40653.7164157007 40669.875 0.999602689108356 1.0268433904822 42 49585 45502.8994032264 40580.5833333333 1.12129732166393 1.08971077997909 43 32747 33989.6387838945 40705.0833333333 0.835021967786033 0.963440659319884 44 33379 31697.6669905338 41391.375 0.76580367263793 1.05304279996280 45 35645 36193.2501727572 42324 0.855147201889169 0.984852143144363 46 37034 41725.0873773427 42991.8333333333 0.970535195692423 0.887571538558598 47 35681 35113.1439202198 43508.75 0.80703637590645 1.01617217988428 48 20972 25643.5100431469 43777.75 0.585765829517206 0.817828759195339 49 58552 55725.6535738563 43778.2916666667 1.27290607861445 1.05071894621025 50 54955 51959.8779901297 43896.25 1.18369742267573 1.05764297619096 51 65540 60152.0028963888 43872.1666666667 1.37107436141492 1.08957302906259 52 51570 54270.1133826834 44046.4166666667 1.23211188309341 0.950246770931844 53 51145 44187.9366733794 44205.5 0.999602689108356 1.15744259294215 54 46641 49540.5041756513 44181.4166666667 1.12129732166393 0.941472049509816 55 35704 36816.3273151782 44090.25 0.835021967786033 0.969787119023152 56 33253 33504.4212136912 43750.6666666667 0.76580367263793 0.992495879511314 57 35193 37002.0412700773 43269.7916666667 0.855147201889169 0.951109689952694 58 41668 41619.4203579117 42882.9583333333 0.970535195692423 1.00116723495115 59 34865 34368.0139596485 42585.4583333333 0.80703637590645 1.01446071457417 60 21210 24949.5727904455 42593.0833333333 0.585765829517206 0.850114756599057 61 56126 NA 42935.2083333333 NA NA 62 49231 NA 43154 NA NA 63 59723 NA 43260.7916666667 NA NA 64 48103 NA 43525.5833333333 NA NA 65 47472 NA 43792.25 NA NA 66 50497 NA NA NA NA 67 40059 NA NA NA NA 68 34149 NA NA NA NA 69 36860 NA NA NA NA 70 46356 NA NA NA NA 71 36577 NA NA NA NA

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/05/t12099996060sx4tmozik95kj5/1usfb1209999539.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/05/t12099996060sx4tmozik95kj5/1usfb1209999539.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/05/t12099996060sx4tmozik95kj5/2olpk1209999539.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/05/t12099996060sx4tmozik95kj5/2olpk1209999539.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/05/t12099996060sx4tmozik95kj5/3ixwr1209999539.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/05/t12099996060sx4tmozik95kj5/3ixwr1209999539.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/05/t12099996060sx4tmozik95kj5/41eo81209999539.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/05/t12099996060sx4tmozik95kj5/41eo81209999539.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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