Van Passen Glenn 2mar01-A oefening 9 eigen reeks

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

Title produced by software: Classical Decomposition

Date of computation: Sun, 18 May 2008 09:32:38 -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/18/t1211124844f3y0m73i15emrrr.htm/, Retrieved Sun, 18 May 2008 17:34:09 +0200

User-defined keywords:

Dataseries X:

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11835.70 11542.20 13093.70 11180.20 12035.70 12112.00 10875.20 9897.30 11672.10 12385.70 11405.60 9830.90 11025.10 10853.80 12252.60 11839.40 11669.10 11601.40 11178.40 9516.40 12102.80 12989.00 11610.20 10205.50 11356.20 11307.10 12648.60 11947.20 11714.10 12192.50 11268.80 9097.40 12639.80 13040.10 11687.30 11191.70 11391.90 11793.10 13933.20 12778.10 11810.30 13698.40 11956.60 10723.80 13938.90 13979.80 13807.40 12973.90 12509.80 12934.10 14908.30 13772.10 13012.60 14049.90 11816.50 11593.20 14466.20 13615.90 14733.90 13880.70 13527.50 13584.00 16170.20 13260.60 14741.90 15486.50 13154.50 12621.20 15031.60 15452.40 15428.00 13105.90 14716.80 14180.00 16202.20 14392.40 15140.60 15960.10 14729.90 13705.20 15728.50 17315.60 16152.80

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 1 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Classical Decomposition by Moving Averages t Observations Fit Trend Seasonal Random 1 11835.7 NA NA -462.27265625 NA 2 11542.2 NA NA -338.468489583334 NA 3 13093.7 NA NA 1328.41067708333 NA 4 11180.2 NA NA 435.015885416665 NA 5 12035.7 NA NA -145.143489583333 NA 6 12112 NA NA 612.026302083333 NA 7 10875.2 10668.3825520833 11455.0833333333 -786.70078125 206.817447916666 8 9897.3 9229.04296875 11392.625 -2163.58203125 668.257031249999 9 11672.1 12150.2908854167 11328.8958333333 821.395052083334 -478.190885416667 10 12385.7 12206.37421875 11321.3166666667 885.057552083335 179.325781249998 11 11405.6 11725.2533854167 11333.5083333333 391.745052083335 -319.653385416668 12 9830.9 10719.4752604167 11296.9583333333 -577.483072916667 -888.575260416666 13 11025.1 10826.0440104167 11288.3166666667 -462.27265625 199.055989583334 14 10853.8 10946.6106770833 11285.0791666667 -338.468489583334 -92.8106770833328 15 12252.6 12615.56484375 11287.1541666667 1328.41067708333 -362.96484375 16 11839.4 11765.2533854167 11330.2375 435.015885416665 74.1466145833328 17 11669.1 11218.7565104167 11363.9 -145.143489583333 450.343489583334 18 11601.4 12000.0596354167 11388.0333333333 612.026302083333 -398.659635416667 19 11178.4 10630.73671875 11417.4375 -786.70078125 547.663281250001 20 9516.4 9286.53880208333 11450.1208333333 -2163.58203125 229.861197916671 21 12102.8 12306.9033854167 11485.5083333333 821.395052083334 -204.103385416665 22 12989 12391.5575520833 11506.5 885.057552083335 597.442447916668 23 11610.2 11904.61171875 11512.8666666667 391.745052083335 -294.411718749998 24 10205.5 10961.8877604167 11539.3708333333 -577.483072916667 -756.387760416665 25 11356.2 11105.4940104167 11567.7666666667 -462.27265625 250.705989583334 26 11307.1 11215.6065104167 11554.075 -338.468489583334 91.493489583334 27 12648.6 12887.40234375 11558.9916666667 1328.41067708333 -238.802343749998 28 11947.2 12018.51171875 11583.4958333333 435.015885416665 -71.3117187500011 29 11714.1 11443.6940104167 11588.8375 -145.143489583333 270.405989583332 30 12192.5 12245.16796875 11633.1416666667 612.026302083333 -52.66796875 31 11268.8 10889.0200520833 11675.7208333333 -786.70078125 379.779947916666 32 9097.4 9533.87630208333 11697.4583333333 -2163.58203125 -436.476302083331 33 12639.8 12592.6283854167 11771.2333333333 821.395052083334 47.1716145833343 34 13040.1 12744.43671875 11859.3791666667 885.057552083335 295.663281250001 35 11687.3 12289.7533854167 11898.0083333333 391.745052083335 -602.453385416668 36 11191.7 11387.2794270833 11964.7625 -577.483072916667 -195.579427083336 37 11391.9 11593.8940104167 12056.1666666667 -462.27265625 -201.994010416667 38 11793.1 11814.1231770833 12152.5916666667 -338.468489583334 -21.0231770833307 39 13933.2 13602.8981770833 12274.4875 1328.41067708333 330.301822916668 40 12778.1 12802.78671875 12367.7708333333 435.015885416665 -24.6867187499993 41 11810.3 12350.1190104167 12495.2625 -145.143489583333 -539.819010416666 42 13698.4 13269.8846354167 12657.8583333333 612.026302083333 428.515364583332 43 11956.6 11991.9950520833 12778.6958333333 -786.70078125 -35.3950520833332 44 10723.8 10709.2346354167 12872.8166666667 -2163.58203125 14.5653645833299 45 13938.9 13782.3825520833 12960.9875 821.395052083334 156.517447916667 46 13979.8 13928.0908854167 13043.0333333333 885.057552083335 51.709114583331 47 13807.4 13526.2908854167 13134.5458333333 391.745052083335 281.109114583334 48 12973.9 12621.8044270833 13199.2875 -577.483072916667 352.095572916667 49 12509.8 12745.8231770833 13208.0958333333 -462.27265625 -236.023177083334 50 12934.1 12900.01484375 13238.4833333333 -338.468489583334 34.0851562500011 51 14908.3 14625.08984375 13296.6791666667 1328.41067708333 283.210156249999 52 13772.1 13738.5033854167 13303.4875 435.015885416665 33.5966145833354 53 13012.6 13181.7856770833 13326.9291666667 -145.143489583333 -169.185677083331 54 14049.9 14015.34296875 13403.3166666667 612.026302083333 34.5570312500004 55 11816.5 12696.8033854167 13483.5041666667 -786.70078125 -880.303385416666 56 11593.2 11389.40546875 13552.9875 -2163.58203125 203.794531250000 57 14466.2 14454.0408854167 13632.6458333333 821.395052083334 12.1591145833354 58 13615.9 14548.9700520833 13663.9125 885.057552083335 -933.070052083334 59 14733.9 14106.39921875 13714.6541666667 391.745052083335 627.500781249997 60 13880.7 13269.08359375 13846.5666666667 -577.483072916667 611.616406249999 61 13527.5 13499.90234375 13962.175 -462.27265625 27.59765625 62 13584 13722.28984375 14060.7583333333 -338.468489583334 -138.289843749997 63 16170.2 15455.5606770833 14127.15 1328.41067708333 714.639322916668 64 13260.6 14662.2450520833 14227.2291666667 435.015885416665 -1401.64505208333 65 14741.9 14187.52734375 14332.6708333333 -145.143489583333 554.372656250001 66 15486.5 14941.3346354167 14329.3083333333 612.026302083333 545.165364583336 67 13154.5 13559.8783854167 14346.5791666667 -786.70078125 -405.378385416665 68 12621.2 12257.3846354167 14420.9666666667 -2163.58203125 363.815364583337 69 15031.6 15268.5283854167 14447.1333333333 821.395052083334 -236.928385416664 70 15452.4 15380.6825520833 14495.625 885.057552083335 71.7174479166642 71 15428 14951.1408854167 14559.3958333333 391.745052083335 476.859114583332 72 13105.9 14018.25859375 14595.7416666667 -577.483072916667 -912.358593750001 73 14716.8 NA 14681.1166666667 NA NA 74 14180 NA 14791.925 NA NA 75 16202.2 NA 14866.1291666667 NA NA 76 14392.4 NA 14972.8 NA NA 77 15140.6 NA 15080.6333333333 NA NA 78 15960.1 NA NA NA NA 79 14729.9 NA NA NA NA 80 13705.2 NA NA NA NA 81 15728.5 NA NA NA NA 82 17315.6 NA NA NA NA 83 16152.8 NA NA NA NA

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/18/t1211124844f3y0m73i15emrrr/1eig01211124755.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/18/t1211124844f3y0m73i15emrrr/1eig01211124755.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/18/t1211124844f3y0m73i15emrrr/26ckc1211124755.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/18/t1211124844f3y0m73i15emrrr/26ckc1211124755.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/18/t1211124844f3y0m73i15emrrr/3hzu91211124755.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/18/t1211124844f3y0m73i15emrrr/3hzu91211124755.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/18/t1211124844f3y0m73i15emrrr/4wyrf1211124755.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/18/t1211124844f3y0m73i15emrrr/4wyrf1211124755.ps (open in new window)

Parameters (Session):

par1 = additive ; par2 = 12 ;

Parameters (R input):

par1 = additive ; 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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