Opgave 9 Oefening 2

*Unverified author*

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

Title produced by software: Classical Decomposition

Date of computation: Sat, 29 May 2010 16:59:25 +0000

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2010/May/29/t1275152428svo3fall0h6uiq1.htm/, Retrieved Sat, 29 May 2010 19:00:28 +0200

BibTeX entries for LaTeX users:

@Manual{KEY, author = {{YOUR NAME}}, publisher = {Office for Research Development and Education}, title = {Statistical Computations at FreeStatistics.org, URL http://www.freestatistics.org/blog/date/2010/May/29/t1275152428svo3fall0h6uiq1.htm/}, year = {2010}, } @Manual{R, title = {R: A Language and Environment for Statistical Computing}, author = {{R Development Core Team}}, organization = {R Foundation for Statistical Computing}, address = {Vienna, Austria}, year = {2010}, note = {{ISBN} 3-900051-07-0}, url = {http://www.R-project.org}, }

Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords:

KDGP2W52

Dataseries X:

» Textbox « » Textfile « » CSV «

22577.0 22792.0 23932.0 22321.0 21102.0 22824.0 23129.0 23604.0 24746.0 26911.0 27909.0 28922.0 29800.0 30506.0 30771.0 31976.0 33749.0 34371.0 33246.0 35072.0 35762.0 36179.0 37433.0 38298.0 37559.0 37511.0 39364.0 40084.0 42712.0 41938.0 40799.0 38568.0 41134.0 43955.0 43607.0 45082.0 46464.0 46496.0 46774.0 47890.0 45740.0 42660.0 39190.0 39010.0 41150.0 42530.0 44710.0 46620.0 44560.0 46120.0 48060.0 51970.0 57720.0 63490.0 65370.0 64260.0 58700.0 58630.0 59803.0 59266.0 60570.0 63062.0 63846.0 64726.0 63460.0 65220.0 66659.0 66871.0 65672.0 67182.0 68292.0 68318.0 69530.0 70500.0 72044.0 73811.0 76018.0 77818.0 79455.0 81408.0 81815.0

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 22577 NA NA 0.990463704266789 NA 2 22792 NA NA 0.99267095171111 NA 3 23932 NA NA 0.99949955870283 NA 4 22321 NA NA 1.01850702746857 NA 5 21102 NA NA 1.03515696144163 NA 6 22824 NA NA 1.03236886858370 NA 7 23129 24556.2357464479 24531.7083333333 1.00099982491155 0.941878887253543 8 23604 24724.7301921269 25154.0833333333 0.982931075821097 0.95467169172654 9 24746 25056.7174145449 25760.4583333333 0.97268135101937 0.987599436534153 10 26911 26048.9939724584 26447.7083333333 0.984924426878513 1.03309172048844 11 27909 27187.5518341622 27376.9583333333 0.993081536054336 1.02653597389859 12 28922 28291.7886622731 28385.0416666667 0.996714713140509 1.02227541514784 13 29800 29008.4120853186 29287.7083333333 0.990463704266789 1.02728821944315 14 30506 29965.8407418826 30187.0833333333 0.99267095171111 1.01802583357397 15 30771 31108.340973437 31123.9166666667 0.99949955870283 0.98915593172503 16 31976 32560.7360367285 31969.0833333333 1.01850702746857 0.98204168247091 17 33749 33903.5470642163 32752.0833333333 1.03515696144163 0.995441566514456 18 34371 34625.2216986019 33539.5833333333 1.03236886858370 0.99265790409041 19 33246 34287.7892109338 34253.5416666667 1.00099982491155 0.96961632012712 20 35072 34273.5369945754 34868.7083333333 0.982931075821097 1.02329677866486 21 35762 34548.3041513504 35518.625 0.97268135101937 1.03513040302449 22 36179 35668.5456571919 36214.5 0.984924426878513 1.01431105006955 23 37433 36670.3219083558 36925.7916666667 0.993081536054336 1.02079823824700 24 38298 37490.9671072034 37614.5416666667 0.996714713140509 1.02152606227759 25 37559 37879.8304071522 38244.5416666667 0.990463704266789 0.991530310360322 26 37511 38421.2464633992 38704.9166666667 0.99267095171111 0.976308773213115 27 39364 39054.8622149038 39074.4166666667 0.99949955870283 1.00791547498990 28 40084 40355.5400691167 39622.25 1.01850702746857 0.993271306277858 29 42712 41616.9328993184 40203.5 1.03515696144163 1.02631301790862 30 41938 42062.2349664008 40743.4166666667 1.03236886858370 0.997046401207639 31 40799 41438.5148768415 41397.125 1.00099982491155 0.98456713811433 32 38568 41423.2138182521 42142.5416666667 0.982931075821097 0.931072131902185 33 41134 41655.7273116385 42825.6666666667 0.97268135101937 0.987475256217822 34 43955 42804.4872839979 43459.6666666667 0.984924426878513 1.02687832021836 35 43607 43607.2860864766 43911.0833333333 0.993081536054336 0.999993439479906 36 45082 43922.5595022005 44067.3333333333 0.996714713140509 1.02639738009215 37 46464 43610.4883227558 44030.375 0.990463704266789 1.06543177540517 38 46496 43659.4056304201 43981.75 0.99267095171111 1.06497097998978 39 46774 43978.8134992234 44000.8333333333 0.99949955870283 1.06355756961988 40 47890 44755.3631144025 43942.125 1.01850702746857 1.07003935768736 41 45740 45473.1082383888 43928.7083333333 1.03515696144163 1.00586922187531 42 42660 45464.2345113403 44038.75 1.03236886858370 0.938319988415492 43 39190 44067.5157919936 44023.5 1.00099982491155 0.889317205557574 44 39010 43178.6877642071 43928.5 0.982931075821097 0.903454968641667 45 41150 42765.3135628139 43966.4166666667 0.97268135101937 0.96222841765345 46 42530 43523.8104237615 44190 0.984924426878513 0.97716628176427 47 44710 44548.8101394508 44859.1666666667 0.993081536054336 1.00361827532643 48 46620 46074.3835083114 46226.25 0.996714713140509 1.01184207905007 49 44560 47725.4935900952 48185 0.990463704266789 0.933672899912089 50 46120 49959.0609351374 50327.9166666667 0.99267095171111 0.923155862754872 51 48060 52085.1713784529 52111.25 0.99949955870283 0.922719436800816 52 51970 54503.7060632683 53513.3333333333 1.01850702746857 0.953513141650823 53 57720 56740.1016590399 54813.0416666667 1.03515696144163 1.01726994334357 54 63490 57780.4811442828 55968.8333333333 1.03236886858370 1.09881397216925 55 65370 57219.9861581148 57162.8333333333 1.00099982491155 1.14243299219550 56 64260 57536.6896324178 58535.8333333333 0.982931075821097 1.11685257547028 57 58700 58263.1265853848 59899.5 0.97268135101937 1.00749828305172 58 58630 60167.8020824748 61088.75 0.984924426878513 0.974441444938161 59 59803 61431.4445227586 61859.4166666667 0.993081536054336 0.973491677830313 60 59266 61966.4181924208 62170.6666666667 0.996714713140509 0.956421263787825 61 60570 61702.380883535 62296.4583333333 0.990463704266789 0.981647695480789 62 63062 62001.1936116346 62458.9583333333 0.99267095171111 1.01710945106977 63 63846 62826.7931358322 62858.25 0.99949955870283 1.01622248746589 64 64726 64680.3736549774 63505.0833333333 1.01850702746857 1.00070541251456 65 63460 66472.7336735942 64215.125 1.03515696144163 0.954677150959552 66 65220 67048.2285390367 64946 1.03236886858370 0.972732634718719 67 66659 65762.1849973016 65696.5 1.00099982491155 1.01363724460699 68 66871 65246.7190802355 66379.75 0.982931075821097 1.02489444592252 69 65672 65200.0468105172 67031.25 0.97268135101937 1.00723854065403 70 67182 66729.9841921062 67751.375 0.984924426878513 1.00677380361117 71 68292 68178.1922083277 68653.1666666667 0.993081536054336 1.00166926971787 72 68318 69472.3444588443 69701.3333333333 0.996714713140509 0.983384115393887 73 69530 NA 70759.4166666666 NA NA 74 70500 NA 71898.2916666667 NA NA 75 72044 NA 73176.625 NA NA 76 73811 NA NA NA NA 77 76018 NA NA NA NA 78 77818 NA NA NA NA 79 79455 NA NA NA NA 80 81408 NA NA NA NA 81 81815 NA NA NA NA

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/May/29/t1275152428svo3fall0h6uiq1/1v18l1275152362.png (open in new window) http://www.freestatistics.org/blog/date/2010/May/29/t1275152428svo3fall0h6uiq1/1v18l1275152362.ps (open in new window) http://www.freestatistics.org/blog/date/2010/May/29/t1275152428svo3fall0h6uiq1/2v18l1275152362.png (open in new window) http://www.freestatistics.org/blog/date/2010/May/29/t1275152428svo3fall0h6uiq1/2v18l1275152362.ps (open in new window) http://www.freestatistics.org/blog/date/2010/May/29/t1275152428svo3fall0h6uiq1/3ns761275152362.png (open in new window) http://www.freestatistics.org/blog/date/2010/May/29/t1275152428svo3fall0h6uiq1/3ns761275152362.ps (open in new window) http://www.freestatistics.org/blog/date/2010/May/29/t1275152428svo3fall0h6uiq1/4ns761275152362.png (open in new window) http://www.freestatistics.org/blog/date/2010/May/29/t1275152428svo3fall0h6uiq1/4ns761275152362.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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