opdracht 9 - Eigen cijferreeks - Sophie Van Landeghem

*Unverified author*

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

Title produced by software: Classical Decomposition

Date of computation: Tue, 17 May 2011 15:05:05 +0000

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2011/May/17/t1305644474z2csgwj6dywkik3.htm/, Retrieved Tue, 17 May 2011 17:01:19 +0200

Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords:

KDGP2W92

Dataseries X:

» Textbox « » Textfile « » CSV «

126.304 125.511 125.495 130.133 126.257 110.323 98.417 105.749 120.665 124.075 127.245 146.731 144.979 148.210 144.670 142.970 142.524 146.142 146.522 148.128 148.798 150.181 152.388 155.694 160.662 155.520 158.262 154.338 158.196 160.371 154.856 150.636 145.899 141.242 140.834 141.119 139.104 134.437 129.425 123.155 119.273 120.472 121.523 121.983 123.658 124.794 124.827 120.382 117.395 115.790 114.283 117.271 117.448 118.764 120.550 123.554 125.412 124.182 119.828 115.361 114.226 115.214 115.864 114.276 113.469 114.883 114.172 111.225 112.149 115.618 118.002 121.382 120.663

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 'Herman Ole Andreas Wold' @ www.yougetit.org Classical Decomposition by Moving Averages t Observations Fit Trend Seasonal Random 1 126.304 NA NA 3.37157708333334 NA 2 125.511 NA NA 1.75565208333334 NA 3 125.495 NA NA 0.447585416666668 NA 4 130.133 NA NA -1.50977291666667 NA 5 126.257 NA NA -1.58227291666667 NA 6 110.323 NA NA 0.650393750000002 NA 7 98.417 123.482677083333 123.020208333333 0.46246874999999 -25.0656770833333 8 105.749 121.214435416667 124.744125 -3.52968958333333 -15.4654354166667 9 120.665 124.48901875 126.488875 -1.99985624999999 -3.82401875000001 10 124.075 128.26266875 127.822708333333 0.439960416666667 -4.18766875000001 11 127.245 129.867410416667 129.035375 0.832035416666663 -2.62241041666668 12 146.731 131.86754375 131.205625 0.661918749999997 14.86345625 13 144.979 138.074035416667 134.702458333333 3.37157708333334 6.90496458333334 14 148.21 140.228277083333 138.472625 1.75565208333334 7.98172291666668 15 144.67 141.858210416667 141.410625 0.447585416666668 2.81178958333334 16 142.97 142.160810416667 143.670583333333 -1.50977291666667 0.809189583333335 17 142.524 144.223685416667 145.805958333333 -1.58227291666667 -1.69968541666665 18 146.142 147.877435416667 147.227041666667 0.650393750000002 -1.73543541666666 19 146.522 148.716427083333 148.253958333333 0.46246874999999 -2.19442708333335 20 148.128 145.682310416667 149.212 -3.52968958333333 2.44568958333329 21 148.798 148.083060416667 150.082916666667 -1.99985624999999 0.714939583333347 22 150.181 151.562877083333 151.122916666667 0.439960416666667 -1.38187708333336 23 152.388 153.08161875 152.249583333333 0.832035416666663 -0.693618749999985 24 155.694 154.157377083333 153.495458333333 0.661918749999997 1.53662291666669 25 160.662 157.807160416667 154.435583333333 3.37157708333334 2.85483958333336 26 155.52 156.642985416667 154.887333333333 1.75565208333334 -1.12298541666664 27 158.262 155.318627083333 154.871041666667 0.447585416666668 2.94337291666665 28 154.338 152.86801875 154.377791666667 -1.50977291666667 1.46998124999996 29 158.196 151.94164375 153.523916666667 -1.58227291666667 6.25435625 30 160.371 153.085602083333 152.435208333333 0.650393750000002 7.28539791666668 31 154.856 151.392135416667 150.929666666667 0.46246874999999 3.4638645833333 32 150.636 145.62326875 149.152958333333 -3.52968958333333 5.01273125 33 145.899 145.073102083333 147.072958333333 -1.99985624999999 0.825897916666662 34 141.242 145.012085416667 144.572125 0.439960416666667 -3.77008541666666 35 140.834 142.483077083333 141.651041666667 0.832035416666663 -1.64907708333331 36 141.119 139.028710416667 138.366791666667 0.661918749999997 2.09028958333334 37 139.104 138.687035416667 135.315458333333 3.37157708333334 0.416964583333339 38 134.437 134.488360416667 132.732708333333 1.75565208333334 -0.0513604166666539 39 129.425 131.059710416667 130.612125 0.447585416666668 -1.63471041666665 40 123.155 127.490310416667 129.000083333333 -1.50977291666667 -4.33531041666666 41 119.273 126.06551875 127.647791666667 -1.58227291666667 -6.79251875 42 120.472 126.767185416667 126.116791666667 0.650393750000002 -6.29518541666668 43 121.523 124.810677083333 124.348208333333 0.46246874999999 -3.28767708333334 44 121.983 119.13701875 122.666708333333 -3.52968958333333 2.84598125000002 45 123.658 119.258977083333 121.258833333333 -1.99985624999999 4.39902291666667 46 124.794 120.822710416667 120.38275 0.439960416666667 3.97128958333336 47 124.827 120.893577083333 120.061541666667 0.832035416666663 3.93342291666666 48 120.382 120.576252083333 119.914333333333 0.661918749999997 -0.194252083333325 49 117.395 123.174202083333 119.802625 3.37157708333334 -5.77920208333333 50 115.79 121.58319375 119.827541666667 1.75565208333334 -5.79319375 51 114.283 120.41366875 119.966083333333 0.447585416666668 -6.13066875000001 52 117.271 118.50389375 120.013666666667 -1.50977291666667 -1.23289374999999 53 117.448 118.197602083333 119.779875 -1.58227291666667 -0.749602083333329 54 118.764 120.01276875 119.362375 0.650393750000002 -1.24876875 55 120.55 119.48359375 119.021125 0.46246874999999 1.06640625000003 56 123.554 115.33539375 118.865083333333 -3.52968958333333 8.21860625000001 57 125.412 116.907102083333 118.906958333333 -1.99985624999999 8.50489791666666 58 124.182 119.288002083333 118.848041666667 0.439960416666667 4.89399791666666 59 119.828 119.38949375 118.557458333333 0.832035416666663 0.438506250000003 60 115.361 118.891877083333 118.229958333333 0.661918749999997 -3.53087708333332 61 114.226 121.174077083333 117.8025 3.37157708333334 -6.94807708333333 62 115.214 118.77869375 117.023041666667 1.75565208333334 -3.56469375 63 115.864 116.40429375 115.956708333333 0.447585416666668 -0.540293749999975 64 114.276 113.537477083333 115.04725 -1.50977291666667 0.738522916666668 65 113.469 113.032060416667 114.614333333333 -1.58227291666667 0.436939583333327 66 114.883 115.43951875 114.789125 0.650393750000002 -0.556518750000009 67 114.172 115.770677083333 115.308208333333 0.46246874999999 -1.5986770833333 68 111.225 NA NA -3.52968958333333 NA 69 112.149 NA NA -1.99985624999999 NA 70 115.618 NA NA 0.439960416666667 NA 71 118.002 NA NA 0.832035416666663 NA 72 121.382 NA NA 0.661918749999997 NA 73 120.663 NA NA NA NA

Charts produced by software:

http://www.freestatistics.org/blog/date/2011/May/17/t1305644474z2csgwj6dywkik3/1v6yz1305644703.png (open in new window) http://www.freestatistics.org/blog/date/2011/May/17/t1305644474z2csgwj6dywkik3/1v6yz1305644703.ps (open in new window) http://www.freestatistics.org/blog/date/2011/May/17/t1305644474z2csgwj6dywkik3/2n6bo1305644703.png (open in new window) http://www.freestatistics.org/blog/date/2011/May/17/t1305644474z2csgwj6dywkik3/2n6bo1305644703.ps (open in new window) http://www.freestatistics.org/blog/date/2011/May/17/t1305644474z2csgwj6dywkik3/3fcnt1305644703.png (open in new window) http://www.freestatistics.org/blog/date/2011/May/17/t1305644474z2csgwj6dywkik3/3fcnt1305644703.ps (open in new window) http://www.freestatistics.org/blog/date/2011/May/17/t1305644474z2csgwj6dywkik3/4vtj61305644703.png (open in new window) http://www.freestatistics.org/blog/date/2011/May/17/t1305644474z2csgwj6dywkik3/4vtj61305644703.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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