Multiplicatief Decompositie Lederen Laarzen - Christine Van Bergen

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

Title produced by software: Classical Decomposition

Date of computation: Thu, 22 May 2008 11:25:26 -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/22/t1211477206mzwfzahlilkus93.htm/, Retrieved Thu, 22 May 2008 19:26:46 +0200

User-defined keywords:

Dataseries X:

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101.22 101.25 101.25 101.26 101.26 101.26 101.29 101.31 101.31 101.31 101.32 101.34 101.34 101.34 101.34 101.34 101.34 101.34 101.34 101.39 102.16 102.19 102.31 102.32 102.32 102.32 102.36 102.36 102.37 102.37 102.37 102.37 103.45 103.8 103.81 103.81 103.81 103.84 103.9 103.91 103.92 103.92 103.93 104 104.51 105 105.01 105.01 105.01 105.01 105.13 105.14 105.15 105.22 105.23 105.23 105.57 106.05 106.09 106.09 106.19 106.2 106.2 106.22 106.22 106.23 106.23 106.61 106.95 107.74 107.8 107.8

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 1 101.22 NA NA 1.00109775315131 NA 2 101.25 NA NA 1.00037964963614 NA 3 101.25 NA NA 1.00008361516197 NA 4 101.26 NA NA 0.999223613556161 NA 5 101.26 NA NA 0.998338022513017 NA 6 101.26 NA NA 0.99754716636901 NA 7 101.29 100.945513216978 101.286666666667 0.996631802971544 1.00341260123451 8 101.31 100.885197375769 101.295416666667 0.995950267994375 1.0042107527693 9 101.31 101.456114473598 101.302916666667 1.00151227439419 0.998559825848284 10 101.31 101.691037934657 101.31 1.00376110882101 0.996252984113493 11 101.32 101.642484113857 101.316666666667 1.00321583267501 0.996827270440424 12 101.34 101.552211877040 101.323333333333 1.00225889275626 0.997910317529108 13 101.34 101.439983954630 101.32875 1.00109775315131 0.999014353603654 14 101.34 101.372638146171 101.334166666667 1.00037964963614 0.99967803791272 15 101.34 101.381392979513 101.372916666667 1.00008361516197 0.999591710290253 16 101.34 101.366239477205 101.445 0.999223613556161 0.999741141850185 17 101.34 101.354187864754 101.522916666667 0.998338022513017 0.999860016985458 18 101.34 101.355779838923 101.605 0.99754716636901 0.999844312391968 19 101.34 101.344165938166 101.686666666667 0.996631802971544 0.999958893162445 20 101.39 101.356198856674 101.768333333333 0.995950267994375 1.00033348866381 21 102.16 102.005694334172 101.851666666667 1.00151227439419 1.00151271619526 22 102.19 102.320061562851 101.936666666667 1.00376110882101 0.99872887524827 23 102.31 102.350169282490 102.022083333333 1.00321583267501 0.999607530864178 24 102.32 102.338567499981 102.107916666667 1.00225889275626 0.99981856791203 25 102.32 102.305933511106 102.19375 1.00109775315131 1.00013749436041 26 102.32 102.316329615661 102.2775 1.00037964963614 1.00003587290859 27 102.36 102.380643191663 102.372083333333 1.00008361516197 0.999798368216694 28 102.36 102.413342555577 102.492916666667 0.999223613556161 0.99947914447233 29 102.37 102.451943715342 102.6225 0.998338022513017 0.99920017412681 30 102.37 102.495061831847 102.747083333333 0.99754716636901 0.998779825782705 31 102.37 102.524759361436 102.87125 0.996631802971544 0.998490517194087 32 102.37 102.579557769194 102.996666666667 0.995950267994375 0.997957119588432 33 103.45 103.280118703339 103.124166666667 1.00151227439419 1.00164485961862 34 103.8 103.641262122337 103.252916666667 1.00376110882101 1.00153160888253 35 103.81 103.714542814928 103.382083333333 1.00321583267501 1.00092038380040 36 103.81 103.745070812816 103.51125 1.00225889275626 1.00062585322537 37 103.81 103.754605384729 103.640833333333 1.00109775315131 1.00053390030318 38 103.84 103.813147666429 103.77375 1.00037964963614 1.00025866023885 39 103.9 103.894519764114 103.885833333333 1.00008361516197 1.00005274807467 40 103.91 103.899271337570 103.98 0.999223613556161 1.00010326022784 41 103.92 103.907021383155 104.08 0.998338022513017 1.00012490606190 42 103.92 103.924463792324 104.18 0.99754716636901 0.999957047723311 43 103.93 103.928764413873 104.28 0.996631802971544 1.00001188877915 44 104 103.956044035418 104.37875 0.995950267994375 1.00042283221712 45 104.51 104.636750538362 104.47875 1.00151227439419 0.998788661366969 46 105 104.974591461887 104.58125 1.00376110882101 1.0002420446487 47 105.01 105.020395423793 104.68375 1.00321583267501 0.999901015190897 48 105.01 105.025874156184 104.789166666667 1.00225889275626 0.999848854805431 49 105.01 105.012651561189 104.8975 1.00109775315131 0.999974750078685 50 105.01 105.042780985773 105.002916666667 1.00037964963614 0.999687927285764 51 105.13 105.107121147498 105.098333333333 1.00008361516197 1.00021767176431 52 105.14 105.104584821422 105.18625 0.999223613556161 1.00033695179557 53 105.15 105.100035320058 105.275 0.998338022513017 1.00047540117175 54 105.22 105.106557184471 105.365 0.99754716636901 1.00107931244794 55 105.23 105.103959414877 105.459166666667 0.996631802971544 1.00119919921024 56 105.23 105.130435393095 105.557916666667 0.995950267994375 1.00094705787656 57 105.57 105.811858273651 105.652083333333 1.00151227439419 0.99771426116508 58 106.05 106.139372581915 105.741666666667 1.00376110882101 0.99915796956642 59 106.09 106.171585591787 105.83125 1.00321583267501 0.999231568490451 60 106.09 106.157173881383 105.917916666667 1.00225889275626 0.999367222403096 61 106.19 106.118030330294 106.001666666667 1.00109775315131 1.00067820397233 62 106.2 106.141114476103 106.100833333333 1.00037964963614 1.00055478524215 63 106.2 106.224714587442 106.215833333333 1.00008361516197 0.999767336748914 64 106.22 106.261186154113 106.34375 0.999223613556161 0.999612406414763 65 106.22 106.308440301475 106.485416666667 0.998338022513017 0.99916807827089 66 106.23 106.366376126664 106.627916666667 0.99754716636901 0.998717864313607 67 106.23 NA NA 0.996631802971544 NA 68 106.61 NA NA 0.995950267994375 NA 69 106.95 NA NA 1.00151227439419 NA 70 107.74 NA NA 1.00376110882101 NA 71 107.8 NA NA 1.00321583267501 NA 72 107.8 NA NA 1.00225889275626 NA

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/22/t1211477206mzwfzahlilkus93/1ii4r1211477121.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/22/t1211477206mzwfzahlilkus93/1ii4r1211477121.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/22/t1211477206mzwfzahlilkus93/2wn4g1211477121.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/22/t1211477206mzwfzahlilkus93/2wn4g1211477121.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/22/t1211477206mzwfzahlilkus93/3vect1211477121.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/22/t1211477206mzwfzahlilkus93/3vect1211477121.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/22/t1211477206mzwfzahlilkus93/4nq5m1211477121.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/22/t1211477206mzwfzahlilkus93/4nq5m1211477121.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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