Klassieke decompositie (additief model)

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

Title produced by software: Classical Decomposition

Date of computation: Wed, 14 May 2008 08:16:02 -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/14/t12107746504taaaziosuarr3l.htm/, Retrieved Wed, 14 May 2008 16:17:30 +0200

User-defined keywords:

Dataseries X:

» Textbox « » Textfile « » CSV «

1.74 1.73 1.74 1.73 1.74 1.73 1.73 1.74 1.75 1.73 1.74 1.74 1.74 1.74 1.76 1.76 1.78 1.77 1.78 1.79 1.83 1.82 1.84 1.83 1.83 1.84 1.83 1.83 1.83 1.83 1.83 1.84 1.83 1.84 1.84 1.83 1.84 1.85 1.85 1.85 1.85 1.84 1.84 1.85 1.84 1.84 1.84 1.84 1.85 1.84 1.85 1.84 1.84 1.84 1.84 1.84 1.83 1.83 1.82 1.83 1.83 1.83 1.87 1.87 1.86 1.87 1.87 1.89 1.89 1.88 1.88 1.87

Text written by user:

Eigen reeks (prijzen kattenvoeding). Veronique Van Hoof 2MAR02

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 4 seconds R Server 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 Classical Decomposition by Moving Averages t Observations Fit Trend Seasonal Random 1 1.74 NA NA 0.00031250000000003 NA 2 1.73 NA NA 0.000625000000000024 NA 3 1.74 NA NA 0.00375000000000001 NA 4 1.73 NA NA -0.000625000000000005 NA 5 1.74 NA NA 0.00249999999999987 NA 6 1.73 NA NA -0.00427083333333335 NA 7 1.73 1.73302083333333 1.73666666666667 -0.00364583333333348 -0.00302083333333303 8 1.74 1.7390625 1.73708333333333 0.00197916666666662 0.00093750000000048 9 1.75 1.74072916666667 1.73833333333333 0.00239583333333336 0.00927083333333356 10 1.73 1.74052083333333 1.74041666666667 0.000104166666666775 -0.0105208333333335 11 1.74 1.74395833333333 1.74333333333333 0.000625000000000024 -0.00395833333333351 12 1.74 1.74291666666667 1.74666666666667 -0.00374999999999988 -0.0029166666666669 13 1.74 1.75072916666667 1.75041666666667 0.00031250000000003 -0.0107291666666667 14 1.74 1.75520833333333 1.75458333333333 0.000625000000000024 -0.0152083333333333 15 1.76 1.76375 1.76 0.00375000000000001 -0.00374999999999992 16 1.76 1.76645833333333 1.76708333333333 -0.000625000000000005 -0.00645833333333323 17 1.78 1.7775 1.775 0.00249999999999987 0.00249999999999995 18 1.77 1.77864583333333 1.78291666666667 -0.00427083333333335 -0.00864583333333324 19 1.78 1.78677083333333 1.79041666666667 -0.00364583333333348 -0.00677083333333295 20 1.79 1.8003125 1.79833333333333 0.00197916666666662 -0.0103124999999997 21 1.83 1.8078125 1.80541666666667 0.00239583333333336 0.0221875000000000 22 1.82 1.81135416666667 1.81125 0.000104166666666775 0.00864583333333346 23 1.84 1.816875 1.81625 0.000625000000000024 0.0231249999999998 24 1.83 1.81708333333333 1.82083333333333 -0.00374999999999988 0.0129166666666667 25 1.83 1.82572916666667 1.82541666666667 0.00031250000000003 0.00427083333333345 26 1.84 1.83020833333333 1.82958333333333 0.000625000000000024 0.00979166666666664 27 1.83 1.83541666666667 1.83166666666667 0.00375000000000001 -0.0054166666666664 28 1.83 1.831875 1.8325 -0.000625000000000005 -0.00187499999999985 29 1.83 1.83583333333333 1.83333333333333 0.00249999999999987 -0.00583333333333336 30 1.83 1.8290625 1.83333333333333 -0.00427083333333335 0.000937500000000036 31 1.83 1.83010416666667 1.83375 -0.00364583333333348 -0.000104166666666350 32 1.84 1.8365625 1.83458333333333 0.00197916666666662 0.00343749999999976 33 1.83 1.83822916666667 1.83583333333333 0.00239583333333336 -0.00822916666666673 34 1.84 1.83760416666667 1.8375 0.000104166666666775 0.00239583333333315 35 1.84 1.83979166666667 1.83916666666667 0.000625000000000024 0.000208333333333366 36 1.83 1.83666666666667 1.84041666666667 -0.00374999999999988 -0.0066666666666666 37 1.84 1.8415625 1.84125 0.00031250000000003 -0.00156249999999991 38 1.85 1.84270833333333 1.84208333333333 0.000625000000000024 0.00729166666666647 39 1.85 1.84666666666667 1.84291666666667 0.00375000000000001 0.00333333333333341 40 1.85 1.84270833333333 1.84333333333333 -0.000625000000000005 0.0072916666666667 41 1.85 1.84583333333333 1.84333333333333 0.00249999999999987 0.00416666666666665 42 1.84 1.83947916666667 1.84375 -0.00427083333333335 0.000520833333333526 43 1.84 1.8409375 1.84458333333333 -0.00364583333333348 -0.000937500000000258 44 1.85 1.8465625 1.84458333333333 0.00197916666666662 0.00343749999999998 45 1.84 1.8465625 1.84416666666667 0.00239583333333336 -0.00656250000000003 46 1.84 1.84385416666667 1.84375 0.000104166666666775 -0.00385416666666671 47 1.84 1.84354166666667 1.84291666666667 0.000625000000000024 -0.00354166666666655 48 1.84 1.83875 1.8425 -0.00374999999999988 0.00125000000000020 49 1.85 1.8428125 1.8425 0.00031250000000003 0.00718750000000035 50 1.84 1.84270833333333 1.84208333333333 0.000625000000000024 -0.00270833333333331 51 1.85 1.845 1.84125 0.00375000000000001 0.00500000000000012 52 1.84 1.83979166666667 1.84041666666667 -0.000625000000000005 0.000208333333333588 53 1.84 1.84166666666667 1.83916666666667 0.00249999999999987 -0.00166666666666671 54 1.84 1.83364583333333 1.83791666666667 -0.00427083333333335 0.00635416666666688 55 1.84 1.83302083333333 1.83666666666667 -0.00364583333333348 0.00697916666666676 56 1.84 1.83739583333333 1.83541666666667 0.00197916666666662 0.00260416666666674 57 1.83 1.83822916666667 1.83583333333333 0.00239583333333336 -0.00822916666666651 58 1.83 1.83802083333333 1.83791666666667 0.000104166666666775 -0.00802083333333314 59 1.82 1.840625 1.84 0.000625000000000024 -0.0206250000000001 60 1.83 1.83833333333333 1.84208333333333 -0.00374999999999988 -0.0083333333333333 61 1.83 1.84489583333333 1.84458333333333 0.00031250000000003 -0.0148958333333333 62 1.83 1.84854166666667 1.84791666666667 0.000625000000000024 -0.0185416666666669 63 1.87 1.85625 1.8525 0.00375000000000001 0.0137500000000002 64 1.87 1.85645833333333 1.85708333333333 -0.000625000000000005 0.013541666666667 65 1.86 1.86416666666667 1.86166666666667 0.00249999999999987 -0.00416666666666665 66 1.87 1.8615625 1.86583333333333 -0.00427083333333335 0.00843750000000032 67 1.87 NA NA -0.00364583333333348 NA 68 1.89 NA NA 0.00197916666666662 NA 69 1.89 NA NA 0.00239583333333336 NA 70 1.88 NA NA 0.000104166666666775 NA 71 1.88 NA NA 0.000625000000000024 NA 72 1.87 NA NA -0.00374999999999988 NA

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/14/t12107746504taaaziosuarr3l/1pwgc1210774556.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/14/t12107746504taaaziosuarr3l/1pwgc1210774556.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/14/t12107746504taaaziosuarr3l/2lldm1210774556.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/14/t12107746504taaaziosuarr3l/2lldm1210774556.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/14/t12107746504taaaziosuarr3l/3bico1210774556.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/14/t12107746504taaaziosuarr3l/3bico1210774556.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/14/t12107746504taaaziosuarr3l/4795a1210774556.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/14/t12107746504taaaziosuarr3l/4795a1210774556.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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