Decompositie Additief model Prijs per liter diesel Inez Van Dijck

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

Title produced by software: Classical Decomposition

Date of computation: Thu, 15 May 2008 07:13:06 -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/15/t1210857221e467xhooptshu1i.htm/, Retrieved Thu, 15 May 2008 15:13:46 +0200

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Dataseries X:

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0.73 0.74 0.75 0.74 0.76 0.76 0.78 0.79 0.89 0.88 0.88 0.84 0.76 0.77 0.76 0.77 0.78 0.79 0.78 0.76 0.78 0.76 0.74 0.73 0.72 0.71 0.73 0.75 0.75 0.72 0.72 0.72 0.74 0.78 0.74 0.74 0.75 0.78 0.81 0.75 0.7 0.71 0.71 0.73 0.74 0.74 0.75 0.74 0.74 0.73 0.76 0.8 0.83 0.81 0.83 0.88 0.89 0.93 0.91 0.9 0.86 0.88 0.93 0.98 0.97 1.03 1.06 1.06 1.08 1.09 1.04 1

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 'George Udny Yule' @ 72.249.76.132 Classical Decomposition by Moving Averages t Observations Fit Trend Seasonal Random 1 0.73 NA NA -0.0182812499999999 NA 2 0.74 NA NA -0.0147395833333333 NA 3 0.75 NA NA 0.00182291666666670 NA 4 0.74 NA NA 0.00380208333333343 NA 5 0.76 NA NA 0.00046874999999999 NA 6 0.76 NA NA -0.00796875000000005 NA 7 0.78 0.789114583333333 0.79625 -0.0071354166666667 -0.00911458333333315 8 0.79 0.801927083333333 0.79875 0.00317708333333333 -0.0119270833333334 9 0.89 0.815677083333333 0.800416666666667 0.0152604166666667 0.0743229166666667 10 0.88 0.828385416666667 0.802083333333333 0.0263020833333333 0.0516145833333334 11 0.88 0.808802083333333 0.804166666666667 0.00463541666666653 0.0711979166666667 12 0.84 0.79890625 0.80625 -0.00734374999999995 0.0410937499999999 13 0.76 0.78921875 0.8075 -0.0182812499999999 -0.0292187499999998 14 0.77 0.791510416666667 0.80625 -0.0147395833333333 -0.0215104166666666 15 0.76 0.802239583333333 0.800416666666667 0.00182291666666670 -0.0422395833333332 16 0.77 0.794635416666667 0.790833333333333 0.00380208333333343 -0.0246354166666667 17 0.78 0.78046875 0.78 0.00046874999999999 -0.000468749999999907 18 0.79 0.761614583333333 0.769583333333333 -0.00796875000000005 0.0283854166666667 19 0.78 0.756197916666667 0.763333333333333 -0.0071354166666667 0.0238020833333333 20 0.76 0.76234375 0.759166666666667 0.00317708333333333 -0.00234375000000009 21 0.78 0.770677083333333 0.755416666666667 0.0152604166666667 0.00932291666666685 22 0.76 0.779635416666667 0.753333333333333 0.0263020833333333 -0.0196354166666666 23 0.74 0.755885416666667 0.75125 0.00463541666666653 -0.0158854166666667 24 0.73 0.739739583333333 0.747083333333333 -0.00734374999999995 -0.00973958333333336 25 0.72 0.723385416666667 0.741666666666667 -0.0182812499999999 -0.0033854166666667 26 0.71 0.722760416666667 0.7375 -0.0147395833333333 -0.0127604166666667 27 0.73 0.735989583333333 0.734166666666667 0.00182291666666670 -0.00598958333333333 28 0.75 0.737135416666667 0.733333333333333 0.00380208333333343 0.0128645833333334 29 0.75 0.734635416666667 0.734166666666667 0.00046874999999999 0.0153645833333332 30 0.72 0.726614583333333 0.734583333333333 -0.00796875000000005 -0.0066145833333332 31 0.72 0.729114583333333 0.73625 -0.0071354166666667 -0.00911458333333326 32 0.72 0.74359375 0.740416666666667 0.00317708333333333 -0.0235937499999999 33 0.74 0.761927083333333 0.746666666666667 0.0152604166666667 -0.0219270833333334 34 0.78 0.776302083333333 0.75 0.0263020833333333 0.00369791666666675 35 0.74 0.752552083333333 0.747916666666667 0.00463541666666653 -0.0125520833333334 36 0.74 0.738072916666667 0.745416666666667 -0.00734374999999995 0.00192708333333336 37 0.75 0.726302083333333 0.744583333333333 -0.0182812499999999 0.0236979166666665 38 0.78 0.72984375 0.744583333333333 -0.0147395833333333 0.05015625 39 0.81 0.746822916666667 0.745 0.00182291666666670 0.0631770833333334 40 0.75 0.747135416666667 0.743333333333333 0.00380208333333343 0.00286458333333339 41 0.7 0.742552083333333 0.742083333333333 0.00046874999999999 -0.0425520833333333 42 0.71 0.73453125 0.7425 -0.00796875000000005 -0.02453125 43 0.71 0.734947916666666 0.742083333333333 -0.0071354166666667 -0.0249479166666665 44 0.73 0.742760416666667 0.739583333333333 0.00317708333333333 -0.0127604166666666 45 0.74 0.750677083333333 0.735416666666667 0.0152604166666667 -0.0106770833333333 46 0.74 0.76171875 0.735416666666667 0.0263020833333333 -0.0217187499999999 47 0.75 0.747552083333333 0.742916666666667 0.00463541666666653 0.00244791666666677 48 0.74 0.74515625 0.7525 -0.00734374999999995 -0.00515624999999986 49 0.74 0.743385416666667 0.761666666666666 -0.0182812499999999 -0.00338541666666659 50 0.73 0.758177083333333 0.772916666666667 -0.0147395833333333 -0.0281770833333334 51 0.76 0.787239583333333 0.785416666666667 0.00182291666666670 -0.0272395833333332 52 0.8 0.803385416666667 0.799583333333333 0.00380208333333343 -0.00338541666666659 53 0.83 0.814635416666667 0.814166666666667 0.00046874999999999 0.0153645833333333 54 0.81 0.81953125 0.8275 -0.00796875000000005 -0.00953124999999988 55 0.83 0.83203125 0.839166666666667 -0.0071354166666667 -0.00203124999999993 56 0.88 0.85359375 0.850416666666667 0.00317708333333333 0.0264062500000001 57 0.89 0.879010416666667 0.86375 0.0152604166666667 0.0109895833333334 58 0.93 0.904635416666667 0.878333333333334 0.0263020833333333 0.0253645833333332 59 0.91 0.896302083333333 0.891666666666667 0.00463541666666653 0.0136979166666666 60 0.9 0.899322916666667 0.906666666666667 -0.00734374999999995 0.000677083333333495 61 0.86 0.907135416666667 0.925416666666667 -0.0182812499999999 -0.0471354166666667 62 0.88 0.927760416666667 0.9425 -0.0147395833333333 -0.0477604166666665 63 0.93 0.959739583333333 0.957916666666667 0.00182291666666670 -0.0297395833333333 64 0.98 0.976302083333333 0.9725 0.00380208333333343 0.00369791666666675 65 0.97 0.985052083333333 0.984583333333333 0.00046874999999999 -0.0150520833333334 66 1.03 0.986197916666667 0.994166666666667 -0.00796875000000005 0.0438020833333335 67 1.06 NA NA -0.0071354166666667 NA 68 1.06 NA NA 0.00317708333333333 NA 69 1.08 NA NA 0.0152604166666667 NA 70 1.09 NA NA 0.0263020833333333 NA 71 1.04 NA NA 0.00463541666666653 NA 72 1 NA NA -0.00734374999999995 NA

Charts produced by software:

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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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