iko Guy Hendrickx opgave 6 BIS oef 2 stap 2

*Unverified author*

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

Title produced by software: (Partial) Autocorrelation Function

Date of computation: Thu, 19 May 2011 14:35:21 +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/19/t130581557218xcm2sfcajq66s.htm/, Retrieved Thu, 19 May 2011 16:32:54 +0200

Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords:

KDGP2W12

Dataseries X:

» Textbox « » Textfile « » CSV «

12,94 12,79 12,82 12,85 12,85 12,72 12,62 12,67 12,6 12,54 12,64 12,67 12,51 12,59 12,52 12,5 12,58 12,51 12,47 12,44 12,51 12,27 12,51 12,41 12,35 12,39 12,31 12,31 12,21 12,1 12,01 11,85 12,12 11,96 11,99 11,93 11,91 11,83 11,92 11,86 11,94 11,87 11,86 11,92 11,82 11,85 11,77 11,82 11,61 11,56 11,45 11,4 11,38 11,33 11,19 11,15 10,98 10,92 10,99 11 10,9 10,99 11,04 11,03 10,99 11 10,87 10,88 10,91 10,92 10,83 10,9 10,82 10,79 10,77 10,72 10,71 10,63 10,61 10,57 10,65 10,57 10,57 10,57 10,52 10,43 10,35 10,2 10,2 10,17 10,14 10,05 10,12 10,12

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 'Gwilym Jenkins' @ www.wessa.org Autocorrelation Function Time lag k ACF(k) T-STAT P-value 1 -0.398296 -3.841 0.000112 2 0.129517 1.249 0.107397 3 0.012632 0.1218 0.451653 4 0.017506 0.1688 0.433151 5 -0.116748 -1.1259 0.131558 6 0.09672 0.9327 0.176685 7 0.014233 0.1373 0.445562 8 -0.125679 -1.212 0.114291 9 0.037546 0.3621 0.359058 10 0.013656 0.1317 0.447756 11 -0.162403 -1.5662 0.060354 12 0.100751 0.9716 0.166883 13 -0.031901 -0.3076 0.379522 14 -0.160432 -1.5472 0.062611 15 0.025208 0.2431 0.404232 16 0.002689 0.0259 0.489685 17 -0.124695 -1.2025 0.116109 18 0.085387 0.8234 0.20618 19 -0.011167 -0.1077 0.457238 20 0.002377 0.0229 0.490879 21 0.068486 0.6605 0.255296 22 0.0039 0.0376 0.485039 23 0.042236 0.4073 0.34236 24 -0.09844 -0.9493 0.172458 25 0.188599 1.8188 0.036082 26 -0.201634 -1.9445 0.027429 27 0.139972 1.3498 0.090172 28 -0.115716 -1.1159 0.133664 29 0.096682 0.9324 0.17678 30 0.056991 0.5496 0.291955 31 0.029084 0.2805 0.389866 32 -0.129241 -1.2464 0.107882 33 0.107131 1.0331 0.152111 34 -0.147414 -1.4216 0.079242 35 0.011942 0.1152 0.454283 36 0.108757 1.0488 0.148492 37 0.015746 0.1519 0.439816 38 -0.127867 -1.2331 0.110322 39 0.095838 0.9242 0.17888 40 -0.045646 -0.4402 0.330408 41 -0.060896 -0.5873 0.279226 42 0.047876 0.4617 0.322688 43 0.026145 0.2521 0.400747 44 -0.100292 -0.9672 0.167981 45 0.041478 0.4 0.345037 46 -0.029456 -0.2841 0.388495 47 -0.047412 -0.4572 0.324288 48 0.086699 0.8361 0.202623 Partial Autocorrelation Function Time lag k PACF(k) T-STAT P-value 1 -0.398296 -3.841 0.000112 2 -0.034614 -0.3338 0.36964 3 0.062137 0.5992 0.275239 4 0.058057 0.5599 0.288453 5 -0.116755 -1.1259 0.131543 6 0.000441 0.0043 0.498308 7 0.081922 0.79 0.215761 8 -0.099661 -0.9611 0.169499 9 -0.077848 -0.7507 0.227352 10 0.003277 0.0316 0.487429 11 -0.15012 -1.4477 0.075531 12 -0.015958 -0.1539 0.439013 13 -0.006978 -0.0673 0.473246 14 -0.194667 -1.8773 0.031805 15 -0.128268 -1.237 0.109607 16 -0.061384 -0.592 0.277656 17 -0.13966 -1.3468 0.090654 18 -0.02241 -0.2161 0.414686 19 -0.059139 -0.5703 0.284918 20 -0.015802 -0.1524 0.439605 21 0.087989 0.8485 0.19916 22 -0.031955 -0.3082 0.379323 23 0.04194 0.4045 0.343404 24 -0.118002 -1.138 0.129028 25 0.035096 0.3385 0.367892 26 -0.115331 -1.1122 0.134458 27 -0.028614 -0.2759 0.391603 28 -0.172287 -1.6615 0.049992 29 -0.013209 -0.1274 0.449455 30 0.138415 1.3348 0.092595 31 0.070995 0.6847 0.247634 32 -0.152321 -1.4689 0.072613 33 -0.014662 -0.1414 0.443934 34 -0.127842 -1.2329 0.110367 35 -0.080532 -0.7766 0.219675 36 0.150485 1.4512 0.075041 37 0.104698 1.0097 0.157637 38 -0.08284 -0.7989 0.213198 39 -0.013262 -0.1279 0.449254 40 -0.037865 -0.3652 0.357912 41 -0.088736 -0.8557 0.197171 42 -0.072179 -0.6961 0.244062 43 -0.074776 -0.7211 0.236325 44 0.015925 0.1536 0.439138 45 -0.027645 -0.2666 0.395184 46 -0.105486 -1.0173 0.155832 47 -0.028421 -0.2741 0.392315 48 0.00887 0.0855 0.466009

Charts produced by software:

http://www.freestatistics.org/blog/date/2011/May/19/t130581557218xcm2sfcajq66s/19zef1305815718.png (open in new window) http://www.freestatistics.org/blog/date/2011/May/19/t130581557218xcm2sfcajq66s/19zef1305815718.ps (open in new window) http://www.freestatistics.org/blog/date/2011/May/19/t130581557218xcm2sfcajq66s/2zj461305815718.png (open in new window) http://www.freestatistics.org/blog/date/2011/May/19/t130581557218xcm2sfcajq66s/2zj461305815718.ps (open in new window) http://www.freestatistics.org/blog/date/2011/May/19/t130581557218xcm2sfcajq66s/3gph91305815718.png (open in new window) http://www.freestatistics.org/blog/date/2011/May/19/t130581557218xcm2sfcajq66s/3gph91305815718.ps (open in new window)

Parameters (Session):

par1 = 48 ; par2 = 1 ; par3 = 1 ; par4 = 0 ; par5 = 12 ; par6 = White Noise ; par7 = 0.95 ;

Parameters (R input):

par1 = 48 ; par2 = 1 ; par3 = 1 ; par4 = 0 ; par5 = 12 ; par6 = White Noise ; par7 = 0.95 ;

R code (references can be found in the software module):

if (par1 == 'Default') { par1 = 10*log10(length(x)) } else { par1 <- as.numeric(par1) } par2 <- as.numeric(par2) par3 <- as.numeric(par3) par4 <- as.numeric(par4) par5 <- as.numeric(par5) if (par6 == 'White Noise') par6 <- 'white' else par6 <- 'ma' par7 <- as.numeric(par7) if (par8 != '') par8 <- as.numeric(par8) ox <- x if (par8 == '') { if (par2 == 0) { x <- log(x) } else { x <- (x ^ par2 - 1) / par2 } } else { x <- log(x,base=par8) } if (par3 > 0) x <- diff(x,lag=1,difference=par3) if (par4 > 0) x <- diff(x,lag=par5,difference=par4) bitmap(file='picts.png') op <- par(mfrow=c(2,1)) plot(ox,type='l',main='Original Time Series',xlab='time',ylab='value') if (par8=='') { mytitle <- paste('Working Time Series (lambda=',par2,', d=',par3,', D=',par4,')',sep='') mysub <- paste('(lambda=',par2,', d=',par3,', D=',par4,', CI=', par7, ', CI type=',par6,')',sep='') } else { mytitle <- paste('Working Time Series (base=',par8,', d=',par3,', D=',par4,')',sep='') mysub <- paste('(base=',par8,', d=',par3,', D=',par4,', CI=', par7, ', CI type=',par6,')',sep='') } plot(x,type='l', main=mytitle,xlab='time',ylab='value') par(op) dev.off() bitmap(file='pic1.png') racf <- acf(x, par1, main='Autocorrelation', xlab='time lag', ylab='ACF', ci.type=par6, ci=par7, sub=mysub) dev.off() bitmap(file='pic2.png') rpacf <- pacf(x,par1,main='Partial Autocorrelation',xlab='lags',ylab='PACF',sub=mysub) dev.off() (myacf <- c(racf$acf)) (mypacf <- c(rpacf$acf)) lengthx <- length(x) sqrtn <- sqrt(lengthx) load(file='createtable') a<-table.start() a<-table.row.start(a) a<-table.element(a,'Autocorrelation Function',4,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Time lag k',header=TRUE) a<-table.element(a,hyperlink('http://www.xycoon.com/basics.htm','ACF(k)','click here for more information about the Autocorrelation Function'),header=TRUE) a<-table.element(a,'T-STAT',header=TRUE) a<-table.element(a,'P-value',header=TRUE) a<-table.row.end(a) for (i in 2:(par1+1)) { a<-table.row.start(a) a<-table.element(a,i-1,header=TRUE) a<-table.element(a,round(myacf[i],6)) mytstat <- myacf[i]*sqrtn a<-table.element(a,round(mytstat,4)) a<-table.element(a,round(1-pt(abs(mytstat),lengthx),6)) a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a,'Partial Autocorrelation Function',4,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Time lag k',header=TRUE) a<-table.element(a,hyperlink('http://www.xycoon.com/basics.htm','PACF(k)','click here for more information about the Partial Autocorrelation Function'),header=TRUE) a<-table.element(a,'T-STAT',header=TRUE) a<-table.element(a,'P-value',header=TRUE) a<-table.row.end(a) for (i in 1:par1) { a<-table.row.start(a) a<-table.element(a,i,header=TRUE) a<-table.element(a,round(mypacf[i],6)) mytstat <- mypacf[i]*sqrtn a<-table.element(a,round(mytstat,4)) a<-table.element(a,round(1-pt(abs(mytstat),lengthx),6)) a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable1.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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