*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 23:10:13 +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/20/t13058464215c684k2dvyhwbwg.htm/, Retrieved Fri, 20 May 2011 01:07:01 +0200

Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords:

KDGP2W12

Dataseries X:

» Textbox « » Textfile « » CSV «

90 51 47 59 54 79 59 80 46 62 55 77 72 72 71 50 66 78 59 52 71 98 70 84 90 98 98 78 59 0 58 55 62 80 91 86 61 49 61 56 73 85 82 32 39 30 51 48 57 59 32 56 54 74 62 78 72 48 59 61 80 69 58 63 27 23 34 45 51 51 73 37 35 66 54 30 66 61 37 55 64 53 63 70 72 52 53 50 60 73 66 78

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 5 seconds R Server 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 Autocorrelation Function Time lag k ACF(k) T-STAT P-value 1 0.402081 3.8566 0.000106 2 0.174078 1.6697 0.049189 3 0.024938 0.2392 0.405742 4 -0.058102 -0.5573 0.28934 5 -0.089763 -0.861 0.195745 6 0.048948 0.4695 0.319914 7 0.297807 2.8565 0.002648 8 0.054849 0.5261 0.300044 9 0.039063 0.3747 0.354381 10 0.021199 0.2033 0.419661 11 -0.058714 -0.5632 0.287346 12 -0.072478 -0.6952 0.244346 13 0.053751 0.5156 0.3037 14 0.28737 2.7564 0.003524 15 0.081638 0.783 0.217806 16 0.035675 0.3422 0.366498 17 -0.013333 -0.1279 0.449258 18 -0.026238 -0.2517 0.400929 19 0.00675 0.0647 0.47426 20 0.067753 0.6499 0.258699 21 0.294692 2.8266 0.002886 22 -0.023358 -0.224 0.41161 23 -0.096205 -0.9228 0.179271 24 -0.201877 -1.9363 0.027948 25 -0.08813 -0.8453 0.200065 26 -0.088609 -0.8499 0.198791 27 0.027855 0.2672 0.394966 28 0.202105 1.9385 0.027812 29 0.009766 0.0937 0.462788 30 -0.031624 -0.3033 0.381164 31 -0.1308 -1.2546 0.106403 32 -0.130765 -1.2543 0.106463 33 -0.076923 -0.7378 0.231252 34 0.007054 0.0677 0.473101 35 0.238088 2.2837 0.012347 36 0.099255 0.952 0.171792 37 -0.0105 -0.1007 0.459999 38 -0.10055 -0.9644 0.168677 39 -0.145798 -1.3984 0.082671 40 -0.158644 -1.5217 0.065762 41 -0.145994 -1.4003 0.08239 42 0.042843 0.4109 0.341036 43 -0.06441 -0.6178 0.269116 44 -0.140862 -1.3511 0.089988 45 -0.195833 -1.8784 0.031749 46 -0.065271 -0.6261 0.266414 47 -0.109993 -1.055 0.14709 48 -0.055464 -0.532 0.298008 49 0.068221 0.6543 0.25726 50 -0.057411 -0.5507 0.291599 51 -0.128611 -1.2336 0.110248 52 -0.139246 -1.3356 0.092487 53 -0.093444 -0.8963 0.18622 54 -0.142486 -1.3667 0.087528 55 -0.056068 -0.5378 0.296012 56 0.085653 0.8216 0.206728 57 -0.001076 -0.0103 0.495895 58 -0.024128 -0.2314 0.408749 59 -0.031169 -0.299 0.382821 60 -0.059887 -0.5744 0.283544 Partial Autocorrelation Function Time lag k PACF(k) T-STAT P-value 1 0.402081 3.8566 0.000106 2 0.014802 0.142 0.443705 3 -0.059621 -0.5719 0.284406 4 -0.060728 -0.5825 0.280832 5 -0.044855 -0.4302 0.334016 6 0.135674 1.3013 0.098196 7 0.299861 2.8762 0.002501 8 -0.241488 -2.3163 0.011382 9 0.017492 0.1678 0.433561 10 0.04834 0.4637 0.321993 11 -0.049701 -0.4767 0.317347 12 0.01923 0.1844 0.427036 13 0.069788 0.6694 0.252463 14 0.213458 2.0474 0.021734 15 -0.108325 -1.039 0.150761 16 -0.077652 -0.7448 0.229142 17 -0.005708 -0.0548 0.478227 18 0.10846 1.0403 0.150461 19 0.093228 0.8942 0.18677 20 -0.052427 -0.5029 0.308131 21 0.172641 1.6559 0.050574 22 -0.24687 -2.3679 0.009992 23 -0.100107 -0.9602 0.169738 24 -0.121533 -1.1657 0.123374 25 0.209973 2.014 0.023465 26 -0.023047 -0.2211 0.412768 27 -0.037377 -0.3585 0.360392 28 -0.043684 -0.419 0.338097 29 -0.007445 -0.0714 0.471615 30 0.007522 0.0721 0.47132 31 -0.093238 -0.8943 0.186746 32 -0.021755 -0.2087 0.417586 33 0.140901 1.3515 0.089928 34 -0.066076 -0.6338 0.263897 35 0.048559 0.4658 0.321243 36 0.043642 0.4186 0.338243 37 -0.063245 -0.6066 0.272798 38 0.027019 0.2592 0.398045 39 -0.143052 -1.3721 0.086683 40 -0.035694 -0.3424 0.36643 41 -0.061166 -0.5867 0.279428 42 -0.043611 -0.4183 0.338349 43 0.009411 0.0903 0.464136 44 -0.143008 -1.3717 0.086748 45 -0.005939 -0.057 0.47735 46 0.064987 0.6233 0.267304 47 -0.064974 -0.6232 0.267344 48 -0.030326 -0.2909 0.385902 49 -0.049139 -0.4713 0.319262 50 -0.054727 -0.5249 0.300449 51 -0.008815 -0.0846 0.466401 52 0.001573 0.0151 0.493997 53 -0.018037 -0.173 0.431515 54 -0.083247 -0.7985 0.213324 55 0.023708 0.2274 0.410308 56 -0.056368 -0.5407 0.295023 57 0.085521 0.8203 0.207086 58 0.123804 1.1875 0.119047 59 0.070565 0.6768 0.250104 60 -0.097936 -0.9394 0.175

Charts produced by software:

http://www.freestatistics.org/blog/date/2011/May/20/t13058464215c684k2dvyhwbwg/1dyyh1305846606.png (open in new window) http://www.freestatistics.org/blog/date/2011/May/20/t13058464215c684k2dvyhwbwg/1dyyh1305846606.ps (open in new window) http://www.freestatistics.org/blog/date/2011/May/20/t13058464215c684k2dvyhwbwg/25xrz1305846606.png (open in new window) http://www.freestatistics.org/blog/date/2011/May/20/t13058464215c684k2dvyhwbwg/25xrz1305846606.ps (open in new window) http://www.freestatistics.org/blog/date/2011/May/20/t13058464215c684k2dvyhwbwg/3lcec1305846606.png (open in new window) http://www.freestatistics.org/blog/date/2011/May/20/t13058464215c684k2dvyhwbwg/3lcec1305846606.ps (open in new window)

Parameters (Session):

par1 = 60 ; par2 = 1 ; par3 = 0 ; par4 = 0 ; par5 = 12 ; par6 = White Noise ; par7 = 0.95 ;

Parameters (R input):

par1 = 60 ; par2 = 1 ; par3 = 0 ; 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')

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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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