Jonas Cloots, geboortes autocorrelation d=1

*Unverified author*

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

Title produced by software: (Partial) Autocorrelation Function

Date of computation: Mon, 11 Apr 2011 13:20:00 +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/Apr/11/t1302527833s9w63bvx6q0s7tc.htm/, Retrieved Mon, 11 Apr 2011 15:17:15 +0200

Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords:

Dataseries X:

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193.230 199.068 195.076 191.563 191.067 186.665 185.508 184.371 183.046 175.714 175.768 171.029 170.465 170.102 156.389 124.291 99.360 86.675 85.056 128.236 164.257 162.401 152.779 156.005 153.387 153.190 148.840 144.211 145.953 145.542 150.271 147.489 143.824 134.754 131.736 126.304 125.511 125.495 130.133 126.257 110.323 98.417 105.749 120.665 124.075 127.245 146.731 144.979 148.210 144.670 142.970 142.524 146.142 146.522 148.128 148.798 150.181 152.388 155.694 160.662 155.520 158.262 154.338 158.196 160.371 154.856 150.636 145.899 141.242 140.834 141.119 139.104 134.437 129.425 123.155 119.273 120.472 121.523 121.983 123.658 124.794 124.827 120.382 117.395 115.790 114.283 117.271 117.448 118.764 120.550 123.554 125.412 124.182 119.828 115.361 114.226 115.214 115.864 114.276 113.469

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 'Herman Ole Andreas Wold' @ www.yougetit.org Autocorrelation Function Time lag k ACF(k) T-STAT P-value 1 0.491434 4.8897 2e-06 2 -0.021601 -0.2149 0.415134 3 -0.238656 -2.3746 0.009748 4 -0.289226 -2.8778 0.002453 5 -0.221704 -2.2059 0.014851 6 -0.021704 -0.216 0.414736 7 0.036634 0.3645 0.358129 8 0.00531 0.0528 0.478986 9 0.038054 0.3786 0.352886 10 0.016253 0.1617 0.43593 11 0.01133 0.1127 0.455236 12 -0.013099 -0.1303 0.448283 13 -0.052589 -0.5233 0.300984 14 -0.13493 -1.3425 0.091247 15 -0.070541 -0.7019 0.242202 16 -0.038874 -0.3868 0.349872 17 0.001805 0.018 0.492854 18 0.122437 1.2182 0.113015 19 0.065152 0.6483 0.25916 20 -0.08687 -0.8643 0.194743 21 -0.193737 -1.9277 0.028381 22 -0.070722 -0.7037 0.241642 23 0.11363 1.1306 0.130476 24 0.148858 1.4811 0.070875 25 0.135689 1.3501 0.090034 26 0.139413 1.3871 0.084256 27 0.057461 0.5717 0.284401 28 -0.100611 -1.0011 0.159617 29 -0.09327 -0.928 0.177827 30 -0.134766 -1.3409 0.091509 31 -0.096621 -0.9614 0.169356 32 -0.006211 -0.0618 0.475423 33 0.069062 0.6872 0.246794 34 0.049387 0.4914 0.312118 35 0.022978 0.2286 0.409813 36 -0.008553 -0.0851 0.466174 37 -0.043118 -0.429 0.334422 38 0.023596 0.2348 0.407434 39 0.003002 0.0299 0.488117 40 -0.018532 -0.1844 0.427043 41 -0.039081 -0.3888 0.349111 42 -0.006567 -0.0653 0.474017 43 -0.018497 -0.184 0.427178 44 0.03249 0.3233 0.373586 45 0.01827 0.1818 0.428063 46 -0.05411 -0.5384 0.295757 47 -0.056215 -0.5593 0.288599 48 -0.072055 -0.7169 0.237551 Partial Autocorrelation Function Time lag k PACF(k) T-STAT P-value 1 0.491434 4.8897 2e-06 2 -0.346883 -3.4514 0.000411 3 -0.080765 -0.8036 0.211777 4 -0.157787 -1.57 0.059808 5 -0.069886 -0.6954 0.24423 6 0.072289 0.7193 0.236835 7 -0.131048 -1.3039 0.097643 8 -0.039096 -0.389 0.349056 9 0.048395 0.4815 0.315603 10 -0.074399 -0.7403 0.230446 11 0.052439 0.5218 0.301502 12 -0.082382 -0.8197 0.207181 13 -0.037954 -0.3776 0.353252 14 -0.120234 -1.1963 0.117216 15 0.046422 0.4619 0.322586 16 -0.111755 -1.1119 0.134427 17 0.00752 0.0748 0.470254 18 0.101682 1.0117 0.157068 19 -0.168376 -1.6753 0.048514 20 -0.065004 -0.6468 0.259634 21 -0.162549 -1.6173 0.054493 22 0.12567 1.2504 0.10705 23 0.105912 1.0538 0.147267 24 -0.143932 -1.4321 0.077632 25 0.13684 1.3615 0.088218 26 0.077102 0.7672 0.222408 27 0.01239 0.1233 0.45107 28 -0.1193 -1.187 0.119029 29 0.07127 0.7091 0.239955 30 -0.138618 -1.3792 0.085465 31 0.059391 0.5909 0.277957 32 0.00705 0.0701 0.472111 33 -0.061501 -0.6119 0.270993 34 0.029378 0.2923 0.385333 35 -0.089123 -0.8868 0.188678 36 0.003292 0.0328 0.486969 37 0.006882 0.0685 0.472771 38 0.042403 0.4219 0.337004 39 0.015182 0.1511 0.440117 40 -0.006296 -0.0626 0.475086 41 -0.10287 -1.0235 0.154274 42 0.026677 0.2654 0.395615 43 0.051291 0.5103 0.305474 44 -0.024246 -0.2412 0.404932 45 -0.080371 -0.7997 0.212906 46 -0.029589 -0.2944 0.38453 47 0.010919 0.1086 0.456854 48 -0.087502 -0.8706 0.193029

Charts produced by software:

http://www.freestatistics.org/blog/date/2011/Apr/11/t1302527833s9w63bvx6q0s7tc/1zndr1302527999.png (open in new window) http://www.freestatistics.org/blog/date/2011/Apr/11/t1302527833s9w63bvx6q0s7tc/1zndr1302527999.ps (open in new window) http://www.freestatistics.org/blog/date/2011/Apr/11/t1302527833s9w63bvx6q0s7tc/29r731302527999.png (open in new window) http://www.freestatistics.org/blog/date/2011/Apr/11/t1302527833s9w63bvx6q0s7tc/29r731302527999.ps (open in new window) http://www.freestatistics.org/blog/date/2011/Apr/11/t1302527833s9w63bvx6q0s7tc/3fgke1302527999.png (open in new window) http://www.freestatistics.org/blog/date/2011/Apr/11/t1302527833s9w63bvx6q0s7tc/3fgke1302527999.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')

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Software written by Ed van Stee & Patrick Wessa

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