ACF - versie 2 -Serie 4 - paper
| Summary of compuational transaction | |
| Raw Input | view raw input (R code) |
| Raw Output | view raw output of R engine |
| Computing time | 2 seconds |
| R Server | 'Gwilym Jenkins' @ 72.249.127.135 |
| Autocorrelation Function | |||
| Time lag k | ACF(k) | T-STAT | P-value |
| 0 | 1 | 6.9282 | 0 |
| 1 | 0.173416 | 1.2015 | 0.117734 |
| 2 | 0.113646 | 0.7874 | 0.217471 |
| 3 | 0.337638 | 2.3392 | 0.011767 |
| 4 | -0.037148 | -0.2574 | 0.601002 |
| 5 | 0.1279 | 0.8861 | 0.189987 |
| 6 | 0.25846 | 1.7907 | 0.039827 |
| 7 | -0.112313 | -0.7781 | 0.779844 |
| 8 | 0.196452 | 1.3611 | 0.089926 |
| 9 | 0.223151 | 1.546 | 0.064332 |
| 10 | -0.150604 | -1.0434 | 0.849008 |
| 11 | 0.022831 | 0.1582 | 0.437491 |
| 12 | -0.16127 | -1.1173 | 0.865289 |
| 13 | -0.244235 | -1.6921 | 0.951446 |
| 14 | 0.108932 | 0.7547 | 0.227057 |
| 15 | -0.094023 | -0.6514 | 0.741055 |
| 16 | -0.134039 | -0.9287 | 0.821142 |
| 17 | 0.203574 | 1.4104 | 0.082435 |
| 18 | -0.209045 | -1.4483 | 0.922984 |
| 19 | -0.17308 | -1.1991 | 0.881819 |
| 20 | -0.038313 | -0.2654 | 0.604096 |
| 21 | -0.224526 | -1.5556 | 0.936809 |
| 22 | -0.127111 | -0.8806 | 0.808552 |
| 23 | 0.028484 | 0.1973 | 0.422197 |
| 24 | -0.295514 | -2.0474 | 0.976942 |
| 25 | -0.127955 | -0.8865 | 0.810116 |
| 26 | -0.079941 | -0.5539 | 0.708874 |
| 27 | -0.222635 | -1.5425 | 0.935236 |
| 28 | -0.067416 | -0.4671 | 0.678718 |
| 29 | -0.12999 | -0.9006 | 0.81385 |
| 30 | -0.131311 | -0.9098 | 0.81625 |
| 31 | -0.000516 | -0.0036 | 0.50142 |
| 32 | -0.031866 | -0.2208 | 0.586899 |
| 33 | -0.041424 | -0.287 | 0.612324 |
| 34 | -0.005687 | -0.0394 | 0.515632 |
| 35 | -0.041636 | -0.2885 | 0.612882 |
| 36 | 0.040705 | 0.282 | 0.389573 |
| 37 | 0.108596 | 0.7524 | 0.227749 |
| 38 | 0.049646 | 0.344 | 0.366191 |
| 39 | 0.036024 | 0.2496 | 0.401988 |
| 40 | 0.101687 | 0.7045 | 0.24226 |
| 41 | 0.029861 | 0.2069 | 0.418489 |
| 42 | 0.078262 | 0.5422 | 0.295089 |
| 43 | 0.040269 | 0.279 | 0.390726 |
| 44 | 0.011439 | 0.0792 | 0.468582 |
| 45 | 0.051297 | 0.3554 | 0.361924 |
| 46 | 0.024364 | 0.1688 | 0.433333 |
| 47 | 0.014965 | 0.1037 | 0.458927 |
| Partial Autocorrelation Function | |||
| Time lag k | PACF(k) | T-STAT | P-value |
| 0 | 0.173416 | 1.2015 | 0.117734 |
| 1 | 0.086164 | 0.597 | 0.276669 |
| 2 | 0.316483 | 2.1927 | 0.016606 |
| 3 | -0.162095 | -1.123 | 0.866494 |
| 4 | 0.130731 | 0.9057 | 0.184802 |
| 5 | 0.143035 | 0.991 | 0.163333 |
| 6 | -0.155552 | -1.0777 | 0.856723 |
| 7 | 0.180769 | 1.2524 | 0.108245 |
| 8 | 0.089676 | 0.6213 | 0.268674 |
| 9 | -0.158151 | -1.0957 | 0.860662 |
| 10 | -0.117073 | -0.8111 | 0.789345 |
| 11 | -0.251947 | -1.7455 | 0.956355 |
| 12 | -0.05884 | -0.4077 | 0.657331 |
| 13 | 0.104107 | 0.7213 | 0.23712 |
| 14 | -0.011241 | -0.0779 | 0.530876 |
| 15 | 0.012649 | 0.0876 | 0.465267 |
| 16 | 0.173665 | 1.2032 | 0.117403 |
| 17 | -0.189911 | -1.3157 | 0.902745 |
| 18 | -0.087649 | -0.6073 | 0.726727 |
| 19 | -0.09821 | -0.6804 | 0.750245 |
| 20 | 0.096439 | 0.6682 | 0.253619 |
| 21 | -0.128811 | -0.8924 | 0.811692 |
| 22 | -0.030605 | -0.212 | 0.583513 |
| 23 | -0.22009 | -1.5248 | 0.933067 |
| 24 | -0.154984 | -1.0738 | 0.85585 |
| 25 | -0.08181 | -0.5668 | 0.713253 |
| 26 | 0.115219 | 0.7983 | 0.214326 |
| 27 | 0.062463 | 0.4328 | 0.333566 |
| 28 | 0.016773 | 0.1162 | 0.453987 |
| 29 | 0.027997 | 0.194 | 0.423509 |
| 30 | -0.166985 | -1.1569 | 0.873483 |
| 31 | 0.037548 | 0.2601 | 0.397935 |
| 32 | 0.109559 | 0.759 | 0.225768 |
| 33 | -0.077248 | -0.5352 | 0.702505 |
| 34 | 0.054171 | 0.3753 | 0.354543 |
| 35 | -0.072001 | -0.4988 | 0.689913 |
| 36 | -0.020041 | -0.1388 | 0.554925 |
| 37 | -0.05053 | -0.3501 | 0.636097 |
| 38 | 0.016916 | 0.1172 | 0.453596 |
| 39 | 0.066031 | 0.4575 | 0.324696 |
| 40 | 0.060526 | 0.4193 | 0.338421 |
| 41 | -0.004491 | -0.0311 | 0.512347 |
| 42 | -0.110121 | -0.7629 | 0.775385 |
| 43 | -0.138577 | -0.9601 | 0.82909 |
| 44 | -0.001297 | -0.009 | 0.503567 |
| 45 | -0.073492 | -0.5092 | 0.693515 |
| 46 | -0.000109 | -8e-04 | 0.500301 |
| 47 | NA | NA | NA |
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| http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Dec/18/t1197963281tpq55zzan50iirj/2ojr11197964220.png (open in new window) |
| http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Dec/18/t1197963281tpq55zzan50iirj/2ojr11197964220.ps (open in new window) |
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 (par2 == 0) {
x <- log(x)
} else {
x <- (x ^ par2 - 1) / par2
}
if (par3 > 0) x <- diff(x,lag=1,difference=par3)
if (par4 > 0) x <- diff(x,lag=par5,difference=par4)
bitmap(file='pic1.png')
racf <- acf(x,par1,main='Autocorrelation',xlab='lags',ylab='ACF')
dev.off()
bitmap(file='pic2.png')
rpacf <- pacf(x,par1,main='Partial Autocorrelation',xlab='lags',ylab='PACF')
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 1:par1) {
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(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-1,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(mytstat,lengthx),6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable1.tab')
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