ACF 6
| 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 | 8.2462 | 0 |
| 1 | 0.188082 | 1.551 | 0.062776 |
| 2 | 0.162806 | 1.3425 | 0.091944 |
| 3 | 0.019712 | 0.1626 | 0.435676 |
| 4 | 0.235949 | 1.9457 | 0.027914 |
| 5 | 0.274588 | 2.2643 | 0.013374 |
| 6 | 0.049529 | 0.4084 | 0.342122 |
| 7 | -0.073704 | -0.6078 | 0.72732 |
| 8 | -0.060777 | -0.5012 | 0.691067 |
| 9 | 0.121543 | 1.0023 | 0.159883 |
| 10 | 0.100802 | 0.8312 | 0.204375 |
| 11 | -0.013081 | -0.1079 | 0.54279 |
| 12 | -0.348235 | -2.8716 | 0.99728 |
| 13 | -0.059668 | -0.492 | 0.68786 |
| 14 | -0.104374 | -0.8607 | 0.803783 |
| 15 | -0.014612 | -0.1205 | 0.547776 |
| 16 | -0.156108 | -1.2873 | 0.898823 |
| 17 | -0.181692 | -1.4983 | 0.930655 |
| 18 | -0.049935 | -0.4118 | 0.659099 |
| 19 | 0.069029 | 0.5692 | 0.28554 |
| 20 | -0.008643 | -0.0713 | 0.528305 |
| 21 | -0.010857 | -0.0895 | 0.535538 |
| 22 | -0.120447 | -0.9932 | 0.83794 |
| 23 | 0.000955 | 0.0079 | 0.496869 |
| 24 | -0.091117 | -0.7514 | 0.772488 |
| 25 | 0.006313 | 0.0521 | 0.479317 |
| 26 | -0.012491 | -0.103 | 0.540868 |
| 27 | -0.111771 | -0.9217 | 0.820026 |
| 28 | -0.017036 | -0.1405 | 0.555654 |
| 29 | -0.112594 | -0.9285 | 0.821778 |
| 30 | 0.021561 | 0.1778 | 0.429705 |
| 31 | -0.146018 | -1.2041 | 0.883636 |
| 32 | -0.026694 | -0.2201 | 0.586783 |
| 33 | -0.209613 | -1.7285 | 0.955782 |
| 34 | -0.044767 | -0.3692 | 0.643421 |
| 35 | 0.006476 | 0.0534 | 0.478783 |
| 36 | 0.082537 | 0.6806 | 0.249214 |
| 37 | -0.1315 | -1.0844 | 0.858986 |
| 38 | -0.004456 | -0.0367 | 0.514603 |
| 39 | 0.017954 | 0.1481 | 0.441369 |
| 40 | 0.107016 | 0.8825 | 0.190314 |
| 41 | 0.141283 | 1.1651 | 0.124034 |
| 42 | -0.045473 | -0.375 | 0.645579 |
| 43 | 0.049444 | 0.4077 | 0.342378 |
| 44 | 0.080244 | 0.6617 | 0.255197 |
| 45 | 0.197759 | 1.6308 | 0.053782 |
| 46 | 0.07043 | 0.5808 | 0.281655 |
| 47 | -0.059625 | -0.4917 | 0.687735 |
| 48 | -0.044857 | -0.3699 | 0.643698 |
| 49 | 0.132132 | 1.0896 | 0.13987 |
| 50 | 0.053646 | 0.4424 | 0.329812 |
| 51 | 0.068252 | 0.5628 | 0.287704 |
| 52 | -0.114982 | -0.9482 | 0.826799 |
| 53 | -0.049661 | -0.4095 | 0.658275 |
| 54 | 0.034964 | 0.2883 | 0.386991 |
| 55 | 0.008384 | 0.0691 | 0.472542 |
| 56 | -0.036581 | -0.3017 | 0.618083 |
| 57 | -0.114386 | -0.9432 | 0.825553 |
| 58 | -0.0618 | -0.5096 | 0.694015 |
| 59 | -0.03343 | -0.2757 | 0.608182 |
| Partial Autocorrelation Function | |||
| Time lag k | PACF(k) | T-STAT | P-value |
| 0 | 0.188082 | 1.551 | 0.062776 |
| 1 | 0.132104 | 1.0894 | 0.139921 |
| 2 | -0.033456 | -0.2759 | 0.608265 |
| 3 | 0.228724 | 1.8861 | 0.031776 |
| 4 | 0.222697 | 1.8364 | 0.035335 |
| 5 | -0.098879 | -0.8154 | 0.791149 |
| 6 | -0.14037 | -1.1575 | 0.874445 |
| 7 | -0.058715 | -0.4842 | 0.68509 |
| 8 | 0.079002 | 0.6515 | 0.258469 |
| 9 | 0.039965 | 0.3296 | 0.371372 |
| 10 | -0.016665 | -0.1374 | 0.554448 |
| 11 | -0.338179 | -2.7887 | 0.996571 |
| 12 | 0.033316 | 0.2747 | 0.392179 |
| 13 | -0.088906 | -0.7331 | 0.767004 |
| 14 | -0.05017 | -0.4137 | 0.659808 |
| 15 | 0.034309 | 0.2829 | 0.389049 |
| 16 | 0.023872 | 0.1969 | 0.422264 |
| 17 | 0.023382 | 0.1928 | 0.423841 |
| 18 | 0.102661 | 0.8466 | 0.200105 |
| 19 | -0.06312 | -0.5205 | 0.697797 |
| 20 | 0.097283 | 0.8022 | 0.21261 |
| 21 | -0.048804 | -0.4024 | 0.655691 |
| 22 | -0.02506 | -0.2066 | 0.581549 |
| 23 | -0.269606 | -2.2232 | 0.985237 |
| 24 | 0.081521 | 0.6722 | 0.251853 |
| 25 | 0.005168 | 0.0426 | 0.483067 |
| 26 | -0.122938 | -1.0138 | 0.842857 |
| 27 | -0.012371 | -0.102 | 0.540477 |
| 28 | -0.136294 | -1.1239 | 0.867499 |
| 29 | 0.023908 | 0.1971 | 0.42215 |
| 30 | -0.075146 | -0.6197 | 0.731226 |
| 31 | 0.021086 | 0.1739 | 0.431237 |
| 32 | -0.091386 | -0.7536 | 0.773151 |
| 33 | 0.022275 | 0.1837 | 0.427405 |
| 34 | 0.156486 | 1.2904 | 0.100638 |
| 35 | 0.007271 | 0.06 | 0.476181 |
| 36 | -0.130564 | -1.0767 | 0.857281 |
| 37 | 0.121817 | 1.0045 | 0.159342 |
| 38 | -0.069605 | -0.574 | 0.716062 |
| 39 | 0.045448 | 0.3748 | 0.354498 |
| 40 | 0.035103 | 0.2895 | 0.386551 |
| 41 | -0.014401 | -0.1187 | 0.547088 |
| 42 | -0.025677 | -0.2117 | 0.583527 |
| 43 | 0.066773 | 0.5506 | 0.291847 |
| 44 | -0.076285 | -0.6291 | 0.734291 |
| 45 | -0.056087 | -0.4625 | 0.677402 |
| 46 | -0.037914 | -0.3126 | 0.622247 |
| 47 | -0.052804 | -0.4354 | 0.667687 |
| 48 | 0.019623 | 0.1618 | 0.435965 |
| 49 | 0.073706 | 0.6078 | 0.272673 |
| 50 | -0.015432 | -0.1273 | 0.550442 |
| 51 | 0.066996 | 0.5525 | 0.29122 |
| 52 | -0.064032 | -0.528 | 0.700399 |
| 53 | -0.053166 | -0.4384 | 0.668765 |
| 54 | -0.050832 | -0.4192 | 0.661793 |
| 55 | 0.080015 | 0.6598 | 0.255798 |
| 56 | -0.040975 | -0.3379 | 0.631755 |
| 57 | -0.040757 | -0.3361 | 0.631081 |
| 58 | -0.077797 | -0.6415 | 0.738331 |
| 59 | -0.00575 | -0.0474 | 0.518841 |
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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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