autocorrelation dollar
| Summary of compuational transaction | |
| Raw Input | view raw input (R code) |
| Raw Output | view raw output of R engine |
| Computing time | 1 seconds |
| R Server | 'Gwilym Jenkins' @ 72.249.127.135 |
| Autocorrelation Function | |||
| Time lag k | ACF(k) | T-STAT | P-value |
| 0 | 1 | 8.4261 | 0 |
| 1 | 0.343616 | 2.8954 | 0.002515 |
| 2 | -0.094715 | -0.7981 | 0.786259 |
| 3 | -0.175819 | -1.4815 | 0.928548 |
| 4 | 0.055289 | 0.4659 | 0.321366 |
| 5 | -0.007435 | -0.0626 | 0.524887 |
| 6 | -0.156075 | -1.3151 | 0.903648 |
| 7 | 0.000678 | 0.0057 | 0.49773 |
| 8 | 0.110928 | 0.9347 | 0.176558 |
| 9 | 0.149661 | 1.2611 | 0.105706 |
| 10 | 0.125832 | 1.0603 | 0.146306 |
| 11 | 0.070229 | 0.5918 | 0.277944 |
| 12 | -0.045382 | -0.3824 | 0.648346 |
| 13 | -0.086769 | -0.7311 | 0.766447 |
| 14 | -0.107295 | -0.9041 | 0.815496 |
| 15 | -0.157479 | -1.3269 | 0.905611 |
| 16 | -0.160103 | -1.3491 | 0.909197 |
| 17 | 0.108316 | 0.9127 | 0.182248 |
| 18 | 0.115195 | 0.9707 | 0.167508 |
| 19 | 0.045741 | 0.3854 | 0.35054 |
| 20 | -0.093903 | -0.7912 | 0.784281 |
| 21 | -0.043852 | -0.3695 | 0.643574 |
| 22 | -0.045821 | -0.3861 | 0.64971 |
| 23 | -0.195752 | -1.6494 | 0.948262 |
| 24 | -0.151584 | -1.2773 | 0.897165 |
| 25 | -0.152833 | -1.2878 | 0.899 |
| 26 | -0.015977 | -0.1346 | 0.553354 |
| 27 | 0.070406 | 0.5933 | 0.277449 |
| 28 | 0.093603 | 0.7887 | 0.216452 |
| 29 | 0.065902 | 0.5553 | 0.290218 |
| 30 | -0.053343 | -0.4495 | 0.672772 |
| 31 | -0.07487 | -0.6309 | 0.734923 |
| 32 | -0.165117 | -1.3913 | 0.915761 |
| 33 | -0.191737 | -1.6156 | 0.944693 |
| 34 | -0.082811 | -0.6978 | 0.756203 |
| 35 | 0.003937 | 0.0332 | 0.486816 |
| Partial Autocorrelation Function | |||
| Time lag k | PACF(k) | T-STAT | P-value |
| 0 | 0.343616 | 2.8954 | 0.002515 |
| 1 | -0.241275 | -2.033 | 0.977105 |
| 2 | -0.063226 | -0.5328 | 0.702067 |
| 3 | 0.155965 | 1.3142 | 0.096507 |
| 4 | -0.158803 | -1.3381 | 0.907433 |
| 5 | -0.118724 | -1.0004 | 0.83974 |
| 6 | 0.182332 | 1.5364 | 0.064447 |
| 7 | -0.029654 | -0.2499 | 0.598296 |
| 8 | 0.087675 | 0.7388 | 0.231244 |
| 9 | 0.168261 | 1.4178 | 0.080312 |
| 10 | -0.037324 | -0.3145 | 0.622968 |
| 11 | -0.072641 | -0.6121 | 0.728781 |
| 12 | 0.046634 | 0.3929 | 0.347767 |
| 13 | -0.130423 | -1.099 | 0.862252 |
| 14 | -0.151617 | -1.2776 | 0.897215 |
| 15 | -0.028309 | -0.2385 | 0.593925 |
| 16 | 0.181226 | 1.527 | 0.065597 |
| 17 | -0.163945 | -1.3814 | 0.91426 |
| 18 | 0.059616 | 0.5023 | 0.308493 |
| 19 | -0.036875 | -0.3107 | 0.621536 |
| 20 | -0.111851 | -0.9425 | 0.825427 |
| 21 | -0.018067 | -0.1522 | 0.560285 |
| 22 | -0.107718 | -0.9076 | 0.816433 |
| 23 | -0.040404 | -0.3405 | 0.63274 |
| 24 | -0.144434 | -1.217 | 0.886187 |
| 25 | 0.03953 | 0.3331 | 0.370026 |
| 26 | 0.05252 | 0.4425 | 0.329721 |
| 27 | -0.082592 | -0.6959 | 0.755628 |
| 28 | 0.09574 | 0.8067 | 0.21126 |
| 29 | -0.065129 | -0.5488 | 0.707564 |
| 30 | -0.097815 | -0.8242 | 0.793707 |
| 31 | -0.083914 | -0.7071 | 0.759083 |
| 32 | -0.04732 | -0.3987 | 0.654353 |
| 33 | -0.041311 | -0.3481 | 0.635598 |
| 34 | -0.054988 | -0.4633 | 0.677729 |
| 35 | -0.113487 | -0.9563 | 0.828908 |
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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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