partial autocorrelation
| 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 | 7.8102 | 0 |
| 1 | 0.49422 | 3.86 | 0.000138 |
| 2 | 0.367366 | 2.8692 | 0.002823 |
| 3 | 0.464685 | 3.6293 | 0.000291 |
| 4 | 0.52391 | 4.0919 | 6.4e-05 |
| 5 | 0.455599 | 3.5583 | 0.000365 |
| 6 | 0.540748 | 4.2234 | 4.1e-05 |
| 7 | 0.304817 | 2.3807 | 0.010209 |
| 8 | 0.418691 | 3.2701 | 0.000885 |
| 9 | 0.363474 | 2.8388 | 0.003071 |
| 10 | 0.17353 | 1.3553 | 0.090158 |
| 11 | 0.278607 | 2.176 | 0.016719 |
| 12 | 0.564627 | 4.4099 | 2.1e-05 |
| 13 | 0.181209 | 1.4153 | 0.081035 |
| 14 | 0.121448 | 0.9485 | 0.173299 |
| 15 | 0.142037 | 1.1093 | 0.135818 |
| 16 | 0.14714 | 1.1492 | 0.12748 |
| 17 | 0.153146 | 1.1961 | 0.118141 |
| 18 | 0.175832 | 1.3733 | 0.087344 |
| 19 | -0.018527 | -0.1447 | 0.557287 |
| 20 | 0.087888 | 0.6864 | 0.247523 |
| 21 | 0.022375 | 0.1748 | 0.430926 |
| 22 | -0.159867 | -1.2486 | 0.891709 |
| 23 | 0.002369 | 0.0185 | 0.492648 |
| 24 | 0.129299 | 1.0099 | 0.158276 |
| 25 | -0.129895 | -1.0145 | 0.842827 |
| 26 | -0.157975 | -1.2338 | 0.889 |
| 27 | -0.17135 | -1.3383 | 0.907115 |
| 28 | -0.136239 | -1.0641 | 0.854252 |
| 29 | -0.121609 | -0.9498 | 0.827019 |
| 30 | -0.149066 | -1.1642 | 0.87557 |
| 31 | -0.267547 | -2.0896 | 0.979585 |
| 32 | -0.142184 | -1.1105 | 0.864427 |
| 33 | -0.231124 | -1.8051 | 0.962005 |
| 34 | -0.321491 | -2.5109 | 0.992646 |
| 35 | -0.214638 | -1.6764 | 0.950608 |
| Partial Autocorrelation Function | |||
| Time lag k | PACF(k) | T-STAT | P-value |
| 0 | 0.49422 | 3.86 | 0.000138 |
| 1 | 0.162901 | 1.2723 | 0.104048 |
| 2 | 0.315612 | 2.465 | 0.008266 |
| 3 | 0.283356 | 2.2131 | 0.015322 |
| 4 | 0.137229 | 1.0718 | 0.144018 |
| 5 | 0.303938 | 2.3738 | 0.010384 |
| 6 | -0.236795 | -1.8494 | 0.965379 |
| 7 | 0.193356 | 1.5102 | 0.068083 |
| 8 | -0.197438 | -1.542 | 0.935882 |
| 9 | -0.343617 | -2.6837 | 0.99532 |
| 10 | 0.141109 | 1.1021 | 0.137374 |
| 11 | 0.383651 | 2.9964 | 0.001973 |
| 12 | -0.293979 | -2.296 | 0.987438 |
| 13 | -0.060724 | -0.4743 | 0.6815 |
| 14 | -0.097061 | -0.7581 | 0.774337 |
| 15 | -0.158998 | -1.2418 | 0.89047 |
| 16 | -0.092104 | -0.7194 | 0.762666 |
| 17 | 0.020895 | 0.1632 | 0.435453 |
| 18 | 0.145186 | 1.1339 | 0.130628 |
| 19 | -0.090366 | -0.7058 | 0.758494 |
| 20 | -0.019255 | -0.1504 | 0.559523 |
| 21 | -0.071926 | -0.5618 | 0.711831 |
| 22 | 0.050422 | 0.3938 | 0.347549 |
| 23 | -0.107546 | -0.84 | 0.797895 |
| 24 | 0.003521 | 0.0275 | 0.489077 |
| 25 | -0.006185 | -0.0483 | 0.519185 |
| 26 | -0.099698 | -0.7787 | 0.780406 |
| 27 | 0.102404 | 0.7998 | 0.213465 |
| 28 | -0.182221 | -1.4232 | 0.920112 |
| 29 | 0.025528 | 0.1994 | 0.421315 |
| 30 | -0.015241 | -0.119 | 0.547182 |
| 31 | -0.009898 | -0.0773 | 0.530685 |
| 32 | 0.003321 | 0.0259 | 0.489697 |
| 33 | 0.080478 | 0.6286 | 0.265993 |
| 34 | -0.105255 | -0.8221 | 0.792881 |
| 35 | 0.009182 | 0.0717 | 0.471533 |
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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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