Autocorrelation Function - grondstoffen (2)
| 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 | 8.1854 | 0 |
| 1 | 0.941593 | 7.7073 | 0 |
| 2 | 0.888364 | 7.2716 | 0 |
| 3 | 0.846644 | 6.9301 | 0 |
| 4 | 0.805104 | 6.5901 | 0 |
| 5 | 0.767613 | 6.2832 | 0 |
| 6 | 0.731486 | 5.9875 | 0 |
| 7 | 0.706919 | 5.7864 | 0 |
| 8 | 0.672033 | 5.5008 | 0 |
| 9 | 0.646872 | 5.2949 | 1e-06 |
| 10 | 0.625722 | 5.1218 | 1e-06 |
| 11 | 0.590729 | 4.8353 | 4e-06 |
| 12 | 0.537833 | 4.4024 | 2e-05 |
| 13 | 0.484294 | 3.9641 | 9.1e-05 |
| 14 | 0.440444 | 3.6052 | 0.000297 |
| 15 | 0.389351 | 3.187 | 0.001092 |
| 16 | 0.329789 | 2.6994 | 0.004392 |
| 17 | 0.2812 | 2.3017 | 0.012233 |
| 18 | 0.235763 | 1.9298 | 0.028933 |
| 19 | 0.188853 | 1.5458 | 0.063428 |
| 20 | 0.153689 | 1.258 | 0.106379 |
| 21 | 0.115299 | 0.9438 | 0.17434 |
| 22 | 0.072805 | 0.5959 | 0.276613 |
| 23 | 0.024708 | 0.2022 | 0.420168 |
| 24 | -0.021934 | -0.1795 | 0.570973 |
| 25 | -0.061115 | -0.5002 | 0.690729 |
| 26 | -0.103283 | -0.8454 | 0.799555 |
| 27 | -0.136649 | -1.1185 | 0.86633 |
| 28 | -0.17385 | -1.423 | 0.920315 |
| 29 | -0.208404 | -1.7059 | 0.953667 |
| 30 | -0.234919 | -1.9229 | 0.970627 |
| 31 | -0.261779 | -2.1428 | 0.982115 |
| 32 | -0.280864 | -2.299 | 0.987684 |
| 33 | -0.303059 | -2.4806 | 0.992184 |
| 34 | -0.324184 | -2.6536 | 0.995031 |
| 35 | -0.341543 | -2.7956 | 0.996624 |
| Partial Autocorrelation Function | |||
| Time lag k | PACF(k) | T-STAT | P-value |
| 0 | 0.941593 | 7.7073 | 0 |
| 1 | 0.015577 | 0.1275 | 0.449463 |
| 2 | 0.075229 | 0.6158 | 0.270065 |
| 3 | -0.012476 | -0.1021 | 0.540517 |
| 4 | 0.023131 | 0.1893 | 0.425201 |
| 5 | -0.004579 | -0.0375 | 0.514893 |
| 6 | 0.090791 | 0.7432 | 0.229992 |
| 7 | -0.095176 | -0.779 | 0.780652 |
| 8 | 0.083679 | 0.6849 | 0.247871 |
| 9 | 0.011246 | 0.0921 | 0.463466 |
| 10 | -0.109156 | -0.8935 | 0.8126 |
| 11 | -0.190174 | -1.5566 | 0.937867 |
| 12 | -0.056482 | -0.4623 | 0.677328 |
| 13 | 0.005316 | 0.0435 | 0.482712 |
| 14 | -0.082819 | -0.6779 | 0.749916 |
| 15 | -0.139901 | -1.1451 | 0.871887 |
| 16 | 0.014962 | 0.1225 | 0.451448 |
| 17 | -0.026187 | -0.2144 | 0.584537 |
| 18 | -0.041237 | -0.3375 | 0.631617 |
| 19 | 0.044263 | 0.3623 | 0.359132 |
| 20 | -0.068533 | -0.561 | 0.711654 |
| 21 | -0.020217 | -0.1655 | 0.565469 |
| 22 | -0.047363 | -0.3877 | 0.650261 |
| 23 | -0.057836 | -0.4734 | 0.681269 |
| 24 | 0.015609 | 0.1278 | 0.449359 |
| 25 | -0.005226 | -0.0428 | 0.516995 |
| 26 | 0.046261 | 0.3787 | 0.353069 |
| 27 | -0.080054 | -0.6553 | 0.742731 |
| 28 | -0.021593 | -0.1767 | 0.569879 |
| 29 | 0.036401 | 0.298 | 0.38333 |
| 30 | -0.036092 | -0.2954 | 0.61571 |
| 31 | 0.027632 | 0.2262 | 0.410876 |
| 32 | -0.003725 | -0.0305 | 0.512117 |
| 33 | -0.021009 | -0.172 | 0.56801 |
| 34 | 0.006133 | 0.0502 | 0.480057 |
| 35 | -0.099348 | -0.8132 | 0.790509 |
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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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