PACF en ACF totaal
| 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 | 11.3137 | 0 |
| 1 | 0.170602 | 1.9301 | 0.027901 |
| 2 | -0.003256 | -0.0368 | 0.514663 |
| 3 | 0.17639 | 1.9956 | 0.024049 |
| 4 | -0.025388 | -0.2872 | 0.612802 |
| 5 | -0.159626 | -1.806 | 0.963363 |
| 6 | -0.100355 | -1.1354 | 0.870832 |
| 7 | -0.035597 | -0.4027 | 0.656093 |
| 8 | -0.125282 | -1.4174 | 0.920603 |
| 9 | -0.130846 | -1.4804 | 0.929382 |
| 10 | -0.040994 | -0.4638 | 0.678209 |
| 11 | -0.014179 | -0.1604 | 0.563598 |
| 12 | -0.346562 | -3.9209 | 0.999928 |
| 13 | 0.126046 | 1.426 | 0.078145 |
| 14 | 0.18545 | 2.0981 | 0.01893 |
| 15 | -0.119007 | -1.3464 | 0.909725 |
| 16 | 0.061926 | 0.7006 | 0.242407 |
| 17 | 0.080179 | 0.9071 | 0.183023 |
| 18 | 0.080345 | 0.909 | 0.182529 |
| 19 | 0.083296 | 0.9424 | 0.173884 |
| 20 | 0.032696 | 0.3699 | 0.35603 |
| 21 | 0.04835 | 0.547 | 0.292659 |
| 22 | 0.055451 | 0.6274 | 0.265772 |
| 23 | -0.126044 | -1.426 | 0.921852 |
| 24 | -0.15055 | -1.7033 | 0.954529 |
| 25 | -0.151517 | -1.7142 | 0.955545 |
| 26 | -0.105706 | -1.1959 | 0.883033 |
| 27 | 0.077204 | 0.8735 | 0.192024 |
| 28 | -0.032674 | -0.3697 | 0.643876 |
| 29 | 0.001246 | 0.0141 | 0.494385 |
| 30 | -0.019895 | -0.2251 | 0.588864 |
| 31 | 0.0462 | 0.5227 | 0.301045 |
| 32 | 0.110556 | 1.2508 | 0.106645 |
| 33 | 0.025504 | 0.2885 | 0.386696 |
| 34 | -0.055757 | -0.6308 | 0.735358 |
| 35 | 0.074588 | 0.8439 | 0.200159 |
| Partial Autocorrelation Function | |||
| Time lag k | PACF(k) | T-STAT | P-value |
| 0 | 0.170602 | 1.9301 | 0.027901 |
| 1 | -0.033331 | -0.3771 | 0.646636 |
| 2 | 0.188335 | 2.1308 | 0.017511 |
| 3 | -0.096147 | -1.0878 | 0.860633 |
| 4 | -0.134439 | -1.521 | 0.934638 |
| 5 | -0.091658 | -1.037 | 0.849153 |
| 6 | 0.008195 | 0.0927 | 0.463139 |
| 7 | -0.080715 | -0.9132 | 0.818569 |
| 8 | -0.083282 | -0.9422 | 0.826074 |
| 9 | -0.040259 | -0.4555 | 0.675233 |
| 10 | 0.001452 | 0.0164 | 0.493457 |
| 11 | -0.370388 | -4.1905 | 0.999974 |
| 12 | 0.279256 | 3.1594 | 0.000986 |
| 13 | 0.036084 | 0.4082 | 0.341888 |
| 14 | -0.080738 | -0.9134 | 0.818637 |
| 15 | -0.010726 | -0.1214 | 0.5482 |
| 16 | -0.07854 | -0.8886 | 0.812052 |
| 17 | 0.136845 | 1.5482 | 0.062018 |
| 18 | 0.095544 | 1.081 | 0.140875 |
| 19 | -0.087587 | -0.9909 | 0.838206 |
| 20 | 0.032773 | 0.3708 | 0.355704 |
| 21 | 0.035822 | 0.4053 | 0.342974 |
| 22 | -0.117974 | -1.3347 | 0.907832 |
| 23 | -0.281069 | -3.1799 | 0.999076 |
| 24 | 0.071149 | 0.805 | 0.211169 |
| 25 | 0.091616 | 1.0365 | 0.150959 |
| 26 | 0.035975 | 0.407 | 0.342341 |
| 27 | -0.055561 | -0.6286 | 0.734633 |
| 28 | -0.059943 | -0.6782 | 0.750559 |
| 29 | -0.047653 | -0.5391 | 0.704633 |
| 30 | 0.16814 | 1.9023 | 0.02969 |
| 31 | -0.039697 | -0.4491 | 0.672947 |
| 32 | 0.00039 | 0.0044 | 0.498242 |
| 33 | -0.075104 | -0.8497 | 0.801463 |
| 34 | -0.025397 | -0.2873 | 0.612839 |
| 35 | -0.058366 | -0.6603 | 0.744887 |
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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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