gewone + partiële autocorrelatie
| 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 | 7.4162 | 0 |
| 1 | -0.2444 | -1.8125 | 0.962317 |
| 2 | -0.116178 | -0.8616 | 0.803676 |
| 3 | 0.207662 | 1.5401 | 0.06464 |
| 4 | -0.157735 | -1.1698 | 0.876436 |
| 5 | 0.132444 | 0.9822 | 0.165144 |
| 6 | 0.149472 | 1.1085 | 0.136232 |
| 7 | -0.317803 | -2.3569 | 0.988992 |
| 8 | 0.048143 | 0.357 | 0.361216 |
| 9 | 0.329477 | 2.4435 | 0.008892 |
| 10 | -0.405073 | -3.0041 | 0.997998 |
| 11 | 0.113144 | 0.8391 | 0.202523 |
| 12 | -0.048378 | -0.3588 | 0.639433 |
| 13 | -0.209375 | -1.5528 | 0.936892 |
| 14 | 0.186322 | 1.3818 | 0.08631 |
| 15 | -0.107305 | -0.7958 | 0.785213 |
| 16 | -0.18085 | -1.3412 | 0.907319 |
| 17 | 0.250274 | 1.8561 | 0.034401 |
| 18 | 0.036803 | 0.2729 | 0.392961 |
| 19 | -0.233329 | -1.7304 | 0.955418 |
| 20 | 0.056207 | 0.4168 | 0.339208 |
| 21 | -0.052574 | -0.3899 | 0.65094 |
| 22 | 0.102284 | 0.7586 | 0.225677 |
| 23 | 0.188661 | 1.3991 | 0.083691 |
| 24 | -0.224298 | -1.6634 | 0.949044 |
| 25 | -0.145502 | -1.0791 | 0.857367 |
| 26 | 0.283887 | 2.1054 | 0.01992 |
| 27 | -0.142442 | -1.0564 | 0.852293 |
| 28 | 0.046518 | 0.345 | 0.365711 |
| 29 | 0.072785 | 0.5398 | 0.29576 |
| 30 | -0.164285 | -1.2184 | 0.885858 |
| 31 | 0.20101 | 1.4907 | 0.070873 |
| 32 | 0.039693 | 0.2944 | 0.384791 |
| 33 | -0.163663 | -1.2138 | 0.884986 |
| 34 | 0.052152 | 0.3868 | 0.350209 |
| 35 | 0.092629 | 0.687 | 0.247499 |
| Partial Autocorrelation Function | |||
| Time lag k | PACF(k) | T-STAT | P-value |
| 0 | -0.2444 | -1.8125 | 0.962317 |
| 1 | -0.187085 | -1.3875 | 0.91455 |
| 2 | 0.141325 | 1.0481 | 0.14959 |
| 3 | -0.096048 | -0.7123 | 0.760359 |
| 4 | 0.131941 | 0.9785 | 0.166056 |
| 5 | 0.172919 | 1.2824 | 0.10254 |
| 6 | -0.20251 | -1.5019 | 0.930573 |
| 7 | -0.10467 | -0.7763 | 0.779538 |
| 8 | 0.297871 | 2.2091 | 0.015677 |
| 9 | -0.269129 | -1.9959 | 0.974548 |
| 10 | -0.040942 | -0.3036 | 0.618725 |
| 11 | -0.124309 | -0.9219 | 0.819696 |
| 12 | -0.095548 | -0.7086 | 0.759217 |
| 13 | -0.100095 | -0.7423 | 0.769476 |
| 14 | -0.07165 | -0.5314 | 0.701348 |
| 15 | -0.022745 | -0.1687 | 0.566668 |
| 16 | 0.103907 | 0.7706 | 0.222122 |
| 17 | 0.109157 | 0.8095 | 0.210849 |
| 18 | -0.053099 | -0.3938 | 0.652373 |
| 19 | -0.240857 | -1.7862 | 0.960214 |
| 20 | 0.031558 | 0.234 | 0.407912 |
| 21 | 0.120256 | 0.8918 | 0.188182 |
| 22 | 0.071904 | 0.5333 | 0.298002 |
| 23 | -0.075289 | -0.5584 | 0.710568 |
| 24 | -0.248501 | -1.8429 | 0.964635 |
| 25 | 0.054963 | 0.4076 | 0.342569 |
| 26 | -0.152124 | -1.1282 | 0.86793 |
| 27 | 0.130236 | 0.9659 | 0.169173 |
| 28 | 0.140738 | 1.0437 | 0.150586 |
| 29 | -0.008555 | -0.0634 | 0.525178 |
| 30 | 0.03758 | 0.2787 | 0.390759 |
| 31 | -0.065067 | -0.4826 | 0.684335 |
| 32 | 0.024603 | 0.1825 | 0.427947 |
| 33 | -0.019532 | -0.1449 | 0.557322 |
| 34 | -0.027444 | -0.2035 | 0.580264 |
| 35 | 0.054848 | 0.4068 | 0.342881 |
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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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