partial 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 | 0 |
| 1 | 0.145342 | 1.0174 | 0.156982 |
| 2 | 0.270729 | 1.8951 | 0.031993 |
| 3 | 0.490847 | 3.4359 | 0.000606 |
| 4 | 0.188361 | 1.3185 | 0.096729 |
| 5 | 0.234867 | 1.6441 | 0.053282 |
| 6 | 0.231779 | 1.6225 | 0.055562 |
| 7 | -0.10943 | -0.766 | 0.776327 |
| 8 | 0.14572 | 1.02 | 0.15636 |
| 9 | 0.039782 | 0.2785 | 0.390911 |
| 10 | -0.190456 | -1.3332 | 0.905683 |
| 11 | -0.096137 | -0.673 | 0.747932 |
| 12 | -0.170146 | -1.191 | 0.880308 |
| 13 | -0.195356 | -1.3675 | 0.911144 |
| 14 | -0.031182 | -0.2183 | 0.585938 |
| 15 | -0.241685 | -1.6918 | 0.951482 |
| 16 | -0.269086 | -1.8836 | 0.96722 |
| 17 | -0.008944 | -0.0626 | 0.524834 |
| 18 | -0.076149 | -0.533 | 0.701794 |
| 19 | -0.192721 | -1.349 | 0.908238 |
| 20 | -0.122901 | -0.8603 | 0.803095 |
| 21 | 0.037538 | 0.2628 | 0.396916 |
| 22 | -0.194054 | -1.3584 | 0.909717 |
| 23 | 0.087585 | 0.6131 | 0.271325 |
| 24 | -0.109174 | -0.7642 | 0.775799 |
| 25 | -0.179524 | -1.2567 | 0.892584 |
| 26 | 0.072714 | 0.509 | 0.306518 |
| 27 | -0.077593 | -0.5432 | 0.705256 |
| 28 | -0.169232 | -1.1846 | 0.87906 |
| 29 | 0.011623 | 0.0814 | 0.467743 |
| 30 | -0.138903 | -0.9723 | 0.832166 |
| 31 | -0.042344 | -0.2964 | 0.615914 |
| 32 | -0.032796 | -0.2296 | 0.59031 |
| 33 | -0.176977 | -1.2388 | 0.889347 |
| 34 | 0.00641 | 0.0449 | 0.482195 |
| 35 | 0.042636 | 0.2985 | 0.383309 |
| Partial Autocorrelation Function | |||
| Time lag k | PACF(k) | T-STAT | P-value |
| 0 | 0.145342 | 1.0174 | 0.156982 |
| 1 | 0.254991 | 1.7849 | 0.040231 |
| 2 | 0.463787 | 3.2465 | 0.001055 |
| 3 | 0.09802 | 0.6861 | 0.24793 |
| 4 | 0.020492 | 0.1434 | 0.443264 |
| 5 | -0.061614 | -0.4313 | 0.66593 |
| 6 | -0.396305 | -2.7741 | 0.996095 |
| 7 | -0.08769 | -0.6138 | 0.728917 |
| 8 | 0.004885 | 0.0342 | 0.486431 |
| 9 | -0.045057 | -0.3154 | 0.623101 |
| 10 | -0.115153 | -0.8061 | 0.787952 |
| 11 | -0.135703 | -0.9499 | 0.826593 |
| 12 | -0.011852 | -0.083 | 0.532892 |
| 13 | 0.214784 | 1.5035 | 0.069566 |
| 14 | 0.121853 | 0.853 | 0.198913 |
| 15 | -0.142141 | -0.995 | 0.837685 |
| 16 | -0.053082 | -0.3716 | 0.644094 |
| 17 | 0.120537 | 0.8438 | 0.201452 |
| 18 | -0.020126 | -0.1409 | 0.55573 |
| 19 | -0.215648 | -1.5095 | 0.931208 |
| 20 | 0.10416 | 0.7291 | 0.2347 |
| 21 | -0.27261 | -1.9083 | 0.968888 |
| 22 | 0.081531 | 0.5707 | 0.2854 |
| 23 | -0.016051 | -0.1124 | 0.5445 |
| 24 | -0.123129 | -0.8619 | 0.80353 |
| 25 | -0.058943 | -0.4126 | 0.65915 |
| 26 | -0.060598 | -0.4242 | 0.663357 |
| 27 | 0.019639 | 0.1375 | 0.44561 |
| 28 | 0.022567 | 0.158 | 0.437566 |
| 29 | 0.093406 | 0.6538 | 0.258135 |
| 30 | -0.043704 | -0.3059 | 0.619523 |
| 31 | -0.173165 | -1.2122 | 0.884367 |
| 32 | 0.005511 | 0.0386 | 0.484691 |
| 33 | 0.146612 | 1.0263 | 0.1549 |
| 34 | 0.024021 | 0.1681 | 0.433581 |
| 35 | -0.0477 | -0.3339 | 0.630061 |
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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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