ACF 7
| Summary of compuational transaction | |
| Raw Input | view raw input (R code) |
| Raw Output | view raw output of R engine |
| Computing time | 3 seconds |
| R Server | 'Herman Ole Andreas Wold' @ 193.190.124.10:1001 |
| Autocorrelation Function | |||
| Time lag k | ACF(k) | T-STAT | P-value |
| 0 | 1 | 8.1854 | 0 |
| 1 | -0.441316 | -3.6123 | 0.999709 |
| 2 | 0.072503 | 0.5935 | 0.277434 |
| 3 | -0.015645 | -0.1281 | 0.550757 |
| 4 | 0.03109 | 0.2545 | 0.399952 |
| 5 | -0.024183 | -0.1979 | 0.578157 |
| 6 | 0.083129 | 0.6804 | 0.249286 |
| 7 | -0.115549 | -0.9458 | 0.826177 |
| 8 | 0.096442 | 0.7894 | 0.216328 |
| 9 | -0.136697 | -1.1189 | 0.866414 |
| 10 | -0.015045 | -0.1232 | 0.548822 |
| 11 | 0.172613 | 1.4129 | 0.081157 |
| 12 | -0.358028 | -2.9306 | 0.997689 |
| 13 | 0.323409 | 2.6472 | 0.005054 |
| 14 | -0.293613 | -2.4033 | 0.990489 |
| 15 | 0.135917 | 1.1125 | 0.134944 |
| 16 | -0.051282 | -0.4198 | 0.661997 |
| 17 | -0.007243 | -0.0593 | 0.523549 |
| 18 | -0.134282 | -1.0991 | 0.86218 |
| 19 | 0.248706 | 2.0357 | 0.022867 |
| 20 | -0.133036 | -1.089 | 0.859961 |
| 21 | 0.076872 | 0.6292 | 0.26567 |
| 22 | -0.066062 | -0.5407 | 0.704761 |
| 23 | 0.064903 | 0.5313 | 0.298501 |
| 24 | 0.002461 | 0.0201 | 0.491994 |
| 25 | -0.118263 | -0.968 | 0.831744 |
| 26 | 0.144137 | 1.1798 | 0.121123 |
| 27 | -0.046006 | -0.3766 | 0.646161 |
| 28 | 0.008268 | 0.0677 | 0.473123 |
| 29 | 0.009439 | 0.0773 | 0.469323 |
| 30 | 0.029167 | 0.2387 | 0.406016 |
| 31 | -0.114225 | -0.935 | 0.82342 |
| 32 | 0.0977 | 0.7997 | 0.213353 |
| 33 | -0.084836 | -0.6944 | 0.755087 |
| 34 | 0.090427 | 0.7402 | 0.230889 |
| 35 | -0.068094 | -0.5574 | 0.710435 |
| 36 | -0.093599 | -0.7661 | 0.776858 |
| 37 | 0.070185 | 0.5745 | 0.28378 |
| 38 | 0.013902 | 0.1138 | 0.454871 |
| 39 | 0.0228 | 0.1866 | 0.42626 |
| 40 | 0.040113 | 0.3283 | 0.371841 |
| 41 | -0.046247 | -0.3785 | 0.646889 |
| 42 | 0.034062 | 0.2788 | 0.390626 |
| 43 | 0.004592 | 0.0376 | 0.485064 |
| 44 | -0.031747 | -0.2599 | 0.602115 |
| 45 | 0.066851 | 0.5472 | 0.293031 |
| 46 | -0.007799 | -0.0638 | 0.525355 |
| 47 | -0.009427 | -0.0772 | 0.530637 |
| 48 | 0.044851 | 0.3671 | 0.357344 |
| 49 | -0.054749 | -0.4481 | 0.67225 |
| 50 | 0.013252 | 0.1085 | 0.456973 |
| 51 | 0.005712 | 0.0468 | 0.481425 |
| 52 | -0.029256 | -0.2395 | 0.594263 |
| 53 | 0.003304 | 0.027 | 0.489252 |
| 54 | -0.011476 | -0.0939 | 0.53728 |
| 55 | 0.044123 | 0.3612 | 0.359558 |
| 56 | -0.050917 | -0.4168 | 0.660911 |
| 57 | -0.007137 | -0.0584 | 0.523204 |
| 58 | -0.000337 | -0.0028 | 0.501095 |
| 59 | 0.025881 | 0.2118 | 0.416436 |
| Partial Autocorrelation Function | |||
| Time lag k | PACF(k) | T-STAT | P-value |
| 0 | -0.441316 | -3.6123 | 0.999709 |
| 1 | -0.151826 | -1.2427 | 0.890853 |
| 2 | -0.058211 | -0.4765 | 0.682357 |
| 3 | 0.013345 | 0.1092 | 0.456671 |
| 4 | -0.001684 | -0.0138 | 0.505478 |
| 5 | 0.094089 | 0.7702 | 0.221957 |
| 6 | -0.047574 | -0.3894 | 0.650896 |
| 7 | 0.03191 | 0.2612 | 0.397373 |
| 8 | -0.113024 | -0.9251 | 0.820894 |
| 9 | -0.161529 | -1.3222 | 0.904697 |
| 10 | 0.120631 | 0.9874 | 0.163497 |
| 11 | -0.309073 | -2.5299 | 0.993116 |
| 12 | 0.111425 | 0.9121 | 0.182505 |
| 13 | -0.228966 | -1.8742 | 0.967366 |
| 14 | -0.019291 | -0.1579 | 0.562495 |
| 15 | -0.047964 | -0.3926 | 0.652069 |
| 16 | -0.110694 | -0.9061 | 0.815926 |
| 17 | -0.160182 | -1.3111 | 0.902858 |
| 18 | 0.035343 | 0.2893 | 0.386625 |
| 19 | 0.101735 | 0.8327 | 0.203976 |
| 20 | -0.05709 | -0.4673 | 0.6791 |
| 21 | -0.014046 | -0.115 | 0.545593 |
| 22 | 0.038835 | 0.3179 | 0.375783 |
| 23 | -0.122112 | -0.9995 | 0.839434 |
| 24 | -0.081107 | -0.6639 | 0.745481 |
| 25 | -0.136699 | -1.1189 | 0.866416 |
| 26 | 0.022807 | 0.1867 | 0.426235 |
| 27 | -0.047953 | -0.3925 | 0.652037 |
| 28 | 0.044798 | 0.3667 | 0.357502 |
| 29 | -0.058127 | -0.4758 | 0.682114 |
| 30 | -0.056723 | -0.4643 | 0.678031 |
| 31 | -0.086507 | -0.7081 | 0.759326 |
| 32 | -0.072113 | -0.5903 | 0.721502 |
| 33 | -0.021187 | -0.1734 | 0.568579 |
| 34 | -0.038348 | -0.3139 | 0.62271 |
| 35 | -0.232156 | -1.9003 | 0.96915 |
| 36 | -0.046313 | -0.3791 | 0.64709 |
| 37 | -0.073813 | -0.6042 | 0.72612 |
| 38 | -0.014222 | -0.1164 | 0.546162 |
| 39 | 0.034499 | 0.2824 | 0.38926 |
| 40 | 0.011824 | 0.0968 | 0.461593 |
| 41 | -0.023844 | -0.1952 | 0.577076 |
| 42 | -0.056949 | -0.4661 | 0.678688 |
| 43 | -0.022106 | -0.1809 | 0.571521 |
| 44 | -0.083252 | -0.6814 | 0.751031 |
| 45 | -0.00283 | -0.0232 | 0.509206 |
| 46 | -0.009811 | -0.0803 | 0.531884 |
| 47 | -0.060173 | -0.4925 | 0.688025 |
| 48 | -0.018037 | -0.1476 | 0.558464 |
| 49 | -0.129081 | -1.0566 | 0.852749 |
| 50 | 0.009326 | 0.0763 | 0.469688 |
| 51 | -0.053826 | -0.4406 | 0.669534 |
| 52 | -0.095886 | -0.7849 | 0.782349 |
| 53 | -0.04038 | -0.3305 | 0.628982 |
| 54 | 0.107152 | 0.8771 | 0.191789 |
| 55 | 0.000446 | 0.0037 | 0.498549 |
| 56 | -0.061221 | -0.5011 | 0.691032 |
| 57 | -0.024856 | -0.2035 | 0.580302 |
| 58 | -0.049462 | -0.4049 | 0.656565 |
| 59 | -0.024782 | -0.2028 | 0.580065 |
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| http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Dec/25/t1198585367amk9359675m1f1v/2sazo1198586518.ps (open in new window) |
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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