G3ACFhuw
| Summary of compuational transaction | |
| Raw Input | view raw input (R code) |
| Raw Output | view raw output of R engine |
| Computing time | 2 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 | 6.9282 | 0 |
| 1 | -0.359702 | -2.4921 | 0.991897 |
| 2 | -0.043887 | -0.3041 | 0.618801 |
| 3 | 0.314529 | 2.1791 | 0.017131 |
| 4 | -0.193175 | -1.3384 | 0.906457 |
| 5 | 0.019842 | 0.1375 | 0.445618 |
| 6 | 0.129126 | 0.8946 | 0.187729 |
| 7 | -0.135308 | -0.9374 | 0.823387 |
| 8 | -0.096373 | -0.6677 | 0.746236 |
| 9 | 0.168727 | 1.169 | 0.124093 |
| 10 | -0.082649 | -0.5726 | 0.715209 |
| 11 | -0.065908 | -0.4566 | 0.674999 |
| 12 | 0.115571 | 0.8007 | 0.213626 |
| 13 | -0.204149 | -1.4144 | 0.918148 |
| 14 | 0.219563 | 1.5212 | 0.067388 |
| 15 | -0.070795 | -0.4905 | 0.686987 |
| 16 | -0.099951 | -0.6925 | 0.754013 |
| 17 | 0.00772 | 0.0535 | 0.478783 |
| 18 | -0.011812 | -0.0818 | 0.532442 |
| 19 | -0.148832 | -1.0311 | 0.846177 |
| 20 | 0.122533 | 0.8489 | 0.200066 |
| 21 | -0.044056 | -0.3052 | 0.619245 |
| 22 | -0.200985 | -1.3925 | 0.9149 |
| 23 | 0.225938 | 1.5653 | 0.062036 |
| 24 | -0.130113 | -0.9014 | 0.814075 |
| 25 | -0.182615 | -1.2652 | 0.894045 |
| 26 | 0.272018 | 1.8846 | 0.032774 |
| 27 | -0.129577 | -0.8977 | 0.813097 |
| 28 | -0.010414 | -0.0722 | 0.528609 |
| 29 | 0.138916 | 0.9624 | 0.170326 |
| 30 | -0.056031 | -0.3882 | 0.650207 |
| 31 | -0.015859 | -0.1099 | 0.543516 |
| 32 | 0.080212 | 0.5557 | 0.290489 |
| 33 | -0.050379 | -0.349 | 0.635707 |
| 34 | -0.037329 | -0.2586 | 0.601485 |
| 35 | 0.034911 | 0.2419 | 0.404954 |
| Partial Autocorrelation Function | |||
| Time lag k | PACF(k) | T-STAT | P-value |
| 0 | -0.359702 | -2.4921 | 0.991897 |
| 1 | -0.199024 | -1.3789 | 0.912837 |
| 2 | 0.267915 | 1.8562 | 0.034787 |
| 3 | 0.023725 | 0.1644 | 0.435063 |
| 4 | -0.01042 | -0.0722 | 0.528626 |
| 5 | 0.041077 | 0.2846 | 0.388592 |
| 6 | -0.032634 | -0.2261 | 0.588957 |
| 7 | -0.191853 | -1.3292 | 0.904966 |
| 8 | 0.025125 | 0.1741 | 0.431272 |
| 9 | 0.054455 | 0.3773 | 0.353815 |
| 10 | -0.011026 | -0.0764 | 0.530286 |
| 11 | 0.002227 | 0.0154 | 0.493876 |
| 12 | -0.17503 | -1.2126 | 0.8844 |
| 13 | 0.180176 | 1.2483 | 0.108989 |
| 14 | -0.017526 | -0.1214 | 0.54807 |
| 15 | -0.048907 | -0.3388 | 0.631896 |
| 16 | -0.186598 | -1.2928 | 0.898863 |
| 17 | -0.036822 | -0.2551 | 0.600137 |
| 18 | -0.191825 | -1.329 | 0.904935 |
| 19 | 0.048168 | 0.3337 | 0.370023 |
| 20 | 0.007085 | 0.0491 | 0.480526 |
| 21 | -0.099561 | -0.6898 | 0.753173 |
| 22 | 0.019921 | 0.138 | 0.445403 |
| 23 | -0.120589 | -0.8355 | 0.796203 |
| 24 | -0.208685 | -1.4458 | 0.922636 |
| 25 | 0.02463 | 0.1706 | 0.432612 |
| 26 | 0.099822 | 0.6916 | 0.246265 |
| 27 | 0.042005 | 0.291 | 0.386144 |
| 28 | 0.01036 | 0.0718 | 0.471539 |
| 29 | -0.030009 | -0.2079 | 0.58191 |
| 30 | 0.061543 | 0.4264 | 0.335867 |
| 31 | -0.086462 | -0.599 | 0.724014 |
| 32 | -0.029231 | -0.2025 | 0.579815 |
| 33 | -0.068 | -0.4711 | 0.680155 |
| 34 | -0.097704 | -0.6769 | 0.749145 |
| 35 | -0.103286 | -0.7156 | 0.761144 |
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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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