workshop 7 Q2 I
| 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 | 8.775 | 0 |
| 1 | 0.361626 | 3.1733 | 0.001083 |
| 2 | -0.274048 | -2.4048 | 0.990708 |
| 3 | -0.618236 | -5.425 | 1 |
| 4 | -0.187875 | -1.6486 | 0.948348 |
| 5 | 0.20111 | 1.7647 | 0.040787 |
| 6 | 0.314178 | 2.7569 | 0.003641 |
| 7 | 0.153447 | 1.3465 | 0.091048 |
| 8 | -0.240869 | -2.1136 | 0.981107 |
| 9 | -0.462604 | -4.0593 | 0.999941 |
| 10 | -0.293262 | -2.5734 | 0.994004 |
| 11 | 0.258092 | 2.2648 | 0.01317 |
| 12 | 0.582795 | 5.114 | 1e-06 |
| 13 | 0.288516 | 2.5317 | 0.006694 |
| 14 | -0.157544 | -1.3824 | 0.914583 |
| 15 | -0.413523 | -3.6286 | 0.999745 |
| 16 | -0.172071 | -1.5099 | 0.932421 |
| 17 | 0.063528 | 0.5575 | 0.289416 |
| 18 | 0.248709 | 2.1824 | 0.016064 |
| 19 | 0.131125 | 1.1506 | 0.126726 |
| 20 | -0.077698 | -0.6818 | 0.751293 |
| 21 | -0.259053 | -2.2732 | 0.987098 |
| 22 | -0.197917 | -1.7367 | 0.956781 |
| 23 | 0.135546 | 1.1894 | 0.118966 |
| 24 | 0.335225 | 2.9416 | 0.002156 |
| 25 | 0.264999 | 2.3254 | 0.011344 |
| 26 | -0.122396 | -1.074 | 0.856915 |
| 27 | -0.31249 | -2.7421 | 0.996206 |
| 28 | -0.21331 | -1.8718 | 0.967483 |
| 29 | 0.075221 | 0.6601 | 0.255592 |
| 30 | 0.212159 | 1.8617 | 0.033232 |
| 31 | 0.132264 | 1.1606 | 0.124694 |
| 32 | -0.079994 | -0.7019 | 0.757584 |
| 33 | -0.230469 | -2.0224 | 0.976694 |
| 34 | -0.134912 | -1.1838 | 0.879943 |
| 35 | 0.126327 | 1.1085 | 0.135545 |
| Partial Autocorrelation Function | |||
| Time lag k | PACF(k) | T-STAT | P-value |
| 0 | 0.361626 | 3.1733 | 0.001083 |
| 1 | -0.465726 | -4.0867 | 0.999947 |
| 2 | -0.447427 | -3.9262 | 0.999907 |
| 3 | 0.202329 | 1.7754 | 0.039889 |
| 4 | -0.091524 | -0.8031 | 0.78781 |
| 5 | -0.084582 | -0.7422 | 0.769889 |
| 6 | 0.207478 | 1.8206 | 0.036275 |
| 7 | -0.381092 | -3.3441 | 0.999361 |
| 8 | -0.366948 | -3.22 | 0.999061 |
| 9 | 0.059447 | 0.5216 | 0.301707 |
| 10 | 0.072276 | 0.6342 | 0.263909 |
| 11 | 0.108085 | 0.9484 | 0.172936 |
| 12 | -0.001617 | -0.0142 | 0.505642 |
| 13 | 0.172447 | 1.5132 | 0.06716 |
| 14 | -0.070426 | -0.618 | 0.730795 |
| 15 | -0.065732 | -0.5768 | 0.717121 |
| 16 | -0.071613 | -0.6284 | 0.734198 |
| 17 | -0.075556 | -0.663 | 0.745345 |
| 18 | -0.060008 | -0.5266 | 0.699997 |
| 19 | 0.105733 | 0.9278 | 0.178205 |
| 20 | 0.09421 | 0.8267 | 0.205484 |
| 21 | -0.039308 | -0.3449 | 0.634455 |
| 22 | 0.128499 | 1.1276 | 0.1315 |
| 23 | 0.057209 | 0.502 | 0.308548 |
| 24 | -0.016353 | -0.1435 | 0.556864 |
| 25 | -0.149186 | -1.3091 | 0.902803 |
| 26 | -0.03266 | -0.2866 | 0.612402 |
| 27 | 0.00917 | 0.0805 | 0.468039 |
| 28 | 0.024381 | 0.2139 | 0.415577 |
| 29 | 0.023895 | 0.2097 | 0.417235 |
| 30 | 0.125534 | 1.1016 | 0.137044 |
| 31 | -0.103003 | -0.9038 | 0.815552 |
| 32 | -0.078466 | -0.6885 | 0.753408 |
| 33 | 0.090047 | 0.7902 | 0.21593 |
| 34 | -0.036504 | -0.3203 | 0.625204 |
| 35 | -0.037723 | -0.331 | 0.629237 |
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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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