Workshop 5 vraag 1
| 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 | 8.4261 | 0 |
| 1 | -0.355295 | -2.9938 | 0.998105 |
| 2 | -0.27199 | -2.2918 | 0.987558 |
| 3 | 0.082346 | 0.6939 | 0.245017 |
| 4 | 0.093485 | 0.7877 | 0.216742 |
| 5 | -0.174678 | -1.4719 | 0.927262 |
| 6 | 0.267616 | 2.255 | 0.01361 |
| 7 | -0.240727 | -2.0284 | 0.976864 |
| 8 | 0.208144 | 1.7539 | 0.041886 |
| 9 | 0.062908 | 0.5301 | 0.298856 |
| 10 | -0.341115 | -2.8743 | 0.99733 |
| 11 | -0.174438 | -1.4698 | 0.926988 |
| 12 | 0.712814 | 6.0063 | 0 |
| 13 | -0.272791 | -2.2986 | 0.987762 |
| 14 | -0.152408 | -1.2842 | 0.898379 |
| 15 | -0.001081 | -0.0091 | 0.503622 |
| 16 | 0.059549 | 0.5018 | 0.308692 |
| 17 | -0.06451 | -0.5436 | 0.705781 |
| 18 | 0.161995 | 1.365 | 0.088283 |
| 19 | -0.210236 | -1.7715 | 0.959614 |
| 20 | 0.245021 | 2.0646 | 0.021308 |
| 21 | -0.019615 | -0.1653 | 0.565405 |
| 22 | -0.296552 | -2.4988 | 0.992611 |
| 23 | -0.032756 | -0.276 | 0.608327 |
| 24 | 0.446967 | 3.7662 | 0.000169 |
| 25 | -0.170994 | -1.4408 | 0.922984 |
| 26 | -0.037577 | -0.3166 | 0.623774 |
| 27 | -0.136641 | -1.1514 | 0.873276 |
| 28 | 0.135179 | 1.139 | 0.129258 |
| 29 | -0.054348 | -0.4579 | 0.675804 |
| 30 | 0.064775 | 0.5458 | 0.293455 |
| 31 | -0.095178 | -0.802 | 0.787381 |
| 32 | 0.17326 | 1.4599 | 0.074362 |
| 33 | -0.04339 | -0.3656 | 0.642129 |
| 34 | -0.179657 | -1.5138 | 0.932744 |
| 35 | -0.053249 | -0.4487 | 0.672487 |
| Partial Autocorrelation Function | |||
| Time lag k | PACF(k) | T-STAT | P-value |
| 0 | -0.355295 | -2.9938 | 0.998105 |
| 1 | -0.455757 | -3.8403 | 0.999868 |
| 2 | -0.318173 | -2.681 | 0.995439 |
| 3 | -0.214966 | -1.8113 | 0.962841 |
| 4 | -0.414448 | -3.4922 | 0.999586 |
| 5 | -0.023819 | -0.2007 | 0.579249 |
| 6 | -0.394715 | -3.3259 | 0.999301 |
| 7 | 0.104604 | 0.8814 | 0.190536 |
| 8 | 0.287479 | 2.4223 | 0.008988 |
| 9 | -0.082724 | -0.697 | 0.755975 |
| 10 | -0.509212 | -4.2907 | 0.999972 |
| 11 | 0.182312 | 1.5362 | 0.064468 |
| 12 | 0.149 | 1.2555 | 0.106707 |
| 13 | 0.153962 | 1.2973 | 0.099363 |
| 14 | -0.077627 | -0.6541 | 0.742419 |
| 15 | -0.068839 | -0.58 | 0.718141 |
| 16 | -0.008754 | -0.0738 | 0.529297 |
| 17 | -0.036564 | -0.3081 | 0.620544 |
| 18 | 0.021671 | 0.1826 | 0.427816 |
| 19 | -0.025785 | -0.2173 | 0.58569 |
| 20 | -0.161466 | -1.3605 | 0.911018 |
| 21 | -0.160375 | -1.3513 | 0.909562 |
| 22 | 0.017851 | 0.1504 | 0.440433 |
| 23 | -0.065488 | -0.5518 | 0.708596 |
| 24 | -0.077824 | -0.6558 | 0.74295 |
| 25 | 0.033033 | 0.2783 | 0.39078 |
| 26 | -0.131872 | -1.1112 | 0.864879 |
| 27 | 0.14281 | 1.2033 | 0.116421 |
| 28 | -0.019327 | -0.1629 | 0.564451 |
| 29 | 0.000813 | 0.0068 | 0.497278 |
| 30 | 0.065194 | 0.5493 | 0.292251 |
| 31 | -0.163362 | -1.3765 | 0.913506 |
| 32 | 0.04949 | 0.417 | 0.338963 |
| 33 | 0.019087 | 0.1608 | 0.436343 |
| 34 | -0.038739 | -0.3264 | 0.627468 |
| 35 | -0.062132 | -0.5235 | 0.698885 |
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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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