WS 4 - Q2 - methode 2 - Reeks 1
| 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 | 7.746 | 0 |
| 1 | 0.117048 | 0.9066 | 0.18411 |
| 2 | -0.049566 | -0.3839 | 0.64881 |
| 3 | 0.390477 | 3.0246 | 0.00183 |
| 4 | -0.172458 | -1.3359 | 0.906678 |
| 5 | -0.007289 | -0.0565 | 0.522419 |
| 6 | 0.47323 | 3.6656 | 0.000262 |
| 7 | -0.134659 | -1.0431 | 0.849449 |
| 8 | -0.098005 | -0.7591 | 0.77463 |
| 9 | 0.288111 | 2.2317 | 0.014691 |
| 10 | -0.263201 | -2.0387 | 0.977056 |
| 11 | 0.012515 | 0.0969 | 0.461549 |
| 12 | 0.551792 | 4.2742 | 3.5e-05 |
| 13 | -0.069539 | -0.5386 | 0.703939 |
| 14 | -0.012567 | -0.0973 | 0.53861 |
| 15 | 0.214065 | 1.6581 | 0.051255 |
| 16 | -0.260437 | -2.0173 | 0.97593 |
| 17 | -0.021481 | -0.1664 | 0.565796 |
| 18 | 0.24705 | 1.9136 | 0.03022 |
| 19 | -0.235204 | -1.8219 | 0.963271 |
| 20 | -0.114443 | -0.8865 | 0.810549 |
| 21 | 0.096926 | 0.7508 | 0.227857 |
| 22 | -0.272793 | -2.113 | 0.980617 |
| 23 | 0.050794 | 0.3934 | 0.347691 |
| 24 | 0.289267 | 2.2407 | 0.014381 |
| 25 | -0.108733 | -0.8422 | 0.798502 |
| 26 | 0.022961 | 0.1779 | 0.429719 |
| 27 | 0.169281 | 1.3112 | 0.097386 |
| 28 | -0.173539 | -1.3442 | 0.908033 |
| 29 | 0.031793 | 0.2463 | 0.403158 |
| 30 | 0.148729 | 1.152 | 0.126935 |
| 31 | -0.117593 | -0.9109 | 0.816996 |
| 32 | -0.013995 | -0.1084 | 0.542981 |
| 33 | -0.008226 | -0.0637 | 0.525297 |
| 34 | -0.235607 | -1.825 | 0.96351 |
| 35 | -0.005697 | -0.0441 | 0.517526 |
| 36 | 0.132938 | 1.0297 | 0.153635 |
| 37 | -0.100904 | -0.7816 | 0.781239 |
| 38 | -0.056781 | -0.4398 | 0.669176 |
| 39 | -0.023436 | -0.1815 | 0.571719 |
| 40 | -0.192632 | -1.4921 | 0.929547 |
| 41 | -0.073501 | -0.5693 | 0.714374 |
| 42 | -0.035804 | -0.2773 | 0.608763 |
| 43 | -0.106684 | -0.8264 | 0.794065 |
| 44 | -0.035009 | -0.2712 | 0.606409 |
| 45 | -0.062817 | -0.4866 | 0.685835 |
| 46 | -0.186483 | -1.4445 | 0.923099 |
| 47 | -0.090505 | -0.701 | 0.757008 |
| Partial Autocorrelation Function | |||
| Time lag k | PACF(k) | T-STAT | P-value |
| 0 | 0.117048 | 0.9066 | 0.18411 |
| 1 | -0.064145 | -0.4969 | 0.689451 |
| 2 | 0.411466 | 3.1872 | 0.001141 |
| 3 | -0.349633 | -2.7082 | 0.9956 |
| 4 | 0.234992 | 1.8202 | 0.036855 |
| 5 | 0.275576 | 2.1346 | 0.018444 |
| 6 | -0.178493 | -1.3826 | 0.914044 |
| 7 | -0.065217 | -0.5052 | 0.692356 |
| 8 | 0.092312 | 0.715 | 0.238676 |
| 9 | -0.162173 | -1.2562 | 0.893041 |
| 10 | 0.174187 | 1.3493 | 0.091163 |
| 11 | 0.356583 | 2.7621 | 0.003806 |
| 12 | -0.041104 | -0.3184 | 0.624354 |
| 13 | -0.088577 | -0.6861 | 0.752357 |
| 14 | -0.144972 | -1.1229 | 0.867033 |
| 15 | 0.070208 | 0.5438 | 0.294286 |
| 16 | -0.102692 | -0.7954 | 0.785256 |
| 17 | -0.12126 | -0.9393 | 0.824322 |
| 18 | -0.022695 | -0.1758 | 0.569477 |
| 19 | -0.098068 | -0.7596 | 0.774776 |
| 20 | -0.025012 | -0.1937 | 0.576483 |
| 21 | 0.100605 | 0.7793 | 0.219438 |
| 22 | 0.14971 | 1.1596 | 0.125393 |
| 23 | -0.043257 | -0.3351 | 0.630628 |
| 24 | 0.011068 | 0.0857 | 0.465982 |
| 25 | -0.014774 | -0.1144 | 0.545363 |
| 26 | 0.161029 | 1.2473 | 0.108563 |
| 27 | -0.074584 | -0.5777 | 0.717195 |
| 28 | -0.034575 | -0.2678 | 0.60512 |
| 29 | -0.065229 | -0.5053 | 0.692387 |
| 30 | 0.173556 | 1.3444 | 0.091947 |
| 31 | -0.051737 | -0.4008 | 0.654988 |
| 32 | -0.117303 | -0.9086 | 0.816409 |
| 33 | -0.0709 | -0.5492 | 0.707544 |
| 34 | -0.06147 | -0.4761 | 0.682149 |
| 35 | 0.019165 | 0.1485 | 0.441241 |
| 36 | -0.060329 | -0.4673 | 0.679014 |
| 37 | -0.116055 | -0.899 | 0.813867 |
| 38 | -0.173024 | -1.3402 | 0.907389 |
| 39 | -0.053286 | -0.4128 | 0.65937 |
| 40 | -0.028118 | -0.2178 | 0.585839 |
| 41 | -0.043046 | -0.3334 | 0.630014 |
| 42 | 0.02987 | 0.2314 | 0.408906 |
| 43 | 0.034708 | 0.2688 | 0.394485 |
| 44 | 0.051152 | 0.3962 | 0.346673 |
| 45 | -0.003604 | -0.0279 | 0.511088 |
| 46 | 0.040869 | 0.3166 | 0.376333 |
| 47 | -0.151739 | -1.1754 | 0.877755 |
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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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