aanpassing lambda 1 autocorrelation inschrijvingen
| Summary of compuational transaction | |
| Raw Input | view raw input (R code) |
| Raw Output | view raw output of R engine |
| Computing time | 5 seconds |
| R Server | 'Gwilym Jenkins' @ 72.249.127.135 |
| Autocorrelation Function | |||
| Time lag k | ACF(k) | T-STAT | P-value |
| 0 | 1 | 7.746 | 0 |
| 1 | 0.463085 | 3.587 | 0.000337 |
| 2 | 0.22618 | 1.752 | 0.042443 |
| 3 | 0.046338 | 0.3589 | 0.360452 |
| 4 | -0.134537 | -1.0421 | 0.849232 |
| 5 | -0.21879 | -1.6947 | 0.952346 |
| 6 | -0.359085 | -2.7815 | 0.996389 |
| 7 | -0.272818 | -2.1132 | 0.980626 |
| 8 | -0.231103 | -1.7901 | 0.960759 |
| 9 | -0.105638 | -0.8183 | 0.791779 |
| 10 | -0.024923 | -0.1931 | 0.576216 |
| 11 | 0.17072 | 1.3224 | 0.095529 |
| 12 | 0.604631 | 4.6835 | 8e-06 |
| 13 | 0.256043 | 1.9833 | 0.025958 |
| 14 | 0.121119 | 0.9382 | 0.175955 |
| 15 | -0.009193 | -0.0712 | 0.528264 |
| 16 | -0.129202 | -1.0008 | 0.839527 |
| 17 | -0.164386 | -1.2733 | 0.896093 |
| 18 | -0.288273 | -2.233 | 0.985353 |
| 19 | -0.215717 | -1.6709 | 0.950028 |
| 20 | -0.190206 | -1.4733 | 0.927055 |
| 21 | -0.087181 | -0.6753 | 0.74896 |
| 22 | 0.028149 | 0.218 | 0.414069 |
| 23 | 0.216886 | 1.68 | 0.049079 |
| 24 | 0.526334 | 4.077 | 6.8e-05 |
| 25 | 0.281684 | 2.1819 | 0.016522 |
| 26 | 0.144915 | 1.1225 | 0.133059 |
| 27 | 0.033734 | 0.2613 | 0.397378 |
| 28 | -0.084036 | -0.6509 | 0.741214 |
| 29 | -0.147593 | -1.1433 | 0.871262 |
| 30 | -0.242206 | -1.8761 | 0.967247 |
| 31 | -0.173811 | -1.3463 | 0.908371 |
| 32 | -0.172378 | -1.3352 | 0.906578 |
| 33 | -0.126297 | -0.9783 | 0.83407 |
| 34 | -0.038699 | -0.2998 | 0.617302 |
| 35 | 0.073849 | 0.572 | 0.28472 |
| 36 | 0.312472 | 2.4204 | 0.009274 |
| 37 | 0.144728 | 1.1211 | 0.133364 |
| 38 | 0.06124 | 0.4744 | 0.318481 |
| 39 | -0.000785 | -0.0061 | 0.502417 |
| 40 | -0.029273 | -0.2267 | 0.589304 |
| 41 | -0.110453 | -0.8556 | 0.802179 |
| 42 | -0.168568 | -1.3057 | 0.901685 |
| 43 | -0.136948 | -1.0608 | 0.853483 |
| 44 | -0.127593 | -0.9883 | 0.836521 |
| 45 | -0.075408 | -0.5841 | 0.719331 |
| 46 | -0.020191 | -0.1564 | 0.561879 |
| 47 | 0.060098 | 0.4655 | 0.321623 |
| 48 | 0.245819 | 1.9041 | 0.030847 |
| 49 | 0.16203 | 1.2551 | 0.107158 |
| 50 | 0.089272 | 0.6915 | 0.245961 |
| 51 | 0.029646 | 0.2296 | 0.409578 |
| 52 | -0.005512 | -0.0427 | 0.516958 |
| 53 | -0.076682 | -0.594 | 0.72262 |
| 54 | -0.103791 | -0.804 | 0.787703 |
| 55 | -0.115696 | -0.8962 | 0.81313 |
| 56 | -0.131388 | -1.0177 | 0.843553 |
| 57 | -0.10688 | -0.8279 | 0.794494 |
| 58 | -0.09889 | -0.766 | 0.77666 |
| 59 | -0.074841 | -0.5797 | 0.717863 |
| Partial Autocorrelation Function | |||
| Time lag k | PACF(k) | T-STAT | P-value |
| 0 | 0.463085 | 3.587 | 0.000337 |
| 1 | 0.014935 | 0.1157 | 0.454144 |
| 2 | -0.081176 | -0.6288 | 0.734063 |
| 3 | -0.166487 | -1.2896 | 0.898931 |
| 4 | -0.110269 | -0.8541 | 0.801788 |
| 5 | -0.240654 | -1.8641 | 0.966399 |
| 6 | -0.006126 | -0.0475 | 0.518846 |
| 7 | -0.095975 | -0.7434 | 0.769935 |
| 8 | 0.015901 | 0.1232 | 0.451192 |
| 9 | -0.055515 | -0.43 | 0.665639 |
| 10 | 0.170976 | 1.3244 | 0.0952 |
| 11 | 0.554345 | 4.2939 | 3.3e-05 |
| 12 | -0.448308 | -3.4726 | 0.999519 |
| 13 | -0.112264 | -0.8696 | 0.806005 |
| 14 | -0.022801 | -0.1766 | 0.569798 |
| 15 | -0.009103 | -0.0705 | 0.527989 |
| 16 | 0.024438 | 0.1893 | 0.42525 |
| 17 | -0.021081 | -0.1633 | 0.564582 |
| 18 | -0.017953 | -0.1391 | 0.555066 |
| 19 | -0.028981 | -0.2245 | 0.588429 |
| 20 | 0.014796 | 0.1146 | 0.454568 |
| 21 | 0.24202 | 1.8747 | 0.032854 |
| 22 | 0.093554 | 0.7247 | 0.235737 |
| 23 | -0.089047 | -0.6898 | 0.753495 |
| 24 | 0.007227 | 0.056 | 0.477771 |
| 25 | -0.077648 | -0.6015 | 0.725099 |
| 26 | 0.084883 | 0.6575 | 0.256687 |
| 27 | 0.018242 | 0.1413 | 0.444053 |
| 28 | -0.079512 | -0.6159 | 0.729855 |
| 29 | 0.068077 | 0.5273 | 0.299957 |
| 30 | 0.032303 | 0.2502 | 0.401636 |
| 31 | 0.019255 | 0.1492 | 0.440967 |
| 32 | -0.003525 | -0.0273 | 0.510845 |
| 33 | -0.09092 | -0.7043 | 0.758004 |
| 34 | -0.155646 | -1.2056 | 0.883653 |
| 35 | 0.003274 | 0.0254 | 0.489926 |
| 36 | -0.016495 | -0.1278 | 0.550622 |
| 37 | 0.087694 | 0.6793 | 0.249787 |
| 38 | -0.086157 | -0.6674 | 0.746451 |
| 39 | 0.106551 | 0.8253 | 0.206225 |
| 40 | -0.086713 | -0.6717 | 0.747816 |
| 41 | 0.008112 | 0.0628 | 0.475053 |
| 42 | -0.06621 | -0.5129 | 0.695034 |
| 43 | 0.024595 | 0.1905 | 0.424774 |
| 44 | 0.000667 | 0.0052 | 0.497947 |
| 45 | -0.080191 | -0.6212 | 0.731575 |
| 46 | -0.014812 | -0.1147 | 0.545481 |
| 47 | 0.031278 | 0.2423 | 0.404694 |
| 48 | 0.106501 | 0.825 | 0.206333 |
| 49 | -0.010307 | -0.0798 | 0.531685 |
| 50 | -0.012766 | -0.0989 | 0.53922 |
| 51 | -0.125449 | -0.9717 | 0.832456 |
| 52 | 0.068388 | 0.5297 | 0.299127 |
| 53 | 0.013103 | 0.1015 | 0.459749 |
| 54 | 0.004099 | 0.0317 | 0.487388 |
| 55 | -0.105109 | -0.8142 | 0.790617 |
| 56 | -0.113294 | -0.8776 | 0.808161 |
| 57 | -0.032007 | -0.2479 | 0.597482 |
| 58 | -0.040156 | -0.311 | 0.621578 |
| 59 | NA | NA | NA |
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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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