WS 4 - Q2 - methode 2 - reeks 2 (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 | 7.746 | 0 |
| 1 | 0.297583 | 2.3051 | 0.012319 |
| 2 | -0.107937 | -0.8361 | 0.796785 |
| 3 | 0.092746 | 0.7184 | 0.237647 |
| 4 | -0.039306 | -0.3045 | 0.619087 |
| 5 | 0.164687 | 1.2757 | 0.103496 |
| 6 | 0.398932 | 3.0901 | 0.001516 |
| 7 | 0.075396 | 0.584 | 0.2807 |
| 8 | -0.073207 | -0.5671 | 0.713604 |
| 9 | -0.010405 | -0.0806 | 0.531986 |
| 10 | -0.219053 | -1.6968 | 0.952539 |
| 11 | 0.208699 | 1.6166 | 0.055608 |
| 12 | 0.610199 | 4.7266 | 7e-06 |
| 13 | 0.069231 | 0.5363 | 0.29688 |
| 14 | -0.13891 | -1.076 | 0.856881 |
| 15 | -0.025764 | -0.1996 | 0.578752 |
| 16 | -0.124036 | -0.9608 | 0.82974 |
| 17 | 0.05171 | 0.4005 | 0.34509 |
| 18 | 0.232282 | 1.7992 | 0.038505 |
| 19 | 0.009503 | 0.0736 | 0.470782 |
| 20 | -0.082685 | -0.6405 | 0.737848 |
| 21 | -0.072144 | -0.5588 | 0.71082 |
| 22 | -0.237079 | -1.8364 | 0.964374 |
| 23 | 0.119459 | 0.9253 | 0.179252 |
| 24 | 0.371911 | 2.8808 | 0.002747 |
| 25 | -0.007958 | -0.0616 | 0.524475 |
| 26 | -0.110842 | -0.8586 | 0.803006 |
| 27 | -0.103058 | -0.7983 | 0.786074 |
| 28 | -0.145543 | -1.1274 | 0.867963 |
| 29 | 0.003406 | 0.0264 | 0.489519 |
| 30 | 0.073774 | 0.5715 | 0.284915 |
| 31 | -0.052261 | -0.4048 | 0.656471 |
| 32 | -0.079258 | -0.6139 | 0.729208 |
| 33 | -0.142198 | -1.1015 | 0.862453 |
| 34 | -0.211002 | -1.6344 | 0.946296 |
| 35 | 0.058655 | 0.4543 | 0.325611 |
| 36 | 0.203677 | 1.5777 | 0.059949 |
| 37 | 0.013523 | 0.1047 | 0.458462 |
| 38 | -0.115327 | -0.8933 | 0.812371 |
| 39 | -0.114542 | -0.8872 | 0.810753 |
| 40 | -0.11073 | -0.8577 | 0.802767 |
| 41 | -0.053172 | -0.4119 | 0.659048 |
| 42 | -0.00427 | -0.0331 | 0.513137 |
| 43 | -0.058569 | -0.4537 | 0.67415 |
| 44 | -0.102648 | -0.7951 | 0.785158 |
| 45 | -0.146969 | -1.1384 | 0.870265 |
| 46 | -0.13085 | -1.0136 | 0.842568 |
| 47 | 0.007284 | 0.0564 | 0.477597 |
| 48 | 0.035134 | 0.2721 | 0.393222 |
| 49 | -0.050784 | -0.3934 | 0.65228 |
| 50 | -0.085139 | -0.6595 | 0.743946 |
| 51 | -0.095596 | -0.7405 | 0.769054 |
| 52 | -0.103977 | -0.8054 | 0.788116 |
| 53 | -0.089064 | -0.6899 | 0.753537 |
| 54 | -0.081434 | -0.6308 | 0.734712 |
| 55 | -0.065307 | -0.5059 | 0.692597 |
| 56 | -0.074051 | -0.5736 | 0.715807 |
| 57 | -0.090512 | -0.7011 | 0.757026 |
| 58 | -0.038759 | -0.3002 | 0.617479 |
| 59 | -0.003445 | -0.0267 | 0.5106 |
| Partial Autocorrelation Function | |||
| Time lag k | PACF(k) | T-STAT | P-value |
| 0 | 0.297583 | 2.3051 | 0.012319 |
| 1 | -0.215584 | -1.6699 | 0.949926 |
| 2 | 0.225461 | 1.7464 | 0.042928 |
| 3 | -0.214 | -1.6576 | 0.948695 |
| 4 | 0.398494 | 3.0867 | 0.001531 |
| 5 | 0.130611 | 1.0117 | 0.15787 |
| 6 | -0.00978 | -0.0758 | 0.530068 |
| 7 | -0.044444 | -0.3443 | 0.634073 |
| 8 | -0.048438 | -0.3752 | 0.645584 |
| 9 | -0.278669 | -2.1586 | 0.982553 |
| 10 | 0.497852 | 3.8563 | 0.000142 |
| 11 | 0.23571 | 1.8258 | 0.036429 |
| 12 | -0.112942 | -0.8748 | 0.807426 |
| 13 | -0.103245 | -0.7997 | 0.786489 |
| 14 | -0.046886 | -0.3632 | 0.641126 |
| 15 | -0.056667 | -0.4389 | 0.668858 |
| 16 | -0.180721 | -1.3999 | 0.916648 |
| 17 | 0.008932 | 0.0692 | 0.472534 |
| 18 | 0.093617 | 0.7252 | 0.235587 |
| 19 | -0.013032 | -0.1009 | 0.540036 |
| 20 | 0.088776 | 0.6877 | 0.247159 |
| 21 | -0.077568 | -0.6008 | 0.724894 |
| 22 | 0.023592 | 0.1827 | 0.427809 |
| 23 | -0.054407 | -0.4214 | 0.662528 |
| 24 | 0.021458 | 0.1662 | 0.434274 |
| 25 | -0.017911 | -0.1387 | 0.554939 |
| 26 | -0.122989 | -0.9527 | 0.827711 |
| 27 | 0.11396 | 0.8827 | 0.190453 |
| 28 | -0.094 | -0.7281 | 0.765314 |
| 29 | -0.105643 | -0.8183 | 0.79179 |
| 30 | -0.001913 | -0.0148 | 0.505888 |
| 31 | -0.006667 | -0.0516 | 0.520506 |
| 32 | -0.030634 | -0.2373 | 0.593379 |
| 33 | 0.061566 | 0.4769 | 0.317587 |
| 34 | -0.065337 | -0.5061 | 0.692678 |
| 35 | 0.132889 | 1.0294 | 0.153722 |
| 36 | -0.059269 | -0.4591 | 0.676087 |
| 37 | -0.040032 | -0.3101 | 0.621214 |
| 38 | 0.028776 | 0.2229 | 0.412186 |
| 39 | -0.120857 | -0.9362 | 0.823527 |
| 40 | 0.004061 | 0.0315 | 0.487506 |
| 41 | -0.066706 | -0.5167 | 0.696369 |
| 42 | -0.0233 | -0.1805 | 0.571307 |
| 43 | -0.02829 | -0.2191 | 0.586356 |
| 44 | 0.019846 | 0.1537 | 0.439171 |
| 45 | 0.119909 | 0.9288 | 0.178354 |
| 46 | -0.12615 | -0.9772 | 0.833792 |
| 47 | -0.173995 | -1.3478 | 0.908599 |
| 48 | 0.018215 | 0.1411 | 0.444135 |
| 49 | 0.050107 | 0.3881 | 0.349647 |
| 50 | -0.049011 | -0.3796 | 0.647223 |
| 51 | -0.05746 | -0.4451 | 0.671069 |
| 52 | 0.048113 | 0.3727 | 0.355349 |
| 53 | -0.041156 | -0.3188 | 0.624505 |
| 54 | -0.04651 | -0.3603 | 0.640044 |
| 55 | 0.031638 | 0.2451 | 0.403619 |
| 56 | 0.014932 | 0.1157 | 0.454153 |
| 57 | -0.032384 | -0.2508 | 0.598604 |
| 58 | 0.090161 | 0.6984 | 0.243818 |
| 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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