autocorrelatie lpg
| Summary of compuational transaction | |
| Raw Input | view raw input (R code) |
| Raw Output | view raw output of R engine |
| Computing time | 2 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 | 0.375957 | 2.9122 | 0.002517 |
| 2 | 0.313436 | 2.4279 | 0.009102 |
| 3 | 0.24296 | 1.882 | 0.032347 |
| 4 | 0.045075 | 0.3491 | 0.3641 |
| 5 | 0.000928 | 0.0072 | 0.497146 |
| 6 | -0.020031 | -0.1552 | 0.561392 |
| 7 | 0.027917 | 0.2162 | 0.414765 |
| 8 | 0.112163 | 0.8688 | 0.194207 |
| 9 | 0.289597 | 2.2432 | 0.014294 |
| 10 | 0.200251 | 1.5511 | 0.063064 |
| 11 | 0.248541 | 1.9252 | 0.029475 |
| 12 | 0.34973 | 2.709 | 0.004391 |
| 13 | 0.223379 | 1.7303 | 0.04436 |
| 14 | 0.143801 | 1.1139 | 0.134886 |
| 15 | 0.085826 | 0.6648 | 0.254362 |
| 16 | 0.003608 | 0.0279 | 0.488898 |
| 17 | -0.095735 | -0.7416 | 0.769376 |
| 18 | -0.137519 | -1.0652 | 0.854477 |
| 19 | -0.095808 | -0.7421 | 0.769547 |
| 20 | -0.096466 | -0.7472 | 0.771076 |
| 21 | 0.078834 | 0.6106 | 0.271871 |
| 22 | 0.046775 | 0.3623 | 0.359194 |
| 23 | 0.220101 | 1.7049 | 0.046694 |
| 24 | 0.159513 | 1.2356 | 0.110715 |
| 25 | 0.067145 | 0.5201 | 0.302452 |
| 26 | -0.027151 | -0.2103 | 0.582932 |
| 27 | -0.116793 | -0.9047 | 0.815371 |
| 28 | -0.181861 | -1.4087 | 0.917956 |
| 29 | -0.22953 | -1.7779 | 0.959759 |
| 30 | -0.138764 | -1.0749 | 0.856629 |
| 31 | -0.150603 | -1.1666 | 0.875999 |
| 32 | -0.031933 | -0.2474 | 0.59726 |
| 33 | -0.026891 | -0.2083 | 0.582149 |
| 34 | -0.071184 | -0.5514 | 0.708293 |
| 35 | 0.003646 | 0.0282 | 0.488782 |
| 36 | 0.014996 | 0.1162 | 0.453958 |
| 37 | -0.055857 | -0.4327 | 0.666597 |
| 38 | -0.140277 | -1.0866 | 0.859216 |
| 39 | -0.087992 | -0.6816 | 0.750937 |
| 40 | -0.141918 | -1.0993 | 0.861985 |
| 41 | -0.231813 | -1.7956 | 0.961204 |
| 42 | -0.240647 | -1.864 | 0.966395 |
| 43 | -0.193605 | -1.4997 | 0.930527 |
| 44 | -0.130343 | -1.0096 | 0.841636 |
| 45 | -0.096802 | -0.7498 | 0.771856 |
| 46 | -0.049944 | -0.3869 | 0.649886 |
| 47 | -0.102419 | -0.7933 | 0.784644 |
| 48 | -0.017545 | -0.1359 | 0.553824 |
| 49 | -0.032923 | -0.255 | 0.600211 |
| 50 | -0.1681 | -1.3021 | 0.90107 |
| 51 | -0.10255 | -0.7943 | 0.784938 |
| 52 | -0.092112 | -0.7135 | 0.760848 |
| 53 | -0.16473 | -1.276 | 0.896562 |
| 54 | -0.093754 | -0.7262 | 0.764735 |
| 55 | -0.081652 | -0.6325 | 0.735259 |
| 56 | -0.060083 | -0.4654 | 0.678336 |
| 57 | -0.026581 | -0.2059 | 0.581215 |
| 58 | -0.017052 | -0.1321 | 0.55232 |
| 59 | -0.005211 | -0.0404 | 0.516033 |
| Partial Autocorrelation Function | |||
| Time lag k | PACF(k) | T-STAT | P-value |
| 0 | 0.375957 | 2.9122 | 0.002517 |
| 1 | 0.20042 | 1.5525 | 0.062907 |
| 2 | 0.089047 | 0.6898 | 0.246503 |
| 3 | -0.136408 | -1.0566 | 0.852538 |
| 4 | -0.0611 | -0.4733 | 0.681134 |
| 5 | -0.004254 | -0.0329 | 0.513088 |
| 6 | 0.095146 | 0.737 | 0.231999 |
| 7 | 0.135716 | 1.0512 | 0.14868 |
| 8 | 0.265814 | 2.059 | 0.021922 |
| 9 | -0.017223 | -0.1334 | 0.552842 |
| 10 | 0.039712 | 0.3076 | 0.379723 |
| 11 | 0.190441 | 1.4752 | 0.0727 |
| 12 | 0.060361 | 0.4676 | 0.320899 |
| 13 | -0.033519 | -0.2596 | 0.601985 |
| 14 | -0.034923 | -0.2705 | 0.606154 |
| 15 | -0.048037 | -0.3721 | 0.644432 |
| 16 | -0.140481 | -1.0882 | 0.859562 |
| 17 | -0.16027 | -1.2414 | 0.890364 |
| 18 | -0.016646 | -0.1289 | 0.551082 |
| 19 | -0.092276 | -0.7148 | 0.761238 |
| 20 | 0.048515 | 0.3758 | 0.354197 |
| 21 | -0.056279 | -0.4359 | 0.667776 |
| 22 | 0.18983 | 1.4704 | 0.073337 |
| 23 | -0.052035 | -0.4031 | 0.655832 |
| 24 | -0.076511 | -0.5926 | 0.722178 |
| 25 | -0.096848 | -0.7502 | 0.771962 |
| 26 | -0.012389 | -0.096 | 0.538067 |
| 27 | -0.067258 | -0.521 | 0.697851 |
| 28 | -0.006327 | -0.049 | 0.519464 |
| 29 | 0.071416 | 0.5532 | 0.291096 |
| 30 | -0.025956 | -0.2011 | 0.579333 |
| 31 | -0.010578 | -0.0819 | 0.532515 |
| 32 | -0.046188 | -0.3578 | 0.639115 |
| 33 | -0.093687 | -0.7257 | 0.764577 |
| 34 | -0.029992 | -0.2323 | 0.591458 |
| 35 | 0.055813 | 0.4323 | 0.333529 |
| 36 | 0.012276 | 0.0951 | 0.462281 |
| 37 | -0.116479 | -0.9022 | 0.814733 |
| 38 | 0.047708 | 0.3695 | 0.356513 |
| 39 | 0.041977 | 0.3251 | 0.373099 |
| 40 | -0.062554 | -0.4845 | 0.685116 |
| 41 | -0.155622 | -1.2054 | 0.883618 |
| 42 | 0.071531 | 0.5541 | 0.290791 |
| 43 | -0.034197 | -0.2649 | 0.604 |
| 44 | -0.050885 | -0.3942 | 0.652567 |
| 45 | -0.026222 | -0.2031 | 0.580134 |
| 46 | -0.087184 | -0.6753 | 0.748968 |
| 47 | -0.015981 | -0.1238 | 0.549051 |
| 48 | 0.072587 | 0.5623 | 0.288018 |
| 49 | 0.02049 | 0.1587 | 0.437212 |
| 50 | 0.043641 | 0.338 | 0.368255 |
| 51 | 0.048646 | 0.3768 | 0.353822 |
| 52 | -0.06694 | -0.5185 | 0.696997 |
| 53 | 0.064942 | 0.503 | 0.308388 |
| 54 | -0.014487 | -0.1122 | 0.544486 |
| 55 | 0.039012 | 0.3022 | 0.381776 |
| 56 | 0.041775 | 0.3236 | 0.373688 |
| 57 | -0.005803 | -0.0449 | 0.517851 |
| 58 | 0.037675 | 0.2918 | 0.385713 |
| 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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