Partial Autocorrelation Function - industriële productie 2
| 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 | 7.3485 | 0 |
| 1 | -0.544662 | -4.0024 | 0.999904 |
| 2 | -0.059321 | -0.4359 | 0.667684 |
| 3 | 0.240273 | 1.7656 | 0.041555 |
| 4 | -0.237364 | -1.7443 | 0.956598 |
| 5 | 0.091279 | 0.6708 | 0.252616 |
| 6 | 0.212507 | 1.5616 | 0.062112 |
| 7 | -0.334986 | -2.4616 | 0.991472 |
| 8 | 0.021558 | 0.1584 | 0.43736 |
| 9 | 0.412135 | 3.0286 | 0.001882 |
| 10 | -0.495898 | -3.6441 | 0.999698 |
| 11 | 0.268188 | 1.9708 | 0.026942 |
| 12 | -0.019677 | -0.1446 | 0.557215 |
| 13 | -0.18799 | -1.3814 | 0.913583 |
| 14 | 0.246433 | 1.8109 | 0.03786 |
| 15 | -0.055043 | -0.4045 | 0.656273 |
| 16 | -0.252102 | -1.8526 | 0.965294 |
| 17 | 0.29334 | 2.1556 | 0.017795 |
| 18 | 0.016124 | 0.1185 | 0.45306 |
| 19 | -0.215903 | -1.5866 | 0.940774 |
| 20 | 0.145245 | 1.0673 | 0.145286 |
| 21 | -0.08527 | -0.6266 | 0.733221 |
| 22 | 0.027032 | 0.1986 | 0.421643 |
| 23 | 0.180995 | 1.33 | 0.094549 |
| 24 | -0.153054 | -1.1247 | 0.867157 |
| 25 | -0.187966 | -1.3813 | 0.913557 |
| 26 | 0.355826 | 2.6148 | 0.005774 |
| 27 | -0.241473 | -1.7745 | 0.959188 |
| 28 | 0.067417 | 0.4954 | 0.31116 |
| 29 | 0.102841 | 0.7557 | 0.22655 |
| 30 | -0.243687 | -1.7907 | 0.960528 |
| 31 | 0.175504 | 1.2897 | 0.101328 |
| 32 | 0.03438 | 0.2526 | 0.400752 |
| 33 | -0.131 | -0.9627 | 0.829993 |
| 34 | 0.016754 | 0.1231 | 0.451235 |
| 35 | 0.158274 | 1.1631 | 0.124957 |
| 36 | -0.237208 | -1.7431 | 0.956498 |
| 37 | 0.199567 | 1.4665 | 0.074155 |
| 38 | -0.108903 | -0.8003 | 0.786469 |
| 39 | -0.017862 | -0.1313 | 0.55197 |
| 40 | 0.120334 | 0.8843 | 0.190235 |
| 41 | -0.090555 | -0.6654 | 0.745699 |
| 42 | -0.006236 | -0.0458 | 0.518191 |
| 43 | 0.038104 | 0.28 | 0.390272 |
| 44 | 0.003372 | 0.0248 | 0.490161 |
| 45 | -0.013598 | -0.0999 | 0.539613 |
| 46 | 0.023752 | 0.1745 | 0.431047 |
| 47 | -0.072131 | -0.5301 | 0.700877 |
| Partial Autocorrelation Function | |||
| Time lag k | PACF(k) | T-STAT | P-value |
| 0 | -0.544662 | -4.0024 | 0.999904 |
| 1 | -0.506123 | -3.7192 | 0.999762 |
| 2 | -0.160665 | -1.1806 | 0.87854 |
| 3 | -0.286604 | -2.1061 | 0.98007 |
| 4 | -0.251017 | -1.8446 | 0.964707 |
| 5 | 0.155488 | 1.1426 | 0.129124 |
| 6 | -0.00982 | -0.0722 | 0.528631 |
| 7 | -0.332704 | -2.4449 | 0.991108 |
| 8 | 0.260111 | 1.9114 | 0.030632 |
| 9 | -0.031664 | -0.2327 | 0.591555 |
| 10 | 0.008113 | 0.0596 | 0.476341 |
| 11 | -0.055753 | -0.4097 | 0.658177 |
| 12 | -0.028807 | -0.2117 | 0.583427 |
| 13 | -0.036081 | -0.2651 | 0.604045 |
| 14 | -0.059459 | -0.4369 | 0.668049 |
| 15 | -0.196586 | -1.4446 | 0.922827 |
| 16 | -0.077752 | -0.5714 | 0.714937 |
| 17 | 0.075763 | 0.5567 | 0.290002 |
| 18 | 0.186708 | 1.372 | 0.087865 |
| 19 | -0.149935 | -1.1018 | 0.862282 |
| 20 | -0.054759 | -0.4024 | 0.655508 |
| 21 | 0.094375 | 0.6935 | 0.245481 |
| 22 | 0.159021 | 1.1686 | 0.123857 |
| 23 | 0.235438 | 1.7301 | 0.044661 |
| 24 | -0.060786 | -0.4467 | 0.671555 |
| 25 | 0.117017 | 0.8599 | 0.196825 |
| 26 | -0.124115 | -0.9121 | 0.817102 |
| 27 | -0.103027 | -0.7571 | 0.773858 |
| 28 | 0.09533 | 0.7005 | 0.243302 |
| 29 | -0.078728 | -0.5785 | 0.717345 |
| 30 | -0.053601 | -0.3939 | 0.652393 |
| 31 | -0.196095 | -1.441 | 0.922321 |
| 32 | -0.024971 | -0.1835 | 0.572454 |
| 33 | 0.045196 | 0.3321 | 0.370543 |
| 34 | -0.028815 | -0.2117 | 0.583448 |
| 35 | 0.055216 | 0.4058 | 0.343263 |
| 36 | 0.029839 | 0.2193 | 0.413634 |
| 37 | -0.06852 | -0.5035 | 0.691675 |
| 38 | 0.129601 | 0.9524 | 0.172576 |
| 39 | -0.186848 | -1.373 | 0.912293 |
| 40 | -0.107106 | -0.7871 | 0.782658 |
| 41 | 0.020492 | 0.1506 | 0.440431 |
| 42 | 0.0412 | 0.3028 | 0.381618 |
| 43 | -0.040621 | -0.2985 | 0.616766 |
| 44 | -0.036576 | -0.2688 | 0.605437 |
| 45 | -0.03837 | -0.282 | 0.610475 |
| 46 | 0.006444 | 0.0474 | 0.481203 |
| 47 | 0.013311 | 0.0978 | 0.46122 |
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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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Software written by Ed van Stee & Patrick Wessa
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