Partial Autocorrelation Function - grondstoffen
| 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 | 7.3485 | 0 |
| 1 | 0.027953 | 0.2054 | 0.419012 |
| 2 | -0.359559 | -2.6422 | 0.994623 |
| 3 | -0.112425 | -0.8262 | 0.793824 |
| 4 | 0.089685 | 0.6591 | 0.256332 |
| 5 | 0.321494 | 2.3625 | 0.010893 |
| 6 | 0.026999 | 0.1984 | 0.421737 |
| 7 | -0.216654 | -1.5921 | 0.941397 |
| 8 | -0.216598 | -1.5917 | 0.941351 |
| 9 | -0.074644 | -0.5485 | 0.707203 |
| 10 | 0.341892 | 2.5124 | 0.007506 |
| 11 | 0.144609 | 1.0627 | 0.146333 |
| 12 | -0.391075 | -2.8738 | 0.997106 |
| 13 | -0.244656 | -1.7978 | 0.961104 |
| 14 | 0.050981 | 0.3746 | 0.354701 |
| 15 | 0.249315 | 1.8321 | 0.036228 |
| 16 | 0.005647 | 0.0415 | 0.483527 |
| 17 | -0.130067 | -0.9558 | 0.828281 |
| 18 | -0.190704 | -1.4014 | 0.91659 |
| 19 | -0.036485 | -0.2681 | 0.605182 |
| 20 | 0.327435 | 2.4061 | 0.009787 |
| 21 | 0.110938 | 0.8152 | 0.209263 |
| 22 | -0.085928 | -0.6314 | 0.734791 |
| 23 | -0.201614 | -1.4816 | 0.927865 |
| 24 | -0.026757 | -0.1966 | 0.577568 |
| 25 | 0.252742 | 1.8573 | 0.034365 |
| 26 | 0.048467 | 0.3562 | 0.361555 |
| 27 | -0.162135 | -1.1914 | 0.880655 |
| 28 | -0.114273 | -0.8397 | 0.797619 |
| 29 | -0.07914 | -0.5816 | 0.718358 |
| 30 | 0.139249 | 1.0233 | 0.155371 |
| 31 | 0.099297 | 0.7297 | 0.23437 |
| 32 | -0.122313 | -0.8988 | 0.813628 |
| 33 | -0.120047 | -0.8822 | 0.809201 |
| 34 | -0.06554 | -0.4816 | 0.683989 |
| 35 | 0.168395 | 1.2374 | 0.110638 |
| Partial Autocorrelation Function | |||
| Time lag k | PACF(k) | T-STAT | P-value |
| 0 | 0.027953 | 0.2054 | 0.419012 |
| 1 | -0.360623 | -2.65 | 0.994732 |
| 2 | -0.102004 | -0.7496 | 0.771618 |
| 3 | -0.041377 | -0.3041 | 0.618872 |
| 4 | 0.284706 | 2.0922 | 0.02057 |
| 5 | 0.041539 | 0.3053 | 0.380674 |
| 6 | -0.011702 | -0.086 | 0.534103 |
| 7 | -0.203115 | -1.4926 | 0.929317 |
| 8 | -0.219053 | -1.6097 | 0.943353 |
| 9 | 0.158107 | 1.1618 | 0.125205 |
| 10 | 0.084988 | 0.6245 | 0.267454 |
| 11 | -0.209507 | -1.5396 | 0.935245 |
| 12 | -0.121221 | -0.8908 | 0.811503 |
| 13 | -0.124084 | -0.9118 | 0.817043 |
| 14 | 0.00344 | 0.0253 | 0.489962 |
| 15 | -0.119686 | -0.8795 | 0.80849 |
| 16 | 0.129256 | 0.9498 | 0.173214 |
| 17 | -0.125667 | -0.9235 | 0.820062 |
| 18 | -0.033739 | -0.2479 | 0.597436 |
| 19 | 0.104846 | 0.7705 | 0.222194 |
| 20 | -0.063828 | -0.469 | 0.679534 |
| 21 | 0.166944 | 1.2268 | 0.112613 |
| 22 | -0.044985 | -0.3306 | 0.628875 |
| 23 | -0.050007 | -0.3675 | 0.642651 |
| 24 | -0.070181 | -0.5157 | 0.695924 |
| 25 | -0.08429 | -0.6194 | 0.730873 |
| 26 | -0.09459 | -0.6951 | 0.75501 |
| 27 | -0.036328 | -0.267 | 0.60474 |
| 28 | -0.025865 | -0.1901 | 0.575016 |
| 29 | -0.110687 | -0.8134 | 0.790213 |
| 30 | -0.043342 | -0.3185 | 0.624332 |
| 31 | -0.049829 | -0.3662 | 0.642164 |
| 32 | -0.011884 | -0.0873 | 0.534635 |
| 33 | 0.012815 | 0.0942 | 0.46266 |
| 34 | 0.02636 | 0.1937 | 0.423567 |
| 35 | -0.159211 | -1.17 | 0.876422 |
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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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