PACF en ACF suiker
| 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 | 11.3137 | 0 |
| 1 | 0.100048 | 1.1319 | 0.129894 |
| 2 | -0.176395 | -1.9957 | 0.975954 |
| 3 | 0.15034 | 1.7009 | 0.045695 |
| 4 | 0.054201 | 0.6132 | 0.270411 |
| 5 | -0.212596 | -2.4053 | 0.991204 |
| 6 | -0.001404 | -0.0159 | 0.506326 |
| 7 | 0.207547 | 2.3481 | 0.0102 |
| 8 | -0.10509 | -1.189 | 0.881672 |
| 9 | -0.041922 | -0.4743 | 0.681951 |
| 10 | 0.237224 | 2.6839 | 0.00412 |
| 11 | 0.085324 | 0.9653 | 0.1681 |
| 12 | -0.537062 | -6.0762 | 1 |
| 13 | -0.114238 | -1.2925 | 0.900735 |
| 14 | 0.071598 | 0.81 | 0.20971 |
| 15 | -0.180501 | -2.0421 | 0.978404 |
| 16 | -0.059796 | -0.6765 | 0.750033 |
| 17 | 0.064046 | 0.7246 | 0.23501 |
| 18 | -0.043429 | -0.4913 | 0.687986 |
| 19 | -0.11193 | -1.2663 | 0.896155 |
| 20 | 0.123573 | 1.3981 | 0.082255 |
| 21 | 0.003708 | 0.042 | 0.483299 |
| 22 | -0.110731 | -1.2528 | 0.893715 |
| 23 | -0.072522 | -0.8205 | 0.793269 |
| 24 | 0.068168 | 0.7712 | 0.220994 |
| 25 | 0.01644 | 0.186 | 0.426368 |
| 26 | -0.05039 | -0.5701 | 0.715194 |
| 27 | 0.072227 | 0.8172 | 0.207678 |
| 28 | -0.011149 | -0.1261 | 0.550088 |
| 29 | 0.04999 | 0.5656 | 0.286337 |
| 30 | 0.163669 | 1.8517 | 0.033186 |
| 31 | 0.130328 | 1.4745 | 0.071403 |
| 32 | -0.03572 | -0.4041 | 0.656603 |
| 33 | -0.009991 | -0.113 | 0.544912 |
| 34 | -0.059173 | -0.6695 | 0.747798 |
| 35 | -0.052637 | -0.5955 | 0.723725 |
| Partial Autocorrelation Function | |||
| Time lag k | PACF(k) | T-STAT | P-value |
| 0 | 0.100048 | 1.1319 | 0.129894 |
| 1 | -0.188289 | -2.1303 | 0.982468 |
| 2 | 0.199131 | 2.2529 | 0.012984 |
| 3 | -0.02903 | -0.3284 | 0.628439 |
| 4 | -0.163451 | -1.8492 | 0.966635 |
| 5 | 0.036699 | 0.4152 | 0.339345 |
| 6 | 0.140996 | 1.5952 | 0.056568 |
| 7 | -0.106593 | -1.206 | 0.884972 |
| 8 | 0.059188 | 0.6696 | 0.252149 |
| 9 | 0.138502 | 1.567 | 0.059796 |
| 10 | 0.058691 | 0.664 | 0.253939 |
| 11 | -0.525521 | -5.9456 | 1 |
| 12 | -0.003066 | -0.0347 | 0.51381 |
| 13 | -0.103044 | -1.1658 | 0.87707 |
| 14 | 0.026113 | 0.2954 | 0.38407 |
| 15 | -0.028891 | -0.3269 | 0.62785 |
| 16 | -0.161569 | -1.8279 | 0.965057 |
| 17 | -0.040683 | -0.4603 | 0.676949 |
| 18 | 0.076616 | 0.8668 | 0.193833 |
| 19 | 0.008385 | 0.0949 | 0.462283 |
| 20 | -0.050343 | -0.5696 | 0.715015 |
| 21 | 0.115472 | 1.3064 | 0.096877 |
| 22 | 0.018464 | 0.2089 | 0.41743 |
| 23 | -0.251032 | -2.8401 | 0.997376 |
| 24 | -0.001702 | -0.0193 | 0.507669 |
| 25 | -0.069816 | -0.7899 | 0.78447 |
| 26 | 0.013695 | 0.1549 | 0.438555 |
| 27 | -0.048558 | -0.5494 | 0.708146 |
| 28 | 0.015036 | 0.1701 | 0.432597 |
| 29 | 0.130519 | 1.4767 | 0.071112 |
| 30 | 0.140187 | 1.586 | 0.057599 |
| 31 | -0.002404 | -0.0272 | 0.510828 |
| 32 | -0.08415 | -0.9521 | 0.828568 |
| 33 | -0.088107 | -0.9968 | 0.839634 |
| 34 | 0.03287 | 0.3719 | 0.355297 |
| 35 | -0.114982 | -1.3009 | 0.902181 |
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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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