ACF - Serie 1 - D=1 - paper
| 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 | 'Gwilym Jenkins' @ 72.249.127.135 |
| Autocorrelation Function | |||
| Time lag k | ACF(k) | T-STAT | P-value |
| 0 | 1 | 6.9282 | 0 |
| 1 | 0.479209 | 3.3201 | 0.000862 |
| 2 | 0.351099 | 2.4325 | 0.009387 |
| 3 | 0.174582 | 1.2095 | 0.116189 |
| 4 | 0.072476 | 0.5021 | 0.308938 |
| 5 | 0.155777 | 1.0793 | 0.142934 |
| 6 | 0.1191 | 0.8252 | 0.206684 |
| 7 | 0.049116 | 0.3403 | 0.367562 |
| 8 | 0.052504 | 0.3638 | 0.358818 |
| 9 | 0.046047 | 0.319 | 0.375547 |
| 10 | -0.185884 | -1.2878 | 0.898012 |
| 11 | -0.334713 | -2.319 | 0.98765 |
| 12 | -0.435596 | -3.0179 | 0.997967 |
| 13 | -0.362122 | -2.5089 | 0.992229 |
| 14 | -0.203192 | -1.4078 | 0.917176 |
| 15 | -0.090338 | -0.6259 | 0.732821 |
| 16 | -0.209836 | -1.4538 | 0.923743 |
| 17 | -0.069847 | -0.4839 | 0.684677 |
| 18 | -0.047865 | -0.3316 | 0.62919 |
| 19 | -0.103752 | -0.7188 | 0.762131 |
| 20 | -0.045735 | -0.3169 | 0.623638 |
| 21 | -0.153186 | -1.0613 | 0.853069 |
| 22 | -0.054213 | -0.3756 | 0.645564 |
| 23 | 0.116261 | 0.8055 | 0.212258 |
| 24 | 0.157439 | 1.0908 | 0.140409 |
| 25 | 0.139582 | 0.9671 | 0.169182 |
| 26 | 0.09635 | 0.6675 | 0.253814 |
| 27 | -0.041642 | -0.2885 | 0.612898 |
| 28 | -0.010271 | -0.0712 | 0.528217 |
| 29 | -0.064939 | -0.4499 | 0.672601 |
| 30 | -0.070301 | -0.4871 | 0.685784 |
| 31 | -0.014658 | -0.1016 | 0.540232 |
| 32 | 0.040951 | 0.2837 | 0.388923 |
| 33 | 0.098599 | 0.6831 | 0.248909 |
| 34 | -0.006323 | -0.0438 | 0.51738 |
| 35 | -0.041724 | -0.2891 | 0.613115 |
| 36 | -0.136338 | -0.9446 | 0.825198 |
| 37 | -0.089943 | -0.6231 | 0.731931 |
| 38 | 0.011315 | 0.0784 | 0.46892 |
| 39 | 0.005041 | 0.0349 | 0.486141 |
| 40 | 0.061812 | 0.4282 | 0.335195 |
| 41 | 0.029893 | 0.2071 | 0.418401 |
| 42 | 0.023192 | 0.1607 | 0.436511 |
| 43 | 0.030163 | 0.209 | 0.417677 |
| 44 | -0.027567 | -0.191 | 0.575331 |
| 45 | 0.00716 | 0.0496 | 0.48032 |
| 46 | -0.02336 | -0.1618 | 0.563944 |
| 47 | 0.005677 | 0.0393 | 0.484394 |
| Partial Autocorrelation Function | |||
| Time lag k | PACF(k) | T-STAT | P-value |
| 0 | 0.479209 | 3.3201 | 0.000862 |
| 1 | 0.157664 | 1.0923 | 0.140071 |
| 2 | -0.056834 | -0.3938 | 0.652248 |
| 3 | -0.047724 | -0.3306 | 0.628823 |
| 4 | 0.172598 | 1.1958 | 0.118826 |
| 5 | 0.014978 | 0.1038 | 0.458891 |
| 6 | -0.097326 | -0.6743 | 0.74832 |
| 7 | 0.032327 | 0.224 | 0.411867 |
| 8 | 0.062073 | 0.4301 | 0.334541 |
| 9 | -0.337092 | -2.3354 | 0.988126 |
| 10 | -0.308932 | -2.1403 | 0.981282 |
| 11 | -0.144184 | -0.9989 | 0.838582 |
| 12 | 0.022632 | 0.1568 | 0.438029 |
| 13 | 0.025179 | 0.1744 | 0.431124 |
| 14 | 0.097536 | 0.6757 | 0.251222 |
| 15 | -0.1555 | -1.0773 | 0.856642 |
| 16 | 0.193167 | 1.3383 | 0.093551 |
| 17 | 0.154908 | 1.0732 | 0.144267 |
| 18 | -0.111232 | -0.7706 | 0.777651 |
| 19 | -0.033192 | -0.23 | 0.59045 |
| 20 | -0.106042 | -0.7347 | 0.766946 |
| 21 | -0.11362 | -0.7872 | 0.782478 |
| 22 | -0.036029 | -0.2496 | 0.598025 |
| 23 | -0.011386 | -0.0789 | 0.531273 |
| 24 | 0.026077 | 0.1807 | 0.428694 |
| 25 | -0.03125 | -0.2165 | 0.585246 |
| 26 | -0.166274 | -1.152 | 0.872482 |
| 27 | 0.026783 | 0.1856 | 0.426785 |
| 28 | -0.027112 | -0.1878 | 0.574103 |
| 29 | 0.026541 | 0.1839 | 0.427441 |
| 30 | 0.026417 | 0.183 | 0.427774 |
| 31 | -0.07084 | -0.4908 | 0.687095 |
| 32 | 0.079062 | 0.5478 | 0.293199 |
| 33 | -0.071858 | -0.4978 | 0.689568 |
| 34 | -0.037437 | -0.2594 | 0.601771 |
| 35 | 0.036345 | 0.2518 | 0.401133 |
| 36 | -0.057491 | -0.3983 | 0.653916 |
| 37 | 0.024807 | 0.1719 | 0.432133 |
| 38 | -0.048785 | -0.338 | 0.631579 |
| 39 | -0.046017 | -0.3188 | 0.624376 |
| 40 | -0.050411 | -0.3493 | 0.635789 |
| 41 | -0.029777 | -0.2063 | 0.581287 |
| 42 | -0.023898 | -0.1656 | 0.565404 |
| 43 | 0.024995 | 0.1732 | 0.431624 |
| 44 | 0.091884 | 0.6366 | 0.263709 |
| 45 | -0.100912 | -0.6991 | 0.756081 |
| 46 | -0.050616 | -0.3507 | 0.636317 |
| 47 | NA | NA | NA |
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| http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Dec/14/t1197628080pbxhojcwxcddxna/2pqal1197628942.ps (open in new window) |
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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