G3ACFwerkl
| 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 | 6.8557 | 0 |
| 1 | 0.110776 | 0.7594 | 0.225691 |
| 2 | 0.095498 | 0.6547 | 0.257926 |
| 3 | -0.135449 | -0.9286 | 0.821077 |
| 4 | -0.154926 | -1.0621 | 0.853195 |
| 5 | 0.003515 | 0.0241 | 0.490438 |
| 6 | -0.08943 | -0.6131 | 0.728617 |
| 7 | -0.075751 | -0.5193 | 0.697013 |
| 8 | 0.072453 | 0.4967 | 0.310854 |
| 9 | 0.218838 | 1.5003 | 0.070115 |
| 10 | -0.153932 | -1.0553 | 0.851659 |
| 11 | -0.098628 | -0.6762 | 0.748873 |
| 12 | -0.205408 | -1.4082 | 0.917175 |
| 13 | 0.06248 | 0.4283 | 0.33518 |
| 14 | 0.304212 | 2.0856 | 0.021236 |
| 15 | 0.084412 | 0.5787 | 0.282778 |
| 16 | 0.108368 | 0.7429 | 0.23061 |
| 17 | -0.114583 | -0.7855 | 0.78196 |
| 18 | 0.137964 | 0.9458 | 0.174535 |
| 19 | -0.035827 | -0.2456 | 0.596475 |
| 20 | 0.016265 | 0.1115 | 0.455844 |
| 21 | -0.007722 | -0.0529 | 0.520999 |
| 22 | 0.02257 | 0.1547 | 0.438847 |
| 23 | 0.116943 | 0.8017 | 0.213374 |
| 24 | -0.086244 | -0.5913 | 0.721409 |
| 25 | -0.1434 | -0.9831 | 0.834704 |
| 26 | -0.222279 | -1.5239 | 0.932878 |
| 27 | 0.055108 | 0.3778 | 0.35364 |
| 28 | -0.090879 | -0.623 | 0.731864 |
| 29 | 0.060479 | 0.4146 | 0.340153 |
| 30 | -0.07671 | -0.5259 | 0.699284 |
| 31 | -0.033659 | -0.2308 | 0.590747 |
| 32 | 0.049134 | 0.3368 | 0.368868 |
| 33 | -0.014223 | -0.0975 | 0.538632 |
| 34 | -0.019092 | -0.1309 | 0.551789 |
| 35 | 0.048343 | 0.3314 | 0.370899 |
| Partial Autocorrelation Function | |||
| Time lag k | PACF(k) | T-STAT | P-value |
| 0 | 0.110776 | 0.7594 | 0.225691 |
| 1 | 0.084261 | 0.5777 | 0.283125 |
| 2 | -0.157508 | -1.0798 | 0.857133 |
| 3 | -0.13751 | -0.9427 | 0.824678 |
| 4 | 0.06623 | 0.4541 | 0.325941 |
| 5 | -0.09046 | -0.6202 | 0.730926 |
| 6 | -0.115291 | -0.7904 | 0.783367 |
| 7 | 0.109232 | 0.7489 | 0.228836 |
| 8 | 0.230796 | 1.5823 | 0.060149 |
| 9 | -0.319579 | -2.1909 | 0.983275 |
| 10 | -0.128423 | -0.8804 | 0.808445 |
| 11 | 0.002805 | 0.0192 | 0.49237 |
| 12 | 0.129155 | 0.8854 | 0.190213 |
| 13 | 0.234363 | 1.6067 | 0.057408 |
| 14 | 0.012589 | 0.0863 | 0.465795 |
| 15 | 0.016034 | 0.1099 | 0.45647 |
| 16 | -0.190144 | -1.3036 | 0.900634 |
| 17 | 0.224347 | 1.538 | 0.065371 |
| 18 | 0.117265 | 0.8039 | 0.212743 |
| 19 | 0.055025 | 0.3772 | 0.35385 |
| 20 | 0.065201 | 0.447 | 0.328465 |
| 21 | -0.111164 | -0.7621 | 0.775096 |
| 22 | -0.062618 | -0.4293 | 0.665161 |
| 23 | -0.010637 | -0.0729 | 0.528911 |
| 24 | 0.038065 | 0.261 | 0.397631 |
| 25 | -0.056536 | -0.3876 | 0.649967 |
| 26 | -0.031042 | -0.2128 | 0.583804 |
| 27 | -0.226859 | -1.5553 | 0.936704 |
| 28 | -0.106967 | -0.7333 | 0.7665 |
| 29 | -0.019275 | -0.1321 | 0.552281 |
| 30 | 0.068794 | 0.4716 | 0.319686 |
| 31 | -0.112407 | -0.7706 | 0.777607 |
| 32 | -0.014542 | -0.0997 | 0.539494 |
| 33 | -0.150836 | -1.0341 | 0.846805 |
| 34 | 0.057616 | 0.395 | 0.347316 |
| 35 | 0.026904 | 0.1844 | 0.42723 |
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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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