paper auto 5
| 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 | -0.570817 | -3.9133 | 0.999854 |
| 2 | 0.074491 | 0.5107 | 0.305982 |
| 3 | 0.160236 | 1.0985 | 0.138785 |
| 4 | -0.279488 | -1.9161 | 0.969275 |
| 5 | 0.161245 | 1.1054 | 0.137298 |
| 6 | 0.145352 | 0.9965 | 0.16206 |
| 7 | -0.276491 | -1.8955 | 0.967909 |
| 8 | 0.110569 | 0.758 | 0.226111 |
| 9 | 0.152545 | 1.0458 | 0.150503 |
| 10 | -0.262614 | -1.8004 | 0.960891 |
| 11 | 0.216797 | 1.4863 | 0.071941 |
| 12 | -0.165776 | -1.1365 | 0.869247 |
| 13 | -0.005198 | -0.0356 | 0.514138 |
| 14 | 0.079283 | 0.5435 | 0.294664 |
| 15 | -0.015074 | -0.1033 | 0.540934 |
| 16 | -0.044009 | -0.3017 | 0.617897 |
| 17 | 0.024929 | 0.1709 | 0.432517 |
| 18 | 0.078889 | 0.5408 | 0.295587 |
| 19 | -0.208641 | -1.4304 | 0.920387 |
| 20 | 0.161252 | 1.1055 | 0.137287 |
| 21 | -0.029306 | -0.2009 | 0.579183 |
| 22 | -0.064864 | -0.4447 | 0.670705 |
| 23 | 0.179932 | 1.2335 | 0.111752 |
| 24 | -0.148681 | -1.0193 | 0.843362 |
| 25 | -0.057358 | -0.3932 | 0.652036 |
| 26 | 0.149246 | 1.0232 | 0.155731 |
| 27 | -0.036189 | -0.2481 | 0.597431 |
| 28 | -0.114751 | -0.7867 | 0.782296 |
| 29 | 0.21821 | 1.496 | 0.070673 |
| 30 | -0.284132 | -1.9479 | 0.971294 |
| 31 | 0.251954 | 1.7273 | 0.045339 |
| 32 | -0.040112 | -0.275 | 0.607736 |
| 33 | -0.123145 | -0.8442 | 0.798594 |
| 34 | 0.102138 | 0.7002 | 0.243621 |
| 35 | -0.075 | -0.5142 | 0.695231 |
| Partial Autocorrelation Function | |||
| Time lag k | PACF(k) | T-STAT | P-value |
| 0 | -0.570817 | -3.9133 | 0.999854 |
| 1 | -0.372818 | -2.5559 | 0.993058 |
| 2 | 0.009989 | 0.0685 | 0.472846 |
| 3 | -0.209557 | -1.4367 | 0.92128 |
| 4 | -0.168659 | -1.1563 | 0.873293 |
| 5 | 0.206768 | 1.4175 | 0.081462 |
| 6 | 0.01412 | 0.0968 | 0.461648 |
| 7 | -0.185335 | -1.2706 | 0.894937 |
| 8 | 0.170452 | 1.1686 | 0.124238 |
| 9 | 0.064884 | 0.4448 | 0.329245 |
| 10 | -0.031503 | -0.216 | 0.58503 |
| 11 | -0.206507 | -1.4157 | 0.918277 |
| 12 | -0.04941 | -0.3387 | 0.631842 |
| 13 | -0.129488 | -0.8877 | 0.810396 |
| 14 | -0.109265 | -0.7491 | 0.77123 |
| 15 | -0.016471 | -0.1129 | 0.544712 |
| 16 | -0.047708 | -0.3271 | 0.627467 |
| 17 | 0.145766 | 0.9993 | 0.161378 |
| 18 | -0.132514 | -0.9085 | 0.815868 |
| 19 | -0.152209 | -1.0435 | 0.848972 |
| 20 | 0.060087 | 0.4119 | 0.34113 |
| 21 | 0.004312 | 0.0296 | 0.488272 |
| 22 | 0.069278 | 0.4749 | 0.318512 |
| 23 | -0.020963 | -0.1437 | 0.55683 |
| 24 | -0.082444 | -0.5652 | 0.712691 |
| 25 | -0.100295 | -0.6876 | 0.752455 |
| 26 | 0.070036 | 0.4801 | 0.316676 |
| 27 | -0.057341 | -0.3931 | 0.651993 |
| 28 | -0.003308 | -0.0227 | 0.508999 |
| 29 | -0.114486 | -0.7849 | 0.781767 |
| 30 | 0.097537 | 0.6687 | 0.253484 |
| 31 | 0.000884 | 0.0061 | 0.497596 |
| 32 | -0.009726 | -0.0667 | 0.526439 |
| 33 | 0.026503 | 0.1817 | 0.428303 |
| 34 | -0.043028 | -0.295 | 0.615348 |
| 35 | -0.117271 | -0.804 | 0.787269 |
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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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