paper auto 7
| 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 | 0.221186 | 1.5324 | 0.065992 |
| 2 | 0.027262 | 0.1889 | 0.425492 |
| 3 | 0.14209 | 0.9844 | 0.16492 |
| 4 | -0.036928 | -0.2558 | 0.600418 |
| 5 | -0.04521 | -0.3132 | 0.622266 |
| 6 | -0.041552 | -0.2879 | 0.612662 |
| 7 | -0.132939 | -0.921 | 0.81918 |
| 8 | -0.246302 | -1.7064 | 0.952804 |
| 9 | -0.004763 | -0.033 | 0.513094 |
| 10 | -0.122635 | -0.8496 | 0.800129 |
| 11 | -0.093363 | -0.6468 | 0.739591 |
| 12 | -0.364253 | -2.5236 | 0.99251 |
| 13 | -0.245037 | -1.6977 | 0.951977 |
| 14 | 0.024503 | 0.1698 | 0.432956 |
| 15 | -0.126268 | -0.8748 | 0.806982 |
| 16 | -0.04049 | -0.2805 | 0.609859 |
| 17 | 0.068134 | 0.472 | 0.319517 |
| 18 | 0.009755 | 0.0676 | 0.473198 |
| 19 | -0.009928 | -0.0688 | 0.527276 |
| 20 | 0.294027 | 2.0371 | 0.023588 |
| 21 | 0.066474 | 0.4605 | 0.323603 |
| 22 | 0.11622 | 0.8052 | 0.212339 |
| 23 | 0.241112 | 1.6705 | 0.050668 |
| 24 | -0.057142 | -0.3959 | 0.653031 |
| 25 | -0.027624 | -0.1914 | 0.575485 |
| 26 | 0.029595 | 0.205 | 0.419205 |
| 27 | 0.055703 | 0.3859 | 0.35063 |
| 28 | -0.007608 | -0.0527 | 0.52091 |
| 29 | 0.013364 | 0.0926 | 0.463309 |
| 30 | 0.022586 | 0.1565 | 0.438156 |
| 31 | 0.029505 | 0.2044 | 0.419445 |
| 32 | -0.055253 | -0.3828 | 0.648224 |
| 33 | -0.105243 | -0.7291 | 0.765271 |
| 34 | -0.088177 | -0.6109 | 0.727928 |
| 35 | -0.192044 | -1.3305 | 0.905183 |
| Partial Autocorrelation Function | |||
| Time lag k | PACF(k) | T-STAT | P-value |
| 0 | 0.221186 | 1.5324 | 0.065992 |
| 1 | -0.022775 | -0.1578 | 0.562358 |
| 2 | 0.148288 | 1.0274 | 0.154698 |
| 3 | -0.108441 | -0.7513 | 0.771931 |
| 4 | -0.00871 | -0.0603 | 0.523935 |
| 5 | -0.057086 | -0.3955 | 0.652888 |
| 6 | -0.099377 | -0.6885 | 0.752775 |
| 7 | -0.209456 | -1.4512 | 0.92338 |
| 8 | 0.111688 | 0.7738 | 0.221422 |
| 9 | -0.154519 | -1.0705 | 0.855135 |
| 10 | 0.031017 | 0.2149 | 0.415381 |
| 11 | -0.494145 | -3.4235 | 0.999363 |
| 12 | -0.006959 | -0.0482 | 0.519127 |
| 13 | -0.047272 | -0.3275 | 0.627646 |
| 14 | -0.091485 | -0.6338 | 0.735398 |
| 15 | -0.147826 | -1.0242 | 0.844556 |
| 16 | 0.047704 | 0.3305 | 0.371229 |
| 17 | -0.197957 | -1.3715 | 0.911699 |
| 18 | -0.070523 | -0.4886 | 0.686323 |
| 19 | 0.007082 | 0.0491 | 0.480535 |
| 20 | -0.042204 | -0.2924 | 0.614377 |
| 21 | 0.087696 | 0.6076 | 0.273165 |
| 22 | 0.004969 | 0.0344 | 0.486339 |
| 23 | -0.336247 | -2.3296 | 0.987959 |
| 24 | -0.164149 | -1.1373 | 0.869461 |
| 25 | -0.050265 | -0.3482 | 0.635412 |
| 26 | 0.056207 | 0.3894 | 0.349347 |
| 27 | -0.074299 | -0.5148 | 0.695457 |
| 28 | -0.052739 | -0.3654 | 0.641785 |
| 29 | 0.032477 | 0.225 | 0.411464 |
| 30 | -0.115696 | -0.8016 | 0.786623 |
| 31 | -0.093059 | -0.6447 | 0.738915 |
| 32 | -0.076205 | -0.528 | 0.700021 |
| 33 | -0.061985 | -0.4294 | 0.66524 |
| 34 | -0.010086 | -0.0699 | 0.527708 |
| 35 | -0.169067 | -1.1713 | 0.876376 |
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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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