workshop 7 Q2 M
| 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 | 8.9443 | 0 |
| 1 | 0.148089 | 1.3246 | 0.094546 |
| 2 | -0.128383 | -1.1483 | 0.872865 |
| 3 | 0.114037 | 1.02 | 0.155407 |
| 4 | 0.021524 | 0.1925 | 0.423912 |
| 5 | 0.088238 | 0.7892 | 0.216157 |
| 6 | 0.183984 | 1.6456 | 0.051884 |
| 7 | 0.073797 | 0.6601 | 0.255556 |
| 8 | 0.055223 | 0.4939 | 0.311355 |
| 9 | 0.05655 | 0.5058 | 0.307195 |
| 10 | -0.25098 | -2.2448 | 0.98623 |
| 11 | 0.03005 | 0.2688 | 0.394396 |
| 12 | 0.552338 | 4.9403 | 2e-06 |
| 13 | 0.021924 | 0.1961 | 0.422517 |
| 14 | -0.211737 | -1.8938 | 0.969069 |
| 15 | 0.040427 | 0.3616 | 0.359305 |
| 16 | -0.084761 | -0.7581 | 0.774698 |
| 17 | -0.028832 | -0.2579 | 0.601421 |
| 18 | 0.050935 | 0.4556 | 0.324963 |
| 19 | 0.014296 | 0.1279 | 0.449286 |
| 20 | -0.038909 | -0.348 | 0.635626 |
| 21 | 0.014639 | 0.1309 | 0.448076 |
| 22 | -0.279875 | -2.5033 | 0.99283 |
| 23 | -0.010126 | -0.0906 | 0.535968 |
| 24 | 0.408097 | 3.6501 | 0.000233 |
| 25 | -0.042356 | -0.3788 | 0.647096 |
| 26 | -0.17479 | -1.5634 | 0.939043 |
| 27 | -0.049931 | -0.4466 | 0.671812 |
| 28 | -0.126197 | -1.1287 | 0.868809 |
| 29 | -0.076227 | -0.6818 | 0.751331 |
| 30 | -0.054494 | -0.4874 | 0.686348 |
| 31 | -0.125113 | -1.119 | 0.866764 |
| 32 | -0.112403 | -1.0054 | 0.841123 |
| 33 | -0.05747 | -0.514 | 0.695674 |
| 34 | -0.221581 | -1.9819 | 0.974536 |
| 35 | -0.061213 | -0.5475 | 0.707223 |
| Partial Autocorrelation Function | |||
| Time lag k | PACF(k) | T-STAT | P-value |
| 0 | 0.148089 | 1.3246 | 0.094546 |
| 1 | -0.153684 | -1.3746 | 0.913452 |
| 2 | 0.166215 | 1.4867 | 0.070517 |
| 3 | -0.052621 | -0.4707 | 0.680415 |
| 4 | 0.147014 | 1.3149 | 0.096144 |
| 5 | 0.125982 | 1.1268 | 0.131595 |
| 6 | 0.062311 | 0.5573 | 0.289431 |
| 7 | 0.063624 | 0.5691 | 0.285452 |
| 8 | 0.01836 | 0.1642 | 0.434987 |
| 9 | -0.299026 | -2.6746 | 0.995465 |
| 10 | 0.116298 | 1.0402 | 0.15069 |
| 11 | 0.476506 | 4.262 | 2.7e-05 |
| 12 | -0.146687 | -1.312 | 0.903365 |
| 13 | -0.194 | -1.7352 | 0.956721 |
| 14 | 0.024949 | 0.2232 | 0.411992 |
| 15 | -0.067873 | -0.6071 | 0.72724 |
| 16 | -0.053422 | -0.4778 | 0.682959 |
| 17 | -0.082429 | -0.7373 | 0.768442 |
| 18 | 0.063704 | 0.5698 | 0.285211 |
| 19 | -0.155449 | -1.3904 | 0.915866 |
| 20 | 0.08902 | 0.7962 | 0.214129 |
| 21 | -0.045898 | -0.4105 | 0.658741 |
| 22 | 0.080921 | 0.7238 | 0.235656 |
| 23 | 0.080274 | 0.718 | 0.237427 |
| 24 | -0.035529 | -0.3178 | 0.624259 |
| 25 | -0.003762 | -0.0336 | 0.513378 |
| 26 | -0.158197 | -1.415 | 0.91952 |
| 27 | -0.021364 | -0.1911 | 0.575528 |
| 28 | -0.037621 | -0.3365 | 0.63131 |
| 29 | -0.171574 | -1.5346 | 0.935587 |
| 30 | -0.162001 | -1.449 | 0.924374 |
| 31 | -0.100518 | -0.8991 | 0.81434 |
| 32 | 0.027308 | 0.2443 | 0.40383 |
| 33 | 0.087447 | 0.7822 | 0.218216 |
| 34 | -0.008849 | -0.0791 | 0.531443 |
| 35 | -0.024779 | -0.2216 | 0.587417 |
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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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