Paper XVI AcorF lag60
| 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 | 7.6811 | 0 |
| 1 | 0.070485 | 0.5414 | 0.295134 |
| 2 | -0.103707 | -0.7966 | 0.785558 |
| 3 | -0.076146 | -0.5849 | 0.719573 |
| 4 | -0.116022 | -0.8912 | 0.811775 |
| 5 | 0.062956 | 0.4836 | 0.315238 |
| 6 | -0.151161 | -1.1611 | 0.874859 |
| 7 | -0.171366 | -1.3163 | 0.903416 |
| 8 | -0.06623 | -0.5087 | 0.693577 |
| 9 | 0.053376 | 0.41 | 0.341649 |
| 10 | 0.109409 | 0.8404 | 0.202043 |
| 11 | 0.179178 | 1.3763 | 0.086967 |
| 12 | 0.063508 | 0.4878 | 0.313745 |
| 13 | 0.003193 | 0.0245 | 0.490259 |
| 14 | -0.046872 | -0.36 | 0.639945 |
| 15 | -0.138502 | -1.0639 | 0.854134 |
| 16 | -0.035811 | -0.2751 | 0.607888 |
| 17 | -0.028088 | -0.2157 | 0.585036 |
| 18 | -0.091618 | -0.7037 | 0.757815 |
| 19 | -0.078803 | -0.6053 | 0.726348 |
| 20 | 0.155944 | 1.1978 | 0.117887 |
| 21 | 0.145859 | 1.1204 | 0.133549 |
| 22 | 0.109343 | 0.8399 | 0.202184 |
| 23 | -0.074108 | -0.5692 | 0.714322 |
| 24 | 0.094578 | 0.7265 | 0.235213 |
| 25 | 0.083763 | 0.6434 | 0.261231 |
| 26 | 0.034448 | 0.2646 | 0.396121 |
| 27 | -0.085363 | -0.6557 | 0.742712 |
| 28 | -0.104451 | -0.8023 | 0.787201 |
| 29 | -0.128407 | -0.9863 | 0.835997 |
| 30 | -0.026041 | -0.2 | 0.578925 |
| 31 | 0.076725 | 0.5893 | 0.278943 |
| 32 | -0.056061 | -0.4306 | 0.665839 |
| 33 | 0.007656 | 0.0588 | 0.476652 |
| 34 | -0.030992 | -0.2381 | 0.593667 |
| 35 | 0.048892 | 0.3755 | 0.3543 |
| 36 | 0.09896 | 0.7601 | 0.225103 |
| 37 | -0.01483 | -0.1139 | 0.545153 |
| 38 | -0.052324 | -0.4019 | 0.655399 |
| 39 | -0.101563 | -0.7801 | 0.780781 |
| 40 | -0.040611 | -0.3119 | 0.621908 |
| 41 | 0.072651 | 0.558 | 0.289462 |
| 42 | 0.07101 | 0.5454 | 0.293753 |
| 43 | -0.099946 | -0.7677 | 0.777135 |
| 44 | 0.007186 | 0.0552 | 0.478083 |
| 45 | -0.006832 | -0.0525 | 0.520837 |
| 46 | -0.004008 | -0.0308 | 0.512229 |
| 47 | 0.061185 | 0.47 | 0.320054 |
| 48 | -0.005342 | -0.041 | 0.516295 |
| 49 | -0.030269 | -0.2325 | 0.591524 |
| 50 | -0.064519 | -0.4956 | 0.688983 |
| 51 | -0.011974 | -0.092 | 0.536484 |
| 52 | 0.012752 | 0.0979 | 0.461153 |
| 53 | -0.019528 | -0.15 | 0.559362 |
| 54 | -0.012255 | -0.0941 | 0.537337 |
| 55 | -0.037728 | -0.2898 | 0.613506 |
| 56 | -0.014826 | -0.1139 | 0.54514 |
| 57 | 0.006084 | 0.0467 | 0.481441 |
| 58 | -0.002839 | -0.0218 | 0.508662 |
| 59 | NA | NA | NA |
| Partial Autocorrelation Function | |||
| Time lag k | PACF(k) | T-STAT | P-value |
| 0 | 0.070485 | 0.5414 | 0.295134 |
| 1 | -0.109218 | -0.8389 | 0.797549 |
| 2 | -0.061373 | -0.4714 | 0.680458 |
| 3 | -0.119538 | -0.9182 | 0.818871 |
| 4 | 0.066749 | 0.5127 | 0.305034 |
| 5 | -0.1975 | -1.517 | 0.9327 |
| 6 | -0.156834 | -1.2047 | 0.883428 |
| 7 | -0.102624 | -0.7883 | 0.783152 |
| 8 | 0.015471 | 0.1188 | 0.452904 |
| 9 | 0.012479 | 0.0959 | 0.461982 |
| 10 | 0.16279 | 1.2504 | 0.108042 |
| 11 | 0.044393 | 0.341 | 0.36716 |
| 12 | 0.021356 | 0.164 | 0.43513 |
| 13 | -0.057551 | -0.4421 | 0.669969 |
| 14 | -0.114658 | -0.8807 | 0.808974 |
| 15 | -0.027287 | -0.2096 | 0.582647 |
| 16 | 0.012564 | 0.0965 | 0.461723 |
| 17 | -0.056343 | -0.4328 | 0.666623 |
| 18 | -0.072326 | -0.5555 | 0.709688 |
| 19 | 0.155934 | 1.1978 | 0.117901 |
| 20 | 0.04406 | 0.3384 | 0.36812 |
| 21 | 0.042038 | 0.3229 | 0.373957 |
| 22 | -0.10757 | -0.8263 | 0.794008 |
| 23 | 0.191909 | 1.4741 | 0.072887 |
| 24 | 0.046378 | 0.3562 | 0.361468 |
| 25 | 0.153179 | 1.1766 | 0.122042 |
| 26 | -0.022092 | -0.1697 | 0.567084 |
| 27 | 0.093638 | 0.7192 | 0.237414 |
| 28 | -0.173098 | -1.3296 | 0.905615 |
| 29 | 0.010619 | 0.0816 | 0.467635 |
| 30 | -0.024024 | -0.1845 | 0.572885 |
| 31 | -0.050939 | -0.3913 | 0.651497 |
| 32 | -0.071399 | -0.5484 | 0.707265 |
| 33 | -0.051374 | -0.3946 | 0.652724 |
| 34 | -0.000592 | -0.0045 | 0.501805 |
| 35 | 0.015335 | 0.1178 | 0.453317 |
| 36 | -0.037722 | -0.2897 | 0.613487 |
| 37 | -0.063872 | -0.4906 | 0.687238 |
| 38 | 0.000421 | 0.0032 | 0.498716 |
| 39 | -0.022416 | -0.1722 | 0.568059 |
| 40 | 0.089481 | 0.6873 | 0.247289 |
| 41 | 0.011595 | 0.0891 | 0.464668 |
| 42 | -0.026659 | -0.2048 | 0.580771 |
| 43 | -0.052313 | -0.4018 | 0.655368 |
| 44 | -0.060065 | -0.4614 | 0.676883 |
| 45 | -0.169667 | -1.3032 | 0.901222 |
| 46 | 0.024404 | 0.1875 | 0.425975 |
| 47 | -0.049408 | -0.3795 | 0.647165 |
| 48 | 0.039687 | 0.3048 | 0.380781 |
| 49 | -0.098956 | -0.7601 | 0.774889 |
| 50 | 0.046425 | 0.3566 | 0.361331 |
| 51 | -0.104533 | -0.8029 | 0.787382 |
| 52 | 0.007479 | 0.0574 | 0.477192 |
| 53 | 0.0274 | 0.2105 | 0.417014 |
| 54 | 0.019199 | 0.1475 | 0.441632 |
| 55 | -0.010001 | -0.0768 | 0.530487 |
| 56 | -0.001386 | -0.0106 | 0.50423 |
| 57 | -0.02442 | -0.1876 | 0.574074 |
| 58 | NA | NA | NA |
| 59 | NA | NA | NA |
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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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