| 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 | 9.5917 | 0 |
| 1 | 0.284676 | 2.7305 | 0.003789 |
| 2 | 0.052198 | 0.5007 | 0.308901 |
| 3 | 0.142631 | 1.3681 | 0.087311 |
| 4 | -0.020495 | -0.1966 | 0.577705 |
| 5 | -0.304248 | -2.9182 | 0.997788 |
| 6 | -0.184722 | -1.7718 | 0.96013 |
| 7 | -0.086861 | -0.8331 | 0.796539 |
| 8 | -0.12315 | -1.1812 | 0.879719 |
| 9 | -0.108707 | -1.0427 | 0.850085 |
| 10 | 0.09502 | 0.9114 | 0.182234 |
| 11 | 0.130213 | 1.249 | 0.107425 |
| 12 | 0.067257 | 0.6451 | 0.260233 |
| 13 | 0.132045 | 1.2665 | 0.104261 |
| 14 | 0.226148 | 2.1691 | 0.016325 |
| 15 | -0.116631 | -1.1187 | 0.866906 |
| 16 | 0.025221 | 0.2419 | 0.404693 |
| 17 | -0.034873 | -0.3345 | 0.630614 |
| 18 | -0.047375 | -0.4544 | 0.674694 |
| 19 | 0.032867 | 0.3153 | 0.376642 |
| 20 | 0.121947 | 1.1697 | 0.122577 |
| 21 | 0.042148 | 0.4043 | 0.343476 |
| 22 | 0.016518 | 0.1584 | 0.43723 |
| 23 | -0.045919 | -0.4404 | 0.669676 |
| 24 | -0.097571 | -0.9359 | 0.824104 |
| 25 | -0.145555 | -1.3961 | 0.91698 |
| 26 | -0.07311 | -0.7012 | 0.75754 |
| 27 | -0.048163 | -0.462 | 0.677402 |
| 28 | -0.045047 | -0.4321 | 0.666652 |
| 29 | -0.163279 | -1.5661 | 0.939622 |
| 30 | 0.042201 | 0.4048 | 0.343289 |
| 31 | -0.000619 | -0.0059 | 0.502362 |
| 32 | 0.145908 | 1.3995 | 0.082513 |
| 33 | 0.083749 | 0.8033 | 0.211939 |
| 34 | 0.012124 | 0.1163 | 0.453837 |
| 35 | -0.011554 | -0.1108 | 0.544 |
| 36 | 0.005067 | 0.0486 | 0.48067 |
| 37 | -0.155656 | -1.493 | 0.930571 |
| 38 | -0.092536 | -0.8876 | 0.811459 |
| 39 | 0.004841 | 0.0464 | 0.481532 |
| 40 | -0.00284 | -0.0272 | 0.510836 |
| 41 | 0.018555 | 0.178 | 0.429569 |
| 42 | 0.089279 | 0.8563 | 0.197019 |
| 43 | -0.021757 | -0.2087 | 0.582423 |
| 44 | 0.003614 | 0.0347 | 0.48621 |
| 45 | 0.008077 | 0.0775 | 0.469209 |
| 46 | 0.086399 | 0.8287 | 0.204707 |
| 47 | -0.043007 | -0.4125 | 0.659536 |
| 48 | 0.036393 | 0.3491 | 0.363917 |
| 49 | 0.059596 | 0.5716 | 0.284486 |
| 50 | 0.036074 | 0.346 | 0.365062 |
| 51 | -0.044425 | -0.4261 | 0.664489 |
| 52 | 0.057604 | 0.5525 | 0.290968 |
| 53 | 0.071531 | 0.6861 | 0.247187 |
| 54 | -0.010875 | -0.1043 | 0.541424 |
| 55 | -0.032556 | -0.3123 | 0.622227 |
| 56 | -0.022153 | -0.2125 | 0.583901 |
| 57 | -0.039222 | -0.3762 | 0.646186 |
| 58 | -0.009704 | -0.0931 | 0.536978 |
| 59 | 0.014376 | 0.1379 | 0.445314 |
| Partial Autocorrelation Function | |||
| Time lag k | PACF(k) | T-STAT | P-value |
| 0 | 0.284676 | 2.7305 | 0.003789 |
| 1 | -0.031386 | -0.301 | 0.617969 |
| 2 | 0.148401 | 1.4234 | 0.079 |
| 3 | -0.112905 | -1.083 | 0.85917 |
| 4 | -0.296997 | -2.8487 | 0.997292 |
| 5 | -0.045235 | -0.4339 | 0.667303 |
| 6 | -0.015506 | -0.1487 | 0.558952 |
| 7 | -0.011905 | -0.1142 | 0.545331 |
| 8 | -0.062752 | -0.6019 | 0.725639 |
| 9 | 0.073821 | 0.7081 | 0.240348 |
| 10 | 0.052015 | 0.4989 | 0.309516 |
| 11 | 0.011782 | 0.113 | 0.455135 |
| 12 | 0.057734 | 0.5538 | 0.290541 |
| 13 | 0.125496 | 1.2037 | 0.115893 |
| 14 | -0.234921 | -2.2533 | 0.986691 |
| 15 | 0.187344 | 1.7969 | 0.037813 |
| 16 | -0.147707 | -1.4168 | 0.920034 |
| 17 | 0.146041 | 1.4008 | 0.082323 |
| 18 | 0.142076 | 1.3627 | 0.088144 |
| 19 | 0.056704 | 0.5439 | 0.293917 |
| 20 | 0.017054 | 0.1636 | 0.435212 |
| 21 | -0.053887 | -0.5169 | 0.696756 |
| 22 | -0.104564 | -1.0029 | 0.84074 |
| 23 | -0.095551 | -0.9165 | 0.819097 |
| 24 | -0.052107 | -0.4998 | 0.690792 |
| 25 | 0.05278 | 0.5062 | 0.306946 |
| 26 | -0.0208 | -0.1995 | 0.578847 |
| 27 | -0.021675 | -0.2079 | 0.582115 |
| 28 | -0.186977 | -1.7934 | 0.961905 |
| 29 | -0.031966 | -0.3066 | 0.620084 |
| 30 | -0.011267 | -0.1081 | 0.542912 |
| 31 | 0.158366 | 1.519 | 0.066097 |
| 32 | -0.094875 | -0.91 | 0.817401 |
| 33 | -0.09617 | -0.9224 | 0.820642 |
| 34 | -0.038125 | -0.3657 | 0.64228 |
| 35 | 0.037664 | 0.3613 | 0.359365 |
| 36 | -0.058301 | -0.5592 | 0.71131 |
| 37 | 0.047048 | 0.4513 | 0.326429 |
| 38 | 0.021119 | 0.2026 | 0.41996 |
| 39 | 0.001356 | 0.013 | 0.494826 |
| 40 | 0.088527 | 0.8491 | 0.19901 |
| 41 | 0.014751 | 0.1415 | 0.443897 |
| 42 | -0.033985 | -0.326 | 0.627406 |
| 43 | -0.004687 | -0.045 | 0.51788 |
| 44 | 0.092454 | 0.8868 | 0.188752 |
| 45 | -0.004038 | -0.0387 | 0.515407 |
| 46 | 0.042077 | 0.4036 | 0.343725 |
| 47 | 0.096601 | 0.9266 | 0.178289 |
| 48 | 0.047344 | 0.4541 | 0.325409 |
| 49 | 0.072995 | 0.7001 | 0.242803 |
| 50 | -0.046023 | -0.4414 | 0.670035 |
| 51 | -0.078026 | -0.7484 | 0.771937 |
| 52 | 0.045968 | 0.4409 | 0.330156 |
| 53 | -0.013953 | -0.1338 | 0.553085 |
| 54 | 0.0048 | 0.046 | 0.481688 |
| 55 | -0.019 | -0.1822 | 0.572101 |
| 56 | 0.073155 | 0.7017 | 0.242325 |
| 57 | -0.07907 | -0.7584 | 0.77493 |
| 58 | 0.009322 | 0.0894 | 0.464473 |
| 59 | -0.035817 | -0.3435 | 0.634015 |
 |
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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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