workshop 7 Q2 R
| Summary of compuational transaction | |
| Raw Input | view raw input (R code) |
| Raw Output | view raw output of R engine |
| Computing time | 3 seconds |
| R Server | 'Herman Ole Andreas Wold' @ 193.190.124.10:1001 |
| Autocorrelation Function | |||
| Time lag k | ACF(k) | T-STAT | P-value |
| 0 | 1 | 8.2462 | 0 |
| 1 | 0.343577 | 2.8332 | 0.00303 |
| 2 | 0.345497 | 2.849 | 0.002898 |
| 3 | 0.375084 | 3.093 | 0.001437 |
| 4 | 0.187787 | 1.5485 | 0.063069 |
| 5 | 0.298005 | 2.4574 | 0.008275 |
| 6 | 0.18573 | 1.5316 | 0.065135 |
| 7 | -0.026847 | -0.2214 | 0.587273 |
| 8 | 0.057977 | 0.4781 | 0.31706 |
| 9 | 0.009586 | 0.079 | 0.468614 |
| 10 | -0.066076 | -0.5449 | 0.70619 |
| 11 | -0.075615 | -0.6235 | 0.732492 |
| 12 | -0.188189 | -1.5518 | 0.937329 |
| 13 | -0.079503 | -0.6556 | 0.742853 |
| 14 | -0.006826 | -0.0563 | 0.52236 |
| 15 | -0.030768 | -0.2537 | 0.59976 |
| 16 | -0.094702 | -0.7809 | 0.781223 |
| 17 | 0.055826 | 0.4604 | 0.323366 |
| 18 | 0.068518 | 0.565 | 0.286963 |
| 19 | 0.096288 | 0.794 | 0.214974 |
| 20 | 0.120394 | 0.9928 | 0.162165 |
| 21 | 0.122918 | 1.0136 | 0.157181 |
| 22 | 0.001707 | 0.0141 | 0.494407 |
| 23 | 0.169931 | 1.4013 | 0.082837 |
| 24 | -0.019492 | -0.1607 | 0.56361 |
| 25 | 0.017204 | 0.1419 | 0.443802 |
| 26 | 0.108605 | 0.8956 | 0.186819 |
| 27 | -0.050237 | -0.4143 | 0.660009 |
| 28 | 0.015177 | 0.1252 | 0.450385 |
| 29 | -0.045137 | -0.3722 | 0.644552 |
| 30 | -0.122261 | -1.0082 | 0.841532 |
| 31 | -0.109969 | -0.9068 | 0.816149 |
| 32 | 0.002005 | 0.0165 | 0.493429 |
| 33 | -0.12379 | -1.0208 | 0.844516 |
| 34 | -0.014198 | -0.1171 | 0.546429 |
| 35 | 0.002231 | 0.0184 | 0.492688 |
| Partial Autocorrelation Function | |||
| Time lag k | PACF(k) | T-STAT | P-value |
| 0 | 0.343577 | 2.8332 | 0.00303 |
| 1 | 0.257896 | 2.1267 | 0.01854 |
| 2 | 0.240965 | 1.987 | 0.025474 |
| 3 | -0.045397 | -0.3743 | 0.645345 |
| 4 | 0.144759 | 1.1937 | 0.118369 |
| 5 | -0.019936 | -0.1644 | 0.565046 |
| 6 | -0.232851 | -1.9201 | 0.970482 |
| 7 | -0.046186 | -0.3809 | 0.647752 |
| 8 | 0.014878 | 0.1227 | 0.451358 |
| 9 | -0.054411 | -0.4487 | 0.672456 |
| 10 | -0.086357 | -0.7121 | 0.760586 |
| 11 | -0.097538 | -0.8043 | 0.787992 |
| 12 | 0.100249 | 0.8267 | 0.205656 |
| 13 | 0.119156 | 0.9826 | 0.164646 |
| 14 | 0.078857 | 0.6503 | 0.258853 |
| 15 | -0.098099 | -0.8089 | 0.789316 |
| 16 | 0.158345 | 1.3057 | 0.09802 |
| 17 | 0.087942 | 0.7252 | 0.235413 |
| 18 | -0.009179 | -0.0757 | 0.530058 |
| 19 | -0.021473 | -0.1771 | 0.57001 |
| 20 | 0.099908 | 0.8239 | 0.206449 |
| 21 | -0.204082 | -1.6829 | 0.95151 |
| 22 | 0.027287 | 0.225 | 0.41132 |
| 23 | -0.180078 | -1.485 | 0.928912 |
| 24 | 0.064198 | 0.5294 | 0.29913 |
| 25 | 0.10472 | 0.8635 | 0.195437 |
| 26 | -0.016491 | -0.136 | 0.553884 |
| 27 | -0.036248 | -0.2989 | 0.617039 |
| 28 | 0.001169 | 0.0096 | 0.49617 |
| 29 | 0.011645 | 0.096 | 0.46189 |
| 30 | -0.146434 | -1.2075 | 0.884294 |
| 31 | 0.154308 | 1.2725 | 0.103773 |
| 32 | -0.003364 | -0.0277 | 0.511024 |
| 33 | 0.018022 | 0.1486 | 0.44115 |
| 34 | 0.050203 | 0.414 | 0.340095 |
| 35 | -0.062334 | -0.514 | 0.695547 |
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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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