Workshop8_q1b
| 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 | 'Herman Ole Andreas Wold' @ 193.190.124.10:1001 |
| Autocorrelation Function | |||
| Time lag k | ACF(k) | T-STAT | P-value |
| 0 | 1 | 9.1652 | 0 |
| 1 | 0.218316 | 2.0009 | 0.024316 |
| 2 | 0.271162 | 2.4852 | 0.007465 |
| 3 | 0.253482 | 2.3232 | 0.011293 |
| 4 | 0.20813 | 1.9075 | 0.029934 |
| 5 | 0.15716 | 1.4404 | 0.076737 |
| 6 | 0.113764 | 1.0427 | 0.150047 |
| 7 | -0.101923 | -0.9341 | 0.823544 |
| 8 | -0.041187 | -0.3775 | 0.646617 |
| 9 | -0.039409 | -0.3612 | 0.640568 |
| 10 | -0.182959 | -1.6768 | 0.951355 |
| 11 | -0.058004 | -0.5316 | 0.701803 |
| 12 | -0.387893 | -3.5551 | 0.999688 |
| 13 | -0.224421 | -2.0569 | 0.978598 |
| 14 | -0.107517 | -0.9854 | 0.836375 |
| 15 | -0.079921 | -0.7325 | 0.767044 |
| 16 | -0.279516 | -2.5618 | 0.993901 |
| 17 | -0.030679 | -0.2812 | 0.610368 |
| 18 | -0.055931 | -0.5126 | 0.695218 |
| 19 | 0.015681 | 0.1437 | 0.443032 |
| 20 | 0.140636 | 1.289 | 0.100477 |
| 21 | 0.025731 | 0.2358 | 0.40707 |
| 22 | 0.058374 | 0.535 | 0.297028 |
| 23 | 0.109871 | 1.007 | 0.158417 |
| 24 | -0.037852 | -0.3469 | 0.63524 |
| 25 | 0.010856 | 0.0995 | 0.460491 |
| 26 | 0.066196 | 0.6067 | 0.272846 |
| 27 | -0.052029 | -0.4769 | 0.682647 |
| 28 | 0.027863 | 0.2554 | 0.39953 |
| 29 | 0.014846 | 0.1361 | 0.446048 |
| 30 | -0.097164 | -0.8905 | 0.812137 |
| 31 | -0.08266 | -0.7576 | 0.774593 |
| 32 | -0.132893 | -1.218 | 0.88668 |
| 33 | -0.123592 | -1.1327 | 0.869726 |
| 34 | -0.107542 | -0.9856 | 0.836429 |
| 35 | -0.116239 | -1.0654 | 0.855115 |
| 36 | -0.204401 | -1.8734 | 0.967752 |
| 37 | -0.019231 | -0.1763 | 0.569741 |
| 38 | -0.062752 | -0.5751 | 0.716629 |
| 39 | -0.127323 | -1.1669 | 0.87673 |
| 40 | 0.023717 | 0.2174 | 0.414222 |
| 41 | -0.050167 | -0.4598 | 0.676573 |
| 42 | -0.027328 | -0.2505 | 0.598581 |
| 43 | 0.077928 | 0.7142 | 0.238535 |
| 44 | -0.009605 | -0.088 | 0.534969 |
| 45 | -0.002002 | -0.0183 | 0.507298 |
| 46 | 0.062418 | 0.5721 | 0.284402 |
| 47 | -0.016402 | -0.1503 | 0.559565 |
| 48 | 0.136657 | 1.2525 | 0.106936 |
| 49 | 0.048998 | 0.4491 | 0.327268 |
| 50 | -0.003837 | -0.0352 | 0.513984 |
| 51 | 0.090017 | 0.825 | 0.20585 |
| 52 | 0.030536 | 0.2799 | 0.390133 |
| 53 | -0.002007 | -0.0184 | 0.507315 |
| 54 | 0.055512 | 0.5088 | 0.30612 |
| 55 | 0.023336 | 0.2139 | 0.415579 |
| 56 | 0.020175 | 0.1849 | 0.426875 |
| 57 | 0.075225 | 0.6895 | 0.246219 |
| 58 | 0.026926 | 0.2468 | 0.40284 |
| 59 | 0.042286 | 0.3876 | 0.349663 |
| Partial Autocorrelation Function | |||
| Time lag k | PACF(k) | T-STAT | P-value |
| 0 | 0.218316 | 2.0009 | 0.024316 |
| 1 | 0.234686 | 2.1509 | 0.017177 |
| 2 | 0.1744 | 1.5984 | 0.056854 |
| 3 | 0.094373 | 0.8649 | 0.194767 |
| 4 | 0.025994 | 0.2382 | 0.406138 |
| 5 | -0.013202 | -0.121 | 0.548009 |
| 6 | -0.236742 | -2.1698 | 0.983576 |
| 7 | -0.095281 | -0.8733 | 0.807496 |
| 8 | -0.005524 | -0.0506 | 0.520129 |
| 9 | -0.130578 | -1.1968 | 0.882617 |
| 10 | 0.066559 | 0.61 | 0.271747 |
| 11 | -0.326593 | -2.9933 | 0.998188 |
| 12 | -0.072857 | -0.6677 | 0.746936 |
| 13 | 0.098325 | 0.9012 | 0.185039 |
| 14 | 0.146791 | 1.3454 | 0.091065 |
| 15 | -0.156366 | -1.4331 | 0.922233 |
| 16 | 0.085271 | 0.7815 | 0.218347 |
| 17 | 0.093219 | 0.8544 | 0.197666 |
| 18 | -0.021496 | -0.197 | 0.577852 |
| 19 | 0.135001 | 1.2373 | 0.109711 |
| 20 | 0.012846 | 0.1177 | 0.453281 |
| 21 | -0.08697 | -0.7971 | 0.786178 |
| 22 | -0.026087 | -0.2391 | 0.594192 |
| 23 | -0.303265 | -2.7795 | 0.996641 |
| 24 | -0.147592 | -1.3527 | 0.91011 |
| 25 | 0.058727 | 0.5382 | 0.295918 |
| 26 | 0.132592 | 1.2152 | 0.113843 |
| 27 | -0.082295 | -0.7542 | 0.773594 |
| 28 | 0.052602 | 0.4821 | 0.315494 |
| 29 | 0.014436 | 0.1323 | 0.447528 |
| 30 | -0.113776 | -1.0428 | 0.849978 |
| 31 | -0.068948 | -0.6319 | 0.735423 |
| 32 | 0.063238 | 0.5796 | 0.281873 |
| 33 | -0.075459 | -0.6916 | 0.75445 |
| 34 | -0.001051 | -0.0096 | 0.503832 |
| 35 | -0.246424 | -2.2585 | 0.986747 |
| 36 | -0.053152 | -0.4871 | 0.686289 |
| 37 | 0.083486 | 0.7652 | 0.223159 |
| 38 | -0.015625 | -0.1432 | 0.556766 |
| 39 | -0.067274 | -0.6166 | 0.730409 |
| 40 | -0.03189 | -0.2923 | 0.614601 |
| 41 | -0.005993 | -0.0549 | 0.521837 |
| 42 | 0.011153 | 0.1022 | 0.459414 |
| 43 | -0.047582 | -0.4361 | 0.668056 |
| 44 | 0.066005 | 0.6049 | 0.273424 |
| 45 | -0.064667 | -0.5927 | 0.722507 |
| 46 | -0.080094 | -0.7341 | 0.767526 |
| 47 | -0.123696 | -1.1337 | 0.869926 |
| 48 | -0.075521 | -0.6922 | 0.754629 |
| 49 | 0.075084 | 0.6882 | 0.246626 |
| 50 | 0.102884 | 0.9429 | 0.174206 |
| 51 | -0.122755 | -1.1251 | 0.868117 |
| 52 | -0.017477 | -0.1602 | 0.563436 |
| 53 | 0.055817 | 0.5116 | 0.305147 |
| 54 | 0.015806 | 0.1449 | 0.442581 |
| 55 | 0.019345 | 0.1773 | 0.429849 |
| 56 | 0.075494 | 0.6919 | 0.245448 |
| 57 | -0.051246 | -0.4697 | 0.6801 |
| 58 | -0.037132 | -0.3403 | 0.632768 |
| 59 | -0.126569 | -1.16 | 0.875337 |
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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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