Workshop8_q1a
| 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 | 9.798 | 0 |
| 1 | 0.358893 | 3.5164 | 0.000335 |
| 2 | 0.15575 | 1.526 | 0.065145 |
| 3 | 0.049037 | 0.4805 | 0.315995 |
| 4 | -0.183934 | -1.8022 | 0.962672 |
| 5 | -0.315277 | -3.0891 | 0.998687 |
| 6 | -0.469839 | -4.6035 | 0.999994 |
| 7 | -0.344287 | -3.3733 | 0.999464 |
| 8 | -0.20416 | -2.0004 | 0.975857 |
| 9 | -0.02239 | -0.2194 | 0.586589 |
| 10 | 0.054845 | 0.5374 | 0.296127 |
| 11 | 0.254731 | 2.4958 | 0.007136 |
| 12 | 0.786474 | 7.7058 | 0 |
| 13 | 0.294149 | 2.8821 | 0.002437 |
| 14 | 0.124383 | 1.2187 | 0.112972 |
| 15 | 0.025368 | 0.2486 | 0.402117 |
| 16 | -0.163729 | -1.6042 | 0.944024 |
| 17 | -0.295181 | -2.8922 | 0.997634 |
| 18 | -0.417121 | -4.0869 | 0.999955 |
| 19 | -0.310596 | -3.0432 | 0.99849 |
| 20 | -0.185417 | -1.8167 | 0.963809 |
| 21 | -0.045211 | -0.443 | 0.670611 |
| 22 | 0.032518 | 0.3186 | 0.375359 |
| 23 | 0.224948 | 2.204 | 0.014956 |
| 24 | 0.669373 | 6.5585 | 0 |
| 25 | 0.266935 | 2.6154 | 0.005175 |
| 26 | 0.111063 | 1.0882 | 0.139617 |
| 27 | 0.0092 | 0.0901 | 0.464181 |
| 28 | -0.133983 | -1.3128 | 0.903803 |
| 29 | -0.241133 | -2.3626 | 0.989918 |
| 30 | -0.355241 | -3.4806 | 0.999623 |
| 31 | -0.248544 | -2.4352 | 0.991636 |
| 32 | -0.140279 | -1.3744 | 0.913748 |
| 33 | -0.018577 | -0.182 | 0.572023 |
| 34 | 0.020763 | 0.2034 | 0.419614 |
| 35 | 0.148018 | 1.4503 | 0.07512 |
| 36 | 0.520109 | 5.096 | 1e-06 |
| 37 | 0.210149 | 2.059 | 0.0211 |
| 38 | 0.074053 | 0.7256 | 0.234934 |
| 39 | 0.004082 | 0.04 | 0.484089 |
| 40 | -0.126745 | -1.2418 | 0.891339 |
| 41 | -0.195319 | -1.9137 | 0.970683 |
| 42 | -0.290426 | -2.8456 | 0.99729 |
| 43 | -0.201952 | -1.9787 | 0.974643 |
| 44 | -0.118171 | -1.1578 | 0.875098 |
| 45 | -0.027641 | -0.2708 | 0.606447 |
| 46 | 0.025164 | 0.2466 | 0.402888 |
| 47 | 0.120846 | 1.184 | 0.119659 |
| 48 | 0.434714 | 4.2593 | 2.4e-05 |
| 49 | 0.185525 | 1.8178 | 0.036109 |
| 50 | 0.068214 | 0.6684 | 0.252753 |
| 51 | 0.013676 | 0.134 | 0.446844 |
| 52 | -0.111357 | -1.0911 | 0.861014 |
| 53 | -0.149338 | -1.4632 | 0.926661 |
| 54 | -0.23737 | -2.3257 | 0.988931 |
| 55 | -0.164429 | -1.6111 | 0.944776 |
| 56 | -0.086785 | -0.8503 | 0.801367 |
| 57 | -0.019715 | -0.1932 | 0.576382 |
| 58 | 0.006786 | 0.0665 | 0.473563 |
| 59 | 0.074699 | 0.7319 | 0.233008 |
| Partial Autocorrelation Function | |||
| Time lag k | PACF(k) | T-STAT | P-value |
| 0 | 0.358893 | 3.5164 | 0.000335 |
| 1 | 0.03093 | 0.3031 | 0.381252 |
| 2 | -0.01865 | -0.1827 | 0.572302 |
| 3 | -0.230043 | -2.254 | 0.986763 |
| 4 | -0.219975 | -2.1553 | 0.983181 |
| 5 | -0.34035 | -3.3347 | 0.999393 |
| 6 | -0.10714 | -1.0498 | 0.851767 |
| 7 | -0.069414 | -0.6801 | 0.750965 |
| 8 | 0.044994 | 0.4408 | 0.330158 |
| 9 | -0.100054 | -0.9803 | 0.835305 |
| 10 | 0.087357 | 0.8559 | 0.197087 |
| 11 | 0.7081 | 6.9379 | 0 |
| 12 | -0.305156 | -2.9899 | 0.998228 |
| 13 | -0.180562 | -1.7691 | 0.959977 |
| 14 | -0.110054 | -1.0783 | 0.8582 |
| 15 | 0.079799 | 0.7819 | 0.218109 |
| 16 | -0.06109 | -0.5986 | 0.72456 |
| 17 | 0.085996 | 0.8426 | 0.200777 |
| 18 | 0.008193 | 0.0803 | 0.468094 |
| 19 | -0.047776 | -0.4681 | 0.679615 |
| 20 | -0.067982 | -0.6661 | 0.746522 |
| 21 | 0.088254 | 0.8647 | 0.194676 |
| 22 | 0.047779 | 0.4681 | 0.320374 |
| 23 | 0.038382 | 0.3761 | 0.35385 |
| 24 | -0.075212 | -0.7369 | 0.768518 |
| 25 | -0.050167 | -0.4915 | 0.687914 |
| 26 | -0.06518 | -0.6386 | 0.737708 |
| 27 | 0.07163 | 0.7018 | 0.242243 |
| 28 | 0.115233 | 1.129 | 0.130846 |
| 29 | -0.00327 | -0.032 | 0.512745 |
| 30 | 0.002649 | 0.026 | 0.489673 |
| 31 | -0.008954 | -0.0877 | 0.534865 |
| 32 | 0.083891 | 0.822 | 0.206568 |
| 33 | -0.063984 | -0.6269 | 0.733899 |
| 34 | -0.157494 | -1.5431 | 0.936954 |
| 35 | -0.082535 | -0.8087 | 0.789648 |
| 36 | 0.058198 | 0.5702 | 0.284931 |
| 37 | 0.030604 | 0.2999 | 0.382467 |
| 38 | 0.111204 | 1.0896 | 0.139314 |
| 39 | -0.084643 | -0.8293 | 0.795511 |
| 40 | -0.027202 | -0.2665 | 0.604796 |
| 41 | -0.042184 | -0.4133 | 0.659853 |
| 42 | 0.031655 | 0.3102 | 0.378558 |
| 43 | -0.061843 | -0.6059 | 0.727006 |
| 44 | -0.079023 | -0.7743 | 0.779661 |
| 45 | 0.047475 | 0.4652 | 0.321436 |
| 46 | 0.063635 | 0.6235 | 0.267218 |
| 47 | 0.046129 | 0.452 | 0.326153 |
| 48 | -0.008287 | -0.0812 | 0.532274 |
| 49 | 0.025798 | 0.2528 | 0.400496 |
| 50 | -0.050929 | -0.499 | 0.690541 |
| 51 | -0.040246 | -0.3943 | 0.652894 |
| 52 | 0.032889 | 0.3222 | 0.373983 |
| 53 | 0.021295 | 0.2086 | 0.417583 |
| 54 | -0.063701 | -0.6241 | 0.73299 |
| 55 | 0.007148 | 0.07 | 0.472157 |
| 56 | 0.017134 | 0.1679 | 0.433515 |
| 57 | -0.025278 | -0.2477 | 0.597541 |
| 58 | -0.00931 | -0.0912 | 0.536246 |
| 59 | -0.100315 | -0.9829 | 0.835932 |
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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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