ACF 5
| 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 | 8.1854 | 0 |
| 1 | -0.360598 | -2.9516 | 0.997823 |
| 2 | 0.092366 | 0.756 | 0.226136 |
| 3 | -0.118434 | -0.9694 | 0.832089 |
| 4 | -0.172742 | -1.414 | 0.918998 |
| 5 | 0.031362 | 0.2567 | 0.399096 |
| 6 | 0.065797 | 0.5386 | 0.295984 |
| 7 | -0.082065 | -0.6717 | 0.747968 |
| 8 | 0.084898 | 0.6949 | 0.244755 |
| 9 | 0.260866 | 2.1353 | 0.018199 |
| 10 | -0.163866 | -1.3413 | 0.907825 |
| 11 | 0.021101 | 0.1727 | 0.431697 |
| 12 | -0.295548 | -2.4192 | 0.990861 |
| 13 | -0.074553 | -0.6102 | 0.728117 |
| 14 | 0.037405 | 0.3062 | 0.380212 |
| 15 | 0.189092 | 1.5478 | 0.063192 |
| 16 | -0.054245 | -0.444 | 0.67077 |
| 17 | 0.037756 | 0.309 | 0.379121 |
| 18 | 0.103731 | 0.8491 | 0.199431 |
| 19 | -0.179667 | -1.4706 | 0.926965 |
| 20 | 0.084381 | 0.6907 | 0.246074 |
| 21 | -0.13427 | -1.099 | 0.862159 |
| 22 | -0.106957 | -0.8755 | 0.80778 |
| 23 | 0.22803 | 1.8665 | 0.033174 |
| 24 | 0.034924 | 0.2859 | 0.387932 |
| 25 | 0.095206 | 0.7793 | 0.219274 |
| 26 | 0.048592 | 0.3977 | 0.346041 |
| 27 | -0.14188 | -1.1613 | 0.875188 |
| 28 | -0.090572 | -0.7414 | 0.769468 |
| 29 | 0.111641 | 0.9138 | 0.182044 |
| 30 | -0.136765 | -1.1195 | 0.866531 |
| 31 | 0.111595 | 0.9134 | 0.182143 |
| 32 | -0.005612 | -0.0459 | 0.518252 |
| 33 | 0.105974 | 0.8674 | 0.1944 |
| 34 | -0.023598 | -0.1932 | 0.576291 |
| 35 | -0.001691 | -0.0138 | 0.5055 |
| 36 | -0.156038 | -1.2772 | 0.897035 |
| 37 | -0.111752 | -0.9147 | 0.818192 |
| 38 | 0.150529 | 1.2321 | 0.111104 |
| 39 | -0.102085 | -0.8356 | 0.796826 |
| 40 | 0.173019 | 1.4162 | 0.080672 |
| 41 | -0.039766 | -0.3255 | 0.627092 |
| 42 | -0.010089 | -0.0826 | 0.532784 |
| 43 | -0.000604 | -0.0049 | 0.501965 |
| 44 | -0.017487 | -0.1431 | 0.556695 |
| 45 | -0.107784 | -0.8822 | 0.809601 |
| 46 | 0.070236 | 0.5749 | 0.283639 |
| 47 | 0.039746 | 0.3253 | 0.37297 |
| 48 | -0.02549 | -0.2086 | 0.582319 |
| 49 | 0.184203 | 1.5078 | 0.068157 |
| 50 | -0.156518 | -1.2812 | 0.897723 |
| 51 | 0.081682 | 0.6686 | 0.253025 |
| 52 | -0.062491 | -0.5115 | 0.694661 |
| 53 | -0.023307 | -0.1908 | 0.575362 |
| 54 | 0.016116 | 0.1319 | 0.447723 |
| 55 | 0.007124 | 0.0583 | 0.476838 |
| 56 | -0.012329 | -0.1009 | 0.54004 |
| 57 | 0.040461 | 0.3312 | 0.370767 |
| 58 | -0.00764 | -0.0625 | 0.52484 |
| 59 | -0.017241 | -0.1411 | 0.555901 |
| Partial Autocorrelation Function | |||
| Time lag k | PACF(k) | T-STAT | P-value |
| 0 | -0.360598 | -2.9516 | 0.997823 |
| 1 | -0.043295 | -0.3544 | 0.637918 |
| 2 | -0.114353 | -0.936 | 0.823687 |
| 3 | -0.29306 | -2.3988 | 0.990381 |
| 4 | -0.176523 | -1.4449 | 0.923428 |
| 5 | -0.006165 | -0.0505 | 0.520048 |
| 6 | -0.155925 | -1.2763 | 0.896872 |
| 7 | -0.10147 | -0.8306 | 0.795417 |
| 8 | 0.34386 | 2.8146 | 0.003203 |
| 9 | 0.108826 | 0.8908 | 0.188119 |
| 10 | -0.055488 | -0.4542 | 0.67442 |
| 11 | -0.276281 | -2.2615 | 0.986509 |
| 12 | -0.256777 | -2.1018 | 0.980334 |
| 13 | -0.272284 | -2.2287 | 0.985405 |
| 14 | -0.057094 | -0.4673 | 0.679111 |
| 15 | -0.121541 | -0.9949 | 0.838307 |
| 16 | -0.245748 | -2.0115 | 0.975853 |
| 17 | 0.006506 | 0.0533 | 0.478844 |
| 18 | -0.080183 | -0.6563 | 0.743068 |
| 19 | -0.036584 | -0.2994 | 0.617238 |
| 20 | 0.089821 | 0.7352 | 0.232387 |
| 21 | -0.095197 | -0.7792 | 0.780703 |
| 22 | -0.029867 | -0.2445 | 0.596193 |
| 23 | -0.139531 | -1.1421 | 0.871263 |
| 24 | -0.03793 | -0.3105 | 0.621415 |
| 25 | 0.026039 | 0.2131 | 0.415934 |
| 26 | -0.038932 | -0.3187 | 0.624517 |
| 27 | -0.144401 | -1.182 | 0.879303 |
| 28 | -0.027082 | -0.2217 | 0.58738 |
| 29 | -0.020296 | -0.1661 | 0.565721 |
| 30 | 0.030104 | 0.2464 | 0.403058 |
| 31 | -0.080734 | -0.6608 | 0.744509 |
| 32 | 0.084818 | 0.6943 | 0.244958 |
| 33 | -0.03917 | -0.3206 | 0.625252 |
| 34 | -0.027146 | -0.2222 | 0.587583 |
| 35 | -0.026592 | -0.2177 | 0.585824 |
| 36 | -0.095104 | -0.7785 | 0.780482 |
| 37 | 0.022522 | 0.1843 | 0.427149 |
| 38 | -0.103873 | -0.8502 | 0.800888 |
| 39 | -0.052221 | -0.4274 | 0.664787 |
| 40 | -0.04981 | -0.4077 | 0.657607 |
| 41 | -0.007075 | -0.0579 | 0.523005 |
| 42 | -0.004417 | -0.0362 | 0.514366 |
| 43 | -0.083952 | -0.6872 | 0.752829 |
| 44 | 0.035956 | 0.2943 | 0.384715 |
| 45 | 0.053469 | 0.4377 | 0.331518 |
| 46 | 0.070115 | 0.5739 | 0.283973 |
| 47 | -0.089511 | -0.7327 | 0.766845 |
| 48 | -0.027101 | -0.2218 | 0.587439 |
| 49 | -0.077686 | -0.6359 | 0.736493 |
| 50 | -0.028948 | -0.237 | 0.593292 |
| 51 | 0.007144 | 0.0585 | 0.476773 |
| 52 | -0.013378 | -0.1095 | 0.543435 |
| 53 | 0.042535 | 0.3482 | 0.364403 |
| 54 | 0.012124 | 0.0992 | 0.460623 |
| 55 | -0.103891 | -0.8504 | 0.80093 |
| 56 | 0.054726 | 0.448 | 0.327816 |
| 57 | -0.072008 | -0.5894 | 0.721217 |
| 58 | -0.001035 | -0.0085 | 0.503367 |
| 59 | -0.046571 | -0.3812 | 0.647871 |
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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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