ACF 1
| 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 | 8.9443 | 0 |
| 1 | 0.394179 | 3.5256 | 0.000351 |
| 2 | 0.184643 | 1.6515 | 0.051278 |
| 3 | 0.043425 | 0.3884 | 0.349372 |
| 4 | -0.139185 | -1.2449 | 0.8916 |
| 5 | -0.213576 | -1.9103 | 0.97016 |
| 6 | -0.336436 | -3.0092 | 0.998249 |
| 7 | -0.23692 | -2.1191 | 0.981407 |
| 8 | -0.127127 | -1.1371 | 0.870546 |
| 9 | 0.002778 | 0.0248 | 0.49012 |
| 10 | 0.081554 | 0.7294 | 0.233932 |
| 11 | 0.265303 | 2.3729 | 0.010023 |
| 12 | 0.723962 | 6.4753 | 0 |
| 13 | 0.285594 | 2.5544 | 0.006267 |
| 14 | 0.137223 | 1.2274 | 0.111644 |
| 15 | 0.004837 | 0.0433 | 0.482801 |
| 16 | -0.156144 | -1.3966 | 0.9168 |
| 17 | -0.208298 | -1.8631 | 0.966939 |
| 18 | -0.327506 | -2.9293 | 0.997788 |
| 19 | -0.232428 | -2.0789 | 0.979585 |
| 20 | -0.145817 | -1.3042 | 0.902051 |
| 21 | -0.051392 | -0.4597 | 0.676498 |
| 22 | 0.022496 | 0.2012 | 0.420522 |
| 23 | 0.199747 | 1.7866 | 0.038896 |
| 24 | 0.547191 | 4.8942 | 3e-06 |
| 25 | 0.219318 | 1.9616 | 0.026642 |
| 26 | 0.092029 | 0.8231 | 0.20644 |
| 27 | -0.028065 | -0.251 | 0.598779 |
| 28 | -0.149765 | -1.3395 | 0.907906 |
| 29 | -0.172858 | -1.5461 | 0.936985 |
| 30 | -0.28241 | -2.526 | 0.993244 |
| 31 | -0.200346 | -1.7919 | 0.961539 |
| 32 | -0.13356 | -1.1946 | 0.882113 |
| 33 | -0.063218 | -0.5654 | 0.71332 |
| 34 | 0.016926 | 0.1514 | 0.440025 |
| 35 | 0.151041 | 1.351 | 0.09026 |
| 36 | 0.416101 | 3.7217 | 0.000183 |
| 37 | 0.165969 | 1.4845 | 0.070807 |
| 38 | 0.073625 | 0.6585 | 0.256047 |
| 39 | -0.006678 | -0.0597 | 0.523742 |
| 40 | -0.122591 | -1.0965 | 0.861923 |
| 41 | -0.172333 | -1.5414 | 0.936416 |
| 42 | -0.247262 | -2.2116 | 0.985075 |
| 43 | -0.170905 | -1.5286 | 0.934848 |
| 44 | -0.101441 | -0.9073 | 0.816518 |
| 45 | -0.068506 | -0.6127 | 0.729105 |
| 46 | -0.007592 | -0.0679 | 0.526985 |
| 47 | 0.077805 | 0.6959 | 0.24425 |
| 48 | 0.320805 | 2.8694 | 0.002629 |
| 49 | 0.109597 | 0.9803 | 0.164956 |
| 50 | 0.036965 | 0.3306 | 0.370897 |
| 51 | -0.02163 | -0.1935 | 0.576457 |
| 52 | -0.091972 | -0.8226 | 0.793415 |
| 53 | -0.143938 | -1.2874 | 0.899171 |
| 54 | -0.20914 | -1.8706 | 0.967472 |
| 55 | -0.162468 | -1.4532 | 0.924954 |
| 56 | -0.099637 | -0.8912 | 0.812247 |
| 57 | -0.067307 | -0.602 | 0.725565 |
| 58 | -0.028434 | -0.2543 | 0.600051 |
| 59 | 0.033523 | 0.2998 | 0.382537 |
| Partial Autocorrelation Function | |||
| Time lag k | PACF(k) | T-STAT | P-value |
| 0 | 0.394179 | 3.5256 | 0.000351 |
| 1 | 0.03465 | 0.3099 | 0.378714 |
| 2 | -0.048 | -0.4293 | 0.66558 |
| 3 | -0.174216 | -1.5582 | 0.938437 |
| 4 | -0.121309 | -1.085 | 0.859413 |
| 5 | -0.225928 | -2.0208 | 0.976675 |
| 6 | -0.018068 | -0.1616 | 0.563987 |
| 7 | 0.000897 | 0.008 | 0.496808 |
| 8 | 0.058392 | 0.5223 | 0.301463 |
| 9 | 0.00116 | 0.0104 | 0.495872 |
| 10 | 0.192091 | 1.7181 | 0.044822 |
| 11 | 0.658944 | 5.8938 | 0 |
| 12 | -0.317164 | -2.8368 | 0.997115 |
| 13 | -0.082178 | -0.735 | 0.767762 |
| 14 | -0.026481 | -0.2369 | 0.593313 |
| 15 | 0.008787 | 0.0786 | 0.468775 |
| 16 | -0.044241 | -0.3957 | 0.653312 |
| 17 | 0.000959 | 0.0086 | 0.496587 |
| 18 | 0.014473 | 0.1295 | 0.448663 |
| 19 | -0.112015 | -1.0019 | 0.840292 |
| 20 | -0.039497 | -0.3533 | 0.637592 |
| 21 | 0.05438 | 0.4864 | 0.314011 |
| 22 | 0.049258 | 0.4406 | 0.330355 |
| 23 | -0.021106 | -0.1888 | 0.574629 |
| 24 | -0.039352 | -0.352 | 0.637109 |
| 25 | -0.06417 | -0.574 | 0.716196 |
| 26 | -0.029441 | -0.2633 | 0.603512 |
| 27 | 0.033989 | 0.304 | 0.380957 |
| 28 | 0.057752 | 0.5166 | 0.303447 |
| 29 | -0.01132 | -0.1012 | 0.540197 |
| 30 | -0.044009 | -0.3936 | 0.65255 |
| 31 | -0.018208 | -0.1629 | 0.564478 |
| 32 | 0.011663 | 0.1043 | 0.45859 |
| 33 | 0.054481 | 0.4873 | 0.313691 |
| 34 | -0.045903 | -0.4106 | 0.658757 |
| 35 | -0.021715 | -0.1942 | 0.576753 |
| 36 | -0.049015 | -0.4384 | 0.668863 |
| 37 | 0.026867 | 0.2403 | 0.405352 |
| 38 | 0.081014 | 0.7246 | 0.235403 |
| 39 | -0.024527 | -0.2194 | 0.586541 |
| 40 | -0.132895 | -1.1886 | 0.880952 |
| 41 | 0.007141 | 0.0639 | 0.474616 |
| 42 | 0.026213 | 0.2345 | 0.407616 |
| 43 | 0.054573 | 0.4881 | 0.313401 |
| 44 | -0.046505 | -0.416 | 0.66072 |
| 45 | -0.061429 | -0.5494 | 0.707883 |
| 46 | -0.124557 | -1.1141 | 0.865709 |
| 47 | 0.067565 | 0.6043 | 0.27367 |
| 48 | 0.001435 | 0.0128 | 0.494897 |
| 49 | -0.007932 | -0.0709 | 0.528192 |
| 50 | -0.086069 | -0.7698 | 0.778164 |
| 51 | 0.035395 | 0.3166 | 0.376193 |
| 52 | -0.019393 | -0.1735 | 0.568634 |
| 53 | -0.012716 | -0.1137 | 0.545132 |
| 54 | -0.02928 | -0.2619 | 0.60296 |
| 55 | -0.030542 | -0.2732 | 0.607288 |
| 56 | -0.020867 | -0.1866 | 0.573794 |
| 57 | -0.045494 | -0.4069 | 0.657419 |
| 58 | 0.02015 | 0.1802 | 0.428716 |
| 59 | -0.108009 | -0.9661 | 0.831538 |
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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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