partial autocorrelatio,
| 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 | 8.4853 | 0 |
| 1 | 0.722331 | 6.1292 | 0 |
| 2 | 0.585763 | 4.9704 | 2e-06 |
| 3 | 0.513133 | 4.3541 | 2.2e-05 |
| 4 | 0.413731 | 3.5106 | 0.000388 |
| 5 | 0.418788 | 3.5535 | 0.000338 |
| 6 | 0.457715 | 3.8838 | 0.000113 |
| 7 | 0.344332 | 2.9218 | 0.002323 |
| 8 | 0.323384 | 2.744 | 0.003828 |
| 9 | 0.271331 | 2.3023 | 0.012106 |
| 10 | 0.19074 | 1.6185 | 0.054966 |
| 11 | 0.128001 | 1.0861 | 0.140524 |
| 12 | 0.018927 | 0.1606 | 0.436428 |
| 13 | 0.036748 | 0.3118 | 0.378042 |
| 14 | 0.04972 | 0.4219 | 0.337182 |
| 15 | -0.02521 | -0.2139 | 0.584389 |
| 16 | -0.055801 | -0.4735 | 0.68135 |
| 17 | -0.138126 | -1.172 | 0.877477 |
| 18 | -0.19268 | -1.6349 | 0.946787 |
| 19 | -0.204676 | -1.7367 | 0.956645 |
| 20 | -0.229669 | -1.9488 | 0.972392 |
| 21 | -0.265826 | -2.2556 | 0.986433 |
| 22 | -0.307819 | -2.6119 | 0.994525 |
| 23 | -0.339369 | -2.8796 | 0.99738 |
| 24 | -0.394118 | -3.3442 | 0.999344 |
| 25 | -0.402784 | -3.4177 | 0.999479 |
| 26 | -0.377565 | -3.2037 | 0.998989 |
| 27 | -0.312331 | -2.6502 | 0.995058 |
| 28 | -0.284019 | -2.41 | 0.990745 |
| 29 | -0.252384 | -2.1415 | 0.982193 |
| 30 | -0.273969 | -2.3247 | 0.988544 |
| 31 | -0.310091 | -2.6312 | 0.9948 |
| 32 | -0.321485 | -2.7279 | 0.995999 |
| 33 | -0.298502 | -2.5329 | 0.993254 |
| 34 | -0.250307 | -2.1239 | 0.981444 |
| 35 | -0.252802 | -2.1451 | 0.98234 |
| 36 | -0.19083 | -1.6193 | 0.945117 |
| 37 | -0.210388 | -1.7852 | 0.96078 |
| 38 | -0.212628 | -1.8042 | 0.96231 |
| 39 | -0.172586 | -1.4644 | 0.926286 |
| 40 | -0.14094 | -1.1959 | 0.882174 |
| 41 | -0.126092 | -1.0699 | 0.855887 |
| 42 | -0.071259 | -0.6046 | 0.726343 |
| 43 | -0.044691 | -0.3792 | 0.647178 |
| 44 | 0.00325 | 0.0276 | 0.489039 |
| 45 | 0.024938 | 0.2116 | 0.416507 |
| 46 | 0.010426 | 0.0885 | 0.464876 |
| 47 | 0.059595 | 0.5057 | 0.307314 |
| Partial Autocorrelation Function | |||
| Time lag k | PACF(k) | T-STAT | P-value |
| 0 | 0.722331 | 6.1292 | 0 |
| 1 | 0.133826 | 1.1356 | 0.129955 |
| 2 | 0.106405 | 0.9029 | 0.1848 |
| 3 | -0.035027 | -0.2972 | 0.61642 |
| 4 | 0.170316 | 1.4452 | 0.076373 |
| 5 | 0.17135 | 1.454 | 0.075152 |
| 6 | -0.186868 | -1.5856 | 0.941397 |
| 7 | 0.060449 | 0.5129 | 0.304787 |
| 8 | -0.046516 | -0.3947 | 0.652884 |
| 9 | -0.052741 | -0.4475 | 0.672079 |
| 10 | -0.129558 | -1.0993 | 0.862359 |
| 11 | -0.203268 | -1.7248 | 0.955572 |
| 12 | 0.210433 | 1.7856 | 0.039189 |
| 13 | -0.014938 | -0.1267 | 0.550254 |
| 14 | -0.158771 | -1.3472 | 0.908932 |
| 15 | -0.034866 | -0.2958 | 0.615901 |
| 16 | -0.090916 | -0.7715 | 0.778518 |
| 17 | 0.07417 | 0.6294 | 0.265554 |
| 18 | -0.171355 | -1.454 | 0.924853 |
| 19 | -0.021556 | -0.1829 | 0.572307 |
| 20 | 0.004772 | 0.0405 | 0.483906 |
| 21 | -0.159092 | -1.3499 | 0.909368 |
| 22 | 0.000734 | 0.0062 | 0.497523 |
| 23 | -0.244126 | -2.0715 | 0.979051 |
| 24 | 0.139027 | 1.1797 | 0.121005 |
| 25 | 0.06658 | 0.565 | 0.286931 |
| 26 | 0.030329 | 0.2574 | 0.39882 |
| 27 | 0.072791 | 0.6177 | 0.269376 |
| 28 | -0.074341 | -0.6308 | 0.734916 |
| 29 | 0.089347 | 0.7581 | 0.225422 |
| 30 | -0.180398 | -1.5307 | 0.934891 |
| 31 | -0.08975 | -0.7616 | 0.775593 |
| 32 | 0.067839 | 0.5756 | 0.283328 |
| 33 | -0.064655 | -0.5486 | 0.707517 |
| 34 | -0.080833 | -0.6859 | 0.752508 |
| 35 | -0.0187 | -0.1587 | 0.562815 |
| 36 | -0.099806 | -0.8469 | 0.800066 |
| 37 | 0.120574 | 1.0231 | 0.154842 |
| 38 | -0.057129 | -0.4848 | 0.685339 |
| 39 | 0.05396 | 0.4579 | 0.324214 |
| 40 | -0.015442 | -0.131 | 0.551942 |
| 41 | -0.063948 | -0.5426 | 0.705465 |
| 42 | 0.08949 | 0.7593 | 0.225062 |
| 43 | -0.054591 | -0.4632 | 0.677697 |
| 44 | 0.002317 | 0.0197 | 0.492184 |
| 45 | -0.06453 | -0.5476 | 0.707154 |
| 46 | 0.029161 | 0.2474 | 0.402635 |
| 47 | 0.00981 | 0.0832 | 0.466946 |
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| http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/28/t1196275210mimpxqzygnn9lrq/20fka1196275825.png (open in new window) |
| http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Nov/28/t1196275210mimpxqzygnn9lrq/20fka1196275825.ps (open in new window) |
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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