qStat(p, n,
type = c("Greenwood", "Jackson", "logLRGPD", "logLRLomax",
"logLRGEV", "logLRFrechet"),
outNorm = FALSE)p < 0.01)
or very large ones (p > 0.99) will be truncated as
0.00 or 1.00 to maintain a realistic level of
precision.TRUE the output is normalized in a such fashion
that its distribution is the asymptotic one (i.e. standard normal in
practice). When FALSE, the quantiles are given in the true
scale of the statistic: $\tex"Greenwood", the statistic isGreenwood's
statistic. The distribution is that of the squared coefficient of
variation$\textrm{CV}^2$of a sample of sizenfrom the exponential distribution as computed byCV2."Jackson", the statistic is Jackson's
statistic, seeJackson."logLRGPD"and"logLRLomax", the
statistic is the log of the likelihood ratio of a sample
from the exponential distribution. The log-likelihoods are
for an exponential distribution compared to a GPD with
non-zero shape, or to a GPD withpositive shape(equivalently, a Lomax distribution)."logLRGEV"and"logLRFrechet", the
statistic is the log of the likelihood ratio of a sample
from the Gumbel distribution. The log-likelihoods are for a
Gumbel distribution compared to a GEV with non-zero shape,
or to a GEV withpositive shape(equivalently, a
Fréchet distribution).2, so that they
compare to a chi-square statistic with one degree of freedom.res <- qStat(n = 40, type = "Greenwood")
plot(res$q, res$p, type = "o")Run the code above in your browser using DataLab