Package 'extRemes' - What are R and CRAN?

12 ci.fevd
Value
likelihood, and smaller values curiously tend to be better, but not too small! Smaller values are also
more efficient). Further, one should really look at the plot of the profile-likelihood to make sure that
this is the case, and that resulting CIs are accurately estimated (perhaps using the locator function
to be sure). Nevertheless, an attempt is made to find the limits automatically. To look at the plot
along with the horizontal line, m - q, and vertical lines through the MLE (thin black dashed) and the
CIs (thick dashed blue), use the verbose = TRUE argument in the call to ci. This is not an explicit
argument, but available nonetheless (see examples below).
See any text on EVA/EVT for more details (e.g., Coles 2001; Beirlant et al 2004; de Haan and
Ferreira 2006).
Either a numeric vector of length 3 (if only one parameter/return level is used) or a matrix. In either
case, they will have class “ci”.
Author(s)
Eric Gilleland
References
Beirlant, J., Goegebeur, Y., Teugels, J. and Segers, J. (2004). Statistics of Extremes: Theory and
Applications. Chichester, West Sussex, England, UK: Wiley, ISBN 9780471976479, 522pp.
Coles, S. (2001). An introduction to statistical modeling of extreme values, London: Springer-
Verlag.
de Haan, L. and Ferreira, A. (2006). Extreme Value Theory: An Introduction. New York, NY, USA:
Springer, 288pp.
See Also
fevd, ci.rl.ns.fevd.bayesian, ci
Examples
data(Fort)
fit