tcplot | R Documentation |
Plots of parameter estimates at various thresholds for peaks over threshold modelling, using the Generalized Pareto or Point Process representation.
tcplot(data, u.range, cmax = FALSE, r = 1,
ulow = -Inf, rlow = 1, nt = 25, which = 1:npar, conf = 0.95,
lty = 1, lwd = 1, type = "b", cilty = 1, ask = nb.fig <
length(which) && dev.interactive(), ...)
data |
A numeric vector. |
u.range |
A numeric vector of length two, giving the limits for the thresholds at which the model is fitted. |
cmax |
Logical; if |
r , ulow , rlow |
Arguments used for the identification of clusters
of exceedances. Ignored if |
nt |
The number of thresholds at which the model is fitted. |
which |
If a subset of the plots is required, specify a
subset of the numbers |
conf |
The (pointwise) confidence coefficient for the plotted confidence intervals. Use zero to suppress. |
lty , lwd |
The line type and width of the line connecting the parameter estimates. |
type |
The form taken by the line connecting the parameter
estimates and the points denoting these estimates. Possible
values include |
cilty |
The line type of the lines depicting the confidence intervals. |
ask |
Logical; if |
... |
Other arguments to be passed to the model fit
function |
For each of the nt
thresholds a peaks over threshold model
is fitted using the function fitgpd
. The maximum likelihood
estimates for the shape and the modified scale parameter (modified by
subtracting the shape multiplied by the threshold) are plotted against
the thresholds. If the threshold u
is a valid threshold to be
used for peaks over threshold modelling, the parameter estimates
depicted should be approximately constant above u
.
A list is invisibly returned. Each component is a matrix with three columns giving parameter estimates and confidence limits.
Stuart Coles and Alec Stephenson
Coles, S. (2001) An Introduction to Statistical Modelling of Extreme Values. Springer Series in Statistics. London.
fitgpd
, mrlplot
data(ardieres)
ardieres <- clust(ardieres, 4, 10 / 365, clust.max = TRUE)
flows <- ardieres[, "obs"]
par(mfrow=c(1,2))
tcplot(flows, u.range = c(0, 15) )
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