tcplot(data, tlim, model = c("gpd","pp"), 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(), ...)
"gpd"
(the default) or
"pp"
, for the Generalized Pareto or Point Process
representations respectively.FALSE
(the default), the models
are fitted using all exceedences over the thresholds. If
TRUE
, the models are fitted using cluster maxima, using
clusters of exceedences derived from clusters
.clusters
).
Ignored if cmax
is FALSE
(the default).1:npar
, where npar
is
the number of parameters, so that npar = 2
when
model = "gpd"
(the default) and npar = 3
"b"
(the default) for points joined by
lines, "o"
for overplotted points and lines, and
TRUE
, the user is asked before
each plot.fpot
.nt
thresholds a peaks over threshold model
is fitted using the function fpot
. When model = "gpd"
(the default), 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.
When model = "pp"
the maximum likelihood estimates for the
location, scale and shape parameters are plotted against the
thresholds. (The modified scale parameter in the "gpd"
case
is equivalent to the scale parameter in the "pp"
case.)
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
.fpot
, mrlplot
,
clusters
data(portpirie)
tlim <- c(3.6, 4.2)
tcplot(portpirie, tlim)
tcplot(portpirie, tlim, nt = 100, lwd = 3, type = "l")
tcplot(portpirie, tlim, model = "pp")
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