plot.mcpot {POT} | R Documentation |
Plot several graphics to judge goodness of fit of the fitted model.
## S3 method for class 'mcpot': plot(x, opy, exi, mains, which = 1:4, ask = nb.fig < length(which) && dev.interactive(), acf.type = "partial", ...)
x |
An object of class "bvpot" . Most often, the object
returned by the fitbvgpd function. |
opy |
Numeric. The number of Observation Per Year (or more generally per block). If missing, the function warns and set it to 365. |
exi |
Numeric. The extremal index value. If missing, the estimator of Ferro and Segers (2003) is used. |
mains |
May be missing. If present a 4–vector of character strings which gives the titles of the plots. |
which |
a numeric vector which specifies which plot must be
drawn: '1' for the auto correlation plot, '2' for
Pickands' Dependence Function plot, '3' for the spectral
density plot and '4' for a bivariate return level plot. |
ask |
Logical. If TRUE , user is asked before each plot. |
acf.type |
The type of auto correlation to be plotted. Must be
one of "correlation" , "covariance" or "partial"
(the default). See the acf function. |
... |
Other parameters to pass to the plot
function. |
Several plots and returns invisibly the return level function.
See the warning for the return level estimation in documentation of
the retlev.mcpot
function.
For the return level plot, the observations are not plotted as these
are dependent realisations. In particular, the return periods computed
using the prob2rp
are inaccurate.
Mathieu Ribatet
Ferro, C. and Segers, J. (2003). Inference for clusters of extreme values. Journal of the Royal Statistical Society B. 65: 545–556.
mc <- simmc(200, alpha = 0.5) mc <- qgpd(mc, 0, 1, 0.25) Mclog <- fitmcgpd(mc, 1) par(mfrow=c(2,2)) rlMclog <- plot(Mclog) rlMclog(T = 3)