NC.diag {mev} | R Documentation |
Score and likelihood ratio tests fit of equality of shape over multiple thresholds
Description
The function returns a P-value path for the score testand/or likelihood ratio test for equality of the shape parameters over multiple thresholds under the generalized Pareto model.
Usage
NC.diag(
xdat,
u,
GP.fit = c("Grimshaw", "nlm", "optim", "ismev"),
do.LRT = FALSE,
size = NULL,
plot = TRUE,
...,
xi.tol = 0.001
)
Arguments
xdat |
numeric vector of raw data |
u |
|
GP.fit |
function used to optimize the generalized Pareto model. |
do.LRT |
boolean indicating whether to perform the likelihood ratio test (in addition to the score test) |
size |
level at which a horizontal line is drawn on multiple threshold plot |
plot |
logical; if |
... |
additional parameters passed to |
xi.tol |
numerical tolerance for threshold distance; if the absolute value of |
Details
The default method is 'Grimshaw'
using the reduction of the parameters to a one-dimensional
maximization. Other options are one-dimensional maximization of the profile the nlm
function or optim
.
Two-dimensional optimisation using 2D-optimization ismev
using the routine
from gpd.fit
from the ismev
library, with the addition of the algebraic gradient.
The choice of GP.fit
should make no difference but the options were kept.
Warning: the function will not recover from failure of the maximization routine, returning various error messages.
Value
a plot of P-values for the test at the different thresholds u
Author(s)
Paul J. Northrop and Claire L. Coleman
References
Grimshaw (1993). Computing Maximum Likelihood Estimates for the Generalized Pareto Distribution, Technometrics, 35(2), 185–191.
Northrop & Coleman (2014). Improved threshold diagnostic plots for extreme value analyses, Extremes, 17(2), 289–303.
Wadsworth & Tawn (2012). Likelihood-based procedures for threshold diagnostics and uncertainty in extreme value modelling, J. R. Statist. Soc. B, 74(3), 543–567.
Examples
## Not run:
data(nidd)
u <- seq(65,90, by = 1L)
NC.diag(nidd, u, size = 0.05)
## End(Not run)