fit.rlarg: Maximum likelihood estimates of point process for the...

In mev: Modelling of Extreme Values

Maximum likelihood estimates of point process for the r-largest observations

Description

This uses a constrained optimization routine to return the maximum likelihood estimate based on an n by r matrix of observations. Observations should be ordered, i.e., the r-largest should be in the last column.

Usage

fit.rlarg(
  xdat,
  start = NULL,
  method = c("nlminb", "BFGS"),
  show = FALSE,
  fpar = NULL,
  warnSE = FALSE
)

Arguments

xdat	a numeric vector of data to be fitted.
start	named list of starting values
method	the method to be used. See Details. Can be abbreviated.
show	logical; if TRUE (the default), print details of the fit.
fpar	a named list with fixed parameters, either scale or shape
warnSE	logical; if TRUE, a warning is printed if the standard errors cannot be returned from the observed information matrix when the shape is less than -0.5.

Value

a list containing the following components:

• estimate a vector containing all the maximum likelihood estimates.

• std.err a vector containing the standard errors.

• vcov the variance covariance matrix, obtained as the numerical inverse of the observed information matrix.

• method the method used to fit the parameter.

• nllh the negative log-likelihood evaluated at the parameter estimate.

• convergence components taken from the list returned by auglag. Values other than 0 indicate that the algorithm likely did not converge.

• counts components taken from the list returned by auglag.

• xdat an n by r matrix of data

Examples

xdat <- rrlarg(n = 10, loc = 0, scale = 1, shape = 0.1, r = 4)
fit.rlarg(xdat)

mev documentation built on Sept. 11, 2024, 8:14 p.m.