lax (version 1.1.0)

ismev_refits: Maximum-likelihood (Re-)Fitting using the ismev package

Description

These are a slightly modified versions of the gev.fit, gpd.fit, pp.fit and rlarg.fit functions in the ismev package. The modification is to add to the returned object regression design matrices for the parameters of the model. That is, xdat, ydat, mulink, siglink, shlink and matrices mumat, sigmat, shmat for the location, scale and shape parameters gev.fit, pp.fit and rlarg.fit, and xdat, ydat, siglink, shlink and matrices sigmat, shmat for the scale and shape parameters for gpd.fit.

Usage

gev_refit(
  xdat,
  ydat = NULL,
  mul = NULL,
  sigl = NULL,
  shl = NULL,
  mulink = identity,
  siglink = identity,
  shlink = identity,
  muinit = NULL,
  siginit = NULL,
  shinit = NULL,
  show = TRUE,
  method = "Nelder-Mead",
  maxit = 10000,
  ...
)
gpd_refit(
  xdat,
  threshold,
  npy = 365,
  ydat = NULL,
  sigl = NULL,
  shl = NULL,
  siglink = identity,
  shlink = identity,
  siginit = NULL,
  shinit = NULL,
  show = TRUE,
  method = "Nelder-Mead",
  maxit = 10000,
  ...
)
pp_refit(
  xdat,
  threshold,
  npy = 365,
  ydat = NULL,
  mul = NULL,
  sigl = NULL,
  shl = NULL,
  mulink = identity,
  siglink = identity,
  shlink = identity,
  muinit = NULL,
  siginit = NULL,
  shinit = NULL,
  show = TRUE,
  method = "Nelder-Mead",
  maxit = 10000,
  ...
)
rlarg_refit(
  xdat,
  r = dim(xdat)[2],
  ydat = NULL,
  mul = NULL,
  sigl = NULL,
  shl = NULL,
  mulink = identity,
  siglink = identity,
  shlink = identity,
  muinit = NULL,
  siginit = NULL,
  shinit = NULL,
  show = TRUE,
  method = "Nelder-Mead",
  maxit = 10000,
  ...
)

Arguments

xdat

A numeric vector of data to be fitted.

ydat

A matrix of covariates for generalized linear modelling of the parameters (or NULL (the default) for stationary fitting). The number of rows should be the same as the length of xdat.

mul

Numeric vectors of integers, giving the columns of ydat that contain covariates for generalized linear modelling of the location, scale and shape parameters repectively (or NULL (the default) if the corresponding parameter is stationary).

sigl

Numeric vectors of integers, giving the columns of ydat that contain covariates for generalized linear modelling of the location, scale and shape parameters repectively (or NULL (the default) if the corresponding parameter is stationary).

shl

Numeric vectors of integers, giving the columns of ydat that contain covariates for generalized linear modelling of the location, scale and shape parameters repectively (or NULL (the default) if the corresponding parameter is stationary).

mulink

Inverse link functions for generalized linear modelling of the location, scale and shape parameters repectively.

siglink

Inverse link functions for generalized linear modelling of the location, scale and shape parameters repectively.

shlink

Inverse link functions for generalized linear modelling of the location, scale and shape parameters repectively.

muinit

numeric of length equal to total number of parameters used to model the location, scale or shape parameter(s), resp. See Details section for default (NULL) initial values.

siginit

numeric of length equal to total number of parameters used to model the location, scale or shape parameter(s), resp. See Details section for default (NULL) initial values.

shinit

numeric of length equal to total number of parameters used to model the location, scale or shape parameter(s), resp. See Details section for default (NULL) initial values.

show

Logical; if TRUE (the default), print details of the fit.

method

The optimization method (see optim for details).

maxit

The maximum number of iterations.

...

Other control parameters for the optimization. These are passed to components of the control argument of optim.

threshold

The threshold; a single number or a numeric vector of the same length as xdat.

npy

The number of observations per year/block.

r

The largest r order statistics are used for the fitted model.

References

Heffernan, J. E. and Stephenson, A. G. (2018). ismev: An Introduction to Statistical Modeling of Extreme Values. R package version 1.42. https://CRAN.R-project.org/package=ismev.

Examples

# NOT RUN {
# We need the ismev package
got_ismev <- requireNamespace("ismev", quietly = TRUE)
if (got_ismev) {
  library(ismev)
  fit1 <- gev.fit(revdbayes::portpirie, show = FALSE)
  ls(fit1)
  fit2 <- gev_refit(revdbayes::portpirie, show = FALSE)
  ls(fit2)

  data(rain)
  fit1 <- gpd.fit(rain, 10)
  ls(fit1)
  fit2 <- gpd_refit(rain, 10)
  ls(fit2)

  fit1 <- pp.fit(rain, 10, show = FALSE)
  ls(fit1)
  fit2 <- pp_refit(rain, 10, show = FALSE)
  ls(fit2)

  data(venice)
  fit1 <- rlarg.fit(venice[, -1], muinit = 120.54, siginit = 12.78,
                   shinit = -0.1129, show = FALSE)
  ls(fit1)
  fit2 <- rlarg_refit(venice[, -1], muinit = 120.54, siginit = 12.78,
                   shinit = -0.1129, show = FALSE)
  ls(fit2)
}
# }