glod.fit: Maximum Likelihood Fitting for the GLO distribution

In ilapros/ilaprosUtils: A Miscellaneous Collection of Functions I Tend to Use

Maximum Likelihood Fitting for the GLO distribution

Description

Maximum-likelihood fitting for the generalized logistic distribution, including generalized linear modelling of each parameter. This function has the same structure as the gevd.fit and is inspired by ismev::gev.fit. The function allows any parameter to be kept fixed and to not be estimated.

Usage

glod.fit(
  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",
  optimPars = NULL,
  maxit = 10000,
  fixedPars = list(mu = NULL, sig = NULL, sh = NULL),
  ...
)

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 parameter (or NULL (the default) if the corresponding parameter is stationary)
sigl	As mul for the scale parameter
shl	As mul for the shape parameter
mulink	the link function for the location parameter - default to identity
siglink	the link function for the scale parameter - default to identity
shlink	the link function for the shape parameter - default to identity
muinit	initial values for the location parameter
siginit	initial values for the scale parameter
shinit	initial values for the shape parameter
show	Logical; if TRUE (the default), print details of the fit.
method	The optimization method (see optim for details).
optimPars	A string with other parameters to pass into optim. For example, depending on method, one could have "lower = 10, upper = 20"
maxit	The maximum number of iterations
fixedPars	a named list to fix any of the distribution parameter to a given value. When the named parameter is set to NULL its value is estimated.
...	Other control parameters for the optimization. These are passed to components of the control argument of optim.

Details

The distribution is discussed in the Hosking and Wallis book and is used as the default distribution for flood frequency estimation in the UK

Value

An object of the glo.fit class - with values which mirror the ones of the gev.fit class in ismev

References

Hosking, J.R.M. and Wallis, J.R., 2005. Regional frequency analysis: an approach based on L-moments. Cambridge university press.

Coles, S., 2001. An introduction to statistical modeling of extreme values. London: Springer.

See Also

dglo

Examples

set.seed(12)
x <- runif(500)
y <- rglo(500,loc = 40+4*x,scale = 6,sh = 0.2)
fit1 <- glod.fit(y, show=FALSE)
fit1
## now add a regression model for the location
fit2 <- glod.fit(y, ydat = cbind(x), mul=1, show=FALSE)
fit2
## now a fit with a fixed shape parameter 
fitf <- glod.fit(y, show=FALSE, fixedPars = list(sh = 0.2))
fitf ## only two parameters are estimated 

ilapros/ilaprosUtils documentation built on April 6, 2023, 4:44 a.m.