ismev (version 1.3)

gum.fit: Maximum-likelihood Fitting of the Gumbel Distribution

Description

Maximum-likelihood fitting for the gumbel distribution, including generalized linear modelling of each parameter.

Usage

gum.fit(xdat, ydat = NULL, mul = NULL, sigl = NULL, mulink = identity,
    siglink = identity, muinit = NULL, siginit = 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, sigl

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

mulink, siglink

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

muinit, siginit

numeric giving initial parameter estimates. See Details section for information about default values (NULL).

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.

Value

• A list containing the following components. A subset of these components are printed after the fit. If show is TRUE, then assuming that successful convergence is indicated, the components nllh, mle and se are always printed.
• trans
  An logical indicator for a non-stationary fit.
• model
  A list with components mul and sigl.
• link
  A character vector giving inverse link functions.
• conv
  The convergence code, taken from the list returned by optim. A zero indicates successful convergence.
• nllh
  The negative logarithm of the likelihood evaluated at the maximum likelihood estimates.
• data
  The data that has been fitted. For non-stationary models, the data is standardized.
• mle
  A vector containing the maximum likelihood estimates.
• cov
  The covariance matrix.
• se
  A vector containing the standard errors.
• vals
  A matrix with two columns containing the maximum likelihood estimates of the location and scale parameters at each data point.

Details

For non-stationary fitting it is recommended that the covariates within the generalized linear models are (at least approximately) centered and scaled (i.e. the columns of ydat should be approximately centered and scaled).

Let m=mean(xdat) and s=sqrt(6*var(xdat))/pi. Then, initial values assigend when 'muinit' is NULL are m - 0.57722 * s (stationary case). When 'siginit' is NULL, the initial value is taken to be s, and when 'shinit' is NULL. When covariates are introduced (non-stationary case), these same initial values are used by default for the constant term, and zeros for all other terms. For example, if a Gumbel( mu(t)=mu0+mu1*t, sigma) is being fitted, then the initial value for mu0 is m - 0.57722 * s, and 0 for mu1.

See Also

gum.diag, optim, gev.fit

Examples

data(portpirie)
gum.fit(portpirie[,2])