alik_optim {geoBayes}

Log-likelihood maximisation

Description

Approximate log-likelihood maximisation

Usage

alik_optim(
  paroptim,
  formula,
  family = "gaussian",
  data,
  weights,
  subset,
  offset,
  atsample,
  corrfcn = "matern",
  np,
  betm0,
  betQ0,
  ssqdf,
  ssqsc,
  dispersion = 1,
  longlat = FALSE,
  control = list()
)

Arguments

paroptim

A named list with the components "linkp", "phi", "omg", "kappa". Each component must be numeric with length 1, 2, or 3 with elements in increasing order. If the compontent's length is 1, then the corresponding parameter is considered to be fixed at that value. If 2, then the two numbers denote the lower and upper bounds for the optimisation of that parameter (infinities are allowed). If 3, these correspond to lower bound, starting value, upper bound for the estimation of that parameter.

formula

A representation of the model in the form response ~ terms.

family

The distribution of the response.

data

An optional data frame containing the variables in the model.

weights

An optional vector of weights. Number of replicated samples for Gaussian and gamma, number of trials for binomial, time length for Poisson.

subset

An optional vector specifying a subset of observations to be used in the fitting process.

offset

See lm.

atsample

A formula in the form ~ x1 + x2 + ... + xd with the coordinates of the sampled locations.

corrfcn

Spatial correlation function. See geoBayes_correlation for details.

np

The number of integration points for the spatial variance parameter sigma^2. The total number of points will be 2*np + 1.

betm0

Prior mean for beta (a vector or scalar).

betQ0

Prior standardised precision (inverse variance) matrix. Can be a scalar, vector or matrix. The first two imply a diagonal with those elements. Set this to 0 to indicate a flat improper prior.

ssqdf

Degrees of freedom for the scaled inverse chi-square prior for the partial sill parameter.

ssqsc

Scale for the scaled inverse chi-square prior for the partial sill parameter.

dispersion

The fixed dispersion parameter.

longlat

How to compute the distance between locations. If FALSE, Euclidean distance, if TRUE Great Circle distance. See spDists.

control

A list of control parameters for the optimisation. See optim.

Details

Uses the "L-BFGS-B" method of the function optim to maximise the log-likelihood for the parameters linkp, phi, omg, kappa.

Value

The output from the function optim. The "value" element is the log-likelihood, not the negative log-likelihood.

References

Evangelou, E., & Roy, V. (2019). Estimation and prediction for spatial generalized linear mixed models with parametric links via reparameterized importance sampling. Spatial Statistics, 29, 289-315.