hib_fit_inla: Fit an INLA mixed-effects model

This function fits an INLA model using the supplied database, formula and other function arguments tuning the model fitting process

Usage

hib_fit_inla(
  obj,
  response_fixed_form,
  random_covar,
  family = "gaussian",
  fit_settings,
  incl_spatial_term = FALSE,
  input_mesh = NULL,
  mesh_settings = list(mesh_dim = "2d", max.edge = c(75, 100), cutoff = 1e-12, offset =
    NULL),
  scale_specs = list(covars_to_scale = NULL, scale_func = NULL),
  relevel_specs = c(NULL),
  prior_list
)

Arguments

obj

A HibDatabase object.

response_fixed_form

A formula with the response and fixed effects

random_covar

A formula with random, non-spatial, 'iid' effects specified

family

A character indicating the model family, e.g., "gaussian"

fit_settings

In development

incl_spatial_term

A logical, whether or not to include a spatial random effect

input_mesh

Optionally provide an INLA mesh object

mesh_settings

Optionally specify INLA mash settings, e.g., max.edge

scale_specs

A named list containing "covars_to_scale" and "scale_func".

covars_to_scale is a character vector of numeric covariates that should be scaled prior to modelling.
scale_func is a function that will be used to scale the specified covariates - if NULL, base::scale() will be used.

relevel_specs

A vector where each item is: "<covar_to_relevel_name>" = "<base_level>".

prior_list

In development

Value

HibModelINLA

Details

This function takes the supplied database object (and various other arguments), fits the specified model and returns a HibModelINLA object. Still in development. Weighted data integration currently not implemented.

References

Havard Rue, Sara Martino, and Nicholas Chopin (2009), Approximate Bayesian Inference for Latent Gaussian Models Using Integrated Nested Laplace Approximations (with discussion), Journal of the Royal Statistical Society B, 71, 319-39
H. Rue, A. Riebler, S. H. Sorbye, J. B. Illian, D. P. Simpson, and F. K. Lindgren. Bayesian computing with INLA: A review. Annual Reviews of Statistics and Its Applications, 4(March):395-421, 2017. URL http://arxiv.org/abs/1604.00860

Examples

if (FALSE) { # \dontrun{
  mod <- hib_fit_inla(obj, y ~ -1 + Intercept + x, ~ f(a, model = "iid"))
} # }