alik_inla {geoBayes} | R Documentation |
Log-likelihood approximation
Description
Log-likelihood approximation.
Usage
alik_inla(
par_vals,
formula,
family = "gaussian",
data,
weights,
subset,
offset,
atsample,
corrfcn = "matern",
np,
betm0,
betQ0,
ssqdf,
ssqsc,
tsqdf,
tsqsc,
dispersion = 1,
longlat = FALSE
)
Arguments
par_vals |
A data frame with the components "linkp", "phi", "omg", "kappa". The approximation will be computed at each row of the data frame. |
formula |
A representation of the model in the form
|
family |
The distribution of the response. Can be one of the
options in |
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 |
atsample |
A formula in the form |
corrfcn |
Spatial correlation function. Can be one of the
choices in |
np |
The number of integration points for the spatial
variance parameter sigma^2. The total number of points will be
|
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. |
tsqdf |
Degrees of freedom for the scaled inverse chi-square prior for the measurement error parameter. |
tsqsc |
Scale for the scaled inverse chi-square prior for the measurement error parameter. |
dispersion |
The fixed dispersion parameter. |
longlat |
How to compute the distance between locations. If
|
Details
Computes and approximation to the log-likelihood for the given parameters using integrated nested Laplace approximations.
Value
A list with components
-
par_vals
A data frame of the parameter values. -
aloglik
The approximate log-likelihood at thos parameter values.
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.