Create a new INLA priorSource:
For any fixed and random effect INLA supports a range of different priors of exponential distributions.
Currently supported for INLA in ibis.iSDM are the following priors that can
be specified via
"gaussian": Priors on normal distributed and set to specified variable. Required parameters are a mean and a precision estimate provided to
"hyper". Note that precision is not equivalent (rather the inverse) to typical standard deviation specified in Gaussian priors. Defaults are set to a mean of
0and a precision of
"clinear": Prior that places a constraint on the linear coefficients of a model so as that the coefficient is in a specified interval
"c(lower,upper)". Specified through hyper these values can be negative, positive or infinite.
'prior.sigma': Specification of penalized complexity priors which can be added to a SPDE spatial random effect added via
add_latent_spatial(). Here the range of the penalized complexity prior can be specified through
'prior.range'and the uncertainty via
'prior.sigma'both supplied to the options 'type' and 'hyper'.
Other priors available in INLA
might also work, but have not been tested!
INLAPrior(variable, type = "normal", hyper = c(0, 0.001), ...) # S4 method for character,character,ANY INLAPrior(variable,type,hyper,...)
charactermatched against existing predictors or latent effects.
characterspecifying the type of prior to be set.
Variables passed on to prior object.
Compared to other engines, INLA does unfortunately does not support
priors related to more stringent parameter regularization such as Laplace or
Horseshoe priors, which limits the capability of
regularization. That being said many of the default uninformative priors act
already regularize the coefficients to some degree.
Rue, H., Riebler, A., Sørbye, S. H., Illian, J. B., Simpson, D. P., & Lindgren, F. K. (2017). Bayesian computing with INLA: a review. Annual Review of Statistics and Its Application, 4, 395-421.
Simpson, D., Rue, H., Riebler, A., Martins, T. G., & Sørbye, S. H. (2017). Penalising model component complexity: A principled, practical approach to constructing priors. Statistical science, 32(1), 1-28.