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evaluate_model

Evaluate or sample from a posterior result given a model and locations

Description

Evaluate or sample from a posterior result given a model and locations

Usage

evaluate_model(
  model,
  state,
  data = NULL,
  A = NULL,
  predictor = NULL,
  format = NULL,
  include = NULL,
  exclude = NULL,
  ...
)

evaluate_state(
  model,
  result,
  property = "mode",
  n = 1,
  seed = 0L,
  num.threads = NULL,
  internal_hyperpar = FALSE,
  ...
)

Arguments

`model`	A bru model
`state`	list of lists, as generated by `evaluate_state()`
`data`	A `list`, `data.frame`, or `Spatial*DataFrame`, with coordinates and covariates needed to evaluate the predictor.
`A`	Precomputed A-matrices
`predictor`	A formula or an expression to be evaluated given the posterior or for each sample thereof. The default (`NULL`) returns a `data.frame` containing the sampled effects. In case of a formula the right hand side is used for evaluation.
`format`	character; determines the storage format of predictor output. Available options: `"auto"` If the first evaluated result is a vector or single-column matrix, the "matrix" format is used, otherwise "list". `"matrix"` A matrix where each column contains the evaluated predictor expression for a state. `"list"` A list where each element contains the evaluated predictor expression for a state.
`include`	Character vector of component labels that are needed by the predictor expression; Default: NULL (include all components that are not explicitly excluded)
`exclude`	Character vector of component labels that are not used by the predictor expression. The exclusion list is applied to the list as determined by the `include` parameter; Default: NULL (do not remove any components from the inclusion list)
`...`	Additional arguments passed on to `inla.posterior.sample`
`result`	A `bru` object from `bru()` or `lgcp()`
`property`	Property of the model components to obtain value from. Default: "mode". Other options are "mean", "0.025quant", "0.975quant", "sd" and "sample". In case of "sample" you will obtain samples from the posterior (see `n` parameter). If `result` is `NULL`, all-zero vectors are returned for each component.
`n`	Number of samples to draw.
`seed`	If seed != 0L, the random seed
`num.threads`	Specification of desired number of threads for parallel computations. Default NULL, leaves it up to INLA. When seed != 0, overridden to "1:1"
`internal_hyperpar`	logical; If `TRUE`, return hyperparameter properties on the internal scale. Currently ignored when `property="sample"`. Default is `FALSE`.

Details

evaluate_model is a wrapper to evaluate model state, A-matrices, effects, and predictor, all in one call.

evaluate_state evaluates model state properties or samples

inlabru

Bayesian Latent Gaussian Modelling using INLA and Extensions

v2.3.1

GPL (>= 2)

Authors

Finn Lindgren [aut, cre, cph] (<https://orcid.org/0000-0002-5833-2011>, Finn Lindgren continued development of the main code), Fabian E. Bachl [aut, cph] (Fabian Bachl wrote the main code), David L. Borchers [ctb, dtc, cph] (David Borchers wrote code for Gorilla data import and sampling, multiplot tool), Daniel Simpson [ctb, cph] (Daniel Simpson wrote the basic LGCP sampling method), Lindesay Scott-Howard [ctb, dtc, cph] (Lindesay Scott-Howard provided MRSea data import code), Seaton Andy [ctb] (Andy Seaton provided testing and bugfixes)

Initial release

evaluate_model

Description

Usage

Arguments

Details

inlabru

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