evaluate_predictor: Evaluate component effects or expressions
In inlabru: Bayesian Latent Gaussian Modelling using INLA and Extensions

evaluate_predictor

R Documentation

Evaluate component effects or expressions

Description

Evaluate component effects or expressions, based on a bru model and one or several states of the latent variables and hyperparameters.

Usage

evaluate_predictor(
  model,
  state,
  data,
  data_extra,
  effects,
  predictor,
  used = NULL,
  format = "auto",
  n_pred = NULL
)

Arguments

`state`	A list where each element is a list of named latent state information, as produced by `evaluate_state()`
`data`	A `list`, `data.frame`, or `Spatial*DataFrame`, with coordinates and covariates needed to evaluate the model.
`data_extra`	Additional data for the predictor evaluation. Variables with the same name as in `data` will be ignored, unless accessed via `.data_extra.[["name"]]` or `.data_extra.$name`, or via pronouns; see Details.
`effects`	A list where each element is list of named evaluated effects, each computed by `evaluate_effect_single_state.bru_comp_list()`
`predictor`	Either a formula or bru_pred_expr expression
`used`	A `bru_used()` object, or NULL (default)
`format`	character; determines the storage format of the 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 column contains the evaluated predictor expression for a state. Default: "auto"
`n_pred`	integer. If provided, scalar predictor results are expanded to vectors of length `n_pred`.

Details

For each component, e.g. "name", the latent state values are available as name_latent, and arbitrary evaluation can be done with name_eval(...), see bru_comp_eval().

The evaluation supports several rlang::as_data_pronoun() data masking pronouns, to access variables from different data sources, and some of these also have corresponding full objects, with an appended . in the name. The full objects can be passed as arguments to functions.

.effect/.effect.: refers to the effects vectors
.latent/.latent.: refers to the latent state vectors
.data/.data.: refers to the main data argument
.data_extra/.data_extra.: refers to the data_extra argument
.env: refers to the evaluation environment of the predictor