predict.inla_metric_graph_spde: Predict method for 'inlabru' fits on Metric Graphs

Description

Auxiliar function to obtain predictions of the field using 'inlabru'.

Usage

# S3 method for inla_metric_graph_spde
predict(
  object,
  cmp,
  bru_fit,
  newdata = NULL,
  formula = NULL,
  data_coords = c("PtE", "euclidean"),
  normalized = TRUE,
  repl = NULL,
  repl_col = NULL,
  group = NULL,
  group_col = NULL,
  n.samples = 100,
  seed = 0L,
  probs = c(0.025, 0.5, 0.975),
  return_original_order = TRUE,
  num.threads = NULL,
  include = NULL,
  exclude = NULL,
  drop = FALSE,
  tolerance_merge = 1e-05,
  ...,
  data = deprecated()
)

Value

A list with predictions.

Arguments

object: An inla_metric_graph_spde object built with the graph_spde() function.
cmp: The 'inlabru' component used to fit the model.
bru_fit: A fitted model using 'inlabru' or 'INLA'.
newdata: A data.frame of covariates needed for the prediction. The locations must be normalized PtE.
formula: A formula where the right hand side defines an R expression to evaluate for each generated sample. If NULL, the latent and hyperparameter states are returned as named list elements. See Details for more information.
data_coords: It decides which coordinate system to use. If PtE, the user must provide the locations as a data frame with the first column being the edge number and the second column as the distance on edge, otherwise if euclidean, the user must provide a data frame with the first column being the x Euclidean coordinates and the second column being the y Euclidean coordinates.
normalized: if TRUE, then the distances in distance on edge are assumed to be normalized to (0,1). Default TRUE. Will not be used if data_coords is euclidean.
repl: Which replicates? If there is no replicates, one can set repl to NULL. If one wants all replicates, then one sets to repl to .all.
repl_col: Column containing the replicates. If the replicate is the internal group variable, set the replicates to ".group". If not replicates, set to NULL.
group: Which groups? If there is no groups, one can set group to NULL. If one wants all groups, then one sets to group to .all.
group_col: Which "column" of the data contains the group variable?
n.samples: Integer setting the number of samples to draw in order to calculate the posterior statistics. The default is rather low but provides a quick approximate result.
seed: Random number generator seed passed on to inla.posterior.sample()
probs: A numeric vector of probabilities with values in the standard unit interval to be passed to stats::quantile
return_original_order: Should the predictions be returned in the original order?
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"
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)
drop: logical; If keep=FALSE, data is a SpatialDataFrame, and the prediciton summary has the same number of rows as data, then the output is a SpatialDataFrame object. Default FALSE.
tolerance_merge: Tolerance for merging prediction points into original points to increase stability.
...: Additional arguments passed on to inla.posterior.sample().
data: Use newdata instead.