Prediction of a mixed effects regression model on a metric graph.

Usage

# S3 method for rspde_lme
predict(
  object,
  newdata = NULL,
  loc = NULL,
  mesh = FALSE,
  which_repl = NULL,
  compute_variances = FALSE,
  posterior_samples = FALSE,
  n_samples = 100,
  sample_latent = FALSE,
  edge_number = "edge_number",
  distance_on_edge = "distance_on_edge",
  normalized = FALSE,
  return_as_list = FALSE,
  return_original_order = TRUE,
  ...,
  data = deprecated()
)

Arguments

object: The fitted object with the rspde_lme() function
newdata: A data.frame or a list containing the covariates, the edge number and the distance on edge for the locations to obtain the prediction.
loc: Prediction locations. Can either be a data.frame, a matrix or a character vector, that contains the names of the columns of the coordinates of the locations. For models using metric_graph objects, plase use edge_number and distance_on_edge instead.
mesh: Obtain predictions for mesh nodes? The graph must have a mesh, and either only_latent is set to TRUE or the model does not have covariates.
which_repl: Which replicates to use? If NULL all replicates will be used.
compute_variances: Set to also TRUE to compute the kriging variances.
posterior_samples: If TRUE, posterior samples will be returned.
n_samples: Number of samples to be returned. Will only be used if sampling is TRUE.
sample_latent: Do posterior samples only for the random effects?
edge_number: Name of the variable that contains the edge number, the default is edge_number.
distance_on_edge: Name of the variable that contains the distance on edge, the default is distance_on_edge.
normalized: Are the distances on edges normalized?
return_as_list: Should the means of the predictions and the posterior samples be returned as a list, with each replicate being an element?
return_original_order: Should the results be return in the original (input) order or in the order inside the graph?
...: Not used.
data: Use newdata instead.

Value

A list with elements mean, which contains the means of the predictions, fe_mean, which is the prediction for the fixed effects, re_mean, which is the prediction for the random effects, variance (if compute_variance is TRUE), which contains the variances of the predictions, samples (if posterior_samples is TRUE), which contains the posterior samples.