Simulation of models on metric graphs — simulate.graph

The function samples a Gaussian random field based on a fitted model using graph_lme().

Usage

# S3 method for class 'graph_lme'
simulate(
  object,
  nsim = 1,
  seed = NULL,
  sample_latent = FALSE,
  posterior = FALSE,
  which_repl = NULL,
  ...
)

Arguments

object: A graph_lme object
nsim: The number of simulations.
seed: an object specifying if and how the random number generator should be initialized (‘seeded’).
sample_latent: If FALSE, samples for the response variable will be generated. If TRUE, samples for the latent model will be generated. The default is FALSE.
posterior: Should posterior samples be generated? If FALSE, samples will be computed based on the estimated prior distribution. The default is FALSE.
which_repl: Which replicates to generate the samples. If NULL samples will be generated for all replicates. Default is NULL.
...: Currently not used.

Value

A list containing elements samples, edge_number and distance_on_edge. Each of them is a list, whose indexes are the replicates, and in samples a matrix is given with nsim columns, each one being a sample. edge_number and distance_on_edges contain the respective edge numbers and distances on edge for each sampled element. The locations of the samples are the location of the data in which the model was fitted.