simulate.graph_lme: Simulation of models on metric graphs

In MetricGraph: Random Fields on Metric Graphs

Simulation of models on metric graphs

Description

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.

MetricGraph documentation built on April 3, 2025, 10:34 p.m.