graph_starting_values: Starting values for random field models on metric graphs

Description

Computes appropriate starting values for optimization of Gaussian random field models on metric graphs.

Usage

graph_starting_values(
  graph,
  model = c("alpha1", "alpha2", "isoExp", "GL1", "GL2"),
  data = TRUE,
  data_name = NULL,
  range_par = FALSE,
  nu = FALSE,
  manual_data = NULL,
  like_format = FALSE,
  log_scale = FALSE,
  model_options = list(),
  rec_tau = TRUE,
  factor_start_range = 0.3,
  type_start_range_bbox = "diag"
)

Value

A vector, c(start_sigma_e, start_sigma, start_kappa)

Arguments

graph: A metric_graph object.
model: Type of model, "alpha1", "alpha2", "isoExp", "GL1", and "GL2" are supported.
data: Should the data be used to obtain improved starting values?
data_name: The name of the response variable in graph$data.
range_par: Should an initial value for range parameter be returned instead of for kappa?
nu: Should an initial value for nu be returned?
manual_data: A vector (or matrix) of response variables.
like_format: Should the starting values be returned with sigma.e as the last element? This is the format for the likelihood constructor from the 'rSPDE' package.
log_scale: Should the initial values be returned in log scale?
model_options: List object containing the model options.
rec_tau: Should a starting value for the reciprocal of tau be given?
factor_start_range: Factor to multiply the max/min/diagonal dimension of the bounding box to obtain a starting value for range. Default is 0.5.
type_start_range_bbox: Which dimension from the bounding box should be used? The options are 'diag', the default, 'max' and 'min'.