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,
max_dim_start_range = TRUE
)Arguments
- graph
A
metric_graphobject.- 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 dimension of the bounding box to obtain a starting value for range. Default is 0.3.
- max_dim_start_range
Should the maximum between the dimensions of the bounding box of the metric graph be used? If
FALSE, the minimum will be used.