Generate multiple starting points for niche model optimization

Creates a set of starting parameter vectors on the math scale for use with the log-likelihood functions. Ensures all starting values are strictly numeric and finite, even if env_data is supplied as a data frame.

Usage

start_theta_multiple(
  env_data,
  num_starts = 100,
  quant_vec = c(0.1, 0.5, 0.9),
  method = "sobol",
  skew = FALSE,
  skew_t = FALSE
)

Arguments

env_data: Environmental data (matrix or data frame). Must be numeric.
num_starts: Integer, number of starting points.
quant_vec: Quantiles for mu ranges.
method: "sobol" (Sobol design) or "uniform".
skew: Logical. If TRUE, append p skewness parameters (alpha_1, ..., alpha_p) to each starting vector with the default range [-3, 3]. Use this for the "skew_normal" / "skew_normal_weighted" likelihoods. Default FALSE.
skew_t: Logical. If TRUE, additionally append a single log_r parameter (default range [log 2, log 100]) after the alpha block. Use for "skew_t" / "skew_t_weighted". Implies skew = TRUE.

Value

A data frame of dimension num_starts × num_parameters.

Examples

starts <- start_theta_multiple(
  env_data   = example_env_occ_2d,
  num_starts = 5L,
  method     = "uniform"
)
dim(starts)
#> [1] 5 5