Negative log-likelihood (inverse-probability-weighted normal, integrated C++)

Low-level C++ bridge for the inverse-probability-weighted (IPW) normal niche model. Computes KDE weights and Mahalanobis distances entirely in C++ to minimise R overhead. This is the workhorse called by loglik_niche_math_ip_weighted.

Usage

loglik_niche_math_ip_weighted_integrated(
  theta,
  env_occ,
  env_m,
  eta = 1,
  neg = TRUE,
  den_idx = NULL,
  kde_idx = NULL,
  precomp_w_den = NULL
)

Arguments

theta

Numeric vector of unconstrained parameters (math scale): \([\mu, \log\sigma, v]\) of length \(2p + p(p-1)/2\).

env_occ

Data frame or matrix (\(n \times p\)) of environmental values at presence points.

env_m

Data frame or matrix (\(n_m \times p\)) of background environmental values from the accessible area M.

eta

Numeric scalar, shape parameter for the LKJ C-vine prior on the correlation matrix (default 1 = uniform over correlations).

neg

Logical. If TRUE (default), returns the negative log-likelihood (suitable for minimisation).

den_idx

Integer vector of 1-based row indices for the denominator subsample. If NULL (default), uses all rows of env_m.

kde_idx

Integer vector of 1-based row indices for the KDE reference subsample. If NULL, uses all rows of env_m.

precomp_w_den

Numeric vector of precomputed KDE weights for the denominator points (must match den_idx in length). If provided, KDE for the denominator is not recomputed.

Value

Scalar numeric: the (negative) log-likelihood.

Details

The objective function is described in detail in loglik_niche_math_ip_weighted.

This function was formerly called loglik_niche_math_kde_bias_corrected_integrated.

See also

[loglik_niche_math_ip_weighted()] for the user-facing wrapper with automatic subsampling.

Examples

# \donttest{
theta <- start_theta(example_env_occ_2d)
ll <- loglik_niche_math_ip_weighted_integrated(
  theta   = theta,
  env_occ = example_env_occ_2d,
  env_m   = example_env_m_2d,
  eta     = 1,
  neg     = TRUE
)
print(ll)
#> [1] 633.8731
# }