logLik.xnicher: Log-likelihood of a fitted xnicher model

In xnicher: Estimates Ecological Niche Models Using Ellipses

Log-likelihood of a fitted xnicher model

Description

Returns the **un-penalised** log-likelihood of a "xnicher" fit at the converged \theta. This is the appropriate quantity for likelihood-based model comparison via AIC or BIC: even when optimize_niche minimises a ridge-penalised objective -\log L(\theta) + \mathrm{penalty}(\theta), the comparison metric is the bare \log L(\theta) evaluated at the optimised \theta.

Usage

## S3 method for class 'xnicher'
logLik(object, ...)

Arguments

object	A "xnicher" object returned by optimize_niche.
...	Ignored.

Details

For penalised fits this means the value returned is **not** the same as x$best$loglik: best$loglik is the negative of the penalised objective at the optimum (and is monotone in fit quality but not directly comparable across penalty strengths), while logLik(x) is the bare data log-likelihood. logLik(x) is what AIC/BIC require.

Stored on x$best$loglik_unpenalised by optimize_niche; older fits without that field are detected and an informative error is raised.

Value

An object of class "logLik" with attributes df (number of free parameters) and nobs (number of occurrence rows used in the fit).

Effective degrees of freedom

df is reported naively as length(theta): 2p + p(p-1)/2 for the Gaussian families, 3p + p(p-1)/2 for the skew-normal families, 3p + p(p-1)/2 + 1 for the skew-t families. This **ignores** the shrinkage induced by the ridge penalties (prior_mu_lambda, prior_log_sigma_lambda, prior_alpha_lambda). For weak penalties (\lambda \le 1) the bias is small; for stronger penalties the effective df is lower than length(theta) so the penalty (2k or k \log n) is conservative — IC values will under-favour the more-regularised model. Quantifying effective df via the trace of the influence matrix is left to a future release.

Examples

fit <- optimize_niche(env_occ = example_env_occ_2d,
                      env_m   = example_env_m_2d,
                      num_starts = 5L, likelihood = "weighted")
logLik(fit)
AIC(fit)
BIC(fit)

xnicher documentation built on Sept. 15, 2026, 5:08 p.m.