Akaike information criterion for a xnicher fit — AIC.xnicher • xnicher

Computes \(-2 \log L + 2 k\), where \(\log L\) is the un-penalised log-likelihood (see logLik.xnicher) and \(k\) is the number of free parameters.

Usage

# S3 method for class 'xnicher'
AIC(object, ..., k = 2)

Arguments

object: a fitted model object for which there exists a logLik method to extract the corresponding log-likelihood, or an object inheriting from class logLik.
...: Additional fitted model objects.
k: Numeric, the penalty per parameter; default 2 (AIC). Pass k = log(nobs(fit)) to recover BIC.

Value

Numeric scalar, or a data.frame when multiple objects are passed.

Examples

# \donttest{
fit <- optimize_niche(env_occ = example_env_occ_2d,
                      env_m   = example_env_m_2d,
                      num_starts = 5L, likelihood = "weighted")
AIC(fit)
#> [1] 1310.801
# }