Estimate Stock Composition Using Direct Maximum Likelihood (Theta5)

Applies an EM algorithm to obtain the direct Maximum Likelihood Estimate (MLE) of stock proportions (Theta_5 in HISEA). This uses the likelihood of each individual mixed fishery sample fish belonging to each stock. The likelihood maximized is:

Usage

estimate_ml_theta5(
  individual_likelihoods,
  np,
  freq = NULL,
  use_accelerated_em = TRUE,
  ...
)

Arguments

individual_likelihoods: A numeric matrix (N_mixed_samples x NP) where `individual_likelihoods[i,j]` is $P(Data_i | Stock_j)$, the likelihood of observing the data for mixed sample fish `i` given it came from stock `j`.
np: Integer, the number of populations (stocks).
freq: Optional numeric vector of frequencies/weights for each sample (row) in `individual_likelihoods`. Defaults to `rep(1, nrow(individual_likelihoods))`.
use_accelerated_em: Logical, if TRUE, uses `accel_em_algorithm`, otherwise `em_algorithm`. Default TRUE.
...: Additional arguments passed to the chosen EM algorithm (e.g., `max_iter`, `tol`, `verbose`, `save_theta_path`).

Value

A numeric vector of estimated stock proportions (Theta5).

Examples

# Direct ML estimation from individual likelihoods
lik <- matrix(runif(50), 25, 2)
lik <- lik / rowSums(lik)

theta5 <- estimate_ml_theta5(lik, np = 2)
print(theta5)
#> [1] 0.4768657 0.5231343