Skip to contents

Calculate log-likelihood for Binomial-distributed data

Source: R/calc_log_likelihood_distributions.R
calc_log_likelihood_binomial.Rd

Computes the total log-likelihood for integer counts of successes under the Binomial distribution, optionally weighting each observation via weights.

Usage

calc_log_likelihood_binomial(
  observed,
  estimated,
  trials,
  weights = NULL,
  verbose = TRUE
)

Arguments

observed

Integer vector of observed successes (e.g., number of positives).

estimated

Numeric vector of expected probabilities of success in (0, 1).

trials

Integer vector of total trials (same length as observed).

weights

Optional numeric vector of non-negative weights, same length as observed. Default is NULL, which sets all weights to 1.

verbose

Logical; if TRUE, prints total log-likelihood and checks for data consistency.

Value

A scalar log-likelihood (numeric).

Details

The Binomial distribution models the number of successes in fixed trials: $$ Y \\sim \\text{Binomial}(n, p). $$ Weighted by weights[i] for each observation i.

Examples

calc_log_likelihood_binomial(c(3, 4, 2), c(0.3, 0.5, 0.25), c(10, 10, 8))
#> Binomial log-likelihood: -4.07
#> [1] -4.071992