Evaluate log-likelihoods of behavioral data

Compute the summed log-likelihood by default, or trial-wise log-likelihoods when sum = FALSE. Build the context and prepare the observations once, then reuse them when evaluating candidate parameter matrices for the same model.

Usage

log_likelihood(
  context,
  data,
  parameters,
  ok = NULL,
  sum = TRUE,
  min_ll = log(1e-10)
)

Arguments

context

Context created with make_context().

data

Prepared data created with prepare_data().

parameters

Numeric matrix from build_param_matrix(), with one accumulator block per prepared trial in matching order.

ok

Optional logical vector with one value per prepared trial. TRUE evaluates that trial; FALSE assigns min_ll. These assigned values are included in the sum. For compressed data, use the retained trial order.

sum

If TRUE, return the summed log-likelihood. If FALSE, return trial-wise log-likelihood values.

min_ll

Minimum log-likelihood value used for excluded or impossible trials.

Value

A summed log-likelihood by default, or a numeric vector of trial-wise log-likelihood values when sum = FALSE.

Details

Response-time observations contribute densities, so a log-likelihood can be positive. Missing responses and censoring contribute probability masses according to the observation rules.

Use prepare_data() and build_param_matrix() for the same model. Evaluation assumes matching layouts and valid parameters and does not repeat preparation checks.

Examples

spec <- race_spec()
spec <- add_accumulator(spec, "A", "lognormal")
spec <- add_outcome(spec, "A_win", "A")
structure <- finalize_model(spec)
params_df <- build_param_matrix(
  structure,
  c(m = 0, s = 0.1),
  n_trials = 2
)
data_df <- simulate(structure, params_df, seed = 1)
prepared <- prepare_data(structure, data_df)
ctx <- make_context(structure)
log_likelihood(ctx, prepared, params_df)
#> [1] 2.481128