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Simulate behavioral data from a model

Source: R/generator.R
simulate.Rd

Generate one trial for each accumulator block in params_df, using the model's response rules, timing dependencies, triggers, and mixture weights.

Usage

simulate(
  structure,
  params_df,
  trial_df = NULL,
  seed = NULL,
  keep_detail = FALSE,
  keep_component = NULL
)

Arguments

structure

Finalized model structure.

params_df

Parameter matrix from build_param_matrix(), with one row per accumulator per trial, in the order accumulators were added to the model.

trial_df

Optional trial-by-accumulator conditioning table containing complete trials/racer blocks in model order. It may supply per-trial component and per-accumulator onset values. Repeat a component label across all rows of its trial; NA draws a component from the mixture. An onset value replaces a fixed onset or adds a delay to a chained onset; NA uses the onset specified in the model.

seed

Optional seed passed to set.seed(). If NULL, use the current state of R's random-number generator.

keep_detail

If TRUE, attach a details list containing latent source times and outcome candidates for each trial. Inactive or unreachable sources have infinite completion times.

keep_component

Whether to keep the chosen mixture component in the output when the model has multiple components. If NULL, fixed mixtures keep the component label and sampled mixtures drop it.

Value

A data frame with one row per trial and columns trials, R, and rt. A trial with no observed response has R = NA and rt = NA. If n_outcomes > 1, additional ordered response pairs such as R2/rt2 are included; unobserved later ranks are NA.

Details

Use the same model to build params_df and simulate the data. Supply valid parameter values and keep the matrix and conditioning table in matching row order; simulation does not check their layout or domains.

See also

prepare_data(), log_likelihood(), set_mixture()

Examples

spec <- race_spec()
spec <- add_accumulator(spec, "A", "lognormal")
spec <- add_outcome(spec, "A_win", "A")
structure <- finalize_model(spec)
params <- c(m = 0, s = 0.1)
df <- build_param_matrix(structure, params, n_trials = 3)
simulate(structure, df, seed = 123)
#>   trials     R       rt
#> 1      1 A_win 1.083347
#> 2      2 A_win 1.168675
#> 3      3 A_win 1.131959