compute_likelihood: Compute Likelihood for Behavioural Models

In ggdmcLikelihood: Likelihood Computation for 'ggdmc' Package

Compute Likelihood for Behavioural Models

Description

Computes the likelihood for multiple subjects by aggregating results from individual subject computations.

Usage

compute_likelihood(dmis, parameter_r, debug = FALSE)

compute_subject_likelihood(dmi, parameter_r, debug = FALSE)

Arguments

dmis	A list of S4 data model instances (one per subject)
parameter_r	A list (one per subject) of or one numeric vector containing model parameters
debug	Logical flag for debug mode (default = FALSE)
dmi	One S4 data model instance (for one subject)

Details

These functions compute likelihoods for behavioural models, with compute_subject_likelihood handling a single subject and compute_likelihood handling multiple subjects.

These functions provide access to the internal mechanism of the design-based likelihood computation. They primarily intended to initialise new 'samples' or to verify that the likelihood evaluations, when associated with a particular design, are computed accurately.

Value

with:

• compute_likelihood returns a list. Each element is the likelihood for a subject. The element in the inner list is the likelihood for a condition.

• compute_subject_likelihood returns also a list. Each element is the likelihood for a condition.

Examples

# Example dataset
hdat <- data.frame(
  RT = round(runif(15, min = 0.4, max = 1.2), 7),
  R  = sample(c("r1", "r2", "r3"), size = 15, replace = TRUE),
  s  = rep(1:3, each = 5),
  S  = rep(c("s1", "s2", "s3"), each = 5),
  stringsAsFactors = FALSE
)
dat <- hdat[hdat$s==1, ]

p_vector <- c(A = .75, B = 1.25, mean_v.false = 1.5, mean_v.true = 2.5, t0 = .15)
nsubject <- length(unique(hdat$s))

if(requireNamespace("ggdmcModel", quietly = TRUE)) {
    BuildModel <- getFromNamespace("BuildModel", "ggdmcModel")
    BuildDMI   <- getFromNamespace("BuildDMI", "ggdmcModel")

    model <- BuildModel(
        p_map = list(A = "1", B = "1", t0 = "1", mean_v = "M", sd_v = "1", st0 = "1"),
        match_map = list(M = list(s1 = "r1", s2 = "r2")),
        factors = list(S = c("s1", "s2")),
        constants = c(st0 = 0, sd_v = 1),
        accumulators = c("r1", "r2"),
        type = "lba")
    pop_dmis <- BuildDMI(hdat, model)
    sub_dmis <- BuildDMI(dat, model)

    parameters <- list()
    for (i in seq_len(nsubject)) {
        new_p_vector <- p_vector[model@pnames]
        parameters[[i]] <- new_p_vector
    }

    result1 <- compute_subject_likelihood(sub_dmis[[1]], parameters[[1]], FALSE)
    result2 <- compute_likelihood(pop_dmis, parameters, FALSE)
}

print(result1)
print(result2)

ggdmcLikelihood documentation built on Aug. 8, 2025, 6:10 p.m.