Likelihood Computation for 'ggdmc' Package

Efficient computation of likelihoods in design-based choice response time models, including the Decision Diffusion Model, is supported. The package enables rapid evaluation of likelihood functions for both single- and multi-subject models across trial-level data. It also offers fast initialisation of starting parameters for genetic sampling with many Markov chains, facilitating estimation in complex models typically found in experimental psychology and behavioural science. These optimisations help reduce computational overhead in large-scale model fitting tasks.

This package helps researchers analyse how people make decisions over time by modelling both their choices and the speed with which they respond (i.e., response times). This genre of analysis, commonly referred to as choice response time modelling, includes well-known frameworks, such as the Decision Diffusion Model (DDM). The package support data from both individuals and groups, and is optimised for speed and scalability, making it well-suited for fitting complex models efficiently. It is particularly valuable for experimental psychologists and behavioural scientists who work with large datasets and require fast, accurate model estimation.

Getting Started

This package is mainly intended to work alongside 'ggdmc', and it integrates most smoothly when used as part of the ggdmc workflow.

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

dmis <- ggdmcModel::BuildDMI(hdat, model)

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

result <- compute_subject_likelihood(sub_dmis[[1]], parameters[[1]], F)
result

sll <- sum(sapply(result, function(x) {
    sum(log(x))
}))
print(sll)

result <- compute_likelihood(pop_dmis, parameters, F)
n_subject <- length(pop_dmis)
for (i in seq_len(n_subject)) {
    res <- result[[i]]
    sll <- sum(sapply(res, function(x) {
        sum(log(x))
    }))
    cat("Subject ", i, "results in summed log likelihood = ", sll, "\n")
}

Prerequisites

R (>= 3.5.0), Rcpp (>= 1.0.7), RcppArmadillo (>= 0.10.7.5.0), and ggdmcHeaders.

Installation

From CRAN:

install.packages("ggdmcLikelihood")