LDDMM: Drift Diffusion Model Fit

Description

Main function for the Gibbs sampler for the drift-diffusion model. Note that priors are noninformative and calibrated so that, for the most stable performance, the response times (variable r_time in the data dataframe) should lie between 0 and 10.

Usage

LDDMM(
  data,
  hypers,
  cluster = TRUE,
  boundaries = "flexible",
  Niter = 5000,
  burnin = 2000,
  thin = 5
)

Value

List with the following MCMC posterior samples:

post_mean_delta: posterior samples for the population offset parameters
post_mean_mu: posterior samples for the population drift parameters
post_mean_b: posterior samples for the population boundary parameters
post_ind_delta: posterior samples for the individual offset parameters
post_ind_mu: posterior samples for the individual drift parameters
post_ind_b: posterior samples for the individual boundary parameters
sigma2_mu_us: posterior samples for the random effects drift smoothness parameters
sigma2_mu_ua: posterior samples for the random effects drift variance parameters
sigma2_b_us: posterior samples for the random effects boundary smoothness parameters
sigma2_b_ua: posterior samples for the random effects boundary variance parameters
sigma2_1_mu: posterior samples for the drift smoothness parameters
sigma2_1_b: posterior samples for the boundary smoothness parameters
pred_ans: predicted population-level categories
pred_time: predicted population-level response times
pred_ans_ind: predicted individual-level categories
pred_time_ind: predicted individual-level response times

Arguments

data

dataframe with the following columns:

subject: vector of size n containing the participant labels
block: vector of size n containing the training blocks (longitudinal units)
s: vector of size n containing the stimuli
d: vector of size n containing the decisions
r_time: vector of size n containing the response times. To avoid numerical issues, the unit of measurement should be such that the numerical values of most response times should lie between 0 and 10
cens: vector of size n containing the censoring indicators (1 censored, 0 non censored)

hypers

hyperparameters of the MCMC: list containing "s_sigma_mu" and "s_sigma_b", which are the smoothness parameters for drifts and boundaries, respectively)

cluster

should clustering be used? (default = TRUE)

boundaries

whether to fit the unrestricted model (flexible), assume constant boundaries over time (constant) or fix the boundaries to the same level across predictors (fixed)

Niter

total number of iterations

burnin

burnin of the chain

thin

thinning factor