ezdm_dist: Distribution functions for the EZ-Diffusion Model (ezdm)

In bmm: Easy and Accessible Bayesian Measurement Models Using 'brms'

Distribution functions for the EZ-Diffusion Model (ezdm)

Description

Density and random generation functions for the EZ-Diffusion Model. The model operates on aggregated data: mean reaction time, variance of reaction time, and number of responses to the upper boundary.

Usage

dezdm(
  mean_rt,
  var_rt,
  n_upper,
  n_trials,
  drift,
  bound,
  ndt,
  zr = 0.5,
  s = 1,
  version = c("3par", "4par"),
  log = TRUE
)

rezdm(
  n,
  n_trials,
  drift,
  bound,
  ndt,
  zr = 0.5,
  s = 1,
  version = c("3par", "4par")
)

Arguments

mean_rt	Observed mean reaction time(s) in seconds. For version "3par", a numeric vector or single value. For version "4par", either a vector of length 2 (c(mean_rt_upper, mean_rt_lower)) for single observation, or a matrix with 2 columns for multiple observations.
var_rt	Observed variance of reaction times in seconds^2. For version "3par", a numeric vector or single value. For version "4par", either a vector of length 2 (c(var_rt_upper, var_rt_lower)) for single observation, or a matrix with 2 columns for multiple observations.
n_upper	Number of responses to the upper boundary
n_trials	Total number of trials
drift	Drift rate (evidence accumulation rate; can be positive or negative for below-chance performance).
bound	Boundary separation (distance between decision thresholds).
ndt	Non-decision time (seconds).
zr	Relative starting point (0 to 1). Only used for version "4par".
s	Diffusion constant (standard deviation of noise), default = 1.
version	Character; either "3par" (default) or "4par"
log	Logical; if TRUE, values are returned on the log scale.
n	Number of samples to generate

Value

dezdm gives the log-density of the observed summary statistics under the EZDM, and rezdm generates random summary statistics from the implied sampling distributions.

References

Wagenmakers, E.-J., Van Der Maas, H. L. J., & Grasman, R. P. P. P. (2007). An EZ-diffusion model for response time and accuracy. Psychonomic Bulletin & Review, 14(1), 3-22.

Chávez De la Peña, A. F., & Vandekerckhove, J. (2025). An EZ Bayesian hierarchical drift diffusion model for response time and accuracy. Psychonomic Bulletin & Review.

Examples

# 3-parameter version (single observation)
dezdm(
  mean_rt = 0.5, var_rt = 0.02, n_upper = 80, n_trials = 100,
  drift = 2, bound = 1.5, ndt = 0.3
)

# 3-parameter version (vectorized)
dezdm(
  mean_rt = c(0.5, 0.55), var_rt = c(0.02, 0.025),
  n_upper = c(80, 75), n_trials = c(100, 100),
  drift = 2, bound = 1.5, ndt = 0.3
)

# 4-parameter version (single observation)
dezdm(
  mean_rt = c(0.45, 0.55), var_rt = c(0.018, 0.025),
  n_upper = 80, n_trials = 100,
  drift = 2, bound = 1.5, ndt = 0.3, zr = 0.55, version = "4par"
)

# generate random summary statistics
rezdm(n = 100, n_trials = 100, drift = 2, bound = 1.5, ndt = 0.3)
rezdm(
  n = 100, n_trials = 100, drift = 2, bound = 1.5, ndt = 0.3,
  zr = 0.55, version = "4par"
)

bmm documentation built on March 30, 2026, 5:08 p.m.