CSTM_T: Custom Time-Dependent Drift Diffusion Model

In ream: Density, Distribution, and Sampling Functions for Evidence Accumulation Models

Custom Time-Dependent Drift Diffusion Model

Description

Density (PDF), distribution function (CDF), and random sampler for a custom time-dependent (CSTM_T) drift diffusion model.

Usage

dCSTM_T(rt, resp, phi, x_res = "default", t_res = "default")

pCSTM_T(rt, resp, phi, x_res = "default", t_res = "default")

rCSTM_T(n, phi, dt = 1e-05)

Arguments

rt	vector of response times
resp	vector of responses ("upper" and "lower")
phi	parameter vector in your specified order
x_res	spatial/evidence resolution
t_res	time resolution
n	number of samples
dt	step size of time. We recommend 0.00001 (1e-5)

Value

For the density a list of PDF values, log-PDF values, and the sum of the log-PDFs, for the distribution function a list of of CDF values, log-CDF values, and the sum of the log-CDFs, and for the random sampler a list of response times (rt) and response thresholds (resp).

Author(s)

Raphael Hartmann & Matthew Murrow

References

Murrow, M., & Holmes, W. R. (2023). PyBEAM: A Bayesian approach to parameter inference for a wide class of binary evidence accumulation models. Behavior Research Methods, 1-21.

Examples

# Probability density function
dCSTM_T(rt = c(1.2, 0.6, 0.4), resp = c("upper", "lower", "lower"),
      phi = c(0.3, 0.5, 1.0, 1.0, 0.75, 0.0, 0.0, 1.0))

ream documentation built on Oct. 7, 2024, 1:20 a.m.