Simulate from a racing diffusion model

Draws from the generative process directly: a uniform start point per accumulator, then that accumulator's inverse-Gaussian first-passage time, and whichever arrives first. This is the joint draw of choice and time, which is what the model produces and what a fit needs; simulate() on a fitted object instead redraws only the time, holding each row's observed choice.

Usage

rdm_simulate(n, v, A = 0.5, k = 0.5, ndt = 0.2)

Arguments

n

Number of trials.

v

Drift rates, one per accumulator, all positive. Its length sets the number of accumulators. May be a matrix with one row per trial, for a drift that varies with a covariate.

A

Upper end of the start-point range.

k

Distance from the top of the start-point range to the threshold, so the threshold is A + k.

ndt

Non-decision time.

Value

A data frame with choice (the accumulator that reached the threshold, from 1) and rt.

Examples

set.seed(1)
dat <- rdm_simulate(500, v = c(3.0, 2.0, 1.2), A = 0.5, k = 0.5,
                    ndt = 0.2)
table(dat$choice)
#> 
#>   1   2   3 
#> 247 164  89 
tapply(dat$rt, dat$choice, mean)
#>         1         2         3 
#> 0.3541706 0.3660322 0.3619880