g_minus: Calculate Drift-diffusion Probability Density

In TasCL/ggdmc: Dynamic Model of Choice with Parallel Computation, and C++ Capabilities

Description

g_minus and g_plus implement A1 to A4 equations in Voss, Rothermund, and Voss (2004). These equations calculate Ratcliff's drift-diffusion model (1978). This source codes are derived from Voss & Voss's fast-dm 30.2 in density.c.

Usage

g_minus(pVec)

g_plus(pVec)

g_minus_parallel(pVec)

g_plus_parallel(pVec)

Arguments

pVec	a 9-element parameter (double) vector. The user has to follow the sequence strictly. a, v, zr, d, sz, sv, t0, st0, RT, precision.

Details

Two parallel functions g_minus_parallel and g_plus_parallel, using OpenMP libraries to do numerical integration. They resolve the problem when high precision (> 10) is required.

References

Voss, A., Rothermund, K., & Voss, J. (2004). Interpreting the parameters of the diffusion model: A empirical validation Memory and Cognition, 32(7), 1206–1220.

Ratcliff, R (1978). A theory of memory retrieval. Psychology Review, 85(2), 59–108.

Examples

pVec1 <- c(a=2, v=2.5, zr=0.5, d=0, sz=0.3, sv=1, t0=0.3, st0=0,
           RT=.550, precision=2.5)
g_minus(pVec1)
## [1] 0.04965882
g_plus(pVec1)
## [1] 2.146265
pVec2 <- c(a=2, v=2.5, zr=0.5, d=0, sz=0.3, sv=1, t0=0.3, st0=0,
           RT=.550, precision=20)
## system.time(d1 <- g_plus_parallel(pVec2))
##     user  system elapsed
##  135.708   0.000  12.022
## system.time(d2 <- g_plus(pVec2))
##    user  system elapsed
## 104.516   0.000 104.490

TasCL/ggdmc documentation built on May 9, 2019, 4:19 p.m.