# g_minus: Calculate Drift-diffusion Probability Density In ggdmc: Cognitive Models

## 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

 ```1 2 3``` ```g_minus(pVec) g_plus(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

 ``` 1 2 3 4 5 6 7 8 9 10``` ```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) ## 0.04965882 g_plus(pvec1) ## 2.146265 pvec2 <- c(a=2, v=2.5, zr=0.5, d=.2, sz=0.3, sv=1, t0=0.3, st0=.1, RT=.550, precision=2.5) g_minus(pvec2) ## 0.04194364 g_plus(pvec2) ## 1.94957 ```

ggdmc documentation built on Sept. 2, 2018, 1:03 a.m.