dcddm {CircularDDM}

The Circular Drift-diffusion Distribution

Description

Density function and random generation for the circular drift-diffusion model with theta vector equal to pVec. dcddm is the equation (23) on page 433 in Smith (2016).

Usage

dcddm(x, pVec, k = 141L)

rcddm(n, pVec, p = 0.15)

Arguments

x

a matrix storing a first column as RT and a second column of continuous responses/reports/outcomes. Each row is a trial.

pVec

a parameter vector with the order [a, vx, vy, t0, s], or [thresh, mu1, mu2, ndt, sigmasq]. The order matters.

k

a precision for calculating the infinite series in dcddm. The larger the k is, the larger the memory space is required. Default is 141.

n

number of observations.

p

a precision for random walk step in rcddm. Default is 0.15 second

Value

dcddm gives a log-likelihood vector. rddm generates random deviates, returning a n x 3 matrix with the columns: RTs, choices and then angles.

References

Smith, P. L. (2016). Diffusion Theory of Decision Making in Continuous Report, Psychological Review, 123 (4), 425–451.

Examples

## dcddm example
x <- cbind(
RT= c(1.2595272, 0.8693937, 0.8009044, 1.0018933, 2.3640007, 1.0521304),
R = c(1.9217430, 1.7844653, 0.2662521, 2.1569724, 1.7277440, 0.8607271)
)
pVec <- c(a=2.45, vx=1.5, vy=1.25, t0=.1, s=1)
dcddm(x, pVec)

## rcddm example
pVec <- c(a=2, vx=1.5, vy=1.25, t0=.25, s=1)
den  <- rcddm(1e3, pVec);
hist(den[,1], breaks = "fd", xlab="Response Time",  main="Density")
hist(den[,3], breaks = "fd", xlab="Response Angle", main="Density")