Distribution functions for the two-parameter mixture model (mixture2p)

Density, distribution, and random generation functions for the two-parameter mixture model with the location of mu, precision of memory representations kappa and probability of recalling items from memory p_mem.

Usage

dmixture2p(x, mu = 0, kappa = 5, p_mem = 0.6, log = FALSE)

pmixture2p(q, mu = 0, kappa = 7, p_mem = 0.8)

qmixture2p(p, mu = 0, kappa = 5, p_mem = 0.6)

rmixture2p(n, mu = 0, kappa = 5, p_mem = 0.6)

Arguments

x

Vector of observed responses

mu

Vector of locations

kappa

Vector of precision values

p_mem

Vector of probabilities for memory recall

log

Logical; if TRUE, values are returned on the log scale.

q

Vector of quantiles

p

Vector of probability

n

Number of observations to generate data for

Value

dmixture2p gives the density of the two-parameter mixture model, pmixture2p gives the cumulative distribution function of the two-parameter mixture model, qmixture2p gives the quantile function of the two-parameter mixture model, and rmixture2p gives the random generation function for the two-parameter mixture model.

References

Zhang, W., & Luck, S. J. (2008). Discrete fixed-resolution representations in visual working memory. Nature, 453.

Examples

# generate random samples from the mixture2p model and overlay the density
r <- rmixture2p(10000, mu = 0, kappa = 4, p_mem = 0.8)
x <- seq(-pi,pi,length.out=10000)
d <- dmixture2p(x, mu = 0, kappa = 4, p_mem = 0.8)
hist(r, breaks=60, freq=FALSE)
lines(x,d,type="l", col="red")