mixture2p: Two-parameter mixture model by Zhang and Luck (2008).

In bmm: Easy and Accessible Bayesian Measurement Models Using 'brms'

Two-parameter mixture model by Zhang and Luck (2008).

Description

Two-parameter mixture model by Zhang and Luck (2008).

Usage

mixture2p(resp_error, ...)

Arguments

resp_error	The name of the variable in the provided dataset containing the response error. The response Error should code the response relative to the to-be-recalled target in radians. You can transform the response error in degrees to radian using the deg2rad function.
...	used internally for testing, ignore it

Details

• Domain: Visual working memory

• Task: Continuous reproduction

• Name: Two-parameter mixture model by Zhang and Luck (2008).

• Citation:

  • Zhang, W., & Luck, S. J. (2008). Discrete fixed-resolution representations in visual working memory. Nature, 453(7192), 233-235

• Requirements:

  • The response vairable should be in radians and represent the angular error relative to the target

• Parameters:

  • mu1: Location parameter of the von Mises distribution for memory responses (in radians). Fixed internally to 0 by default.

  • kappa: Concentration parameter of the von Mises distribution

  • thetat: Mixture weight for target responses

• Fixed parameters:

  • mu1 = 0

  • mu2 = 0

  • kappa2 = -100

• Default parameter links:

  • mu1 = tan_half; kappa = log; thetat = identity

• Default priors:

  • mu1:

    • main: student_t(1, 0, 1)

  • kappa:

    • main: normal(2, 1)

    • effects: normal(0, 1)

  • thetat:

    • main: logistic(0, 1)

Value

An object of class bmmodel

Examples

# generate artificial data
dat <- data.frame(y = rmixture2p(n=2000))

# define formula
ff <- bmmformula(kappa ~ 1, thetat ~ 1)

model <- mixture2p(resp_error = "y")

# fit the model
fit <- bmm(formula = ff,
           data = dat,
           model = model,
           cores = 4,
           iter = 500,
           backend = 'cmdstanr')

bmm documentation built on May 29, 2024, 11:52 a.m.