bayes4psy (version 1.2.13)

b_reaction_time: b_reaction_time

Description

Bayesian model for comparing reaction times.

Usage

b_reaction_time(
  t,
  s,
  priors = NULL,
  warmup = 1000,
  iter = 2000,
  chains = 4,
  seed = NULL,
  refresh = NULL,
  control = NULL,
  suppress_warnings = TRUE
)

Value

An object of class `reaction_time_class`

Arguments

t

a vector containing reaction times for each measurement.

s

a vector containing subject indexes. Starting index should be 1 and the largest subject index should equal the number of subjects.

priors

List of parameters and their priors - b_prior objects. You can put a prior on the mu_m (mean), sigma_m (variance of mu_m), mu_s (variance), sigma_s (variance of mu_s), mu_l (mean of the exponent factor) and sigma_l (variance of mu_l) parameters (default = NULL).

warmup

Integer specifying the number of warmup iterations per chain (default = 1000).

iter

Integer specifying the number of iterations (including warmup, default = 2000).

chains

Integer specifying the number of parallel chains (default = 4).

seed

Random number generator seed (default = NULL).

refresh

Frequency of output (default = NULL).

control

A named list of parameters to control the sampler's behavior (default = NULL).

suppress_warnings

Suppress warnings returned by Stan (default = TRUE).

Examples

# \donttest{
# priors
mu_prior <- b_prior(family="normal", pars=c(0, 100))
sigma_prior <- b_prior(family="uniform", pars=c(0, 500))
lambda_prior <- b_prior(family="uniform", pars=c(0.05, 5))

# attach priors to relevant parameters
priors <- list(c("mu_m", mu_prior),
              c("sigma_m", sigma_prior),
              c("mu_s", sigma_prior),
              c("sigma_s", sigma_prior),
              c("mu_l", lambda_prior),
              c("sigma_l", sigma_prior))

# generate data
s <- rep(1:5, 20)
rt <- emg::remg(100, mu=10, sigma=1, lambda=0.4)

# fit
fit <- b_reaction_time(t=rt, s=s, priors=priors, chains=1)
# }