b_reaction_time function

b_reaction_time

Bayesian model for comparing reaction times.

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

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).

Returns

An object of class reaction_time_class

Examples

# 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)