b_success_rate function

b_success_rate

Bayesian model for comparing test success rate.

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

Arguments

• r: a vector containing test results (0 - test was not solved successfully, 1 - test was solved successfully).
• 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 p (mean probability of success) and tau (variance) 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 success_rate_class.

Examples

# priors
p_prior <- b_prior(family="beta", pars=c(1, 1))
tau_prior <- b_prior(family="uniform", pars=c(0, 500))

# attach priors to relevant parameters
priors <- list(c("p", p_prior),
               c("tau", tau_prior))

# generate data
s <- rep(1:5, 20)
data <- rbinom(100, size=1, prob=0.6)

# fit
fit <- b_success_rate(r=data, s=s, priors=priors, chains=1)