Run Bayesian Sigmoid Antagonist Model — sigmoid_antagonist

For additional information on additional function arguments, reference: <https://paul-buerkner.github.io/brms/reference/brm.html> or <https://rdrr.io/cran/rstan/man/stan.html>

Usage

sigmoid_antagonist_model(
  data,
  formula = sigmoid_antagonist_formula(),
  prior = sigmoid_antagonist_prior(),
  init = sigmoid_antagonist_init(),
  iter = 8000,
  control = list(adapt_delta = 0.99),
  stanvar_function = sigmoid_stanvar,
  expose_functions = TRUE,
  ...
)

sigmoid_antagonist_model(
  data,
  formula = sigmoid_antagonist_formula(),
  prior = sigmoid_antagonist_prior(),
  init = sigmoid_antagonist_init(),
  iter = 8000,
  control = list(adapt_delta = 0.99),
  stanvar_function = sigmoid_stanvar,
  expose_functions = TRUE,
  ...
)

Arguments

data: data.frame of experimental data. must contain columns sponse and any predictors specified in the formula.
formula: brmsformula object. To create a dose-response brmsformula, (default: BayesPharma::sigmoid__formula().
prior: brmspriors data.frame for ec50, hill, top, and bottom. Use one of the priors functions provided to create priors to use here. (default: BayesPharma::sigmoid_antagonist_prior()
init: initial values of the parameters being modeled (default = BayesPharma::sigmoid_antagonist_init()
iter: number of iterations the model runs. Increasing iter can help with model convergence (default: 8000).
control: a named list of parameters to control the sampler's behavior. Adding max_treedepth and giving a greater value than 10 can improve model convergence (default: list(adapt_delta = 0.99)).
stanvar_function: stan code for the model (default: BayesPharma::sigmoid_stanvar)
expose_functions: boolean. Expose the BayesPharma functions for the model [default: TRUE].
...: additional arguments passed to brms::brm

Value

brmsfit object

Examples

if (FALSE) {
  sigmoid_antagonist_model(data,
   formula = sigmoid_antagonist_formula(predictors = 0 + drug))
}
if (FALSE) {
  sigmoid_antagonist_model(data,
   formula = sigmoid_antagonist_formula(predictors = 0 + drug))
}