Fit a response curve model using brms — fit

This function fits a response curve model using the brms package.

Usage

fit_response(
  data,
  x = NULL,
  y = NULL,
  auto = TRUE,
  type = "gompertz",
  scale_data = TRUE,
  scale_method = "min_max",
  prior = NULL,
  chains = 4,
  iter = 4000,
  warmup = 1000,
  control = list(adapt_delta = 0.95),
  infer_xrange = NULL,
  infer_length = 1000,
  cost_per_unit = 1,
  response_rate = 1,
  ...
)

Arguments

data: A data frame containing the data to be fitted.
x: The name of the independent variable (predictor).
y: The name of the dependent variable (response).
auto: Logical indicating whether to automatically scale the data and set priors. Default is TRUE.
type: The type of response curve model to fit. Options are "gompertz", "logistic", "weibull", or "exponential".
infer_xrange: Optional range of x values for inference. If NULL, uses the range of x in the data.
infer_length: The number of points to generate for inference. Default is 1000.
cost_per_unit: The cost per unit of the independent variable. Default is 1.0.
response_rate: The response rate to be used in return calculations. Default is 1.0.
...: Additional arguments to be passed to the brms::brm function.
infer_scaled: Logical indicating whether to return scaled inference results. Default is TRUE.

Value

A fitted model object.

Details

The function fits a response curve model using the specified type and returns the fitted model object.