Create a risk preference object directly from known parameters

Use this when you already know the utility function parameters — for example, from a published study or a previous elicitation session.

Usage

risk_preference(
  model = c("CRRA", "CARA"),
  param,
  val_min,
  val_max,
  outcome_name = "outcome",
  maximize = TRUE,
  param_ci = NULL,
  posterior = NULL
)

Arguments

model: Character: "CRRA" or "CARA".
param: Numeric scalar. The risk aversion parameter (gamma for CRRA, alpha for CARA). Positive values = risk-averse.
val_min: Numeric. Minimum plausible outcome in original units.
val_max: Numeric. Maximum plausible outcome in original units.
outcome_name: Character. Label for the outcome (e.g. "frogs").
maximize: Logical. TRUE if higher outcomes are better.
param_ci: Optional length-2 numeric vector giving the 95% credible interval for the parameter, e.g. from a previous elicitation.
posterior: Optional named list with elements grid (parameter grid) and weights (posterior weights). Carries full Bayesian posterior from elicitation.

Value

An S3 object of class risk_preference.

Examples

rp <- risk_preference(model = "CRRA", param = 1.5,
                      val_min = 0, val_max = 200,
                      outcome_name = "frogs", maximize = TRUE)