confint.beezdemand_hurdle: Confidence Intervals for Hurdle Demand Model Parameters

In beezdemand: Behavioral Economic Easy Demand

Confidence Intervals for Hurdle Demand Model Parameters

Description

Computes confidence intervals for fixed effect parameters from a TMB-based hurdle demand model using the asymptotic normal approximation.

Usage

## S3 method for class 'beezdemand_hurdle'
confint(
  object,
  parm = NULL,
  level = 0.95,
  report_space = c("internal", "natural"),
  ...
)

Arguments

object	A beezdemand_hurdle object from fit_demand_hurdle().
parm	Character vector of parameter names to compute CIs for. Default includes all fixed effect parameters.
level	Confidence level (default 0.95).
report_space	Character. Reporting space for parameters: "internal": parameters on internal/fitting scale (log for Q0, alpha) "natural": back-transformed to natural scale
...	Additional arguments (ignored).

Details

Confidence intervals are computed using the asymptotic normal approximation based on standard errors from TMB::sdreport(). For parameters estimated on the log scale (Q0, alpha, k), intervals can be back-transformed to the natural scale using report_space = "natural".

The transformation uses:

• For log-scale parameters: exp(estimate +/- z * SE)

Value

A tibble with columns: term, estimate, conf.low, conf.high, level, component, estimate_scale.

Examples

data(apt)
fit <- fit_demand_hurdle(apt, y_var = "y", x_var = "x", id_var = "id")
confint(fit)

beezdemand documentation built on March 3, 2026, 9:07 a.m.