cbc_power: Estimate power analysis for choice experiment designs

In cbcTools: Design and Analyze Choice-Based Conjoint Experiments

Estimate power analysis for choice experiment designs

Description

This function estimates the same model multiple times using different sample sizes to assess statistical power. It returns both the estimated models and a summary of coefficient estimates, standard errors, and power statistics.

Usage

cbc_power(
  data,
  outcome = "choice",
  obsID = "obsID",
  pars = NULL,
  randPars = NULL,
  n_breaks = 10,
  n_q = NULL,
  panelID = NULL,
  alpha = 0.05,
  return_models = FALSE,
  n_cores = NULL,
  ...
)

Arguments

data	A data frame containing choice data. Can be a cbc_choices object or any data frame with the required columns.
outcome	Name of the outcome variable column (1 for chosen, 0 for not). Defaults to "choice".
obsID	Name of the observation ID column. Defaults to "obsID".
pars	Names of the parameters to estimate. If NULL (default), will auto-detect from column names for cbc_choices objects.
randPars	Named vector of random parameters and their distributions ('n' for normal, 'ln' for log-normal). Defaults to NULL.
n_breaks	Number of sample size groups to test. Defaults to 10.
n_q	Number of questions per respondent. Auto-detected for cbc_choices objects if not specified.
panelID	Name of the panel ID column for panel data. Auto-detected as "respID" for multi-respondent cbc_choices objects.
alpha	Significance level for power calculations. Defaults to 0.05.
return_models	If TRUE, includes full model objects in returned list. Defaults to FALSE.
n_cores	Number of cores for parallel processing. Defaults to parallel::detectCores() - 1.
...	Additional arguments passed to logitr::logitr().

Value

A cbc_power object containing:

• power_summary: Data frame with sample sizes, coefficients, estimates, standard errors, t-statistics, and power

• models: List of estimated models (if return_models = TRUE)

• sample_sizes: Vector of sample sizes tested

• n_breaks: Number of breaks used

• alpha: Significance level used

Examples

library(cbcTools)

# Create profiles and design
profiles <- cbc_profiles(
  price = c(1, 2, 3),
  type = c("A", "B", "C"),
  quality = c("Low", "High")
)

design <- cbc_design(profiles, n_alts = 2, n_q = 6)

# Simulate choices
priors <- cbc_priors(profiles, price = -0.1, type = c(0.5, 0.2), quality = 0.3)
choices <- cbc_choices(design, priors)

# Run power analysis
power_results <- cbc_power(choices, n_breaks = 8)

# View results
print(power_results)
plot(power_results)

cbcTools documentation built on Aug. 21, 2025, 6:03 p.m.