This function creates a ggplot2 object comparing the power curves of different designs. Each design is color coded and each facet (sub plot) is a model coefficient.
plot_compare_power(...)Any number of data frame containing power results obtained from
the cbc_power() function, separated by commas.
A plot comparing the power curves of different designs.
if (FALSE) {
library(cbcTools)
# Generate all possible profiles
profiles <- cbc_profiles(
price = c(1, 1.5, 2, 2.5, 3),
type = c("Fuji", "Gala", "Honeycrisp"),
freshness = c('Poor', 'Average', 'Excellent')
)
# Make designs to compare: full factorial vs bayesian d-efficient
design_random <- cbc_design(
profiles = profiles,
n_resp = 100, n_alts = 3, n_q = 6
)
# Same priors will be used in bayesian design and simulated choices
priors <- list(
price = -0.1,
type = c(0.1, 0.2),
freshness = c(0.1, 0.2)
)
design_bayesian <- cbc_design(
profiles = profiles,
n_resp = 100, n_alts = 3, n_q = 6, n_start = 1, method = "CEA",
priors = priors, parallel = FALSE
)
# Obtain power for each design by simulating choices
power_random <- design_random |>
cbc_choices(obsID = "obsID", priors = priors) |>
cbc_power(
pars = c("price", "type", "freshness"),
outcome = "choice", obsID = "obsID", nbreaks = 5, n_q = 6, n_cores = 2
)
power_bayesian <- design_bayesian |>
cbc_choices(obsID = "obsID", priors = priors) |>
cbc_power(
pars = c("price", "type", "freshness"),
outcome = "choice", obsID = "obsID", nbreaks = 5, n_q = 6, n_cores = 2
)
# Compare power of each design
plot_compare_power(power_bayesian, power_random)
}