Some code to simulate a conjoint

conjoint_dd.R

# Load packages
library(tidyverse)
library(DeclareDesign)

# helper
Y_function <- function(data) {
  data %>%
    group_by(pair) %>%
    mutate(Y = if_else(E == max(E), 1, 0)) %>%
    ungroup
}

# design declaration
# You can change the number of subjects, or pairs to be rated
# You can also change how many attributes and what their levels are.  
# You can change what your beliefs are about what the effects will be
design <- 
  declare_population(
    subject = add_level(N = 500),
    pair = add_level(N = 4),
    candidate = add_level(N = 2, U = runif(N))
  ) +
  declare_assignment(assignment_variable = "A1") +
  declare_assignment(assignment_variable = "A2", 
                     conditions = c("young", "middle", "old")) +
  declare_assignment(assignment_variable = "A3")  +
  declare_step(
    E = 
      0.05 * A1 + 
      0.04 * (A2 == "middle") + 
      0.08 * (A2 == "old") + 
      0.02 * A3 + U,
    handler = fabricate) +
  declare_measurement(handler = Y_function) +
  declare_estimator(Y ~ A1 + A2 + A3,
                    model = lm_robust, term = TRUE)


# Simulate the experiment 500 times
simulations <- simulate_design(design)

# Calculate diagnosands -- here i just did the power
simulations %>%
  group_by(term) %>%
  summarise(power = mean(p.value <= 0.05))