rlikert

gistfile1.txt

# Load packages using the 'librarian' package
librarian::shelf(tidyverse,
                 tidybayes,
                 brms,
                 here)
                 
# Create function to simulate the outcome scale for arbitrary
# prior distributions
rlikert <- function(n = 1e3,
                    t1 = -0.84,
                    t2 = -0.25,
                    t3 = 0.25,
                    t4 = 0.84,
                    mean = 0,
                    disc = 0){
  
  require(tidyverse)
  
  # Calculate standard deviation from discrimination
  sd <- 1/exp(disc)
  
  # Create empty dataframe
  dta <- tibble(n = 1:n)
  
  # Calculate probabilities of each response for each individual
  dta <- 
    dta %>% 
    mutate(
      p1 = pnorm(t1, mean, sd),
      p2 = pnorm(t2, mean, sd) - pnorm(t1, mean, sd),
      p3 = pnorm(t3, mean, sd) - pnorm(t2, mean, sd),
      p4 = pnorm(t4, mean, sd) - pnorm(t3, mean, sd),
      p5 = 1 - pnorm(t4, mean, sd)
    )
  
  # For each individual, sample a single response category
  dta <- dta %>% mutate(resp = NA)
  for (i in 1:nrow(dta)) {
    dta$resp[i] <- 
      sample(
        x = c(1:5), 
        size = 1, 
        prob = c(dta$p1[i], dta$p2[i], dta$p3[i], dta$p4[i], dta$p5[i])
      )
  }
  
  # Return
  dta$resp
  
}