Power calculation for inattentive raters

inatt.R

library(faux)
library(tidyverse)

inatt <- function(within, between, n, mu, sd, r, prop_bad = 0) {
  good_n <- n * (1-prop_bad)
  bad_n <- n * prop_bad
  good_dat <- sim_design(within, between, n = good_n, mu, sd, r)
  if (bad_n > 0) {
    # should r be 0 for inattentive responders?
    bad_dat <- sim_design(within, between, n = bad_n, unlist(mu)%>% mean(), sd, r) %>%
      dplyr::mutate(sub_id = make_id(nrow(.), "B"))
  } else {
    bad_dat <- good_dat[FALSE,]
  }
  all_dat <- dplyr::bind_rows(good_dat, bad_dat)
}

within <- list(
  condition = c("pre", "post")
)
between <- list(
  pet = c("dog", "cat")
)
mu <- list(
  dog = c(pre = 100, post = 110),
  cat = c(pre = 100, post = 120)
)
sd <- 30

r <- 0.5

sim_dat <- purrr::map_df(1:100, ~{
  dat <- inatt(within, between, 150, mu, sd, r, .10) %>%
    tidyr::gather(condition, val, pre, post)
  suppressMessages(
    analysis <- afex::aov_4(val ~ pet + (condition | sub_id), data = dat)
  )
  analysis$aov %>% broom::tidy()
})

alpha <- 0.05
sim_dat %>%
  dplyr::filter(term != "Residuals") %>%
  dplyr::group_by(term) %>%
  dplyr::summarise(power = mean(p.value < alpha))