sim <- function(subj_n = 10, item_n = 10,
b0 = 0, b1 = 0, # fixed effects
u0s_sd = 1, u0i_sd = 1, # random intercepts
u1i_sd = 1, r01i = 0, # random slope and cor
sigma_sd = 2, # error term
... # helps the function work with pmap() below
) {
# set up data structure
data <- add_random(subj = subj_n, item = item_n) %>%
# add and recode categorical variables
add_between("subj", cond = c("control", "test")) %>%
add_recode("cond", "cond.t", control = 0, test = 1) %>%
# add random effects
add_ranef("subj", u0s = u0s_sd) %>%
add_ranef("item", u0i = u0i_sd, u1i = u1i_sd, .cors = r01i) %>%
add_ranef(sigma = sigma_sd) %>%
# calculate DV
mutate(dv = b0 + u0s + u0i + (b1 + u1i) * cond.t + sigma)
# run mixed effect model and return relevant values
m <- lmer(dv ~ cond.t + (1 | subj) + (1 + cond.t | item), data = data)
broom.mixed::tidy(m)
}