MB1_mixedmodel_es.R

# starts at line 716 of paper.Rmd

d_lmer_scale <- d %>%
  filter(trial_type != "train") %>%
  mutate(log_lt = log(looking_time),
         age_mo = scale(age_mo, scale = FALSE),
         trial_num = trial_num - 8.5, 
         item = paste0(stimulus_num, trial_type)) %>%
  filter(!is.na(log_lt), !is.infinite(log_lt))

contrasts(d_lmer_scale$nae) <- c(-.5,.5)
d_lmer_scale$method <- factor(d_lmer_scale$method)
contrasts(d_lmer_scale$method) <- contr.sum(3)

mod_lmer <- lmer(log_lt ~ trial_type * method +
                   trial_type * trial_num +
                   age_mo * trial_num +
                   trial_type * age_mo * nae +
                   (1 | subid_unique) +
                   (1 | item) + 
                   (1 | lab), 
                 data = d_lmer_scale)

# fixed effect / residual standard deviation
fixef(mod_lmer)[2]/summary(mod_lmer)[[11]]