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JimGrange/brm.R

Last active November 20, 2023 14:09
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interaction term in brms
Raw
brm.R
library(brms)
library(tidyverse)
set.seed(123)
# generate some subject-averaged data
n_subjects <- 100
data <- tibble(
id = 1:n_subjects,
congruent_informative = rnorm(n_subjects, 500, 60),
incongruent_informative = rnorm(n_subjects, 500, 60),
congruent_noninformative = rnorm(n_subjects, 500, 60),
incongruent_noninformative = rnorm(n_subjects, 500, 60),
) |>
pivot_longer(cols = congruent_informative:incongruent_noninformative,
names_sep = "_",
names_to = c("congruency", "cue"),
values_to = "rt")
# fit the model
# of course, ex-G is bad choice for this subject-averaged simulated data
model <- brm(rt ~ congruency * cue + (1|id),
data = data,
family = exgaussian(),
cores = 4)
summary(model)
# Population-Level Effects:
# Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
# Intercept 505.59 5.79 494.38 517.35 1.00 3755 2505
# congruencyincongruent -12.24 8.33 -29.04 3.86 1.00 3511 2635
# cuenoninformative 1.68 8.26 -14.45 17.67 1.00 3552 2613
# congruencyincongruent:cuenoninformative 2.53 11.57 -20.01 25.80 1.00 3098 2293
#
# Family Specific Parameters:
# Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
# sigma 55.01 3.71 46.66 60.88 1.00 2638 2786
# beta 15.10 9.73 2.03 35.04 1.00 1975 2215
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