## Not run:
# fit <- brm(count ~ log_Age_c + log_Base4_c * Trt_c + (1 | patient),
# data = epilepsy, family = poisson())
# ## plot all marginal effects
# plot(marginal_effects(fit), ask = FALSE)
# ## only plot the marginal interaction effect of 'log_Base4_c:Trt_c'
# ## for different values for 'log_Age_c'
# mdata <- data.frame(log_Age_c = c(-0.3, 0, 0.3))
# plot(marginal_effects(fit, effects = "log_Base4_c:Trt_c",
# data = mdata))
# ## also incorporate random effects variance over patients
# ## and add a rug representation of predictor values
# plot(marginal_effects(fit, effects = "log_Base4_c:Trt_c",
# data = mdata, re_formula = NULL), rug = TRUE)
# ## End(Not run)
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