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@andrewheiss
Last active July 22, 2024 02:27
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library(tidyverse)
library(brms)
library(marginaleffects)
library(tidybayes)
library(ggh4x)
library(scales)
# Ordered logit model
ologit_priors <- c(
prior(student_t(1, 0, 3), class = Intercept),
prior(student_t(1, 0, 3), class = b)
)
model3_bayes <- brm(
bf(c7_internal_movement ~ derogation_ineffect*panbackdichot +
new_cases_z + new_deaths_z + cumulative_cases_z + cumulative_deaths_z + v2x_rule),
data = derog,
family = "cumulative",
prior = ologit_priors,
chains = 4, seed = 1234,
threads = threading(2)
)
# Basic normal way with pointranges
plot_predictions(model3_bayes, condition = c("derogation_ineffect", "panbackdichot")) +
facet_wrap(vars(factor(group, levels = levels(derog$c7_internal_movement)))) +
theme_pandem() +
theme(legend.position = "bottom")
preds_model3 <- model3_bayes |>
epred_draws(
datagrid(model = model3_bayes, derogation_ineffect = unique, panbackdichot = unique),
ndraws = 500, seed = 1234) |>
ungroup() |>
mutate(
derogation_ineffect = factor(derogation_ineffect, labels = c("No derogation", "Derogation"), ordered = TRUE),
panbackdichot = factor(panbackdichot, labels = c("No backsliding", "Backsliding"), ordered = TRUE)
)
# Fancy way with fuzzy stacked bars
ggplot(preds_model3, aes(x = .draw, y = .epred)) +
geom_area(aes(fill = .category), position = position_stack()) +
scale_x_continuous(breaks = NULL, expand = c(0, 0)) +
scale_y_continuous(labels = label_percent(), expand = c(0, 0)) +
scale_fill_manual(values = clrs[c(7, 4, 2)]) +
labs(
x = NULL, y = "Cumulative outcome probabilities",
fill = "Internal movement measures") +
facet_nested_wrap(
vars(panbackdichot, derogation_ineffect),
strip = strip_nested(background_x = list(element_rect(fill = "grey92"), NULL), by_layer_x = TRUE),
nrow = 1
) +
theme_pandem() +
theme(legend.title.position = "left", legend.position = "top")
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