paper/JOSS paper files/Figure2.R

In DominiqueMakowski/bayestestR: Understand and Describe Bayesian Models and Posterior Distributions

library(bayestestR)
library(tidyverse)
library(strengejacke) # What the hell is that ^^
library(ggplot2)

set.seed(123)
posterior <- distribution_chisquared(100, 3)
dat <- as.data.frame(density(posterior))

p1 <- dat %>%
  mutate(fill = ifelse(x < hdi(posterior)$CI_low, "low",
    ifelse(x > hdi(posterior)$CI_high, "high", "middle")
  )) %>%
  ggplot(aes(x = x, y = y, fill = fill)) +
  geom_ribbon(aes(ymin = 0, ymax = y)) +
  geom_vline(xintercept = 0, linetype = "dotted") +
  theme_classic(base_size = 20) +
  scale_y_continuous(expand = c(0, 0), limits = c(0, .25)) +
  scale_fill_manual(values = c("high" = "#FFC107", "low" = "#FFC107", "middle" = "#E91E63"), guide = FALSE) +
  annotate("text", x = 2.5, y = .05, label = "The 89% HDI", color = "white", size = 10) +
  xlab(NULL) +
  ylab("Probability Density\n")

# ggsave("paper/Figure2.png", width = 13, height = 8, units = "in", dpi = 300)


p2 <- dat %>%
  mutate(fill = ifelse(x < ci(posterior)$CI_low, "low",
    ifelse(x > ci(posterior)$CI_high, "high", "middle")
  )) %>%
  ggplot(aes(x = x, y = y, fill = fill)) +
  geom_ribbon(aes(ymin = 0, ymax = y)) +
  geom_vline(xintercept = 0, linetype = "dotted") +
  theme_classic(base_size = 20) +
  scale_y_continuous(expand = c(0, 0), limits = c(0, .25)) +
  scale_fill_manual(values = c("high" = "#FFC107", "low" = "#FFC107", "middle" = "#2196F3"), guide = FALSE) +
  annotate("text", x = 2.9, y = .05, label = "The 89% ETI", color = "white", size = 10) +
  xlab("\nParameter Value") +
  ylab("Probability Density\n")

p <- see::plots(p1, p2, tags = TRUE)

ggsave("Figure2.png", plot = p, width = 13, height = 8, units = "in", dpi = 450)