Is there an easy non-modelling way for visualizing different prior distributions?

Modeling
,
2

You can plot simulations from any available probability distribution, or alternatively, you can directly plot the density function for a given distribution. For example:

library(tidyverse)
library(VGAM)  # For laplace distribution
library(patchwork) # For plot layouts
theme_set(theme_bw())

# Sample from distribution
p1=ggplot(tibble(x=rt(1000, df=4)), aes(x)) +
  geom_density()

p2=ggplot(tibble(x=rlaplace(1000, location=0, scale=4)), aes(x)) +
  geom_density()

p3=ggplot(tibble(x=rlnorm(1000, meanlog=0, sdlog=0.5)), aes(x)) +
  geom_density()

# Plot multiple sets of random draws from a distribution
p4=ggplot() +
  replicate(10, geom_density(data=tibble(x=rlaplace(100, location=0, scale=4)), aes(x)))

list(p1,p2,p3,p4) %>% wrap_plots(nrow=2)


# Plot density function
p5 = ggplot() +
  stat_function(fun=dcauchy, args=list(location=1, scale=2), 
                xlim=c(-10,10))

p6=ggplot() +
  stat_function(fun=dnorm, args=list(mean=2, sd=3), 
                xlim=c(-10,15))

p7=ggplot() +
  stat_function(fun=dt, args=list(df=4), 
                xlim=c(-20,20), n=1000)

p8=ggplot() +
  stat_function(fun=dlaplace, args=list(location=0, scale=4), 
                xlim=c(-20,20), n=1000)

list(p5,p6,p7,p8) %>% wrap_plots(nrow=2)

Created on 2021-08-01 by the reprex package (v2.0.0)

7 Likes