Plot method for density estimation of posterior samples — plot.see_estimate

The plot() method for the bayestestR::estimate_density() function.

Usage

# S3 method for class 'see_estimate_density'
plot(
  x,
  stack = TRUE,
  show_intercept = FALSE,
  n_columns = 1,
  priors = FALSE,
  alpha_priors = 0.4,
  alpha_posteriors = 0.7,
  linewidth = 0.9,
  size_point = 2,
  centrality = "median",
  ci = 0.95,
  ...
)

Arguments

x: An object.
stack: Logical. If TRUE, densities are plotted as stacked lines. Else, densities are plotted for each parameter among each other.
show_intercept: Logical, if TRUE, the intercept-parameter is included in the plot. By default, it is hidden because in many cases the intercept-parameter has a posterior distribution on a very different location, so density curves of posterior distributions for other parameters are hardly visible.
n_columns: For models with multiple components (like fixed and random, count and zero-inflated), defines the number of columns for the panel-layout. If NULL, a single, integrated plot is shown.
priors: Logical. If TRUE, prior distributions are simulated (using bayestestR::simulate_prior()) and added to the plot.
alpha_priors: Numeric value specifying alpha for the prior distributions.
alpha_posteriors: Numeric value specifying alpha for the posterior distributions.
linewidth: Numeric value specifying size of line geoms.
size_point: Numeric specifying size of point-geoms.
centrality: Character specifying the point-estimate (centrality index) to compute. Can be "median", "mean" or "MAP".
ci: Numeric value of probability of the CI (between 0 and 1) to be estimated. Default to 0.95.
...: Arguments passed to or from other methods.

Value

A ggplot2-object.

Examples

library(rstanarm)
library(bayestestR)
set.seed(123)
m <<- suppressWarnings(stan_glm(Sepal.Length ~ Petal.Width * Species, data = iris, refresh = 0))
result <- estimate_density(m)
plot(result)