plot.see_p_significance: Plot method for practical significance

In see: Model Visualisation Toolbox for 'easystats' and 'ggplot2'

Plot method for practical significance

Description

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

Usage

## S3 method for class 'see_p_significance'
plot(
  x,
  data = NULL,
  show_intercept = FALSE,
  priors = FALSE,
  priors_alpha = 0.4,
  n_columns = 1,
  ...
)

Arguments

x	An object.
data	The original data used to create this object. Can be a statistical model.
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.
priors	Logical. If TRUE, prior distributions are simulated (using bayestestR::simulate_prior()) and added to the plot.
priors_alpha	Numeric value specifying alpha for the prior distributions.
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.
...	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 <- p_significance(m)
plot(result)

see documentation built on Sept. 11, 2024, 5:51 p.m.