print.see_performance_pp_check: Plot method for posterior predictive checks

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

Plot method for posterior predictive checks

Description

The plot() method for the performance::check_predictions() function.

Usage

## S3 method for class 'see_performance_pp_check'
print(
  x,
  size_line = 0.5,
  size_point = 2,
  size_bar = 0.7,
  size_axis_title = base_size,
  size_title = 12,
  base_size = 10,
  line_alpha = 0.15,
  style = theme_lucid,
  colors = unname(social_colors(c("green", "blue"))),
  type = c("density", "discrete_dots", "discrete_interval", "discrete_both"),
  x_limits = NULL,
  ...
)

## S3 method for class 'see_performance_pp_check'
plot(
  x,
  size_line = 0.5,
  size_point = 2,
  size_bar = 0.7,
  size_axis_title = base_size,
  size_title = 12,
  base_size = 10,
  line_alpha = 0.15,
  style = theme_lucid,
  colors = unname(social_colors(c("green", "blue"))),
  type = c("density", "discrete_dots", "discrete_interval", "discrete_both"),
  x_limits = NULL,
  ...
)

Arguments

x	An object.
size_line	Numeric value specifying size of line geoms.
size_point	Numeric specifying size of point-geoms.
size_bar	Size of bar geoms.
base_size, size_axis_title, size_title	Numeric value specifying size of axis and plot titles.
line_alpha	Numeric value specifying alpha of lines indicating yrep.
style	A ggplot2-theme.
colors	Character vector of length two, indicating the colors (in hex-format) for points and line.
type	Plot type for the posterior predictive checks plot. Can be "density" (default), "discrete_dots", "discrete_interval" or "discrete_both" (the ⁠discrete_*⁠ options are appropriate for models with discrete - binary, integer or ordinal etc. - outcomes).
x_limits	Numeric vector of length 2 specifying the limits of the x-axis. If not NULL, will zoom in the x-axis to the specified limits.
...	Arguments passed to or from other methods.

Value

A ggplot2-object.

See Also

See also the vignette about check_model().

Examples

library(performance)

model <- lm(Sepal.Length ~ Species * Petal.Width + Petal.Length, data = iris)
check_predictions(model)

# dot-plot style for count-models
d <- iris
d$poisson_var <- rpois(150, 1)
model <- glm(
  poisson_var ~ Species + Petal.Length + Petal.Width,
  data = d,
  family = poisson()
)
out <- check_predictions(model)
plot(out, type = "discrete_dots")

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