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Plot method for posterior predictive checks

Source: R/plot.check_predictions.R
print.see_performance_pp_check.Rd

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")