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Plot method for checking model assumptions

Source: R/plot.check_model.R
plot.see_check_model.Rd

The plot() method for the performance::check_model() function. Diagnostic plots for regression models.

Usage

# S3 method for class 'see_check_model'
plot(
  x,
  theme = NULL,
  colors = NULL,
  type = c("density", "discrete_dots", "discrete_interval", "discrete_both"),
  n_columns = 2,
  ...
)

Arguments

x

An object.

theme

A ggplot2-theme function, e.g. theme = theme_lucid() or theme = ggplot2::theme_dark().

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).

n_columns

Number of columns to align plots.

...

Arguments passed to or from other methods.

Value

A ggplot2-object.

Details

Larger models (with many observations) may take a longer time to render. Thus, the number of data points is limited to 2000 by default. Use plot(check_model(), maximum_dots = <number>) (or check_model(maximum_dots = <number>)) to define the number of data points that should be shown in the plots.

See also

See also the vignette about check_model().

Examples

if (FALSE) { # require("patchwork") && FALSE
library(performance)

model <- lm(qsec ~ drat + wt, data = mtcars)
plot(check_model(model))
}