Plot interaction effects

Usage

tl_plot_interaction(
  model,
  var1,
  var2,
  n_points = 100,
  fixed_values = NULL,
  confidence = TRUE,
  ...
)

Arguments

model: A tidylearn model object
var1: First variable in the interaction
var2: Second variable in the interaction
n_points: Number of points to use for continuous variables
fixed_values: Named list of values for other variables in the model
confidence: Logical; whether to show a 95\ band is drawn when one variable is numeric and the other categorical, and needs a model whose underlying fit is an lm or glm; for a glm it is built on the link scale and transformed to the response scale. For any other fit a message says no band was drawn.
...: Additional arguments to pass to predict()

Value

A ggplot object. Two numeric variables are drawn as a filled contour of the prediction; a numeric and a categorical variable as one line per category; two categorical variables as dodged bars.

Examples

# \donttest{
model <- tl_model(mtcars, mpg ~ wt * hp, method = "linear")

# Two numeric variables are drawn as a filled contour over both ranges
tl_plot_interaction(model, var1 = "wt", var2 = "hp")


# A numeric by categorical interaction is drawn as one line per level,
# each with a confidence band
am_model <- tl_model(transform(mtcars, am = factor(am)), mpg ~ wt * am,
  method = "linear")
tl_plot_interaction(am_model, var1 = "wt", var2 = "am")


# Coarser grid, no band
tl_plot_interaction(am_model, var1 = "wt", var2 = "am",
  n_points = 20, confidence = FALSE)

# }