Find important interactions automatically

Usage

tl_auto_interactions(
  data,
  formula,
  top_n = 3,
  min_r2_change = 0.01,
  max_p_value = 0.05,
  exclude_vars = NULL
)

Arguments

data: A data frame containing the data
formula: A formula specifying the base model without interactions
top_n: Number of top interactions to return, a whole number. With 0 none is added.
min_r2_change: Minimum change in R-squared to consider
max_p_value: Maximum p-value for significance
exclude_vars: Character vector of predictor variables that may not appear in a selected interaction. They stay in the model as main effects. Every name must be a predictor in formula.

Value

A tidylearn model object (class "tidylearn_model") fitted with the top significant interaction terms added to the formula. The interaction test results and selected interactions are stored as attributes "interaction_tests" and "selected_interactions", data frames in the layout tl_test_interactions returns. Both are present when no interaction is added, with no rows where there is nothing to report.

Examples

# \donttest{
model <- tl_auto_interactions(mtcars, mpg ~ wt + hp + cyl, top_n = 2)
# }