Calculate partial effects based on a model with interactions

Calculate partial effects based on a model with interactions

Usage

tl_interaction_effects(model, var, by_var, at_values = NULL, intervals = TRUE)

Arguments

model

A tidylearn model object

var

Variable to calculate effects for

by_var

Variable to calculate effects by (interaction variable)

at_values

Named list of values at which to hold other variables

intervals

Logical; whether to include confidence intervals

Value

For numeric var: a list with effects (data frame of predicted values across the variable range for each level of by_var) and slopes (data frame with the slope of var at each level of by_var). For categorical var: a data frame of predicted values at each factor level for each level of by_var.

slopes$slope_se is the standard error of a straight line fitted to the prediction grid, not the sampling uncertainty of the marginal effect. For a linear model the grid is exactly linear in var, so this is near zero by construction and should not be read as a precise estimate. Use summary(model$fit) for inference on the interaction coefficient itself.