Governed prediction grids can be explored as one-dimensional profiles, finite-difference predictive gradients, two-dimensional surfaces, and two-level contrast profiles.
profile <- create_prediction_profile(
fit,
variable = "trial_index",
type = "expected"
)
prediction_gradient_table(profile)
plot_prediction_profile(profile)
plot_prediction_gradient(profile)
surface <- create_prediction_surface(
fit,
x = "trial_index",
y = "standardized_covariate"
)
plot_prediction_surface(surface)
plot_prediction_surface_uncertainty(surface)
contrast <- create_prediction_contrast_profile(
fit,
variable = "trial_index",
contrast_variable = "condition",
contrast_levels = c("control", "treatment"),
measure = "difference"
)
plot_prediction_contrast_profile(contrast)These are fitted predictive descriptions, not causal response curves or automatic interaction tests.