# Prediction Contrasts, Rankings, and Groups

Prediction grids can be summarised by observed design variables without expanding the approved model-family scope.

    grid <- create_prediction_grid(
      fit,
      at = list(condition = c("control", "treatment"))
    )

    pred <- predict_model(
      fit,
      newdata = grid,
      type = "expected",
      include_group_effects = FALSE
    )

    prediction_pairwise_contrasts(pred)
    prediction_interval_width(pred)
    prediction_rank_probabilities(pred)

The ranking function is deliberately descriptive. A probability of rank one is not converted into an automatic selection.

When the prediction data contain multiple rows per substantive group:

    grouped <- group_prediction_summary(pred, by = "condition")
    grouped
    plot_group_predictions(grouped, "condition")

This makes aggregation explicit and reproducible rather than hiding it inside plotting code.