Pairwise model correlations — model_pairwise

Computes Pearson and/or Spearman correlations between every pair of models on a shared set of units. This is a diagnostic complement to the omnibus metrics in model_agreement(): it reveals which models diverge.

Usage

model_pairwise_cor(
  data,
  outcome = "mean_outcome",
  unit_by = c("book_id", "chapter_id", "group"),
  group_by = NULL,
  model_col = "model",
  methods = c("pearson", "spearman")
)

Arguments

data: A data frame with one row per model-by-unit combination.
outcome: Character string naming the score column (default "mean_outcome").
unit_by: Character vector of columns that jointly identify a unit (default c("book_id", "chapter_id", "group")).
group_by: Optional character vector. If provided, agreement metrics are computed separately within each level of these columns (e.g., "group" to get separate estimates for Democrats and Republicans).
model_col: Character string naming the model column (default "model").
methods: Character vector of correlation types. One or both of "pearson" and "spearman" (default both).

Value

A tibble with columns: any group_by columns, plus model_a, model_b, method, correlation, and n_units.

Examples

if (FALSE) { # \dontrun{
pw <- model_pairwise_cor(agg, outcome = "mean_rating",
  unit_by = c("book_id", "chapter_id", "group"))
plot_model_agreement(pw, type = "heatmap")
} # }