Pairwise model correlations at a chosen analysis level

Aggregates lower-level rows to the requested unit level, then calls model_pairwise_cor(). This is a convenience wrapper for cases where data still contain chapter, party, or simulation-detail rows but the researcher wants correlations at a broader level, such as book-level correlations.

Usage

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

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).
drop_missing: Logical. Whether to drop rows with missing model, unit, or grouping identifiers before aggregating (default TRUE).

Value

Output of model_pairwise_cor() for the requested level.

Examples

if (FALSE) { # \dontrun{
# Book-level pairwise correlations from chapter-party-level aggregated data
pw_book <- pairwise_for_level(
  agg,
  outcome = "mean_delta_gap",
  unit_by = "book",
  model_col = "model",
  methods = "pearson"
)

summarize_model_correlations(pw_book, method = "pearson")
} # }