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Simulation-study assessment helpers

Source: R/helpers.R
assess_recovery.Rd

assess_recovery() compares a point estimate and optional posterior draws to a known loading truth, returning RMSE, per-factor RMSE, bias, Tucker's congruence, credible-interval coverage, width, and Gneiting-Raftery interval score. assess_classification() compares a logical flag matrix to the true factor assignments using optimal permutation matching.

Usage

assess_recovery(Lambda_hat, Lambda_true, Lambda_draws = NULL, prob = 0.95)

assess_classification(flags, Lambda_true)

Arguments

Lambda_hat

Estimated loading matrix (N x K).

Lambda_true

True loading matrix.

Lambda_draws

Optional array of shape [T, N, K] of posterior draws, used for coverage / interval metrics.

prob

Credible-interval probability.

flags

Logical matrix of factor assignments.

Value

assess_recovery() returns a list of metrics; assess_classification() returns a list with accuracy and per_factor.