Evaluate Model Selection Results — evaluate_model

Comprehensive evaluation of model selection results, computing all relevant metrics including ARI, VAF, BIC, logLik, and selection accuracy.

Usage

evaluate_model_selection(
  list_of_data,
  true_cluster,
  K_true,
  r_true,
  select_result,
  runtime_sec = NA_real_
)

Arguments

list_of_data: A list of data matrices, each (T_i x M).
true_cluster: Integer vector of true cluster assignments.
K_true: True number of clusters.
r_true: True factor dimension.
select_result: Result from select_K_r.
runtime_sec: Runtime in seconds (optional).

Value

A list with evaluation metrics:

K_hat: Estimated number of clusters.
r_hat: Estimated factor dimension.
K_correct: Logical: K_hat == K_true.
r_correct: Logical: r_hat == r_true.
Kr_correct: Logical: both K and r correct.
ARI: Adjusted Rand Index.
VAF: Variance Accounted For.
logLik: Log-likelihood.
BIC: Bayesian Information Criterion.
runtime_sec: Runtime in seconds.

Examples

if (FALSE) { # \dontrun{
# Generate data
data <- generate_synergy_data(N = 30, K = 3, r = 3, seed = 123)

# Run model selection
start_time <- Sys.time()
select_result <- select_K_r(data$list_of_data, method = "MFA", strategy = "grid")
runtime <- as.numeric(difftime(Sys.time(), start_time, units = "secs"))

# Evaluate
eval_result <- evaluate_model_selection(
  data$list_of_data, data$true_cluster,
  K_true = 3, r_true = 3,
  select_result, runtime_sec = runtime
)
} # }