Evaluate imputation performance against known values

Computes type-specific metrics for each trait on the validation and test splits. When a trait_map is supplied, metrics are dispatched per trait type; otherwise the function falls back to continuous-only metrics (RMSE, Pearson r, 95% coverage).

Usage

evaluate_imputation(pred, truth, splits, pred_se = NULL, trait_map = NULL)

Arguments

pred

predicted values: either a numeric matrix in latent scale (same dimensions as truth), or a "pigauto_pred" object from predict.pigauto_fit.

truth

numeric matrix of true values in latent scale (from pigauto_data$X_scaled).

splits

list (output of make_missing_splits).

pred_se

numeric matrix of prediction SEs (same scale as pred). Used for 95\ when pred is a pigauto_pred (uses pred$se).

trait_map

list of trait descriptors (from pigauto_data). If NULL and pred is not a pigauto_pred, the v0.1 all-continuous evaluation is used.

Value

A data.frame with columns split, trait, type, n, and type-specific metric columns.

Details

Metrics by trait type:

continuous

RMSE, Pearson r, 95\ supplied)

proportion

RMSE, Pearson r, 95\ supplied)

count

RMSE, MAE, Pearson r

ordinal

RMSE, Spearman rho

binary

Accuracy, Brier score

categorical

Accuracy

zi_count

RMSE, MAE, Pearson r, zero-accuracy, Brier score (on gate)

For binary and categorical traits the function accepts either a pigauto_pred object (preferred, gives access to probabilities) or raw matrices (latent scale).

Examples

# \donttest{
truth <- matrix(c(1, 2, 3, 4), nrow = 2L)
splits <- list(test_idx = 1L, val_idx = 2L, n = 2L, p = 2L)
evaluate_imputation(truth, truth, splits)
#>      split  trait       type n rmse pearson_r coverage_95 mae spearman_rho
#> val    val trait1 continuous 1    0        NA          NA  NA           NA
#> test  test trait1 continuous 1    0        NA          NA  NA           NA
#>      accuracy brier zero_accuracy aitchison rmse_clr simplex_mae
#> val        NA    NA            NA        NA       NA          NA
#> test       NA    NA            NA        NA       NA          NA
# }