Score selbal-selected single-balance discriminator

Scores each row of X independently with the frozen balance learned by fit_bal_selbal. For one specimen, the self-contained balance is computed over the frozen numerator/denominator groups using only the features that are PRESENT in X and strictly POSITIVE in that specimen: $$B = \sqrt{r s / (r + s)} \,\bigl(\overline{\log v_N} - \overline{\log v_D}\bigr),$$ with \(r\), \(s\) the per-specimen counts of present-positive numerator and denominator features. The returned score is the frozen logistic linear predictor \(intercept + slope \cdot B\); larger is more case-like.

If a specimen has fewer than model$min_group_coverage present-positive features in EITHER group (so the balance is undefined/degenerate), it receives the documented neutral score 0. If selbal selected an empty/degenerate balance at fit time (model$degenerate), every row receives 0. The balance is exactly invariant to per-sample positive scaling, and scoring uses no random numbers and no scored-batch statistics.

Usage

score_bal_selbal(model, X, meta = NULL)

Arguments

model

A bal_selbal_model object returned by fit_bal_selbal.

X

Numeric matrix (samples \(\times\) features) of non-negative abundance values. Columns must be named feature ids.

meta

Optional per-sample metadata. Accepted for interface uniformity and ignored by this method.

Value

Plain finite numeric vector of length nrow(X). Scores use only each row's own values plus the frozen model, with no scored-batch centering, quantiles, or renormalization.

References

Rivera-Pinto J, Egozcue JJ, Pawlowsky-Glahn V, Paredes R, Noguera-Julian M, Calle ML. (2018) Balances: a new perspective for microbiome analysis. mSystems 3: e00053-18.

Examples

if (FALSE) { # \dontrun{
set.seed(1)
X <- matrix(stats::rgamma(120 * 15, shape = 30, rate = 2), nrow = 120,
            dimnames = list(NULL, paste0("f", sprintf("%02d", seq_len(15)))))
y <- rep(c(0, 1), each = 60)
X[y == 1, c("f01", "f02", "f03")] <- X[y == 1, c("f01", "f02", "f03")] * 1.8
X[y == 1, c("f13", "f14", "f15")] <- X[y == 1, c("f13", "f14", "f15")] / 1.8
model <- fit_bal_selbal(X, y, hp = list(logit_acc = "Dev"))
score_bal_selbal(model, X[1, , drop = FALSE])
} # }