Score class-conditional Gaussian-copula LRT discriminator

Scores each row of X independently with the frozen model learned by fit_lrt_copula. For one specimen the abundances are mapped to the self-contained per-sample rCLR representation $z$, the present features are PIT-transformed under each class's frozen marginal and mapped to Gaussian scores $q_c = \Phi^{-1}(\mathrm{PIT}_c(z))$, and the score is the Gaussian-copula log-density ratio $$S(z) = -\tfrac{1}{2} q_{\mathrm{case}}^\top (R_{\mathrm{case}}^{-1}-I) q_{\mathrm{case}} - \tfrac{1}{2}\log\det R_{\mathrm{case}} + \tfrac{1}{2} q_{\mathrm{control}}^\top (R_{\mathrm{control}}^{-1}-I) q_{\mathrm{control}} + \tfrac{1}{2}\log\det R_{\mathrm{control}}.$$

At scoring time the present feature set is intersect(model$feature_universe, colnames(X)). The class copula (per-feature marginals and correlation) is re-derived over exactly that present set from the frozen raw anchors – using only frozen training data, never a scored-batch statistic – which keeps partial-overlap scores consistent and equal to the fit-time representation at full overlap. The score of a row therefore depends only on that row and the frozen model, and is exactly invariant to per-sample scaling. If fewer than model$hp$min_features features are present, the documented neutral score 0 is returned for every row.

Usage

score_lrt_copula(model, X, meta = NULL)

Arguments

model: An lrt_copula_model object returned by fit_lrt_copula.
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). Larger values are more case-like (the specimen's feature dependence matches the case copula better than the control copula). Scoring uses only each row's own values plus the frozen anchors and hyperparameters.

References

Sklar A. (1959) Fonctions de repartition a n dimensions et leurs marges. Publications de l'Institut de Statistique de l'Universite de Paris 8: 229-231.

Joe H. (2014) Dependence Modeling with Copulas. Chapman and Hall/CRC.

Examples

if (FALSE) { # \dontrun{
set.seed(1)
n <- 120; p <- 30
L <- matrix(stats::rnorm(n * p, 4, 0.6), nrow = n,
            dimnames = list(NULL, paste0("miR-", seq_len(p))))
y <- rep(c(0, 1), each = n / 2)
f <- stats::rnorm(sum(y == 1))
L[y == 1, 1:8] <- L[y == 1, 1:8] + outer(f, rep(1.4, 8))
X <- exp(L)
model <- fit_lrt_copula(X, y)
score_lrt_copula(model, X[1, , drop = FALSE])
} # }