Score class-conditional vine-copula LRT discriminator — score_lrt

Scores each row of X independently with the frozen model learned by fit_lrt_vinecopula. 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 to pseudo-observations $u_c = \mathrm{PIT}_c(z)$, and the score is the vine-copula log-density ratio $$S(z) = \log c_{\mathrm{case}}(u_{\mathrm{case}}) - \log c_{\mathrm{control}}(u_{\mathrm{control}}).$$

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

Usage

score_lrt_vinecopula(model, X, meta = NULL)

Arguments

model: An lrt_vinecopula_model object returned by fit_lrt_vinecopula.
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 vine better than the control vine). Scoring uses only each row's own values plus the frozen anchors and hyperparameters.

References

Aas K., Czado C., Frigessi A., Bakken H. (2009) Pair-copula constructions of multiple dependence. Insurance: Mathematics and Economics 44(2): 182-198.

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_vinecopula(X, y)
score_lrt_vinecopula(model, X[1, , drop = FALSE])
} # }