Score per-feature KDE naive-Bayes class-conditional LRT discriminator

Scores each row of X independently with the frozen model learned by fit_lrt_nbkde. For one specimen the abundances are mapped to the self-contained per-sample rCLR representation $z$, and the score is the naive-Bayes class-conditional marginal log-likelihood ratio $$S(z) = \sum_{j \in \mathrm{present}} \big[\log f_{\mathrm{case},j}(z_j) - \log f_{\mathrm{control},j}(z_j)\big],$$ where each $\log f_{c,j}$ is the frozen Gaussian-kernel KDE log-density of class $c$ for feature $j$ evaluated at the specimen's own coordinate $z_j$ via a numerically stable log-sum-exp.

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

Usage

score_lrt_nbkde(model, X, meta = NULL)

Arguments

model: An lrt_nbkde_model object returned by fit_lrt_nbkde.
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 per-feature marginals match the case KDEs better than the control KDEs). Scoring uses only each row's own values plus the frozen anchors, bandwidths and hyperparameters.

References

Silverman B.W. (1986) Density Estimation for Statistics and Data Analysis. Chapman and Hall, London.

Hand D.J., Yu K. (2001) Idiot's Bayes – not so stupid after all? International Statistical Review 69(3): 385-398.

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)
L[y == 1, 1:8] <- L[y == 1, 1:8] + 1.2
X <- exp(L)
model <- fit_lrt_nbkde(X, y)
score_lrt_nbkde(model, X[1, , drop = FALSE])
} # }