Score REO-kTSP within-sample pair-order votes — score_reo

Scores each row of X independently with the frozen oriented pairs learned by fit_reo_ktsp. For the pairs whose two features are present in X, the score is the k-TSP vote fraction: the mean of feature_a < feature_b votes over usable pairs. This is the standard k-TSP decision statistic and supplies a continuous margin score for DeLong comparisons. If no retained pair has both features present, the function returns the neutral vote fraction 0.5 for every row.

Usage

score_reo_ktsp(model, X, meta = NULL)

Arguments

model: A reo_ktsp_model object returned by fit_reo_ktsp.
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

Numeric vector of one finite vote-fraction score per row of X. Scores use only each row's own values plus the frozen model, with no scored-batch centering, quantiles, or renormalization. On a fully present feature set the result is identical to predict(model$ktsp, X).

References

Geman D, d'Avignon C, Naiman DQ, Winslow RL. (2004) Classifying gene expression profiles from pairwise mRNA comparisons. Statistical Applications in Genetics and Molecular Biology 3: Article19.

Tan AC, Naiman DQ, Xu L, Winslow RL, Geman D. (2005) Simple decision rules for classifying human cancers from gene expression profiles. Bioinformatics 21: 3896-3904.

Examples

if (FALSE) { # \dontrun{
X <- matrix(stats::rgamma(40 * 12, shape = 2), nrow = 40,
            dimnames = list(NULL, paste0("miR-", seq_len(12))))
y <- rep(c(0, 1), each = 20)
X[y == 0, "miR-1"] <- X[y == 0, "miR-1"] + 5
X[y == 1, "miR-2"] <- X[y == 1, "miR-2"] + 5
model <- fit_reo_ktsp(X, y, hp = list(k = 3L))
score_reo_ktsp(model, X[1, , drop = FALSE])
} # }