Score REO-pairratio within-sample log-ratio discriminator — score_reo

Scores each row of X independently with the frozen pairwise log-ratio coefficients learned by fit_reo_pairratio. For one specimen and each stored pair (a, b), if both features are present in X, the term is log(X[i, a] + pseudocount) - log(X[i, b] + pseudocount). If either feature is absent, that pair is dropped for that specimen. The returned score is the frozen linear predictor, not plogis() of it.

If the model is intercept-only, or if none of the stored pairs is usable for a scored specimen, the score is the intercept. Scoring uses no random numbers and no scored-batch statistics.

Usage

score_reo_pairratio(model, X, meta = NULL)

Arguments

model: A reo_pairratio_model object returned by fit_reo_pairratio.
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 linear-predictor score per row of X. Larger scores are more case-like.

References

Aitchison J. (1986) The Statistical Analysis of Compositional Data. Chapman and Hall.

Friedman J, Hastie T, Tibshirani R. (2010) Regularization paths for generalized linear models via coordinate descent. Journal of Statistical Software 33: 1-22.

Examples

if (FALSE) { # \dontrun{
set.seed(1)
X <- matrix(stats::rgamma(60 * 20, shape = 2), nrow = 60,
            dimnames = list(NULL, paste0("miR-", seq_len(20))))
y <- rep(c(0, 1), each = 30)
X[y == 1, "miR-1"] <- X[y == 1, "miR-1"] + 10
X[y == 0, "miR-2"] <- X[y == 0, "miR-2"] + 10
model <- fit_reo_pairratio(X, y, hp = list(m_features = 8L, nfolds = 5L))
score_reo_pairratio(model, X[1, , drop = FALSE])
} # }