Score REO-kTSP within-sample pair-order votes

Scores each specimen using the oriented pairs learned by fit_reo_ktsp. With the default strict = FALSE, only pairs with both features present contribute. The score is the mean of feature_a < feature_b votes over these available pairs. Missing pairs reduce the denominator: this is a reduced-support score, not the complete fitted model's score. Its discrimination and calibration on a reduced panel are not established by the complete-panel fit.

Empty or constant fitted support, absence of all required features, all-nondetect rows, and panels with no complete pair raise a singlesample_input_error; see score_specimen. Scoring uses only each specimen and the frozen model.

Usage

score_reo_ktsp(model, X, meta = NULL, strict = FALSE)

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.

strict

Logical scalar. The default FALSE preserves scoring over available pairs for backward compatibility. Set TRUE to reject any missing feature from a fitted pair. Unused training features are not required. Existing invalid-input and unusable-support checks apply in both modes.

Value

Numeric vector of one finite vote-fraction score per row. With all fitted-pair features present, numeric values equal predict(model$ktsp, X). The support attribute is a data frame with one row per score: n_available counts fitted pairs with both named columns present, n_fitted counts all fitted pairs, and full_support reports whether these counts agree. Counts describe column availability, not detection or assay quality. Save attr(score, "support") before subsetting or numeric coercion, which can discard attributes.

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

# An illustrative frozen two-pair model.
pairs <- data.frame(feature_a = c("a", "c"), feature_b = c("b", "d"))
model <- structure(list(ktsp = list(pairs = pairs)), class = "reo_ktsp_model")
X <- rbind(c(a = 2, b = 1, c = 1, d = 3), c(a = 1, b = 2, c = 4, d = 2))
full <- score_reo_ktsp(model, X, strict = TRUE)
partial <- score_reo_ktsp(model, X[, c("a", "b"), drop = FALSE])
attr(partial, "support")  # 1 available / 2 fitted pairs; full_support = FALSE
#>   n_available n_fitted full_support
#> 1           1        2        FALSE
#> 2           1        2        FALSE
stopifnot(identical(as.numeric(partial), c(0, 1)))
tryCatch(score_reo_ktsp(model, X[, c("a", "b"), drop = FALSE], strict = TRUE),
         singlesample_input_error = function(e) e$code)
#> [1] "MISSING_REQUIRED_FEATURES"