Score GLCM Haralick texture (inv-glcm) within-sample discriminator

Scores each row of X independently with the frozen model from fit_inv_glcm. The model-universe features present in X are taken in the frozen canonical order; for one specimen its within-sample rCLR landscape is quantised with the frozen edges, a 1-D GLCM is built, the Haralick descriptor is computed, standardised by the frozen head, and the frozen LDA score is returned. Larger = more case-like.

Scoring uses only each row's own values plus the frozen model; no test-batch renormalisation, no cross-row coupling, no statistic estimated from X. If fewer than model$hp$min_features model-universe features are present in X, or a single specimen has fewer than model$hp$min_features positive values, the documented neutral score 0 is returned.

Usage

score_inv_glcm(model, X, meta = NULL)

Arguments

model

An inv_glcm_model object returned by fit_inv_glcm.

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; ignored.

Value

Plain finite numeric vector of length nrow(X). Larger values are more case-like. Each score depends only on that row's values and the frozen model. It is invariant to per-sample positive scaling: the rCLR landscape is exactly scale-invariant, so the frozen-edge quantised levels and hence the score are bit-identical, except on the measure-zero set where an rCLR value lies within floating-point rounding of a frozen edge.

References

Haralick RM, Shanmugam K, Dinstein I. (1973) Textural features for image classification. IEEE Transactions on Systems, Man, and Cybernetics SMC-3(6): 610-621.

Examples

if (FALSE) { # \dontrun{
set.seed(1)
p <- 50
X <- matrix(stats::rgamma(120 * p, shape = 20, rate = 0.2), nrow = 120,
            dimnames = list(NULL, paste0("miR-", sprintf("%03d", seq_len(p)))))
y <- rep(c(0, 1), each = 60)
X[y == 1, 11:20] <- X[y == 1, 11:20] + 150
model <- fit_inv_glcm(X, y)
score_inv_glcm(model, X[1, , drop = FALSE])
} # }