summary.nmfkc: Summary method for objects of class 'nmfkc'

In nmfkc: Non-Negative Matrix Factorization with Kernel Covariates

Summary method for objects of class nmfkc

Description

Produces a summary of an nmfkc object, including matrix dimensions, runtime, fit statistics, and diagnostics.

Usage

## S3 method for class 'nmfkc'
summary(object, ...)

Arguments

object	An object of class nmfkc, i.e., the return value of nmfkc.
...	Additional arguments (currently unused).

Details

Separability measures how close the basis is to having an anchor row for every basis – a row of X that carries that basis alone. For each basis q take the largest share it has in any row, \max_j \tilde x_{jq} / \sum_r \tilde x_{jr}, with \tilde X the basis rescaled so that its columns sum to one (so the value does not depend on X.restriction); these are anchor.purity, attained at rows anchor.row. The separability is their minimum: 1 when every basis has a row of its own, 1/Q when every row mixes all bases equally, and NA for rank = 1. The same three values are stored on the fit, in fit$criterion.

Anchors for every basis, together with \mathrm{rank}(B) = Q, make the factorization unique up to scale, so a value near 1 says the solution is nearly unique. A fit with X.anchor scores exactly 1. The measure looks at X only, however: zeros in B or in \Theta can pin the solution as well, so a low value does not show that it is not unique.

Value

An object of class summary.nmfkc, containing summary statistics. Among them, separability, anchor.purity and anchor.row; see Details.

See Also

nmfkc, nmfkc.inference, plot.nmfkc

Examples

Y <- matrix(cars$dist, nrow = 1)
A <- rbind(1, cars$speed)
result <- nmfkc(Y, A, rank = 1)
summary(result)

nmfkc documentation built on Oct. 30, 2026, 9:06 a.m.