nmfkc.signed.rank: Rank selection for nmfkc.signed (concise diagnostics)

In nmfkc: Non-Negative Matrix Factorization with Kernel Covariates

Rank selection for nmfkc.signed (concise diagnostics)

Description

Fits nmfkc.signed across a range of ranks and reports r.squared, the effective rank, and the element-wise CV error sigma.ecv, with the same concise plot as nmfkc.rank.

Usage

nmfkc.signed.rank(
  Y,
  A,
  rank = 1:5,
  detail = c("full", "fast"),
  plot = TRUE,
  ...
)

Arguments

Y	Observation matrix (may contain negative entries).
A	Covariate matrix (may be signed).
rank	Integer vector of ranks to evaluate.
detail	"full" (default) also runs element-wise CV (sigma.ecv); "fast" skips it (plots r.squared and eff.rank only, and recommends the R-squared elbow).
plot	Logical; draw the diagnostics plot (default TRUE).
...	Passed on to nmfkc.signed and nmfkc.signed.ecv (e.g.\ maxit, nfolds, seed).

Value

A list with rank.best and criteria (rank, effective.rank, effective.rank.ratio, r.squared, sigma.ecv).

References

Roy, O., & Vetterli, M. (2007). The effective rank: A measure of effective dimensionality. Proc. EUSIPCO, 606–610. (effective.rank) Wold, S. (1978). Cross-validatory estimation of the number of components in factor and principal components models. Technometrics, 20(4), 397–405. (sigma.ecv)

See Also

nmfkc.signed, nmfkc.rank

nmfkc documentation built on July 14, 2026, 1:07 a.m.