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

In nmfkc: Non-Negative Matrix Factorization with Kernel Covariates

Rank selection for nmfkc.net (concise diagnostics)

Description

Fits nmfkc.net across a range of ranks and reports the three rank-selection criteria – r.squared, the effective rank (utilization), and the element-wise CV error sigma.ecv – with the same concise diagnostics plot as nmfkc.rank.

Usage

nmfkc.net.rank(
  Y,
  rank = 1:5,
  type = c("tri", "bi", "signed"),
  detail = c("full", "fast"),
  plot = TRUE,
  ...
)

Arguments

Y	Symmetric (network) observation matrix.
rank	Integer vector of ranks to evaluate.
type	One of "tri", "bi", "signed".
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.net and nmfkc.net.ecv (e.g.\ nstart, maxit, nfolds, seed).

Value

A list with rank.best (ECV minimum, or the R-squared elbow under detail = "fast") and criteria (data frame: 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.net, nmfkc.net.ecv, nmfkc.rank

Examples

Y <- matrix(c(0,1,1,0,0,0, 1,0,1,0,0,0, 1,1,0,1,0,0,
              0,0,1,0,1,1, 0,0,0,1,0,1, 0,0,0,1,1,0), 6, 6)
nmfkc.net.rank(Y, rank = 1:3, type = "tri", nstart = 5, nfolds = 3)

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