plot.nmfkc.consensus: Plot a consensus rank-selection (nmfkc.consensus) object

In nmfkc: Non-Negative Matrix Factorization with Kernel Covariates

Plot a consensus rank-selection (nmfkc.consensus) object

Description

Two views of a nmfkc.consensus result:

• type = "criteria" (default): the stability curves cophenetic (blue) and dispersion (red) against rank. No "best" marker is drawn – a stable rank tends to be a coarse one, so the curves are shown for inspection rather than as an optimum.

• type = "heatmap": the consensus matrix of each requested rank, reordered by average-linkage hierarchical clustering of 1 - \bar C (blue = 0 / different cluster, red = 1 / same cluster). Requires keep.consensus = TRUE at compute time.

Usage

## S3 method for class 'nmfkc.consensus'
plot(
  x,
  type = c("criteria", "heatmap"),
  rank = NULL,
  mfrow = NULL,
  mar = NULL,
  col = grDevices::hcl.colors(50, "Blue-Red"),
  main = NULL,
  ...
)

Arguments

x	An object of class "nmfkc.consensus".
type	"criteria" or "heatmap".
rank	For type = "heatmap", the rank(s) to display. NULL (default) shows every rank stored in x.
mfrow	Panel layout for multiple heatmaps, as c(nrow, ncol). NULL (default) uses n2mfrow for a near-square grid.
mar	Per-panel margins c(b, l, t, r) for the heatmap view. NULL (default) uses tight margins for a grid and wider margins (for sample labels) when a single rank is shown.
col	Heatmap colour palette (length-50 blue-to-red by default).
main	Plot title for the "criteria" view (heatmap panels are titled per rank).
...	Further arguments passed to the underlying plot / image.

Value

x, invisibly.

See Also

nmfkc.consensus

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